A two-tier game bidding method for energy storage entities to participate in the frequency regulation auxiliary service market

Abstract

The present invention discloses a double-layer game bidding method for energy storage entities to participate in the frequency regulation auxiliary service market, which belongs to the field of power systems. The method includes constructing a double-layer optimization model, wherein the upper layer optimizes the centralized clearing process, and the lower layer optimizes the energy storage power station quotation process. The upper layer optimizes the centralized clearing process of the frequency regulation auxiliary service market, with the goal of minimizing the social frequency regulation cost in each time period, and the lower layer model aims to maximize the profit of the energy storage power station, and the constraints include energy storage declared power constraints, state of charge constraints, etc. The present invention nests and iterates the solution of the frequency regulation auxiliary service market clearing model and the bidding model, which can not only obtain the clearing price of the frequency regulation auxiliary service market, but also unify the bidding strategies of different energy storage entities on the power supply side, power grid measurement and load side, reduce the clearing cost of the frequency regulation auxiliary service market, increase the benefits of energy storage power stations participating in the frequency regulation of the power system, and thus improve the operational safety of the access of new energy to the power system.

Description

Technical Field

[0001] The present invention belongs to the field of electric power systems, and in particular relates to a double-layer game bidding method for energy storage entities to participate in a frequency regulation auxiliary service market. Background Art

[0002] Energy storage resources participate in the frequency regulation auxiliary service market as an independent market entity, which can be divided into two stages: the first is the bidding stage of energy storage, in which the bidding frequency regulation capacity and quotation are declared; the second is the market pre-clearing stage, in which the winning energy storage resources are determined according to the ranking of the quotations, and the quotation of the marginal unit is used as the clearing marginal price. In the energy storage bidding stage, its goal is to maximize the frequency regulation benefits of energy storage; in the market pre-clearing stage, its goal is to minimize the frequency regulation expenditure of the power grid. The power supply side energy storage is mostly combined with traditional thermal power units and new energy power stations for frequency regulation. In order to give full play to its advantages of rapid response, the power supply side energy storage mainly bears the high-frequency components in the ACE signal. On this basis, the bidding strategies such as thermal storage combined frequency regulation and wind storage combined frequency regulation are simplified into two parts: 1) the bidding strategy of traditional units participating in the low-frequency component of the ACE signal; 2) the bidding strategy of energy storage participating in the high-frequency component of the ACE signal. The bidding game model of energy storage participating in the high-frequency component of the ACE signal is divided into two stages: "energy storage quotation" and "market pre-clearing", and its goals are to maximize the frequency regulation profit of energy storage and minimize the frequency regulation expenditure of the power grid. Load aggregators integrate small-scale distributed energy storage resources and make unified bids, thereby participating in the frequency regulation auxiliary service market and bidding with other units participating in frequency regulation. The profit distribution rules for load aggregators managing small-capacity energy storage power stations will be formulated by load aggregators. Similarly, the bidding of load aggregators in the frequency regulation market is divided into two stages: "energy storage bidding" and "market pre-clearing".

[0003] The frequency regulation auxiliary service market is divided into three time scales, namely day-ahead quotation, pre-hour clearing, and real-time clearing and settlement. Figure 1As shown in the figure, in the day-ahead quotation stage, the units participating in frequency regulation report capacity quotations and mileage quotations as well as the bid frequency regulation capacity, adjust the quotations according to the historical frequency regulation performance, and use the estimated real-time LMP (Location Marginal Price) and operating costs to calculate the frequency regulation opportunity cost. In the pre-hour clearing stage, the ranking price is obtained according to the adjusted quotations and opportunity costs, and the prices are cleared in order from low to high until the sum of the winning capacity meets the total frequency regulation demand. In the real-time clearing stage, the opportunity cost of the winning frequency regulation resources is recalculated according to the actual LMP of each 5-minute scheduling stage to obtain a new ranking price. The final ranking price of the marginal frequency regulation resources is the clearing price. The arithmetic average of the prices of the 12 scheduling periods is the settlement price for a certain hour. The clearing and settlement process of the frequency regulation auxiliary service market mechanism is as follows: the units participating in frequency regulation report quotations and frequency regulation capacity on the day-ahead, sort them according to the quotations (or quotations adjusted based on the historical frequency regulation performance), and determine the winning units for the next day from low to high until the sum of the winning capacities of the units meets the frequency regulation demand. The quotation of the marginal unit is used as the clearing marginal price, and the settlement price is determined according to different market rules, and is generally obtained by using the clearing marginal price within the settlement period.

[0004] Because there is a game behavior among the energy storage power stations. In the process of the energy storage power station game, each energy storage power station continuously optimizes its bidding strategy with the goal of maximizing its profit until a Nash equilibrium is formed between each other. The problem of solving the optimal solution of the game is actually a multi-objective planning problem, but there are often contradictions between multiple goals. It is impossible to require all goals to be the optimal solution. Therefore, the multi-objective planning problem often only solves its effective solution. Therefore, when the game reaches the Nash equilibrium, the quotation of each energy storage power station is usually not the highest price in history that appears in the game iteration process, but the price at equilibrium, that is, the price that all energy storage power stations participating in the frequency regulation market have no intention to deviate from. Therefore, a bidding method for energy storage power stations of a new market entity to participate in the frequency regulation auxiliary service market is needed to solve the problem of low benefits of energy storage power stations participating in the frequency regulation of the power system. Summary of the invention

[0005] Based on the above shortcomings, the present invention provides a double-layer game bidding method for energy storage entities to participate in the frequency regulation auxiliary service market, which is used to ensure that energy storage power stations distributed on the power supply side, power grid side and load side actively participate in the frequency regulation auxiliary service market.

[0006] The technical solution adopted by the present invention is as follows: a two-layer game bidding method for energy storage entities to participate in the frequency regulation auxiliary service market, the steps are as follows:

[0007] S1: Constructing the upper centralized clearing model: It is used to simulate the centralized clearing process of the frequency regulation market. Its goal is to minimize the social frequency regulation cost in each period. The objective function is shown in formula (1).

[0008]

[0009] In the formula, λ θ,t is the clearing marginal price of the FM market at time t; i,t is the quotation of energy storage power station i at time t; is the increased power of the energy storage power station i that won the bid at time t; is the power reduction of energy storage station i at time t, which should satisfy

[0010] Define α i,t is a 0-1 variable used to indicate the winning bid of energy storage power station i at time t, and is defined as shown in formula (2).

[0011]

[0012] Using alpha i,t The power of energy storage station i at time t There are two situations:

[0013] 1) When the energy storage power station i is not the clearing marginal unit θ:

[0014]

[0015] 2) When the energy storage power station i is the clearing marginal unit θ:

[0016]

[0017] In the formula, is the power increase or decrease reported by energy storage power station i at time t;

[0018] Therefore, the clearing marginal price of the FM market at time t is θ,t By α i,t The value of is defined as shown in formula (5).

[0019] λ θ,t =max{α i,t λ i,t} (5)

[0020] The market concentration clearing stage is subject to the power balance constraint, that is, the bidding capacity of the energy storage power station and the frequency regulation demand should be balanced in real time, as shown in formula (6):

[0021]

[0022] Where D t is the frequency regulation demand of the power grid at time t;

[0023] S2: Construct the bidding game model of the lower-level energy storage power station: The goal is to maximize the profit of each energy storage power station within the settlement period. Since there are I energy storage power stations participating in the frequency regulation market, the strategy set S of the day-ahead market quotation has I elements, as shown in formula (7).

[0024] S={s1 s2 … s I} (7)

[0025] The bidding strategy reported by energy storage power station i to the dispatching and trading center is s i , its profit at time t is defined as E i,t (s i ,s -i ) is shown in formula (8),

[0026]

[0027] In the formula, s -i It represents the quotation collection of the remaining energy storage power stations except the energy storage power station i that is, s -i ={s1 … s i-1 s i+1 …s I}; Indicates the winning bid power of energy storage station i and The corresponding frequency modulation cost;

[0028] Costs in the frequency regulation ancillary services market Including capacity cost and mileage cost. The capacity cost includes the average annual investment cost and annual operation cost of the energy storage power station. The mileage cost includes the energy loss cost caused by continuous charging and discharging and the cycle life attenuation cost caused by the depth of discharge.

[0029] According to the clearing results of the day-ahead market, each energy storage power station continuously optimizes its own bid to achieve Nash equilibrium and obtain the final bidding strategy. The bidding strategy of energy storage power station i after optimization is defined as s i ′, then the objective function of the game model is as shown in formula (9):

[0030]

[0031] In the formula, Where λ θ,t 'and is the updated market clearing price and winning bid up or down power of energy storage power station i at time t;

[0032] S3: Construct the constraints of the lower-level model, including declared power constraints and state of charge constraints.

[0033] 1) Declare power constraints

[0034]

[0035] Where P i b is the rated power of the energy storage station i. Whether the energy storage is charging or discharging, its power cannot exceed the rated power;

[0036] 2) State of charge constraints

[0037]

[0038] In the formula, SOC i,t is the state of charge of the i-th energy storage station at time t, with SOC constraints, Q i,t The calculation formula of is shown in formula (12):

[0039]

[0040]

[0041] Where η i down is the charging efficiency of the i-th energy storage power station; η i up is the discharge efficiency of the i-th energy storage power station; is the charging power of the i-th energy storage power station at time t-Δt; is the discharge power of the i-th energy storage power station; Q i,t is the capacity of the i-th energy storage power station at time t; Q i,t-Δt is the capacity of the i-th energy storage power station at time t-Δt; is the rated capacity of the i-th energy storage power station.

[0042] In order to ensure that the energy storage power station i can continuously participate in the frequency regulation market, the SOC is selected as 50% to cope with the subsequent charge and discharge response, as shown in formula (14):

[0043] SOC i,0 =SOC i,T =50% (14)

[0044] In the formula, SOC i,0 is the state of charge of the i-th energy storage power station at the beginning of the market clearing period; SOC i,T is the charge state of the i-th energy storage power station at the end of the market clearing period T.

[0045] On the other hand, the present invention also provides a double-layer game bidding system for energy storage entities to participate in the frequency regulation auxiliary service market, including:

[0046] Centralized clearing module: It is used to simulate the centralized clearing process of the frequency regulation market. Its goal is to minimize the social frequency regulation cost in each period. The objective function is shown in formula (1).

[0047]

[0048] In the formula, λ θ,t is the clearing marginal price of the FM market at time t; i,t is the quotation of energy storage power station i at time t; is the increased power of the energy storage power station i that won the bid at time t; is the power reduction of energy storage station i at time t, which should satisfy

[0049] Define α i,t is a 0-1 variable used to indicate the winning bid of energy storage power station i at time t, and is defined as shown in formula (2).

[0050]

[0051] Using alpha i,t The power of energy storage station i at time t There are two situations:

[0052] 1) When the energy storage power station i is not the clearing marginal unit θ:

[0053]

[0054] 2) When the energy storage power station i is the clearing marginal unit θ:

[0055]

[0056] In the formula, is the power increase or decrease reported by energy storage power station i at time t;

[0057] Therefore, the clearing marginal price of the FM market at time t is θ,t By α i,t The value of is defined as shown in formula (5).

[0058] λ θ,t =max{α i,t λ i,t} (5)

[0059] The market concentration clearing stage is subject to the power balance constraint, that is, the bidding capacity of the energy storage power station and the frequency regulation demand should be balanced in real time, as shown in formula (6):

[0060]

[0061] Where D tis the frequency regulation demand of the power grid at time t;

[0062] Bidding game module: The goal is to maximize the profit of each energy storage power station within the settlement period. Since there are I energy storage power stations participating in the frequency regulation market, the strategy set S of the day-ahead market quotation has I elements, as shown in formula (7):

[0063] S={s1 s2 … s I} (7)

[0064] The bidding strategy reported by energy storage power station i to the dispatching and trading center is s i , its profit at time t is defined as E i,t (s i ,s -i ) is shown in formula (8),

[0065]

[0066] In the formula, s -i It represents the quotation collection of the remaining energy storage power stations except the energy storage power station i that is, s -i ={s1 … s i-1 s i+1 …s I}; Indicates the winning bid power of energy storage station i and The corresponding frequency modulation cost;

[0067] Costs in the frequency regulation ancillary services market Including capacity cost and mileage cost. The capacity cost includes the average annual investment cost and annual operation cost of the energy storage power station. The mileage cost includes the energy loss cost caused by continuous charging and discharging and the cycle life attenuation cost caused by the depth of discharge.

[0068] According to the clearing results of the day-ahead market, each energy storage power station continuously optimizes its own bid to achieve Nash equilibrium and obtain the final bidding strategy. The bidding strategy of energy storage power station i after optimization is defined as s i ′, then the objective function of the game model is as shown in formula (9):

[0069]

[0070] In the formula, Where λ θ,t 'and is the updated market clearing price and winning bid up or down power of energy storage power station i at time t;

[0071] 1) Constraint module: declare power constraints

[0072]

[0073] Where P i b is the rated power of the energy storage station i. Whether the energy storage is charging or discharging, its power cannot exceed the rated power;

[0074] 2) State of charge constraints

[0075]

[0076] In the formula, SOC i,t is the state of charge of the i-th energy storage station at time t, with SOC constraints, Q i,t The calculation formula of is shown in formula (12):

[0077]

[0078]

[0079] Where η i down is the charging efficiency of the i-th energy storage power station; η i up is the discharge efficiency of the i-th energy storage power station; is the charging power of the i-th energy storage power station at time t-Δt; is the discharge power of the i-th energy storage power station; Q i,t is the capacity of the i-th energy storage power station at time t; Q i,t-Δt is the capacity of the i-th energy storage power station at time t-Δt; is the rated capacity of the i-th energy storage power station.

[0080] In order to ensure that the energy storage power station i can continuously participate in the frequency regulation market, the SOC is selected as 50% to cope with the subsequent charge and discharge response, as shown in formula (14):

[0081] SOC i,0 =SOC i,T =50% (14)

[0082] In the formula, SOC i,0 is the state of charge of the i-th energy storage power station at the beginning of the market clearing period; SOC i,T is the charge state of the i-th energy storage power station at the end of the market clearing period T.

[0083] On the other hand, the purpose of the present invention is achieved through a quotation determination device for an energy storage entity to participate in a double-layer game bidding in a frequency regulation auxiliary service market. The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, it implements the above-mentioned method for an energy storage entity to participate in a double-layer game bidding in a frequency regulation auxiliary service market.

[0084] On the other hand, the purpose of the present invention is achieved through a computer-readable storage medium, on which an information transmission implementation program is stored. When the program is executed by a processor, the steps of a two-tier game bidding method for a storage entity to participate in the frequency regulation auxiliary service market as described above are implemented.

[0085] To achieve the above objectives, the present invention also provides a quotation determination device for an energy storage entity to participate in a double-layer game bidding in a frequency regulation auxiliary service market, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the above-mentioned method for an energy storage entity to participate in a double-layer game bidding in a frequency regulation auxiliary service market.

[0086] To achieve the above objectives, the present invention also provides a computer-readable storage medium, on which is stored an implementation program for information transmission, and when the program is executed by a processor, the steps of the above-mentioned method for energy storage entities to participate in a two-tier game bidding method in the frequency regulation auxiliary service market are implemented.

[0087] Advantages and beneficial effects of the present invention: The present invention nests and iterates the frequency regulation auxiliary service market clearing model and the bidding model to obtain the clearing price of the frequency regulation auxiliary service market, and unifies the bidding strategies of different energy storage entities on the power supply side, grid measurement and load side, thereby reducing the clearing cost of the frequency regulation auxiliary service market, increasing the benefits of energy storage power stations participating in the frequency regulation of the power system, and further improving the operational safety of new energy access to the power system, which has important reference value for improving the market competitiveness of energy storage power stations in future new power systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0088] Figure 1 A flow chart for clearing and settlement of the FM market;

[0089] Figure 2 This is a diagram of the architecture of the double-layer optimization game method of the present invention. DETAILED DESCRIPTION

[0090] The present invention is further described below with reference to examples in the accompanying drawings of the specification, and the present invention is described through some embodiments. It is known to those skilled in the art that various changes or equivalent substitutions may be made to these features and embodiments without departing from the spirit and scope of the present invention.

[0091] Example 1

[0092] Energy storage power stations distributed on the power supply side, grid measurement and load side are new market entities that participate in the two-tier game bidding in the frequency regulation auxiliary service market. The entire game bidding process is divided into two stages: the first is the bidding stage for energy storage, where the bid frequency regulation capacity and quotation are declared; the second is the market pre-clearing stage, where the winning energy storage resources are determined based on the ranking of quotations, and the quotation of the marginal unit is used as the clearing marginal price. The transaction of new energy storage entities participating in the frequency regulation auxiliary service market is simplified into a two-tier optimization model, where the upper layer optimizes the centralized clearing process and the lower layer optimizes the energy storage power station quotation process. The upper layer optimizes the centralized clearing process of the frequency regulation auxiliary service market, with the goal of minimizing the social frequency regulation cost in each period, and the lower layer model aims to maximize the profit of energy storage power stations. Constraints include energy storage declared power constraints and charge state constraints.

[0093] like Figure 2 As shown, this embodiment provides a method for energy storage entities to participate in a double-layer game bidding in the frequency regulation auxiliary service market, and the steps are as follows:

[0094] S1: Constructing the upper centralized clearing model: It is used to simulate the centralized clearing process of the frequency regulation market. Its goal is to minimize the social frequency regulation cost in each period. The objective function is shown in formula (1).

[0095]

[0096] In the formula, λ θ,t is the clearing marginal price of the FM market at time t; i,t is the quotation of energy storage power station i at time t; is the increased power of the energy storage power station i that won the bid at time t; is the power reduction of energy storage station i at time t, which should satisfy

[0097] Define α i,t is a 0-1 variable used to indicate the winning bid of energy storage power station i at time t, and is defined as shown in formula (2).

[0098]

[0099] Using alpha i,t The power of energy storage station i at time t There are two situations:

[0100] 1) When the energy storage power station i is not the clearing marginal unit θ:

[0101]

[0102] 2) When the energy storage power station i is the clearing marginal unit θ:

[0103]

[0104] In the formula, is the power increase or decrease reported by energy storage power station i at time t;

[0105] Therefore, the clearing marginal price of the FM market at time t is θ,t By α i,t The value of is defined as shown in formula (5).

[0106] λ θ,t =max{α i,t λ i,t} (5)

[0107] The market concentration clearing stage is subject to the power balance constraint, that is, the bidding capacity of the energy storage power station and the frequency regulation demand should be balanced in real time, as shown in formula (6):

[0108]

[0109] Where D t is the frequency regulation demand of the power grid at time t;

[0110] S2: Construct the bidding game model of the lower-level energy storage power station: The goal is to maximize the profit of each energy storage power station within the settlement period. Since there are I energy storage power stations participating in the frequency regulation market, the strategy set S of the day-ahead market quotation has I elements, as shown in formula (7).

[0111] S={s1 s2 … s I} (7)

[0112] The bidding strategy reported by energy storage power station i to the dispatching and trading center is s i , its profit at time t is defined as E i,t (s i ,s -i ) is shown in formula (8),

[0113]

[0114] In the formula, s -i It represents the quotation collection of the remaining energy storage power stations except the energy storage power station i that is, s -i ={s1 … s i-1 s i+1 …sI}; Indicates the winning bid power of energy storage station i and The corresponding frequency modulation cost;

[0115] Costs in the frequency regulation ancillary services market Including capacity cost and mileage cost. The capacity cost includes the average annual investment cost and annual operation cost of the energy storage power station. The mileage cost includes the energy loss cost caused by continuous charging and discharging and the cycle life attenuation cost caused by the depth of discharge.

[0116] According to the clearing results of the day-ahead market, each energy storage power station continuously optimizes its own bid to achieve Nash equilibrium and obtain the final bidding strategy. The bidding strategy of energy storage power station i after optimization is defined as s i ′, then the objective function of the game model is as shown in formula (9):

[0117]

[0118] In the formula, Where λ θ,t 'and is the updated market clearing price and winning bid up or down power of energy storage power station i at time t;

[0119] S3: Construct the constraints of the lower-level model, including declared power constraints and state of charge constraints.

[0120] 1) Declare power constraints

[0121]

[0122] Where P i b is the rated power of the energy storage station i. Whether the energy storage is charging or discharging, its power cannot exceed the rated power;

[0123] 2) State of charge constraints

[0124]

[0125] In the formula, SOC i,t is the state of charge of the i-th energy storage station at time t, with SOC constraints, Q i,t The calculation formula of is shown in formula (12):

[0126]

[0127]

[0128] Where η i downis the charging efficiency of the i-th energy storage power station; η i up is the discharge efficiency of the i-th energy storage power station; is the charging power of the i-th energy storage power station at time t-Δt; is the discharge power of the i-th energy storage power station; Q i,t is the capacity of the i-th energy storage power station at time t; Q i,t-Δt is the capacity of the i-th energy storage power station at time t-Δt; is the rated capacity of the i-th energy storage power station.

[0129] In order to ensure that the energy storage power station i can continuously participate in the frequency regulation market, the SOC is selected as 50% to cope with the subsequent charge and discharge response, as shown in formula (14):

[0130] SOC i,0 =SOC i,T =50% (14)

[0131] In the formula, SOC i,0 is the state of charge of the i-th energy storage power station at the beginning of the market clearing period; SOC i,T is the charge state of the i-th energy storage power station at the end of the market clearing period T.

[0132] Example 2

[0133] This embodiment provides a two-tier game bidding system for energy storage entities to participate in the frequency regulation auxiliary service market, including:

[0134] Centralized clearing module: It is used to simulate the centralized clearing process of the frequency regulation market. Its goal is to minimize the social frequency regulation cost in each period. The objective function is shown in formula (1).

[0135]

[0136] In the formula, λ θ,t is the clearing marginal price of the FM market at time t; i,t is the quotation of energy storage power station i at time t; is the increased power of the energy storage power station i that won the bid at time t; is the power reduction of energy storage station i at time t, which should satisfy

[0137] Define α i,t is a 0-1 variable used to indicate the winning bid of energy storage power station i at time t, and is defined as shown in formula (2).

[0138]

[0139] Using alpha i,tThe power of energy storage station i at time t There are two situations:

[0140] 1) When the energy storage power station i is not the clearing marginal unit θ:

[0141]

[0142] 2) When the energy storage power station i is the clearing marginal unit θ:

[0143]

[0144] In the formula, is the power increase or decrease reported by energy storage power station i at time t;

[0145] Therefore, the clearing marginal price of the FM market at time t is θ,t By α i,t The value of is defined as shown in formula (5).

[0146] λ θ,t =max{α i,t λ i,t} (5)

[0147] The market concentration clearing stage is subject to the power balance constraint, that is, the bidding capacity of the energy storage power station and the frequency regulation demand should be balanced in real time, as shown in formula (6):

[0148]

[0149] Where D t is the frequency regulation demand of the power grid at time t;

[0150] Bidding game module: The goal is to maximize the profit of each energy storage power station within the settlement period. Since there are I energy storage power stations participating in the frequency regulation market, the strategy set S of the day-ahead market quotation has I elements, as shown in formula (7):

[0151] S={s1 s2 … s I} (7)

[0152] The bidding strategy reported by energy storage power station i to the dispatching and trading center is s i , its profit at time t is defined as E i,t (s i ,s -i ) is shown in formula (8),

[0153]

[0154] In the formula, s -i It represents the quotation collection of the remaining energy storage power stations except the energy storage power station i that is, s -i={s1 … s i-1 s i+1 …s I}; Indicates the winning bid power of energy storage station i and The corresponding frequency modulation cost;

[0155] Costs in the frequency regulation ancillary services market Including capacity cost and mileage cost. The capacity cost includes the average annual investment cost and annual operation cost of the energy storage power station. The mileage cost includes the energy loss cost caused by continuous charging and discharging and the cycle life attenuation cost caused by the depth of discharge.

[0156] According to the clearing results of the day-ahead market, each energy storage power station continuously optimizes its own bid to achieve Nash equilibrium and obtain the final bidding strategy. The bidding strategy of energy storage power station i after optimization is defined as s i ′, then the objective function of the game model is as shown in formula (9):

[0157]

[0158] In the formula, Where λ θ,t 'and is the updated market clearing price and winning bid up or down power of energy storage power station i at time t;

[0159] 1) Constraint module: declare power constraints

[0160]

[0161] Where P i b is the rated power of the energy storage station i. Whether the energy storage is charging or discharging, its power cannot exceed the rated power;

[0162] 2) State of charge constraints

[0163]

[0164] In the formula, SOC i,t is the state of charge of the i-th energy storage station at time t, with SOC constraints, Q i,t The calculation formula of is shown in formula (12):

[0165]

[0166]

[0167] Where η i down is the charging efficiency of the i-th energy storage power station; ηi up is the discharge efficiency of the i-th energy storage power station; is the charging power of the i-th energy storage power station at time t-Δt; is the discharge power of the i-th energy storage power station; Q i,t is the capacity of the i-th energy storage power station at time t; Q i,t-Δt is the capacity of the i-th energy storage power station at time t-Δt; is the rated capacity of the i-th energy storage power station.

[0168] In order to ensure that the energy storage power station i can continuously participate in the frequency regulation market, the SOC is selected as 50% to cope with the subsequent charge and discharge response, as shown in formula (14):

[0169] SOC i,0 =SOC i,T =50% (14)

[0170] In the formula, SOC i,0 is the state of charge of the i-th energy storage power station at the beginning of the market clearing period; SOC i,T is the charge state of the i-th energy storage power station at the end of the market clearing period T.

[0171] Example 3

[0172] The present embodiment provides a quotation determination device for energy storage entities to participate in double-layer game bidding in the frequency regulation auxiliary service market, including: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, a method for energy storage entities to participate in double-layer game bidding in the frequency regulation auxiliary service market and a method for double-layer game bidding in the frequency regulation auxiliary service market are implemented as described in the embodiment.

[0173] Example 4

[0174] This embodiment provides a computer-readable storage medium, on which an implementation program for information transmission is stored. When the program is executed by a processor, the steps of a two-tier game bidding method for a storage entity to participate in a frequency regulation auxiliary service market as described in Embodiment 1 are implemented.

[0175] It is to be understood that the present invention is described by some embodiments, and it is known to those skilled in the art that various changes or equivalent substitutions may be made to these features and embodiments without departing from the spirit and scope of the present invention. In addition, under the teachings of the present invention, these features and embodiments may be modified to adapt to specific circumstances and materials without departing from the spirit and scope of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed herein, and all embodiments falling within the scope of the claims of this application are within the scope of protection of the present invention.

Claims

1. A two-tier game bidding method for energy storage entities to participate in the frequency regulation auxiliary service market, characterized in that: Here are the steps: S1: Constructing the upper centralized clearing model: It is used to simulate the centralized clearing process of the frequency regulation market. Its goal is to minimize the social frequency regulation cost in each period. The objective function is shown in formula (1). In the formula, λ θ,t is the clearing marginal price of the FM market at time t; i,t is the quotation of energy storage power station i at time t; is the increased power of the energy storage power station i that won the bid at time t; is the power reduction of energy storage station i at time t, which should satisfy Define α i,t is a 0-1 variable used to indicate the winning bid of energy storage power station i at time t, and is defined as shown in formula (2). Using alpha i,t The power of energy storage station i at time t There are two situations: 1) When the energy storage power station i is not the clearing marginal unit θ: 2) When the energy storage power station i is the clearing marginal unit θ: In the formula, is the power increase or decrease reported by energy storage power station i at time t; Therefore, the clearing marginal price of the FM market at time t is θ,t By α i,t The value of is defined as shown in formula (5). l θ,t =max{α i,t l i,t } (5) The market concentration clearing stage is subject to the power balance constraint, that is, the bidding capacity of the energy storage power station and the frequency regulation demand should be balanced in real time, as shown in formula (6): Where D t is the frequency regulation demand of the power grid at time t; S2: Construct the bidding game model of the lower-level energy storage power station: The goal is to maximize the profit of each energy storage power station within the settlement period. Since there are I energy storage power stations participating in the frequency regulation market, the strategy set S of the day-ahead market quotation has I elements, as shown in formula (7). S={s1 s2 …s I } (7) The bidding strategy reported by energy storage power station i to the dispatching and trading center is s i , its profit at time t is defined as E i,t (s i ,s -i ) is shown in formula (8), In the formula, s -i It represents the quotation collection of the remaining energy storage power stations except the energy storage power station i that is, s -i ={s1…s i-1 s i+1 …s I }; Indicates the winning bid power of energy storage station i and The corresponding frequency modulation cost; Costs in the frequency regulation ancillary services market Including capacity cost and mileage cost. The capacity cost includes the average annual investment cost and annual operation cost of the energy storage power station. The mileage cost includes the energy loss cost caused by continuous charging and discharging and the cycle life attenuation cost caused by the depth of discharge. According to the clearing results of the day-ahead market, each energy storage power station continuously optimizes its own bid to achieve Nash equilibrium and obtain the final bidding strategy. The bidding strategy of energy storage power station i after optimization is defined as s i ′, then the objective function of the game model is as shown in formula (9): In the formula, Where λ θ,t 'and is the updated market clearing price and winning bid up or down power of energy storage power station i at time t; S3: Construct the constraints of the lower-level model, including declared power constraints and state of charge constraints. 1) Declare power constraints In the formula, is the rated power of the energy storage station i. Whether the energy storage is charging or discharging, its power cannot exceed the rated power; 2) State of charge constraints In the formula, SOC i,t is the state of charge of the i-th energy storage station at time t, with SOC constraints, Q i,t The calculation formula of is shown in formula (12): In the formula, is the charging efficiency of the i-th energy storage power station; is the discharge efficiency of the i-th energy storage power station; is the charging power of the i-th energy storage power station at time t-Δt; is the discharge power of the i-th energy storage power station; Q i,t is the capacity of the i-th energy storage power station at time t; Q i,t-Δt is the capacity of the i-th energy storage power station at time t-Δt; is the rated capacity of the i-th energy storage power station; In order to ensure that the energy storage station i can continuously participate in the frequency regulation market, the SOC is selected as 50% to cope with the subsequent charge and discharge response, as shown in formula (14): SOCIETY i,0 =SOC i,T =50% (14) In the formula, SOC i,0 is the state of charge of the i-th energy storage power station at the beginning of the market clearing period; SOC i,T is the charge state of the i-th energy storage power station at the end of the market clearing period T.

2. A two-tier game bidding system for energy storage entities to participate in the frequency regulation auxiliary service market, characterized in that: include: Centralized clearing module: It is used to simulate the centralized clearing process of the frequency regulation market. Its goal is to minimize the social frequency regulation cost in each period. The objective function is shown in formula (1). In the formula, λ θ,t is the clearing marginal price of the FM market at time t; i,t is the quotation of energy storage power station i at time t; is the increased power of the energy storage power station i that won the bid at time t; is the power reduction of energy storage station i at time t, which should satisfy Define α i,t is a 0-1 variable used to indicate the winning bid of energy storage power station i at time t, and is defined as shown in formula (2). Using alpha i,t The power of energy storage station i at time t There are two situations: 1) When the energy storage power station i is not the clearing marginal unit θ: 2) When the energy storage power station i is the clearing marginal unit θ: In the formula, is the power increase or decrease reported by energy storage power station i at time t; Therefore, the clearing marginal price of the FM market at time t is θ,t By α i,t The value of is defined as shown in formula (5). l θ,t =max{α i,t l i,t } (5) The market concentration clearing stage is subject to the power balance constraint, that is, the bidding capacity of the energy storage power station and the frequency regulation demand should be balanced in real time, as shown in formula (6): Where D t is the frequency regulation demand of the power grid at time t; Bidding game module: The goal is to maximize the profit of each energy storage power station within the settlement period. Since there are I energy storage power stations participating in the frequency regulation market, the strategy set S of the day-ahead market quotation has I elements, as shown in formula (7): S={s1 s2 …s I } (7) The bidding strategy reported by energy storage power station i to the dispatching and trading center is s i , its profit at time t is defined as E i,t (s i ,s -i ) is shown in formula (8), In the formula, s -i It represents the quotation collection of the remaining energy storage power stations except the energy storage power station i that is, s -i ={s1…s i-1 s i+1 …s I }; Indicates the winning bid power of energy storage station i and The corresponding frequency modulation cost; Costs in the frequency regulation ancillary services market Including capacity cost and mileage cost. The capacity cost includes the average annual investment cost and annual operation cost of the energy storage power station. The mileage cost includes the energy loss cost caused by continuous charging and discharging and the cycle life attenuation cost caused by the depth of discharge. According to the clearing results of the day-ahead market, each energy storage power station continuously optimizes its own bid to achieve Nash equilibrium and obtain the final bidding strategy. The bidding strategy of energy storage power station i after optimization is defined as s i ′, then the objective function of the game model is as shown in formula (9): In the formula, Where λ θ,t 'and is the updated market clearing price and winning bid up or down power of energy storage power station i at time t; 1) Constraint module: declare power constraints In the formula, is the rated power of the energy storage station i. Whether the energy storage is charging or discharging, its power cannot exceed the rated power; 2) State of charge constraints In the formula, SOC i,t is the state of charge of the i-th energy storage station at time t, with SOC constraints, Q i,t The calculation formula of is shown in formula (12): In the formula, is the charging efficiency of the i-th energy storage power station; is the discharge efficiency of the i-th energy storage power station; is the charging power of the i-th energy storage power station at time t-Δt; is the discharge power of the i-th energy storage power station; Q i,t is the capacity of the i-th energy storage power station at time t; Q i,t-Δt is the capacity of the i-th energy storage power station at time t-Δt; is the rated capacity of the i-th energy storage power station; In order to ensure that the energy storage power station i can continuously participate in the frequency regulation market, the SOC is selected as 50% to cope with the subsequent charge and discharge response, as shown in formula (14): SOCIETY i,0 =SOC i,T =50% (14) In the formula, SOC i,0 is the state of charge of the i-th energy storage power station at the beginning of the market clearing period; SOC i,T is the charge state of the i-th energy storage power station at the end of the market clearing period T.

3. A quotation determination device for energy storage entities participating in double-layer game bidding in the frequency regulation auxiliary service market, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the computer program is executed by the processor, the method for energy storage entities to participate in a double-tier game bidding in a frequency regulation auxiliary service market and a double-tier game bidding in a frequency regulation auxiliary service market as described in claim 1 are implemented.

4. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores an implementation program for information transmission, and when the program is executed by the processor, the steps of a two-tier game bidding method for energy storage entities to participate in the frequency regulation auxiliary service market as described in claim 1 are implemented.

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