Novel power distribution network multi-layer flexible planning method and system based on Hemma optimization algorithm

Abstract

The invention discloses a power distribution network multilayer flexible planning method and system based on a Hemma optimization algorithm, and the method is characterized in that the method comprises the steps: building a physical layer model of a demand response and energy storage system; establishing a value layer model of the power distribution network on the basis of the demand response and the physical layer model of the energy storage system; establishing an operation layer model of the power distribution network; and solving the established value layer model and the operation layer model by adopting a He-Make algorithm. The multi-layer model constructed by the invention also fully considers the mutual influence between a physical layer and a power grid operation layer and a value layer, and strengthens the synergistic effect between different layers. The utilization efficiency of each level of resources is improved, and information sharing and decision optimization are promoted, so that the overall performance and flexibility of a power grid are comprehensively improved.

Description

Technical Field

[0001] The present invention belongs to the field of power system planning and optimization, and specifically relates to a novel distribution network multi-layer flexible planning method and system based on a Hippo optimization algorithm. Background Art

[0002] In the new distribution network, energy storage and demand-side response can effectively alleviate and solve the uncertainty impact of distributed power sources on the distribution network. By configuring energy storage, power supply fluctuations can be smoothed, supply and demand balance can be adjusted, peak loads can be reduced and valley loads can be filled, and peak load regulation capacity and power supply reliability can be improved; demand-side response can achieve real-time response to power demand by actively adjusting user power consumption. However, in practical applications, it is necessary to comprehensively consider the physical layer characteristics of energy storage and load sides, while also taking into account the reliability of the distribution network operation layer and the economy of the value layer to ensure the optimal configuration and coordinated operation of various resources. How to achieve optimal resource allocation between multiple levels and resolve the contradictions between various constraints is a major project, and a flexible and efficient new distribution network planning model and optimization solution algorithm are urgently needed.

[0003] The prior art document (CN110502814A) discloses a multi-objective planning method for active distribution networks that considers energy storage and load management technologies. The method in this document does not adequately integrate demand response with energy storage systems, and does not fully consider the complex relationship between electricity prices and loads; energy storage utilization fails to fully utilize the advantages of multiple time scales; constraints are not perfect; the algorithm has low solution efficiency and poor accuracy, making it difficult to cope with complex multi-layer models, affecting the planning effect of the distribution network. Summary of the invention

[0004] In order to solve the deficiencies in the prior art, the present invention proposes a new multi-layer flexible planning method for distribution networks based on the Hippo optimization algorithm, which can comprehensively coordinate the participation of energy storage and demand-side response in the physical layer, and fully consider the mutual influence of the grid operation layer and the value layer. This model integrates a variety of influencing factors and utilizes the superiority of the Hippo optimization algorithm in processing complex multivariate data and multidimensional optimization problems. It can accurately balance the allocation of resources at different levels and quickly converge to the global optimal solution. By efficiently solving resource coordination and constraint conflicts, the accuracy and operating efficiency of resource allocation can be significantly improved, and the stability and sustainability of the distribution network can be enhanced.

[0005] The first aspect of the present invention discloses a novel multi-layer flexible planning method for distribution network based on Hippo optimization algorithm, comprising:

[0006] Based on electricity costs, load fluctuations and energy storage costs, a physical layer model of the demand response and energy storage system is established to provide data support for establishing a value layer model for the distribution network;

[0007] Based on the physical layer models of demand response and energy storage systems, a distribution network value layer model is established;

[0008] Establish a distribution network operation layer model based on energy costs, user demand and grid load rate;

[0009] The Hippo optimization algorithm is used to solve the value layer model and operation layer model of the distribution network, and the optimal solution is used as the distribution network planning scheme.

[0010] Preferably, the objective function of the demand-side response can be expressed by the following formula:

[0011]

[0012] Among them, T is the total number of time periods, C t is the electricity price in period t, P t is the load demand in the tth period (kW), λ is the penalty coefficient used to smooth the load curve, f(P t ) is a function for smoothing load fluctuations.

[0013] Preferably, the objective function of the energy storage system can be expressed by the following formula:

[0014]

[0015] Among them, C battery is the battery charging and discharging cost, P battery (t) represents the power output of the battery in the tth period, C hydrogen represents the charging and discharging cost of the hydrogen energy storage system, P hydrogen (t) represents the power output of the hydrogen energy storage system at time period t, λ1×f(P battery (t),P hydrogen (t)) is the penalty term for smoothing power output fluctuations.

[0016] Preferably, the objective function of the distribution network value layer model can be expressed by the following formula:

[0017] F=χ×f1+ε×f2+λ×f3 (3)

[0018] Among them, f1 represents the economic target; f2 represents energy efficiency, f3 represents reliability, χ, ε, λ are weight coefficients, and χ+ε+λ=1.

[0019] Preferably, the economic target f1, energy efficiency f2, and reliability f3 can be expressed by the following formula:

[0020]

[0021] f2=1-λ reliability

[0022] f3=U level

[0023] Among them, C line is the line investment and operation and maintenance cost, C DG is the investment and operation and maintenance cost of distributed power generation, C loss Network loss costs, is the annual comprehensive investment cost of energy storage batteries, is the annual operating cost of the energy storage battery, C c is the controllable load cost of demand response, C n For renewable energy environmental protection and energy saving benefits, reliability For power supply reliability, U level It represents the value of voltage quality evaluation function at distribution network node.

[0024] Preferably, the distribution network operation layer model objective function can be expressed as follows:

[0025] F lower =γ×F cost +δ×F satisfaction +ε×F load (5)

[0026] Among them, γ, δ, ε are weight coefficients, and γ+δ+ε=1, F cost is the minimum user energy cost objective function; F satisfaction is the maximum user satisfaction objective function, F load is the maximum grid load rate objective function.

[0027] Preferably, the minimum user energy cost objective function F cost The objective function of the maximum grid load rate can be expressed as follows:

[0028] F cost =min(C user )=min(C purchase +C transmission +C storage ) (6)

[0029] F load =max(L load ) (7)

[0030] Among them, C user is the user energy cost, C purchase is the energy purchase cost, C transmission is the energy transmission and distribution cost, C storage is the energy storage cost, L load is the grid load rate.

[0031] Preferably, the Hippo optimization algorithm is used to solve the distribution network value layer model and the operation layer model as follows:

[0032] Collect the original economic and technical data of distribution network planning, perform population initialization of Hippo optimization algorithm and generate network topology;

[0033] Perform power flow calculation and check whether the power flow calculation results meet the opportunity constraints. If they meet the conditions, solve and calculate the objective functions of the distribution network value layer and the operation layer and eliminate the solutions. If they do not meet the conditions, modify the decision variables of the operation layer model.

[0034] According to the results of the power flow calculation, the hippopotamus individuals and the optimal solution set are updated;

[0035] Adjust the network structure, perform power flow calculation on the adjusted network structure, and determine whether the adjusted network structure meets the opportunity constraint conditions. If not, start the optimization adjustment of the operating layer model; if it does, calculate the fitness, update the individual optimal and population optimal solutions, and continue iterative optimization;

[0036] The optimal solution set is stored in the archive set in order, the inferior solution is eliminated, and it is determined whether the maximum number of iterations has been reached. If it has been reached, the iteration is stopped and the archive set is output; if it has not been reached, individual update, solution set update and network structure adjustment are executed cyclically until the iteration ends;

[0037] The TOPSIS method is used to comprehensively rank the individuals in the archive set, and the optimal individual is used as the multi-layer planning scheme for the distribution network.

[0038] Preferably, updating the hippopotamus individual and the optimal solution set is specifically as follows:

[0039] Exploration phase: Simulate the random swimming behavior of hippos in water, allowing individuals to conduct extensive exploration in the solution space. By randomly perturbing the energy storage capacity, electricity price, and load rate, the diversity of the search is increased to prevent the algorithm from falling into the local optimal solution too early.

[0040] Defense phase: Combine the user cost and load rate penalty in the operation layer objective function to optimize the individual position and ensure that the individual moves towards the global optimal solution.

[0041] Development stage: In this stage, the results of power flow calculation are used to ensure that the key operating parameters of the power grid nodes meet the constraints.

[0042] The second aspect of the present invention discloses a distribution network multi-layer flexible planning system based on the Hippo optimization algorithm, and runs the above-mentioned distribution network multi-layer flexible planning method based on the Hippo optimization algorithm, including:

[0043] Physical layer module, used to build physical layer models of demand response and energy storage systems;

[0044] The value layer module is used to establish the value layer model of the distribution network based on the physical layer model;

[0045] Operation layer module, used to establish the operation layer model of the distribution network;

[0046] The model solving module is used to solve the established distribution network value layer model and operation layer model to obtain the distribution network planning solution.

[0047] The beneficial effect of the present invention is that, compared with the prior art, the present invention proposes a new multi-layer flexible planning model for distribution networks based on the Hippo optimization algorithm, which coordinates the participation of energy storage and demand-side response at the physical layer to ensure the rational allocation of resources. The multi-layer model also fully considers the mutual influence between the physical layer and the grid operation layer and the value layer, and strengthens the synergy between different levels. It not only improves the utilization efficiency of resources at all levels, but also promotes information sharing and decision-making optimization, thereby helping to comprehensively improve the overall performance and flexibility of the power grid.

[0048] The Hippo algorithm is used to adaptively adjust the resolution and search speed of the search space to achieve accurate calculation and rapid convergence of multi-layer planning models under multivariate coupling and nested multi-layer models, solve the coordination and constraint conflict problems of distributed power sources, energy storage, and demand response resources in distribution networks, and can quickly converge to the global optimal solution. Through dynamic adjustment and real-time optimization, the accuracy and efficiency of new distribution network resource configuration are significantly improved, ensuring stability and reliability under different operating conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 It is a new type of distribution network multi-layer planning model and solution flow chart;

[0050] Figure 2 This is the wiring diagram of the actual example;

[0051] Figure 3 This is the wiring diagram after modification of the actual example. DETAILED DESCRIPTION

[0052] In order to make the purpose, technical scheme and advantages of the present invention clearer, the technical scheme of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. The embodiments described in this application are only part of the embodiments of the present invention, not all of them. Based on the spirit of the present invention, other embodiments obtained by ordinary technicians in this field without creative work are all within the scope of protection of the present invention.

[0053] The first embodiment of the present invention provides a novel multi-layer flexible planning method and system for distribution network based on Hippo optimization algorithm, such as Figure 1 As shown, the following steps are included:

[0054] Step 1: Establish a physical layer model of the demand response and energy storage system to provide data support for the subsequent value layer and provide a basis for setting constraints at the value layer.

[0055] The goal of demand-side response is to minimize electricity costs and load fluctuations and achieve real-time balance of electricity as much as possible. The objective function of demand-side response can be expressed as follows:

[0056]

[0057] Among them, T is the total number of time periods, C t is the electricity price in period t, P t is the load demand in the tth period (kW), λ is the penalty coefficient used to smooth the load curve, f(P t ) is a function to smooth load fluctuations, usually taking the square of load fluctuations, which can be expressed by the following formula:

[0058] f(P t )=(P t -P t-1 ) 2 (2)

[0059] The constraints of the demand response model should comprehensively consider power demand constraints, power supply constraints, equipment operation constraints, as well as factors such as grid reliability, cost optimization and environmental impact, so as to achieve efficient management and flexible scheduling of the power system.

[0060] Considering the multi-time scale characteristics of the energy storage system, the main goal of the hybrid energy storage model composed of batteries and hydrogen storage devices is to minimize the energy storage cost and improve the operation efficiency of the distribution network. The objective function can be expressed as follows:

[0061]

[0062] Among them, C battery is the battery charging and discharging cost, P battery (t) represents the power output of the battery in the tth period, in kW, C hydrogen represents the charging and discharging cost of the hydrogen energy storage system, P hydrogen (t) represents the power output of the hydrogen energy storage system at time period t, in kW, λ1×f(P battery (t),P hydrogen (t)) is the penalty term for smoothing power output fluctuations and is usually expressed as the square of the power fluctuation.

[0063] In the hybrid energy storage model composed of batteries and hydrogen energy storage devices, the optimization of the objective function can maximize the system operation efficiency and economy. In order to ensure the feasibility and reliability of the optimization results in practical applications, it is also necessary to comprehensively consider factors such as battery charging and discharging constraints, hydrogen energy storage system operation constraints, and power balance constraints to ensure that the system maintains stability and safety while achieving optimal performance.

[0064] Step 2: Based on the physical layer model of the demand response and energy storage system established in step 1, a distribution network value layer model is established.

[0065] The objective function of the distribution network value layer model can be expressed as follows:

[0066] F=χ×f1+ε×f2+λ×f3 (4)

[0067] Among them, f1 represents the economic goal, f2 represents energy efficiency, and f3 represents reliability. χ, ε, and λ are weight coefficients, and χ+ε+λ=1.

[0068] The economic goal f1, energy efficiency f2, and reliability f3 can be expressed by the following formula:

[0069]

[0070] f2=1-λ reliability

[0071] f3=U level

[0072] Among them, C line is the line investment and operation and maintenance cost, C DG is the investment and operation and maintenance cost of distributed power generation, C loss Network loss costs, is the annual comprehensive investment cost of energy storage batteries, is the annual operating cost of the energy storage battery, C c Demand response controllable load cost, C n It is a renewable energy source with environmental protection and energy saving benefits. reliability That is, power supply reliability. For the distribution network, the greater the power supply reliability, the better. Here, f2 is used to convert the power supply reliability index into a form where the smaller the better. level It represents the value of the voltage quality evaluation function of the distribution network node, which has important evaluation significance. That is, the smaller the value is, the better the voltage quality of the distribution network is.

[0073] In the distribution network value layer model, the optimization of the objective function can maximize the economy and operation efficiency. At the same time, in order to ensure the feasibility and reliability of the optimization results in practical applications, it is also necessary to take the physical layer model of the demand response and energy storage system as the basis, and comprehensively consider factors such as power supply and demand balance constraints, voltage level constraints, power balance constraints, and renewable energy access constraints to ensure that the system maintains stability and sustainability while achieving optimal value.

[0074] Step 3: Establish the distribution network operation layer model.

[0075] The objective function of the distribution network operation layer model can be expressed as follows:

[0076] F lower =γ×F cost +δ×F satisfaction +ε×F load (6)

[0077] Among them, γ, δ, and ε are weight coefficients used to balance the importance of the three objectives, and γ+δ+ε=1, F cost represents the minimum user energy cost objective function; F satisfaction It represents the maximum user satisfaction objective function, F load It represents the maximum grid load rate objective function.

[0078] Minimum user energy cost objective function F cost It can be expressed by the following formula:

[0079] F cost =min(C user )=min(C purchase +C transmission +C storage ) (7)

[0080] Among them, C user For energy costs, C purchase is the energy purchase cost, C transmission is the energy transmission and distribution cost, C storage The cost of energy storage.

[0081] The maximum grid load rate objective function can be expressed as follows:

[0082] F load =max(L load ) (8)

[0083] Among them, L load is the grid load rate, which can be expressed by the following formula:

[0084]

[0085] Among them, P actual (t) is the actual power of the power grid at time t, P max is the maximum power of the grid.

[0086] In the distribution network operation layer model, the system operation efficiency and user service quality can be improved by optimizing the objective function. In order to ensure the feasibility and rationality of the optimization results in practical applications, factors such as user demand constraints, electricity price signal constraints, and incentive mechanism constraints must also be considered to ensure that the system meets user needs and adapts to the market environment while achieving optimal performance.

[0087] Step 4: Use the Hippo optimization algorithm to solve the model.

[0088] There are multivariate couplings in the multi-layer programming model, and the multi-layer models are nested with each other, which easily leads to the problem of difficulty in accurately solving the decision variables and fast convergence. The particle swarm optimization algorithm in the prior art does not consider the computational efficiency and convergence speed in the optimization configuration process, which leads to a slow solution process and difficulty in obtaining the optimal solution. The Hippo optimization algorithm can quickly and accurately find the optimal solution by adaptively adjusting the resolution and search speed of the search space. It has the characteristics of fast convergence speed and high solution accuracy. The solution steps of the Hippo optimization algorithm for the multi-layer programming model are:

[0089] Step 4.1, data import and parameter setting.

[0090] Collect original economic and technical data for distribution network planning, including electricity price signals, user demand, transmission capacity ceiling and other data.

[0091] Step 4.2: Population initialization and network topology generation.

[0092] Complete the population initialization of the Hippo optimization algorithm, limit the range of energy storage capacity, load rate, electricity price, and other data, and create conditions for searching for the optimal solution. Use the minimum tree algorithm based on Kruskal's idea to build the initial radial network topology to ensure reasonable and smooth network connections, and lay the initial network infrastructure for power flow calculations and subsequent optimization operations.

[0093] Step 4.3, power flow calculation and constraint condition verification.

[0094] With the help of MATPOWER tool, power flow calculation is carried out to accurately obtain key operating parameters such as voltage and power of network nodes. These parameters are important basis for subsequent evaluation and optimization. Check whether the power flow calculation results meet the opportunity constraints. If they meet the conditions, proceed to step 4.4 to perform multi-objective value calculation and scheme elimination. If they do not meet the conditions, optimize the operating layer model by modifying the decision variables of the operating layer model, including but not limited to the variables related to the minimum user energy cost objective function, the maximum user satisfaction objective function and the maximum grid load rate objective function. The optimized operating layer model provides more accurate operating data for the value layer model objective function, including user power consumption levels and energy storage system charging and discharging strategies, and then optimizes the value layer model to promote efficient, stable and sustainable operation of the distribution network.

[0095] Step 4.4, calculate the multi-objective values ​​and eliminate the solutions.

[0096] Comprehensively calculate the multi-objective function values, comprehensively consider the economy, reliability, and stability goals of the distribution network value layer, as well as the minimum user energy cost, maximum user satisfaction, and increase grid load rate goals in the operation layer, solve the distribution network value layer model established in step 2 and the distribution network operation layer model established in step 3, calculate the multi-objective values, and eliminate the solutions.

[0097] Step 4.5, update the hippo individuals and the optimal solution set.

[0098] Exploration phase: Simulate the random swimming behavior of hippos in water, allowing individuals to conduct extensive exploration in the solution space. By randomly perturbing key parameters such as energy storage capacity, electricity price, and load rate, the diversity of the search is increased to prevent the algorithm from falling into a local optimal solution too early.

[0099] Defense phase: Combine the user cost and load rate penalty in the operation layer objective function to optimize the individual position and ensure that the individual moves towards the global optimal solution.

[0100] Development stage: In this stage, the results of power flow calculation are used to ensure that key operating parameters such as voltage and power of power grid nodes meet the constraints.

[0101] Update the Pareto optimal solution set, select the individual optimal and population optimal solutions from the current individuals, build and update the Pareto optimal solution set, and record the non-inferior solutions in the optimization process.

[0102] Step 4.6: Network structure adjustment and recalculation verification.

[0103] Modify the line parameters, recalculate the branch weights, and obtain a new network structure based on the new parameters and weights. Perform power flow calculations again to determine whether the new network structure meets the opportunity constraints. If not, start the optimization adjustment of the operating layer model; if it is satisfied, calculate the fitness, update the individual optimal and population optimal solutions, and continue iterative optimization.

[0104] Step 4.7, optimal solution set management and iteration termination judgment.

[0105] The Pareto optimal solution set is stored in the archive set in order, and the inferior solution is eliminated to ensure the high quality and non-inferiority of the solution. It is determined whether the maximum number of iterations has been reached. If it has been reached, the iteration is stopped and the archive set is output; if it has not been reached, the steps of individual update, solution set update, network structure adjustment, etc. (steps 4.5-4.7) are executed repeatedly until the iteration ends.

[0106] Step 4.8, determine the optimal planning solution.

[0107] The TOPSIS method is used to comprehensively rank the individuals in the Pareto archive set, weigh the importance of each objective, select the optimal solution from the non-inferior solutions as the basis for multi-layer planning decision-making of distribution network, and provide a planning solution.

[0108] As a prominent substantive feature of the present invention and one of the significant advances in the prior art, the present invention deeply analyzes demand response and energy storage characteristics in model construction, finely balances multiple objectives and constraints, and uses the Hippo optimization algorithm to solve in stages to improve search diversity and constraint compliance. The present invention can effectively improve resource allocation accuracy and operating efficiency, reduce costs, improve energy utilization, enhance the scientificity and timeliness of decision-making, improve the stability and reliability of the distribution network, and at the same time strengthen the coordination of various layers to support the sustainable development of the distribution network.

[0109] A second embodiment of the present invention provides a multi-layer flexible planning system for a distribution network based on a Hippo optimization algorithm, comprising:

[0110] Physical layer module, used to build physical layer models of demand response and energy storage systems;

[0111] The value layer module is used to establish the value layer model of the distribution network based on the physical layer model;

[0112] Operation layer module, used to establish the operation layer model of the distribution network;

[0113] The model solving module is used to solve the established distribution network value layer model and operation layer model to obtain the distribution network planning solution.

[0114] The third embodiment of the present invention uses a part of a city's 110kV power grid as an actual example to verify the effectiveness and practicality of the model. The actual example wiring diagram and example node data are shown in Figure 2 , as shown in Table 1.

[0115] Table 1 Load node parameters of 9-node distribution network system

[0116]

[0117] Table 2 9-node system operating parameters

[0118]

[0119] First, in terms of line construction, the total length of the planned new and renovated lines is 14km. The new lines will be connected after node 6, and nodes 10-12 will be added.

[0120] like Figure 3 As shown, the new lines to be built are branches 9-15. For photovoltaic (PV) and wind turbines (WG), the installation base capacity is set to 100kW, the unit distributed generation (DG) capacity is 0.1MW, and the maximum penetration rate is set to 50%. In terms of equipment installation nodes, the candidate nodes for WG include 1, 3, 5, and 6, and the candidate nodes for PV include 2, 3, 4, and 9. At the same time, in order to reasonably plan the equipment layout, the upper limit of the number of installations for each node is set to 20.

[0121] In addition, in terms of economic related parameters, line operation and maintenance has a certain cost rate standard, which is set at 0.03. The fixed investment payback period varies for different equipment and lines. The fixed investment payback period of the overall line is 20 years, with a discount rate of 10%; while the fixed investment payback period of PV and WG is 10 years.

[0122] Other related parameters are shown in the following table.

[0123] Table 3 Distributed power supply parameters

[0124]

[0125] Table 4 Improved Hippo optimization algorithm parameter settings

[0126]

[0127] Analysis of planning results:

[0128] Table 5 shows the decision contents of some optimal solution centralized planning schemes.

[0129] After a lot of experiments, the weight of the economic index is determined to be 0.3, the weight of the stability index is 0.4, and the weight of the reliability index is 0.4. It is worth noting that in Table 5, under the DG (distributed generation) planning decision, the numbers outside the brackets represent the installation node number, and the numbers inside the brackets are the number of corresponding installed DGs.

[0130] Table 5 Planning results

[0131]

[0132]

[0133] After a comprehensive evaluation of the solution set, it was finally determined that solution 1 was the best planning solution. Five PVs need to be installed at node 2; eight WGs need to be installed at node 3; eight WGs need to be installed at node 4; and eight WGs need to be installed at node 6. The DG's waiting access locations are set to nodes 2, 3, 4, and 6.

[0134] On this basis, consider the case where the candidate access points of energy storage are the same as the candidate access points of distributed power generation. Therefore, the optional nodes of energy storage devices are 2, 3, 4, and 6 accordingly. In addition, each energy storage device is set to have certain performance parameters, and its total charging capacity is 1.6 / MW.h, and the maximum charging and discharging power per unit time is 0.2 / MWh.

[0135] The solution based on the model can obtain the optimal solution set for planning. In the distribution network value layer objective function F = χ × f1 + ε × f2 + λ × f3, the economic index weight χ = ​​0.5, the energy efficiency weight index χ = 0.4, and the reliability index weight χ = ​​0.1. The operation layer objective function F lower =γ×F cost +δ×F satisfaction +ε×F load In the equation, the economic index weight χ is taken as 0.6, the satisfaction index weight χ is taken as 0.3, and the load factor index weight χ is taken as 0.1.

[0136] Table 6 Planning results

[0137]

[0138] After a detailed evaluation of the solution set, Scheme 2 is determined to be the optimal planning scheme. Five PVs (photovoltaic devices) are installed at node 2, eight WGs (wind turbines) are installed at node 3, seven WGs are installed at node 4, and ten PVs are installed at node 5. Photovoltaic energy storage devices are installed at nodes 2, 3, 4, and 5. After the model calculation, the optimal load values ​​of all load nodes are presented in Table 7.

[0139] Table 7 Optimal node load values

[0140]

[0141] The present disclosure may be a system, a method and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present disclosure.

[0142] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents, and any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A multi-layer flexible planning method for distribution network based on Hippo optimization algorithm, characterized in that: include: Based on electricity costs, load fluctuations and energy storage costs, a physical layer model of the demand response and energy storage system is established to provide data support for establishing a value layer model for the distribution network; Based on the physical layer models of demand response and energy storage systems, a distribution network value layer model is established; Establish a distribution network operation layer model based on energy costs, user demand and grid load rate; The Hippo optimization algorithm is used to solve the value layer model and operation layer model of the distribution network, and the optimal solution is used as the distribution network planning scheme.

2. A multi-layer flexible planning method for distribution network based on Hippo optimization algorithm as claimed in claim 1, characterized in that: The objective function of demand-side response can be expressed as follows: Among them, T is the total number of time periods, C t is the electricity price in period t, P t is the load demand in the tth period (kW), λ is the penalty coefficient used to smooth the load curve, f(P t ) is a function for smoothing load fluctuations.

3. A multi-layer flexible planning method for distribution network based on Hippo optimization algorithm as claimed in claim 1, characterized in that: The objective function of the energy storage system can be expressed as follows: Among them, C battery is the battery charging and discharging cost, P battery (t) represents the power output of the battery in the tth period, C hydrogen represents the charging and discharging cost of the hydrogen energy storage system, P hydrogen (t) represents the power output of the hydrogen energy storage system at time period t, λ1×f(P battery (t),P hydrogen (t)) is the penalty term for smoothing power output fluctuations.

4. A multi-layer flexible planning method for distribution network based on Hippo optimization algorithm as claimed in claim 1, characterized in that: The objective function of the distribution network value layer model can be expressed as follows: F=χ×f1+ε×f2+λ×f3 (3) Among them, f1 represents the economic target; f2 represents energy efficiency, f3 represents reliability, χ, ε, λ are weight coefficients, and χ+ε+λ=1.

5. A multi-layer flexible planning method for distribution network based on Hippo optimization algorithm as claimed in claim 4, characterized in that: The economic goal f1, energy efficiency f2, and reliability f3 can be expressed by the following formula: f2=1-λ reliability f3=U level Among them, C line is the line investment and operation and maintenance cost, C DG is the investment and operation and maintenance cost of distributed power generation, C loss Network loss costs, is the annual comprehensive investment cost of energy storage batteries, is the annual operating cost of the energy storage battery, C c is the controllable load cost of demand response, C n For renewable energy environmental protection and energy saving benefits, reliability For power supply reliability, U level It represents the value of voltage quality evaluation function at distribution network node.

6. A multi-layer flexible planning method for distribution network based on Hippo optimization algorithm as claimed in claim 1, characterized in that: The objective function of the distribution network operation layer model can be expressed as follows: F lower =γ×F cost +δ×F satisfaction +ε×F load (5) Among them, γ, δ, ε are weight coefficients, and γ+δ+ε=1, F cost is the minimum user energy cost objective function; F satisfaction is the maximum user satisfaction objective function, F load is the maximum grid load rate objective function.

7. A multi-layer flexible planning method for distribution network based on Hippo optimization algorithm as claimed in claim 6, characterized in that: Minimum user energy cost objective function F cost and the maximum grid load factor objective function F load It can be expressed by the following formula: F cost =min(C user )=min(C purchase +C transmission +C storage ) (6) F load =max(L load ) (7) Among them, C user For energy costs, C purchase is the energy purchase cost, C transmission is the energy transmission and distribution cost, C storage is the energy storage cost, L load is the grid load rate.

8. A method for multi-layer flexible planning of distribution network based on Hippo optimization algorithm as claimed in claim 1, characterized in that: The Hippo optimization algorithm is used to solve the distribution network value layer model and operation layer model as follows: Collect the original economic and technical data of distribution network planning, perform population initialization of Hippo optimization algorithm and generate network topology; Perform power flow calculation and check whether the power flow calculation results meet the opportunity constraints. If they meet the conditions, solve and calculate the objective functions of the distribution network value layer model and the operation layer model and eliminate the solutions. If they do not meet the conditions, optimize the operation layer model. The optimized operation layer model provides operation data for the value layer model, and then optimizes the value layer model. According to the results of the power flow calculation, the hippopotamus individuals and the optimal solution set are updated; Adjust the network structure, perform power flow calculation on the adjusted network structure, and determine whether the adjusted network structure meets the opportunity constraint conditions. If not, start the optimization adjustment of the distribution network operation layer model; if it does, calculate the fitness, update the individual optimal and population optimal solutions, and continue iterative optimization; The optimal solution set is stored in the archive set in order, the inferior solution is eliminated, and it is determined whether the maximum number of iterations has been reached. If it has been reached, the iteration is stopped and the archive set is output; if it has not been reached, individual update, solution set update and network structure adjustment are executed cyclically until the iteration ends; The TOPSIS method is used to comprehensively rank the individuals in the archive set, and the optimal individual is used as the distribution network planning scheme.

9. A multi-layer flexible planning method for distribution network based on Hippo optimization algorithm as claimed in claim 8, characterized in that: The specific steps to update the hippopotamus individual and the optimal solution set are: Exploration phase: Simulate the random swimming behavior of hippos in water, allow individuals to conduct extensive exploration in the solution space, and randomly perturb the energy storage capacity, electricity price, and load rate to prevent the algorithm from falling into the local optimal solution too early; Defense phase: Combine the user cost and load rate penalty in the objective function of the distribution network operation layer model to optimize the position of the individual and ensure that the individual moves towards the global optimal solution; Development stage: In this stage, the results of power flow calculation are used to ensure that the voltage and power of the grid nodes meet the constraints.

10. A distribution network multi-layer flexible planning system based on the Hippo optimization algorithm, running a distribution network multi-layer flexible planning method based on the Hippo optimization algorithm as described in any one of claims 1 to 9, characterized in that: include: Physical layer module, used to build physical layer models of demand response and energy storage systems; The value layer module is used to establish the value layer model of the distribution network based on the physical layer model; Operation layer module, used to establish the operation layer model of the distribution network; The model solving module is used to solve the established distribution network value layer model and operation layer model to obtain the distribution network planning solution.

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