A multi-power power dispatching strategy optimization method and system, and a storage medium

Abstract

The present application relates to a kind of multi-power power dispatching strategy optimization method and system, storage medium, comprising the following steps: to multi-power power dispatching system is carried out power grid aggregation and power source cluster division processing, and set up working state constraint condition to each type of power supply after division;Based on the constraint condition set up by each type of power supply, constructs the power dispatching strategy optimization model with the highest source load matching degree of each type of power supply and the maximum renewable energy power generation as objective function;To power dispatching strategy optimization model is solved, obtains strategy optimization result, source load matching degree and renewable energy power generation solution result;Finally, strategy optimization result is simulated and checked and index evaluation.The strategy optimization method provided in the present application reduces the complexity of model construction and the difficulty of calculation, effectively reduces the deviation between actual optimization result and expectation.

Description

Technical Field

[0001] The present invention relates to the technical field of power system optimization and dispatching, and in particular to a multi-power supply power dispatching strategy optimization method and system, and a storage medium. Background Art

[0002] Vigorously developing new energy power, mainly wind power and photovoltaic power generation, has become a future trend. From the perspective of the power production process, new energy power represented by wind power and photovoltaic power generation has the characteristics of zero carbon dioxide emissions and is a typical low-carbon power source. Therefore, vigorously developing new energy is of great significance to promoting the development of a low-carbon economy and adjusting the energy structure.

[0003] However, due to the randomness, volatility, and intermittency of renewable energy sources like wind power and photovoltaics, the large-scale, high-proportion integration of renewable energy has significantly impacted the power system, posing significant challenges to the safe and stable operation of the grid. Furthermore, due to imperfect market mechanisms, unclear local consumption responsibilities, and insufficient system power flexibility, the "three abandonments" of clean energy (wind, solar, and hydro) are becoming increasingly prominent, and the power system is facing serious challenges in accommodating new energy sources. Therefore, rationally allocating the installed capacity of various power sources in a multi-source power system is an essential step in promoting the development and construction of renewable energy at this stage.

[0004] There are many types of power sources in the multi-energy power dispatching system, with complex structures and large scale. There are also technical difficulties in large-scale system optimization algorithms, modeling and computing efficiency. If a single power source and a single power station are used as the management basis for unified optimization across the entire region, the complexity of the power production dispatching strategy modeling and the solution efficiency of the optimization algorithm will be greatly increased, affecting the accuracy of the optimization results and bringing great uncertainty to the grid power generation plan formulation and dispatching operation. Summary of the Invention

[0005] The present invention provides a multi-power source power dispatching strategy optimization method and system, and a storage medium. The method aggregates and groups multiple types of power sources involved in power dispatching, constructs an optimization model, and solves the objective function with the highest source-load matching degree of the multi-source power system and the maximization of hydropower, wind, and solar renewable energy power generation to obtain an optimized dispatching plan. Finally, the matching degree of the optimized dispatching plan is verified through simulation calculation, solving the problems of high complexity and low accuracy in establishing large-scale system optimization modeling.

[0006] The present invention solves the above technical problems with the following solution: A multi-power supply power dispatching strategy optimization method comprises the following steps:

[0007] Aggregate multiple subgrids in a multi-power dispatch system to form power clusters, divide the power clusters based on power generation type, and set working status constraints for each type of power after division;

[0008] Based on the working state constraints set by various power sources, a power dispatch strategy optimization model is constructed with the highest source-load matching of various power sources and the maximization of renewable energy power generation as the objective function;

[0009] Solve the power dispatch strategy optimization model to obtain strategy optimization results, source-load matching and renewable energy power generation results;

[0010] Based on the actual power distribution and grid topology of the multi-power dispatching system, the strategy optimization results are simulated and calculated to obtain the simulation results of source-load matching and renewable energy power generation;

[0011] The solution results of source-load matching and renewable energy power generation are compared with the simulation results of source-load matching and renewable energy power generation. If the solution results of source-load matching and renewable energy power generation are not optimal, the working state constraints and / or objective functions of various power sources are changed to solve the power dispatch strategy optimization model again until the solution results of source-load matching and renewable energy power generation are optimal. The optimal solution is that the source-load matching solution result is greater than or equal to the source-load matching simulation result, and at the same time, the renewable energy power generation solution result is greater than or equal to the renewable energy power generation simulation result.

[0012] In order to reduce the modeling complexity and calculation difficulty of the optimization model, the multi-power source power dispatching system is generalized, including grid aggregation and power cluster division processing; based on the constraints established by various power sources, a power dispatching strategy optimization model is established. By solving the power dispatching strategy optimization model, the optimized power organization strategy of the multi-power source power dispatching system can be obtained, that is, the power generation plan of various power sources is obtained, and the optimization strategy is simulated and verified to further improve the accuracy of the results.

[0013] Preferably, the multi-power source dispatch strategy optimization method further comprises: calculating, through simulation, comprehensive indicators of various optimization results in the strategy optimization results, wherein the indicators include power generation characteristics indicators, efficiency indicators, reliability indicators, flexibility indicators, and environmental benefit indicators. The indicator calculation is performed to support the dispatcher's comprehensive evaluation of the organizational strategy.

[0014] Preferably, the setting of working state constraints for various power sources specifically includes:

[0015] 1) Based on the operating power of each thermal power unit, the thermal power units are classified and the following constraints are set:

[0016] X g (t)P g，min ≤P g (t)≤X g (t)P g，max

[0017] Where: P g (t) is the output of the thermal power unit; P g，min and P g，max is the minimum and maximum unit output; X g (t) represents the operating status of the unit, which is a binary variable. 0 means the unit is shut down, and 1 means the unit is running.

[0018] Unit ramp rate constraint:

[0019] P g (t+1)-P g (t)≤ΔP g，up ΔT

[0020] P g (t)-P g (t+1)≤ΔP g，d ΔT

[0021] Where: ΔP g，up , ΔP g，d They are the unit's ramp-up rate and ramp-down rate respectively.

[0022] Thermal power start-up and shutdown constraints:

[0023]

[0024] Where: Y(t) and Z(t) are the start and stop instructions of the system in period t. Considering the actual scheduling situation, the start and stop instructions are only issued to the units in the network once a week.

[0025] 2) Based on the operating power of each hydropower unit, classify the hydropower units and set the following constraints:

[0026] P h，min ≤P h (t)≤P h，max

[0027] Where: P h (t) is the output of the hydropower unit; P h，min and P h，max Provides the minimum and maximum technical output for the unit.

[0028]

[0029] Where: Z j,end is the end-of-day water level of the j-th reservoir; Z j,begin is the initial water level of the jth reservoir, Z j,allow is the daily water level fluctuation allowed for the j-th reservoir.

[0030] Downflow flow restriction:

[0031]

[0032] Where: Q j,t is the discharge flow of the j-th hydropower station in the t-th period; is the maximum discharge of the j-th hydropower station in the t-th period; is the minimum discharge flow of the j-th hydropower station in the t-th period.

[0033] Power generation flow constraints:

[0034]

[0035] Where: is the maximum power generation flow of the j-th hydropower station in the t-th period; is the minimum power generation flow of the j-th hydropower station in the t-th period; the other parameters have the same meanings as above.

[0036] Unit daily power generation constraints:

[0037]

[0038] Where: P h (t) is the output of the hydropower unit at time t, ΔT is the step length t, Q h The daily power generation of the unit.

[0039] 3) Set the following constraints on the energy storage units in the aggregated grid:

[0040] Charge and discharge state constraints:

[0041] a(t)+b(t)≤1

[0042] Where: a(t) is the charging state variable, 1 indicates energy storage charging, 0 indicates non-charging; b(t) is the discharging state variable, 1 indicates discharging, 0 indicates non-discharging.

[0043] Energy storage capacity limitations:

[0044] E es，min ≤E es (t-1)-P es (t)ΔT≤E es，max

[0045] Where: E es，min and E es，max are the minimum and maximum energy storage values ​​of the energy storage power station, which are calculated by multiplying the energy storage SOC limit and the rated capacity; P es (t) and E es (t) is the output and energy storage value of period t. Each energy storage power station is given an initial SOC at the beginning of the calculation.

[0046] Energy storage output constraints:

[0047]

[0048] Where: and are the minimum and maximum charging power of the energy storage power station respectively; and are the minimum and maximum discharge power of the energy storage power station respectively.

[0049] 4) Set the following constraints on the pumped storage units in the aggregated grid:

[0050] Pumping and releasing water state constraints:

[0051] a(t)+b(t)≤1

[0052] Where: a(t) is the pumping state variable, 1 indicates that the pumped storage unit is pumping, and 0 indicates that it is not pumping; b(t) is the discharge state variable, 1 indicates discharge, and 0 indicates that it is not discharge.

[0053] Reservoir capacity limits:

[0054] E ph，min ≤E ph (t-1)-P ph (t)ΔT≤E ph，max

[0055] Where: E ph，min and E ph，max are the minimum and maximum energy storage values ​​of the pumped storage power station respectively; P ph (t) and E ph (t) is the output and energy storage value of period t. Each pumped storage power station is given an initial energy storage value at the beginning of the calculation.

[0056] Unit output constraints:

[0057] Power generation conditions:

[0058] Pumping conditions:

[0059] Where: and They are respectively the pumping power and discharging power of the pumped storage unit. The power generation power can be adjusted between 0.5 and 1 times the maximum power generation power, and the pumping power is a constant value.

[0060] 5) Set the following constraints on wind power in the aggregated grid:

[0061]

[0062] Where: It is the theoretical wind power output of the aggregated grid n when the installed capacity is constant during period t.

[0063] 6) Set the following constraints on photovoltaic power in the aggregated grid:

[0064]

[0065] Where: It is the theoretical photovoltaic output of the aggregated grid n when the installed capacity is constant during period t.

[0066] 7) Establish constraints on the total load, line transmission capacity, and reserve capacity within the aggregated grid, as follows:

[0067] Inter-regional line transmission capacity constraints:

[0068] -P ι，max ≤P l (t)≤P l，max

[0069] Where: P l (t) is the transmission power of the transmission line, and the reference direction of the current is set as the positive direction for the inflow area and the negative direction for the outflow area; and P l，max and -P l，max are the upper and lower limits of the transmission capacity of the transmission line respectively.

[0070] Total load balancing constraints:

[0071]

[0072] Where: P ld，n (t) is the total load of zone n.

[0073] System spinning reserve capacity constraints:

[0074]

[0075] Where: P j，max (t, n)·S j (t, n) is the sum of the total power of all conventional units in the grid during the tth period.

[0076] P re and N re are the minimum requirements for positive spinning reserve and negative spinning reserve respectively; P j，max (t, n) and P j，min (t, n) are the upper and lower output limits of the j-th unit in the aggregated grid n.

[0077] Preferably, the power dispatch strategy optimization model with the highest source-load matching degree of various power sources and the maximization of renewable energy power generation as the objective function utilizes the regulation capabilities of thermal power, pumped storage, energy storage, and hydropower to smooth out the intermittency, randomness, and volatility of wind power and photovoltaic power generation to the greatest extent possible, so that the total output power is closer to the grid load level and has the least impact on grid operation. Define the load tracking coefficient ρ to quantitatively evaluate the characteristics of multiple power sources tracking the load. The closer the load tracking coefficient is to 1, the more consistent the change characteristics of the complementary power generation power of multiple energy sources and the load power within the time scale of the investigation, and the better the output power of the power generation system tracks the load, as follows:

[0078]

[0079] Where: ρ is the load tracking coefficient, t is the time period variable, T is the total number of time periods, N r，t is the total power generation output of hydropower, wind power and solar power in period t, L t is the electricity load in period t, is the average load level, l, k, j are the numbers of wind power stations, solar power stations and hydropower stations respectively; L, K, J are the total number of wind power stations, solar power stations and hydropower stations respectively; wN l，t is the output of the lth wind power station in the tth period; sN k，t hN is the output of the k-th photovoltaic power station in the t-th period; j，t is the output of the j-th hydropower station in the t-th period; N n，t is the total power generation output of thermal power, pumped storage and energy storage in period t; m, o, c are the numbers of thermal power, pumped storage and energy storage power stations respectively; M, O, C are the total number of thermal power, pumped storage and energy storage power stations respectively; gN m，t is the output of the mth thermal power station in the tth period; pN o，t is the output of the oth pumped storage power station in the tth period; eN c，t is the output of the cth energy storage power station in the tth period; E is the total renewable energy power generation; m t is the number of hours in period t.

[0080] Preferably, the power dispatching strategy optimization model is solved to obtain the strategy optimization result, the source-load matching degree and the renewable energy power generation result. Specifically, the power dispatching strategy optimization model is solved by the PSO algorithm of the Sigmoid function to obtain the strategy optimization result, the source-load matching degree and the renewable energy power generation result. The PSO algorithm of the Sigmoid function is used to generate the initial population, which can greatly improve the convergence speed of the algorithm and save calculation time. The steps include:

[0081] The load demand of the power grid and the output forecast curves of each power source obtained from various power stations are input into the PSO algorithm of the Sigmoid function;

[0082] Based on the Sigmoid function, the reservoir storage curve is initialized in the variable domain, and the water level is obtained as the initial state of the particle swarm;

[0083] Calculate the initial fitness of particles and determine the initial global extreme value and initial individual extreme value;

[0084] The particle positions and velocities are updated based on the particle swarm update formula until all types of power sources meet the working state constraints;

[0085] Calculate the fitness value of the updated particle position and velocity, compare it with the historical individual extreme value and the global extreme value, determine the current individual optimal position and the current global optimal position, output the obtained results and put them into the result cluster; repeatedly update the particle position and velocity until the maximum number of cycles is reached;

[0086] After the cycle is completed, the optimal result is selected from the result cluster to obtain the strategy optimization result, source-load matching and renewable energy power generation solution results.

[0087] Preferably, the simulation calculation of the strategy optimization results is performed based on the actual power distribution and grid topology of the system to obtain the simulation results of source-load matching and renewable energy power generation, specifically including:

[0088] Based on the actual power distribution and grid topology of the system, the power balance theory of the power grid and the working state constraints of various power sources, the optimization results of the strategy optimization results are synchronously input into the simulation calculation to obtain the simulation results of the first source-load matching degree and renewable energy power generation;

[0089] Based on the actual power distribution and grid topology of the system, based on the power grid power balance theory and the working state constraints of various power sources, the optimization results of the strategy optimization results are input one by one for simulation calculation, and multiple second source-load matching and renewable energy power generation simulation results are obtained (specifically, under the premise of keeping other conditions unchanged, a single optimization result of the strategy optimization result is input for simulation to obtain the first simulation result; then, keeping other conditions unchanged, another single optimization result of the strategy optimization result is input for simulation to obtain the second simulation result, and this is repeated until all single results of the strategy optimization results are simulated and the nth simulation result is obtained);

[0090] The strategy optimization results include the water level utilization mode of pumped storage reservoirs and hydropower reservoirs, water level control range, charging and discharging plan of energy storage power station, start-up and shutdown plan and startup mode of thermal power units, power generation output plan of pumped storage hydropower and conventional hydropower units, and system wind power and photovoltaic output plan.

[0091] Preferably, the solution results of the source-load matching and renewable energy power generation are compared with the simulation results of the source-load matching and renewable energy power generation. If the solution results of the source-load matching and renewable energy power generation are not the optimal solution, the power dispatch strategy optimization model is re-adjusted or the parameters are changed and solved again until the solution results of the source-load matching and renewable energy power generation are the optimal solution. The optimal solution is that the source-load matching solution result is greater than or equal to the source-load matching simulation result, and at the same time, the renewable energy power generation solution result is greater than or equal to the renewable energy power generation simulation result, specifically including:

[0092] Comparing the first source-load matching and renewable energy power generation simulation result with the source-load matching and renewable energy power generation solution result, if the source-load matching and renewable energy power generation solution result is not the optimal solution, adjusting the various power supply working state constraints or objective functions to reconstruct the power dispatch strategy optimization model, the optimal solution is that the source-load matching solution result is greater than or equal to the first source-load matching simulation result, and at the same time, the renewable energy power generation solution result is greater than or equal to the first renewable energy power generation simulation result;

[0093] Compare multiple second source-load matching and renewable energy power generation simulation results with the source-load matching and renewable energy power generation solution results. If the source-load matching and renewable energy power generation solution results are not the optimal solution, adjust the power cluster division method, grid load demand or each power output forecast curve to re-solve the power dispatch strategy optimization model until the source-load matching and renewable energy power generation solution results are the optimal solution. The optimal solution is that the source-load matching solution result is greater than or equal to any second source-load matching simulation result, and at the same time, the renewable energy power generation solution result is greater than or equal to any second renewable energy power generation simulation result.

[0094] The optimization results are verified through simulation calculations, which effectively reduces the deviation between the actual optimization results and the expected ones, so that the trend can be kept consistent when applied to the optimization of actual power operation organizations.

[0095] The present invention also provides a multi-power supply power dispatching strategy optimization system, comprising:

[0096] The system generalization constraint module is used to aggregate multiple sub-grids in a multi-power dispatch system to form power clusters, divide the power clusters based on power generation type, and establish working state constraints for each type of power after division;

[0097] The strategy optimization model establishment module is used to build a power dispatch strategy optimization model based on the operating status constraints of various power sources, with the objective function of maximizing the source-load matching of various power sources and maximizing the power generation of renewable energy;

[0098] The strategy optimization model solving module is used to solve the power dispatch strategy optimization model to obtain the strategy optimization results, source-load matching and renewable energy power generation results;

[0099] The simulation calculation module is used to simulate and calculate the strategy optimization results based on the actual power distribution and grid topology of the system, and obtain the simulation results of source-load matching and renewable energy power generation;

[0100] The strategy optimization result verification module is used to compare the solution results of source-load matching and renewable energy power generation with the simulation results of source-load matching and renewable energy power generation. If the solution results of source-load matching and renewable energy power generation are not optimal, the working state constraints and / or objective functions of various power sources are changed to solve the power dispatch strategy optimization model again until the solution results of source-load matching and renewable energy power generation are optimal. The optimal solution is that the source-load matching solution result is greater than or equal to the source-load matching simulation result, and at the same time, the renewable energy power generation solution result is greater than or equal to the renewable energy power generation simulation result.

[0101] The present invention also provides a computer storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the multi-power supply power dispatching strategy optimization method described above are implemented.

[0102] The present invention also provides an electronic device, comprising a memory and a processor: the memory is used to store computer-executable instructions, the processor is used to execute the computer-executable instructions, and when the computer-executable instructions are executed by the processor, the steps of the multi-power supply power scheduling strategy optimization method as described above are implemented.

[0103] The beneficial effects of the present invention are as follows: The present invention utilizes multi-source power system object generalization technology to aggregate multiple subgrids into an equivalent large power grid, significantly reducing the number of grids required for modeling and lowering the modeling complexity and computational difficulty of the optimization model. Taking the highest source-load matching in the multi-source power system and maximizing the generation of renewable energy sources such as hydropower, wind, and solar power as the objective functions, a multi-source power dispatch strategy optimization model is established based on constraints such as various power unit constraints, line transmission constraints, load balance constraints, and positive and negative reserve constraints. Solving this model yields an optimized power organization strategy for the multi-source power system, and a power generation plan for a multi-source power system that satisfies the constraints, including thermal power, hydropower, pumped storage, energy storage, and new energy. This improves system source-load matching and clean energy utilization, reduces curtailed hydropower, wind, and solar power, and achieves multi-power system resource integration with clean energy, conventional units, pumped storage, and energy storage systems. Through simulation verification and evaluation of the optimization results, the deviation between the actual optimization results and the expected results is effectively reduced, ensuring consistent trends when applied to actual power operation organization optimization, while also supporting dispatchers' comprehensive evaluation of the organizational strategy.

[0104] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention and to implement it according to the contents of the description, the following preferred embodiments of the present invention are described in detail with reference to the accompanying drawings. The specific implementation methods of the present invention are given in detail by the following embodiments and the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0105] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0106] Figure 1 This is a flow chart of a multi-power supply power dispatching strategy optimization method according to Example 1;

[0107] Figure 2 A module diagram of a multi-power supply power dispatching strategy optimization system according to Example 2;

[0108] Figure 3 This is a functional diagram of the power dispatching strategy simulation module in Example 2. DETAILED DESCRIPTION

[0109] The principles and features of the present invention are described below with reference to the accompanying drawings. The examples given are only used to explain the present invention and are not used to limit the scope of the present invention.

[0110] like Figure 1 As shown, the present invention provides a multi-power supply power dispatching strategy optimization method, comprising the following steps:

[0111] S1. Aggregate multiple subgrids in a multi-power dispatching system to form a power cluster, divide the power clusters based on power generation type, and set working state constraints for each type of divided power source. Specifically, the following steps are included:

[0112] S11. Grid aggregation processing is performed on the multi-power source power dispatching system to form a power cluster, following the following principles: 1) The power balance analysis of the power grid is not affected before and after the establishment of the grid aggregation model; 2) The detailed grid topology is not considered in the grid aggregation model, and each power source and load is no longer affected by its physical location; 3) The grid aggregation model should accurately describe the power generation restriction situation; 4) The grid aggregation model is the basis for subsequent research, and the calculation target grid can include one or more aggregated grids; 5) The logical relationship between multiple aggregated grids should be consistent with the actual grid operation; 6) The loads in the aggregated grid should be combined into the total load.

[0113] S12. Power cluster division processing, as follows:

[0114] 1) Thermal power units: Units above 300MW are modeled separately, and small units below 300MW are modeled as one unit.

[0115] 2) Hydropower units: Hydropower units above 100MW are modeled separately, and small units below 100MW are modeled as one unit. The small units participate in intraday optimization based on the given daily power generation.

[0116] 3) Energy storage: The energy storage in the aggregated power grid is modeled as an equivalent energy storage system.

[0117] 4) Pumped storage: The units are modeled separately.

[0118] 5) Wind power: Centralized wind power in the aggregated power grid is modeled as one unit; distributed wind power is modeled as one power station.

[0119] 6) Photovoltaic: Centralized photovoltaic systems in the aggregated power grid are modeled as one unit; distributed photovoltaic systems are modeled as one power station.

[0120] S13. Establish constraints on various power sources, including:

[0121] 1) Set the following constraints for thermal power units:

[0122] X g (t)P g，min ≤P g (t)≤X g (t)P g，max

[0123] Where: P g (t) is the output of the thermal power unit; P g，min and P g，max is the minimum and maximum unit output; X g (t) represents the operating status of the unit, which is a binary variable. 0 means the unit is shut down, and 1 means the unit is running.

[0124] Unit ramp rate constraint:

[0125] P g (t+1)-P g (t)≤ΔP g，up ΔT

[0126] P g (t)-P g (t+1)≤ΔP g，d ΔT

[0127] Where: ΔP g，up PP g，d They are the unit's ramp-up rate and ramp-down rate respectively.

[0128] Thermal power start-up and shutdown constraints:

[0129]

[0130] Where: Y(t) and Z(t) are the start and stop instructions of the system in period t. Considering the actual scheduling situation, the start and stop instructions are only issued to the units in the network once a week.

[0131] 2) The following constraints are set for the hydropower unit:

[0132] P h，min ≤P h (t)≤P h，max

[0133] Where: P h (t) is the output of the hydropower unit; P h，min and P h，max Provides the minimum and maximum technical output for the unit.

[0134]

[0135] Where: Z j,end is the end-of-day water level of the j-th reservoir; Z j,begin is the initial water level of the jth reservoir, Z j,allow is the daily water level fluctuation allowed for the j-th reservoir.

[0136] Downflow flow restriction:

[0137]

[0138] Where: Q j,t is the discharge flow of the j-th hydropower station in the t-th period; is the maximum discharge of the j-th hydropower station in the t-th period; is the minimum discharge flow of the j-th hydropower station in the t-th period.

[0139] Power generation flow constraints:

[0140]

[0141] Where: is the maximum power generation flow of the j-th hydropower station in the t-th period; is the minimum power generation flow of the j-th hydropower station in the t-th period; the other parameters have the same meanings as above.

[0142] Unit daily power generation constraints:

[0143]

[0144] Where: P h(t) is the output of the hydropower unit at time t, ΔT is the step length t, Q h The daily power generation of the unit.

[0145] 3) Set the following constraints on the energy storage units in the aggregated grid:

[0146] Charge and discharge state constraints:

[0147] a(t)+b(t)≤1

[0148] Where: a(t) is the charging state variable, 1 indicates energy storage charging, 0 indicates non-charging; b(t) is the discharging state variable, 1 indicates discharging, 0 indicates non-discharging.

[0149] Energy storage capacity limitations:

[0150] E es，min ≤E es (t-1)-P es (t)ΔT≤E es，max

[0151] Where: E es，min and E es，max are the minimum and maximum energy storage values ​​of the energy storage power station, which are calculated by multiplying the energy storage SOC limit and the rated capacity; P es (t) and E es (t) is the output and energy storage value of period t. Each energy storage power station is given an initial SOC at the beginning of the calculation.

[0152] Energy storage output constraints:

[0153]

[0154] Where: and are the minimum and maximum charging power of the energy storage power station respectively; and are the minimum and maximum discharge power of the energy storage power station respectively.

[0155] 4) Set the following constraints on the pumped storage units in the aggregated grid:

[0156] Pumping and releasing water state constraints:

[0157] a(t)+b(t)≤1

[0158] Where: a(t) is the pumping state variable, 1 indicates that the pumped storage unit is pumping, and 0 indicates that it is not pumping; b(t) is the discharge state variable, 1 indicates discharge, and 0 indicates that it is not discharge.

[0159] Reservoir capacity limits:

[0160] Eph，min ≤E ph (t-1)-P ph (t)ΔT≤E ph，max

[0161] Where: E ph，min and E ph，max are the minimum and maximum energy storage values ​​of the pumped storage power station respectively; P ph (t) and E ph (t) is the output and energy storage value of period t. Each pumped storage power station is given an initial energy storage value at the beginning of the calculation.

[0162] Unit output constraints:

[0163] Power generation conditions:

[0164] Pumping conditions:

[0165] Where: and They are respectively the pumping power and discharging power of the pumped storage unit. The power generation power can be adjusted between 0.5 and 1 times the maximum power generation power, and the pumping power is a constant value.

[0166] 5) Set the following constraints on wind power in the aggregated grid:

[0167]

[0168] Where: It is the theoretical wind power output of the aggregated grid n when the installed capacity is constant during period t.

[0169] 6) Set the following constraints on photovoltaic power in the aggregated grid:

[0170]

[0171] Where: It is the theoretical photovoltaic output of the aggregated grid n when the installed capacity is constant during period t.

[0172] 7) Establish constraints on the total load, line transmission capacity, and reserve capacity within the aggregated grid, as follows:

[0173] Inter-regional line transmission capacity constraints:

[0174] -P l，max ≤P l (t)≤P l，max

[0175] Where: P l (t) is the transmission power of the transmission line, and the reference direction of the current is set as the positive direction for the inflow area and the negative direction for the outflow area; and P l，maxand -P l，max are the upper and lower limits of the transmission capacity of the transmission line respectively.

[0176] Total load balancing constraints:

[0177]

[0178] Where: P ld，n (t) is the total load of zone n.

[0179] System spinning reserve capacity constraints:

[0180]

[0181] Where: P j，max (t, n)·S j (t, n) is the sum of the total power of all conventional units in the grid during the tth period.

[0182] P re and N re are the minimum requirements for positive spinning reserve and negative spinning reserve respectively; P j，max (t, n) and P j，min (t, n) are the upper and lower output limits of the j-th unit in the aggregated grid n.

[0183] S2. Based on the constraints set by various power sources, a power dispatch strategy optimization model is constructed with the highest source-load matching of various power sources and the maximization of renewable energy power generation as the objective function (in this embodiment, the goal is to maximize the total power generation of wind power, hydropower, and photovoltaic power to reduce the abandonment rate of wind and photovoltaic power). The specific model is as follows:

[0184]

[0185] Where: ρ is the load tracking coefficient, t is the time period variable, T is the total number of time periods, N r，t is the total power generation output of hydropower, wind power and solar power in period t, L t is the electricity load in period t, is the average load level, l, k, j are the numbers of wind power stations, solar power stations and hydropower stations respectively; L, K, J are the total number of wind power stations, solar power stations and hydropower stations respectively; wN l，t is the output of the lth wind power station in the tth period; sN k，t hN is the output of the k-th photovoltaic power station in the t-th period; j，t is the output of the j-th hydropower station in the t-th period; N n，t is the total power generation output of thermal power, pumped storage and energy storage in period t; m, o, c are the numbers of thermal power, pumped storage and energy storage power stations respectively; M, O, C are the total number of thermal power, pumped storage and energy storage power stations respectively; gN m，t is the output of the mth thermal power station in the tth period; pNo，t is the output of the oth pumped storage power station in the tth period; eN c，t is the output of the cth energy storage power station in the tth period; E is the total renewable energy power generation; m t is the number of hours in period t.

[0186] S3. Solve the power dispatch strategy optimization model to obtain the strategy optimization results, source-load matching, and renewable energy power generation results, as follows:

[0187] S31, inputting the load demand of the power grid and the output forecast curves of each power source obtained from various power source stations into the PSO algorithm of the Sigmoid function;

[0188] S32, based on the Sigmoid function, the reservoir storage curve is initialized in the variable domain, and the water level is obtained as the initial state of the particle swarm;

[0189] S33, calculating the initial fitness of the particles, and determining the initial global extreme value and the initial individual extreme value;

[0190] S34. Update the position and velocity of the particles based on the particle swarm update formula until all types of power supplies meet the working state constraints;

[0191] S35. Calculate the fitness value of the particle position and velocity after the update, compare it with the historical individual extreme value and the global extreme value, determine the current individual optimal position and the current global optimal position, output the obtained results and put them into the result cluster; repeatedly update the particle position and velocity until the maximum number of cycles is reached;

[0192] S36. After the cycle is completed, the optimal result is selected from the result cluster to obtain the strategy optimization result, the source-load matching degree and the renewable energy power generation result.

[0193] S4. Based on the actual power distribution and grid topology of the system, the strategy optimization results are simulated and calculated to obtain the simulation results of source-load matching and renewable energy power generation. Specifically, the following steps are included:

[0194] S41. Based on the actual power distribution and grid topology of the system, the power balance theory of the power grid, and the operating state constraints of various power sources, the optimization results of the strategy optimization results are synchronously input into the simulation calculation to obtain the simulation results of the first source-load matching degree and renewable energy power generation;

[0195] S42. Based on the actual power distribution and grid topology of the system, the power balance theory of the power grid, and the operating transition constraints of various power sources, the optimization results of the strategy optimization are inputted one by one for simulation calculation to obtain multiple simulation results of the second source-load matching degree and renewable energy power generation;

[0196] The optimization results of the strategy optimization results include the water level utilization method of pumped storage reservoirs and hydropower reservoirs, water level control range, charging and discharging plan of energy storage power station, start-up and shutdown plan and startup method of thermal power units, power generation output plan of pumped storage hydropower and conventional hydropower units, and system wind power and photovoltaic output plan.

[0197] S5. Compare the solution results of the source-load matching and renewable energy power generation with the simulation results of the source-load matching and renewable energy power generation. If the solution results of the source-load matching and renewable energy power generation are not optimal, change the working state constraints and / or objective functions of each type of power source to solve the power dispatch strategy optimization model again until the solution results of the source-load matching and renewable energy power generation are optimal. The optimal solution is that the solution result of the source-load matching is greater than or equal to the simulation result of the source-load matching, and at the same time, the solution result of renewable energy power generation is greater than or equal to the simulation result of renewable energy power generation, specifically including:

[0198] S51. Compare the first source-load matching and renewable energy power generation simulation result with the source-load matching and renewable energy power generation solution result. If the source-load matching and renewable energy power generation solution result is not an optimal solution, adjust the operating state constraints or objective functions of various power sources to reconstruct the power dispatch strategy optimization model. The optimal solution is that the source-load matching solution result is greater than or equal to the first source-load matching simulation result, and at the same time, the renewable energy power generation solution result is greater than or equal to the first renewable energy power generation simulation result.

[0199] S52. Compare the multiple second source-load matching and renewable energy power generation simulation results with the source-load matching and renewable energy power generation solution results. If the source-load matching and renewable energy power generation solution results are not the optimal solution, adjust the power cluster division method, grid load demand or each power output forecast curve to re-solve the power dispatch strategy optimization model until the source-load matching and renewable energy power generation solution results are the optimal solution. The optimal solution is that the source-load matching solution result is greater than or equal to any second source-load matching simulation result, and at the same time, the renewable energy power generation solution result is greater than or equal to any second renewable energy power generation simulation result.

[0200] S6. Calculate comprehensive indicators of each optimization result from the strategy optimization results through simulation, including power generation characteristics, efficiency, reliability, flexibility, and environmental benefit. This indicator calculation is used to support the dispatcher's comprehensive evaluation of the organization's strategy.

[0201] Power generation characteristic indicators include power extreme difference rate, output extreme difference rate, integrated complementary coefficient, integrated load rate, etc.; efficiency indicators include peak-shaving contribution rate, channel utilization rate, network loss rate, power abandonment rate, clean energy utilization rate, hydropower utilization rate, wind power utilization rate, photovoltaic utilization rate, etc.; reliability indicators include source-load matching degree, failure probability, etc.; flexibility indicators include peak-shaving capacity, peak-shaving depth, etc.; environmental benefit indicators include clean energy contribution rate, etc.

[0202] Example 2

[0203] like Figure 2 As shown, this embodiment provides a multi-power supply power dispatch strategy optimization system, including:

[0204] The system generalization constraint module is used to aggregate multiple sub-grids in a multi-power dispatch system to form power clusters, divide the power clusters based on power generation type, and establish working state constraints for each type of power after division;

[0205] The strategy optimization model establishment module is used to build a power dispatch strategy optimization model based on the operating status constraints of various power sources, with the objective function of maximizing the source-load matching of various power sources and maximizing the power generation of renewable energy;

[0206] The strategy optimization model solving module is used to solve the power dispatch strategy optimization model to obtain the strategy optimization results, source-load matching and renewable energy power generation results;

[0207] The simulation calculation module is used to simulate and calculate the strategy optimization results based on the actual power distribution and grid topology of the system, and obtain the simulation results of source-load matching and renewable energy power generation;

[0208] The strategy optimization result verification module is used to compare the solution results of source-load matching and renewable energy power generation with the simulation results of source-load matching and renewable energy power generation. If the solution results of source-load matching and renewable energy power generation are not optimal, the power dispatch strategy optimization model is adjusted or the parameters are changed and solved again until the solution results of source-load matching and renewable energy power generation are optimal. The optimal solution is that the source-load matching solution result is greater than or equal to the source-load matching simulation result, and at the same time, the renewable energy power generation solution result is greater than or equal to the renewable energy power generation simulation result.

[0209] In this embodiment, a result comprehensive index calculation module is also included, which is used to calculate the comprehensive index of each optimization result in the strategy optimization result through simulation calculation.

[0210] like Figure 3As shown, in this embodiment, the simulation calculation module, the strategy optimization result verification module, and the comprehensive index calculation module are integrated into the power dispatching strategy simulation module, and a parameter configuration function is further provided to realize the import of power grid models, power supply models, basic equipment information, power grids and various power station operation data, including historical operation data, real-time operation data and forecast plan data, and support manual modification of imported data; support the input and modification of various constraints of simulation calculation, including various power unit constraints, line transmission constraints, load balance constraints and positive and negative standby constraints; support the input and modification of simulation parameters, including simulation objects, simulation time, and simulation range; support manual modification and maintenance of power grid model and power supply model data.

[0211] The simulation calculation module includes the functions of result import, simulation calculation, and result comparison. The result import can select the optimization results stored in the library, and import the weekly water level operation mode of the pumped storage reservoir and hydropower reservoir, the weekly water level control range, the weekly charge and discharge plan of the energy storage power station, the weekly start and stop plan and weekly startup mode of the thermal power unit, the weekly power generation output plan of the pumped storage hydropower and conventional hydropower units, and the weekly output plan of the system wind power and photovoltaic power units. The operating data used in the optimization calculation is selected. The simulation calculation is based on the power balance theory of the power grid and various constraints. The simulation calculation results in the simulation environment are obtained. The simulation calculation results are used to recalculate the objective function, that is, the highest source-load matching and the maximization of the power generation of water, wind, and solar renewable energy. The result comparison compares the objective function calculation results of the simulation calculation with the objective function results of the optimization calculation to analyze the differences.

[0212] The strategy optimization result verification module includes result import, result modification and result verification. Result import allows you to select the optimization results stored in the library, as well as the objective function results calculated based on the result data; result modification allows you to select the weekly water level operation mode of pumped storage reservoirs and hydropower reservoirs, weekly water level control range, weekly charge and discharge plan of energy storage power stations, weekly start-up and shutdown plan and weekly startup mode of thermal power units, weekly power generation output plan of pumped storage hydropower and conventional hydropower units, and weekly output plan of system wind power and photovoltaic power units, and modify the selected optimization results in positive and negative directions in turn. After each modification, re-simulate the objective function calculation, and then obtain two optimization objective functions based on the simulation calculation: the highest source-load matching degree and the maximum power generation of water, wind, and solar renewable energy; result verification compares the original objective function results with the series of calculation results after the modified data, and verifies through trend comparison analysis whether the original optimization results are the optimal values ​​in the simulation environment.

[0213] The comprehensive indicator calculation module includes power generation characteristic indicators, efficiency indicators, reliability indicators, flexibility indicators, and environmental benefit indicators. These comprehensive indicators are calculated based on the simulation results of the power dispatch strategy, supporting dispatchers in their comprehensive evaluation of the organization's strategy. Power generation characteristic indicators include power extreme difference rate, output extreme difference rate, integrated complementarity coefficient, and integrated load rate; efficiency indicators include peak-shaving contribution rate, channel utilization rate, network loss rate, power curtailment rate, clean energy utilization rate, hydropower utilization rate, wind power utilization rate, and photovoltaic utilization rate; reliability indicators include source-load matching and probability of mismatch; flexibility indicators include peak-shaving capacity and peak-shaving depth; and environmental benefit indicators include clean energy contribution rate.

[0214] Example 3

[0215] This embodiment provides a computer storage medium, wherein the computer-readable storage medium stores a computer program. When the computer program is executed by a processor, the steps of the multi-power supply power dispatching strategy optimization method as described in Example 1 are implemented.

[0216] Example 4

[0217] This embodiment provides an electronic device, including a memory and a processor: the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the multi-power supply power scheduling strategy optimization method as described in Example 1 are implemented.

[0218] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Any ordinary technician in this industry can smoothly implement the present invention as shown in the drawings and described above. However, any equivalent changes, modifications and evolutions made by technicians familiar with this profession without departing from the scope of the technical solution of the present invention using the technical content disclosed above are all equivalent embodiments of the present invention. At the same time, any equivalent changes, modifications and evolutions made to the above embodiments based on the essential technology of the present invention are still within the scope of protection of the technical solution of the present invention.

[0219] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0220] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0221] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0222] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

Claims

1. A multi-power supply power dispatching strategy optimization method, characterized in that: The following steps are involved: Aggregate multiple subgrids in a multi-power dispatch system to form power clusters, divide the power clusters based on power generation type, and set working status constraints for each type of power after division; Based on the working state constraints set by various power sources, a power dispatch strategy optimization model is constructed with the highest source-load matching of various power sources and the maximization of renewable energy power generation as the objective function. The details are as follows: Where: ρ is the load tracking coefficient, t is the time period variable, T is the total number of time periods, N r，t is the total power generation output of hydropower, wind power and solar power in period t, L t is the electricity load in period t, is the average load level, l, k, j are the numbers of wind power stations, solar power stations and hydropower stations respectively; L, K, J are the total number of wind power stations, solar power stations and hydropower stations respectively; wN l，t is the output of the lth wind power station in the tth period; sN k，t hN is the output of the k-th photovoltaic power station in the t-th period; j，t is the output of the j-th hydropower station in the t-th period; N n，t is the total power generation output of thermal power, pumped storage and energy storage in period t; m, o, c are the numbers of thermal power, pumped storage and energy storage power stations respectively; M, O, C are the total number of thermal power, pumped storage and energy storage power stations respectively; gN m，t is the output of the mth thermal power station in the tth period; pN o，t is the output of the oth pumped storage power station in the tth period; eN c，t is the output of the cth energy storage power station in the tth period; E is the total renewable energy power generation; m t is the number of hours in period t; Solve the power dispatch strategy optimization model to obtain strategy optimization results, source-load matching and renewable energy power generation results; Based on the actual power distribution and grid topology of the system, the power balance theory of the power grid and the working state constraints of various power sources, the optimization results of the strategy optimization results are synchronously input into the simulation calculation to obtain the simulation results of the first source-load matching degree and renewable energy power generation; Based on the actual power distribution and grid topology of the system, the power balance theory of the power grid and the working state constraints of various power sources, the optimization results of the strategy optimization results are inputted one by one for simulation calculation to obtain multiple simulation results of the second source-load matching degree and renewable energy power generation; The strategy optimization results include the water level operation mode of pumped storage reservoirs and hydropower reservoirs, water level control range, charging and discharging plan of energy storage power station, start-up and shutdown plan and startup mode of thermal power units, power generation output plan of pumped storage hydropower and conventional hydropower units, and system wind power and photovoltaic output plan; Comparing the first source-load matching and renewable energy power generation simulation result with the source-load matching and renewable energy power generation solution result, if the source-load matching and renewable energy power generation solution result is not the optimal solution, adjusting the various power supply working state constraints or objective functions to reconstruct the power dispatch strategy optimization model, the optimal solution is that the source-load matching solution result is greater than or equal to the first source-load matching simulation result, and at the same time, the renewable energy power generation solution result is greater than or equal to the first renewable energy power generation simulation result; Compare multiple second source-load matching and renewable energy power generation simulation results with the source-load matching and renewable energy power generation solution results. If the source-load matching and renewable energy power generation solution results are not the optimal solution, adjust the power cluster division method, grid load demand or each power output forecast curve to re-solve the power dispatch strategy optimization model until the source-load matching and renewable energy power generation solution results are the optimal solution. The optimal solution is that the source-load matching solution result is greater than or equal to any second source-load matching simulation result, and at the same time, the renewable energy power generation solution result is greater than or equal to any second renewable energy power generation simulation result.

2. A multi-power supply power dispatching strategy optimization method according to claim 1, characterized in that: The method further includes: calculating comprehensive indicators of various optimization results in the strategy optimization results through simulation calculation, wherein the comprehensive indicators include power generation characteristic indicators, efficiency indicators, reliability indicators, flexibility indicators, and environmental benefit indicators.

3. The multi-power supply scheduling strategy optimization method according to claim 1, characterized in that: The setting of working state constraints for various power sources specifically includes: Based on the operating power of each thermal power unit, the thermal power units are classified and the following constraints are set: X g (t)P g，min ≤P g (t)≤X g (t)P g,max Where: P g (t) is the output of the thermal power unit; P g,min and P g,max is the minimum and maximum unit output; X g (t) indicates the operating status of the unit; Thermal power unit ramp rate constraints: P g (t+1)-P g (t)≤ΔP g，up ΔT P g (t)-P g (t+1)≤ΔP g，d ΔT Where: ΔP g，up , ΔP g，d are the unit's ramp-up rate and ramp-down rate respectively; Thermal power start-up and shutdown constraints: Where: Y(t) and Z(t) are the start command and stop command of the system in period t; Based on the operating power of each hydropower unit, the hydropower units are classified and the following constraints are set: P h，min ≤P h (t)≤P h，max Where: P h (t) is the output of the hydropower unit; P h,min and P h,max is the minimum and maximum output of the hydropower unit; Where: Z j,end is the end-of-day water level of the j-th reservoir; Z j,begin is the initial water level of the jth reservoir, Z j,allow is the daily water level fluctuation allowed for the jth reservoir; Downflow flow restriction: Where: Q j,t is the discharge flow of the j-th hydropower station in the t-th period; is the maximum discharge of the j-th hydropower station in the t-th period; is the minimum discharge flow of the j-th hydropower station in the t-th period; Constraints on the power generation flow of hydropower units: Where: is the maximum power generation flow of the j-th hydropower station in the t-th period; is the minimum power generation flow of the j-th hydropower station in the t-th period; Constraints on daily power generation of hydropower units: Where: P h (t) is the output of the hydropower unit at time t, ΔT is the step length t, Q h is the daily power generation of the unit; The following constraints are set for energy storage units in the aggregated grid: Energy storage unit charging and discharging state constraints: a(t)+b(t)≤1 Where: a(t) is the charging state variable, 1 indicates energy storage charging, 0 indicates non-charging; b(t) is the discharging state variable, 1 indicates discharging, 0 indicates non-discharging; Energy storage capacity limitations: E es，min ≤E es (t-1)-P es (t)ΔT≤E es，max Where: E es，min and E es，max are the minimum and maximum energy storage values ​​of the energy storage power station, which are calculated by multiplying the energy storage SOC limit and the rated capacity; P es (t) and E es (t) is the output and energy storage value in period t. Each energy storage power station is given an initial SOC at the beginning of the calculation; Energy storage output constraints: Where: and are the minimum and maximum charging power of the energy storage power station respectively; and are the minimum and maximum discharge power of the energy storage power station respectively; The following constraints are set for the pumped storage units in the aggregated grid: Pumping and releasing water state constraints: a(t)+b(t)≤1 Where: a(t) is the pumping state variable, 1 indicates that the pumped storage unit is pumping water, and 0 indicates that it is not pumping water; b(t) is the discharge state variable, 1 indicates discharge water, and 0 indicates that it is not discharge water; Reservoir capacity limits: E ph，min ≤E ph (t-1)-P ph (t)ΔT≤E ph，max Where: E ph，min and E ph，max are the minimum and maximum energy storage values ​​of the pumped storage power station respectively; P ph (t) and E ph (t) is the output and energy storage value of period t. Each pumped storage power station is given an initial energy storage value at the beginning of the calculation; Unit output constraints: Power generation conditions: Pumping conditions: Where: and are the pumping power and discharging power of the pumped storage unit respectively; The following constraints are set for wind power in the aggregated grid: Where: The theoretical output of wind power in the aggregated grid n when the installed capacity is constant during time period t; The following constraints are set for PV in the aggregated grid: Where: The theoretical photovoltaic output of the aggregated grid n when the installed capacity is constant during time period t; Constraints are set on the total load, line transmission capacity, and reserve capacity within the aggregated grid, as follows: Inter-regional line transmission capacity constraints: -P l，max ≤P l (t)≤P l，max Where: P l (t) is the transmission power of the transmission line, and the reference direction of the current is set as the positive direction for the inflow area and the negative direction for the outflow area; and P l，max and -P l，max are the upper and lower limits of the transmission capacity of the transmission line respectively; Total load balancing constraint: Where: P ld，n (t) is the total load of region n; System spinning reserve capacity constraints: Where: P j，max (t, n)·S j (t, n) is the sum of the total power of all conventional units in the grid during the tth period; P re and N re are the minimum requirements for positive spinning reserve and negative spinning reserve respectively; P j，max (t, n) and P j,min (t, n) are the upper and lower output limits of the j-th unit in the aggregated grid n.

4. The multi-power supply scheduling strategy optimization method according to claim 1, characterized in that: The power dispatch strategy optimization model is solved to obtain the strategy optimization results, source-load matching and renewable energy power generation results, specifically including: The load demand of the power grid and the output forecast curves of each power source obtained from various power stations are input into the PSO algorithm of the Sigmoid function; Based on the Sigmoid function, the reservoir storage curve is initialized in the variable domain, and the water level is obtained as the initial state of the particle swarm; Calculate the initial fitness of particles and determine the initial global extreme value and initial individual extreme value; The particle positions and velocities are updated based on the particle swarm update formula until all types of power sources meet the working state constraints; Calculate the fitness value of the updated particle position and velocity, compare it with the historical individual extreme value and the global extreme value, determine the current individual optimal position and the current global optimal position, output the obtained results and put them into the result cluster; repeatedly update the particle position and velocity until the maximum number of cycles is reached; After the cycle is completed, the optimal result is selected from the result cluster to obtain the strategy optimization result, source-load matching and renewable energy power generation solution results.

5. A system for implementing the multi-power supply power dispatching strategy optimization method according to claim 1, characterized in that: include: The system generalization constraint module is used to aggregate multiple sub-grids in a multi-power dispatch system to form power clusters, divide the power clusters based on power generation type, and establish working state constraints for each type of power after division; The strategy optimization model establishment module is used to build a power dispatch strategy optimization model based on the operating status constraints of various power sources, with the objective function of maximizing the source-load matching of various power sources and maximizing the power generation of renewable energy; The strategy optimization model solving module is used to solve the power dispatch strategy optimization model to obtain the strategy optimization results, source-load matching and renewable energy power generation results; The simulation calculation module is used to simulate and calculate the strategy optimization results based on the actual power distribution and grid topology of the system, and obtain the simulation results of source-load matching and renewable energy power generation; The strategy optimization result verification module is used to compare the solution results of source-load matching and renewable energy power generation with the simulation results of source-load matching and renewable energy power generation. If the solution results of source-load matching and renewable energy power generation are not optimal, the working state constraints and / or objective functions of various power sources are changed to solve the power dispatch strategy optimization model again until the solution results of source-load matching and renewable energy power generation are optimal. The optimal solution is that the source-load matching solution result is greater than or equal to the source-load matching simulation result, and at the same time, the renewable energy power generation solution result is greater than or equal to the renewable energy power generation simulation result.

6. A computer storage medium, wherein the computer readable storage medium stores a computer program, characterized in that: When the computer program is executed by a processor, the steps of the multi-power supply power dispatching strategy optimization method according to any one of claims 1 to 4 are implemented.

7. An electronic device, characterized in that: It comprises a memory and a processor: the memory is used to store computer-executable instructions, the processor is used to execute the computer-executable instructions, and when the computer-executable instructions are executed by the processor, the steps of the multi-power supply power dispatching strategy optimization method as described in any one of claims 1 to 4 are implemented.

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