Power distribution network economic reactive power coordinated dispatching optimization method and device, terminal and medium

Abstract

The application discloses a power distribution network economic reactive power coordinated dispatch optimization method and device, a terminal and a medium. The scheme forms a closed-loop feedback of economic and reactive power optimization by constructing a cooperative optimization framework, and dynamically adjusts the optimization direction in the iteration process. At the same time, the combination of the Pareto solution and the second-order cone programming ensures the comprehensiveness of multi-objective optimization and overcomes the difficulty in solving non-convex optimization problems. In combination with the cross-iteration mechanism, the adaptability of the system to distributed power output fluctuation is enhanced, and the coordinated control of flexible load and energy storage improves the operation flexibility of the power distribution network. Therefore, the feasibility of the optimization scheme can be maintained under complex operation scenarios.

Description

Technical Field

[0001] This application relates to the field of power distribution network technology, and in particular to a method, device, terminal and medium for optimizing economic reactive power coordination and dispatch in power distribution networks. Background Technology

[0002] Reactive power optimization can significantly reduce active power losses in a system. In power distribution networks, the presence of reactive power during power transmission leads to voltage drops and additional energy losses on lines. Through proper reactive power optimization, reactive power flow in transmission lines can be reduced, thereby lowering line losses and improving the system's energy utilization efficiency.

[0003] However, while existing reactive power optimization has been widely extended to multi-objective optimization models that take into account multiple dimensions such as economic efficiency, environmental benefits, and carbon emissions, both the economic and reactive power optimization stages mainly focus on single-stage scheduling optimization. This makes it difficult to effectively balance power generation and minimize system operating costs and network losses. Summary of the Invention

[0004] This application provides a method, device, terminal, and medium for optimizing the economic reactive power coordination and dispatch of a power distribution network, which aims to minimize both system operating costs and network losses.

[0005] To address the aforementioned technical problems, the first aspect of this application provides a method for optimizing the economic reactive power coordinated dispatch of a distribution network, comprising:

[0006] Based on the equipment nodes in the distribution network, a distribution network collaborative optimization model is constructed. The distribution network collaborative optimization model includes an economic optimization sub-model and a reactive power optimization sub-model. The equipment nodes include distributed power generation nodes, energy storage nodes, and flexible load nodes. The optimization objective of the economic optimization sub-model is to minimize carbon emissions and distribution network operating costs. The optimization objective of the reactive power optimization sub-model is to minimize distribution network losses while ensuring that node voltage does not exceed limits.

[0007] The economic optimization sub-model is solved using Pareto optimization and heuristic optimization methods to obtain the optimized operation scheme of the equipment node. The optimized operation scheme is then passed to the reactive power optimization sub-model as the boundary condition of the reactive power optimization sub-model.

[0008] The reactive power optimization sub-model is solved using second-order cone programming and CPLEX methods to obtain a distribution network voltage adjustment scheme that minimizes network losses, and the distribution network voltage adjustment scheme is fed back to the economic optimization sub-model.

[0009] Cross-iterative optimization is performed based on the economic optimization sub-model and the reactive power optimization sub-model. When the preset iterative optimization conditions are met, the coordinated scheduling optimization scheme of the distribution network is determined based on the optimization parameters in the distribution network collaborative optimization model.

[0010] Preferably, the objective function of the economic optimization sub-model is:

[0011]

[0012]

[0013] In the formula, The cost of purchasing and selling electricity between the distribution network and the main grid. , , and These are the operation and maintenance costs of flexible loads, energy storage systems, photovoltaic power, and wind power in the distribution network. For network loss cost, ρ e t For carbon emission density, P e t The power exchanged between the distribution network and the main grid is represented by T, which indicates the optimization period.

[0014] Preferably, the constraints of the economic optimization sub-model include:

[0015]

[0016]

[0017]

[0018]

[0019]

[0020]

[0021] In the formula, P L,t P represents the power absorbed by the load. grid,t P represents the power injected from the main grid into the distribution network at time t. ess,t E represents the power of the energy storage system. pv,T E wt,T and E L,T E represents the electricity generated by photovoltaic and wind power loads within period T. ess,dis,T and E ess,ch,T P represents the discharge and charging capacity of the energy storage system within period T. pv,max A represents the maximum power of photovoltaic power. t P indicates the operating status of photovoltaics.wt,max B represents the maximum power output of the wind turbine. t Indicates the operating status of wind power, R t To reserve load power, P grid,max β represents the maximum exchange power between the distribution network and the main grid. τ P tran max and P tran min T represents the state of the transferable load and the upper and lower power limits of the transferable load, respectively. kp and z τ re T is the reduction time and reduction state for load reduction. re max and T re min P represents the maximum and minimum values ​​of the reduction time; ess max P ess min These represent the power of the energy storage system and the SoC, respectively. ess max and SoC ess min These represent the upper and lower limits of the depth of charge and discharge, respectively.

[0022] Preferably, the objective function of the reactive power optimization sub-model includes:

[0023]

[0024]

[0025]

[0026] In the formula, P loss U represents the network loss after reactive power optimization. d The node voltage deviation is represented by ω1 and ω2, where ω1 and ω2 represent weights, P0 represents the network loss before reactive power optimization, U0 is the average voltage deviation, and N is the average voltage deviation. k G represents the number of branches. ij U represents the electrical conductance between node i and node j. i U j θ represents the voltage magnitude at node i and node j, respectively. ij γ is the phase difference; γ1 is the penalty factor; U i U is the actual voltage at node i. Ni Where is the rated voltage and n is the number of nodes.

[0027] Preferably, the constraints of the reactive power optimization sub-model include:

[0028]

[0029]

[0030]

[0031]

[0032]

[0033] In the formula, z ij and I ij S represents the impedance and the current in branch ij. ij S represents the power from node i to node j; pv.j S ess.j and S L.j U represents the photovoltaic, energy storage, and load power injected into node j, respectively. j,min and U j,max I represents the lower and upper limits of the node voltage. ij,max Q represents the maximum current in branch ij; pvmin and Q pvmax Q represents the lower and upper limits of photovoltaic reactive power; wtmin and Q wtmax This represents the lower and upper limits of reactive power from wind power.

[0034] Preferably, the step of solving the economic optimization sub-model using Pareto and heuristic optimization methods to obtain the optimized operation scheme of the equipment node includes:

[0035] A Pareto population is randomly generated, and then the Pareto population is sorted based on non-dominated sorting and crowding distance sorting. The Pareto population is then split into a first subpopulation and a second subpopulation.

[0036] Based on the first subpopulation, a global search is performed using the NSGA-II algorithm and a preset iteration factor. Based on the second subpopulation, a local optimization is performed using the MOPSO algorithm and a preset mutation operator, through particle velocity / position updates. Based on the search and optimization results of the first and second subpopulations, the Pareto solution set of the economic optimization sub-model is obtained.

[0037] Based on the Pareto solution set, the optimized operation scheme of the device node is obtained, wherein the optimized operation scheme of the device node includes: distributed power output curve, energy storage charging and discharging plan, and flexible load adjustment amount.

[0038] Preferably, the reactive power optimization sub-model is solved using second-order cone programming and CPLEX methods to obtain a distribution network voltage adjustment scheme that minimizes network losses, including:

[0039] The power balance relationship in the reactive power optimization sub-model is transformed into a linear equation in the form of a second-order cone by using the second-order cone relaxation method, and the voltage capacity relationship in the reactive power optimization sub-model is transformed into a convex constraint in the form of a second-order cone.

[0040] Based on the linear equation and the convex constraint, the reactive power optimization sub-model is solved using CPLEX to obtain a distribution network voltage adjustment scheme that minimizes network losses.

[0041] The second aspect of this application provides a distribution network economic reactive power coordination and dispatch optimization device, comprising:

[0042] The collaborative optimization model construction unit is used to construct a collaborative optimization model for the distribution network based on the equipment nodes in the distribution network. The collaborative optimization model includes an economic optimization sub-model and a reactive power optimization sub-model. The equipment nodes include distributed power generation nodes, energy storage nodes, and flexible load nodes. The optimization objective of the economic optimization sub-model is to minimize carbon emissions and distribution network operating costs. The optimization objective of the reactive power optimization sub-model is to minimize distribution network losses while ensuring that node voltage does not exceed limits.

[0043] The first sub-model solving unit is used to solve the economic optimization sub-model in multiple objectives through Pareto and heuristic optimization methods to obtain the optimized operation scheme of the equipment node, and to pass the optimized operation scheme to the reactive power optimization sub-model as the boundary condition of the reactive power optimization sub-model.

[0044] The second sub-model solving unit is used to solve the reactive power optimization sub-model using second-order cone programming and CPLEX methods to obtain a distribution network voltage adjustment scheme that minimizes network losses, and then feeds the distribution network voltage adjustment scheme back to the economic optimization sub-model.

[0045] The collaborative optimization execution unit is used to perform cross-iterative optimization based on the economic optimization sub-model and the reactive power optimization sub-model. When the preset iterative optimization conditions are met, the unit determines the coordinated scheduling optimization scheme of the distribution network based on the optimization parameters in the distribution network collaborative optimization model.

[0046] The third aspect of this application provides a distribution network economic reactive power coordination and dispatch optimization terminal, including: a memory and a processor;

[0047] The memory is used to store program code, which corresponds to the distribution network economic reactive power coordinated dispatch optimization method provided in the first aspect of this application.

[0048] The processor is used to read and execute the program code to implement the distribution network economic reactive power coordination and scheduling optimization method.

[0049] The fourth aspect of this application provides a computer-readable storage medium containing program code for being read and executed by a processor to implement the distribution network economic reactive power coordination and scheduling optimization method provided in the first aspect of this application.

[0050] As can be seen from the above technical solutions, this application has the following advantages:

[0051] This scheme constructs two collaborative sub-models: an economic optimization sub-model and a reactive power optimization sub-model. The economic optimization sub-model aims to minimize carbon emissions and operating costs, generating equipment output plans through a multi-objective algorithm. This plan, as a boundary condition input to the reactive power optimization sub-model, is then combined with voltage constraints to solve for the minimum network loss. When the voltage adjustment plan is fed back to the economic optimization, the equipment output plan can be modified to adapt to voltage stability requirements. This cross-iteration process continues until the economic and voltage quality indicators reach a synergistically optimal state. By constructing a collaborative optimization framework, this scheme creates a closed-loop feedback between economic and reactive power optimization, dynamically adjusting the optimization direction during the iteration process. Simultaneously, the combination of Pareto solving and second-order cone programming ensures the comprehensiveness of multi-objective optimization while overcoming the difficulties of solving non-convex optimization problems. Furthermore, the cross-iteration mechanism enhances the system's adaptability to distributed power generation fluctuations, and the coordinated control of flexible loads and energy storage improves the operational flexibility of the distribution network. This ensures the feasibility of the optimized scheme even under complex operating scenarios. Attached Figure Description

[0052] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0053] Figure 1 This is a flowchart illustrating an embodiment of an economic reactive power coordination and scheduling optimization method for power distribution networks provided in this application.

[0054] Figure 2 This is a schematic diagram of an IEEE 33-node distribution network containing distributed generation sources.

[0055] Figure 3 A fitted curve of wind power and solar power output based on wind speed and solar irradiance data on the dispatch day.

[0056] Figure 4 This is a schematic diagram of the power balance after optimized scheduling.

[0057] Figure 5 This is a schematic diagram comparing the distribution network load before and after optimization based on the optimized scheduling strategy proposed in this application.

[0058] Figure 6 This diagram illustrates the comparison of the voltage at the lowest point of the distribution network before and after optimization based on the optimized scheduling strategy of this application.

[0059] Figure 7 This diagram illustrates the iterative characteristics of power distribution network operating costs and carbon emissions for different algorithms.

[0060] Figure 8 This is a schematic diagram of an embodiment of an economic reactive power coordination and dispatch optimization device for a power distribution network provided in this application.

[0061] Figure 9 This is a schematic diagram of the structure of an embodiment of an economic reactive power coordination and dispatch optimization terminal for a distribution network provided in this application. Detailed Implementation

[0062] In existing technologies, distribution network optimization and dispatch typically employs a single-stage optimization model, which struggles to effectively reconcile the conflict between economic efficiency and voltage stability. As the penetration rate of distributed generation continues to increase, its output volatility exacerbates the uncertainty of system operation. Traditional methods, when dealing with multi-objective optimization problems, often consider economic indicators and voltage quality indicators separately, leading to optimization results that fail to meet actual operational needs. For example, in areas with significant fluctuations in wind and solar resources, simply pursuing minimum operating costs may cause node voltage exceedances, while overemphasizing voltage stability can result in economic losses.

[0063] In view of this, to solve the above problems, researchers discovered a coupling relationship between economic optimization and reactive power optimization, where the output of a single optimization stage may impose boundary condition constraints on another stage. By analyzing the interaction mechanism of equipment nodes in the distribution network, a phased collaborative optimization is gradually formed. Therefore, embodiments of this application provide a method, device, terminal, and medium for economic reactive power coordinated scheduling optimization of distribution networks, used to achieve the invention objective of minimizing both system operating costs and network losses.

[0064] To make the inventive objectives, features, and advantages of this application more apparent and understandable, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described below are only some embodiments of this application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0065] First, a detailed description of an embodiment of the economic reactive power coordinated dispatch optimization method for distribution networks provided in this application is as follows:

[0066] Please see Figure 1 The distribution network economic reactive power coordinated dispatch optimization method provided in this application includes:

[0067] Step 101: Based on the equipment nodes in the distribution network, construct a distribution network collaborative optimization model, which includes an economic optimization sub-model and a reactive power optimization sub-model.

[0068] The equipment nodes include distributed power generation nodes, energy storage nodes, and flexible load nodes. The optimization objective of the economic optimization sub-model is to minimize carbon emissions and distribution network operating costs. The optimization objective of the reactive power optimization sub-model is to minimize distribution network losses while ensuring that node voltage does not exceed limits.

[0069] Step 102: Solve the economic optimization sub-model using Pareto and heuristic optimization methods to obtain the optimized operation scheme of the equipment nodes, and then pass the optimized operation scheme to the reactive power optimization sub-model as the boundary condition of the reactive power optimization sub-model.

[0070] Step 103: Solve the reactive power optimization sub-model using second-order cone programming and CPLEX to obtain the distribution network voltage adjustment scheme that minimizes network losses, and feed the distribution network voltage adjustment scheme back to the economic optimization sub-model.

[0071] Step 104: Perform cross-iterative optimization based on the economic optimization sub-model and the reactive power optimization sub-model. When the preset iterative optimization conditions are met, determine the coordinated dispatch optimization scheme of the distribution network based on the optimization parameters in the distribution network collaborative optimization model.

[0072] The equipment nodes include distributed power generation nodes, energy storage nodes, and flexible load nodes. These nodes constitute the basic units of energy interaction in the distribution network, and their operating status directly affects the overall system performance. When constructing the collaborative optimization model, by dividing the objective functions of economic optimization and reactive power optimization, cost control and voltage quality improvement can be focused on respectively. Pareto solving is used to handle multi-objective conflict problems in economic optimization, such as finding a balance between carbon emissions and operating costs. Second-order cone programming transforms non-convex optimization problems into a solvable form, and combined with the CPLEX solver, improves computational efficiency. The cross-iteration mechanism enables dynamic adaptation between economic dispatch schemes and voltage adjustment strategies through bidirectional information transmission.

[0073] Specifically, a node model incorporating distributed power sources, energy storage, and flexible loads is first established to form mathematical constraints reflecting the operating characteristics of the equipment. The economic optimization sub-model, aiming to minimize carbon emissions and operating costs, generates equipment output schemes using a multi-objective algorithm. This scheme, as a boundary condition input to the reactive power optimization sub-model, is then combined with voltage constraints to solve for the minimum network loss scheme. When the voltage adjustment scheme is fed back to the economic optimization, the equipment output plan can be modified to adapt to voltage stability requirements. This cross-iterative process continues until the economic indicators and voltage quality indicators reach a mutually optimal state.

[0074] This scheme constructs a collaborative optimization framework, creating a closed-loop feedback between economic and reactive power optimization, dynamically adjusting the optimization direction during the iteration process. Simultaneously, it employs a combination of Pareto principles and second-order cone programming, ensuring the comprehensiveness of multi-objective optimization while overcoming the difficulties of solving non-convex optimization problems. Through these technical solutions, collaborative optimization of economic efficiency and voltage stability is achieved, effectively suppressing voltage fluctuations while reducing operating costs. The cross-iteration mechanism enhances the system's adaptability to distributed power generation output fluctuations, and the coordinated control of flexible loads and energy storage improves the operational resilience of the distribution network. The collaborative optimization model maintains the feasibility of the optimization scheme even under complex operating scenarios by dynamically adjusting equipment output and reactive power compensation strategies.

[0075] Furthermore, the objective function of the economic optimization sub-model provided in this application is specifically as follows:

[0076]

[0077]

[0078] In the formula, The cost of purchasing and selling electricity between the distribution network and the main grid. , , and These are the operation and maintenance costs of flexible loads, energy storage systems, photovoltaic power, and wind power in the distribution network, respectively. For network loss cost, ρ et For carbon emission density, P et The power exchanged between the distribution network and the main grid is represented by T, which indicates the optimization period.

[0079] It should be noted that the economic optimization sub-model provided in this embodiment includes two objective functions, one of which is the operating cost function. This includes the operation and maintenance costs of photovoltaic, energy storage, and flexible loads; the costs of purchasing and selling electricity between the distribution network and the main grid; and network loss costs. The specific expressions for the above costs are as follows:

[0080]

[0081] In the formula, c buy and c sell These are the electricity purchase price and the electricity sales price, respectively, P buy and P sell These are the electricity purchased and the electricity sold, respectively.

[0082]

[0083] In the formula, P pv,t λ represents the power of the photovoltaic system at time t. pv This represents the operation and maintenance cost coefficient for photovoltaic systems.

[0084]

[0085] In the formula, P wt,t λ represents the power of photovoltaic and wind power at time t. wt This represents the operation and maintenance cost coefficient for photovoltaic and wind power.

[0086]

[0087] In the formula, T represents the optimization period, and P... cha,t and P dis,t Let λ represent the charging and discharging power of the energy storage system at time t, respectively. ess This represents the operation and maintenance cost coefficient for the energy storage system.

[0088] Flexible loads can be specifically classified into shiftable loads, transferable loads, and reduceable loads. The specific formulas are as follows:

[0089]

[0090] In the formula, c shift c tran and c re Indicates the load factor for translation, transferability, and load reduction; P τ shift and P τ tran These represent the power consumed by the movable and transferable loads, respectively; P τ re and P τ re* To reduce the power consumed by the load before and after dispatching; [t] sh- , t sh+ ] and [t tr- , t tr+ [This refers to the operating range for loads that can be shifted or transferred; t] D To reduce the duration of the load, α τ β τ and γ τ This indicates the status of three types of loads.

[0091] Another objective function is the carbon emission function. The specific expression is as shown above.

[0092] More specifically, the constraints of the economic optimization sub-model include the following:

[0093] 1) Power balance constraints:

[0094]

[0095] In the formula, P L,t P represents the power absorbed by the load. grid,t P represents the power injected from the main grid into the distribution network at time t. ess,t The power of the energy storage system;

[0096] 2) Power balance constraint conditions:

[0097]

[0098] In the formula, E pv,T E wt,T and E L,T E represents the electricity generated by photovoltaic and wind power loads within period T. ess,dis,T and E ess,ch,T This represents the discharge and charging capacity of the energy storage system within period T.

[0099] 3) Other constraints, including: system reserve constraints, distribution network power purchase constraints, flexible load constraints, and energy storage capacity and SOC constraints.

[0100]

[0101]

[0102]

[0103]

[0104] Among them, P pv,max A represents the maximum power of photovoltaic power. t P indicates the operating status of photovoltaics. wt,max B represents the maximum power output of the wind turbine. t Indicates the operating status of wind power, R t To reserve load power, P grid,max β represents the maximum exchange power between the distribution network and the main grid. τ P tran max and P tran minT represents the state of the transferable load and its upper and lower power limits, respectively. kp and z τ re T is the reduction time and reduction state for load reduction. re max and T re min P represents the maximum and minimum values ​​of the reduction time; ess max P ess min SoC ess max and SoC ess min These represent the upper and lower limits of the energy storage system's power and depth of charge / discharge, respectively.

[0105] Furthermore, the objective function of the reactive power optimization sub-model provided in this application is specifically as follows:

[0106]

[0107] Where ω1 and ω2 represent weights, P0 represents the network loss before reactive power optimization, U0 is the average voltage deviation, and P loss and U d These represent the network loss and node voltage deviation after optimization by the reactive power optimization sub-model, respectively, and their specific expressions are as follows:

[0108]

[0109]

[0110] Where, N k G represents the number of branches. ij U represents the electrical conductance between node i and node j. i U j θ represents the voltage magnitude at node i and node j, respectively. ij γ is the phase difference; γ1 is the penalty factor; U i U is the actual voltage at node i. Ni Where is the rated voltage and n is the number of nodes.

[0111] More specifically, the constraints of the reactive power optimization sub-model include: power constraints, node voltage constraints, branch current constraints, and reactive power constraints of distributed generation sources, as expressed in the following expressions:

[0112]

[0113]

[0114]

[0115]

[0116]

[0117] Among them, z ij and I ij S represents the impedance and the current in branch ij. ij S represents the power from node i to node j; pv.j S ess.j and S L.j U represents the photovoltaic, energy storage, and load power injected into node j, respectively. This power characteristic can be used as a constraint based on the results calculated by the economic optimization model in step 1. j,min and U j,max I represents the lower and upper limits of the node voltage. ij,max Q represents the maximum current in branch ij; pvmin and Q pvmax Q represents the lower and upper limits of photovoltaic reactive power; wtmin and Q wtmax These parameters represent the lower and upper limits of wind power reactive power. Through these parameters, the reactive power optimization model can carry out optimization based on the actual operation plan of economic optimization, avoiding the contradiction between the reactive power optimization results and the actual output of DG / energy storage.

[0118] Furthermore, regarding the aforementioned economic optimization sub-model, this application also proposes a multi-objective solution for the economic optimization sub-model using Pareto optimization and heuristic optimization methods, resulting in optimized operation schemes for equipment nodes, including:

[0119] A Pareto population is randomly generated and then sorted based on non-dominated sorting and crowding distance sorting. The Pareto population is then split into a first subpopulation and a second subpopulation. Based on the first subpopulation, a global search is performed using the NSGA-II algorithm and a preset iteration factor. Based on the second subpopulation, a local optimization is performed using the MOPSO algorithm and a preset mutation operator, with particle velocity / position updates. The Pareto solution set of the economic optimization sub-model is obtained based on the search and optimization results of the first and second subpopulations. Based on the Pareto solution set, the optimized operation scheme of the equipment nodes is obtained. The optimized operation scheme of the equipment nodes includes: distributed power output curve, energy storage charging and discharging plan, and flexible load adjustment amount.

[0120] It should be noted that, regarding the multi-objective solution in the economic optimization stage: the two sub-objective functions in the economic optimization stage are contradictory, namely, reducing carbon emissions f 12 This will require a reduction in the electricity contribution from the main grid, while the electricity generated by photovoltaic and energy storage systems will increase, leading to higher operating costs. 11Pareto optimization is required. The NSGAII-MOPSO algorithm is used to obtain the Pareto solution set and solve the multi-objective optimization in the economic optimization scheduling stage. After the solution is completed, key variables such as the output curve of distributed power sources, energy storage charging and discharging plans, and flexible load adjustment are synchronously transferred to the reactive power optimization sub-model.

[0121] In this context, the Pareto population refers to a set of potentially feasible solutions generated through random initialization, specifically using Monte Carlo methods or Latin hypercube sampling, to cover the solution space of multi-objective optimization problems. Non-dominated sorting refers to stratifying the population based on the dominance of solutions, specifically implemented using a fast non-dominated sorting algorithm, to select individuals with high fitness. Crowding distance sorting refers to sorting solutions within the same non-dominated layer based on their distribution density, specifically achieved by calculating the density differences of solutions surrounding an individual, to maintain population diversity. The NSGA-II algorithm is a multi-objective optimization method based on genetic algorithms, specifically using crossover, mutation, and selection operations to achieve global search, to explore potential optimal solutions in the solution space. The MOPSO algorithm is a multi-objective optimization method based on particle swarm optimization, specifically using particle velocity and position update mechanisms to achieve local optimization, to refine the search within a specific region. The Pareto solution set refers to the set of solutions in a multi-objective optimization problem that cannot be dominated by other solutions, specifically formed by selecting non-dominated solutions, to provide a trade-off between various optimization schemes.

[0122] Specifically, this embodiment improves the algorithm by introducing iteration factors (MaxIt-iter) / MaxIt and iter / MaxIt to achieve adaptive evolution of the population and accelerate the convergence speed;

[0123]

[0124]

[0125] Where p cagv p cmax p cmin p magv p m max and p mmin Crossover rate p c and the rate of variation p m The average, maximum, and minimum values ​​of the congestion level are given. `iter` and `MaxIt` represent the current and maximum number of iterations, respectively. `d(i)` and `dagv(i)` represent the current and average congestion levels, respectively.

[0126] In the MOPSO algorithm, a mutation operator is used to perform the search by adjusting the individual learning coefficients c1 and the global learning coefficients c2. The specific update process is as follows:

[0127]

[0128]

[0129] Where X(t) and V(t) represent the position and velocity of the particle at time t. X(t+1) and V(t+1) represent the updated position and velocity of the particle, respectively; pBest(t) represents the optimal position of the particle at time t; pLeader(t) represents the position of the dominant particle; ω is the inertia weight factor; γ1 and γ2 represent random numbers in the range [0,1].

[0130] Next, the NSGA-II algorithm and the MOPSO algorithm are combined for solving the problem, specifically including:

[0131] The initial Pareto population is generated randomly, covering the feasible solution space within the objective function and constraints of the economic optimization sub-model. After non-dominated sorting, the Pareto population is divided into different levels, with the first level being the non-dominated solution set. Individuals within the same level are further prioritized by crowding distance sorting to ensure population diversity during evolution. The first sub-population uses the NSGA-II algorithm for global search through crossover and mutation operations; for example, the crossover probability can be set to 0.8 and the mutation probability to 0.1 to balance exploration and exploitation capabilities. The second sub-population uses the MOPSO algorithm for local optimization by updating particle velocity and position; for example, the inertia weight can be set to a dynamic decay mode to accelerate convergence to a local optimum. The synergy of the two algorithms combines global search with refined local search, ultimately forming a Pareto solution set containing multiple optimization schemes. This solution set is further used to determine specific operating parameters such as distributed power output curves, energy storage charging and discharging plans, and flexible load adjustments through decision-maker preferences or automated screening mechanisms.

[0132] This scheme, by splitting the population and applying different algorithms to each segment, retains the broad global exploration capability of NSGA-II while leveraging the fast convergence characteristics of MOPSO in local regions, effectively improving the diversity and convergence accuracy of the solution set. Furthermore, existing technologies often lack dynamic adjustment mechanisms for generating Pareto solutions, making it difficult to cope with the uncertainties caused by fluctuations in distributed generation output. This scheme, through a hybrid algorithm framework, can adaptively adjust the search strategy during iteration, enhancing the robustness of the optimization scheme. These technical solutions address the difficulty of traditional single-stage optimization methods in balancing multi-objective conflicts and handling uncertainties in distributed generation. Generating Pareto solutions through a hybrid algorithm framework provides decision-makers with multiple trade-offs for optimal operation schemes, enabling synergistic optimization of objectives such as economic efficiency, carbon emissions, and network losses. Simultaneously, the combination of global and local searches significantly improves solution efficiency, ensuring the rapid acquisition of feasible and high-quality scheduling schemes under complex constraints, providing reliable technical support for the economic and coordinated optimization of distribution networks.

[0133] Furthermore, regarding the aforementioned reactive power optimization sub-model, this application also proposes a solution method using second-order cone programming and CPLEX to obtain a distribution network voltage adjustment scheme that minimizes network losses. Specifically, this includes:

[0134] The power balance relationship in the reactive power optimization sub-model is transformed into a linear equation in the form of a second-order cone by using the second-order cone relaxation method, and the voltage-capacity relationship is transformed into a convex constraint in the form of a second-order cone. Based on the linear equation and the convex constraint, the reactive power optimization sub-model is solved by CPLEX to obtain the distribution network voltage adjustment scheme that minimizes network losses.

[0135] It should be noted that, regarding the solution of the reactive power optimization sub-model, the non-convex nonlinear problem in the reactive power optimization model is transformed into a mixed-integer second-order cone programming problem using the second-order cone relaxation method. CPLEX is then used for solving this problem, generating a voltage adjustment scheme that minimizes network losses. After the solution is completed, the node voltage deviation and network loss characteristics output by the reactive power optimization model are transformed into cost parameters identifiable by the economic optimization model and incorporated into its objective function. Specifically, the network loss P is... loss Based on the electricity price, the network loss cost C is calculated. loss The node voltage deviation is used as the penalty cost ΔC for the operation and maintenance of distributed power sources. pv and ΔC wt By leveraging these two costs, the economic optimization model will naturally avoid solutions that lead to high network losses or large voltage deviations when making decisions, thus achieving a synergy between economic efficiency and physical security.

[0136] Second-order cone programming is a mathematical programming method for handling non-convex optimization problems. Specifically, it uses relaxation techniques to transform the original non-convex constraints into convex constraints, thus converting complex nonlinear optimization problems into efficiently solvable convex optimization forms. CPLEX solving involves using a mathematical optimization solver to perform numerical calculations on the model, specifically using branch and bound or interior point methods, to quickly obtain the optimal solution that satisfies the constraints. Second-order cone relaxation is a technique that transforms quadratic constraints into cone constraints by introducing auxiliary variables, specifically using variable substitution and relaxation conditions, to eliminate non-convexity in the model while ensuring solution accuracy.

[0137] Specifically, in the reactive power optimization sub-model, the power balance relationship typically includes nonlinear terms, such as the square of the node voltage magnitude or the product of the current magnitude. These nonlinear equations can be transformed into linear equations using the second-order cone relaxation method. For example, the square of the voltage magnitude can be defined as a new variable, and the original equations can be rewritten as a linear combination. Simultaneously, the quadratic inequalities in the voltage capacity constraint can be relaxed into convex constraints of the second-order cone form, for example, the upper and lower limits of the square of the voltage magnitude can be transformed into cone constraints. After the model transformation, the transformed convex optimization problem is solved using the CPLEX solver, for example, by iteratively calculating the optimal solution using the interior-point method, ultimately obtaining a voltage regulation scheme that satisfies the objective of minimizing network losses.

[0138] The power balance constraint can be transformed into:

[0139]

[0140]

[0141] Where P pv.j and Q pv.j P represents the active and reactive power of the photovoltaic system injected into node j. ess.j and Q ess.j P represents the active and reactive power injected into the energy storage system at node j. L.j and Q L.j The active and reactive power injected into node j by the load.

[0142] Voltage capacity constraints can be transformed into:

[0143]

[0144]

[0145] Among them, U N S is the rated voltage of the branch circuit. B This represents the maximum capacity of the branch line. and This indicates the upper and lower limits of the voltage after reactive power optimization.

[0146] This scheme accurately handles nonlinear constraints using the second-order cone relaxation method, combined with the efficient computational capabilities of the CPLEX solver, thus improving solution speed while ensuring solution quality. For example, existing techniques may neglect the impact of the squared term of voltage amplitude on network losses, while this scheme fully preserves this physical relationship through variable substitution. Through these technical solutions, the accuracy loss problem caused by model simplification in traditional reactive power optimization methods can be effectively solved, ensuring that the voltage adjustment scheme minimizes network losses while satisfying node voltage constraints. This scheme accurately describes the physical characteristics of the distribution network using mathematical programming methods, avoiding errors caused by model linearization, thereby improving the feasibility and economy of the optimization results.

[0147] The above is a detailed description of the process embodiment of the distribution network economic reactive power coordination scheduling optimization method provided in this application. To more clearly demonstrate the advantages of the technical solution of this application, this application also provides a verification example in the IEEE 33-bus system based on the above optimization method. The performance of the proposed optimized scheduling strategy is compared with that of the traditional scheduling strategy, and the scheduling effect of the proposed strategy and the efficiency and accuracy of the optimization algorithm used are evaluated. The experimental topology is as follows: Figure 2 As shown, the results are as follows:

[0148] Please see Figures 3 to 7 ,in, Figure 5 (a) and Figure 5 Figure (b) shows a comparison of the distribution network load before and after adopting the optimized scheduling strategy proposed in this application. As can be seen from the figure, the shiftable load is shifted from the peak periods of noon and evening to the low-load period of 7:00-9:00 AM; the significant reduction in shiftable load during the 10:00-20:00 period is transferred to the 4:00-8:00 AM period through scheduling; and the load that can be reduced almost disappears during the peak load period of 11:00-15:00, being distributed to other time periods or directly reduced. This demonstrates that the scheduling strategy proposed in this application can effectively regulate the three types of loads, perform peak shaving and valley filling, achieve balanced load distribution, and further improve the overall operating efficiency of the distribution network, proving that this strategy has a significant effect on load optimization.

[0149] To further verify the adaptability of the NSGAII-MOPSO algorithm used in this application to the proposed optimization model, this application compares the NSGAII-MOPSO algorithm with the NSGA-II and MOPSO algorithms. The results are shown in Table 1 below:

[0150]

[0151] Table 1 compares the computational resource consumption of different algorithms. The results show that the NSGAII-MOPSO algorithm consumes fewer computational resources than MOPSO and NSGA-II. The NSGAII-MOPSO algorithm combines the global search capability of NSGA-II with the local search capability of MOPSO, enabling it to find a high-quality solution set in a shorter time. In multi-objective optimization, NSGAII-MOPSO reduces unnecessary computation, achieving an 85.8% improvement in solution efficiency compared to MOPSO and NSGA-II algorithms.

[0152] Based on the calculation results of the embodiments, the proposed model in this application achieves power system economic efficiency and reactive power coordination optimization, and can significantly reduce carbon emissions from the distribution network compared to traditional single-stage optimization scheduling models. The proposed optimization scheduling strategy can be effectively solved using the NSGAII-MOPSO algorithm and Yalmip-CPLEX. Compared with MOPSO and NSGA-II, the computational efficiency of the NSGAII-MOPSO algorithm is improved by 85.8%.

[0153] The above are examples of verification instructions based on the present invention provided in this application. The following is a detailed description of the embodiment of the distribution network economic reactive power coordination and dispatch optimization device provided in this application.

[0154] Please see Figure 8 This application provides a distribution network economic reactive power coordination and dispatch optimization device, comprising:

[0155] The collaborative optimization model construction unit 201 is used to construct a collaborative optimization model for the distribution network based on the equipment nodes in the distribution network. The collaborative optimization model includes an economic optimization sub-model and a reactive power optimization sub-model. The equipment nodes include distributed power generation nodes, energy storage nodes, and flexible load nodes. The optimization objective of the economic optimization sub-model is to minimize carbon emissions and distribution network operating costs. The optimization objective of the reactive power optimization sub-model is to minimize distribution network losses while ensuring that node voltage does not exceed limits.

[0156] The first sub-model solving unit 202 is used to solve the economic optimization sub-model in multiple objectives through Pareto and heuristic optimization methods, obtain the optimized operation scheme of the equipment nodes, and pass the optimized operation scheme to the reactive power optimization sub-model as the boundary condition of the reactive power optimization sub-model.

[0157] The second sub-model solving unit 203 is used to solve the reactive power optimization sub-model through second-order cone programming and CPLEX solution methods, to obtain the distribution network voltage adjustment scheme with minimal network loss, and to feed the distribution network voltage adjustment scheme back to the economic optimization sub-model.

[0158] The collaborative optimization execution unit 204 is used to perform cross-iterative optimization based on the economic optimization sub-model and the reactive power optimization sub-model. When the preset iterative optimization conditions are met, it determines the coordinated scheduling optimization scheme of the distribution network based on the optimization parameters in the distribution network collaborative optimization model.

[0159] The collaborative optimization model construction unit is a module that establishes a multi-objective collaborative optimization framework by integrating the operational characteristics of distributed power sources, energy storage, and flexible loads. Specifically, it can be implemented using node topology analysis algorithms and multi-objective modeling tools, providing a unified model foundation for subsequent optimization. The first sub-model solution unit is a hybrid solution module that integrates Pareto front search and heuristic algorithms, implemented using a combined optimizer of NSGA-II and MOPSO, balancing conflicting economic objectives. The second sub-model solution unit is a computational module that transforms the non-convex optimization problem into a solvable convex form, implemented using second-order cone relaxation techniques and the commercial solver CPLEX, ensuring rapid convergence of the reactive power optimization model. The collaborative optimization execution unit is a control module that coordinates the iterative processes of economic and reactive power optimization, implemented using convergence condition judgment algorithms and parameter update mechanisms, achieving dynamic balance of the global optimization objectives.

[0160] Specifically, the device establishes a two-tiered model framework encompassing economic and reactive power optimization through a collaborative optimization model building unit. The economic optimization sub-model aims to minimize carbon emissions and operating costs, generating optimized schemes for distributed power generation, energy storage charging / discharging, and load adjustment through a first sub-model solving unit. This scheme, used as boundary conditions, is then input into the reactive power optimization sub-model, where a second sub-model solving unit generates a voltage adjustment strategy based on voltage constraints and network loss targets. The collaborative optimization execution unit achieves cross-iteration between the two sub-models through bidirectional data interaction. For example, if the voltage adjustment scheme causes the economic objective to deviate from a threshold, the economic optimization parameters are recalculated. During the iteration process, a preset convergence condition (e.g., the rate of change of the objective function is less than a set value) is used as the termination criterion, ultimately outputting a coordinated scheduling scheme that satisfies multiple objective constraints.

[0161] In some embodiments, the objective function of the economic optimization sub-model is specifically:

[0162]

[0163]

[0164] In the formula, The cost of purchasing and selling electricity between the distribution network and the main grid. , , and These are the operation and maintenance costs of flexible loads, energy storage systems, photovoltaic power, and wind power in the distribution network. For network loss cost, ρ e t For carbon emission density, P e t The power exchanged between the distribution network and the main grid is represented by T, which indicates the optimization period.

[0165] In some embodiments, the constraints of the economic optimization sub-model include:

[0166]

[0167]

[0168]

[0169]

[0170]

[0171]

[0172] In the formula, P L,t P represents the power absorbed by the load. grid,t P represents the power injected from the main grid into the distribution network at time t. ess,t E represents the power of the energy storage system. pv,T E wt,T and E L,T E represents the electricity generated by photovoltaic and wind power loads within period T. ess,dis,T and E ess,ch,T P represents the discharge and charging capacity of the energy storage system within period T. pv,max A represents the maximum power of photovoltaic power. t P indicates the operating status of photovoltaics. wt,max B represents the maximum power output of the wind turbine. t R indicates the operating status of wind power. t To reserve load power, P grid,max β represents the maximum exchange power between the distribution network and the main grid. τ P tran max and P tran min T represents the state of the transferable load and the upper and lower power limits of the transferable load, respectively. kp and z τ re T is the reduction time and reduction state for load reduction. re max and T re minP represents the maximum and minimum values ​​of the reduction time; ess max P ess min These represent the power of the energy storage system and the SoC, respectively. ess max and SoC ess min These represent the upper and lower limits of the depth of charge and discharge, respectively.

[0173] In some embodiments, the objective function of the reactive power optimization sub-model includes:

[0174]

[0175]

[0176]

[0177] In the formula, P loss U represents the network loss after reactive power optimization. d The node voltage deviation is represented by ω1 and ω2, where ω1 and ω2 represent weights, P0 represents the network loss before reactive power optimization, U0 is the average voltage deviation, and N is the average voltage deviation. k G represents the number of branches. ij U represents the electrical conductance between node i and node j. i U j θ represents the voltage magnitude at node i and node j, respectively. ij γ is the phase difference; γ1 is the penalty factor; U i U is the actual voltage at node i. Ni Where is the rated voltage and n is the number of nodes.

[0178] In some embodiments, the constraints of the reactive power optimization sub-model include:

[0179]

[0180]

[0181]

[0182]

[0183]

[0184] In the formula, z ij and I ij S represents the impedance and the current in branch ij. ij S represents the power from node i to node j; pv.j S ess.j and S L.jU represents the photovoltaic, energy storage, and load power injected into node j, respectively. j,min and U j,max I represents the lower and upper limits of the node voltage. ij,max Q represents the maximum current in branch ij; pvmin and Q pvmax Q represents the lower and upper limits of photovoltaic reactive power; wtmin and Q wtmax This represents the lower and upper limits of reactive power from wind power.

[0185] In some embodiments, the step of solving the economic optimization sub-model using Pareto and heuristic optimization methods to obtain the optimized operation scheme of the equipment node includes:

[0186] A Pareto population is randomly generated, and then the Pareto population is sorted based on non-dominated sorting and crowding distance sorting. The Pareto population is then split into a first subpopulation and a second subpopulation.

[0187] Based on the first subpopulation, a global search is performed using the NSGA-II algorithm and a preset iteration factor. Based on the second subpopulation, a local optimization is performed using the MOPSO algorithm and a preset mutation operator, through particle velocity / position updates. Based on the search and optimization results of the first and second subpopulations, the Pareto solution set of the economic optimization sub-model is obtained.

[0188] Based on the Pareto solution set, the optimized operation scheme of the device node is obtained, wherein the optimized operation scheme of the device node includes: distributed power output curve, energy storage charging and discharging plan, and flexible load adjustment amount.

[0189] In some embodiments, the reactive power optimization sub-model is solved using second-order cone programming and CPLEX methods to obtain a distribution network voltage adjustment scheme that minimizes network losses, including:

[0190] The power balance relationship in the reactive power optimization sub-model is transformed into a linear equation in the form of a second-order cone by using the second-order cone relaxation method, and the voltage capacity relationship in the reactive power optimization sub-model is transformed into a convex constraint in the form of a second-order cone.

[0191] Based on the linear equation and the convex constraint, the reactive power optimization sub-model is solved using CPLEX to obtain a distribution network voltage adjustment scheme that minimizes network losses.

[0192] Furthermore, such as Figure 9As shown, this application also provides a distribution network economic coordination and optimization scheduling terminal, including a memory 33 and a processor 31; the memory 33 and the processor 31 can be connected through a communication bus 34, the memory is used to store program code, the program code corresponds to the distribution network economic coordination and optimization scheduling method; the processor is used to read and execute the program code to implement the distribution network economic coordination and optimization scheduling method.

[0193] In this context, memory refers to the hardware module used to store program code, which can be implemented using solid-state drives (SSDs) or flash memory chips. It is used to persistently store the computational logic and data parameters required for the optimization scheduling algorithm. The processor refers to the computing unit that executes the program code, which can be implemented using multi-core central processing units (CPUs) or graphics processing units (GPUs). It accelerates the solution process of the optimization model through parallel computing, improving the efficiency of generating scheduling schemes.

[0194] Specifically, the program code stored in the memory contains computer instructions for steps such as constructing a coordinated optimization model of the distribution network, executing multi-objective solution algorithms, and handling cross-iterative optimization logic. During runtime, the processor first loads the program code from memory, parses the constraints and objective functions of the economic optimization sub-model and the reactive power optimization sub-model, and then calls algorithm modules such as Pareto optimization, heuristic optimization, and second-order cone programming to perform alternating optimization calculations on the two sub-models. During this process, the processor achieves dynamic feedback between the economic optimization results and the reactive power optimization results through memory interaction until the preset convergence conditions are met, ultimately outputting the coordinated scheduling scheme.

[0195] More specifically, the distribution network economic coordination and optimization scheduling method corresponding to the program code includes:

[0196] Based on the equipment nodes in the distribution network, a distribution network collaborative optimization model is constructed. The distribution network collaborative optimization model includes an economic optimization sub-model and a reactive power optimization sub-model. The equipment nodes include distributed power generation nodes, energy storage nodes, and flexible load nodes. The optimization objective of the economic optimization sub-model is to minimize carbon emissions and distribution network operating costs. The optimization objective of the reactive power optimization sub-model is to minimize distribution network losses while ensuring that node voltage does not exceed limits.

[0197] The economic optimization sub-model is solved using Pareto optimization and heuristic optimization methods to obtain the optimized operation scheme of the equipment node. The optimized operation scheme is then passed to the reactive power optimization sub-model as the boundary condition of the reactive power optimization sub-model.

[0198] The reactive power optimization sub-model is solved using second-order cone programming and CPLEX methods to obtain a distribution network voltage adjustment scheme that minimizes network losses, and the distribution network voltage adjustment scheme is fed back to the economic optimization sub-model.

[0199] Cross-iterative optimization is performed based on the economic optimization sub-model and the reactive power optimization sub-model. When the preset iterative optimization conditions are met, the coordinated scheduling optimization scheme of the distribution network is determined based on the optimization parameters in the distribution network collaborative optimization model.

[0200] In some embodiments, the objective function of the economic optimization sub-model is specifically:

[0201]

[0202]

[0203] In the formula, The cost of purchasing and selling electricity between the distribution network and the main grid. , , and These are the operation and maintenance costs of flexible loads, energy storage systems, photovoltaic power, and wind power in the distribution network. For network loss cost, ρ e t For carbon emission density, P e t The power exchanged between the distribution network and the main grid is represented by T, which indicates the optimization period.

[0204] In some embodiments, the constraints of the economic optimization sub-model include:

[0205]

[0206]

[0207]

[0208]

[0209]

[0210]

[0211] In the formula, P L,t P represents the power absorbed by the load. grid,t P represents the power injected from the main grid into the distribution network at time t. ess,t E represents the power of the energy storage system. pv,T E wt,T and E L,T E represents the electricity generated by photovoltaic and wind power loads within period T. ess,dis,T and E ess,ch,T P represents the discharge and charging capacity of the energy storage system within period T. pv,maxA represents the maximum power of photovoltaic power. t P indicates the operating status of photovoltaics. wt,max B represents the maximum power output of the wind turbine. t Indicates the operating status of wind power, R t To reserve load power, P grid,max β represents the maximum exchange power between the distribution network and the main grid. τ P tran max and P tran min T represents the state of the transferable load and the upper and lower power limits of the transferable load, respectively. kp and z τ re T is the reduction time and reduction state for load reduction. re max and T re min P represents the maximum and minimum values ​​of the reduction time; ess max P ess min These represent the power of the energy storage system and the SoC, respectively. ess max and SoC ess min These represent the upper and lower limits of the depth of charge and discharge, respectively.

[0212] In some embodiments, the objective function of the reactive power optimization sub-model includes:

[0213]

[0214]

[0215]

[0216] In the formula, P loss U represents the network loss after reactive power optimization. d The node voltage deviation is represented by ω1 and ω2, where ω1 and ω2 represent weights, P0 represents the network loss before reactive power optimization, U0 is the average voltage deviation, and N is the average voltage deviation. k G represents the number of branches. ij U represents the electrical conductance between node i and node j. i U j θ represents the voltage magnitude at node i and node j, respectively. ij γ is the phase difference; γ1 is the penalty factor; U i U is the actual voltage at node i. Ni Where is the rated voltage and n is the number of nodes.

[0217] In some embodiments, the constraints of the reactive power optimization sub-model include:

[0218]

[0219]

[0220]

[0221]

[0222]

[0223] In the formula, z ij and I ij S represents the impedance and the current in branch ij. ij S represents the power from node i to node j; pv.j S ess.j and S L.j U represents the photovoltaic, energy storage, and load power injected into node j, respectively. j,min and U j,max I represents the lower and upper limits of the node voltage. ij,max Q represents the maximum current in branch ij; pvmin and Q pvmax Q represents the lower and upper limits of photovoltaic reactive power; wtmin and Q wtmax This represents the lower and upper limits of reactive power from wind power.

[0224] In some embodiments, the step of solving the economic optimization sub-model using Pareto and heuristic optimization methods to obtain the optimized operation scheme of the equipment node includes:

[0225] A Pareto population is randomly generated, and then the Pareto population is sorted based on non-dominated sorting and crowding distance sorting. The Pareto population is then split into a first subpopulation and a second subpopulation.

[0226] Based on the first subpopulation, a global search is performed using the NSGA-II algorithm and a preset iteration factor. Based on the second subpopulation, a local optimization is performed using the MOPSO algorithm and a preset mutation operator, through particle velocity / position updates. Based on the search and optimization results of the first and second subpopulations, the Pareto solution set of the economic optimization sub-model is obtained.

[0227] Based on the Pareto solution set, the optimized operation scheme of the device node is obtained, wherein the optimized operation scheme of the device node includes: distributed power output curve, energy storage charging and discharging plan, and flexible load adjustment amount.

[0228] In some embodiments, the reactive power optimization sub-model is solved using second-order cone programming and CPLEX methods to obtain a distribution network voltage adjustment scheme that minimizes network losses, including:

[0229] The power balance relationship in the reactive power optimization sub-model is transformed into a linear equation in the form of a second-order cone by using the second-order cone relaxation method, and the voltage capacity relationship in the reactive power optimization sub-model is transformed into a convex constraint in the form of a second-order cone.

[0230] Based on the linear equation and the convex constraint, the reactive power optimization sub-model is solved using CPLEX to obtain a distribution network voltage adjustment scheme that minimizes network losses.

[0231] Furthermore, this application also proposes an embodiment of a computer-readable storage medium storing program code that is read and executed by a processor to implement an economically coordinated and optimized scheduling method for a power distribution network.

[0232] Computer-readable storage media refers to physical carriers capable of persistently storing program code. Specifically, they can be solid-state drives (SSDs), hard disk drives (HDDs), or flash memory chips. Data is recorded electromagnetically or optically to ensure the program code is retained even after power loss. Program code refers to a sequence of instructions executable by a processor. It can be written in a high-level programming language and compiled into machine code. It includes the construction logic of the distribution network collaborative optimization model, multi-objective solution algorithms, and cross-iterative optimization processes, used to control the processor to execute optimization scheduling methods.

[0233] Specifically, after the program code stored in the computer-readable storage medium is read by the processor, it drives the processor to perform the following operations: First, a collaborative model incorporating economic optimization and reactive power optimization is constructed based on the parameters of distributed power sources, energy storage, and flexible load nodes in the distribution network; then, equipment operation schemes are generated through Pareto solving and heuristic optimization, and these schemes are input as boundary conditions into the reactive power optimization model; further, a voltage adjustment scheme is calculated using second-order cone programming and the CPLEX solver, and the results are fed back to the economic optimization model for cross-iteration; finally, a coordinated and optimized scheduling scheme is output when the iteration termination condition is met. During this process, the program code achieves a dynamic balance between economic objectives and voltage stability through pre-defined algorithm modules.

[0234] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the terminals, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0235] In the several embodiments provided in this application, it should be understood that the disclosed terminals, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, or indirect coupling or communication connection between devices or units, and may be electrical, mechanical, or other forms.

[0236] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a particular order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the application described herein can be implemented, for example, in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0237] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0238] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0239] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0240] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0241] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit it. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for optimizing the economic reactive power coordinated dispatch of a distribution network, characterized in that, include: Based on the equipment nodes in the distribution network, a distribution network collaborative optimization model is constructed. The distribution network collaborative optimization model includes an economic optimization sub-model and a reactive power optimization sub-model. The equipment nodes include distributed power generation nodes, energy storage nodes, and flexible load nodes. The optimization objective of the economic optimization sub-model is to minimize carbon emissions and distribution network operating costs. The optimization objective of the reactive power optimization sub-model is to minimize distribution network losses while ensuring that node voltage does not exceed limits. The economic optimization sub-model is solved using Pareto optimization and heuristic optimization methods to obtain the optimized operation scheme of the equipment node. The optimized operation scheme is then passed to the reactive power optimization sub-model as the boundary condition of the reactive power optimization sub-model. The reactive power optimization sub-model is solved using second-order cone programming and CPLEX methods to obtain a distribution network voltage adjustment scheme that minimizes network losses, and the distribution network voltage adjustment scheme is fed back to the economic optimization sub-model. Cross-iterative optimization is performed based on the economic optimization sub-model and the reactive power optimization sub-model. When the preset iterative optimization conditions are met, the coordinated scheduling optimization scheme of the distribution network is determined according to the optimization parameters in the distribution network collaborative optimization model.

2. The method for optimizing economic reactive power coordination and dispatch in a distribution network according to claim 1, characterized in that, The objective function of the economic optimization sub-model is specifically: In the formula, and These are the outputs of the two objective functions of the economic optimization sub-model, respectively. The cost of purchasing and selling electricity between the distribution network and the main grid. , , and These are the operation and maintenance costs of flexible loads, energy storage systems, photovoltaic power, and wind power in the distribution network. For network loss cost, ρ e t For carbon emission density, P e t The power exchanged between the distribution network and the main grid is represented by T, which indicates the optimization period.

3. The method for optimizing economic reactive power coordination and dispatch in a distribution network according to claim 2, characterized in that, The constraints of the economic optimization sub-model include: In the formula, P L,t P represents the power absorbed by the load. grid,t P represents the power injected from the main grid into the distribution network at time t. ess,t E represents the power of the energy storage system. pv,T E wt,T and E L,T E represents the electricity generated by photovoltaic and wind power loads within period T. ess,dis,T and E ess,ch,T P represents the discharge and charging capacity of the energy storage system within period T. pv,t P represents the real-time photovoltaic power at time t. pv,max A represents the maximum power of photovoltaic power. t P indicates the operating status of photovoltaics. wt,t P represents the real-time wind power output at time t. wt,max B represents the maximum power output of the wind turbine. t R indicates the operating status of wind power. t To reserve load power, P grid,max This represents the maximum exchange power between the distribution network and the main grid. T represents the state of the transferable load and the upper and lower power limits of the transferable load, respectively. kp and z τ re The reduction time and reduction status are for load reduction. and This indicates the maximum and minimum values ​​of the reduction time; , These represent the maximum and minimum power values ​​of the energy storage system, respectively. and These represent the maximum and minimum depths of charge and discharge, respectively.

4. The method for optimizing economic reactive power coordination and dispatch in a distribution network according to claim 1, characterized in that, The objective function of the reactive power optimization sub-model includes: In the formula, P is the output of the objective function of the reactive power optimization sub-model. loss U represents the network loss after reactive power optimization. d The node voltage deviations between each node voltage and its rated voltage, ω1 and ω2 represent weights, P0 represents the network loss before reactive power optimization, U0 is the average voltage deviation, and N... k G represents the number of branches. ij U represents the electrical conductance between node i and node j. i U j θ represents the voltage magnitude at node i and node j, respectively. ij γ is the phase difference; γ1 is the penalty factor; U i U is the actual voltage at node i. Ni Where is the rated voltage and n is the number of nodes.

5. The method for optimizing economic reactive power coordination and dispatch in a distribution network according to claim 4, characterized in that, The constraints of the reactive power optimization sub-model include: In the formula, z ij and I ij S represents the impedance and the current in branch ij. ij S represents the power from node i to node j; pv.j S ess.j and S L.j U represents the photovoltaic, energy storage, and load power injected into node j, respectively. j,min and U j,max I represents the lower and upper limits of the node voltage. ij,max Q represents the maximum current in branch ij; pvmin and Q pvmax Q represents the lower and upper limits of photovoltaic reactive power; wtmin and Q wtmax This represents the lower and upper limits of reactive power from wind power.

6. The method for optimizing economic reactive power coordination and dispatch in a distribution network according to claim 3, characterized in that, The process of solving the economic optimization sub-model using Pareto and heuristic optimization methods to obtain the optimized operation scheme for the equipment nodes includes: A Pareto population is randomly generated, and then the Pareto population is sorted based on non-dominated sorting and crowding distance sorting. The Pareto population is then split into a first subpopulation and a second subpopulation. Based on the first subpopulation, a global search is performed using the NSGA-II algorithm and a preset iteration factor. Based on the second subpopulation, a local optimization is performed using the MOPSO algorithm and a preset mutation operator, through particle velocity / position updates. Based on the search and optimization results of the first and second subpopulations, the Pareto solution set of the economic optimization sub-model is obtained. Based on the Pareto solution set, the optimized operation scheme of the device node is obtained, wherein the optimized operation scheme of the device node includes: distributed power output curve, energy storage charging and discharging plan, and flexible load adjustment amount.

7. The method for optimizing economic reactive power coordination and dispatch in a distribution network according to claim 5, characterized in that, Solving the reactive power optimization sub-model using second-order cone programming and CPLEX yields distribution network voltage adjustment schemes that minimize network losses, including: The power balance relationship in the reactive power optimization sub-model is transformed into a linear equation in the form of a second-order cone by using the second-order cone relaxation method, and the voltage capacity relationship in the reactive power optimization sub-model is transformed into a convex constraint in the form of a second-order cone. Based on the linear equation and the convex constraint, the reactive power optimization sub-model is solved using CPLEX to obtain a distribution network voltage adjustment scheme that minimizes network losses.

8. A distribution network economic reactive power coordination and dispatch optimization device, characterized in that, include: The collaborative optimization model construction unit is used to construct a collaborative optimization model for the distribution network based on the equipment nodes in the distribution network. The collaborative optimization model includes an economic optimization sub-model and a reactive power optimization sub-model. The equipment nodes include distributed power generation nodes, energy storage nodes, and flexible load nodes. The optimization objective of the economic optimization sub-model is to minimize carbon emissions and distribution network operating costs. The optimization objective of the reactive power optimization sub-model is to minimize distribution network losses while ensuring that node voltage does not exceed limits. The first sub-model solving unit is used to solve the economic optimization sub-model in multiple objectives through Pareto and heuristic optimization methods to obtain the optimized operation scheme of the equipment node, and to pass the optimized operation scheme to the reactive power optimization sub-model as the boundary condition of the reactive power optimization sub-model. The second sub-model solving unit is used to solve the reactive power optimization sub-model using second-order cone programming and CPLEX methods to obtain a distribution network voltage adjustment scheme that minimizes network losses, and then feeds the distribution network voltage adjustment scheme back to the economic optimization sub-model. The collaborative optimization execution unit is used to perform cross-iterative optimization based on the economic optimization sub-model and the reactive power optimization sub-model. When the preset iterative optimization conditions are met, the unit determines the coordinated scheduling optimization scheme of the distribution network based on the optimization parameters in the distribution network collaborative optimization model.

9. A distribution network economic reactive power coordination and dispatch optimization terminal, characterized in that, include: Memory and processor; The memory is used to store program code, which corresponds to the distribution network economic reactive power coordinated dispatch optimization method as described in any one of claims 1 to 7; The processor is used to read and execute the program code to implement the distribution network economic reactive power coordination and scheduling optimization method.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium contains program code that is read and executed by a processor to implement the distribution network economic reactive power coordination and scheduling optimization method as described in any one of claims 1 to 7.

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