Capacity configuration method and system based on energy storage participating in spot joint market income

Abstract

The invention discloses a capacity configuration method and system based on participation of energy storage in spot joint market income, and the method comprises the steps: building a capacity planning model framework based on participation of an energy storage power station in electric power spot joint market income based on consideration of uncertainty conditions of wind power output; establishing a bidding model that the energy storage power station participates in the day-ahead energy-frequency modulation combined market under a master-slave game bidding strategy, and calculating the optimal configuration of an economic benefit guide energy storage power station capacity planning model; converting the bidding model into a single-layer equilibrium constraint mathematical programming problem according to a strong duality theory and a KKT condition; and solving the global energy storage power station capacity planning model by adopting a mode of nesting a CPLEX solver in a particle swarm algorithm. The method has the beneficial effects that the economic benefit of the energy storage power station can be maximized through scientific capacity configuration and a market bidding strategy, the market competitiveness of the energy storage power station is effectively improved, and wide application of an energy storage technology in a modern power system is promoted.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage power station configuration optimization, and specifically to a capacity configuration method and system based on the benefits of energy storage participating in a spot joint market. Background Art

[0002] Driven by the "dual carbon" goal, new energy is developing rapidly and the proportion of installed capacity is increasing. As a flexible resource, energy storage can not only cope with the uncertainty of new energy output, but also provide different types of services when deployed in different locations of the power grid and play a corresponding role in the safe and stable operation of the power grid. Therefore, in order to ensure the safe, economical, stable and flexible operation of the power system, the demand for energy storage construction has increased significantly.

[0003] The implementation plan for the development of new energy storage proposes to promote the coordinated optimization of new energy storage with new energy and conventional power sources, and to support new power systems with new energy storage. There are two main forms of energy storage applied to power grids: centralized energy storage and distributed energy storage. Centralized energy storage is achieved by building energy storage power stations. At present, research on energy storage is focused on the source, grid and load side. Existing technologies propose to improve the absorption of new energy by establishing a two-layer optimization configuration model of energy storage power stations and microgrids / integrated energy systems. On the grid side, the regulation capacity and flexibility of the power grid are improved by combining distributed energy storage with thermal power units for joint frequency modulation. Apply energy storage power stations to the economic optimization scheduling on the user side to reduce the user's electricity costs.

[0004] In the research on energy storage capacity configuration, energy storage is configured on the source, grid and load sides to promote the consumption of new energy, achieve friendly grid connection of new energy, improve the safety and flexibility of the power grid, and reduce the electricity costs of users. However, the above research on energy storage capacity configuration is mostly focused on the capacity configuration method that meets the technical requirements of energy storage. In view of the problems that restrict the commercial development of energy storage in energy storage construction, such as long investment recovery period, high cost and low benefit, it is necessary to take into account the improvement of energy storage economic benefits in the research on the reasonable configuration of energy storage resources. Summary of the invention

[0005] In view of the above-mentioned problems, the present invention is proposed.

[0006] Therefore, the technical problem solved by the present invention is that most existing studies focus on optimizing the bidding operation strategy of energy storage power stations in the electricity spot market under a certain plan, but there is insufficient research on how energy storage power stations can achieve optimal configuration of planned capacity based on the optimal bidding strategy in the spot market environment.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions: a capacity configuration method based on the benefits of energy storage participating in the spot joint market, comprising:

[0008] Taking into account the uncertainty of wind power output, a capacity planning model framework based on the benefits of energy storage power stations participating in the electricity spot joint market is constructed;

[0009] Establish a bidding model for energy storage power stations to participate in the day-ahead energy-frequency regulation joint market under the master-slave game bidding strategy, and calculate the optimal configuration of the energy storage power station capacity planning model guided by economic benefits;

[0010] According to the strong duality theory and KKT conditions, the bidding model is transformed into a single-layer equilibrium constraint mathematical programming problem;

[0011] The particle swarm algorithm is nested with the CPLEX solver to solve the global energy storage power station capacity planning model.

[0012] As a preferred solution of the capacity configuration method based on the benefits of energy storage participating in the spot joint market described in the present invention, wherein: the capacity planning model framework based on the benefits of energy storage power stations participating in the electricity spot joint market includes, on the basis of taking into account the typical output scenarios of new energy units, establishing a capacity planning model framework for energy storage power stations, wherein the goal is to consider the investment C within the planning period inv 、Maintenance feeM ess and operating income R sm The annual operating cost is minimized; the composition and optimization process of the framework will be elaborated in detail through the energy storage power station bidding model.

[0013] As a preferred solution of the capacity configuration method based on the benefits of energy storage participating in the spot joint market described in the present invention, wherein: the bidding model of the energy storage power station participating in the day-ahead energy-frequency regulation joint market under the master-slave game bidding strategy includes that the energy storage power station participates in the electricity spot market bidding as an independent market entity, and its bidding and quotation process is coupled with the market clearing process of the power system dispatching center, which is converted into a master-slave game problem;

[0014] Among them, the energy storage power station operator, as the main player in the game, aims to maximize the benefits of the energy storage power station in the joint market. On the basis of considering its own charging and discharging power, state of charge, declared power, and declared price constraints, it fully considers the market's reaction to its own bidding plan, predicts the possible clearing situation in the power market in advance, and formulates the best bidding plan as the best bidding strategy;

[0015] As a game player, the power system dispatching center aims to minimize the market electricity purchase cost. When determining the electricity purchase plan, it uses the electricity prices declared by various market players as known conditions and takes into account the safety constraints of power grid operation.

[0016] As a preferred solution of the capacity configuration method based on the benefits of energy storage participating in the spot joint market described in the present invention, wherein: the grid operation safety constraints taken into account include the constraints of the bidding decision model of the energy storage power station of the game subject and the market clearing model constraints of the power system dispatching center of the game slave;

[0017] The constraints of the bidding decision model of the energy storage power station of the game subject include the power reported by the energy storage power station, the price reported by the energy storage power station, the charging and discharging power of the energy storage power station, the capacity and power ratio of the energy storage power station, the charging and discharging state of the energy storage power station, and the state of charge of the energy storage power station.

[0018] The market clearing model of the power system dispatching center of the game slave body adopts a joint clearing method to minimize the market power purchase cost and construct the objective function of the game slave body for the clearing problem of the day-ahead power energy market and the day-ahead auxiliary service market in the power market.

[0019] In the bidding strategy for energy storage power stations, the game subject model will decide the quantity and price pairs of energy storage in each market and pass them to the game slave model. The slave model will decide the clearing electricity price of each market and the winning bid quantity of energy storage in each market and pass them back to the subject model. The subject model will then adjust the strategy and pass it downward until equilibrium is reached and the game ends.

[0020] As a preferred solution of the capacity configuration method based on the benefits of energy storage participating in the spot joint market described in the present invention, wherein: the conversion of the bidding model into a single-layer equilibrium constrained mathematical programming problem according to the strong duality theory and the KKT condition includes that the capacity planning model of the energy storage power station can be regarded as a two-layer planning problem, wherein the upper-layer model optimizes the capacity configuration of the energy storage power station with the goal of minimizing the annual operating cost of the energy storage power station, and the lower-layer model optimizes the bidding strategy of the energy storage power station participating in the electricity spot joint market through master-slave game; the two-layer model is solved by using a particle swarm algorithm nested in a CPLEX solver, that is, the upper-layer energy storage power station planning model is based on the converted lower-layer bidding model and the benefit value obtained by calling the CPLEX solver to obtain the optimal planned capacity and power.

[0021] As a preferred solution of the capacity configuration method based on the benefits of energy storage participating in the spot joint market described in the present invention, the method of nesting the CPLEX solver with the particle swarm algorithm to solve the global energy storage power station capacity planning model includes solving the energy storage power station capacity planning model based on the particle swarm optimization algorithm, firstly initializing a group of random particles to represent the planned capacity and power of the energy storage power station, and then calculating the fitness of each particle, based on which the individual optimal solution and the global optimal solution are screened out, and then iterating continuously to find the optimal solution; in each iteration, the particle updates itself by tracking the above two extreme values, and the formula is expressed as:

[0022]

[0023] ν jd =wν jd +c1r1(p jd -x jd )+c2r2(g jd -x jd

[0024] x jd =x jd +ν jd

[0025] Where w represents the inertia factor; C1 and C2 represent the acceleration factors; r1 and r2 represent random numbers between the interval [0,1]; p jd represents the d-th dimension component in the optimal position vector of the j-th particle; g jd Represents the d-th dimension component in the population's optimal position vector.

[0026] As a preferred solution of the capacity configuration method based on the benefits of energy storage participating in the spot joint market described in the present invention, wherein: the solution of the global energy storage power station capacity planning model includes the solution of the master-slave game model, and the master-slave game bidding model of the energy storage power station participating in the power spot joint market can be described by a double-layer model. When solving the double-layer model, the double-layer model is a mixed integer nonlinear problem, and the KKT condition is used to first convert the double-layer model into a single-layer model;

[0027] Since there is a product term of the decision variable and the dual variable in the complementary relaxation constraint, the big M method is used to perform precise linear processing on it, and the resulting formula is expressed as:

[0028]

[0029] Wherein, M represents a sufficiently large constant; v g,t represents binary variables; there are a large number of nonlinear terms of the product of two variables in the objective function of the single-layer MPEC model, so the strong duality theorem is used to deal with it; through the KKT condition and linearization processing, the model is finally transformed into a mixed integer linear programming problem.

[0030] Another object of the present invention is to provide a capacity configuration system based on the benefits of energy storage participating in the spot joint market, wherein: a data acquisition and monitoring module collects data from the energy storage system, renewable energy resources, and the electricity spot market in real time, including electricity prices, power generation, demand, and energy storage status, for real-time decision-making and strategy adjustment;

[0031] The capacity optimization and planning module uses a two-level planning model to optimize the capacity configuration of the energy storage system. The upper model focuses on capacity configuration, while the lower model deals with the bidding strategy of the energy storage system participating in the day-ahead energy market and frequency regulation market;

[0032] The bidding strategy and market interaction module interacts with the electricity spot market to formulate the bidding strategy of the energy storage system in the energy market and frequency regulation market;

[0033] The simulation and verification module verifies the effectiveness of the optimization model through simulations of different scenarios, and tests how uncertain factors such as new energy penetration and market fluctuations affect the revenue and capacity configuration of the energy storage system.

[0034] A computer device includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of a capacity configuration method based on the benefits of energy storage participating in a spot joint market.

[0035] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a capacity configuration method based on the benefits of energy storage participating in a spot joint market.

[0036] Beneficial effects of the present invention: The energy storage power station capacity optimization configuration method based on the optimal bidding strategy proposed in this article not only provides an innovative solution to the problem of investment recovery of energy storage power stations, but also provides theoretical support and practical guidance for the efficient operation of energy storage power stations in the electricity spot market. Research has shown that energy storage power stations can not only maximize their economic benefits through scientific capacity configuration and market bidding strategies, but also effectively improve their market competitiveness, thereby promoting the widespread application of energy storage technology in modern power systems. This research provides an important reference for future investment and operation decisions of energy storage power stations, and demonstrates the key role of energy storage power stations under the high proportion of renewable energy access. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0038] Figure 1 An overall flow chart of a capacity configuration method based on energy storage participating in a spot joint market revenue provided by the first embodiment of the present invention;

[0039] Figure 2A master-slave game bidding strategy model for energy storage power stations participating in the day-ahead electric energy-frequency regulation joint market based on a capacity configuration method for energy storage participating in the spot joint market revenue provided in the first embodiment of the present invention;

[0040] Figure 3 A flow chart of a particle swarm optimization algorithm based on bi-level programming for a capacity configuration method based on energy storage participating in a spot joint market revenue provided by the first embodiment of the present invention;

[0041] Figure 4 A comparison chart of annual net income of energy storage power stations with different capacity configurations in two scenarios according to a capacity configuration method based on energy storage participating in the spot joint market income provided by the second embodiment of the present invention;

[0042] Figure 5 A comparison chart of the day-ahead clearing electricity prices in various markets under two scenarios of a capacity configuration method based on energy storage participating in the spot joint market revenue provided in the second embodiment of the present invention;

[0043] Figure 6 A schematic diagram of the bidding strategy of energy storage power stations in various markets on the day before under scenario 1 of a capacity configuration method based on energy storage participating in spot joint market benefits provided in a second embodiment of the present invention. DETAILED DESCRIPTION

[0044] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0045] Example 1, reference Figure 1-3 , which is an embodiment of the present invention, provides a capacity configuration method based on the benefits of energy storage participating in the spot joint market, including:

[0046] S1: Taking into account the uncertainty of wind power output, a capacity planning model framework is constructed based on the benefits of energy storage power stations participating in the electricity spot joint market.

[0047] The main goal of optimizing the capacity of energy storage power stations in this paper is to make full use of the independent market entity status of energy storage and the technical advantages of rapid charging and discharging, actively increase the clearing volume and overall net profit level by participating in the bidding in the day-ahead electricity energy-frequency regulation joint market, and thus guide the rational investment in energy storage.

[0048] As an important flexibility support for the operation of the power system, the benefits of energy storage are closely related to the system flexibility requirements affected by the fluctuation level of the output of new energy units. Therefore, based on the typical output scenario of new energy units, the framework of the energy storage power station capacity planning model is established as shown in the following formula:

[0049]

[0050] st H(U,M,Ψ)=0

[0051] G(U,M,Ψ)≥0

[0052] P(U,M,Ψ)≥0

[0053] Among them, the goal is to consider investing C during the planning period inv 、Maintenance feeM ess and operating income R sm The annual operating cost is the smallest. sce is the number of typical output scenarios of new energy units. If the probability corresponding to the kth typical output scenario is P(k), then the number of days corresponding to this scenario in a year is T k As shown in formula (2). U is a state variable, including the charging state and discharging state of the energy storage power station; M is a control variable, including the winning bid output of the energy storage power station and thermal power unit, the joint market clearing price, and the declared electricity price of the energy storage power station participating in the joint market; Ψ is a decision variable, including the configuration capacity and maximum charging and discharging power of the energy storage power station. Constraint G represents the relevant technical constraints in the operation of the energy storage power station, and constraint P represents the constraints of the power market clearing process.

[0054] T k =365P(k)

[0055] C im =η P P max +η E E max

[0056] M ess =η M P max

[0057] Among them, C inv is the annual investment cost of the energy storage power station. ess is the annual maintenance cost. max is the maximum capacity of the energy storage power station, P max is its maximum charge and discharge power; η P and η E are the power cost and capacity cost of the energy storage power station respectively; η M is the maintenance cost of the energy storage power station. It should be noted that R smAfter the energy storage power station participates in the electricity spot joint market bidding, the comprehensive daily income in a typical new energy output scenario, its composition and optimization process will be elaborated in detail through the energy storage power station bidding model.

[0058] S2: Establish a bidding model for energy storage power stations to participate in the day-ahead energy-frequency regulation joint market under the master-slave game bidding strategy, and calculate the economic benefits to guide the optimal configuration of the energy storage power station capacity planning model.

[0059] Furthermore, as an independent market player, energy storage power stations participate in the electricity spot market bidding. There is a coupling relationship between their bidding and quotation process and the market clearing process of the power system dispatching center. Therefore, it can be regarded as a master-slave game problem. Among them, the energy storage power station operator, as the game subject, aims to maximize the benefits of the energy storage power station in the joint market. It fully considers the market's reaction to its own bidding plan on the basis of considering its own charging and discharging power, state of charge, declared power, declared price and other constraints, predicts the possible clearing situation in the power market in advance, and formulates the optimal bidding plan as its optimal bidding strategy. As a game slave, the power system dispatching center aims to minimize the market power purchase cost. When determining the power purchase plan, it takes the declared electricity price of each market player as a known condition, takes into account the safety constraints of the power grid operation, and makes decisions that are conducive to maximizing the interests of the whole society.

[0060] Game subject - Energy storage power station bidding decision model, energy storage power station participates in the day-ahead electricity energy-frequency regulation joint market, and benefits from "low charging and high discharge" in the day-ahead electricity energy market, and benefits from providing upward and downward frequency regulation capacity in the day-ahead frequency regulation auxiliary service market. Therefore, the bidding decision target of the energy storage power station is the comprehensive daily income R in the above two markets. sm Maximum, the formula is:

[0061]

[0062] Where T is the number of dispatching periods of the energy storage power station in a day, 24h; λ t , and They are the upper and lower frequency regulation clearing electricity prices in the day-ahead electric energy market and the day-ahead frequency regulation ancillary service market respectively; and They are respectively the charging and discharging power of the energy storage power station that won the bid at time t in the day-ahead electricity market; and They are respectively the upper and lower frequency regulation capacities of the energy storage power station in the charging state that won the bid at time t in the day-ahead frequency regulation auxiliary service market; and They are respectively the upper and lower frequency regulation capacities in the discharge state of the energy storage power station that won the bid at time t in the day-ahead frequency regulation auxiliary service market.

[0063] The bidding decision-making process of energy storage power stations should meet the following constraints: power constraints declared by energy storage power stations, price constraints declared by energy storage power stations, charging and discharging power constraints of energy storage power stations, capacity and power ratio constraints of energy storage power stations, charging and discharging state constraints of energy storage power stations, and state of charge constraints of energy storage power stations. Specifically, they include:

[0064] Energy storage power stations report power constraints.

[0065]

[0066] in, and They are respectively the charging and discharging power reported by the energy storage power station at time t in the day-ahead electricity market; and They are respectively the upper and lower frequency regulation capacities of the charging state reported by the energy storage power station at time t in the day-ahead frequency regulation auxiliary service market; and They are respectively the upper and lower frequency regulation capacities in the discharge state reported by the energy storage power station at time t in the day-ahead frequency regulation auxiliary service market; and They are 0-1 variables for the charging and discharging status of the energy storage power station.

[0067] Energy storage power station declared price constraints:

[0068] 0≤α s,t ≤α max

[0069]

[0070] Among them, α s,t The charging and discharging electricity price reported by the energy storage power station at time t in the day-ahead electricity market; They are the upper and lower frequency regulation capacity prices of the charging state reported by the energy storage power station in the frequency regulation auxiliary service market at time t; are the upper and lower frequency regulation capacity prices of the energy storage power station in the discharge state reported at time t in the frequency regulation auxiliary service market; α max The upper limit of the price that energy storage power stations can bid in the electricity market.

[0071] Energy storage power station charging and discharging power constraints.

[0072]

[0073]

[0074] Energy storage power station capacity and power ratio constraints:

[0075] E max =2P max

[0076] Energy storage power station charging and discharging state constraints:

[0077]

[0078] The charge state constraint formula of the energy storage power station is expressed as:

[0079]

[0080] E init =E 24

[0081] Among them, E t is the energy stored in the energy storage power station at time t; E init Initial storage energy for the energy storage power station; E min is the minimum energy that can be stored in the energy storage power station; η ch and η dis They are the charging efficiency and discharging efficiency of the energy storage power station respectively; u is the self-discharge rate of the energy storage power station, which can generally be ignored; Δt is the duration of the unit scheduling period.

[0082] It should be noted that the game slave - the power system dispatching center market clearing model, aims at the clearing problem of the day-ahead electricity energy market and the day-ahead ancillary service market in the power market. This paper adopts a joint clearing method to construct the objective function of the game slave with the goal of minimizing the market electricity purchase cost.

[0083]

[0084] Where N is the number of thermal power units; β g,t and are the bids of thermal power unit g in the day-ahead electricity market and frequency regulation auxiliary service market at time t respectively; P g,t is the winning bid amount of thermal power unit g at time t in the day-ahead electricity market; and They are respectively the upper and lower frequency regulation capacities won by thermal power unit g in the day-ahead frequency regulation auxiliary service market at time t.

[0085] S3: According to the strong duality theory and KKT conditions, the bidding model is transformed into a single-layer equilibrium constrained mathematical programming problem.

[0086] Furthermore, the energy storage power station capacity planning model in this paper can be regarded as a two-level planning problem, in which the upper-level model optimizes the energy storage power station capacity configuration with the goal of minimizing the annual operating cost of the energy storage power station, and the lower-level model optimizes the bidding strategy of the energy storage power station participating in the electricity spot joint market through master-slave game. The particle swarm algorithm is nested in the CPLEX solver to solve the two-level model, that is, the upper-level energy storage power station planning model is based on the converted lower-level bidding model. The benefit value obtained by calling the CPLEX solver is continuously iterated and updated to obtain the optimal planning capacity and power.

[0087] Furthermore, since the capacity planning of energy storage power stations is usually carried out on an annual basis, this paper adopts typical scenarios of renewable energy output in a year to characterize the uncertainty of its output.

[0088] This paper takes wind power as an example. First, based on the historical annual wind power output data at different times, different wind speeds and different weather conditions in the region, the Gaussian distribution is used to predict the future wind power output. Then, the Monte Carlo method is used to simulate the uncertainty of wind power output and generate a large number of wind power output samples. The power system dispatching center should meet the following constraints for market clearing: Thermal power unit ramp constraints:

[0089]

[0090] in, and The maximum downward and upward ramping power of the thermal power unit, respectively. The winning power constraint of the thermal power unit, the coupling constraint of the power energy and reserve capacity of the thermal power unit:

[0091]

[0092] Power constraints for winning bids for energy storage power stations.

[0093]

[0094] Coupling constraints between the electric energy and reserve capacity of energy storage power stations, and balancing constraints on the frequency regulation capacity of the ancillary service market.

[0095]

[0096] It should be noted that k-means clustering is finally used to obtain typical scenarios and corresponding expected weights to represent wind power output in a year, so as to realize the construction of energy storage power station capacity planning model based on the obtained typical scenarios.

[0097] S4: The particle swarm algorithm is nested with the CPLEX solver to solve the global energy storage power station capacity planning model.

[0098] Furthermore, the particle swarm algorithm is nested in the CPLEX solver to solve the global energy storage power station capacity planning model. The energy storage power station capacity planning model is solved based on the particle swarm optimization algorithm. First, a group of random particles are initialized to represent the planned capacity and power of the energy storage power station. Then, the fitness of each particle is calculated. Based on this, the individual optimal solution and the global optimal solution are screened out, and then the optimal solution is found through continuous iteration. In each iteration, the particle updates itself by tracking the above two extreme values. The formula is expressed as:

[0099]

[0100] Where w represents the inertia factor; C1 and C2 represent the acceleration factors; r1 and r2 represent random numbers between the interval [0,1]; p jd represents the d-th dimension component in the optimal position vector of the j-th particle; g jd Represents the d-th dimension component in the population's optimal position vector.

[0101] Furthermore, the solution of the global energy storage power station capacity planning model includes the solution of the master-slave game model. The master-slave game bidding model of the energy storage power station participating in the electricity spot joint market can be described by a two-layer model. When solving the two-layer model, it is a mixed integer nonlinear problem. The KKT condition is used to first convert the two-layer model into a single-layer model.

[0102] Furthermore, since there is a product term of the decision variable and the dual variable in the complementary relaxation constraint, the big M method is used to perform precise linear processing on it, and the resulting formula is expressed as:

[0103] 0≤ μ g,t ≤M v g,t

[0104]

[0105] Wherein, M represents a sufficiently large constant; v g,t represents binary variables; there are a large number of nonlinear terms of the product of two variables in the objective function of the single-layer MPEC model, so the strong duality theorem is used to deal with it; through the KKT condition and linearization processing, the model is finally converted into a mixed integer linear programming problem for solution.

[0106] Furthermore, there are a lot of nonlinear terms of the product of two variables in the objective function of the single-layer MPEC model, so the strong duality theorem is used to deal with them:

[0107]

[0108] It should be noted that the model is finally transformed into a mixed integer linear programming problem through KKT conditions and linearization processing. Combining the energy storage power station capacity planning model framework and the transformed master-slave game model, the solution process of energy storage power station capacity planning is as follows: Figure 3 As shown.

[0109] Example 2, reference Figure 4-Figure 6 , which is an embodiment of the present invention, provides a capacity configuration method based on the benefits of energy storage participating in the spot joint market. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0110] To verify the effectiveness of the proposed model, this paper sets up a power simulation system including 6 thermal power units G1-G6, 1 energy storage power station Es, and 1 wind farm W1 with an installed capacity of 10MW. The 6 thermal power units and the energy storage power station participate in the electricity spot joint market bidding on the power supply side. The initial state of charge of the energy storage power station is set to 0.1, the maximum and minimum states of charge are set to 0.9 and 0.1 respectively, the charging and discharging efficiency is set to 0.9, the capacity cost is set to 1.1 million yuan / (MW·h), the power cost is set to 1 million yuan / (MW·h), the operation and maintenance cost is set to 72,000 yuan / (a·MW), and the life payback period is set to 10 years[5]. The declared parameters of the thermal power units are detailed in Appendix Table B1. The wind power output samples generated by the Monte Carlo method and the four typical scenarios and probabilities obtained by k-means clustering are detailed in Appendix Figures B1 and B2 respectively.

[0111] Results and analysis of optimal configuration of energy storage power station capacity,In order to illustrate the effect of the proposed method on the optimal configuration of energy storage power station capacity, the following two scenarios are set for comparative analysis.

[0112] Scenario 1: The model background of this article, that is, energy storage power stations participate in the bidding in the electricity spot joint market as pricing participants.

[0113] Scenario 2: Comparison scenario, that is, the energy storage power station acts as a price acceptor and does not participate in the electricity spot joint market bidding.

[0114] 5.2.1 Capacity configuration results and net benefit comparison

[0115] The particle swarm algorithm is nested in CPLEX to solve the capacity configuration and annual net income of the energy storage power station under the two scenarios, as shown in the figure: Figure 4 As shown in the figure, the optimal capacity configuration in scenario 1 is 105.9MW, corresponding to a maximum annual net income of 1.451 million yuan. The optimal capacity configuration in scenario 2 is 8.2MW, and the maximum annual net income is 187,000 yuan.

[0116] Figure 4 Comparison of annual net benefits of energy storage power stations with different capacity configurations in two scenarios. Figure 4It can be seen that in both scenarios, the annual net income level corresponding to the optimal energy storage capacity is the largest. At the same time, the annual net income of scenario 1 is much higher than that of scenario 2, that is, after the energy storage power station participates in the electricity spot joint market bidding as an independent entity, it can significantly accelerate the cost recovery of the energy storage power station operator. The following detailed analysis of the bidding strategy explains the main reasons for the differences in the capacity determination results and income levels of the two scenarios.

[0117] Taking the wind power scenario 1 with the highest probability as the base date, a comparative analysis is made on the operation strategies and output results of the energy storage power station under the above two scenarios to explain the reasons for the different economic benefits of the two scenarios.

[0118] Comparison of overall output share and revenue of various markets recently

[0119] Table 1 Comparison of output ratio and revenue of energy storage power stations in two scenarios

[0120]

[0121] As can be seen from Table 1, the proportion of energy storage power stations in the overall winning bids in various markets under scenario 1 is much higher than the proportion of energy storage power stations in the overall dispatched output in various markets under scenario 2. This is because energy storage power stations participating in the electricity spot market bidding as independent entities will reduce the number of high-priced thermal power units winning bids and increase their own charging and discharging power. In the electric energy market, thermal power units are still the main force in the supply of electric energy, and their output can basically meet the system load demand. Therefore, the proportion and income of energy storage output in the electric energy market are relatively low. However, in the frequency regulation auxiliary service market, energy storage, as a flexible resource, can quickly change its own charging and discharging state and flexibly provide up and down frequency regulation capacity. Therefore, its proportion and income in the frequency regulation auxiliary service market have increased significantly.

[0122] Comparison of the clearing prices of various markets a day ago. The clearing prices of various markets a day ago under the two scenarios are as follows: Figure 5 shown. Figure 5 Comparison of the day-ahead clearing electricity prices in various markets under the two scenarios.

[0123] In scenario 1, the bidding strategies of energy storage power stations in various markets on the day before are as follows: Figure 6 shown. Figure 6 The bidding strategy of energy storage power stations in various markets on the day before under scenario 1.

[0124] from Figure 5 It can be seen that energy storage power stations participating in the electricity spot market bidding as independent entities will reduce the system clearing electricity price. Figure 6 It can be seen that in the electric energy market, the energy storage power station is in a discharging state at 11-12 and 18-19, and the quotes are both 0, corresponding to Figure 5At these times, the clearing electricity price of scenario 1 is lower than that of scenario 2. In the frequency regulation auxiliary service market, the frequency regulation quotation of the energy storage power station at each time is equal to the clearing electricity price, corresponding to Figure 5 At 11-13 and 16-17, the upward frequency adjustment clearing electricity price in scenario 1 was lower than that in scenario 2. The above shows that when the energy storage power station is charging and discharging, the market bid volume is increased by adjusting its own quotation, thereby reducing the bid volume of high-priced thermal power units, promoting market competition, and reducing the overall market clearing electricity price.

[0125] Embodiment 3, the following is an embodiment of the present invention, which provides a computing platform load balancing system based on a particle swarm genetic algorithm, including: a system based on a capacity configuration method for energy storage participating in a spot joint market income,

[0126] The data acquisition and monitoring module collects data in real time from the energy storage system, renewable energy resources and the electricity spot market, including electricity prices, power generation, demand and energy storage status, for real-time decision-making and strategy adjustment.

[0127] The capacity optimization and planning module uses a two-level planning model to optimize the capacity configuration of the energy storage system. The upper model focuses on capacity configuration, while the lower model deals with the bidding strategy of the energy storage system participating in the day-ahead energy market and frequency regulation market.

[0128] The bidding strategy and market interaction module interacts with the electricity spot market to formulate bidding strategies for energy storage systems in the energy market and frequency regulation market.

[0129] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0130] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0131] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0132] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc. It should be noted that the above embodiments are only used to illustrate the technical solution of the present invention and are not limited. Although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention, which should be included in the scope of the claims of the present invention.

[0133] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A capacity allocation method based on the benefits of energy storage participating in the spot joint market, characterized in that: include: Taking into account the uncertainty of wind power output, a capacity planning model framework based on the benefits of energy storage power stations participating in the electricity spot joint market is constructed; Establish a bidding model for energy storage power stations to participate in the day-ahead energy-frequency regulation joint market under the master-slave game bidding strategy, and calculate the optimal configuration of the energy storage power station capacity planning model guided by economic benefits; According to the strong duality theory and KKT conditions, the bidding model is transformed into a single-layer equilibrium constraint mathematical programming problem; The particle swarm algorithm is nested with the CPLEX solver to solve the global energy storage power station capacity planning model.

2. The capacity configuration method based on the benefits of energy storage participating in the spot joint market as claimed in claim 1 is characterized by: The capacity planning model framework based on the benefits of energy storage power stations participating in the electricity spot joint market includes establishing a capacity planning model framework for energy storage power stations based on typical output scenarios of new energy units, wherein the goal is to consider investment C during the planning period. inv 、Maintenance feeM ess and operating income R sm The annual operating cost is minimized; the composition and optimization process of the framework will be elaborated in detail through the energy storage power station bidding model.

3. The capacity configuration method based on the benefits of energy storage participating in the spot joint market as claimed in claim 2 is characterized by: The bidding model of the energy storage power station participating in the day-ahead energy-frequency regulation joint market under the master-slave game bidding strategy includes that the energy storage power station participates in the electricity spot market bidding as an independent market entity, and its bidding and quotation process is coupled with the market clearing process of the power system dispatching center, which is converted into a master-slave game problem; Among them, the energy storage power station operator, as the main player in the game, aims to maximize the benefits of the energy storage power station in the joint market. On the basis of considering its own charging and discharging power, state of charge, declared power, and declared price constraints, it fully considers the market's reaction to its own bidding plan, predicts the possible clearing situation in the power market in advance, and formulates the best bidding plan as the best bidding strategy; As a game player, the power system dispatching center aims to minimize the market electricity purchase cost. When determining the electricity purchase plan, it uses the electricity prices declared by various market players as known conditions and takes into account the safety constraints of power grid operation.

4. The capacity configuration method based on energy storage participating in the spot joint market revenue according to claim 3 is characterized by: The grid operation safety constraints include the constraints of the bidding decision model of the energy storage power station as the main player and the market clearing model constraints of the power system dispatching center as the secondary player; The constraints of the bidding decision model of the energy storage power station of the game subject include the power reported by the energy storage power station, the price reported by the energy storage power station, the charging and discharging power of the energy storage power station, the capacity and power ratio of the energy storage power station, the charging and discharging state of the energy storage power station, and the state of charge of the energy storage power station. The market clearing model of the power system dispatching center of the game slave body adopts a joint clearing method to minimize the market power purchase cost and construct the objective function of the game slave body for the clearing problem of the day-ahead power energy market and the day-ahead auxiliary service market in the power market. In the bidding strategy for energy storage power stations, the game subject model will decide the quantity and price pairs of energy storage in each market and pass them to the game slave model. The slave model will decide the clearing electricity price of each market and the winning bid quantity of energy storage in each market and pass them back to the subject model. The subject model will then adjust the strategy and pass it downward until equilibrium is reached and the game ends.

5. The capacity configuration method based on the benefits of energy storage participating in the spot joint market as claimed in claim 4 is characterized by: The method of converting the bidding model into a single-layer equilibrium constrained mathematical programming problem according to the strong duality theory and the KKT condition includes that the capacity planning model of the energy storage power station can be regarded as a two-layer planning problem, wherein the upper-layer model optimizes the capacity configuration of the energy storage power station with the goal of minimizing the annual operating cost of the energy storage power station, and the lower-layer model optimizes the bidding strategy of the energy storage power station participating in the electricity spot joint market through the master-slave game; the two-layer model is solved by using the particle swarm algorithm nested in the CPLEX solver, that is, the upper-layer energy storage power station planning model is based on the converted lower-layer bidding model, and the benefit value obtained by calling the CPLEX solver is continuously iteratively updated to obtain the optimal planned capacity and power.

6. The capacity configuration method based on the benefits of energy storage participating in the spot joint market as claimed in claim 5 is characterized by: The method of nesting the CPLEX solver with the particle swarm algorithm to solve the global energy storage power station capacity planning model includes solving the energy storage power station capacity planning model based on the particle swarm optimization algorithm, firstly initializing a group of random particles to represent the planned capacity and power of the energy storage power station, then calculating the fitness of each particle, based on which the individual optimal solution and the global optimal solution are screened out, and then continuously iterating to find the optimal solution; in each iteration, the particle updates itself by tracking the above two extreme values, and the formula is expressed as: n jd =wν jd +c1r1(p jd -x jd )+c2r2(g jd -x jd x jd =x jd +n jd Where w represents the inertia factor; C1 and C2 represent the acceleration factors; r1 and r2 represent random numbers between the interval [0,1]; p jd represents the d-th dimension component in the optimal position vector of the j-th particle; g jd Represents the d-th dimension component in the population's optimal position vector.

7. The capacity configuration method based on energy storage participating in the spot joint market revenue according to claim 6 is characterized by: The solution of the global energy storage power station capacity planning model includes the solution of the master-slave game model. The master-slave game bidding model of the energy storage power station participating in the electricity spot joint market can be described by a double-layer model. When solving the double-layer model, the double-layer model is a mixed integer nonlinear problem. The KKT condition is used to first convert the double-layer model into a single-layer model. Since there is a product term of the decision variable and the dual variable in the complementary relaxation constraint, the big M method is used to perform precise linear processing on it, and the resulting formula is expressed as: Wherein, M represents a sufficiently large constant; ν g,t represents binary variables; there are a large number of nonlinear terms of the product of two variables in the objective function of the single-layer MPEC model, so the strong duality theorem is used to deal with it; through the KKT condition and linearization processing, the model is finally converted into a mixed integer linear programming problem for solution.

8. A system using the capacity configuration method based on energy storage participating in the spot joint market revenue as described in any one of claims 1 to 7, characterized in that: Data acquisition and monitoring module, which collects data from energy storage systems, renewable energy resources and the electricity spot market in real time, including electricity prices, power generation, demand and energy storage status, for real-time decision-making and strategy adjustment; The capacity optimization and planning module uses a two-layer planning model to optimize the capacity configuration of the energy storage system. The upper layer model focuses on capacity configuration, while the lower layer model deals with the bidding strategy of the energy storage system participating in the day-ahead energy market and frequency regulation market. The bidding strategy and market interaction module interacts with the electricity spot market to formulate the bidding strategy of the energy storage system in the energy market and frequency regulation market; The simulation and verification module verifies the effectiveness of the optimization model through simulations of different scenarios, and tests how uncertain factors such as new energy penetration and market fluctuations affect the revenue and capacity configuration of the energy storage system.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the capacity configuration method based on the benefits of energy storage participating in the spot joint market as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the capacity configuration method based on the benefits of energy storage participating in the spot joint market as described in any one of claims 1 to 7 are implemented.

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