Patent Publication Number: US-2019181680-A1

Title: Control device optimizing evaluation of energy management in in-plant energy network

Description:
TECHNICAL FIELD 
     The present invention relates in general to control devices optimizing evaluation of energy management in an in-plant energy network by exploiting both electric and thermal energy in such a manner as to achieve desirable overall performance and in particular to control devices, optimizing evaluation of energy management in an in-plant energy network, that improve energy facility efficiency and are applicable in residential areas (e.g., general households), manufacturing industry areas (e.g., factories), and tertiary areas (e.g., office buildings, hotels, hospitals, schools, and swimming pools) for which it is desirable to reduce at least one of energy supply cost, CO 2  (carbon dioxide) emission, and primary energy consumption. 
     BACKGROUND ART 
     An in-plant energy network that manages both electric and thermal energy for desirable overall performance connects to a large-scale electric power network (e.g., commercial electric power system) and includes an energy generation unit (for example, a combined heat and power device (CHP) and a renewable energy source (RES)), an energy storage unit (specifically, an electric storage unit and/or a thermal accumulation unit), and an energy load (specifically, an electric load and/or a thermal load). The in-plant energy network may further include a backup thermal energy supply unit. 
     These in-plant energy networks could fail to reduce at least one of energy supply cost, CO 2  emission, and primary energy consumption to expected levels even if the energy generation unit installed in the plant has relatively high efficiency. This is especially so when the energy generation unit needs to be integrated with another piece of equipment, such as an energy storage unit or an energy load (or a backup thermal energy supply unit). Some of the factors in this failure are facility managers&#39; insufficient experience or competence in managing the equipment, such as the energy generation unit, the energy storage unit, and the energy load (possibly also, the backup thermal energy supply unit) and facility managers&#39; insufficient understanding or knowledge in highly complex facilities and interactions between the installed pieces of equipment. In view of these problems, optimal management is desirable that reduces at least one of energy supply cost, CO 2  emission, and primary energy consumption in in-plant energy networks by specifying value settings for each piece of equipment, including the energy generation unit, the energy storage unit, and the energy load (possibly also, the backup thermal energy supply unit) even if the facility manager has insufficient knowledge or competence. 
     In relation to this desirable optimal management, related technical document 1 (US2014/0257584 A1) discloses a configuration that involves use of a genetic algorithm to optimize charging/discharging of rechargeable batteries in an energy network to which electrical appliances, electric loads, thermal appliances, and thermal loads are connected. 
     SUMMARY OF INVENTION 
     Technical Problems 
     However, as described in related technical document 1, according to conventional configurations, when there exist energy storage units (specifically, an electric storage unit and/or a thermal accumulation unit), even if a predetermined operation period (e.g., 1 day) is divided into a plurality of time steps, and an operation status of apparatus is calculated that results in an individual evaluation value representing an evaluation value for an individual time step (best evaluation value for each time step), optimal operation is not generally achieved when energy management is evaluated over the entire operation period by means of each transitional evaluation value representing an evaluation value for a transition from value settings for one time step to value settings for a next, consecutive time step. 
     Accordingly, the present invention has an object to provide a control device capable of practically achieving optimal operation of each piece of equipment, such as an energy generation unit, an energy storage unit, and an energy load, throughout an entire predetermined operation period. 
     Examples of other technical documents related to the present invention include: 
     “Smart energy management system for optimal microgrid operation”, by authors 
     Chen, Duan, Cai, Liu, Hu, from IET Renewable Power Generation, Volume 5, Issue 3, pp. 258-267, 2011. This technical document presents a Smart Energy Management System (SEMS), composed by several modules (for power forecasting, energy storage management, optimization), whose optimization is in the shape if a single-object optimization problem aiming to economically optimize the management of the storage, while the loads are managed by means of a real-coded genetic algorithm. 
     “Online management genetic algorithms of microgrid for residential application”, by Mohamed, Koivo, found in Energy Conversion and Management, Volume 64, pp. 562-568, 2012. The technique presented in this document performs the optimization of the microgrid by means of genetic algorithm only and in addition only economically optimizes the electric power generation, regardless of loads and thermal energy. 
     “Probabilistic energy and operation management of a microgrid containing wind/photovoltaic/fuel cell generation and energy storage devices based on point estimate method and self-adaptive gravitational search algorithm”, by Niknam, Golestaneh, Malekpour, in Energy, Volume 43, Issue 1, pp. 427-437, 2012. In this technical document, microgrid optimization is performed only for the electric side (neglecting the thermal one). 
     “Power source scheduling and adaptive load management via a genetic algorithm embedded neural network”, by Chih-Hsien, Devaney, Chung-Ming, Chih-Ming, from Instrumentation and Measurement Technology Conference (IMTC), 2000. This technical document presents an energy management system with a user-friendly interface. 
     “An algorithm for intelligent home energy management and demand response analysis”, by authors Pipattanasomporn, Kuzlu, Rahman, published in IEEE Transactions on Smart Grid, Volume 3, Issue 4, pp. 2166-2173, 2012. This technical document presents a method for the only demand side management, and only for the electric loads. 
     “Optimal and autonomous incentive-based energy consumption scheduling algorithm for smart grid”, by Mohsenian-Rad, Wong, Jatskevich, Schober, from Innovative Smart Grid Technologies (ISGT), 2010. In this technical document, management is only on the load side, and only for the electric power consumptions. 
     “Energy management and operational planning of a microgrid with a PV-based active generator for smart grid applications”, by authors Kanchev, Di, Colas, Lazarov, Francois, published in IEEE Transactions on Industrial Electronics, Volume 58, Issue 10, pp. 4583-4592, 2011. This technical document proposes a system that works considering two different space scales, global for the smart grid and local for the single user, optimizing the operations of both generators and storages, but regardless the load management and only considering the electric side. 
     “Autonomous demand-side management based on game-theoretic energy consumption scheduling for the future smart grid”, by Mohsenian-Rad, Wong, Jatskevich, Schober, Leon-Garcia, from IEEE Transactions on Smart Grid, Volume 1, Issue 3, pp. 320-331, 2010. This technical document presents a game-theoretic optimization carried out only on the electric loads. 
     “Application of a game-theoretic energy management algorithm in a hybrid predictive-adaptive scenario”, by Dave, Sooriyabandara, Luyang, by 2nd IEEE PES International Conference and Exhibition on Innovative Smart Grid Technologies (ISGT), 2011. This technical document presents, similarly to the previous one, an optimization carried out only for the electric loads by means of a game-theoretic-based algorithm. 
     “Demand side management in smart grid using heuristic optimization”, by authors Logenthiran, Srinivasan, Tan, published in IEEE Transactions on Smart Grid, Volume 3, Issue 3, pp. 1244-1252, 2012. This technical document presents management that is only carried out on the loads, without considering the generation side. 
     “Experimental validation of a real-time energy management system using multi-period gravitational search algorithm for microgrids in islanded mode”, by authors Marzband, Ghadimi, Sumper, Dominguez-Garcia, in Applied Energy, Volume 128, pp. 164-174, 2014. This technical document presents an optimization only carried out for the electric generation and loads. 
     “Real-time tool for management of smart polygeneration grids including thermal energy storage”, by authors Ferrari, Pascenti, Sorce, Traverso, Massardo, published in Applied Energy, Volume 130, pp. 670-678, 2014. This technical document presents an optimization carried out by a Simulink software toolbox. The operation of the plant is only managed with real-time signals. 
     “Plant management tools tested with a small-scale distributed generation laboratory”, by Ferrari, Traverso, Pascenti, Massardo, in Energy Conversion and Management, Volume 78, pp. 105-113, 2014. This technical document presents, similarly to the previously cited system, a system related to energy conversion and management. 
     Examples of software related to the technology of the present invention include Energy AnalityX by Iconics, Energy Sentinel Web and ES3 Evo by EnergyTeam, DEXCell Energy Manager by DEXMA, Wattics by Wattics, eSight by eSightenergy, AVReporter by KNOsys International, ENMAT Energy Management by ENMAT Energy, Acotel Energy Management by Acotel Net, Agentis Platform by Agentis Energy, ATLAS Energy Monitoring System by NewFound Energy, SIMATIC Powerrate by Siemens, SAP Eenergy Management Software by SAP, EnergyCAP by EnergyCAP, PowerLogic ION EEM by Schneider Electric, C3 Energy Customer Engagement Applications (by C3 Energy), and Energy Management Suite by Cisco. 
     A few more examples of software related to the technology of the present invention include: 
     Events2HVAC by Streamside Solutions, managing air conditioning, room heating, lighting systems, and door locking of single offices or rooms in large buildings once the occupation schedule is inputted. 
     Energy Management System Software by ETAP, aiming to manage large power production plants with an economic optimization based on power quality inputs from the field (voltage, frequency, and the like). 
     ASPEC by AlbaSystem, only managing generators with a real-time procedure aiming to minimize energy supply costs by deciding the priority interventions of a group of generators, with the objective of achieving the self-consumptions of the plant. 
     Energy Efficiency Platform Software by InspiringSoftware, claiming to manage and optimize the operations of both generators and loads (it is not completely clear how they do it). 
     As detailed so far, there exist technologies related to the present invention, none of which discloses the present invention. 
     Solution to Problem 
     A control device in accordance with the present invention, to address the problems, is a control device optimizing evaluation of energy management in an in-plant energy network, the control device including an input section, a computing section, and an output section, the in-plant energy network being connected to a large-scale electric power network and including an energy generation unit, an energy storage unit, and an energy load, the input section being configured to allow input of input information for calculation of value settings for optimizing the evaluation of energy management in the in-plant energy network, the input information including vector information that is changeable over an entire predetermined operation period, scalar information that does not change throughout the entire operation period, and technical information representing characteristics of the energy generation unit and the energy storage unit, the computing section being configured to: compute, based on the input information, an output value of the energy generation unit and an output value of the energy storage unit and when the energy load includes a controllable energy load, also a demand of the controllable energy load, as the value settings by means of a predetermined optimization algorithm for each one of time steps into which the operation period is divided and for each one of predetermined levels of the energy storage unit; give a weight to each edge from the levels of the energy storage unit for one of the time steps to the levels of the energy storage unit for a next one of the time steps based on one of transitional evaluation values obtained by means of a fitness function; determine, out of combinations of the value settings resulting in the transitional evaluation values for the time steps, a combination of the value settings for which a total sum of the transitional evaluation values for a first one of the time steps to a last one of the time steps is most positively evaluated, by means of a graph-theoretic shortest-path problem solving algorithm; and select the combination of the value settings determined by means of the shortest-path problem solving algorithm as operational instruction values for each one of the time steps, the output section being configured to transmit the selected combination of the value settings as the operational instruction values to the energy generation unit and the energy storage unit and when the energy load includes the controllable energy load, also to the controllable energy load, for each one of the time steps. 
     In one aspect of the present invention, the shortest-path problem solving algorithm may be a Dijkstra algorithm. 
     In another aspect of the present invention, the optimization algorithm may be a genetic algorithm. 
     In a further aspect of the present invention, the vector information may include: at least one of an energy price, a time step-specific electric power price, an electric power demand, and a thermal power demand. 
     In still another aspect of the present invention, the scalar information may include at least one of a set of value settings in an operation season mode and a set of value settings for a last time step of an immediately preceding operation period. 
     The energy generation unit may include, for example, an electric energy generation unit and/or a thermal energy generation unit. More specifically, the energy generation unit may be a combined heat and power device (CHP) running on fuel gas, a renewable energy source (RES), or a cold thermal source. For example, in yet another aspect of the present invention, the energy generation unit may include a CHP and/or an RES. 
     The CHP may be, for example, a gas engine-based power generator, a fuel cell, or an organic rankine cycle (ORC). The RES may be, for example, a renewable electric energy source and/or a renewable thermal energy source. The renewable electric energy source may be, for example, a photovoltaic power generator or a wind-powered electric generator. The renewable thermal energy source may be, for example, a solar thermal collector. 
     The energy storage unit may be, for example, an electric storage unit and/or a thermal accumulation unit. The electric storage unit may be, for example, a rechargeable battery or a capacitor. 
     Thermal energy may include, for example, hot thermal energy and/or cold thermal energy. Therefore, the thermal accumulation unit may be, for example, a hot thermal accumulation unit and/or a cold thermal accumulation unit. The hot thermal accumulation unit may be, for example, a thermal accumulator (e.g., thermal water storage tank). The cold thermal accumulation unit may be, for example, a cooling storage device (e.g., cooling water tank). 
     The energy load may be an electric load and/or a thermal load. The electric load may be an electric lamp or an electrical appliance (e.g., electric cooling device). The thermal load may be, for example, a cold thermal load at lower-than-normal temperature in place of, or in addition to, a hot thermal load at higher-than-normal temperature. In other words, the thermal load may include a hot thermal load and/or a cold thermal load. The hot thermal load may be, for example, an air heating device. The cold thermal load may be, for example, an air cooling device. 
     In yet another aspect of the present invention, the technical information may include at least one of a rated output of the CHP, a conversion parameter for conversion of an electric energy output of the CHP to a fuel gas consumption of the CHP, and a conversion parameter for conversion of the electric energy output of the CHP to a thermal energy output of the CHP. 
     In another aspect of the present invention, the in-plant energy network may further include a backup thermal energy supply unit, the technical information may further include technical information representing characteristics of the backup thermal energy supply unit, the computing section may compute the output value of the energy generation unit, the output value of the energy storage unit, and an output value of the backup thermal energy supply unit and when the energy load includes the controllable energy load, also compute the demand of the controllable energy load, as the value settings by means of the optimization algorithm for each one of the time steps and for each one of the levels of the energy storage unit, and the output section may transmit the selected combination of the value settings as the operational instruction values to the energy generation unit, the energy storage unit, and the backup thermal energy supply unit and when the energy load includes the controllable energy load, also to the controllable energy load, for each one of the time steps. 
     The backup thermal energy supply unit may include a backup hot thermal energy supply unit and/or a backup cold thermal energy supply unit. The backup hot thermal energy supply unit may be, for example, a gas boiler or an electric boiler. The backup cold thermal energy supply unit may be, for example, an electric cooling device or an absorption cooling device. 
     In another aspect of the present invention, the transitional evaluation values may be derived from an evaluation condition including at least one of an energy supply cost, a CO 2  emission, and a primary energy consumption. 
     Advantageous Effects of Invention 
     The present invention can practically achieve optimal operation of each piece of equipment, such as an energy generation unit, an energy storage unit, and an energy load, throughout an entire predetermined operation period. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         FIG. 1  is a schematic illustration of an exemplary large-scale electric power network connected to an in-plant energy network in accordance with an embodiment of the present invention. 
         FIG. 2  is a schematic illustration of an exemplary in-plant energy network in accordance with an embodiment of the present invention. 
         FIG. 3  is a schematic block diagram illustrating in detail the in-plant energy network shown in  FIG. 2 . 
         FIG. 4  is a schematic block diagram illustrating a control device and related apparatus for the in-plant energy network shown in  FIGS. 2 and 3 . 
         FIG. 5  is a diagram representing an example of individual evaluation values for individual time steps into which an operation period is divided, depicting that the individual evaluation values for the time steps fail to achieve optimal operation when energy management is evaluated over the entire operation period. 
         FIG. 6  is a diagram representing an example of transitional evaluation values for evaluation by means of a combination of value settings for equipment, such as an energy generation unit, an energy storage unit, and an energy load, over an entire operation period, depicting that individual evaluation values for time steps into which an operation period is divided fail to achieve optimal operation when energy management is evaluated over the entire operation period. 
         FIG. 7  is a schematic illustration of a control structure of a control section in the control device shown in  FIG. 4 . 
         FIG. 8 , related to the table in  FIG. 6  of transitional evaluation values for evaluation by means of a combination of value settings in a transition from one time step to a next time step over an entire operation period, is a table of the output ratios of a CHP, an RES, and a backup thermal energy supply unit and the consumption ratios of an electric load and a thermal load, along with the output ratios of an electric storage unit and a thermal accumulation unit, for that one time step. 
         FIG. 9 , related to the table in  FIG. 6  of transitional evaluation values for evaluation by means of a combination of value settings in a transition from one time step to a next time step over an entire operation period, is a table of the output ratios of a CHP, an RES, and a backup thermal energy supply unit and the consumption ratios of an electric load and a thermal load, along with the output ratios of an electric storage unit and a thermal accumulation unit, for the next time step. 
         FIG. 10  is a flow chart representing a first half of an exemplary energy management evaluation optimization process performed by the control section in the control device shown in  FIG. 7 . 
         FIG. 11  is a flow chart representing a second half of the exemplary energy management evaluation optimization process performed by the control section in the control device shown in  FIG. 7 . 
         FIG. 12  is a schematic diagram illustrating an exemplary replacement process by which some individuals in a population are replaced, the left half representing the population before the replacement, the right half representing the population after the replacement. 
         FIG. 13  is a schematic diagram illustrating an exemplary selection process by which parent individuals are selected from two or more individuals in a population, the left half representing the population before the selection, the right half representing the population after the selection. 
         FIG. 14  is a diagram illustrating an exemplary roulette selection process, the left half representing selection rates, the right half representing a roulette wheel prepared according to the selection rates. 
         FIG. 15  is a schematic diagram illustrating an exemplary one-point crossover operation and an exemplary two-point crossover operation, the upper half representing a one-point crossover operation, the lower half representing a two-point crossover operation. 
         FIG. 16  is an illustration of local solutions that are likely to be found other than an optimal solution during the generation of a next-generation population. 
         FIG. 17  is a schematic diagram illustrating an exemplary single-locus mutation operation. 
         FIG. 18  is a schematic diagram of another exemplary in-plant energy network in accordance with an embodiment of the present invention. 
         FIG. 19  is a schematic diagram of another exemplary in-plant energy network in accordance with an embodiment of the present invention. 
         FIG. 20  is a schematic diagram of another exemplary in-plant energy network in accordance with an embodiment of the present invention. 
         FIG. 21  is a schematic diagram of another exemplary in-plant energy network in accordance with an embodiment of the present invention. 
         FIG. 22  is a schematic diagram of another exemplary in-plant energy network in accordance with an embodiment of the present invention. 
         FIG. 23  is a schematic diagram of another exemplary in-plant energy network in accordance with an embodiment of the present invention. 
         FIG. 24  is a schematic diagram of another exemplary in-plant energy network in accordance with an embodiment of the present invention. 
         FIG. 25  is a schematic diagram of another exemplary in-plant energy network in accordance with an embodiment of the present invention. 
         FIG. 26  is a schematic diagram of another exemplary in-plant energy network in accordance with an embodiment of the present invention. 
         FIG. 27  is a schematic diagram of another exemplary in-plant energy network in accordance with an embodiment of the present invention. 
         FIG. 28  is a schematic diagram of another exemplary in-plant energy network in accordance with an embodiment of the present invention. 
         FIG. 29  is a schematic diagram of another exemplary in-plant energy network in accordance with an embodiment of the present invention. 
     
    
    
     DESCRIPTION OF EMBODIMENTS 
     The following will describe embodiments in accordance with the present invention in reference to drawings. 
     In-Plant Energy Network 
       FIG. 1  is a schematic illustration of an exemplary large-scale electric power network  200  to which is connected an in-plant energy network  100  in accordance with an embodiment of the present invention. 
     The large-scale electric power network  200  is a large-scale commercial electric power system operating as an energy network (in this example, a smart energy network (smart grid)). A smart energy network enables exchange of electric power supply/demand information and also of various electric power-related information between electric power suppliers, such as electric power companies, and electric power consumers, such as residential areas (e.g., general households), manufacturing industry areas (e.g., factories), and tertiary areas (e.g., office buildings, hotels, hospitals, schools, and swimming pools) by using information communications technology. The energy network may be a non-smart energy network. 
     In the example shown in  FIG. 1 , the large-scale electric power network  200  connects a central power plant  201 , a general household  211  as a residential area  210 , a factory  221  as a manufacturing industry area  220 , and an office  231  as a tertiary area  230 , as well as a small-scale wind-powered plant  240 , an electric vehicle  250 , a combined heat and power system (CHP)  260 , an electric storage unit  270 , fuel cells  280 , and a large-scale wind-powered plant  290  in such a manner as to enable exchange of electric power supply/demand information and various electric power-related information between the electric power suppliers and the electric power consumers. 
     At least one of the residential area  210 , the manufacturing industry area  220 , and the tertiary area  230  (in the example shown in  FIG. 1 , all of the residential area  210 , the manufacturing industry area  220 , and the tertiary area  230 ) includes the in-plant energy network  100 . 
     In-Plant Energy Network 
       FIG. 2  is a schematic illustration of an example of the in-plant energy network  100  in accordance with an embodiment of the present invention.  FIG. 3  is a schematic block diagram illustrating in detail the in-plant energy network  100  shown in  FIG. 2 . 
     As illustrated in  FIGS. 2 and 3 , the in-plant energy network  100  is applicable in at least one of the residential area  210 , the manufacturing industry area  220 , and the tertiary area  230  and operates as an electric and thermal energy network that manages both electric and thermal energy for desirable overall performance. 
     The in-plant energy network  100  is connected to the large-scale electric power network  200  and includes an energy generation unit (in this example, a combined heat and power device  110  (CHP  110 ) and a renewable energy source  120  (RES  120 )), an energy storage unit (in this example, an electric storage unit  130  and a thermal accumulation unit  140 ), a backup thermal energy supply unit  150 , and an energy load (in this example, an electric load  160  and a thermal load  170 ). Under the control of a control device  300 , the in-plant energy network  100  operates the CHP  110 , the RES  120 , the electric storage unit  130 , the backup thermal energy supply unit  150 , the electric load  160 , and the thermal load  170 . 
     As illustrated in  FIG. 3 , the CHP  110  is a gas-powered electric generator in the present embodiment. The CHP  110  includes an open/close valve  111 , a gas engine  112 , an electric power generator  113 , an electric power converter  114  for the electric power generator  113 , an AC circuit breaker  115 , a waste heat recovery boiler  116 , and an adjusting valve  117 . 
     In the CHP  110 , the gas engine  112  runs on fuel supplied from a fuel supply source F via the open/close valve  111 . The rotation of the gas engine  112  drives the electric power generator  113  for electric power generation. Generated electric energy P 1  is fed to an electric power supply line PL via the AC circuit breaker  115 . Also in the CHP  110 , heat (waste heat) Q generated by the gas engine  112  heats up the waste heat recovery boiler  116 . Thermal energy Q 1  of obtained water vapor or hot water is fed to a heat supply line TL. Fuel, throughout this specification, may be, for example, gas fuel, such as natural gas, city gas, or biogas. 
     The CHP  110  is connected to an output system of the control device  300  so that the CHP  110  can change outputs thereof (electric energy P 1  and thermal energy Q 1 ) in response to an operational instruction value C 1  from the control device  300  (i.e., an operational instruction value for an output ratio R 1  (R 11 ) with respect to the rated electric energy output, specifically an operational instruction value C 11  for the duty ratio of a switching element in the electric power converter  114 , and an operational instruction value for an output ratio R 1  (R 12 ) with respect to the rated thermal energy output, specifically an operational instruction value C 12  for the opening degree of the adjusting valve  117  disposed on the heat supply line TL of the waste heat recovery boiler  116 ). As an example, for a rated electric energy output of 10 kWh and an output ratio R 1  (R 11 ) of 50%, the CHP  110  generates 5 kWh (electric energy P 1 ) in response to an input from the control device  300  of the operational instruction value C 11  dictating that the output ratio R 1  (R 11 ) with respect to the 10-kWh rated electric energy output be 50%. In addition, for a rated thermal energy output of 36,000 kJ and an output ratio R 1  (R 12 ) of 50%, the CHP  110  generates 18,000 kJ (thermal energy Q 1 ) in response to an input from the control device  300  of the operational instruction value C 12  dictating that the output ratio R 1  (R 12 ) with respect to the 36,000-kJ rated thermal energy output be 50%. 
     The RES  120  runs on natural energy to output electric energy and is connected to the output system of the control device  300 . The RES  120  can change an output thereof in response to an operational instruction value C 2  from the control device  300  (i.e., an operational instruction value for an output ratio R 2  with respect to a maximum output) because the output of the RES  120  may need to be restricted to maintain predetermined electric power demand levels (e.g., an electric power exchange profile described later in detail) for the large-scale electric power network  200 . 
     The RES  120  includes a photovoltaic power generator  121  and a wind-powered electric generator  122  in the present embodiment. 
     The photovoltaic power generator  121  includes a solar cell panel  121   a,  a solar-generated electric power inverter  121   b,  and an AC circuit breaker  121   c.  In the photovoltaic power generator  121 , the solar cell panel  121   a  generates electric power under sunlight. The generated electric power is converted to AC electric energy P 2  (P 21 ) by the solar-generated electric power inverter  121   b.  The converted electric energy P 2  (P 21 ) is fed to the electric power supply line PL via the AC circuit breaker  121   c.  The photovoltaic power generator  121  is connected to the output system of the control device  300  so that the photovoltaic power generator  121  can change an output thereof (electric energy P 21 ) in response to the operational instruction value C 2  (C 21 ) from the control device  300  (i.e., an operational instruction value for an output ratio R 2  (R 21 ) with respect to a maximum output of the solar cell panel  121   a  that depends on the intensity of sunlight, specifically an operational instruction value for the duty ratio of a switching element in the solar-generated electric power inverter  121   b ). As an example, where the solar cell panel  121   a  has a rated output of 10 kWh, but the solar cell panel  121   a  has a maximum output of 7 kWh as being determined by the intensity of sunlight, and the output ratio R 21  is 50%, the photovoltaic power generator  121  delivers 3.5 kWh (electric energy output P 2  (P 21 )) at the output of the solar-generated electric power inverter  121   b  in response to an input from the control device  300  of the operational instruction value C 21  dictating that the output ratio R 21  with respect to the 7-kWh maximum output be 50%. 
     The wind-powered electric generator  122  includes a wind turbine  122   a,  a wind-generated electric power inverter  122   b,  and an AC circuit breaker  122   c.  In the wind-powered electric generator  122 , the wind turbine  122   a  rotates with wind for electric power generation. The generated electric power is converted to AC electric energy P 2  (P 22 ) by the wind-generated electric power inverter  122   b.  The converted electric energy P 2  (P 22 ) is fed to the electric power supply line PL via the AC circuit breaker  122   c.  The wind-powered electric generator  122  is connected to the output system of the control device  300  so that the wind-powered electric generator  122  can change an output thereof (electric energy P 22 ) in response to the operational instruction value C 2  (C 22 ) from the control device  300  (i.e., an operational instruction value for an output ratio R 2  (R 22 ) with respect to a maximum output of the wind turbine  122   a  that depends on the wind air flow, specifically an operational instruction value for the duty ratio of a switching element in the wind-generated electric power inverter  122   b ). As an example, where the wind turbine  122   a  has a rated output of 5 kWh, but the wind turbine  122   a  has a maximum output of 3 kWh as being determined by the wind air flow, and the output ratio R 22  is 50%, the wind-powered electric generator  122  delivers 1.5 kWh (electric energy output P 2  (P 22 )) at the output of the wind-generated electric power inverter  122   b  in response to an input from the control device  300  of the operational instruction value C 22  dictating that the output ratio R 22  with respect to the 3-kWh maximum output be 50%. 
     In the present embodiment, the RES  120  exploits natural energy for electric energy output. This is however for illustrative purposes only. Examples of the RES  120  include those that exploit solar heat, geothermal heat, or another form of natural energy for thermal energy output. 
     The electric storage unit  130  in the present embodiment includes a rechargeable battery  131 , an electric storage inverter  132 , and an AC circuit breaker  133 . The electric storage unit  130  is connected to the output system of the control device  300 . In the electric storage unit  130 , the rechargeable battery  131  is charged by an electric energy supply from the electric power supply line PL via a rectifier circuit (not shown) in response to an operational instruction value C 3  from the control device  300  (i.e., an operational instruction value for an output ratio R 3  with respect to a rated electric energy input). The rechargeable battery  131  also discharges DC electric energy, which is converted to AC electric energy P 3  by the electric storage inverter  132  and fed to the electric power supply line PL via the AC circuit breaker  133 , in response to the operational instruction value C 3  from the control device  300  (i.e., an operational instruction value for the output ratio R 3  with respect to a rated electric energy output). 
     The thermal accumulation unit  140 , in the present embodiment, includes a thermal accumulator  141  (e.g., a thermal water storage tank) and a thermal energy regulation unit  142  (specifically, a thermal energy regulation valve). In the thermal accumulation unit  140 , the thermal accumulator  141  stores thermal energy Q 4  (e.g., in the form of water vapor or hot water) supplied from the heat supply line TL. The thermal accumulator  141  also delivers the thermal energy Q 4  (e.g., in the form of water vapor or hot water) to the heat supply line TL via the thermal energy regulation unit  142 . 
     The thermal accumulation unit  140  is not directly controlled by the control device  300 , but indirectly controlled based on the balance of thermal energies Q 1 , Q 7 , and Q 5  of thermal energy-related pieces of equipment (specifically, in this example, the CHP  110 , the thermal load  170 , and the backup thermal energy supply unit  150 ). As an example, assuming that the thermal load  170  is the thermal energy Q 7  of 54,000 kJ, the control device  300  controls the CHP  110  so that the output of the CHP  110  is 36,000 kJ (thermal energy Q 1 ). If the backup thermal energy supply unit  150  supplies no thermal energy Q 5  (i.e., if the backup thermal energy supply unit  150  is controlled so that the backup thermal energy supply unit  150  outputs 0-kJ thermal energy Q 5 ), then the thermal accumulation unit  140  is controlled to output 18,000 kJ at an output ratio R 4  (e.g., 50% when the rated thermal energy output is 36,000 kJ). 
     The thermal accumulator  141  may include a cooling storage device (e.g., ice-making tank) capable of storing thermal energy at lower-than-normal temperature in place of, or in addition to, a heating storage device (e.g., thermal water storage tank) capable of storing thermal energy at higher-than-normal temperature. 
     The backup thermal energy supply unit  150  is a gas boiler in the present embodiment. The backup thermal energy supply unit  150  includes a fuel flow rate regulation valve  151 , a boiler  152 , and an adjusting valve  153 . The backup thermal energy supply unit  150  heats the boiler  152  using fuel supplied from the fuel supply source F via the fuel flow rate regulation valve  151 . The thermal energy Q 5  of obtained water vapor or hot water is fed to the heat supply line TL in response to an operational instruction value C 5  from the control device  300  (i.e., an operational instruction value for the output ratio R 5  with respect to the rated thermal energy output, specifically an operational instruction value C 5  for the opening degree of the adjusting valve  153  disposed on the heat supply line TL of the boiler  152 ). As an example, for a rated thermal energy output of 36,000 kJ and an output ratio R 5  of 50%, the backup thermal energy supply unit  150  generates 18,000 kJ (thermal energy Q 5 ) in response to an input from the control device  300  of the operational instruction value C 5  dictating that the output ratio R 5  with respect to the 36,000-kJ rated thermal energy output be 50%. 
     Examples of the electric load  160  include an electric lamp and an electrical appliance (e.g., electric cooling device). Examples of the thermal load  170  include an absorption cooling device, an air cooling device, and an air heating device. Conceptually, the thermal load  170  encompasses cold thermal loads at lower-than-normal temperature as well as hot thermal loads at higher-than-normal temperature. In other words, the thermal energy Q 7  in the current context refers to both hot thermal energy and cold thermal energy. 
     The electric load  160  and the thermal load  170  may be classified into categories (a) to (d) as below. 
     (a) Non-Adjustable Loads (First Loads L 1 ) that Consume Uninterruptible Energy Consumption for Energy Demand 
     First loads L 1  are, for example, electric and air-conditioning appliances installed in a test laboratory where endurance tests are conducted, medical and air-conditioning appliances installed in a medical room (operating room, intensive care unit, and the like), and power supply and air-conditioning appliances installed in a computer room. The first load L 1  is given highest priority because it needs to continuously consume electric and thermal energy. The first load L 1  is therefore not controllable by the control device  300 . 
     (b) Interruptible and Adjustable Loads (Second Loads L 2 ) 
     Second loads L 2  are, for example, electrical appliances, such as illuminating devices, and air-conditioning appliances installed in general residential areas, manufacturing industry areas, and tertiary areas. The second load L 2  is a common load capable of alternating between consumption of electric and thermal energy and discontinuation of the consumption at any given time. The second load L 2  is therefore controllable by the control device  300 . The second load L 2  is connected to the output system of the control device  300  so that the second load L 2  can change electric and thermal energy consumption thereof in response to operational instruction values C 6  (C 62 ) and C 7  (C 72 ) from the control device  300  (i.e., operational instruction values for consumption ratios R 6  (R 62 ) and R 7  (R 72 ) of the second load L 2  with respect to its rated energy consumption). 
     (c) Manageable Loads (Third Loads L 3 ) that are, After Starting to Operate, Allowed to Discontinue Their Operation if a Total Operation Time is Reached Within a Predetermined Period of Time (e.g., on the Same Day) 
     Third loads L 3  are, for example, washing machines in an industrial laundry facility, batch process machines, drilling machines, and battery chargers for electrical appliances. The third load L 3  is capable of alternating between consumption of electric and thermal energy and discontinuation of the consumption at any given time if a predetermined total operation time is reached before or at the end of a predetermined period (e.g., at the end of the day). The third load L 3  is therefore controllable by the control device  300 . The third load L 3  is connected to the output system of the control device  300  so that the third load L 3  can change electric and thermal energy consumption thereof in response to operational instruction values C 6  (C 63 ) and C 7  (C 73 ) from the control device  300  (operational instruction values for consumption ratios R 6  (R 63 ) and R 7  (R 73 ) of the third load L 3  with respect to its rated energy consumption). 
     (d) Manageable Loads (Fourth Loads L 4 ) that are not Allowed to Discontinue Their Operation Until a Total Operation Time is Reached Once They Start to Operate 
     Fourth loads L 4  are, for example, electric ovens, electric melting furnaces in foundries, and experimental devices, such as artificial climate chambers. The fourth load L 4  is capable of alternating between consumption of electric and thermal energy and discontinuation of the consumption, but not allowed to discontinue its energy consumption for a certain continuous period of time once it starts to operate. The fourth load L 4  is therefore controllable by the control device  300 . The fourth load L 4  is connected to the output system of the control device  300  so that the fourth load L 4  can change electric and thermal energy consumption thereof in response to operational instruction values C 6  (C 64 ) and C 7  (C 74 ) from the control device  300  (operational instruction values for consumption ratios R 6  (R 64 ) and R 7  (R 74 ) of the fourth load L 4  with respect to its rated energy consumption). 
     The electric power supply line PL is connected electrically to the CHP  110 , the RES  120 , the electric storage unit  130 , the electric load  160 , and the large-scale electric power network  200 . The heat supply line TL is connected thermally to the CHP  110 , the thermal accumulation unit  140 , the backup thermal energy supply unit  150 , and the thermal load  170 . If the RES  120  exploits natural energy for thermal energy output, the heat supply line TL is also connected thermally to the RES  120 . 
     Control Device 
     The control device  300  controls the whole in-plant energy network  100 . 
       FIG. 4  is a schematic block diagram illustrating the control device  300  and related apparatus for the in-plant energy network  100  shown in  FIGS. 2 and 3 . 
     As illustrated in  FIG. 4 , the control device (computer)  300  is provided with external devices (man/machine interfaces) including an input section  310 , such as a keyboard and a pointing device, and a display unit  320 , such as a display device. 
     The control device  300 , in this embodiment of the present invention, may be connected to the Internet  400 , as well as to the in-plant energy network  100 . 
     The control device  300  is provided with internal devices including a control section  330 , a memory section  340 , a first reading section  350 , and a second reading section  360 . The control section  330  is, for example, a CPU (central processing unit) and executes a computer program PR, computations, and other various processes. The memory section  340  contains a volatile memory  341 , such as a RAM (random access memory), and a non-volatile memory  342 , such as a ROM (read-only memory) and/or an electrically rewritable non-volatile ROM. 
     The control section  330  controls the display unit  320  to display various input screens so that the control section  330  can receive input of necessary information from the user operating the input section  310 . 
     The volatile memory  341  is used, for example, as working memory when needed in the computations and other various processes executed by the control section  330 . The non-volatile memory  342  includes, for example, a hard disk device and a flash memory. 
     The first reading section  350  is capable of reading a computer-readable storage medium M, such as a CD (compact disc)-ROM, containing the computer program PR. The non-volatile memory  342  pre-records software including the computer program PR which has been read by the first reading section  350 . The storage medium M may be a USB (universal serial bus) memory or an SD (secure digital) memory card. The computer program PR may be downloaded over the Internet  400 . The second reading section  360 , in the present embodiment, reads an external storage medium ME. The external storage medium ME is typically a USB memory, but by no means limited to this example. 
     Energy demand data DT 1 , unit energy price data DT 2 , and weather condition data DT 3  shown in  FIG. 4  will be described later in detail. 
     As in the conventional configuration described earlier, even if the output values of the energy generation units (in this example, the CHP  110  and the RES  120 ), the energy storage units (in this example, the electric storage unit  130  and the thermal accumulation unit  140 ), and the backup thermal energy supply unit  150  that result in an individual evaluation value (best evaluation value) for each time step are calculated, and when there additionally exist controllable energy loads (in this example, the electric load  160  and the thermal load  170 ), also even if the demands of the controllable energy loads (in this example, the electric load  160  and the thermal load  170 ) that result in an individual evaluation value (best evaluation value) for each time step are calculated, optimal operation is not generally achieved when energy management is evaluated over the entire operation period by means of each transitional evaluation value for a transition from value settings for one time step to value settings for a next, consecutive time step. 
       FIGS. 5 and 6  are tables depicting that individual evaluation values for a plurality of time steps UT( 1 ) to UT(n) into which an operation period T is divided fail to achieve optimal operation when energy management is evaluated over the entire operation period T.  FIG. 5  represents an example of individual evaluation values for individual time steps UT( 1 ) to UT(n).  FIG. 6  represents an example of transitional evaluation values for evaluation by means of a combination of value settings for each piece of equipment, such as energy generation units, energy storage units, and energy loads, over the entire operation period T. In this context, “n” is an integer greater than or equal to 2. In this example, the operation period T is 1 day, and the unitary time steps UT( 1 ) to UT(n) are each 15 minutes. Therefore, “n” is 96, and “i” is an integer from 2 to 95. In addition, in this example, the evaluation values for time steps UT( 1 ) to UT(n) are given as 7 numbers, [1] to [7]. A smaller evaluation value indicates more positive evaluation. 
       FIG. 5  shows, among individual time steps UT( 1 ) to UT(n), for example, an individual evaluation value of [1] for a value setting α 2  for time step UT(i−1) (e.g., 11 hours 45 minutes 00 seconds (“11:45:00”)) and an individual evaluation value of [6] for a value setting α 3  for next, consecutive time step UT(i) (e.g., 12:00:00). In this example, the evaluation value, when energy management is evaluated over the entire operation period T, is equal to the sum of these values (e.g., . . . +[1]+[6]+ . . . =X). 
     Meanwhile,  FIG. 6  shows transitional evaluation values (e.g., [1], [2], [3], . . . ) for a transition from value settings (e.g., β 2 , β 7 , β 12 ) for time step UT(i−1) (e.g., 11:45:00) to value settings (e.g., β 3 , β 8 , β 13 ) for next, consecutive time step UT(i) (e.g., 12:00:00) and transitional evaluation values (e.g., [6], [5], [4], [2], [3], . . . ) for a transition from value settings (e.g., β 3 , β 8 , β 13 ) for time step UT(i) (e.g., 12:00:00) to value settings (e.g., β 4 , β 9 , β 14 ) for further next, consecutive time step UT(i+1) (e.g., 12:15:00). When energy management is evaluated over the entire operation period T (in this example, 1 day) by means of a combination of these transitional evaluation values, the best evaluation value for this combination of value settings when energy management is evaluated over the entire operation period T (time steps UT( 1 ) to UT(n)) is equal to the sum (for example, . . . +[3]+[2]+ . . . =Y (Y&lt;X)) of the transitional evaluation value from time step UT(i−1) to next time step UT(i) (e.g., [3]) and the transitional evaluation value from next time step UT(i) to further next time step UT(i+1) (e.g., [2]). Throughout this specification, the transitional evaluation values are derived by means of a fitness function designed to reduce at least one of energy supply cost, CO 2  emission, and primary energy consumption in a transition from value settings for a time step to value settings for a next time step by using, as parameters, value settings for each piece of equipment, such as an energy generation unit, an energy storage unit, and a controllable energy load, as well as a backup thermal energy supply unit in this example. 
     As described above, an individual evaluation value is determined for each time step UT( 1 ) to UT(n) in the conventional configuration. The evaluation value over the entire operation period T is therefore equal to the sum of these individual evaluation values for individual time steps UT( 1 ) to UT(n) (in the example shown in  FIG. 5 , . . . +[1]+[6]+ . . . =X). This evaluation value over the entire operation period T generally and inherently represents less positive evaluation than the transitional evaluation values (in the example shown in  FIG. 6 , . . . +[3]+[2]+ . . . =Y&lt;X), which is inconvenient. 
     Therefore, the conventional configuration does not practically achieve optimal operation of each piece of equipment, such as an energy generation unit, an energy storage unit, and energy loads, throughout the entire operation period T. 
     In the present embodiment, the following optimization of evaluation of energy management in the in-plant energy network  100  is performed by the control device  300  running the computer program PR. 
     Software Configuration for Computer Program 
       FIG. 7  is a schematic illustration of a control structure of the control section  330  in the control device  300  shown in  FIG. 4 . 
     The computer program PR enables the control section  330  in the control device  300  to function as various means including input means PR 1  (an example of the input section), computing means PR 2  (an example of a computing section), and output means PR 3  (an example of an output section). In other words, the computer program PR enables the control device  300  (particularly, the control section  330 ) to perform steps including an input step corresponding to the input means PR 1 , a computing step corresponding to the computing means PR 2 , and an output step corresponding to the output means PR 3 . In other words, the control device  300  (specifically, the control section  330 ) functions as various means including the input means PR 1 , the computing means PR 2 , and the output means PR 3 . 
     Input Step 
     In the input step, predetermined input information is inputted to calculate value settings for optimizing the evaluation of energy management in the in-plant energy network  100 . The predetermined input information includes vector information, scalar information, and technical information. The vector information may change over the entire predetermined operation period T (in this example, 1 day). The scalar information does not change throughout the entire operation period T. The technical information represents characteristics of the CHP  110 , the RES  120 , the electric storage unit  130 , and the backup thermal energy supply unit  150 . 
     Specifically, the vector information includes at least one of an energy price, a time step-specific electric power price, an electric power demand, and a thermal demand. 
     The scalar information includes at least one of a set of value settings in an operation season mode (e.g., an operation mode for air cooling and air heating) and a set of value settings (in this example, the output ratios R 3  and R 4  of the electric storage unit  130  and the thermal accumulation unit  140 ) for last time step UT(n) of an immediately preceding operation period T (in this example, the preceding day). 
     The technical information includes at least one of the rated output of the CHP  110 , a conversion parameter for conversion of the electric energy output P 1  of the CHP  110  to the fuel gas consumption of the CHP  110 , and a conversion parameter for conversion of the electric energy output P 1  of the CHP  110  to the thermal energy output Q 1  of the CHP  110 . The conversion parameter for conversion of the electric energy output P 1  of the CHP  110  to the fuel gas consumption of the CHP  110  may be, for example, a coefficient (conversion coefficient) in a predetermined function (conversion formula) that yields a fuel gas consumption that corresponds to an engine load ratio. In addition, the thermal energy output Q 1  of the CHP  110  varies, for example, depending on the electric power output of the CHP  110  (there exists a correlation with the electric power output of the CHP  110 ). Once the electric power output (specifically, engine rotational speed) is determined, the thermal waste of the CHP  110  is determined automatically. Therefore, the conversion parameter for conversion of the electric energy output P 1  of the CHP  110  to the thermal energy output Q 1  of the CHP  110  may be, for example, a coefficient (conversion coefficient) in a predetermined function (conversion formula) that yields a thermal waste of the CHP  110  that corresponds to the electric power output of the CHP  110 . 
     In the input step, the control device  300  receives the energy demand data DT 1  on forecast energy demands (see  FIG. 4 ), the unit energy price data DT 2  on unit energy prices (see  FIG. 4 ), and the weather condition data DT 3  on forecast weather conditions of the geographical region covered by the in-plant energy network  100  (see  FIG. 4 ). 
     In the present embodiment, the energy demand data DT 1  is directly inputted through the input section  310  by the user and obtained by the control device  300 . The unit energy price data DT 2  and the weather condition data DT 3  are automatically obtained from a Web site on the Internet  400  by the control device  300 . 
     The memory section  340  (e.g., the non-volatile memory  342 ) pre-records the energy demand data DT 1  obtained by the input section  310 . The memory section  340  (e.g., the non-volatile memory  342 ) also pre-records the unit energy price data DT 2  and the weather condition data DT 3  both obtained from a Web site on the Internet  400 . 
     Each of the energy demand data DT 1 , the unit energy price data DT 2 , and the weather condition data DT 3  is sets of time-sequential data on the processing target to be processed, covering a predetermined operation period T (e.g., 24 hours), with each set covering a predetermined time step UT (e.g., 15 minutes, or 0.25 hours) (unit time). The energy demand data DT 1 , the unit energy price data DT 2 , and the weather condition data DT 3  therefore each consist of N sets of time-sequential data to be processed, where N=T/UT, or the operation period T divided by the unitary time step UT. For example, the energy demand data DT 1 , the unit energy price data DT 2 , and the weather condition data DT 3  may cover a next single whole day (e.g., for a time step UT of 15 minutes, there are 24 hours/0.25 hours=96 processing units), half of a next day (either from midnight to noon or from noon to midnight) (e.g., for a time step UT of 15 minutes, there are 12 hours/0.25 hours=48 processing units), a shorter period of time, or a period of time that is longer than a single day. 
     The control device  300  optimizes energy management in advance (e.g., either collectively sometime on the preceding day (December 24) so that the optimization is completed by 00:00:00, December 25 or immediately before every time step UT on the day of interest (December 25) as necessary) before a forecast period for the energy demand data DT 1  and the weather condition data DT 3  (e.g., from 00:00:00 to 23:59:59, the next day (December 25)). The control device  300  also performs energy management for the in-plant energy network  100  in accordance with the processing units (e.g., 96 processing units when the time step UT is 15 minutes) of the corresponding time steps UT in the forecast period for the energy demand data DT 1  and the weather condition data DT 3  (e.g., 00:00:00 to 23:59:59, the day of interest (December 25)). 
     The energy demand data DT 1  indicates an energy consumption for every time step UT in the forecast period including an uninterruptible energy consumption for energy demand (energy consumption of the first load L 1 ). 
     The energy demand data DT 1  indicates forecast electric power demands (forecast electric energy demands at equal time intervals) and forecast thermal energy demands (forecast thermal energy demands at equal time intervals). The energy demand data DT 1  may indicate an electric power exchange profile for the large-scale electric power network  200 . An electric power exchange profile in this context is information representing forecast electric power demands (forecast electric energy demands at equal time intervals) as agreed upon with a utility company (electric power company). If a consumer fails to follow the profile, he/she could be given a penalty. 
     In the present embodiment, the energy demand data DT 1  is directly inputted to the control device  300  by the user. Alternatively, the energy demand data DT 1  may be inputted to the control device  300  from a Web site on the Internet  400  or using the external storage medium ME (see  FIG. 4 ). If the energy demand data DT 1  is inputted from a Web site on the Internet  400 , the memory section  340  (e.g., the non-volatile memory  342 ) pre-records the energy demand data DT 1  inputted from the Web site. If the energy demand data DT 1  is inputted using the external storage medium ME, the second reading section  360  reads the external storage medium ME containing the energy demand data DT 1 , and the memory section  340  (e.g., the non-volatile memory  342 ) pre-records the energy demand data DT 1  which has been read by the second reading section  360 . 
     The unit energy price data DT 2  and the weather condition data DT 3  are obtained from a Web site on the Internet  400 . Alternatively, the unit energy price data DT 2  and the weather condition data DT 3  may be directly inputted to the control device  300  by the user and/or using the external storage medium ME. If the unit energy price data DT 2  and/or the weather condition data DT 3  are/is directly inputted by the user, the memory section  340  (e.g., the non-volatile memory  342 ) pre-records the unit energy price data DT 2  and/or the weather condition data DT 3  which have/has been directly inputted by the user. If the unit energy price data DT 2  and/or the weather condition data DT 3  are/is inputted using the external storage medium ME, the second reading section  360  reads the external storage medium ME containing the unit energy price data DT 2  and/or the weather condition data DT 3 , and the memory section  340  (e.g., the non-volatile memory  342 ) pre-records the unit energy price data DT 2  and/or the weather condition data DT 3  which have/has been read by the second reading section  360 . 
     Typical examples of the unit energy price data DT 2  include a unit electric energy price per kilowatt-hour as an electric utility fee for the large-scale electric power network  200  and a unit thermal energy price per cubic meter of gas as a gas fee for the CHP  110  and the backup thermal energy supply unit  150 . In the present embodiment, the CHP  110  and the backup thermal energy supply unit  150  use gas as their fuel and may use solid fuel. When this is the case, the unit energy price data DT 2  may be, for example, a unit thermal energy price per unit weight of the solid fuel. 
     The weather condition data DT 3  may be, as in the present embodiment, the angle of the sun and cloudiness if the RES  120  is the photovoltaic power generator  121  and the wind direction and speed if the RES  120  is the wind-powered electric generator  122 . The weather condition data DT 3  is weather condition data for the geographical region covered by the in-plant energy network  100 , specifically weather condition data for the geographical region where the RES  120  (in this example, the photovoltaic power generator  121  and the wind-powered electric generator  122 ) is installed. 
     Computing Step 
     Based on the input information fed in the input step, the computing step computes the output values of the CHP  110 , the RES  120 , the electric storage unit  130 , the thermal accumulation unit  140 , and the backup thermal energy supply unit  150  (in this example, the output ratios R 1 , R 2 , R 3 , R 4 , and R 5 ), and when there additionally exist a controllable electric load  160  and a controllable thermal load  170 , also computes the demands of the controllable electric load  160  and thermal load  170  (in this example, the consumption ratios R 6  and R 7 ), as value settings by means of a predetermined optimization algorithm for each unitary time step UT( 1 ) to UT(n) into which the operation period T is divided and for each one of predetermined levels of the electric storage unit  130  and the thermal accumulation unit  140  (in this example, 16 combinations of four levels, 20%, 50%, 70%, and 100%, of the electric storage unit  130  and four levels, 20%, 50%, 70%, and 100%, of the thermal accumulation unit  140 ). 
     The optimization algorithm may be any publicly known, conventional technique. Typical examples include sequential quadratic programming algorithms using information on the slope of an objective function and genetic algorithms that emulate biological evolution. The optimization algorithm in this example is a genetic algorithm. 
     The computing step repeatedly generates two or more energy balance candidates for each level (in this example, four levels: 20%, 50%, 70%, and 100%) of the electric storage unit  130  and each level (in this example, four levels: 20%, 50%, 70%, and 100%) of the thermal accumulation unit  140  for each time step UT (e.g., every 15 minutes) for the output (electric energy Pp) of the large-scale electric power network  200  and the output (thermal energy Q 5 ) of the backup thermal energy supply unit  150  by combining varied values of the output ratio R 1  of the CHP  110 , the output ratio R 2  of the RES  120 , the output ratio R 3  of the electric storage unit  130 , the output ratio R 4  of the thermal accumulation unit  140 , the output ratio R 5  of the backup thermal energy supply unit  150 , the consumption ratio R 6  of the electric load  160 , and the consumption ratio R 7  of the thermal load  170  until the convergence of the individual evaluation values of the two or more candidates reaches predetermined convergence criteria. 
     More particularly, the entire range of the output ratio R 1  (from zero output (0%) to rated output (100%)) of the CHP  110  is evenly divided so that the range can be represented by a number of values (levels) that is equal to a predetermined division count DI (e.g., 2 8 =256 values or levels). Specifically, the output ratio R 1  is 0% for “00000000” (=0 in decimal form), 1×100%/255=0.39% for “00000001” (=1 in decimal form), 2×100%/255=0.78% for “00000010” (=2 in decimal form), . . . , 254×100%/255=99.6% for “11111110” (=254 in decimal form), and 255×100%/255=100% for “11111111” (=255 in decimal form). Then, in the computing step, the output ratio R 1  of the CHP  110  is converted to a value selected from the values obtained by the foregoing division of the range (specifically, those values that divide the range, 0% (00000000) to 100% (11111111), into 1/255 =0.39% intervals). 
     The same procedures may be applied to the entire ranges of the output ratio R 2  of the RES  120  and the output ratio R 5  of the backup thermal energy supply unit  150  so that the ranges can be evenly divided and represented by a number of values (levels) that is equal to a predetermined division count DI (e.g., 2 8 =256 values or levels). 
     Due to the performance of the CHP  110 , the output ratio R 2  of the CHP  110  may be set to 0%, or “00000000,” if it is less than or equal to a predetermined ratio (e.g., 10%). 
     The entire range of the consumption ratio R 6  (from zero consumption (0%) to rated energy consumption (100%)) of the electric load  160  is evenly divided so that the range can be represented by a number of values (levels) that is equal to a predetermined division count DI (e.g., 2 8 =256 values or levels). Specifically, the consumption ratio R 6  is 0% for “00000000” (=0 in decimal form), 1×100%/255=0.39% for “00000001” (=1 in decimal form), 2×100%/255=0.78% for “00000010” (=2 in decimal form), . . . , 254×100%/255=99.6% for “11111110” (=254 in decimal form), and 255×100%/255=100% for “11111111” (=255 in decimal form). Then, in the computing step, the consumption ratio R 6  of the electric load  160  is converted to a value selected from the values obtained by the foregoing division of the range (specifically, those values that divide the range, 0% (00000000) to 100% (11111111), into 1/255=0.39% intervals). 
     The same procedures may be applied to the entire range of the consumption ratio R 7  of the thermal load  170  so that the range can be evenly divided and represented by a number of values (levels) that is equal to a predetermined division count DI (e.g., 2 8 =256 values or levels). 
     The same procedures may be applied to the entire ranges of the output ratio R 3  of the electric storage unit  130  and the resultant output ratio R 4  of the thermal accumulation unit  140  so that the ranges (from zero output (0%) to rated output (100%)) can be evenly divided by a number of values (levels) that is equal to a predetermined division count DI (e.g., 2 8 =256 values or levels). However, taking into consideration the processing times of the optimization algorithm and the shortest-path problem solving algorithm, the output range of the electric storage unit  130  and the resultant output range of the thermal accumulation unit  140  are divided into approximately two to ten levels (in this example, four levels: 20%, 50%, 70%, and 100%). Specifically, the output ratios R 3  and R 4  are 51×100%/255=20% for “00110011” (=51 in decimal form), 128×100%/255=50.1% for “01000000” (=128 in decimal form), 179×100%/255=70.1% for “10110011” (=179 in decimal form), and 255×100%/255=100% for “11111111” (=255 in decimal form). Then, in the computing step, the output ratio R 3  of the electric storage unit  130  and the resultant output ratio R 4  of the thermal accumulation unit  140  are converted to values selected from the values obtained by the foregoing division of the range (specifically, those values that divide the range, 20% (00110011) to 100% (11111111), into 30.1%, 20%, and 29.9% intervals) so that the output range of the electric storage unit  130  and the resultant output range of the thermal accumulation unit  140  are represented by a number of values (levels) that is equal to a predetermined division count DJ. 
     The division count DI may assume different values for the output range of the CHP  110 , the output range of the RES  120 , the output range of the backup thermal energy supply unit  150 , the consumption range of the electric load  160 , and the consumption range of the thermal load  170 . The division count DJ may assume different values for the output range of the electric storage unit  130  and the output range of the thermal accumulation unit  140 . 
     The computing step includes a first calculation process, a second calculation process, an evaluation process, a first selection process, a weighting process, a shortest-path problem solving process, and a second selection process. 
     First Calculation Process 
     In the first calculation process, the outputs of the CHP  110  (electric energy P 1  and thermal energy Q 1 ) are calculated by multiplying the rated output of the CHP  110  and the output ratio R 1  of the CHP  110  together. 
     Specifically, in the first calculation process, the entire range of the output ratio R 11  of the electric energy P 1  of the CHP  110  is evenly divided so that the range can be represented by 256 values. For example, when the rated electric energy output of the CHP  110  is 25.1 kWh, and the output ratio R 11  of the CHP  110  is represented by “00111101” (=61 in decimal form), the output ratio R 11  is 23.9%, and the electric energy P 1  of the CHP  110  is 25.1 kWh×23.9%=6 kWh. The thermal energy Q 1  of the CHP  110  can be similarly determined by evenly dividing the entire range of the thermal energy Q 1  of the CHP  110  so that the output ratio R 12  can be represented by 256 values. 
     Also in the first calculation process, the output of the RES  120  (in this example, the electric energy P 2 ) is calculated by multiplying the maximum output of the RES  120  and the output ratio R 2  of the RES  120 , both based on weather conditions, together. 
     Specifically, in the first calculation process, if the RES  120  is the photovoltaic power generator  121 , the entire range of the output ratio R 21  of the photovoltaic power generator  121  is evenly divided so that the range can be represented by 256 values. The maximum output of the photovoltaic power generator  121  is then calculated using a photovoltaic cell conversion formula or table. The conversion formula and table are designed so that by using either of them, one can determine the maximum output of the photovoltaic power generator  121  from the intensity of sunlight given in the weather condition data DT 3  in the form of the total solar radiation and ambient temperature. For example, when the maximum output of the photovoltaic power generator  121  is determined to be 2.51 kWh, and the output ratio R 21  of the photovoltaic power generator  121  is represented by “00111101” (=61 in decimal form), the output ratio R 21  is 23.9%, and the output (electric energy P 21 ) of the photovoltaic power generator  121  is 2.51 kWh×23.9%=0.6 kWh. Likewise, if the RES  120  is the wind-powered electric generator  122 , the output of the RES  120  is similarly determined using a wind-powered generator conversion formula or table that are designed so that by using either of them, one can determine the maximum output of the wind-powered electric generator  122  from the wind air flow given in the weather condition data DT 3  in the form of wind direction and speed. If the RES  120  exploits natural energy for thermal energy output, its output (thermal energy) is similarly determined. The conversion formulas and tables may be predetermined (and stored in the memory section  340 ) based on, for example, the specifications of the RES  120 . 
     Also in the first calculation process, the output of the backup thermal energy supply unit  150  (thermal energy Q 5 ) is calculated by multiplying the rated output of the backup thermal energy supply unit  150  and the output ratio R 5  of the backup thermal energy supply unit  150  together. 
     Specifically, in the first calculation process, the entire range of the output ratio R 5  of the backup thermal energy supply unit  150  is evenly divided so that the range can be represented by 256 values. For example, when the rated thermal energy output of the backup thermal energy supply unit  150  is 86,000 kJ, and the output ratio R 5  of the backup thermal energy supply unit  150  is represented by “00111101” (=61 in decimal form), the output ratio R 5  is 23.9%, and the thermal energy output Q 5  of the backup thermal energy supply unit  150  is 86,000 kJ×23.9%=20,554 kJ. 
     Also in the first calculation process, the output of the electric storage unit  130  (the electric energy P 3 ) is calculated by multiplying the maximum output of the electric storage unit  130  and the output ratio R 3  of the electric storage unit  130 , based on the charge conditions, together. 
     Specifically, in the first calculation process, the entire range of the output ratio R 3  of the electric storage unit  130  is evenly divided so that the range can be represented by 256 values. The maximum output of the electric storage unit  130  is then calculated based on the state of charge of the electric storage unit  130  and on the maximum energy content of the electric storage unit  130  so that by using either of them, one can determine the maximum output of the electric storage unit  130 . For example, when the state of charge of the electric storage unit  130  is determined to be 50%, and the maximum energy content of the electric storage unit  130  is 5 kWh, the output ratio R 3  of the electric storage unit  130  is represented by “00110011” (=51 in decimal form), the output ratio R 3  is 20%, and the output (electric energy P 3 ) of the electric storage unit  130  is 5 kWh×50%×20%=0.5 kWh. 
     Further in the first calculation process, the energy consumption Q 6  (electric energy consumption) of the electric load  160  is calculated by multiplying the rated power consumption of the electric load  160  and the consumption ratio R 6  of the electric load  160  together, and the energy consumption Q 7  (thermal energy consumption) of the thermal load  170  is calculated by multiplying the rated power consumption of the thermal load  170  and the consumption ratio R 7  of the thermal load  170  together. In this context, the energy consumption of the electric load  160  and that of the thermal load  170  are respectively parts other than the energy consumption of the first load L 1 . 
     Specifically, in the first calculation process, the entire range of the consumption ratio R 6  of the electric load  160  is evenly divided so that the range can be represented by 256 values. For example, when the rated power consumption of the electric load  160  is 25.1 kWh, and the consumption ratio R 6  of the electric load  160  is represented by “00111101” (=61 in decimal form), the consumption ratio R 6  is 23.9%, and the energy consumption Q 6  of the electric load  160  is 25.1 kWh×23.9%=6 kWh. The energy consumption Q 7  of the thermal load  170  is similarly determined by evenly dividing the entire range of the consumption ratio R 7  of the thermal load  170  so that the range can be represented by 256 values. 
     The output (thermal energy Q 4 ) of the thermal accumulation unit  140  results from the supply/demand balance between the thermal energy Q 7  consumed by the thermal load  170  and a sum (thermal energy Q 1 +Q 5 ) of the thermal energy Q 1  supplied from the CHP  110  and the thermal energy Q 5  supplied from the backup thermal energy supply unit  150 . Therefore, in the first calculation process, the output ratio R 4  of the thermal accumulation unit  140  can be obtained by subtracting this sum (thermal energy Q 1 +Q 5 ) from the thermal energy consumption Q 7  of the thermal load  170  to obtain a differential thermal energy and dividing the differential thermal energy by the thermal charge level of the thermal accumulation unit  140  (=Maximum Thermal Charge Level of Thermal Accumulation Unit  140 ×Thermal Charge Ratio of Thermal Accumulation Unit  140 ). For example, assuming that the rated thermal energy output of the thermal accumulation unit  140  is 18,000 kJ, the thermal charge ratio of the thermal accumulation unit  140  is determined to be 50%. When the output ratio R 4  of the thermal accumulation unit  140  is represented by “00110011” (=51 in decimal form), the output ratio R 4  is 20%, and the output (thermal energy Q 4 ) of the thermal accumulation unit  140  is 18,000 kJ×50%×20%=1,800 kJ. 
     Second Calculation Process 
     In the second calculation process, a difference between a total electric energy (total electric energy consumption) and another total electric energy (total electric energy supply) is calculated as an output (electric energy Pp) of the large-scale electric power network  200 . The total electric energy consumption is a sum of the uninterruptible electric energy consumption manually fed in the input step and given in the energy demand data DT 1  and the electric energy consumption Q 6  of the electric load  160  obtained in the first calculation process. The total electric energy supply is a sum of the output (electric energy P 1 ) of the CHP  110  obtained in the first calculation process, the output (electric energy P 2 ) of the RES  120  obtained in the first calculation process, and the output (electric energy P 3 ) of the electric storage unit  130  obtained in the first calculation process. Also in the second calculation process, a difference between a total thermal energy (total thermal energy consumption) and another total thermal energy (total thermal energy supply) is calculated as an output (thermal energy Q 5 ) of the backup thermal energy supply unit  150 . The total thermal energy consumption is a sum of the uninterruptible thermal energy consumption manually fed in the input step and given in the energy demand data DT 1  and the thermal energy consumption Q 7  of the thermal load  170  obtained in the first calculation process. The total thermal energy supply is a sum of the output (thermal energy Q 1 ) of the CHP  110  and the output (thermal energy Q 4 ) of the thermal accumulation unit  140  obtained in the first calculation process. 
     The uninterruptible electric energy consumption given in the energy demand data DT 1  is the energy consumed by the first load L 1 , a part of the electric load  160 . The uninterruptible thermal energy consumption given in the energy demand data DT 1  is energy consumed by the first load L 1  which is part of the thermal load  170 . 
     The large-scale electric power network  200  covers an electric energy shortage in view of a total electric energy (total electric energy consumption; the sum of the uninterruptible electric energy consumption of the first load L 1  given in the energy demand data DT 1  and the electric energy consumptions of the second load L 2 , the third load L 3 , and the fourth load L 4 , each being a part of the electric load  160 ) and another total electric energy (total electric energy supply; the sum of the electric energy supply P 1  from the CHP  110 , the electric energy supply P 2  from the RES  120 , and the electric energy supply P 3  from the electric storage unit  130 ). The backup thermal energy supply unit  150  covers a thermal energy shortage in view of a total thermal energy (total thermal energy consumption; the sum of the uninterruptible thermal energy consumption of the first load L 1  given in the energy demand data DT 1  and the thermal energy consumptions of the second load L 2 , the third load L 3 , and the fourth load L 4  each of which is part of the thermal load  170 ) and another total thermal energy (total thermal energy supply; the sum of the thermal energy supply Q 1  from the CHP  110  and the thermal energy supply Q 4  from the thermal accumulation unit  140 ). 
     If the RES  120  exploits natural energy for thermal energy output, the total thermal energy (total thermal energy supply) is inclusive of the thermal energy supply from the RES  120 . 
     Evaluation Process 
     In the evaluation process, two or more energy balance candidates for each time step UT are evaluated by an optimization algorithm (in this example, a genetic algorithm) in accordance with evaluation criteria including an electric energy supply cost based on a unit electric energy price, a thermal energy supply cost based on a unit thermal energy price, a CO 2  emission based on the electric energy output P 1  of the CHP  110  and the electric energy output Pp of the large-scale electric power network  200 , a CO 2  emission based on the thermal energy output Q 1  of the CHP  110  and the thermal energy output Q 5  of the backup thermal energy supply unit  150 , a primary energy consumption based on the electric energy output P 1  of the CHP  110  and the electric energy output Pp of the large-scale electric power network  200 , and a primary energy consumption based on the thermal energy output Q 1  of the CHP  110  and the thermal energy output Q 5  of the backup thermal energy supply unit  150 . In this context, primary energy is the energy directly obtainable from natural resources, like fossil fuels (e.g., coal, crude oil, and natural gas), hydraulic power, solar and geothermal power, and uranium, and expressed in kWh. 
     More particularly, the energy supply cost is obtained by adding a first electric energy supply cost, a second electric energy supply cost, a first thermal energy supply cost, and a second thermal energy supply cost together. The first electric energy supply cost is calculated by multiplying the electric energy output P 1  of the CHP  110  obtained in the first calculation process and the unit electric energy price obtained in the input step together. The first thermal energy supply cost is calculated by multiplying the thermal energy output Q 1  of the CHP  110  obtained in the first calculation process and the unit thermal energy price obtained in the input step together. The second electric energy supply cost is calculated by multiplying the electric energy output Pp of the large-scale electric power network  200  obtained in the second calculation process and the unit electric energy price obtained in the input step together. The second thermal energy supply cost is calculated by multiplying the thermal energy output Q 5  of the backup thermal energy supply unit  150  obtained in the second calculation process and the unit thermal energy price obtained in the input step together. 
     The CO 2  emission is obtained by adding first, second and third CO 2  emissions together. The first CO 2  emission is obtained by converting the electric energy output P 1  of the CHP  110  obtained in the first calculation process using a CO 2  emission conversion formula or table. The second CO 2  emission is obtained by converting the electric energy output Pp of the large-scale electric power network  200  obtained in the second calculation process using a CO 2  emission conversion formula or table. The third CO 2  emission is obtained by converting the thermal energy output Q 5  of the backup thermal energy supply unit  150  obtained in the second calculation process using a CO 2  emission conversion formula or table. The CO 2  emission conversion formulas and tables are pre-recorded in the memory section  340 . The conversion of the electric energy output P 1  of the CHP  110  to a CO 2  emission, the conversion of the electric energy output Pp of the large-scale electric power network  200  to a CO 2  emission, the conversion of the thermal energy output Q 1  of the CHP  110  to a CO 2  emission, and the conversion of the thermal energy output Q 5  of the backup thermal energy supply unit  150  to a CO 2  emission can be carried out by a publicly known, conventional technique; no detailed description is given here. 
     The primary energy consumption is obtained by adding first, second and third primary energy consumptions together. The first primary energy consumption is obtained by converting the electric energy output P 1  of the CHP  110  obtained in the first calculation process using a primary energy consumption conversion formula or table. The second primary energy consumption is obtained by converting the electric energy output Pp of the large-scale electric power network  200  obtained in the second calculation process using a primary energy consumption conversion formula or table. The third primary energy consumption is obtained by converting the thermal energy output Q 5  of the backup thermal energy supply unit  150  obtained in the second calculation process using a primary energy consumption conversion formula or table. The primary energy consumption conversion formulas and tables are pre-recorded in the memory section  340 . The conversion of the electric energy output P 1  of the CHP  110  to a primary energy consumption, the conversion of the electric energy output Pp of the large-scale electric power network  200  to a primary energy consumption, the conversion of the thermal energy output Q 1  of the CHP  110  to a primary energy consumption, and the conversion of the thermal energy output Q 5  of the backup thermal energy supply unit  150  to a primary energy consumption can be carried out by a publicly known, conventional technique; no detailed description is given here. 
     In the evaluation process, two or more energy balance candidates for each time step UT are evaluated based on the energy supply costs, CO 2  emissions, and primary energy consumptions obtained in the above manner. 
     More particularly, in the evaluation process, an individual evaluation value is determined from an energy supply cost, a CO 2  emission, and a primary energy consumption by means of an objective function. A candidate will be evaluated progressively reliably with a greater or smaller individual evaluation value. In the present embodiment, a smaller individual evaluation value indicates that the candidate is more reliably evaluated. The objective function is pre-recorded in the memory section  340 . The objective function includes as its variables an energy supply cost, a CO 2  emission, and a primary energy consumption. For example, in the evaluation process, the individual evaluation values of two or more energy balance candidates for each time step UT may be determined by plugging in their energy supply costs, CO 2  emissions, and primary energy consumptions into a predetermined evaluation formula. Alternatively, the individual evaluation values may be determined by ranking the energy supply costs, CO 2  emissions, and primary energy consumptions and summing up the ranks of the energy supply costs, the ranks of the CO 2  emissions, and the ranks of the primary energy consumptions. The evaluation process is however by no means limited to these examples. 
     When the evaluation process uses an electric power exchange profile as another evaluation criterion, progressively positive evaluation is given to a better matching between the total electric energy output of the large-scale electric power network  200  and the electric power demand in the electric power exchange profile. The power exchange profile requested by the large-scale electric power network can be satisfied by incorporating the electric power exchange profile into the evaluation process. 
     First Selection Process 
     In the first selection process, a most positively evaluated candidate is selected from the two or more energy balance candidates for each time step UT. The most positively evaluated candidate is one of these candidates that yields the most positive evaluation when the convergence of the individual evaluation values of the two or more candidates has reached predetermined convergence criteria. In the first selection process, the convergence of the individual evaluation values of the two or more candidates may be taken as having reached convergence criteria when the variance statistically calculated from the individual evaluation values of the two or more candidates comes to converge within convergence criteria. In the first selection process, a most positively evaluated candidate may be selected that yields the most positive evaluation when the evaluation is repeated a predetermined number of times (repetition count) even if the convergence of the individual evaluation values of the two or more candidates has not reached the convergence criteria. When this is the case, the most positively evaluated candidate that yields the most positive evaluation as a result of the combination of the energy supply cost, CO 2  emission, and primary energy consumption yielding the smallest or largest (in this example, the smallest) individual evaluation value (e.g., the evaluation value obtained from an evaluation formula or the evaluation value obtained from ranking) is selected from the two or more candidates in the first selection process. 
     Weighting Process 
     Related to the table in  FIG. 6  of transitional evaluation values for evaluation by means of a combination of a plurality of value settings β 1  to β 16  in a transition from one time step UT(i−1) to next time step UT(i) over the entire operation period T,  FIGS. 8 and 9  show tables of the output ratios R 1 , R 2 , and R 5  of the CHP  110 , the RES  120 , and the backup thermal energy supply unit (BACKUP)  150  and the consumption ratios R 6  and R 7  of the electric load  160  and the thermal load  170  along with the output ratios R 3  and R 4  of the electric storage unit  130  and the thermal accumulation unit  140 .  FIG. 8  represents an example for time step UT(i−1), whereas  FIG. 9  represents an example for next time step UT(i). 
     In the present embodiment, based on one of the transitional evaluation values obtained by means of a fitness function, the computing step gives a weight to each edge (in this example, at maximum, a total of 16×16×95=24320 edges: β 1 →β 1 , β 1 →β 2 , . . . , β 16 →β 15 , and β 16 →β 16 ) from a plurality of levels (in this example, 16 combinations of four levels, 20%, 50%, 70%, and 100%, of the electric storage unit  130  and four levels, 20%, 50%, 70%, and 100%, of the thermal accumulation unit  140 ) of the energy storage units (in this example, the electric storage unit  130  and the thermal accumulation unit  140 ) for one of time steps UT( 1 ) to UT(n) (specifically, time step UT(i−1)) to a plurality of levels (in this example, 16 combinations of four levels, 20%, 50%, 70%, and 100%, of the electric storage unit  130  and four levels, 20%, 50%, 70%, and 100%, of the thermal accumulation unit  140 ) of the energy storage units (in this example, the electric storage unit  130  and the thermal accumulation unit  140 ) for next time step UT(i) (see  FIG. 6 ). 
     The transitional evaluation values are derived from evaluation conditions including at least one of the energy supply cost, the CO 2  emission, and the primary energy consumption. 
     More particularly, the transitional evaluation values are derived by means of a fitness function designed to reduce energy supply cost, CO 2  emission, and primary energy consumption in a transition (in this example, β 1 →β 1 , β 1 →β 2 , . . . , β 16 →β 15 , and β 16 →β 16 ) from the value settings (in this example, β 1  to β 16 ) for one time step UT(i−1) to the value settings for next time step UT(i) by using, as parameters, the value settings for the CHP  110 , the RES  120 , the electric storage unit  130 , the thermal accumulation unit  140 , the electric load  160 , the thermal load  170 , and the backup thermal energy supply unit (BACKUP)  150 . The fitness function, similarly to the evaluation process, can be determined in advance by including in evaluation criteria an electric energy supply cost based on a unit electric energy price, a thermal energy supply cost based on a unit thermal energy price, a CO 2  emission based on the electric energy output P 1  of the CHP  110  and the electric energy output Pp of the large-scale electric power network  200 , a CO 2  emission based on the thermal energy output Q 1  of the CHP  110  and the thermal energy output Q 5  of the backup thermal energy supply unit  150 , a primary energy consumption based on the electric energy output P 1  of the CHP  110  and the electric energy output Pp of the large-scale electric power network  200 , and a primary energy consumption based on the thermal energy output Q 1  of the CHP  110  and the thermal energy output Q 5  of the backup thermal energy supply unit  150 . The fitness function is pre-recorded in the memory section  340 . For example, letting FF represent the fitness function, fitness function FF=γ 1 ×|R 1 (i)−R 1 (i−1)|+γ 2 ×|R 2 (i)−R 2 (i−1)|+γ 3 ×|R 3 (i)−R 3 (i−1)|+γ 4 ×|R 4 (i)−R 4 (i−1)|+γ 5 ×|R 5 (i)−R 5 (i−1)|+γ 6 ×|R 6 (i)−R 6 (i−1)|+γ 7 ×|R 7 (i)−R 7 (i−1)|, where γ 1  to γ 7  are respective evaluation coefficients for the CHP  110 , the RES  120 , the electric storage unit  130 , the thermal accumulation unit  140 , the backup thermal energy supply unit  150 , the electric load  160 , and the thermal load  170  with the evaluation criteria above taken into consideration; R 1 (i) to R 5 (i), R 6 (i), and R 7 (i) are the output ratios R 1  to R 5  and the consumption ratios R 6  and R 7  respectively for time step UT(i); and R 1 (i−1) to R 5 (i−1), R 6 (i−1), and R 7 (i−1) are the output ratios R 1  to R 5  and the consumption ratios R 6  and R 7  respectively for time step UT(i−1). 
     Some of the combinations (in this example, 256 combinations: β 1 →β 1 , β 1 →β 2 , . . . , β 16 →β 15 , and β 16 →β 16 ) of value settings (in this example, β 1  to β 16 ) in the transition from one time step UT(i−1) to next time step UT(i) do not allow for the transition from one time step UT(i−1) to next time step UT(i) due to, for example, technical constraints. As an example, the electric storage unit  130  only allows a limited temperature rise from one time step UT(i−1) to next time step UT(i) (in this example, 15 minutes) and may not be able to transition from the output ratio, 20%, for one time step UT(i−1) to the output ratio, 100%, for next time step UT(i). When this is actually the case, the transitional evaluation value is set to an impossible value (a value that is none of [1] to [7] in this example (e.g., [999])). 
     Shortest-Path Problem Solving Process 
     In addition, out of the combinations of value settings (in this example, the output ratios R 1 , R 2 , R 3 , R 4 , and R 5  of the CHP  110 , the RES  120 , the electric storage unit  130 , the thermal accumulation unit  140 , and the backup thermal energy supply unit  150  and the consumption ratios R 6  and R 7  of the electric load  160  and the thermal load  170 ) resulting in the transitional evaluation values for time steps UT( 1 ) to UT(n), the computing step determines, by a graph-theoretic shortest-path problem solving algorithm, a combination of value settings (in the example shown in  FIG. 6 , the value settings β 2  for one time step UT(i−1), the value settings β 13  for next time step UT(i), and the value settings β 9  for further next time step UT(i+1)) for which the total sum of the transitional evaluation values for first time step UT( 1 ) to last time step UT(n) is most positively evaluated (in the example shown in  FIG. 6 , . . . +[3]+[2]+ . . . ) (see  FIG. 6 ). 
     The graph-theoretic shortest-path problem solving algorithm may be a publicly known, conventional technique. Typical examples include the Dijkstra algorithm, which addresses a single-starting-point shortest-path problem with no negative cycle (i.e., a cycle whose edges sum to a negative value), and the Bellman-Ford algorithm, which addresses a single-starting-point shortest-path problem with a negative cycle. The shortest-path problem solving algorithm in this example is a Dijkstra algorithm. The Dijkstra algorithm is a publicly known, conventional technique and therefore is not detailed here. 
     Second Selection Process 
     Then, in the computing step, the combination of value settings (in the example shown in  FIG. 6 , the value settings β 2  for one time step UT(i−1), the value settings β 13  for next time step UT(i), and the value settings β 9  for further next time step UT(i+1)) determined by the shortest-path problem solving algorithm is selected for each time step UT( 1 ) to UT(n) as the operational instruction values C 1  (C 11  and C 12 ), C 2  (C 21  and C 22 ), C 5 , C 3 , C 6  (C 62  to C 64 ), and C 7  (C 72  to C 74 ). 
     Output Step 
     In the output step, the value settings (in this example, output ratios R 1  (R 11  and R 12 ), R 2  (R 21  and R 22 ), R 5 , and R 3  and the consumption ratios R 6  (R 62  to R 64 ) and R 7  (R 72  to R 74 )) are transmitted for each time step UT( 1 ) to UT(n) to the CHP  110 , the RES  120 , the electric storage unit  130 , and the backup thermal energy supply unit  150 , and when there additionally exist a controllable electric load  160  and a controllable thermal load  170 , also to the controllable electric load  160  and thermal load  170 , as the operational instruction values C 1  (C 11  and C 12 ), C 2  (C 21  and C 22 ), C 5 , C 3 , C 6  (C 62  to C 64 ), and C 7  (C 72  to C 74 ). 
     Then, in the output step, the operational instruction values C 1  (C 11  and C 12 ), C 2  (C 21  and C 22 ), C 5 , C 3 , C 6  (C 62  to C 64 ), and C 7  (C 72  to C 74 ) obtained in the second selection process for each time step UT are transmitted (outputted) respectively to the CHP  110 , the RES  120 , the backup thermal energy supply unit  150 , the electric storage unit  130 , the electric load  160 , and the thermal load  170  as instructions to the CHP  110 , the RES  120 , the backup thermal energy supply unit  150 , the electric storage unit  130 , the electric load  160 , and the thermal load  170 . 
     More particularly, in the output step, the operational instruction values C 1  (C 11  and C 12 ), C 2  (C 21  and C 22 ), C 5 , C 3 , C 6  (C 62  to C 64 ), and C 7  (C 72  to C 74 ) corresponding to the output ratios R 1  (R 11  and R 12 ) of the CHP  110 , the output ratios R 2  (R 21  and R 22 ) of the RES  120 , the output ratio R 5  of the backup thermal energy supply unit  150 , the output ratio R 3  of the electric storage unit  130 , the consumption ratios R 6  (R 62  to R 64 ) of the electric load  160 , and the consumption ratios R 7  (R 72  to R 74 ) of the thermal load  170 , all obtained in the second selection process, are transmitted respectively to the CHP  110 , the RES  120 , the backup thermal energy supply unit  150 , the electric storage unit  130 , the electric load  160 , and the thermal load  170 . 
     Specifically, in the output step, as the operational instruction values obtained in the second selection process as a result of the evaluation over the entire operation period T (time steps UT( 1 ) to UT(n)), the operational instruction values C 11 ( 1 ) to C 11 (N) corresponding respectively to the output ratios R 11 ( 1 ) to R 11 (N) of the electric energy P 1  of the CHP  110  are transmitted to the CHP  110 , the operational instruction values C 12 ( 1 ) to C 12 (N) corresponding respectively to the output ratios R 12 ( 1 ) to R 12 (N) of the thermal energy Q 1  of the CHP  110  are transmitted to the CHP  110 , the operational instruction values C 21 ( 1 ) to C 21 (N) corresponding respectively to the output ratios R 21 ( 1 ) to R 21 (N) of the photovoltaic power generator  121  (RES  120 ) are transmitted to the photovoltaic power generator  121 , the operational instruction values C 22 ( 1 ) to C 22 (N) corresponding respectively to the output ratios R 22 ( 1 ) to R 22 (N) of the wind-powered electric generator  122  (RES  120 ) are transmitted to the wind-powered electric generator  122 , the operational instruction values C 5 ( 1 ) to C 5 (N) corresponding respectively to the output ratios R 5 ( 1 ) to R 5 (N) of the backup thermal energy supply unit  150  are transmitted to the backup thermal energy supply unit  150 , and the operational instruction values C 3 ( 1 ) to C 3 (N) corresponding respectively to the output ratios R 3 ( 1 ) to R 3 (N) of the electric storage unit  130  are transmitted to the electric storage unit  130 . 
     N is an integer greater than or equal to 2 and, as mentioned earlier, indicates the number of processing units (e.g., 96 processing units) that is obtained by dividing the operation period to be processed (e.g., 1 day=24 hours) by the time step UT (e.g., 15 minutes=0.25 hours). 
     In the output step, as the operational instruction values obtained in the second selection process as a result of the evaluation over the entire operation period T (time steps UT( 1 ) to UT(n)), the operational instruction values C 62 ( 1 ) to C 62 (N) corresponding respectively to the consumption ratios R 62 ( 1 ) to R 62 (N) of the second load L 2 , a part of the electric load  160 , are transmitted to the second load L 2  when the second load L 2  consumes electric energy, the operational instruction values C 63 ( 1 ) to C 63 (N) corresponding respectively to the consumption ratios R 63 ( 1 ) to R 63 (N) of the third load L 3 , a part of the electric load  160 , are transmitted to the third load L 3  when the third load L 3  consumes electric energy, and the operational instruction values C 64 ( 1 ) to C 64 (N) corresponding respectively to the consumption ratios R 64 ( 1 ) to R 64 (N) of the fourth load L 4 , a part of the electric load  160 , are transmitted to the fourth load L 4  when the fourth load L 4  consumes electric energy. 
     Also in the output step, as the operational instruction values obtained in the second selection process as a result of the evaluation over the entire operation period T (time steps UT( 1 ) to UT(n)), the operational instruction values C 72 ( 1 ) to C 72 (N) corresponding respectively to the consumption ratios R 72 ( 1 ) to R 72 (N) of the second load L 2 , a part of the thermal load  170 , are transmitted to the second load L 2  when the second load L 2  consumes thermal energy, the operational instruction values C 73 ( 1 ) to C 73 (N) corresponding respectively to the consumption ratios R 73 ( 1 ) to R 73 (N) of the third load L 3 , a part of the thermal load  170 , are transmitted to the third load L 3  when the third load L 3  consumes thermal energy, and the operational instruction values C 74 ( 1 ) to C 74 (N) corresponding respectively to the consumption ratios R 74 ( 1 ) to R 74 (N) of the fourth load L 4 , a part of the thermal load  170 , are transmitted to the fourth load L 4  when the fourth load L 4  consumes thermal energy. 
     In the present embodiment, there is provided the single CHP  110 . Alternatively, there may be provided a plurality of CHPs  110 . In addition, although there are provided the two RES&#39;s  120 , there may be provided a single RES  120  or three or more RES&#39;s  120 . 
     As described above, according to the present embodiment, the output values (in this example, the output ratios R 1 , R 2 , R 3 , R 4 , and R 5 ) of the CHP  110 , the RES  120 , the electric storage unit  130 , the thermal accumulation unit  140 , and the backup thermal energy supply unit  150 , and when there additionally exist a controllable electric load  160  and a controllable thermal load  170 , also the demands (in this example, the consumption ratios R 6  and R 7 ) of the controllable electric load  160  and thermal load  170  are computed as value settings by means of an optimization algorithm based on the input information for each time step UT( 1 ) to UT(n) and for each level of the electric storage unit  130  and the thermal accumulation unit  140  (in this example, 16 combinations of four levels, 20%, 50%, 70%, and 100%, of the electric storage unit  130  and four levels, 20%, 50%, 70%, and 100%, of the thermal accumulation unit  140 ). Each edge (in this example, a total of 16×16=256 edges: β 1 →β 1 , β 1 →β 2 , . . . , β 16 →β 15 , and β 16 →β 16 ) from a plurality of levels (in this example, 16 combinations of four levels, 20%, 50%, 70%, and 100%, of the electric storage unit  130  and four levels, 20%, 50%, 70%, and 100%, of the thermal accumulation unit  140 ) of the energy storage units (in this example, the electric storage unit  130  and the thermal accumulation unit  140 ) for one of time steps UT( 1 ) to UT(n) (specifically, time step UT(i−1)) to a plurality of levels (in this example, 16 combinations of four levels, 20%, 50%, 70%, and 100%, of the electric storage unit  130  and four levels, 20%, 50%, 70%, and 100%, of the thermal accumulation unit  140 ) of the energy storage units (in this example, the electric storage unit  130  and the thermal accumulation unit  140 ) for next time step UT(i) is given a weight based on one of the transitional evaluation values obtained by means of a fitness function. Out of the combinations of value settings resulting in the transitional evaluation values for time steps UT( 1 ) to UT(n), a combination of value settings (in the example shown in  FIG. 6 , the value settings  132  for one time step UT(i−1), the value settings β 13  for next time step UT(i), and the value settings β 9  for further next time step UT(i+1)) for which the total sum of the transitional evaluation values for first time step UT( 1 ) to last time step UT(n) is most positively evaluated (in the example shown in  FIG. 6 , . . . +[3]+[2]+ . . . ) is determined by a graph-theoretic shortest-path problem solving algorithm. Then, the combination of value settings determined by the shortest-path problem solving algorithm is selected for the time steps UT( 1 ) to UT(n) as the operational instruction values C 1  (C 11  and C 12 ), C 2  (C 21  and C 22 ), C 5 , C 3 , C 6  (C 62  to C 64 ), and C 7  (C 72  to C 74 ). The configuration of the present embodiment can hence practically achieve optimal operation of each piece of equipment, such as the energy generation units (in this example, the CHP  110  and the RES  120 ), the energy storage units (in this example, the electric storage unit  130  and the thermal accumulation unit  140 ), and the energy loads (in this example, the electric load  160  and the thermal load  170 ), throughout the entire operation period T. 
     In addition, the shortest-path problem solving algorithm in the present embodiment is a Dijkstra algorithm. Therefore, the present embodiment can reliably determine, out of the combinations of value settings resulting in the transitional evaluation values for time steps UT( 1 ) to UT(n), a combination of value settings for which the total sum of the transitional evaluation values for first time step UT( 1 ) to last time step UT(n) is most positively evaluated. 
     In addition, the optimization algorithm in the present embodiment is a genetic algorithm. Therefore, the present embodiment can quickly compute an output value for each level (in this example, four levels: 20%, 50%, 70%, and 100%) of energy generation units (in this example, the CHP  110  and the RES  120 ) and energy storage units (in this example, the electric storage unit  130  and the thermal accumulation unit  140 ) and for each time step UT( 1 ) to UT(n), and when there additionally exist controllable energy loads (in this example, the electric load  160  and the thermal load  170 ), also quickly compute a demand for each level (e.g., four levels: 20%, 50%, 70%, and 100%) of the energy loads (in this example, the electric load  160  and the thermal load  170 ) and for each time step UT( 1 ) to UT(n). 
     In addition, the scalar information in the present embodiment includes at least one of a set of value settings in an operation season mode and a set of value settings (in this example, the output ratios R 1 , R 2 , R 3 , R 4 , and R 5  of the CHP  110 , the RES  120 , the electric storage unit  130 , the thermal accumulation unit  140 , and the backup thermal energy supply unit  150  and the consumption ratios R 6  and R 7  of the electric load  160  and the thermal load  170 ) for last time step UT(n) of an immediately preceding operation period (in this example, 1 day). Therefore, operation can be started by using the value settings for first time step UT( 1 ) of a next operation period T (in this example, the operation period T for the next day) as initial values. 
     In addition, the vector information in the present embodiment includes at least one of an energy price, a time step-specific electric power price, an electric power demand, and a thermal power demand. Therefore, the present embodiment can obtain accurate transitional evaluation values by means of a fitness function. 
     In addition, the energy generation unit in the present embodiment includes the fuel gas-powered CHP  110  and/or the RES  120  (in this example, both the CHP  110  and the RES  120 ). Therefore, the present embodiment can efficiently supply thermal energy and electric energy. 
     In addition, the technical information in the present embodiment includes at least one of the rated output of the CHP  110 , the conversion parameter for conversion of the electric energy output P 1  of the CHP  110  to the fuel gas consumption of the CHP  110 , and the conversion parameter for conversion of the electric energy output P 1  of the CHP  110  to the thermal energy output Q 1  of the CHP  110 . Therefore, the present embodiment can obtain accurate individual evaluation values and transitional evaluation values by means of a fitness function. 
     In addition, the in-plant energy network  100  in the present embodiment further includes the backup thermal energy supply unit  150 . Therefore, the backup thermal energy supply unit  150  can cover a thermal energy shortage in the in-plant energy network  100 . 
     In addition, the transitional evaluation values in the present embodiment are derived from evaluation conditions including at least one of an energy supply cost, a CO 2  emission, and a primary energy consumption. Therefore, the present embodiment can carry out transitional evaluation based on at least one of an energy supply cost, a CO 2  emission, and a primary energy consumption. 
     In addition, in the present embodiment, in the input step (according to the input means PR 1 ), forecast energy demands, unit energy prices, and forecast weather conditions are received. Then, in the first calculation process of the computing step (carried out by the computing means PR 2 ), the outputs (electric energy P 1  and thermal energy Q 1 ) of the CHP  110  are calculated from the rated output of the CHP  110  and the output ratios R 1  (R 11  and R 12 ) of the CHP  110 ; the outputs (in this example, electric energies P 21  and P 22 ) of the RES  120  are calculated from the maximum output of the RES  120  and the output ratios R 2  (R 21  and R 22 ) of the RES  120 , both based on weather conditions; the output (thermal energy Q 5 ) of the backup thermal energy supply unit  150  is calculated from the rated output of the backup thermal energy supply unit  150  and the output ratio R 5  of the backup thermal energy supply unit  150 ; the output (electric energy P 3 ) of the electric storage unit  130  is calculated from the maximum output of the electric storage unit  130  and the output ratio R 3  of the electric storage unit  130 , both based on the state of charge; the electric energy consumption Q 6  of the electric load  160  is calculated from the rated power consumption of the electric load  160  and the consumption ratio R 6  of the electric load  160 ; and the thermal energy consumption Q 7  of the thermal load  170  is calculated from the rated power consumption of the thermal load  170  and the consumption ratio R 7  of the thermal load  170 . In the second calculation process of the computing step, a difference between a total energy, or the sum of the outputs (electric energy P 1  and thermal energy Q 1 ) of the CHP  110 , the outputs (in this example, electric energies P 21  and P 22 ) of the RES  120 , the output (thermal energy Q 4 ) of the thermal accumulation unit  140 , and the output (electric energy P 3 ) of the electric storage unit  130 , and another total energy, or the sum of the uninterruptible electric and thermal energy consumption for energy demand, the electric energy consumption Q 6  of the electric load  160 , and the thermal energy consumption Q 7  of the thermal load  170 , is calculated as the output (electric energy Pp) of the large-scale electric power network  200  and the energy that can be supplied from the backup thermal energy supply unit  150 . In the evaluation process, two or more candidates are evaluated using evaluation criteria including the energy supply cost (electric energy supply cost and thermal energy supply cost) based on a unit energy price, the outputs (electric energy P 1  and thermal energy Q 1 ) of the CHP  110 , and the CO 2  emission and primary energy consumption based on the output (electric energy Pp) of the large-scale electric power network  200  and the output (thermal energy Q 5 ) of the backup thermal energy supply unit  150 . In the selection process, from the two or more energy balance candidates for each unit time, a most positively evaluated candidate is selected that yields the most positive evaluation when the convergence of the individual evaluation values of the two or more candidates has reached convergence criteria. In the output step (according to the output means PR 3 ), the operational instruction values C 1 , C 2 , C 5 , C 3 , C 6 , and C 7  for each unit time based on the most positively evaluated candidate are transmitted respectively to the CHP  110 , the RES  120 , the backup thermal energy supply unit  150 , the electric storage unit  130 , the electric load  160 , and the thermal load  170 . Therefore, the present embodiment is capable of reducing CO 2  emission and primary energy consumption, as well as energy supply cost, to lowest possible levels in optimal energy management. 
     Furthermore, in the present embodiment, an optimal scheduling for (the combination of) the output ratios R 1 , R 2 , R 5 , and R 3  of the CHP  110 , the RES  120 , the backup thermal energy supply unit  150 , and the electric storage unit  130  and the consumption ratios R 6  and R 7  of the electric load  160  and the thermal load  170  is found for the CHP  110 , the RES  120 , the backup thermal energy supply unit  150 , the electric storage unit  130 , the electric load  160 , and the thermal load  170  in accordance with inputted energy demands, unit energy prices, and weather conditions by means of an objective function that takes into consideration energy supply costs, CO 2  emissions, and primary energy consumptions (in this example, to minimize the individual evaluation values obtained by means of the objective function) in residential areas, manufacturing industry areas, and tertiary areas. Hence, the present embodiment can specify optimal operation conditions for the CHP  110 , the RES  120 , the backup thermal energy supply unit  150 , the electric storage unit  130 , the electric load  160 , and the thermal load  170 . 
     In addition, in the present embodiment, the electric load  160  and the thermal load  170  can be classified into non-adjustable loads L 1  that require uninterruptible energy consumption for energy demand, interruptible and adjustable loads L 2 , manageable loads L 3  that, after starting to operate, may discontinue their operation if a total operation time is reached within a predetermined period, and manageable loads L 4  that are not allowed to discontinue their operation until a total operation time is reached once it starts to operate. Hence, in the first calculation process, the calculations related to the energy consumption Q 6  of the electric load  160  and the energy consumption Q 7  of the thermal load  170  can be carried out in a manner more suitable to the actual circumstances. Accordingly, the output (electric energy Pp) of the large-scale electric power network  200  and the output (thermal energy Q 4 ) supplied from the thermal accumulation unit  140  can be precisely calculated in the second calculation process. 
     As mentioned earlier, if a consumer fails to follow the electric power exchange profile, he/she is given a penalty from the utility company. The present embodiment is however capable of effectively preventing the consumer from being given a penalty from the utility company because the computing step (computing means PR 2 ) includes as one of evaluation criteria how well the output (electric energy Pp) of the large-scale electric power network  200  matches the electric power exchange profile, so that evaluation can be done based on two or more electric power exchange profile candidates. 
     According to the present embodiment, in the computing step, the output ratio R 1  of the CHP  110 , the output ratio R 2  of the RES  120 , the output ratio R 5  of the backup thermal energy supply unit  150 , the consumption ratio R 6  of the electric load  160 , and the consumption ratio R 7  of the thermal load  170  are coded as parts of a chromosome CH including a predetermined number (division count DI: for example, 2 8 =256) of genes GE; the output ratio R 3  of the electric storage unit  130  and the output ratio R 4  of the thermal accumulation unit  140  are coded as parts of the chromosome CH including a predetermined number (division count DJ: for example, 2 2 =4 and 2 4 =4) of genes GE; two or more candidates are coded as individuals ID; and two or more individuals ID are repeatedly generated for each one of the 16 combinations of the output ratio R 3  of the electric storage unit  130  and the output ratio R 4  of the thermal accumulation unit  140  based on a genetic algorithm (see  FIG. 12  which will be described later in detail). 
     Referring to the flow charts shown in  FIGS. 10 and 11 , the following will describe the control device  300  of the in-plant energy network  100  in accordance with an embodiment of the present invention by taking a Dijkstra algorithm as an example of the shortest-path problem solving algorithm used in the shortest-path problem solving process and a genetic algorithm as an example of the optimization algorithm used in the evaluation process. 
       FIGS. 10 and 11  are flow charts representing an exemplary energy management evaluation optimization process performed by the control section  330  in the control device  300  shown in  FIG. 7 .  FIG. 10  represents a first half of the exemplary process, and  FIG. 11  represents a second half of the exemplary process. 
     The control section  330  optimizes energy management a number of times that is equal to the number of processing units (e.g., 96 processing units) that is obtained by dividing the operation period to be processed (e.g., 1 day=24 hours) by the time step UT (e.g., 15 minutes=0.25 hours) according to the genetic algorithm shown in  FIGS. 10 and 11  (in this example, on the preceding day; specifically, collectively sometime on December 24 so that the optimization is completed by 00:00:00, December 25) before the forecast period for the energy demand data DT 1  and the weather condition data DT 3  (e.g., from 00:00:00 to 23:59:59, December 25). 
     In the optimization process represented in  FIGS. 10 and 11 , a set of two or more candidates is termed a population PP (group of individuals), and each candidate in the population PP is termed an individual ID. 
     Prior to implementing the optimization process represented in  FIGS. 10 and 11 , the following set of data is specified (stored) in advance in the memory section  340 : the number of two or more individuals ID in the population PP (e.g., 1,000); the number of types of chromosomes CH (e.g., 13, or the sum of the “2” types of output ratios R 11  and R 12  of the CHP  110 , the “2” types of output ratios R 21  and R 22  of the RES  120 , the “1” type of output ratio R 5  of the backup thermal energy supply unit  150 , the “1” type of output ratio R 3  of the electric storage unit  130 , the “1” type of output ratio R 4  of the thermal accumulation unit  140 , the “3” types of consumption ratios R 62  to R 64  of the electric load  160 , and the “3” types of consumption ratios R 72  to R 74  of the thermal load  170 ); and the number of genes GE (e.g., 256=8 bits in binary form, or the division count DI for the output ratios R 11  and R 12  of the CHP  110 , the output ratios R 21  and R 22  of the RES  120 , the output ratio R 5  of the backup thermal energy supply unit  150 , the consumption ratios R 62  to R 64  of the electric load  160 , and the consumption ratios R 72  to R 74  of the thermal load  170 , and 4=2 bits in binary form, or the division count DJ for the output ratio R 3  of the electric storage unit  130  and the output ratio R 4  of the thermal accumulation unit  140 ). Specified individuals ID and an existence ratio, a replacement count, selection rates, a crossover rate and the type of crossover operation, a mutation ratio and genetic loci, and convergence criteria and a repetition count, all of which will be described later in detail, are also specified (stored) in advance in the memory section  340 . The control device  300  allows the user to readily specify these values and conditions. This configuration enables the control device  300  to be readily applicable in environments in residential areas, manufacturing industry areas, and tertiary areas in which the in-plant energy network  100  is installed. 
     Step S 1 : Data Input 
     First of all, as illustrated in  FIG. 10 , the control section  330  receives the energy demand data DT 1  for each time step UT (e.g., 15 minutes) in relation to forecast energy demands, the unit energy price data DT 2  for each time step UT (e.g., 15 minutes) in relation to unit energy prices, and the weather condition data DT 3  for each time step UT (e.g., 15 minutes) in relation to forecast weather conditions of the geographical region covered by the in-plant energy network  100  (step S 1 ). In this example, the control section  330  also receives the electric power exchange profile of the large-scale electric power network  200  in step S 1  as part of the energy demand data DT 1  for each time step UT (e.g., 15 minutes). 
     Specifically, the control section  330  obtains, as the energy demand data DT 1 , the forecast electric power demands, forecast thermal energy demands, and electric power exchange profile directly inputted by the user through the input section  310 . The control section  330  automatically obtains, as the unit energy price data DT 2 , a unit electric energy price and a unit thermal energy price from a Web site on the Internet  400 . The control section  330  automatically obtains, as the weather condition data DT 3 , the angle of the sun and cloudiness in the geographical region where the photovoltaic power generator  121  is installed and the wind direction and speed in the geographical region where the wind-powered electric generator  122  is installed from a Web site on the Internet  400 . 
     Step S 2 : Incrementing Generation Count 
     Next, the control section  330  increments (increases by one) a generation count G (initially set to 0; G=G+1) (step S 2 ). 
     Step S 3 : Generating Initial Population 
     Next, the control section  330 , during the first generation (G=1), generates an initial (first generation) population PP from all combinations of the output ratios R 11  and R 12  of the CHP  110 , the output ratios R 1  and R 22  of the RES  120 , the output ratio R 5  of the backup thermal energy supply unit  150 , the output ratio R 3  of the electric storage unit  130 , the output ratio R 4  of the thermal accumulation unit  140 , the consumption ratios R 62  to R 64  of the electric load  160 , and the consumption ratios R 72  to R 74  of the thermal load  170  (step S 3 ). In step  3 , the output ratio R 4  of the thermal accumulation unit  140  is logically dictated by the balance of the thermal energies Q 1 , Q 7 , and Q 5  of the CHP  110 , the thermal load  170 , and the backup thermal energy supply unit  150 . Therefore, the output ratio R 12  of the CHP  110 , the consumption ratios R 72  to R 74  of the thermal load  170 , and the output ratio R 5  of the backup thermal energy supply unit  150  can assume a limited number of values in accordance with the 16 combinations of the four levels, 20%, 50%, 70%, and 100%, of the output ratio R 3  of the electric storage unit  130  and the four levels, 20%, 50%, 70%, and 100%, of the output ratio R 4  of the thermal accumulation unit  140 . The control section  330  stores the generated population PP in the memory section  340 . 
     Specifically, the control section  330  selects two or more (e.g., 1,000) individuals ID, as the initial population PP, from all the individuals (e.g., 2 92 =256 11 ×4 2  individuals), that is, all the combinations of as many output ratios R 11  and R 12  of the electric energy P 1  and the thermal energy Q 1  of the CHP  110  as the division count DI (e.g., 2 8 =256) by which the entire output range of the CHP  110  is divided, as many output ratios R 21  and R 22  of the RES  120  (in this example, the photovoltaic power generator  121  and the wind-powered electric generator  122 ) as the division count DI by which the entire output range of the RES  120  is divided, as many output ratios R 5  of the backup thermal energy supply unit  150  as the division count DI by which the entire output range of the backup thermal energy supply unit  150  is divided, as many output ratios R 3  of the electric storage unit  130  as the division count DJ (e.g., 2 2 =4) by which the entire output range of the electric storage unit  130  is divided, as many output ratios R 4  of the thermal accumulation unit  140  as the division count DJ (e.g., 2 2 =4) by which the entire output range of the thermal accumulation unit  140  is divided, as many consumption ratios R 62  to R 64  of the electric load  160  (the second load L 2  to the fourth load L 4 ) as the division count DI by which the entire consumption range of the electric load  160  is divided, and as many consumption ratios R 72  to R 74  of the thermal load  170  (the second load L 2  to the fourth load L 4 ) as the division count DI by which the entire consumption range of the thermal load  170  is divided, so that the initial population PP can be statistically distributed (specifically, the initial population PP can have randomness) for each one of the 16 combinations of a plurality of levels (in this example, four levels: 20%, 50%, 70%, and 100%) of the electric storage unit  130  and a plurality of levels (in this example, four levels: 20%, 50%, 70%, and 100%) of the thermal accumulation unit  140 . 
     Furthermore, the control section  330  deliberately specifies predetermined individuals that are to exist in advance at a predetermined existence ratio in the initial population PP. For example, when there are 1,000 individuals ID with an existence ratio of 5%, 50 out of the 1,000 individuals ID are the specified individuals existent in advance. 
     In the present embodiment, for example, the specified individuals consist of the following three types of individuals. 
     1. Individuals that lead to electric power-led operation of the CHP  110  (the CHP  110  is operated following the electric power demand of the electric load  160 ) 
     2. Individuals that lead to thermal power-led operation of the CHP  110  (the CHP  110  is operated following the thermal power demand of the thermal load  170 ) 
     3. Individuals that lead to such operation of the CHP  110  and the RES  120  that all the electric power demand of the electric load  160  is fulfilled by the supply of the electric energy Pp from the large-scale electric power network  200  and all the thermal power demand of the thermal load  170  is fulfilled by the supply of the thermal energy Q 5  from the backup thermal energy supply unit  150 . 
     To do this, the control section  330  refers to the memory section  340  to retrieve the number (e.g., 1,000) of the two or more (e.g., 1,000) individuals ID, the number of types of chromosomes CH (in this example, 10 (types)), the number of genes GE (e.g., 8 bits in binary form), the specified individuals existent in advance (in this example, three types of individuals), and the existence ratio of the specified individuals to the individuals ID in the population PP, all being stored in the memory section  340 . 
     Step S 4 : Evaluation for Replacement Process and Selection Process 
     Next, the control section  330  evaluates two or more (e.g., 1,000) individuals ID in the current generation population PP by means of an objective function to carry out a replacement process in step S 5  and a selection process in step S 6  (step S 4 ). 
     Specifically, the control section  330  determines individual evaluation values for the two or more (e.g., 1,000) individuals ID either by plugging in their energy supply costs, CO 2  emissions, and primary energy consumptions into a predetermined evaluation formula in the evaluation process or by ranking the energy supply costs, CO 2  emissions, and primary energy consumptions and summing up the rank of the energy supply cost, the rank of the CO 2  emission, and the rank of the primary energy consumption for each individual ID in the evaluation process. In addition, in this example, the control section  330  incorporates the electric power exchange profile as one of evaluation criteria; therefore, the control section  330  gives a progressively positive evaluation to a better matching between the total electric energy output Pp of the large-scale electric power network  200  and the electric power demand in the electric power exchange profile (e.g., if a smaller evaluation value represents a progressively positive evaluation, the evaluation value is multiplied by an evaluation ratio that decreases with a better matching). 
     To do this, the control section  330  refers to the memory section  340  to retrieve the unit energy price data DT 2 , the CO 2  emission conversion formula or table, the primary energy consumption conversion formula or table, the objective function, and the electric power exchange profile, all stored in the memory section  340 . 
     Step S 5 : Replacing Most Negatively Evaluated Individuals with Most Positively Evaluated Individuals 
     Next, except for the first generation (G=1), the control section  330  carries out a replacement process of replacing most negatively evaluated individuals with most positively evaluated individuals of the two or more (e.g., 1,000) individuals ID in the population PP based on the evaluation (fitness) obtained in step S 4  (step S 5 ). The control section  330  then stores the post-replacement population PP in the memory section  340 . 
       FIG. 12  is a schematic diagram illustrating an exemplary replacement process by which some of the individuals ID in the population PP are replaced, the left half representing the population before the replacement, the right half representing the population after the replacement. Note that in the example shown in  FIG. 12 , for ease of description, the two or more individuals ID include ten individuals in the population PP, and the evaluation values (fitness) of the ten individuals  1  to  10  are given as  1 ,  2 ,  3 ,  3 ,  3 ,  4 ,  4 ,  5 ,  6 , and  7 . The evaluation is progressively positive with a smaller value. This applies to  FIGS. 13 and 14 , which will be described later in detail. 
     Specifically, as illustrated in  FIG. 12 , the control section  330  replaces as many individuals that were most negatively evaluated (fitness) in step S 4  as the predetermined replacement count (two in the example shown in  FIG. 12 : individual [ 9 ] evaluated “ 6 ” and individual [ 10 ] evaluated “ 7 ” in the example shown in  FIG. 12 ) out of the two or more (ten in the example shown in  FIG. 12 ) individuals ID with as many individuals that were most positively evaluated as the replacement count (two in the example shown in  FIG. 12 : individual [ 1 ] evaluated “ 1 ” and individual [ 2 ] evaluated “ 2 ” in the example shown in  FIG. 12 ). 
     To do this, the control section  330  refers to the memory section  340  to retrieve the predetermined replacement count (in this example, two) stored in the memory section  340 . 
     Step S 6 : Selecting Parent Individuals 
     Next, the control section  330  carries out a selection process of selecting parent individuals from the two or more (e.g., 1,000) individuals ID in the population PP based on the evaluation (fitness) obtained in step S 4  (step S 6 ). The control section  330  stores the selected population PP in the memory section  340 . 
       FIG. 13  is a schematic diagram illustrating an exemplary selection process by which parent individuals are selected from the two or more individuals ID in the population PP, the left half representing the population before the selection, the right half representing the population after the selection. 
     Specifically, as illustrated in  FIG. 13 , the control section  330  selects parent individuals (5 sets of parents in the example shown in  FIG. 13 ) from the two or more (ten in the example shown in  FIG. 13 ) individuals ID so that relatively positively evaluated individuals are more likely to be selected as parent individuals whereas relatively negatively evaluated individuals are less likely to be selected as parent individuals. This selection process may be any publicly known, conventional selection process and may typically be a roulette selection process, a ranking selection process, a tournament selection process, or an elite selection process. The roulette selection process utilizes a roulette wheel prepared according to the predetermined selection rates that are proportional to evaluation values, to enable a random selection. 
       FIG. 14  is a diagram illustrating an exemplary roulette selection process, the left half representing selection rates, the right half representing a roulette wheel prepared according to the selection rates. 
     As an example, suppose that ten individuals  1 ,  2 ,  3 ,  4 ,  5 ,  6 ,  7 ,  8 ,  1 , and  2  before the selection process are evaluated  1 ,  2 ,  3 ,  3 ,  3 ,  4 ,  4 ,  5 ,  1 , and  2  respectively as shown in the left half of  FIG. 13  and given selection rates 40% (evaluation “ 1 ”), 30% (evaluation “ 2 ”), 15% (evaluation “ 3 ”), 10% (evaluation “ 4 ”), and 5% (evaluation “ 5 ”) as shown in the left half of  FIG. 14 . A roulette wheel is prepared using these selection rates as shown in the right half of  FIG. 14 . Since there are ten individuals, the roulette wheel is, for example, rotated 10 times to select ten post-selection individuals  1 ,  5 ,  2 ,  1 ,  8 ,  2 ,  1 ,  2 ,  6 , and  1  shown in the right half of  FIG. 13 . 
     To do this, the control section  330  refers to the memory section  340  to retrieve the selection rates stored in the memory section  340 . 
     Step S 7 : Crossover of Selected Parent Individuals 
     Next, the control section  330  carries out a crossover operation of exchanging some of the genes GE between arbitrarily (specifically, randomly) selected parent individuals (two individuals or chromosomes CH) at a predetermined crossover rate, or a probability at which the parent individuals selected in step S 6  undergo crossover, to generate children (two next-generation individuals) (step S 7 ). This process generates children that possess some of the parent individuals&#39; genes GE. A next-generation population PP is obtained in this manner. The control section  330  then stores the generated next-generation population PP in the memory section  340 . In addition, the control section  330  leaves, in the memory section  340 , the current generation population PP that was selected in the selection process in step S 7 , as well as the next-generation population PP. 
     Specifically, the control section  330  exchanges some genes GE between the parent individuals (two individuals) at one or multiple crossover points to generate children (two next-generation individuals). This crossover operation may be a publicly known, conventional crossover operation and may be typically one-point crossover, multiple-point crossover, or uniform crossover. One-point crossover is an operation of randomly designating a single site as a crossover point in the chromosomes CH and exchanging the genes GE preceding or succeeding those sites between the parent individuals. Multiple-point crossover is an operation randomly designating a plurality of sites as crossover points in the chromosomes CH and exchanging the genes GE preceding or succeeding those sites between the parent individuals. Multiple-point crossover is typically two-point crossover. The control section  330 , in the present embodiment, allows the user to specify any one of crossover operations: one-point crossover and two-point crossover. 
       FIG. 15  is a schematic diagram illustrating an exemplary one-point crossover operation and an exemplary two-point crossover operation, the upper half representing a one-point crossover operation, the lower half representing a two-point crossover operation. In the examples shown in  FIG. 15 , there are provided eight (8 bits in binary form) genes GE in each individual ID for ease of description. 
     In the one-point crossover operation example shown in the upper half of  FIG. 15 , with the crossover point being located between the fifth and sixth bits from the left, the genes GE either from the first to fifth bits or from the sixth to eighth bits are exchanged between the parent individuals (two individuals). 
     In the two-point crossover operation example shown in the lower half of  FIG. 15 , with one of the crossover points being located between the second and third bits from the left and the other crossover point being located between the fifth and sixth bits from the left, the genes GE either from the third to fifth bits or from the first to second bits and from the sixth to eighth bits are exchanged between the parent individuals (two individuals). 
     To do this, the control section  330  refers to the memory section  340  to retrieve the crossover rate and the type of crossover operation (in this example, either one-point crossover or two-point crossover), both stored in the memory section  340 . 
     Step S 8 : Gene Mutation in Individuals 
       FIG. 16  is an illustration of local solutions γb that are likely to be found along with an optimal solution γa during the generation of a next-generation population PP. 
     If the control section  330  was capable only of crossover operations, limited child variants would be generated depending on the genes GE of the parent individuals. If so, the control section  330  could fail to give diversity to the population PP, and the algorithm would likely come up with a local solution (see γb in  FIG. 16 ) that is not the desirable optimal solution (see γa in  FIG. 16 ). 
     Regarding this point, however, the control section  330  carries out a mutation operation of replacing some of the genes GE in an arbitrarily (specifically, randomly) selected individual ID (chromosome CH) with their allelic genes at a predetermined mutation ratio, or a probability at which mutations occur in a next-generation population PP that underwent the selection operation in step S 6  and the crossover operation in step S 7  as illustrated in  FIG. 11  (step S 8 ). This operation gives diversity to the population PP, hence preventing the algorithm from coming up with a local solution that is not the desirable optimal solution. 
     Specifically, the control section  330  changes the value of a selected gene GE in a parent individual (chromosome CH) to a different value (e.g., reverses the selected bit in binary form). The mutation operation may be a publicly known, conventional mutation operation and may be typically a single-locus mutation operation, an inversion operation, or a translocation operation. Single-locus mutation is an operation of replacing a gene with its allelic gene at a single genetic locus. In the present embodiment, the control section  330  allows the user to specify genetic loci in a mutation operation. 
       FIG. 17  is a schematic diagram illustrating an exemplary single-locus mutation operation. In the example shown in  FIG. 17 , there are provided eight (8 bits in binary form) genes GE in an individual ID for ease of description. 
     In the single-locus mutation operation example shown in  FIG. 17 , the 0 in the third bit (genetic locus) is reversed to a 1. 
     To do this, the control section  330  refers to the memory section  340  to retrieve the mutation ratio and the genetic locus (in this example, the third bit), both stored in the memory section  340 . 
     Step S 9 : Establishing Next-Generation Population 
     Next, the control section  330  establishes a next-generation population PP that underwent the crossover operation in step S 7  and the mutation operation in step S 8  (step S 9 ). The control section  330  then stores the established next-generation population PP in the memory section  340 . 
     Step S 10 : Evaluation for Convergence Check 
     Next, the control section  330  evaluates two or more (e.g., 1,000) individuals ID in the current generation population PP obtained in step S 7  by means of an objective function to carry out a convergence check process in step S 11  (step S 10 ). 
     Specifically, the control section  330  determines individual evaluation values for the two or more (e.g., 1,000) individuals ID either by plugging in their energy supply costs, CO 2  emissions, and primary energy consumptions into a predetermined evaluation formula in the evaluation process or by ranking the energy supply costs, CO 2  emissions, and primary energy consumptions and summing up the rank of the energy supply cost, the rank of the CO 2  emission, and the rank of the primary energy consumption for each individual ID in the evaluation process. In addition, in this example, the control section  330  incorporates the electric power exchange profile as one of evaluation criteria; therefore, the control section  330  gives a progressively positive evaluation to a better matching between the total electric energy output Pp of the large-scale electric power network  200  and the electric power demand in the electric power exchange profile (e.g., if a smaller evaluation value represents a progressively positive evaluation, the evaluation value is multiplied by an evaluation ratio that decreases with a better matching). 
     To do this, the control section  330  refers to the memory section  340  to retrieve the unit energy price data DT 2 , the CO 2  emission conversion formula or table, the primary energy consumption conversion formula or table, the objective function, and the electric power exchange profile, all stored in the memory section  340 . 
     Step S 11 : Convergence Check Process 
     Next, the control section  330  carries out a convergence check process of determining whether or not the changes of the individual evaluation values (fitness) of the two or more (e.g., 1,000) candidates in the current generation population PP evaluated in step S 10  have reached convergence criteria and whether or not the generation count G has reached a predetermined repetition count (step S 11 ). If the convergence of the individual evaluation values has not reached the convergence criteria and the generation count G has not reached the predetermined repetition count (“No” in step S 11 ), the control section  330  proceeds to and carries out step S 2  shown in  FIG. 10  with the next-generation population PP established in step S 9  being designated as the current generation population PP and repeats steps S 2  to S 11 . At least either if the convergence of the individual evaluation values has reached the convergence criteria or if the generation count G has reached the predetermined repetition count (“Yes” in step S 11 ), the control section  330  proceeds to and carries out step S 12 . 
     To do this, the control section  330  retrieves the predetermined convergence criteria and the predetermined repetition count. 
     Step S 12 : Selecting Most Positively Evaluated Individual 
     Next, the control section  330  selects a most positively evaluated individual that is to be a most positively evaluated (best fitness) individual (step S 12 ). 
     Step S 13 : Initializing Generation Count 
     Next, the control section  330  initializes the generation count G (sets the generation count G to 0) (step S 13 ). 
     Step S 14 : Operations for Each Processing Unit 
     Next, the control section  330  carries out steps S 1  to S 14  of the genetic algorithm for each processing unit (e.g., 96 processing units), or time step UT (e.g., 15 minutes) (“No” in step S 14 ). 
     As explained above, in the present embodiment, the computing step (computing means) is capable of converging the convergence of the individual evaluation values of the two or more individuals within the convergence criteria in a short period of time, by coding the output ratio R 1  of the CHP  110 , the output ratio R 2  of the RES  120 , the output ratio R 5  of the backup thermal energy supply unit  150 , the output ratio R 3  of the electric storage unit  130 , the output ratio R 4  of the thermal accumulation unit  140 , the consumption ratio R 6  of the electric load  160 , and the consumption ratio R 7  of the thermal load  170  into a chromosome CH, coding two or more (e.g., 1,000) candidates into individuals ID, and repeatedly generating two or more (e.g., 1,000) individuals ID according to a genetic algorithm for each one of the 16 combinations of the output ratio R 3  of the electric storage unit  130  and the output ratio R 4  of the thermal accumulation unit  140 . 
     Step S 15 : Weighting Process Based on Transitional Evaluation Values by Means of Fitness Function 
     Following the completion of the process for each processing unit (“Yes” in step S 14 ), the control section  330  gives weights to a total of 163316=256 edges, β 1 →β 1 , β 1 →β 2 , . . . , β 16 →β 15 , and β 16 →β 16 , from the 16 combinations of the four levels, 20%, 50%, 70%, and 100%, of the electric storage unit  130  and the four levels, 20%, 50%, 70%, and 100%, of the thermal accumulation unit  140  for one of time steps UT( 1 ) to UT(n) (specifically, time step UT(i−1)) to the 16 combinations of the four levels, 20%, 50%, 70%, and 100%, of the electric storage unit  130  and the four levels, 20%, 50%, 70%, and 100%, of the thermal accumulation unit  140  for next time step UT(i) based on the transitional evaluation values obtained by means of a fitness function (step S 15 ). 
     Step S 16 : Dijkstra Algorithm Process 
     Next, out of the combinations of value settings (in this example, the output ratios R 1 , R 2 , R 3 , R 4 , and R 5  of the CHP  110 , the RES  120 , the electric storage unit  130 , the thermal accumulation unit  140 , and the backup thermal energy supply unit  150  and the consumption ratios R 6  and R 7  of the electric load  160  and the thermal load  170 ) resulting in the transitional evaluation values for time steps UT( 1 ) to UT(n), the control section  330  determines, by means of a Dijkstra algorithm, a combination of value settings (in the example shown in  FIG. 6 , the value settings β 2  for one time step UT(i−1), the value settings β 13  for next time step UT(i), and the value settings β 9  for further next time step UT(i+1)) for which the total sum of the transitional evaluation values for first time step UT( 1 ) to last time step UT(n) is most positively evaluated (in the example shown in  FIG. 6 , . . . +[3]+[2]+ . . . ) (step S 16 ). 
     Step S 17 : Selecting Value Settings 
     Next, the control section  330  selects the combination of value settings (in the example shown in  FIG. 6 , the value settings β 2  for one time step UT(i−1), the value settings β 13  for next time step UT(i), and the value settings β 9  for further next time step UT(i+1)) determined by means of the Dijkstra algorithm as the operational instruction values C 1  (C 11  and C 12 ), C 2  (C 21  and C 22 ), C 5 , C 3 , C 6  (C 62  to C 64 ), and C 7  (C 72  to C 74 ) for the time steps UT( 1 ) to UT(n) (step S 17 ). 
     Steps S 1  to S 17  may be implemented before the processing for the day of interest is started (e.g., sometime between approximately 20:00, December 24 (preceding day) and when the processing is started on the day of interest). 
     Step S 18 : Output of Operational Instruction Values 
     Next, the control section  330  transmits (outputs), respectively to the CHP  110 , the RES  120  ( 121 ,  122 ), the backup thermal energy supply unit  150 , the electric storage unit  130 , the electric load  160  (from the second load L 2  to the fourth load L 4 ), and the thermal load  170  (from the second load L 2  to the fourth load L 4 ), the operational instruction values C 1  (C 11  and C 12 ), C 2  (C 21  and C 22 ), C 5 , C 3 , C 6  (C 62  to C 64 ), and C 7  (C 72  to C 74 ) corresponding to the combination, yielding the most positively evaluated individual for the time steps UT, of the output ratios R 1  (R 11  and R 12 ) of the CHP  110 , the output ratios R 2  (R 21  and R 22 ) of the RES  120 , the output ratio R 5  of the backup thermal energy supply unit  150 , the output ratio R 3  of the electric storage unit  130 , the consumption ratios R 6  (R 62  to R 64 ) of the electric load  160 , and the consumption ratios R 7  (R 72  to R 74 ) of the thermal load  170 , in accordance with the processing units (e.g., 96 processing units) of the corresponding time steps UT (e.g., 15 minutes) before the forecast period (e.g., from 23:45:00 to 23:59:59, the preceding day (December 24) to 23:44:59, the day of interest (December 25)) (step S 18 ). Then, the control section  330  ends the energy management evaluation optimization process. 
     Steps S 1  to S 17  may be further implemented on the day of interest after updating the input data acquired in step S 1 . Specifically, the control section  330  may update the latest energy management evaluation optimization process on the day of interest based on the latest information for each time step UT (e.g., 15 minutes) from current time step UT(i) to last time step TU (n). This configuration enables use of the latest energy demand data DT 1 , unit energy price data DT 2 , and weather condition data DT 3 , which in turn allows for a more accurate energy management evaluation optimization process. 
     There are provided seven individual evaluation values and seven transitional evaluation values in the embodiment described above. Alternatively, more or less than 7 values may be provided. In addition, the number of individual evaluation values may differ from the number of transitional evaluation values. In addition, a smaller evaluation value indicates more positive evaluation in the embodiment described above. Alternatively, a greater evaluation value may indicate more positive evaluation. 
     The division count DI, in the embodiment described above, for the output ratio R 1  of the CHP  110 , the output ratio R 2  of the RES  120 , the output ratio R 5  of the backup thermal energy supply unit  150 , the consumption ratio R 6  of the electric load  160 , and the consumption ratio R 7  of the thermal load  170  is 256. Alternatively, the division count DI may be greater or smaller than 256. In addition, the division count DJ for the output ratios R 3  and R 4  of the electric storage unit  130  and the thermal accumulation unit  140  is 4 in the embodiment described above. Alternatively, the division count DJ may be greater or smaller than 4. 
     The in-plant energy network  100 , in the embodiment described above, includes the CHP  110 , the RES  120 , the electric storage unit  130 , the thermal accumulation unit  140 , the backup thermal energy supply unit  150 , the electric load  160 , and the thermal load  170 . Alternatively, the in-plant energy network  100  may be configured as shown in  FIGS. 18 to 29 .  FIG. 18  is a schematic diagram of another exemplary in-plant energy network  100  in accordance with another embodiment of the present invention.  FIGS. 19 to 29  are schematic diagrams of other exemplary in-plant energy networks  100  in accordance with other embodiments of the present invention. 
     In the example shown in  FIG. 18 , the in-plant energy network  100  includes an energy generation unit, a thermal accumulation unit (hot thermal accumulation unit)  140 , and a thermal load (hot thermal load)  170 . The in-plant energy network  100 , in this example, is configured to manage hot thermal energy for desirable overall performance without including a backup thermal energy supply unit (backup hot thermal energy supply unit)  150 . 
     In the example shown in  FIG. 19 , the in-plant energy network  100  includes an energy generation unit, a thermal accumulation unit (hot thermal accumulation unit)  140 , a backup thermal energy supply unit (backup hot thermal energy supply unit)  150 , and a thermal load (hot thermal load)  170 . The in-plant energy network  100 , in this example, is configured to manage hot thermal energy for desirable overall performance, with the backup thermal energy supply unit (backup hot thermal energy supply unit)  150  included in the in-plant energy network  100 . 
     In the example shown in  FIG. 20 , the in-plant energy network  100  includes a thermal load (cold thermal load)  170 , a thermal accumulation unit (cold thermal accumulation unit)  140 , a cold thermal source  180 , and an energy generation unit. The in-plant energy network  100 , in this example, is configured to manage cold thermal energy for desirable overall performance without including a backup thermal energy supply unit (backup cold thermal energy supply unit)  150 . 
     In the example shown in  FIG. 21 , the in-plant energy network  100  includes a thermal load (cold thermal load)  170 , a backup thermal energy supply unit (backup cold thermal energy supply unit)  150 , a thermal accumulation unit (cold thermal accumulation unit)  140 , a cold thermal source  180 , and an energy generation unit. The in-plant energy network  100 , in this example, is configured to manage cold thermal energy for desirable overall performance, with the backup thermal energy supply unit (backup cold thermal energy supply unit)  150  included in the in-plant energy network  100 . 
     In the example shown in  FIG. 22 , the in-plant energy network  100  includes a thermal load (cold thermal load)  170 , a thermal accumulation unit (cold thermal accumulation unit)  140 , a cold thermal source  180 , an energy generation unit, a thermal accumulation unit (hot thermal accumulation unit)  140 , and a thermal load (hot thermal load)  170 . The in-plant energy network  100 , in this example, is configured to manage cold thermal energy and hot thermal energy for desirable overall performance without including a backup thermal energy supply unit (backup cold thermal energy supply unit)  150  or a backup thermal energy supply unit (backup hot thermal energy supply unit)  150 . 
     In the example shown in  FIG. 23 , the in-plant energy network  100  includes a thermal load (cold thermal load)  170 , a thermal accumulation unit (cold thermal accumulation unit)  140 , a cold thermal source  180 , an energy generation unit, a thermal accumulation unit (hot thermal accumulation unit)  140 , a backup thermal energy supply unit (backup hot thermal energy supply unit)  150 , and a thermal load (hot thermal load)  170 . The in-plant energy network  100 , in this example, is configured to manage cold thermal energy and hot thermal energy for desirable overall performance without including a backup thermal energy supply unit (backup cold thermal energy supply unit)  150 , but with the backup thermal energy supply unit (backup hot thermal energy supply unit)  150  included in the in-plant energy network  100 . 
     In the example shown in  FIG. 24 , the in-plant energy network  100  includes a thermal load (cold thermal load)  170 , a backup thermal energy supply unit (backup cold thermal energy supply unit)  150 , a thermal accumulation unit (cold thermal accumulation unit)  140 , a cold thermal source  180 , an energy generation unit, a thermal accumulation unit (hot thermal accumulation unit)  140 , and a thermal load (hot thermal load)  170 . The in-plant energy network  100 , in this example, is configured to manage cold thermal energy and hot thermal energy for desirable overall performance without including a backup thermal energy supply unit (backup hot thermal energy supply unit)  150 , but with the backup thermal energy supply unit (backup cold thermal energy supply unit)  150  included in the in-plant energy network  100 . 
     In the example shown in  FIG. 25 , the in-plant energy network  100  includes a thermal load (cold thermal load)  170 , a backup thermal energy supply unit (backup cold thermal energy supply unit)  150 , a thermal accumulation unit (cold thermal accumulation unit)  140 , a cold thermal source  180 , an energy generation unit, a thermal accumulation unit (hot thermal accumulation unit)  140 , a backup thermal energy supply unit (backup hot thermal energy supply unit)  150 , and a thermal load (hot thermal load)  170 . The in-plant energy network  100 , in this example, is configured to manage cold thermal energy and hot thermal energy for desirable overall performance, with both the backup thermal energy supply unit (backup cold thermal energy supply unit)  150  and the backup thermal energy supply unit (backup hot thermal energy supply unit)  150  included in the in-plant energy network  100 . 
     The in-plant energy network  100  in the example shown in  FIG. 26  is configured similarly to the example shown in  FIG. 24 , but includes no thermal accumulation unit (cold thermal accumulation unit)  140  and has the energy generation unit directly connected to the thermal accumulation unit (hot thermal accumulation unit)  140 . 
     The in-plant energy network  100  in the example shown in  FIG. 27  is configured similarly to the example shown in  FIG. 25 , but includes no thermal accumulation unit (cold thermal accumulation unit)  140  and has the energy generation unit directly connected to the thermal accumulation unit (hot thermal accumulation unit)  140 . 
     The in-plant energy network  100  in the example shown in  FIG. 28  is configured similarly to the example shown in  FIG. 25 , but has the energy generation unit directly connected to the thermal accumulation unit (hot thermal accumulation unit)  140 . 
     The in-plant energy network  100  in the example shown in  FIG. 29  is configured similarly to the example shown in  FIG. 25 , but includes no thermal accumulation unit (cold thermal accumulation unit)  140 . 
     Alternative Embodiment 
     In the embodiments, for example, the operations of the electric storage unit  130  and the thermal accumulation unit  140  may be carried out as described below. 
     The operations of the electric storage unit  130  and the thermal accumulation unit  140  are divided into different levels (energy levels). If considering, for example, 5 levels, then the charge differences between all the levels in time step UT(i) and all the levels in time step UT(i−1) are written into a matrix; these differences determine an energy balance: this energy balance is summed (with its sign, negative or positive) so that new fictional loads are calculated. After that, these fictional loads are optimized by the genetic algorithm, giving back a matrix with the fitness values for each possible configuration and connection between time step UT(i) and time step UT(i−1). After all the possible configurations for all the time steps UT (UT( 1 ) to UT(n)) in the operation period T are optimized by the genetic algorithm, Dijkstra algorithm evaluates the optimized path during the operation period T, by calculating the minimum path (in terms of at least one of energy supply cost, CO 2  emission, and primary energy consumption) and selecting just one of the possible configurations for each time step UT. The electric storage unit  130  and the thermal accumulation unit  140  are never considered in the genetic algorithm, so the operating levels of the electric storage unit  130  and the thermal accumulation unit  140  are never assigned a value between 0 and 255. 
     By combining the columns for the output ratios R 3  and R 4  of the electric storage unit  130  and the thermal accumulation unit  140  in  FIGS. 8 and 9 , and by the knowledge of the maximum energy content of the electric storage unit  130  and the thermal accumulation unit  140 , the power for passing from one state to another can be evaluated. 
     For example, assume the 20% value of the electric storage unit  130  for time step UT(i−1) and 50% for time step UT(i) and also assume the 70% value of the thermal accumulation unit  140  for time step UT(i−1) and 50% for time step UT(i). Considering that the electric storage unit  130  has a maximum capacity of 15 kWh and that the thermal accumulation unit  140  has a maximum capacity of 20 kWh, the resulting energy difference is +4.5 kWh (=15 kWh×0.5-15 kWh×0.2) for the electric storage unit  130  and −4 kWh (=20 kWh×0.5-20 kWh×0.7) for the thermal accumulation unit  140 ; if the duration of the time step UT is 15 minutes, the corresponding required power is +18 kW and −16 kW, respectively. These power values are respectively summed to the electric load  160  and the thermal load  170 , and the results are the fictional loads that are optimized by the genetic algorithm, that never explicitly considers the energy storage units. 
     After all the possible passages are plotted in a matrix and optimized by the genetic algorithm, fitness results are plotted in another matrix, and the Dijkstra algorithm evaluates the best passage from one UT to another, corresponding to the best possible use of both the energy storage units and the energy generation units. 
     The above-described combination of levels is a mere example. Any other combinations are possible. The present embodiment is therefore not limited to that combination. 
     The present invention is by no means limited to the foregoing embodiments and may be implemented in many other forms. Therefore, the embodiments are merely illustrative in every point and should not be interpreted in a limiting manner. The scope of the present invention is defined in the claims and is by no means bound by the specification at all. Furthermore, the scope of the present invention encompasses all the changes and modifications that are equivalents of the claims. 
     INDUSTRIAL APPLICABILITY 
     The present invention relates to control devices for optimizing evaluation of energy management in an in-plant energy network and is especially applicable for the purpose of practically achieving optimal operation of each piece of equipment, such as energy generation units, energy storage units, and energy loads, throughout an entire predetermined operation period. 
     REFERENCE SIGNS LIST 
     
         
           100  In-plant Energy Network 
           110  Combined Heat and Power Device (CHP) 
           111  Open/close Valve 
           112  Gas Engine 
           113  Electric Power Generator 
           114  Electric Power Converter 
           115  AC Circuit Breaker 
           116  Waste Heat Recovery Boiler 
           117  Adjusting Valve 
           120  Renewable Energy Source (RES) 
           121  Photovoltaic Power Generator 
           121   a  Solar Cell Panel 
           121   b  Solar-generated Electric Power Inverter 
           121   c  AC Circuit Breaker 
           122  Wind-powered Electric Generator 
           122   a  Wind Turbine 
           122   b  Wind-generated Electric Power Inverter 
           122   c  AC Circuit Breaker 
           130  Electric Storage Unit 
           131  Rechargeable Battery 
           132  Electric Storage Inverter 
           133  AC Circuit Breaker 
           140  Thermal Accumulation Unit 
           141  Thermal Accumulator 
           142  Thermal Energy Regulation Unit 
           150  Backup Thermal Energy Supply Unit 
           151  Fuel Flow Rate Regulation Valve 
           152  Boiler 
           153  Adjusting Valve 
           160  Electric Load 
           170  Thermal Load 
           180  Cold Thermal Source 
           200  Large-scale Electric Power Network 
           201  Central Power Plant 
           210  Residential Area 
           211  General Household 
           220  Manufacturing Industry Area 
           221  Factory 
           230  Tertiary Area 
           231  Office 
           240  Small-scale Wind-powered Plant 
           250  Electric Vehicle 
           270  Electric Storage Unit 
           280  Fuel Cell 
           290  Large-scale Wind-powered Plant 
           300  Control Device 
           310  Input Section 
           320  Display Unit 
           330  Control Section 
           340  Memory Section 
           341  Volatile Memory 
           342  Non-volatile Memory 
           350  First Reading Section 
           360  Second Reading Section 
           400  Internet 
         C 1  Operational Instruction Value 
         C 11  Operational Instruction Value 
         C 12  Operational Instruction Value 
         C 2  Operational Instruction Value 
         C 21  Operational Instruction Value 
         C 22  Operational Instruction Value 
         C 3  Operational Instruction Value 
         C 5  Operational Instruction Value 
         C 6  Operational Instruction Value 
         C 62  Operational Instruction Value 
         C 63  Operational Instruction Value 
         C 64  Operational Instruction Value 
         C 72  Operational Instruction Value 
         C 73  Operational Instruction Value 
         C 74  Operational Instruction Value 
         CH Chromosome 
         DT 1  Energy Demand Data 
         DT 2  Unit Energy Price Data 
         DT 3  Weather Condition Data 
         F Fuel Supply Source 
         GE Gene 
         ID Individual 
         L 1  First Load 
         L 2  Second Load 
         L 3  Third Load 
         L 4  Fourth Load 
         M Storage Medium 
         ME External Storage Medium 
         P 1  Electric Energy 
         P 2  Electric Energy 
         P 3  Electric Energy 
         PL Electric Power Supply Line 
         PP Population 
         PR Computer Program 
         PR 1  Input Means 
         PR 2  Computing Means 
         PR 3  Output Means 
         Q 1  Thermal Energy 
         Q 4  Thermal Energy 
         Q 5  Thermal Energy 
         T Operation Period 
         TL Heat Supply Line 
         UT Time Step