# A Novel Day Optimal Scheduling Strategy for Integrated Energy System including Electric Vehicle and Multisource Energy Storage

Authors: Xiao Ren; Yang Zeng; Zhang Sun; Jun Wang
Journal: International Transactions on Electrical Energy Systems
 (2023)

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## Abstract

Aiming at problems such as high operating costs, the low utilization efficiency of renewable energy, and the increase in the peak-valley difference in user load caused by disordered charging of electric vehicles (EVs), the operation mode of “setting electricity by heat” or “setting heat by electricity” is adopted in the integrated energy system of intelligent residential areas on the distribution side. In this paper, a method is proposed to use multiple energy storage, demand response technology, and make EV orderly charge and discharge plan to consume renewable resources and realize optimal economic scheduling in the integrated energy system of combined heat and power (CHP). Firstly, the demand response model of electricity load adjusting according to renewable energy output and time-of-use electricity prices has been established. Secondly, considering the comfort of the space thermal load, the orderly charge and discharge plan of electric energy storage (ESS), thermal energy storage (HSS), and EV and aiming at the minimum operating cost of the integrated energy system, the optimal output operation of each equipment was scheduled. Finally, through simulation analysis and comparative experiments, it is verified that the proposed method can promote the local consumption of renewable energy, realize the decoupling of thermoelectric constraints, and reduce the operating cost of the integrated energy system.


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## Body

## 1. Introduction

In the grim situation of an increasing energy crisis and environmental pollution, clean energy power generation technologies have been widely concerned and vigorously developed. Renewable energy power generation systems, such as photovoltaic and wind power, have developed relatively maturely, and the installed capacity on the user side is also increasing. Developing renewable energy power generation technology is an important strategy for the development of the current energy industry [1]. However, there are still some problems to be solved in the application of renewable energy, such as the intermittent fluctuation and the mismatch between the peak period of power generation output and the peak period of user load, which will result in energy waste from abandoning wind and light [2, 3]. In addition, with the increasing penetration of EVs, EVs have become an important load on the power grid. Affected by the rules of vehicle usage [4, 5], EV charging presents the characteristics of randomness and aggregation. Overlapping charging times and charging during peak hours of electricity consumption will lead to excessive load and an increase in the peak-to-valley difference, which will aggravate the burden on the distribution network [6].The integrated energy system is an important part of the energy Internet and an important way of energy consumption in the future. The integrated energy system with the cogeneration microgrid as the core unit dispatches the grid power, the natural gas energy, and the distributed energy in a unified manner [7]. Under the condition of meeting various load demands, improving the efficiency of energy utilization and realizing the economic operation of the system are the key technologies to achieve sustainable development, which has a very broad development prospect.At present, there has been extensive discussion on the concept, basic framework, and operational optimization of integrated energy systems. Reference [8] comprehensively summarizes the development trends and hotspots of integrated energy systems and the energy Internet, as well as the existing problems. Reference [9] adds thermal storage to the integrated energy system. The electric heating device can effectively reduce the peak-to-valley difference of the power grid, improve the use efficiency of electric heating equipment, and greatly reduce the heating operation cost of users. Reference [10] established a regional integrated energy system model, including energy conversion equipment and energy storage equipment, and verified that the thermoelectric operation constraints can be effectively decoupled through energy storage equipment. References [11–13] introduce power-to-gas (P2G) technology and multiple energy storage devices into the integrated energy system to increase the local consumption of renewable energy and improve the utilization rate of wind turbines. Reference [14] introduced the organic Rankine cycle system into CHP based on the introduction of electricity to gas to break the thermoelectric coupling, and improved the system economy through the coordinated operation of electricity to gas and CHP. However, the investment cost of P2G and energy storage devices is high, and the energy conversion efficiency of P2G is low, so it has not been widely used. Reference [15] proposes a master-slave game model for power trading between community operators and consumers and verifies that the model can improve operator and consumer revenue and optimize system load characteristics. However, its microcombustion unit adopts the operation modes of heat and electricity and does not have scheduling flexibility. Reference [16] proposes a coordinated optimal scheduling scheme for flexible electric vehicles and distributed power sources, and uses the wind power to reasonably adjust the charging behavior of electric vehicles, which can provide an effective way for wind power consumption. Reference [17] established a generalized demand response interactive optimization model based on multienergy complementation to realize the multiparty interaction between the power grid, users, and CCHP units. Reference [18] proposes adding an electric heat pump to the source side of the regional integrated energy system, and on the load side, the electric load should respond to the demand for wind power according to the wind power output and electricity price in order to absorb wind power, so that the heat load could vary within a certain range. According to reference [19], electricity and heat loads should respond to demands based on customized electricity and heat prices, which can reduce the demand for electricity and heat in the microgrid at peak electricity prices as well as system operating costs. The above research studies did not add the large number of existing energy storage resources, such as EVs, into the comprehensive energy system and make full use of their energy storage resources to further absorb renewable energy and reduce system operating costs.In summary, this paper discusses and studies the optimization problems of the integrated energy system of the combined heat and power smart residential area, including PV, wind power generation, electric boiler, CHP unit, EV, ESS, HSS, thermal load, and electric load, and proposes a method. Using multiple energy storage systems, demanding response technology, and formulating EV orderly charging and discharging plans to consume renewable resources and achieve optimal economic dispatch of the system, first a demand response model is established, in which the electric load can be adjusted according to the output of the renewable energy and the time-of-use electricity price. Secondly, the comfort of space thermal load, ESS, and HSS are considered, and EVs are added to the integrated energy system. In order to achieve the goal of minimizing the operating cost of the integrated energy system, this paper formulates an orderly charge-discharge plan according to its behavior characteristics and interacts with other equipment in the system to optimize the scheduling. On this basis, the CPLEX commercial solver is used to solve the problem, and the optimal output of each equipment is obtained. Finally, the feasibility and economy of the proposed method are verified by a simulation example.


## 2. Integrated Energy System Structure of Intelligent Community

The structure of the integrated energy system of the smart community is shown in Figure1, which includes photovoltaic, wind energy, electric energy, thermal energy, and natural gas energy. The user-side loads include electricity and heat, and the heat load is divided into the hot water load and the space heat load, which, respectively, meet the user’s demands for indoor temperature and hot water. The electric load is satisfied by PV, fans, the power grid, CHP unit electric output, and ESS or EV discharge, while the thermal load is satisfied by electric boiler outputs, CHP unit heat output, and heat energy released by a heat storage tank. The day scheduling process is to formulate the day scheduling plan of the smart community according to PV and wind power outputs, electricity price information, load demand, and an EV travel plan, and arrange the optimal operation output of each equipment to meet the user’s electricity and heat needs while minimizing the operating cost of the system.Figure 1
Block diagram of intelligent community integrated energy system.


### 2.1. Renewable Energy Output and User Load

The output power of photovoltaic and wind power generation in smart communities depends on the installed capacity and the external environment. The predicted output of wind and optical power is expressed as follows [20], and T is the total number of optimal scheduling time periods:(1)Pwind≜Pwind1,Pwind2,...,PwindT,PPV≜PPV1,PPV2,...,PPVT.The predicted demand power of electric load and hot water load is shown in equations (2) and (3). The space heat load is determined by factors such as outdoor temperature and indoor demand temperature, which is defined as Qte(t).(2)Pave≜Pave1,Pave2,...,PaveT,(3)Qha≜Qha1,Qha2,...,QhaT.


### 2.2. CHP Unit

The CHP unit is composed of a gas boiler and a waste heat boiler. Natural gas can be converted into electric energy and heat energy through the CHP unit. The mathematical model of its operation, as well as the ramp rate and rated power constraints, is as follows [18]:(4)QGTt=PGTt1−λGT−λLossεGTμGTλGT,0≤PGTt≤PGTmax,∆PGTmin≤∆PGTt≤∆PGTmax.In the formula,QGTtandPGTt are the heat generation and power generation of the CHP unit in the t period, t = 1, 2, …, T. λGT,λLoss,εGT, and μGT are the power generation efficiency, heat dissipation loss coefficient, waste heat recovery rate, and heating efficiency of the CHP unit, respectively. PGTmax,∆PGTt,∆PGTmax, and ∆PGTmin are the maximum output of the CHP unit’s electrical power, the rate of change of the electrical power between the t period and the previous period, and the upper and lower limits of the ramp rate of the electrical power.The cost of natural gas consumed by CHP units in t period is as follows:(5)CGTt=ρgasPGTtLλGT.In the formula,ρgas and L are the unit price of natural gas and the low calorific value of natural gas, respectively.


### 2.3. Electric Boiler

Electric boilers can convert electrical energy into thermal energy. The mathematical model of an electric boiler in operation, as well as the ramp rate and rated power constraints, is as follows:(6)QEBt=PEBtλEB,0≤PEBt≤PEBmax,∆PEBmin≤∆PEBt≤∆PEBmax.In the formula,QEBtandPEBt are the heating power and the input electric power of the electric boiler in the t period, respectively. λEB is the heating efficiency of the electric boiler. PEBmax,∆PEBt,∆PEBmax, and ∆PEBmin are the maximum value of the electric boiler input electric power, the rate of change of the input electric power, and the upper and lower limits of the ramp rate, respectively.


### 2.4. HSS/ESS

HSS/ESS can store energy when there is excess heat/electricity and release it when there is a shortage of heat/electricity. Its mathematical model and the constraints to be satisfied are as follows [11]:(7)WYt=WYt−1+QYchtηYch−QYdischtηYdisch∆tWYt−1=WYint,t=1,(8)0.2WYmax≤Wt≤0.9WYmax,(9)0≤QYcht≤QYchmaxηYch,0≤QYdischt≤QYdischmaxηYdisch.In the formula,Y is the type of energy storage device, WY (t), WYint,QYcht, and QYdischt, respectively, are the energy storage energy of the energy storage device t period, the initial storage energy, the t period storage, and release of energy. QYchmax,QYdischmax,ηYch, and ηYdisch, respectively, are maximum energy storage power, maximum discharge power, energy storage, and efficiency of the energy storage device. Set capacity constraints (8) to prevent overcharging and overdischarging and extend the life of the energy storage unit. In order to achieve the next optimization cycle of the energy storage device, there must be enough capacity to participate in the optimal operation, and the heat storage/gas storage after one cycle of operation of the energy storage device is restored to the original heat storage/gas volume, and its constraints are as follows:(10)WYT=WYint.In the formula,WYT is the heat storage/electricity in the last period of the optimization cycle.


### 2.5. EV

As a special energy storage device, electric vehicles are only connected to the grid for part of the day. Orderly charging and discharging operations can be performed during the time period when EVs are connected to the grid, reducing power purchase costs and promoting renewable energy consumption. Assuming that the total amount of EV isI, the mathematical model and constraints to be satisfied are as follows [21]:(11)WEV.it=WEV.it−1+PEV.ichtηEVch−PEV.idischtηEVdisch∆tWEV.it=WEV.iint,t∈td+1,ta,(12)0.2WEVmax≤WEV.it≤0.9WEVmax,(13)0≤PEV.icht≤PEVchmaxηEVch,0≤PEV.idischt≤PEVdischmaxηEVdisch.In the formula, for convenient calculation, the maximum storage capacity, maximum charge, and discharge power, charge, and discharge efficiency of all EVs in the cell are the same, which are expressed byWEVmax,PEVchmax,PEVdischmax,ηEVch and ηEVdisch, respectively. WEV⋅it,WEV.iint,PEV.icht, and PEV.idischt, respectively, represent the storage capacity of the i EV, the initial storage capacity, the charging power, and discharging power of the t period, i = 1, 2, …, I.ta and td are the times when the EV arrives and leaves. The EV also sets the capacity constraint of equation (12).The capacity of the EV to leave must meet the user’s travel plan. The capacity of the EV at departure needs to be equal to the desired capacity, which is set to 0.9 and charged to the minimum of the maximum SOC during the dwell period, as shown in equation (14). Among them, (td−ta) represents the total period of EV access to the grid, and SOCEV.ita and SOCEV.itd represent the capacity of the i-th EV at the time of arrival and departure, respectively.(14)SOCEV.itd=min0.9,td−taPEVchmaxηEVchWEVmax+SOCEV.ita.


## 2.1. Renewable Energy Output and User Load

The output power of photovoltaic and wind power generation in smart communities depends on the installed capacity and the external environment. The predicted output of wind and optical power is expressed as follows [20], and T is the total number of optimal scheduling time periods:(1)Pwind≜Pwind1,Pwind2,...,PwindT,PPV≜PPV1,PPV2,...,PPVT.The predicted demand power of electric load and hot water load is shown in equations (2) and (3). The space heat load is determined by factors such as outdoor temperature and indoor demand temperature, which is defined as Qte(t).(2)Pave≜Pave1,Pave2,...,PaveT,(3)Qha≜Qha1,Qha2,...,QhaT.


## 2.2. CHP Unit

The CHP unit is composed of a gas boiler and a waste heat boiler. Natural gas can be converted into electric energy and heat energy through the CHP unit. The mathematical model of its operation, as well as the ramp rate and rated power constraints, is as follows [18]:(4)QGTt=PGTt1−λGT−λLossεGTμGTλGT,0≤PGTt≤PGTmax,∆PGTmin≤∆PGTt≤∆PGTmax.In the formula,QGTtandPGTt are the heat generation and power generation of the CHP unit in the t period, t = 1, 2, …, T. λGT,λLoss,εGT, and μGT are the power generation efficiency, heat dissipation loss coefficient, waste heat recovery rate, and heating efficiency of the CHP unit, respectively. PGTmax,∆PGTt,∆PGTmax, and ∆PGTmin are the maximum output of the CHP unit’s electrical power, the rate of change of the electrical power between the t period and the previous period, and the upper and lower limits of the ramp rate of the electrical power.The cost of natural gas consumed by CHP units in t period is as follows:(5)CGTt=ρgasPGTtLλGT.In the formula,ρgas and L are the unit price of natural gas and the low calorific value of natural gas, respectively.


## 2.3. Electric Boiler

Electric boilers can convert electrical energy into thermal energy. The mathematical model of an electric boiler in operation, as well as the ramp rate and rated power constraints, is as follows:(6)QEBt=PEBtλEB,0≤PEBt≤PEBmax,∆PEBmin≤∆PEBt≤∆PEBmax.In the formula,QEBtandPEBt are the heating power and the input electric power of the electric boiler in the t period, respectively. λEB is the heating efficiency of the electric boiler. PEBmax,∆PEBt,∆PEBmax, and ∆PEBmin are the maximum value of the electric boiler input electric power, the rate of change of the input electric power, and the upper and lower limits of the ramp rate, respectively.


## 2.4. HSS/ESS

HSS/ESS can store energy when there is excess heat/electricity and release it when there is a shortage of heat/electricity. Its mathematical model and the constraints to be satisfied are as follows [11]:(7)WYt=WYt−1+QYchtηYch−QYdischtηYdisch∆tWYt−1=WYint,t=1,(8)0.2WYmax≤Wt≤0.9WYmax,(9)0≤QYcht≤QYchmaxηYch,0≤QYdischt≤QYdischmaxηYdisch.In the formula,Y is the type of energy storage device, WY (t), WYint,QYcht, and QYdischt, respectively, are the energy storage energy of the energy storage device t period, the initial storage energy, the t period storage, and release of energy. QYchmax,QYdischmax,ηYch, and ηYdisch, respectively, are maximum energy storage power, maximum discharge power, energy storage, and efficiency of the energy storage device. Set capacity constraints (8) to prevent overcharging and overdischarging and extend the life of the energy storage unit. In order to achieve the next optimization cycle of the energy storage device, there must be enough capacity to participate in the optimal operation, and the heat storage/gas storage after one cycle of operation of the energy storage device is restored to the original heat storage/gas volume, and its constraints are as follows:(10)WYT=WYint.In the formula,WYT is the heat storage/electricity in the last period of the optimization cycle.


## 2.5. EV

As a special energy storage device, electric vehicles are only connected to the grid for part of the day. Orderly charging and discharging operations can be performed during the time period when EVs are connected to the grid, reducing power purchase costs and promoting renewable energy consumption. Assuming that the total amount of EV isI, the mathematical model and constraints to be satisfied are as follows [21]:(11)WEV.it=WEV.it−1+PEV.ichtηEVch−PEV.idischtηEVdisch∆tWEV.it=WEV.iint,t∈td+1,ta,(12)0.2WEVmax≤WEV.it≤0.9WEVmax,(13)0≤PEV.icht≤PEVchmaxηEVch,0≤PEV.idischt≤PEVdischmaxηEVdisch.In the formula, for convenient calculation, the maximum storage capacity, maximum charge, and discharge power, charge, and discharge efficiency of all EVs in the cell are the same, which are expressed byWEVmax,PEVchmax,PEVdischmax,ηEVch and ηEVdisch, respectively. WEV⋅it,WEV.iint,PEV.icht, and PEV.idischt, respectively, represent the storage capacity of the i EV, the initial storage capacity, the charging power, and discharging power of the t period, i = 1, 2, …, I.ta and td are the times when the EV arrives and leaves. The EV also sets the capacity constraint of equation (12).The capacity of the EV to leave must meet the user’s travel plan. The capacity of the EV at departure needs to be equal to the desired capacity, which is set to 0.9 and charged to the minimum of the maximum SOC during the dwell period, as shown in equation (14). Among them, (td−ta) represents the total period of EV access to the grid, and SOCEV.ita and SOCEV.itd represent the capacity of the i-th EV at the time of arrival and departure, respectively.(14)SOCEV.itd=min0.9,td−taPEVchmaxηEVchWEVmax+SOCEV.ita.


## 3. Electric/Heat Load Demand Response Model

### 3.1. Load Demand Response Model That Considers User Comfort

The user’s electricity load is adjusted according to the renewable energy output and time-of-use electricity price, and the controllable load is adjusted to different time intervals, but the total electricity demand remains unchanged, so that the average power in each period deviates from the original initial valuePavet.The average power consumption of each time period after adjustment is defined as xavet. Adjusting the power consumption behavior will affect the user’s electrical comfort, and the electrical discomfort is taken into account in the demand response model, as shown in the following equation:(15)d=α∑t=1Txavet−Pavet2,∑t=1Txavet=∑t=1TPavet.In the formula,d is the discomfort degree of electricity consumption. The more the adjusted average power xavet deviates from the original average power Pavet in each period, the greater the discomfort value. α is the comfort coefficient. The larger the α value, the higher the requirement for electricity comfort. The electricity prices for buying and selling electricity at each moment are represented by ρt and τ, respectively. Integrating the electricity purchase cost and electricity discomfort of the community, the utility cost function of the demand response of the electricity load is obtained as follows:(16)Cele=∑t=1Tρtxavet−PPVt−Pwindt+xavet−PPVt−Pwindt2+∑t=1Tτxavet−PPVt−Pwindt−xavet−PPVt−Pwindt2+d.


### 3.2. Space Heat Load Demand Response Model considering User Satisfaction

The relationship between the indoor temperature change of the building and the space thermal power is as follows:(17)Qtet=NRθint+1−e−∆t/τθint1−e−∆t/τ−θoutt,τ=RCair.In the formula,θint and θoutt are the indoor and outdoor temperatures of the building during the t-time period, respectively; R, N, and Cair represent the equivalent thermal resistance of the building, the number of heating users, and the indoor air heat capacity of the building, respectively. The user sets the best indoor temperature θcom, and the temperature variable range [θmin,θmax]. The indoor temperature can vary within the set range, but deviations from the optimal temperature will make users feel uncomfortable, and the more the deviation, the stronger the discomfort. The discomfort cost is as follows:(18)H=∑t=1Tbθint−θcom.In the formula,H is the room temperature discomfort, b is the discomfort conversion coefficient, and the room temperature discomfort caused by the temperature difference is converted into the cost of electricity.


## 3.1. Load Demand Response Model That Considers User Comfort

The user’s electricity load is adjusted according to the renewable energy output and time-of-use electricity price, and the controllable load is adjusted to different time intervals, but the total electricity demand remains unchanged, so that the average power in each period deviates from the original initial valuePavet.The average power consumption of each time period after adjustment is defined as xavet. Adjusting the power consumption behavior will affect the user’s electrical comfort, and the electrical discomfort is taken into account in the demand response model, as shown in the following equation:(15)d=α∑t=1Txavet−Pavet2,∑t=1Txavet=∑t=1TPavet.In the formula,d is the discomfort degree of electricity consumption. The more the adjusted average power xavet deviates from the original average power Pavet in each period, the greater the discomfort value. α is the comfort coefficient. The larger the α value, the higher the requirement for electricity comfort. The electricity prices for buying and selling electricity at each moment are represented by ρt and τ, respectively. Integrating the electricity purchase cost and electricity discomfort of the community, the utility cost function of the demand response of the electricity load is obtained as follows:(16)Cele=∑t=1Tρtxavet−PPVt−Pwindt+xavet−PPVt−Pwindt2+∑t=1Tτxavet−PPVt−Pwindt−xavet−PPVt−Pwindt2+d.


## 3.2. Space Heat Load Demand Response Model considering User Satisfaction

The relationship between the indoor temperature change of the building and the space thermal power is as follows:(17)Qtet=NRθint+1−e−∆t/τθint1−e−∆t/τ−θoutt,τ=RCair.In the formula,θint and θoutt are the indoor and outdoor temperatures of the building during the t-time period, respectively; R, N, and Cair represent the equivalent thermal resistance of the building, the number of heating users, and the indoor air heat capacity of the building, respectively. The user sets the best indoor temperature θcom, and the temperature variable range [θmin,θmax]. The indoor temperature can vary within the set range, but deviations from the optimal temperature will make users feel uncomfortable, and the more the deviation, the stronger the discomfort. The discomfort cost is as follows:(18)H=∑t=1Tbθint−θcom.In the formula,H is the room temperature discomfort, b is the discomfort conversion coefficient, and the room temperature discomfort caused by the temperature difference is converted into the cost of electricity.


## 4. Day Optimal Scheduling Method for Integrated Energy System

First, the equation (16) is minimized, and the electric load demand response is performed to obtain the optimized electric load. Then, the optimized electrical load is added to the optimization model, and the room temperature discomfort, the operation and maintenance cost of each unit, and the EV loss cost are added to the operating cost objective function. By minimizing the operating cost of the entire system, the optimal output of each unit is solved, and the scheduling plan is formulated. The objective function is as follows:(19)minC=∑t=1TCgridt+CGTt+COCt+H,(20)Cgridt=ρtPgridt+Pgridt2+τPgridt−Pgridt2∆t,(21)COCt=∑f=1XPftDf+∑k=1YQk.cht+Qk.dischtDk∆t+∑j=1ZPjtDj+∑i=1IPEV.icht+PEV.idischtDEV∆t.In the formula,Cgridt, COCt, and Pgridt are the electricity purchase cost, equipment operation and maintenance and loss cost, and electricity purchase and sale power in period t, respectively. Df, Dk,Dj, and DEV are unit O&M and loss cost, such as the controllable unit f, the energy storage device k, the renewable energy unit j, and EV, and X, Y, and Z are the types of controllable units, energy storage device, and the renewable energy units.In formula (20), the first part is the cost of purchasing electricity, and the second part is the cost of selling electricity.The entire integrated energy system needs to meet the following constraints:(1)
Electrical power balance constraints are as follows:(22)Pgridt=xavet−Pwindt−PPVt+PEBt−PGTt+QESS.cht−QESS.discht+∑i=1IPEV.icht−∑i=1IPEV.idischt.(2)
Thermal power balance constraints are as follows:(23)QGTt+QEBt=QHSS.cht−QHSS.discht+Qhat+Qtet.(3)
Power purchase constraints are as follows:(24)Pgridt≤Pgridmax.In the formula,Pgridmax is the maximum purchased power.


## 5. Example Analysis

### 5.1. Basic Data

An integrated energy system of intelligent residential area, including EV, ESS, HSS, electric boiler, CHP unit, PV, and wind power renewable energy is taken as the research object. Its electrical load and hot water load are shown in Figure2; the predicted output of renewable energy is shown in Figure 3; and the typical outdoor temperature in winter is shown in Figure 4. The time-of-use electricity price is from 10:00 to 15:00, and the peak time is from 18:00 to 22:00, ρt = 0.9 yuan/(kWh); from 0:00 to 7:00, it is the valley electricity price, ρt = 0.3 yuan/(kWh); the rest period is the electricity price in normal times, ρt = 0.5 yuan/(kWh). The electricity price for selling electricity is a certain value, τ = 0.15 yuan/(kWh), and the comfort coefficient α = 0.005.Figure 2
Electric load and hot water load.Figure 3
Renewable energy contribution.Figure 4
Typical outdoor temperature in winter.The power generation efficiency of the CHP unit isλGT = 0.35, the heat dissipation loss coefficient λLoss = 0.15, the waste heat recovery rate εGT = 0.85, and the heating efficiency μGT = 1.2. The unit price of natural gas is ρgas = 3.45 yuan/m3, and the low calorific value of natural gas is L = 9.7 kWh/m3. Building equivalent thermal resistance R = 16, number of heating users N = 320, air heat capacity Cair = 0.525, optimum temperature θcom = 21°C, upper and lower temperature limits θmax = 22°C, θmin = 20°C, and discomfort conversion coefficient b = 4. The relevant data of EVs and energy storage devices are shown in Tables 1 and 2, which show the relevant parameters of each controllable unit and renewable energy unit. Based on the U.S. Department of Transportation’s survey results on family vehicles in the United States [22], a Monte Carlo simulation stochastic model can be used to obtain the travel plan of the EV group. According to the data processing method in [23], the probability distribution functions of EV arrival time, departure time, and travel distance can be obtained by fitting. According to the obtained arrival time, departure time, and travel distance, the initial capacity of the EV when it is connected to the grid can be calculated. The maximum limit power Pmax for power purchase is set to 800 kW, and the optimization flowchart is shown in Figure 5.Table 1
Parameters of EVs and energy storage devices.
EVParameterEnergy storage deviceHSS parametersESS parametersPEVchmax (kW)4QY.ichmax (kW)100150PEVdischmax (kW)4QY.idischmax (kW)100150CEVmax (kW·h)30WY.imax (kW·h)500400ηEVch0.92ηY.ich0.950.92ηEVdisch0.92ηY.idisch0.950.92DEV0.06Dk0.040.052Table 2
Parameters of controllable unit and renewable energy unit.
Parameter nameCHP unitGas boilerWind turbinePhotovoltaicUpper limit of power (kW)500200800850Efficiency (kW)0.350.97——Climb rate upper limit (kW/min)83——Lower limit of climbing rate (kW/min)−7−3——Operation and maintenance cost (yuan/(kW·h))0.0530.0260.0250.029Figure 5
Optimization flowchart.


### 5.2. Simulation Results

On the MATLAB2015a platform, the CPELX software is called through the YALMIP toolbox to solve the calculation examples. The recently optimized electric power dispatching situation is shown in Figure6.Figure 6
Electric power dispatching.It can be seen from the simulation results that part of the electricity load during the peak period of electricity consumption is transferred to the period when the renewable energy generation is sufficient and the electricity price is low, which alleviates the electricity consumption pressure during the peak period of electricity consumption. It promotes the timely and on-site consumption of renewable energy power generation. Electric boilers can use renewable energy and electricity generated by the CHP units so as to generate heat to supply heat load demand and reduce the CHP output, thereby reducing the cost of purchasing natural gas and consuming part of the excess electricity. During the peak electricity consumption period and electricity price peak period (18:00–21:00), the EV uses the remaining capacity after arrival to discharge to meet the electricity load demand during the peak electricity consumption period and then at night when the wind power generation is sufficient and the electricity price is low, so as to realize the local consumption of wind power generation and reduce the cost of electricity consumption. The thermal power balance of the system is shown in Figure7, the indoor temperature and space thermal power are shown in Figure 8, and the HSS charge and discharge heat power and capacity curves are shown in Figure 9.Figure 7
Thermal power balance.Figure 8
Indoor temperature and space thermal power.Figure 9
HSS charge-discharge power and capacity.During the period of sufficient photovoltaic power generation (10:00–15:00), the electric boiler runs at rated power, and at this time, there is a surplus of electric power. The room temperature is reduced by the response to the heat load demand, and the HSS discharges energy, reducing the CHP’s electricity and heat output. At 15:00–16:00, the HSS cannot continue to discharge, and the room temperature is further lowered to reduce the thermal load. Excess power is consumed by EV and ESS charging and sold to the power grid. During the peak period of electricity price and user load (18:00–21:00), in order to reduce the electricity purchase cost, the output of the electric boiler is reduced, the output of the CHP unit is increased to meet the electricity load demand, and the excess heat energy is charged and enhanced by the HSS. Consume at room temperature to achieve multienergy complementation. Since there are heat storage tanks, electric boilers, and space heat loads in the system for demand response, the operation mode of CHP units based on heat and electricity is decoupled, and the flexible scheduling operation of the entire system is realized.


### 5.3. Comparison and Analysis

In order to verify the economy of the proposed day scheduling method for the integrated energy system of the intelligent community and the effectiveness of realizing the local consumption of renewable energy, four comparative simulation experiments are designed as follows: (1) EV carries out disordered charging; (2) orderly charging of EV; (3) electricity/heat load demand response is not considered; and (4) electric/thermal energy storage systems are not considered.


#### 5.3.1. Scenario 1 Scheduling

Unordered charging of EVs means that the EVs are charged immediately after arriving at home until they reach the expected capacity. The scheduling situation is shown in Figure10. After the EVs arrive home in the afternoon, they will be connected to the power grid for charging one after another, and 18:00–22:00 is the peak period of electrical load, which overlaps with the EV charging time, resulting in insufficient power in the integrated energy system and the need to purchase power from the power grid. But this is the peak period for electricity prices, which increases the operating cost of the system. During the peak period of wind power generation from 1:00 to 6:00 at night, EVs have no remaining capacity to consume wind power and can only sell the excess wind energy to the grid, which reduces the economic benefits of the system.Figure 10
Electric power dispatching of scenario 1.


#### 5.3.2. Scenario 2 Scheduling

EV orderly charging means that after the EV arrives home, the time period with the lower electricity price is selected for charging under the condition of meeting the constraints. The scheduling situation is shown in Figure11. It can be seen from the figure that most EVs are charged at the beginning of the valley of the electricity price at 23:00, which leads to a large peak value of power purchase at this time. Because EVs delay the charging time, they can absorb some of the excess wind power and CHP power output during peak wind power generation periods, but there is still more surplus power to be sold to the grid.Figure 11
Electric power dispatching of scenario 2.


#### 5.3.3. Scenario 3 Scheduling

The electric load demand response is not considered in the system, and the indoor temperature is kept at the optimal temperature. The scheduling situation is shown in Figure12. At this time, the indoor temperature and space heat load are shown in Figure 13. There is no demand response for electrical loads, which reduces the on-site consumption of renewable energy. The room temperature remains stable, and it is impossible to further increase or decrease the output of the CHP unit during certain periods, thereby reducing the flexibility and economy of the operation of the integrated energy system.Figure 12
Electric power dispatching of scenario 3.Figure 13
Indoor temperature and space heat load.


#### 5.3.4. Scenario 4 Scheduling

The ESS and HSS are removed from the system, and the electrical power scheduling is shown in Figure14, and the thermal power balance is shown in Figure 15.Figure 14
Electric power dispatching of scenario 4.Figure 15
Thermal power balance of scenario 4.Without considering the ESS, the power output by the renewable energy power generation system and the power generated by the CHP cannot be stored and can only be sold to the grid. When the power consumption peaks and the power price is high, it is unable to provide electric energy for the system, so it can only purchase electricity from the grid or increase the output of CHP units to meet the electric energy consumption. Without considering HSS, thermal energy cannot be stored when the output of the CHP unit is increased, and at the same time, thermal energy cannot be provided when the output of the CHP unit is reduced and the user needs thermal energy. Without considering the electric/thermal energy storage system, the thermodecoupled operation of the system cannot be realized, which reduces the operating flexibility of the system and increases the operating cost.The operating costs of each comparison scenario are shown in Table3. Among them, scenario 5 is the model and method proposed in this paper. It can be seen from Table 3 that when EVs are charged in an orderly or disorderly manner, the electricity purchase cost and electricity sales revenue of the entire system are relatively large, which cannot effectively consume renewable energy. EVs are charged in an orderly manner, and the total cost is lower than that of disordered charging because charging is started during the low electricity price period. In Scenario 3, the CHP unit needs to provide more heat output to maintain a stable room temperature, so the gas purchase cost increases. In scenario 4, since the ESS and HSS are not considered, the equipment operation and maintenance costs are the lowest, but the power purchase cost increases. Scenario 5 is the optimization method proposed in this paper. In this scenario, the revenue from electricity sales is the lowest, which shows that renewable energy has been effectively consumed locally and the power back to the grid has been reduced. At the same time, through the thermoelectrolytic coupling operation and multienergy complementary effect of the CHP unit, the gas purchase cost is reduced, as is the operating cost of the entire system.Table 3
Operating costs.
ScenarioPower purchase costGas purchase costPower sales revenueOperation and maintenance costTotal costScenario 11551.99340.9345.351130.711678.15Scenario 21194.49337.1313.741129.711344.46Scenario 3238.379393.6128.7114010643.27Scenario 4323.79149.5161.91075.610386.9Scenario 5273.18820.382.81122.110132.7Unit: Yuan


## 5.1. Basic Data

An integrated energy system of intelligent residential area, including EV, ESS, HSS, electric boiler, CHP unit, PV, and wind power renewable energy is taken as the research object. Its electrical load and hot water load are shown in Figure2; the predicted output of renewable energy is shown in Figure 3; and the typical outdoor temperature in winter is shown in Figure 4. The time-of-use electricity price is from 10:00 to 15:00, and the peak time is from 18:00 to 22:00, ρt = 0.9 yuan/(kWh); from 0:00 to 7:00, it is the valley electricity price, ρt = 0.3 yuan/(kWh); the rest period is the electricity price in normal times, ρt = 0.5 yuan/(kWh). The electricity price for selling electricity is a certain value, τ = 0.15 yuan/(kWh), and the comfort coefficient α = 0.005.Figure 2
Electric load and hot water load.Figure 3
Renewable energy contribution.Figure 4
Typical outdoor temperature in winter.The power generation efficiency of the CHP unit isλGT = 0.35, the heat dissipation loss coefficient λLoss = 0.15, the waste heat recovery rate εGT = 0.85, and the heating efficiency μGT = 1.2. The unit price of natural gas is ρgas = 3.45 yuan/m3, and the low calorific value of natural gas is L = 9.7 kWh/m3. Building equivalent thermal resistance R = 16, number of heating users N = 320, air heat capacity Cair = 0.525, optimum temperature θcom = 21°C, upper and lower temperature limits θmax = 22°C, θmin = 20°C, and discomfort conversion coefficient b = 4. The relevant data of EVs and energy storage devices are shown in Tables 1 and 2, which show the relevant parameters of each controllable unit and renewable energy unit. Based on the U.S. Department of Transportation’s survey results on family vehicles in the United States [22], a Monte Carlo simulation stochastic model can be used to obtain the travel plan of the EV group. According to the data processing method in [23], the probability distribution functions of EV arrival time, departure time, and travel distance can be obtained by fitting. According to the obtained arrival time, departure time, and travel distance, the initial capacity of the EV when it is connected to the grid can be calculated. The maximum limit power Pmax for power purchase is set to 800 kW, and the optimization flowchart is shown in Figure 5.Table 1
Parameters of EVs and energy storage devices.
EVParameterEnergy storage deviceHSS parametersESS parametersPEVchmax (kW)4QY.ichmax (kW)100150PEVdischmax (kW)4QY.idischmax (kW)100150CEVmax (kW·h)30WY.imax (kW·h)500400ηEVch0.92ηY.ich0.950.92ηEVdisch0.92ηY.idisch0.950.92DEV0.06Dk0.040.052Table 2
Parameters of controllable unit and renewable energy unit.
Parameter nameCHP unitGas boilerWind turbinePhotovoltaicUpper limit of power (kW)500200800850Efficiency (kW)0.350.97——Climb rate upper limit (kW/min)83——Lower limit of climbing rate (kW/min)−7−3——Operation and maintenance cost (yuan/(kW·h))0.0530.0260.0250.029Figure 5
Optimization flowchart.


## 5.2. Simulation Results

On the MATLAB2015a platform, the CPELX software is called through the YALMIP toolbox to solve the calculation examples. The recently optimized electric power dispatching situation is shown in Figure6.Figure 6
Electric power dispatching.It can be seen from the simulation results that part of the electricity load during the peak period of electricity consumption is transferred to the period when the renewable energy generation is sufficient and the electricity price is low, which alleviates the electricity consumption pressure during the peak period of electricity consumption. It promotes the timely and on-site consumption of renewable energy power generation. Electric boilers can use renewable energy and electricity generated by the CHP units so as to generate heat to supply heat load demand and reduce the CHP output, thereby reducing the cost of purchasing natural gas and consuming part of the excess electricity. During the peak electricity consumption period and electricity price peak period (18:00–21:00), the EV uses the remaining capacity after arrival to discharge to meet the electricity load demand during the peak electricity consumption period and then at night when the wind power generation is sufficient and the electricity price is low, so as to realize the local consumption of wind power generation and reduce the cost of electricity consumption. The thermal power balance of the system is shown in Figure7, the indoor temperature and space thermal power are shown in Figure 8, and the HSS charge and discharge heat power and capacity curves are shown in Figure 9.Figure 7
Thermal power balance.Figure 8
Indoor temperature and space thermal power.Figure 9
HSS charge-discharge power and capacity.During the period of sufficient photovoltaic power generation (10:00–15:00), the electric boiler runs at rated power, and at this time, there is a surplus of electric power. The room temperature is reduced by the response to the heat load demand, and the HSS discharges energy, reducing the CHP’s electricity and heat output. At 15:00–16:00, the HSS cannot continue to discharge, and the room temperature is further lowered to reduce the thermal load. Excess power is consumed by EV and ESS charging and sold to the power grid. During the peak period of electricity price and user load (18:00–21:00), in order to reduce the electricity purchase cost, the output of the electric boiler is reduced, the output of the CHP unit is increased to meet the electricity load demand, and the excess heat energy is charged and enhanced by the HSS. Consume at room temperature to achieve multienergy complementation. Since there are heat storage tanks, electric boilers, and space heat loads in the system for demand response, the operation mode of CHP units based on heat and electricity is decoupled, and the flexible scheduling operation of the entire system is realized.


## 5.3. Comparison and Analysis

In order to verify the economy of the proposed day scheduling method for the integrated energy system of the intelligent community and the effectiveness of realizing the local consumption of renewable energy, four comparative simulation experiments are designed as follows: (1) EV carries out disordered charging; (2) orderly charging of EV; (3) electricity/heat load demand response is not considered; and (4) electric/thermal energy storage systems are not considered.


### 5.3.1. Scenario 1 Scheduling

Unordered charging of EVs means that the EVs are charged immediately after arriving at home until they reach the expected capacity. The scheduling situation is shown in Figure10. After the EVs arrive home in the afternoon, they will be connected to the power grid for charging one after another, and 18:00–22:00 is the peak period of electrical load, which overlaps with the EV charging time, resulting in insufficient power in the integrated energy system and the need to purchase power from the power grid. But this is the peak period for electricity prices, which increases the operating cost of the system. During the peak period of wind power generation from 1:00 to 6:00 at night, EVs have no remaining capacity to consume wind power and can only sell the excess wind energy to the grid, which reduces the economic benefits of the system.Figure 10
Electric power dispatching of scenario 1.


### 5.3.2. Scenario 2 Scheduling

EV orderly charging means that after the EV arrives home, the time period with the lower electricity price is selected for charging under the condition of meeting the constraints. The scheduling situation is shown in Figure11. It can be seen from the figure that most EVs are charged at the beginning of the valley of the electricity price at 23:00, which leads to a large peak value of power purchase at this time. Because EVs delay the charging time, they can absorb some of the excess wind power and CHP power output during peak wind power generation periods, but there is still more surplus power to be sold to the grid.Figure 11
Electric power dispatching of scenario 2.


### 5.3.3. Scenario 3 Scheduling

The electric load demand response is not considered in the system, and the indoor temperature is kept at the optimal temperature. The scheduling situation is shown in Figure12. At this time, the indoor temperature and space heat load are shown in Figure 13. There is no demand response for electrical loads, which reduces the on-site consumption of renewable energy. The room temperature remains stable, and it is impossible to further increase or decrease the output of the CHP unit during certain periods, thereby reducing the flexibility and economy of the operation of the integrated energy system.Figure 12
Electric power dispatching of scenario 3.Figure 13
Indoor temperature and space heat load.


### 5.3.4. Scenario 4 Scheduling

The ESS and HSS are removed from the system, and the electrical power scheduling is shown in Figure14, and the thermal power balance is shown in Figure 15.Figure 14
Electric power dispatching of scenario 4.Figure 15
Thermal power balance of scenario 4.Without considering the ESS, the power output by the renewable energy power generation system and the power generated by the CHP cannot be stored and can only be sold to the grid. When the power consumption peaks and the power price is high, it is unable to provide electric energy for the system, so it can only purchase electricity from the grid or increase the output of CHP units to meet the electric energy consumption. Without considering HSS, thermal energy cannot be stored when the output of the CHP unit is increased, and at the same time, thermal energy cannot be provided when the output of the CHP unit is reduced and the user needs thermal energy. Without considering the electric/thermal energy storage system, the thermodecoupled operation of the system cannot be realized, which reduces the operating flexibility of the system and increases the operating cost.The operating costs of each comparison scenario are shown in Table3. Among them, scenario 5 is the model and method proposed in this paper. It can be seen from Table 3 that when EVs are charged in an orderly or disorderly manner, the electricity purchase cost and electricity sales revenue of the entire system are relatively large, which cannot effectively consume renewable energy. EVs are charged in an orderly manner, and the total cost is lower than that of disordered charging because charging is started during the low electricity price period. In Scenario 3, the CHP unit needs to provide more heat output to maintain a stable room temperature, so the gas purchase cost increases. In scenario 4, since the ESS and HSS are not considered, the equipment operation and maintenance costs are the lowest, but the power purchase cost increases. Scenario 5 is the optimization method proposed in this paper. In this scenario, the revenue from electricity sales is the lowest, which shows that renewable energy has been effectively consumed locally and the power back to the grid has been reduced. At the same time, through the thermoelectrolytic coupling operation and multienergy complementary effect of the CHP unit, the gas purchase cost is reduced, as is the operating cost of the entire system.Table 3
Operating costs.
ScenarioPower purchase costGas purchase costPower sales revenueOperation and maintenance costTotal costScenario 11551.99340.9345.351130.711678.15Scenario 21194.49337.1313.741129.711344.46Scenario 3238.379393.6128.7114010643.27Scenario 4323.79149.5161.91075.610386.9Scenario 5273.18820.382.81122.110132.7Unit: Yuan


## 5.3.1. Scenario 1 Scheduling

Unordered charging of EVs means that the EVs are charged immediately after arriving at home until they reach the expected capacity. The scheduling situation is shown in Figure10. After the EVs arrive home in the afternoon, they will be connected to the power grid for charging one after another, and 18:00–22:00 is the peak period of electrical load, which overlaps with the EV charging time, resulting in insufficient power in the integrated energy system and the need to purchase power from the power grid. But this is the peak period for electricity prices, which increases the operating cost of the system. During the peak period of wind power generation from 1:00 to 6:00 at night, EVs have no remaining capacity to consume wind power and can only sell the excess wind energy to the grid, which reduces the economic benefits of the system.Figure 10
Electric power dispatching of scenario 1.


## 5.3.2. Scenario 2 Scheduling

EV orderly charging means that after the EV arrives home, the time period with the lower electricity price is selected for charging under the condition of meeting the constraints. The scheduling situation is shown in Figure11. It can be seen from the figure that most EVs are charged at the beginning of the valley of the electricity price at 23:00, which leads to a large peak value of power purchase at this time. Because EVs delay the charging time, they can absorb some of the excess wind power and CHP power output during peak wind power generation periods, but there is still more surplus power to be sold to the grid.Figure 11
Electric power dispatching of scenario 2.


## 5.3.3. Scenario 3 Scheduling

The electric load demand response is not considered in the system, and the indoor temperature is kept at the optimal temperature. The scheduling situation is shown in Figure12. At this time, the indoor temperature and space heat load are shown in Figure 13. There is no demand response for electrical loads, which reduces the on-site consumption of renewable energy. The room temperature remains stable, and it is impossible to further increase or decrease the output of the CHP unit during certain periods, thereby reducing the flexibility and economy of the operation of the integrated energy system.Figure 12
Electric power dispatching of scenario 3.Figure 13
Indoor temperature and space heat load.


## 5.3.4. Scenario 4 Scheduling

The ESS and HSS are removed from the system, and the electrical power scheduling is shown in Figure14, and the thermal power balance is shown in Figure 15.Figure 14
Electric power dispatching of scenario 4.Figure 15
Thermal power balance of scenario 4.Without considering the ESS, the power output by the renewable energy power generation system and the power generated by the CHP cannot be stored and can only be sold to the grid. When the power consumption peaks and the power price is high, it is unable to provide electric energy for the system, so it can only purchase electricity from the grid or increase the output of CHP units to meet the electric energy consumption. Without considering HSS, thermal energy cannot be stored when the output of the CHP unit is increased, and at the same time, thermal energy cannot be provided when the output of the CHP unit is reduced and the user needs thermal energy. Without considering the electric/thermal energy storage system, the thermodecoupled operation of the system cannot be realized, which reduces the operating flexibility of the system and increases the operating cost.The operating costs of each comparison scenario are shown in Table3. Among them, scenario 5 is the model and method proposed in this paper. It can be seen from Table 3 that when EVs are charged in an orderly or disorderly manner, the electricity purchase cost and electricity sales revenue of the entire system are relatively large, which cannot effectively consume renewable energy. EVs are charged in an orderly manner, and the total cost is lower than that of disordered charging because charging is started during the low electricity price period. In Scenario 3, the CHP unit needs to provide more heat output to maintain a stable room temperature, so the gas purchase cost increases. In scenario 4, since the ESS and HSS are not considered, the equipment operation and maintenance costs are the lowest, but the power purchase cost increases. Scenario 5 is the optimization method proposed in this paper. In this scenario, the revenue from electricity sales is the lowest, which shows that renewable energy has been effectively consumed locally and the power back to the grid has been reduced. At the same time, through the thermoelectrolytic coupling operation and multienergy complementary effect of the CHP unit, the gas purchase cost is reduced, as is the operating cost of the entire system.Table 3
Operating costs.
ScenarioPower purchase costGas purchase costPower sales revenueOperation and maintenance costTotal costScenario 11551.99340.9345.351130.711678.15Scenario 21194.49337.1313.741129.711344.46Scenario 3238.379393.6128.7114010643.27Scenario 4323.79149.5161.91075.610386.9Scenario 5273.18820.382.81122.110132.7Unit: Yuan


## 6. Conclusion

Aiming at the problems of increased peak-to-valley difference of user load and low utilization efficiency of renewable energy due to the coupling operation between different forms of energy and the disordered charging of EVs in the integrated energy system of smart residential areas with combined heat and power, this paper proposes a scheduling method that utilizes multiple energy storage, demand response technology, and formulates an EV orderly charging and discharging plan. With the goal of achieving the lowest operating cost of the integrated energy system, the operating output of each unit in the decision-making system is optimized. Through simulation experiments and comparative analysis, it is verified that the proposed method in this paper can effectively promote the local consumption of renewable energy, realize the safe and orderly access of EVs to the power grid, avoid large load peak-to-valley differences, realize the thermoelectrolytic coupling operation of the integrated energy system, and improve the efficiency of energy utilization. Moreover, it reduces the cost of purchasing electricity and gas and realizes the economic and stable operation of the integrated energy system.



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*Source: 3880254-2023-02-28.xml*