Electric quantity and frequency modulation coupling clearing method with participation of new energy storage combined station

Abstract

The invention provides an electric quantity and frequency modulation coupling clearing method participated by a new energy and energy storage combined station. The method comprises the following steps: establishing a bidding model of the new energy and energy storage combined station; dividing a plurality of new energy storage combined stations; each new energy storage joint station and each thermal power station are used as market subjects, and bidding information is reported to a market operator based on a bidding model of the market subjects; the market operator determines bid winning information by solving the electric quantity-frequency modulation mixed market clearing model; each market main body participates in electric quantity and frequency modulation coupling clearing based on the received bid winning information, and each new energy energy storage joint station carries out clearing scheduling on the included new energy power generation unit and energy storage unit based on the autonomous decision model. The method provided by the invention has relatively high competitiveness in a high-proportion new energy environment, and is beneficial to long-term win-win operation of market operators and market subjects.

Description

Technical Field

[0001] The present application belongs to the field of power system planning and regulation technology, and specifically relates to a method for clearing the amount of electricity and frequency regulation coupled by a new energy storage joint station. Background Art

[0002] A high proportion of renewable energy is an important evolutionary feature of the power supply side of the new power system, but its uncertainty brings challenges to the safe operation of the system. At the same time, a fair and efficient market operation model is an important direction for the reform of the power system. In the context of a diversified power market, how new energy operators optimize bidding strategies, improve service quality, and fully form market competitiveness is the key to achieving cost recovery and is of great significance to promoting the sustainable development of the new energy industry. In addition to using the power grid as a direct way to make profits, renewable energy power generation can also provide capacity security services. However, due to the strong volatility and low inertia of renewable energy, it is possible to equip renewable energy power generation equipment with energy storage units and provide new energy leveling, rapid frequency regulation, black start and other services to enhance the ability of renewable energy power generation to participate in frequency regulation services, so that it can flexibly participate in multiple types of power market services.

[0003] The clearing model of the hybrid electricity market, including the electricity volume and frequency regulation markets, can be divided into two categories: sequential clearing and coupled clearing. Compared with sequential clearing, the coupled clearing model carefully depicts the coupling relationship between bidding strategies of different power services, which is more conducive to achieving the optimal economic operation state of the power system. At present, there are many methods for the coupled clearing of independent energy storage participating in the hybrid electricity commodity market. For example, Li Guoqing et al. (Li Guoqing, Yan Kefei, Fan Gaofeng, et al. Research on the trading decision of energy storage participating in the spot electricity energy-frequency regulation ancillary service market [J]. Power System Protection and Control, 2022, 50(17): 45-54.) proposed a coupled clearing framework for the electricity-frequency regulation market and characterized the trading decision-making behavior of energy storage participating in the hybrid market; for example, Wang Aoer et al. (Wang Aoer, Zhao Shuqiang, Song Jinli, et al. A joint market clearing model considering the participation of new energy and energy storage in frequency regulation [J]. Acta Energiae Solaris Sinica, 2024, 45(03): 367-376.) proposed to use the hierarchical analysis method to generate frequency regulation performance weights and to correct the clearing costs of multiple entities participating in the electricity-frequency regulation market including new energy and energy storage.

[0004] However, existing solutions for the participation of new energy and energy storage in hybrid electricity market transactions and clearing mostly discuss the situations where new energy and energy storage are independent of each other or where new energy leasing energy storage participates in the market. There are few targeted models for the joint participation of new energy and energy storage in bidding and coordinated scheduling. There is also no further refinement of the decomposition of the winning bids of market players (i.e., the problem of coordinated scheduling of electricity and frequency regulation). Summary of the invention

[0005] To solve the problems existing in the above-mentioned prior art, this application provides, through an embodiment, a method for clearing electricity quantity and frequency modulation coupling participated by a new energy storage combined station, and the method includes the following steps:

[0006] S1. Establish a bidding model for a new energy storage combined station, where each new energy storage combined station includes at least one new energy power generation unit and at least one energy storage unit;

[0007] S2. Divide the new energy power generation units and energy storage units participating in the clearing of electricity quantity and frequency modulation coupling into multiple new energy storage combined stations;

[0008] S3. Each market entity reports its bidding information to the market operator based on its bidding model, where the market entities include each of the new energy storage combined stations and the thermal power stations participating in the clearing of electricity quantity and frequency modulation coupling;

[0009] S4. The market operator determines the winning bid information of each market entity participating in the clearing of electricity quantity and frequency modulation coupling by solving the electricity quantity - frequency modulation hybrid market clearing model;

[0010] S5. Each market entity participates in the clearing of electricity quantity and frequency modulation coupling based on the winning bid information it receives. Among them, each new energy storage combined station conducts clearing dispatch on the new energy power generation unit and the energy storage unit it includes by solving its autonomous decision-making model.

[0011] The method for clearing electricity quantity and frequency modulation coupling participated by the new energy storage combined station provided by the embodiment of this application aims at the problem that it is difficult to coordinate and give full play to the advantages when multiple new energy power generation units and energy storage units independently participate in the integrated market clearing in the prior art. First, the independent new energy power generation equipment and energy storage equipment are integrated into a combined station, and then the feasible bidding strategies are standardized based on the power generation and frequency modulation characteristics of the new energy - energy storage equipment, and their frequency modulation capacity characteristics are further analyzed. Based on their feasible bidding strategies and frequency modulation capacity range, their bidding models are constructed to jointly participate in the electricity quantity - frequency modulation coupling market clearing with the thermal power stations. Compared with the fixed charge - discharge plan and the fixed frequency modulation capacity strategy, the method provided by this application constructs a combined station by combining new energy power generation units and energy storage units to participate in the hybrid market clearing, which has strong competitiveness in a high - proportion new energy environment, can obtain rich profits by responding to the dual markets of electricity quantity and frequency modulation, is conducive to accelerating the cost recovery of the station, and can effectively reduce the bid - withdrawal rate of the combined station, which is conducive to the long - term win - win operation of the market operator and market entities. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 It is a flowchart of the method for clearing electricity quantity and frequency modulation coupling participated by the new energy storage combined station provided by the embodiment of this application;

[0013] Figure 2 It is the electricity - frequency hybrid market trading framework for new - energy energy - storage combined stations and thermal power stations as market entities;

[0014] Figure 3 It is the specific implementation flowchart of step S1 provided according to the embodiments of the present application;

[0015] Figure 4A It is a schematic diagram of the implementable bidding strategy 1 for the new - energy energy - storage combined station provided according to the embodiments of the present application;

[0016] Figure 4B It is a schematic diagram of the implementable bidding strategy 2 for the new - energy energy - storage combined station provided according to the embodiments of the present application;

[0017] Figure 4C It is a schematic diagram of the implementable bidding strategy 3 for the new - energy energy - storage combined station provided according to the embodiments of the present application;

[0018] Figure 4D It is a schematic diagram of the implementable bidding strategy 4 for the new - energy energy - storage combined station provided according to the embodiments of the present application;

[0019] Figure 5A It is a schematic diagram of the relationship between the operating power and the frequency - modulation reserved capacity when the energy - storage unit of the combined station is in the charging state provided according to the embodiments of the present application;

[0020] Figure 5B It is a schematic diagram of the relationship between the operating power and the frequency - modulation reserved capacity when the energy - storage unit of the combined station is in the discharging state provided according to the embodiments of the present application;

[0021] Figure 6 It is a schematic diagram of the market - environment configuration provided by the first specific embodiment of the present application;

[0022] Figure 7A It is a schematic diagram of the electricity - market winning / losing results of bidding plan 1 provided by the first specific embodiment of the present application;

[0023] Figure 7B It is a schematic diagram of the electricity - market winning / losing results of bidding plan 2 provided by the first specific embodiment of the present application;

[0024] Figure 7C It is a schematic diagram of the electricity - market winning / losing results of bidding plan 3 provided by the first specific embodiment of the present application;

[0025] Figure 8A It is a schematic diagram of the frequency - modulation - market winning / losing results of bidding plan 1 provided by the first specific embodiment of the present application;

[0026] Figure 8BSchematic diagram of the winning / losing results of the bidding plan 2 in the frequency modulation market provided by the first specific embodiment of the present application;

[0027] Figure 8C Schematic diagram of the winning / losing results of the bidding plan 3 in the frequency modulation market provided by the first specific embodiment of the present application;

[0028] Figure 9A Schematic diagram of the market allocation in scenario 1 provided by the second specific embodiment of the present application;

[0029] Figure 9B Schematic diagram of the market allocation in scenario 2 provided by the second specific embodiment of the present application;

[0030] Figure 9C Schematic diagram of the market allocation in scenario 4 provided by the second specific embodiment of the present application;

[0031] Figure 10A Schematic diagram of the winning / losing results of the electricity market in scenario 1 provided by the second specific embodiment of the present application;

[0032] Figure 10B Schematic diagram of the winning / losing results of the electricity market in scenario 2 provided by the second specific embodiment of the present application;

[0033] Figure 10C Schematic diagram of the winning / losing results of the electricity market in scenario 4 provided by the second specific embodiment of the present application;

[0034] Figure 11A Schematic diagram of the winning / losing results of the frequency modulation market in scenario 1 provided by the second specific embodiment of the present application;

[0035] Figure 11B Schematic diagram of the winning / losing results of the frequency modulation market in scenario 2 provided by the second specific embodiment of the present application;

[0036] Figure 11C Schematic diagram of the winning / losing results of the frequency modulation market in scenario 4 provided by the second specific embodiment of the present application;

[0037] Figure 12A Schematic diagram of the comparison between the clearing model and the autonomous decision-making model solved in scenario 1 provided by the second specific embodiment of the present application;

[0038] Figure 12B Schematic diagram of the comparison between the clearing model and the autonomous decision-making model solved in scenario 2 provided by the second specific embodiment of the present application;

[0039] Figure 12C Schematic diagram of the comparison between the clearing model and the autonomous decision-making model solved in scenario 3 provided by the second specific embodiment of the present application;

[0040] Figure 12D This is a comparison schematic diagram for solving the clearing model and the autonomous decision-making model in scenario 4 provided by the second specific embodiment of the present application. Detailed implementation manners

[0041] The following further describes the present application based on preferred implementation manners and with reference to the accompanying drawings.

[0042] The terms used in this specification are for the purpose of describing the embodiments of the present application, but are not intended to limit the present application. It should also be noted that unless otherwise clearly specified and defined, if the terms "set", "connected", and "coupled" are used, they should be understood in a broad sense. For example, they can be fixedly connected, detachably connected, or integrally connected; they can be mechanically connected, directly connected, or indirectly connected through an intermediate medium, and can be the internal communication of two components. For those skilled in the art, the specific meanings of the above terms in the present application can be specifically understood.

[0043] The embodiment of the present application provides a method for clearing the electricity quantity and frequency modulation coupling of a new energy storage combined station. This clearing method fully establishes a joint dispatching mechanism for new energy power generation and energy storage units, effectively reduces the bid rejection rate of the new energy and energy storage combined station while improving social welfare, has strong competitiveness in a high-proportion new energy environment, can obtain substantial profits by responding to the electricity quantity and frequency modulation dual markets, and is conducive to accelerating the cost recovery of the station.

[0044] Figure 1 FIG. is a flowchart of a method for clearing the electricity quantity and frequency modulation coupling participated by a new energy storage combined station according to an embodiment of the present application. Figure 2 FIG. is a trading framework for the electricity quantity - frequency modulation hybrid market for performing coupled clearing using this method. Refer to Figure 1 and Figure 2 , the trading framework of the electricity quantity - frequency modulation hybrid market includes a market operator at the upper layer and various market entities at the lower layer. Among them, the upper layer is the clearing model for the electricity quantity - frequency modulation hybrid market. The market operator at the upper layer receives the bidding information of the electricity quantity and frequency modulation market entities, takes the winning bid quantity of each market entity as the decision variable, takes the bidding quantity of each market entity as the decision boundary, performs electricity quantity - frequency modulation coupled clearing according to the principle of maximizing social welfare, and issues the winning bid electricity quantity / capacity and clearing price to each market entity; each market entity at the lower layer includes a combined station composed of new energy and energy storage, and traditional thermal power stations. Each market entity responds to the upper layer clearing result, takes the output of the autonomous equipment as the decision variable, takes the winning bid electricity quantity / capacity as the decision boundary, and makes a decision and dispatch according to the principle of minimizing the net cost of market participation.

[0045] Refer to Figure 1 , the above-mentioned bidding and clearing process for the electricity quantity - frequency modulation hybrid market can be executed by the following steps:

[0046] S1. Establish a bidding model for the new - energy energy - storage combined station, where each new - energy energy - storage combined station includes at least one new - energy power - generation unit and at least one energy - storage unit;

[0047] S2. Divide the new - energy power - generation units and energy - storage units participating in the power - quantity and frequency - modulation coupled clearing into multiple new - energy energy - storage combined stations;

[0048] S3. Each market entity reports its bidding information to the market operator based on its bidding model, where the market entities include each of the new - energy energy - storage combined stations and the thermal - power stations participating in the power - quantity and frequency - modulation coupled clearing;

[0049] S4. The market operator determines the winning bid information of each market entity participating in the power - quantity and frequency - modulation coupled clearing by solving the power - quantity - frequency - modulation hybrid market clearing model;

[0050] S5. Each market entity participates in the power - quantity and frequency - modulation coupled clearing based on the winning bid information it receives. Among them, each new - energy energy - storage combined station conducts clearing scheduling for the new - energy power - generation units and energy - storage units it includes by solving its autonomous decision - making model.

[0051] In the implementation process of the above - mentioned method, a complete bidding and clearing process can be divided into the following steps: 1) Establish a combined - station bidding model in which new - energy power - generation units and energy - storage units cooperate to participate in clearing; 2) Divide the new - energy power - generation units and energy - storage units participating in the power - market clearing to form multiple combined stations; 3) Use the different power - generation and frequency - modulation characteristics of new - energy power - generation units and energy - storage units to formulate the bidding decisions of each combined station, and then participate in the bidding as market entities together with thermal - power stations; 4) The upper - layer market operator receives the bidding information and determines the winning bid information of each bidding entity according to the principle of maximizing social welfare; 5) Each market entity executes the clearing task according to the winning bid quantity. Among them, the new - energy energy - storage combined station converts the bidding model into an autonomous decision - making model according to the winning bid information it receives, and schedules the clearing tasks executed by the new - energy power - generation units and energy - storage units it includes by solving the autonomous decision - making model.

[0052] The following will elaborate on the above steps in combination with specific implementation manners.

[0053] I. Step S1

[0054] Step S1 is used to establish a bidding model for the new - energy energy - storage combined station. In the embodiment of the present application, each new - energy energy - storage combined station includes at least one new - energy power - generation unit (where the new - energy power - generation unit can be new - energy power - generation equipment such as a photovoltaic power - generation device or a wind turbine) and at least one energy - storage unit. As Figure 3 shown, establish the bidding model of the new - energy energy - storage combined station according to the following steps:

[0055] Step S11: Establish the rules for the power - frequency modulation coupled clearing of the new - energy energy - storage combined station participating in the hybrid power market.

[0056] Specifically, it can be stipulated that the new - energy energy - storage combined station participating in the hybrid - power - market coupled clearing shall abide by the following rules:

[0057] Under the background of the increasing proportion of new - energy power - generation units, in order to ensure the supply - demand balance and promote the consumption, each new - energy energy - storage combined station is not allowed to be in a static state. Therefore, it can be stipulated that the new - energy energy - storage combined station participating in the hybrid - power - market coupled clearing shall abide by Rule 1: Only selling electricity or buying electricity is allowed, and initiating both types of bids simultaneously is prohibited.

[0058] Considering the strong uncertainty of new - energy power - generation units, it can be stipulated that the new - energy energy - storage combined station participating in the hybrid - power - market coupled clearing shall abide by Rule 2: The new - energy energy - storage combined station relies on the energy - storage unit to participate in the frequency - modulation market. The new - energy energy - storage combined station has the frequency - modulation ability if and only if there is a direct transaction between the energy - storage unit and the market.

[0059] In order to more freely determine the quantity and price bids, promote the self - consumption of new - energy electricity, and efficiently participate in the power - frequency modulation hybrid market, it can be stipulated that the new - energy energy - storage combined station participating in the hybrid - power - market coupled clearing shall abide by Rule 3: Priority is given to guiding the new - energy energy - storage combined station to make full use of the energy - storage unit to self - consume the new - energy electricity.

[0060] Considering the actual physical - condition limitations, it can be stipulated that the new - energy energy - storage combined station participating in the hybrid - power - market coupled clearing shall abide by Rule 4: At the same moment, the energy - storage unit has only three working states: charging, discharging, and static.

[0061] Step S12: Determine the compliant bidding strategies that can be adopted by the new - energy energy - storage combined station when participating in the power - frequency modulation coupled clearing of the hybrid power market.

[0062] For any specific new - energy energy - storage combined station JS j (where the subscript j is the number of the new - energy energy - storage combined station, hereinafter referred to as the j - th new - energy energy - storage combined station), according to the rules determined in Step S11, as Figures 4A to 4D shown, there are only 4 compliant bidding strategies:

[0063] Bidding strategy 1: The energy - storage unit is static, and only the new - energy power - generation unit sells electricity;

[0064] Bidding strategy 2: The new - energy power - generation unit and the energy - storage unit sell electricity together;

[0065] Bidding strategy 3: The new - energy power - generation unit sells electricity in full, and the energy - storage unit consumes the remaining electricity;

[0066] Bidding strategy 4: When the power generation of the new energy power generation unit is insufficient and the market meets the charging shortage of the energy storage unit, the energy storage unit purchases electricity.

[0067] Figures 4A to 4D Among them, are respectively the winning bid power for selling electricity, the winning bid power for purchasing electricity, the winning bid capacity for upward regulation, and the winning bid capacity for downward regulation of the j-th new energy energy storage combined station, are respectively the charging power and the grid-connected power supplied by the new energy power generation unit in the j-th new energy energy storage combined station, are respectively the charging power and the discharging power of the energy storage unit in the j-th new energy energy storage combined station.

[0068] Step S13: Establish a bidding strategy constraint model for the new energy energy storage combined station based on the compliance bidding strategy.

[0069] The bidding strategy constraint model is used to reasonably constrain the bidding and clearing decisions according to the frequency regulation characteristics of the energy storage unit and its cooperation with the new energy power generation unit. In some preferred embodiments, the bidding strategy constraint model consists of a state and bidding strategy relationship model and bidding strategy constraint conditions.

[0070] Specifically, 0-1 state variables can be used to respectively represent the charging state, discharging state of the energy storage unit in the j-th new energy energy storage combined station, and the power purchase state of the j-th new energy energy storage combined station. Then each feasible compliance bidding strategy can be represented by a set of values of. Integrating the above 4 feasible compliance bidding strategies, the state and bidding strategy relationship model of the combined station described by the state variable relationship table in Table 1 can be obtained:

[0071] Table 1 State variable relationship table

[0072]

[0073] In the above table, each set of values of respectively corresponds to a feasible bidding strategy. For example, When both are 0, it represents bidding strategy 1. Similarly, bidding strategies 2-4 respectively correspond to a group of state variable combinations.

[0074] Obviously, except for the state variable combinations in Table 1, other combinations should not be used as feasible bidding strategies. Therefore, it is also necessary to constrain the state combinations through bidding strategy constraint conditions. Specifically, it can be set that Through the bidding strategy constraint conditions shown in formula (1), constrain Other values of [[]] are used to prohibit the \(j\)th new - energy energy - storage combined station from adopting other bidding strategies:

[0075]

[0076] Among them, \(\chi\) j,t,1 and \(\chi\) j,t,2 are dual variables.

[0077] In addition, in some preferred embodiments, the winning bid scalar can also be constrained according to the frequency - regulation ability of the energy - storage unit. Figure 5A and Figure 5B respectively show the relationship between the operating power of the energy - storage unit and the winning - bid frequency - regulation capacity when there is a direct power exchange between the energy - storage unit of the \(j\)th new - energy energy - storage combined station and the market. The black dashed line is the maximum operating power of the energy - storage unit, the red dashed line is its maximum available power, and the blue dashed line is its minimum available power. As Figure 5A shown, when the energy - storage unit is in the (a) charging state, it is necessary to ensure that its charging power available for electricity bidding is greater than the upward winning - bid capacity and less than the maximum operating power minus the downward winning - bid capacity On the contrary, as Figure 5B shown, when it is in the (b) discharging state, it is necessary to ensure that its available discharging power is greater than the downward winning - bid capacity and less than the maximum operating power minus the upward winning - bid capacity. In addition, when the energy - storage unit is static or does not directly participate in the market, the new - energy energy - storage combined station does not have the frequency - regulation ability, and the upward and downward winning - bid capacities should both be set to 0.

[0078] Step S14: Establish a bidding model for the new - energy energy - storage combined station constrained by the bidding - strategy constraint model.

[0079] Specifically, for any new - energy energy - storage combined station \(j\), its bidding model can be established through the following steps:

[0080] The first step: Determine the electricity - market revenue the frequency - regulation market revenue the electricity - market cost the operating cost of the new - energy power - generation unit and the operating cost of the energy - storage unit

[0081]

[0082] Among them, \(t\) is the market - clearing period, \(\Omega\) T is the set of market - clearing periods, \(T\) is the total number of periods, \(\lambda\) t is the electricity - market - clearing price, is the winning - bid power for selling electricity, is the upward - clearing price, To increase the winning bid capacity, To decrease the clearing price, To decrease the winning bid capacity, To be the winning bid power of power purchase, To be the charging power supplied by the new energy power generation unit, To be the grid-connected power of the new energy power generation unit, To be the operating cost coefficient, To be the charge and discharge power of the energy storage unit, To be the operating cost coefficient (in this application, increasing and decreasing refer to upward frequency modulation and downward frequency modulation respectively).

[0083] In the second step, with the goal of minimizing the net cost of market participation, determine the objective function of the new energy storage integrated station bidding model shown in equation (7):

[0084]

[0085] Among them, To be the cost function of the j-th new energy storage integrated station.

[0086] In the third step, determine the various constraint conditions that the new energy storage integrated station satisfies when participating in the hybrid power market bidding:

[0087] 1) Establish the balance constraint condition of the new energy storage integrated station shown in equation (8):

[0088]

[0089] In equation (8), To be the expected charge and discharge power of the energy storage unit, To be the selling and buying power bidding of integrated station j, To be the expected power supply of the new energy power generation unit, To be the expected grid-connected power of the new energy power generation unit, β j,t,1 β j,t,2 To be the dual variables of the constraint, separated by a colon.

[0090] 2) Introduce the bidding strategy constraint condition shown in equation (1):

[0091]

[0092] 3) Based on the bidding strategy constraint model and the relationship between the operating power of the energy storage unit and the frequency modulation winning bid capacity, determine the regulation constraint condition of the energy storage unit shown in equation (9), the selling and buying power constraint condition of the integrated station shown in equation (10), and the electric energy constraint condition of the new energy power generation unit shown in equation (11):

[0093]

[0094] Among them, are respectively the upward bidding capacity, downward bidding capacity, maximum upward capacity, maximum downward capacity, maximum charge-discharge power of the energy storage unit, available power of the energy storage unit, upper limit of the available power of the energy storage unit, and lower limit of the available power of the energy storage unit of the jth new energy storage combined station, are respectively the charging efficiency and discharging efficiency, β j,t,3 , χ j,t,3 ~χ j,t,12 are dual variables.

[0095]

[0096] Among them, is the power of the new energy generation unit in the jth new energy storage combined station, χ j,t,13 ~χ j,t,20 are dual variables.

[0097]

[0098] Among them, χ j,t,21 ~χ j,t,26 are dual variables.

[0099] 4) Establish the bidding range constraint condition shown in formula (12):

[0100]

[0101] In the above formulas (9) to (12), are respectively the selling electricity bidding price, upper limit of the selling electricity bidding price, lower limit of the selling electricity bidding price, purchasing electricity bidding price, upper limit of the purchasing electricity bidding price, lower limit of the purchasing electricity bidding price, upward bidding price, upper limit of the upward bidding price, lower limit of the upward bidding price, downward bidding price, upper limit of the downward bidding price, lower limit of the downward bidding price of the jth new energy storage combined station, χ j,t,27 ~χ j,t,34 are dual variables.

[0102] It can be seen from formulas (9) to (12) that in the embodiment of the present application, the bidding model of the new energy storage combined station established in step S1 optimizes the bidding strategies that the combined station can adopt by comprehensively considering the coupling effect between the new energy generation unit and the energy storage unit, and on this basis, reasonable constraint conditions are established, so as to ensure that the combined station makes full and reasonable use of the characteristics of the new energy generation unit and the energy storage unit, and while promoting the self-consumption of new energy electricity, efficiently participates in the electricity-frequency modulation hybrid market.

[0103] II. Step S2

[0104] In step S2, each distributed new - energy power generation unit and energy storage unit included in the system are reasonably divided to construct several new - energy energy - storage combined stations.

[0105] In some preferred embodiments, the division of the combined stations comprehensively considers factors such as the geographical location, power generation capacity and volatility, electricity storage and frequency regulation capacity of each new - energy power generation unit and energy storage unit for matching, so as to improve the competitiveness of each combined station.

[0106] III. Step S3

[0107] In step S3, each market entity reports its bidding information to the market operator based on its bidding model.

[0108] See Figure 2 , in the embodiments of the present application, the market entities include each new - energy energy - storage combined station and the thermal power stations participating in the coupled clearing of electricity quantity and frequency regulation. Among them, the division method of the new - energy energy - storage combined stations and the bidding models used by each combined station have been introduced above and will not be elaborated here.

[0109] The thermal power stations obtain revenue from the electricity market by adjusting their own output and obtain revenue from the frequency regulation market by reserving capacity. In some specific embodiments, for a specific thermal power station TP g (g is the number of the thermal power station, hereinafter referred to as the g - th thermal power station), its bidding / decision - making model can be established through the following steps:

[0110] The first step is to determine the electricity - market revenue frequency - regulation market revenue and the fuel cost consumed by thermal power

[0111]

[0112] where are respectively the winning bid power for electricity sales, the winning bid capacity for upward regulation, and the winning bid capacity for downward regulation of the g - th thermal power station, is the fuel - cost coefficient.

[0113] The second step is to determine the objective function of the bidding model of the thermal power station shown in formula (16) with the goal of minimizing the net cost of market participation:

[0114]

[0115] where is the cost function of the g - th thermal power station.

[0116] Step 3: Determine the power-capacity constraint conditions and bidding range constraint conditions for thermal power plants participating in the electricity-frequency hybrid market bidding as shown in equations (17) and (18):

[0117]

[0118] In equation (17), are respectively the electricity selling bidding power and its upper limit of the g-th thermal power plant, are respectively the upward regulation bidding capacity and its upper limit of the g-th thermal power plant, are respectively the downward regulation bidding capacity and its upper limit of the g-th thermal power plant, ε g,t,1 ~ε g,t,8 are dual variables;

[0119]

[0120] In equation (18),

[0121] are respectively the electricity selling bidding price, electricity selling bidding price upper limit, electricity selling bidding price lower limit, upward regulation bidding price, upward regulation bidding price upper limit, upward regulation bidding price lower limit, downward regulation bidding price, downward regulation bidding price upper limit, downward regulation bidding price lower limit of the g-th thermal power plant, ε g,t,9 ~ε g,t,14 are dual variables.

[0122] IV. Step S4

[0123] In step S4, the market operator aggregates the bidding information of each market entity and determines the winning bid quantity of each market entity by solving the electricity-frequency hybrid market clearing model. Among them, the electricity-frequency hybrid market clearing model determines the objective function based on the principle of maximizing the social total welfare and establishes the constraint conditions according to the supply-demand balance and electricity-frequency coupling clearing.

[0124] In some specific embodiments, the market operator can determine the social welfare maximization objective function of its electricity-frequency coupling clearing based on equation (19):

[0125]

[0126] Wherein, f ISO is the cost function of the electricity-frequency coupling clearing in the hybrid market, Ω J , Ω G are respectively the set of new energy energy storage combined power stations and the set of thermal power plants.

[0127] In some specific embodiments, the market operator can determine the power balance constraint condition, the frequency modulation capacity balance constraint condition, the clearing constraint condition of the new energy energy storage combined station, and the clearing constraint condition of the thermal power station based on equations (20) to (23):

[0128]

[0129] In equation (20), is the total user load at time t;

[0130]

[0131] In equation (21), r t up is the upward frequency modulation demand at time t, and r t down is the downward frequency modulation demand at time t;

[0132]

[0133] The process by which the market operator determines the winning bids of each market entity is the process of solving the two-layer electricity-frequency modulation coupled clearing model composed of equations (1), (5)-(12), (16)-(18), and (19)-(23). The above equations involve two levels: the market entity (lower layer) and the market operator (upper layer), including the inter-layer coupling constraint and the 0-1 constraint established by the bidding strategy constraint model of the combined station, which belongs to a two-layer mixed-integer nonlinear programming problem (Bilevel Mixed-Integer Nonlinear Programming, BMNLP).

[0134] In some embodiments of the present application, according to the KKT (Karush-Kuhn-Tucker) conditions, the two-layer mixed-integer nonlinear programming can be transformed into a single-layer mixed-integer nonlinear programming, and then a solver is called to solve it. The specific steps include constructing the gradient condition and the complementary slackness condition of the optimality of the lower-layer model, and adding the above conditions and the equality constraints to the upper-layer model.

[0135] Specifically, the Lagrangian dual function of the combined station can be established:

[0136]

[0137] Taking the partial derivative of it to obtain the gradient condition:

[0138] β j,t,2 -χ j,t,15 +χ j,t,16 -χ j,t,19 +χ j,t,20 = 0 (24)

[0139] β j,t,1 -χ j,t,13 +χ j,t,14 -χ j,t,17 +χ j,t,18 =0 (25)

[0140]

[0141] -χ j,t,27 +χ j,t,28 =0 (28)

[0142] -χ j,t,29 +χ j,t,30 =0 (29)

[0143] -χ j,t,31 +χ j,t,32 =0 (30)

[0144] -χ j,t,33 +χ j,t,34 =0 (31)

[0145]

[0146] β j,t,1 -χ j,t,21 +χ j,t,22 -χ j,t,25 +χ j,t,26 =0(34)

[0147] -β j,t,2 -χ j,t,23 +χ j,t,24 -χ j,t,25 +χ j,t,26 =0 (35)

[0148]

[0149] The inequality constraints in equations (1) and (7) to (12) can be transformed to obtain the complementary slackness conditions related to the combined station, and the transformation operation can be carried out in an implementation manner known to those skilled in the art:

[0150] For any inequality constraint, assume its constraint form is g(x) ≥ 0, where x is the decision variable, and assume its dual variable is Then the complementary slackness conditions for the constraint g(x) ≥ 0 are as follows:

[0151]

[0152] For example, the inequality constraint in equation (11) The complementary slackness conditions are:

[0153]

[0154] Complementary slackness introduces the product term of the dual variable and the constraint, which will exacerbate the nonlinearity of the model. Usually, the "big M method" is adopted for further relaxation. Its core idea is to introduce an auxiliary 0-1 variable and a relatively large number, and split the aforementioned product term into two constraints. The big M relaxation form of Equation (38) is as follows:

[0155]

[0156] Among them, v j,t,22 is the auxiliary 0-1 variable, which characterizes whether the constraint takes effect, and M is a relatively large integer selected according to experience.

[0157] By adopting the above method, the complementary slackness conditions of the inequality constraints involved in Equation (1) and Equations (7)-(12) can be constructed one by one.

[0158] In the same way, the gradient conditions and complementary slackness conditions of the thermal power plant can be obtained.

[0159] After adding the gradient conditions, complementary slackness conditions, and equality constraints of all market entities to the upper-layer power-frequency modulation coupled clearing model, commercial solvers such as Gurobi can be called for clearing and solving.

[0160] V. Step S5

[0161] After solving the power-frequency modulation coupled clearing result of the hybrid market through Step S4, the winning bid quantity and clearing price of each market entity can be returned in Step S5, and each market entity will participate in the power and frequency modulation coupled clearing based on the winning bid information it receives.

[0162] For each thermal power plant, its participation in market clearing can be controlled based on the implementation methods known to those skilled in the art. For each new energy storage combined station, the bidding model of the new energy storage combined station established in Step S1 can be converted into an autonomous decision-making model, and by solving the autonomous decision-making model, the clearing tasks of the new energy power generation unit and the energy storage unit therein can be scheduled.

[0163] Specifically, for any new energy storage combined station j, the winning bid information it obtains includes the winning bid power for power sale and purchase winning bid power for power purchase winning bid capacity for upward regulation winning bid capacity for downward regulation power clearing price λ t , upward clearing price downward clearing price The clearing tasks that need to be completed by the new energy power generation unit and the energy storage unit it includes are determined through the following steps:

[0164] First, based on equations (1) and (7)-(12), substitute the electricity price information λ t 、 into the objective function of the bidding model for the new energy storage combined station, and assign the winning bid power / capacity to the bidding power / capacity, then the autonomous decision-making model for the new energy storage combined station is obtained.

[0165] The variables to be solved in the above autonomous decision-making model are all autonomous variables of the combined station, including the charging power of the energy storage unit discharge power the power supply of the new energy power generation unit the power fed into the grid by the new energy power generation unit the available electricity of the energy storage unit and the 0-1 state variable

[0166] Secondly, solve the above autonomous decision-making model. Specifically, since the above autonomous decision-making model is a mixed integer programming problem, a commercial solver such as gurobi can be called to solve the model, and the new energy power generation unit and the energy storage unit are scheduled according to the solution situation to complete the clearing task.

[0167] It should be noted that since in the autonomous decision-making model, the state variable is still a variable to be solved, that is, in the solution result of the above self-made decision-making model, the value may be different from the value during bidding, indicating that after receiving the clearing task, each new energy storage combined station can also adjust the strategy for executing the clearing task according to the real-time status of the new energy power generation unit and the energy storage unit in it, so as to further optimize the overall cost of the combined station. Specific Example 1

[0169] To verify the effectiveness of the proposed bidding strategy for the combined station, a case study is carried out based on the IEEE 39-node system. The case includes 10 thermal power plants with different output characteristics and 3 new energy stations equipped with energy storage. Based on the steady-state power flow of the IEEE 39 standard case, the daily load curve and the new energy wind power output trend are generated with reference to the actual load trend. The average output is set at 50% of the average daily load. The time granularity of the mixed market clearing is 1 h, the power base value is 100 MVA, the electrical energy base value is 100 MVAh, and the partial quotation limits of thermal power plants and new energy-storage combined stations are shown in Table 2. The electricity price quotation refers to the electricity price levels of some provinces in China, and the proportional relationship between the frequency modulation electricity price and the electricity price is obtained through public data.

[0170] Table 2 Quotation Limits of Market Entities

[0171]

[0172]

[0173] To verify the effectiveness of the proposed bidding strategy, the following three bidding schemes are designed to compare and analyze the market competitiveness. Among them, Scheme 1 designs the charge and discharge plan according to the experience of new - energy peak - valley periods, with the sold - and - purchased bid power and the frequency - regulation bid capacity as decision variables; Scheme 2 reserves a fixed frequency - regulation capacity for energy storage, with the sold - and - purchased bid power and the charge - and - discharge state of energy storage as decision variables; Scheme 3 uses the scheme provided in this application for bidding.

[0174] Figure 6 For the schematic diagram of the market environment where this embodiment is located, under Figure 6 the market - environment configuration relationship shown, the clearing models of the three bidding schemes are solved, and the electricity - market bid / win results of each bidding scheme are as shown in Figures 7A to 7C shown, and the up - and - down frequency - regulation market bid / win results are as shown in Figures 8A to 8C shown.

[0175] From Figures 7A to 7C it can be seen that due to the advantage of the new - energy - energy - storage combined power station in selling - electricity quotation, it is favored by the power - purchasing party throughout the day, and the new - energy power generation is preferentially consumed by the market, and the clearing marginal entity is thermal power. During the periods when new energy is highly generated, such as 1 - 6 and 22 - 24 hours, the system's power - generation capacity can fully meet the load demand, and the combined power station shows the decision - making characteristics of "two sales and one purchase, high and low intermittently"; during peak - load periods such as 8 - 10 and 13 - 16 hours, the system's power - generation capacity is relatively tight, and the combined power station sells electricity simultaneously to maintain the supply - demand balance; in horizontal comparison, the overall competitiveness of Scheme 1 in selling and purchasing electricity by the combined power station is weaker than that of Scheme 2 and Scheme 3, and the competitiveness levels of Scheme 2 and Scheme 3 are close.

[0176] From Figures 8A to 8C it can be seen that due to the relative disadvantage of the energy - storage frequency - regulation cost, the frequency - regulation market preferentially purchases thermal - power capacity for frequency - regulation reserve, and the energy storage only participates in the frequency - regulation response during periods when frequency - regulation resources are scarce (such as the 11th, 12th, 17th - 19th hours). In horizontal comparison of each scheme, compared with Scheme 1 and Scheme 3, Scheme 2 needs to reserve more capacity to participate in frequency - regulation bidding, but the winning bid result has no significant difference from that of Scheme 3.

[0177] Furthermore, based on two indicators of unequal funds and bidding non - winning - bid rate, a quantitative analysis is made on the clearing results of the mixed market under the three schemes. The unequal funds are calculated by splitting and combining each sub - item in the market - clearing objective function formula (19), and the bidding non - winning - bid rate is calculated according to the following method.

[0178]

[0179] Among them, y t is the winning bid quantity of power services, Y t is the bidding quantity of power services, k yThe bid - losing rate for power service y.

[0180] The results of the market evaluation indicators under each scheme are shown in Table 3. It can be seen from Table 3 that Scheme 3 is slightly superior to other schemes in terms of the unequal funds in the electricity market, is not inferior to other schemes as a whole in terms of the upward - and downward - adjusted market unequal funds, and has the smallest total unequal funds in the mixed market, which proves that the proposed scheme has a positive effect on promoting the maximization of social welfare. In terms of the bid - losing rate for selling electricity, each scheme has a 0 bid - losing rate, reflecting the clearing advantage brought by the low power generation cost of new energy. Compared with Scheme 1, Scheme 3 reduces the bid - losing rate for purchasing electricity by 17%, indicating that energy storage under this scheme can better ensure the purchase of electricity as scheduled to respond to the market demand in other periods. In terms of the upward - adjusted bid - losing rate, Scheme 3 is 37.9% lower than Scheme 1 and 50.1% lower than Scheme 2, showing a significant advantage and effectively enhancing the competitiveness of upward frequency modulation. In terms of the downward - adjusted bid - losing rate, the differences among the schemes are not significant, and the overall level is relatively high.

[0181] Table 3 Comparison of market evaluation indicators under each scheme

[0182] Index Scheme 1 Scheme 2 Scheme 3 Electricity quantity inequality funds / 10,000 yuan 34.373 34.171 34.167 Upward adjustment inequality funds / 10,000 yuan 0.106 0.107 0.107 Downward adjustment inequality funds / 10,000 yuan 0.102 0.103 0.102 Total inequality funds / 10,000 yuan 34.581 34.380 34.376 Field station selling current bid failure rate / % 0 0 0 Field station purchasing current bid failure rate / % 28.6 10.8 11.6 Field station upward adjustment bid failure rate / % 77.1 89.3 39.2 Field station downward adjustment bid failure rate / % 93.2 94.0 93.5

[0183] It can be seen from the above specific embodiments that the method provided by this application has strong competitiveness in a high - proportion new - energy environment compared with the fixed charge - discharge plan and the fixed frequency - modulation capacity strategy. It can obtain substantial profits by responding to the dual markets of electricity quantity and frequency modulation, which is conducive to accelerating the cost recovery of the power station. At the same time, it can effectively reduce the bid - losing rate of the combined power station while improving social welfare, which is conducive to the long - term win - win operation of market operators and market players. Specific Embodiment Two

[0185] In this embodiment, the following 4 scenarios are designed to verify the effectiveness and flexibility of the method proposed in this application in the face of different market scenarios.

[0186] Scenario 1: High proportion of thermal power, medium - low proportion of new - energy power generation, and relatively low system frequency - modulation demand level.

[0187] Scenario 2: High proportion of thermal power, medium - low proportion of new - energy power generation, and relatively high system frequency - modulation demand level.

[0188] Scenario 3: Medium - low proportion of thermal power, high proportion of new - energy power generation, and relatively low system frequency - modulation demand level.

[0189] Scenario 4: Medium - low proportion of thermal power, high proportion of new - energy power generation, and relatively high system frequency - modulation demand level.

[0190] Among them, the calculation example conditions of Scenario 3 are the same as those of Bid Scheme 3 in Specific Embodiment One. The market allocation situations under Scenarios 1, 2, and 4 are as Figures 9A to 9C shown, and the bid - winning / losing results in the electricity market under Scenarios 1, 2, and 4 are as Figures 10A to 10CAs shown, the winning / losing results of the frequency modulation market are as Figures 11A to 11C shown.

[0191] It can be seen from the above figures that in the scenario of a high proportion of thermal power, the new energy storage combined power station mainly makes profits through the electricity market. When the new energy resources are sufficient and the system's frequency modulation demand is high, the combined power station has strong competitiveness in the frequency modulation market and can further profit by participating in frequency modulation response.

[0192] Figures 12A to 12D shows the results obtained by the market operator solving the clearing model under four scenarios ( Figures 12A to 12D the upper row of figures above) and the autonomous variable scheduling results obtained by each new energy storage combined power station solving its autonomous decision-making model ( Figures 12A to 12D the lower row of figures below). Through Figures 12A to 12D it can be seen that since the power supply from new energy to energy storage is not considered in the clearing, the values of the combined power station's electricity purchase, new energy power generation unit power supply, and energy storage unit charging obtained by the market operator solving the clearing model do not strictly satisfy the station balance constraint equation (7), and their values are meaningless. However, the variables obtained by each combined power station solving its own autonomous decision-making model strictly satisfy the station balance constraint equation (7), indicating that the combined power station can further optimize the scheduling of the new energy power generation unit and the energy storage unit based on the autonomous decision-making model on the basis of completing the winning clearing task, and also proves that involving the variables in the solution of the autonomous decision-making model can more flexibly adjust the scheduling of the new energy power generation unit and the energy storage unit according to the actual situation.

[0193] The above has made a detailed introduction to the specific implementation manners of the present application. For those skilled in the art of this technology, without departing from the principle of the present application, several improvements and modifications can still be made to the present application, and these improvements and modifications also fall within the protection scope of the claims of the present application.

Claims

1. A method for clearing electricity and frequency modulation coupling involving a new energy storage joint station, characterized in that: The following steps are involved: S1, establishing a bidding model for a new energy storage joint station, wherein each new energy storage joint station includes at least one new energy power generation unit and at least one energy storage unit; S2, divide the new energy generation units and energy storage units involved in the electricity and frequency regulation coupling clearing into multiple new energy storage joint sites; S3, each market player reports the bidding information to the market operator based on its bidding model, wherein the market players include each of the new energy storage joint stations and the thermal power stations participating in the electricity and frequency regulation coupling clearing; S4, the market operator determines the winning bid information of each market player participating in the electricity and frequency regulation coupling clearing by solving the electricity-frequency regulation hybrid market clearing model; S5, each market player participates in the electricity and frequency coupling clearing based on the winning bid information it receives, wherein each new energy and energy storage joint station performs clearing and dispatching of the new energy power generation units and energy storage units it includes by solving its autonomous decision-making model.

2. The method for clearing the electricity and frequency modulation coupling of the new energy storage joint station according to claim 1 is characterized in that: Step S1 includes the following steps: S11, establishing rules for the new energy storage joint station to participate in the hybrid power market electricity-frequency coupling clearing; S12, determining a compliance bidding strategy that can be adopted when the new energy storage joint station participates in the hybrid power market electricity-frequency coupling clearing; S13, establishing a bidding strategy constraint model for the new energy storage joint site based on the compliance bidding strategy; S14, establishing a bidding model for a new energy storage joint station that is constrained by the bidding strategy constraint model.

3. The method for clearing the electricity and frequency modulation coupling of the new energy storage joint station according to claim 2 is characterized in that: The new energy storage joint station participates in the hybrid power market and the frequency regulation coupling clearing complies with the following rules: Rule 1: The new energy storage joint station is only allowed to sell electricity or purchase electricity, and is prohibited from launching two bids at the same time; Rule 2: The new energy storage joint station relies on the energy storage unit to participate in the frequency regulation market. The new energy storage joint station has the frequency regulation capability only when and only when there is a direct transaction between the energy storage unit and the market; Rule 3: Prioritize the use of energy storage units to self-consume new energy electricity by the new energy storage joint stations; Rule 4: At the same time, the energy storage unit only has three working states: charging, discharging, and static.

4. The method for clearing the electricity and frequency modulation coupling of the new energy storage joint station according to claim 3 is characterized in that: The compliant bidding strategies include: Bidding strategy 1: the energy storage unit is stationary, and only the new energy generation unit sells electricity; Bidding strategy 2: new energy generation units and energy storage units jointly sell electricity; Bidding strategy 3: the new energy generation unit sells sufficient electricity, and the energy storage unit consumes the surplus electricity; Bidding strategy 4: the new energy power generation unit generates insufficient electricity, and the market meets the charging shortage of the energy storage unit, and the energy storage unit purchases electricity.

5. The method for clearing the electricity and frequency modulation coupling of the new energy storage joint station according to claim 4 is characterized in that: The bidding strategy constraint model includes: The relationship model between status and bidding strategy and the constraints of bidding strategy; The state and bidding strategy relationship model is used to characterize Relationship to the compliant bidding strategy, where: They are the charging state and discharging state of the energy storage unit of the new energy storage joint station. is the electricity purchasing status of the new energy storage joint station, j is the number of the new energy storage joint station, t is the clearing period, All are 0-1 state variables; The bidding strategy constraints are as follows: in, χ j,t,1 , χ j,t,2 is the dual variable.

6. The method for clearing the electricity and frequency modulation coupling of the new energy storage joint station according to claim 5 is characterized in that: The objective function of the new energy storage joint site bidding model is: in, is the cost function of the j-th new energy storage joint station, They are the electricity market revenue, frequency regulation market revenue, electricity market cost, new energy power generation unit operating cost and energy storage unit operating cost of the j-th new energy energy storage joint station; In addition, the constraints satisfied by the new energy storage joint station participating in the hybrid power market bidding include state variables 7. The method for clearing the electricity and frequency modulation coupling of the new energy storage joint station according to claim 6 is characterized in that: The objective function of the bidding model of the thermal power plant is: in, is the cost function of the g-th thermal power station, They are respectively the electricity market revenue, frequency regulation market revenue and thermal power consumption fuel cost of the g-th thermal power station.

8. The method for clearing the electricity and frequency modulation coupling of the new energy storage joint station according to claim 7 is characterized in that: In step S4, the objective function of the electricity-frequency modulation hybrid market clearing model is: Among them, f ISO is the cost function of hybrid market electricity-frequency coupling clearing, The power sales winning bid power, power purchase winning bid power, upward bid capacity, downward bid capacity, power sales bidding price, power purchase bidding price, upward bid price, downward bid price of the j-th new energy storage joint station, are the power sales winning power, upward bid capacity, downward bid capacity, power sales bidding price, upward bid price, downward bid price of the g-th thermal power station, Ω T ,Ω J ,Ω G They are the clearing period set, the new energy storage joint site set and the thermal power site set.

9. The method for clearing the electricity and frequency modulation coupling of the new energy storage joint station according to claim 5 is characterized in that: In step S4, the market operator determines the winning bid amount of each market player's participating electricity-frequency coupling clearing by solving a two-layer mixed integer nonlinear programming problem including inter-layer coupling constraints and 0-1 constraints, wherein the 0-1 constraints are determined by the bidding strategy constraint model.

10. The method for clearing the electricity and frequency modulation coupling of the new energy storage joint station according to claim 5, characterized in that: In step S5, the new energy storage joint station substitutes the received winning bid information into its bidding model to obtain its autonomous decision-making model, wherein: The winning bid information of the new energy storage joint station includes the winning bid power of the new energy storage joint station Power purchase winning bid power Increase the winning bid capacity Lower the bid capacity Electricity clearing price λ t , raise the clearing price Lower the clearance price The independent variables of the autonomous decision-making model include the charging power of the energy storage unit in the jth new energy storage joint station Discharge power of energy storage unit Power supply of new energy power generation unit Grid-connected power of renewable energy power generation units Available power of the energy storage unit as well as

Images