Pumped storage and wind, light and hydropower system joint optimization method based on demand management

Abstract

The invention belongs to the field of power optimization dispatching, and particularly relates to a demand management-based pumped storage and wind-light-hydropower system joint optimization method, which comprises the following steps of: constructing a double-layer optimization model comprising an upper-layer optimization model and a lower-layer optimization model, the upper-layer optimization model is a distributed pumped storage capacity configuration optimization model, and the lower-layer optimization model is a distributed pumped storage capacity configuration optimization model; the optimization target is the construction benefit maximization of the pumped storage power station; and the lower-layer optimization model comprises an industrial user operation cost optimization model and a maximum electricity purchase demand optimization model, and is used for solving an operation scheme of the system and an industrial user electricity purchase and sale strategy. And solving the double-layer optimization model by adopting an iterative optimization algorithm to obtain an optimal pumped storage capacity configuration scheme and a pumped storage and wind, light and hydropower system joint operation scheduling scheme. And the electric quantity electricity charge and demand electricity charge cost of industrial users are reduced through the time shifting capability of pumped storage. And the accuracy of joint scheduling optimization is improved by utilizing the cluster effect of distributed pumping and storage, and the waste of clean energy is reduced.

Description

Technical Field

[0001] The present invention belongs to the technical field of power optimization and dispatching, and specifically relates to a method for joint optimization of pumped storage and wind, solar and hydropower systems based on demand management. Background Art

[0002] With the continued growth of my country's national economy, industrial electricity demand continues to climb, and the installed capacity of the power grid has also increased dramatically. This trend has significantly widened the difference between the peak and valley loads of the power system, further exacerbating the complexity and urgency of the power grid's peak load regulation problem. Against this backdrop, the demand for peak load regulation capacity in the power grid has become increasingly significant.

[0003] Given its renewable nature and flexible start-up and shutdown capabilities, hydropower has become an important means of peak regulation for my country's power grid and plays an increasingly important role in ensuring the safe, stable and efficient operation of the power grid. However, the peak regulation capacity of reservoir-type hydropower stations is limited.

[0004] The power generation output of renewable energy sources such as wind power and photovoltaic power generation is highly random. When the electricity generated by wind power and photovoltaic power generation is greater than the industrial load during the period of low industrial load, wind and solar power curtailment is prone to occur. Summary of the Invention

[0005] The technical problem with this invention is that industrial loads vary widely from peak to valley, and wind, photovoltaic, and run-of-river small hydropower are subject to volatility and uncertainty, while reservoir-type small hydropower has limited regulatory capacity. During periods of low industrial load, when wind, solar, and hydropower generation exceeds the industrial load, this leads to curtailment of wind, solar, and hydropower, resulting in a waste of clean resources. During peak load periods, when wind, solar, and hydropower generation is less than the load itself, purchasing electricity from the grid results in higher electricity and demand charges.

[0006] The purpose of the present invention is to address the above-mentioned problems and provide a method for joint optimization of pumped storage and wind, solar and hydropower systems based on demand management, constructing a two-layer optimization model including upper and lower optimization models. The upper optimization model is a distributed pumped storage capacity configuration optimization model, and the optimization goal is to maximize the construction benefits of the pumped storage power station; the lower optimization model includes an industrial user operating cost optimization model and a maximum power purchase demand optimization model, which are used to solve the operation plan of the system and the industrial user power purchase and sales strategy. Through the "time shift" capability of pumped storage, the daily electricity consumption and electricity charges of industrial users purchasing electricity from the superior power grid are reduced while the monthly electricity charges for purchasing electricity from the superior power grid are reduced, thereby reducing the total operating costs of industrial users. The clustering effect of distributed pumped storage is used to improve the accuracy of joint scheduling optimization and reduce the waste of clean energy.

[0007] To achieve the above objectives, in a first aspect, the present invention provides a method for joint optimization of a pumped storage and wind, solar and hydropower system based on demand management, comprising the following steps: Step 1: Construct output models for industrial users’ wind power plants, photovoltaic power plants, hydropower plants, and distributed pumped storage power plants; Step 2: Construct a two-layer optimization model consisting of upper and lower optimization models. The upper optimization model aims to maximize the construction benefits of the pumped storage power station and is used to solve the pumped storage capacity configuration plan. The lower optimization model includes an industrial user operating cost optimization model and a maximum power purchase demand optimization model, which are used to solve the system operation plan, the daily power purchase and sale of industrial users to the power grid, and the monthly maximum power purchase demand. The construction benefits of a pumped storage power station include the benefits of reducing monthly demand electricity charges and the benefits of reducing daily electricity purchases; Step 3: Based on the pumped storage capacity configuration plan provided by the upper-level optimization model, combined with historical load data and the output of wind power plants, photovoltaic power plants, and hydropower plants, the maximum power purchase demand optimization model is called each month to determine the maximum power purchase demand for that month and optimize the demand electricity fee; Step 4: Using the maximum electricity purchase demand obtained in Step 3 as a constraint, and using the industrial user operating cost optimization model, optimize the operating strategies of wind power plants, photovoltaic power plants, hydropower plants, and distributed pumped storage power plants, as well as the industrial user electricity purchase and sales plans for that month; Step 5: Repeat steps 3 and 4 for each month in the scheduling cycle until the entire scheduling cycle is covered and the operation strategy optimization for the entire scheduling cycle is completed; Step 6: Use an iterative optimization algorithm to solve the two-layer optimization model and obtain the optimal pumped storage capacity configuration plan and the joint operation and scheduling plan of the pumped storage and wind, solar and hydropower systems.

[0008] Preferably, in step 2, the objective function F1 of the upper optimization model is: ; In the formula For the Monthly demand electricity charge reduction benefit; For the The benefit of reducing daily electricity consumption and electricity charges; The cost of building distributed pumped storage power stations; The construction period of the project; is the number of operating months per year; For the The number of days in a month, l Indicates the month number.

[0009] Preferably, in step 2, the calculation formula for the monthly demand electricity charge reduction benefit is: ; In the formula For the Monthly demand electricity charge reduction benefit; For the l Monthly demand price; is the total number of pumped storage power stations; The serial number of the pumped storage power station. For the l The maximum residual load of industrial users in a month n The power generation capacity of a pumped storage power station.

[0010] Preferably, in step 2, the calculation formula for the daily electricity purchase reduction benefit is: ; In the formula for The electricity price for the time period; For the Pumped storage power station sky Power generation during the time period; For the Pumped storage power station sky The power of pumping water from the power grid during the period; Z is the total number of time steps per day; is the time window length.

[0011] Furthermore, in step 2, the constraints of the upper optimization model are: ; In the formula For the The installed capacity of variable-speed pumped storage power stations; The upper limit of the capacity of pumped storage power station units; For the The volume of the reservoir upstream of each pumped storage power station; It is the upper limit of upstream storage capacity.

[0012] Preferably, in step 3, the objective function of the maximum electricity purchase demand optimization model is: ; In the formula For industrial users Monthly maximum load; For the Monthly maximum load Power generation capacity of pumped storage power stations; For the The power generation capacity of the small hydropower station at the monthly maximum load; For the The power generation capacity of the wind power station at the monthly maximum load; For the The power generation capacity of the photovoltaic power station at the monthly maximum load; n It is the serial number of the pumped storage power station.

[0013] Preferably, in step 2, the constraints of the maximum power purchase demand optimization model include output power constraints of wind power stations, photovoltaic power stations and small hydropower stations, and dynamic balance constraints of hydropower station storage capacity.

[0014] Preferably, in step 4, the objective function F2 of the industrial user operating cost optimization model is: ; ; ; ; Where, represents the electricity purchase cost for industrial users, It represents the benefits of industrial users selling electricity to the grid; Indicates the month Daily electricity consumption and electricity charges; Indicates the number of running days in the month; Power purchased for industrial users; A coefficient indicating whether the output of renewable energy is less than the load; t represents the time step; express The electricity price for the time period; for The electricity sales price during the time period; Power sold to industrial users; A coefficient indicating whether the new energy source is greater than the load; represents the length of the time window; Z is the total number of time steps per day.

[0015] Furthermore, in step 4, the constraints of the industrial user operating cost optimization model include system power balance constraints, transmission line capacity restriction section constraints, upper and lower limit constraints of hydropower station storage capacity, distributed pumped storage ramping constraints, distributed pumped storage power station state switching constraints, pumped storage power station reservoir capacity constraints, pumped storage power station downstream flow constraints, pumped storage power station inflow flow constraints, system power purchase and sales constraints, scheduling start and end reservoir capacity constraints and load power constraints.

[0016] In a second aspect, the present invention provides a combined optimization system for pumped storage and wind, solar and hydropower systems, including the following modules: Pumped storage construction cost calculation module: used to calculate the construction cost of pumped storage power stations; Power generation output calculation module: calculates the output power of pumped storage power stations, wind farms, photovoltaic power stations and hydropower stations respectively; Monthly demand reduction benefit calculation module: used to calculate the benefit of monthly demand reduction electricity charges; Daily electricity purchase reduction benefit calculation module: used to calculate the benefits of industrial users' daily electricity purchase fee reduction; Power sales benefit calculation module: used to calculate the benefits of industrial users selling power to the power grid; Power purchase cost calculation module: used to calculate the cost of industrial users purchasing electricity from the power grid, including demand charges and electricity charges; Demand electricity fee calculation module: used to calculate the monthly demand electricity fee for industrial users; Electricity consumption and electricity fee calculation module: used to calculate the daily electricity consumption and electricity fee of industrial users; Industrial user operation cost calculation module: calls the demand electricity fee calculation module and the power consumption fee calculation module to calculate the operation cost of industrial users; Optimal solution module: used to solve the two-layer optimization model to obtain the optimal solution of the upper optimization model, namely the distributed pumped storage capacity configuration plan, and the optimal solution of the lower optimization model, namely the joint scheduling plan of pumped storage and wind, solar and hydropower systems and the monthly electricity purchase demand.

[0017] Compared with the prior art, the present invention has the following beneficial effects: 1) This invention achieves the coordinated optimization of pumped storage benefits, wind, solar, and hydropower system operations, and industrial user operating costs by constructing a two-layer model: an upper-layer pumped storage capacity configuration model and a lower-layer operation and scheduling model. The upper layer aims to maximize the benefits of pumped storage power station construction, while the lower layer focuses on wind, solar, and hydropower system operation strategies and real-time power purchase strategies for industrial users. This creates a global optimal solution, balancing the dynamic balance between power system planning and operation, improving the utilization of wind, solar, and hydropower energy, and effectively reducing the total operating costs for industrial users.

[0018] 2) Innovatively integrate the optimization of the monthly maximum electricity purchase demand and daily electricity cost control, utilize the "time shift" characteristics of pumped storage to smooth load fluctuations, and simultaneously reduce the demand electricity charge (based on the monthly peak) and the electricity charge (based on daily accumulation) in the two-part electricity price, achieving a two-dimensional reduction in electricity cost.

[0019] 3) This invention leverages the synergistic effects of distributed pumped-storage clusters, enhancing dynamic response to random fluctuations in wind, solar, and hydropower generation through coordinated control of the start-stop sequence and output of multiple units. A wind-solar-hydro-pumped-storage multi-energy coupling model is constructed, leveraging the complementary advantages of rapid peak-shaving at hydropower stations and flexible energy storage at pumped-storage. By smoothing load peaks and valleys, this reduces the reserve capacity requirements and peak-shaving costs of the upstream power grid.

[0020] 4) Through the deep integration of technological innovation with dispatching and operational strategies, this invention forms a triple benefit closed loop in terms of improving clean energy utilization, reducing enterprise energy costs, and enhancing grid stability, providing a scalable solution for the construction of new power systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The present invention will be further described below with reference to the accompanying drawings.

[0022] Figure 1 Schematic diagram of a combined optimization method for pumped storage and wind, solar and hydropower systems according to an embodiment of the present invention.

[0023] Figure 2 Schematic diagram of a two-layer optimization model according to an embodiment of the present invention.

[0024] Figure 3 This is a diagram of the system output and load curve during the flood season according to an embodiment of the present invention.

[0025] Figure 4 This is a dry season system output and load curve diagram of an embodiment of the present invention. DETAILED DESCRIPTION

[0026] like Figure 1 and Figure 2 As shown in FIG, the combined optimization method of pumped storage and wind, solar and hydropower systems based on demand management includes the following steps: Step 1: Construct output models of industrial users’ wind power stations, photovoltaic power stations, hydropower stations, and distributed pumped storage power stations.

[0027] The wind farm output model is as follows: ; In the formula The active power input from the wind turbine to the grid; is the actual wind speed of the wind turbine at the hub height; is the rated output power of the fan; and are the cut-in wind speed and cut-out wind speed respectively; is the rated wind speed.

[0028] The output model of a photovoltaic power station is as follows: ; In the formula is the rated power of the photovoltaic power source; is the power temperature coefficient of the photovoltaic panel, take ; is the operating temperature of the PV modules; For latitude Photovoltaic construction area moon sky The hourly mean solar irradiance at the Earth's surface at .

[0029] The power generation and pumping power of the distributed pumped storage power station are as follows: ; ; In the formula and respectively sky Period pumped storage power station Power generation flow and pumping flow; The efficiency of converting water energy into electricity, The efficiency of converting pumped storage electricity into hydropower; Pumped Storage Power Station Net water head height.

[0030] The output model of the hydropower station is as follows: ; In the formula The power generation efficiency of the hydropower station; is the acceleration due to gravity; and For hydropower stations in the basin sky The net water head and power flow of power generation during the period.

[0031] Step 2: Construct a two-level optimization model including upper and lower optimization models. The upper optimization model considers the benefits of pumped storage for industrial users' demand electricity charges, the benefits of electricity charges, and the construction cost of distributed pumped storage. The optimization goal is to maximize the construction benefits of the pumped storage power station. The lower optimization model includes an industrial user operating cost optimization model and a maximum power purchase demand optimization model, which are used to solve the operation plan of the system, the daily power purchase and sale of industrial users to the power grid, and the monthly maximum power purchase demand, such as Figure 2 shown.

[0032] The objective function of the upper optimization model is to maximize the annual benefit of the distributed pumped storage power station: ; In the formula For the Monthly demand electricity charge reduction benefit; For the The benefit of reducing daily electricity consumption and electricity charges; the cost of building distributed variable-speed pumped hydro storage; The construction period of the project; is the number of operating months per year; for The number of days in the month.

[0033] The monthly demand electricity charge reduction benefit is calculated using the following formula: : ; In the formula For the Monthly demand price; is the total number of pumped storage power stations; is the power station serial number, For the Pumped storage power station at the monthly maximum residual load of monthly users of generated power.

[0034] Use the following formula to calculate the daily electricity consumption and electricity bill reduction benefits : ; In the formula for The electricity price for the time period; For the Power station No. sky Power generation during the time period; For the Pumped storage power station sky The power of water pumping purchased from the power grid during the period; is the total number of time steps per day; is the time window length, t Represents the time step.

[0035] The total cost of distributed pumped storage construction is calculated using the following formula: : ; In the formula the cost of distributed variable-speed pumped hydro storage; is the replacement cost of the turbine; The operation and maintenance costs of variable-speed pumped storage power stations; 、 and The calculation formulas are: ; ; ; In the formula For the Unit price per kilowatt of installed capacity of reversible pump turbine in pumped storage power station; For the installed capacity of variable-speed pumped storage power stations; The maintenance cost of the variable speed pumped storage power station reservoir capacity; Cost of constructing water pipelines; is the unit operation and maintenance cost of installed capacity; is the discount rate; For the The storage capacity of a variable-speed pumped storage power station; is the operation and maintenance cost per unit storage capacity; For the Unit capacity replacement cost of turbines in variable-speed pumped storage power stations; For the entire life cycle of the turbine; The number of replacements that occur within the project life.

[0036] The constraints of the upper-level optimization model include: Distributed pumped storage capacity and power constraints: ; ; In the formula The upper limit of the capacity of pumped storage power station units; For the The volume of the reservoir upstream of each pumped storage power station; It is the upper limit of upstream storage capacity.

[0037] Step 3: Based on the pumped storage capacity configuration plan provided by the upper-level optimization model, combined with historical load data and the output of wind power plants, photovoltaic power plants, and hydropower plants, the maximum power purchase demand optimization model is called each month to determine the maximum power purchase demand for that month and optimize the demand electricity fee; The maximum electricity purchase demand optimization model is: ; In the formula Indicates industrial users Monthly demand electricity charge; For industrial users Monthly maximum load; For the At the maximum load of the month, Power generation capacity of pumped storage power stations; For the The power generation capacity of the small hydropower station at the monthly maximum load; For the Wind power generation capacity at the monthly maximum load; For the Photovoltaic power generation at the monthly maximum load; It is the serial number of the hydropower station.

[0038] The constraints of the maximum electricity purchase demand optimization model include: Output power constraints for wind power plants, photovoltaic power plants, and small hydropower plants: ; In the formula is the upper limit of the output power of the photovoltaic power station; is the upper limit of the output power of the wind farm; is the maximum power generation capacity of the hydropower station; is the minimum power generation capacity of the hydropower station.

[0039] Dynamic balance constraints of hydropower station storage capacity: ; In the formula For the hydropower station sky Inbound traffic during the time period; For hydropower stations sky Reservoir capacity during the period; At the reservoir sky Reservoir capacity during the period; For hydropower stations sky Outbound flow during the period.

[0040] Step 4: Using the maximum electricity purchase demand obtained in step 3 as a constraint, and using the industrial user operating cost optimization model, optimize the operating strategies of wind power stations, photovoltaic power stations, hydropower stations, and distributed pumped storage power stations, as well as the industrial user electricity purchase and sales plans for that month.

[0041] The optimization model of industrial user operating costs is: ; The electricity purchase cost The calculation formula is as follows: ; ; ; In the formula For the month Daily electricity consumption and electricity charges; is the number of operating days in the month; For the sky Industrial user load during the time period; For the sky The power generation capacity of the hydropower station during the period; For the sky Wind power generation during the time period; For the sky Photovoltaic power generation during the period; Power purchased for industrial users; Indicates the relationship between renewable energy output and load. When the renewable energy output is less than the power required by the load, the system is in the power purchasing state and the value is 1. Otherwise, the value is 0.

[0042] Electricity sales benefits The calculation formula is as follows: ; ; In the formula for The electricity sales price during the time period; Power sold to industrial users; It represents the relationship between new energy and load. When the total output of new energy is greater than the load, it is in the power selling state and the value is 1, otherwise it is 0.

[0043] The constraints of the industrial user operating cost optimization model include: System power balance: ; Electricity generation is subject to cross-sectional constraints imposed by the capacity of transmission lines: ; In the formula The maximum power transmitted by the output lines of the new energy systems of industrial and mining enterprises.

[0044] Upper and lower limit constraints on hydropower station storage capacity: ; In the formula and They are the lower and upper limits of the reservoir flow of the hydropower station respectively.

[0045] Distributed pumped storage ramping constraints: ; In the formula and are the minimum and maximum conversion speeds of pumped storage water, respectively; and They are the minimum conversion speed and maximum conversion speed of pumped storage power generation respectively.

[0046] To ensure a smooth transition between pumping and generating modes, a pumped-storage power station must undergo a shutdown phase as an intermediate stage. This constraint aims to prevent instantaneous state reversals, preventing the system from being in generating mode one moment and then immediately switching to pumping mode the next, or vice versa. Therefore, the following constraints are set as state constraints for the pumped-storage power station.

[0047] Distributed pumped storage power station state switching constraints: ; To ensure the dispatching demand in the next period, it is necessary to ensure that the reservoir capacity at the beginning and end of the dispatching is the same. The reservoir capacity constraint of the pumped storage power station is: ; In the formula 、 Pumped storage power station In the The reservoir capacity at time period t and t+1, 、 、 Pumped storage power station In the sky The inflow, abandoned water and power generation discharge flows in each period.

[0048] Constraints on discharge flow of pumped storage power generation: ; Where, is the minimum discharge volume of the pumped storage power station; It is the maximum discharge volume of the hydropower station.

[0049] Pumped storage power station inflow constraints: ; In the formula It is the maximum value of the inflow flow into the pumped storage power station.

[0050] The system cannot be in the state of purchasing and selling electricity at the same time. The system's power purchasing and selling constraints are: ; Power transmission constraints for purchasing and selling electricity: ; In the formula The maximum power allowed to be transmitted between industrial users and the upper-level power grid.

[0051] Reservoir capacity constraints at the beginning and end of dispatching: ; In the formula Pumped storage power station at the initial dispatching time Storage capacity; To dispatch the pumped storage power station at the end of the storage capacity.

[0052] Load power constraint: ; Step 5: Repeat steps 3 and 4 for each month in the scheduling cycle until the entire scheduling cycle is covered and the operation strategy optimization within the entire scheduling cycle is completed.

[0053] Step 6: Use an iterative optimization algorithm to solve the two-layer optimization model and obtain the optimal pumped storage capacity configuration plan and the joint operation and scheduling plan of the pumped storage and wind, solar and hydropower systems.

[0054] In the upper-level model, the decision variable is the pumped storage system capacity allocation plan, which aims to maximize the system's economic benefits while meeting power system demand. To achieve this goal, the upper-level model receives the monthly maximum demand value and the daily electricity price from the lower-level model. These variables reflect the power system's changing demand for pumped storage capacity in different months.

[0055] The lower-level model optimizes the daily operation of the pumped storage and wind, solar and hydropower systems. Its goal is to optimize the monthly demand electricity charges and daily electricity charges by adjusting the operation strategies of the pumped storage and wind, solar and hydropower systems based on the capacity configuration plan provided by the upper-level model. The optimization results are transmitted to the upper level in the form of pumped storage output at different time periods, which serves as an important basis for evaluating the economic feasibility of different capacity configuration plans.

[0056] The improved NSGA-II algorithm is used for hierarchical iterative solution, and load clustering and scenario reduction technology are introduced. The computational efficiency is improved by 40% compared with the traditional method, and the Pareto solution set coverage rate exceeds 90%, ensuring global convergence under complex constraints.

[0057] Example: To verify the effectiveness of this method, the installed power capacity was set according to the proportion of each power source in a certain region: 392 MW of photovoltaic power, 610 MW of wind power, and 120 MW of hydropower. Two distributed pumped-storage power stations were set, each with an initial installed capacity of 0 MW and an iteration step of 5 MW. Solving the pumped-storage constraint yielded a capacity range of [0 MW, 70 MW]. The proposed method yielded the optimal distributed pumped-storage capacity of 55 MW and 70 MW.

[0058] Depend on Figure 3As can be seen, during the flood season, hydropower station output increases significantly, while wind power output is relatively low. During the 2:00-4:00 AM and 11:00-14:00 PM periods of the daily load curve, the combined output of wind, solar, and small hydropower exceeds the system load demand. Consequently, the pumped storage system enters pumping mode, and the hydropower station's output decreases, reducing power transmission to the upstream grid. From 5:00 AM to 7:00 AM, the combined output of wind, solar, and small hydropower falls below the load demand, causing the pumped storage system to enter generating mode. Through the regulatory role of pumped storage, the system is able to meet the load demand as much as possible, reducing the need to purchase power from the upstream grid.

[0059] Depend on Figure 4 As can be seen, during the dry season, hydropower station output is low, while wind power output is relatively high. From 5:00 AM to 8:00 AM, the combined output of wind, solar, and small hydropower is insufficient to meet load demand, and pumped storage systems enter power generation mode to fill the power gap. From 8:00 AM to 12:00 PM, the combined output of wind, solar, and small hydropower exceeds load demand, and pumped storage systems enter pumping mode. This energy storage reduces the amount of electricity purchased from the upper grid, thereby lowering electricity purchase costs.

[0060] When responding to changes in industrial load, rapid start-stop linkage is achieved through sequential coordination between the speed change units between power stations. During the dry season from 10:00 AM to 11:00 AM, to achieve rapid conversion between pumped storage and power generation, when the total output of wind, solar, and small hydropower exceeds the load requirements, one pumped storage power station can be prioritized for pumping, and then another pumped storage power station will generate power in the next period to ensure the flexibility of the pumped storage power station output. This demonstrates the superiority of the proposed method in reducing the total operating costs of industrial users, improving the accuracy of joint scheduling optimization, and thus reducing the waste of clean energy.

[0061] Implementation results show that this invention utilizes the synergistic effect of distributed pumped-storage clusters to enhance the dynamic response capability to random fluctuations in wind, solar, and hydropower through coordinated control of the start-stop sequence and output of multiple units. Compared with centralized pumped-storage, the distributed structure can more accurately match regional load characteristics, increase the renewable energy absorption rate by 10%-18%, and reduce the phenomenon of wind and solar power curtailment. By constructing a wind-solar-hydro-pumped-storage multi-energy coupling model, the complementary advantages of the rapid peak-shaving of hydropower stations and the flexible energy storage of pumped-storage are brought into play. This combination can increase the system's peak-shaving capacity utilization by 20%-30% and reduce the demand for backup capacity.

[0062] The upper-level model reduces unit storage construction costs through capacity optimization, while the lower-level model reduces users' average annual electricity bills by 8%-12%, improving overall economic efficiency in both directions. By smoothing peak-to-valley load variations, the upper-level grid's reserve capacity requirements are reduced by approximately 11%-16%, deferring investment in transmission and distribution infrastructure upgrades. Application of this model in one regional power grid increased peak-hour load factors by 7.2 percentage points, reducing peak-shaving costs by 18%.

Claims

1. A joint optimization method for pumped storage and wind, solar and hydropower systems based on demand management, characterized by: The following steps are involved: Step 1: Establish output models for industrial users’ wind power plants, photovoltaic power plants, hydropower plants, and distributed pumped storage power plants; Step 2: Construct a two-level optimization model consisting of upper and lower optimization models. The optimization objective of the upper optimization model is to maximize the construction benefits of the pumped storage power station, which is used to solve the pumped storage capacity configuration plan. The lower optimization model includes the industrial user operation cost optimization model and the maximum power purchase demand optimization model; Step 3: Based on the pumped storage capacity configuration plan provided by the upper-level optimization model, combined with historical load data and the output of wind power plants, photovoltaic power plants, and hydropower plants, the maximum power purchase demand optimization model is called each month to determine the maximum power purchase demand for that month and optimize the demand electricity fee; Step 4: Using the maximum electricity purchase demand obtained in Step 3 as a constraint, and using the industrial user operating cost optimization model, optimize the operating strategies of wind power plants, photovoltaic power plants, hydropower plants, and distributed pumped storage power plants, as well as the industrial user electricity purchase and sales plans for that month; Step 5: Repeat steps 3 and 4 for each month in the scheduling cycle until the entire scheduling cycle is covered and the operation strategy optimization for the entire scheduling cycle is completed; Step 6: Use the iterative optimization algorithm to solve the two-layer optimization model and obtain the joint scheduling plan of pumped storage and wind, solar and hydropower systems.

2. The method for joint optimization of pumped storage and wind, solar and hydropower systems based on demand management according to claim 1, characterized in that: In step 2, the objective function F1 of the upper optimization model is: ； In the formula For the Monthly demand electricity charge reduction benefit; For the The benefit of reducing daily electricity consumption and electricity charges; The cost of building distributed pumped storage power stations; The construction period of the project; is the number of operating months per year; For the The number of days in a month, l Indicates the month number.

3. The method for joint optimization of pumped storage and wind, solar and hydropower systems based on demand management according to claim 2, characterized in that: In step 2, the calculation formula for the monthly demand electricity charge reduction benefit is: ； In the formula For the Monthly demand electricity charge reduction benefit; For the l Monthly demand price; is the total number of pumped storage power stations; The serial number of the pumped storage power station. For the l The maximum residual load of industrial users in a month n The power generation capacity of a pumped storage power station.

4. The method for joint optimization of pumped storage and wind, solar and hydropower systems based on demand management according to claim 3 is characterized in that: In step 2, the calculation formula for the benefit of reducing daily purchased electricity is: ； In the formula for The electricity price for the time period; For the Pumped storage power station sky Power generation during the time period; For the Pumped storage power station sky The power of pumping water from the power grid during the period; Z is the total number of time steps per day; is the time window length, and t represents the time step.

5. The method for joint optimization of pumped storage and wind, solar and hydropower systems based on demand management according to claim 4 is characterized in that: In step 2, the constraints of the upper optimization model are: ； ； In the formula For the The installed capacity of variable-speed pumped storage power stations; The upper limit of the capacity of pumped storage power station units; For the The volume of the reservoir upstream of each pumped storage power station; It is the upper limit of upstream storage capacity.

6. The method for joint optimization of pumped storage and wind, solar and hydropower systems based on demand management according to claim 5, characterized in that: In step 3, the objective function of the maximum electricity purchase demand optimization model is: ； In the formula For industrial users Monthly maximum load; For the The power generation capacity of the nth pumped storage power station at the monthly maximum load; For the The power generation capacity of the small hydropower station at the monthly maximum load; For the The power generation capacity of the wind power station at the monthly maximum load; For the The power generation capacity of the photovoltaic power station at the maximum load of the month; n is the serial number of the pumped-storage power station; M is the total number of pumped-storage power stations.

7. The method for joint optimization of pumped storage and wind, solar and hydropower systems based on demand management according to claim 6, characterized in that: The constraints of the maximum power purchase demand optimization model include output power constraints of wind power stations, photovoltaic power stations and small hydropower stations, and dynamic balance constraints of hydropower station storage capacity.

8. The method for joint optimization of pumped storage and wind, solar and hydropower systems based on demand management according to claim 7, characterized in that: In step 4, the objective function F2 of the industrial user operating cost optimization model is: ； ； ； ； Where, represents the electricity purchase cost for industrial users, It represents the benefits of industrial users selling electricity to the grid; Indicates the month Daily electricity consumption and electricity charges; Indicates the number of running days in the month; Power purchased for industrial users; A coefficient indicating whether the output of renewable energy is less than the load; t represents the time step; express The electricity price for the time period; for The electricity sales price during the time period; Power sold to industrial users; A coefficient indicating whether the new energy source is greater than the load; represents the length of the time window; Z is the total number of time steps per day.

9. The method for joint optimization of pumped storage and wind, solar and hydropower systems based on demand management according to claim 8, characterized in that: In step 4, the constraints of the industrial user operating cost optimization model include system power balance constraints, transmission line capacity restriction section constraints, upper and lower limit constraints of hydropower station storage capacity, distributed pumped storage ramping constraints, distributed pumped storage power station state switching constraints, pumped storage power station reservoir capacity constraints, pumped storage power station downstream flow constraints, pumped storage power station inflow flow constraints, system power purchase and sales constraints, scheduling start and end reservoir capacity constraints and load power constraints.

10. The system of the combined optimization method of pumped storage and wind, solar and hydropower systems according to any one of claims 1 to 9, characterized in that: The system includes the following modules: Pumped storage construction cost calculation module: used to calculate the construction cost of pumped storage power stations; Power generation output calculation module: calculates the output power of pumped storage power stations, wind farms, photovoltaic power stations and hydropower stations respectively; Monthly demand reduction benefit calculation module: used to calculate the benefit of monthly demand reduction electricity charges; Daily electricity purchase reduction benefit calculation module: used to calculate the benefits of industrial users' daily electricity purchase fee reduction; Demand electricity fee calculation module: used to calculate the monthly demand electricity fee for industrial users; Electricity consumption and electricity fee calculation module: used to calculate the daily electricity consumption and electricity fee of industrial users; Power purchase cost calculation module: used to calculate the cost of industrial users purchasing electricity from the power grid, including demand charges and electricity charges; Power sales benefit calculation module: used to calculate the benefits of industrial users selling power to the power grid; Industrial user operation cost calculation module: calls the demand electricity fee calculation module, the electricity charge calculation module and the electricity sales benefit calculation module to calculate the operation cost of industrial users; Optimal solution module: used to solve the two-layer optimization model to obtain the optimal solution of the upper optimization model, namely the distributed pumped storage capacity configuration plan, and the optimal solution of the lower optimization model, namely the joint scheduling plan of pumped storage and wind, solar and hydropower systems and the monthly electricity purchase demand.

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