Pumped storage capacity time sequence configuration method, device and equipment and storage medium

Abstract

The invention relates to the technical field of energy storage optimal configuration, in particular to a pumped storage capacity time sequence configuration method, device and equipment and a storage medium, and the method comprises the steps: orderly configuring a pumped storage capacity time sequence; a double-layer loop iteration optimization configuration model is constructed and comprises an inner-layer model and an outer-layer model, the inner-layer model is based on capacity balance, the minimum sum of wind curtailment and light curtailment is taken as the target, a planning scheme of the pumping and storage capacity is provided for the outer-layer model, and the outer-layer model is based on optimization scheduling and the economical efficiency and peak-valley difference index are taken as the target; introducing a dynamic feedback mechanism and a multi-target adaptive weight, dynamically correcting a power abandoning penalty weight of an inner-layer model through an economic evaluation result of an outer-layer model, and realizing closed-loop collaborative optimization of capacity planning and a scheduling strategy; and carrying out loop iteration solution on the double-layer loop iteration optimization configuration model by adopting an improved grey wolf optimization algorithm of a composite chaotic mapping Logistic-Tent to obtain a pumped storage time sequence capacity configuration scheme.

Description

Technical Field

[0001] This invention relates to the field of energy storage optimization configuration technology, and in particular to a method, apparatus, equipment and storage medium for time-series configuration of pumped hydro storage capacity. Background Technology

[0002] With the large-scale grid connection of new energy sources, their intermittency and volatility pose a significant threat to the stable operation of the power system, requiring the grid to have more capacity to balance power. Pumped storage, as a technologically mature and economically viable safe regulating power source, can effectively improve the anti-peak-shaving characteristics caused by large-scale wind power grid connection, playing a role in peak shaving and valley filling. In addition to ensuring the safe and stable operation of the power system, it can also help to absorb large-scale new energy sources and reduce wind and solar curtailment.

[0003] Currently, the most common method for planning pumped storage capacity is to use the system's operational economy, technicality, and reliability as indicators, combined with the load peak-valley difference in the region to balance conventional units, and to determine the pumped storage capacity allocation through evaluation or operational optimization methods. Pumped storage capacity is generally allocated based on target years, which may lead to problems such as early-stage capacity shortages and wind / solar curtailment, followed by later-stage overcapacity and resource waste, thus hindering the economic efficiency of pumped storage construction.

[0004] The information disclosed in this background section is intended only to enhance the understanding of the general background of the invention and should not be construed as an admission or in any way implying that the information constitutes prior art known to those skilled in the art. Summary of the Invention

[0005] This invention provides a method, apparatus, equipment, and storage medium for timing configuration of pumped storage capacity, thereby effectively solving the problems in the background art.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is: a method for timing configuration of pumped storage capacity, comprising the following steps:

[0007] When planning and allocating pumped storage capacity each year, the capacity is increased based on the planned capacity of the previous year to establish a sequence of pumped storage power stations.

[0008] A two-layer iterative optimization configuration model is constructed, which includes an inner layer model and an outer layer model. The inner layer model is based on capacity balance and aims to minimize the sum of wind curtailment and solar curtailment, providing a planning scheme for pumped storage capacity for the outer layer model. The outer layer model is based on optimized scheduling and aims to refine the capacity of the inner layer model with economic efficiency and peak-valley difference indicators.

[0009] By introducing a dynamic feedback mechanism and multi-objective adaptive weights, the curtailment penalty weights of the inner model are dynamically adjusted based on the economic evaluation results of the outer model, thereby achieving closed-loop collaborative optimization of capacity planning and scheduling strategies.

[0010] An improved gray wolf optimization algorithm based on the composite chaotic mapping Logistic-Tent is used to iteratively solve the two-layer cyclic iterative optimization configuration model to obtain the pumped storage time-series capacity configuration scheme.

[0011] Furthermore, the objective function of the inner model is:

[0012]

[0013] In the formula, μ1 and μ2 are the penalty weights for wind curtailment and solar curtailment, respectively, which are dynamically adjusted. These represent the power of wind and solar power curtailed during time period t, respectively.

[0014]

[0015] In the formula, These represent the available capacity of wind power and solar power during time period t, respectively. These represent the actual absorption of wind power and solar power during time period t, respectively.

[0016] Furthermore, the inner model also includes the following constraints:

[0017] Minimum power constraint:

[0018] P ps,t ≥P qw,t +P qpv,t ;

[0019] In the formula, P ps,t For the pumped storage unit output during time period t; P qw,t P qpv,t These represent the power of wind and solar power curtailed during time period t, respectively.

[0020] Output constraints:

[0021]

[0022] In the formula, These represent the minimum and maximum power outputs of the pumped storage unit under power generation conditions, respectively. P represents the minimum and maximum power of the pumped storage unit under pumping conditions, respectively. t psG P t psP These represent the power generation and pumping power of the pumped storage unit during time period t, respectively. This is a Boolean variable indicating whether the pumped storage unit is in power generation mode at time t. This is a Boolean variable indicating whether the pumped storage unit is in pumping mode at time t;

[0023] Energy balance constraints:

[0024]

[0025] In the formula, τ is the energy conversion efficiency of the pumped storage unit;

[0026] Power balance constraints:

[0027] P L,t =P G,t +P wind,t +P pv,t +P he,t +P ps,t ;

[0028] In the formula, P L,t Let P be the load during time period t. G,t P wind,t P pv,t P he,t These represent the output of thermal power, wind power, photovoltaic power, and nuclear power during time period t.

[0029] Furthermore, the objective function of the outer model includes:

[0030] Minimize grid operator costs:

[0031] mincost d =cost G +cost green ;

[0032] In the formula, cost d Cost represents the cost of a power grid operator. G This refers to the operating cost of a thermal power plant; cost green This indicates the cost of curtailing wind and solar power.

[0033] Maximizing the revenue from pumped storage two-part tariffs:

[0034] maxshouyi c =shouyi l +shouyi r ;

[0035]

[0036] shouyi r =p rong ·P ps ;

[0037] In the formula, shouyi c This indicates the revenue from the pumped storage two-part electricity pricing system; shouyi l This indicates the revenue from pumped storage electricity price; shouyi r This represents the revenue from pumped storage capacity electricity price; p ranmei p rong These represent the price of coal-fired power and the price of capacity-based power, respectively. P represents a 0-1 variable indicating whether the pumped storage unit is in power generation or pumping mode at time t; t psG P t psP P represents the power generation and pumping power of the pumped storage unit during time period t, respectively. ps This represents the total installed capacity of the pumped storage power station.

[0038] Minimize the peak-to-valley difference of the load curve:

[0039] minf pl =pl max -pl min ;

[0040] In the formula, f pl This represents the peak-to-valley difference in the power grid load curve; pl max This indicates the peak load on the power grid during the day; pl min This indicates the lowest load on the power grid during the day.

[0041] Furthermore, the operating costs of the aforementioned thermal power plant include:

[0042] cost G =cost G,P +cost G,switch ;

[0043]

[0044] In the formula, cost G,P Cost indicates the cost of thermal power generation. G,switch This represents the start-up and shutdown costs, where a, b, and c are cost parameters; c switch Cost per start / stop; u switch,t This indicates whether the units of a thermal power plant change their start-up or shutdown status during time period t; it is a 0-1 variable.

[0045] Furthermore, the costs of wind and solar power curtailment include:

[0046]

[0047] In the formula, p s Indicates the probability of scenario s occurring; This represents the amount of wind and solar power curtailment in scenario s; This represents the penalty for abandoning wind and light in scenario s.

[0048] Furthermore, the outer model also includes the following constraints:

[0049] Power output constraints of wind and solar turbines:

[0050]

[0051] In the formula, These represent the actual power absorbed by wind power and solar power during time period t, respectively.

[0052] Thermal power unit constraints:

[0053] u switch,t =|u G,t -u G,t-1 |;

[0054]

[0055] P G,down ≤P G,t -P G,t-1 ≤P G,up ;

[0056] In the formula, u switch,t This is a Boolean variable representing the switching of the operating state of a thermal power unit during time period t; u G,t A Boolean variable representing whether a thermal power unit is in operation during time period t; u G,0 Indicates the initial start-up and shutdown status of the thermal power unit; These represent the minimum and maximum output of the thermal power unit, respectively; P G,down P G,up These are the downhill and uphill ramp rates for thermal power units, respectively.

[0057] Nuclear power unit constraints:

[0058]

[0059] P he,down ≤P he,t -P he,t-1 ≤P he,up ;

[0060] In the formula, These represent the minimum and maximum output of the nuclear power unit, respectively; P he,down P he,up These are the downhill and uphill ramp rates for nuclear power units, respectively.

[0061] Pumped storage unit operating reservoir capacity constraints:

[0062]

[0063] In the formula, V represents the maximum and minimum reservoir capacity of a pumped storage power station, respectively; t u V t d These represent the upper and lower reservoir capacities at time t, respectively. These represent the maximum and minimum reservoir capacity of the pumped storage power station, respectively. These represent the water consumption of the pumped storage unit at time t under power generation and pumping conditions, respectively.

[0064] Pumped storage unit state constraints:

[0065]

[0066] Furthermore, when planning and allocating pumped storage capacity each year, the planned pumped storage capacity for the previous year is increased to establish a sequence of pumped storage power stations, including:

[0067] The sequence P of pumped storage power stations configured each year is as follows:

[0068] P = [P1, P2, ..., P i ,...,P n ];

[0069] In the formula, P i P represents the set of pumped storage capacities to be configured in year i; i+1 It is in P i ,P i-1 ..., the set of incremental pumped storage capacity based on P1;

[0070] In the first year, plan and configure P1 to meet the load demand of the first year; in the second year, plan and configure P2 based on P1 to meet the demand of the second year due to load growth; and so on, in the i-th year, based on the already configured P1∪P2···∪P i-1 Based on the capacity set, plan and configure P i To meet the load demand in year i, and up to year n, the planned configuration P is... n .

[0071] Furthermore, the step of dynamically correcting the curtailment penalty weight of the inner model based on the economic evaluation results of the outer model includes the following steps:

[0072] Calculating the economic indicators of the outer layer model pumped storage capacity configuration scheme includes:

[0073]

[0074] In the formula, ROI (k) NPV represents the return on investment in the k-th iteration. (k) Net present value; r is the discount rate;

[0075] Based on the degree to which economic indicators deviate from the target value, the inner-layer curtailment penalty weights are updated according to the following non-linear relationship:

[0076]

[0077] In the formula, α and β are feedback gain coefficients, controlling the weight adjustment rate; tanh(·) and sigmoid(·) functions are used to smooth weight abrupt changes and avoid oscillations; when ROI (k) <ROI targ When economic viability is insufficient, increase μ1 to prioritize reducing wind power curtailment and alleviate capacity investment pressure; when NPV (k) >NPV thre When economic conditions permit, the μ2 limit should be relaxed to increase the capacity for photovoltaic power consumption.

[0078] The outer model feeds back the economic evaluation results to the inner model, dynamically adjusting the curtailment penalty weights μ1 and μ2 in the objective function of the inner model.

[0079] Furthermore, the improved gray wolf optimization algorithm using the composite chaotic mapping Logistic-Tent to iteratively solve the two-layer iterative optimization configuration model includes the following steps:

[0080] Input basic data, including design variables, load data, and initial parameters;

[0081] Randomly generate x n-dimensional vectors and iteratively generate chaotic vectors, expanding the chaotic vectors to their value range;

[0082] Calculate the objective function value of each chaotic vector, and superimpose multiple objective functions;

[0083] Select N chaotic vectors to output as the chaotic optimization results to form the initial population of the Grey Wolf Optimization Algorithm;

[0084] The individual fitness of each gray wolf was calculated, and the gray wolf population hierarchy and hunting behavior were simulated to select leaders and determine α, β, and δ wolves.

[0085] Determine if the maximum number of iterations has been reached. If so, output the optimal solution. Otherwise, update the individual positions of the gray wolves, calculate the fitness function, and update the leader until the maximum number of iterations is reached.

[0086] Furthermore, in the simulated gray wolf population hierarchy mechanism and hunting behavior, the improved Logistic mapping is used to update the gray wolf's location, including:

[0087]

[0088] In the formula, x m This represents the position of the gray wolf after the m-th iteration; This represents the XOR operation.

[0089] Furthermore, updating the individual gray wolf positions, calculating the fitness function, and updating the leader include:

[0090] Initial decision variables Mapping to chaotic variables between 0 and 1

[0091]

[0092] In the formula, These are the upper and lower limits of the search for the j-th dimension variable, respectively;

[0093] The chaotic variables for the next iteration are calculated. And transform it into a new decision variable. And based on decision variables Calculate the fitness value of the new solution:

[0094]

[0095] The present invention also includes a pumped storage capacity timing configuration device, using the method described above, the device comprising:

[0096] The time sequence unit is used to increase the pumped storage capacity based on the planned pumped storage capacity of the previous year when planning and configuring the pumped storage capacity each year, and to establish a sequence of pumped storage power stations.

[0097] The modeling unit is used to construct a two-layer cyclic iterative optimization configuration model, which includes an inner layer model and an outer layer model. The inner layer model is based on capacity balance and aims to minimize the sum of wind and solar curtailment, providing a planning scheme for pumped storage capacity for the outer layer model. The outer layer model is based on optimized scheduling and aims to refine the capacity of the inner layer model with economic efficiency and peak-valley difference indicators.

[0098] The correction unit is used to introduce a dynamic feedback mechanism and multi-objective adaptive weights, and dynamically correct the curtailment penalty weights of the inner model through the economic evaluation results of the outer model, so as to realize the closed-loop collaborative optimization of capacity planning and scheduling strategies.

[0099] The solution unit is used to iteratively solve the two-layer cyclic iterative optimization configuration model using the improved gray wolf optimization algorithm with composite chaotic mapping Logistic-Tent, so as to obtain the pumped storage time-series capacity configuration scheme.

[0100] The present invention also includes a computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the method as described above.

[0101] The present invention also includes a storage medium having a computer program stored thereon, which, when executed by a processor, implements the method as described above.

[0102] The beneficial effects of this invention are as follows: By considering the development of new energy sources and the growth of load, the pumped storage capacity is configured in an orderly manner, with the capacity allocated year by year. The resulting configuration scheme better satisfies the needs of new energy absorption and the economic efficiency of capacity allocation, effectively avoiding the problems caused by allocating pumped storage capacity according to the target year. It improves the new energy absorption rate in the early stages of planning, while avoiding saturation of new energy absorption in the later stages of planning, and avoiding the clustering of pumped storage construction. Furthermore, a dynamic feedback mechanism and multi-objective adaptive weights are introduced. The curtailment penalty weights of the inner model are dynamically corrected based on the economic evaluation results of the outer model, realizing closed-loop collaborative optimization of capacity planning and scheduling strategies, forming a closed-loop optimization loop, improving the accuracy of capacity time-series configuration. An improved gray wolf optimization algorithm based on the composite chaotic mapping Logistic-Tent is used to iteratively solve the double-layer cyclic iterative optimization configuration model, improving the randomness and anti-truncation ability of the sequence, enhancing ergodicity, preventing the situation of getting trapped in local optima, obtaining the optimal configuration scheme, and improving the solution efficiency. Attached Figure Description

[0103] 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 recorded in the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0104] Figure 1 This is a flowchart of the method in Example 1;

[0105] Figure 2 This is a schematic diagram of the device in Example 1;

[0106] Figure 3 This is a flowchart of the capacity configuration method for double-loop iterative optimization in Example 2;

[0107] Figure 4 This is a schematic diagram of the pumped storage capacity configuration method considering the construction sequence in Example 2;

[0108] Figure 5 This is a system structure diagram from Example 2;

[0109] Figure 6 The flowchart shows the solution process of the improved gray wolf optimization algorithm for the Logistic-Tent composite chaotic mapping in Example 2.

[0110] Figure 7 To contribute to the wind power and photovoltaic projects in Example 2;

[0111] Figure 8 The results are typical daily power grid dispatching results in Example 2;

[0112] Figure 9 This is a typical daily load curve from Example 2;

[0113] Figure 10 This represents the output of a typical Japanese thermal power unit in Example 2;

[0114] Figure 11 This is a schematic diagram of the structure of the computer device of the present invention. Detailed Implementation

[0115] 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.

[0116] Example 1:

[0117] like Figure 1 As shown: A method for timing configuration of pumped storage capacity, comprising the following steps:

[0118] When planning and allocating pumped storage capacity each year, the capacity is increased based on the planned capacity of the previous year to establish a sequence of pumped storage power stations.

[0119] A two-layer iterative optimization configuration model is constructed, which includes an inner layer model and an outer layer model. The inner layer model is based on capacity balance and aims to minimize the sum of wind and solar curtailment, providing a planning scheme for pumped storage capacity for the outer layer model. The outer layer model is based on optimal scheduling and aims to refine the capacity of the inner layer model with economic efficiency and peak-valley difference indicators.

[0120] By introducing a dynamic feedback mechanism and multi-objective adaptive weights, the curtailment penalty weights of the inner model are dynamically adjusted based on the economic evaluation results of the outer model, thereby achieving closed-loop collaborative optimization of capacity planning and scheduling strategies.

[0121] An improved gray wolf optimization algorithm based on the composite chaotic mapping Logistic-Tent is used to iteratively solve the two-level cyclic iterative optimization configuration model to obtain the pumped storage time-series capacity configuration scheme.

[0122] By considering the development of new energy sources and the growth of load, the pumped storage capacity is configured in an orderly manner, with the capacity allocated year by year. The resulting configuration scheme effectively satisfies both the new energy absorption effect and the economic efficiency of capacity allocation, effectively avoiding the problems caused by allocating pumped storage capacity according to the target year. This improves the new energy absorption rate in the early stages of planning while preventing the absorption of new energy from reaching saturation in the later stages of planning, and avoids the clustering of pumped storage construction. Furthermore, a dynamic feedback mechanism and multi-objective adaptive weights are introduced. The curtailment penalty weights of the inner model are dynamically adjusted based on the economic evaluation results of the outer model, achieving closed-loop collaborative optimization of capacity planning and scheduling strategies, forming a closed-loop optimization loop, and improving the accuracy of capacity time-series configuration. An improved gray wolf optimization algorithm based on the composite chaotic mapping Logistic-Tent is used to iteratively solve the two-layer cyclic iterative optimization configuration model, improving the randomness and anti-truncation ability of the sequence, enhancing ergodicity, preventing getting trapped in local optima, obtaining the optimal configuration scheme, and improving the solution efficiency.

[0123] In this embodiment, the objective function of the inner model is:

[0124]

[0125] In the formula, μ1 and μ2 are the penalty weights for wind curtailment and solar curtailment, respectively, which are dynamically adjusted. These represent the power of wind and solar power curtailed during time period t, respectively.

[0126]

[0127] In the formula, These represent the available capacity of wind power and solar power during time period t, respectively. These represent the actual absorption of wind power and solar power during time period t, respectively.

[0128] The inner model also includes the following constraints:

[0129] Minimum power constraint:

[0130] P ps,t ≥P qw,t +P qpv,t ;

[0131] In the formula, P ps,t For the pumped storage unit output during time period t; P qw,t P qpv,t These represent the power of wind and solar power curtailed during time period t, respectively.

[0132] Output constraints:

[0133]

[0134] In the formula, These represent the minimum and maximum power outputs of the pumped storage unit under power generation conditions, respectively. P represents the minimum and maximum power of the pumped storage unit under pumping conditions, respectively. t psG P t psP These represent the power generation and pumping power of the pumped storage unit during time period t, respectively. This is a Boolean variable indicating whether the pumped storage unit is in power generation mode at time t. This is a Boolean variable indicating whether the pumped storage unit is in pumping mode at time t;

[0135] Energy balance constraints:

[0136]

[0137] In the formula, τ is the energy conversion efficiency of the pumped storage unit;

[0138] Power balance constraints:

[0139] P L,t =P G,t +P wind,t +P pv,t +P he,t +P ps,t ;

[0140] In the formula, P L,t Let P be the load during time period t. G,t P wind,t P pv,t P he,t These represent the output of thermal power, wind power, photovoltaic power, and nuclear power during time period t.

[0141] The objective function of the outer model includes:

[0142] Minimize grid operator costs:

[0143] mincost d =cost G +cost green ;

[0144] In the formula, cost d Cost represents the cost of a power grid operator. G This refers to the operating cost of a thermal power plant; cost green This indicates the cost of curtailing wind and solar power.

[0145] Maximizing the revenue from pumped storage two-part tariffs:

[0146] maxshouyi c =shouyil +shouyi r ;

[0147]

[0148] shouyi r =p rong ·P ps ;

[0149] In the formula, shouyi c This indicates the revenue from the pumped storage two-part electricity pricing system; shouyi l This indicates the revenue from pumped storage electricity price; shouyi r This represents the revenue from pumped storage capacity electricity price; p ranmei p rong These represent the price of coal-fired power and the price of capacity-based power, respectively. P represents a 0-1 variable indicating whether the pumped storage unit is in power generation or pumping mode at time t; t psG P t psP P represents the power generation and pumping power of the pumped storage unit during time period t, respectively. ps This represents the total installed capacity of the pumped storage power station.

[0150] Minimize the peak-to-valley difference of the load curve:

[0151] minf pl =pl max -pl min ;

[0152] In the formula, f pl This represents the peak-to-valley difference in the power grid load curve; pl max This indicates the peak load on the power grid during the day; pl min This indicates the lowest load on the power grid during the day.

[0153] The operating costs of a thermal power plant include:

[0154] cost G =cost G,P +cost G,switch ;

[0155]

[0156] In the formula, cost G,P Cost indicates the cost of thermal power generation. G,switch This represents the start-up and shutdown costs, where a, b, and c are cost parameters; c switch Cost per start / stop; u switch,t This indicates whether the units of a thermal power plant change their start-up or shutdown status during time period t; it is a 0-1 variable.

[0157] The costs of wind and solar power curtailment include:

[0158]

[0159] In the formula, p s Indicates the probability of scenario s occurring; This represents the amount of wind and solar power curtailment in scenario s; This represents the penalty for abandoning wind and light in scenario s.

[0160] As a preferred embodiment of the above, the outer model further includes the following constraints:

[0161] Power output constraints of wind and solar turbines:

[0162]

[0163] In the formula, These represent the actual power absorbed by wind power and solar power during time period t, respectively.

[0164] Thermal power unit constraints:

[0165] u switch,t =|u G,t -u G,t-1 |;

[0166]

[0167] P G,down ≤P G,t -P G,t-1 ≤P G,up ;

[0168] In the formula, u switch,t This is a Boolean variable representing the switching of the operating state of a thermal power unit during time period t; u G,t A Boolean variable representing whether a thermal power unit is in operation during time period t; u G,0 Indicates the initial start-up and shutdown status of the thermal power unit; These represent the minimum and maximum output of the thermal power unit, respectively; P G,down P G,up These are the downhill and uphill ramp rates for thermal power units, respectively.

[0169] Nuclear power unit constraints:

[0170]

[0171] P he,down ≤P he,t -P he,t-1 ≤P he,up ;

[0172] In the formula, These represent the minimum and maximum output of the nuclear power unit, respectively; P he,down P he,up These are the downhill and uphill ramp rates for nuclear power units, respectively.

[0173] Pumped storage unit operating reservoir capacity constraints:

[0174]

[0175] In the formula, V represents the maximum and minimum reservoir capacity of a pumped storage power station, respectively; t u V t d These represent the upper and lower reservoir capacities at time t, respectively. These represent the maximum and minimum reservoir capacities of the pumped storage power station's lower reservoir, respectively. These represent the water consumption of the pumped storage unit at time t under power generation and pumping conditions, respectively.

[0176] Pumped storage unit state constraints:

[0177]

[0178] In this embodiment, when planning and configuring pumped storage capacity each year, the planned pumped storage capacity for the previous year is increased to establish a sequence of pumped storage power stations, including:

[0179] The sequence P of pumped storage power stations configured each year is as follows:

[0180] P = [P1, P2, ..., P i ,...,P n ];

[0181] In the formula, P i P represents the set of pumped storage capacities to be configured in year i; i+1 It is in P i ,P i-1 ..., the set of incremental pumped storage capacity based on P1;

[0182] In the first year, plan and configure P1 to meet the load demand of the first year; in the second year, plan and configure P2 based on P1 to meet the demand of the second year due to load growth; and so on, in the i-th year, based on the already configured P1∪P2···∪P i-1 Based on the capacity set, plan and configure P i To meet the load demand in year i, and continue until year n, the planned configuration P is... n .

[0183] As a preferred embodiment of the above, dynamically adjusting the curtailment penalty weights of the inner model based on the economic evaluation results of the outer model includes the following steps:

[0184] Calculate the economic indicators of the outer layer model pumped storage capacity configuration scheme, including:

[0185]

[0186] In the formula, ROI (k) NPV represents the return on investment in the k-th iteration. (k) Net present value; r is the discount rate;

[0187] Based on the degree to which economic indicators deviate from the target value, the inner-layer curtailment penalty weights are updated according to the following non-linear relationship:

[0188]

[0189] In the formula, α and β are feedback gain coefficients, controlling the weight adjustment rate; tanh(·) and sigmoid(·) functions are used to smooth weight abrupt changes and avoid oscillations; when ROI (k) <ROI targ When economic viability is insufficient, increase μ1 to prioritize reducing wind power curtailment and alleviate capacity investment pressure; when NPV (k) >NPV thre When economic conditions permit, the μ2 limit should be relaxed to increase the capacity for photovoltaic power consumption.

[0190] The outer model feeds back the economic evaluation results to the inner model, dynamically adjusting the curtailment penalty weights μ1 and μ2 in the objective function of the inner model.

[0191] As a preferred embodiment of the above, the improved gray wolf optimization algorithm based on the composite chaotic mapping Logistic-Tent is used to iteratively solve the two-level loop iterative optimization configuration model, including the following steps:

[0192] Input basic data, including design variables, load data, and initial parameters;

[0193] Randomly generate x n-dimensional vectors and iteratively generate chaotic vectors, expanding the chaotic vectors to their value range;

[0194] Calculate the objective function value for each chaotic vector, and superimpose multiple objective functions;

[0195] Select N chaotic vectors to output as the chaotic optimization results to form the initial population of the Grey Wolf Optimization Algorithm;

[0196] The individual fitness of each gray wolf was calculated, and the gray wolf population hierarchy and hunting behavior were simulated to select leaders and determine α, β, and δ wolves.

[0197] Determine if the maximum number of iterations has been reached. If so, output the optimal solution. Otherwise, update the individual positions of the gray wolves, calculate the fitness function, and update the leader until the maximum number of iterations is reached.

[0198] In simulating the gray wolf population hierarchy and hunting behavior, an improved Logistic mapping is used to update the gray wolf's location, including:

[0199]

[0200] In the formula, x m This represents the position of the gray wolf after the m-th iteration. This represents the XOR operation.

[0201] Update the individual gray wolf positions, calculate the fitness function, and update the leader, including:

[0202] Initial decision variables Mapping to chaotic variables between 0 and 1

[0203]

[0204] In the formula, These are the upper and lower limits of the search for the j-th dimension variable, respectively;

[0205] The chaotic variables for the next iteration are calculated. And transform it into a new decision variable. And based on decision variables Calculate the fitness value of the new solution:

[0206]

[0207] like Figure 2 As shown, this embodiment also includes a pumped storage capacity timing configuration device, using the method described above. The device includes:

[0208] The time sequence unit is used to increase the pumped storage capacity based on the planned pumped storage capacity of the previous year when planning and configuring the pumped storage capacity each year, and to establish a sequence of pumped storage power stations.

[0209] The modeling unit is used to construct a two-layer iterative optimization configuration model, which includes an inner model and an outer model. The inner model is based on capacity balance and aims to minimize the sum of wind and solar curtailment, providing a planning scheme for pumped storage capacity for the outer model. The outer model is based on optimal scheduling and aims to refine the capacity of the inner model with economic efficiency and peak-valley difference indicators.

[0210] The correction unit is used to introduce a dynamic feedback mechanism and multi-objective adaptive weights. It dynamically corrects the curtailment penalty weights of the inner model based on the economic evaluation results of the outer model, thereby achieving closed-loop collaborative optimization of capacity planning and scheduling strategies.

[0211] The solution unit is used to iteratively solve the two-level cyclic iterative optimization configuration model using the improved gray wolf optimization algorithm with composite chaotic mapping Logistic-Tent, so as to obtain the pumped storage time-series capacity configuration scheme.

[0212] Example 2:

[0213] like Figure 3 As shown, this embodiment includes a method for timing configuration of pumped storage capacity, comprising the following steps:

[0214] 1) Construct a two-layer cyclic iterative optimization configuration model. The inner layer of the model is based on capacity balance, with the goal of minimizing the sum of wind and solar power curtailment, to provide a planning scheme for pumped storage capacity for the outer layer. The outer layer is based on optimized scheduling, with the goal of economic efficiency and peak-valley difference, to refine the capacity of the inner layer.

[0215] 2) Introduce a dynamic feedback mechanism and multi-objective adaptive weights to achieve closed-loop collaborative optimization of capacity planning and scheduling strategies;

[0216] 3) The improved gray wolf optimization algorithm based on Logistic-Tent composite chaotic mapping is used to iteratively solve the model until a better solution is obtained, which is the final pumping time-series capacity configuration scheme.

[0217] The above-mentioned method for time-series configuration of pumped storage capacity to adapt to the development of new energy sources, the inner-layer model established in step (1) is as follows:

[0218] Objective function: Minimize the sum of curtailed wind and solar power.

[0219]

[0220] In the formula, μ1 and μ2 are the penalty weights for wind curtailment and solar curtailment, respectively, which are dynamically adjusted. These represent the power of wind and solar power curtailed during time period t, respectively.

[0221]

[0222] In the formula, These represent the available capacity of wind power and solar power during time period t, respectively. These represent the actual absorption of wind power and solar power during time period t, respectively.

[0223] The constraints are as follows:

[0224] (1) Pumped storage unit;

[0225] Minimum power constraint. At any given moment, the power output of pumped hydro storage should be greater than the amount of wind and solar power curtailed at that moment.

[0226] P ps,t ≥P qw,t +P qpv,t ;

[0227] In the formula, P ps,t For the pumped storage unit output during time period t; P qw,t P qpv,t These represent the power of wind and solar power curtailed during time period t.

[0228] Output constraint. Used to constrain the maximum and minimum operating power of pumped storage units when operating in pumping and power generation modes:

[0229]

[0230] In the formula, These represent the minimum and maximum power outputs of the pumped storage unit under power generation conditions, respectively. P represents the minimum and maximum power of the pumped storage unit under pumping conditions, respectively. t psG P t psP These represent the power generation and pumping power of the pumped storage unit during time period t, respectively. This is a Boolean variable indicating whether the pumped storage unit is in power generation mode at time t. This is a Boolean variable indicating whether the pumped storage unit is in pumping mode at time t.

[0231] Energy balance constraints:

[0232]

[0233] In the formula, τ is the energy conversion efficiency of the pumped storage unit.

[0234] (2) Power balance constraints;

[0235] P L,t =P G,t +P wind,t +P pv,t +P he,t +P ps,t ;

[0236] In the formula, P L,t Let P be the load during time period t. G,t P wind,t P pv,t P he,tThese represent the output of thermal power, wind power, photovoltaic power, and nuclear power during time period t.

[0237] The objective function and constraints of the outer model established in step (1) consider the scenario. The outer model established in step (1) is as follows:

[0238] The model objective function is as follows:

[0239] Objective function 1: Minimize grid operator costs;

[0240] mincost d =cost G +cost green ;

[0241] In the formula, cost d Cost represents the cost of a power grid operator. G This refers to the operating cost of a thermal power plant; cost green This indicates the cost of curtailing wind and solar power.

[0242] The costs of each part specifically include:

[0243] The cost of a thermal power plant;

[0244] In the grid optimization problem, grid operators pay thermal power plants for power generation costs, including thermal power generation costs and start-up and shutdown costs.

[0245] cost G =cost G,P +cost G,switch ;

[0246]

[0247] In the formula, cost G,P Cost indicates the cost of thermal power generation. G,switch This represents the start-up and shutdown costs, where a, b, and c are cost parameters; c switch Cost per start / stop; u switch,t This indicates whether the units of a thermal power plant change their start-up or shutdown status during time period t; it is a 0-1 variable.

[0248] Costs of curtailing wind and solar power;

[0249] The cost of curtailing wind and solar power is proportional to the amount of wind and solar power curtailed in the power grid.

[0250]

[0251] In the formula, p s Indicates the probability of scenario s occurring; This represents the amount of wind and solar power curtailment in scenario s; This represents the penalty for abandoning wind and light in scenario s.

[0252] Objective function two: Maximize the revenue from the pumped storage two-part tariff;

[0253] maxshouyi c =shouyi l +shouyi r ;

[0254]

[0255] shouyi r =p rong ·P ps ;

[0256] In the formula, shouyi c This indicates the revenue from the pumped storage two-part electricity pricing system; shouyi l This indicates the revenue from pumped storage electricity price; shouyi r This represents the revenue from pumped storage capacity electricity price; p ranmei p rong These represent the price of coal-fired power and the price of capacity-based power, respectively. P represents a 0-1 variable indicating whether the pumped storage unit is in power generation or pumping mode at time t; t psG P t psP P represents the power generation and pumping power of the pumped storage unit during time period t, respectively. ps This represents the total installed capacity of the pumped storage power station.

[0257] Objective function 3: Minimize the peak-to-valley difference of the load curve;

[0258] minf pl =pl max -pl min ;

[0259] In the formula, f pl This represents the peak-to-valley difference in the power grid load curve; pl max This indicates the peak load on the power grid during the day; pl min This indicates the lowest load on the power grid during the day.

[0260] The constraints are as follows:

[0261] (1) Output constraints of wind and solar turbine units;

[0262]

[0263] In the formula, These represent the actual power absorbed by wind and solar power during time period t.

[0264] (2) Constraints of thermal power units;

[0265] u switch,t =|u G,t -u G,t-1 |;

[0266]

[0267] In the formula, u switch,t This is a Boolean variable representing the switching of the operating state of a thermal power unit during time period t; u G,t A Boolean variable representing whether a thermal power unit is in operation during time period t; u G,0 Indicates the initial start-up and shutdown status of the thermal power unit; These represent the minimum and maximum output of the thermal power unit, respectively; P G,down P G,up These represent the downhill and uphill ramp rates of thermal power units, respectively.

[0268] (3) Nuclear power unit constraints;

[0269]

[0270] P he,down ≤P he,t -P he,t-1 ≤P he,up ;

[0271] In the formula, These represent the minimum and maximum output of the nuclear power unit, respectively; P he,down P he,up These represent the downhill and uphill ramp rates of the nuclear power unit, respectively.

[0272] (4) Constraints of pumped storage units;

[0273] Reservoir capacity constraints. Pumped storage power stations must meet the water volume constraints of both the upper and lower reservoirs during operation:

[0274]

[0275] In the formula, V represents the maximum and minimum reservoir capacity of a pumped storage power station, respectively; t u V t d These represent the upper and lower reservoir capacities at time t, respectively. These represent the maximum and minimum reservoir capacities of the pumped storage power station's lower reservoir, respectively. These represent the water consumption of the pumped storage unit at time t under power generation and pumping conditions, respectively.

[0276] State constraints. A single pumped storage unit can only operate in a single mode:

[0277]

[0278] (5) Consider the constraints of the scenario;

[0279] In random scenario s, unit standby needs to meet various constraints including power balance, unit output, unit ramp-up, and system safety. The specific expressions are similar to those in typical scenarios and will not be repeated here.

[0280] (6) The amount of wind curtailment is less than 10% of the natural output of wind power, and the amount of unexpected load loss is less than 1% of the total load.

[0281] In the model constructed in step (1), considering the development of new energy sources and the growth of load, the pumped storage capacity is configured in an orderly manner according to the time sequence. The schematic diagram of the pumped storage capacity configuration considering the construction time sequence is shown below. Figure 4 As shown, for Figure 5 The system performs capacity timing configuration.

[0282] The load level is the predicted peak load for the year, such as Figure 4 Green load curve description.

[0283] The sequence P of pumped storage power stations configured each year is:

[0284] P = [P1, P2, ..., P i ,...,P n ];

[0285] In the formula, P i P represents the set of pumped storage capacities to be configured in year i; i+1 It is in P i ,P i-1 The set of incremental pumped storage capacity based on P1, ..., P1.

[0286] The capacity configuration approach for pumped storage power stations, taking into account the construction sequence, is as follows: Figure 4 As shown in the following section, in the first year, P1 is planned and configured to meet the load demand of the first year; then, in the second year, P2 is planned and configured based on P1 to meet the demand of the second year due to load growth; and so on, in the i-th year, based on the already configured P1∪P2···∪P i-1 Based on the capacity set, plan and configure P i To meet the load demand in year i, and continue until year n, the planned configuration P is... n .

[0287] In step (2), a two-layer dynamic feedback mechanism is introduced. Its core is to dynamically correct the curtailment penalty weight of the inner layer model through the economic evaluation results of the outer layer model, forming a closed-loop optimization circuit.

[0288] The specific process is as follows:

[0289] 1) Economic evaluation of the outer layer model;

[0290] The outer-layer optimization scheduling model calculates the economic indicators of pumped storage capacity allocation schemes, including:

[0291]

[0292] In the formula, ROI (k) NPV represents the return on investment in the k-th iteration. (k) is the net present value; r is the discount rate.

[0293] 2) Dynamic weight adjustment rules;

[0294] Based on the degree to which economic indicators deviate from the target value, the inner-layer curtailment penalty weights are updated according to the following non-linear relationship:

[0295]

[0296] In the formula, α and β are feedback gain coefficients, controlling the weight adjustment rate; tanh(·) and sigmoid(·) functions are used to smooth weight abrupt changes and avoid oscillations; when ROI (k) <ROI targ When economic viability is insufficient, increase μ1 to prioritize reducing wind power curtailment and alleviate capacity investment pressure; when NPV (k) >NPV thre When economic conditions are sufficient, the μ2 limit should be appropriately relaxed to increase the photovoltaic absorption capacity.

[0297] 3) Construct a capacity-economic feedback loop;

[0298] The outer layer feeds back the economic evaluation results to the inner layer, and dynamically corrects the curtailment penalty weights μ1 and μ2 in the objective function of the inner layer model in step (1).

[0299] In step (3), the improved Grey Wolf algorithm based on Logistic-Tent composite chaotic mapping is used to solve the two-layer objective function.

[0300] The improved Logistic-Tent composite chaotic mapping is:

[0301]

[0302] In the formula, x m This represents the position of the gray wolf after the m-th iteration. It represents the XOR operation, which improves the randomness and resistance to truncation of the sequence and enhances its traversal.

[0303] Initial decision variables Mapping to chaotic variables between 0 and 1

[0304]

[0305] In the formula, These represent the upper and lower limits of the search for the j-th dimension variable, respectively.

[0306] The chaotic variables for the next iteration are calculated based on the above formula. And transform it into a new decision variable. And based on decision variables Calculate the fitness value of the new solution:

[0307]

[0308] The flowchart of the improved Grey Wolf algorithm for solving the Logistic-Tent composite chaotic map is as follows: Figure 6 As shown.

[0309] (2) Example Demonstration

[0310] The system of the example is as follows Figure 5 As shown in Table 1, the results of the pumped storage capacity configuration in this example are shown in Table 2. A comparison of the renewable energy absorption rates between the conventional method of configuring pumped storage power station capacity according to the target year without considering time sequence and the pumped storage capacity time sequence configuration model of this invention is shown in Table 2.

[0311] Table 1 shows the configuration results.

[0312]

[0313] Table 2 Comparison of non-time-series and time-series renewable energy absorption rates (%)

[0314] years 2026 2027 2028 2029 2030 Non-time sequence 90.81 93.26 95.78 97.92 98.02 Time series 95.30 95.98 96.75 97.67 98.02

[0315] Taking actual power grid data as an example, Figure 6 This is a two-layer, progressively refined capacity configuration method process. Figure 7 This is wind and solar power generation data for a typical day. Figure 8-10 The results are the optimized scheduling results for a typical day's operation, among which Figure 8 For the results of power grid dispatching, Figure 9 To predict the load and take into account the load curve after destorage, Figure 10 This refers to the output status of thermal power units.

[0316] As shown in Table 2, if the capacity of pumped-storage power stations is configured according to the target year without considering the time sequence, it will lead to insufficient capacity configuration in the early stage of planning and a low renewable energy absorption rate; in the later stage of planning, the renewable energy absorption rate will be very high, close to saturation, which may result in resource waste. However, the annual configuration that takes into account the construction sequence adopted in this invention can effectively avoid these problems, improve the renewable energy absorption rate in the early stage of planning, and avoid the renewable energy absorption reaching saturation in the later stage of planning, thus avoiding the clustering of pumped-storage power station construction.

[0317] Depend on Figure 8-10 It can be seen that the thermal power units do not frequently start and stop and maintain a relatively economical output level, thereby reducing the electricity purchase cost of the power grid. Meanwhile, during peak electricity price periods (10:00 AM to 12:00 PM and 2:00 PM to 7:00 PM), pumped storage power stations are basically operating at full capacity, while during off-peak periods (midnight to 8:00 AM), they are basically pumping water, thus maximizing the operating benefits of the pumped storage power stations. Furthermore, the consideration of minimizing the peak-to-valley difference in the load curve during operation also reduces the overall peak-to-valley load difference of the power grid. Therefore, the pumped storage time-series capacity configuration method proposed in this embodiment has good practicality, ensuring both the configuration effect and a certain degree of economy.

[0318] Please see Figure 11 The diagram shows a structural schematic of a computer device provided in an embodiment of this application. An embodiment of this application provides a computer device 400, including a processor 410 and a memory 420. The memory 420 stores a computer program executable by the processor 410. When the computer program is executed by the processor 410, it performs the method described above.

[0319] This application embodiment also provides a storage medium 430, on which a computer program is stored, and the computer program is executed by a processor 410 to perform the above method.

[0320] The storage medium 430 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0321] In the description of this invention, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. "A plurality of" means two or more, unless otherwise explicitly specified.

[0322] In this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," "linking," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0323] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Furthermore, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0324] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process, and the scope of the preferred embodiments of the invention includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as will be understood by those skilled in the art to which embodiments of the invention pertain.

[0325] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0326] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0327] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0328] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for sequentially configuring pumped storage capacity, characterized in that, Includes the following steps: When planning and allocating pumped storage capacity each year, the capacity is increased based on the planned capacity of the previous year to establish a sequence of pumped storage power stations. A two-layer iterative optimization configuration model is constructed, which includes an inner layer model and an outer layer model. The inner layer model is based on capacity balance and aims to minimize the sum of wind curtailment and solar curtailment, providing a planning scheme for pumped storage capacity for the outer layer model. The outer layer model is based on optimized scheduling and aims to refine the capacity of the inner layer model with economic efficiency and peak-valley difference indicators. By introducing a dynamic feedback mechanism and multi-objective adaptive weights, the curtailment penalty weights of the inner model are dynamically adjusted based on the economic evaluation results of the outer model, thereby achieving closed-loop collaborative optimization of capacity planning and scheduling strategies. An improved gray wolf optimization algorithm based on the composite chaotic mapping Logistic-Tent is used to iteratively solve the two-layer cyclic iterative optimization configuration model to obtain the pumped storage time-series capacity configuration scheme.

2. The method for sequential configuration of pumped storage capacity according to claim 1, characterized in that, The objective function of the inner layer model is: In the formula, μ1 and μ2 are the penalty weights for wind curtailment and solar curtailment, respectively, which are dynamically adjusted. These represent the power of wind and solar power curtailed during time period t, respectively. In the formula, These represent the available capacity of wind power and solar power during time period t, respectively. These represent the actual absorption of wind power and solar power during time period t, respectively.

3. The method for sequential configuration of pumped storage capacity according to claim 2, characterized in that, The inner model also includes the following constraints: Minimum power constraint: P ps,t ≥P qw,t +P qpv,t ; In the formula, P ps,t For the pumped storage unit output during time period t; P qw,t P qpv,t These represent the power of wind and solar power curtailed during time period t, respectively. Output constraints: In the formula, These represent the minimum and maximum power outputs of the pumped storage unit under power generation conditions, respectively. These represent the minimum and maximum power values ​​of the pumped storage unit under pumping conditions, respectively. These represent the power generation and pumping power of the pumped storage unit during time period t, respectively. This is a Boolean variable indicating whether the pumped storage unit is in power generation mode at time t. This is a Boolean variable indicating whether the pumped storage unit is in pumping mode at time t; Energy balance constraints: In the formula, τ is the energy conversion efficiency of the pumped storage unit; Power balance constraints: P L,t =P G,t +P wind,t +P pv,t +P he,t +P ps,t ; In the formula, P L,t Let P be the load during time period t. G,t P wind,t P pv,t P he,t These represent the output of thermal power, wind power, photovoltaic power, and nuclear power during time period t.

4. The method for sequential configuration of pumped storage capacity according to claim 1, characterized in that, The objective function of the outer model includes: Minimize grid operator costs: mincost d =cost G +cost green ; In the formula, cost d Cost represents the cost of a power grid operator. G This refers to the operating cost of a thermal power plant; cost green This indicates the cost of curtailing wind and solar power. Maximizing the revenue from pumped storage two-part tariffs: maxshouyi c =shouyi l +shouyi r ; shouyi r =p rong ·P ps ; In the formula, shouyi c This indicates the revenue from the pumped storage two-part electricity pricing system; shouyi l This indicates the revenue from pumped storage electricity price; shouyi r This represents the revenue from pumped storage capacity electricity price; p ranmei p rong These represent the price of coal-fired power and the price of power generated by capacity, respectively. These represent 0-1 variables indicating whether the pumped storage unit is in power generation or pumping mode at time t; P represents the power generation and pumping power of the pumped storage unit during time period t, respectively. ps This represents the total installed capacity of the pumped storage power station. Minimize the peak-to-valley difference of the load curve: minf pl =pl max -pl min ; In the formula, f pl This represents the peak-to-valley difference in the power grid load curve; pl max This indicates the peak load on the power grid during the day; pl min This indicates the lowest load on the power grid during the day.

5. The method for sequential configuration of pumped storage capacity according to claim 4, characterized in that, The operating costs of the thermal power plant include: cost G =cost G,P +cost G,switch ; In the formula, cost G,P Cost indicates the cost of thermal power generation. G,switch This represents the start-up and shutdown costs, where a, b, and c are cost parameters; c switch Cost per start / stop; u switch,t This indicates whether the generator units of a thermal power plant change their start-up or shutdown status during time period t; it is a 0-1 variable.

6. The method for sequential configuration of pumped storage capacity according to claim 4, characterized in that, The costs of wind and solar power curtailment include: In the formula, p s Indicates the probability of scenario s occurring; This represents the amount of wind and solar power curtailment in scenario s; This represents the penalty for abandoning wind and light in scenario s.

7. The method for sequential configuration of pumped storage capacity according to claim 4, characterized in that, The outer model also includes the following constraints: Power output constraints of wind and solar turbines: In the formula, These represent the actual power absorbed by wind power and solar power during time period t, respectively. Thermal power unit constraints: in switch,t =|in G,t -in G,t-1 |; P G,down ≤P G,t -P G,t-1 ≤P G,up ; In the formula, u switch,t This is a Boolean variable representing the switching of the operating state of a thermal power unit during time period t; u G,t A Boolean variable representing whether a thermal power unit is in operation during time period t; u G,0 Indicates the initial start-up and shutdown status of the thermal power unit; These represent the minimum and maximum output of the thermal power unit, respectively; P G,down P G,up These are the downhill and uphill ramp rates for thermal power units, respectively. Nuclear power unit constraints: P he,down ≤P he,t -P he,t-1 ≤P he,up ; In the formula, These represent the minimum and maximum output of the nuclear power unit, respectively; P he,down P he,up These are the downhill and uphill ramp rates for nuclear power units, respectively. Pumped storage unit operating capacity constraints: In the formula, These represent the maximum and minimum reservoir capacities of the upper reservoir of a pumped storage power station, respectively. These represent the upper and lower reservoir capacities at time t, respectively. These represent the maximum and minimum reservoir capacities of the pumped storage power station's lower reservoir, respectively. These represent the water consumption of the pumped storage unit at time t under power generation and pumping conditions, respectively. Pumped storage unit state constraints:

8. Let's move this point to the second point. According to the method for sequential configuration of pumped storage capacity as described in claim 1, the characteristic is that... When planning and allocating pumped storage capacity each year, the planned capacity for the previous year is increased to establish a sequence of pumped storage power stations, including: The sequence P of pumped storage power stations configured each year is as follows: P=[P1,P2,...,P i ,...,P n ]; In the formula, P i P represents the set of pumped storage capacities planned for year i; i+1 It is in P i ,P i-1 ..., the set of incremental pumped storage capacity based on P1; In the first year, plan and configure P1 to meet the load demand of the first year; in the second year, plan and configure P2 based on P1 to meet the demand of the second year due to load growth; and so on, in the i-th year, based on the already configured P1∪P2···∪P i-1 Based on the capacity set, plan and configure P i To meet the load demand in year i, and up to year n, the planned configuration P is... n .

9. The method for sequential configuration of pumped storage capacity according to claim 2, characterized in that, The step of dynamically adjusting the curtailment penalty weights of the inner model based on the economic evaluation results of the outer model includes the following steps: Calculating the economic indicators of the outer layer model pumped storage capacity configuration scheme includes: In the formula, ROI (k) NPV represents the return on investment in the k-th iteration. (k) Net present value; r is the discount rate; Based on the degree to which economic indicators deviate from the target value, the inner-layer curtailment penalty weights are updated according to the following non-linear relationship: In the formula, α and β are feedback gain coefficients, controlling the weight adjustment rate; tanh(·) and sigmoid(·) functions are used to smooth weight abrupt changes and avoid oscillations; when ROI (k) <ROI targ When economic viability is insufficient, increase μ1 to prioritize reducing wind power curtailment and alleviate capacity investment pressure; when NPV (k) >NPV thre When economic conditions permit, the μ2 limit should be relaxed to increase the capacity for photovoltaic power consumption. The outer model feeds back the economic evaluation results to the inner model, dynamically adjusting the curtailment penalty weights μ1 and μ2 in the objective function of the inner model.

10. The method for sequential configuration of pumped storage capacity according to claim 1, characterized in that, The improved gray wolf optimization algorithm using the composite chaotic mapping Logistic-Tent is used to iteratively solve the two-level loop iterative optimization configuration model, including the following steps: Input basic data, including design variables, load data, and initial parameters; Randomly generate x n-dimensional vectors and iteratively generate chaotic vectors, expanding the chaotic vectors to their value range; Calculate the objective function value of each chaotic vector, and superimpose multiple objective functions; Select N chaotic vectors to output as the chaotic optimization results to form the initial population of the Grey Wolf Optimization Algorithm; The individual fitness of each gray wolf was calculated, and the gray wolf population hierarchy and hunting behavior were simulated to select leaders and determine α, β, and δ wolves. Determine if the maximum number of iterations has been reached. If so, output the optimal solution. Otherwise, update the individual positions of the gray wolves, calculate the fitness function, and update the leader until the maximum number of iterations is reached.

11. The method for sequential configuration of pumped storage capacity according to claim 10, characterized in that, In the simulated gray wolf population hierarchy mechanism and hunting behavior, an improved Logistic mapping is used to update the gray wolf's location, including: In the formula, x m This represents the position of the gray wolf after the m-th iteration. This represents the XOR operation.

12. The method for sequential configuration of pumped storage capacity according to claim 10, characterized in that, The steps of updating the individual gray wolf positions, calculating the fitness function, and updating the leader include: Initial decision variables Mapping to chaotic variables between 0 and 1 In the formula, These are the upper and lower limits of the search for the j-th dimension variable, respectively; The chaotic variables for the next iteration are calculated. And transform it into a new decision variable. And based on decision variables Calculate the fitness value of the new solution:

13. A pumped storage capacity timing configuration device, characterized in that, Using the method as described in any one of claims 1 to 12, the apparatus comprises: The time sequence unit is used to increase the pumped storage capacity based on the planned pumped storage capacity of the previous year when planning and configuring the pumped storage capacity each year, and to establish a sequence of pumped storage power stations. The modeling unit is used to construct a two-layer cyclic iterative optimization configuration model, which includes an inner layer model and an outer layer model. The inner layer model is based on capacity balance and aims to minimize the sum of wind and solar curtailment, providing a planning scheme for pumped storage capacity for the outer layer model. The outer layer model is based on optimized scheduling and aims to refine the capacity of the inner layer model with economic efficiency and peak-valley difference indicators. The correction unit is used to introduce a dynamic feedback mechanism and multi-objective adaptive weights, and dynamically correct the curtailment penalty weights of the inner model through the economic evaluation results of the outer model, so as to realize the closed-loop collaborative optimization of capacity planning and scheduling strategies. The solution unit is used to iteratively solve the two-layer cyclic iterative optimization configuration model using the improved gray wolf optimization algorithm with composite chaotic mapping Logistic-Tent, so as to obtain the pumped storage time-series capacity configuration scheme.

14. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method as described in any one of claims 1-12.

15. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1-12.

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