Multi-scene light storage cooperative cluster regulation and control energy storage planning method and device

Abstract

The invention provides a multi-scene light storage cooperative cluster regulation and control energy storage planning method, which comprises the following steps of: clustering by adopting an ISODATA method to obtain a photovoltaic-load scene, and carrying out time-phased cluster division on each scene by taking modularity, power supply rate and inter-cluster transmission quantity as targets; establishing a DESS addressing and sizing double-layer coordinated planning model for cluster regulation and control; solving the DESS locating and sizing double-layer coordinated planning model by adopting a double-layer nested multi-target particle swarm algorithm to obtain a Pareto solution set; and evaluating the scheme in the Pareto solution set based on a TOPSIS method of an entropy weight method, and determining a final planning scheme.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network fault identification, and in particular to a method and device for planning energy storage by means of photovoltaic-storage collaborative cluster regulation in multiple scenarios. Background Art

[0002] Energy storage systems have the characteristics of flexible storage of electric energy and adjustment of charging and discharging power, which can effectively alleviate the safety operation problems caused by the mismatch between distributed photovoltaic output and load demand timing. At present, a large number of 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 economy, environmental protection and reliability, a photovoltaic and energy storage site selection and sizing planning model based on multiple scenarios was established, and the access scheme of photovoltaic and energy storage was optimized by combining gravity reverse learning and particle swarm algorithm. For another example, taking system load fluctuation, energy storage cost and energy storage charge deviation as the objective function, the energy storage site selection and sizing optimization model was constructed by combining the multi-objective particle swarm optimization (MOPSO) algorithm, and the Pareto solution set containing the energy storage access location and capacity scheme was obtained through optimization, and the ordinal preference method based on information entropy was used to select the optimal access scheme of the energy storage system. For another example, based on the robust idea, a scenario reduction method was proposed, and a distributed photovoltaic scenario set with significant impact on the distribution network was selected. With the goal of minimizing the maximum charging and discharging power of energy storage at each moment, a linear optimization model for the site selection and sizing of medium-voltage distribution network energy storage was proposed.

[0003] As the penetration rate of distributed photovoltaics continues to increase, it is difficult for centralized control to achieve effective control in the face of numerous decentralized distributed photovoltaic nodes. The use of a cluster control system with photovoltaic-energy storage collaboration is an inevitable trend to ensure the "four-can" operation of distributed photovoltaics, improve the system's photovoltaic absorption capacity, and ensure the safe operation of the power grid. Therefore, it is necessary to consider the cluster control problem in the subsequent operation stage and optimize the planning of the DESS energy storage configuration. However, existing studies have considered the control division problem of centralized transportation in a single scenario. However, in the group control system, energy storage planning is a long-term planning problem. The energy storage planning scheme considering a single scenario is difficult to meet its cluster control needs in multiple scenarios throughout the year.

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

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

[0006] In order to achieve the above-mentioned object, the first aspect of the present invention provides a method for energy storage planning of photovoltaic and energy storage collaborative cluster regulation in multiple scenarios, comprising the following steps: The PV-load scenario is obtained by clustering using the ISODATA method; Taking modularity, power supply rate and inter-cluster transmission volume as targets, each scenario is divided into time-segment clusters; A cluster-controlled two-layer coordinated planning model for DESS site selection and sizing is established. The external planning layer of the DESS site selection and sizing two-layer coordinated planning model takes the cluster as the basic object, the cluster supply rate, the comprehensive cost of energy storage and the node voltage deviation as the optimization objectives, and the DESS installed capacity constraint, the maximum charging and discharging power constraint of energy storage, and the energy storage access location constraint as the constraint conditions; the internal simulation operation layer of the DESS site selection and sizing two-layer coordinated planning model takes the minimization of the comprehensive operating cost of energy storage operation, power purchase and sale, abandoned light and network loss as the optimization objective, and takes the node-accessed DESS capacity, power, distribution network flow, node voltage and branch power as the constraint conditions; The double-layer nested multi-objective particle swarm algorithm is used to solve the DESS site selection and capacity double-layer coordinated planning model and obtain the Pareto solution set. The TOPSIS method based on entropy weight method evaluates the solutions in the Pareto solution set and determines the final planning solution.

[0007] In order to achieve the above-mentioned object, the second aspect of the present invention provides a storage energy planning device for photovoltaic and storage collaborative cluster control in multiple scenarios, comprising: PV-scenario clustering module, used to obtain PV-load scenarios by clustering using ISODATA method; The cluster division module is used to divide each scenario into clusters in different time periods based on modularity, power supply rate and inter-cluster transmission volume; A module for establishing a two-layer coordinated planning model for DESS site selection and capacity determination is used to establish a two-layer coordinated planning model for DESS site selection and capacity determination for cluster control. The external planning layer of the two-layer coordinated planning model for DESS site selection and capacity determination takes the cluster as the basic object, takes the cluster supply rate, the comprehensive cost of energy storage and the node voltage deviation as the optimization objectives, and takes the DESS installed capacity constraint, the maximum charge and discharge power constraint of energy storage, and the energy storage access location constraint as the constraint conditions; the internal simulation operation layer of the two-layer coordinated planning model for DESS site selection and capacity determination takes the minimization of the comprehensive operation cost of energy storage operation, power purchase and sale, abandoned light quantity and network loss as the optimization objective, and takes the DESS capacity, power, distribution network flow, node voltage and branch power of the node access as the constraint conditions; The model solving module is used to solve the DESS site selection and capacity two-layer coordinated planning model using a two-layer nested multi-objective particle swarm algorithm to obtain the Pareto solution set; The scheme evaluation module is used to evaluate the schemes in the Pareto solution set based on the TOPSIS method of the entropy weight method and determine the final planning scheme.

[0008] In order to achieve the above-mentioned purpose, the 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; the processor is used to execute the program stored in the memory, and implement the steps of the method for energy storage planning of photovoltaic and storage collaborative cluster regulation in multiple scenarios as described in the first aspect above.

[0009] In order to achieve the above-mentioned objectives, the fourth aspect of the present invention provides a computer-readable storage medium having instructions stored thereon, which, when executed by one or more processors, enables the processor to execute the steps of the method for energy storage planning of photovoltaic-storage collaborative cluster regulation in multiple scenarios as described in the first aspect above.

[0010] In order to achieve the above-mentioned purpose, the fifth aspect of the present invention provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of the method for energy storage planning of photovoltaic-storage collaborative cluster regulation in multiple scenarios as described in the first aspect above.

[0011] The beneficial effects of the present invention are: 1) The present invention considers the energy storage configuration planning strategy under the operation of multi-scenario photovoltaic clusters. Compared with the conventional single-layer planning scheme, the planning scheme obtained by the proposed method takes into account the cluster division and multi-scenario operation, and has better operation capability for multi-scenario operation, thereby improving the distribution network's photovoltaic absorption capacity and reducing the number of times the cluster photovoltaic supply is oversupplied during normal operation.

[0012] 2) The method proposed in the present invention takes into account the simulated operation of the distribution network in the planning stage, reduces the voltage deviation during the operation of the distribution network, and makes the system operation more stable and reliable. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 It is a flow chart of the method for energy storage planning of photovoltaic-storage collaborative cluster regulation in multiple scenarios of the present invention; Figure 2 This is the 8760 photovoltaic output curve for a certain place in Belgium throughout the year; Figure 3 It is the 8760 load curve for a certain place in Belgium throughout the year; Figure 4 It is the output curve of typical load scenario; Figure 5 It is the photovoltaic output curve corresponding to the typical load scenario; Figure 6 It is the inner and outer particle encoding; Figure 7 It is the flow chart of the double-layer multi-objective particle swarm optimization algorithm; Figure 8 It is the IEEE33 node distribution network structure; Fig. 9 It is the cluster division result at a certain moment in a typical scenario; Fig.10 It is the radar chart of the objective function of different schemes; Fig.11 It is the radar chart of the objective function of different schemes; Fig.12 is the voltage deviation in each scenario of different schemes; Fig.13 It is the energy storage planning access diagram of different solutions; Fig.14 It is a cluster supply diagram under different scenarios; Fig.15 It is the voltage deviation diagram under different scenarios. DETAILED DESCRIPTION

[0014] The present invention proposes a method for energy storage planning under multi-scenario photovoltaic-storage coordinated cluster regulation. First, the ISODATA method is used to cluster the photovoltaic output and load change trends throughout the year. Then, each scenario is clustered by time period based on modularity, power supply rate and inter-cluster transmission volume. The short-term system operation is incorporated into the long-term planning of energy storage configuration, and a two-layer coordination optimization model combining outer energy storage configuration with inner simulation operation is constructed. According to the characteristics of the two-layer planning model, a two-layer nested multi-objective particle swarm algorithm is used to solve the problem, and TOPSIS based on the entropy weight method is used to evaluate the solutions in the Pareto solution set. Finally, the analysis of different types of energy storage, the number of energy storage connections and different planning schemes shows that the planning scheme obtained by the proposed method can improve the distribution network's photovoltaic absorption capacity, reduce the voltage deviation during system operation, and effectively improve the autonomy of the cluster during operation when operating in multiple scenarios.

[0015] The technical solution of the present invention is further described in detail below through specific implementation methods.

[0016] Example 1 This embodiment provides a method for energy storage planning by PV-storage collaborative cluster regulation in multiple scenarios, including the following steps: Step 1: Use ISODATA method to cluster and obtain photovoltaic-load scenarios.

[0017] Specifically, this embodiment uses the photovoltaic output and load data of a certain area in Belgium for 8760 hours throughout the year, such as Figure 2 and Figure 3 As shown, its annual load data presents a "mountain" shape. The load demand is relatively dense in the 3000h-6000h period of the year, while the annual photovoltaic output at the corresponding time is in an "M" shape. The photovoltaic-load situation is very complicated in different time periods.

[0018] By clustering the PV output and load data for 8760 hours in a year in a certain area of ​​Belgium, we can get the corresponding 16 typical load scenarios and the corresponding PV output scenarios. 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. Such scenarios have a large demand for energy storage. Scenarios 7 and 10 are scenarios of insufficient photovoltaic supply with low photovoltaic output and high load demand. Such scenarios have a large demand for energy storage. The rest are normal photovoltaic-load operation scenarios. When conducting energy storage planning, it is necessary to fully consider the proportion and energy storage demand of different scenarios to obtain a more accurate and economical planning solution.

[0019] Step 2: Based on the modularity, power supply rate and inter-cluster transmission volume, each scenario is divided into time-segment clusters.

[0020] In this embodiment, the cluster division principles involved are the basis for ensuring the integrity and independence of the network division scheme: 1) Logical principle: Nodes in the same cluster must be able to connect directly or indirectly to ensure connectivity within the cluster and avoid nodes that need to be connected through other clusters. In addition, in the cluster division of the power network, isolated nodes should be avoided and there should be no overlap between clusters.

[0021] 2) Structural principle: The application of clusters includes collaboration and division of labor. Within the cluster, strong electrical coupling between nodes should be ensured, so that the internal nodes are closely connected and the structure is stable, so as to achieve mutual collaboration. Between clusters, weak electrical coupling should be maintained to minimize the mutual influence between clusters to facilitate their respective division of labor.

[0022] 3) Functional principle: The function of a cluster is determined by the comprehensive attributes of each node within it. If the cluster needs to cooperate with each other to optimize efficiency or improve the economy of system operation, it is necessary to ensure that the functional attributes of each node within it have a certain degree of similarity or complementarity.

[0023] Therefore, in this embodiment, modularity is constructed based on reactive power-voltage sensitivity as a structural indicator, and power supply rate and inter-cluster transmission volume are used as functional indicators. In this way, the network can be decoupled to the maximum extent into cluster units with strong coupling within the cluster and weak coupling between clusters. Not only can the local balancing demand of power within the cluster be met when the network is operating normally, the power exchange between clusters can be reduced, thereby reducing network losses; when the cluster is off-grid, it can also ensure the normal execution of the control strategy and improve the autonomy of the cluster.

[0024] Step 3: Establish a two-layer coordinated planning model for DESS site selection and capacity determination based on cluster control.

[0025] The DESS site selection and capacity determination two-layer coordinated planning model includes an external planning layer and an internal simulation operation layer.

[0026] Specifically, the outer planning model uses the sum of the construction investment cost of energy storage and the operating cost and operating income brought by the simulated operation after energy storage access as economic indicators, and uses the voltage deviation and cluster power supply rate after energy storage access as operating indicators to plan the planned access location, capacity, and rated power of DESS.

[0027] In this embodiment, the investment cost of energy storage and the benefits of energy storage to system operation and scheduling are comprehensively considered, and the following three indicators are selected as the objective function of the outer energy storage planning.

[0028] (1) Node voltage deviation. Voltage is one of the important indicators that characterize the quality of system operation. When the system is operating normally, the voltage of each node should be maintained within a certain range without large voltage deviation. After distributed photovoltaics are connected to the distribution network, photovoltaics will produce large voltage fluctuations when there is oversupply or undersupply. 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: (1) (2) Cluster photovoltaic power oversupply index. In order to ensure that the cluster can implement the control strategy normally during operation, ensure the stability of internal power supply, and reduce the situation of photovoltaic oversupply, while meeting the power balance requirements within the cluster as much as possible, reducing power exchange between clusters, and thus reducing network losses during transmission, it is necessary to effectively coordinate photovoltaics with energy storage to keep the photovoltaic power supply rate of the cluster within a healthy operating range.

[0029] Therefore, the cluster photovoltaic power supply excess index is set based on the cluster power supply rate. f 2 as follows: (2) in S It is the scene time of 24h; T is the number of typical scenes; g ij for i Scenario j The number of over-powered clusters at the time instant.

[0030] (3) Comprehensive cost of energy storage access to distribution networks (3) Where: I 1 is the annual equivalent installation cost of energy storage, I 2is the annual operating cost after energy storage is connected.

[0031] The annual equivalent installation cost is: (4) Where: N DESS is the number of clusters; r is the discount rate; y They are DPV、ESS The useful life of 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.

[0032] The outer energy storage planning objective function that comprehensively considers system voltage deviation, cluster power supply, and comprehensive energy storage operation costs is as follows: (5) Furthermore, the constraints of the outer 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.

[0033] (6) (7) (8) S j For energy storage j Rated capacity, S min , S max are the minimum and maximum rated capacities of energy storage, respectively; P pmax 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 The access location; L min , L max They are the minimum and maximum access position numbers of the energy storage respectively.

[0034] (9) g ij，N For i In the scenario j The cluster power supply rate of cluster N at the moment, g min , g max are the lower and upper limits of the cluster power supply rate respectively.

[0035] The objective function of the inner simulation operation model is to minimize the total operating cost, including the energy storage operation and maintenance cost, system network loss cost, the cost of purchasing and selling electricity from the upper network, and the photovoltaic power curtailment income reduced by energy storage. Through this model, the aim is to solve the operating cost of the system after connecting to energy storage in a typical scenario.

[0036] (10) Where: I OM is the energy storage operation and maintenance cost, I pb The cost of purchasing and selling electricity for the upper-level network, I p To store energy and reduce the cost of photovoltaic power curtailment, I loss is the system network loss cost, N is the number of typical scenes, w i For a typical scenario i The proportion of days in a year.

[0037] The energy storage operation and maintenance cost is: (11) Where: C DESS is the operation and maintenance cost per unit energy storage charge and discharge capacity, P ij，DESS is the charging and discharging power of energy storage z at scene i and time j, N is the number of typical scenes, T The total time of the scene is 24 hours. Z is the total number of energy storage units.

[0038] The cost of purchasing and selling electricity from the upper-level network is: (12) Where: C t,buy , C t,sell for t The current electricity purchase and sale prices with the upper-level network, P it，buy ,Pit，sell For the scene i Next, time t The power purchased and sold by the lower and upper power grids.

[0039] The benefits of energy storage in reducing photovoltaic power curtailment are: (13) Where: C t,sell for t The electricity price sold at the moment with the upper network, P it，pv For the scene i Next, time t Photovoltaic output with energy storage.

[0040] (14) Where: C t,sell for t The electricity price sold at the moment with the upper network, P it，loss For the scene i Next, time t The system is running with network loss.

[0041] The constraints of the inner simulation operation model include power balance constraints, distribution network flow constraints, node voltage constraints, and DESS charging and discharging power constraints.

[0042] Among them, the power flow and power balance constraints of the distribution network are as follows: (15) (16) P pv , P loss、 , P grid , P DESS , P L Respectively in t The photovoltaic output, system network loss, and interaction power with the upper network at the moment. DESS Charging and discharging power and total load; P i , Q i Node i Active and reactive power injection; U i , U j Node i , j The voltage amplitude;G ij , B ij For Node i , j The branch admittance of θ ij For Node i , j The voltage phase angle difference between them.

[0043] Node voltage constraints: (17) V ij For i Nodes in the scene j The node voltage, V min , V max are the lower and upper limits of the system node voltage respectively.

[0044] DESS charging and discharging power constraints: (18) P DESS Contribute to energy storage, SOC min , SOC max are the minimum and maximum values ​​of the energy storage charge state, respectively.

[0045] Specifically, the external planning layer obtains the initial planning scheme with the DESS capacity, power and access location of the access system as decision variables according to the constraints, and imports it into the internal simulation operation layer. The internal simulation operation layer takes the minimization of the comprehensive operating cost of energy storage operation, power purchase and sale, abandoned light, and network loss as the optimization goal, and takes the DESS capacity, power, distribution network flow, node voltage and branch power of the node access as constraints. And taking the 24-hour charging and discharging power of the node in the group accessing the DESS as the decision variable, the charging and discharging power of the energy storage under 16 scenarios is simulated to obtain the comprehensive operating cost. Then the comprehensive operating cost, voltage and cluster supply parameters obtained by the simulation operation are fed back to the outer layer, and the target value is calculated. The outer layer iteratively modifies the initial planning scheme according to the target value, and collaboratively optimizes the DESS capacity, power and access location of the access system.

[0046] Step 4: Use a double-layer nested multi-objective particle swarm algorithm to solve the DESS site selection and capacity double-layer coordinated planning model to obtain the Pareto solution set. In view of the large number of objective functions and variables, high dimensions, and difficulty in unifying variable types in the double-layer energy storage planning model, a double-layer iterative multi-objective particle swarm optimization (BMPSO) algorithm is used for optimization and solution. The inner and outer layer particles are encoded as follows: Figure 6 shown.

[0047] The particles in the external planning layer include the access location of DESS, the maximum capacity of DESS, and the maximum charging and discharging power of DESS; the particles in the internal simulation operation layer include: the charging and discharging power of DESS at each moment, the actual net output of photovoltaics, and the interactive power with the main grid.

[0048] Specifically, Figure 7 As shown in the figure, the process of the double-layer nested multi-objective particle swarm algorithm is as follows: 1) Raw data input: Input cluster division data, PV output and load curves under 16 scenarios, distribution network branch impedance and load data.

[0049] 2) Initialize the parameters of the outer particle swarm. According to the upper and lower limits of the outer decision variables, initialize the initial speed and position of the particles in the particle swarm.

[0050] 3) The outer layer parameters are input into the inner layer algorithm for calculation. Follow the steps below: ① Initialize the inner particle group. Use the outer particles as external input parameters to initialize the speed and position of the lower particles of the corresponding dimensions of each cluster in parallel.

[0051] ② Calculate the fitness of the lower-layer particles. According to the lower-layer particle data, update the DESS charging and discharging power and photovoltaic output data connected to the upper-level network exchange power and the distribution network flow program, and obtain the simulated operation cost after the flow calculation to obtain the fitness of the lower-layer particle group.

[0052] ③ Update the individual optimal particles and the population optimal particles in the inner layer. Compare the fitness of the particle group with the current corresponding individual optimal fitness in turn, and update the individual optimal particles. Then compare the individual optimal fitness with the current group optimal fitness in turn, and update the population optimal particles.

[0053] ④ The inner particle group updates its position and speed. Update the speed and position of the inner particles and determine whether the updated values ​​meet the constraints. If there is an out-of-bounds situation, constrain the out-of-bounds particles.

[0054] ⑤ Iteration number judgment. Judge whether the conditions are met to reach the maximum number of iterations. If not, return to ②; take the current group optimal value and group optimal fitness as the optimization result and go to step 4.

[0055] 4) Calculate the fitness of the outer layer particles. According to the current population particle data and the derived inner layer optimal fitness, the fitness of the outer layer particles is obtained.

[0056] 5) Update the outer individual optimal particles and the population optimal particles. Compare the fitness of the particle group with the current corresponding individual optimal fitness in turn, and update the individual optimal particle. Then compare the individual optimal fitness with the current group optimal fitness in turn, and update the population optimal particle.

[0057] 6) The outer particle swarm updates its position and speed. The speed and position of the outer particles are updated. The particles corresponding to the DESS access position are optimized using the binary particle swarm formula, and it is determined whether the updated value meets the constraint conditions. If an out-of-bounds situation occurs, the out-of-bounds particles are constrained.

[0058] 7) Iteration number judgment: Determine whether the condition is met to reach the maximum number of iterations. If not, return to step 4; otherwise, output the Pareto solution set containing the double-layer location and capacity optimization results.

[0059] Step 5: The TOPSIS method based on the entropy weight method is used to evaluate the solutions in the Pareto solution set and determine the final planning solution.

[0060] The optimization scheme based on the BMPSO algorithm is a set of Pareto solution sets, from which decision makers need to select the optimal solution based on actual needs and priorities, which is essentially a multi-attribute decision-making problem. To this end, the TOPSIS method (Technique for Order Preference by Similarity to an Ideal Solution) based on the entropy weight method is used to select the optimal solution for 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) of each indicator, and scores the solution according to the distance between the solution set and the positive and negative ideal values ​​to obtain the evaluation value of each solution.

[0061] In the specific implementation, the N solutions in the Pareto solution set constitute N alternatives, where the number of attributes of each alternative is the number of objective functions n, and the mth attribute value of the xith solution is In order to eliminate the difference in the dimensions of each objective function, the formula (x) is used to normalize the attributes in the solution set: (19) The TOPSIS method needs to assign different weights to the target values ​​during calculation, and the selection of indicator weights usually depends on the decision maker's personal experience and ability. In order 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 as follows: First, calculate the proportion of the i-th solution to the indicator under the j-th attribute value: (20) Then substitute the specific gravity band calculated by the above formula into the following formula to calculate the j The weight of the item attribute value.

[0062] (twenty one) in ,satisfy .

[0063] The entropy weight method determines the weight of the target by analyzing the degree of difference between indicators. When the difference of an indicator in the solution set is small, it means that the amount of information it reflects is also reduced accordingly, so the weight corresponding to the indicator should be reduced. This method effectively reduces the interference of subjective factors and improves the objectivity and scientificity of weight allocation.

[0064] After calculating the weight of each indicator through the entropy weight method, the TOPSIS method is used to calculate the distance of the scheme, obtain the score of each scheme, and select the best scheme according to the score.

[0065] plan x i The relative distance d ( x i ) is calculated as follows: (twenty two) (twenty three) (twenty four) In the formula ：d + ( x i ), d - ( x i ) are respectively x i The distance from the ideal optimal solution and the ideal worst solution; ɑ m For attributes f m The weight of ɑ m <1, the sum of weights is 1;f m + 、f m - are the optimal and worst values ​​of the normalized attributes of the solutions in the solution set.

[0066] Verification Example by Figure 8 The IEEE 33-node distribution network system is shown for verification.

[0067] like Figure 8 As shown in the figure, the grid structure of the system contains 32 load nodes and 9 photovoltaic nodes. The total photovoltaic capacity is 3300+j1084.65kVA, and the total network load is 3715+j2315kVA. Photovoltaic power stations with rated capacities of 300kW, 300kW, 300kW, 300kW, 500kW, 300kW, 500kW, 300kW, and 300kW are connected to nodes 4, 7, 10, 14, 17, 20, 24, 28, and 32 of the IEEE33 system, respectively, to establish a high-penetration photovoltaic distribution network. The method of considering the comprehensive index of cluster division is adopted to cluster the distribution network in each typical scenario and time period. The cluster division results under a certain scenario and a certain time period are shown as follows: Fig. 9 shown.

[0068] In the double-layer multi-objective particle swarm optimization algorithm (BMOPSO), the outer MOSPO parameter settings are: the number of particles is 30, the number of iterations is 50 times, and the inner particle swarm parameter settings are: the number of particles is 20, and the number of iterations is 30 times.

[0069] Analysis of the impact of energy storage types on energy storage planning Different types of energy storage systems have significant differences in energy density, power density, charge and discharge efficiency, life cycle cost, etc. Their different characteristics directly affect the economy of the energy storage planning scheme and the stable and safe operation of the later access system. In order to verify the impact of energy storage type on the planning results and select the most suitable energy storage type for the corresponding system, this embodiment selects four types of energy storage, namely sodium sulfur battery (NAS), all-vanadium redox battery (VRB), lithium-ion battery (Li-ion), and lead-acid battery (Value-regulated lead-acid battery, VRLA) for analysis. The planning method proposed in this embodiment is used to plan the configuration of DESS for the above-mentioned IEEE33 node system. The relevant parameters of each type of energy storage are shown in Table 1 below. The planning results obtained by using four types of energy storage schemes based on the connection of two energy storage units are shown in Table 2 below: Table 1 Related parameters of various types of energy storage parameter NAS VRB VRLA Li-ion Unit power cost / (yuan / kW) 1650 2815 1980 2830 Unit capacity cost / (yuan / kW) 1270 660 980 1390 Operation and maintenance cost per unit of power generation / (yuan / kW) 0.080 0.080 0.093 0.087 Charge and discharge efficiency 0.80 0.70 0.85 0.90 Use life 15 15 10 15 Table 2 Planning results of different energy storage types parameter Installation location Installed capacity(MW) Rated power(MW) Comprehensive cost (yuan) Daily average equivalent network loss (kW) Daily average converted photovoltaic income (yuan) 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 From Table 2 and Fig.10 It can be seen that for the energy storage planning of the IEEE33 system, the NAS solution performs best in terms of economy, and in the process of cluster power supply, its cluster oversupply is the least, showing excellent performance in maintaining stable supply in cluster control. In contrast, the VRB solution performs best in terms of network loss, but its voltage fluctuation suppression ability is weak, which is not conducive to the long-term stable operation of the system. The VRLA solution has the highest photovoltaic absorption benefit and the lowest voltage fluctuation deviation, but the overall cost is the highest, and it is difficult to balance cost and benefit. The Li-ion solution did not show obvious advantages in indicators such as comprehensive cost, network loss, photovoltaic absorption benefit, voltage offset and cluster supply. Each indicator was analyzed separately. In terms of comprehensive cost, NAS was 5.71%, 5.31%, and 9.24% lower than VRB, VRLA, and Li-ion, respectively. In terms of network loss, VRB, VRLA, and Li-ion were 6.33%, 3.71%, and 2.99 lower than NAS, respectively. In terms of voltage offset, VRB, VRLA, and Li-ion were 24.9%, 20.00%, and 12.88% lower than NAS, respectively. In terms of cluster oversupply, VRB, VRLA, and Li-ion were 9.8%, 0.65%, and 54.90% higher than NAS, respectively.

[0070] Although the NAS solution has slightly higher network loss during operation, its comprehensive cost is much lower than the other three types of energy storage, showing superior economic efficiency. At present, the main reason restricting the access of distributed energy storage systems (DESS) to the distribution network is the high investment cost of energy storage. Therefore, considering the low cost, relatively reasonable power loss and good power supply control capability of the NAS solution, it can be regarded as the optimal energy storage configuration solution for the IEEE33-node system under the operation of multiple scenarios including photovoltaic clusters.

[0071] Analysis of the impact of the number of energy storage connections on energy storage planning Through the analysis in the previous section, compared with other solutions, the use of NAS energy storage is more in line with the planning requirements of the system in this embodiment. However, when the energy storage is connected and operated, its connection location and number will affect the system's cluster supply, voltage fluctuations, and operating losses. In order to select the number of energy storage connections with better comprehensive performance, this embodiment will analyze four access solutions of 1 to 4 energy storages. The planning results are shown in Table 3: Table 3 Planning results of different energy storage connection number solutions Installation location Installed capacity(MW) Rated power(MW) Comprehensive cost (ten thousand yuan) Daily average equivalent network loss (kW) Daily average converted photovoltaic income (yuan) 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 From Table 3 and Fig.11 It can be seen that in the energy storage planning for the IEEE33 system, the comprehensive cost of Scheme 2 was reduced by 17.638% and 20.452% respectively compared with Scheme 3 and Scheme 4; the daily average equivalent network loss was reduced by 8.76% and 20.01% respectively; the daily average equivalent photovoltaic income was increased by 10.41% and 12.66% respectively; the voltage deviations differed by 40.54% and 41.87% respectively; and the number of cluster oversupply was reduced by 15.69% and 26.57% respectively. It can be seen that Scheme 2, while ensuring economy, has a more stable and controllable cluster power supply, and lower network losses in long-term operation. Fig.12 It can be seen that although the total voltage deviation is large, it can still ensure that the voltage is within the normal fluctuation range when it is dispersed in various typical scenarios. Compared with the 1-unit solution, the cost of Solution 2 is slightly higher, but it shows different degrees of advantages in other indicators.

[0072] In summary, Scheme 2 performs well in key indicators such as cost, network loss and revenue. Although it is slightly disadvantaged 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 regulation.

[0073] Energy storage planning performance and operation index analysis In the IEEE33 node of this embodiment, the comprehensive performance of connecting two NAS energy storage is optimal. In order to verify the advantages of the double-layer energy storage configuration planning strategy proposed in this embodiment considering multiple scenarios including photovoltaic cluster operation, this embodiment constructs three planning schemes respectively and conducts comparative analysis with the results.

[0074] Planning scheme 1: Planning without considering the cluster and multi-scenario operation of the distribution network. A single-layer planning model is adopted, with nodes as the basic unit, and the energy storage connected to each node is directly planned.

[0075] Planning Scheme 2: Consider the cluster operation of the distribution network for planning, and adopt a single load and photovoltaic scenario. Based on the cluster division results and the single load-PV scenario, a two-layer planning model is adopted, with nodes as the basic unit, to plan the energy storage connected to the system.

[0076] Planning scheme three: Consider multiple scenarios and cluster operation for planning. Based on the cluster division results, a two-layer energy storage planning model is adopted. The inner layer simulates the operation of multiple scenarios of the system, and the outer layer model uses nodes as the basic unit to plan the location, capacity and rated power of energy storage access.

[0077] Table 4 Planning results of different energy storage planning strategies plan Equivalent annual comprehensive cost / million yuan Daily average network loss / kW Total scene voltage deviation 1 126.484 1192.580 0.2667 2 118.891 1123.437 0.1643 3 114.150 1203.021 0.1470

[0078] Depend on Fig.13 , 14 , 15, Table 4 Analysis of the three planning strategies shows that in planning scheme 1, only single-scenario operation is considered, and cluster operation planning is not considered. Compared with schemes 2 and 3, the energy storage access location is single, and the capacity planning is not accurate enough, resulting in its high comprehensive cost and poor adaptability to multi-scenario operation. The voltage deviation in each scenario is much higher than that of the other two planning schemes. In addition, the single access location leads to too many cluster oversupply times in each scenario, which is not conducive to subsequent cluster regulation.

[0079] In planning scheme 2, when single scenario and cluster operation planning are considered, the voltage deviation is lower than that of scheme 1 and slightly higher than that of scheme 2. The network loss in each scenario is low and varies less. The total network loss is the lowest, and the cluster oversupply is also lower than that of scheme 1. The overall cost is moderate among the three schemes.

[0080] Planning Scheme 3 takes multiple scenarios and cluster operation into consideration. Since it considers cluster operation in multiple scenarios, it has good adaptability to the operation of multiple scenarios and the number of cluster oversupply in each scenario is the least, which can ensure good performance in subsequent cluster regulation. Compared with Schemes 1 and 2, the total scenario voltage deviation is also the lowest, and the deviation is the smallest in most scenarios. Although its total network loss is higher, the network loss in most scenarios is lower than that of the other two schemes. Only a small part of the network loss is larger due to the high transmission power to ensure stable cluster supply. Since Scheme 3 takes into account its operation in multiple scenarios, the planning of access location and capacity is more accurate. On the basis of ensuring economy, various planning indicators perform better.

[0081] Based on the above analysis, this section compares and analyzes the three planning schemes through various indicators. The planning strategy proposed in this embodiment not only ensures the economy and the safe operation of the system after the energy storage is connected to the system, but also provides good operation and control conditions for subsequent cluster control.

[0082] It should be understood that, although the steps in the flowcharts involved in the above embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear description in the present embodiment, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the above embodiments may include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of the steps or stages in other steps.

[0083] Example 2 Based on the same inventive concept, the embodiment of the present application also provides a distribution network section positioning device based on multi-dimensional feature clustering of fault phase current for implementing the above-mentioned method for energy storage planning under multi-scenario photovoltaic and storage collaborative cluster regulation. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above-mentioned method, so the specific limitations in the embodiments of one or more distribution network section positioning devices based on multi-dimensional feature clustering of fault phase current provided below can be referred to the limitations of the energy storage planning method under multi-scenario photovoltaic and storage collaborative cluster regulation, and will not be repeated here.

[0084] Specifically, the photovoltaic and energy storage collaborative cluster control energy storage planning device under multiple scenarios includes: PV-scenario clustering module, used to obtain PV-load scenarios by clustering using ISODATA method; The cluster division module is used to divide each scenario into clusters in different time periods based on modularity, power supply rate and inter-cluster transmission volume; A module for establishing a two-layer coordinated planning model for DESS site selection and capacity determination is used to establish a two-layer coordinated planning model for DESS site selection and capacity determination for cluster control. The external planning layer of the two-layer coordinated planning model for DESS site selection and capacity determination takes the cluster as the basic object, takes the cluster supply rate, the comprehensive cost of energy storage and the node voltage deviation as the optimization objectives, and takes the DESS installed capacity constraint, the maximum charge and discharge power constraint of energy storage, and the energy storage access location constraint as the constraint conditions; the internal simulation operation layer of the two-layer coordinated planning model for DESS site selection and capacity determination takes the minimization of the comprehensive operation cost of energy storage operation, power purchase and sale, abandoned light quantity and network loss as the optimization objective, and takes the DESS capacity, power, distribution network flow, node voltage and branch power of the node access as the constraint conditions; The model solving module is used to solve the DESS site selection and capacity two-layer coordinated planning model using a two-layer nested multi-objective particle swarm algorithm to obtain the Pareto solution set; The scheme evaluation module is used to evaluate the schemes in the Pareto solution set based on the TOPSIS method of the entropy weight method and determine the final planning scheme.

[0085] Example 3 The embodiment of the present application is 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, the memory and the input / output interface are connected through a system bus, and the communication interface, the display unit and the input device are connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a method for energy storage planning for photovoltaic storage coordinated cluster regulation in multiple scenarios is implemented. The display unit of the computer device is used to form a visually visible picture, which can be a display screen, a projection device or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device shell, or an external keyboard, touchpad or mouse.

[0086] Example 4 Based on the above embodiments, this embodiment provides a readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of the method for regulating energy storage planning in a photovoltaic-storage collaborative cluster under multiple scenarios described in Embodiment 1 are implemented.

[0087] Example 5 Based on the above embodiments, this embodiment provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of the method for regulating energy storage planning of a photovoltaic-storage collaborative cluster in multiple scenarios described in Embodiment 1.

[0088] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium 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), magnetoresistive 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. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0089] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, 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.

[0090] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

[0091] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or some technical features can be replaced by equivalents without departing from the spirit of the technical solution of the present invention, which should be included in the scope of the technical solution for protection of the present invention.

Claims

1. A method for energy storage planning for photovoltaic and energy storage collaborative cluster control in multiple scenarios, characterized in that: The following steps are involved: The PV-load scenario is obtained by clustering using the ISODATA method; Taking modularity, power supply rate and inter-cluster transmission volume as targets, each scenario is divided into time-segment clusters; A two-layer coordinated planning model for DESS site selection and capacity determination based on cluster control is established. The outer planning layer of the two-layer coordinated planning model for DESS site selection and capacity determination takes the cluster as the basic object, the cluster supply rate, the comprehensive cost of energy storage and the node voltage deviation as the optimization objectives, and the DESS installed capacity constraint, the maximum charging and discharging power constraint of energy storage and the energy storage access location constraint as the constraint conditions. The internal simulation operation layer of the DESS site selection and capacity two-layer coordinated planning model takes the minimization of the comprehensive operation cost of energy storage operation, power purchase and sale, abandoned light, and network loss as the optimization goal, and takes the DESS capacity, power, distribution network flow, node voltage and branch power connected to the node as the constraint conditions; The double-layer nested multi-objective particle swarm algorithm is used to solve the DESS site selection and capacity double-layer coordinated planning model and obtain the Pareto solution set. The TOPSIS method based on entropy weight method evaluates the solutions in the Pareto solution set and determines the final planning solution.

2. According to the method of claim 1, the method is characterized in that: When the double-layer nested multi-objective particle swarm algorithm is used to solve the double-layer coordinated planning model for DESS site selection and capacity determination, the particles in the outer planning layer include the access location of DESS, the maximum capacity of DESS, and the maximum charging and discharging power of DESS; The particles of the internal simulation operation layer include: the charging and discharging power of DESS at each moment, the actual net output of photovoltaics, and the interactive power with the main grid.

3. A method for energy storage planning for photovoltaic and energy storage collaborative cluster control in multiple scenarios according to claim 1 or 2, characterized in that: The steps of solving the DESS site selection and capacity two-layer coordinated planning model using a two-layer nested multi-objective particle swarm algorithm include: 1) Raw data input: input cluster division data, photovoltaic output and load curves under 16 scenarios, distribution network branch impedance and load data; 2) Initialize the parameters of the outer particle swarm: Initialize the initial speed and position of the particles in the particle swarm according to the upper and lower limits of the outer decision variables; 3) The outer layer parameters are input into the inner layer algorithm for calculation. The steps are as follows: ① Initialize the inner particle group: use the outer particles as external input parameters to initialize the speed and position of the lower particles of each cluster in the corresponding dimension in parallel; ② Calculate the fitness of the lower-layer particles: According to the lower-layer particle data, update the DESS charging and discharging power and photovoltaic output data connected to the upper-level network exchange power and the distribution network flow program, obtain the simulated operation cost after the flow calculation, and obtain the fitness of the lower-layer particle group; ③ Update the individual optimal particles and population optimal particles in the inner layer: compare the fitness of the particle group with the current corresponding individual optimal fitness in turn, update the individual optimal particles, and then compare the individual optimal fitness with the current group optimal fitness in turn, and update the population optimal particles; ④ Update position and speed of the inner particle group: Update the speed and position of the inner particles, and determine whether the updated values ​​meet the constraints; if there is an out-of-bounds situation, constrain the out-of-bounds particles; ⑤ Iteration number judgment: judge whether the conditions are met to reach the maximum number of iterations. If not, return to ②; take the current group optimal value and group optimal fitness as the optimization result and go to step 4; 4) Calculate the fitness of the outer layer particles: According to the current population particle data and the derived optimal fitness of the inner layer, the fitness of the outer layer particles is obtained; 5) Update the outer individual optimal particles and the population optimal particles: compare the fitness of the particle group with the current corresponding individual optimal fitness in turn, update the individual optimal particles, and then compare the individual optimal fitness with the current group optimal fitness in turn, and update the population optimal particles; 6) Update position and speed of the outer particle swarm: Update the speed and position of the outer particles. The particles corresponding to the DESS access position are optimized using the binary particle swarm formula, and it is determined whether the updated values ​​meet the constraints. If there is an out-of-bounds situation, the out-of-bounds particles are constrained. 7) Determine the number of iterations to determine whether the condition is met to reach the maximum number of iterations. If not, return to step 4; otherwise, output the Pareto solution set containing the double-layer site selection and fixed capacity optimization results.

4. According to the method of claim 3, the method is characterized in that: The TOPSIS method based on entropy weight method evaluates the solutions in the Pareto solution set, including: The N solutions in the Pareto solution set constitute N alternatives, where the number of attributes of each alternative is the number of objective functions n, and the mth attribute value of the xith solution is f m ( x i ); The entropy weight method is used to assign weights to each objective function, and the TOPSIS method is used to calculate the distance between the solutions to obtain the scores of each solution, and the optimal solution is selected based on the scores; plan x i The relative distance d ( x i ) is calculated as follows: In the formula ：d + ( x i ), d - ( x i ) are respectively x i The distance from the ideal optimal solution and the ideal worst solution; ɑ m For attributes f m The weight of ɑ m <1, the sum of weights is 1; f m + 、f m - are the optimal and worst values ​​of the normalized attributes of the solutions in the solution set.

5. A device for energy storage planning by synergistic cluster control of photovoltaic and energy storage in multiple scenarios, characterized in that: include: PV-scenario clustering module, used to obtain PV-load scenarios by clustering using ISODATA method; The cluster division module is used to divide each scenario into clusters in different time periods based on modularity, power supply rate and inter-cluster transmission volume; A DESS site selection and capacity determination double-layer coordinated planning model establishment module, which is used to establish a cluster-controlled DESS site selection and capacity determination double-layer coordinated planning model. The outer planning layer of the DESS site selection and capacity determination double-layer coordinated planning model takes the cluster as the basic object, the cluster supply rate, the comprehensive cost of energy storage and the node voltage deviation as the optimization target, and the DESS installed capacity constraint, the maximum charge and discharge power constraint of energy storage and the energy storage access location constraint as the constraint conditions; The internal simulation operation layer of the DESS site selection and capacity two-layer coordinated planning model takes the minimization of the comprehensive operation cost of energy storage operation, power purchase and sale, abandoned light, and network loss as the optimization goal, and takes the DESS capacity, power, distribution network flow, node voltage and branch power connected to the node as the constraint conditions; The model solving module is used to solve the DESS site selection and capacity two-layer coordinated planning model using a two-layer nested multi-objective particle swarm algorithm to obtain the Pareto solution set; The scheme evaluation module is used to evaluate the schemes in the Pareto solution set based on the TOPSIS method of the entropy weight method and determine the final planning scheme.

6. A computer device, characterized in that: It includes a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus; Memory, used to store computer programs; The processor is used to implement the energy storage planning method for photovoltaic-storage collaborative cluster regulation in multiple scenarios as described in any one of claims 1 to 4 when executing the program stored in the memory.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for energy storage planning of photovoltaic-storage collaborative cluster regulation in multiple scenarios according to any one of claims 1 to 4 are implemented.

8. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method for planning energy storage by regulating a photovoltaic-storage collaborative cluster in multiple scenarios according to any one of claims 1 to 4 are implemented.

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