Source network load storage collaborative planning method and device considering safe operation of power distribution network, and medium

Abstract

The invention relates to a source network load storage collaborative planning method and device considering safe operation of a power distribution network and a medium, and the method comprises the steps: carrying out the clustering of the historical data of distributed renewable energy power generation and load in the power distribution network, and obtaining a typical scene of the operation of the power distribution network and a corresponding probability; adopting Monte Carlo simulation random sampling and a demand elastic matrix to construct an electric vehicle charging power model based on electricity price excitation; according to the power distribution network grid structure parameters, the energy storage parameters, the electric vehicle charging power model and the power distribution network operation typical scene, a multi-target source network load storage collaborative planning model with the comprehensive operation cost and the safety target as the lowest comprehensive operation risk index is established; and solving the source network load storage collaborative planning model by using a particle swarm algorithm to obtain an optimal planning scheme. Compared with the prior art, the method provided by the invention can realize source-network-load-storage collaborative planning, and minimizes the comprehensive operation cost while ensuring the safe operation of the power distribution network.

Description

Technical Field

[0001] The present invention relates to the field of source-grid-load-storage collaborative planning of distribution networks, and in particular to a source-grid-load-storage collaborative planning method, device and medium that take into account the safe operation of distribution networks. Background Art

[0002] In modern power systems, with the large-scale access of renewable energy generation, distributed power sources and energy storage equipment, the operation of distribution networks has become increasingly complex. Traditional distribution network planning methods mainly focus on power planning and load growth forecasting, but due to the uncertainty and volatility of renewable energy, existing methods are difficult to fully guarantee the safety and stability of the system. At the same time, the flexibility resources on the load side (such as demand response and distributed energy storage) are not fully utilized, resulting in the lack of elastic adjustment capabilities of the distribution network in response to peak loads and emergencies.

[0003] In order to improve the safety and stability of the distribution network, the concept of source-grid-load-storage collaborative planning has been proposed in recent years, that is, by coordinating the resources of the power supply side (source), distribution network side (grid), load side (load) and energy storage equipment (storage), optimizing the configuration and scheduling to minimize the overall cost. However, the existing collaborative planning methods still have shortcomings in considering the safety of the distribution network, and fail to effectively integrate the role of energy storage equipment and demand response resources. Therefore, a new source-grid-load-storage collaborative planning method is urgently needed to effectively integrate various resources to ensure the safety and stability of the distribution network in various operating scenarios, thereby achieving the system's economic, reliability and environmental friendliness goals. Summary of the invention

[0004] The purpose of the present invention is to overcome the defects of the above-mentioned prior art and provide a source-grid-load-storage collaborative planning method, device and medium taking into account the safe operation of the distribution network.

[0005] The purpose of the present invention can be achieved by the following technical solutions:

[0006] As a first aspect of the present invention, a source-grid-load-storage collaborative planning method considering the safe operation of a distribution network is provided, the steps comprising:

[0007] Collect historical data on distributed renewable energy generation and load in the distribution network, and cluster the historical data to obtain typical scenarios and corresponding probabilities of distribution network operation;

[0008] Monte Carlo simulation random sampling and demand elasticity matrix are used to build an electric vehicle charging power model based on electricity price incentives;

[0009] According to the distribution network grid structure parameters, energy storage parameters, electric vehicle charging power model and typical distribution network operation scenarios, a multi-objective source-grid-load-storage collaborative planning model is established with the lowest comprehensive operation cost and the lowest comprehensive operation risk index as the goal, and energy storage site selection and capacity determination, distributed power source site selection and capacity determination, energy storage charging and discharging behavior and demand side response as decision variables;

[0010] The particle swarm algorithm is used to solve the source-grid-load-storage collaborative planning model and obtain the optimal planning scheme.

[0011] As an optimal technical solution, the process of clustering historical data to obtain typical scenarios of distribution network operation and corresponding probabilities is as follows: the Calinski-Harabasz index is used as the selection criterion for the number of clusters, and the clustering algorithm is used to cluster the historical data; the center of the cluster is used as the typical operation scenario, and the proportion of each category is used as the probability of the typical scenario.

[0012] As a preferred technical solution, the steps of using Monte Carlo simulation random sampling and demand elasticity matrix to construct an electric vehicle charging power model based on electricity price incentives are specifically as follows:

[0013] Based on time-of-use electricity price data, a day is divided into peak period, normal period and valley period according to electricity price level;

[0014] Calculate the increase in electricity prices during peak hours and the decrease in electricity prices during valley hours, and construct the demand elasticity matrix Z:

[0015]

[0016] In the formula, Δ peak Indicates the increase in electricity price during peak hours; Δ valley Indicates the reduction in electricity price during off-peak hours; c t represents the electricity price at time t; T flat 、T peak , and T valley They represent normal time, peak time and valley time respectively;

[0017] Construct the initial load vector and calculate the load adjustment after excitation. The calculation formula is as follows:

[0018] Q=Q0+ΔQ

[0019] ΔQ=Z·Q0

[0020] Q0=[Q peak ,Q flat ,Q valley ] T

[0021]

[0022] P ev =[p1,p2,...,p24]

[0023] In the formula, Q represents the charging load after excitation; Q0 represents the initial charging load; ΔQ represents the load change; P ev represents the electric vehicle charging power before optimization;

[0024] The proportion of electric vehicles participating in demand response is set, and the adjusted electric vehicle charging power is obtained by random sampling using Monte Carlo simulation.

[0025] As a preferred technical solution, the optimization objectives of the source-grid-load-storage collaborative planning model include economic objectives and safety objectives, wherein the economic objective is to minimize the comprehensive operating cost; the safety objective is to minimize the comprehensive operating risk index, including voltage over-limit risk and power flow overload risk. The objective function of the multi-objective optimization model is:

[0026]

[0027] In the formula, Planned investment costs for energy storage devices; The operation and maintenance cost of the energy storage device; Plan investment costs for distributed generation (DG); is the operation and maintenance cost of DG; C IL Compensation for interrupted load; C grid The cost of purchasing and selling electricity to the superior power grid; C loss is the network operation loss cost; P i , Sev i They represent the voltage over-limit risk probability and the severity of voltage over-limit at node i respectively; P l , Sev l They represent the power overload probability and the severity of power overload of line l respectively.

[0028] As the preferred technical solution, the economic objective function with the lowest comprehensive operating cost as the economic goal is as follows:

[0029]

[0030] In the formula, Planned investment costs for energy storage devices; The operation and maintenance cost of the energy storage device; Plan investment costs for DG; is the operation and maintenance cost of DG; C IL Compensation for interrupted load; C grid The cost of purchasing and selling electricity to the superior power grid; C loss is the network operation loss cost; D is the estimated number of days for energy storage use; c in,iis the unit capacity configuration cost of the i-th energy storage; S i is the capacity of the i-th energy storage; r is the discount rate of energy storage; N is the expected service life of energy storage; c om is the unit power operation and maintenance cost; P ESS,i (t) is the charging and discharging power of the i-th energy storage; d is the discount rate of the distributed power source; y is the expected service life of the distributed power source; is the unit power construction cost of the kth distributed power source; P DGk,j is the rated power of the jth distributed generation of the kth type; is the unit power operation and maintenance cost of the kth distributed power source; T k is the operating time of the distributed generation; N type is the number of types of distributed power sources; a IL and b IL Adjust the cost factor for interruptible load; P IL is the regulated power of the interruptible load; is the electricity price at time t; is the power purchased and sold from the grid at time t; is the unit network loss cost at time t; is the network loss power at time t.

[0031] As the preferred technical solution, including voltage over-limit risk and power flow overload risk, the safety objective function with the lowest comprehensive operation risk index is as follows:

[0032]

[0033] In the formula, Risk is the operation risk of the distribution network; k is the risk factor, θ(V i ) is the voltage offset indicator; V Base is the reference voltage, V max and V min PR is the upper and lower limits of the voltage allowable value; l is the load factor of line l, PR max It is the maximum value allowed for the line load rate.

[0034] As an optimal technical solution, the constraints of the source-grid-load-storage collaborative planning model include: power balance constraints, energy storage equipment constraints, energy storage operation constraints, node voltage constraints, line power constraints and interruptible load constraints.

[0035] As a preferred technical solution, the process of using the particle swarm algorithm to solve the source-grid-load-storage collaborative planning model is as follows:

[0036] The location and capacity of energy storage, the location and capacity of distributed generation, the location of electric vehicle charging stations, the charging and discharging behavior of energy storage, the electric vehicle charging incentives, and the interruptible load are encoded as the location of each particle of the particle swarm;

[0037] Set the particle swarm parameters, initialize the positions of the particles in the particle swarm, the individual optimal positions, and the global optimal positions; initialize the archive, and add the non-dominated solutions in the initial population to the archive;

[0038] The two objective functions of source-grid-load-storage collaborative planning are used as the two fitness functions of the particle swarm algorithm, the fitness value of each particle is calculated, and its non-dominated situation in the target space is judged;

[0039] Add the non-dominated solutions of the current particle to the archive, and update the archive according to the dominance relationship, retain the non-dominated solutions, delete the dominated solutions, and if the number of archives exceeds the maximum archive limit, prune them by the crowding distance method to retain the well-distributed solutions;

[0040] For each particle, a non-dominated solution is randomly selected in the archive as the leader particle, and then the velocity and position of each particle are calculated, and boundary restrictions are imposed on out-of-bounds particles;

[0041] Update the individual optimal position. For each particle, if the current solution is better than its historical optimal solution or is a non-dominated solution, update its individual optimal position.

[0042] Repeat the update iteration until the termination condition is reached, output the solution set of the Pareto frontier, and obtain the optimal source-grid-load-storage coordinated planning and the scheduling strategy of energy storage and interruptible loads in typical scenarios.

[0043] As a second aspect of the present invention, there is provided an electronic device, comprising:

[0044] one or more processors;

[0045] A memory for storing one or more programs;

[0046] When the one or more programs are executed by the one or more processors, the one or more processors implement the source-grid-load-storage collaborative planning method that takes into account the safe operation of the distribution network as described above.

[0047] As a third aspect of the present invention, a computer-readable storage medium is provided, which stores a computer program. When the computer program is executed by a processor, the steps of the source-grid-load-storage collaborative planning method considering the safe operation of the distribution network as described above are implemented.

[0048] Compared with the prior art, the present invention has the following beneficial effects:

[0049] 1) The present invention provides a source-grid-load-storage collaborative planning method that takes into account the safe operation of the distribution network. By comprehensively considering multiple resources such as source, grid, load, and storage, a multi-objective source-grid-load-storage collaborative planning model is established with the economic goal of minimizing the comprehensive operating cost and the safety goal of minimizing the comprehensive operating risk index. The model takes into account the comprehensive cost of the system and the risk indicators of the distribution network under various typical operating scenarios, effectively integrates key factors such as the site selection and sizing of distributed power sources and energy storage, the reasonable scheduling of energy storage systems, and load demand response, ensures the coordinated and optimized operation of various devices, and guarantees the safety, stability, and economy of the distribution network under various operating scenarios.

[0050] 2) The load demand response of the present invention takes into account the charging incentives and interruptible loads of electric vehicles. Monte Carlo simulation random sampling and demand elasticity matrix are used to construct an electric vehicle charging power model based on electricity price incentives, which significantly improves the flexibility of source-grid-load-storage coordinated planning and the safety of the distribution network. On the one hand, it effectively smoothes the load curve and reduces the cost of purchasing electricity and network losses; on the other hand, it improves the system's adaptability to load fluctuations and enhances the safety and economy of operation.

[0051] 3) The present invention adopts a particle swarm algorithm to solve the multi-objective scheduling model, and encodes the location and capacity of energy storage, the location of DG, the location of electric vehicle charging stations, the charging and discharging behavior of energy storage, and the interruptible load as the position of each particle of the particle swarm; the two objective functions of the source-grid-load-storage collaborative planning are used as the two fitness functions of the particle swarm algorithm respectively; and finally, the optimal source-grid-load-storage collaborative planning and the scheduling strategy of energy storage and interruptible load in typical scenarios are obtained through iteration, which fully considers the synergy of each device and ensures the rapidity and accuracy of the solution process. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 A flow chart of the steps of the source-grid-load-storage collaborative planning method considering the safe operation of the distribution network of the present invention;

[0053] Figure 2 It is a flowchart of the specific steps of the particle swarm algorithm of the present invention;

[0054] Figure 3 It is the IEEE33 node power distribution system diagram;

[0055] Figure 4 This is a typical scenario diagram for photovoltaic operation;

[0056] Figure 5 This is the charging power curve of the electric vehicle before and after incentive. DETAILED DESCRIPTION

[0057] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. This embodiment is implemented based on the technical solution of the present invention, and provides a detailed implementation method and specific operation process, but the protection scope of the present invention is not limited to the following embodiments.

[0058] Example 1

[0059] like Figure 1 As shown in the figure: A source-grid-load-storage collaborative planning method considering the safe operation of the distribution network, the steps are as follows:

[0060] S1. Collect historical data on distributed renewable energy generation and load in the distribution network, and cluster the historical data to obtain typical scenarios and corresponding probabilities of distribution network operation;

[0061] S2, using Monte Carlo simulation random sampling and demand elasticity matrix to build an electric vehicle charging power model based on electricity price incentives;

[0062] S3. Establish a source-grid-load-storage collaborative planning model based on distribution network structure parameters, energy storage parameters, electric vehicle charging power model and typical operation scenarios;

[0063] S4. Use particle swarm algorithm to solve the source-grid-load-storage collaborative planning model and obtain the optimal planning solution;

[0064] In step S1, the historical data is clustered to obtain the typical scenarios of distribution network operation and the corresponding probability calculation process is as follows: first, the historical data is clustered using a clustering algorithm such as k-means, the center of the cluster is taken as the typical operation scenario, and the proportion of each category is taken as the probability of the typical scenario. The selection standard for the number of clusters uses the Calinski-Harabasz index, which is also called the variance ratio criterion. It calculates the ratio of the compactness within the cluster to the separation between clusters. The larger the value, the better the clustering effect. The calculation formula is as follows:

[0065]

[0066] Where tr(B) is the trace of the sum of squared deviations between clusters; tr(W) is the trace of the sum of squared deviations within clusters; k is the number of clusters; and n is the total number of data.

[0067] In step S2, the steps of using Monte Carlo simulation random sampling and demand elasticity matrix to construct an electric vehicle charging power model based on electricity price incentives are:

[0068] S211. Based on the time-of-use electricity price data, 24 hours are divided into peak period, normal period and valley period according to the electricity price level;

[0069] S212. Calculate the increase in electricity price during the peak period and the decrease in electricity price during the valley period, and construct a demand elasticity matrix. The calculation formula is as follows:

[0070]

[0071] In the formula, c t represents the electricity price at time t; T flat 、T peak , and T valley Respectively represent normal time, peak time and valley time; Δ peak Indicates the increase in electricity price during peak hours; Δ valley represents the reduction in electricity price during off-peak hours; Z represents the demand elasticity matrix;

[0072] S213, construct an initial load vector, and calculate the load adjustment after excitation. The calculation formula is as follows:

[0073] Q=Q0+ΔQ

[0074] ΔQ=Z·Q0

[0075] Q0=[Q peak ,Q flat ,Q valley ] T

[0076]

[0077] P ev =[p1,p2,...,p 24 ]

[0078] Where P ev represents the charging power of the electric vehicle before optimization; Q0 represents the initial charging load; ΔQ represents the load change; Q represents the charging load after excitation;

[0079] S214. Set the proportion of electric vehicles participating in demand response, and use random sampling of Monte Carlo simulation to obtain the adjusted charging power of electric vehicles.

[0080] In step S3, the optimization objectives of the source-grid-load-storage collaborative planning model include economic objectives and safety objectives, among which the economic objective is to minimize the comprehensive operating cost. The decision variables are energy storage site selection and capacity determination, distributed generation site selection and capacity determination, energy storage charging and discharging behavior, and demand-side response. The objective function is:

[0081]

[0082] In the formula, Planned investment costs for energy storage devices; The operation and maintenance cost of the energy storage device; Plan investment costs for DG; is the operation and maintenance cost of DG; C IL Compensation for interrupted load; Cgrid The cost of purchasing and selling electricity to the superior power grid; C loss is the network operation loss cost; D is the estimated number of days for energy storage use; c in,i is the unit capacity configuration cost of the i-th energy storage; S i is the capacity of the i-th energy storage; r is the discount rate of energy storage; N is the expected service life of energy storage; c om is the unit power operation and maintenance cost; P ESS,i (t) is the charging and discharging power of the i-th energy storage; d is the discount rate of the distributed power source; y is the expected service life of the distributed power source; is the unit power construction cost of the kth distributed power source; P DGk,j is the rated power of the jth distributed generation of the kth type; is the unit power operation and maintenance cost of the kth distributed power source; T k is the operating time of the distributed generation; N type is the number of types of distributed power sources; a IL and b IL Adjust the cost factor for interruptible load; P IL is the regulated power of the interruptible load; is the electricity price at time t; is the power purchased and sold from the grid at time t; is the unit network loss cost at time t; is the network loss power at time t.

[0083] The safety goal is to minimize the comprehensive operation risk index, including voltage over-limit risk and power flow overload risk. The objective function is:

[0084]

[0085] Where Risk is the operation risk of the distribution network; P i , Sev i They represent the voltage over-limit risk probability and the severity of voltage over-limit at node i respectively; P l , Sev l They represent the power overload probability and severity of power overload of line l respectively; k is the risk factor, the larger the value, the more sensitive it is to risk, θ(V i ) is the voltage offset indicator; V Base is the reference voltage, V max and V min PR is the upper and lower limits of the voltage allowable value; l is the load factor of line l, PR max is the maximum value allowed for the line load rate;

[0086] In step S3, the source-grid-load-storage collaborative planning model is a multi-objective optimization model, and its objective function is:

[0087]

[0088] The constraints of the source-grid-load-storage collaborative planning model include:

[0089] Power balance constraints:

[0090]

[0091] Where P DG,j (t) is the access power of the jth distributed generation at time t; P load (t) is the total load at time t.

[0092] Power flow balance constraints:

[0093]

[0094] Where P i and Q i are the active and reactive powers injected into node i respectively; V i and V j are the voltage amplitudes of nodes i and j respectively; j∈i represents all nodes directly connected to node i; G ij and B ij are the real and imaginary parts of the node admittance matrix respectively; θ ij is the phase angle difference between node i and node j.

[0095] Energy storage equipment constraints:

[0096]

[0097] In the formula, S SOC,max and S SOC,min are the maximum and minimum values ​​of the state of charge of the energy storage device, S SOC (t) is the state of charge at time t; P ESS,max and P ESS,min are the maximum and minimum values ​​of the charging and discharging power of the energy storage device, P ESS (t) is the charge and discharge power at time t.

[0098] Energy storage operation constraints:

[0099]

[0100] In the formula, δ is the self-discharge rate of energy storage; η is the charge and discharge efficiency of energy storage; S SOC,i (t start ) and S SOC,i (t end ) represent the charge state at the start and end of a cycle respectively.

[0101] Node voltage constraints:

[0102] V lower ≤V i (t)≤V upper

[0103] Where V upper and V lower are the maximum and minimum boundary values ​​of the node voltage amplitude respectively.

[0104] Line power constraints:

[0105] P ij ≤P ij,max

[0106] Where P ij,max is the maximum transmission power of the line from node i to node j.

[0107] Interruptible load constraints:

[0108]

[0109] In the formula, is the reduction amount of the i-th interruptible load at time t; is the maximum reduction amount of the i-th interruptible load at time t.

[0110] In step S4, Figure 2 As shown, the specific steps of the particle swarm algorithm are as follows:

[0111] S411, setting the particle swarm parameters, including the population size, the maximum number of iterations, the learning factor, the inertia weight, the position range and the speed range; initializing the positions of the particles in the particle swarm, the individual optimal positions and the global optimal positions; initializing the archive, and adding the non-dominated solutions in the initial population to the archive;

[0112] S412, calculating the fitness value of each particle and determining its non-dominance status in the target space;

[0113] S413, adding the non-dominated solutions of the current particle to the archive, and updating the archive according to the dominance relationship, retaining the non-dominated solutions and deleting the dominated solutions. If the number of archives exceeds the maximum archive limit, pruning is performed by the crowding distance method to retain the well-distributed solutions;

[0114] S414, randomly selecting a non-dominated solution in the archive as a leader particle for each particle, then the speed and position of each particle, and performing boundary restrictions on out-of-bounds particles;

[0115] S415, update the individual optimal position. For each particle, if the current solution is better than its historical optimal solution (or is a non-dominated solution), update its individual optimal position;

[0116] S416, determine whether the algorithm reaches the termination condition; if so, output the result and the algorithm ends; if not, return to S412;

[0117] S417. Output the results, output the solution set of the Pareto frontier.

[0118] The condition for judging whether solution x dominates solution y is that solution x is not inferior to solution y in all objectives, that is, for all i = 1, 2, ...., m, there is f i (x)≤f i (y); solution x is better than solution y in at least one objective, that is, for all j∈{1,2,...,m}, f i (x)<f i (y), where m is the number of targets.

[0119] The particle speed and position are updated as follows:

[0120]

[0121] in, is the velocity of particle i at the kth iteration, is the velocity of particle i at the k-1th iteration, is the position of particle i at the kth iteration, is the position of particle i at the k-1th iteration, ω is the inertia weight, is the individual optimal position of particle i, is the leader particle position of the current particle, c1, c2 are learning factors, r1, r2 are random numbers in [0, 1].

[0122] The process of solving the source-grid-load-storage collaborative planning model using the particle swarm algorithm in step S4 is as follows:

[0123] S421, encoding the location and capacity of energy storage; the location and capacity of distributed power sources; the location of electric vehicle charging stations; the charging and discharging behavior of energy storage; and the demand-side response, i.e., the electric vehicle charging incentive and the interruptible load, as the location of each particle in the particle group;

[0124] S422, using the two objective functions of the source-grid-load-storage collaborative planning as two fitness functions of the particle swarm algorithm respectively;

[0125] S423. According to the above steps, the particle swarm algorithm is iterated to obtain the optimal source-grid-load-storage coordination planning, as well as the scheduling strategy of energy storage and demand-side response in typical scenarios.

[0126] Example 2

[0127] This embodiment provides a specific example of the source-grid-load-storage collaborative planning method considering the safe operation of the distribution network as described above to further illustrate the solution of the present invention.

[0128] The simulation example and calculation parameters are as follows:

[0129] like Figure 3 As shown, this implementation example adopts the IEEE 33-node distribution system. The base voltage of the distribution network is 12.66 kV, the maximum and minimum values ​​of the node voltage are set to 1.07 pu and 0.93 pu; the maximum value of the line power is set to the maximum rated power.

[0130] The photovoltaic power generation data of a certain region for one year is used to cluster the scenarios. The clustering results are as follows: Figure 4 As shown, the corresponding probabilities are 0.23, 0.33, 0.31 and 0.13 respectively.

[0131] The parameters of the energy storage device and the electricity price are shown in the following table:

[0132] Table 1 Energy storage device parameters

[0133]

[0134] Table 2 Time-of-use electricity prices

[0135]

[0136]

[0137] Demand-side response takes into account interruptible loads and electricity price incentives for electric vehicles. The charging power of electric vehicles before and after electricity price incentives is as follows: Figure 5 shown.

[0138] This simulation sets up five simulation schemes for comparison, as shown in Table 3-4 below:

[0139] Table 3 Five simulation schemes

[0140]

[0141] Table 4 Results of five schemes

[0142]

[0143] Scheme 1 does not configure DG, energy storage and demand-side response, and the comprehensive cost reaches 442,040 yuan, which is the highest among all schemes. The risk value is 0.6043, which is also the highest, indicating that the system operation cost and risk without optimization measures are high; after adding DG configuration to Scheme 2, the purchase and sale cost of electricity dropped significantly to 286,330 yuan, which is 33.3% lower than that of Scheme 1. Although DG configuration increases construction and operation and maintenance costs, the economic benefits brought by power generation reduce the comprehensive cost to 309,850 yuan, which is 29.9% less than that of Scheme 1, and the risk value is also slightly reduced. Therefore, the introduction of DG configuration can effectively reduce the cost of purchasing electricity, but the risk optimization effect is limited; Scheme 3 adds energy storage on the basis of Scheme 2. Although the energy storage construction and operation and maintenance costs are increased by 19,340 yuan, the overall comprehensive cost is only 278,690 yuan, which is 36.9% less than that of Scheme 1, and the comprehensive risk is reduced to 0.2604, which is 56.9% less than that of Scheme 1. The introduction of energy storage can effectively balance the load demand of the system, significantly reduce the cost of electricity purchase and the risk of system operation; Scheme 4 does not introduce energy storage, and only balances the load demand through demand-side response. The comprehensive cost is 278,120 yuan, slightly lower than Scheme 3, but the risk value is 79.6% higher than Scheme 3. This shows that it is difficult to effectively reduce system risks through demand-side response alone, and energy storage is still needed to optimize the operational stability of the system; Scheme 5 comprehensively considers DG, energy storage and demand-side response, and the cost and risk are minimized. The comprehensive cost is 268,570 yuan, a decrease of 39.3% compared with Scheme 1, and the comprehensive risk is only 0.113, a decrease of 81.3%. It can be seen that the synergy of source, grid, load and storage significantly optimizes the system cost and operational risk, making the system operation more economical and reliable.

[0144] In summary, the present invention proposes a source-grid-load-storage collaborative planning method for the safe operation of the distribution network. By analyzing the historical data of wind power, photovoltaics, loads, etc. in the distribution network, the clustering method is used to select typical operating scenarios and corresponding probabilities, and the modeling of multiple possible operating states is realized. On this basis, a multi-objective optimization scheduling model of source-grid-load-storage is constructed with the goal of reducing the operating risk of the distribution network and minimizing the comprehensive cost. It effectively integrates the key factors such as the site selection and capacity determination of distributed power sources and energy storage, charging incentives for electric vehicles, reasonable scheduling of energy storage systems, and load demand response, ensuring the collaborative optimization operation of various devices. The method proposed in the present invention fully considers the synergy of various devices, and solves the scheduling model through the particle swarm optimization algorithm to ensure the rapidity and accuracy of the solution process. Finally, the method of the present invention realizes the source-grid-load-storage collaborative planning, significantly reduces the comprehensive operating cost while ensuring the safe and reliable operation of the distribution network, and provides important support for the efficient utilization of distributed energy and the safe and economical operation of the power grid.

[0145] Example 3

[0146] As a second aspect of the present invention, the present application also provides an electronic device, comprising: one or more processors; a memory for storing one or more programs; when the one or more programs are executed by the one or more processors, the one or more processors implement the source-grid-load-storage collaborative planning method considering the safe operation of the distribution network as described above. In addition to the above-mentioned processor, memory and interface, any device with data processing capability in which the device in the embodiment is located may also include other hardware, generally according to the actual function of the device with data processing capability, which will not be described in detail.

[0147] Example 4

[0148] As a third aspect of the present invention, the present application also provides a computer-readable storage medium having computer instructions stored thereon, which, when executed by a processor, implements the source-grid-load-storage collaborative planning method for considering the safe operation of the distribution network as described above. The computer-readable storage medium may be an internal storage unit of any device with data processing capabilities as described in any of the foregoing embodiments, such as a hard disk or a memory. The computer-readable storage medium may also be an external storage device, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), an SD card, a flash memory card (FlashCard), etc. equipped on the device. Furthermore, the computer-readable storage medium may also include both an internal storage unit and an external storage device of any device with data processing capabilities. The computer-readable storage medium is used to store the computer program and other programs and data required by any device with data processing capabilities, and may also be used to temporarily store data that has been output or is to be output.

[0149] The preferred specific embodiments of the present invention are described in detail above. It should be understood that a person skilled in the art can make many modifications and changes based on the concept of the present invention without creative work. Therefore, any technical solution that can be obtained by a person skilled in the art through logical analysis, reasoning or limited experiments based on the concept of the present invention on the basis of the prior art should be within the scope of protection determined by the claims.

Claims

1. A source-grid-load-storage collaborative planning method considering the safe operation of a distribution network, characterized in that the steps include: Collect historical data on distributed renewable energy generation and load in the distribution network, and cluster the historical data to obtain typical scenarios and corresponding probabilities of distribution network operation; Monte Carlo simulation random sampling and demand elasticity matrix are used to build an electric vehicle charging power model based on electricity price incentives; According to the distribution network grid structure parameters, energy storage parameters, electric vehicle charging power model and typical distribution network operation scenarios, a multi-objective source-grid-load-storage collaborative planning model is established with the lowest comprehensive operation cost and the lowest comprehensive operation risk index as the goal, and energy storage site selection and capacity determination, distributed power source site selection and capacity determination, energy storage charging and discharging behavior and demand side response as decision variables; The particle swarm algorithm is used to solve the source-grid-load-storage collaborative planning model and obtain the optimal planning scheme.

2. A source-grid-load-storage collaborative planning method considering the safe operation of the distribution network according to claim 1, characterized in that: The process of clustering historical data to obtain typical scenarios of distribution network operation and corresponding probabilities is specifically as follows: using the Calinski-Harabasz index as the selection criterion for the number of clusters, and using a clustering algorithm to cluster historical data; taking the center of the cluster as the typical operation scenario, and taking the proportion of each category as the probability of the typical scenario.

3. A source-grid-load-storage collaborative planning method considering the safe operation of the distribution network according to claim 1, characterized in that: The steps of using Monte Carlo simulation random sampling and demand elasticity matrix to construct an electric vehicle charging power model based on electricity price incentives are specifically as follows: Based on time-of-use electricity price data, a day is divided into peak period, normal period and valley period according to electricity price level; Calculate the increase in electricity prices during peak hours and the decrease in electricity prices during valley hours, and construct the demand elasticity matrix Z: In the formula, Δ peak Indicates the increase in electricity price during peak hours; Δ valley Indicates the reduction in electricity price during off-peak hours; c t represents the electricity price at time t; T flat 、T peak , and T valley They represent normal time, peak time and valley time respectively; Construct the initial load vector and calculate the load adjustment after excitation. The calculation formula is as follows: Q=Q0+ΔQ ΔQ=Z·Q0 Q0=[Q peak ,Q flat ,Q valley ] T P ev =[p1,p2,...,p24] In the formula, Q represents the charging load after excitation; Q0 represents the initial charging load; ΔQ represents the load change; P ev represents the electric vehicle charging power before optimization; The proportion of electric vehicles participating in demand response is set, and the adjusted electric vehicle charging power is obtained by random sampling using Monte Carlo simulation.

4. A source-grid-load-storage collaborative planning method considering the safe operation of a distribution network according to claim 1, characterized in that: The optimization objectives of the source-grid-load-storage collaborative planning model include economic objectives and safety objectives, wherein the economic objective is to minimize the comprehensive operating cost; The safety goal is to minimize the comprehensive operation risk index, including voltage over-limit risk and power flow overload risk. The objective function of the multi-objective optimization model is: In the formula, Planned investment costs for energy storage devices; The operation and maintenance cost of the energy storage device; Plan investment costs for distributed generation (DG); is the operation and maintenance cost of DG; C IL Compensation for interrupted load; C grid The cost of purchasing and selling electricity to the superior power grid; C loss is the network operation loss cost; P i , Sev i They represent the voltage over-limit risk probability and the severity of voltage over-limit at node i respectively; P l , Sev l They represent the power overload probability and the severity of power overload of line l respectively.

5. A source-grid-load-storage collaborative planning method considering the safe operation of the distribution network according to claim 4, characterized in that: The economic objective function with the economic goal as the lowest comprehensive operating cost is as follows: In the formula, Planned investment costs for energy storage devices; The operation and maintenance cost of the energy storage device; Plan investment costs for DG; is the operation and maintenance cost of DG; C IL Compensation for interrupted load; C grid The cost of purchasing and selling electricity to the superior power grid; C loss The network operation loss cost; D is the estimated number of days for energy storage use; c in,i is the unit capacity configuration cost of the i-th energy storage; S i is the capacity of the i-th energy storage; r is the discount rate of energy storage; N is the expected service life of energy storage; c om is the unit power operation and maintenance cost; P ESS,i (t) is the charging and discharging power of the i-th energy storage; d is the discount rate of the distributed power source; y is the expected service life of the distributed power source; is the unit power construction cost of the kth distributed power source; P DGk,j is the rated power of the jth distributed generation of the kth type; is the unit power operation and maintenance cost of the kth distributed power source; T k is the operating time of the distributed generation; N type is the number of types of distributed power sources; a IL and b IL Adjust the cost factor for interruptible load; P IL is the regulated power of the interruptible load; is the electricity price at time t; is the power purchased and sold from the grid at time t; is the unit network loss cost at time t; is the network loss power at time t.

6. A source-grid-load-storage collaborative planning method considering the safe operation of the distribution network according to claim 4, characterized in that: Including voltage over-limit risk and power flow overload risk, the safety objective function with the lowest comprehensive operation risk index is as follows: In the formula, Risk is the operation risk of the distribution network; k is the risk factor, θ(V i ) is the voltage offset indicator; V Base is the reference voltage, V max and V min PR is the upper and lower limits of the voltage allowable value; l is the load factor of line l, PR max It is the maximum value allowed for the line load rate.

7. A source-grid-load-storage collaborative planning method considering the safe operation of a distribution network according to claim 1, characterized in that: The constraints of the source-grid-load-storage collaborative planning model include: power balance constraints, energy storage equipment constraints, energy storage operation constraints, node voltage constraints, line power constraints, and interruptible load constraints.

8. A source-grid-load-storage collaborative planning method considering the safe operation of a distribution network according to claim 1, characterized in that: The specific process of using the particle swarm algorithm to solve the source-grid-load-storage collaborative planning model is as follows: The location and capacity of energy storage, the location and capacity of distributed generation, the location of electric vehicle charging stations, the charging and discharging behavior of energy storage, the electric vehicle charging incentives, and the interruptible load are encoded as the location of each particle of the particle swarm; Set the particle swarm parameters, initialize the positions of the particles in the particle swarm, the individual optimal positions, and the global optimal positions; initialize the archive, and add the non-dominated solutions in the initial population to the archive; The two objective functions of source-grid-load-storage collaborative planning are used as the two fitness functions of the particle swarm algorithm, the fitness value of each particle is calculated, and its non-dominated situation in the target space is judged; Add the non-dominated solutions of the current particle to the archive, and update the archive according to the dominance relationship, retain the non-dominated solutions, delete the dominated solutions, and if the number of archives exceeds the maximum archive limit, prune them by the crowding distance method to retain the well-distributed solutions; For each particle, a non-dominated solution is randomly selected in the archive as the leader particle, and then the velocity and position of each particle are calculated, and boundary restrictions are imposed on out-of-bounds particles; Update the individual optimal position. For each particle, if the current solution is better than its historical optimal solution or is a non-dominated solution, update its individual optimal position. Repeat the update iteration until the termination condition is reached, output the solution set of the Pareto frontier, and obtain the optimal source-grid-load-storage coordinated planning and the scheduling strategy of energy storage and interruptible loads in typical scenarios.

9. An electronic device, characterized in that: include: one or more processors; A memory for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the source-grid-load-storage collaborative planning method that takes into account the safe operation of the distribution network as described in any one of claims 1-8.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by the processor, the steps of the source-grid-load-storage collaborative planning method considering the safe operation of the distribution network as described in any one of claims 1-8 are implemented.

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