Operation optimization method for long-term hydrogen energy storage to participate in joint transactions of multiple types of electricity markets

Abstract

This invention provides an operational optimization method for long-term hydrogen energy storage participating in joint transactions of multiple electricity markets, belonging to the field of electricity market operation optimization technology. It solves the problem of the lack of existing operational optimization methods for long-term hydrogen energy storage in joint transactions of multiple electricity markets. The method includes: constructing a capacity market revenue decision model with the objective of maximizing the total revenue of the long-term hydrogen energy storage operator; constructing a capacity market clearing model with the objective of minimizing the total procurement cost of the market operator; constructing a joint clearing optimization model for the electricity-frequency regulation market with the objective of minimizing the sum of electricity purchase cost and ancillary service fees; transforming the capacity market clearing model and the joint clearing optimization model into constraints for the capacity market revenue decision model, and forming a single-layer optimization model with the transformed constraints; solving the single-layer optimization model to obtain the operational optimization results of the joint transactions.

Description

Technical Field

[0001] This invention relates to the field of power market operation optimization technology, and in particular to an operation optimization method for long-term hydrogen energy storage participating in joint transactions of multiple power markets. Background Technology

[0002] With the accelerated pace of global energy transition, the proportion of renewable energy in the power system is continuously increasing. However, the intermittency and volatility of renewable energy pose challenges to the stable operation of the power system, and energy storage technology, as a key means to solve this problem, has received widespread attention. Among them, long-term hydrogen energy storage technology, due to its advantages such as large-scale energy storage, long-cycle energy storage, and cross-energy form conversion, can effectively solve the problem of insufficient renewable energy output for extended periods, supporting the efficient operation of the energy system. However, its high investment cost and relatively low efficiency make its economic viability highly dependent on maximizing returns in the electricity market.

[0003] Currently, although there has been some research and practice regarding the participation of energy storage technologies in the electricity market, the optimization methods for the operation of long-duration hydrogen energy storage, a special form of energy storage, in the joint trading of multiple electricity markets are still in the exploratory stage. Traditional energy storage optimization and dispatch methods are mainly aimed at short-duration energy storage technologies such as battery energy storage, and are difficult to apply directly to long-duration hydrogen energy storage systems. In addition, the complexity and uncertainty of multiple electricity markets also increase the difficulty of optimizing the operation of long-duration hydrogen energy storage. Summary of the Invention

[0004] Based on the above analysis, the embodiments of the present invention aim to provide an operation optimization method for long-term hydrogen energy storage to participate in joint transactions of multiple types of electricity markets, in order to solve the problem that there is a lack of operation optimization methods for long-term hydrogen energy storage in joint transactions of multiple types of electricity markets in the prior art.

[0005] This invention discloses an operation optimization method for long-term hydrogen energy storage participating in joint trading of multiple types of electricity markets, the method comprising:

[0006] To maximize the total revenue of long-term hydrogen energy storage operators, a capacity market revenue decision-making model is constructed.

[0007] A capacity market clearing model is constructed with the goal of minimizing the total procurement cost for market operators.

[0008] To minimize the sum of electricity purchase costs and ancillary service fees, a joint clearing optimization model for the electricity-frequency regulation market is constructed.

[0009] The capacity market clearing model and the joint clearing optimization model are transformed into constraints for the capacity market revenue decision model. The capacity market revenue decision model and the transformed constraints form a single-layer optimization model. Solving the single-layer optimization model yields the operational optimization results of the joint transaction.

[0010] Based on the above solution, the present invention also makes the following improvements:

[0011] Furthermore, the objective function of the capacity market revenue decision model is expressed as:

[0012] (1)

[0013] in, The total revenue for long-term hydrogen energy storage operators; This represents the winning bid capacity for long-duration hydrogen energy storage power stations in the capacity market. , Long-term hydrogen energy storage power station The discharge and charging power that are constantly won in the electric energy market; , Long-term hydrogen energy storage power station FM capacity and FM mileage won in the FM market; The clearing price for long-duration hydrogen energy storage power plants in the capacity market; For the trading cycle; for The clearing price of electricity in the current electricity market; , They are respectively The clearing price of frequency modulation capacity and the clearing price of frequency modulation mileage in the frequency modulation market at any given time; , These are the fixed operation and maintenance costs and real-time operation and maintenance costs of long-term hydrogen energy storage power stations, respectively.

[0014] Furthermore, the constraints of the capacity market revenue decision-making model include: charging and discharging constraints of long-term hydrogen energy storage power stations, operational constraints, application capacity constraints in the frequency regulation market and capacity market, capacity obligation constraints, bidding constraints, and capacity bidding constraints.

[0015] Furthermore, the objective function of the capacity market clearing model is expressed as:

[0016] (2)

[0017] in, This represents the total procurement cost for the market operator; , The quotes are for the capacity of long-duration hydrogen energy storage power stations and conventional generator sets, respectively. This represents the winning bid capacity for conventional generator sets in the capacity market.

[0018] Furthermore, the constraints of the capacity market clearing model include system capacity balance constraints and upper and lower limits constraints on the winning bid capacity of generating units.

[0019] Furthermore, the objective function of the joint clearing optimization model is expressed as:

[0020] (3)

[0021] In the formula, The sum of electricity purchase cost and ancillary service fees. For conventional generator sets The amount of electricity generated that is constantly won in the electricity market. , They are conventional generator sets Constantly winning bids for FM capacity and FM mileage in the FM market; , , They are conventional generator sets The power generation price, frequency regulation capacity price, and frequency regulation mileage price declared at all times; , , , Long-duration hydrogen energy storage power stations The charging price, discharging price, frequency regulation capacity price, and frequency regulation mileage price are declared at all times.

[0022] Furthermore, the constraints of the joint clearing optimization model include: market clearing rules, grid operation constraints, frequency regulation capacity and mileage demand constraints.

[0023] Furthermore, the constraints for transforming the capacity market clearing model and the joint clearing optimization model into the capacity market revenue decision model are executed as follows:

[0024] By using KKT conditions and duality theory, the gradient conditions of the capacity market clearing model and the joint clearing optimization model are transformed into the equality constraints of the capacity market revenue decision model.

[0025] Handling complementary relaxation constraints in KKT conditions;

[0026] The equality constraint and the complementary relaxation constraint are used together as the transformed constraint conditions.

[0027] Furthermore, the gurobi solver is used to solve the single-layer optimization model.

[0028] Furthermore, the operational optimization results of the joint transaction include:

[0029] Clearing prices in the capacity market, electricity market, and frequency regulation market;

[0030] The price, power, capacity, frequency regulation capacity and frequency regulation mileage declared by long-term hydrogen energy storage power stations in the capacity market, electricity market and frequency regulation market, as well as the price, power, capacity, frequency regulation capacity and frequency regulation mileage of the winning bids.

[0031] Compared with the prior art, the present invention can achieve at least one of the following beneficial effects.

[0032] 1. Market Adaptation and Mechanism Alignment: Focusing on three key electricity markets, including capacity market, power energy market, and frequency regulation market, the three-layer optimized architecture is used to accurately match the multi-device coupling characteristics of long-term hydrogen energy storage power stations and deeply align with cross-market trading mechanisms, making the participation mode of long-term hydrogen energy storage in market trading more in line with the actual operation logic of the power system.

[0033] 2. Advantages of interactive closed loop and decoupling: Construct a closed loop interaction of "upper-level operator bidding - mid-level capacity market clearing - lower-level electricity and frequency regulation market clearing - price reaction benefits", while effectively decoupling the technical constraints of hydrogen energy storage equipment (such as electrolyzer power, hydrogen storage tank state of charge, etc.) from the power system-level market rules (such as clearing rules, frequency regulation requirements, etc.), avoiding the constraint confusion problem that is prone to occur in single-layer models, and ensuring the scientific nature of strategy formulation.

[0034] 3. Improved system efficiency: The upper layer aims to maximize operator revenue by optimizing multi-market pricing strategies; the middle and lower layers allocate resources with the goal of minimizing total costs. This not only enables the long-term hydrogen energy storage system to operate economically, but also ensures a stable and reliable power supply to the power system by mitigating renewable energy fluctuations and flexibly allocating energy, thereby improving overall operational efficiency.

[0035] 4. The capacity market revenue decision-making model for long-term hydrogen energy storage power stations in the first layer can comprehensively consider various factors such as the operating characteristics, costs, and revenues of long-term hydrogen energy storage power stations. It also covers various electricity market products such as capacity market, electricity energy market, and frequency regulation market. It can fully explore the multiple values ​​of long-term hydrogen energy storage, formulate the optimal pricing strategy for hydrogen energy storage power stations, and thus obtain higher returns in the electricity market and achieve optimal resource allocation.

[0036] 5. The second and third layer capacity market clearing models and joint clearing optimization models can simultaneously consider the demands of the capacity, electricity, and frequency regulation markets, achieving joint clearing of all three and improving the efficiency and accuracy of market clearing. Furthermore, the optimized results of joint trading can accurately reflect market supply and demand, promoting fair competition and rational resource allocation.

[0037] 6. The objective function for hydrogen energy storage considers capacity market revenue as well as revenue from the electricity and frequency regulation markets, reflecting the value of hydrogen energy storage as a special form of energy storage in providing long-term energy storage and participating in various power ancillary services. Traditional energy storage revenue calculations in the electricity market typically do not involve long-term capacity market revenue.

[0038] In this invention, the above-described technical solutions can be combined with each other to achieve more preferred combinations. Other features and advantages of this invention will be set forth in the following description, and some advantages may become apparent from the description or be learned by practicing the invention. The objects and other advantages of this invention can be realized and obtained from what is particularly pointed out in the description and drawings. Attached Figure Description

[0039] The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Throughout the drawings, the same reference numerals denote the same parts.

[0040] Figure 1 A flowchart illustrating the operational optimization method for long-term hydrogen energy storage participating in joint transactions of multiple electricity markets, provided in an embodiment of the present invention. Detailed Implementation

[0041] Preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings, which form part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, but are not intended to limit the scope of the present invention.

[0042] A specific embodiment of the present invention discloses an operation optimization method for long-term hydrogen energy storage to participate in joint trading of multiple types of electricity markets, the flowchart of which is shown below. Figure 1 As shown, the method includes the following steps.

[0043] Step S1: Construct a capacity market revenue decision model with the goal of maximizing the total revenue of long-term hydrogen energy storage operators.

[0044] In this embodiment, the capacity market revenue decision model is used to determine the investment capacity and bidding price of long-term hydrogen energy storage power stations in the capacity market. The capacity market revenue decision model is used as the first-level model.

[0045] The objective function of the capacity market revenue decision model is expressed as:

[0046] (1)

[0047] in, The total revenue for long-term hydrogen energy storage operators; This represents the winning bid capacity for long-duration hydrogen energy storage power stations in the capacity market. , Long-term hydrogen energy storage power station The discharge and charging power that are constantly won in the electric energy market; , Long-term hydrogen energy storage power station FM capacity and FM mileage won in the FM market (i.e., the FM ancillary services market); The clearing price for the capacity market is obtained from the capacity market clearing model (second-level model) in step S2; For the trading cycle; for The clearing price of electricity in the current electricity market; , They are respectively The clearing price of frequency modulation capacity and the clearing price of frequency modulation mileage in the frequency modulation market at any given time are obtained by clearing (third-level model) using the joint clearing optimization model in step S3. This represents the fixed operation and maintenance cost of a long-term hydrogen energy storage power station; This indicates the real-time operation and maintenance cost of a long-term hydrogen energy storage power station.

[0048] (2)

[0049] in, , These are the real-time operation and maintenance costs per unit power of the electrolyzer and the hydrogen gas turbine, respectively. The storage and maintenance cost per unit capacity of the hydrogen storage tank; For the electrolytic cell in Operating power at any given time; For hydrogen gas turbines in Output power at any given moment; For hydrogen storage tanks in The equivalent state of charge at time t.

[0050] The constraints of the capacity market revenue decision-making model include: charging and discharging constraints of long-term hydrogen energy storage power stations, operational constraints, application capacity constraints in the frequency regulation market and capacity market, capacity obligation constraints, bidding constraints, and capacity bidding constraints.

[0051] (1) Charge and discharge constraints of long-term hydrogen energy storage power stations

[0052] (3)

[0053] In the formula, , Long-term hydrogen energy storage power station The discharge power and charging power that are constantly declared in the electricity market; , These are the maximum discharge power and maximum charging power of the long-duration hydrogen energy storage power station, respectively. , Long-duration hydrogen energy storage power stations The discharge and charging states at any given time.

[0054] , All are 0-1 variables. This indicates that long-term hydrogen energy storage power stations are in Discharge operation is in progress at all times. This indicates that long-term hydrogen energy storage power stations are in No discharge operation was performed at any time; This indicates that long-term hydrogen energy storage power stations are in Charging is currently in progress. This indicates that long-term hydrogen energy storage power stations are in No charging operation was performed at any time.

[0055] (2) Operational constraints of long-term hydrogen energy storage power stations

[0056] The long-term hydrogen energy storage power station includes an electrolyzer sub-model, a hydrogen storage tank model, and a hydrogen gas turbine sub-model.

[0057] 1) Electrolytic cell sub-model

[0058] The electrolyzer sub-model, through a piecewise function, considers the impact of different power ranges on hydrogen production, thus more accurately reflecting the frequent power variations in the electrolyzer during actual operation and helping to optimize the operation strategy of the energy storage system. The expression for the electrolyzer sub-model is:

[0059] (4)

[0060] In the formula, For the electrolytic cell in Hydrogen production at any given time For the operating efficiency of the electrolytic cell; The amount of electricity consumed per unit of hydrogen gas. This is the rated power of the electrolytic cell.

[0061] 2) Hydrogen storage tank model

[0062] The hydrogen storage tank model serves as a hydrogen storage device within a coupled network. The equivalent state of charge (SOC) at time t is determined by The state of charge (SOC) at any given time is determined by the amount of gas added or released from the hydrogen storage tank. Therefore, the expression for the hydrogen storage tank model is:

[0063] (5)

[0064] In the formula, For hydrogen storage tanks in The equivalent state of charge at any given moment; For example, the gas loss ratio per unit hour of the hydrogen storage tank, ; This refers to the capacity of the hydrogen storage tank; , The hydrogen storage tanks are respectively The amount of hydrogen added and released at any given time; , These represent the hydrogen filling efficiency and hydrogen release efficiency of the hydrogen storage tank, respectively.

[0065] 3) Hydrogen gas turbine sub-model

[0066] The expression for the hydrogen gas turbine sub-model is:

[0067] (6)

[0068] In the formula: For hydrogen gas turbines in The amount of hydrogen consumed at any given time; For the operating efficiency of hydrogen gas turbines; for example, ; Hydrogen consumed per unit of electricity produced by a hydrogen gas turbine.

[0069] Therefore, the operating constraints of long-duration hydrogen energy storage power stations are expressed as follows:

[0070] (7)

[0071] (8)

[0072] In the formula, , These are the lower and upper limits of the equivalent state of charge of the hydrogen storage tank, respectively.

[0073] (3) Capacity constraints for long-term hydrogen energy storage power stations in the frequency regulation market

[0074] (9)

[0075] In the formula, , These represent long-term hydrogen energy storage power stations. The frequency modulation capacity and frequency modulation mileage are constantly being declared in the frequency modulation market; It is the frequency regulation mileage multiplier for long-term hydrogen energy storage power stations.

[0076] (4) Capacity constraints for long-duration hydrogen energy storage power stations in the capacity market

[0077] (10)

[0078] in, This indicates the capacity declared by a long-duration hydrogen energy storage power station in the capacity market. This represents the maximum capacity of a long-duration hydrogen energy storage power station.

[0079] (5) Capacity obligations constraint

[0080] (11)

[0081] in, For long-term hydrogen energy storage power stations during peak hours Whether it is available is a binary variable, represented by 1 if available and 0 if unavailable; This refers to the collection of peak hours.

[0082] (6) Pricing constraints for long-term hydrogen energy storage power stations

[0083] (12)

[0084] In the formula, , , , Long-duration hydrogen energy storage power stations The charging price, discharging price, frequency regulation capacity price, and frequency regulation mileage price are declared at all times; , , , Long-duration hydrogen energy storage power stations The upper limit of the charging price, discharging price, frequency regulation capacity price, and frequency regulation mileage price that are declared at all times.

[0085] (7) Capacity pricing constraints for long-duration hydrogen energy storage power stations:

[0086] (13)

[0087] in, Quotation for the capacity of long-duration hydrogen energy storage power stations; This represents the maximum capacity bid for a long-duration hydrogen energy storage power station.

[0088] The trading decision model is used to generate the optimal bidding strategy for hydrogen energy storage power plants. Specifically, in the trading decision model, , , , , , , , , These are the decision variables for the capacity market revenue decision-making model.

[0089] Step S2: Construct a capacity market clearing model with the goal of minimizing the total procurement cost for market operators.

[0090] In this embodiment, the capacity market clearing model is used to optimize the clearing process and output the clearing price of the capacity market.

[0091] The objective function of the capacity market clearing model is expressed as:

[0092] (14)

[0093] in, This represents the total procurement cost for the market operator; Quotation for the capacity of a conventional generator set. This represents the winning bid capacity for conventional generator sets in the capacity market.

[0094] The constraints of the capacity market clearing model include system capacity balance constraints and upper and lower limits of the unit's winning bid capacity.

[0095] (1) System capacity balance constraints

[0096] The expression for the system capacity balance constraint is as follows:

[0097] (15)

[0098] in, The system peak load (MW); The system's reserve rate; It is the dual variable of the system capacity balance constraint, and also the clearing price of the capacity market.

[0099] (2) Upper and lower limits of the winning bid capacity of the generating units

[0100] The expressions for the upper and lower limits of the unit's awarded capacity are as follows:

[0101] (16)

[0102] in, This refers to the capacity declared by conventional generator sets in the capacity market. These are the dual variables of the lower and upper limits of the capacity awarded to long-term hydrogen energy storage power plants in the capacity market; The dual variables of the lower and upper limits of the capacity bid for conventional generator sets in the capacity market are respectively defined.

[0103] The decision variables of the capacity market clearing model are , as well as .

[0104] Step S3: Construct a joint clearing optimization model for the electricity-frequency regulation market with the objective of minimizing the sum of electricity purchase costs and ancillary service fees.

[0105] Considering that both long-duration hydrogen energy storage power plants and basic conventional generator sets (other than long-duration hydrogen energy storage conventional generator sets) can bid in the energy and frequency regulation markets, the objective function of the joint clearing optimization model is expressed as:

[0106] (17)

[0107] In the formula, The sum of electricity purchase cost and ancillary service fees. For conventional generator sets The amount of electricity generated that is constantly won in the electricity market. , They are conventional generator sets Constantly winning bids for FM capacity and FM mileage in the FM market; , , They are conventional generator sets The power generation price, frequency regulation capacity price, and frequency regulation mileage price are declared at all times.

[0108] The constraints of the joint clearing optimization model include: market clearing rules, power grid operation constraints, frequency regulation capacity and mileage demand constraints.

[0109] (1) Market clearing rules

[0110] The expression for the market clearing rule constraint is as follows:

[0111] (18)

[0112] (19)

[0113] (20)

[0114]

[0115] In the formula, , The electrolyzers of the long-term hydrogen energy storage power station are respectively located in The dual variables of the lower and upper bound constraints of the input power at time t; , The hydrogen gas turbines of the long-term hydrogen energy storage power station are respectively The dual variables of the lower and upper bound constraints on the output power at any given time; , Long-term hydrogen energy storage power station The dual variables of the lower and upper limits of the frequency modulation capacity that is constantly won in the frequency modulation market; , For long-term hydrogen energy storage power stations The dual variables of the lower and upper limits of the frequency modulation mileage that is constantly won in the frequency modulation market; , For conventional generator sets The dual variables of the frequency modulation capacity and the upper limit constraint of frequency modulation mileage at any given time; , For conventional generator sets The dual variables of the frequency modulation capacity and the lower limit constraint of frequency modulation mileage capability at any given time; , For conventional generator sets The dual variables of the upper and lower limits of output constraints at any given time; , For the dual variable corresponding to constraint (19), , For the dual variable corresponding to constraint (20); , These are the upper and lower limits of the output of conventional generator sets; It is the frequency regulation mileage multiplier for conventional generator sets.

[0116] (2) Power grid operation constraints

[0117] The expression for the power grid operation constraints is:

[0118] (twenty two)

[0119] , (twenty three)

[0120] (twenty four)

[0121] In the formula: Indicates nodes in the power grid The set of all connected nodes; Represents all nodes Connecting side roads; Indicates that the power grid is The workload of the moment; For nodes , Line admittance between; , Representing nodes respectively , exist Phase angle at any given moment; Indicates the line The transmission capacity limit; For nodes At any moment The dual variables of the power balance constraint; , The lines are respectively exist The dual variables of the time-to-time upper and lower bound constraints on power flow. For nodes connecting long-duration hydrogen energy storage power plants to the grid. ,satisfy .

[0122] (3) Frequency modulation capacity and mileage requirements constraints

[0123] The expressions for the frequency regulation capacity and mileage requirement constraints are as follows:

[0124] (25)

[0125] In the formula: , Representing the system Constant frequency modulation capacity and FM mileage requirements; dual variables , For the system in Frequency regulation capacity at any given time, and mileage clearing price. The decision variables for the joint clearing optimization model include... , , , , , , , , And each dual variable.

[0126] Step S4: Transform the capacity market clearing model and the joint clearing optimization model into constraints for the capacity market revenue decision model. The capacity market revenue decision model and the transformed constraints form a single-layer optimization model. Solve the single-layer optimization model to obtain the operational optimization results of the joint transaction.

[0127] Because of the strong coupling between the three-layer models and the presence of nonlinear terms, they are difficult to solve directly. Furthermore, in the constructed three-layer model, the second and third layers are linear programming problems involving market clearing. Therefore, KKT conditions and duality theory are used to transform the constraints of the second-layer capacity market clearing model and the third-layer joint clearing optimization model into the constraints of the first-layer capacity market revenue decision model, resulting in a single-layer optimization model. This reduces the complexity of the model and facilitates solution and analysis.

[0128] According to the duality theory of linear programming, the optimal solutions of the second-level and third-level models satisfy the KKT conditions (including feasibility, complementary slackness, gradient conditions, etc.). Therefore, by simply adding the KKT conditions of the second-level and third-level models as constraints to the first-level model, the original three-level coupling relationship can be replaced, realizing the transformation from a three-level model to a single-level model. The specific implementation process is described below.

[0129] Step S41: Using KKT conditions and duality theory, transform the gradient conditions of the capacity market clearing model and the joint clearing optimization model into equality constraints for the capacity market revenue decision model. The specific steps are as follows:

[0130] 1) Define dual variables for each constraint of the original problem, construct the Lagrangian function and the dual problem, and establish the relationship between the original problem and the dual variables; 2) Calculate the partial derivatives of the decision variables of the original problem with respect to the Lagrangian function and set them to 0, and extract the KKT gradient conditions. That is, first construct the Lagrangian function: combine the objective function with all constraints (multiplied by the corresponding multipliers) to form an augmented function; then calculate the gradient of the decision variables, that is, calculate the partial derivatives of each decision variable; set the gradient to zero: then, at the optimal solution, the gradient vector is zero. This is the basic step of solving the KKT conditions. This process can ensure that the second and third layer models run in the optimal state, specifically corresponding to formulas (28)-(37).

[0131] Step S42: Handle the complementary relaxation constraints in the KKT conditions.

[0132] The equality constraint and the complementary relaxation constraint are used together as the transformed constraint conditions.

[0133] The complementary slackness in KKT conditions means that the dual variable of a constraint and the constraint deviation cannot be positive at the same time (i.e., if the dual variable is non-zero, the constraint must be equal; if the constraint is not equal, the dual variable must be zero). Such constraints are shown in equations (38)-(63). The single-layer model obtained after transformation is as follows.

[0134] (26)

[0135] (27)

[0136] (28)

[0137] (29)

[0138] (30)

[0139] (31)

[0140] (32)

[0141] (33)

[0142] (34)

[0143] (35)

[0144] (36)

[0145] (37)

[0146] (38)

[0147] (39)

[0148] (40)

[0149] (41)

[0150] (42)

[0151] (43)

[0152] (44)

[0153] (45)

[0154] (46)

[0155] (47)

[0156] (48)

[0157] (49)

[0158] (50)

[0159] (51)

[0160] (52)

[0161] (53)

[0162] (54)

[0163] (55)

[0164] (56)

[0165] (57)

[0166] (58)

[0167] (59)

[0168] (60)

[0169] (61)

[0170] (62)

[0171] (63)

[0172] To linearize the above nonlinear terms, the Big M method is used to linearize equations (33)-(54). Specifically, for each general complementary relaxation condition:

[0173] (64)

[0174] All of these can be equivalently replaced by the following four sets of linear constraints:

[0175] (65)

[0176] (66)

[0177] in, As dual variables; The relaxation function of the original constraints; It is a newly introduced binary auxiliary variable; For a sufficiently large constant, its value needs to be greater than and An upper bound on all possible values.

[0178] For the objective function (26), the first objective function is linearized by strong duality theory and KKT optimality conditions to obtain the linearized objective function.

[0179] Step S43: Use the gurobi solver to solve the single-layer optimization model and obtain the operational optimization results of the joint transaction.

[0180] The operational optimization results of joint trading refer to key information such as the clearing prices of the capacity market, the electricity market, the frequency regulation market, and the bidding strategies of long-term hydrogen energy storage power stations.

[0181] Specifically, the operational optimization results of the joint transaction include: the clearing prices of the capacity market, the electricity market, and the frequency regulation market; the prices, power, capacity, frequency regulation capacity, and frequency regulation mileage declared by long-term hydrogen energy storage power stations in the capacity market, the electricity market, and the frequency regulation market; and the prices, power, capacity, frequency regulation capacity, and frequency regulation mileage of the winning bids.

[0182] Those skilled in the art will understand that all or part of the processes of the methods described in the above embodiments can be implemented by a computer program instructing related hardware, and the program can be stored in a computer-readable storage medium. The computer-readable storage medium may be a disk, optical disk, read-only memory, or random access memory, etc.

[0183] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for operation optimization of long-time hydrogen energy participating in joint transaction of multi-product power market, characterized in that, The method comprises: A capacity market revenue decision model is constructed to maximize the total revenue of the long-time hydrogen storage energy operator; A capacity market clearing model is constructed to minimize the total procurement cost of the market operator; A joint clearing optimization model of the electricity energy-frequency regulation market is constructed to minimize the sum of the electricity purchase cost and the auxiliary service cost; The capacity market clearing model and the joint clearing optimization model are converted into constraint conditions of the capacity market revenue decision model, and a single-layer optimization model is formed according to the capacity market revenue decision model and the converted constraint conditions; the single-layer optimization model is solved to obtain the operation optimization result of the joint transaction; The objective function of the capacity market revenue decision model is expressed as: (1) wherein, is the total revenue of the long-duration hydrogen energy storage operator; is the winning capacity of the long-duration hydrogen energy storage power station in the capacity market; , are respectively the fixed operation and maintenance cost and the real-time operation and maintenance cost of the long-duration hydrogen energy storage power station is the discharging power and the charging power of the long-duration hydrogen energy storage power station in the electricity market at the moment; , are respectively the fixed operation and maintenance cost and the real-time operation and maintenance cost of the long-duration hydrogen energy storage power station is the frequency modulation capacity and the frequency modulation mileage of the long-duration hydrogen energy storage power station in the frequency modulation market; is the clearing price of the capacity market; is the trading period; is is the clearing price of the electricity market at the moment; , are respectively is the frequency modulation capacity clearing price and the frequency modulation mileage clearing price of the frequency modulation market at the moment; , are respectively the fixed operation and maintenance cost and the real-time operation and maintenance cost of the long-duration hydrogen energy storage power station The objective function of the capacity market clearing model is expressed as: (2) wherein, represents the total cost of procurement of the market operator; , respectively the capacity offer of the long-duration hydrogen storage power plant, the conventional generator unit, is the winning capacity of the conventional generator unit in the capacity market. The constraint conditions of the capacity market clearing model include system capacity balance constraints and upper and lower limits of the winning capacity of the unit; wherein, The expression of the system capacity balance constraint is as follows: (3) wherein, is the system peak load (MW); is the reserve margin of the system capacity; is the dual variable of the system capacity balance constraint, also the clearing price of the capacity market; The expression of the upper and lower limits of the winning capacity of the unit is as follows: (4) wherein, denotes the capacity declared by the conventional generator set in the capacity market, are the dual variables of the lower bound constraint and the upper bound constraint of the long-time hydrogen storage energy station's winning capacity in the capacity market, respectively; are the dual variables of the lower bound constraint and the upper bound constraint of the conventional generator set's winning capacity in the capacity market, respectively.

2. The operation optimization method for long-time hydrogen storage participating in multi-product power market joint transaction according to claim 1, characterized in that, The constraint conditions of the capacity market revenue decision model include the charge and discharge constraints of the long-time hydrogen storage energy power station, the operation constraints, the declared capacity constraints in the frequency regulation market and the capacity market, the capacity obligation constraints, the bidding constraints and the capacity bidding constraints.

3. The operation optimization method for long-time hydrogen storage participating in multi-product power market joint transaction according to claim 2, characterized in that, The objective function of the joint clearing optimization model is expressed as: (5) In the formula, The sum of electricity purchase cost and ancillary service fees. For conventional generator sets The amount of electricity generated that is constantly won in the electricity market. , They are conventional generator sets Constantly winning bids for FM capacity and FM mileage in the FM market; , , They are conventional generator sets The power generation price, frequency regulation capacity price, and frequency regulation mileage price declared at all times; , , , Long-duration hydrogen energy storage power stations The charging price, discharging price, frequency regulation capacity price, and frequency regulation mileage price are declared at all times.

4. The operation optimization method for long-time hydrogen storage participating in multi-product power market joint transaction according to claim 3, characterized in that, The constraint conditions of the joint clearing optimization model include market clearing rules, power grid operation constraints and frequency regulation capacity and mileage demand constraints.

5. The operation optimization method for long-time hydrogen storage participating in multi-product power market joint transaction according to claim 4, characterized in that, The capacity market clearing model and the joint clearing optimization model are converted into constraint conditions of the capacity market revenue decision model, and the following is executed: The gradient conditions of the capacity market clearing model and the joint clearing optimization model are converted into equality constraints of the capacity market revenue decision model through the KKT condition and the dual theory; The complementary relaxation constraints in the KKT condition are processed; The equality constraints and the complementary relaxation constraints are jointly used as the converted constraint conditions.

6. The operation optimization method for long-time hydrogen storage participating in multi-product power market joint transaction according to claim 5, characterized in that, The single-layer optimization model is solved by using a gurobi solver.

7. The operation optimization method for long-time hydrogen storage participating in multi-product power market joint transaction according to claim 6, characterized in that, The operation optimization result of the joint transaction includes: The clearing prices of the capacity market, the electricity energy market and the frequency regulation market; The declared prices, powers, capacities, frequency regulation capacities and frequency regulation mileages of the long-time hydrogen storage energy power station in the capacity market, the electricity energy market and the frequency regulation market, and the winning prices, powers, capacities, frequency regulation capacities and frequency regulation mileages.

Images