System and method for accelerated assessment of operational uncertainties in electrical power distribution systems

A system for accelerated assessment of operational uncertainties in an electrical power distribution system includes a plurality of utility assets, and a distribution analysis (“DA”) system. DA system includes a preparation module configured to identify a first network model and a reduced network model for the electrical power distribution system. DA system also includes an input module configured to identify a plurality of scenarios, and a reduced-model-analysis module configured to analyze the reduced network model using the plurality of scenarios, generating a first set of results, and to select a subset of scenarios based on the first set of results. DA system further includes a full-model-analysis module configured to analyze the first network model using the subset of scenarios, generating a second set of results. DA system also includes a command module configured to dispatch configuration commands to utility assets based on the second set of results.

BACKGROUND

The embodiments described herein relate generally to electrical power distribution systems and, more particularly, to techniques for accelerated assessment of operational uncertainties in electrical power distribution systems.

Known electric power grids typically include power generation plants, transmission and distribution lines, transformers, and other devices that facilitate electric power transmission, and power delivery. After electric power is generated in the generating plants, it is transmitted for extended distances through the high voltage transmission lines to sub-transmission/distribution substations. From the substations, power is then transmitted through a feeder to an end customer through an electrical power distribution system.

Most known electrical power distribution systems include a plurality of feeders coupled to the substation transformer. The electrical power distribution systems may also include at least one capacitor bank, at least one voltage regulator, and at least one distributed generation (DG) device, e.g., a diesel generator and a photovoltaic source. The feeder is divided into smaller units via bus-bars, disconnect switches, reclosers, sectionalizers, and fuses, wherein such smaller units are referred to as segments. Each segment may have any number of DG devices coupled thereto.

The distribution networks now often include multiple power sources due to an increase in DG. The recent proliferation of wind and solar power sources, for example, has added significant complexity to the management of electrical power distribution systems. Not only do these generators represent power sources within the distribution network, but generators such as wind and solar farms represent a less predictable source of power. Their outputs change with weather patterns, a variable controlled by nature. This variability adds a complexity to analyzing electrical power distribution systems.

Known mathematical modeling techniques are typically used to model and analyze electrical circuits. As circuits get larger and more complex, modeling analysis of electrical circuits becomes significantly more complex as well. In modeling electrical power distribution systems with DG, full-model analysis can become computationally intensive and infeasible to adequately support the decision-making needs of system operations managers.

BRIEF DESCRIPTION

In one aspect, a system for accelerated assessment of operational uncertainties in an electrical power distribution system is provided. The system includes a plurality of utility assets. The system also includes a distribution analysis system comprising a preparation module configured to identify a first network model and a reduced network model for the electrical power distribution system. The system further includes an input module configured to identify a plurality of scenarios. The system also includes a reduced-model-analysis module configured to analyze the reduced network model using the plurality of scenarios, generating a first set of results, and to select a subset of scenarios from the plurality of scenarios at least partially based on the first set of results. The system further includes a full-model-analysis module configured to analyze the first network model using the subset of scenarios, generating a second set of results. The system also includes a command module configured to dispatch at least one configuration command to at least one of the plurality of assets at least partially based on the second set of results.

In a further aspect, a method of accelerated assessment of operational uncertainties in an electrical power distribution system is provided. The electrical power distribution system includes a plurality of utility assets. The method includes identifying a first network model and a reduced network model for an electrical power distribution system. The method also includes identifying a plurality of scenarios. The method further includes analyzing the reduced network model using the plurality of scenarios, and selecting a subset of scenarios from the plurality of scenarios to generate a first set of results. The method also includes analyzing the first network model using the subset of scenarios to generate a second set of results. The method further includes dispatching at least one configuration command to at least one utility asset at least partially based on the second set of results.

In another aspect, a method for accelerated assessment of operational uncertainties in an electrical power distribution system is provided. The electrical power distribution system includes a plurality of utility assets. The method includes identifying a reduced network model for the electrical power distribution system. The method also includes identifying a plurality of scenarios. The method further includes analyzing the reduced network model using the plurality of scenarios, and generating a first set of results. The method also includes executing a configuration change within the electrical power distribution system based at least in part on the first set of results.

Unless otherwise indicated, the drawings provided herein are meant to illustrate key inventive features of the invention. These key inventive features are believed to be applicable in a wide variety of systems comprising one or more embodiments of the invention. As such, the drawings are not meant to include all conventional features known by those of ordinary skill in the art to be required for the practice of the invention.

DETAILED DESCRIPTION

As used herein, the terms “software” and “firmware” are interchangeable, and include any computer program stored in memory for execution by devices that include, without limitation, mobile devices, clusters, personal computers, workstations, clients, and servers.

As used herein, the term “operator” includes any person in any capacity associated with operating and maintaining electric distribution system, including, without limitation, users of the systems described herein, shift operations personnel, maintenance technicians, and system supervisors.

A power network is traditionally thought of as having a transmission network and one or more distribution networks. As used herein, the terms “power distribution system,” “distribution system,” “power distribution network,” and “distribution network” are used synonymously throughout, and are meant to refer generally to that part of the network with lower voltage, localized, numerous small to medium consumers, and normally separated from the transmission part of the power network by an electrical substation.

As used herein, the terms “scenario” and “scenarios” are used, generally, to refer to situational variables or conditions that may occur within electrical power distribution system, and the terms are used more specifically to refer to those variables or conditions as the inputs to the model analysis systems and methods described herein.

As used herein, the term “utility asset” refers to electrical components that facilitate electric power delivery in an electrical power distribution system, such as, without limitation, distribution lines, transformers, capacitor banks, voltage regulators, switches, and distributed power generators, such as, without limitation, diesel generators, coal plants, photovoltaic farms, and wind farms.

As used herein, the term “bus” refers, generally, to a node within an electrical power distribution network. For example, and without limitation, a “bus” may be a source of load, or a source of power generation. As used herein, the term “line segment” is used to refer to the electrical cabling connecting buses, i.e., each “line segment” is terminated by two or more “buses”. As used herein, the terms “line segment”, “feeder line”, and “section” are used interchangeably.

The exemplary systems and methods described herein overcome disadvantages to known methods of analyzing electrical power distribution systems by greatly reducing the time required to perform scenario-based analysis of network models. More specifically, during operational and planning analysis of electrical power distribution systems, a reduced network model is generated from a full network model, and utilized to analyze numerous scenarios. A reduced network model allows for quicker simulation, because the time required to run a simulation is proportional to the number of buses in the network. The reduction of a full network model to a reduced network model greatly reduces the time needed to simulate scenarios, enabling an operator to investigate numerous scenarios, and facilitates responsiveness required when dealing with faults during real-world operations. The inaccuracies that may have been introduced with the use of reduced network models may be at least partially overcome with a final analysis of a select few scenarios with the full network model. Therefore, use of a reduced network model is more efficient with respect to computational efficiency during systems planning, and enables richer analysis in the more time-critical environment of electrical power distribution systems management.

FIG. 1is a general schematic diagram of an exemplary electrical power network100. Electrical power network100typically includes power plants102outputting power through a transmission grid103, which includes an extra high voltage transmission grid104and a high voltage transmission grid106through which power is transmitted to an exemplary electrical power distribution system110. Electrical power network100may include, without limitation, any number, type and configuration of extra high voltage transmission grids104, high voltage transmission grids106, and electrical power distribution systems110, as well as any number of consumers within electrical power distribution system110, high voltage transmission grid106, e.g., greater than 110-265 kilovolts (kV), and extra high voltage grid104, e.g., greater than 265 kV. Factory116is an example of a consumer coupled to high voltage transmission grid106.

Electrical power distribution system110includes low wattage consumers112and industrial medium wattage consumers114. Electrical power distribution system110also includes distributed generators130, including a city power plant132, a solar farm134, and a wind farm136. While electrical power distribution system110is shown with an exemplary number and type of distributed generators130, electrical power distribution system110may include any number and type of distributed generators130, including, without limitation, diesel generators, micro-turbines, solar collector arrays, photo-voltaic arrays, and wind turbines.

FIG. 2is a block diagram of an exemplary distribution analysis (DA) system120used to analyze electrical power distribution system110(shown inFIG. 1). Alternatively, any computer architecture that enables operation of DA system120as described herein may be used. DA system120facilitates collecting, storing, analyzing, displaying, and transmitting data and operational commands associated with configuration, operation, monitoring and maintenance of components in electrical power distribution system110.

Also, in the exemplary embodiment, DA system120includes a memory device150and a processor152operatively coupled to memory device150for executing instructions. In some embodiments, executable instructions are stored in memory device150. DA system120is configurable to perform one or more operations described herein by programming processor152. For example, processor152may be programmed by encoding an operation as one or more executable instructions and providing the executable instructions in memory device150. Processor152may include one or more processing units, e.g., without limitation, in a multi-core configuration.

Further, in the exemplary embodiment, memory device150is one or more devices that enable storage and retrieval of information such as executable instructions and/or other data. Memory device150may include one or more tangible, non-transitory computer-readable media, such as, without limitation, random access memory (RAM), dynamic random access memory (DRAM), static random access memory (SRAM), a solid state disk, a hard disk, read-only memory (ROM), erasable programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), and/or non-volatile RAM (NVRAM) memory. The above memory types are exemplary only, and are thus not limiting as to the types of memory usable for storage of a computer program.

Also, in the exemplary embodiment, memory device150may be configured to store a variety of static operational data associated with components and operational data transmitted from sensing devices (not shown) associated with utility assets in electrical power distribution system110including, without limitation, values of electric power transmitted through regulators (not shown inFIG. 2), output values of electric power generators within the distribution network such as, without limitation, solar farm134and wind farm136, bus lengths of individual buses (not shown inFIG. 2), and values of various consumer loads such as, without limitation, low wattage consumers112and medium wattage consumers114.

In some embodiments, DA system120includes a presentation interface154coupled to processor152. Presentation interface154presents information, such as a user interface and/or an alarm, to a user156. For example, presentation interface154may include a display adapter (not shown) that may be coupled to a display device (not shown), such as a cathode ray tube (CRT), a liquid crystal display (LCD), an organic LED (OLED) display, and/or a hand-held device with a display. In some embodiments, presentation interface154includes one or more display devices. In addition, or alternatively, presentation interface154may include an audio output device (not shown), e.g., an audio adapter and/or a speaker.

In some embodiments, DA system120includes a user input interface158. In the exemplary embodiment, user input interface158is coupled to processor152and receives input from user156. User input interface158may include, for example, a keyboard, a pointing device, a mouse, a stylus, and/or a touch sensitive panel, e.g., a touch pad or a touch screen. A single component, such as a touch screen, may function as both a display device of presentation interface154and user input interface158.

Further, a communication interface160is coupled to processor152and is configured to be coupled in communication with one or more other devices, such as, without limitation, components in electrical power distribution system110, another DA system120, and any device capable of accessing DA system120including, without limitation, a portable laptop computer, a personal digital assistant (PDA), and a smart phone. Communication interface160may include, without limitation, a wired network adapter, a wireless network adapter, a mobile telecommunications adapter, a serial communication adapter, and/or a parallel communication adapter. Communication interface160may receive data from and/or transmit data to one or more remote devices. For example, a communication interface160of one DA system120may transmit transaction information to communication interface160of another DA system120. DA system120may be web-enabled for remote communications, for example, with a remote desktop computer (not shown).

Also, presentation interface154and/or communication interface160are both capable of providing information suitable for use with the methods described herein, e.g., to user156or another device. Accordingly, presentation interface154and communication interface160may be referred to as output devices. Similarly, user input interface158and communication interface160are capable of receiving information suitable for use with the methods described herein and may be referred to as input devices.

Further, processor152and/or memory device150may also be operatively coupled to a storage device162. Storage device162is any computer-operated hardware suitable for storing and/or retrieving data, such as, but not limited to, data associated with a database164. In the exemplary embodiment, storage device162is integrated in DA system120. For example, DA system120may include one or more hard disk drives as storage device162. Moreover, for example, storage device162may include multiple storage units such as hard disks and/or solid state disks in a redundant array of inexpensive disks (RAID) configuration. Storage device162may include a storage area network (SAN), a network attached storage (NAS) system, and/or cloud-based storage. Alternatively, storage device162is external to DA system120and may be accessed by a storage interface (not shown).

Moreover, in the exemplary embodiment, database164contains a variety of static and dynamic operational data associated with components, some of which may be transmitted from sensing devices (not shown) associated with components in electrical power distribution system110including, without limitation, values of electric power transmitted through regulators, output values of electric power generators within the distribution network such as, without limitation, solar farm134and wind farm136, bus lengths of individual buses, and values of various consumer loads such as, without limitation, low wattage consumers112and medium wattage consumers114.

The embodiments illustrated and described herein as well as embodiments not specifically described herein but within the scope of aspects of the disclosure, constitute exemplary means for recording, storing, retrieving, and displaying operational data associated with an electrical power distribution system. For example, DA system120, and any other similar computer device added thereto or included within, when integrated together, include sufficient computer-readable storage media that is/are programmed with sufficient computer-executable instructions to execute processes and techniques with a processor as described herein. Specifically, DA system120and any other similar computer device added thereto or included within, when integrated together, constitute an exemplary means for recording, storing, retrieving, and displaying operational data associated with an electrical power distribution system110.

FIG. 3is a block diagram of DA system120used to monitor, analyze, and/or control the operation of electrical power distribution system110. Electrical power distribution system110includes a plurality of utility assets200. DA system120includes a preparation module202that identifies a full network model204and a reduced network model206for electrical power distribution system110. Further, DA system120includes an input module208that identifies a plurality of scenarios210for analysis.

Also, in the exemplary embodiment, DA system120includes a reduced-model-analysis module212, a full-model-analysis module214, and a command module216. In operation, reduced-model-analysis module212analyzes reduced network model206using plurality of scenarios210. Based on the results of the reduced network model206analysis, reduced-model-analysis module212selects a subset of scenarios for further analysis. Alternatively, full-model-analysis module214may select the subset of scenarios for further analysis. Full-model-analysis module214then analyzes the subset of scenarios, generating a second set of results. Based on the second set of results, command module216dispatches configuration commands to utility assets200in electrical power distribution system110. Operations of DA system120are described in greater detail below.

FIG. 4is a graphical view400showing an exemplary full network model401of electrical power distribution system110(shown inFIG. 1). During operation, in the exemplary embodiment, a user156(shown inFIG. 2) may view graphical view400using presentation interface154(shown inFIG. 2), as well as dispatch configuration commands to various devices in electrical power distribution system110using user input interface158(shown inFIG. 2), facilitated by communication interface160(shown inFIG. 2). Full network model401is identified by preparation module202(shown inFIG. 3).

Graphical view400is a visual depiction of electrical power distribution system110, and is represented as a virtual model existing in memory device150of DA system120(shown inFIG. 1). Graphical view400includes a y-axis410and an x-axis412, both representing distance, in feet, from a point of origin414on a Cartesian plane416representing a real-world landscape in two dimensions. Full network model401includes line segments402, regulators403, a photovoltaic power generator404, capacitors405, and loads406that may be, without limitation, observed, analyzed and modified with DA system120. Alternatively, full network model401may include any type or combination of utility assets appropriate for electrical power distribution system110. Line segments402represent all sections of electrical power distribution system110. Loads406come in various sizes, with larger loads shown as larger-diameter circles. Loads406represent, without limitation, the consumers of power within electrical power distribution system110. In the exemplary embodiment, full network model401includes approximately 2,462 buses.

FIG. 5is an exemplary graphical view500of electrical power distribution system110(shown inFIG. 1) as represented by full network model401(shown inFIG. 4), but including only certain retained circuit elements.FIG. 5is an interim model501of electrical power distribution system110generated while creating a reduced network model206(shown inFIG. 3) by preparation module202(shown inFIG. 3). For illustrative purposes,FIG. 5shows all of the sections402of full network model401, though they are not included as a part of interim model501. Interim model501includes a subset of electrical equipment from full network model401. Interim model501includes regulators403, photovoltaic power generator404, and capacitors405from full network model401. Interim model501also includes retained segments506, representing significant individual segments. Retained segments506represent segments with significant losses and significant voltage drop. In some embodiments, a threshold value for losses and voltage drop may be used to identify significant segments. For example, and without limitation, all segments that contribute to 50% of the total system losses may be retained, and segments which have a voltage drop of more than 0.01 per unit may be retained, i.e., voltage drop that is 1% of the normal system voltage. In some embodiments, user156(shown inFIG. 2) may input these thresholds. The utility assets retained at this stage represent components so significant in the network that they are retained without reduction. Alternatively, any subset of utility assets within full network model401which facilitates model reduction may be retained.

FIG. 6is a graphical view600of electrical power distribution system110(shown inFIG. 1) as represented by full network model401(shown inFIG. 4), but shows an interim model601including the retained circuit elements ofFIG. 5, and further including merged and retained bus segments606.FIG. 6represents a continuation of the creation of a reduced network model that was started inFIG. 5. For illustrative purposes,FIG. 6shows all of the sections402of full network model401, though they are not included as a part of interim model601.FIG. 6includes many of the same circuit elements asFIG. 5, including regulators403, photovoltaic power generator404, and capacitors405. But additionally, interim model601further includes merged and retained bus segments606. Segments between two retained segments506(shown inFIG. 5) are merged in a process whereby the impedances of multiple segments are combined into one. The losses of the insignificant laterals are also transferred to these line segments. Each merged and retained segment606represents an aggregation of numerous segments into a single segment, which, computationally, will serve to approximate the actions and responses of the numerous smaller segments, thereby aggregating the network without consideration of load.

FIG. 7is a graphical view700showing an exemplary reduced network model701of electrical power distribution system110(shown inFIG. 1) as represented by full network model401(shown inFIG. 4), with the model modifications shown inFIGS. 5 and 6, and further including lumped loads708.FIG. 7, and more specifically reduced network model701, represents the final result of the process that was started inFIG. 5.FIG. 7includes many of the same circuit elements asFIGS. 5 and 6, including regulators403, photovoltaic power generator404, and capacitors405, and merged and retained segments606. Additionally,FIG. 7further includes lumped loads708. Each lumped load708represents one or more individual loads aggregated into a single large load. Lumped loads708come in various sizes, with larger loads shown as larger-diameter circles. The lumping step uses a threshold value to limit lumping, such as, without limitation, allowing no more than 5% of total load to accumulate under a single lumped load708. In some embodiments, user156(shown inFIG. 2) may input this threshold value. In the exemplary embodiment, load aggregation is started from a leaf node or end node, i.e., a child node. The load at the child node is transferred to its parent node, and the segment between the parent node and child node is excluded from the network. This process continues until the total accumulated load becomes more than a threshold, or until a retained segment is reached, eliminating the insignificant laterals from the system. Unlike a traditional “Wards method” approach, the lumped loads are not bundled with network impedances. This approach allows the load in the reduced network model to be adjusted without requiring recreation of new reduced network models.

In the exemplary embodiment,FIG. 7shows the final reduced network model701, which includes regulators403, photovoltaic power generator404, capacitors405, merged and retained segments606, and lumped loads708. For illustrative purposes,FIG. 7shows only the retained elements of the reduced network model701, and does not show all sections402(shown inFIGS. 4-6). Full network model401has been reduced by preparation module202(shown inFIG. 3) from approximately 2,462 buses, as shown inFIG. 4, down to approximately 53 buses as represented by reduced network model701inFIG. 7.

In the exemplary embodiment,FIGS. 4-7are visual representations of network models stored in data structures within memory device150of DA system120. As used herein, the phrases “full network model”, “first network model”, “interim model”, and “reduced network model”, and any other references to models are used, without limitation, to either refer to the models themselves, as logically represented in memory device150(shown inFIG. 2), or to their visual representations as they may be displayed on presentation interface154(shown inFIG. 2).

FIG. 8is a flowchart of an exemplary method800of accelerated assessment of operational uncertainties in electrical power distribution system110(shown inFIG. 1) using distribution analysis system120(shown inFIG. 2). Full network model401(shown inFIG. 4) for electrical power distribution system110(shown inFIG. 1) is identified802. Electrical power distribution system110is virtualized as a model in data structures within a computer system such as DA system120to facilitate computation and display. Reduced network model701(shown inFIG. 7) for electrical power distribution system110is identified804. In the exemplary embodiment, the reduced network model is constructed in a multi-step process graphically illustrated inFIGS. 5-7, and described above. Alternatively, any reduction technique appropriate for distribution networks that enables operation of the systems and methods described herein may be used.

Also, in the exemplary embodiment, a plurality of scenarios210(shown inFIG. 3) to be analyzed are identified806by input module208(shown inFIG. 3). In some embodiments, scenario variables used are the output value of any electric power generator within the distribution network, faults occurring within particular feeders, certain configurations of switches within the distribution network, uncertainty due to circuit load, or certain individual loads or aggregate loads rising or falling to specific values or percentages of their forecasted values. Alternatively, any other scenario variables associated with an electric power distribution system that enables operation of the systems and methods described herein may be used. For example, and without limitation, a scenario variable may be a particular DG such as photovoltaic power generator404(shown inFIG. 4) operating at a particular output value, such as 20% of max output value. In a further example, and without limitation, a scenario variable may include a load scenario such that loads406(shown inFIG. 4) are drawing power at a particular value. In some embodiments, the plurality of scenarios is identified806by user156(shown inFIG. 1). In other embodiments, the plurality of scenarios includes scenario data received from user156, or stored in memory device150(shown inFIG. 1), or calculated by processor152(shown inFIG. 1). Alternatively, any other way of identifying806scenario variables that enables operation of the systems and methods described herein may be used.

Further, in the exemplary embodiment, the scenarios are analyzed808by reduced-model-analysis module212(shown inFIG. 3) with reduced network model701. In operation, analysis of reduced network model701approximates how the plurality of scenarios each affect a parameter within electrical power distribution system110, including, without limitation, how many buses' voltage goes out of tolerance, and whether line losses or reactive power flows have increased or decreased.

Moreover, in the exemplary embodiment, a subset of scenarios is selected810for further analysis by reduced-model-analysis module212. In some embodiments, the subset of scenarios is sorted, based on performance objective. A performance objective may be defined, for example, and without limitation, as a tolerance level on bus voltage levels or a tolerance level on line losses or reactive power flow. In operation, the operator, user156(shown inFIG. 1), selects810the most challenging scenarios in terms of the performance objective. The subset of scenarios selected810are then analyzed812by full-model-analysis module214(shown inFIG. 3) with full network model401. With the results of full network model analysis812, configuration commands are dispatched814by command module216(shown inFIG. 3) to a utility asset200(shown inFIG. 3) by user156. In some embodiments, utility assets200may accept dispatched814configuration commands from DA system120(shown inFIG. 2) through, without limitation, communications interface160(shown inFIG. 2). Other devices may not be able to accept configuration commands from DA system120, and thus must have a human operator dispatched814to apply configuration commands to device.

FIG. 9is a flow chart of another exemplary method900of accelerated assessment of operational uncertainties in electrical power distribution system110(shown inFIG. 1) using distribution analysis system120(shown inFIG. 2). Full network model401(shown inFIG. 4) for electrical power distribution system110is identified902by preparation module202(shown inFIG. 3). Electrical power distribution system110is virtualized as a model in data structures within a computer system such as DA system120to facilitate computation and display. Reduced network model701(shown inFIG. 7) for electrical power distribution system110is identified904by preparation module202. In the exemplary embodiment, reduced network model701(shown inFIG. 7) is constructed in a multi-step process graphically illustrated inFIGS. 5-7, and described above. Alternatively, any reduction technique appropriate for distribution networks that enables operation of the systems and methods described herein may be used.

Also, in the exemplary embodiment, a plurality of scenarios210(shown inFIG. 3) to be analyzed are identified906by input module208(shown inFIG. 3) and then analyzed908using reduced network model701by reduced-model-analysis module212(shown inFIG. 3). In some embodiments, iterative methods may be used to identify the pluralities of scenarios for analysis. For example, without limitation, the Monte Carlo method may be used to select several random sets of variables to analyze a scenario for a given performance objective. Iterative methods may be used numerous times in a given scenario, to quantify the performance of that scenario.

Further, in the exemplary embodiment, after analysis908, a configuration change to electrical power distribution system110is executed910. A configuration change includes, for example, without limitation, installation of a utility asset, and installation of one or more line segments. In operation, an operator in a planning phase may wish to analyze electrical power distribution network110using an iterative method such as the Monte Carlo method. This method is computationally expensive due to its numerous iterations needed to aggregate and achieve a reasonably representative approximation. Using reduced order model701, the operator may execute hundreds or thousands of iterations, allowing convergence on a representative approximation using just the reduced model.

Moreover, in the exemplary embodiment, full network model401is analyzed912by full-model-analysis module214(shown inFIG. 3) with at least the results of the reduced network model analysis. In some embodiments, execution910is performed after analysis908of the reduced model. In other embodiments, execution910is performed after full network model401analysis912. In operation, the operator can perform actions in the planning stage to reinforce electrical power distribution system110executing a configuration change, such as, without limitation, installing a utility a voltage regulator or a capacitor bank, or installing one or more additional line segments.

FIG. 10is a table1000of an exemplary list of evaluation scenarios to be analyzed with reduced network model701(shown inFIG. 7), along with associated resulting distribution graphs1002,1004. The Monte Carlo method is analyzed using X buses, for n iterations. At each iteration, each Bus 1 to Bus X is assigned a random load value from its own load profile and range of uncertainty. A power flow is then run, outputting the voltage expected on each bus. This simulation is repeated n times, creating a distribution of voltages expected, from each of the n random scenarios, for each individual bus. Distribution graph1002shows a voltage distribution for Bus 1, and distribution graph1004shows a voltage distribution for Bus X. In operation, such an analysis helps the grid planner to make an informed decision on installation of a voltage regulator or a capacitor bank at or in the vicinity of Bus X in order to improve the voltage profile, or otherwise improve on the performance objective. Alternatively, the grid planner may also plan on reinforcing the capacity of the line with the objective of improving the voltage profile, or otherwise improving on the performance objective.

In operation, in the exemplary embodiment, a grid operator working under operational time constraints may benefit by quickly assessing the capability of distribution feeders to handle load transfer after a fault has occurred on a nearby distribution feeder. Reduced circuit analysis may inform the grid operator on the load that may be picked by the adjacent feeders while still maintaining a specified performance objective, such as limits on bus voltages or line losses.

The above-described system and method provides a time saving analysis. During operational analysis of electrical power distribution systems, time is a factor. During planning analysis of electrical power distribution systems, certain iterative and computational techniques may be computationally prohibitive when using a only full network model. A reduced network model allows for quicker simulation. The time required to run a simulation is proportional to the number of buses in the network. The embodiments described herein facilitate analysis of an approximately 2,462 bus network by analyzing a reduced network model of using an approximately 53 bus network simulation. This reduction greatly reduces the time needed to simulate scenarios, enabling an operator's investigation of numerous scenarios feasible, and facilitating responsiveness required when dealing with faults during real-world operations. Loads are not bundled with network impedances, so the reduced network model may be examined with different loading scenarios, or used for Monte Carlo-type studies. The inaccuracies that may have been introduced with the use of reduced network models may be at least partially overcome with a final analysis of a select few scenarios with the full network model.

An exemplary technical effect of the methods, and systems described herein includes at least one of: (a) increased speed through the use of reduced network model simulations; (b) increased breadth of coverage through the numerous scenarios operators run with a reduced network model in the time it would have taken to run a single simulation with a full model; and (c) greater accuracy through scenario selection and full-model analysis of the select few scenarios of most interest after the reduced-model analysis.

Exemplary embodiments of systems and methods for accelerated assessment of operational uncertainties in electrical power distribution systems are described above in detail. The systems and methods described herein are not limited to the specific embodiments described herein, but rather, components of systems and/or steps of the methods may be utilized independently and separately from other components and/or steps described herein. For example, the methods may also be used in combination with other systems requiring concept extraction systems and methods, and are not limited to practice with only the text processing system and concept extraction system and methods as described herein. Rather, the exemplary embodiments can be implemented and utilized in connection with many other distribution analysis systems.

Although specific features of various embodiments may be shown in some drawings and not in others, this is for convenience only. In accordance with the principles of the systems and methods described herein, any feature of a drawing may be referenced and/or claimed in combination with any feature of any other drawing.