Multi-scenario light-storage collaborative cluster regulation and energy storage planning method and device

Abstract

The application provides a multi-scene light-storage coordination cluster regulation and energy storage planning method, including the following steps: obtaining a photovoltaic-load scene by adopting an ISODATA method clustering, and performing time period cluster division on each scene by taking modularity, power supply rate and inter-cluster transmission as targets; establishing a DESS site selection and capacity determination double-layer coordination planning model for cluster regulation; solving the DESS site selection and capacity determination double-layer coordination planning model by adopting a double-layer nested multi-objective particle swarm optimization algorithm to obtain a Pareto solution set; and evaluating the schemes in the Pareto solution set by adopting a TOPSIS method based on an entropy weight method to determine a final planning scheme.

Description

Technical Field

[0001] This invention relates to the field of power distribution network fault identification technology, and more specifically, to a method and device for planning energy storage coordinated cluster regulation under multiple scenarios. Background Technology

[0002] Energy storage systems, with their flexible energy storage and adjustable charging and discharging power, can effectively alleviate safety issues caused by the mismatch between distributed photovoltaic (PV) output and load demand. Currently, numerous scholars have conducted in-depth research on the energy storage planning problem of Distributed Energy Storage Systems (DESS) considering distributed power sources. For example, considering economic efficiency, environmental friendliness, and reliability, a multi-scenario-based photovoltaic and energy storage site selection and capacity planning model has been established, combining centroid back-learning and particle swarm optimization to optimize PV and energy storage access schemes. Another example is the construction of an energy storage site selection and capacity optimization model using system load fluctuations, energy storage costs, and energy storage charge deviation as objective functions, combined with the Multi-Objective Particle Swarm Optimization (MOPSO) algorithm. Through optimization, a Pareto solution set including energy storage access locations and capacity schemes is obtained, and the optimal access scheme for the energy storage system is selected from this set using an ordinal preference method based on information entropy. Yet another example is the proposal of a scenario reduction method based on robustness, selecting a set of distributed PV scenarios that significantly impact the distribution network, and proposing a linear optimization model for energy storage site selection and capacity determination in medium-voltage distribution networks with the objective of minimizing the maximum charging and discharging power of energy storage at each moment.

[0003] With the continuous increase in the penetration rate of distributed photovoltaic (PV) power, centralized control is insufficient to effectively regulate numerous dispersed PV nodes. Adopting a PV-storage synergistic cluster control system is an inevitable trend to ensure the "four-fold" operation of distributed PV, improve the system's PV absorption capacity, and guarantee the safe operation of the power grid. Therefore, it is necessary to consider the cluster control issues in the subsequent operation phase and optimize the planning of DESS (Distributed Energy Storage System) energy storage configuration. However, existing research only considers the control allocation problem in a single scenario. In a cluster control system, energy storage planning is a long-term planning issue, and energy storage planning schemes considering single scenarios cannot meet the cluster control needs across multiple scenarios throughout the year.

[0004] In order to solve the above problems, people have been seeking an ideal technological solution. Summary of the Invention

[0005] Based on this, it is necessary to provide a method and device for planning energy storage and regulation of photovoltaic-storage collaborative clusters in multiple scenarios to address the above-mentioned technical problems. When operating in multiple scenarios, it can improve the distribution network's ability to absorb photovoltaics, reduce voltage deviation during system operation, and effectively enhance the autonomous operation capability of the cluster.

[0006] To achieve the above objectives, the first aspect of the present invention provides a method for planning energy storage regulation and control in a multi-scenario photovoltaic-storage collaborative cluster, comprising the following steps:

[0007] Photovoltaic-load scenarios were obtained using ISODATA clustering.

[0008] Based on the objectives of modularity, power supply rate, and inter-group transmission volume, time-segmented clusters are divided for each scenario;

[0009] A two-layer coordinated planning model for DESS (Digital Energy Storage System) location and capacity determination is established for cluster-based regulation. The outer planning layer of the model takes the cluster as the basic object, with cluster supply rate, comprehensive energy storage cost, and node voltage deviation as optimization objectives, and DESS installed capacity constraints, maximum energy storage charging and discharging power constraints, and energy storage access location constraints as constraints. The inner simulation operation layer of the model takes minimizing the comprehensive operating cost of energy storage operation, power purchase and sale, curtailment of solar power, and network losses as the optimization objective, and the capacity and power of DESS connected to nodes, distribution network power flow, node voltage, and branch power as constraints.

[0010] A double-nested multi-objective particle swarm optimization algorithm was used to solve the DESS location and capacity two-level coordination planning model to obtain the Pareto solution set.

[0011] The TOPSIS method based on entropy weight is used to evaluate the solutions in the Pareto solution set and determine the final planning scheme.

[0012] To achieve the above objectives, a second aspect of the present invention provides a multi-scenario photovoltaic-storage collaborative cluster regulation and energy storage planning device, comprising:

[0013] The photovoltaic-scenario clustering module is used to obtain photovoltaic-load scenarios by clustering using the ISODATA method.

[0014] The cluster partitioning module is used to partition clusters in different time periods for various scenarios based on modularity, power supply rate, and inter-group transmission volume.

[0015] The DESS (Digital Energy Storage and Power Supply) site selection and capacity determination two-layer coordinated planning model establishment module is used to establish a DESS site selection and capacity determination two-layer coordinated planning model for cluster regulation. The outer planning layer of the DESS site selection and capacity determination two-layer coordinated planning model takes the cluster as the basic object, and uses the cluster supply rate, energy storage comprehensive cost and node voltage deviation as optimization objectives, and uses DESS installed capacity constraints, energy storage maximum charging and discharging power constraints, and energy storage access location constraints as constraints. The inner simulation operation layer of the DESS site selection and capacity determination two-layer coordinated planning model takes minimizing the comprehensive operating cost of energy storage operation, power purchase and sale, curtailment of solar power, and network loss as optimization objectives, and uses the capacity and power of DESS connected to the node, distribution network power flow, node voltage and branch power as constraints.

[0016] The model solving module is used to solve the DESS location and capacity two-level coordination planning model using a two-level nested multi-objective particle swarm algorithm to obtain the Pareto solution set;

[0017] The scheme evaluation module is used to evaluate schemes in the Pareto solution set based on the TOPSIS method using the entropy weight method, and to determine the final planning scheme.

[0018] To achieve the above objectives, a third aspect of the present invention provides a computer device, including a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; the memory is used to store computer programs; and the processor is used to execute the program stored in the memory to implement the steps of the multi-scenario photovoltaic-storage collaborative cluster regulation and energy storage planning method as described in the first aspect above.

[0019] To achieve the above objectives, a fourth aspect of the present invention provides a computer-readable storage medium having instructions stored thereon that, when executed by one or more processors, cause the processors to perform the steps of the multi-scenario photovoltaic-storage collaborative cluster regulation and energy storage planning method described in the first aspect above.

[0020] To achieve the above objectives, the fifth aspect of the present invention provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the multi-scenario photovoltaic-storage collaborative cluster regulation and energy storage planning method as described in the first aspect above.

[0021] The beneficial effects of this invention are as follows:

[0022] 1) This invention considers the energy storage configuration planning strategy under multiple scenarios including photovoltaic cluster operation. Compared with the conventional single-layer planning scheme, the planning scheme obtained by the proposed method takes into account cluster division and multi-scenario operation, and has better operation capability for multi-scenario operation, improves the distribution network's ability to absorb photovoltaics, and reduces the number of times the cluster photovoltaic supply is excessive during normal operation.

[0023] 2) The method proposed in this invention takes into account the simulated operation of the distribution network during the planning stage, which reduces the voltage deviation during the operation of the distribution network and makes the system operation more stable and reliable. Attached Figure Description

[0024] Figure 1 This is a flowchart illustrating the multi-scenario photovoltaic-storage collaborative cluster regulation and energy storage planning method of the present invention;

[0025] Figure 2 This is the annual photovoltaic power output curve of 8760 units in a certain location in Belgium;

[0026] Figure 3 This is the annual load curve of 8760 in a certain area of ​​Belgium;

[0027] Figure 4 This is the output curve for a typical load scenario;

[0028] Figure 5 These are photovoltaic output curves corresponding to typical load scenarios;

[0029] Figure 6 It is encoded by inner and outer layer particles;

[0030] Figure 7 This is a flowchart of a two-layer multi-objective particle swarm optimization algorithm;

[0031] Figure 8 It is an IEEE 33-node distribution network structure;

[0032] Figure 9 This is the cluster partitioning result at a certain moment in a typical scenario;

[0033] Figure 10 These are radar charts of the objective functions for different schemes;

[0034] Figure 11 These are radar charts of the objective functions for different schemes;

[0035] Figure 12 It refers to the voltage deviation under different scenarios for different solutions;

[0036] Figure 13 These are energy storage planning and access diagrams for different schemes;

[0037] Figure 14 These are cluster supply diagrams for different scenarios;

[0038] Figure 15 These are voltage deviation diagrams under different scenarios; Detailed Implementation

[0039] This invention proposes a multi-scenario photovoltaic-storage coordinated cluster regulation energy storage planning method. First, the ISODATA method is used to cluster the annual photovoltaic output and load change trends across scenarios. Then, based on modularity, power supply rate, and inter-cluster transmission volume as objectives, each scenario is divided into time-period clusters. Short-term system operation is incorporated into the long-term planning of energy storage configuration, constructing a two-layer coordinated optimization model combining outer-layer energy storage configuration and inner-layer simulated operation. Based on the characteristics of the two-layer planning model, a two-layer nested multi-objective particle swarm optimization algorithm is used for solving the problem, and TOPSIS based on the entropy weight method is used to evaluate the solutions in the Pareto solution set. Finally, analysis of different types of energy storage, the number of connected energy storage units, and different planning schemes shows that the planning scheme obtained by the proposed method can improve the distribution network's ability to absorb photovoltaic power, reduce voltage deviation during system operation, and effectively enhance the autonomous capability of the cluster operation in multiple scenarios.

[0040] The technical solution of the present invention will be further described in detail below through specific embodiments. Example 1

[0041] This embodiment provides a method for planning energy storage regulation and control in a multi-scenario photovoltaic-storage collaborative cluster, including the following steps:

[0042] Step 1: Use the ISODATA method to cluster and obtain the photovoltaic-load scenario.

[0043] Specifically, this embodiment uses photovoltaic power output and load data from a certain region in Belgium over 8760 hours per year, such as... Figure 2 and Figure 3 As shown, its annual load data presents a "mountain" shape, with the load demand being more concentrated in the 3000h-6000h period of the year, while the annual photovoltaic output at the corresponding time period presents an "M" shape, indicating that the photovoltaic-load situation is very complex in different time periods.

[0044] By clustering photovoltaic (PV) output and load data from a region in Belgium over a year of 8760 hours, 16 typical load scenarios and their corresponding PV output scenarios were obtained, as follows: Figure 4 and Figure 5 As shown, scenarios 1, 2, 3, 6, 14, and 16 correspond to scenarios of excessive photovoltaic supply with high photovoltaic output and low load demand. These scenarios have a high demand for energy storage. Scenarios 7 and 10 correspond to scenarios of insufficient photovoltaic supply with low photovoltaic output and high load demand. These scenarios also have a high demand for energy storage. The rest are normal photovoltaic-load operation scenarios. When planning energy storage, it is necessary to fully consider the proportion of different scenarios and energy storage demand to obtain a more accurate and economical planning solution.

[0045] Step 2: Based on the modularity, power supply rate and inter-group transmission volume as objectives, perform time-segmented cluster division for each scenario.

[0046] In this embodiment, the cluster partitioning principle involved is the basis for ensuring the integrity and independence of the network partitioning scheme:

[0047] 1) Logical Principle: Nodes within the same cluster must be directly or indirectly connected to ensure cluster connectivity and avoid nodes that require connections through other clusters. Furthermore, in the clustering of power networks, isolated nodes should be avoided, and clusters should not overlap.

[0048] 2) Structural Principles: Cluster applications involve both collaboration and division of labor. Within a cluster, strong electrical coupling between nodes should be ensured to maintain close connections and structural stability, thereby enabling mutual collaboration. Between clusters, weak electrical coupling should be maintained to minimize mutual interference and facilitate the division of labor.

[0049] 3) Functional Principle: The function of a cluster is determined by the combined attributes of its internal nodes. If the cluster needs to cooperate with each other to optimize efficiency or improve the economics of system operation, it is necessary to ensure that the functional attributes of the internal nodes have a certain degree of similarity or complementarity.

[0050] Therefore, in this embodiment, the modularity is constructed based on reactive power-voltage sensitivity as a structural indicator, while power supply rate and inter-cluster transmission volume are used as functional indicators. This maximizes the decoupling of the network into cluster units with strong intra-cluster coupling and weak inter-cluster coupling. This not only meets the local power balancing requirements within the cluster and reduces power exchange between clusters during normal network operation, thereby reducing network losses, but also ensures the normal execution of control strategies when the cluster is off-grid, enhancing the cluster's autonomy.

[0051] Step 3: Establish a two-layer coordinated planning model for DESS location and capacity determination for cluster regulation.

[0052] The DESS site selection and capacity determination two-layer coordinated planning model includes an external planning layer and an internal simulation operation layer.

[0053] Specifically, the outer planning model uses the sum of the construction investment cost of energy storage and the operating cost and operating benefits brought by the simulated operation after the energy storage is connected as economic indicators, and uses the voltage deviation and cluster power supply rate after the energy storage is connected as operating indicators to plan the connection location, capacity and rated power of DESS.

[0054] In this embodiment, taking into account both the investment cost of energy storage and the benefits that energy storage brings to system operation and scheduling, the following three indicators are selected as the objective function for outer layer energy storage planning.

[0055] (1) Node Voltage Deviation. Voltage is one of the important indicators characterizing the operating quality of a system. During normal system operation, the voltage of each node should be maintained within a certain range without significant voltage deviation. After distributed photovoltaic (PV) grid connection, large voltage fluctuations will occur when PV supply is excessive or insufficient. Therefore, the sum of the voltage deviations of each node under all scenarios is selected as the objective function for DESS site selection and capacity determination. f 1 The formula is as follows:

[0056] (1)

[0057] (2) Excessive photovoltaic power supply index of the cluster. In order to ensure that the cluster can implement the control strategy normally during operation, ensure the internal power supply stability, reduce the situation of excessive photovoltaic power supply, and at the same time meet the power balance needs within the cluster as much as possible, reduce the power exchange between clusters, and thus reduce the network loss during transmission, it is necessary to effectively coordinate photovoltaic power and energy storage to keep the photovoltaic power supply rate of the cluster within a healthy operating range.

[0058] Therefore, an oversupply index for cluster photovoltaic power is set based on the cluster power supply rate. f 2 as follows:

[0059] (2)

[0060] in S This refers to the scene time within a 24-hour period; T Number of typical scenarios; g ij for i Scene j The number of clusters with excessive power supply at any given time.

[0061] (3) Overall cost of energy storage connected to the distribution network

[0062] (3)

[0063] In the formula: I 1 The annual equivalent installation cost of energy storage, I 2 This represents the annual operating cost after energy storage is integrated.

[0064] The annual equivalent installation cost is:

[0065] (4)

[0066] In the formula: N DESS Number of clusters; r The discount rate; y They are respectively DPV, ESS Service life; I e 、I p for DESS Investment cost per unit capacity and per unit power; E DESS 、P DESS For installation DESS Rated capacity and rated power.

[0067] The objective function for outer layer energy storage planning, taking into account system voltage deviation, cluster power supply, and overall energy storage operating costs, is as follows:

[0068] (5)

[0069] Furthermore, the constraints of the outer layer energy storage planning model include DESS access location constraints, installed capacity constraints, DESS charging and discharging power constraints, node voltage constraints, and cluster power supply rate constraints.

[0070] ,

[0071] S j For energy storage j Rated capacity, S min , S max These are the minimum and maximum rated capacities of the energy storage, respectively. P pmax This is the maximum rated power of the energy storage. P p，j For energy storage j Rated charge and discharge power, L j For energy storage j Access location; L min , L max These are the minimum and maximum access point numbers for energy storage, respectively.

[0072] (9)

[0073] g ij，N In order to be in i In the scene j The cluster power supply rate of cluster N at time point, g min , g max These represent the lower and upper limits of the cluster power supply rate, respectively.

[0074] The objective function of the inner-layer simulation operation model is to minimize the total operating cost, including energy storage operation and maintenance costs, system network loss costs, upstream network electricity purchase and sales costs, and the revenue from reduced photovoltaic curtailment due to energy storage. This model aims to solve for the operating cost of the system after integrating energy storage in typical scenarios.

[0075] (10)

[0076] In the formula: I OM For energy storage operation and maintenance costs, I pb The cost of purchasing and selling electricity for the superior network. I p To reduce the revenue from curtailed solar power, I loss For system network loss costs,N Number of typical scenarios w i Typical scenario i The proportion of days in the year.

[0077] The energy storage operation and maintenance cost is:

[0078] (11)

[0079] In the formula: C DESS The cost of operation and maintenance per unit of energy storage charge and discharge capacity. P ij，DESS The charging and discharging power of energy storage z under scenario i and time j. N Number of typical scenarios T The total time for the scene is 24 hours. Z This represents the total number of energy storage units.

[0080] The cost of purchasing and selling electricity from the upstream network is:

[0081] (12)

[0082] In the formula: C t,buy , C t,sell for t The current electricity purchase and sale price with the upstream network, P it，buy ,P it，sell For the scene i Next, at the moment t The power purchased and sold between the downstream and upstream power grids.

[0083] The benefits of energy storage in reducing solar power curtailment are: (13)

[0084] In the formula: C t,sell for t The electricity price at present and the price sold by the upstream network. P it，pv For the scene i Next, at the moment t Photovoltaic power output for energy storage and consumption.

[0085] (14)

[0086] In the formula: C t,sell for t The electricity price at present and the price sold by the upstream network. P it，loss For the scene iNext, at the moment t The system suffers from network loss during operation.

[0087] The constraints of the inner-layer simulation operation model include power balance constraints, distribution network power flow constraints, node voltage constraints, and DESS charging and discharging power constraints.

[0088] The power flow and power balance constraints of the distribution network are as follows:

[0089] ,

[0090] P pv , P loss、 , P grid , P DESS , P L They are respectively in t At any given moment, the photovoltaic output, system grid loss, and power exchanged with the upstream network are all measured in terms of power output. DESS Charge / discharge power and total load; P i , Q i They are nodes i The injection of active and reactive power; U i , U j They are nodes i , j The voltage amplitude; G ij , B ij For nodes i , j Branch admittance; θ ij For nodes i , j The voltage phase angle difference between them.

[0091] Node voltage constraints:

[0092] (17)

[0093] V ij In order to be in i Nodes in the scene j node voltage, V min , V max These represent the lower and upper limits of the system node voltage, respectively.

[0094] DESS charge / discharge power constraints:

[0095] (18)

[0096] P DESS To contribute to energy storage, SOC min , SOC max These are the minimum and maximum values ​​of the energy storage state of charge, respectively.

[0097] Specifically, the external planning layer, based on constraints, obtains an initial planning scheme with the capacity, power, and access location of the DESS (Distributed Energy Storage System) connected to the system as decision variables, and imports it into the internal simulation operation layer. The internal simulation operation layer aims to minimize the comprehensive operating cost of energy storage operation, power purchase and sale, curtailment of solar power, and network losses, using the capacity and power of the DESS connected to the nodes, distribution network power flow, node voltage, and branch power as constraints. It simulates the charging and discharging power of energy storage under 16 scenarios using the 24-hour charging and discharging power of nodes connected to the DESS within the cluster as decision variables to obtain the comprehensive operating cost. Then, the comprehensive operating cost, voltage, and cluster supply parameters obtained from the simulation are fed back to the external layer, and a target value is calculated. The external layer iteratively corrects the initial planning scheme based on the target value, and collaboratively optimizes the capacity, power, and access location of the DESS connected to the system.

[0098] Step 4: A two-layer nested multi-objective particle swarm optimization (BMPSO) algorithm is used to solve the DESS site selection and capacity determination two-layer coordination planning model to obtain the Pareto solution set. To address the challenges of the two-layer energy storage planning model, such as a large number of objective functions and variables, high dimensionality, and difficulty in unifying variable types, a two-layer iterative multi-objective particle swarm optimization (BMPSO) algorithm is used for optimization. The inner and outer layer particle encodings are as follows: Figure 6 As shown.

[0099] The particles in the external planning layer include the DESS connection location, the maximum capacity of the DESS, and the maximum charging and discharging power of the DESS; the particles in the internal simulation operation layer include: the charging and discharging power of the DESS at various times, the actual net output of photovoltaic power, and the interaction power with the main grid.

[0100] Specifically, such as Figure 7 As shown, the flow of the double-nested multi-objective particle swarm optimization algorithm is as follows:

[0101] 1) Raw data input. Input cluster partitioning data, as well as photovoltaic output and load curves, distribution network branch impedance and load data for 16 scenarios.

[0102] 2) Initialize the outer particle swarm parameters. Based on the upper and lower limits of the outer decision variables, initialize the initial velocity and position of the particles in the particle swarm.

[0103] 3) Input the outer layer parameters into the inner layer algorithm for calculation. Run the algorithm according to the following steps:

[0104] ① Initialize the inner particle swarm. Using the outer particles as external input parameters, initialize the velocity and position of the lower-level particles in the corresponding dimension of each swarm in parallel.

[0105] ② Calculate the fitness of the lower-level particle swarm. Based on the lower-level particle data, update the power exchange with the upper-level network and the DESS charging and discharging power and photovoltaic output data accessed in the distribution network power flow program. After performing power flow calculations, obtain the simulated operating cost and the fitness of the lower-level particle swarm.

[0106] ③ Update the inner layer's best individual particle and the population's best particle. Compare the swarm's fitness sequentially with the current best individual fitness, and update the best individual particle. Then, compare the best individual fitness sequentially with the current population's best fitness, and update the population's best particle.

[0107] ④ Update the position and velocity of the inner particle swarm. Update the velocity and position of the inner particles and determine whether the updated values ​​meet the constraints; if an out-of-bounds situation occurs, constrain the out-of-bounds particles.

[0108] ⑤ Iteration count determination. Determine if the condition for reaching the maximum number of iterations is met. If not, return to step ②; use the current optimal value and the optimal fitness of the population as the optimization result, and proceed to step 4.

[0109] 4) Calculate the fitness of outer particles. Based on the current population particle data and the derived optimal fitness of the inner layer, calculate the fitness of outer particles.

[0110] 5) Update the best individual particle and the best particle in the outer layer. Compare the fitness of the particle swarm with the current best individual fitness, and update the best individual particle. Then compare the best individual fitness with the current best population fitness, and update the best population particle.

[0111] 6) Update the position and velocity of the outer particle swarm. Update the velocity and position of the outer particles. The particles corresponding to the DESS access position are optimized using the binary particle swarm optimization formula, and it is determined whether the updated values ​​meet the constraints. If an out-of-bounds situation occurs, constraints are applied to the out-of-bounds particles.

[0112] 7) Iteration count determination. Determine whether the condition for reaching the maximum number of iterations is met. If not, return to step 4; otherwise, output the Pareto solution set containing the results of the two-layer addressing and sizing optimization.

[0113] Step 5: Evaluate the schemes in the Pareto solution set using the TOPSIS method based on the entropy weight method, and determine the final planning scheme.

[0114] The optimization schemes based on the BMPSO algorithm constitute a set of Pareto solutions. Decision-makers need to select the optimal solution from this set based on actual needs and priorities, which is essentially a multi-attribute decision problem. Therefore, the TOPSIS method (Technique for Order Preference by Similarity to an Ideal Solution) based on entropy weighting is used to select the optimal solution from the generated Pareto solution set. The TOPSIS method first determines the optimal ideal value (positive ideal value) and the worst ideal value (negative ideal value) for each indicator. Then, it scores each solution based on its distance from the positive and negative ideal values, obtaining an evaluation value for each solution.

[0115] In practical implementation, N solutions from the Pareto solution set constitute N alternative schemes, where the number of attributes of each alternative scheme is equal to the number of objective functions n, and the x-th attribute... i The value of the m-th attribute of the solution is To eliminate the differences in the dimensions of each objective function, the attributes in the solution set are normalized using formula (19):

[0116] (19)

[0117] The TOPSIS method requires assigning different weights to the target values ​​during calculation, and the selection of these weights often depends on the decision-maker's experience and ability. To avoid the influence of the decision-maker's subjective factors on the final solution selection, this embodiment uses the entropy weight method to assign weights to each objective function. The weights calculated using the entropy weight method are shown below:

[0118] First, calculate the proportion of the i-th option for the j-th attribute value in that index:

[0119] (20)

[0120] Then, substitute the specific gravity obtained from the above formula into the following formula to calculate the first... j The weight of the attribute value.

[0121] (twenty one)

[0122] in ,satisfy .

[0123] The entropy weight method determines the weight of an objective by analyzing the degree of difference between indicators. When the difference between an indicator in the solution set is small, it means that the amount of information it reflects is also reduced, so the weight of that indicator should be decreased. This method effectively reduces the interference of subjective factors and improves the objectivity and scientific nature of weight allocation.

[0124] After calculating the weights of each indicator using the entropy weight method, the TOPSIS method is then used to calculate the distance between the solutions, resulting in a score for each solution. The optimal solution is then selected based on the score.

[0125] plan x i relative distance d ( x i The calculation formula is as follows:

[0126] ,

[0127] In the formula ：d + ( x i ), d - ( x i ) are schemes x i The distance from the ideal optimal solution and the ideal worst solution; a m For attributes f m The weight, 0 < a m If the sum of the weights is less than 1, the total weights are 1. f m+ 、f m- These are the optimal and worst values ​​of the normalized properties of the solutions in the solution set, respectively.

[0128] Verification Implementation Examples

[0129] by Figure 8 The IEEE 33-node power distribution network system shown was used for verification.

[0130] like Figure 8As shown, the system's network structure contains 32 load nodes and 9 photovoltaic nodes, with a total photovoltaic capacity of 3300+j1084.65kVA and a total network load of 3715+j2315kVA. Photovoltaic power plants with rated capacities of 300kW, 300kW, 300kW, 300kW, 500kW, 300kW, 500kW, and 300kW are connected to nodes 4, 7, 10, 14, 17, 20, 24, 28, and 32 of the IEEE 33 system, respectively, to establish a high-penetration photovoltaic distribution network. A method considering comprehensive clustering indicators is used to cluster the distribution network for each typical scenario and time period. The clustering results for a specific scenario and time period are shown below. Figure 9 As shown.

[0131] In the two-layer multi-objective particle swarm optimization (BMOPSO) algorithm, the outer MOSPO parameters are set as follows: number of particles is 30 and number of iterations is 50. The inner particle swarm parameters are set as follows: number of particles is 20 and number of iterations is 30.

[0132] A. Impact Analysis of Energy Storage Type on Energy Storage Planning

[0133] Different types of energy storage systems exhibit significant differences in energy density, power density, charge / discharge efficiency, and lifecycle cost. These distinct characteristics directly impact the economic viability of energy storage planning schemes and the stable and safe operation of subsequently integrated systems. To verify the impact of energy storage type on planning results and to select the most suitable energy storage type for the corresponding system, this embodiment analyzes four types of energy storage: sodium-sulfur batteries (NAS), vanadium redox batteries (VRB), lithium-ion batteries (Li-ion), and value-regulated lead-acid batteries (VRLA). The planning method proposed in this embodiment is used to perform DESS configuration planning for the aforementioned IEEE 33-node system. The relevant parameters for each type of energy storage are shown in Table 1 below. The planning results obtained using the four energy storage types with two energy storage units as a baseline are shown in Table 2 below.

[0134] Table 1 Relevant parameters for various types of energy storage

[0135] parameter NAS VRB VRLA Li-ion Unit power cost (RMB / kW) 1650 2815 1980 2830 Unit capacity cost (RMB / kW) 1270 660 980 1390 Operation and maintenance cost per unit of power generation (RMB / kW) 0.080 0.080 0.093 0.087 Charge and discharge efficiency 0.80 0.70 0.85 0.90 Service life 15 15 10 15

[0136] Table 2 Planning Results for Different Energy Storage Types

[0137] parameter Installation location Installed capacity (MW) Rated power (MW) Total cost (RMB) Average daily equivalent network loss (kW) Average daily revenue from photovoltaic power generation (RMB) NAS 1,32 0.248,0.900 0.062,0.255 1141503.098 1203.021 249.782 VRB 3,24 0.471,0.900 0.118,0.225 1210656.801 1127.013 249.782 VRLA 1,32 0.875,0.900 0.101,0.225 1205484.156 1158.358 254.241 Li-ion 6,22 0.596,0.621 0.149,0.128 1257664.079 1166.831 238.954

[0138] From Table 2 and Figure 10It can be seen that, for the energy storage planning of this IEEE 33 system, the NAS scheme performs best in terms of economy, and has the fewest instances of oversupply during cluster power supply, demonstrating excellent performance in maintaining stable supply during cluster control. In contrast, the VRB scheme performs best in terms of network losses, but its voltage fluctuation suppression capability is weak, which is not conducive to the long-term stable operation of the system. The VRLA scheme has the highest photovoltaic integration benefit and the lowest voltage fluctuation deviation, but its overall cost is the highest, making it difficult to achieve a balance between cost and benefit. The Li-ion scheme does not show a significant advantage in terms of overall cost, network losses, photovoltaic integration benefit, voltage deviation, and cluster supply. Analyzing each indicator separately, in terms of overall cost, NAS reduces costs by 5.71%, 5.31%, and 9.24% compared to VRB, VRLA, and Li-ion, respectively. In terms of network loss, VRB, VRLA, and Li-ion reduce costs by 6.33%, 3.71%, and 2.99% compared to NAS, respectively. In terms of voltage offset, VRB, VRLA, and Li-ion differ from NAS by 24.9%, 20.00%, and 12.88%, respectively. In terms of cluster overload, VRB, VRLA, and Li-ion increase costs by 9.8%, 0.65%, and 54.90% compared to NAS, respectively.

[0139] Although the NAS solution has slightly higher network losses during operation, its overall cost is far lower than the other three energy storage types, demonstrating superior economic efficiency. Currently, the main reason limiting the integration of distributed energy storage systems (DESS) into the distribution network is the excessively high investment cost of energy storage. Therefore, considering the low cost, relatively reasonable power loss, and good power supply control capabilities of the NAS solution, it can be regarded as the optimal energy storage configuration for the IEEE 33-node system operating in multiple scenarios including photovoltaic clusters.

[0140] B. Impact Analysis of the Number of Energy Storage Units Connected on Energy Storage Planning

[0141] Based on the analysis in the previous section, using NAS energy storage better meets the planning requirements of this embodiment compared to other solutions. However, when energy storage is connected and operational, its location and number will affect the system's cluster power supply, voltage fluctuations, and operating losses. To select the optimal number of energy storage units for overall performance, this embodiment will analyze four connection schemes with 1 to 4 energy storage units. The planning results are shown in Table 3:

[0142] Table 3 Planning Results for Different Number of Energy Storage Units Connected

[0143] Installation location Installed capacity (MW) Rated power (MW) Total cost (ten thousand yuan) Average daily equivalent network loss (kW) Average daily revenue from photovoltaic power generation (RMB) 1 unit 9 0.367 0.178 109.431 1231.068 241.419 2 units 1,32 0.248,0.900 0.062,0.255 114.150 1203.021 249.782 3 units 16,20,28 0,721,0.335,0.559 0.119,0.192,0.222 138.519 1308.216 226.219 4 units 5,6,16,21 0.328,0.450,0.479,0.634 0,189,0.142,0.113,0.138 143.499 1442.342 221.701

[0144] From Table 3 and Figure 11It can be seen that in the energy storage planning for this IEEE33 system, Scheme 2 reduces the overall cost by 17.638% and 20.452% compared to Schemes 3 and 4, respectively; the average daily equivalent network loss is reduced by 8.76% and 20.01%, respectively; the average daily equivalent photovoltaic revenue is increased by 10.41% and 12.66%, respectively; the voltage deviation is reduced by 40.54% and 41.87%, respectively; and the number of times the cluster power supply is over-supplied is reduced by 15.69% and 26.57%, respectively. Therefore, Scheme 2, while ensuring economic efficiency, provides a more stable and controllable cluster power supply and lowers network losses in long-term operation. Figure 12 It can be seen that although the total voltage deviation is relatively large, it can still ensure that the voltage remains within the normal fluctuation range when distributed across various typical scenarios. Compared to the single-unit solution, although the cost of solution 2 is slightly higher, it shows advantages in other indicators to varying degrees.

[0145] In summary, Scheme 2 performs well in key indicators such as cost, network loss, and revenue. Although it is slightly inferior in terms of voltage deviation, overall, Scheme 2 achieves a balance between functionality and economy. While ensuring economy and stability, it can provide a more solid foundation for subsequent cluster control.

[0146] C. Energy Storage Planning Performance and Operation Index Analysis

[0147] In the IEEE33 node of this embodiment, the overall performance of connecting two NAS energy storage units is optimal. To verify the advantages of the dual-layer energy storage configuration planning strategy proposed in this embodiment, which considers multiple scenarios including photovoltaic cluster operation, three planning schemes are constructed and compared with the results.

[0148] Planning Scheme 1: Planning without considering the clustering and multi-scenario operation of the distribution network. A single-layer planning model is adopted, with nodes as the basic unit, and energy storage connected to each node is planned directly.

[0149] Scheme Two: This scheme considers the cluster operation of the distribution network, employing a single load and photovoltaic (PV) scenario. Based on the cluster partitioning results and the single load-PV scenario, a two-layer planning model is used, with nodes as the basic unit, to plan the energy storage connected to the system.

[0150] Planning Scheme 3: Planning considering multiple scenarios and cluster operation. Based on the cluster partitioning results, a two-layer energy storage planning model is adopted. The inner layer simulates the operation of the system in multiple scenarios, while the outer layer model uses nodes as basic units to plan the location, capacity, and rated power of energy storage access.

[0151] Table 4. Planning Results of Different Energy Storage Planning Strategies

[0152] plan Annual comprehensive cost per million yuan Average daily network loss per kW Total voltage deviation in the scene 1 126.484 1192.580 0.2667 2 118.891 1123.437 0.1643 3 114.150 1203.021 0.1470

[0153] Depend on Figure 13 , 14 Table 4 analyzes the three planning strategies and shows that, compared with Schemes 2 and 3, Scheme 1 only considers single-scenario operation and does not consider cluster operation planning. The energy storage access location is singular, the capacity planning is not accurate enough, resulting in higher overall cost and poor adaptability to multi-scenario operation. The voltage deviation in each scenario is much higher than the other two planning schemes. Moreover, the singular access location leads to too many times of cluster oversupply in each scenario, which is not conducive to subsequent cluster regulation.

[0154] In Scheme 2, considering both single-scenario and cluster operation planning, the voltage deviation is lower than that of Scheme 1, but slightly higher than that of Scheme 2. The network loss is lower and less variable in each scenario, resulting in the lowest total network loss. The number of cluster oversupply is also lower than that of Scheme 1, and the overall cost is moderate among the three schemes.

[0155] Scheme 3 considers multiple scenarios and cluster operation in its planning. Because it considers cluster operation under multiple scenarios, it has good adaptability to multiple scenarios and the fewest cluster oversupply in each scenario. This ensures good performance in subsequent cluster control. Compared with Schemes 1 and 2, it also has the lowest total scenario voltage deviation, with the smallest deviation in most scenarios. Although its total network loss is higher, the network loss in most scenarios is lower than the other two schemes. Only a small part of the network loss is higher due to the higher transmission power to ensure the stability of the cluster supply. Scheme 3 considers operation under multiple scenarios and the planning of access location and capacity is more accurate. While ensuring economy, it performs better in various planning indicators.

[0156] In summary, this section compares and analyzes the three planning schemes using various indicators. The planning strategy proposed in this embodiment, while ensuring economic efficiency and the safe operation of the system after energy storage is connected to the system, can also provide good operational control conditions for subsequent cluster regulation.

[0157] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated in this embodiment, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages in other steps.

[0158] Example 2

[0159] Based on the same inventive concept, this application also provides a distribution network section location device based on multi-dimensional feature clustering of fault phase current for implementing the above-mentioned multi-scenario photovoltaic-storage collaborative cluster regulation and energy storage planning method. The solution provided by this device is similar to the implementation scheme described in the above method. Therefore, the specific limitations of one or more distribution network section location device embodiments based on multi-dimensional feature clustering of fault phase current provided below can be found in the limitations of the multi-scenario photovoltaic-storage collaborative cluster regulation and energy storage planning method described above, and will not be repeated here.

[0160] Specifically, the multi-scenario photovoltaic-storage collaborative cluster regulation and energy storage planning device includes:

[0161] The photovoltaic-scenario clustering module is used to obtain photovoltaic-load scenarios by clustering using the ISODATA method.

[0162] The cluster partitioning module is used to partition clusters in different time periods for various scenarios based on modularity, power supply rate, and inter-group transmission volume.

[0163] The DESS (Digital Energy Storage and Power Supply) site selection and capacity determination two-layer coordinated planning model establishment module is used to establish a DESS site selection and capacity determination two-layer coordinated planning model for cluster regulation. The outer planning layer of the DESS site selection and capacity determination two-layer coordinated planning model takes the cluster as the basic object, and uses the cluster supply rate, energy storage comprehensive cost and node voltage deviation as optimization objectives, and uses DESS installed capacity constraints, energy storage maximum charging and discharging power constraints, and energy storage access location constraints as constraints. The inner simulation operation layer of the DESS site selection and capacity determination two-layer coordinated planning model takes minimizing the comprehensive operating cost of energy storage operation, power purchase and sale, curtailment of solar power, and network loss as optimization objectives, and uses the capacity and power of DESS connected to the node, distribution network power flow, node voltage and branch power as constraints.

[0164] The model solving module is used to solve the DESS location and capacity two-level coordination planning model using a two-level nested multi-objective particle swarm algorithm to obtain the Pareto solution set;

[0165] The scheme evaluation module is used to evaluate schemes in the Pareto solution set based on the TOPSIS method using the entropy weight method, and to determine the final planning scheme.

[0166] Example 3

[0167] This application embodiment discloses a computer device, which includes a processor, a memory, an input / output interface, a communication interface, a display unit, and an input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interface. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The input / output interface is used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it implements a multi-scenario photovoltaic-storage collaborative cluster regulation energy storage planning method. The display unit is used to form a visually visible image and can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer device, or external keyboards, touchpads, or mice, etc. Example 4

[0168] Based on the above embodiments, this embodiment provides a readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the multi-scenario photovoltaic-storage collaborative cluster regulation and energy storage planning method described in Embodiment 1. Example 5

[0169] Based on the above embodiments, this embodiment provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the multi-scenario photovoltaic-storage collaborative cluster regulation and energy storage planning method described in Embodiment 1.

[0170] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0171] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0172] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

[0173] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them; although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications can still be made to the specific implementation of the present invention or equivalent substitutions can be made to some technical features without departing from the spirit of the technical solutions of the present invention, and all such modifications and substitutions should be covered within the scope of the technical solutions claimed in the present invention.

Claims

1. A multi-scenario light storage coordination cluster regulation energy storage planning method, characterized in that, The method comprises the following steps: A photovoltaic-load scene is obtained by clustering using an ISODATA method; Modularity, power supply rate and inter-cluster transmission amount are taken as targets to perform time period clustering division on each scene; A DESS site selection and capacity determination double-layer coordination planning model is established, an external planning layer of the DESS site selection and capacity determination double-layer coordination planning model takes a cluster as a basic object, takes cluster supply rate, comprehensive cost of energy storage and node voltage deviation as optimization targets, and takes DESS installed capacity constraint, maximum charge and discharge power constraint of energy storage, access position constraint of energy storage as constraint conditions; An internal simulation running layer of the DESS site selection and capacity determination double-layer coordination planning model takes minimization of comprehensive running cost of energy storage running, power purchase and sale, abandoned light amount and network loss as an optimization target, and takes DESS capacity, power, power flow of a distribution network, node voltage and branch power accessed by a node as constraint conditions; A double-layer nested multi-objective particle swarm optimization algorithm is used to solve the DESS site selection and capacity determination double-layer coordination planning model to obtain a Pareto solution set, when the double-layer nested multi-objective particle swarm optimization algorithm is used to solve the DESS site selection and capacity determination double-layer coordination planning model, particles of the external planning layer include an access position of the DESS, maximum capacity of the DESS and maximum charge and discharge power of the DESS; Particles of the internal simulation running layer include charge and discharge power of the DESS at each moment, actual net output of photovoltaic and interactive power with a main network; A TOPSIS method based on an entropy weight method is used to evaluate schemes in the Pareto solution set to determine a final planning scheme.

2. The multi-scenario light storage coordination cluster regulation and energy storage planning method according to claim 1, characterized in that, The steps of solving the DESS site selection and capacity determination double-layer coordination planning model by using the double-layer nested multi-objective particle swarm optimization algorithm comprise: 1) original data input: input cluster division data, photovoltaic output and load curves under 16 scenes, impedance and load data of a distribution network branch; 2) initialization of external particle swarm parameters: initial speed and position of a particle swarm particle are initialized according to upper and lower limits of external decision variables; 3) the external parameters are input into an internal algorithm for calculation, and the steps are as follows: ① initialization of internal particle swarm: the external particles are taken as external input parameters, and the speed and position of the lower layer particles corresponding to the dimensions of each cluster are initialized in parallel; ② calculation of lower layer particle fitness: according to the lower layer particle data, the DESS charge and discharge power and photovoltaic output data accessed in the power exchange program and power flow program of the upper network are updated, the simulation running cost is obtained after the power flow calculation, and the fitness of the lower layer particle swarm is obtained; ③ update of individual optimal particle and population optimal particle of the internal layer: the fitness of the particle swarm is compared with the current corresponding individual optimal fitness in turn, and the individual optimal particle is updated, and then the individual optimal fitness is compared with the current population optimal fitness in turn, and the population optimal particle is updated; ④ update of position and speed of the internal layer particle swarm: the speed and position of the internal layer particle are updated, and whether the updated value meets the constraint condition is judged; if the out-of-bound condition occurs, the constraint is performed on the out-of-bound particle; ⑤ iteration number judgment: whether the condition meets the maximum iteration number is judged, if not, return to ②; the current population optimal value and population optimal fitness are taken as optimization results, and step 4 is turned to. 4) Calculate the outer layer particle fitness: according to the current population particle data and the derived inner layer optimal fitness, the outer layer particle fitness is calculated; 5) Update the individual optimal particle and the population optimal particle of the outer layer: compare the fitness of the particle swarm with the current corresponding individual optimal fitness in turn, update the individual optimal particle, and then compare the individual optimal fitness with the current population optimal fitness in turn, update the population optimal particle; 6) Update the position and speed of the outer layer particle swarm: update the speed and position of the outer layer particle, wherein the DESS access position corresponding particle adopts the binary particle swarm formula for optimization, and it is judged whether the updated value meets the constraint condition; if the boundary condition occurs, the boundary particle is constrained; 7) Iteration number judgment: judge whether the maximum iteration number is reached, if not, return to step 4; otherwise, output the Pareto solution set containing the double-layer site selection and constant optimization results.

3. The multi-scenario light storage coordination cluster regulation and energy storage planning method according to claim 2, characterized in that, Based on the entropy weight method, the TOPSIS method is used to evaluate the schemes in the Pareto solution set, including: N solutions from the Pareto solution set, where the number of attributes of each alternative is the number of objective functions n, the mth attribute value of the xth i solution is f m ( x i ). The entropy weight method is used to weight each objective function, the TOPSIS method is used to calculate the distance of the scheme, the score of each scheme is obtained, and the optimal scheme is selected according to the score; Scheme x i the relative distance d ( x i ) is calculated as follows: ， where ：d + ( x i ), d - ( x i ) are the ideal best and worst solutions, respectively. x i the distance from the ideal best and worst solutions; ɑ m are the weights of the attributes f m , 0 ɑ m <1, the sum of the weights is 1. f m+ 、f m- are the normalized best and worst values of the attributes of the solutions in the solution set, respectively.

4. A multi-scenario light storage collaborative cluster regulation and control energy storage planning device, characterized in that, Including: A photovoltaic-scenario clustering module is used to cluster photovoltaic-load scenarios by using the ISODATA method; A cluster division module is used to divide clusters for each scenario in time periods, with the objectives of modularity, power supply rate and inter-cluster transmission amount; A DESS site selection and constant double-layer coordination planning model establishment module is used to establish a cluster regulation DESS site selection and constant double-layer coordination planning model, wherein the external planning layer of the DESS site selection and constant double-layer coordination planning model takes a cluster as a basic object, takes a cluster supply rate, a comprehensive cost of energy storage and a node voltage deviation as optimization objectives, and takes a DESS installed capacity constraint, a maximum charge-discharge power constraint of energy storage, and an energy storage access position constraint as constraint conditions; The internal simulation running layer of the DESS site selection and constant double-layer coordination planning model takes minimization of comprehensive operation cost of energy storage operation, power purchase and sale, abandoned light amount and network loss as an optimization objective, and takes a DESS capacity, power, power flow of a distribution network, a node voltage and a branch power accessed by a node as constraint conditions; A model solving module is used to solve the DESS site selection and constant double-layer coordination planning model by using a double-layer nested multi-objective particle swarm optimization algorithm to obtain a Pareto solution set; when the DESS site selection and constant double-layer coordination planning model is solved by using the double-layer nested multi-objective particle swarm optimization algorithm, the particles of the external planning layer include a DESS access position, a maximum capacity of the DESS, and a maximum charge-discharge power of the DESS; The particles of the internal simulation running layer include: DESS charge-discharge power at each time, actual net output of photovoltaic, and interactive power with a main network; A scheme evaluation module is used to evaluate the schemes in the Pareto solution set based on the entropy weight method TOPSIS method to determine a final planning scheme.

5. A computer device, characterized by: The processor, the communication interface, and the memory complete mutual communication through the communication bus; The memory is used to store a computer program. A processor is configured to implement the method for planning energy storage of a multi-scenario photovoltaic and energy storage coordinated cluster according to any one of claims 1 to 3 when executing a program stored in a memory.

6. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is configured to implement the steps of the method for planning energy storage of a multi-scenario photovoltaic and energy storage coordinated cluster according to any one of claims 1 to 3 when executed by a processor.

7. A computer program product comprising a computer program, characterized in that, The computer program is configured to implement the steps of the method for planning energy storage of a multi-scenario photovoltaic and energy storage coordinated cluster according to any one of claims 1 to 3 when executed by a processor.

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