Energy storage device power configuration method and system considering economy and voltage stability

Abstract

The invention belongs to the technical field of power optimization configuration, and provides an energy storage device power configuration method and system giving consideration to economy and voltage stability, and the method comprises the steps: determining an energy storage device needing to be configured according to the network loss sensitivity and the voltage sensitivity; establishing a multi-objective optimization model by taking the lowest total cost of energy storage equipment, the minimum system load margin difference value and the minimum static voltage stability L index as objectives; solving the multi-objective optimization model by adopting a multi-objective function solving optimization algorithm; according to the network loss sensitivity and the voltage sensitivity, a scientific and appropriate energy storage equipment position is selected, and the problems of voltage out-of-limit, network loss increase and the like caused by large-scale distributed wind and light power supplies and system loads can be efficiently solved; on the basis of considering economical efficiency and voltage stability targets, a multi-objective function solution optimization algorithm is adopted, and the problem that the weight of each sub-objective function is difficult to select is solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of power optimization configuration, and in particular relates to a power configuration method and system for an energy storage device that takes both economy and voltage stability into consideration. Background Art

[0002] Grid-following energy storage using current source control relies on the AC grid to provide voltage source support. ESS (Energy Storage System) has a fast energy response speed. Its advantage of quickly adjusting active and reactive power can also improve peak shaving and valley filling, voltage deviation, and static voltage stability.

[0003] Although the current power configuration method for energy storage devices can improve static voltage stability, it does not take into account economic efficiency and does not solve problems such as voltage over-limit and increased network losses caused by large-scale distributed wind and solar power sources and system loads. Specifically, when taking into account both economic efficiency and voltage stability, it is necessary to consider multi-objective optimization problems. If the multi-objective optimization problem is converted into a single-objective optimization problem for optimal solution, normalization processing is required to unify the dimensions of the multiple objectives into one dimension, which will make the weight selection of each sub-objective function extremely difficult and difficult to have general universality. Summary of the Invention

[0004] In order to solve the above problems, the present invention proposes a power configuration method and system for energy storage devices that take into account both economy and voltage stability. The present invention determines the energy storage device that needs to be configured based on network loss sensitivity and voltage sensitivity, selects a scientific and appropriate location for the energy storage device, and can efficiently solve problems such as voltage over-limit and increased network loss caused by large-scale distributed wind and solar power sources and system loads. At the same time, with the goals of minimizing the total cost of energy storage equipment, minimizing the system load margin difference, and minimizing the static voltage stability L index, a multi-objective optimization model is established, and a multi-objective function solution optimization algorithm is used to solve the multi-objective optimization model. On the basis of taking into account the goals of economy and voltage stability, a multi-objective function solution optimization algorithm is adopted to solve the problem of difficulty in selecting the weights of each sub-objective function.

[0005] In order to achieve the above object, the present invention is implemented through the following technical solutions: In a first aspect, the present invention provides a method for configuring power of an energy storage device that takes into account both economy and voltage stability, comprising: Obtain network loss sensitivity and voltage sensitivity of nodes in the power system; Determine the energy storage device that needs to be configured based on grid loss sensitivity and voltage sensitivity; According to the energy storage devices configured as needed, a multi-objective optimization model is established with the goals of minimizing the total cost of energy storage equipment, minimizing the system load margin difference, and minimizing the static voltage stability L index; A multi-objective function solving and optimization algorithm is adopted to solve the multi-objective optimization model, obtain the power configuration scheme of the energy storage device, and perform power configuration.

[0006] Furthermore, the total cost of the energy storage equipment includes the investment cost of the energy storage equipment, the operation and maintenance cost of the energy storage equipment, the penalty cost for the system abandonment of wind and solar power, the arbitrage income of the energy storage equipment, and the income obtained by the energy storage equipment from reducing network losses; the system load margin difference is equal to the sum of the initial load margin minus the system load margin under the optimal and worst cases.

[0007] Furthermore, the constraints of the energy storage capacity configuration and operation strategy model include system flow balance constraints, energy storage equipment operation constraints, system voltage constraints, line capacity constraints, and conventional wind and solar power output constraints.

[0008] Furthermore, the system power flow balance constraint is: ; in, Node i Active load and reactive load; Includes nodes i Active charging and discharging power of ESS, output power of photovoltaic and wind power; Includes nodes i Reactive charging and discharging power of ESS; For nodes i Voltage at is the number of system nodes; is a node; For nodes Voltage at For the line i Conductivity; For the line i Phase angle, For the line i Electrical Susceptance.

[0009] Furthermore, the energy storage equipment operation constraints are: ; in, is the PCS power capacity. The reactive output capability of ESS is mainly limited by the active output of PCS and its ESS. Energy storage device ess Output active power; Energy storage device ess Output reactive power; for t +1 period ess state of charge; Charging power for ess; is the ess discharge power; for t ESS charge state at the moment; E B The capacity configuration of ESS; 、 are ESS charging and discharging efficiency respectively; are the minimum and maximum values ​​of ESS state of charge, respectively; is the state of charge of ESS at the initial moment; is the state of charge of the ESS at the end moment; 、 They are respectively the active charging and discharging states of ESS; 、 They are the reactive charging and discharging states of ESS respectively.

[0010] Furthermore, nodes in the system whose power-affecting voltage and network loss are greater than the corresponding preset values ​​are selected for energy storage device configuration; the network loss sensitivity of the node is the ratio of the total change in network loss to the power change of the node, and the voltage sensitivity of the node is the ratio of the change in node voltage to the power change of the node.

[0011] Furthermore, a multi-objective function solving optimization algorithm is used to solve the multi-objective optimization model. First, the basic parameters of the algorithm are set, including the size N of the population and the maximum number of iterations; an initial population is generated while satisfying the constraints, and the current number of iterations is set to 0; then, the evolutionary generation is increased by one, and the population with a population size of N is merged to generate a new population with a population size of 2N for fast non-dominated sorting to form several non-dominated layers; then, uniformly distributed reference points are set to form a reference point evaluation system based on the number of objective functions in the optimization model; then, the new population with a population size of 2N is selected, crossed over, and mutated to generate a daughter population; finally, the generated daughter population is subjected to an elite strategy, the non-dominated layer of the population individuals is elite sorted and retained, and the population size is retained to N before entering the next step; it is determined whether to terminate the iteration. If the evolutionary generation has not reached the maximum value, the iteration is returned to continue, otherwise the iteration is terminated.

[0012] In a second aspect, the present invention further provides a power configuration system for an energy storage device that takes into account both economy and voltage stability, comprising: The data acquisition module is configured to: obtain network loss sensitivity and voltage sensitivity of nodes in the power system; The energy storage device determination module is configured to: determine the energy storage device to be configured based on the network loss sensitivity and the voltage sensitivity; The model building module is configured to: establish a multi-objective optimization model based on the energy storage devices configured as needed, with the goals of minimizing the total cost of the energy storage equipment, minimizing the system load margin difference, and minimizing the static voltage stability L index; The configuration module is configured to: adopt a multi-objective function solving optimization algorithm to solve the multi-objective optimization model, obtain a power configuration plan for the energy storage device, and perform power configuration.

[0013] In a third aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the power configuration method for an energy storage device that takes into account both economy and voltage stability as described in the first aspect.

[0014] In a fourth aspect, the present invention also provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor. When the processor executes the program, the steps of the power configuration method for an energy storage device that takes into account both economy and voltage stability as described in the first aspect are implemented.

[0015] In a fifth aspect, the present invention further provides a computer program product, which includes a computer program. When the computer program is executed by a processor, it implements the steps of the power configuration method of the energy storage device that takes into account both economy and voltage stability as described in the first aspect.

[0016] Compared with the prior art, the present invention has the following beneficial effects: In the present invention, first, the energy storage device that needs to be configured is determined based on the network loss sensitivity and voltage sensitivity; then, a multi-objective optimization model is established with the goals of minimizing the total cost of the energy storage equipment, minimizing the system load margin difference and minimizing the static voltage stability L index; finally, a multi-objective function solving and optimization algorithm is used to solve the multi-objective optimization model to obtain a power configuration plan for the energy storage device; based on the network loss sensitivity and voltage sensitivity, the energy storage device that needs to be configured is determined, and a scientific and appropriate location of the energy storage device is selected, which can effectively solve the problems of voltage over-limit and network loss increase caused by large-scale distributed wind and photovoltaic power sources and system loads; at the same time, with the goals of minimizing the total cost of the energy storage equipment, minimizing the system load margin difference and minimizing the static voltage stability L index, a multi-objective optimization model is established, and a multi-objective function solving and optimization algorithm is used to solve the multi-objective optimization model. On the basis of taking into account the goals of economy and voltage stability, the multi-objective function solving and optimization algorithm is adopted to solve the problem of difficulty in selecting the weights of each sub-objective function. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings constituting a part of the specification of this embodiment are used to provide a further understanding of this embodiment. The schematic embodiments and descriptions of this embodiment are used to explain this embodiment and do not constitute an improper limitation on this embodiment.

[0018] Figure 1 This is a flow chart of the method of Example 1 of the present invention; Figure 2 This is a flow chart of NSGA-III according to Example 1 of the present invention; Figure 3 This is a diagram of the IEEE-33 node distribution network system according to embodiment 1 of the present invention; Figure 4a This is a typical wind power output scenario of Example 1 of the present invention; Figure 4b This is the photovoltaic-typical output scenario of Example 1 of the present invention; Figure 5a The active-reactive network loss sensitivity of embodiment 1 of the present invention; Figure 5b The active-reactive voltage sensitivity of embodiment 1 of the present invention; Figure 6a The Pareto optimal solution set of energy storage active power in Example 1 of the present invention; Figure 6b The energy storage active-reactive Pareto optimal solution set of embodiment 1 of the present invention; Figure 7a The optimal boundary diagram of the economic-system load margin difference in embodiment 1 of the present invention; Figure 7b The economic-static voltage stability L index optimal boundary diagram of Example 1 of the present invention; Figure 7c The optimal boundary diagram of the system load margin difference-static voltage stability L index according to the first embodiment of the present invention; Figure 8a The Pareto optimal solution set of energy storage active power in Example 1 of the present invention; Figure 8b The energy storage active-reactive Pareto optimal solution set of embodiment 1 of the present invention; Figure 9a The optimal boundary diagram of the economic-system load margin difference in embodiment 1 of the present invention; Figure 9b The economic-static voltage stability L index optimal boundary diagram of Example 1 of the present invention; Figure 9c The optimal boundary diagram of the system load margin difference-static voltage stability L index according to the first embodiment of the present invention; Figure 10a is the system load margin of node 5 in embodiment 1 of the present invention; Figure 10b is the system load margin of node 7 in embodiment 1 of the present invention; Figure 10c is the system load margin of node 11 in embodiment 1 of the present invention; Figure 10d is the system load margin of node 14 in embodiment 1 of the present invention; Figure 11 is the static voltage stability L index of each node in embodiment 1 of the present invention; Figure 12a This is the voltage distribution before optimization of Example 1 of the present invention; Figure 12b This is the optimized voltage distribution of Example 1 of the present invention. DETAILED DESCRIPTION

[0019] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0020] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present application. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the present application belongs.

[0021] Example 1: In recent years, with the large-scale grid integration of renewable energy sources, such as wind and photovoltaic power, the power system has shifted from a traditional single-source power source to a complex, multi-source power system—a new type of power system. The random, fluctuating, and intermittent output of renewable energy sources like wind and solar has led to increased peak-to-valley variations in the system, increased network losses, increased curtailment of wind and solar power, and instability in the system's static voltage stability, all of which urgently require solutions.

[0022] In applications such as wind and solar power storage on the power supply side and centralized energy storage systems (ESS) on the grid side, energy storage plays only a supporting role, primarily addressing renewable energy power fluctuations, peak-load shifting, and primary frequency regulation. Essentially, ESS utilizes current-source-controlled grid-following energy storage, relying on the AC grid for voltage source support. ESS boasts a fast energy response and rapid active-reactive power regulation, simultaneously improving peak-load shifting, voltage deviation, and static voltage stability.

[0023] Therefore, this paper focuses on the advantages and disadvantages of ESS in improving static voltage stability. ESS participates in regulating active-reactive power and improving the economic efficiency and static voltage stability of the distribution network. To further improve the economic, safe and stable operation of the distribution network, it is necessary to scientifically analyze and determine the appropriate ESS power and capacity configuration and operation strategy.

[0024] This embodiment provides a power configuration method for energy storage devices that takes into account both economy and voltage stability, based on a typical output scenario obtained by reducing wind and solar power scenarios. For large-scale distributed wind and solar power and other renewable power sources connected to the distribution network, a multi-objective optimization capacity configuration method and operation strategy model considering the total cost of ESS equipment and the system static voltage stability index is proposed; the static voltage stability index includes the system load margin difference index in the load-voltage aspect and the static voltage stability L index based on the flow solvability. The ESS comprehensively adjusts the active and reactive power, and uses a non-dominated sorting multi-objective optimization genetic algorithm with an elite strategy to solve it. The method is applied to IEEE-33 and IEEE-14 node systems. Specifically: S1. Establishment of energy storage capacity configuration and operation strategy model: The energy storage capacity configuration and operation strategy model proposed in this embodiment is a multi-objective optimization capacity configuration and operation strategy model, that is, a multi-objective optimization model, and its overall objective function is as shown in formula (1). It mainly includes the economic objectives of ESS equipment cost and its benefits, and the penalty cost of abandoned wind and solar power, which constitute the objective function of the energy storage capacity configuration and operation strategy model. f 1. Mainly includes the technical target of system load margin difference, which constitutes the objective function of the energy storage capacity configuration and operation strategy model f 2. Including the technical goal of the static voltage stability L index based on the flow solution, which constitutes the objective function of the energy storage capacity configuration and operation strategy model f 3.

[0025] (1) in, f 1 is the total cost of ESS equipment, which is composed of economic targets, including ESS investment cost, ESS operation and maintenance cost, system curtailment penalty cost of wind and solar power, ESS arbitrage income, and income from ESS reducing network losses; f 2 is the system load margin difference, The smaller the value, the smaller the system load difference, that is, the better the static voltage stability; f 3 is the static voltage stability L index. The smaller the L index is, the more stable the system static voltage is. When the L index is greater than 1, the system voltage collapses.

[0026] S2. Determination of economic objectives: The economic objectives include ESS investment costs, ESS operation and maintenance costs, system wind and solar power curtailment penalty costs, ESS peak-shaving and valley-filling arbitrage profits, and ESS profits from reducing network losses.

[0027] S3. Determination of technical objectives: S3.1. System load margin difference: The system load margin mentioned in the static voltage stability of distribution networks generally refers to the optimal load margin under device access regulation. Because the ESS has flexible power throughput, the system load margin can increase or decrease under ESS regulation, meaning there are optimal and worst-case load margins. Therefore, this embodiment proposes a system load margin difference. The sum of the initial load margin and the optimal and worst-case system load margins is used as the technical objective function for the system load margin difference. Specifically, it is: (2) in, They are the system initial load margin, the system worst load margin under ESS regulation, and the system optimal load margin under ESS regulation; The smaller it is, the further away the optimal load margin is from the initial load margin, and the closer the worst load margin is to the initial load margin, that is, the better the static voltage stability of the system.

[0028] S3.2, Static voltage stability L index: This embodiment proposes a static voltage stability L index based on power flow solvability as one of the technical objective functions, specifically: (3) Among them, L represents that the corresponding branch is the weakest link in the system; when L is greater than 1, the overall stability of the system deteriorates, and voltage collapse has occurred at this time; when L is equal to 1, the system voltage is in a critical stable state and is about to collapse, starting from the weakest branch of the system; when L is less than 1, the overall stability of the system improves, and the lower L is, the better the overall stability of the system.

[0029] S4. Constraints: In this embodiment, the constraints of the energy storage capacity configuration and operation strategy model are divided into equality and inequality constraints. The equality constraints include: system power flow balance; the inequality constraints include: ESS operation, system voltage, line capacity, and conventional wind and solar output constraints.

[0030] S4.1. System power flow balance constraints: (4) in, Node i Active load and reactive load; Includes nodes i Active charging and discharging power of ESS, output power of photovoltaic and wind power; Includes nodes i Reactive charging and discharging power of ESS; For nodes i Voltage at is the number of system nodes; is a node; For nodes Voltage at For the line i Conductivity; For the line i Phase angle, For the line i Electrical Susceptance.

[0031] S4.2. ESS operation constraints: (5) in, is the PCS power capacity. The reactive output capability of ESS is mainly limited by the active output of PCS and its ESS. Energy storage device ess Output active power; Energy storage device ess Output reactive power; for t +1 period ess State of charge; for ess Charging power; is the ess discharge power; for t ESS charge state at the moment; E B The capacity configuration of ESS; 、 are ESS charging and discharging efficiency respectively; are the minimum and maximum values ​​of ESS state of charge, respectively; is the state of charge of ESS at the initial moment; is the charge state of ESS at the end moment; 、 They are respectively the active charging and discharging states of ESS; 、 They are the reactive charging and discharging states of ESS respectively.

[0032] In order to prevent frequent charging and discharging of the ESS from affecting its cycle life and reducing its economic efficiency, it is necessary to constrain the number of ESS charge and discharge state conversions: (6) in, 、 They are respectively the charging and discharging conditions of energy storage in each period; The maximum number of charge and discharge conversions for the ESS is set to two charges and two discharges per day for both node systems.

[0033] S4.3, System voltage constraints: (7) in, are the minimum and maximum values ​​of the system voltage amplitude respectively; for i node t The system voltage value at the moment. The specific voltage constraint value changes according to the actual situation of the node system.

[0034] S4.4. Line capacity constraints: (8) in, For the line ij exist t Active power at the moment; For the line ij The specific line capacity varies according to the actual situation of the node system.

[0035] S4.5. Wind and solar output constraints: (9) in, They are t The power of wind power and photovoltaic power connected to the grid at any given moment; They are t The specific wind and solar power output varies according to the actual situation of the node system.

[0036] S5. ESS model site selection method: Optimizing the location of equipment in the distribution network of a new power system has a significant impact on resolving issues such as excessive peak-to-valley differences, increased network losses, and intensified voltage fluctuations. Therefore, selecting scientifically appropriate equipment installation locations is key to enabling ESS and SVC to more effectively address voltage overshoots and increased network losses caused by large-scale distributed wind and solar power sources and system loads in the distribution network. Voltage sensitivity and network loss sensitivity are used as the basis for equipment location selection. Nodes in the system where power affects voltage and network losses are significant (for example, greater than a preset value) should be selected for optimal ESS and SVC deployment to maximize their utilization. The selection criteria for this embodiment are as follows: S5.1. Network loss sensitivity: The network loss sensitivity of a node is the ratio of the total change in network loss to the power change of the node. i The network loss sensitivity of a node can be expressed as: (10) in, Nodei Power change The change in total network loss caused by

[0037] S5.2, Voltage Sensitivity: The voltage sensitivity of a node is the ratio of the change in node voltage to the change in power at that node. i The voltage sensitivity of a node can be expressed as: (11) in, For nodes i Power change The voltage change caused.

[0038] Higher grid loss and voltage sensitivities indicate greater grid loss and voltage changes caused by the corresponding power. That is, when ESS and SVC regulate the same power, higher sensitivity leads to greater changes in grid loss and voltage. This leads to more significant improvements and enhances the value of ESS and SVC, ultimately saving costs. Therefore, after calculation, nodes with higher grid loss and voltage sensitivities are selected for optimization by deploying ESS and SVC at these locations.

[0039] S6. Model solution method: After establishing the multi-objective optimization model, the model solution process is detailed. First, different ESS capacity and power configuration sets are set, forming M×N sets. Next, a multi-objective optimization model is constructed based on economic and technical objective sets and equality and inequality constraints. Objective function 1 represents the economic objective, encompassing the ESS initial investment, total operation and maintenance costs, system curtailment penalty costs for wind and solar power after ESS integration, ESS peak-valley arbitrage, and the benefits of reduced grid losses. Objective function 2 represents technical objective 1, consisting of the load-voltage system load margin difference. Objective function 3 represents technical objective 2, consisting of the static voltage stability L index, which can be solved based on power flow. Subsequently, a set of system constraints is determined. In this case, these primarily include equality constraints for power flow balance and inequality constraints for ESS operation, voltage, and line conditions. A multi-objective optimization model for capacity configuration and operation strategy is then established, combining these three objective functions and constraint sets. A multi-objective optimization algorithm is then used to solve and optimize the model, resulting in the optimal Pareto solution set (non-dominated solution set). Finally, the ESS capacity configuration and power charging and discharging conditions for the optimal solution are determined, thereby determining the ESS operation strategy.

[0040] The model in this embodiment is a capacity configuration and operation strategy model for multi-objective optimization. The three objective functions in the model—system economic objectives and technical objectives—are not uniform in their first and second dimensions. If the multi-objective optimization problem is converted to a single-objective optimization problem, the optimization solution requires normalization to unify the dimensions of the three objectives into a single dimension. This makes selecting weights for each sub-objective function extremely difficult and difficult to generalize. Therefore, this embodiment uses a multi-objective function optimization algorithm.

[0041] S6.1. First, set the basic parameters of the NSGA-III algorithm, including the population size N, the maximum number of iterations MaxGen, and other related variables; generate the initial population while meeting the model constraints. , and set the current iteration number to 0.

[0042] S6.2. Then, the evolutionary generation is increased by one, Gen = Gen + 1; and the population with a population size of N is merged to generate a new population with a population size of 2N A fast non-dominated sorting is performed to form several non-dominated layers such as F1, F2, ..., Fl, ..., Fn; then uniformly distributed reference points are set to form a reference point evaluation system based on the number of objective functions in the optimization model.

[0043] S6.3, then, the new population with a population size of 2N It performs selection, crossover, and mutation. Different from NSGA-Ⅱ, it uses the binary tournament method for selection, the PMX / SBX method for crossover, and the adaptive transposition mutation method for mutation. After selection, crossover, and mutation, the offspring population is generated. .

[0044] S6.4. Finally, the generated offspring population Different elite strategies are implemented in NSGA-III and NSGA-II. The non-dominated layer of individuals in the population is sorted and retained by the elites. The population size is retained to N before entering the next step. It is determined whether to terminate the iteration. If the evolutionary generation has not reached the maximum value, the iteration is continued. Otherwise, the iteration is terminated.

[0045] Example 2: This embodiment provides a method for configuring energy storage device power that balances economic efficiency and voltage stability. Optionally, it includes two distribution network node systems: IEEE-33 and IEEE-14. This demonstration focuses on demonstrating the advantages and disadvantages of ESS in improving static voltage stability. Because the static voltage stability indicators set in this embodiment are difficult to represent individually in the IEEE-33 node system and are not clearly comparable to the IEEE-14 node system, this embodiment focuses on analyzing the optimized configuration of the IEEE-14 node system and provides a more general analysis of the IEEE-33 node system.

[0046] To determine the advantages and disadvantages of ESS regulating only active power and ESS regulating both active power and reactive power in solving the economic and multi-faceted static voltage stability problems in the distribution network under the new power system, this embodiment designs the following two solutions for comparative study.

[0047] Solution 1: The ESS participates in the optimization configuration alone and only adjusts the active power.

[0048] Option 2: ESS independently participates in the optimization configuration and comprehensively adjusts the active and reactive power.

[0049] NSGA-III was used to solve the optimal solutions for the multi-objective capacity configuration and operation models for Scenarios 1 and 2, respectively. ESSs were installed and configured at three locations in the distribution network, as shown in the following table. ESS access points were 6, 13, and 28. The ESS parameters for the two scenarios are shown in Tables 1 and 2, with Table 1 describing the configuration and Table 2 describing the optimized operation.

[0050] Table 1 ESS configuration under two schemes

[0051] Table 2 ESS optimization operation under two schemes

[0052] It can be seen from Table 2 that the former in the system load margin is the load margin under the optimal situation, and the one in the brackets is the load margin under the worst situation. The optimal solution obtained by NSGA-Ⅲ for the two schemes is combined with Table 1 and Table 2. It can be seen that Scheme 2 is better than Scheme 1 in terms of economic indicators, namely the total cost of ESS equipment, and technical indicators, namely the system load margin difference and the static voltage stability L index. However, the advantages and disadvantages cannot be summarized and sorted out simply by the optimal solution. Therefore, NSGA-Ⅲ is used to solve the multi-objective optimization model to obtain the Pareto optimal solution set of Schemes 1 and 2, and to more comprehensively and completely compare the advantages and disadvantages of Schemes 1 and 2 in terms of improving the economy and the static voltage stability of the system in the distribution network, and the specific results are as follows. Figure 6a 、 Figure 6b 、 Figure 7a 、 Figure 7b and Figure 7c shown.

[0053] Figure 6a and Figure 6b These are the Pareto optimal solution sets for Option 1 and Option 2, respectively. It can be seen that regardless of whether the total equipment cost is taken into account, the system load margin difference for Option 2 is far superior to that for Option 1. When the total equipment cost is not taken into account, a small number of static voltage stability indicators (L) for Option 1 are superior to those for Option 2, and in most cases, Option 2 is superior to Option 1. Although the number of solutions for these two Pareto optimal solution sets is small, when comparing the advantages and disadvantages of the three objective functions, only the general trend of advantages and disadvantages can be seen, and the specific advantages and disadvantages relationship is not obvious. Therefore, in order to more clearly compare the relationship between the objective functions of Options 1, 2, and 3, a projection on the x, y, and z axes of Figure 6 is made, as shown in the following figure: Figure 7a 、 Figure 7b and Figure 7c The figure shows the optimal boundary diagram between the total equipment cost, the system load margin difference and the static voltage stability L index.

[0054] Figure 7a 、 Figure 7b and Figure 7c The projections of the three objective functions on z, y, and x, respectively, clearly show that, regardless of whether total equipment cost is prioritized, Scheme 2 significantly outperforms Scheme 1 in terms of system load margin difference, achieving a roughly 20% advantage. Furthermore, when total equipment cost is not prioritized, Scheme 1 slightly outperforms Scheme 2 in terms of the static voltage stability L indicator. However, when total equipment cost is prioritized, Scheme 2 largely outperforms Scheme 1, achieving a roughly 6% advantage. Because this embodiment comprehensively considers both economic and technical indicators, it is approximately equivalent to Scheme 2 in terms of the static voltage stability L indicator, outperforming Scheme 1 by approximately 5%. Combining these three sub-graphs, we conclude that Scheme 2 is generally superior to Scheme 1.

[0055] According to Table 3 and Table 4 above, and Figure 6a 、 Figure 6b 、 Figure 7a 、 Figure 7b and Figure 7c It can be generally concluded that the proposed solution 2 is generally superior to the proposed solution 1 in the IEEE-33 bus system. Because the static voltage stability indicators of each node in the IEEE-33 bus system are more complex and less obvious than those in the IEEE-14 bus system, only a general summary is provided here. The following specifically analyzes the optimized configuration of the IEEE-14 bus system.

[0056] Result analysis of IEEE-14 bus system: Design Option 1: ESS alone optimizes configuration and regulates only active power. Option 2: ESS alone optimizes configuration and combines active and reactive power. This approach determines the trade-offs between ESS-only active power regulation and ESS-combined active and reactive power regulation in addressing the economic benefits and multiple aspects of static voltage stability in the distribution network under the new power system.

[0057] NSGA-III was used to solve the optimal solutions for the multi-objective capacity configuration and operation models for Scenarios 1 and 2, respectively. ESSs were installed and configured at two locations in the distribution network, as shown in the following table. ESS access points were 4 and 14. The ESS parameters for the two schemes are shown in Tables 3 and 4, respectively. Table 3 describes the configuration, and Table 4 describes the optimized operation.

[0058] Table 3 ESS configuration under two schemes

[0059] Table 4 ESS optimization operation under two schemes

[0060] It can be seen from Table 4 that the former of the system load margins is the load margin under the optimal situation, and the ones in the brackets are the load margin under the worst situation. The optimal solutions obtained by NSGA-Ⅲ for the two schemes are combined with Table 3 and Table 4. It can be seen that Scheme 2 is better than Scheme 1 in terms of economic indicators, namely the total cost of ESS equipment, and technical indicators, namely the system load margin difference and the static voltage stability L index. However, the advantages and disadvantages cannot be summarized and sorted out simply by the optimal solution. Therefore, NSGA-Ⅲ is used to solve the multi-objective optimization model to obtain the Pareto optimal solution set of Schemes 1 and 2, and to more comprehensively and completely compare the advantages and disadvantages of Schemes 1 and 2 in terms of improving the economy and the static voltage stability of the system in the distribution network, and the specific results are as follows. Figure 8a and Figure 8b shown.

[0061] like Figure 8a and Figure 8b They are the Pareto optimal solution sets of Scheme 1 and 2 respectively. It can be seen that regardless of whether the total equipment cost is taken into account, the system load margin difference of Scheme 2 is much better than that of Scheme 1; and when the total equipment cost is not taken into account, a small part of the static voltage stability L index of Scheme 1 is better than that of Scheme 2, and in most cases, Scheme 2 is better than Scheme 1. At this time, due to the large number of solutions, when comparing the advantages and disadvantages of the three objective functions, only the general advantages and disadvantages trend can be seen, and the specific advantages and disadvantages relationship is not obvious. Therefore, in order to more clearly compare the advantages and disadvantages relationship between the three objective functions in Schemes 1 and 2, the following is made. Figure 8a and Figure 8bThe x-, y-, and z-axis projections show the optimal boundary diagram between the total equipment cost, the system load margin difference, and the static voltage stability L index.

[0062] Figure 9a 、 Figure 9b and Figure 9c These are the projections of the three objective functions on z, y, and x, respectively. A clear comparison shows that, regardless of whether total equipment cost is considered, Scheme 2 outperforms Scheme 1 in terms of system load margin difference, by approximately 50%. Furthermore, when total equipment cost is considered, Scheme 2 outperforms Scheme 1 in terms of the static voltage stability L index, by approximately 8%. When total equipment cost is not considered, Scheme 2 also outperforms Scheme 1 by approximately 12%, equivalent to Scheme 2 outperforming Scheme 1 in terms of the static voltage stability L index by approximately 10%. The last sub-figure clearly shows that, when ignoring total equipment cost, Scheme 2 outperforms Scheme 1 in both the load-voltage system load margin difference and the power flow-based static voltage stability L index. Combining these three sub-figures, we conclude that Scheme 2 is generally superior to Scheme 1.

[0063] In order to obtain the specific methods of adjusting power to change the PV curve and improve the static voltage stability L index of the system in Scheme 1 and Scheme 2, the specific conditions of the system load margin (optimal and worst cases) and the static voltage stability L index of each node are shown as follows when both Schemes 1 and 2 are the optimal solutions of the Pareto optimal solution set. Figure 10a 、 Figure 10b 、 Figure 10c 、 Figure 10d and Figure 11 shown.

[0064] This system is an IEEE-14 node. Nodes 1, 2, 3, 6, and 8 of this system are all generator nodes, and their PV curves remain unchanged. The remaining 9 nodes have varying PV curves. For ease of demonstration, this example selects four nodes for demonstration, namely nodes 4, 7, 11, and 14, which correspond to Figure 10a 、 Figure 10b 、 Figure 10c 、 Figure 10d .from Figure 10a 、 Figure 10b 、 Figure 10c 、 Figure 10d It can be seen that the load margin of the four nodes in the best and worst cases of energy storage is better than that of Scheme 1. That is, the system load margin difference of Scheme 2 is better than that of Scheme 1. When energy storage only adjusts active power, that is, Scheme 1, Figure 10a 、 Figure 10b 、 Figure 10c 、 Figure 10dThe red and green dotted lines in the middle indicate that when the system load margin is larger, the corresponding voltage collapse value is also larger, which is positively correlated. Only the 11-node diagram is opposite. When the energy storage comprehensively regulates active and reactive power, that is, in scheme 2, Figure 10a 、 Figure 10b 、 Figure 10c 、 Figure 10d The solid red and green lines in the middle section show that as the system load margin increases, the corresponding voltage collapse value increases, but does not significantly exceed the initial voltage collapse value. This is positively correlated with the voltage collapse value, with the exception of the 14-node diagram. This leads to the conclusion that when energy storage is used for reactive power regulation, the power factor of the PV curve is altered. When the power factor increases, the system load margin increases, and the corresponding voltage collapse value rises slightly, but does not significantly exceed the initial voltage collapse value, leading to a more stable overall state.

[0065] like Figure 11 As shown in the figure, the system has nodes with static voltage stability L indicators. Node 4 has the largest indicator, representing the static voltage stability L indicator of the system. The static voltage stability L indicators of both Schemes 1 and 2 are smaller than the initial state, and Scheme 2 outperforms Scheme 1.

[0066] The above explanation shows that considering the economic efficiency and static voltage stability of the distribution network, the solution proposed in this embodiment, which comprehensively adjusts the active and reactive power for coordinated optimization, is superior to the ESS only adjusting the active power for optimization. Next, we will analyze the various parameters of the typical day under the optimal solution: the comparison of the system voltage amplitude before and after optimization, as shown in the following figure. Figure 12a and Figure 12b shown.

[0067] Figure 12a and Figure 12b The voltage distribution before system optimization and the voltage distribution after the optimal solution are shown in Figure 2. Both figures show the voltage distribution of the IEEE-14 bus system within one day. In this case, the voltage per unit value is 1.06 and the voltage fluctuation range is -10% to +7%, which means the per unit value is between 0.954 and 1.134. Figure 12a The voltage deviation is obviously below the lower limit and has dropped to about 0.9, which has exceeded the allowable range of voltage fluctuation and caused the voltage to exceed the limit. Figure 12b The lower limit of the voltage deviation is about 1.01, which is within the allowable range of voltage fluctuation. Figure 10a 、 Figure 10b 、 Figure 10c 、 Figure 1 and Figure 11It can be seen that after the ESS regulates active and reactive power coordinated optimization, in addition to improving the static voltage stability of the system, it also avoids the voltage over-limit problem of the system. By discharging at the lower voltage limit during peak load conditions and raising the voltage, and charging at the upper voltage limit during light load conditions and high wind and solar output conditions, the voltage fluctuation of the system is greatly reduced.

[0068] Example 2: This embodiment provides a power configuration system for an energy storage device that takes both economy and voltage stability into consideration, including: The data acquisition module is configured to: obtain network loss sensitivity and voltage sensitivity of nodes in the power system; The energy storage device determination module is configured to: determine the energy storage device to be configured based on the network loss sensitivity and the voltage sensitivity; The model building module is configured to: establish a multi-objective optimization model based on the energy storage devices configured as needed, with the goals of minimizing the total cost of the energy storage equipment, minimizing the system load margin difference, and minimizing the static voltage stability L index; The configuration module is configured to: adopt a multi-objective function solving optimization algorithm to solve the multi-objective optimization model, obtain a power configuration plan for the energy storage device, and perform power configuration.

[0069] The working method of the system is the same as the power configuration method of the energy storage device that takes into account both economy and voltage stability in Example 1, and will not be repeated here.

[0070] Example 3: This embodiment provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the steps of the method for configuring the power of an energy storage device taking into account both economy and voltage stability as described in Example 1 are implemented.

[0071] Example 4: This embodiment provides an electronic device, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor. When the processor executes the program, the steps of the energy storage device power configuration method that takes into account both economy and voltage stability as described in Example 1 are implemented.

[0072] Example 5: This embodiment provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the steps of the energy storage device power configuration method that takes into account both economy and voltage stability as described in Example 1 are implemented.

[0073] The above description is merely a preferred embodiment of this embodiment and is not intended to limit this embodiment. Those skilled in the art will readily appreciate that this embodiment may be modified and varied in various ways. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of this embodiment shall be within the scope of protection of this embodiment.

Claims

1. A power configuration method for an energy storage device that takes into account both economy and voltage stability, characterized in that: include: Obtain network loss sensitivity and voltage sensitivity of nodes in the power system; Determine the energy storage device that needs to be configured based on grid loss sensitivity and voltage sensitivity; According to the energy storage devices configured as needed, a multi-objective optimization model is established with the goals of minimizing the total cost of energy storage equipment, minimizing the system load margin difference, and minimizing the static voltage stability L index; A multi-objective function solving and optimization algorithm is used to solve the multi-objective optimization model, obtain a power configuration scheme for the energy storage device, and perform power configuration.

2. The method for configuring power of an energy storage device taking into account both economy and voltage stability as claimed in claim 1, characterized in that: The total cost of the energy storage equipment includes the investment cost of the energy storage equipment, the operation and maintenance cost of the energy storage equipment, the penalty cost for system curtailment of wind and solar power, the arbitrage income of the energy storage equipment, and the income from the energy storage equipment reducing network losses; the system load margin difference is equal to the sum of the initial load margin minus the system load margin under the optimal and worst cases.

3. The method for power configuration of an energy storage device taking into account both economy and voltage stability as claimed in claim 1, characterized in that: The constraints of the energy storage capacity configuration and operation strategy model include system power flow balance constraints, energy storage equipment operation constraints, system voltage constraints, line capacity constraints, and conventional wind and solar power output constraints.

4. The method for configuring power of an energy storage device taking into account both economy and voltage stability as claimed in claim 3, characterized in that: The system power flow balance constraint is: ； in, Node i Active load and reactive load; Includes nodes i Active charging and discharging power of ESS, output power of photovoltaic and wind power; Includes nodes i Reactive charging and discharging power of ESS; For nodes i Voltage at is the number of system nodes; is a node; For nodes Voltage at For the line i Conductivity; For the line i Phase angle, For the line i Electrical Susceptance.

5. The method for power configuration of an energy storage device taking into account both economy and voltage stability as claimed in claim 4, characterized in that: The operating constraints of energy storage equipment are: ； in, is the PCS power capacity. The reactive output capability of ESS is mainly limited by the active output of PCS and its ESS. Energy storage device ess Output active power; Energy storage device ess Output reactive power; for t +1 period ess state of charge; Charging power for ess; is the ess discharge power; for t ESS charge state at the moment; E B The capacity configuration of ESS; 、 are ESS charging and discharging efficiency respectively; are the minimum and maximum values ​​of ESS state of charge, respectively; is the state of charge of ESS at the initial moment; is the state of charge of the ESS at the end moment; 、 They are respectively the active charging and discharging states of ESS; 、 They are the reactive charging and discharging states of ESS respectively.

6. The method for configuring power of an energy storage device taking into account both economy and voltage stability as claimed in claim 1, characterized in that: Nodes in the system whose power-affecting voltage and network loss are greater than the corresponding preset values ​​are selected for energy storage device configuration; the network loss sensitivity of the node is the ratio of the total change in network loss to the power change of the node, and the voltage sensitivity of the node is the ratio of the change in node voltage to the power change of the node.

7. The method for power configuration of an energy storage device taking into account both economy and voltage stability as claimed in claim 1, characterized in that: A multi-objective function solving optimization algorithm is used to solve the multi-objective optimization model. First, the basic parameters of the algorithm are set, including the size N of the population and the maximum number of iterations. An initial population is generated while satisfying the constraints, and the current number of iterations is set to 0. Then, the evolutionary generation is increased by one, and a new population with a size of 2N is generated after merging the population with a size of N, and a fast non-dominated sorting is performed to form several non-dominated layers. Subsequently, uniformly distributed reference points are set to form a reference point evaluation system based on the number of objective functions in the optimization model. Next, the new population with a size of 2N is subjected to selection, crossover, and mutation to generate a progeny population. Finally, an elite strategy is applied to the generated progeny population, and the non-dominated layer of the individuals in the population is retained by elite sorting. After the population size is retained to N, the next step is entered. Determine whether to terminate the iteration. If the number of evolution generations has not reached the maximum value, return to continue the iteration; otherwise, terminate the iteration.

8. An energy storage device power configuration system that takes into account both economy and voltage stability is characterized by: include: The data acquisition module is configured to: obtain network loss sensitivity and voltage sensitivity of nodes in the power system; The energy storage device determination module is configured to: determine the energy storage device to be configured based on the network loss sensitivity and the voltage sensitivity; The model building module is configured to: establish a multi-objective optimization model based on the energy storage devices configured as needed, with the goals of minimizing the total cost of the energy storage equipment, minimizing the system load margin difference, and minimizing the static voltage stability L index; The configuration module is configured to: adopt a multi-objective function solving optimization algorithm to solve the multi-objective optimization model, obtain a power configuration plan for the energy storage device, and perform power configuration.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that: When the processor executes the program, the steps of the energy storage device power configuration method that takes into account both economy and voltage stability as described in any one of claims 1 to 6 are implemented.

10. A computer program product, characterized in that The computer program product includes a computer program, and when the computer program is executed by a processor, the steps of the energy storage device power configuration method that takes into account both economy and voltage stability as described in any one of claims 1 to 6 are implemented.

Images