Pumped storage-virtual power plant combined operation system and method

Abstract

The invention provides a pumped storage-virtual power plant combined operation system and method, the interior of the pumped storage-virtual power plant combined operation system comprises a constant / variable speed pumped storage operator and a virtual power plant operator, and the operators carry out electric energy and auxiliary service transactions. And through a reasonable benefit distribution strategy, the total benefit of each operator is maximized. The optimization model and method comprises the following steps: firstly, constructing an upper-layer optimization model by taking the minimum construction cost of a combined operation system as a target; then constructing a combined operation system cooperative game model with the lowest operation cost of the combined operation system; on the basis of considering the wind and light uncertainty, a virtual gamer'nature 'is introduced, and a non-cooperative game model is constructed to minimize the influence of the wind and light uncertainty on a planning result; and finally, solving by adopting an improved cuckoo algorithm. According to the invention, the robustness of the system is effectively improved, the income of each operator in the system is improved, and the stable operation of the combined operation system is facilitated.

Description

Technical Field

[0001] The present invention relates to the technical field of virtual power plant operation and dispatching, and mainly to a pumped storage-virtual power plant joint operation system and method. Background Art

[0002] With the access of a large number of new energy sources to the power grid, the power system is gradually developing towards a new power system with a high proportion of new energy and high power electronics. The large-scale grid connection of renewable energy such as wind power and photovoltaic power and the withdrawal of a large number of thermal power units will cause the original volatility and uncertainty to put greater pressure on the power grid. In serious cases, it will undermine the stability of the power system and affect the normal power consumption of users. Virtual power plants achieve effective control of distributed energy by aggregating distributed energy in the region and enabling it to participate in the management system of the power system. It is an effective way to deal with the randomness, volatility and other problems brought by new energy in the new power system.

[0003] Faced with the rapid development of massive distributed energy, traditional single virtual power plants are difficult to independently achieve effective regulation of distributed energy due to the constraints of communication technology, economic benefits and other issues. Therefore, the integration of distributed energy through the joint operation system of multiple virtual power plants can not only fully tap the regulation potential of a single small virtual power plant and give play to the complementary advantages of heterogeneous resource integration, but also achieve rapid regulation of distributed energy and improve the operational stability of the power system. At the same time, as the most widely used, mature and reliable energy storage system, pumped storage power stations are flexible in start and stop, respond quickly, and have multiple functions such as peak-to-valley shifting, frequency modulation, phase modulation, emergency backup and black start. They are regarded as an important way to solve the problem of new energy flexibility needs.

[0004] In view of the randomness problem caused by the grid connection of new energy, robust optimization uses an uncertain set to describe the uncertainty parameters, and takes the minimum cost under the worst scenario in the set as the decision-making goal, so as to suppress the interference caused by uncertainty on the decision to the greatest extent and ensure the feasibility of the decision-making plan within a certain fluctuation range. It is one of the most commonly used methods in the field of uncertainty modeling. However, since robust optimization deals with the impact of uncertainty factors on the entire planning decision from a holistic perspective, and in the planning model of multi-agent game, people make decisions from individuals and then seek the optimal solution through the game between individuals, it is difficult for robust optimization to balance the relationship between individuals. Introducing a virtual subject "Nature" that represents uncertainty, on this basis, the above subjects are taken as game participants. On the one hand, by accurately simulating the game behavior of the subjects in the market, it can ensure that each market subject continuously optimizes its own decision in the game process to maximize its own benefits and enhance market vitality and the effectiveness of planning decisions; on the other hand, by introducing the virtual player "Nature", the impact of uncertainty factors on planning decisions can be fully considered in the planning model based on game theory, and the rationality of planning decisions can be improved through active optimization. Summary of the invention

[0005] In response to the above problems, the present invention provides a pumped storage-virtual power plant joint operation system and method, which formulates operation strategies and configuration plans that maximize the interests of all parties in the form of cooperative game, and introduces the virtual player "Nature" to minimize the impact of wind and solar energy uncertainty. The overall goal is to optimize the power quality and economic benefits of the power system, thereby improving the economic and production benefits of the pumped storage-virtual power plant joint operation system.

[0006] In order to achieve the above technical features, the object of the present invention is achieved as follows: a pumped storage-virtual power plant joint operation system, comprising: a fixed / variable speed pumped storage operator and a virtual power plant operator; The fixed / variable speed pumped storage operator includes a fixed speed pumped storage unit and a variable speed pumped storage unit; The virtual power plant operators include wind turbines, photovoltaic generators, energy storage units, demand response systems and distribution networks; The fixed / variable speed pumped storage operator, the virtual power plant operator, and the demand response system transmit information and energy through the information interaction module and the energy interaction module.

[0007] The present invention also provides a multi-time scale optimization method for a pumped storage-virtual power plant joint operation system, comprising the following steps: Step 1, obtaining the parameter information of the joint operation system, including the parameters of the pumped storage unit, the parameters of the wind turbine unit, the parameters of the photovoltaic unit, various load information, economic costs and safe operation constraints; Step 2: construct an upper-level optimization model with the goal of minimizing the investment and construction cost of the pumped storage-virtual power plant joint operation system, including the construction costs of fixed / variable speed pumped storage operators and virtual power plant operators and related constraints; Step 3: construct a lower-level cooperative game model with the pumped storage-virtual power plant joint operation system with the lowest operating cost, including the operating costs of fixed / variable speed pumped storage operators and virtual power plant operators and related constraints; Step 4, construct a non-cooperative game model of the joint operation system considering the virtual player "Nature"; Step 5: Construct an improved cuckoo algorithm solution model.

[0008] Preferably, in step 2, establishing an upper-level optimization model of the pumped storage-virtual power plant joint operation system includes: Step 2.1, construct the upper model objective function: ; ; Where: The investment cost recovery period of the hybrid energy system at the maximum economic benefit under a certain capacity configuration; is the total cost of the PV power station; is the total cost of the wind power station; is the total cost of the pumped storage power station; The electricity sales revenue obtained through optimized operation of the hybrid energy system within one year; is the number of photovoltaic cells in the photovoltaic power station; is the unit price of photovoltaic cells; is the replacement cost of a unit photovoltaic cell; is the life cycle of the photovoltaic cell; is the operation and maintenance cost of a unit photovoltaic cell; is the discount rate; The period of this project; is the number of wind turbines; is the capacity unit price of the wind turbine generator set; is the replacement cost of a unit of light wind turbine; is the life cycle of the wind turbine; is the operation and maintenance cost of a unit wind turbine; , are the total costs of fixed-speed and variable-speed pumped storage units respectively; , , , are the volumes of upstream and downstream storage capacities of fixed-speed and variable-speed pumped-storage power stations respectively; , The installed capacity of reversible pump turbines for fixed and variable speed pumped storage power stations; , They are the unit price per cubic meter of upstream storage capacity of fixed-speed and variable-speed pumped-storage power stations respectively; , They are the unit price per kilowatt of installed capacity of turbines in fixed-speed and variable-speed pumped-storage power stations; , are the replacement costs of turbines in fixed-speed and variable-speed pumped-storage power stations respectively; , They are the life cycle of turbines in fixed-speed and variable-speed pumped-storage power stations respectively; , They are the operation and maintenance costs per cubic meter of upstream storage capacity of fixed-speed and variable-speed pumped storage power stations, respectively; , The operation and maintenance cost per kilowatt of the pumped storage power station; Step 2.2, determine the upper model constraints: 1) Constraints on the number of photovoltaic cells: ; In the formula, is the upper limit of the number of photovoltaic cells in a photovoltaic power station; 2) Constraints on the number of wind turbines: ; In the formula, The upper limit of the number of wind turbines in a wind power station; 3) Upstream storage capacity constraints of pumped storage power stations: ; In the formula, , , , They are the upper limits of the upstream and downstream storage volumes of fixed-speed and variable-speed pumped-storage power stations, respectively; 4) Constraints on installed capacity of pumped storage power stations: ; In the formula, , They are the upper limits of the installed capacity of turbines in fixed-speed and variable-speed pumped-storage power stations, respectively. Taking the actual situation into consideration, under the premise of simplifying the model, the downstream reservoir of the pumped-storage power station is regarded as the sea level with unlimited storage capacity.

[0009] Preferably, in step 3, establishing a lower-level optimization model of the pumped storage-virtual power plant joint operation system includes: Step 3.1, build a virtual power plant model: The goal of a virtual power plant is to maximize its total operating revenue, namely: 1) Revenue from electricity sales: ; 2) Battery degradation cost: ; 3) Demand response costs: ; 4) Power transmission cost: ; 5) Network loss cost: ; Where: The revenue from electricity sales of distributed energy; is the degradation loss of distributed energy storage; for demand response costs; is the transmission cost between operators; is the network loss cost within the virtual power plant; , Respectively represent i Photovoltaic and wind turbines t The effort of the moment; , Respectively represent i Energy storage units t The charging and discharging status at all times; express The transferable load of the time period; express Interruptible load during the period; Indicates the operator a With operators b between t Energy transfer power at the moment; is the line admittance; is the node power angle; , They represent photovoltaic respectively; represents the unit degradation cost of distributed energy storage; , represents the unit cost of demand response; represents the unit cost of electricity transmission; is the unit cost of network loss; Constraints of the virtual power plant model: 1) Scenery constraints: ; Where: , , , Respectively represent The upper and lower limits of the output of photovoltaic and wind turbines; 2) Distributed energy storage output constraints: ; Where: Indicates Energy storage devices in State of charge during the time period; , Respectively represent the upper and lower limits of the state of charge; Indicates the rated capacity of energy storage; , They represent the charging and discharging efficiency of energy storage respectively; , They represent the upper limits of charging and discharging of energy storage respectively; , represents the state constraints of energy storage; 3) Power transmission constraints between operators: ; Where: Indicates the maximum value of the transmission power; 4) Flexibility load constraints: ; Where: , Respectively represent the upper and lower limits of interruptible load; , Respectively represent the upper and lower limits of the transferable load; For fixed load; is the total load; 4) Power flow constraints: To facilitate calculation, the improved Distflow Model: ; Where: The first node in the AC power grid is The end node set of the branch; is the terminal node in the AC power grid The set of first nodes of the branches; For Node The injected power; 5) Power balance constraints: ; 6) Line flow constraints: ; ; ; Where: For Node n,m The admittance between For Node n The phase angle, is the phase angle of the equilibrium node; Step 3.2, build fixed / variable speed pumped storage operator model: The fixed / variable speed pumped storage operator model is a system consisting of fixed speed pumped storage units and variable speed pumped storage units. After the upper model determines its capacity, it includes the following objective functions in the operation stage: 1) Start-up and shutdown loss costs of pumped storage construction: ; ; Where: represents the total start-stop loss cost of the pumped storage system; , , , They represent the pumping and power generation conversion costs of fixed-speed pumped-storage units and the pumping and power generation conversion costs of variable-speed pumped-storage units respectively; , , , They represent the unit conversion costs of pumping and power generation conditions of fixed-speed pumped-storage units and the unit conversion costs of pumping and power generation conditions of variable-speed pumped-storage units respectively; , , , Respectively represent Pumping and power generation conditions of fixed and variable speed pumped storage units The state variable of the time period takes a Boolean value of 0 or 1. When it is 1, it means that the pumped storage unit is in this state; , Respectively represent the number of fixed-speed and variable-speed pumped storage units; 2) Pumped storage operation cost: ; ; Where: represents the total operating cost of the pumped storage system; , Respectively represent the operating costs of fixed-speed and variable-speed pumped storage units; , They are the unit operating costs of fixed-speed and variable-speed pumped storage units respectively; , , , Respectively The pumping and power generation conditions of fixed and variable speed pumped storage units are Output power during the time period; 3) Revenue from pumped storage electricity sales: ; ; Where: represents the total electricity sales revenue of the pumped storage system; , Respectively represent the electricity sales revenue of fixed-speed and variable-speed pumped storage units; is the electricity sales price of the pumped storage unit; 4) Pumped storage ancillary service income: ; ; Where: represents the total ancillary service revenue of the pumped storage system; , Respectively represent the ancillary service revenue of fixed-speed and variable-speed pumped storage units; The unit price of auxiliary services sold for pumped storage units. , , , Respectively represent Fixed and variable speed pumped storage units The upper and lower spinning reserve capacity of the time period; And also includes the following constraints: 1) Constraints of fixed-speed pumped storage units: ; ; Where: is the rated pumping power of the fixed-speed pumped-storage unit; , Respectively represent the minimum and maximum power generation of the fixed-speed pumped storage unit; 2) Constraints of variable speed pumped storage units: ; ; Where: , , , They represent the minimum pumping power, maximum pumping power, minimum generating power and maximum generating power of the variable speed pumped storage unit respectively; 3) Reservoir capacity constraints: ; Where: , Respectively represent the upper reservoir and the lower reservoir in Water storage capacity during the period; , , , They represent the maximum and minimum values ​​of the water storage capacity of the upper reservoir and the maximum and minimum values ​​of the water storage capacity of the lower reservoir respectively; is the unit time period; , They represent the power-flow conversion coefficient, which is a constant representing the ability of the pumped storage unit to convert power into flow; 4) Spinning reserve constraints: ; ; Step 3.3, construct a cooperative game model for the pumped storage-virtual power plant joint operation system: The Nash negotiation model is used to construct a cooperative game model of the joint operation system; The standard Nash negotiation model is as follows: ; Where: The benefits to the negotiating parties; The benefits of the participants before they cooperate, i.e. the negotiation breakdown point; Based on this, the following cooperative game model is constructed: ; In the formula, , represents the set of pumped storage operators and virtual power plant operators; , They represent the profits after the cooperation between the pumped storage operator and the virtual power plant operator; , They respectively represent the income before the cooperation game between the pumped storage operator and the virtual power plant operator.

[0010] Preferably, in step 4, constructing a non-cooperative game model of a joint operation system considering the virtual player “Nature” includes: The output of distributed power sources is regarded as a special decision variable to characterize uncertainty, and "Nature" is introduced as the corresponding virtual subject. After obtaining the configuration information calculated by the upper model, each operator designs an output strategy with the lowest operating cost and passes the output strategy to the virtual game player "Nature". After learning the capacity and output strategy information of each unit, Nature interferes with the planning of each operator, making each operator make new decisions. The specific process of "Nature" participating in the game is as follows: The robust optimization method is used to deal with the uncertainty of renewable energy output. The fluctuation range of output is described by the uncertainty interval. Considering that renewable energy is wind and solar power generation, when the external environment is determined, its output will only depend on the installed capacity. That is, after determining the fluctuation range of wind and solar power, the specific description of the uncertainty interval is as follows: ; Where: and They are respectively the lower and upper limits of the proportion of new energy output to new energy capacity.

[0011] Preferably, in step 5, an improved cuckoo algorithm solution model is constructed: The cuckoo search algorithm is a meta-heuristic algorithm based on the special reproduction method of cuckoos and the evolution of Levy flight, which aims to quickly find the global optimal solution. In order to facilitate the establishment of the cuckoo search algorithm model, three assumptions are proposed, as follows: (1) The way cuckoos choose their nests is completely random, and each cuckoo lays only one egg at a time, representing a set of solutions to the problem; (2) Among these bird nests, some contain high-quality eggs, which are better solutions. These nests will be retained and hatch the next generation; (3) Assuming the probability of the host discovering foreign bird eggs, once discovered, the host will directly abandon the egg or nest, but the total number of bird nests remains unchanged; According to the characteristics of Levy flight, the position and search path formula of the cuckoo search algorithm are as follows: ; Where: represents a set of solutions to a problem; is the step factor, which is a number greater than zero; are the flight path parameters, is the flight coefficient, , is a normally distributed random number, ; In addition, the traditional cuckoo algorithm is improved as follows: Firstly, a dynamic Levy step factor is used. The large step size in the early stage of the algorithm speeds up the global search speed, and the smaller step size in the later stage improves the accuracy of the algorithm in the local search. Secondly, the idea of ​​genetic algorithm is borrowed to introduce a mutation mechanism in the algorithm evolution process to further increase the population diversity. In order to find a balance between search accuracy and search speed, the Levy step size control amount of the standard cuckoo algorithm is improved as follows: ; Where: is the Levy step size factor; is the minimum step size factor; is the current iteration number, is the maximum number of iterations; The genetic algorithm idea is used to mutate the best individual of each generation of the cuckoo algorithm to further improve the quality of the individual, and the mutation step length decreases with the increase of the number of iterations. The mutation mechanism is as follows: ; Where: It is the best bird nest of the contemporary era; is the location of the bird's nest after mutation; To change the asynchronous length; It follows a standard normal distribution.

[0012] Preferably, the cuckoo algorithm solution process in step 5 is: Step 1: Let the capacity of wind, solar and pumped storage units be the bird's nest, initialize the location information of the bird's nest, and initialize the electricity price information, initial load information, parameters related to the virtual power plant operator and parameters related to the pumped storage operator; Step 2: Solve the objective function of the upper model, calculate the optimal capacity of wind and solar units and pumped storage units, and pass it to the lower model; Step 3: After the lower-level model obtains the capacity information of the wind and solar units and the pumped storage units, the virtual power plant operator obtains the output strategy of energy storage and flexible load that optimizes its own economic benefits based on the wind and solar output strategy obtained in the previous round of game, and passes it to the virtual game player "Nature". After obtaining the output strategy of energy storage and flexible load, the virtual game player "Nature" updates the wind and solar output with the goal of minimizing its own economic benefits, and then passes it to the virtual power plant operator. The above operations are repeated until all outputs remain unchanged and the game reaches equilibrium; Step 4: Take the sum of the objective functions of the upper and lower double-layer models as the optimal fitness of the bird's nest and update the position of the bird's nest; Step 5: Repeat Step 2-Step 4 until the maximum number of iterations is reached, and output the optimal planning scheme for the optimal wind, solar, and pumped storage units.

[0013] The present invention has the following beneficial effects: 1. By constructing a cooperative game model between pumped storage operators and virtual power plant operators, the interests of each operator are maximized while taking into account the balance of interests among operators.

[0014] 2. A non-cooperative game model that takes into account the virtual player "Nature" was constructed to reduce the impact of wind and solar uncertainty and make the optimization results closer to reality. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0016] Figure 1 It is a schematic diagram of the structure of the pumped storage-virtual power plant joint operation system of the present invention.

[0017] Figure 2 This is a flow chart for solving the pumped storage-virtual power plant joint operation system of the present invention.

[0018] Figure 3 It is the iteration curve of each operator in the present invention.

[0019] Figure 4 It is the output curve of each operator of the present invention. DETAILED DESCRIPTION

[0020] The technical solution of the present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments of the specification, but the protection scope of the present invention is not limited to the embodiments.

[0021] Embodiment 1: like Figure 1 As shown, the present invention provides a pumped storage-virtual power plant joint operation system, including: a fixed / variable speed pumped storage operator and a virtual power plant operator; The fixed / variable speed pumped storage operator includes a fixed speed pumped storage unit and a variable speed pumped storage unit; The virtual power plant operators include wind turbines, photovoltaic generators, energy storage units, demand response systems and distribution networks; The fixed / variable speed pumped storage operator, the virtual power plant operator, and the demand response system transmit information and energy through the information interaction module and the energy interaction module.

[0022] The present invention also provides a multi-time scale optimization method for a pumped storage-virtual power plant joint operation system, comprising the following steps: Step 1, obtaining the parameter information of the joint operation system, including the parameters of the pumped storage unit, the parameters of the wind turbine unit, the parameters of the photovoltaic unit, various load information, economic costs and safe operation constraints; Step 2: construct an upper-level optimization model with the goal of minimizing the investment and construction cost of the pumped storage-virtual power plant joint operation system, including the construction costs of fixed / variable speed pumped storage operators and virtual power plant operators and related constraints; Step 2.1, construct the upper model objective function: ; ; Where: The investment cost recovery period of the hybrid energy system at the maximum economic benefit under a certain capacity configuration; is the total cost of the PV power station; is the total cost of the wind power station; is the total cost of the pumped storage power station; The electricity sales revenue obtained through optimized operation of the hybrid energy system within one year; is the number of photovoltaic cells in the photovoltaic power station; is the unit price of photovoltaic cells; is the replacement cost of a unit photovoltaic cell; is the life cycle of the photovoltaic cell; is the operation and maintenance cost of a unit photovoltaic cell; is the discount rate; The period of this project; is the number of wind turbines; is the capacity unit price of the wind turbine generator set; is the replacement cost of a unit of light wind turbine; is the life cycle of the wind turbine; is the operation and maintenance cost of a unit wind turbine; , are the total costs of fixed-speed and variable-speed pumped storage units respectively; , , , are the volumes of upstream and downstream storage capacities of fixed-speed and variable-speed pumped-storage power stations respectively; , The installed capacity of reversible pump turbines for fixed and variable speed pumped storage power stations; , They are the unit price per cubic meter of upstream storage capacity of fixed-speed and variable-speed pumped-storage power stations respectively; , They are the unit price per kilowatt of installed capacity of turbines in fixed-speed and variable-speed pumped-storage power stations; , are the replacement costs of turbines in fixed-speed and variable-speed pumped-storage power stations respectively; , They are the life cycle of turbines in fixed-speed and variable-speed pumped-storage power stations respectively; , They are the operation and maintenance costs per cubic meter of upstream storage capacity of fixed-speed and variable-speed pumped storage power stations, respectively; , The operation and maintenance cost per kilowatt of the pumped storage power station; Step 2.2, determine the upper model constraints: 1) Constraints on the number of photovoltaic cells: ; In the formula, is the upper limit of the number of photovoltaic cells in a photovoltaic power station; 2) Constraints on the number of wind turbines: ; In the formula, The upper limit of the number of wind turbines in a wind power station; 3) Upstream storage capacity constraints of pumped storage power stations: ; In the formula, , , , They are the upper limits of the upstream and downstream storage volumes of fixed-speed and variable-speed pumped-storage power stations, respectively; 4) Constraints on installed capacity of pumped storage power stations: ; In the formula, , They are the upper limits of the installed capacity of turbines in fixed-speed and variable-speed pumped-storage power stations, respectively. Taking the actual situation into consideration, under the premise of simplifying the model, the downstream reservoir of the pumped-storage power station is regarded as the sea level with unlimited storage capacity.

[0023] Step 3: construct a lower-level cooperative game model with the pumped storage-virtual power plant joint operation system with the lowest operating cost, including the operating costs of fixed / variable speed pumped storage operators and virtual power plant operators and related constraints; Step 3.1, build a virtual power plant model: The goal of a virtual power plant is to maximize its total operating revenue, namely: 1) Revenue from electricity sales: ; 2) Battery degradation cost: ; 3) Demand response costs: ; 4) Power transmission cost: ; 5) Network loss cost: ; Where: The revenue from electricity sales of distributed energy; is the degradation loss of distributed energy storage; for demand response costs; is the transmission cost between operators; is the network loss cost within the virtual power plant; , Respectively represent i Photovoltaic and wind turbinest The effort of the moment; , Respectively represent i Energy storage units t The charging and discharging status at all times; express The transferable load of the time period; express Interruptible load during the period; Indicates the operator a With operators b between t Energy transfer power at the moment; is the line admittance; is the node power angle; , They represent photovoltaic respectively; represents the unit degradation cost of distributed energy storage; , represents the unit cost of demand response; represents the unit cost of electricity transmission; is the unit cost of network loss; Constraints of the virtual power plant model: 1) Scenery constraints: ; Where: , , , Respectively represent The upper and lower limits of the output of photovoltaic and wind turbines; 2) Distributed energy storage output constraints: ; Where: Indicates Energy storage devices in State of charge during the time period; , Respectively represent the upper and lower limits of the state of charge; Indicates the rated capacity of energy storage; , They represent the charging and discharging efficiency of energy storage respectively; , They represent the upper limits of charging and discharging of energy storage respectively; , represents the state constraints of energy storage; 3) Power transmission constraints between operators: ; Where: Indicates the maximum value of the transmission power; 4) Flexibility load constraints: ; Where: , Respectively represent the upper and lower limits of interruptible load; , Respectively represent the upper and lower limits of the transferable load; For fixed load; is the total load; 4) Power flow constraints: To facilitate calculation, the improved Distflow Model: ; Where: The first node in the AC power grid is The end node set of the branch; is the terminal node in the AC power grid The set of first nodes of the branches; For Node The injected power; 5) Power balance constraints: ; 6) Line flow constraints: ; ; ; Where: For Node n,m The admittance between For Node n The phase angle, is the phase angle of the equilibrium node; Step 3.2, build fixed / variable speed pumped storage operator model: The fixed / variable speed pumped storage operator model is a system consisting of fixed speed pumped storage units and variable speed pumped storage units. After the upper model determines its capacity, it includes the following objective functions in the operation stage: 1) Start-up and shutdown loss costs of pumped storage construction: ; ; Where: represents the total start-stop loss cost of the pumped storage system; , , , They represent the pumping and power generation conversion costs of fixed-speed pumped-storage units and the pumping and power generation conversion costs of variable-speed pumped-storage units respectively; , , , They represent the unit conversion costs of pumping and power generation conditions of fixed-speed pumped-storage units and the unit conversion costs of pumping and power generation conditions of variable-speed pumped-storage units respectively; , , , Respectively represent Pumping and power generation conditions of fixed and variable speed pumped storage units The state variable of the time period takes a Boolean value of 0 or 1. When it is 1, it means that the pumped storage unit is in this state; , Respectively represent the number of fixed-speed and variable-speed pumped storage units; 2) Pumped storage operation cost: ; ; Where: represents the total operating cost of the pumped storage system; , Respectively represent the operating costs of fixed-speed and variable-speed pumped storage units; , They are the unit operating costs of fixed-speed and variable-speed pumped storage units respectively; , , , Respectively The pumping and power generation conditions of fixed and variable speed pumped storage units are Output power during the time period; 3) Revenue from pumped storage electricity sales: ; ; Where: represents the total electricity sales revenue of the pumped storage system; , Respectively represent the electricity sales revenue of fixed-speed and variable-speed pumped storage units; is the electricity sales price of the pumped storage unit; 4) Pumped storage ancillary service income: ; ; Where: represents the total ancillary service revenue of the pumped storage system; , Respectively represent the ancillary service revenue of fixed-speed and variable-speed pumped storage units; The unit price of auxiliary services sold for pumped storage units. , , , Respectively represent Fixed and variable speed pumped storage units The upper and lower spinning reserve capacity of the time period; And also includes the following constraints: 1) Constraints of fixed-speed pumped storage units: ; ; Where: is the rated pumping power of the fixed-speed pumped-storage unit; , Respectively represent the minimum and maximum power generation of the fixed-speed pumped storage unit; 2) Constraints of variable speed pumped storage units: ; ; Where: , , , They represent the minimum pumping power, maximum pumping power, minimum generating power and maximum generating power of the variable speed pumped storage unit respectively; 3) Reservoir capacity constraints: ; Where: , Respectively represent the upper reservoir and the lower reservoir in Water storage capacity during the period; , , , They represent the maximum and minimum values ​​of the water storage capacity of the upper reservoir and the maximum and minimum values ​​of the water storage capacity of the lower reservoir respectively; is the unit time period; , They represent the power-flow conversion coefficient, which is a constant representing the ability of the pumped storage unit to convert power into flow; 4) Spinning reserve constraints: ; ; Step 3.3, construct a cooperative game model for the pumped storage-virtual power plant joint operation system: This embodiment adopts the Nash negotiation model to construct a cooperative game model of the joint operation system.

[0024] The Nash negotiation optimization model is a cooperative game. After maximizing the interests of all participants, multiple participants distribute the cooperative benefits through mutual negotiation. The Nash negotiation model satisfies a set of axioms, including: symmetry; Pareto optimality. The solution that maximizes the Nash product is the equilibrium solution to the Nash negotiation game problem.

[0025] The standard Nash negotiation model is as follows: ; Where: The benefits to the negotiating parties; The benefits of the participants before they cooperate, i.e. the negotiation breakdown point; Based on this, this embodiment constructs the following cooperative game model: ; In the formula, , represents the set of pumped storage operators and virtual power plant operators; , They represent the profits after the cooperation between the pumped storage operator and the virtual power plant operator; , They respectively represent the income before the cooperation game between the pumped storage operator and the virtual power plant operator.

[0026] Step 4, construct a non-cooperative game model of the joint operation system considering the virtual player "Nature"; The market entities in this embodiment are pumped storage operators and virtual power plant operators. In addition, after the distributed power source is connected, the uncertainty of its output will mainly affect the planning schemes and economic benefits of each operator. Therefore, it is considered to regard the output of the distributed power source as a special decision variable to characterize its uncertainty, and introduce "Nature" as the corresponding virtual subject. After obtaining the configuration information calculated by the upper-level model, each operator designs an output strategy with the lowest operating cost, and passes the output strategy to the virtual player "Nature". After learning about the capacity, output strategy and other information of each unit, Nature interferes with the planning of each operator, causing each operator to make new decisions. The specific process of "Nature" participating in the game is as follows: In view of the interference caused by the fluctuation of the output of new energy within a certain range, people always hope to design the best strategy to minimize the possible cost loss or operation risk and to suppress the adverse effects caused by uncertainty to the greatest extent. This process is consistent with the idea of ​​robust optimization. Therefore, this embodiment adopts the robust optimization method to deal with the uncertainty of the output of new energy, and describes the fluctuation range of the output through the uncertainty interval. Since this article considers that the new energy is wind and solar power generation, when the external environment is determined, its output will only depend on the installed capacity, that is, after determining the fluctuation range of wind and solar, the specific description of the uncertainty interval is as follows: ; Where: and They are respectively the lower and upper limits of the proportion of new energy output to new energy capacity.

[0027] In the above robust optimization problem, the uncertainty of wind and solar power output will reduce the revenue of the distribution network, while the output strategy of the virtual power plant operator is to maximize the revenue, that is, the objective function of "Nature" is the opposite value of the objective function of the virtual power plant operator. From the perspective of game theory, the two decision makers "Nature" and the virtual power plant operator form a zero-sum game relationship, so the robust optimization problem can be transformed into a game problem between "Nature" and the virtual power plant operator. In the face of a player like "Nature", the best way to deal with it is to observe its worst interference first, and then build a response strategy, thereby forming a dynamic game process in which the actions of the two parties in the game round have a sequence. The specific process is as follows: In a round of game, first, "Nature" will continuously adjust the output of the current wind and solar units within the uncertain range to minimize the total benefits of the virtual power plant operator. Therefore, "Nature" can be equivalent to an optimization model with the opposite objective function and the same constraints as the virtual power plant operator. Afterwards, the virtual power plant operator will maximize its own benefits by adjusting energy storage and flexible loads based on the worst planning scenario caused by "Nature", and then update the output strategy of energy storage and flexible loads to enter the next round of game. During the game, when neither "Nature" nor the virtual power plant operator can obtain better payments by changing their strategies, the game reaches equilibrium.

[0028] Step 5: Construct an improved cuckoo algorithm solution model: The cuckoo search algorithm is a meta-heuristic algorithm based on the special reproduction method of cuckoos and the evolution of Levy flight, which aims to quickly find the global optimal solution. In order to facilitate the establishment of the cuckoo search algorithm model, three assumptions are proposed, as follows: (1) The way cuckoos choose their nests is completely random, and each cuckoo lays only one egg at a time, representing a set of solutions to the problem; (2) Among these bird nests, some contain high-quality eggs, which are better solutions. These nests will be retained and hatch the next generation; (3) Assuming the probability of the host discovering foreign bird eggs, once discovered, the host will directly abandon the egg or nest, but the total number of bird nests remains unchanged; According to the characteristics of Levy flight, the position and search path formula of the cuckoo search algorithm are as follows: ; Where: represents a set of solutions to a problem; is the step factor, which is a number greater than zero; are the flight path parameters, is the flight coefficient, , is a normally distributed random number, ; In addition, the traditional cuckoo algorithm is improved as follows: Firstly, a dynamic Levy step size factor is adopted. The large step size in the early stage of the algorithm speeds up the global search speed, and the smaller step size in the later stage improves the accuracy of the algorithm's search in the local range. Secondly, the idea of ​​genetic algorithm is borrowed to introduce a mutation mechanism in the algorithm evolution process to further increase the population diversity. The Levy step size in the standard cuckoo (CS) algorithm is completely random, so the uncertainty of the step size will affect the convergence speed of the algorithm. Although a small step size can improve the accuracy of the solution in the local range, it will reduce the convergence speed of the global algorithm. On the contrary, a large step size has a better global search speed, but it reduces the accuracy of the algorithm for the optimal solution. Therefore, in order to find a balance between search accuracy and search speed, this paper improves the Levy step size control amount of the standard CS algorithm as shown below.

[0029] In order to find a balance between search accuracy and search speed, the Levy step size control amount of the standard cuckoo algorithm is improved as follows: ; Where: is the Levy step size factor; is the minimum step size factor; is the current iteration number, is the maximum number of iterations; Considering that the initial solution obtained by the standard CS algorithm is random, the population size must be increased to improve the quality of the initial solution, and the computer memory occupied will also increase accordingly, which is not conducive to the algorithm optimization. Therefore, this embodiment draws on the idea of ​​genetic algorithm

[14] to mutate the best individual of each generation of CS to further improve the quality of the individual, and the mutation step length decreases with the increase of the number of iterations. The mutation mechanism is as follows: ; Where: It is the best bird nest of the contemporary era; is the location of the bird's nest after mutation; To change the asynchronous length; It follows a standard normal distribution.

[0030] The cuckoo algorithm solution process in this embodiment is as follows: Step 1: Let the capacity of wind, solar and pumped storage units be the bird's nest, initialize the location information of the bird's nest, and initialize the electricity price information, initial load information, parameters related to the virtual power plant operator and parameters related to the pumped storage operator; Step 2: Solve the objective function of the upper model, calculate the optimal capacity of wind and solar units and pumped storage units, and pass it to the lower model; Step 3: After the lower-level model obtains the capacity information of the wind and solar units and the pumped storage units, the virtual power plant operator obtains the output strategy of energy storage and flexible load that optimizes its own economic benefits based on the wind and solar output strategy obtained in the previous round of game, and passes it to the virtual game player "Nature". After obtaining the output strategy of energy storage and flexible load, the virtual game player "Nature" updates the wind and solar output with the goal of minimizing its own economic benefits, and then passes it to the virtual power plant operator. The above operations are repeated until all outputs remain unchanged and the game reaches equilibrium; Step 4: Take the sum of the objective functions of the upper and lower double-layer models as the optimal fitness of the bird's nest and update the position of the bird's nest; Step 5: Repeat Step 2-Step 4 until the maximum number of iterations is reached, and output the optimal planning scheme for the optimal wind, solar, and pumped storage units.

[0031] Embodiment 2: According to the above implementation plan, specific data is input to build a simulation example, and the specific parameters are shown in Table 1: Table 1 Model solution parameters

[0032] The benefits of each operator before and after optimization are shown in Table 2: Table 2 Comparison of results before and after optimization

[0033] The iteration curves and output curves of each operator are as follows: Figure 3 , Figure 4 As shown in Table 2, it can be seen that the solution proposed by the present invention can effectively increase the revenue of each operator.

Claims

1. A pumped storage-virtual power plant joint operation system, characterized in that: include: Fixed / variable speed pumped storage operators and virtual power plant operators; The fixed / variable speed pumped storage operator includes a fixed speed pumped storage unit and a variable speed pumped storage unit; The virtual power plant operators include wind turbines, photovoltaic generators, energy storage units, demand response systems and distribution networks; The fixed / variable speed pumped storage operator, the virtual power plant operator, and the demand response system transmit information and energy through the information interaction module and the energy interaction module.

2. The multi-time scale optimization method of a pumped storage-virtual power plant joint operation system according to claim 1 is characterized in that: The following steps are involved: Step 1, obtaining the parameter information of the joint operation system, including the parameters of the pumped storage unit, the parameters of the wind turbine unit, the parameters of the photovoltaic unit, various load information, economic costs and safe operation constraints; Step 2: construct an upper-level optimization model with the goal of minimizing the investment and construction cost of the pumped storage-virtual power plant joint operation system, including the construction costs of fixed / variable speed pumped storage operators and virtual power plant operators and related constraints; Step 3: construct a lower-level cooperative game model with the pumped storage-virtual power plant joint operation system with the lowest operating cost, including the operating costs of fixed / variable speed pumped storage operators and virtual power plant operators and related constraints; Step 4, construct a non-cooperative game model of the joint operation system considering the virtual player "Nature"; Step 5: Construct an improved cuckoo algorithm solution model.

3. The multi-time scale optimization method for a pumped storage-virtual power plant joint operation system according to claim 1, characterized in that: The upper optimization model of the pumped storage-virtual power plant joint operation system established in step 2 includes: Step 2.1, construct the upper model objective function: ； ； Where: The investment cost recovery period of the hybrid energy system at the maximum economic benefit under a certain capacity configuration; is the total cost of the PV power station; is the total cost of the wind power station; is the total cost of the pumped storage power station; The electricity sales revenue obtained through optimized operation of the hybrid energy system within one year; is the number of photovoltaic cells in the photovoltaic power station; is the unit price of photovoltaic cells; is the replacement cost of a unit photovoltaic cell; is the life cycle of the photovoltaic cell; is the operation and maintenance cost of a unit photovoltaic cell; is the discount rate; The period of this project; is the number of wind turbines; is the capacity unit price of the wind turbine generator set; is the replacement cost of a unit of light wind turbine; is the life cycle of the wind turbine; is the operation and maintenance cost of a unit wind turbine; , are the total costs of fixed-speed and variable-speed pumped storage units respectively; , , , are the volumes of upstream and downstream storage capacities of fixed-speed and variable-speed pumped-storage power stations respectively; , The installed capacity of reversible pump turbines for fixed and variable speed pumped storage power stations; , They are the unit price per cubic meter of upstream storage capacity of fixed-speed and variable-speed pumped-storage power stations respectively; , They are the unit price per kilowatt of installed capacity of turbines in fixed-speed and variable-speed pumped-storage power stations; , are the replacement costs of turbines in fixed-speed and variable-speed pumped-storage power stations respectively; , They are the life cycle of turbines in fixed-speed and variable-speed pumped-storage power stations respectively; , They are the operation and maintenance costs per cubic meter of upstream storage capacity of fixed-speed and variable-speed pumped storage power stations, respectively; , The operation and maintenance cost per kilowatt of the pumped storage power station; Step 2.2, determine the upper model constraints: 1) Constraints on the number of photovoltaic cells: ； In the formula, is the upper limit of the number of photovoltaic cells in a photovoltaic power station; 2) Constraints on the number of wind turbines: ； In the formula, The upper limit of the number of wind turbines in a wind power station; 3) Upstream storage capacity constraints of pumped storage power stations: ； In the formula, , , , They are the upper limits of the upstream and downstream storage volumes of fixed-speed and variable-speed pumped-storage power stations, respectively; 4) Constraints on installed capacity of pumped storage power stations: ； In the formula, , They are the upper limits of the installed capacity of turbines in fixed-speed and variable-speed pumped-storage power stations, respectively. Taking the actual situation into consideration, under the premise of simplifying the model, the downstream reservoir of the pumped-storage power station is regarded as the sea level with unlimited storage capacity.

4. The multi-time scale optimization method of a pumped storage-virtual power plant joint operation system according to claim 3 is characterized by: The lower optimization model of the pumped storage-virtual power plant joint operation system established in step 3 includes: Step 3.1, build a virtual power plant model: The goal of a virtual power plant is to maximize its total operating revenue, namely: 1) Revenue from electricity sales: ； 2) Battery degradation cost: ； 3) Demand response costs: ； 4) Power transmission cost: ； 5) Network loss cost: ； Where: The revenue from electricity sales of distributed energy; is the degradation loss of distributed energy storage; for demand response costs; is the transmission cost between operators; is the network loss cost within the virtual power plant; , Respectively represent i Photovoltaic and wind turbines t The effort of the moment; , Respectively represent i Energy storage units t The charging and discharging status at all times; express The transferable load of the time period; express Interruptible load during the period; Indicates the operator a With operators b between t Energy transfer power at the moment; is the line admittance; is the node power angle; , They represent photovoltaic respectively; represents the unit degradation cost of distributed energy storage; , represents the unit cost of demand response; represents the unit cost of electricity transmission; is the unit cost of network loss; Constraints of the virtual power plant model: 1) Scenery constraints: ； Where: , , , Respectively represent The upper and lower limits of the output of photovoltaic and wind turbines; 2) Distributed energy storage output constraints: ； Where: Indicates Energy storage devices in State of charge during the time period; , Respectively represent the upper and lower limits of the state of charge; Indicates the rated capacity of energy storage; , They represent the charging and discharging efficiency of energy storage respectively; , They represent the upper limits of charging and discharging of energy storage respectively; , represents the state constraints of energy storage; 3) Power transmission constraints between operators: ； Where: Indicates the maximum value of the transmission power; 4) Flexibility load constraints: ； Where: , Respectively represent the upper and lower limits of interruptible load; , Respectively represent the upper and lower limits of the transferable load; For fixed load; is the total load; 4) Power flow constraints: To facilitate calculation, the improved Distflow Model: ； Where: The first node in the AC power grid is The end node set of the branch; is the terminal node in the AC power grid The set of first nodes of the branches; For Node The injected power; 5) Power balance constraints: ； 6) Line flow constraints: ； ； ； Where: For Node n,m The admittance between For Node n The phase angle, is the phase angle of the equilibrium node; Step 3.2, build fixed / variable speed pumped storage operator model: The fixed / variable speed pumped storage operator model is a system consisting of fixed speed pumped storage units and variable speed pumped storage units. After the upper model determines its capacity, the operation stage includes the following objective functions: 1) Start-up and shutdown loss costs of pumped storage construction: ； ； Where: represents the total start-stop loss cost of the pumped storage system; , , , They represent the pumping and power generation conversion costs of fixed-speed pumped-storage units and the pumping and power generation conversion costs of variable-speed pumped-storage units respectively; , , , They represent the unit conversion costs of pumping and power generation conditions of fixed-speed pumped-storage units and the unit conversion costs of pumping and power generation conditions of variable-speed pumped-storage units respectively; , , , Respectively represent Pumping and power generation conditions of fixed and variable speed pumped storage units The state variable of the time period takes a Boolean value of 0 or 1. When it is 1, it means that the pumped storage unit is in this state; , Respectively represent the number of fixed-speed and variable-speed pumped storage units; 2) Pumped storage operation cost: ； ； Where: represents the total operating cost of the pumped storage system; , Respectively represent the operating costs of fixed-speed and variable-speed pumped storage units; , They are the unit operating costs of fixed-speed and variable-speed pumped storage units respectively; , , , Respectively The pumping and power generation conditions of fixed and variable speed pumped storage units are Output power during the time period; 3) Revenue from pumped storage electricity sales: ； ； Where: represents the total electricity sales revenue of the pumped storage system; , Respectively represent the electricity sales revenue of fixed-speed and variable-speed pumped storage units; is the electricity sales price of the pumped storage unit; 4) Pumped storage ancillary service income: ； ； Where: represents the total ancillary service revenue of the pumped storage system; , Respectively represent the ancillary service revenue of fixed-speed and variable-speed pumped storage units; The unit price of auxiliary services sold for pumped storage units. , , , Respectively represent Fixed and variable speed pumped storage units The upper and lower spinning reserve capacity of the time period; And also includes the following constraints: 1) Constraints of fixed-speed pumped storage units: ； ； Where: is the rated pumping power of the fixed-speed pumped-storage unit; , Respectively represent the minimum and maximum power generation of the fixed-speed pumped storage unit; 2) Constraints of variable speed pumped storage units: ； ； Where: , , , They represent the minimum pumping power, maximum pumping power, minimum generating power and maximum generating power of the variable speed pumped storage unit respectively; 3) Reservoir capacity constraints: ； Where: , Respectively represent the upper reservoir and the lower reservoir in Water storage capacity during the period; , , , They represent the maximum and minimum values ​​of the water storage capacity of the upper reservoir and the maximum and minimum values ​​of the water storage capacity of the lower reservoir respectively; is the unit time period; , They represent the power-flow conversion coefficient, which is a constant representing the ability of the pumped storage unit to convert power into flow; 4) Spinning reserve constraints: ； ； Step 3.3, construct a cooperative game model for the pumped storage-virtual power plant joint operation system: The Nash negotiation model is used to construct a cooperative game model of the joint operation system; The standard Nash negotiation model is as follows: ； Where: The benefits to the negotiating parties; The benefits of the participants before they cooperate, i.e. the negotiation breakdown point; Based on this, the following cooperative game model is constructed: ； In the formula, , represents the set of pumped storage operators and virtual power plant operators; , They represent the profits after the cooperation between the pumped storage operator and the virtual power plant operator; , They respectively represent the income before the cooperation game between the pumped storage operator and the virtual power plant operator.

5. The multi-time scale optimization method of a pumped storage-virtual power plant joint operation system according to claim 4, characterized in that: In step 4, the non-cooperative game model of the joint operation system considering the virtual player "Nature" is constructed, including: The output of distributed power sources is regarded as a special decision variable to characterize uncertainty, and "Nature" is introduced as the corresponding virtual subject. After obtaining the configuration information calculated by the upper model, each operator designs an output strategy with the lowest operating cost and passes the output strategy to the virtual game player "Nature". After learning the capacity and output strategy information of each unit, Nature interferes with the planning of each operator, making each operator make a new decision. The specific process of "Nature" participating in the game is as follows: The robust optimization method is used to deal with the uncertainty of renewable energy output. The fluctuation range of output is described by the uncertainty interval. Considering that renewable energy is wind and solar power generation, when the external environment is determined, its output will only depend on the installed capacity. That is, after determining the fluctuation range of wind and solar power, the specific description of the uncertainty interval is as follows: ； Where: and They are respectively the lower and upper limits of the proportion of new energy output to new energy capacity.

6. The multi-time scale optimization method of a pumped storage-virtual power plant joint operation system according to claim 5, characterized in that: In step 5, the improved cuckoo algorithm solution model is constructed: The cuckoo search algorithm is a meta-heuristic algorithm based on the special reproduction method of cuckoos and the evolution of Levy flight, which aims to quickly find the global optimal solution. In order to facilitate the establishment of the cuckoo search algorithm model, three assumptions are proposed, as follows: (1) The way cuckoos choose their nests is completely random, and each cuckoo lays only one egg at a time, representing a set of solutions to the problem; (2) Among these bird nests, some contain high-quality eggs, which are better solutions. These nests will be retained and hatch the next generation; (3) Assuming the probability of the host discovering foreign bird eggs, once discovered, the host will directly abandon the egg or nest, but the total number of bird nests remains unchanged; According to the characteristics of Levy flight, the position and search path formula of the cuckoo search algorithm are as follows: ； Where: represents a set of solutions to a problem; is the step factor, which is a number greater than zero; are the flight path parameters, is the flight coefficient, , is a normally distributed random number, ; In addition, the traditional cuckoo algorithm is improved as follows: Firstly, a dynamic Levy step factor is used. The large step size in the early stage of the algorithm speeds up the global search speed, and the smaller step size in the later stage improves the accuracy of the algorithm in the local search. Secondly, the idea of ​​genetic algorithm is borrowed to introduce a mutation mechanism in the algorithm evolution process to further increase the population diversity. In order to find a balance between search accuracy and search speed, the Levy step size control amount of the standard cuckoo algorithm is improved as follows: ； Where: is the Levy step size factor; is the minimum step size factor; is the current iteration number, is the maximum number of iterations; The genetic algorithm idea is used to mutate the best individual of each generation of the cuckoo algorithm to further improve the quality of the individual, and the mutation step length decreases with the increase of the number of iterations. The mutation mechanism is as follows: ； Where: It is the best bird nest of the contemporary era; is the location of the bird's nest after mutation; To change the asynchronous length; It follows a standard normal distribution.

7. The multi-time scale optimization method of a pumped storage-virtual power plant joint operation system according to claim 6, characterized in that: The cuckoo algorithm solution process in step 5 is: Step 1: Let the capacity of wind, solar and pumped storage units be the bird's nest, initialize the location information of the bird's nest, and initialize the electricity price information, initial load information, parameters related to the virtual power plant operator and parameters related to the pumped storage operator; Step 2: Solve the objective function of the upper model, calculate the optimal capacity of wind and solar units and pumped storage units, and pass it to the lower model; Step 3: After the lower-level model obtains the capacity information of the wind and solar units and the pumped storage units, the virtual power plant operator obtains the output strategy of energy storage and flexible load that optimizes its own economic benefits based on the wind and solar output strategy obtained in the previous round of game, and passes it to the virtual game player "Nature". After obtaining the output strategy of energy storage and flexible load, the virtual game player "Nature" updates the wind and solar output with the goal of minimizing its own economic benefits, and then passes it to the virtual power plant operator. The above operations are repeated until all outputs remain unchanged and the game reaches equilibrium; Step 4: Take the sum of the objective functions of the upper and lower double-layer models as the optimal fitness of the bird's nest and update the position of the bird's nest; Step 5: Repeat Step 2-Step 4 until the maximum number of iterations is reached, and output the optimal planning scheme for the optimal wind, solar, and pumped storage units.

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