Bidding method, device and equipment for energy storage electricity market and storage medium

Abstract

The invention discloses an energy storage electricity market bidding method, device and equipment and a storage medium, and relates to the field of energy storage optimization scheduling, and the method comprises the steps: an independent energy storage bidding decision model sends first quotation data to an electricity market clearing model; the electricity market clearing model determines the total electricity purchase cost based on the first quotation data and the second quotation data of each conventional unit in the plurality of conventional units; the power market clearing model determines bid-winning data of the independent energy storage bidding decision-making model based on the total power purchase cost, and sends the bid-winning data to the independent energy storage bidding decision-making model; the independent energy storage bidding decision model determines the total income of the electric energy market and the frequency modulation auxiliary service market based on the bid winning data; and the independent energy storage bidding decision-making model optimizes the first quotation data based on the total income, and sends the optimized first quotation data to the electricity market clearing model, and the optimization is ended until the reduction of the total electricity purchase cost and the increase of the total income are both smaller than a preset threshold value or the number of iterations is reached.

Description

Technical Field

[0001] The present application relates to the technical field of energy storage optimization dispatching, and particularly to a method, device, equipment and storage medium for competitive bidding in the energy storage power market. Background Art

[0002] With the increase in the penetration rate of new energy and the deepening of power market reform, independent energy storage systems have gradually become an important part of the flexible regulation resources in the power system. Current research mainly focuses on the optimization of bidding strategies for independent energy storage systems in a single market (such as the electricity energy market or the frequency regulation ancillary service market), or the design of an optimization dispatching model based on the price taker assumption.

[0003] However, due to insufficient market coupling and the complex joint clearing mechanism of the electricity energy market and the frequency regulation ancillary service market, the sequential independent clearing method for different markets is mostly adopted in related technologies, which is difficult to reflect the dynamic interaction between markets. Summary of the Invention

[0004] The purpose of the present application is to provide a method, device, equipment, medium and product for competitive bidding in the energy storage power market.

[0005] To achieve the above purpose, the present application provides the following solutions:

[0006] In the first aspect, the present application provides a method for competitive bidding in the energy storage power market, including:

[0007] The independent energy storage bidding decision model sends the first bid data for the electricity energy market and the frequency regulation ancillary service market to the power market clearing model;

[0008] The power market clearing model determines the total power purchase cost based on the first bid data and the second bid data of each of the multiple conventional units;

[0009] The power market clearing model determines the winning bid data of the independent energy storage bidding decision model based on the total power purchase cost, and sends the winning bid data to the independent energy storage bidding decision model;

[0010] The independent energy storage bidding decision model determines the total revenue of the electricity energy market and the frequency regulation ancillary service market based on the winning bid data;

[0011] The independent energy storage bidding decision model optimizes the first bid data based on the total revenue to obtain the optimized first bid data, and sends the optimized first bid data to the power market clearing model until both the decrease in the total power purchase cost and the increase in the total revenue are less than a preset threshold or the iteration times are reached, and then the optimization ends.

[0012] In a second aspect, the present application provides a bidding device for an energy storage power market, including: an independent energy storage bidding decision model and a power market clearing model, where:

[0013] The independent energy storage bidding decision model is configured to send first bid data for the electric energy market and the frequency regulation ancillary service market to the power market clearing model;

[0014] The power market clearing model is configured to determine the total power purchase cost based on the first bid data and the second bid data of each of a plurality of conventional units;

[0015] The power market clearing model is further configured to determine the winning bid data of the independent energy storage bidding decision model based on the total power purchase cost, and send the winning bid data to the independent energy storage bidding decision model;

[0016] The independent energy storage bidding decision model is further configured to determine the total revenue of the electric energy market and the frequency regulation ancillary service market based on the winning bid data;

[0017] The independent energy storage bidding decision model is further configured to optimize the first bid data based on the total revenue to obtain optimized first bid data, and send the optimized first bid data to the power market clearing model until the decrease in the total power purchase cost and the increase in the total revenue are both less than a preset threshold or the iteration times are reached, and then the optimization ends.

[0018] In a third aspect, the present application provides a computer device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor executes the computer program to implement the steps of the bidding method for the energy storage power market described in any one of the above.

[0019] In a fourth aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the bidding method for the energy storage power market described in any one of the above are implemented.

[0020] In a fifth aspect, the present application provides a computer program product, including a computer program, and when the computer program is executed by a processor, the steps of the bidding method for the energy storage power market described in any one of the above are implemented.

[0021] According to the specific embodiments provided by the present application, the following technical effects are disclosed:

[0022] The present application provides a method, device, equipment, medium and product for competitive bidding in the energy storage power market. Through the dynamic interactive quotation optimization between the independent energy storage system and the power market, the coordinated clearing and dynamic interaction of the electricity energy market and the frequency regulation ancillary service market are realized, manual intervention is reduced, and the market operation efficiency is improved; by iteratively optimizing the competition mechanism between the energy storage system quotation and the conventional unit quotation, the power market clearing model is prompted to preferentially select a more economical resource combination, and finally the overall procurement cost of the power system is reduced; the energy storage system realizes the continuous optimization of its own revenue by dynamically adjusting the quotation strategies of the electricity energy market and the frequency regulation service market, and improves the economic feasibility of the energy storage system participating in the power market. The quotation data of the energy storage system and the conventional unit are interacted in real time, promoting the reasonable allocation of resources in the electricity energy market and the frequency regulation ancillary service market of the power system, and improving the economy and reliability of the power grid operation; through the quotation-clearing-feedback mechanism, the revenue trade-off problem of energy storage in multi-market resource allocation is solved, and the maximization of the energy storage system revenue and the efficient allocation of power market resources are realized. Description of the Drawings

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required to be used in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0024] Figure 1 It is an application environment diagram of a method for competitive bidding in the energy storage power market in an embodiment of the present application;

[0025] Figure 2 It is a schematic flowchart of a method for competitive bidding in the energy storage power market provided by an embodiment of the present application;

[0026] Figure 3 It is a schematic flowchart of a method for competitive bidding in the energy storage power market provided by another embodiment of the present application;

[0027] Figure 4 It is a schematic flowchart of a method for competitive bidding in the energy storage power market provided by yet another embodiment of the present application;

[0028] Figure 5 It is a schematic flowchart of a method for competitive bidding in the energy storage power market provided by still another embodiment of the present application;

[0029] Figure 6 It is a schematic composition diagram of a two-layer optimization model for multi-scenario application bidding game of an independent energy storage participating in the power market provided by an embodiment of the present application;

[0030] Figure 7 Schematic flow chart of a method for solving a two - layer optimization model for competitive bidding of an independent energy storage participating in the electricity energy and frequency regulation ancillary service markets;

[0031] Figure 8 Schematic diagram of functional modules of a device for competitive bidding in an energy storage power market provided by an embodiment of the present application;

[0032] Figure 9 Schematic diagram of the structure of a computer device provided by an embodiment of the present application. Detailed implementation manners

[0033] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0034] To make the above - mentioned objects, features, and advantages of the present application more obvious and understandable, the present application will be further described in detail below in conjunction with the drawings and specific implementation manners.

[0035] As a flexible resource, an energy storage system (ESS) has the advantages of flexible configuration of power and energy according to different application requirements, fast response speed, and no geographical resource restrictions. The energy storage system is built in conjunction with a photovoltaic power station to form a photovoltaic - energy storage system, which can effectively suppress the fluctuations of photovoltaic power generation and improve the stability and reliability of photovoltaic grid connection. In addition, the energy storage system can further improve the economic benefits of the photovoltaic - energy storage system by participating in power market transactions.

[0036] Conventional units include coal - fired units, gas - fired units, oil - fired units, nuclear power units, and hydropower units, etc. Coal - fired units generate electricity by burning coal to drive steam turbines, with low costs but high pollution. Gas - fired units use natural gas combustion to drive gas turbines or combined cycles for power generation, with fast startup and low pollution, but high fuel costs. Oil - fired units burn petroleum products for power generation, usually as standby or peaking units, with high fuel costs and high pollution. Nuclear power units generate electricity by using nuclear fission to produce heat energy, with stable operation and low carbon, but high construction costs and nuclear safety risks. Hydropower units generate electricity using water energy, including conventional hydropower stations and pumped - storage power stations, with fast response and no pollution, but are restricted by water resources.

[0037] The electricity energy market and the ancillary service market are two important components in the power market, responsible for different functions respectively; the electricity energy market is the most fundamental market in the power system, mainly trading electric energy, that is, power generation enterprises provide electricity to users or retailers, and users pay corresponding fees; the ancillary service market provides various services required to maintain the stable operation of the power system, such as frequency regulation, peak shaving, reserve, black start, voltage control, etc. The ancillary service market can include a frequency regulation ancillary service market, a peak shaving ancillary service market, a reserve ancillary service market, a black start ancillary service market, etc. according to different services provided. These ancillary service markets jointly ensure the stability and reliability of the power system; the electricity energy market and the ancillary service market cooperate with each other to ensure the reliability and economy of the power system.

[0038] The energy storage power market bidding method provided by the embodiment of the present application can be applied to an application environment as Figure 1 shown. Among them, the process of the independent energy storage system participating in the power market bidding game can be modeled as a master-slave game two-layer optimization model, which is used to simulate the process of the energy storage system participating in the joint bidding of multiple markets. The upper layer of the master-slave game two-layer optimization model is the independent energy storage bidding decision model 11, and the independent energy storage system (also known as the energy storage system or energy storage) is the leader. The lower layer is the power market clearing model 12, and the power trading center is the follower.

[0039] The independent energy storage bidding decision model 11 can send the first bid data 101 of the electricity energy market and the frequency regulation ancillary service market to the power market clearing model 12. The power market clearing model 12 determines the total power purchase cost 102 based on the first bid data 101 and the second bid data of each of the multiple conventional units; the power market clearing model 12 determines the winning bid data 103 of the independent energy storage bidding decision model based on the total power purchase cost 102, and sends the winning bid data 103 to the independent energy storage bidding decision model 11. The independent energy storage bidding decision model 11 determines the total revenue 104 of the electricity energy market and the frequency regulation ancillary service market based on the winning bid data 103; the independent energy storage bidding decision model 11 optimizes the first bid data 101 based on the total revenue 104 to obtain the optimized first bid data 101, and sends the optimized first bid data 101 to the power market clearing model 12 until the decrease in the total power purchase cost 102 and the increase in the total revenue 104 are both less than the preset threshold or the iteration times are reached, and then the optimization ends.

[0040] In an exemplary embodiment, as Figure 2As shown, a bidding method for the energy storage power market is provided. This method is executed by a computer device, which can be specifically executed by a computer device such as a terminal or a server alone, or jointly executed by a terminal and a server. In the embodiments of the present application, taking the application of this method to Figure 1 the energy storage system in

[0041] Step 202: The independent energy storage bidding decision model sends the first bidding data of the electric energy market and the frequency regulation ancillary service market to the power market clearing model;

[0042] Among them, the first bidding data may be the bidding data reported by the energy storage system.

[0043] Step 204: The power market clearing model determines the total power purchase cost based on the first bidding data and the second bidding data of each of the multiple conventional units;

[0044] Among them, the second bidding data may be the bidding data reported by the conventional units, and the total power purchase cost can be determined according to the first bidding data of the energy storage system and the second bidding data of the multiple conventional units.

[0045] Step 206: The power market clearing model determines the winning bid data of the independent energy storage bidding decision model based on the total power purchase cost, and sends the winning bid data to the independent energy storage bidding decision model;

[0046] Among them, since the power trading center hopes to reduce the total power purchase cost to the lowest, the winning bid data can be adjusted according to the total power purchase cost to minimize the total power purchase cost.

[0047] Step 208: The independent energy storage bidding decision model determines the total revenue of the electric energy market and the frequency regulation ancillary service market based on the winning bid data;

[0048] Among them, the total revenue of the energy storage system in the electric energy market and the frequency regulation ancillary service market can be determined according to the winning bid data of the energy storage system.

[0049] Step 210: The independent energy storage bidding decision model optimizes the first bidding data based on the total revenue to obtain the optimized first bidding data, and sends the optimized first bidding data to the power market clearing model until the decrease in the total power purchase cost and the increase in the total revenue are both less than the preset threshold or the iteration times are reached, and then the optimization ends.

[0050] Among them, since the energy storage system hopes to increase the total revenue to the highest, the first bidding data can be adjusted according to the total revenue to maximize the total revenue.

[0051] The process of an independent energy storage system participating in the electricity market bidding game can be understood as a game problem of maximizing revenue and minimizing cost. The independent energy storage system takes the initiative to maximize its own revenue, and then the power trading center selects a strategy to minimize its own cost or maximize its own utility according to the strategy of the independent energy storage system. The final result depends on the equilibrium solution of the game.

[0052] Implement the above steps 202 to 210. Through the dynamic interactive bidding optimization between the independent energy storage system and the electricity market, the coordinated clearing of the electricity energy market and the frequency modulation ancillary service market is realized, with dynamic interaction, reduced manual intervention, and improved market operation efficiency. Through the iterative optimization of the competition mechanism between the energy storage system's bid and the conventional unit's bid, the electricity market clearing model is prompted to preferentially select a more economical resource combination, ultimately reducing the overall procurement cost of the power system. The energy storage system realizes the continuous optimization of its own revenue by dynamically adjusting the bidding strategies in the electricity energy market and the frequency modulation service market, enhancing the economic feasibility of the energy storage system's participation in the electricity market. The bid data of the energy storage system and the conventional units are interacted in real time, promoting the rational allocation of resources in the power system in the electricity energy market and the frequency modulation ancillary service market, and improving the economy and reliability of the power grid operation. Through the bidding-clearing-feedback mechanism, the problem of revenue trade-off in the multi-market resource allocation of energy storage is solved, realizing the maximization of the energy storage system's revenue and the efficient allocation of power market resources.

[0053] In another exemplary embodiment of the present application, the first bid data and each of the second bid data satisfy the first constraint term of the cost minimization objective function of the electricity market clearing model.

[0054] Wherein, the first constraint term may be composed of all the constraint conditions of the total power purchase cost, and the constraint conditions may be equations or inequalities; the cost minimization objective function is used to minimize the total power purchase cost on the premise of satisfying the first constraint term.

[0055] The winning bid data satisfies the second constraint term of the revenue maximization objective function of the independent energy storage bidding decision model.

[0056] Wherein, the second constraint term may be composed of all the constraint conditions of the total revenue, and the revenue maximization objective function is used to maximize the total revenue on the premise of satisfying the second constraint term.

[0057] In the embodiments of the present application, the first quotation data and each second quotation data satisfy the first constraint item of the cost minimization objective function of the electricity market clearing model, which can enable the electricity market clearing model to screen the most economical quotation combinations (including energy storage and conventional units), thereby ensuring the lowest electricity procurement cost and improving the market operation efficiency; the winning bid data satisfies the second constraint item of the revenue maximization objective function of the independent energy storage bidding decision model, ensuring that when the energy storage system participates in the market, its bidding strategy always focuses on revenue optimization, avoiding losses or a decline in market competitiveness caused by blind quotation.

[0058] In another exemplary embodiment of the present application, the first quotation data includes the first charging power, the first discharging power, the first electricity market price in the electricity energy market declared by the energy storage system, and the first frequency regulation capacity, the first frequency regulation capacity price, the first frequency regulation mileage, and the first frequency regulation mileage price declared in the frequency regulation ancillary service market; the second quotation data includes the first power generation amount and the power generation price declared by the conventional unit in the electricity energy market, and the second frequency regulation capacity, the second frequency regulation capacity price, the second frequency regulation mileage, and the second frequency regulation mileage price declared in the frequency regulation ancillary service market;

[0059] Among them, in the electricity energy market, the first charging power can be the charging power of the energy storage system participating in the electricity energy market, the first discharging power can be the discharging power of the energy storage system participating in the electricity energy market, and the electricity energy market price is the price when electric energy is traded as a commodity, which can reflect the electricity supply and demand relationship; in the frequency regulation ancillary service market, the frequency regulation capacity and the frequency regulation mileage are two key indicators that can be used to measure and compensate the resources providing frequency regulation services. The frequency regulation capacity is the maximum frequency regulation power that the resources can provide within a specific time, and the frequency regulation mileage is the total amount of changes in the actual frequency regulation power provided by the resources within a specific time; the frequency regulation capacity price is the fee paid by the power system to maintain frequency stability, and the frequency regulation mileage price is the fee paid by the power system for the electricity actually provided by the frequency regulation service.

[0060] As Figure 3 shown, step 204 can be replaced by the following steps 2041 to 2043:

[0061] Step 2041: The electricity market clearing model determines the first electricity purchase cost of the energy storage system based on the first charging power, the first discharging power, the first electricity market price in the electricity energy market, the first frequency regulation capacity, the first frequency regulation capacity price, the first frequency regulation mileage, and the first frequency regulation mileage price;

[0062]

[0063] As shown in the above formula (1), the first power purchase cost of the energy storage system can also be referred to as the clearing cost of the energy storage system in the electricity energy market and the frequency regulation ancillary service market. The first power purchase cost can be expressed as U1, q e，t is the first electricity energy market price declared by the energy storage system in the electricity energy market at time t, is the second discharge power won by the energy storage system in the electricity energy market at time t, is the second charging power won by the energy storage system in the electricity energy market at time t, is the third frequency regulation capacity won by the energy storage system in the frequency regulation ancillary service market at time t, is the first frequency regulation capacity price declared by the energy storage system in the frequency regulation ancillary service market at time t, is the third frequency regulation mileage won by the energy storage system in the frequency regulation ancillary service market at time t, is the first frequency regulation mileage price declared by the energy storage system in the frequency regulation ancillary service market at time t.

[0064] Step 2042: The electricity market clearing model determines the second power purchase cost of multiple conventional units based on the first power generation amount, the power generation price, the second frequency regulation capacity, the second frequency regulation capacity price, the second frequency regulation mileage, and the second frequency regulation mileage price of multiple conventional units;

[0065]

[0066] As shown in the above formula (2), the second power purchase cost of the conventional unit can also be referred to as the clearing cost of the conventional unit in the electricity energy market and the frequency regulation ancillary service market. The second power purchase cost can be expressed as U2, G is the number of conventional units, q g，t is the power generation price declared by the conventional unit in the electricity energy market at time t, P g，t is the first power generation amount declared by the conventional unit in the electricity energy market at time t, p g，t is the second power generation amount won by the conventional unit in the electricity energy market at time t, is the fourth frequency regulation capacity won by the conventional unit in the frequency regulation ancillary service market at time t, is the second frequency regulation capacity price declared by the conventional unit in the frequency regulation ancillary service market at time t, is the fourth frequency regulation mileage won by the conventional unit in the frequency regulation ancillary service market at time t, is the second frequency regulation mileage price declared by the conventional unit in the frequency regulation ancillary service market at time t.

[0067] Step 2043: Based on the first power purchase cost and the second power purchase cost, the power market clearing model determines the total power purchase cost.

[0068] Among them, in the spot market, after the power trading center aggregates the bidding situations of each market entity, it can conduct joint clearing of the electricity energy and frequency regulation ancillary service markets. In the embodiments of the present application, it is considered that both the energy storage system and the conventional unit can bid in the energy and frequency regulation markets, and the clearing objective is to minimize the sum of the power purchase cost and the ancillary service cost. The lower-layer electricity energy-frequency regulation ancillary service market joint clearing model can be expressed as the following formula (3):

[0069] minU = U1 + U2 (3);

[0070] As shown in the above formula (3), the total power purchase cost can be expressed as U, and the cost minimization objective function min U is used to minimize the total power purchase cost on the premise of satisfying the first constraint item.

[0071] In the embodiments of the present application, through the first quotation data, the first power purchase cost of the energy storage system is determined, and through the second quotation data, the second power purchase cost of the conventional unit is determined. Furthermore, the total power purchase cost of the energy storage system and the conventional unit is determined, so that the total power purchase cost can be determined more accurately.

[0072] In another exemplary embodiment of the present application, the first constraint item includes power flow balance constraint, frequency regulation capacity and mileage demand constraint, and market clearing rule constraint; where:

[0073] The power flow balance constraint is used to constrain the second power generation amount of the conventional unit winning the bid in the electricity energy market, the second charging power and the second discharging power of the energy storage system winning the bid in the electricity energy market through the demand on the load side;

[0074]

[0075] As shown in the above formula (4), is the demand on the load side at time t, is the dual variable of the power system power flow balance constraint, and its physical meaning is the electricity energy clearing price at this moment, which can be obtained through to constrain the second power generation amount p g，t , the second charging power and the second discharging power .

[0076] The frequency regulation capacity and mileage demand constraints are used to constrain the third frequency regulation capacity and the third frequency regulation mileage of the energy storage system in the frequency regulation ancillary service market, and the fourth frequency regulation capacity and the fourth frequency regulation mileage of the conventional unit in the frequency regulation ancillary service market, through the frequency regulation capacity demand and the frequency regulation mileage demand;

[0077]

[0078] As shown in the above formula (5), respectively represent the frequency regulation capacity demand and the frequency regulation mileage demand of the system; the dual variables and are the frequency regulation capacity and mileage clearing prices at time t, and the third frequency regulation capacity can be constrained by the frequency regulation capacity demand and the fourth frequency regulation capacity , and the third frequency regulation mileage and the fourth frequency regulation mileage can be constrained by the frequency regulation mileage demand .

[0079] The market clearing rule constraints are used to constrain the second charging power, the second discharging power, the third frequency regulation capacity, the third frequency regulation mileage, the fourth frequency regulation capacity and the fourth frequency regulation mileage through the market clearing rules.

[0080]

[0081] As shown in the above formulas (6) and (7), is the frequency regulation mileage multiplier of the energy storage system, is the frequency regulation mileage multiplier of the conventional unit g. Since the winning bid quantity should be less than the declared quantity, therefore, the second discharging power can be constrained by the first discharging power declared by the energy storage system in the electricity energy market in the market clearing rules, the second charging power can be constrained by the first charging power declared by the energy storage system in the electricity energy market, the third frequency regulation capacity can be constrained by the first frequency regulation capacity declared by the energy storage system in the frequency regulation ancillary service market, the third frequency regulation mileage can be constrained by the first frequency regulation mileage declared by the energy storage system in the frequency regulation ancillary service market, and the fourth frequency regulation capacity can be constrained by the second frequency regulation capacity Conduct constraints on the second frequency regulation mileage declared by the conventional unit in the frequency regulation ancillary service market Conduct constraints on the fourth frequency regulation mileage Conduct constraints

[0082] Among them, the variables after the colon in each formula in the above-discussed constraints are the dual variables corresponding to the constraint formula, which will be applied when using the KKT (Karush-Kuhn-Tucker) conditions for transformation in the following two-layer model solution process

[0083] In the embodiment of the present application, through the first constraint term, it is prevented that the market clearing deviates from the optimal solution due to the unreasonable quotations of the energy storage system or the conventional unit, and the overall economic benefits of the power market are guaranteed

[0084] In another exemplary embodiment of the present application, the winning bid data includes the second charging power, the second discharging power, the second electricity market price of the energy storage system in the electricity energy market, and the third frequency regulation capacity, the third frequency regulation capacity price, the third frequency regulation mileage, and the third frequency regulation mileage price of the energy storage system in the frequency regulation ancillary service market

[0085] Among them, the second electricity market price is the clearing price of the electricity energy market, the third frequency regulation capacity price is the clearing price of the frequency regulation capacity in the frequency regulation ancillary service market, and the third frequency regulation mileage price is the clearing price of the frequency regulation mileage in the frequency regulation ancillary service market

[0086] As Figure 4 shown, step 208 can be replaced by the following steps 2081 to 2083

[0087] Step 2081: The independent energy storage bidding decision model determines the first revenue of the electricity energy market based on the second charging power, the second discharging power, the second electricity market price, and the preset scheduling duration

[0088]

[0089] As shown in the above formula (8), the first revenue can be expressed as C1 is the second electricity market price (i.e., the clearing price of the electricity energy market) of the electricity energy market at time t is the second discharging power of the energy storage system in the electricity energy market at time t is the second charging power of the energy storage system in the electricity energy market at time t, Δt is the scheduling duration for the energy storage system to participate in the electricity energy market transaction, and the scheduling duration can be 1 hour, 30 minutes, etc

[0090] Step 2082: Based on the third frequency regulation capacity, the third frequency regulation capacity electricity price, the third frequency regulation mileage, the third frequency regulation mileage electricity price, and the preset scheduling duration, the independent energy storage bidding decision model determines the second revenue of the frequency regulation ancillary service market;

[0091]

[0092] As shown in the above formula (9), the second revenue can be expressed as C2, is the third frequency regulation capacity won by the energy storage system in the frequency regulation ancillary service market at time t, is the third frequency regulation capacity electricity price (i.e., the frequency regulation capacity clearing electricity price) of the frequency regulation ancillary service market at time t, is the third frequency regulation mileage won by the energy storage system in the frequency regulation ancillary service market at time t, is the third frequency regulation mileage electricity price (i.e., the frequency regulation mileage clearing electricity price) of the frequency regulation ancillary service market at time t, and Δt is the scheduling duration for the energy storage system to participate in the frequency regulation ancillary service market transaction.

[0093] Step 2083: The independent energy storage bidding decision model determines the total revenue based on the first revenue and the second revenue.

[0094] Among them, the revenue sources of the energy storage system include the electric energy market and the frequency regulation ancillary service market in the day-ahead market, and each revenue source is based on two working modes of the energy storage system, namely charging and discharging. The upper-layer model establishes the revenue maximization objective function shown in the following formula (10) with the goal of maximizing the comprehensive revenue of the energy storage in the two markets:

[0095] max F = C1 + C2 (10);

[0096] As shown in the above formula (10), the total revenue can be expressed as F, and the revenue maximization objective function max F is used to maximize the total revenue on the premise of satisfying the second constraint term.

[0097] In the embodiment of the present application, through the winning bid data of the energy storage system in the electric energy market and the frequency regulation ancillary service market, the first revenue of the energy storage system in the electric energy market and the second revenue in the frequency regulation ancillary service market are respectively determined, and then the total revenue of the energy storage system in the electric energy market and the frequency regulation ancillary service market is determined, so that the total revenue can be determined more accurately.

[0098] In another exemplary embodiment of the present application, the second constraint term includes: energy storage charge and discharge constraint, energy storage state of charge constraint, energy storage declared capacity constraint, energy storage bid price constraint, and opportunity cost constraint; where:

[0099] The energy storage charge and discharge constraint is used to constrain the first charging power, the first discharging power, the charge state variable, and the discharge state variable through the maximum charging power and the maximum discharging power of the energy storage system;

[0100]

[0101] As shown in the above formula (11), is the maximum discharging power of the energy storage system, is the maximum charging power of the energy storage system, is the discharge state variable, is the charge state variable. Both the charge state variable and the discharge state variable are 0-1 variables. When is 1, the energy storage system is in the discharging state. When is 1, the energy storage system is in the charging state. The first discharging power can be constrained by the maximum discharging power and the discharge state variable . The first charging power can be constrained by the maximum charging power and the charge state variable . The purpose of formula (11) is to prohibit the energy storage system from charging and discharging simultaneously, and at the same time limit the charge and discharge power of the energy storage within the allowable range of the energy storage.

[0102] The energy storage state of charge constraint is used to constrain the energy storage state of charge through the maximum capacity, the minimum capacity, the charging efficiency, the discharging efficiency, the first charging power, and the first discharging power of the energy storage system;

[0103]

[0104] As shown in the above formula (12), S is the maximum capacity allowed for the energy storage system, S is the minimum capacity allowed for the energy storage system, S t is the remaining capacity of the energy storage system at time t, η dis is the discharging efficiency of the energy storage system, η ch is the charging efficiency of the energy storage system; the state of charge is the ratio between the remaining capacity and the rated total capacity. By constraining the remaining capacity, the state of charge of the energy storage system can be constrained; the remaining capacity S t can be constrained by the maximum capacity S and the minimum capacity S, and can also be constrained by the first charging power the first discharging power the charging efficiency η ch and the discharging efficiency η disConstrain the change rate of the remaining capacity; with 24 hours as a scheduling period, S0 is the energy value at the start of each scheduling period, and S 24 is the energy value at the end of each scheduling period, and S init is the initial value of each scheduling period. Equation (12) is to limit the state of charge of the energy storage within the allowable range of the energy storage, and in order to ensure the cyclic progress of the energy storage scheduling, the energy value at the end of each scheduling period should return to the initial value.

[0105] The energy storage declared capacity constraint is used to constrain the first frequency regulation capacity and the first frequency regulation mileage through the frequency regulation mileage multiplier, the maximum frequency regulation capacity, the maximum charging power, the maximum discharging power, the first charging power, and the first discharging power of the energy storage system;

[0106]

[0107] As shown in the above formula (13), is the first frequency regulation capacity declared by the energy storage system in the frequency regulation ancillary service market at time t, is the first frequency regulation mileage declared by the energy storage system in the frequency regulation ancillary service market at time t, is the maximum frequency regulation capacity of the energy storage system, is the frequency regulation mileage multiplier of the energy storage system; the first frequency regulation capacity can be constrained by the first charging power and the maximum charging power and can also be constrained by the first discharging power and the maximum discharging power for the first frequency regulation capacity and can also be constrained by the maximum frequency regulation capacity for the first frequency regulation capacity, and the first frequency regulation mileage can be constrained by the maximum frequency regulation capacity and the frequency regulation mileage multiplier for the first frequency regulation mileage

[0108] The energy storage quotation constraint is used to constrain the first electricity energy market price through the first quotation upper limit of the energy storage system in the electricity energy market, to constrain the first frequency regulation capacity price through the second quotation upper limit of the energy storage system in the frequency regulation ancillary service market, and to constrain the first frequency regulation mileage price through the third quotation upper limit of the energy storage system in the frequency regulation ancillary service market;

[0109]

[0110] Among them, as shown in the above formula (14), q e，t is the first electricity market price declared by the energy storage system in the electricity energy market at time t, is the first frequency modulation capacity price declared by the energy storage system in the frequency modulation ancillary service market at time t, is the first frequency modulation mileage price declared by the energy storage system in the frequency modulation ancillary service market at time t, is the first bid ceiling, is the second bid ceiling, is the third bid ceiling, which can be used to constrain the first electricity market price q e，t through the first bid ceiling, constrain the first frequency modulation capacity price through the second bid ceiling, constrain the first frequency modulation mileage price through the third bid ceiling.

[0111] The cost constraint is used to constrain the opportunity cost generated by the energy storage system providing frequency modulation ancillary services through the first revenue, the second revenue, and the third revenue of the energy storage system in the spot market before deducting the frequency modulation declared capacity.

[0112] C op = C e - C1 (15);

[0113] C op ≥ C2 (16);

[0114] Among them, as shown in the above formulas (15) and (16), C op is the opportunity cost generated by the energy storage system providing frequency modulation ancillary services in time period t, C e is the revenue of the energy storage system in the spot market before deducting the frequency modulation declared capacity (i.e., the third revenue), and the opportunity cost C e can be constrained by the first revenue C1, the second revenue C2, and the third revenue C op .

[0115] In the embodiments of the present application, through the setting of the second constraint term, the energy storage system can more accurately adjust its bidding strategies in the electricity energy market and the frequency modulation ancillary service market, improve the winning bid probability and profitability; incorporate the opportunity cost into the energy storage bidding decision-making process, solve the revenue trade-off problem of energy storage in multi-market resource allocation, and achieve the maximization of energy storage revenue and the efficient allocation of market resources.

[0116] In another exemplary embodiment of the present application, asFigure 5 As shown, the two - layer optimization relationship between the independent energy storage bidding decision - making model and the electricity market clearing model is transformed into a single - layer mixed - integer linear programming model through the following steps:

[0117] Step S1: Apply the KKT conditions to the electricity market clearing model, and embed the KKT conditions as equilibrium constraints into the independent energy storage bidding decision - making model to obtain a mathematical programming MPEC model with equilibrium constraints;

[0118] Among them, the KKT (Karush - Kuhn - Tucker) conditions are a set of necessary conditions for solving constrained nonlinear optimization problems. For convex optimization problems, the KKT conditions are also sufficient conditions. The KKT conditions include: Primal feasibility: The solution must satisfy all constraints. Dual feasibility: The Lagrange multipliers must be non - negative. Complementary slackness: The product of the Lagrange multiplier and the corresponding constraint is zero. Gradient condition: The linear combination of the gradients of the objective function and the constraints is zero. The transformation of the KKT conditions converts the two - layer model into a single - layer MPEC (Mathematical Program with Equilibrium Constraints).

[0119] Step S2: Use the big - M method to linearize the complementary slackness condition in the KKT conditions to eliminate the logical constraints in the two - layer model;

[0120] Among them, in the transformation process, non - linear terms (such as the product term in the complementary slackness condition) may be introduced. The big - M method converts these non - linear terms into linear constraints by introducing a sufficiently large constant M, thus eliminating the non - linearity.

[0121] Step S3: Use the McCormick envelope constraints to linearly process the non - linear terms in the MPEC model to obtain a mixed - integer linear programming MILP model;

[0122] Among them, the McCormick envelope is a mathematical programming technique for linearizing non - linear terms such as bilinear terms (the product of two variables) or complementary constraints, and is commonly used for the relaxation or solution of mixed - integer non - linear programming (MINLP) problems. Using the McCormick envelope constraints to linearly process the non - linear terms in the MPEC model can obtain a mixed - integer linear programming MILP (Mixed - Integer Linear Programming) model.

[0123] Step S4: Solve the MILP model on the Matlab platform by calling the Gurobi solver through the Yalmip modeling tool to obtain the optimized first bid data.

[0124] Among them, the solution of the MILP model can be completed on the Matlab platform with the help of the Yamlip+Gurobi professional solver.

[0125] Among them, steps S1 to S4 are the mathematical implementation methods of the energy storage power market bidding method.

[0126] In the embodiment of the present application, the KKT condition transformation converts the bilevel model into a single-level MPEC model. By taking the optimization conditions (such as KKT conditions) of the lower-level problem as the upper-level constraints, the nested optimization structure is avoided; the bilinear terms are converted into linear inequalities through the McCormick envelope constraints, and auxiliary integer variables are introduced to convert the nonlinear problem into a tractable MILP problem; through the professional solver, the transformation from a complex bilevel model to an efficiently solvable MILP model is realized, balancing accuracy, computational efficiency and engineering practicability.

[0127] In another exemplary embodiment of the present application, the process of an independent energy storage participating in the electricity market bidding game can be modeled as a bilevel model, such as Figure 6As shown, a two-layer optimization model for independent energy storage to participate in the bidding game of multiple scenarios in the power market is provided, including: an upper model 31 and a lower model 32, wherein: the upper model 31 is the independent energy storage bidding decision layer, the independent energy storage system pursues the maximum comprehensive revenue (i.e., energy storage income) in the day-ahead market according to its own power, quotation and other constraints, plans its own quantity-price bidding plan and simulates the bidding data to the lower power trading center, the bidding data includes the charging and discharging power declaration and the electricity market quotation of the day-ahead electric energy market, and the frequency regulation capacity declaration, frequency regulation capacity quotation, frequency regulation mileage declaration, and frequency regulation mileage quotation of the day-ahead frequency regulation auxiliary service market; the lower model 32 is the electricity market clearing layer. The power trading center coordinates the bidding data reported by independent energy storage systems, conventional units and other trading entities to clear the day-ahead market. The clearing target is to minimize the cost of electricity purchase in the day-ahead market. Finally, the winning data of the independent energy storage system in each market is returned to the independent energy storage decision layer. The winning data includes the winning amount of charging and discharging power of the independent energy storage system and the clearing price of the electricity market in the day-ahead electric energy market, and the winning amount of frequency regulation capacity, winning amount of frequency regulation mileage, clearing price of frequency regulation capacity and clearing price of frequency regulation mileage of the independent energy storage system in the day-ahead frequency regulation auxiliary service market. Then, the energy storage can optimize the initial bidding strategy and obtain a better bidding plan. In addition, the day-ahead market clearing model of the lower layer represents the clearing of the joint market of day-ahead electric energy and frequency regulation auxiliary services, which is a constraint of the upper layer model. In addition, all the bid declaration values ​​of energy storage are decision variables in the upper layer problem, but when they are passed to the lower layer, they should be regarded as known quantities and parameters from the perspective of market clearing by integrating the data of the entire market by the power trading center.

[0128] In an exemplary embodiment of the present application, a joint bidding strategy for energy storage power stations to participate in the electric energy and frequency regulation auxiliary service market is involved, especially for the quantification and optimization of opportunity costs when energy storage participates in multi-market transactions. The embodiment of the present application constructs a master-slave game two-layer optimization model, incorporates opportunity costs into the energy storage bidding decision-making process, solves the problem of energy storage benefit trade-offs in multi-market resource allocation, and can achieve energy storage benefit maximization and efficient allocation of market resources.

[0129] An embodiment of the present application proposes a master-slave game joint bidding optimization strategy for energy storage considering opportunity cost, aiming to solve problems such as unscientific resource allocation, unquantified opportunity cost, and insufficient market coupling modeling when energy storage participates in the electric energy and frequency regulation markets. By establishing a two-layer optimization model of master-slave game embedded with opportunity cost, combining the strong duality theorem and linearization technology, the embodiment of the present application introduces an opportunity cost constraint in the lower-layer trading center decision model, transforms the potential revenue difference of energy storage between the electric energy and frequency regulation markets into a mathematical expression, and ensures that the bidding strategy takes into account the revenue trade-off of multiple markets when allocating resources. At the same time, the embodiment of the present application reconstructs the two-layer model of master-slave game. The upper-layer model aims to maximize the comprehensive revenue of energy storage, and the lower-layer model aims to minimize the power purchase cost and frequency regulation service cost of the power trading center. The two-layer model is transformed into a single-layer mixed-integer linear programming (MILP) problem through the KKT condition and complementary slackness theory, and the big M method is combined to eliminate the non-linear terms, improving the model solving efficiency. The embodiment of the present application not only significantly improves the comprehensive revenue of energy storage in the electric energy and frequency regulation markets, but also optimizes the allocation of energy storage resources among multiple markets, improving the market resource allocation efficiency.

[0130] In an exemplary embodiment of the present application, as Figure 7 shown, a method for solving a two-layer optimization model of the competitive bidding of an independent energy storage participating in the electric energy and frequency regulation ancillary service markets is provided. The method includes the following steps:

[0131] Step 401: Establish a two-layer optimization model for the competitive bidding of an independent energy storage system participating in the electric energy market and the frequency regulation ancillary service market;

[0132] Among them, the two-layer optimization model involves a mixed-integer non-linear programming (MINLP) problem.

[0133] Step 402: Use the KKT condition to transform the two-layer model into a single-layer MPEC model;

[0134] Among them, the two-layer model is first transformed into a single-layer model by using the KKT condition and the big M method, that is, it becomes a mathematical program with equilibrium constraints (MPEC) model.

[0135] Step 403: Use the McCormick envelope constraint to linearly process the MPEC model to obtain a mixed MILP model;

[0136] Among them, since the MPEC model contains a product term of two variables and belongs to a non-linear problem, the McCormick envelope constraint is further used to linearly process the MPEC model, and finally it is transformed into a Mixed-Integer Linear Programming (MILP) model.

[0137] Step 404: Solve the MILP model on the Matlab platform through the Yamlip+Gurobi professional solver.

[0138] Among them, the solution of the MILP model may include the finally optimized bidding data of the independent energy storage system, the predicted total revenue, the winning bid data, and the predicted total electricity purchase cost.

[0139] Based on the same inventive concept, an embodiment of the present application further provides an energy storage power market bidding device for implementing the energy storage power market bidding method involved above. The solution provided by this device for solving problems is similar to the solution described in the above method. Therefore, the specific limitations in one or more embodiments of the energy storage power market bidding device provided below can refer to the limitations on the energy storage power market bidding method in the above text, and will not be repeated here.

[0140] In an exemplary embodiment, as Figure 8 shown, an energy storage power market bidding device 500 is provided, including: an independent energy storage bidding decision model 51 and a power market clearing model 52, where:

[0141] The independent energy storage bidding decision model 51 is used to send the first bidding data of the electric energy market and the frequency regulation ancillary service market to the power market clearing model 52;

[0142] The power market clearing model 52 is used to determine the total electricity purchase cost based on the first bidding data and the second bidding data of each of the multiple conventional units;

[0143] The power market clearing model 52 is further used to determine the winning bid data of the independent energy storage bidding decision model 51 based on the total electricity purchase cost, and send the winning bid data to the independent energy storage bidding decision model 51;

[0144] The independent energy storage bidding decision model 51 is further used to determine the total revenue of the electric energy market and the frequency regulation ancillary service market based on the winning bid data;

[0145] The independent energy storage bidding decision-making model 51 is further configured to optimize the first bid data based on the total revenue, obtain the optimized first bid data, and send the optimized first bid data to the power market clearing model 52 until the decrease in the total power purchase cost and the increase in the total revenue are both less than a preset threshold or the iteration times are reached, at which point the optimization ends.

[0146] Exemplarily, the energy storage power market bidding device 500 may be the principal-agent game two-layer optimization model itself. In some embodiments, the energy storage power market bidding device 500 may also be a device independent of the principal-agent game two-layer optimization model.

[0147] As an alternative implementation manner, the first bid data and each second bid data satisfy the first constraint term of the cost minimization objective function of the power market clearing model; the winning bid data satisfies the second constraint term of the revenue maximization objective function of the independent energy storage bidding decision-making model.

[0148] As an alternative implementation manner, the first bid data includes the first charging power, the first discharging power, the first electricity market price declared by the energy storage system in the electricity energy market, and the first frequency regulation capacity, the first frequency regulation capacity price, the first frequency regulation mileage, and the first frequency regulation mileage price declared by the energy storage system in the frequency regulation ancillary service market; the second bid data includes the first power generation amount and the power generation price declared by the conventional unit in the electricity energy market, and the second frequency regulation capacity, the second frequency regulation capacity price, the second frequency regulation mileage, and the second frequency regulation mileage price declared by the conventional unit in the frequency regulation ancillary service market;

[0149] The power market clearing model 52 is configured to determine the first power purchase cost of the energy storage system based on the first charging power, the first discharging power, the first electricity market price, the first frequency regulation capacity, the first frequency regulation capacity price, the first frequency regulation mileage, and the first frequency regulation mileage price; the power market clearing model 52 is configured to determine the second power purchase cost of multiple conventional units based on the first power generation amount, the power generation price, the second frequency regulation capacity, the second frequency regulation capacity price, the second frequency regulation mileage, and the second frequency regulation mileage price of the multiple conventional units; the power market clearing model 52 is configured to determine the total power purchase cost based on the first power purchase cost and the second power purchase cost.

[0150] As an alternative implementation, the first constraint item includes a power flow balance constraint, a frequency regulation capacity and mileage demand constraint, and a market clearing rule constraint; where: the power flow balance constraint is used to constrain the second power generation of the conventional unit winning the bid in the electric energy market, the second charging power and the second discharging power of the energy storage system winning the bid in the electric energy market through the demand on the load side; the frequency regulation capacity and mileage demand constraint is used to constrain the third frequency regulation capacity and the third frequency regulation mileage of the energy storage system winning the bid in the frequency regulation ancillary service market, and the fourth frequency regulation capacity and the fourth frequency regulation mileage of the conventional unit winning the bid in the frequency regulation ancillary service market through the frequency regulation capacity demand and the frequency regulation mileage demand; the market clearing rule constraint is used to constrain the second charging power, the second discharging power, the third frequency regulation capacity, the third frequency regulation mileage, the fourth frequency regulation capacity and the fourth frequency regulation mileage through the market clearing rule.

[0151] As an alternative implementation, the winning bid data includes the second charging power, the second discharging power, the second electricity market price of the energy storage system winning the bid in the electric energy market, and the third frequency regulation capacity, the third frequency regulation capacity price, the third frequency regulation mileage, and the third frequency regulation mileage price of the energy storage system winning the bid in the frequency regulation ancillary service market;

[0152] The independent energy storage bidding decision model 51 determines the first revenue of the electric energy market based on the second charging power, the second discharging power, the second electricity market price and a preset scheduling duration; the independent energy storage bidding decision model 51 determines the second revenue of the frequency regulation ancillary service market based on the third frequency regulation capacity, the third frequency regulation capacity price, the third frequency regulation mileage, the third frequency regulation mileage price and the preset scheduling duration; the independent energy storage bidding decision model 51 determines the total revenue based on the first revenue and the second revenue.

[0153] As an alternative implementation manner, the second constraint item includes: energy storage charge and discharge constraint, energy storage state of charge constraint, energy storage declared capacity constraint, energy storage quotation constraint, and opportunity cost constraint; where: the energy storage charge and discharge constraint is used to constrain the first charging power, the first discharging power, the charging state variable, and the discharging state variable through the maximum charging power and the maximum discharging power of the energy storage system; the energy storage state of charge constraint is used to constrain the energy storage state of charge through the maximum capacity, the minimum capacity, the charging efficiency, the discharging efficiency, the first charging power, and the first discharging power of the energy storage system; the energy storage declared capacity constraint is used to constrain the third frequency regulation capacity and the third frequency regulation mileage through the frequency regulation mileage multiplier, the maximum frequency regulation capacity, the maximum charging power, the maximum discharging power, the second charging power, and the second discharging power of the energy storage system; the energy storage quotation constraint is used to constrain the first electricity energy market price through the first quotation upper limit of the energy storage system in the electricity energy market, to constrain the first frequency regulation capacity price through the second quotation upper limit of the energy storage system in the frequency regulation ancillary service market, and to constrain the first frequency regulation mileage price through the third quotation upper limit of the energy storage system in the frequency regulation ancillary service market; the cost constraint is used to constrain the opportunity cost generated by the energy storage system providing frequency regulation ancillary services through the first revenue, the second revenue, and the third revenue of the energy storage system in the spot market before deducting the frequency regulation declared capacity.

[0154] As an alternative implementation manner, the device 500 is configured to impose the KKT conditions on the electricity market clearing model, embed the KKT conditions as equilibrium constraints into the independent energy storage bidding decision model, and obtain a mathematical programming MPEC model with equilibrium constraints; use the big M method to linearize the complementary slackness conditions in the KKT conditions to eliminate the logical constraints in the two-layer model; use the McCormick envelope constraint to linearly process the non-linear terms in the MPEC model to obtain a mixed integer linear programming MILP model; solve the MILP model through the Yalmip modeling tool by calling the Gurobi solver on the Matlab platform to obtain the optimized first quotation data.

[0155] In an exemplary embodiment, a computer device is provided. The computer device can be a server or a terminal, and its internal structure diagram can be as Figure 9As shown in the figure. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O), and a communication interface. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals through a network connection. When the computer program is executed by the processor, it implements a method for competitive bidding in the energy storage power market.

[0156] Those skilled in the art can understand that Figure 9 the structure shown in the figure is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0157] In an exemplary embodiment, a computer device is further provided, including a memory and a processor. A computer program is stored in the memory. When the processor executes the computer program, the steps in the above method embodiments are implemented.

[0158] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program. When the computer program is executed by the processor, the steps in the above method embodiments are implemented.

[0159] In an exemplary embodiment, a computer program product is provided, including a computer program. When the computer program is executed by the processor, the steps in the above method embodiments are implemented.

[0160] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use, and processing of relevant data need to comply with relevant regulations.

[0161] Those of ordinary skill in the art can understand that all or part of the processes of implementing the methods in the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the various embodiments provided in the present application can include at least one of non-volatile and volatile memories. Non-volatile memories can include read-only memory (ROM), magnetic tapes, floppy disks, flash memories, optical memories, high-density embedded non-volatile memories, resistive random access memories (ReRAM), magnetoresistive random access memories (MRAM), ferroelectric random access memories (FRAM), phase change memories (PCM), graphene memories, etc. Volatile memories can include random access memory (RAM) or external cache memories, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0162] The databases involved in the various embodiments provided in the present application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the various embodiments provided in the present application can be general-purpose processors, central processors, graphics processors, digital signal processors, programmable logics, data processing logics based on quantum computing, etc., without limitation.

[0163] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

[0164] In this article, specific examples are used to elaborate on the principles and implementation manners of the present application. The descriptions of the above embodiments are only used to help understand the method and its core idea of the present application; at the same time, for those of ordinary skill in the art, according to the idea of the present application, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation on the present application.

Claims

1. A method for competitive bidding in the energy storage power market, characterized in that, The method for competitive bidding in the energy storage power market includes: The independent energy storage bidding decision model sends the first bid data of the electricity energy market and the frequency regulation ancillary service market to the electricity market clearing model; The electricity market clearing model determines the total electricity purchase cost based on the first bid data and the second bid data of each of the multiple conventional units; The electricity market clearing model determines the winning bid data of the independent energy storage bidding decision model based on the total electricity purchase cost, and sends the winning bid data to the independent energy storage bidding decision model; The independent energy storage bidding decision model determines the total revenue of the electricity energy market and the frequency regulation ancillary service market based on the winning bid data; The independent energy storage bidding decision model optimizes the first bid data based on the total revenue to obtain the optimized first bid data, and sends the optimized first bid data to the electricity market clearing model until the decrease in the total electricity purchase cost and the increase in the total revenue are both less than the preset threshold or the iteration times reach the end of the optimization.

2. The method for competitive bidding in the energy storage power market according to claim 1, wherein The first bid data and each of the second bid data satisfy the first constraint term of the cost minimization objective function of the electricity market clearing model; The winning bid data satisfies the second constraint term of the revenue maximization objective function of the independent energy storage bidding decision model.

3. The energy storage power market competitive bidding method according to claim 2, wherein, The first bid data includes the first charging power, the first discharging power, the first electricity energy market price declared by the energy storage system in the electricity energy market, and the first frequency regulation capacity, the first frequency regulation capacity price, the first frequency regulation mileage, and the first frequency regulation mileage price declared in the frequency regulation ancillary service market; the second bid data includes the first power generation amount and the power generation price declared by the conventional unit in the electricity energy market, and the second frequency regulation capacity, the second frequency regulation capacity price, the second frequency regulation mileage, and the second frequency regulation mileage price declared in the frequency regulation ancillary service market; The electricity market clearing model determines the total electricity purchase cost based on the first bid data and the second bid data of each of the multiple conventional units, including: The electricity market clearing model determines the first electricity purchase cost of the energy storage system based on the first charging power, the first discharging power, the first electricity energy market price, the first frequency regulation capacity, the first frequency regulation capacity price, the first frequency regulation mileage, and the first frequency regulation mileage price; The electricity market clearing model determines the second electricity purchase cost of the multiple conventional units based on the first power generation amount, the power generation price, the second frequency regulation capacity, the second frequency regulation capacity price, the second frequency regulation mileage, and the second frequency regulation mileage price of the multiple conventional units; The electricity market clearing model determines the total electricity purchase cost based on the first electricity purchase cost and the second electricity purchase cost.

4. The energy storage power market competitive bidding method according to claim 3, wherein The first constraint term includes power flow balance constraint, frequency regulation capacity and mileage demand constraint, and market clearing rule constraint; wherein: The power flow balance constraint is used to constrain the second power generation of the conventional unit won in the electric energy market, the second charging power and the second discharging power of the energy storage system won in the electric energy market through the demand on the load side; The frequency regulation capacity and mileage demand constraint is used to constrain the third frequency regulation capacity and the third frequency regulation mileage of the energy storage system won in the frequency regulation ancillary service market, and the fourth frequency regulation capacity and the fourth frequency regulation mileage of the conventional unit won in the frequency regulation ancillary service market through the frequency regulation capacity demand and the frequency regulation mileage demand; The market clearing rule constraint is used to constrain the second charging power, the second discharging power, the third frequency regulation capacity, the third frequency regulation mileage, the fourth frequency regulation capacity and the fourth frequency regulation mileage through the market clearing rules.

5. The energy storage power market competitive bidding method according to claim 3, wherein The winning bid data includes the second charging power, the second discharging power, the second electric energy market electricity price of the energy storage system won in the electric energy market, and the third frequency regulation capacity, the third frequency regulation capacity electricity price, the third frequency regulation mileage, and the third frequency regulation mileage electricity price won in the frequency regulation ancillary service market; Based on the winning bid data, the independent energy storage bidding decision model determines the total revenue of the electric energy market and the frequency regulation ancillary service market, including: The independent energy storage bidding decision model determines the first revenue of the electric energy market based on the second charging power, the second discharging power, the second electric energy market electricity price and the preset scheduling duration; The independent energy storage bidding decision model determines the second revenue of the frequency regulation ancillary service market based on the third frequency regulation capacity, the third frequency regulation capacity electricity price, the third frequency regulation mileage, the third frequency regulation mileage electricity price and the preset scheduling duration; The independent energy storage bidding decision model determines the total revenue based on the first revenue and the second revenue.

6. The energy storage power market competitive bidding method according to claim 5, wherein The second constraint item includes: energy storage charge and discharge constraint, energy storage state of charge constraint, energy storage declared capacity constraint, energy storage bid price constraint and opportunity cost constraint; where: The energy storage charge and discharge constraint is used to constrain the first charging power, the first discharging power, the charge state variable and the discharge state variable through the maximum charging power and the maximum discharging power of the energy storage system; The energy storage state of charge constraint is used to constrain the energy storage state of charge through the maximum capacity, the minimum capacity, the charge efficiency, the discharge efficiency, the first charging power and the first discharging power of the energy storage system; The energy storage declared capacity constraint is used to constrain the first frequency regulation capacity and the first frequency regulation mileage through the frequency regulation mileage multiplier, the maximum frequency regulation capacity, the maximum charging power, the maximum discharging power, the first charging power and the first discharging power of the energy storage system; The energy storage price quotation constraint is used to constrain the first electricity energy market price through the first price ceiling of the energy storage system in the electricity energy market, constrain the first frequency modulation capacity price through the second price ceiling of the energy storage system in the frequency modulation ancillary service market, and constrain the first frequency modulation mileage price through the third price ceiling of the energy storage system in the frequency modulation ancillary service market; The cost constraint is used to constrain the opportunity cost generated by the energy storage system providing frequency modulation ancillary services through the first revenue, the second revenue, and the third revenue of the energy storage system in the spot market before deducting the declared frequency modulation capacity.

7. The energy storage power market competitive bidding method according to any one of claims 2 to 6, characterized in that The double-layer optimization relationship between the independent energy storage bidding decision model and the electricity market clearing model is transformed into a single-layer mixed-integer linear programming model through the following steps: Apply the KKT conditions to the electricity market clearing model and embed the KKT conditions as equilibrium constraints into the independent energy storage bidding decision model to obtain a mathematical programming MPEC model with equilibrium constraints; Use the big M method to linearize the complementary slackness conditions in the KKT conditions to eliminate the logical constraints in the double-layer model; Use the McCormick envelope constraint to linearly process the non-linear terms in the MPEC model to obtain a mixed-integer linear programming MILP model; Solve the MILP model through the Yalmip modeling tool by calling the Gurobi solver on the Matlab platform to obtain the optimized first quotation data.

8. A bidding device for an energy storage power market, characterized in that, The energy storage electricity market bidding device includes: an independent energy storage bidding decision model and an electricity market clearing model, where: The independent energy storage bidding decision model is used to send the first quotation data of the electricity energy market and the frequency modulation ancillary service market to the electricity market clearing model; The electricity market clearing model is used to determine the total electricity purchase cost based on the first quotation data and the second quotation data of each of the multiple conventional units; The electricity market clearing model is further used to determine the winning bid data of the independent energy storage bidding decision model based on the total electricity purchase cost and send the winning bid data to the independent energy storage bidding decision model; The independent energy storage bidding decision model is further used to determine the total revenue of the electricity energy market and the frequency modulation ancillary service market based on the winning bid data; The independent energy storage bidding decision model is further used to optimize the first quotation data based on the total revenue to obtain the optimized first quotation data and send the optimized first quotation data to the electricity market clearing model until the decrease in the total electricity purchase cost and the increase in the total revenue are both less than the preset threshold or the iteration times are reached to end the optimization.

9. A computer device, comprising: A memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the energy storage electricity market bidding method according to any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the energy storage electricity market bidding method according to any one of claims 1-7.

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