Intraday economic optimal dispatching method, system, device, medium and product for active power distribution network

Abstract

The present application relates to the technical field of power systems, and discloses a kind of active power distribution network daily economic optimization scheduling method, system, equipment, medium and product, the present application minimizes the minimum of comprehensive operation cost, minimizes voltage fluctuation, minimizes load peak valley difference and minimizes energy storage life consumption of active power distribution network as multi-optimization target, and determines the constraint condition of the multi-optimization target according to the balance state of cuttable load, the balance state of transferable load and the operation balance state of power distribution network, constructs power distribution network optimization scheduling model, and is optimized to solve to power distribution network optimization scheduling model, determines the daily scheduling optimal strategy of active power distribution network according to optimal solution, so as to comprehensively consider comprehensive operation cost, voltage fluctuation, load peak valley difference and energy storage life consumption, can better cope with the changing load demand and energy resources, improve the resource utilization rate and flexibility of active power distribution network optimization scheduling strategy, guarantee the stability and economy of power supply.

Description

Technical Field

[0001] This invention relates to the field of power system technology, and in particular to a method, system, equipment, medium and product for intraday economic optimization dispatching of active distribution networks. Background Technology

[0002] Traditional distribution networks are gradually transforming into active distribution networks that integrate power generation, grid, load, and storage. Active distribution networks can increase the network's capacity to accommodate renewable energy and improve asset utilization. By coordinating and controlling distributed power sources, energy storage devices, and flexible loads, active distribution networks can utilize renewable energy more effectively. While large-scale renewable energy integration into active distribution networks contributes to the decarbonization and sustainable development of the power system, it also brings numerous problems such as load fluctuations, power quality issues, and wind and solar curtailment. Accurate power forecasting for wind and solar power allows for advance planning of energy storage systems and backup power dispatch strategies, thereby maximizing the absorption of renewable energy. Load forecasting can anticipate changes in electricity demand trends, providing a scientific basis for distribution network dispatch. Through accurate load forecasting, grid operators can rationally arrange power generation plans, reduce reserve capacity requirements, and lower operating costs. Active distribution networks, through deep learning models to predict wind and solar power output and load power, can optimize the charging and discharging strategies of energy storage systems. Simultaneously, combined with optimized dispatching of flexible loads, this can effectively smooth power fluctuations and improve the utilization rate of renewable energy.

[0003] Traditional power system dispatching relies primarily on empirical rules and static models, which are ill-suited to the characteristics of modern power grids: large-scale integration of renewable energy, rapid load changes, and increased uncertainty. Traditional rigid loads cannot meet the grid's real-time balancing and regulation requirements, necessitating flexible loads to provide the necessary flexibility. Demand response, as a crucial controllable resource, is introduced into the distribution network system. Through price signals or direct control, it incentivizes users to consume electricity during off-peak hours or reduce consumption during peak hours. This helps to better integrate renewable energy, reduce grid load, and improve its utilization rate. Simultaneously, the combined effect of demand response and distributed generation, energy storage, and other equipment ensures the safe, stable, and economical operation of the distribution network system.

[0004] In summary, current optimization scheduling strategies for active power distribution networks have poor resource utilization and flexibility, making it difficult to guarantee the stability of power supply. Summary of the Invention

[0005] In view of this, the present invention provides a method, system, equipment, medium and product for intraday economic optimization scheduling of active distribution networks, which solves the technical problems of poor resource utilization and flexibility of current optimization scheduling strategies for active distribution networks, making it difficult to guarantee the stability of power supply.

[0006] The first aspect of this invention provides a method for intraday economic optimization scheduling of an active distribution network, comprising:

[0007] The optimization objectives are to minimize the overall operating cost of the active distribution network, minimize voltage fluctuation, minimize load peak-valley difference, and minimize energy storage life loss. The constraints of the optimization objectives are determined based on the load balance state that can be reduced, the load balance state that can be transferred, and the distribution network operation balance state. A distribution network optimization scheduling model is then constructed.

[0008] The optimal solution is obtained by finding the optimal solution for the distribution network optimization scheduling model, and the optimal intraday scheduling strategy for the active distribution network is determined based on the optimal solution.

[0009] The active distribution network is scheduled according to the intraday optimal scheduling strategy.

[0010] Preferably, the objective function corresponding to the multiple optimization objectives is:

[0011]

[0012] In the formula, The objective function value, , , , Each item has its own weight. To account for overall operating costs, For voltage fluctuations, For the difference between peak and valley loads, This is due to energy storage lifespan loss;

[0013] in,

[0014] In the formula, For load dispatch cost, For the cost of energy storage dispatch, For energy storage revenue, For electricity purchase costs, For network loss costs, For backup costs, Cost of carbon emissions;

[0015] in,

[0016] In the formula, t represents time, T represents the total time period, and E represents the total time period. s (t) and E c (t) represents the amount of load that can be shifted and load that can be cut off, respectively. de Incentive pricing to respond to demand;

[0017]

[0018] In the formula, i is the index of the energy storage device. A collection of energy storage devices. and Let represent the discharge power and charging power of energy storage device i at time t, respectively;

[0019]

[0020] In the formula, The incentive electricity price for energy storage discharge;

[0021]

[0022] In the formula, n is the node index, n b For a set of nodes, Let be the active power output of node n at time t. This refers to the unit price of electricity purchased.

[0023]

[0024] In the formula, l is the line index. For the collection of routes, Let be the square of the current in line l at time t. Let be the resistance of line l;

[0025]

[0026] In the formula, This represents the maximum margin of available power for energy storage. For the charge and discharge cycle of the energy storage device, For the revenue per kilowatt-hour from energy storage;

[0027]

[0028] In the formula, Total energy consumption Carbon emission factor For carbon price;

[0029]

[0030] In the formula, Let be the square of the voltage at the nth node at time t. Let be the square of the voltage at the (n+1)th node at time t;

[0031]

[0032] In the formula, , These represent the weighting coefficients for peak shaving and load smoothing, respectively. This represents the minimum power margin available for energy storage. Rated power, , These represent the total load of the active distribution network at time t and the total load at time t+1, respectively. This represents the total load difference between adjacent time points;

[0033]

[0034] In the formula, This is the lifespan loss coefficient;

[0035] in,

[0036]

[0037] In the formula, N represents the number of charge-discharge cycles of the energy storage device at a depth of discharge of D. Where is the cycle life of the energy storage device, and D is the depth of discharge of the energy storage device.

[0038] Preferably, the constraints include load reduction constraints, load transfer constraints, and distribution network operation balance constraints; wherein, the load reduction constraints include load reduction amount limit constraints, load reduction amount time-series change smoothing constraints, load start-up and shutdown time constraints, and load reduction limit constraints.

[0039] The transferable load constraints include transferable load power limit constraints, minimum continuous operating time constraints, state of charge constraints, state of charge / discharge constraints, charge / discharge power constraints, energy storage balance constraints, and energy storage capacity constraints.

[0040] The power distribution network operation balance constraints include wind power constraints, photovoltaic power constraints, power balance constraints, node voltage constraints, branch power constraints, and line current constraints.

[0041] Preferably, the method further includes:

[0042] Data standardization is performed on each optimization objective within the objective function; wherein, the optimization objectives include comprehensive operating cost, voltage fluctuation, peak-valley load difference, and energy storage life loss;

[0043] The standardized value proportion of each optimization objective is determined based on the standardized optimization objective, and the information entropy of the optimization objective is determined based on the standardized value proportion.

[0044] The weights corresponding to each optimization objective are determined based on the information entropy of each optimization objective.

[0045] Preferably, the optimization solution of the distribution network scheduling model includes:

[0046] The optimal solution for the power distribution network scheduling model is obtained based on the snow ablation optimization algorithm.

[0047] Preferably, the optimization solution of the distribution network scheduling model based on the snow ablation optimization algorithm includes:

[0048] Initialize the population, determine the population size and maximum number of iterations; wherein, the individuals in the population are obtained by encoding multiple intraday scheduling candidate strategies of the active distribution network;

[0049] The objective function is used as the fitness function, and the fitness value of each individual is calculated based on the fitness function.

[0050] Individuals in the population are sorted according to their fitness values, and the individual with the best fitness value is determined.

[0051] Determine whether the individual with the optimal fitness value has met the convergence condition or whether the current iteration number has reached the maximum iteration number;

[0052] If the individual with the best fitness value has not reached the convergence condition and the current iteration number has not reached the maximum iteration number, then the position of each individual is updated according to the snow ablation amount to form a new population, and the current iteration number is updated by 1.

[0053] The process continues with the new population, using the objective function as the fitness function and calculating the fitness value of each individual, until the individual with the optimal fitness value reaches the convergence condition or the current iteration count reaches the maximum iteration count. Finally, the individual with the optimal fitness value is output.

[0054] The optimal solution is determined based on the individual with the best final fitness value.

[0055] Secondly, the present invention also provides an intraday economic optimization dispatching system for active distribution networks, comprising:

[0056] The optimization model construction module is used to construct an optimized scheduling model for the distribution network with multiple optimization objectives, including minimizing the overall operating cost of the active distribution network, minimizing voltage fluctuations, minimizing the load peak-valley difference, and minimizing energy storage lifetime loss. The module also determines the constraints of the multiple optimization objectives based on the load balance state that can be reduced, the load balance state that can be transferred, and the distribution network operation balance state.

[0057] The model solving module is used to find the optimal solution for the distribution network optimization scheduling model and determine the intraday optimal scheduling strategy for the active distribution network based on the optimal solution.

[0058] The scheduling strategy execution module is used to schedule the active distribution network according to the intraday optimal scheduling strategy.

[0059] Thirdly, the present invention also provides an electronic device, the electronic device including a memory and a processor, the memory storing a computer program, the computer program being executed by the processor causing the processor to perform the steps of the active distribution network intraday economic optimization scheduling method as described in the first aspect.

[0060] Fourthly, the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed, implements the steps of the intraday economic optimization scheduling method for active distribution networks as described in the first aspect.

[0061] Fifthly, the present invention also provides a computer program product comprising a computer program stored on a non-transitory computer-readable storage medium, the computer program comprising program instructions, wherein when the program instructions are executed by a computer, the computer performs the steps of the intraday economic optimization scheduling method for active distribution networks as described in the first aspect.

[0062] As can be seen from the above technical solutions, this invention takes minimizing the overall operating cost, voltage fluctuation, load peak-valley difference, and energy storage lifetime loss of the active distribution network as multiple optimization objectives. It determines the constraints of these multiple optimization objectives based on the load balance state that can be reduced, the load balance state that can be transferred, and the distribution network operation balance state. A distribution network optimization scheduling model is constructed, and the optimal solution is obtained from this model. Based on the optimal solution, the optimal intraday scheduling strategy for the active distribution network is determined. This comprehensively considers overall operating cost, voltage fluctuation, load peak-valley difference, and energy storage lifetime loss, enabling better response to constantly changing load demands and energy resources. It improves the resource utilization rate and flexibility of the optimized scheduling strategy for the active distribution network, ensuring the stability and economy of power supply. Attached Figure Description

[0063] To more clearly illustrate the technical solutions in the embodiments of the present invention 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 the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0064] Figure 1 This is an application environment diagram of an intraday economic optimization scheduling method for an active distribution network provided by an embodiment of the present invention;

[0065] Figure 2 A flowchart illustrating an intraday economic optimization scheduling method for an active distribution network, provided as an embodiment of the present invention;

[0066] Figure 3 A graph showing the relationship between energy storage cycle life and depth of discharge provided for embodiments of the present invention;

[0067] Figure 4 This is a structural diagram of the IEEE 33-node power distribution system provided in an embodiment of the present invention;

[0068] Figure 5 The distribution network base load and wind and solar power output curves provided in this embodiment of the invention;

[0069] Figure 6 This is a schematic diagram of flexible load optimization scheduling provided in an embodiment of the present invention;

[0070] Figure 7 Energy storage charge / discharge power diagram provided for embodiments of the present invention;

[0071] Figure 8 The energy storage SOC curve provided for embodiments of the present invention;

[0072] Figure 9 This is a schematic diagram illustrating the energy storage charging and discharging process and the main grid output, provided in an embodiment of the present invention.

[0073] Figure 10 This is a schematic diagram showing the node voltage before and after optimization, provided in an embodiment of the present invention.

[0074] Figure 11 This is a schematic diagram illustrating the average voltage situation provided in an embodiment of the present invention;

[0075] Figure 12 This is a schematic diagram of network losses in a power distribution system provided in an embodiment of the present invention;

[0076] Figure 13 This is a schematic diagram comparing the optimization scheduling effects in different scenarios provided by embodiments of the present invention;

[0077] Figure 14 A schematic diagram of the IEEE 118-node system provided in an embodiment of the present invention;

[0078] Figure 15 A schematic diagram of an improved IEEE 118 power distribution system operation plan provided for embodiments of the present invention;

[0079] Figure 16 A schematic diagram illustrating the improved flexible load scheduling results of a 118-node system provided in an embodiment of the present invention;

[0080] Figure 17 This is a schematic diagram of the charging and discharging power of the energy storage system provided in an embodiment of the present invention;

[0081] Figure 18 This is a schematic diagram of the SOC (State of Charge) of an energy storage system provided in an embodiment of the present invention;

[0082] Figure 19 This is a schematic diagram of the voltage curves of each node before and after optimized scheduling provided in an embodiment of the present invention;

[0083] Figure 20 This is a schematic diagram of network loss curves before and after optimized scheduling provided in an embodiment of the present invention;

[0084] Figure 21 This is a schematic diagram of the structure of an intraday economic optimization dispatching system for an active distribution network provided in an embodiment of the present invention;

[0085] Figure 22 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0086] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0087] The intraday economic optimization scheduling method for active distribution networks provided in this application embodiment can be applied to, for example... Figure 1 In the application environment shown, terminal 101 communicates with server 102 via a network. A data storage system can store the data that server 102 needs to process. The data storage system can be integrated onto server 102 or placed on the cloud or other network servers. Terminal 101 or server 102 uses minimizing the overall operating cost of the active distribution network, minimizing voltage fluctuations, minimizing load peak-to-valley differences, and minimizing energy storage lifespan loss as multiple optimization objectives. It determines the constraints of these multiple optimization objectives based on the load balance state that can be reduced, the load balance state that can be transferred, and the distribution network operation balance state, and constructs a distribution network optimization scheduling model. It then seeks the optimal solution for the distribution network optimization scheduling model, determines the optimal intraday scheduling strategy for the active distribution network based on the optimal solution, and schedules the active distribution network according to the optimal intraday scheduling strategy.

[0088] Terminal 101 can be, but is not limited to, various personal computers, laptops, smartphones, and tablets.

[0089] Server 102 can be a standalone physical server, a server cluster or distributed system consisting of multiple physical servers, or a cloud server that provides cloud computing services.

[0090] like Figure 2As shown in the embodiments of this application, a method for intraday economic optimization scheduling of an active distribution network is provided, which is applied to... Figure 1 Taking terminal 101 or server 102 as an example, the explanation includes the following steps S1 to S3. Wherein:

[0091] Step S1: Minimize the overall operating cost of the active distribution network, minimize voltage fluctuation, minimize load peak-valley difference, and minimize energy storage life loss as multiple optimization objectives. Based on the load balance state that can be reduced, the load balance state that can be transferred, and the distribution network operation balance state, determine the constraints of the multiple optimization objectives and construct the distribution network optimization scheduling model.

[0092] Among these, the objectives are to minimize the overall operating cost of the active distribution network, minimize voltage fluctuations, minimize peak-to-valley load differences, and minimize energy storage lifespan losses. By comprehensively considering these optimization objectives, intraday economic optimization scheduling of the active distribution network can be achieved, improving the network's operating efficiency and economic benefits.

[0093] The reduceable load balance state measures the real-time status of reduceable loads in a distribution network, taking into account both the economics of load dispatching and the stability of grid operation. Specifically, by analyzing the costs of load dispatching, the status of energy storage devices, and the overall operation of the grid, the reduceable load balance state ensures that load demand is met while minimizing the impact of load dispatching on grid operating costs and the environment. This state is a key element in achieving intraday economically optimized dispatching because it directly affects the achievement of optimization objectives such as overall operating costs, voltage fluctuations, peak-to-valley load differences, and energy storage lifespan losses. Through precise control of the reduceable load balance state, the method of this invention can more effectively balance the economy and stability of the grid, thereby improving the overall operating efficiency of active distribution networks.

[0094] Step S2: Find the optimal solution for the distribution network optimization scheduling model, and determine the optimal intraday scheduling strategy for the active distribution network based on the optimal solution.

[0095] Among them, the method for finding the optimal solution of the distribution network optimization scheduling model can be achieved by using mathematical solvers or the like.

[0096] Step S3: Dispatch the active distribution network according to the intraday optimal dispatch strategy.

[0097] Specifically, scheduling instructions are generated based on the optimal intraday scheduling strategy and then sent to the active distribution network for scheduling.

[0098] It should be noted that the embodiments of this application take minimizing the overall operating cost, voltage fluctuation, load peak-valley difference, and energy storage lifetime loss of the active distribution network as multiple optimization objectives. The constraints of the multiple optimization objectives are determined based on the load balance state that can be reduced, the load balance state that can be transferred, and the distribution network operation balance state. A distribution network optimization scheduling model is constructed, and the optimal solution of the distribution network optimization scheduling model is obtained. The optimal intraday scheduling strategy of the active distribution network is determined based on the optimal solution. In this way, by comprehensively considering the overall operating cost, voltage fluctuation, load peak-valley difference, and energy storage lifetime loss, it can better cope with the ever-changing load demand and energy resources, improve the resource utilization rate and flexibility of the optimization scheduling strategy of the active distribution network, and ensure the stability and economy of power supply.

[0099] In some embodiments, the objective function corresponding to multiple optimization objectives is:

[0100]

[0101] In the formula, The objective function value, , , , Each item has its own weight. To account for overall operating costs, For voltage fluctuations, For the difference between peak and valley loads, This is due to energy storage lifespan loss;

[0102] in,

[0103] In the formula, For load dispatch cost, For the cost of energy storage dispatch, For energy storage revenue, For electricity purchase costs, For network loss costs, For backup costs, Cost of carbon emissions;

[0104] in,

[0105] In the formula, t represents time, T represents the total time period, and E represents the total time period. s (t) and E c (t) represents the amount of load that can be shifted and load that can be cut off, respectively. de Incentive pricing to respond to demand;

[0106]

[0107] In the formula, i is the index of the energy storage device. A collection of energy storage devices. and Let represent the discharge power and charging power of energy storage device i at time t, respectively;

[0108]

[0109] In the formula, The incentive electricity price for energy storage discharge;

[0110]

[0111] In the formula, n is the node index, n b For a set of nodes, Let be the active power output of node n at time t. This refers to the unit price of electricity purchased.

[0112]

[0113] In the formula, l is the line index. For the collection of routes, Let be the square of the current in line l at time t. Let be the resistance of line l;

[0114]

[0115] In the formula, This represents the maximum margin of available power for energy storage. For the charge and discharge cycle of the energy storage device, For the revenue per kilowatt-hour from energy storage;

[0116]

[0117] In the formula, Total energy consumption Carbon emission factor For carbon price;

[0118] In distribution network optimization and dispatching, minimizing voltage fluctuations is an important objective function. Voltage fluctuations affect power quality and the safe operation of equipment; therefore, voltage deviation is often included as one of the optimization objectives in optimization and dispatching models. Specifically, the objective function for minimizing voltage fluctuations can be expressed as:

[0119]

[0120] In the formula, Let be the square of the voltage at the nth node at time t. Let be the square of the voltage at the (n+1)th node at time t;

[0121] The objective function of peak shaving and valley filling is mainly used to smooth the load curve, reduce the peak-to-valley difference in load, and thus improve the stability and economy of the power grid. The objective function of peak shaving and valley filling is expressed as follows:

[0122]

[0123] In the formula, , These represent the weighting coefficients for peak shaving and load smoothing, respectively, and can take values ​​of 0.25 and 0.75. This represents the minimum power margin available for energy storage. Rated power, , These represent the total load of the active distribution network at time t and the total load at time t+1, respectively. This represents the total load difference between adjacent time points;

[0124] In the optimized dispatching of active power distribution networks, to ensure the reliability of system operation, energy storage systems may frequently switch between charging and discharging states during operation, accelerating the depletion of energy storage life and hindering the economic viability of energy storage participation in demand response. Therefore, rationally considering lifespan loss helps maintain the good operating condition of energy storage systems and ensure their stable role in the distribution network. The cycle life of energy storage systems is affected by various factors such as temperature, number of charge-discharge cycles, depth of charge-discharge, and peak current, especially the depth of charge-discharge and number of charge-discharge cycles.

[0125] Among them, the energy storage life loss is:

[0126]

[0127] In the formula, This is the lifespan loss coefficient;

[0128] in,

[0129] By employing standardized battery aging test methods, long-term cycle tests were conducted at different DOD levels (20%, 50%, 80%, etc.) to obtain a quantitative relationship curve between battery cycle life and depth of discharge. See details below. Figure 3 The results are shown. Among various energy storage materials, this paper selects lithium batteries, which have high charge-discharge efficiency, long cycle life, high specific energy, and high specific power, as the research object to analyze the cycle loss characteristics of the energy storage system. The characteristic curve is fitted with a fourth-order polynomial function, and the functional relationship is as follows:

[0130]

[0131] In the formula, N represents the number of charge-discharge cycles of the energy storage device at a depth of discharge of D. The cycle life of the energy storage is denoted by D, and the depth of discharge of the energy storage device is denoted by D, which takes the value [0,1].

[0132] To address the volatility and randomness brought about by renewable energy integration, this invention introduces demand-side response constraints for transferable and reduceable loads into the active distribution network optimization scheduling model. By using transferable loads to achieve peak shaving and valley filling, not only can the renewable energy absorption capacity be improved, but also the curtailment of electricity can be avoided. Furthermore, this model can ensure operational stability during grid faults or power shortages, prioritizing the power supply needs of critical loads. Simultaneously, the demand-side response mechanism helps users reduce electricity costs or obtain economic compensation through time-of-use pricing, and supports grid operators in reducing investment and operating costs. By promoting the coordinated operation of transferable and reduceable loads with distributed power sources and energy storage, this invention achieves the goals of optimizing source-load interaction and improving resource integration efficiency.

[0133] In some embodiments, the constraints include load reduction constraints, load transfer constraints, and distribution network operation balance constraints; wherein, load reduction constraints include load reduction amount limit constraints, load reduction amount time-series change smoothing constraints, load start-up and shutdown time constraints, and load reduction limit constraints.

[0134] The load reduction limit constraint restricts the maximum and minimum amount of load that can be reduced, ensuring that excessive load is not used during optimized scheduling, thus preventing disruption to the stable operation of the power grid. This constraint is achieved by setting upper and lower limits for the load that can be reduced, ensuring a balance between the economic efficiency of load scheduling and the stability of power grid operation.

[0135] Specifically, the load reduction limit constraint is characterized by the range of values ​​for the load reduction amount and the state constraint formula, as follows:

[0136]

[0137] In the formula, Let t be the amount of load that can be reduced. The maximum amount of load that can be reduced at time t. This is a binary variable representing the load reduction state at time t. =0 indicates that the load cannot be reduced. =1 indicates that the load can be reduced.

[0138] The constraint on the smoothing of load reduction time series changes requires that the amount of load that can be reduced change smoothly over time to avoid the impact of frequent load starts and stops on the power grid. This constraint is measured by introducing a load dispatch smoothness index to ensure the continuity and stability of load dispatch.

[0139] Specifically, the constraint for smoothing the time-series changes in load that can be reduced is:

[0140]

[0141] In the formula, This represents the amount of load that can be reduced at time t-1. , These represent the upper and lower limits of the load reduction, respectively.

[0142] In practical applications, demand response must strictly adhere to start-up and shutdown time constraints; otherwise, it may lead to a significant increase in load curve volatility, adversely affecting the stable operation of the power system. Setting reasonable start-up and shutdown time constraints is a crucial prerequisite for ensuring smooth demand response and a key factor in suppressing load fluctuations. Therefore, load start-up and shutdown time constraints limit the start-up and shutdown times of loads that can be reduced, preventing frequent start-ups and shutdowns within a short period, which could affect equipment lifespan and grid stability. This constraint is achieved by setting a minimum time interval for load start-up and shutdown, ensuring the rationality and economy of load dispatch.

[0143] Specifically, the load start-up and shutdown time constraints are as follows:

[0144]

[0145] In the formula, , These represent the load start-up and stop times, respectively. , Indicates the minimum start and stop time of the load. This represents the load reduction state at time t-1.

[0146] Throughout the entire dispatch cycle, the total amount of load reduction should be constrained to ensure it does not exceed the overall upper limit of load that can be reduced. By limiting the amount of load reduction through load reduction limits, it ensures that the power supply demand of critical loads is not affected during load reduction. This constraint is achieved by setting a maximum limit for load reduction, thus ensuring the operational stability of the power grid during faults or power shortages.

[0147] Specifically, the load reduction limit constraint is as follows:

[0148]

[0149] In the formula, The maximum load at time t, This represents the maximum load reduction.

[0150] The constraints on transferable loads include transferable load power limits, minimum continuous operating time constraints, state of charge constraints, state of charge / discharge constraints, charge / discharge power constraints, energy storage balance constraints, and energy storage capacity constraints.

[0151] Translatable load (TL) refers to a type of load whose electricity consumption allocation can be flexibly adjusted across different time periods, while its overall electricity demand remains constant. A typical example is electric vehicle charging load, which can be optimally configured according to demand over time. TL can be pre-scheduled in day-ahead dispatching and flexibly adjusted in intraday dispatching; therefore, it is classified as both day-ahead and intraday controllable loads. In optimal dispatching, the control model for translatable loads needs to comprehensively consider the flexibility of time allocation and the constancy of energy demand to improve the operating efficiency of the power system and enhance its adaptability to fluctuations in renewable energy. Therefore, the power limit constraint for translatable loads is set as follows:

[0152]

[0153] In the formula, For transferable load power, , These are the minimum and maximum values ​​of the transferable load power, respectively. These are runtime status variables, and they are binary variables.

[0154] If no appropriate restrictions are set during load transfer, the load may be concentrated in multiple independent short periods, leading to frequent equipment start-ups and shutdowns. This not only reduces system stability but may also adversely affect the service life of the equipment. Therefore, to avoid these problems, it is necessary to reasonably constrain the minimum continuous operating time of the transferred load to ensure balanced load distribution and reliable equipment operation. Therefore, the minimum continuous operating time constraint for transferable loads is set as follows:

[0155]

[0156] In the formula, This is the minimum continuous running time.

[0157] The state of charge constraint is:

[0158]

[0159] In the formula, Let be the charge value at time t. , These are the minimum and maximum values ​​of charge, which can be 0.1 and 0.9 respectively.

[0160] The charge / discharge state constraints are as follows:

[0161]

[0162] In the formula, The energy storage is in a discharging state. The energy storage is in a charging state.

[0163] The charging and discharging power constraints are:

[0164]

[0165]

[0166] In the formula, , These represent the charging and discharging power of energy storage, respectively. , These represent the maximum charging and discharging power of the energy storage system, respectively. , The symbol represents the charging and discharging power coefficient of energy storage.

[0167] The energy balance constraint for energy storage is:

[0168]

[0169] In the formula, , These represent the charging and discharging efficiencies of the energy storage, respectively, with a value of 0.9.

[0170] Energy storage capacity constraints are:

[0171]

[0172] In the formula, For energy storage capacity, This refers to the rated capacity of the energy storage.

[0173] Distribution network operation balance constraints include wind power constraints, photovoltaic power constraints, power balance constraints, node voltage constraints, branch power constraints, and line current constraints.

[0174] Among them, the wind power constraint is:

[0175]

[0176]

[0177] In the formula, P wt Indicates wind power output. Indicates the rated power of the wind turbine. and These represent the minimum and maximum reactive power of the wind turbine, respectively. This indicates the reactive power of the wind turbine.

[0178] Photovoltaic power constraints are:

[0179]

[0180]

[0181] In the formula, P pv Indicates the output power of the photovoltaic unit. This represents the maximum output power of the photovoltaic unit. and These represent the minimum and maximum reactive power of a photovoltaic (PV) unit, respectively, and are typically limited by the rated capacity of the inverter:

[0182]

[0183] In the formula, This refers to the capacity of the photovoltaic inverter.

[0184] The power balance constraint is:

[0185]

[0186] The node voltage constraint is:

[0187]

[0188] In the formula, V min and V max These represent the minimum and maximum node voltages, which can be 0.95 and 1.05, respectively.

[0189] Branch power constraints are:

[0190]

[0191] In the formula, For branch power, , These represent the minimum and maximum power of the branch circuit, respectively.

[0192] The line current constraint is:

[0193]

[0194] In the formula, I ij，t I represents the current in branch ij at time t. max This represents the maximum value of the branch current.

[0195] In some embodiments, the entropy weight method is used to provide objective weights for the objective function by quantifying data differences, thereby avoiding result bias caused by subjective preferences. Specifically, this method further includes:

[0196] S21. Standardize the data for each optimization objective within the objective function; among which, the optimization objectives include comprehensive operating cost, voltage fluctuation, load peak-valley difference, and energy storage life loss.

[0197] Since the dimensions of the optimization objectives within the objective function are inconsistent, the objective function values ​​with different dimensions are converted into comparable dimensionless values, thereby standardizing the data of each optimization objective within the objective function as follows:

[0198]

[0199] In the formula, For the standardized data, For the maximum value of the data, This refers to the data before data standardization. This represents the minimum value of the data.

[0200] S22. Determine the proportion of standardized values ​​for each optimization objective based on the standardized data, and determine the information entropy of the optimization objective based on the proportion of standardized values.

[0201] The proportion of the standardized value of the j-th objective function can be expressed as:

[0202]

[0203] In the formula, p i,j This represents the relative importance of the i-th sample in target j. Furthermore, the information entropy e is calculated. j e j The larger the value, the more uniform the data of target j.

[0204]

[0205] The standardized value proportion (i.e., relative importance) is obtained by dividing the optimization objective after data standardization by the sum of the optimization objectives after all data standardization.

[0206] S23. Determine the weights corresponding to each optimization objective based on the information entropy of each objective.

[0207] Convert information entropy into weights:

[0208]

[0209]

[0210] In the formula, d j The difference coefficient represents the effectiveness of the information about target j.

[0211] In some embodiments, the optimization solution of the distribution network scheduling model includes:

[0212] The optimal solution for the power distribution network scheduling model is found based on the snow ablation optimization algorithm.

[0213] The Snow Ablation Optimization Algorithm (SAO) simulates the snow ablation process in nature. By simulating natural phenomena such as snowflake formation, falling, accumulation, and melting, it solves optimization problems. In distribution network optimization scheduling, the SAO algorithm can efficiently search for optimal solutions, balancing multiple optimization objectives such as overall operating costs, voltage fluctuations, load peak-to-valley differences, and energy storage lifespan losses, ensuring intraday economic optimization scheduling of active distribution networks.

[0214] SAO utilizes a two-population mechanism to divide the molecular population P into two subpopulations P0. a and P b The number of molecules in its population is N. a and N b The algorithm utilizes P a The population traverses the feasible region by simulating the evaporation of liquid water and the Brownian motion of water vapor; it utilizes P b The population simulates the snow melting process to explore the global optimum near a local optimum. a and N b It changes over time according to the following formula.

[0215]

[0216]

[0217]

[0218] In the SAO algorithm, the update rule for Brownian motion is:

[0219]

[0220]

[0221]

[0222]

[0223] In the formula, Z i (t+1) represents the position of the i-th molecule at time t+1. Elite(t) is a random variable derived from G(t), Z second (t), Z third (t) and Z c(t) is randomly selected from four quantities. G(t) is the optimal position of the molecule at time t, and Z is the position of the molecule at time t. second (t) represents the second-most preferred position of the molecule at time t, Z third (t) represents the third position of the molecule at time t, Z c (t) represents the average position of the molecules at time t. θ is a Gaussian distribution based on Brownian motion; θ1 represents a random number randomly selected from the interval [0, 1].

[0224] When molecules exist in the form of snow, the process of snow melting and converting into liquid water is simulated as shown in the following equation:

[0225]

[0226] In the formula, DDF refers to the snow ablation coefficient in the snow ablation model, which ranges from [0.35, 0, 6]. T represents the daily average temperature, and T1 represents the base temperature, which is generally set to 0. The trend of DDF changing over time can be represented by the following formula.

[0227]

[0228] In the formula, t max This indicates the length of the calculation period.

[0229] The snow melting process can be updated in terms of location according to the following formula:

[0230]

[0231] In the formula, M represents the amount of snow melt, and θ2 represents a random number within the range of [-1, 1]. This represents the average value of the solutions within the population.

[0232] To verify the optimization performance of the Snow Ablation Algorithm (SAO), this invention selected the Dung Beetle Optimizer (DBO), Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), and Sparrow Search Algorithm (SSA) as comparative algorithms, and conducted simulation analysis on the IEEE 33-node test system. Simulation results show that the proposed SAO algorithm outperforms the other comparative algorithms in both convergence speed and solution accuracy. Normalizing and summing the four sub-objective functions, including the comprehensive cost, yields the normalized comprehensive objective function:

[0233]

[0234] Simulation results show that the SAO algorithm performs best among all algorithms, reaching the global optimum quickly and maintaining stable convergence. While PSO and GWO also demonstrate fast convergence and good global search capabilities, they still fall short of SAO's convergence efficiency and accuracy. DBO and SSA converge more slowly, and SSA is more prone to getting trapped in local optima; therefore, parameter adjustments may be necessary to improve performance in practical applications.

[0235] Specifically, the optimization solution for the distribution network scheduling model based on the snow ablation optimization algorithm includes:

[0236] Step S201: Initialize the population and determine the population size and maximum number of iterations; wherein, the individuals in the population are obtained by encoding multiple intraday scheduling candidate strategies of the active distribution network.

[0237] Randomly generate intraday scheduling candidate strategies, with each intraday scheduling candidate strategy representing one scheduling strategy:

[0238]

[0239] Step S202: Use the objective function as the fitness function, and calculate the fitness value of each individual based on the fitness function.

[0240] Step S203: Sort the individuals in the population according to their fitness values ​​and determine the individual with the best fitness value.

[0241] Step S204: Determine whether the individual with the best fitness value has reached the convergence condition or whether the current iteration number has reached the maximum iteration number.

[0242] Step S205: If the individual with the best fitness value has not reached the convergence condition and the current iteration number has not reached the maximum iteration number, then update the position of each individual according to the snow ablation amount to form a new population, and update the current iteration number by 1.

[0243] The individual's position (scheduling strategy) is adjusted based on the snow ablation amount M as follows:

[0244]

[0245] In the formula, Di represents the search direction, which can be the gradient direction or a random vector. The individual position before adjustment. This refers to the adjusted individual position.

[0246] Step S206: Based on the new population, proceed to the step of using the objective function as the fitness function and calculating the fitness value of each individual according to the fitness function, until the individual with the best fitness value reaches the convergence condition or the current iteration number reaches the maximum iteration number, and output the individual with the best fitness value.

[0247] Step S207: Determine the optimal solution based on the individual with the best final fitness value.

[0248] To verify the method proposed in this application, such as Figure 4 As shown, an active distribution network model incorporating photovoltaic (PV), wind, and energy storage was constructed based on the IEEE 33-node system. A PV station is connected to node 12; wind farms WT1 and WT2 are connected to nodes 7 and 27, respectively, with wind power capacities of 1.5MW and 1MW. Two energy storage systems (ESS) with capacities of 0.5MW each are connected to node 17, denoted as ESS1 and ESS2. Demand-side loads that can be shifted are connected to nodes 4, 15, and 30, while loads that can be reduced are connected to nodes 10 and 26. During intraday optimization, a 24-hour scheduling cycle is used to balance supply and demand in the active distribution network, optimizing the network's economy and reliability through charging and discharging operations. The charging and discharging strategies of the ESS are determined by the intraday economic optimization scheduling system to ensure that the distribution network's operational balance constraints are met.

[0249] Based on a power prediction model constructed using data on solar irradiance, temperature, wind speed, meteorological data, and historical load data for a given day, the curves for wind power, solar power, and load are obtained as follows: Figure 5 As shown.

[0250] During certain periods, renewable energy sources in the distribution network cannot meet load demand, necessitating energy storage to ensure power generation. Furthermore, when the capacity of the energy storage system is limited and unable to meet the distribution network's load demand or provide sufficient backup capacity, the distribution network needs to draw power from the main grid to ensure stable system operation. Simultaneously, during peak load periods, loads that can be shifted (such as washing machines) can be moved to off-peak periods, and loads that can be reduced (such as air conditioners and lighting) can have their power consumption decreased through proper control, thereby lowering the system's peak load.

[0251] Figure 6The graph illustrates the results of different types of demand-side response scheduling in the distribution network. As can be seen from the graph, during periods such as 01:00-02:00 and 15:00-16:00, transferable loads 1, 2, and 3 show positive power, with transferable load 1 reaching 0.06 MW during 15:00-16:00. By transferring non-rigid loads to this period, the spatiotemporal distribution of loads is optimized. However, during periods of 04:00-08:00 and 21:00-24:00, loads 1 and 2 that can be reduced show significant negative values, with load 2 that can be reduced reaching -0.18 MW at 07:00, directly reducing non-essential load power.

[0252] The charging and discharging power of energy storage is closely related to changes in the output of the main grid. Energy storage systems are typically used to balance fluctuations in renewable energy (wind and solar power) or to provide additional power support during peak grid load periods.

[0253] Simulation results demonstrate a strong synergistic effect between the energy storage system and the main grid output. When grid output is low and load is light, the energy storage system charges, storing excess power for later use. Conversely, when grid load is high and demand exceeds grid output, the energy storage system releases its stored power, alleviating grid load pressure. Particularly during the periods of 5:00-8:00 and 20:00-24:00, the discharge power of the energy storage system matches the increase in grid demand, effectively stabilizing the grid's power supply.

[0254] Figure 7 and Figure 8 The optimized scheduling results of two energy storage systems (ESS1 and ESS2) in the distribution network over a 24-hour period are presented. The scheduling results show that the two energy storage systems exhibit synergistic optimization scheduling characteristics at different times. Due to insufficient distributed generation in the early morning, the energy storage systems are in a discharging state from 0:00 to 5:30 and from 21:00 to 24:00 to meet nighttime load supply and reduce the need to purchase electricity from the main grid. During peak load periods, from 15:00 to 17:00, they are in a charging state to supplement power supply. From 10:00 to 12:30, photovoltaic power generation is at its peak, and the generation of distributed power sources exceeds the current load demand. The excess electricity can be used to charge the energy storage systems, thereby improving the utilization rate of the photovoltaic systems. The energy storage systems are in a charging state from 18:00 to 20:00 because charging costs are lower during this period, thus reducing operating costs.

[0255] Figure 9This diagram illustrates the charging and discharging of energy storage systems in relation to grid output. Calculations show that during the charging phase, the maximum charging power of the energy storage system is 0.295 MW, indicating relatively small fluctuations in charging power and stable system operation. During the discharging phase, the maximum discharging power of the energy storage system approaches 0.9 MW, demonstrating that the system can provide up to 0.9 MW of power support during peak grid load periods. The charging and discharging behavior of the energy storage system plays a crucial role in regulating grid load and improving grid stability. Especially during peak grid load periods, the energy storage system provides approximately 0.9 MW of power through discharging, successfully alleviating peak grid load pressure.

[0256] from Figure 9 The results show that the energy storage system, through flexible scheduling of charging and discharging, complements the grid output and effectively balances grid load fluctuations. During the charging phase, the energy storage system absorbs excess power; during the discharging phase, it provides necessary power support to the grid, ensuring grid stability and flexibility. The dynamic scheduling of the energy storage system can effectively reduce the peak-to-valley difference in the grid, optimize the use of power resources, and play a crucial role in load regulation, especially during peak hours.

[0257] Figure 10 To optimize the voltage conditions of power distribution system nodes before and after dispatching. From Figure 10 The results show a significant difference in voltage distribution before and after optimization. Before optimization, the voltage of some nodes fluctuated significantly, ranging from 0.95 pu to 1.05 pu. After optimization, the voltage stabilized, with most node voltages remaining around 1.02 pu, effectively improving system stability.

[0258] from Figure 11 It can be observed that before optimization, the average voltage of the system exhibited significant fluctuations, especially during the 5-10 hour and 15-20 hour periods, with noticeable drops and rises in voltage values. The minimum value was close to 0.98 pu, and the maximum value was close to 1.03 pu. After optimization, the voltage fluctuations of the system were significantly reduced, and the voltage remained within a more stable range, with a minimum value of 1.00 pu and a maximum value of 1.02 pu. The amplitude of voltage fluctuations was significantly reduced. For example, during the 5-10 hour period, the voltage fluctuation amplitude before optimization was approximately 0.05 pu, while after optimization, this fluctuation amplitude was reduced to 0.02 pu.

[0259] By comparing voltage changes before and after optimization, simulation results show that the optimization strategy significantly improves the voltage stability of the distribution system and reduces voltage fluctuations. In the optimized system, the voltage fluctuation amplitude decreased from 0.05 pu before optimization to 0.02 pu, effectively improving the stability and reliability of the power grid. This provides an important basis for further improving the operating efficiency of the distribution system and ensuring the stability of power supply.

[0260] according to Figure 12 Simulation results show that the intraday optimized dispatch strategy significantly reduces network losses in the active distribution network, especially during periods of high system load, where network loss fluctuations are effectively controlled. Compared to before optimization, the range of network loss fluctuations has decreased by approximately 2 MW, indicating that the optimized dispatch strategy improves system stability, helps increase energy utilization efficiency, and reduces grid operating costs.

[0261] To verify the impact of different types of scheduling resources on the operation of the distribution network, this application sets up the following four scenarios for simulation analysis:

[0262] Scenario 1: No energy storage or flexible loads participate in the optimal dispatch of the distribution network;

[0263] Scenario 2: Optimize load scheduling and balance load fluctuations through demand-side response of flexible loads;

[0264] Scenario 3: Consider the charging and discharging operation of the energy storage system, and improve system stability by regulating the load through energy storage;

[0265] Scenario 4: Simultaneously consider the demand-side response of flexible loads and the charging and discharging operation of energy storage systems.

[0266] Figure 13 The diagram shows a comparison of the optimized scheduling effects in different scenarios. By comparing the scheduling effects of the four scenarios, it can be seen that energy storage systems and flexible load demand-side response play a significant role in optimizing load scheduling. Using flexible load response (Scenario 2) or energy storage regulation (Scenario 3) alone can alleviate load fluctuations to some extent, but the optimal effect is achieved through a combination of both. Scenario 4, by simultaneously considering the scheduling strategies of energy storage systems and flexible loads, achieves the effect of minimizing load fluctuations and maximizing system stability.

[0267] Table 1 shows the objective function values ​​for peak shaving and valley filling under different scenarios. A smaller objective function value (f3) indicates smaller load fluctuations and higher system stability. Scenario 4 shows the best effect in peak shaving and valley filling, with an objective function value of 0.1906, significantly lower than Scenario 1 by 0.0529. In Scenarios 2 and 3, the system also exhibits some peak reduction effect; however, their performance is still significantly lower than that of Scenario 4. Therefore, the integrated regulation strategy (i.e., using energy storage and flexible load response simultaneously) can effectively improve the stability and economy of the distribution network and reduce load fluctuations, making it the optimal choice.

[0268] Table 1. Solution results of the optimized scheduling model under various scenarios

[0269]

[0270] Table 1 shows the multi-objective optimization results of the distribution network optimization scheduling model under different scenarios. By comparing the changes in overall cost, voltage fluctuation, peak-valley load difference, and energy storage lifetime loss under different scenarios, the different effects of optimization strategies can be seen.

[0271] In Scenario 1, without any optimized scheduling measures, the overall cost is the highest, voltage fluctuations are large, and the load peak-to-valley difference is high. After introducing demand response (Scenario 2), the overall cost significantly decreases by approximately 4.9%, load fluctuations improve, and the load peak-to-valley difference also decreases. This indicates that demand response has a certain effect on reducing costs and mitigating load fluctuations, but voltage fluctuations remain large, and ideal system stability has not yet been achieved. When an energy storage system is introduced into the system for regulation (Scenario 3), the overall cost increases by approximately 2.8%, but voltage fluctuations are significantly improved, and the load peak-to-valley difference is further reduced. However, the introduction of the energy storage system increases the overall cost. In Scenario 4, the optimized strategy combining demand response and energy storage regulation achieves the best results. The overall cost is reduced by approximately 6.7% compared to Scenario 3, voltage fluctuations are minimized, and the load peak-to-valley difference reaches its minimum. This shows that through the joint scheduling of demand response and energy storage systems, the system can not only effectively reduce overall costs but also significantly improve the stability and balance of the power system. Energy storage lifetime losses are also controlled, further enhancing the sustainability of this scheme.

[0272] Table 2 presents the economic evaluation results of the distribution network under different scenarios. In Scenario 1, no optimization strategy was adopted, resulting in high electricity purchase costs and network loss costs of ¥19,466.6 and ¥2,033.2, respectively. In Scenario 2, by introducing load dispatching and energy storage systems, the electricity purchase cost was reduced to ¥19,309.3 and the network loss cost to ¥1,810.9, indicating improved system economics. In Scenario 3, after further optimization, the electricity purchase cost was ¥19,055.8 and the network loss cost was reduced to ¥1,765.3, demonstrating the regulatory role of energy storage. In Scenario 4, using the optimal strategy, the electricity purchase cost was reduced to ¥17,365.7 and the network loss cost to ¥1,468.5, achieving the best overall economic performance.

[0273] Table 2. Costs of Active Distribution Networks

[0274]

[0275] In summary, the joint optimization strategy in Scenario 4 achieves an optimal balance in multiple aspects, taking into account the system's economy, stability, and sustainability, making it the best solution for distribution network optimization and scheduling. This demonstrates that the participation of demand response and energy storage systems in distribution network optimization and scheduling can effectively improve the operational efficiency of the distribution network.

[0276] like Figure 14As shown, to verify the effectiveness and applicability of the established optimized scheduling model to more complex active distribution networks, this application establishes an improved IEEE 118-node system. Four PVs of the same capacity are installed at nodes 2, 7, 15, and 40; four WTs of the same capacity are installed at nodes 62, 74, 82, and 94; and two ESSs are installed at nodes 51 and 108. The rated voltage level of the system is 11 kV, and the total load is 22.709 MW + j17.041 MVAR. Other model parameters can be found in the reference. The power changes of the IEEE 118-node distribution system after optimized scheduling are shown in the figure. Figure 15 As shown in the figure, this diagram intuitively demonstrates the effect of the system in balancing power supply and demand by utilizing distributed power sources such as photovoltaics and wind power, along with the demand response mechanism, under the optimized scheduling strategy presented in this paper.

[0277] like Figure 16 As shown, during the early morning period (0:00-04:00), system load fluctuations are relatively small, mainly balancing power through a small amount of transferable load. During the morning peak (08:00-11:00) and evening peak (18:00-22:00), the system employs various flexible load coordination scheduling methods, significantly reducing peak loads, with a maximum reduction of nearly 3.2 MW, effectively alleviating system pressure. During the midday and afternoon periods (12:00-18:00), the load is relatively stable, and the range of flexible load adjustments is relatively small. Overall, this scheduling strategy successfully achieved the synergistic effect of load reduction and transfer during peak load periods, effectively improving the system's load balancing capacity and operational economy.

[0278] like Figure 17 and Figure 18 As shown, the charging and discharging power curves of ESS1 and ESS2 are not completely synchronized, reflecting a differentiated control strategy. For example, when one is deeply discharging at a certain time, the other may be charging or operating at low power. Through complementary cooperation, the grid power fluctuations are smoothed more efficiently, improving the overall system regulation capability. ESS1 has a more prominent charging efficiency between 5:00 and 7:00, while ESS2's discharging process is more continuous. Overall, the two energy storage systems achieve efficient energy storage and release by dynamically adjusting the SOC, effectively verifying the key role of energy storage systems in smoothing power fluctuations and maintaining stable operation in power system energy management.

[0279] After optimization, load dispatch costs decreased from 966.3 to 431.3, a reduction of 55.4%; electricity purchase costs decreased from 594,330 to 480,168, a reduction of 19.2%; and network loss costs decreased from 8,048.9 to 6,902.1, a reduction of 14.2%. Energy storage dispatch costs decreased slightly, while energy storage revenue increased from 5,832.3 to 6,295.1, an increase of 7.9%. This demonstrates that the optimization strategy significantly reduced costs associated with load dispatch, electricity purchase, and network losses, while simultaneously increasing energy storage revenue, effectively validating its effectiveness in improving the economics of distribution networks, optimizing cost control, and unlocking the value of energy storage.

[0280] like Figure 19 As shown, before optimization, the voltage at some nodes fluctuated drastically, with the voltage near node 42 dropping to approximately 0.91, significantly deviating from the rated voltage and posing a risk of voltage exceeding limits, thus threatening power quality. After optimization, the overall node voltage improved, and the fluctuation amplitude decreased significantly. Before optimization, the lowest node voltage was 0.91; after optimization, the lowest voltage increased to 0.96, resulting in a substantial improvement in voltage compliance. Before optimization, the voltage fluctuation range was 0.91-1.0; after optimization, the voltage at most nodes stabilized within the 0.96-1.0 range. Further statistics show that the voltage standard deviation decreased from approximately 0.032 before optimization to 0.015. This indicates that the optimized scheduling strategy, by adjusting the system power distribution, effectively improved node voltage levels, reduced voltage fluctuations, and enhanced the voltage stability and power supply reliability of the distribution network, fully validating the effectiveness of the optimization scheme in voltage regulation.

[0281] like Figure 20 As shown, before the optimized scheduling, network losses fluctuated significantly, with high peak values ​​at multiple times: 5.4 MW at 00:00, approximately 4.2 MW at 06:00, and 5.7 MW at 21:00, indicating that the power loss problem in the distribution network was quite prominent under the traditional operation mode. After optimization, the overall level of network losses was significantly reduced, and the fluctuation amplitude was greatly reduced. Losses at each time period were controlled within a lower range: 3.2 MW at 00:00, a peak of only 3.6 MW at 16:00, and further reduced to 1.6 MW at 21:00. The average network loss before optimization was 2.4 MW, which decreased to 0.8 MW after optimization, a reduction of 66.7%; the standard deviation of losses before optimization was 1.6 MW, which decreased to 0.6 MW after optimization, indicating a significant improvement in loss stability.

[0282] Overall, the comprehensive benefits of the proposed optimized scheduling model were verified through multi-dimensional simulation analysis. In terms of economics, after optimization of the 118-node distribution network, load dispatch costs decreased by 55.4%, electricity purchase costs decreased by 19.2%, and network loss costs decreased by 14.2%, while energy storage revenue increased by 7.9%, significantly optimizing the operating costs of the distribution system. Regarding voltage quality, the minimum node voltage improved from 0.91 to 0.96, and the voltage standard deviation decreased from 0.032 to 0.015, effectively solving the voltage exceedance problem and improving power supply stability. In terms of network losses, the average network loss decreased from 1.2 MW to 0.4 MW after optimization. In conclusion, this optimized scheduling model performs excellently in reducing operating costs, stabilizing node voltage, and reducing network losses, fully verifying its feasibility and effectiveness in improving the overall operating performance of the distribution network.

[0283] Based on the same inventive concept, this application also provides an active distribution network intraday economic optimization scheduling system for implementing the above-mentioned active distribution network intraday economic optimization scheduling method.

[0284] The solution provided by this system is similar to the solution described in the above method. Therefore, the specific limitations of one or more embodiments of the active distribution network intraday economic optimization scheduling system provided below can be found in the limitations of the active distribution network intraday economic optimization scheduling method described above, and will not be repeated here.

[0285] like Figure 21 As shown in the figure, this application provides an intraday economic optimization dispatching system for an active distribution network, including:

[0286] The optimization model construction module 100 is used to construct an optimized scheduling model for the distribution network with multiple optimization objectives, including minimizing the overall operating cost of the active distribution network, minimizing voltage fluctuations, minimizing the load peak-valley difference, and minimizing energy storage life loss. The module also determines the constraints of the multiple optimization objectives based on the load balance state that can be reduced, the load balance state that can be transferred, and the distribution network operation balance state.

[0287] The model solving module 200 is used to find the optimal solution for the distribution network optimization scheduling model and determine the intraday optimal scheduling strategy for the active distribution network based on the optimal solution.

[0288] The scheduling strategy execution module 300 is used to schedule the active distribution network according to the intraday optimal scheduling strategy.

[0289] In some embodiments, the objective function corresponding to multiple optimization objectives is:

[0290]

[0291] In the formula, The objective function value, , , , Each item has its own weight. To account for overall operating costs, For voltage fluctuations, For the difference between peak and valley loads, This is due to energy storage lifespan loss;

[0292] in,

[0293] In the formula, For load dispatch cost, For the cost of energy storage dispatch, For energy storage revenue, For electricity purchase costs, For network loss costs, For backup costs, Cost of carbon emissions;

[0294] in,

[0295] In the formula, t represents time, T represents the total time period, and E represents the total time period. s (t) and E c (t) represents the amount of load that can be shifted and load that can be cut off, respectively. de Incentive pricing to respond to demand;

[0296]

[0297] In the formula, i is the index of the energy storage device. A collection of energy storage devices. and Let represent the discharge power and charging power of energy storage device i at time t, respectively;

[0298]

[0299] In the formula, The incentive electricity price for energy storage discharge;

[0300]

[0301] In the formula, n is the node index, n b For a set of nodes, Let be the active power output of node n at time t. This refers to the unit price of electricity purchased.

[0302]

[0303] In the formula, l is the line index. For the collection of routes, Let be the square of the current in line l at time t. Let be the resistance of line l;

[0304]

[0305] In the formula, This represents the maximum margin of available power for energy storage. For the charge and discharge cycle of the energy storage device, For the revenue per kilowatt-hour from energy storage;

[0306]

[0307] In the formula, Total energy consumption Carbon emission factor For carbon price;

[0308]

[0309] In the formula, Let be the square of the voltage at the nth node at time t. Let be the square of the voltage at the (n+1)th node at time t;

[0310]

[0311] In the formula, , These represent the weighting coefficients for peak shaving and load smoothing, respectively. This represents the minimum power margin available for energy storage. Rated power, , These represent the total load of the active distribution network at time t and the total load at time t+1, respectively. This represents the total load difference between adjacent time points;

[0312]

[0313] In the formula, This is the lifespan loss coefficient;

[0314] in,

[0315]

[0316] In the formula, N represents the number of charge-discharge cycles of the energy storage device at a depth of discharge of D. Where is the cycle life of the energy storage device, and D is the depth of discharge of the energy storage device.

[0317] In some embodiments, the constraints include load reduction constraints, load transfer constraints, and distribution network operation balance constraints; wherein, load reduction constraints include load reduction amount limit constraints, load reduction amount time-series change smoothing constraints, load start-up and shutdown time constraints, and load reduction limit constraints.

[0318] The constraints on transferable loads include transferable load power limit constraints, minimum continuous operating time constraints, state of charge constraints, state of charge / discharge constraints, charge / discharge power constraints, energy storage balance constraints, and energy storage capacity constraints.

[0319] Distribution network operation balance constraints include wind power constraints, photovoltaic power constraints, power balance constraints, node voltage constraints, branch power constraints, and line current constraints.

[0320] In some embodiments, the system further includes a weight determination module, configured to:

[0321] Data standardization is performed on each optimization objective within the objective function; the optimization objectives include overall operating cost, voltage fluctuation, peak-valley load difference, and energy storage life loss.

[0322] Based on the standardized optimization objectives, determine the proportion of standardized values ​​for each optimization objective, and then determine the information entropy of the optimization objective based on the proportion of standardized values.

[0323] The weights corresponding to each optimization objective are determined based on the information entropy of each objective.

[0324] In some embodiments, the model solving module 200 is used for:

[0325] The optimal solution for the power distribution network scheduling model is found based on the snow ablation optimization algorithm.

[0326] In some embodiments, the optimal solution for the distribution network scheduling model is obtained based on the snow ablation optimization algorithm, including:

[0327] Initialize the population, determine the population size and maximum number of iterations; the individuals in the population are obtained by encoding multiple intraday scheduling candidate strategies for the active distribution network;

[0328] The objective function is used as the fitness function, and the fitness value of each individual is calculated based on the fitness function.

[0329] Individuals in the population are sorted according to their fitness values, and the individual with the best fitness value is determined.

[0330] Determine whether the individual with the optimal fitness value has met the convergence condition or whether the current iteration count has reached the maximum iteration count;

[0331] If the individual with the best fitness value has not reached the convergence condition and the current iteration number has not reached the maximum iteration number, then the position of each individual is updated according to the snow ablation amount to form a new population, and the current iteration number is updated by 1.

[0332] Based on the new population, the objective function is used as the fitness function, and the fitness value of each individual is calculated according to the fitness function until the individual with the best fitness value reaches the convergence condition or the current iteration number reaches the maximum iteration number. Finally, the individual with the best fitness value is output.

[0333] The optimal solution is determined based on the individual with the best final fitness value.

[0334] like Figure 22 As shown in the figure, this application provides an electronic device. The electronic device 10 includes a memory 20 and a processor 30. The memory 20 stores a computer program. When the computer program is executed by the processor 30, the processor 30 performs the steps of the active distribution network intraday economic optimization scheduling method as described in the above embodiment.

[0335] This application provides a computer-readable storage medium storing a computer program thereon, which, when executed, implements the steps of the intraday economic optimization scheduling method for active distribution networks as described in the above embodiments.

[0336] This application provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions, wherein when the program instructions are executed by a computer, the computer performs the steps of the intraday economic optimization scheduling method for active distribution networks as described in the above embodiments.

[0337] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, electronic devices, computer storage media, and computer program products described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0338] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented 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.

[0339] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0340] In the several embodiments provided by this invention, it should be understood that the disclosed systems, electronic devices, computer storage media, computer program products, 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, indirect coupling or communication connection between devices or units, and may be electrical, mechanical, or other forms.

[0341] 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.

[0342] 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.

[0343] 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 for executing all or part of the steps of the methods described in the various embodiments of the present invention through a computer device (which may be a personal computer, a server, or a network device, etc.). The aforementioned storage medium includes: USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, optical disks, and other media capable of storing program code.

[0344] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention 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 the present invention.

Claims

1. A method for intraday economic optimization scheduling of an active distribution network, characterized in that, include: The optimization objectives are to minimize the overall operating cost of the active distribution network, minimize voltage fluctuation, minimize load peak-valley difference, and minimize energy storage life loss. The constraints of the optimization objectives are determined based on the load balance state that can be reduced, the load balance state that can be transferred, and the distribution network operation balance state. A distribution network optimization scheduling model is then constructed. The objective function corresponding to the multiple optimization objectives is: ； In the formula, The objective function value, , , , Each item has its own weight. To account for overall operating costs, For voltage fluctuations, For the difference between peak and valley loads, This is due to energy storage lifespan loss; in, ; In the formula, For load dispatch cost, For the cost of energy storage dispatch, For energy storage revenue, For electricity purchase costs, For network loss costs, For backup costs, Cost of carbon emissions; in, ; In the formula, t represents time, T represents the total time period, and E represents the total time period. s (t) and E c (t) represents the amount of load that can be shifted and load that can be cut off, respectively. de Incentive pricing to respond to demand; ； In the formula, i is the index of the energy storage device. A collection of energy storage devices. and Let represent the discharge power and charging power of energy storage device i at time t, respectively; ； In the formula, The incentive electricity price for energy storage discharge; ； In the formula, n is the node index, n b For a set of nodes, Let be the active power output of node n at time t. This refers to the unit price of electricity purchased. ； In the formula, l is the line index. For the collection of routes, Let be the square of the current in line l at time t. Let be the resistance of line l; ； In the formula, This represents the maximum margin of available power for energy storage. For the charge and discharge cycle of the energy storage device, For the revenue per kilowatt-hour from energy storage; ； In the formula, Total energy consumption Carbon emission factor For carbon price; ； In the formula, Let be the square of the voltage at the nth node at time t. Let be the square of the voltage at the (n+1)th node at time t; ； In the formula, , These represent the weighting coefficients for peak shaving and load smoothing, respectively. This represents the minimum power margin available for energy storage. Rated power, , These represent the total load of the active distribution network at time t and the total load at time t+1, respectively. This represents the total load difference between adjacent time points; ； In the formula, This is the lifespan loss coefficient; in, ; ； In the formula, N represents the number of charge-discharge cycles of the energy storage device at a depth of discharge of D. Where is the cycle life of the energy storage device, and D is the depth of discharge of the energy storage device; The optimal solution is obtained by finding the optimal solution for the distribution network optimization scheduling model, and the optimal intraday scheduling strategy for the active distribution network is determined based on the optimal solution. The active distribution network is scheduled according to the intraday optimal scheduling strategy.

2. The intraday economic optimization scheduling method for active distribution networks according to claim 1, characterized in that, The constraints include load reduction constraints, load transfer constraints, and distribution network operation balance constraints; wherein, the load reduction constraints include load reduction amount limit constraints, load reduction amount time sequence change smoothing constraints, load start and stop time constraints, and load reduction limit constraints. The transferable load constraints include transferable load power limit constraints, minimum continuous operating time constraints, state of charge constraints, state of charge / discharge constraints, charge / discharge power constraints, energy storage balance constraints, and energy storage capacity constraints. The power distribution network operation balance constraints include wind power constraints, photovoltaic power constraints, power balance constraints, node voltage constraints, branch power constraints, and line current constraints.

3. The intraday economic optimization scheduling method for active distribution networks according to claim 2, characterized in that, Also includes: Data standardization is performed on each optimization objective within the objective function; wherein, the optimization objectives include comprehensive operating cost, voltage fluctuation, peak-valley load difference, and energy storage life loss; The standardized value proportion of each optimization objective is determined based on the standardized optimization objective, and the information entropy of the optimization objective is determined based on the standardized value proportion. The weights corresponding to each optimization objective are determined based on the information entropy of each optimization objective.

4. The intraday economic optimization scheduling method for active distribution networks according to claim 2 or 3, characterized in that, The optimization solution of the power distribution network scheduling model includes: The optimal solution for the power distribution network scheduling model is obtained based on the snow ablation optimization algorithm.

5. The intraday economic optimization scheduling method for active distribution networks according to claim 4, characterized in that, The optimization solution of the distribution network scheduling model based on the snow ablation optimization algorithm includes: Initialize the population, determine the population size and maximum number of iterations; wherein, the individuals in the population are obtained by encoding multiple intraday scheduling candidate strategies of the active distribution network; The objective function is used as the fitness function, and the fitness value of each individual is calculated based on the fitness function. Individuals in the population are sorted according to their fitness values, and the individual with the best fitness value is determined. Determine whether the individual with the optimal fitness value has met the convergence condition or whether the current iteration number has reached the maximum iteration number; If the individual with the best fitness value has not reached the convergence condition and the current iteration number has not reached the maximum iteration number, then the position of each individual is updated according to the snow ablation amount to form a new population, and the current iteration number is updated by 1. The process continues with the new population, using the objective function as the fitness function and calculating the fitness value of each individual, until the individual with the optimal fitness value reaches the convergence condition or the current iteration count reaches the maximum iteration count. Finally, the individual with the optimal fitness value is output. The optimal solution is determined based on the individual with the best final fitness value.

6. An intraday economic optimization dispatching system for an active distribution network, characterized in that, include: The optimization model construction module is used to construct an optimized scheduling model for the distribution network with multiple optimization objectives, including minimizing the overall operating cost of the active distribution network, minimizing voltage fluctuations, minimizing the load peak-valley difference, and minimizing energy storage lifetime loss. The module also determines the constraints of the multiple optimization objectives based on the load balance state that can be reduced, the load balance state that can be transferred, and the distribution network operation balance state. The objective function corresponding to the multiple optimization objectives is: ； In the formula, The objective function value, , , , Each item has its own weight. To account for overall operating costs, For voltage fluctuations, For the difference between peak and valley loads, This is due to energy storage lifespan loss; in, ; In the formula, For load dispatch cost, For the cost of energy storage dispatch, For energy storage revenue, For electricity purchase costs, For network loss costs, For backup costs, Cost of carbon emissions; in, ; In the formula, t represents time, T represents the total time period, and E represents the total time period. s (t) and E c (t) represents the amount of load that can be shifted and load that can be cut off, respectively. de Incentive pricing to respond to demand; ； In the formula, i is the index of the energy storage device. A collection of energy storage devices. and Let represent the discharge power and charging power of energy storage device i at time t, respectively; ； In the formula, The incentive electricity price for energy storage discharge; ； In the formula, n is the node index, n b For a set of nodes, Let be the active power output of node n at time t. This refers to the unit price of electricity purchased. ； In the formula, l is the line index. For the collection of routes, Let be the square of the current in line l at time t. Let be the resistance of line l; ； In the formula, This represents the maximum margin of available power for energy storage. For the charge and discharge cycle of the energy storage device, For the revenue per kilowatt-hour from energy storage; ； In the formula, Total energy consumption Carbon emission factor For carbon price; ； In the formula, Let be the square of the voltage at the nth node at time t. Let be the square of the voltage at the (n+1)th node at time t; ； In the formula, , These represent the weighting coefficients for peak shaving and load smoothing, respectively. This represents the minimum power margin available for energy storage. Rated power, , These represent the total load of the active distribution network at time t and the total load at time t+1, respectively. This represents the total load difference between adjacent time points; ； In the formula, This is the lifespan loss coefficient; in, ; ； In the formula, N represents the number of charge-discharge cycles of the energy storage device at a depth of discharge of D. Where is the cycle life of the energy storage device, and D is the depth of discharge of the energy storage device; The model solving module is used to find the optimal solution for the distribution network optimization scheduling model and determine the intraday optimal scheduling strategy for the active distribution network based on the optimal solution. The scheduling strategy execution module is used to schedule the active distribution network according to the intraday optimal scheduling strategy.

7. An electronic device, characterized in that, The electronic device includes a memory and a processor. The memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the intraday economic optimization scheduling method for active distribution networks as described in any one of claims 1-5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed, it implements the steps of the intraday economic optimization scheduling method for active distribution networks as described in any one of claims 1-5.

9. A computer program product, characterized in that, The computer program product includes a computer program stored on a non-transitory computer-readable storage medium, the computer program including program instructions, wherein when the program instructions are executed by a computer, the computer performs the steps of the intraday economic optimization scheduling method for active distribution networks as described in any one of claims 1-5.

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