Capacity market multi-energy pricing method and system considering power generation capacity adequacy

Abstract

The invention is suitable for the technical field of capacity market pricing, and provides a capacity market multi-energy pricing method and system considering power generation capacity adequacy. The method comprises the following steps of: constructing a first model by taking the maximum income of power generators of power generation types including thermal power generation, wind power generation, photovoltaic power generation, hydropower and large-scale energy storage power stations in a capacity market as a target; determining active power output constraints, total capacity constraints and power transmission line constraints of the thermal power generating unit, the wind power plant, the photovoltaic power station, the hydroelectric generating unit and the energy storage power station; constructing a second model by taking the maximum difference value between the income of the user in the daily spot transaction market and the cost of the generator as a target; determining a generator set power constraint and a user electric energy constraint; the first model is optimized and solved by using an improved genetic algorithm, and the second model is optimized and solved by using a dynamic programming method based on a duality theory, so that the clearing price corresponding to the active capacity of a power generator is obtained, and the effective operation of the power market is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of capacity market pricing, and in particular to a capacity market multi-energy pricing method and system taking into account the margin of power generation capacity. Background Art

[0002] As wind, light, water and other renewable energy sources are integrated into the power system at a high rate, the low cost of renewable energy generation has greatly challenged the cost recovery of traditional thermal power units. Renewable energy generation mainly depends on weather factors and cannot serve as a strong guarantee for power trading during peak load periods. Therefore, reasonable pricing in the capacity market is the key to ensuring sufficient capacity.

[0003] In the existing technology, when the capacity market pricing is unreasonable for traditional thermal power generation, the supply of power generation capacity may be reduced. When the power consumption peaks or the output of renewable energy is insufficient, the stability of the power system will be threatened, increasing the risk of power outages. For wind power and photovoltaic power, pricing that is too high or too low will affect the stability of the power grid. If it is too high, market participants who purchase capacity will bear unnecessary costs, and if it is too low, it will not encourage wind power and photovoltaic companies to invest in energy storage or complementary power generation facilities. Hydropower can quickly adjust the power generation according to demand in the power system to balance the supply and demand of the power grid. Unreasonable pricing cannot reflect this regulatory value, which will make it difficult for hydropower companies to obtain reasonable returns in the capacity market, and may cause them to reduce their participation in the capacity market, thereby affecting the ability of the power grid to respond to emergencies and adjust the power balance. Energy storage will also be affected under this unreasonable pricing mechanism, which may lead to the lack of sufficient economic motivation for the development of the energy storage industry and its inability to fully play its role in improving the continuity and stability of energy supply, ultimately making the entire power system more vulnerable to fluctuations in energy supply.

[0004] In view of this, a capacity market multi-energy pricing method and system that takes into account the surplus of power generation capacity is needed. Summary of the invention

[0005] The embodiments of the present application provide a capacity market multi-energy pricing method and system that takes into account the margin of power generation capacity, which is used to solve the problem of unstable reasonable pricing in the capacity market.

[0006] A first aspect of an embodiment of the present application provides a capacity market multi-energy pricing method taking into account the margin of power generation capacity, including:

[0007] A first model is constructed with the goal of maximizing the revenue of power generators in the capacity market, wherein the power generation types of the power generators include thermal power generation, wind power generation, photovoltaic power generation, hydropower and large-scale energy storage power stations;

[0008] Determining the constraint conditions of the first model, wherein the constraint conditions include active output constraints, total capacity constraints, and transmission line constraints of thermal power units, wind farms, photovoltaic power stations, hydropower units, and energy storage power stations;

[0009] A second model is constructed with the goal of maximizing the difference between the user's revenue in the daily spot trading market and the power generator's cost;

[0010] Determining constraint conditions of the second model, wherein the constraint conditions include power constraints of the generator set and power constraints of the user;

[0011] Using an improved genetic algorithm to optimize and solve the first model, and inputting the obtained optimal solution into the second model as a boundary condition;

[0012] The second model is optimized and solved based on a dynamic programming method of duality theory to obtain a clearing price corresponding to the active capacity of the power generator.

[0013] Furthermore, the first model is constructed with the goal of maximizing the revenue of power generators in the capacity market, and the power generation types of the power generators include thermal power generation, wind power generation, photovoltaic power generation, hydropower and large-scale energy storage power stations, including:

[0014] The expression of the first model is as follows:

[0015]

[0016] Where: C R represents the economic maximization benefit of the power generator's investment capacity, i, j, k, f, and m represent the number of units, represents the price quote of thermal power unit i, represents the bid selling capacity of thermal power unit i, represents the price quote of hydropower unit j, represents the bid selling capacity of hydropower unit j, represents the bid of wind farm k, represents the bid-sold capacity of wind farm k, represents the price quote of the photovoltaic power station f, represents the bid-sold capacity of the PV power station f, represents the price of energy storage power station m, represents the bid selling capacity of energy storage power station m, represents the quotation of the capacity market demand curve for the lth segment of the xth position area, Indicates the capacity requirement of the lth segment of the xth location area.

[0017] Furthermore, the constraint conditions of the first model are determined, and the constraint conditions include active output constraints, total capacity constraints and transmission line constraints of thermal power units, wind farms, photovoltaic power stations, hydropower units and energy storage power stations, including:

[0018] Active power output constraints of thermal power units:

[0019]

[0020] Where: and They represent the minimum and maximum active output of thermal power unit i, respectively. and They represent the active power output of thermal power unit i at time t+1 and time t respectively, It represents the rate of decrease of unit power of thermal power unit i, It represents the rising rate of the thermal power unit i to increase the unit power, and ΔT represents the response time interval of the demand;

[0021] Active power output constraints of hydropower plants:

[0022]

[0023] Where: and They represent the minimum and maximum active output of hydropower unit j, and They represent the active power output of hydropower unit j at time t+1 and time t respectively, It represents the decreasing rate of unit power of hydropower unit j, It represents the rising rate of unit power increase of hydropower unit j;

[0024] Active power output constraints of wind farms:

[0025]

[0026] Where: Represents the maximum active output of wind farm k, which is obtained through the function The prediction function is obtained by deep learning, t represents time, w k represents the credibility factor;

[0027] Active output constraints of photovoltaic power stations:

[0028]

[0029] Where: It represents the maximum active output of the photovoltaic power station f, which is obtained by the function The prediction function is obtained through deep learning. f Represents the trust factor, which is a discount factor for considering the safety and reliability of the participants in the capacity market;

[0030] Active output constraints of energy storage power stations:

[0031]

[0032] Where: and are the minimum and maximum active output of energy storage unit m, respectively. and They represent the active output of energy storage unit m at time t+1 and time t respectively, It represents the decreasing rate of the energy storage unit m in reducing the unit power, It indicates the rate of increase of unit power of energy storage unit m;

[0033] Active output constraints of total capacity:

[0034]

[0035] Where: γ l,x Indicates the capacity margin requirement;

[0036] Transmission line constraints:

[0037]

[0038] Where: l represents the transmission line from area g to area d, represents the active power on the line from area g to area d, and Indicates the minimum and maximum values ​​allowed for active power on the line from area g to area d.

[0039] Furthermore, the second model is constructed with the goal of maximizing the difference between the user's income in the daily spot trading market and the power generator's cost, including:

[0040] The expression of the second model is as follows:

[0041]

[0042] Where: D represents the optimal target, u represents the user-identified variable, λ u,t represents the price at which user u obtains income in period t, P u,t represents the power of user u in time period t, λ u,t P u,tIt indicates the actual income obtained by user u by using electricity in time period t. Variable i indicates the power generator identification, including thermal power generation, hydropower units, wind farms, photovoltaic power stations, and large-scale energy storage power stations. represents the electricity price of generator i in region x in the spot electricity market during period t, P i,x,t It represents the active power sold by generator i in area x during period t.

[0043] Furthermore, the constraint conditions of the second model are determined, wherein the constraint conditions include the power constraint of the generator set and the power constraint of the user, including:

[0044] Generator power constraints:

[0045]

[0046] Where: u P u,t =∑ i η i,t P i,x,t Indicates that the load is equal to the total power generation, η i,t A Boolean variable indicating whether generator set i is in operation or shut down at time t. It is 1 when in operation and 0 when shut down. minP i,x,t and max P i,x,t Respectively represent P i,x,t The minimum and maximum values ​​of

[0047] User power constraints:

[0048]

[0049] Where: maxP u,t Indicates P u,t The maximum value set, U u,t represents the voltage level of user u in period t, minU u,t and maxU u,t Respectively represent the minimum and maximum values ​​allowed.

[0050] Furthermore, the optimizing and solving the first model by using an improved genetic algorithm, and inputting the obtained optimal solution into the second model as a boundary condition, comprises:

[0051] Population classification and individual settings to obtain preliminary candidate solutions:

[0052]

[0053] Where: P represents the maximum ratio of the number of excellence individuals to the total number, N represents the number of individuals in the population, x max Indicates the optimal number of populations;

[0054] According to the cross-dissimilation process, the optimal solution is iterated to obtain the updated solution:

[0055]

[0056] Where: maxP a Indicates the maximum value in the genetic crossover process, minP a represents the minimum value, and f represents the two values ​​of the maximum priority of individuals in the genetic crossover process, f av represents the average priority value of all populations;

[0057]

[0058] Where: maxP b represents the maximum value in the genetic co-alienation process, minP b Indicates the minimum value.

[0059] Furthermore, the dynamic programming method based on duality theory optimizes and solves the second model to obtain the clearing price corresponding to the active capacity of the power generator, including:

[0060] Rewrite the objective of the second model into dual programming solver format:

[0061]

[0062] Where: ω represents the constraint duality.

[0063] A second aspect of the embodiment of the present application provides a capacity market multi-energy pricing system taking into account the margin of power generation capacity, including:

[0064] A first model building unit is used to build a first model with the goal of maximizing the revenue of power generators in the capacity market, wherein the power generation types of the power generators include thermal power generation, wind power generation, photovoltaic power generation, hydropower and large-scale energy storage power stations;

[0065] A constraint determination unit of the first model, used to determine the constraint of the first model, wherein the constraint includes active output constraint, total capacity constraint and transmission line constraint of thermal power units, wind farms, photovoltaic power stations, hydropower units and energy storage power stations;

[0066] A second model building unit, used for building a second model with the goal of maximizing the difference between the user's income and the power generator's cost in the daily spot trading market;

[0067] A constraint condition determination unit of the second model, used to determine the constraint conditions of the second model, wherein the constraint conditions include power constraints of the generator set and power constraints of the user;

[0068] A first model optimization unit, used for optimizing and solving the first model by using an improved genetic algorithm, and inputting the obtained optimal solution into the second model as a boundary condition;

[0069] The clearing price determination unit corresponding to the active capacity is used to optimize and solve the second model based on the dynamic programming method of the duality theory to obtain the clearing price corresponding to the active capacity of the power generator.

[0070] A third aspect of the embodiments of the present application provides a computer device, including:

[0071] memories, transceivers, processors, and bus systems;

[0072] Wherein, the memory is used to store programs;

[0073] The processor is used to execute the program in the memory, including executing the capacity market multi-energy pricing method taking into account the power generation capacity surplus as described in any one of the above;

[0074] The bus system is used to connect the memory and the processor so that the memory and the processor can communicate with each other.

[0075] A fourth aspect of an embodiment of the present application provides a readable storage medium, including instructions, which, when executed on a computer, enables the computer to execute the capacity market multi-energy pricing method taking into account the surplus of power generation capacity as described in any one of the above.

[0076] It can be seen from the above technical solutions that the embodiments of the present application have the following advantages:

[0077] The present invention first constructs the first model, i.e., the upper model, with the goal of maximizing the revenue of power generators in the capacity market, in which the power generation types of power generators include thermal power generation, wind power generation, photovoltaic power generation, hydropower and large-scale energy storage power stations; determines the constraints; then constructs the second model, i.e., the lower model, with the goal of maximizing the difference between the revenue of users and the cost of power generators in the daily spot trading market; determines the constraints; optimizes and solves the upper model using an improved genetic algorithm, and inputs the obtained optimal solution into the lower model as a boundary condition, and optimizes and solves the lower model based on the dynamic programming method of duality theory to obtain the clearing price corresponding to the active capacity of the power generator. The present invention ensures the adequacy of power generation capacity while improving the effective operation of the power market through the cooperation of multiple energy pricing mechanisms. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] Figure 1 The figure is a flow chart of an embodiment of a capacity market multi-energy pricing method taking into account the power generation capacity surplus in the present invention. DETAILED DESCRIPTION

[0079] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0080] In this embodiment, the capacity market multi-energy pricing method taking into account the power generation capacity surplus is used to improve the effective operation of the power market. In this embodiment, the implementation method can be implemented in the system, can be implemented in the server, and can also be implemented in the terminal, and the specific details are not clearly limited.

[0081] Embodiment 1

[0082] See also Figure 1 An embodiment of a capacity market multi-energy pricing method taking into account the power generation capacity surplus in the present invention comprises the following steps:

[0083] S11. The first model is constructed with the goal of maximizing the revenue of power generators in the capacity market. The power generation types of power generators include thermal power generation, wind power generation, photovoltaic power generation, hydropower and large-scale energy storage power stations;

[0084] In this embodiment, the upper model (first model) for pricing the load demand and capacity characteristics of fire, wind, light, water, and energy storage at the capacity market level is set to be an optimization model for maximizing the revenue of the five types of power generators in the capacity market. The expression is as follows:

[0085]

[0086] Where: C R represents the economic maximization benefit of the power generator's investment capacity, i, j, k, f, and m represent the number of units, represents the price quote of thermal power unit i, represents the bid selling capacity of thermal power unit i, represents the price quote of hydropower unit j, represents the bid selling capacity of hydropower unit j, represents the bid of wind farm k, represents the bid-sold capacity of wind farm k, represents the price quote of the photovoltaic power station f, represents the bid-sold capacity of the PV power station f, represents the price of energy storage power station m, represents the bid selling capacity of energy storage power station m, represents the quotation of the capacity market demand curve for the lth segment of the xth position area, Indicates the capacity requirement of the lth segment of the xth location area.

[0087] S12. Determine the constraints of the first model, the constraints including active output constraints, total capacity constraints and transmission line constraints of thermal power units, wind farms, photovoltaic power stations, hydropower units and energy storage power stations;

[0088] Corresponding to the optimization objective in the above formula, the corresponding constraints need to be obeyed. The constraints of five types of power generators, namely thermal power generation, wind power generation, photovoltaic power generation, hydropower, and large-scale energy storage power stations, are given below:

[0089] 1. Active power output constraints of thermal power units:

[0090]

[0091] Where: and They represent the minimum and maximum active output of thermal power unit i, respectively. and They represent the active power output of thermal power unit i at time t+1 and time t respectively, It represents the rate of decrease of unit power of thermal power unit i, It represents the rate of increase of unit power of thermal power unit i, and ΔT represents the response time interval of demand.

[0092] 2. Active power output constraints of hydropower plants:

[0093]

[0094] Where: and They represent the minimum and maximum active output of hydropower unit j, and They represent the active power output of hydropower unit j at time t+1 and time t respectively, It represents the decreasing rate of unit power of hydropower unit j, It represents the increasing rate of unit power of hydropower unit j.

[0095] 3. Active power output constraints of wind farms:

[0096]

[0097] Where: Represents the maximum active output of wind farm k, which is obtained through the function The prediction function is obtained by deep learning, t represents time, w k Represents the credibility factor.

[0098] 4. Active output constraints of photovoltaic power stations:

[0099]

[0100] Where: It represents the maximum active output of the photovoltaic power station f, which is obtained by the function The prediction function is obtained through deep learning. f Represents the trust factor, which is a discount factor for considering the safety and reliability when participating in the capacity market.

[0101] 5. Active output constraints of energy storage power stations:

[0102]

[0103] Where: and are the minimum and maximum active output of energy storage unit m, respectively. and They represent the active output of energy storage unit m at time t+1 and time t respectively, It represents the decreasing rate of the energy storage unit m in reducing the unit power, It indicates the rate of increase of unit power of energy storage unit m;

[0104] 6. Active output constraints of total capacity:

[0105]

[0106] Where: γ l,x Indicates the capacity margin requirement.

[0107] 7. Transmission line constraints:

[0108]

[0109] Where: l represents the transmission line from area g to area d, represents the active power on the line from area g to area d, and Indicates the minimum and maximum values ​​allowed for active power on the line from area g to area d.

[0110] S13. constructing a second model with the goal of maximizing the difference between the user's revenue and the power generator's cost in the daily spot trading market;

[0111] In this embodiment, the lower-level model (second model) of the joint optimization scheduling of fire, wind, light, water and energy storage at the spot trading market level is set to maximize the buyer's profit (i.e., user side) of the five types of suppliers in the spot trading market and minimize the cost of the power generator. The expression is as follows:

[0112]

[0113] Where: D represents the optimal target, u represents the user-identified variable, λ u,t represents the price at which user u obtains income in period t, P u,t represents the power of user u in time period t, λ u,t P u,t It indicates the actual income obtained by user u by using electricity in time period t. Variable i indicates the power generator identification, including thermal power generation, hydropower units, wind farms, photovoltaic power stations, and large-scale energy storage power stations. represents the electricity price of generator i in region x in the spot electricity market during period t, P i,x,t It represents the active power sold by generator i in area x during period t.

[0114] S14. Determine the constraints of the second model, the constraints including the power constraints of the generator set and the power constraints of the user;

[0115] The optimization objective in the above formula needs to meet certain constraints. The detailed constraints are given below:

[0116] 1. Generator power constraints:

[0117]

[0118] Where: u P u,t =∑ i η i,t P i,x,t Indicates that the load is equal to the total power generation, η i,t A Boolean variable indicating whether generator set i is in operation or shut down at time t. It is 1 when in operation and 0 when shut down. minP i,x,t and maxP i,x,t Respectively represent P i,x,t The minimum and maximum values ​​of .

[0119] 2. User power constraints:

[0120]

[0121] Where: maxP u,t Indicates P u,t The maximum value set, U u,t represents the voltage level of user u in period t, minU u,t and maxU u,t Respectively represent the minimum and maximum values ​​allowed.

[0122] S15. Optimizing and solving the first model using an improved genetic algorithm, and inputting the obtained optimal solution into the second model as a boundary condition;

[0123] It can be seen from the upper and lower optimization objective functions (first model and second model) of the above steps that nonlinear function calculation is involved, which cannot be achieved using conventional linear programming methods. In this regard, this embodiment proposes to use improved dynamic programming combined with intelligent optimization methods for solving. Genetic algorithm is the process of searching for the optimal solution through initial classification, selection, crossover, and collaborative alienation of the population. The initial classification of the population is the basis and key to ensure the subsequent search for the optimal solution, while the traditional genetic algorithm usually selects the first node as the initial population, which is easy to fall into the local search optimum and cannot obtain the global optimal solution. The present invention improves this.

[0124] First, prioritize each individual in the population under various conditions, classify the population, and set the individual to obtain a preliminary candidate solution:

[0125]

[0126] Where: P represents the maximum ratio of the number of excellence individuals to the total number, N represents the number of individuals in the population, x max Indicates the optimal number of populations.

[0127] By optimizing the above formula, we can obtain that n gradually increases in multiple iterations, and the expected n value can be obtained by appropriately adjusting the P value.

[0128] Traditional genetic algorithms use initially set crossover and cooperative alienation, so that subsequent populations use the same rate to search, and the efficiency cannot be adjusted according to the situation. Sometimes the speed is slow and it is impossible to jump out of local iterations. In this regard, the present invention makes corresponding improvements.

[0129] According to the cross-dissimilation process, the optimal solution is iterated to obtain the updated solution:

[0130]

[0131] Where: maxP a Indicates the maximum value in the genetic crossover process, minP a represents the minimum value, and f represents the two values ​​of the maximum priority of individuals in the genetic crossover process, f av Represents the average priority value of all populations.

[0132]

[0133] Where: maxP b represents the maximum value in the genetic co-alienation process, minP b Indicates the minimum value.

[0134] The genetic algorithm improved by the above formula is used to solve the upper optimization objective. First, the population classification and individual setting are carried out, and the preliminary candidate solutions are obtained. Secondly, the optimal solution is iterated according to the cross-differentiation process, and the updated solution is obtained and substituted into the lower optimization objective as the boundary condition.

[0135] S16. The second model is optimized and solved by a dynamic programming method based on duality theory to obtain the clearing price corresponding to the active capacity of the power generator.

[0136] In this embodiment, the dynamic programming method is a commonly used method for current power grid planning. However, the traditional method has problems such as slow convergence speed and local solution. Therefore, the present invention adopts a dynamic programming method based on duality theory to solve the lower-level optimization objectives.

[0137] First, rewrite the lower-level optimization objective into the dual programming solution format:

[0138]

[0139] Where: ω represents the duality of the constraints in the above equation.

[0140] It can be expressed in dual solution format:

[0141]

[0142] According to the above formula, the M method can be used to transform the multiple linear problems in the objective function product into a single-layer linearization problem, and then solve it using the linearization programming method.

[0143] The above embodiment optimizes the active capacity of the power generator as the coordination quantity through the capacity market level and the spot trading market level, and then obtains the active capacity of power generation based on multiple iterations of the two-layer model to solve the model, and then obtains the clearing price. This method avoids power shortages, thereby ensuring the safety and reliability of the power system, and also provides a basis for the stable operation of the power market.

[0144] Embodiment 2

[0145] An embodiment of a capacity market multi-energy pricing system taking into account the surplus of power generation capacity in the present invention comprises the following steps:

[0146] A first model building unit is used to build a first model with the goal of maximizing the revenue of power generators in the capacity market, where the power generation types of power generators include thermal power generation, wind power generation, photovoltaic power generation, hydropower and large-scale energy storage power stations;

[0147] A constraint determination unit of the first model, used to determine the constraint conditions of the first model, wherein the constraint conditions include active output constraints, total capacity constraints, and transmission line constraints of thermal power units, wind farms, photovoltaic power stations, hydropower units, and energy storage power stations;

[0148] A second model building unit is used to build a second model with the goal of maximizing the difference between the user's income and the power generator's cost in the daily spot trading market;

[0149] A constraint condition determination unit of the second model, used to determine the constraint conditions of the second model, the constraint conditions including power constraints of the generator set and power constraints of the user;

[0150] A first model optimization unit, used for optimizing and solving the first model by using an improved genetic algorithm, and inputting the obtained optimal solution into the second model as a boundary condition;

[0151] The clearing price determination unit corresponding to the active capacity is used for dynamic programming based on duality theory to

[0152] The second model is optimized and solved to obtain the clearing price corresponding to the active capacity of the generator.

[0153] For the specific definition of the system, please refer to the definition of the method above, which will not be repeated here. Each module in the above system can be implemented in whole or in part by software, hardware and a combination thereof. The above modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory in the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0154] Embodiment 3

[0155] The present invention provides a computer device, comprising a memory, a processor, and computer-readable instructions stored in the memory and executable on the processor. When the processor executes the computer-readable instructions, the steps of the above method are implemented.

[0156] Those of ordinary skill in the art will appreciate that the units of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition of each example has been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0157] In the embodiments provided by the present invention, it should be understood that the division of units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units can be combined into one unit, one unit can be split into multiple units, or some features can be ignored. In addition, each functional unit in each embodiment of the present invention can be integrated into a processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units.

[0158] If the integrated unit 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, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-0nlyMemory), random access memory (RAM, RandomAccessMemory), mobile hard disk, magnetic disk or optical disk, etc., which can store program code.

[0159] It can be understood 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 above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein by equivalents. These modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and specification of the present invention.

Claims

1. A capacity market multi-energy pricing method taking into account the power generation capacity surplus, characterized in that: include: A first model is constructed with the goal of maximizing the revenue of power generators in the capacity market, wherein the power generation types of the power generators include thermal power generation, wind power generation, photovoltaic power generation, hydropower and large-scale energy storage power stations; Determining the constraint conditions of the first model, wherein the constraint conditions include active output constraints, total capacity constraints, and transmission line constraints of thermal power units, wind farms, photovoltaic power stations, hydropower units, and energy storage power stations; A second model is constructed with the goal of maximizing the difference between the user's revenue in the daily spot trading market and the power generator's cost; Determining constraint conditions of the second model, wherein the constraint conditions include power constraints of the generator set and power constraints of the user; Using an improved genetic algorithm to optimize and solve the first model, and inputting the obtained optimal solution into the second model as a boundary condition; The second model is optimized and solved based on a dynamic programming method of duality theory to obtain a clearing price corresponding to the active capacity of the power generator.

2. The capacity market multi-energy pricing method taking into account the power generation capacity surplus according to claim 1 is characterized in that: The first model is constructed with the goal of maximizing the revenue of power generators in the capacity market. The power generation types of the power generators include thermal power generation, wind power generation, photovoltaic power generation, hydropower and large-scale energy storage power stations, including: The expression of the first model is as follows: Where: C R represents the economic maximization benefit of the power generator's investment capacity, i, j, k, f, and m represent the number of units, represents the price quote of thermal power unit i, represents the bid selling capacity of thermal power unit i, represents the price quote of hydropower unit j, represents the bid selling capacity of hydropower unit j, represents the bid of wind farm k, represents the bid-sold capacity of wind farm k, represents the price quote of the photovoltaic power station f, represents the bid-sold capacity of the PV power station f, represents the price of energy storage power station m, represents the bid selling capacity of energy storage power station m, represents the quotation of the capacity market demand curve for the lth segment of the xth position area, Indicates the capacity requirement of the lth segment of the xth location area.

3. The capacity market multi-energy pricing method taking into account the power generation capacity surplus according to claim 1 is characterized in that: The constraint conditions of the first model are determined, and the constraint conditions include active output constraints, total capacity constraints and transmission line constraints of thermal power units, wind farms, photovoltaic power stations, hydropower units and energy storage power stations, including: Active power output constraints of thermal power units: Where: and They represent the minimum and maximum active output of thermal power unit i, respectively. and They represent the active power output of thermal power unit i at time t+1 and time t respectively, It represents the rate of decrease of unit power of thermal power unit i, It represents the rising rate of the thermal power unit i to increase the unit power, and ΔT represents the response time interval of the demand; Active power output constraints of hydropower plants: Where: and They represent the minimum and maximum active output of hydropower unit j, and They represent the active power output of hydropower unit j at time t+1 and time t respectively, It represents the decreasing rate of unit power of hydropower unit j, It represents the rising rate of unit power increase of hydropower unit j; Active power output constraints of wind farms: Where: Represents the maximum active output of wind farm k, which is obtained through the function The prediction function is obtained by deep learning, t represents time, w k represents the credibility factor; Active output constraints of photovoltaic power stations: Where: It represents the maximum active output of the photovoltaic power station f, which is obtained by the function The prediction function is obtained through deep learning. f Represents the trust factor, which is a discount factor for considering the safety and reliability of the participants in the capacity market; Active output constraints of energy storage power stations: Where: and are the minimum and maximum active output of energy storage unit m, respectively. and They represent the active output of energy storage unit m at time t+1 and time t respectively, It represents the decreasing rate of the energy storage unit m in reducing the unit power, It indicates the rate of increase of unit power of energy storage unit m; Active output constraints of total capacity: Where: γ l,x Indicates the capacity margin requirement; Transmission line constraints: Where: l represents the transmission line from area g to area d, represents the active power on the line from area g to area d, and Indicates the minimum and maximum values ​​allowed for active power on the line from area g to area d.

4. The capacity market multi-energy pricing method taking into account the power generation capacity surplus according to claim 1 is characterized in that: The second model is constructed with the goal of maximizing the difference between the revenue of users in the daily spot trading market and the cost of the power generator, including: The expression of the second model is as follows: Where: D represents the optimal target, u represents the user-identified variable, λ u,t represents the price at which user u obtains income in period t, P u,t represents the power of user u in time period t, λ u,t P u,t It indicates the actual income obtained by user u by using electricity in time period t. Variable i indicates the power generator identification, including thermal power generation, hydropower units, wind farms, photovoltaic power stations, and large-scale energy storage power stations. represents the electricity price of generator i in region x in the spot electricity market during period t, P i,x,t It represents the active power sold by generator i in area x during period t.

5. The capacity market multi-energy pricing method taking into account the power generation capacity surplus according to claim 1 is characterized in that: Determining the constraint conditions of the second model, wherein the constraint conditions include the power constraint of the generator set and the power constraint of the user, including: Generator power constraints: Where: u P u,t =∑ i η i,t P i,x,t Indicates that the load is equal to the total power generation, η i,t A Boolean variable indicating whether generator set i is in operation or shut down at time t. It is 1 when in operation and 0 when shut down. minP i,x,t and max P i,x,t Respectively represent P i,x,t The minimum and maximum values ​​of User power constraints: Where: maxP u,t Indicates P u,t The maximum value set, U u,t represents the voltage level of user u in period t, minU u,t and maxU u,t Respectively represent the minimum and maximum values ​​allowed.

6. The capacity market multi-energy pricing method taking into account the power generation capacity surplus according to claim 1 is characterized in that: The step of optimizing and solving the first model by using an improved genetic algorithm and inputting the obtained optimal solution into the second model as a boundary condition comprises: Population classification and individual settings to obtain preliminary candidate solutions: Where: P represents the maximum ratio of the number of excellence individuals to the total number, N represents the number of individuals in the population, x max Indicates the optimal number of populations; According to the cross-dissimilation process, the optimal solution is iterated to obtain the updated solution: Where: maxP a Indicates the maximum value in the genetic crossover process, minP a represents the minimum value, and f represents the two values ​​of the maximum priority of individuals in the genetic crossover process, f av represents the average priority value of all populations; Where: maxP b represents the maximum value in the genetic co-alienation process, minP b Indicates the minimum value.

7. The capacity market multi-energy pricing method taking into account the power generation capacity surplus according to claim 1 is characterized in that: The dynamic programming method based on duality theory optimizes and solves the second model to obtain the clearing price corresponding to the active capacity of the power generator, including: Rewrite the objective of the second model into dual programming solver format: Where: ω represents the constraint duality.

8. A capacity market multi-energy pricing system taking into account the adequacy of power generation capacity, characterized in that: include: A first model building unit is used to build a first model with the goal of maximizing the revenue of power generators in the capacity market, wherein the power generation types of the power generators include thermal power generation, wind power generation, photovoltaic power generation, hydropower and large-scale energy storage power stations; A constraint determination unit of the first model, used to determine the constraint of the first model, wherein the constraint includes active output constraint, total capacity constraint and transmission line constraint of thermal power units, wind farms, photovoltaic power stations, hydropower units and energy storage power stations; A second model building unit, used for building a second model with the goal of maximizing the difference between the user's income and the power generator's cost in the daily spot trading market; A constraint condition determination unit of the second model, used to determine the constraint conditions of the second model, wherein the constraint conditions include power constraints of the generator set and power constraints of the user; A first model optimization unit, used for optimizing and solving the first model by using an improved genetic algorithm, and inputting the obtained optimal solution into the second model as a boundary condition; The clearing price determination unit corresponding to the active capacity is used to optimize and solve the second model based on the dynamic programming method of the duality theory to obtain the clearing price corresponding to the active capacity of the power generator.

9. A computer device, characterized in that: include: memories, transceivers, processors, and bus systems; Wherein, the memory is used to store programs; The processor is used to execute the program in the memory, including executing the capacity market multi-energy pricing method taking into account the power generation capacity surplus according to any one of claims 1 to 7; The bus system is used to connect the memory and the processor so that the memory and the processor can communicate with each other.

10. A readable storage medium, characterized in that: The method comprises instructions which, when executed on a computer, cause the computer to execute the capacity market multi-energy pricing method taking into account the generation capacity surplus according to any one of claims 1 to 7.

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