Collaborative planning optimization method for distributed power distribution network energy storage system

Abstract

The invention relates to the technical field of power system management, in particular to a collaborative planning optimization method for a distributed power distribution network energy storage system, which comprises the following steps: S1, measuring and calculating an energy storage demand, and generating a regional initial energy storage planning scheme; s2, respectively establishing a regional economic optimization objective function with the lowest energy storage system investment and operation cost of each region; s3, independently optimizing the energy storage system configuration of each sub-region; s4, realizing global optimal configuration of the distributed energy storage system through a data interaction and feedback mechanism; s5, the distributed power distribution network energy storage system is specifically deployed, so that the distributed energy storage system can cooperatively operate under different load conditions; and S6, dynamically adjusting the charging and discharging operation of the energy storage equipment. According to the invention, through real-time monitoring and dynamic adjustment of the operation of the energy storage equipment, the power resource distribution and system stability of the distributed power distribution network are optimized, and the economical efficiency and operation efficiency of the whole power distribution network are significantly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power system management, and in particular to a collaborative planning and optimization method for a distributed distribution network energy storage system. Background Art

[0002] With the widespread use of renewable energy and the continuous growth of electricity demand, modern distribution networks face increasingly complex management challenges. Especially in distributed distribution networks, due to the 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 based on the load characteristics of specific regions, resulting in low system efficiency and uneven resource allocation.

[0003] Existing technologies are deficient in two key areas: First, existing energy storage management systems typically employ static configuration strategies, determining the size and configuration of energy storage equipment during the system design phase. This makes it difficult to adapt to dynamically changing load demands. Second, existing technologies lack effective network-wide collaborative control mechanisms, hindering the dynamic optimization and scheduling of power and energy storage resources across sub-regions. This can easily lead to resource waste or power shortages during peak hours. Therefore, developing a collaborative planning and optimization method for distributed distribution network energy storage systems is essential to improving the operational efficiency and reliability of distributed distribution networks. Summary of the Invention

[0004] Based on the above objectives, the present invention provides a collaborative planning and optimization method for a distributed distribution network energy storage system.

[0005] A method for collaborative planning and optimization of a distributed distribution network energy storage system comprises the following steps:

[0006] S1: Based on the planning and operation requirements of the distributed distribution network and the long-term power balance, the energy storage demand is calculated for each sub-region of the distribution network and an initial regional energy storage planning scheme is generated.

[0007] S2: Based on the initial energy storage planning scheme for each sub-region and combined with the research results on the economics of distributed distribution network energy storage, a regional economic optimization objective function is established to minimize the investment and operating costs of the energy storage system in each region.

[0008] S3: Independently optimize the energy storage system configuration in each sub-region to achieve a local optimum within the constraints of local power load characteristics, power demand, and geographic location.

[0009] S4: After completing the independent optimization of each sub-region in S3, a collaborative control algorithm is used to perform global optimization of the entire network. The collaborative control algorithm combines the optimization results of each region and achieves the global optimal configuration of the distributed energy storage system through data interaction and feedback mechanisms.

[0010] S5: Based on the network-wide optimization results, specific deployment of distributed distribution network energy storage systems is carried out, including hardware deployment of energy storage systems in each sub-region and the configuration of communication control systems, so that the distributed energy storage systems can operate collaboratively under different load conditions.

[0011] S6: During the operation of the energy storage system, the power load in each region, the status of the energy storage system, and the cross-regional power flow are monitored in real time. The charging and discharging operations of the energy storage equipment are dynamically adjusted through the intelligent controller in the region to achieve the optimal allocation of energy storage resources and power balance among the regions.

[0012] Optionally, the S1 specifically includes:

[0013] S11: Collecting planning data and operating parameters of the distributed distribution network. The planning data includes the geographical distribution of the distribution network, load density distribution, and future electricity demand forecasts. The operating parameters include historical load data of each sub-region, seasonal variation patterns, and access to distributed energy resources.

[0014] S12: Based on the collected operating parameters, the power supply and demand balance of each sub-region is calculated using a long-term power balance algorithm;

[0015] S13: Based on the energy storage demand calculated in S12, combined with the physical distribution of each sub-region, the existing power load density distribution, and the future electricity demand forecast, generate a regional initial energy storage planning scheme. The initial energy storage planning scheme includes specific recommendations on the type, capacity configuration, and installation location of the energy storage equipment.

[0016] Optionally, the S12 specifically includes:

[0017] S121: 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 in time t, P s (t) represents the power supply within time t, where t is a time variable;

[0018] S122: Calculate the power gap in the long period, the formula 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 there is a power shortage within the time period; if ΔP(t)<, it means there is a power surplus;

[0019] S123: 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, and ΔP(t) represents the power gap in time t.

[0020] Optionally, the S2 specifically includes:

[0021] S21: 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 and unit operation and maintenance cost of the equipment based on the type of energy storage equipment;

[0022] S22: 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 ;

[0023] S23: Calculate the annual operation and maintenance cost C of the energy storage system in each sub-region om_total ;

[0024] S24: Calculate the operating income R of the energy storage system in each sub-region, where 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;

[0025] S25: Based on the above calculation results, establish the regional economic optimization objective function F of each sub-region's energy storage system. Its goal is to minimize the total investment and operating cost of the energy storage system. The expression of the optimization objective function is:

[0026] F=C total +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-region is determined.

[0027] Optionally, the S3 specifically includes:

[0028] S31: Obtain the power load characteristics of each sub-region, including daily load curves, seasonal load variation patterns, and future load growth forecasts;

[0029] S32: Obtaining geographical location constraints of each sub-region, including geographical environment, grid access conditions, land use restrictions, and layout of existing power infrastructure in the region;

[0030] S33: Based on the data obtained in S31 and S32, a storage system configuration model is constructed for each sub-region. The system configuration model is based on the regional power load characteristics and the power demand function P. d (t) and geographical location constraints as input, and the configuration parameters of the energy storage system X config As the optimization variable, X config Including the capacity of the energy storage system E capacity , power output P output and installation location;

[0031] S34: 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:

[0032] Among them, 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;

[0033] S35: Use distributed optimization algorithm to optimize the above objective function F local To solve, select an 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

[0034] S36: The optimal configuration parameters obtained by optimization The energy storage system configuration scheme applied to each sub-region includes the energy storage system capacity after optimization. Power output and the specific installation location of the energy storage system.

[0035] Optionally, in S35, a genetic algorithm is specifically selected to optimize the objective function F local The specific steps for solving include:

[0036] S351: Initialize the population of the genetic algorithm and define each individual as the 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 in a given range by random selection;

[0037] S352: 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, ∈ is a small constant to prevent the denominator from being zero, F local (X config ) is the defined optimization objective function;

[0038] S353: 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:

[0039]

[0040] Among them, F fitness (X configi ) represents the fitness of the i-th individual, N is the population size, and by selecting individuals with high fitness as parents, the overall quality of the offspring is improved;

[0041] S354: Perform a crossover operation on the selected individuals to generate new offspring using a single-point crossover or multi-point crossover method;

[0042] S355: Perform mutation operation on the offspring individuals generated by crossover, according to the predetermined mutation probability P mut Randomly change the gene value of an individual;

[0043] S356: All new individuals generated by the crossover and mutation operations are combined with the high-fitness individuals in the population to form a new population, and S352 to S355 are repeated until the preset number of iterations is reached or the termination condition is met;

[0044] S357: 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.

[0045] Optionally, the S4 specifically includes:

[0046] S41: Obtaining the optimal energy storage system configuration parameters for each sub-region Including the capacity of the energy storage system Power output and installation location;

[0047] S42: Establish a network-wide data exchange platform to summarize the configuration parameters of each sub-area and operational 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;

[0048] S43: 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. exch ange (t); to calculate the power balance error ΔP of the entire network net (t);

[0049] S44: Based on the feedback information from the network-wide data interaction platform, the configuration parameters of each sub-region 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 adjacent 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;

[0050] S45: During the feedback adjustment process, iteratively update the optimal configuration parameters of each sub-region The updated parameters are applied to the energy storage systems in each region again until the power balance of the entire network is less than ΔP net (t) falls within the preset range.

[0051] Optionally, the S5 specifically includes:

[0052] S51: Based on the network-wide optimization results, determine the energy storage equipment deployment plan for each sub-region, including the type, specifications, quantity, and specific installation location of the energy storage equipment;

[0053] S52: Performing hardware deployment of the energy storage device at the determined installation location, wherein the hardware deployment includes infrastructure construction, installation of batteries or other energy storage media, configuration of inverters, and provision of cooling and protective facilities;

[0054] S53: Configuring a communication control system within the area, wherein the communication control system includes a data acquisition device, a communication module, and a central control unit;

[0055] S54: After hardware deployment and communication control system configuration, system debugging and joint commissioning testing are performed to verify the operation of each sub-region's energy storage system. This includes testing the energy storage system's charging and discharging functions, the communication system's connectivity, and the coordinated operation of the entire network.

[0056] S55: Based on the debugging results, final parameter adjustments and system optimization are carried out to ensure that the energy storage system in each sub-area can match the overall operation requirements of the distribution network. Specifically, this includes optimizing and adjusting the energy storage system operation strategy and correcting the parameters of the communication control system.

[0057] Optionally, the S6 specifically includes:

[0058] S61: Deploy a power load monitoring device in the area, wherein the monitoring device includes a smart meter, a sensor, and a data collector for collecting power load data of each area in real time;

[0059] S62: Installing a status monitoring module on the energy storage device, the module including a voltage sensor, a current sensor, a temperature sensor, and a status indicator for real-time monitoring of the working status of the energy storage device, including the battery's charge and discharge status, remaining capacity, and operating parameters such as temperature changes;

[0060] S63: Deploy an inter-regional power flow monitoring device in each sub-region, wherein the inter-regional power flow monitoring device includes a power sensor and a meter for real-time monitoring of the amount of power transmitted from one region to another;

[0061] S64: The regional intelligent controller calculates the power supply and demand balance of each sub-region based on the real-time power load data, energy storage device status data, and cross-regional power flow data received. If the power supply and demand in a sub-region is unbalanced, the intelligent controller dynamically adjusts the charging and discharging operations of the energy storage device.

[0062] Optionally, the dynamic adjustment in S64 specifically includes:

[0063] S641: The intelligent controller receives the power supply and demand data at the current time point t, including the power demand P d (t) and power supply P s (t), calculate the current power balance state ΔP(t) based on the supply and demand data, where when ΔP(t)>0, it means that the power demand exceeds the supply; when ΔP(t)<0, it means that the power supply exceeds the demand;

[0064] S642: Based on the result of the power balance state ΔP(t), the intelligent controller selects a corresponding operation strategy. The specific strategy includes:

[0065] Strategy 1: When ΔP(t)>0, calculate the required discharge power P dish arge (t), which is calculated as follows:

[0066] P disch arge(t)=min(ΔP(t), P max ), where P max is the maximum discharge power of the energy storage device;

[0067] Strategy 2: When AP(t) < 0, calculate the required charging power P ch arge (t), which is calculated as follows:

[0068] P disch arge (t) = min(|ΔP(t)|, P max ), where |ΔP(t)| is the absolute value of the excess of power supply over demand;

[0069] S643: The intelligent controller dynamically adjusts the charge and discharge operation of the energy storage device according to the calculated charge and discharge power; specifically, when P discharge When (t)>0, the intelligent controller sends a discharge instruction to start the discharge module of the energy storage device, and the output power P disch arge (t) To supplement the electricity gap; if P charge (t)>0, the intelligent controller sends a charging instruction to start the charging module of the energy storage device, absorbs excess power and stores it in the energy storage system;

[0070] S644: The intelligent controller monitors the status of the energy storage device in real time, including the remaining capacity E remain (t) and equipment health status H status (t), after each charge and discharge operation, updating the remaining capacity of the energy storage device;

[0071] S645: When the remaining capacity of the energy storage device approaches the limit value, the intelligent controller will status (t) and future load forecast data to adjust the priority and intensity of charging and discharging operations.

[0072] Beneficial effects of the present invention:

[0073] The present invention significantly improves the operating efficiency and responsiveness of distributed distribution network energy storage systems by introducing advanced collaborative control strategies and intelligent control technologies. In particular, by real-time monitoring of the power load and energy storage status of each region, as well as data on cross-regional power flows, the present invention can ensure that the energy storage system achieves optimal power supply and demand matching and resource allocation between sub-regions. This dynamic adjustment mechanism not only reduces power waste caused by load fluctuations, but also allows for the rapid deployment of resources to meet increased power demand during peak power demand periods, thereby significantly improving the stability and economy of the entire distribution network.

[0074] The present invention, through a collaborative control algorithm, can not only adjust the energy storage configuration within a single area, but also coordinate energy storage resources across regions, so that the economic and environmental benefits of the entire system are significantly improved. This system-level optimization is of great significance for coping with the uncertainty of renewable energy and the instability of grid loads, ensuring the reliability of power supply while reducing energy costs, providing strong technical support for the sustainable development of modern distribution networks. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0076] Figure 1 A schematic diagram of a collaborative planning optimization method according to an embodiment of the present invention;

[0077] Figure 2 Schematic diagram of a method for calculating energy storage requirements in each sub-region according to an embodiment of the present invention. DETAILED DESCRIPTION

[0078] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments. It is also noted that, to provide a more detailed description, the following embodiments are best and preferred embodiments, and those skilled in the art may employ alternative methods for implementing certain known technologies. Furthermore, the accompanying drawings are intended only to provide a more detailed description of the embodiments and are not intended to limit the present invention.

[0079] It should be noted that references in the specification to "one embodiment," "an embodiment," "an exemplary embodiment," "some embodiments," etc. indicate that the described embodiments may include specific features, structures, or characteristics, but not every embodiment necessarily includes such specific features, structures, or characteristics. In addition, when specific features, structures, or characteristics are described in conjunction with an embodiment, it is within the knowledge of persons skilled in the relevant art to implement such features, structures, or characteristics in conjunction with other embodiments (whether or not explicitly described).

[0080] In general, terms can be understood, at least in part, from their use in context. For example, depending at least in part on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular sense, or can be used to describe a combination of features, structures, or characteristics in the plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey an exclusive set of factors, but can instead, depending at least in part on the context, allow for the presence of other factors that are not necessarily explicitly described.

[0081] like Figure 1-Figure 2 As shown, a collaborative planning and optimization method for a distributed distribution network energy storage system includes the following steps:

[0082] S1: Based on the planning and operation requirements of the distributed distribution network and the long-term power balance, the energy storage demand is calculated for each sub-region of the distribution network, and an initial regional energy storage planning scheme is generated as the basis for subsequent optimization.

[0083] S2: Based on the initial energy storage planning scheme for each subregion and combined with research results on the economics of distributed distribution network energy storage, a regional economic optimization objective function is established to minimize the investment and operating costs of the energy storage system in each region. The objective function includes the initial investment cost, operation and maintenance costs, and operating benefits of the energy storage system in the region.

[0084] S3: Independently optimize the energy storage system configuration in each sub-region to achieve a local optimum within the constraints of local power load characteristics, power demand, and geographic location.

[0085] S4: After completing the independent optimization of each sub-region in S3, a collaborative control algorithm is used to perform global optimization of the entire network. The collaborative control algorithm combines the optimization results of each region and achieves the global optimal configuration of the distributed energy storage system through data interaction and feedback mechanisms.

[0086] S5: Based on the network-wide optimization results, specific deployment of distributed distribution network energy storage systems is carried out, including hardware deployment of energy storage systems in each sub-region and the configuration of communication control systems, so that the distributed energy storage systems can operate collaboratively under different load conditions.

[0087] S6: During the operation of the energy storage system, the power load in each region, the status of the energy storage system, and the cross-regional power flow are monitored in real time. The charging and discharging operations of the energy storage equipment are dynamically adjusted through the intelligent controllers within the region to achieve the optimal allocation of energy storage resources and power balance among the regions, thereby ensuring the maximization of the economic benefits of the entire network.

[0088] S1 specifically includes:

[0089] S11: 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 resources.

[0090] S12: Based on the collected operating parameters, a long-term power balance algorithm is used to calculate the power supply and demand balance for each sub-region. This calculation process includes calculating the power gap in each sub-region under different load conditions and determining the energy storage requirement for each sub-region. This ensures that the energy storage system can effectively balance power supply and demand even when there are significant differences in peak and valley loads.

[0091] S13: Based on the energy storage demand calculated in S12, combined with the rational distribution of each sub-region, the existing power load density distribution, and the future electricity demand forecast, a regional initial energy storage planning scheme is generated. The initial energy storage planning scheme includes specific recommendations on the type of energy storage equipment, capacity configuration, and installation location. Through the above steps, the energy storage demand of each sub-region in the distributed distribution network can be accurately calculated, and an accurate initial energy storage planning scheme can be generated based on actual operating data. This method can effectively improve the configuration accuracy of the energy storage system, ensure the reasonable distribution of the energy storage system in each sub-region, thereby optimizing the balance between power supply and demand and improving the economic benefits and operational reliability of the system.

[0092] The calculation of the power supply and demand balance of each sub-region in S12 specifically includes:

[0093] S121: 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 in time t, P s (t) represents the power supply within time t, where t is a time variable;

[0094] S122: Calculate the power gap in the long term.

[0095] The formula 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 there is a power shortage within the time period and the energy storage system is needed to provide power. If ΔP(t)<0, it means there is a power surplus and the excess power can be stored in the energy storage system.

[0096] S123: 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 within the long cycle, and 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 there is a significant difference in peak and valley loads.

[0097] S2 specifically includes:

[0098] S21: 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 ;

[0099] S22: 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 ,

[0100] The calculation formula is: C total =C inv ×E reg , where C total represents the total initial investment cost of the energy storage system, E req The energy storage requirement determined in step S12;

[0101] S23: 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 ,in, Represents the total annual operation and maintenance cost of the energy storage system;

[0102] S24: 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 income of the energy storage system, P sell (t i ) and P buy (t i ) represent the time point t i The selling and buying prices of electricity on sell (t i ) and 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 cycle;

[0103] S25: Based on the above calculation results, establish the regional economic optimization objective function F of each sub-region's energy storage system. Its goal is to minimize the total investment and operating cost of the energy storage system. The expression of the optimization objective function is:

[0104] F=C total +Com_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-region is determined to ensure that the benefits of the energy storage system are maximized while minimizing investment and operation and maintenance costs. Through the above steps, the regional economic optimization objective function of the energy storage system can be accurately established by combining the energy storage demand of each sub-region with the cost characteristics of the energy storage equipment. This method can ensure that the energy storage system in each sub-region obtains the maximum operating benefit while minimizing the initial investment cost and operation and maintenance cost, thereby improving the economy and overall benefits of the energy storage system.

[0105] S3 specifically includes:

[0106] S31: Obtain 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;

[0107] S32: Obtaining geographic location constraints for each sub-region, including the geographical environment, grid access conditions, land use restrictions, and the layout of existing power infrastructure within the region. The geographic location constraints are used to limit the configuration range of the energy storage system.

[0108] S33: Based on the data obtained in S31 and S32, the energy storage system configuration model of each sub-region is constructed. 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 the optimization variable, X config Including the capacity of the energy storage system E capacity , power output P output and installation location;

[0109] S34: 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:

[0110] Among them, 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;

[0111] S35: Use distributed optimization algorithm 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-region reaches the local optimum;

[0112] S36: The optimal configuration parameters obtained by optimization The energy storage system configuration scheme applied to each sub-region includes the energy storage system capacity after optimization. Power output and the specific installation location of the energy storage system to ensure that the energy storage system meets regional needs while maintaining the lowest cost and highest operating efficiency. Through the above steps, the configuration of the energy storage system can be accurately optimized using a distributed optimization algorithm based on the power load characteristics, power demand and geographical location constraints of each sub-region. This method can ensure that the energy storage system achieves a locally optimal configuration within each sub-region, thereby maximizing the operating benefits of the energy storage system, reducing investment and operating costs, and improving the stability and economy of the overall distribution network.

[0113] In S35, a genetic algorithm is specifically selected to optimize the objective function F local The specific steps for solving include:

[0114] S351: Initialize the population of the genetic algorithm and define each individual as the 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 in a given range by random selection;

[0115] S352: 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, ∈ is a small constant to prevent the denominator from being zero, F local (X config ) is the defined optimization objective function, and the fitness of each individual is calculated to evaluate its performance in local optimization;

[0116] S353: 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:

[0117]

[0118] Among them, F fitness (X configi ) represents the fitness of the i-th individual, N is the population size, and by selecting individuals with high fitness as parents, the overall quality of the offspring is improved;

[0119] S354: 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,

[0120] The cross expression is:

[0121] 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 offspring individuals are generated by selecting different crossover points for gene exchange;

[0122] S355: 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 parameters after mutation are: 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 local optimality and improve the global search capability;

[0123] S356: All new individuals generated by the crossover and mutation operations are combined with the high-fitness individuals in the population to form a new population, and S352 to S355 are repeated until the preset number of iterations is reached or the termination condition is met;

[0124] S357: 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.

[0125] S4 specifically includes:

[0126] S41: Obtaining the optimal energy storage system configuration parameters for each sub-region Including the capacity of the energy storage system Power output and installation position, these configuration parameters are obtained by genetic algorithm optimization in the aforementioned step S3 and represent the local optimal configuration results of each sub-area;

[0127] S42: Establish a network-wide data exchange platform to summarize the configuration parameters of each sub-area and operational 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;

[0128] S43: 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. exch ange (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 follows:

[0129] in, represents the power demand of the ith sub-region in time t, is the corresponding power output, is the power exchange volume of 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;

[0130] S44: Based on the feedback information from the network-wide data interaction platform, the configuration parameters of each sub-region 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 adjacent 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;

[0131] S45: During the feedback adjustment process, iteratively update the optimal configuration parameters of each sub-region The updated parameters are applied to the energy storage systems in each region again until the power balance of the entire network is less than ΔP net (t) is reduced to within a preset range, ultimately achieving 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 region, and dynamically adjust the energy storage system configuration parameters of each region through the full network data interaction platform and feedback mechanism to achieve 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 operating efficiency and economy of the energy storage system, and at the same time enhances the stability and reliability of the entire distribution network system.

[0132] S5 specifically includes:

[0133] S51: Based on the network-wide optimization results, determine the energy storage equipment deployment plan for each sub-region, including the type, specifications, quantity, and specific installation location of the energy storage equipment. The deployment plan should take into account the regional power load characteristics, geographical conditions, and the layout of existing power infrastructure to ensure the rational distribution and efficient operation of the energy storage system.

[0134] S52: Energy storage equipment hardware is deployed at the determined installation location. Hardware deployment includes infrastructure construction, installation of batteries or other energy storage media, inverter configuration, and provision of cooling and protective facilities. Hardware deployment in each sub-area should meet regional power load requirements and be adaptable to different environmental conditions.

[0135] S53: Configuring a communication control system within the area, the communication control system including a data acquisition device, a communication module, and a central control unit;

[0136] Data acquisition device: used to monitor the operating status, power output, battery status and environmental parameters of the energy storage device in real time, and transmit the data to the communication module;

[0137] Communication module: This module is used to ensure that the energy storage systems in each sub-area can exchange data with the network control center. The communication module can use optical fiber, wireless communication, or other appropriate communication technologies to ensure real-time and reliable data transmission.

[0138] Central control unit: used to collect and process monitoring data from each sub-area, generate control instructions and send them to the energy storage systems in each sub-area to achieve unified scheduling and coordinated control of the entire network.

[0139] S54: After hardware deployment and communication control system configuration, system debugging and joint commissioning testing are conducted to verify the operation of the energy storage systems in each sub-region and ensure their stable and efficient operation under different load conditions. Debugging includes testing the energy storage system's charging and discharging functions, the communication system's connectivity, and the coordinated operation of the entire network.

[0140] S55: Based on the debugging results, final parameter adjustments and system optimization are carried out to ensure that the energy storage system in each sub-area can match the overall operation requirements of the distribution network. Specifically, this includes optimizing and adjusting the energy storage system operation strategy and modifying the parameters of the communication control system to ensure that the system can achieve the best coordinated operation effect under different load conditions.

[0141] S6 specifically includes:

[0142] S61: Deploy power load monitoring devices within the region. These devices include smart meters, sensors, and data collectors to collect real-time power load data from each region. The power load monitoring devices transmit the load data in a time series format to the smart controller within the region, ensuring that the power load situation at each point in time can be accurately recorded and analyzed.

[0143] S62: Install a status monitoring module on the energy storage device. The module includes a voltage sensor, a current sensor, a temperature sensor, and a status indicator. The module is used to monitor the operating status of the energy storage device in real time, including the battery's charge and discharge status, remaining capacity, and operating parameters such as temperature changes. The monitoring module transmits this data to the intelligent controller for real-time monitoring and management during the operation of the energy storage system.

[0144] S63: Deploy inter-regional power flow monitoring devices in each sub-region. The inter-regional power flow monitoring devices include power sensors and meters for real-time monitoring of the amount of power transmitted from one region to another. The system transmits monitoring data to the network control center and, when necessary, shares information with regional intelligent controllers to achieve real-time monitoring of inter-regional power flow.

[0145] S64: The regional intelligent controller calculates the power supply and demand balance of each sub-region based on the real-time power load data, energy storage device status data and cross-regional power flow data received. If the power supply and demand of a sub-region is unbalanced, the intelligent controller dynamically adjusts the charging and discharging operations of the energy storage device. Through the above steps, comprehensive, real-time monitoring and dynamic control of the distributed energy storage system can be achieved. The intelligent controller adjusts the charging and discharging of the energy storage device based on real-time data and coordinates cross-regional power flow to ensure that the energy storage resources between the sub-regions are optimally allocated, thereby achieving a power supply and demand balance in the distribution network and improving the operating efficiency and reliability of the system.

[0146] Dynamic adjustments in S64 include:

[0147] S641: The intelligent controller receives the power supply and demand data at the current time point t, including the power demand P d (t) and power supply P s (t), calculate the current power balance state ΔP(t) based on the supply and demand data, and the calculation formula is: ΔP(t)=P d (t)-P s (t), where ΔP(t)>0 means that the power demand exceeds the supply and the gap needs to be filled by discharging the energy storage device; when ΔP(t)<0 means that the power supply exceeds the demand and the excess power can be stored by charging the energy storage device;

[0148] S642: Based on the result of the power balance state ΔP(t), the intelligent controller selects a corresponding operation strategy. The specific strategy includes:

[0149] Strategy 1: When ΔP(t)>0, calculate the required discharge power P disch arge (t), which is calculated as follows:

[0150] P disch arge (t)=min(ΔP(t), P max ), where P max The maximum discharge power of the energy storage device, ensuring that the discharge power does not exceed the safe operating range of the device;

[0151] Strategy 2: When ΔP(t) < 0, calculate the required charging power P charge (t), which is calculated as follows:

[0152] P charge (t) = min(|ΔP(t)|, P max ), where |ΔP(t)| is the absolute value of the excess of power supply over demand, ensuring that the charging power does not exceed the maximum charging capacity of the energy storage device;

[0153] S643: The intelligent controller dynamically adjusts the charge and discharge operation of the energy storage device according to the calculated charge and discharge power; specifically, when P dischargt When (t)>0, the intelligent controller sends a discharge instruction to start the discharge module of the energy storage device, and the output power P disch arge (t) To supplement the electricity gap; if P charge (t)>0, the intelligent controller sends a charging instruction to start the charging module of the energy storage device, absorbs excess power and stores it in the energy storage system;

[0154] S644: The intelligent controller monitors the status of the energy storage device in real time, including the remaining capacity E remain (t) and equipment health status H status (t), after each charge and discharge operation, the remaining capacity of the energy storage device is updated, and the update formula is:

[0155] For discharge operation:

[0156] E remain (t+1)=E remain (t)-P disch arge (t) × Δt;

[0157] For charging operation:

[0158] E remain (t+1)=E remain (t)+P charge (t)×Δt, where Δt is the operation time interval;

[0159] S645: The remaining capacity E of the energy storage device remain (t+1) When it approaches the limit value (such as fully filled or completely empty), the intelligent controller will adjust the device according to the health status H of the current device. status (t) and future load forecast data, adjust the priority and intensity of charging and discharging operations to extend the service life of the energy storage equipment and ensure the stability of the system; through the above steps, the intelligent controller can dynamically adjust the charging and discharging operations of the energy storage equipment based on the real-time power supply and demand balance, ensure that the power demand of each region is met and optimize the utilization efficiency of energy storage resources. The algorithms and formulas involved ensure the accuracy and safety of charging and discharging operations, effectively improving the operating efficiency of the energy storage system and the stability of the overall power grid.

[0160] The present invention encompasses any alternatives, modifications, equivalents, and solutions that fall within the spirit and scope of the present invention. To provide a thorough understanding of the present invention, specific details are described in detail below in connection with the preferred embodiments of the present invention, but those skilled in the art will be able to fully understand the present invention without these detailed descriptions. Furthermore, to avoid unnecessary confusion regarding the essence of the present invention, well-known methods, processes, procedures, components, and circuits have not been described in detail.

[0161] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A collaborative planning and optimization method for a distributed distribution network energy storage system, characterized in that: The following steps are involved: S1: Based on the planning and operation requirements of the distributed distribution network and the long-term power balance, the energy storage demand is calculated for each sub-region of the distribution network and an initial regional energy storage planning scheme is generated. S2: Based on the initial energy storage planning scheme for each sub-region and combined with the research results on the economics of distributed distribution network energy storage, a regional economic optimization objective function is established to minimize the investment and operating costs of the energy storage system in each region. S3: Independently optimize the energy storage system configuration in each sub-region to achieve a local optimum within the constraints of local power load characteristics, power demand, and geographic location. S4: After completing the independent optimization of each sub-region in S3, a collaborative control algorithm is used to perform global optimization of the entire network. The collaborative control algorithm combines the optimization results of each region and achieves the global optimal configuration of the distributed energy storage system through data interaction and feedback mechanisms. S5: Based on the network-wide optimization results, specific deployment of distributed distribution network energy storage systems is carried out, including hardware deployment of energy storage systems in each sub-region and the configuration of communication control systems, so that the distributed energy storage systems can operate collaboratively under different load conditions. S6: During the operation of the energy storage system, the power load in each region, the status of the energy storage system, and the cross-regional power flow are monitored in real time. The charging and discharging operations of the energy storage equipment are dynamically adjusted through the intelligent controller in the region to achieve the optimal allocation of energy storage resources and power balance among the regions.

2. A collaborative planning and optimization method for a distributed distribution network energy storage system according to claim 1, characterized in that: Said S1 specifically includes: S11: Collecting planning data and operating parameters of the distributed distribution network. The planning data includes the geographical distribution of the distribution network, load density distribution, and future electricity demand forecasts. The operating parameters include historical load data of each sub-region, seasonal variation patterns, and access to distributed energy resources. S12: Based on the collected operating parameters, the power supply and demand balance of each sub-region is calculated using a long-term power balance algorithm; S13: Based on the energy storage demand calculated in S12, combined with the physical distribution of each sub-region, the existing power load density distribution, and the future electricity demand forecast, generate a regional initial energy storage planning scheme. The initial energy storage planning scheme includes specific recommendations on the type, capacity configuration, and installation location of the energy storage equipment.

3. The collaborative planning and optimization method for a distributed distribution network energy storage system according to claim 2, characterized in that: The S12 specifically includes: S121: 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 in time t, P s (t) represents the power supply within time t, where t is a time variable; S122: Calculate the power gap in the long period, the formula 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 there is a power shortage within the time period; if ΔP(t)<0, it means there is a power surplus; S123: 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, and ΔP(t) represents the power gap in time t.

4. The collaborative planning and optimization method for a distributed distribution network energy storage system according to claim 3, characterized in that: The S2 specifically includes: S21: 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 and unit operation and maintenance cost of the equipment based on the type of energy storage equipment; S22: 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 ; S23: Calculate the annual operation and maintenance cost C of the energy storage system in each sub-region om_total ; S24: Calculate the operating income R of the energy storage system in each sub-region, where 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; S25: Based on the above calculation results, establish the regional economic optimization objective function F of each sub-region's energy storage system. Its goal is to minimize the total investment and operating 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. By minimizing the value of F, the optimal energy storage system configuration for each sub-region is determined.

5. The method for collaborative planning and optimization of a distributed distribution network energy storage system according to claim 1, characterized in that: The S3 specifically includes: S31: Obtain the power load characteristics of each sub-region, including daily load curves, seasonal load variation patterns, and future load growth forecasts; S32: Obtaining geographical location constraints of each sub-region, including geographical environment, grid access conditions, land use restrictions, and layout of existing power infrastructure in the region; S33: Based on the data obtained in S31 and S32, a storage system configuration model is constructed for each sub-region. The system configuration model is based on the regional power load characteristics and the power demand function P. d (t) and geographical location constraints as input, and the configuration parameters of the energy storage system X config As the optimization variable, X config Including the capacity of the energy storage system E capacity , power output P output and installation location; S34: 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 Among them, 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; S35: Use distributed optimization algorithm to optimize the above objective function F local To solve, select an 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 S36: The optimal configuration parameters obtained by optimization The energy storage system configuration scheme applied to each sub-region includes the energy storage system capacity after optimization. Power output and the specific installation location of the energy storage system.

6. A method for collaborative planning and optimization of a distributed distribution network energy storage system according to claim 5, characterized in that: In the step S35, a genetic algorithm is specifically selected to optimize the objective function F local The specific steps for solving include: S351: Initialize the population of the genetic algorithm and define each individual as the 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 in a given range by random selection; S352: 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, ∈ is a small constant to prevent the denominator from being zero, F local (X config ) is the defined optimization objective function; S353: 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 configi ) represents the fitness of the i-th individual, N is the population size, and by selecting individuals with high fitness as parents, the overall quality of the offspring is improved; S354: Perform a crossover operation on the selected individuals to generate new offspring using a single-point crossover or multi-point crossover method; S355: Perform mutation operation on the offspring individuals generated by crossover, according to the predetermined mutation probability P mut Randomly change the gene value of an individual; S356: All new individuals generated by the crossover and mutation operations are combined with the high-fitness individuals in the population to form a new population, and S352 to S355 are repeated until the preset number of iterations is reached or the termination condition is met; S357: 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.

7. A method for collaborative planning and optimization of a distributed distribution network energy storage system according to claim 6, characterized in that: The S4 specifically includes: S41: Obtaining the optimal energy storage system configuration parameters for each sub-region Including the capacity of the energy storage system Power output and installation location; S42: Establish a network-wide data exchange platform to summarize the configuration parameters of each sub-area and operational 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; S43: 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); to calculate the power balance error ΔP of the entire network net (t); S44: Based on the feedback information from the network-wide data interaction platform, the configuration parameters of each sub-region 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 adjacent 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; S45: During the feedback adjustment process, iteratively update the optimal configuration parameters of each sub-region The updated parameters are applied to the energy storage systems in each region again until the power balance of the entire network is less than ΔP net (t) falls within the preset range.

8. The method for collaborative planning and optimization of a distributed distribution network energy storage system according to claim 1, characterized in that: The S5 specifically includes: S51: Based on the network-wide optimization results, determine the energy storage equipment deployment plan for each sub-region, including the type, specifications, quantity, and specific installation location of the energy storage equipment; S52: Performing hardware deployment of the energy storage device at the determined installation location, wherein the hardware deployment includes infrastructure construction, installation of batteries or other energy storage media, configuration of inverters, and provision of cooling and protective facilities; S53: Configuring a communication control system within the area, wherein the communication control system includes a data acquisition device, a communication module, and a central control unit; S54: After hardware deployment and communication control system configuration, system debugging and joint commissioning testing are performed to verify the operation of each sub-region's energy storage system. This includes testing the energy storage system's charging and discharging functions, the communication system's connectivity, and the coordinated operation of the entire network. S55: Based on the debugging results, final parameter adjustments and system optimization are carried out to ensure that the energy storage system in each sub-area can match the overall operation requirements of the distribution network. Specifically, this includes optimizing and adjusting the energy storage system operation strategy and correcting the parameters of the communication control system.

9. The method for collaborative planning and optimization of a distributed distribution network energy storage system according to claim 1, characterized in that: The S6 specifically includes: S61: Deploy a power load monitoring device in the area, wherein the monitoring device includes a smart meter, a sensor, and a data collector for collecting power load data of each area in real time; S62: Installing a status monitoring module on the energy storage device, the module including a voltage sensor, a current sensor, a temperature sensor, and a status indicator for real-time monitoring of the working status of the energy storage device, including the battery's charge and discharge status, remaining capacity, and operating parameters such as temperature changes; S63: Deploy an inter-regional power flow monitoring device in each sub-region, wherein the inter-regional power flow monitoring device includes a power sensor and a meter for real-time monitoring of the amount of power transmitted from one region to another; S64: The regional intelligent controller calculates the power supply and demand balance of each sub-region based on the real-time power load data, energy storage device status data, and cross-regional power flow data received. If the power supply and demand in a sub-region is unbalanced, the intelligent controller dynamically adjusts the charging and discharging operations of the energy storage device.

10. The method for collaborative planning and optimization of a distributed distribution network energy storage system according to claim 1, characterized in that: The dynamic adjustment in S64 specifically includes: S641: The intelligent controller receives the power supply and demand data at the current time point t, including the power demand P d (t) and power supply P s (t), calculate the current power balance state ΔP(t) based on the supply and demand data. When ΔP(t)>0, it means that the power demand exceeds the supply; when ΔP(t)<0, it means that the power supply exceeds the demand; S642: Based on the result of the power balance state ΔP(t), the intelligent controller selects a corresponding operation strategy. The specific strategy includes: Strategy 1: When ΔP(t)>0, calculate the required discharge power P discharge (t), which is calculated as follows: P discharge (t)=min(ΔP(t),P max ), where P max is the maximum discharge power of the energy storage device; Strategy 2: When ΔP(t) < 0, calculate the required charging power P charge (t), which is calculated as follows: P charge (t) = min(|ΔP(t)|, P max ), where |ΔP(t)| is the absolute value of the excess of power supply over demand; S643: The intelligent controller dynamically adjusts the charge and discharge operation of the energy storage device according to the calculated charge and discharge power; specifically, when P discharge When (t)>0, the intelligent controller sends a discharge instruction to start the discharge module of the energy storage device, and the output power P discharge (t) To supplement the electricity gap; if P charge (t)>0, the intelligent controller sends a charging instruction to start the charging module of the energy storage device, absorbs excess power and stores it in the energy storage system; S644: The intelligent controller monitors the status of the energy storage device in real time, including the remaining capacity E remain (t) and equipment health status H status (t), after each charge and discharge operation, updating the remaining capacity of the energy storage device; S645: When the remaining capacity of the energy storage device approaches the limit value, the intelligent controller will status (t) and future load forecast data to adjust the priority and intensity of charging and discharging operations.

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