Power distribution network energy storage multi-objective optimization configuration method for long-period electric power and electric quantity balance

Abstract

The invention relates to a power distribution network energy storage multi-objective optimization configuration method for long-period electric power and electric quantity balance, and the method comprises the following steps: 1, a collaborative planning optimization method of a power distribution network energy storage system: achieving the storage and optimal utilization of energy through the high energy density of energy storage and the renewable characteristic of renewable energy; 2, considering a global collaborative energy storage system optimal configuration method: proposing dual goals of realizing local optimization and global collaboration, performing independent optimization on energy storage system configuration of each sub-region, performing global optimization on the whole network by adopting a collaborative control algorithm, and realizing global optimal configuration of the energy storage system through data interaction and a feedback mechanism; 3, performing instance analysis; the method has the advantages that the initial energy storage plan is generated, the optimization objective function of each region is established, each sub-region is independently optimized, and the cooperative control algorithm is adopted for global optimization.

Description

Technical Field

[0001] The present invention belongs to the technical field of distribution networks, and in particular relates to a multi-objective optimization configuration method for energy storage in distribution networks for long-term electric power and quantity balance. Background Art

[0002] With the widespread application of renewable energy and the continuous growth of electricity demand, modern distribution networks are facing increasingly complex management challenges. Especially in distributed distribution networks, due to multi-source power input and variable load demand, how to effectively manage and optimize the operation of energy storage systems has become a key issue. In distributed distribution networks, the diversity of factors such as power load characteristics, power demand and geographical location in each sub-region increases the complexity of energy storage system management. In existing technologies, although energy storage systems can balance load and power supply to a certain extent, they often lack optimized configuration for specific regional load characteristics, resulting in low system efficiency and uneven resource allocation. The shortcomings of existing technologies are mainly It is reflected in two aspects: first, the existing energy storage management system usually adopts a static configuration strategy, that is, the scale and configuration of the energy storage equipment are determined in the system design stage, which is difficult to adapt to the dynamically changing load demand; second, the existing technology lacks an effective whole-network collaborative control mechanism and cannot achieve dynamic optimization and scheduling of electricity and energy storage resources among sub-regions, which easily leads to resource waste or insufficient power supply during peak hours; therefore, it is very necessary to provide a multi-objective optimization configuration method for distribution network energy storage for long-term power and electricity balance, which generates an initial energy storage plan, establishes optimization objective functions for each region, independently optimizes each sub-region, and uses a collaborative control algorithm for global optimization. Summary of the invention

[0003] The purpose of the present invention is to overcome the shortcomings of the prior art and to provide a multi-objective optimization configuration method for distribution network energy storage for long-term power and electricity balance, which generates an initial energy storage plan, establishes optimization objective functions for each region, independently optimizes each sub-region, and adopts a collaborative control algorithm for global optimization.

[0004] The object of the present invention is achieved by: a multi-objective optimization configuration method for energy storage in a distribution network with long-term power and electricity balance, the method comprising the following steps:

[0005] Step 1: Coordinated planning and optimization method of distribution network energy storage system: Utilize the high energy density of energy storage and the renewable characteristics of renewable energy to achieve energy storage and optimal utilization;

[0006] Step 2: Consider the global collaborative energy storage system optimization configuration method: Propose the dual goals of achieving local optimization and global coordination, independently optimize the energy storage system configuration of each sub-region, and then use the collaborative control algorithm to globally optimize the entire network, and achieve the global optimal configuration of the energy storage system through data interaction and feedback mechanism;

[0007] Step 3: Example analysis.

[0008] The collaborative planning and optimization method of the distribution network energy storage system in step 1 comprises the following steps:

[0009] Step 1.1: Distribution network renewable energy system structure based on energy storage configuration;

[0010] Step 1.2: Long-term power and electricity balance calculation method;

[0011] Step 1.3: Economic optimization of the region with the lowest energy storage investment and operating costs.

[0012] The long-term power and quantity balance calculation method in step 1.2 specifically includes the following steps:

[0013] Step 1.21: Collect planning data and operating parameters of the distributed distribution network. Planning data includes the geographical distribution of the distribution network, load density distribution, and future electricity demand forecasts; operating parameters include historical load data of each sub-region, seasonal variation patterns, and access to distributed energy sources;

[0014] Step 1.22: First determine the power demand P of each sub-region in different time periods d (t) and power supply P s (t), where P d (t) represents the power demand within time t; P s (t) represents the power supply within time t; t is a time variable;

[0015] Step 1.23: Calculate the power deficit in the long term using the formula: ΔP(t) = P d (t)-P s (t), where ΔP(t) represents the power gap within time t;

[0016] Step 1.24: Calculate the required capacity E of the energy storage system based on the power shortage of each sub-region req , the formula is: Among them, E req Represents the required capacity of the energy storage system; t1 and t2 are time intervals in the long cycle; ΔP(t) represents the power gap within time t.

[0017] The regional economic optimization with the lowest energy storage investment and operation cost in step 1.3 is specifically as follows: establishing a regional economic optimization objective function F for each sub-region energy storage system, the goal of which is to minimize the total investment and operation cost of the energy storage system. The expression of the optimization objective function is: F = C total +C om_total -R, where F represents the value of the optimization objective function; C totalRepresents the total initial investment cost of the energy storage system; C om_total Represents the total annual operation and maintenance cost of the energy storage system; R represents the total operating income of the energy storage system; by minimizing the value of F, the optimal energy storage system configuration of each sub-area is determined to ensure that the income of the energy storage system is maximized while minimizing the investment cost and operation and maintenance cost.

[0018] The total initial investment cost of the energy storage system is C total =E based on the energy storage demand E of each sub-region req and equipment configuration, calculate the total investment cost C of each sub-region energy storage system total , the calculation formula is: C total =C inv ×E req , where C inv is the unit investment cost of the equipment; E req The demand for energy storage.

[0019] The total annual operation and maintenance cost of the energy storage system is C om_total To calculate the annual operation and maintenance cost C of the energy storage system in each sub-region om_total , the calculation formula is: C om_total =C om ×E req , where C om The unit operation and maintenance cost of the equipment.

[0020] The total operating income R of the energy storage system is: the operating income R of each sub-regional energy storage system is calculated. The operating income is the income obtained by the energy storage system through the purchase and sale of electricity under the condition of peak and valley electricity price differences. The calculation formula of the operating income is: Among them, P sell (t i ), P buy (t i ) represent the time point t i The selling and buying prices of electricity on E sell (t i ), E buy (t i ) represent the time point t i The amount of electricity sold and bought; n is the number of time nodes in the long period.

[0021] The method for optimizing the configuration of the global collaborative energy storage system in step 2 includes the following steps:

[0022] Step 2.1: Local optimization configuration of energy storage sub-areas based on genetic algorithm;

[0023] Step 2.2: Use the collaborative control algorithm to globally optimize the entire network.

[0024] The local optimization configuration of energy storage sub-regions based on genetic algorithm in step 2.1 is specifically as follows: obtaining the power load characteristics of each sub-region, including daily load curves, seasonal load variation patterns, and future load growth forecasts. The load characteristics are used to define the power demand function P of each sub-region. d (t), where P d (t) represents the power demand in time t;

[0025] Obtain the geographical location constraints of each sub-region, including the geographical environment, grid access conditions, land use restrictions, and the layout of existing power infrastructure in the region. The geographical location constraints are used to limit the configuration range of the energy storage system.

[0026] Construct the energy storage system configuration model for each sub-region. The system configuration model is based on the regional power load characteristics and power demand function P d (t) and geographical location constraints as input, and the configuration parameters of the energy storage system X config As optimization variables, X config Including the capacity of the energy storage system E capacity , power output P output and installation location;

[0027] Define the optimization objective function F local , whose goal is to meet the regional electricity demand P d (t) and geographical location constraints, minimize the configuration cost C of the energy storage system config , the expression of the optimization objective function is: F local =C config (E capacity ,P output ,Location)+λ×∑ t (P d (t)-P output (t)) 2 , where C config (E capacity ,P output , Location) represents the configuration cost of the energy storage system; λ is the penalty factor, which is used to balance the weight between the system configuration cost and the imbalance between power supply and demand; P output (t) is the power output of the energy storage system in time t, and the optimization process aims to make F local minimize;

[0028] The distributed optimization algorithm is used to optimize the above objective function F local To solve, select genetic algorithm or particle swarm optimization algorithm and algorithm with global search capability, combine the load characteristics and geographical location constraints in the region, and iteratively solve the optimal energy storage system configuration parameters To ensure that the energy storage system configuration in each sub-area reaches the local optimum.

[0029] The collaborative control algorithm in step 2.2 is used to globally optimize the entire network, specifically: obtain the optimal energy storage system configuration parameters for each sub-area Including the capacity of the energy storage system Power Output and installation position, these configuration parameters are obtained by optimizing the genetic algorithm in the above step 2.1, and represent the local optimal configuration results of each sub-area;

[0030] Establish a network-wide data interaction platform to summarize the configuration parameters of each sub-area and operating data, including regional power load P d (t), power output P output (t) and energy storage state S storage (t), where S storage (t) represents the storage state of the energy storage system within time t;

[0031] Based on the network-wide data interaction platform, a feedback mechanism is established to monitor the power flow between sub-regions and the operating status of the energy storage system; specifically, the cross-regional power exchange volume P is monitored in real time. exchange (t), which represents the power transmitted from one area to another within time t; to calculate the power balance error ΔP of the entire network net (t), which is calculated as: in, represents the power demand of the ith sub-region in time t; is the corresponding power output; is the power exchange amount in the i-th region; n is the total number of sub-regions, and the power balance error is used to evaluate the supply and demand balance between sub-regions;

[0032] Based on the feedback information from the whole network data interaction platform, the configuration parameters of each sub-area are adjusted in a coordinated manner. When the preset threshold is exceeded, the energy storage power output of the area is adjusted or the amount of power exchange with neighboring areas To balance the power supply and demand of the entire network; dynamically adjust the capacity of the energy storage system for areas with excessive load or insufficient energy storage or location to ensure that insufficient areas can obtain support from the energy storage systems in neighboring areas, thereby achieving the optimal configuration of the entire network.

[0033] Beneficial effects of the invention: The invention is a multi-objective optimization configuration method for energy storage in a distribution network with long-term power and electricity balance. In use, the grid connection of renewable energy power generation needs to meet the requirements of power balance and frequency stability of the power grid, but the existing technology is difficult to meet these requirements, resulting in difficulties in the grid connection of renewable energy power generation. In order to solve the above problems, a multi-objective optimization configuration method for energy storage in a distribution network based on energy storage with power and electricity balance is proposed; firstly, a regional initial energy storage planning scheme is generated based on the long-term power and electricity balance; secondly, on the basis of the initial energy storage planning scheme of each sub-region, combined with the research results of the economic efficiency of distributed distribution network energy storage, regional economic optimization objective functions with the lowest investment and operation cost of energy storage systems in each region are established respectively; then, the energy storage system configuration of each sub-region is independently optimized, so that the energy storage system configuration of each region reaches the local optimum under the constraints of local power load characteristics, power demand and geographical location; finally, a collaborative control algorithm is used to globally optimize the entire network. The collaborative control algorithm combines the optimization results of each region and realizes the global optimal configuration of the distributed energy storage system through data interaction and feedback mechanism. The test results show that: while the multi-objective optimization configuration of distribution network energy storage based on long-term power and electricity balance effectively reduces the investment cost of renewable energy systems, the planning performance of traditional methods is weaker than this method. This method reduces the total energy storage cost of renewable energy systems and promotes the renewable energy power generation of renewable energy systems. The present invention has the advantages of generating initial energy storage planning, establishing optimization objective functions for each region, independently optimizing each sub-region, and using a collaborative control algorithm for global optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 It is a schematic diagram of the structure of the renewable energy system based on energy storage of the present invention.

[0035] Figure 2 This is the IEEE33 node power distribution network topology diagram of the present invention.

[0036] Figure 3 It is a schematic diagram of the energy storage rated capacity of the present invention.

[0037] Figure 4 It is a schematic diagram of the energy storage rated power of the present invention. DETAILED DESCRIPTION

[0038] The present invention will be further described below in conjunction with the accompanying drawings.

[0039] Example 1

[0040] like Figure 1-4 As shown, a multi-objective optimization configuration method for energy storage in a distribution network for long-term power and electricity balance is provided, wherein the method comprises the following steps:

[0041] Step 1: Collaborative planning optimization method for distribution network energy storage system: The collaborative planning method for renewable energy system is a comprehensive planning strategy. The invention aims to achieve sustainable development of energy through the combination of energy storage technology and renewable energy system. Its core idea is to use the high energy density of energy storage and the renewable characteristics of renewable energy to achieve energy storage and optimal utilization. In the collaborative planning process, multiple factors such as energy demand, output characteristics, and energy storage system capacity of renewable energy need to be considered;

[0042] In the present invention, ① the structure of the renewable energy system of the distribution network based on energy storage configuration: the renewable energy power generation system in the distribution network usually includes a wind power generation system and a photovoltaic power generation system, which can complement each other. The battery energy storage system mainly uses lithium batteries. The structure of the renewable energy system designed by the present invention is as follows: Figure 1 As shown; Figure 1 The energy management system is used to monitor and control the operation of the entire system to achieve optimal energy allocation and scheduling; the energy management system can monitor and control the output of renewable energy, energy demand, energy storage system, etc. in real time to ensure the stability and economy of the system; wind turbines and photovoltaic power generation devices: this is the core part of the system, responsible for converting renewable energy such as solar energy and wind energy into electrical energy; common equipment includes solar panels, wind turbines, etc.

[0043] ② Long-term power balance calculation method: Collect planning data and operating parameters of distributed distribution networks. Planning data include the geographical distribution of distribution networks, load density distribution and future electricity demand forecasts; operating parameters include historical load data of each sub-region, seasonal variation patterns and access to distributed energy.

[0044] First determine the power demand P of each sub-area in different time periods d (t) and power supply P s (t), where P d (t) represents the power demand within time t; P s (t) represents the power supply within time t; t is a time variable;

[0045] The formula for calculating the power gap in a long period is: ΔP(t) = P d (t)-P s (t), where ΔP(t) represents the power gap within time t; if ΔP(t)>0, it means that there is a power shortage within the time period and the energy storage system is required to provide power; if ΔP(t)<0, it means that there is a power surplus and the excess power can be stored in the energy storage system;

[0046] According to the power shortage situation of each sub-region, calculate the required capacity E of the energy storage system req , the formula is: Among them, E req represents the required capacity of the energy storage system; t1 and t2 are time intervals in the long cycle; ΔP(t) represents the power gap within time t; the formula is used to calculate the total energy storage demand of the energy storage system within a certain period to ensure that the energy storage system can effectively balance the power supply and demand when the peak and valley loads differ significantly.

[0047] ③ Economic optimization of the region with the lowest energy storage investment and operation cost: Determine the type and configuration of energy storage equipment involved in the initial energy storage planning scheme for each sub-region, and obtain the unit investment cost C of the equipment based on the type of energy storage equipment. inv and unit operation and maintenance cost C om ;

[0048] Based on the energy storage demand E of each sub-region req and equipment configuration, calculate the total investment cost C of each sub-region energy storage system total , the calculation formula is: C total =C inv ×E req , where C total Represents the total initial investment cost of the energy storage system; E req The energy storage demand;

[0049] Calculate the annual operation and maintenance cost C of the energy storage system in each sub-region om_total , the calculation formula is: C om_total =C om ×E req , where C om_total Represents the total annual operation and maintenance cost of the energy storage system;

[0050] Calculate the operating income R of the energy storage system in each sub-region. The operating income is the income obtained by the energy storage system through the purchase and sale of electricity under the condition of peak and valley electricity price differences. The calculation formula for the operating income is: Where R represents the total operating benefit of the energy storage system; P sell (t i ), P buy (t i ) represent the time point t i The selling and buying prices of electricity on E sell (t i ), E buy (t i ) represent the time point t i The amount of electricity sold and bought; n is the number of time nodes in the long period;

[0051] Based on the above calculation results, the regional economic optimization objective function F of each sub-regional energy storage system is established. Its goal is to minimize the total investment and operation cost of the energy storage system. The expression of the optimization objective function is: F = Ctotal +C om_total -R, where F represents the value of the optimization objective function. By minimizing the value of F, the optimal energy storage system configuration for each sub-area is determined to ensure that the benefits of the energy storage system are maximized while minimizing the investment cost and operation and maintenance cost.

[0052] Step 2: Consider the global collaborative energy storage system optimization configuration method: Propose the dual goals of achieving local optimization and global coordination, independently optimize the energy storage system configuration of each sub-region, and then use the collaborative control algorithm to globally optimize the entire network, and achieve the global optimal configuration of the energy storage system through data interaction and feedback mechanism;

[0053] In the present invention, ① based on the genetic algorithm, the local optimization configuration of energy storage sub-regions is: the power load characteristics of each sub-region are obtained, including the daily load curve, seasonal load change law and future load growth forecast. The load characteristics are used to define the power demand function P of each sub-region. d (t), where P d (t) represents the power demand in time t;

[0054] Obtain the geographical location constraints of each sub-region, including the geographical environment, grid access conditions, land use restrictions, and the layout of existing power infrastructure in the region. The geographical location constraints are used to limit the configuration range of the energy storage system.

[0055] Construct the energy storage system configuration model for each sub-region. The system configuration model is based on the regional power load characteristics and power demand function P d (t) and geographical location constraints as input, and the configuration parameters of the energy storage system X config As optimization variables, X config Including the capacity of the energy storage system E capacity , power output P output and installation location;

[0056] Define the optimization objective function F local , whose goal is to meet the regional electricity demand P d (t) and geographical location constraints, minimize the configuration cost C of the energy storage system config , the expression of the optimization objective function is: F local =C config (E capacity ,P output ,Location)+λ×∑ t (P d (t)-P output (t)) 2 , where C config (E capacity ,P output, Location) represents the configuration cost of the energy storage system; λ is the penalty factor, which is used to balance the weight between the system configuration cost and the imbalance between power supply and demand; P output (t) is the power output of the energy storage system in time t, and the optimization process aims to make F local minimize;

[0057] The distributed optimization algorithm is used to optimize the above objective function F local To solve, select an algorithm with global search capabilities such as genetic algorithm or particle swarm optimization algorithm, combine the load characteristics and geographical location constraints in the region, and iteratively solve the optimal energy storage system configuration parameters To ensure that the energy storage system configuration in each sub-area reaches the local optimum.

[0058] Specifically choose the genetic algorithm to optimize the objective function F local The specific steps for solving the problem include:

[0059] S1: Initialize the population of the genetic algorithm and define each individual as a configuration parameter combination X of the energy storage system config , the population size is set to N, that is, N different initial configuration schemes are generated, and each scheme is generated within a given range by random selection;

[0060] S2: For each individual X config , calculate its fitness function F fitness , the fitness function is based on the optimization objective function F local To determine, the expression of the fitness function is: Where ε is a small constant to prevent the denominator from being zero; F local (X config ) is the defined optimization objective function, and the performance of each individual in local optimization is evaluated by calculating its fitness;

[0061] S3: Select the individual with the highest fitness value in the population for reproduction, using the roulette wheel selection method, with a selection probability of P select (X config ) is proportional to the fitness, and its formula is: Among them, F fitness (X config ) represents the fitness of the i-th individual; N is the population size; by selecting individuals with high fitness as parents, the overall quality of the offspring is improved;

[0062] S4: Perform a crossover operation on the selected individuals and generate new offspring using a single-point crossover or multi-point crossover method; let the two parent individuals be X config and X config , then new offspring individuals are produced through crossover Taking single-point crossover as an example, its crossover expression is: in, is the energy storage capacity of the first parent individual; is the power output of the second parent individual; Location1 is the installation location of the first parent individual, and the offspring individuals are generated by selecting different crossover points for gene exchange;

[0063] S5: Perform mutation operation on the offspring individuals generated by crossover according to the predetermined mutation probability P mut Randomly change the gene value of the individual, and let the parameter corresponding to a gene be x j , then the parameter after mutation is: x′ j =x j +Δx, where Δx is a small value randomly generated within a predetermined range. The mutation operation is used to prevent the population from falling into the local optimum and improve the global search capability;

[0064] S6: All new individuals generated by crossover and mutation operations are combined with individuals with high fitness in the population to form a new population, and S2 to S5 are repeated until the preset number of iterations is reached or the termination condition is met;

[0065] After reaching the preset number of iterations or termination conditions, select the individual with the highest fitness value As the final optimal energy storage system configuration parameters; through the above steps, the genetic algorithm can effectively combine the load characteristics and geographical location constraints in the region to iteratively solve the configuration parameters of the energy storage system. This method can gradually optimize the energy storage system configuration parameters to ensure that the final configuration scheme reaches the local optimum while meeting the regional power demand and geographical location constraints, effectively improving the operating efficiency and economy of the energy storage system.

[0066] ②Use collaborative control algorithm to optimize the whole network globally: obtain the optimal energy storage system configuration parameters for each sub-area Including the capacity of the energy storage system Power Output and installation position, these configuration parameters are obtained through the optimization of the genetic algorithm in the above ①, and represent the local optimal configuration results of each sub-area;

[0067] Establish a network-wide data interaction platform to summarize the configuration parameters of each sub-area and operating data, including regional power load P d (t), power output P output (t) and energy storage state S storage (t), where S storage (t) represents the storage state of the energy storage system within time t;

[0068] Based on the network-wide data interaction platform, a feedback mechanism is established to monitor the power flow between sub-regions and the operating status of the energy storage system; specifically, the cross-regional power exchange volume P is monitored in real time. exchange (t), which represents the power transmitted from one area to another within time t; to calculate the power balance error ΔP of the entire network net (t), which is calculated as: in, represents the power demand of the ith sub-region in time t; is the corresponding power output; is the power exchange amount in the i-th region; n is the total number of sub-regions, and the power balance error is used to evaluate the supply and demand balance between sub-regions;

[0069] Based on the feedback information from the whole network data interaction platform, the configuration parameters of each sub-area are adjusted in a coordinated manner. When the preset threshold is exceeded, the energy storage power output of the area is adjusted or the amount of power exchange with neighboring areas To balance the power supply and demand of the entire network; dynamically adjust the capacity of the energy storage system for areas with excessive load or insufficient energy storage or location to ensure that insufficient areas can be supported by energy storage systems in neighboring areas, thereby achieving optimal configuration for the entire network;

[0070] During the feedback adjustment process, the optimal configuration parameters of each sub-region are iteratively updated. The updated parameters are applied to the energy storage systems in each region again until the power balance error of the entire network is ΔP net (t) drops to within a preset range, and finally realizes the global optimal configuration of the distributed energy storage system; through the above steps, the collaborative control algorithm can effectively combine the local optimization results of each area, and dynamically adjust the configuration parameters of the energy storage system in each area through the whole network data interaction platform and feedback mechanism, so as to realize the global optimal configuration of the distributed energy storage system. This method ensures the supply and demand balance of the distribution network under different load conditions, improves the overall operation efficiency and economy of the energy storage system, and enhances the stability and reliability of the entire distribution network system.

[0071] Based on the optimization results of the entire network, the energy storage equipment deployment plan for each sub-region is determined, including the type, specification, quantity and specific installation location of the energy storage equipment. The deployment plan should take into account the power load characteristics, geographical conditions and the layout of the existing power infrastructure in the region to ensure the reasonable distribution and efficient operation of the energy storage system.

[0072] Step 3: Example analysis.

[0073] The present invention is a multi-objective optimization configuration method for energy storage in a distribution network with long-term power and quantity balance. In use, the grid connection of renewable energy power generation needs to meet the requirements of power balance and frequency stability of the power grid, but the existing technology is difficult to meet these requirements, resulting in difficulties in the grid connection of renewable energy power generation. In order to solve the above problems, a multi-objective optimization configuration method for energy storage in a distribution network based on energy storage with power and quantity balance is proposed; firstly, a regional initial energy storage planning scheme is generated based on the long-term power and quantity balance; secondly, on the basis of the initial energy storage planning scheme of each sub-region, combined with the research results of the economic efficiency of distributed distribution network energy storage, regional economic optimization objective functions with the lowest investment and operation cost of energy storage systems in each region are established respectively; then, the energy storage system configuration of each sub-region is independently optimized, so that the energy storage system configuration of each region reaches the local optimum under the constraints of local power load characteristics, power demand and geographical location; finally, a collaborative control algorithm is used to globally optimize the entire network. The collaborative control algorithm combines the optimization results of each region and realizes the global optimal configuration of the distributed energy storage system through data interaction and feedback mechanism. The test results show that: while the multi-objective optimization configuration of distribution network energy storage based on long-term power and electricity balance effectively reduces the investment cost of renewable energy systems, the planning performance of traditional methods is weaker than this method. This method reduces the total energy storage cost of renewable energy systems and promotes the renewable energy power generation of renewable energy systems. The present invention has the advantages of generating initial energy storage planning, establishing optimization objective functions for each region, independently optimizing each sub-region, and using a collaborative control algorithm for global optimization.

[0074] Example 2

[0075] like Figure 1-4 As shown, a multi-objective optimization configuration method for energy storage in a distribution network for long-term power and electricity balance is provided, wherein the method comprises the following steps:

[0076] Step 1: Coordinated planning and optimization method of distribution network energy storage system: Utilize the high energy density of energy storage and the renewable characteristics of renewable energy to achieve energy storage and optimal utilization;

[0077] Step 2: Consider the global collaborative energy storage system optimization configuration method: Propose the dual goals of achieving local optimization and global coordination, independently optimize the energy storage system configuration of each sub-region, and then use the collaborative control algorithm to globally optimize the entire network, and achieve the global optimal configuration of the energy storage system through data interaction and feedback mechanism;

[0078] Step 3: Example analysis.

[0079] In the present invention, case analysis: the present invention selects the IEEE-33 node distribution network system for case analysis, and performs power flow calculation by forward back substitution method. The total number of branches of the distribution network is 32, the total active load of the system is 3715kW (benchmark value), the rated voltage is 12.66kV, and the maximum transmission current of the line is 0.55KA; wind turbines are connected at nodes 7 and 24, and the output curves of the wind turbines are known; photovoltaic generators are connected at nodes 25 and 32, and the output curves of the photovoltaic generators are known, and the minimization of the difference between the average daily operating cost of the distribution network, the carbon emission cost and the peak-valley arbitrage income is used as the objective function of the energy storage configuration.

[0080] Assume that the unit cost of energy storage equipment is 1,500,000 yuan / MWh and 1,000,000 yuan / MW respectively; call Gurobi in matlab2022a software to solve, and configure the energy storage capacity and power at nodes 1, 19, and 26 with higher carbon potential. The final configuration results are as follows: Figure 2-4 shown.

[0081] Among them, energy storage No. 1 corresponds to node 1, energy storage No. 2 corresponds to node 19, and energy storage No. 3 corresponds to node 26.

[0082] According to the above results, energy storage devices with capacities / powers of 23.72MWh / 1.31MW, 31.24MWh / 1.34MW and 26.36MWh / 1.10MW are configured at nodes 1, 19 and 26 respectively, which can effectively reduce the carbon emission cost from the initial 1942 yuan to 738.26 yuan, with good economic returns and environmental benefits; the method of the present invention can significantly reduce the investment and operation costs of the energy storage system, while improving the economy and stability of the overall operation of the distribution network; although the configuration cost of energy storage is relatively high, due to its bidirectional power flow characteristics, it has certain peak shaving and valley filling benefits, so the configuration of energy storage is economically reasonable.

[0083] In summary, the present invention proposes a multi-objective optimization configuration of distribution network energy storage for long-term power and electricity balance. By combining local optimization with global coordination strategies, the volatility and scheduling complexity problems caused by high proportion of renewable energy access are solved; in system design, the high energy density characteristics of energy storage technology and the volatility complementarity of renewable energy are utilized to construct an optimization planning framework for distributed distribution networks, including long-term power balance measurement and energy storage investment and operation cost optimization models; in addition, the present invention realizes the local optimal configuration of sub-regional energy storage systems through genetic algorithms, and combines with collaborative control algorithms to achieve global optimization of the entire network energy storage system.

[0084] Experimental results show that this method can significantly reduce the investment and operating costs of the energy storage system, while improving the economy and stability of the overall operation of the distribution network; compared with traditional methods, the collaborative planning method proposed in this invention is more adaptable to complex scenarios with a high proportion of renewable energy access, and provides a theoretical basis and practical path for realizing a green and low-carbon energy system.

[0085] The present invention is a multi-objective optimization configuration method for energy storage in a distribution network with long-term power and quantity balance. In use, the grid connection of renewable energy power generation needs to meet the requirements of power balance and frequency stability of the power grid, but the existing technology is difficult to meet these requirements, resulting in difficulties in the grid connection of renewable energy power generation. In order to solve the above problems, a multi-objective optimization configuration method for energy storage in a distribution network based on energy storage with power and quantity balance is proposed; firstly, a regional initial energy storage planning scheme is generated based on the long-term power and quantity balance; secondly, on the basis of the initial energy storage planning scheme of each sub-region, combined with the research results of the economic efficiency of distributed distribution network energy storage, regional economic optimization objective functions with the lowest investment and operation cost of energy storage systems in each region are established respectively; then, the energy storage system configuration of each sub-region is independently optimized, so that the energy storage system configuration of each region reaches the local optimum under the constraints of local power load characteristics, power demand and geographical location; finally, a collaborative control algorithm is used to globally optimize the entire network. The collaborative control algorithm combines the optimization results of each region and realizes the global optimal configuration of the distributed energy storage system through data interaction and feedback mechanism. The test results show that: while the multi-objective optimization configuration of distribution network energy storage based on long-term power and electricity balance effectively reduces the investment cost of renewable energy systems, the planning performance of traditional methods is weaker than this method. This method reduces the total energy storage cost of renewable energy systems and promotes the renewable energy power generation of renewable energy systems. The present invention has the advantages of generating initial energy storage planning, establishing optimization objective functions for each region, independently optimizing each sub-region, and using a collaborative control algorithm for global optimization.

Claims

1. A multi-objective optimization configuration method for energy storage in a distribution network for long-term power and electricity balance, characterized by: The method comprises the following steps: Step 1: Coordinated planning and optimization method of distribution network energy storage system: Utilize the high energy density of energy storage and the renewable characteristics of renewable energy to achieve energy storage and optimal utilization; Step 2: Consider the global collaborative energy storage system optimization configuration method: Propose the dual goals of achieving local optimization and global coordination, independently optimize the energy storage system configuration of each sub-region, and then use the collaborative control algorithm to globally optimize the entire network, and achieve the global optimal configuration of the energy storage system through data interaction and feedback mechanism; Step 3: Example analysis.

2. The multi-objective optimization configuration method for energy storage in a distribution network with long-term power and electricity balance according to claim 1 is characterized in that: The collaborative planning and optimization method of the distribution network energy storage system in step 1 comprises the following steps: Step 1.1: Distribution network renewable energy system structure based on energy storage configuration; Step 1.2: Long-term power and electricity balance calculation method; Step 1.3: Economic optimization of the region with the lowest energy storage investment and operating costs.

3. The multi-objective optimization configuration method for energy storage in a distribution network with long-term power and electricity balance as claimed in claim 2 is characterized by: The long-term power and quantity balance calculation method in step 1.2 specifically includes the following steps: Step 1.21: Collect planning data and operating parameters of the distributed distribution network. Planning data includes the geographical distribution of the distribution network, load density distribution, and future electricity demand forecasts; operating parameters include historical load data of each sub-region, seasonal variation patterns, and access to distributed energy sources; Step 1.22: First determine the power demand P of each sub-region in different time periods d (t) and power supply P s (t), where P d (t) represents the power demand within time t; P s (t) represents the power supply within time t; t is a time variable; Step 1.23: Calculate the power deficit in the long term using the formula: ΔP(t) = P d (t)-P s (t), where ΔP(t) represents the power gap within time t; Step 1.24: Calculate the required capacity E of the energy storage system based on the power shortage of each sub-region req , the formula is: Among them, E req Represents the required capacity of the energy storage system; t1 and t2 are time intervals in the long cycle; ΔP(t) represents the power gap within time t.

4. The multi-objective optimization configuration method for energy storage in a distribution network with long-term power and quantity balance as claimed in claim 2 is characterized by: The regional economic optimization with the lowest energy storage investment and operation cost in step 1.3 is specifically as follows: establishing a regional economic optimization objective function F for each sub-region energy storage system, the goal of which is to minimize the total investment and operation cost of the energy storage system. The expression of the optimization objective function is: F = C total +C om_total -R, where F represents the value of the optimization objective function; C total Represents the total initial investment cost of the energy storage system; C om_total Represents the total annual operation and maintenance cost of the energy storage system; R represents the total operating income of the energy storage system; by minimizing the value of F, the optimal energy storage system configuration of each sub-area is determined to ensure that the income of the energy storage system is maximized while minimizing the investment cost and operation and maintenance cost.

5. The multi-objective optimization configuration method for energy storage in a distribution network with long-term power and quantity balance as claimed in claim 4 is characterized by: The total initial investment cost of the energy storage system is C total =E based on the energy storage demand E of each sub-region req and equipment configuration, calculate the total investment cost C of each sub-region energy storage system total , the calculation formula is: C total =C inv ×E req , where C inv is the unit investment cost of the equipment; E req The demand for energy storage.

6. The multi-objective optimization configuration method for energy storage in a distribution network with long-term power and quantity balance according to claim 4, characterized in that: The total annual operation and maintenance cost of the energy storage system is C om_total To calculate the annual operation and maintenance cost C of the energy storage system in each sub-region om_total , the calculation formula is: C om_total =C om ×E req , where C om The unit operation and maintenance cost of the equipment.

7. The multi-objective optimization configuration method for energy storage in a distribution network with long-term power and quantity balance as claimed in claim 4, characterized in that: The total operating income R of the energy storage system is: the operating income R of each sub-regional energy storage system is calculated. The operating income is the income obtained by the energy storage system through the purchase and sale of electricity under the condition of peak and valley electricity price differences. The calculation formula of the operating income is: Among them, P sell (t i ), P buy (t i ) represent the time point t i The selling and buying prices of electricity on E sell (t i )、E buy (t i ) represent the time point t i The amount of electricity sold and bought; n is the number of time nodes in the long period.

8. The multi-objective optimization configuration method for energy storage in a distribution network with long-term power and electricity balance according to claim 1, characterized in that: The method for optimizing the configuration of the global collaborative energy storage system in step 2 includes the following steps: Step 2.1: Local optimization configuration of energy storage sub-areas based on genetic algorithm; Step 2.2: Use the collaborative control algorithm to globally optimize the entire network.

9. The multi-objective optimization configuration method for energy storage in a distribution network with long-term power and quantity balance as claimed in claim 8, characterized in that: The local optimization configuration of energy storage sub-regions based on genetic algorithm in step 2.1 is specifically as follows: obtaining the power load characteristics of each sub-region, including daily load curves, seasonal load variation patterns, and future load growth forecasts. The load characteristics are used to define the power demand function P of each sub-region. d (t), where P d (t) represents the power demand in time t; Obtain the geographical location constraints of each sub-region, including the geographical environment, grid access conditions, land use restrictions, and the layout of existing power infrastructure in the region. The geographical location constraints are used to limit the configuration range of the energy storage system. Construct the energy storage system configuration model for each sub-region. The system configuration model is based on the regional power load characteristics and power demand function P d (t) and geographical location constraints as input, and the configuration parameters of the energy storage system X config As optimization variables, X config Including the capacity of the energy storage system E capacity , power output P output and installation location; Define the optimization objective function F local , whose goal is to meet the regional electricity demand P d (t) and geographical location constraints, minimize the configuration cost C of the energy storage system config , the expression of the optimization objective function is: F local =C config (E capacity ,P output ,Location)+λ×∑ t (P d (t)-P output (t)) 2 , where C config (E capacity ,P output , Location) represents the configuration cost of the energy storage system; λ is the penalty factor, which is used to balance the weight between the system configuration cost and the imbalance between power supply and demand; P output (t) is the power output of the energy storage system in time t, and the optimization process aims to make F local minimize; The distributed optimization algorithm is used to optimize the above objective function F local To solve, select genetic algorithm or particle swarm optimization algorithm and algorithm with global search capability, combine the load characteristics and geographical location constraints in the region, and iteratively solve the optimal energy storage system configuration parameters To ensure that the energy storage system configuration in each sub-area reaches the local optimum.

10. The multi-objective optimization configuration method for energy storage in a distribution network with long-term power and quantity balance according to claim 8, characterized in that: The collaborative control algorithm in step 2.2 is used to globally optimize the entire network, specifically: obtain the optimal energy storage system configuration parameters for each sub-area Including the capacity of the energy storage system Power Output and installation position, these configuration parameters are obtained by optimizing the genetic algorithm in the above step 2.1, and represent the local optimal configuration results of each sub-area; Establish a network-wide data interaction platform to summarize the configuration parameters of each sub-area and operating data, including regional power load P d (t), power output P output (t) and energy storage state S storage (t), where S storage (t) represents the storage state of the energy storage system within time t; Based on the network-wide data interaction platform, a feedback mechanism is established to monitor the power flow between sub-regions and the operating status of the energy storage system; specifically, the cross-regional power exchange volume P is monitored in real time. exchange (t), which represents the power transmitted from one area to another within time t; to calculate the power balance error ΔP of the entire network net (t), which is calculated as: in, represents the power demand of the ith sub-region in time t; is the corresponding power output; is the power exchange amount in the i-th region; n is the total number of sub-regions, and the power balance error is used to evaluate the supply and demand balance between sub-regions; Based on the feedback information from the whole network data interaction platform, the configuration parameters of each sub-area are adjusted in a coordinated manner. When the preset threshold is exceeded, the energy storage power output of the area is adjusted or the amount of electricity exchanged with neighboring areas To balance the power supply and demand of the entire network; dynamically adjust the capacity of the energy storage system for areas with excessive load or insufficient energy storage or location to ensure that insufficient areas can obtain support from the energy storage systems in neighboring areas, thereby achieving the optimal configuration of the entire network.

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