A collaborative scheduling method and system for a centralized energy storage power station to participate in the joint market

Abstract

A collaborative scheduling method and system for a centralized energy storage power station to participate in the joint market. The collaborative scheduling method first forms a joint system of energy storage and new energy power stations, takes the maximization of the revenue of the joint system in the green power and spot markets as the optimization goal, constructs a joint system optimal scheduling model considering the green power trading electricity decomposition curve, and optimally schedules the shared part of the energy storage capacity; then uses the remaining capacity after the sharing of the energy storage to independently participate in the electricity market competition, constructs a two-layer optimization model for the energy storage to participate in the spot-peak regulation-green power joint market, solves the two-layer optimization model to obtain the energy storage operation strategy; calculates the contribution level of each subject in the joint system, calculates the revenue distribution ratio according to the contribution level, and then distributes the revenue to each subject in the joint system. The present invention can give full play to the flexible diversity of the centralized energy storage participating in multiple types of markets and multiple operation modes, thereby improving the energy storage utilization rate and operation revenue.

Description

Technical Field

[0001] The present invention belongs to the technical field of energy storage optimization scheduling, and particularly relates to a collaborative scheduling method and system for a centralized energy storage power station to participate in a joint market. Background Art

[0002] In recent years, the penetration rate of new energy in the power grid has increased significantly. The role of energy storage in suppressing the fluctuations of wind and light and promoting the consumption of new energy has thus been taken seriously. New energy-storage complementary projects such as wind-solar-storage and water-wind-solar-storage have been built in many places. It has become a trend for energy storage and new energy to jointly participate in the power market. Most centralized energy storage meets the conditions for entering the power market and can also participate in the power market as an independent entity. Under the trend of the complementary participation of energy storage and new energy in the power market, existing research has not fully considered the flexible diversity of centralized energy storage participating in multiple types of markets and multiple operation modes, and its profit potential has not been fully explored. Therefore, in the case where the power interaction between centralized energy storage and combined new energy is frequent, and multi-scale and heterogeneous power markets such as the green power market and the spot market are coupled with each other, it is necessary to study a collaborative scheduling method for centralized energy storage that simultaneously participates in multiple power markets and capacity sharing. At the same time, regarding the revenue distribution problem of the joint operation of new energy and energy storage, existing research has not fully considered the impact of the emerging power trading variety of green power trading on the joint operation of new energy and energy storage. Summary of the Invention

[0003] The object of the present invention is to address the above problems existing in the prior art, and provide a collaborative scheduling method and system for a centralized energy storage power station to participate in a joint market, which divides energy storage into two forms, namely a consortium and an independent entity, and participates in the spot-peak regulation-green power joint market with different strategies, thereby improving the utilization rate of energy storage and the operation revenue. To achieve the above object, the technical solution of the present invention is as follows:

[0004] In the first aspect, the present invention proposes a collaborative scheduling method for a centralized energy storage power station to participate in a joint market, and the collaborative scheduling method includes:

[0005] S1. Form a joint system with a new energy power station for energy storage to participate in green power trading and spot trading. Decompose the green power trading electricity based on the spot electricity price to obtain a green power trading electricity decomposition curve. With the maximization of the joint system's revenue in the green power and spot markets as the optimization goal, construct a joint system optimal scheduling model considering the green power trading electricity decomposition curve, and optimize the scheduling of the shared part of the energy storage capacity;

[0006] S2. Independently participate in the electricity market competition by using the remaining capacity after energy storage sharing, and construct a two-layer optimization model for energy storage to participate in the spot-regulation-green power joint market. The two-layer optimization model includes an upper-layer optimization model and a lower-layer joint clearing model. The upper-layer optimization model is constructed with the goal of maximizing the revenue of energy storage participating in peak regulation and the spot electricity market on the basis of the energy storage completing the call demand of new energy power stations in the joint system. Solve the upper-layer optimization model to obtain the energy storage operation strategy, and substitute the obtained energy storage operation strategy as the boundary condition into the lower-layer joint clearing model, and adjust the energy storage operation strategy based on the clearing price feedback by the lower-layer joint clearing model;

[0007] S3. On the basis of the energy storage operation strategy obtained in S2, calculate the contribution level of each subject in the joint system, calculate the revenue distribution ratio according to the contribution level, and distribute the revenue to each subject in the joint system based on the revenue distribution ratio.

[0008] In S1, the method for obtaining the green power trading electricity decomposition curve is as follows:

[0009] Construct a green power trading electricity decomposition model considering the uncertainty of spot electricity prices, and solve the green power trading electricity decomposition model to obtain the green power trading electricity decomposition curve; the objective function of the green power trading electricity decomposition model includes:

[0010] ;

[0011] ; ;

[0012] ;

[0013] In the above formula, is the expected revenue of the joint system in the green power and spot markets; is the number of scenarios of the day-ahead clearing electricity price; is the scheduling period; is the day-ahead electricity price scenario is the occurrence probability of is the green power trading contract electricity price; is decomposed into the green power electricity quantity in the period; 、 are the predicted outputs of photovoltaic and wind power in the period respectively; is the predicted output of photovoltaic and wind power in the sum of the predicted outputs in the period; represents the output of the joint system at the day-ahead planned moment; 、 are the charging power and discharging power of the energy storage at the day-ahead planned moment respectively; For a scenario of the spot market on a certain day Time-of-use electricity price; is the unit time; is the energy storage operation cost; is the unit charge and discharge cost of energy storage; 、 are the charge efficiency and discharge efficiency of energy storage respectively; Scenario refers to the day-ahead clearing electricity price scenario;

[0014] The constraint conditions of the green power trading volume decomposition model include:

[0015] Internal constraints of the combined system:

[0016] ; ; ; ;

[0017] ; ;

[0018] In the above formula, 、 are respectively The lower and upper limits of the green power decomposition volume in a time period; is the planned value of the green power volume decomposed into the scheduling period This planned value is the sum of the green power volumes at each moment decomposed into the scheduling period ; 、 are respectively the charging powers of the photovoltaic power station and the wind farm to the energy storage power station in the moment;

[0019] Charge and discharge power and state of charge constraints:

[0020] ;

[0021] ; ;

[0022] ;

[0023] In the above formula, is the maximum charge and discharge power of the energy storage; is a binary variable, whose value is 1 when the energy storage is in the charging state and 0 when the energy storage is in the discharging state; 、 are respectively the maximum and minimum state of charge of the energy storage; represents the rated capacity of the energy storage; 、 For the energy storage at time period, the stored electricity after the end of the time period; is the initial stored electricity of the energy storage; , are the maximum charging power and discharging power of the energy storage respectively.

[0024] The objective function of the combined system optimization scheduling model includes:

[0025] ;

[0026] In the above formula, is the scheduling period the total revenue of the combined system within; is the electricity selling revenue of the combined system; is the curtailment loss of the combined system; is the deviation cost of the combined system; is the operation cost of the energy storage;

[0027] The electricity selling revenue of the combined system

[0028] ; ; ;

[0029] ;

[0030] ; ;

[0031] ;

[0032] In the above formula, is the green power trading revenue; is the contract electricity price of green power trading; represents the green power quantity decomposed to the time period; is the actually delivered green power quantity during the time period; , are the charging power and discharging power of the energy storage respectively; is the clearing electricity price of the day-ahead spot market during the time period; is the clearing electricity price of the intraday market during the time period; is the spot trading revenue; , are the maximum outputs of the wind farm and the photovoltaic power station respectively during the time period, It is the sum of the maximum outputs of the wind farm and the PV power station during a certain period; represents the actual total output of the combined system during a certain period; is the day-ahead planned total output of the combined system during a certain period; and are the charging powers of the PV power station and the wind farm to the energy storage power station during a certain period, respectively; is the curtailed power of the combined system during a certain period;

[0033] The curtailed power loss of the said combined system is calculated as:

[0034] ;

[0035] In the above formula, is the green certificate conversion coefficient, and 1 MWh of electricity corresponds to one green certificate; is the price of the green certificate;

[0036] The deviation cost of the said combined system is calculated as:

[0037] ;

[0038] ;

[0039] ;

[0040] ;

[0041] In the above formula, is the allowable deviation margin; is the green power deviation assessment electricity during a certain period; is the green power electricity deviation assessment coefficient; is the spot deviation assessment coefficient; and are the positive deviation electricity and negative deviation electricity of the combined system exceeding the deviation assessment margin, respectively.

[0042] The objective function of the upper-layer optimization model includes:

[0043] ;

[0044] In the above formula, represents the revenue of the shared partial energy storage in the day-ahead spot-peak regulation combined market; is the opening period of the electricity spot market; is the current market trading cycle; is the user participation scenario probability; is the user participation scenario day-ahead spot market clearing price; 、 are respectively the charging power of the energy storage as a power purchaser and the discharging power of the energy storage as a power seller during the period; is the day-ahead peaking market clearing price for scenario ; is the time-of-use power selling price of the power grid; is the transmission and distribution price of the power grid; is the charging power of the energy storage bidding in the deep peaking market; is the operation cost of the energy storage; is the green power decomposition power of the integrated system during the period; is the output power of the integrated system after calling the energy storage; is the green power trading contract price signed by the integrated system; Scenario refers to the power user bidding scenario;

[0045] The constraint conditions of the upper-layer optimization model include:

[0046] ; ;

[0047] ;

[0048] ;

[0049] ; ;

[0050] ;

[0051] In the above formula, 、 are respectively the capacities of the energy storage independently participating in the electricity market competition during the period and the period; is the initial stored electricity of the energy storage; is the charging power of the energy storage used by the photovoltaic power station and the wind farm in the integrated system during the period; is the discharging power of the energy storage used by the photovoltaic power station and the wind farm in the integrated system during the period; is the sum of the day-ahead predicted maximum output powers of the photovoltaic power station and the wind farm in the integrated system; is the reserved proportion of the energy storage capacity; is the sum of the rated powers of the PV power station and the wind farm in the combined system; is the rated capacity of the energy storage; is the maximum state of charge of the energy storage;

[0052] The objective function of the lower-layer combined clearing model includes:

[0053] ;

[0054] In the above formula, is the market operation cost, that is, the value of the surplus electricity; is the opening period of the day-ahead electricity spot market; is the trading cycle of the day-ahead market; , , , are the sets of thermal power units, users, PV power stations, and wind farms participating in the electricity market, respectively; is the set of thermal power units participating in the deep peak shaving market; , are the bid section numbers of the thermal power unit and the electricity user, respectively; , are the bid price and output power of the thermal power unit, respectively; , are the bid price and the winning bid load of the user , respectively; , are the cost per unit of electricity and output power of the PV power station , respectively; , are the cost per unit of electricity and output power of the wind turbine , respectively; , are the peak shaving price and output power reduction of the thermal power unit at the th gear, respectively;

[0055] The constraint conditions of the lower-layer combined clearing model include: market power balance constraint, generator set operation constraint, and unit load power constraint.

[0056] S3 includes: S31, calculating the contribution degree levels of each entity in the combined system; the calculation formula for the contribution degree level of the energy storage is:

[0057] ;

[0058] ;

[0059] ;

[0060] ; ;

[0061] ; ;

[0062] ;

[0063] In the above formula, is the energy storage contribution level; are the weights of each index respectively; , , , , are the total energy storage cost, energy storage equivalent power generation, energy storage marginal contribution, green power trading deviation, and output accuracy respectively; , are the opportunity cost of energy storage and the operation cost of energy storage respectively; is the clearing electricity price in the period of the day-ahead spot market; , are the charging power and discharging power of the energy storage respectively; , are the curtailment amounts of the combined system in the period with and without energy storage respectively; represents the marginal revenue of the energy storage; represents the combined system, , , represent the energy storage, photovoltaic power station, and wind farm respectively; represents the combined system revenue; represents the combined system revenue after removing the energy storage; is the green power deviation of the combined system without energy storage; is the green power deviation assessment electricity quantity in the , are the average relative errors of the output of the combined system with and without energy storage respectively; , are the maximum tracking plan errors of the combined system with and without energy storage respectively;

[0064] The calculation formula for the photovoltaic contribution level is:

[0065] ;

[0066] In the above formula, is the photovoltaic contribution level; , are the average relative errors of the output of the combined system with and without PV, respectively; , are the maximum tracking plan errors of the combined system with and without PV, respectively;

[0067] The formula for calculating the wind power contribution level is:

[0068] ;

[0069] In the above formula, is the wind power contribution level; , are the average relative errors of the output of the combined system with and without wind power, respectively; , are the maximum tracking plan errors of the combined system with and without wind power, respectively;

[0070] S32. Calculate the revenue of each entity in the combined system after being corrected by the contribution level according to the following formula:

[0071] ;

[0072] In the above formula, is the revenue of entity in the combined system after being corrected by the contribution level; is the contribution level of entity in the combined system ; is the minimum revenue for entity in the combined system to participate in cooperation; is the maximum revenue for entity in the combined system to participate in cooperation; is the cooperation revenue of the combined system ;

[0073] S33. Calculate the cooperation revenue distribution ratio of each entity in the combined system according to the following formula:

[0074] ;

[0075] In the above formula, is the cooperation revenue distribution ratio of the combined system in the th period; is the probability of scenario , and scenario is obtained by clustering the predicted output of wind and light in the area where the combined system is located during the dispatching period, is the number of scenarios; The revenue of the entity in the combined system after being corrected by the contribution level for a scenario in the combined system for a scenario after being corrected by the contribution level; The minimum revenue for an entity participating in cooperation in the combined system for a scenario; The cooperative revenue of the combined system for a scenario; in the combined system for a scenario;

[0076] S34. Perform revenue distribution to each entity in the combined system according to the cooperative revenue distribution ratio calculated based on S33.

[0077] Second, the present invention proposes a collaborative dispatching system for a centralized energy storage power station to participate in the combined market. The collaborative dispatching system includes:

[0078] An optimized dispatching module for the shared part of the energy storage capacity, which is used to form a combined system with new energy power stations for the energy storage to participate in green power trading and spot trading, decompose the green power trading electricity based on the spot electricity price to obtain the green power trading electricity decomposition curve, construct an optimized dispatching model of the combined system considering the green power trading electricity decomposition curve with the goal of maximizing the revenue of the combined system in the green power and spot markets, and optimize the dispatching of the shared part of the energy storage capacity;

[0079] An operation strategy adjustment module for the remaining energy storage capacity, which is used to independently participate in the power market competition with the remaining energy storage capacity, construct a two-layer optimized model for the energy storage to participate in the combined spot-peak regulation-green power market. The two-layer optimized model includes an upper-layer optimized model and a lower-layer combined clearing model. The upper-layer optimized model is constructed with the goal of maximizing the revenue of the energy storage participating in peak regulation and the spot power market on the basis of the energy storage completing the call requirements of new energy power stations in the combined system. Solve the upper-layer optimized model to obtain the energy storage operation strategy, substitute the obtained energy storage operation strategy into the lower-layer combined clearing model as a boundary condition, and adjust the energy storage operation strategy based on the clearing price fed back by the lower-layer combined clearing model;

[0080] A revenue distribution module, which is used to calculate the contribution level of each entity in the combined system based on the energy storage operation strategy obtained by the operation strategy adjustment module for the remaining energy storage capacity, calculate the revenue distribution ratio according to the contribution level, and perform revenue distribution to each entity in the combined system based on the revenue distribution ratio.

[0081] The optimized dispatching module for the shared part of the energy storage capacity includes a green power trading electricity decomposition curve acquisition module, which is used to construct a green power trading electricity decomposition model considering the uncertainty of the spot electricity price and solve the green power trading electricity decomposition model to obtain the green power trading electricity decomposition curve;

[0082] The objective function of the green power trading electricity quantity decomposition model includes:

[0083] ;

[0084] ; ;

[0085] ;

[0086] In the above formula, is the expected revenue of the combined system in the green power and spot markets; is the number of day-ahead clearing price scenarios; is the scheduling period; is the day-ahead price scenario 's occurrence probability; is the green power trading contract price; is decomposed to the green power quantity in the period; , are the predicted outputs of photovoltaic and wind power respectively in the period; is the sum of the predicted outputs of photovoltaic and wind power in the period; represents the output of the combined system at the day-ahead planned moment; , are the charging power and discharging power of the energy storage at the day-ahead planned moment respectively; is the period price in the day-ahead spot market under scenario ; is the unit time; is the energy storage operation cost; is the unit charge-discharge cost of the energy storage; , are the charging efficiency and discharging efficiency of the energy storage respectively; Scenario refers to the day-ahead clearing price scenario;

[0087] The constraint conditions of the green power trading electricity quantity decomposition model include: Internal constraints of the combined system:

[0088] ; ; ; ;

[0089] ; ;

[0090] In the above formula, , are respectively the lower and upper limits of the green power decomposition volume in a time period; is the planned value of the green power volume decomposed into the scheduling period The planned value is the sum of the green power volumes at each moment within the scheduling period ; and are respectively the charging powers of the energy storage power station by the PV power station and the wind farm in the daily plan at time;

[0091] Constraints on charge-discharge power and state of charge:

[0092] ;

[0093] ; ;

[0094] ;

[0095] In the above formula, is the maximum charge-discharge power of the energy storage; is a binary variable, whose value is 1 when the energy storage is in the charging state and 0 when the energy storage is in the discharging state; and are respectively the maximum and minimum state of charge of the energy storage; represents the rated capacity of the energy storage; and are the stored electricity of the energy storage at the end of the time period time period and time period; is the initial stored electricity of the energy storage; and are respectively the maximum values of the charging power and discharging power of the energy storage.

[0096] The energy storage shared partial capacity optimal scheduling module further includes an optimal scheduling module, which is used to construct a joint system optimal scheduling model considering the green power trading volume decomposition curve to optimize the scheduling of the shared partial capacity of the energy storage;

[0097] The objective function of the joint system optimal scheduling model includes:

[0098] ;

[0099] In the above formula, is the total revenue of the joint system within the scheduling period ; is the electricity sales revenue of the joint system; is the curtailment loss of the joint system; is the deviation cost of the joint system; is the energy storage operation cost;

[0100] The electricity selling revenue of the combined system The calculation formula is:

[0101] ; ; ;

[0102] ;

[0103] ; ;

[0104] ;

[0105] In the above formula, is the green power trading revenue; is the contract electricity price of green power trading; represents the green power quantity decomposed to time period; is the actually delivered green power quantity in the time period; , are the energy storage charging power and discharging power respectively; is the clearing electricity price in the time period of the day-ahead spot market; is the clearing electricity price in the time period of the intraday market; is the spot trading revenue; , are the maximum outputs of the wind farm and the photovoltaic power station respectively in the time period, then is the sum of the maximum outputs of the wind farm and the photovoltaic power station in the time period; represents the actual total output of the combined system in the time period; is the day-ahead planned total output of the combined system in the time period; , are the charging powers of the photovoltaic power station and the wind farm to the energy storage power station respectively in the time period; is the curtailed power quantity of the combined system in the time period;

[0106] The curtailment loss of the combined system The calculation formula is:

[0107] ;

[0108] In the above formula, is the green certificate conversion coefficient, and one green certificate corresponds to 1 MWh of electricity; is the price of the green certificate;

[0109] The deviation cost of the said combined system The calculation formula is:

[0110] ;

[0111] ;

[0112] ;

[0113] ;

[0114] In the above formula, is the allowable deviation margin; is the green power deviation assessment electricity quantity during the period; is the green power quantity deviation assessment coefficient; is the spot deviation assessment coefficient; , are respectively the positive deviation electricity quantity and the negative deviation electricity quantity of the combined system exceeding the deviation assessment margin.

[0115] The objective function of the said upper-layer optimization model includes:

[0116] ;

[0117] In the above formula, represents the revenue of the shared part of the energy storage in the day-ahead spot-peaking combined market; is the period when the electricity spot market is open; is the day-ahead market trading cycle; is the probability of the user participating in scenario ; is the probability of the user participating in scenario the clearing price of the day-ahead spot market; , are respectively the charging power of the energy storage as a power purchaser and the discharging power of the energy storage as a power seller during the period; is the clearing price of the day-ahead peaking market under scenario ; is the grid time-of-use power selling price; is the grid transmission and distribution price; is the charging power of the energy storage bidding in the deep peaking market; is the energy storage operation cost; is the decomposed green power quantity of the combined system during the period; is the output power of the combined system after calling the energy storage; is the price of the green power trading contract signed by the combined system; scenario refers to the electricity user bidding scenario;

[0118] The constraint conditions of the upper-layer optimization model include:

[0119] ;

[0120] ;

[0121] ;

[0122] ;

[0123] ;

[0124] ;

[0125] ;

[0126] In the above formula, 、 are respectively time period, the capacity of the energy storage participating in the electricity market competition independently during the time period; is the initial stored electricity of the energy storage; is the charging power of the photovoltaic power station and the wind farm in the combined system using the energy storage during the time period; is the discharging power of the photovoltaic power station and the wind farm in the combined system using the energy storage during the time period; is the sum of the predicted maximum output powers of the photovoltaic power station and the wind farm in the combined system; is the reserved proportion of the energy storage capacity; is the sum of the rated powers of the photovoltaic power station and the wind farm in the combined system; is the rated capacity of the energy storage; is the maximum state of charge of the energy storage;

[0127] The objective function of the lower-layer combined clearing model includes:

[0128] ;

[0129] In the above formula, is the market operation cost, that is, the value of the surplus electricity; is the opening time period of the day-ahead electricity spot market; is the day-ahead market trading cycle; 、 , , are respectively the set of thermal power units, the set of users, the set of photovoltaic power stations, and the set of wind farms participating in the power market; is the set of thermal power units participating in the deep peak shaving market; , are respectively the quotation section numbers of thermal power units and electricity users; , are respectively the quotation and output power of thermal power units; , are respectively the quotation and winning bid load of users; , are respectively the cost per kilowatt-hour and output power of the photovoltaic power station; , are respectively the cost per kilowatt-hour and output power of the wind turbine; , are respectively the nth peak shaving price and output power reduction of the thermal power unit;

[0130] The constraint conditions of the lower-layer joint clearing model include: market power balance constraint, thermal power unit operation constraint, unit and load power constraint.

[0131] The revenue distribution module includes a contribution level calculation module, a revenue distribution ratio calculation module, and a distribution module;

[0132] The contribution level calculation module is used to calculate the energy storage contribution level according to the following formula:

[0133] ;

[0134] ;

[0135] ;

[0136] ; ;

[0137] ; ;

[0138] ;

[0139] In the above formula, is the energy storage contribution level; are respectively the weights of each index; , , , , are the total energy storage cost, the equivalent power generation of energy storage, the marginal contribution degree of energy storage, the deviation of green power trading, and the output accuracy rate respectively; , are the opportunity cost of energy storage and the operation cost of energy storage respectively; is the clearing electricity price in the period of the day-ahead spot market; , are the charging power and discharging power of energy storage respectively; , are the curtailment amounts of the combined system in the period with and without energy storage respectively; represents the marginal revenue of energy storage; represents the combined system, , , represent energy storage, photovoltaic power station, and wind farm respectively; represents the combined system revenue; represents the combined system revenue after removing energy storage; is the green power deviation of the combined system without energy storage; is the green power deviation assessment electricity quantity in the , are the average relative errors of the output of the combined system with and without energy storage respectively; , are the maximum tracking plan errors of the combined system with and without energy storage respectively;

[0140] The photovoltaic contribution level is calculated according to the following formula:

[0141] ;

[0142] In the above formula, is the photovoltaic contribution level; , are the average relative errors of the output of the combined system with and without photovoltaic respectively; , are the maximum tracking plan errors of the combined system with and without photovoltaic respectively;

[0143] The wind power contribution level is calculated according to the following formula:

[0144] ;

[0145] In the above formula, is the wind power contribution level; , The average relative error of the output of the combined system under wind power and non-wind power conditions respectively; 、 The maximum tracking plan error of the combined system under wind power and non-wind power conditions respectively;

[0146] The revenue distribution ratio calculation module is used to first calculate the revenue of each entity in the combined system after being corrected by the contribution level, and then calculate the cooperative revenue distribution ratio. The formula for the revenue after being corrected by the contribution level is:

[0147] ;

[0148] In the above formula, is the revenue of entity in the combined system after being corrected by the contribution level; is the contribution level of entity in the combined system ; is the minimum revenue for entity in the combined system to participate in cooperation; is the maximum revenue for entity in the combined system to participate in cooperation; is the cooperative revenue of the combined system ;

[0149] The formula for the cooperative revenue distribution ratio of each entity in the combined system is:

[0150] ;

[0151] In the above formula, is the cooperative revenue distribution ratio in the th period of the combined system; is the probability of scenario . The scenario is obtained by clustering the predicted output of wind and light in the area where the combined system is located during the scheduling period, is the number of scenarios; is the revenue of entity in the combined system under scenario after being corrected by the contribution level; is the minimum revenue for entity in the combined system under scenario to participate in cooperation; is the cooperative revenue of the combined system under scenario ;

[0152] The distribution module is used to distribute the benefits to each entity in the joint system based on the cooperative benefit distribution ratio.

[0153] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0154] 1. For the collaborative scheduling method of a centralized energy storage power station participating in the joint market described in the present invention, the energy storage is first divided into two forms, namely the consortium and the independent entity, and participates in the spot - peak regulation - green power joint market with different strategies to obtain the energy storage operation strategy for the collaborative reuse of the energy storage among multiple markets. Then, based on the consideration of the contribution degree correction, the benefits are distributed to each entity in the joint system. On the one hand, this collaborative scheduling method forms a joint system with new energy for the energy storage to participate in green power trading and spot trading, proposes a joint system optimal scheduling model considering the green power trading power decomposition curve, and optimally schedules the shared part of the energy storage capacity. On the other hand, it uses the remaining capacity after the energy storage sharing to independently participate in the power market competition, thereby giving full play to the flexible diversity of the centralized energy storage participating in multiple types of markets and multiple operation modes, and thus improving the energy storage utilization rate and the energy storage operation income.

[0155] 2. For the collaborative scheduling method of a centralized energy storage power station participating in the joint market described in the present invention, the contribution degree levels of the energy storage and new energy participating in medium - and long - term green power and spot trading are quantitatively analyzed from two aspects of cost and benefit. Furthermore, a cooperative benefit distribution method based on the contribution degree level correction is proposed. On the premise of ensuring fairness and rationality, it effectively guarantees that the energy storage fully obtains the return in promoting the realization of the green value of new energy, effectively improves the enthusiasm of the energy storage to cooperate with new energy, and thus improves the overall income. BRIEF DESCRIPTION OF THE DRAWINGS

[0156] Figure 1 It is a flowchart of the collaborative scheduling method described in Embodiment 1.

[0157] Figure 2 It is a structural block diagram of the collaborative scheduling system described in Embodiment 2. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0158] The present invention will be further described in detail below in conjunction with the detailed description of the embodiments and the accompanying drawings.

[0159] Embodiment 1:

[0160] Refer to Figure 1 , a collaborative scheduling method for a centralized energy storage power station participating in the joint market, which is carried out in the following steps in sequence:

[0161] Step S1. Form an energy storage and a new energy power station as Figure 2The combined system shown participates in green power trading and spot trading. Based on the spot electricity price, the green power trading electricity volume is decomposed to obtain the green power trading electricity volume decomposition curve. With the maximization of the combined system's revenue in the green power and spot markets as the optimization goal, an optimal scheduling model of the combined system considering the green power trading electricity volume decomposition curve is constructed to optimize the shared capacity of energy storage; the method for obtaining the green power trading electricity volume decomposition curve is as follows: According to historical electricity price data, multiple representative scenarios of the day-ahead clearing electricity price and their corresponding probabilities are obtained using Latin hypercube sampling and Kantorovich distance scenario reduction methods. A green power trading electricity volume decomposition model considering the uncertainty of spot electricity prices is constructed, and the green power trading electricity volume decomposition curve is obtained by solving the green power trading electricity volume decomposition model; the objective function of the green power trading electricity volume decomposition model includes:

[0162] ;

[0163] ; ;

[0164] ;

[0165] In the above formula, is the expected revenue of the combined system in the green power and spot markets; is the number of day-ahead clearing electricity price scenarios; is the scheduling period, usually set to 24 hours; is the day-ahead electricity price scenario 's occurrence probability; is the contract electricity price of green power trading; is the green power electricity volume decomposed to time period; , are the predicted outputs of photovoltaic and wind power at time period respectively; is the sum of the predicted outputs of photovoltaic and wind power at time period; represents the output of the combined system at the day-ahead plan moment; , are the charging power and discharging power of the energy storage at the day-ahead plan moment respectively; is the electricity price of the day-ahead spot market under scenario at time period; is the unit time, usually set to one hour; is the operating cost of the energy storage; is the unit charge-discharge cost of the energy storage; , are the charging efficiency and discharging efficiency of the energy storage respectively; scenario Indicates the scenario of clearing electricity price on the current day ;

[0166] To avoid excessive speculation of new energy in the spot market and affect the smooth delivery of medium- and long-term green power transactions, it is stipulated that the sum of green power electricity in each period cannot be lower than the electricity volume decomposed from the medium- and long-term green power transaction plan to the day; the settlement electricity volume of green power transactions is determined according to the principle of taking the minimum value among the contract electricity volume, new energy grid-connected electricity volume, and green power user electricity consumption; when formulating the green power decomposition curve, the electricity consumption pattern of green power users needs to be considered, and the green power electricity volume decomposed to each period should be within the range specified by green power users and not exceed the output of wind and solar new energy in that period. Based on the above principles, the constraint conditions of the green power transaction electricity volume decomposition model are set. The constraint conditions of the green power transaction electricity volume decomposition model include: Internal constraints within the combined system:

[0167] ; ; ; ;

[0168] ; ;

[0169] In the above formula, 、 are respectively The lower and upper limits of the green power decomposition volume in the period; Is the planned value of the green power electricity volume decomposed into the dispatching period , and this planned value is the sum of the green power electricity volumes at each moment decomposed into the dispatching period ; 、 Are respectively the charging powers of the energy storage power station by the photovoltaic power station and the wind farm in the pre-dispatch plan at moment;

[0170] Charging and discharging power and state of charge constraints:

[0171] ;

[0172] ; ;

[0173] ;

[0174] In the above formula, Is the maximum charging and discharging power of the energy storage; Is a binary variable, when its value is 1, it indicates that the energy storage is in the charging state, and when its value is 0, it indicates that the energy storage is in the discharging state; 、 Are respectively the maximum and minimum state of charge of the energy storage; Indicates the rated capacity of the energy storage; , is the stored electricity at the end of the time period; is the initial stored electricity of the energy storage; is the initial stored electricity of the energy storage; , are the maximum charging power and discharging power of the energy storage respectively;

[0175] In the combined system, due to the randomness of new energy power generation, the actual power of wind and light fluctuates greatly, resulting in a large deviation from the predicted value. When there is a difference between the power generation plan submitted by the new energy power station and the actual output, it will affect its own power trading compliance and the stable operation of the power system. The energy storage has the characteristic of bidirectional power flow, and the energy storage power compensation can reduce the error assessment cost between the predicted output and the actual output of new energy; based on the above characteristics, the objective function of the combined system optimal scheduling model is designed as: ; In the above formula, is the scheduling period the total revenue of the combined system within; is the electricity sales revenue of the combined system; is the curtailment loss of the combined system; is the deviation cost of the combined system; is the operation cost of the energy storage; the electricity sales revenue of the combined system The calculation formula of is:

[0176] ; ; ;

[0177] ;

[0178] ; ;

[0179] ;

[0180] In the above formula, is the green power trading revenue; is the contract electricity price of green power trading; represents the green power electricity decomposed to the time period; is the actual delivered green power electricity during the time period; , are the charging power and discharging power of the energy storage respectively; is the clearing electricity price in the day-ahead spot market during the time period; is the clearing electricity price in the intraday market during the time period; It is the revenue from spot trading; and are the maximum outputs of the wind farm and the PV power station during the time period, while is the sum of the maximum outputs of the wind farm and the PV power station during the time period; represents the actual total output of the combined system during the time period; is the day-ahead planned total output of the combined system and are the charging powers of the PV power station and the wind farm to the energy storage power station during the time period; is the curtailed electricity of the combined system during the time period;

[0181] The curtailment loss of the said combined system is calculated as:

[0182] ;

[0183] In the above formula, is the green certificate conversion coefficient, and 1 MWh of electricity corresponds to one green certificate; is the price of the green certificate;

[0184] The deviation cost of the said combined system is calculated as:

[0185] ; ;

[0186] ;

[0187] ;

[0188] In the above formula, is the allowable deviation margin; is the green power deviation assessment electricity during the time period; is the green power electricity deviation assessment coefficient; is the spot deviation assessment coefficient; and are the positive deviation electricity and the negative deviation electricity of the combined system exceeding the deviation assessment margin respectively;

[0189] Step S2: Independently participate in the electricity market using the remaining capacity after energy storage sharing, participate in market competition as a price taker, and construct a two-layer optimization model for the energy storage to participate in the spot-peak regulation-green power joint market. The two-layer optimization model includes an upper-layer optimization model and a lower-layer joint clearing model. The upper-layer optimization model is constructed with the goal of maximizing the revenue of the energy storage participating in peak regulation and the spot electricity market on the basis of the energy storage completing the call requirements of new energy power stations in the joint system. Solve the upper-layer optimization model to obtain the energy storage operation strategy, and substitute the obtained energy storage operation strategy as a boundary condition into the lower-layer joint clearing model, and adjust the energy storage operation strategy based on the clearing price feedback by the lower-layer joint clearing model; the objective function of the upper-layer optimization model includes:

[0190] ;

[0191] In the above formula, represents the revenue of the shared part of the energy storage in the day-ahead spot-peak regulation joint market; is the opening period of the electricity spot market; is the trading cycle of the day-ahead market; is the user participation scenario probability; is the user participation scenario day-ahead spot market clearing price; , are respectively the charging power of the energy storage as a power purchaser and the discharging power of the energy storage as a power seller during the period; is the day-ahead peak regulation market clearing price under scenario ; is the grid time-of-use power selling price; is the grid transmission and distribution price; is the charging power of the energy storage bidding in the deep peak regulation market; is the energy storage operation cost; is the green power decomposition power of the joint system during the period; is the output power of the joint system after calling the energy storage; is the green power trading contract price signed by the joint system; Scenario represents the power user bidding scenario , which generates multiple power user bidding scenarios through Latin hypercube sampling, and is used to describe the revenue risk brought by the power user bidding price to the energy storage;

[0192] Reserve part of the energy storage power and capacity for the joint dispatching of new energy power stations, and set the constraint conditions of the upper-layer optimization model as follows:

[0193] ; ;

[0194] ;

[0195] ;

[0196] ; ;

[0197] ;

[0198] In the above formula, and are respectively time period, the capacity of the energy storage participating in the electricity market competition independently during the time period; is the initial stored electricity of the energy storage; is the charging power of the photovoltaic power station and the wind farm in the joint system using the energy storage during time period; is the discharging power of the photovoltaic power station and the wind farm in the joint system using the energy storage during time period; is the sum of the predicted maximum output powers of the photovoltaic power station and the wind farm in the joint system in advance; is the reserved ratio of the energy storage capacity; is the sum of the rated powers of the photovoltaic power station and the wind farm in the joint system; is the rated capacity of the energy storage; is the maximum state of charge of the energy storage;

[0199] The objective function of the lower-layer joint clearing model includes:

[0200] ;

[0201] In the above formula, is the market operation cost, that is, the value of the surplus electricity; is the time period when the electricity spot market is opened on that day; is the trading cycle of the day-ahead market; and and and are respectively the set of thermal power units, the set of users, the set of photovoltaic power stations, and the set of wind farms participating in the electricity market; is the set of thermal power units participating in the deep peak shaving market; and are respectively the quotation section numbers of the thermal power units and the electricity users; and are respectively the quotation and output power of the thermal power unit in the th quotation section; and The user 's quoted price and winning bid load; 、 are the cost per kilowatt-hour and output power of the PV power station respectively; 、 are the cost per kilowatt-hour and output power of the wind turbine respectively; 、 are the peak shaving price and output power reduction of the thermal power unit in the gear respectively;

[0202] The constraint conditions of the lower-layer combined clearing model include: Market power balance constraint:

[0203] ;

[0204] ;

[0205] In the above formula, 、 are the dual multipliers corresponding to the constraints in the period, which are the clearing prices of the spot market and the peak shaving market respectively; 、 are the green power decomposition amounts of the PV power station and the wind farm in the period respectively; is the green power consumption of the electricity user in the period as agreed according to the green power decomposition curve; 、 are the power upper limits of the PV power station and the wind farm in the period respectively; is the set of thermal power units that do not participate in the electricity energy market, and the day-ahead planned output of this type of unit in the period is ; is the net load of the non-market part in the period; is the declared power of the user in the gear;

[0206] Thermal power unit operation constraint:

[0207] For thermal power units participating in the spot market, the power generation in the period is the sum of the powers of all winning bid power segments:

[0208] ;

[0209] For thermal power units participating in the deep peak shaving market, their The generated electricity during a time period needs to comprehensively consider the winning bid power in the electricity energy market and the deep peak shaving market:

[0210] ;

[0211] In the above formula, is the technical output of the thermal power unit during the time period; is the conventional minimum technical output; is the basic peak shaving power of the thermal power unit obligated to provide basic peak shaving services;

[0212] ; ; ;

[0213] In the above formula, , are respectively the minimum and maximum technical outputs of the thermal power unit. For thermal power units not participating in deep peak shaving, is the conventional minimum technical output . For units participating in deep peak shaving, is the maximum peak shaving depth that can be achieved when in the peak shaving state; is the technical output of the thermal power unit during the time period and the time period; , are respectively the upper and lower ramping rates of the thermal power unit;

[0214] Unit load power constraint:

[0215] ; ;

[0216] ; ;

[0217] ;

[0218] In the above formula, is the upper limit of the declared power of the thermal power unit in the th section under the spot market; ; ; is the upper limit of the declared power reduction of the thermal power unit in the th gear under the peak shaving market; is the user's upper limit of the declared power in the

[0219] Step S3: Based on the energy storage operation strategy obtained in Step S2, calculate the contribution level of each entity in the combined system, calculate the revenue distribution ratio according to the contribution level, and distribute the revenue to each entity in the combined system based on the revenue distribution ratio;

[0220] In the multi-entity cooperation model, the unreasonable revenue distribution method among entities will make it difficult to effectively allocate the energy storage cost, resulting in insufficient enthusiasm for energy storage to cooperate with new energy power stations to complete power transactions. Therefore, the collaborative scheduling method proposed in this invention constructs an energy storage contribution index system from two aspects: cost and benefit. The cost aspect includes the opportunity cost of energy storage and the operation cost of energy storage. When calculating the operation cost of energy storage, assuming that energy storage is a price taker, further calculate the revenue generated by the energy storage capacity used by new energy through peak-valley arbitrage in the intraday real-time power market, and regard it as the opportunity cost of energy storage. The benefit aspect includes the equivalent power generation of energy storage, the marginal revenue contribution, the improvement degree of green power trading deviation, and the improvement degree of output accuracy. Energy storage can store the electricity exceeding the power generation plan during the periods of wind and light curtailment, thus avoiding the waste of clean energy and reducing the economic losses of wind and light power stations. This part of the electricity can be regarded as the equivalent power generation of energy storage. Marginal revenue refers to the difference in the coalition revenue between the two cases of a participant joining the coalition and not joining the coalition. The marginal revenue contribution is the ratio of the marginal contribution value to the total coalition revenue. The higher the marginal contribution degree, the more important the participant is in the coalition. On the one hand, the green power trading deviation will reduce the economic benefits of new energy power stations, and on the other hand, it will affect the performance evaluation of green power trading compliance. To evaluate the effectiveness of energy storage in tracking the wind and light output plan, the root mean square error and the maximum tracking plan error are used as the tracking performance evaluation indicators of energy storage, and the difference in the output accuracy of new energy is compared with and without the participation of energy storage. The total cost of energy storage, equivalent power generation, improvement degree of green power deviation, improvement degree of output accuracy, and marginal revenue contribution are used as the contribution evaluation indicators of energy storage, and the weights of each evaluation indicator are determined after normalizing each indicator;

[0221] Step S31: Calculate the contribution level of each entity in the combined system; the calculation formula for the energy storage contribution level is:

[0222] ;

[0223] ; ;

[0224] ; ;

[0225] ; ;

[0226] ;

[0227] ;

[0228] ;

[0229] In the above formula, is the energy storage contribution level; are the weights of each index respectively; 、 、 、 、 are the total cost of energy storage, the equivalent power generation of energy storage, the marginal contribution degree of energy storage, the deviation of green power trading, and the accuracy of output respectively; 、 are the opportunity cost of energy storage and the operating cost of energy storage respectively; is the clearing electricity price in the day-ahead spot market during the period; 、 are the charging power and discharging power of energy storage respectively; 、 are the curtailment amounts of the combined system during the period with and without energy storage respectively; represents the marginal revenue of energy storage; represents the combined system, 、 、 represent energy storage, photovoltaic power station, and wind farm respectively; represents the combined system revenue; represents the combined system revenue after removing energy storage; is the green power deviation of the combined system without energy storage; is the green power deviation assessment electricity quantity during the period; 、 are the average relative errors of the output of the combined system with and without energy storage respectively; 、 are the maximum tracking plan errors of the combined system with and without energy storage respectively; 、 are the maximum outputs of the wind farm and the photovoltaic power station during the period respectively; 、 are the predicted outputs of the photovoltaic power station and the wind farm during the period respectively;

[0230] The calculation formula for the photovoltaic contribution level is: ; In the above formula, is the photovoltaic contribution level; 、 are the average relative errors of the output of the combined system with and without photovoltaic respectively; , The maximum tracking plan errors of the combined system with and without PV respectively;

[0231] The calculation formula for the wind power contribution level is: ; In the above formula, is the wind power contribution level; , The average relative errors of the output of the combined system with and without wind power respectively; , The maximum tracking plan errors of the combined system with and without wind power respectively;

[0232] Step S32: To ensure the effective diversion of energy storage costs while stimulating the enthusiasm of energy storage to actively participate in the operation of the wind-solar-storage combined system, based on the comparison of the economic benefits of the independent and combined operation modes of wind-solar-storage, the maximum-minimum cost method (MCRS method) in cooperative game theory is used to calculate the benefit contributions of energy storage and new energy, and the income distribution is corrected in combination with the contribution degree index system of wind-solar-storage. To achieve fair distribution, by taking the marginal income of individuals as the distribution measurement standard, the gain distribution is carried out according to the proportion of the difference between the maximum and minimum incomes of each stakeholder participating in the cooperation. The minimum income refers to the income obtained when the wind farm, photovoltaic power station, and energy storage power station operate independently. In the independent operation mode, wind power and photovoltaic power generate electricity according to the amount of natural resources and bear the deviation assessment cost by themselves, while the energy storage system independently participates in the power market and obtains income through low storage and high generation. Its net income is calculated by subtracting the operation cost from the market income. The income calculation formula for each subject in the combined system after being corrected by the contribution degree level is:

[0233] ;

[0234] In the above formula, is the income of subject in the combined system after being corrected by the contribution degree level; is the contribution degree level of subject in the combined system ; is the minimum income of subject in the combined system participating in the cooperation; is the maximum income of subject in the combined system participating in the cooperation; is the cooperation income of the combined system ;

[0235] Step S33: To reduce the computational complexity, the K-means clustering method is used to cluster the predicted wind and solar power outputs in the area where the combined system is located during the scheduling period into A typical scenario is used to calculate the cooperative revenue distribution ratio of the joint system in combination with the scenario probability. The calculation formula for the cooperative revenue distribution ratio of each entity in the joint system is as follows:

[0236] ;

[0237] In the above formula, is the cooperative revenue distribution ratio in the th time period of the joint system; is the probability of scenario . The scenario is obtained by clustering the predicted output of wind and light in the area where the joint system is located during the scheduling period. is the number of scenarios; is the revenue of entity in the joint system under scenario after being corrected by the contribution level; is the minimum revenue of entity in the joint system participating in cooperation under scenario ; is the cooperative revenue of the joint system under scenario ;

[0238] Step S34: Distribute the revenue to each entity in the joint system based on the cooperative revenue distribution ratio obtained in Step S33.

[0239] The following takes a certain area as an example for simulation analysis. In a certain area, energy storage signs long-term contracts with a wind farm and a photovoltaic power station at the same node to form a wind-solar-storage joint system. The specific operating parameters of the energy storage are shown in Table 1. The parameters such as the predicted output power, actual output power, and clearing price of the power market for wind and light are set as follows: The wind turbines can generate electricity throughout the day, and the output power is relatively high in the 18-24h time period; The photovoltaic power generation is distributed in the 7-18h time period and reaches the peak at noon; The overall gap between the actual power and the predicted power of wind and light is not large; The clearing price of the power market reaches the peak at 9h in the morning and 20h in the evening, and reaches the trough at 14h in the afternoon.

[0240] Table 1 Equipment parameter settings of the energy storage system

[0241]

[0242] (1) The simulation results are as follows: For the decomposition curve of the day-ahead green power trading curve, since the green power price has been determined in the long term, within the limited green power decomposition area, the wind-solar-storage integrated system will decompose the green power to each low-price period according to the predicted day-ahead electricity price. The final green power decomposition curve is negatively correlated with the predicted day-ahead electricity price. For example, 2-3h and 12-16h are low-price periods, and the wind-solar-storage integrated system tends to decompose more green power to the corresponding periods. The decomposed green power reaches the upper limit set by the green power users. On the contrary, for 8-11h and 17-21h, the decomposed green power in the corresponding periods drops to the lower limit set by the green power users. In addition, in the day-ahead stage, due to the consideration of electricity price uncertainty and the loss cost in the energy storage charging and discharging process, the energy storage scheduling strategy is relatively conservative, and the overall charging and discharging volume is small. The energy storage only plans to absorb part of the electricity in the low-price period and resell it to the high-price period. For the energy storage scheduling strategy, the energy storage is only called to participate in green power trading and spot trading during the low and high clearing electricity price periods, and the energy storage is not called during 5-11h and 21-23h. In addition, although each new energy power station has the motivation to decompose the green power trading electricity to the low-price period, the total decomposed green power in each period is relatively average in the end. This is because the new energy power stations tend to decompose the green power trading electricity within their output range to avoid the risk of green power trading deviation assessment, and the green power users also set the green power decomposition range. Finally, the green power trading electricity is evenly distributed to each period, leaving enough bidding space for the day-ahead spot market. Since the behavior of the energy storage in the spot and peaking markets will affect the market clearing price, the energy storage disperses its bidding volume to each period. For example, at 20-21h, the discharging bidding volume of the energy storage in the day-ahead spot market is lower than that at 19h, the highest electricity price period, because too much bidding volume of the energy storage will pull down the clearing electricity price and reduce the revenue. At the same time, to obtain the maximum revenue, the energy storage will also disperse its charging bidding volume to the two day-ahead markets. For example, at 14h, to avoid its charging behavior from raising the spot electricity price, the energy storage charges in the spot market and uses part of its capacity to charge in the deep peaking market at the same time to obtain peaking compensation while minimizing its power purchase cost.

[0243] (2) To verify the rationality of the revenue distribution method proposed in step S3 of the present invention, the revenue of the wind-solar-storage integrated system is distributed by the electricity quantity distribution method, the MCRS method, and step S3 of the present invention respectively, and the revenues of each entity in the wind-solar-storage integrated system under different revenue distribution methods are compared. The comparison results are shown in Table 3.

[0244] Table 2 Revenue Comparison of Different Alliances

[0245]

[0246] Table 3 Revenue Comparison of Each Entity in the Integrated System under Different Distribution Methods

[0247]

[0248] As can be seen from Table 2, compared with the scenario without energy storage, considering the operating cost of energy storage, the net income of the wind-solar-storage integrated system is still 12.33% higher than that of the wind-solar integrated system. As can be seen from Table 3, when allocating according to the power generation, since the actual power generation of wind power is the largest and the income obtained is the most, and since energy storage is not a power source, when using power generation as the allocation basis, the proportion of the income allocated to energy storage is the lowest among other methods. This is because when allocating income according to the power quantity, the marginal contributions of each entity are not considered. When using the MCRS method, the incomes of the three parties of wind, solar, and storage after allocation are all higher than their respective incomes when participating in the market independently, meeting the rational conditions for the formation of a wind-solar-storage integrated system. And because the marginal contribution value income distribution result is considered, it is more fair than allocating according to the power generation. The income distribution method proposed in S3 of the present invention further considers the contribution levels of each entity to the alliance system. The participation of energy storage in wind-solar power generation can improve its power deviation and enhance the competitiveness of wind and solar in bilateral negotiation transactions such as green power trading. Both the wind farm and the photovoltaic power station give concessions to energy storage. Therefore, the income of energy storage after being corrected by the contribution level has increased, rising by 28.19% compared with the MCRS method. However, due to the large deviation between the actual power curve and the predicted power curve of the photovoltaic power station, and the actual power generation being lower than the daily plan amount, with a large amount of deviation assessment power, the incremental income distribution ratio is the lowest after the contribution degree correction, and the proportion of the incremental income distribution has decreased by 12.34% compared with the MCRS method. To sum up, the income distribution method proposed in S3 of the present invention not only considers the contribution of energy storage to improving the overall internal economy of the integrated system, but also adjusts the income distribution of wind-solar new energy power stations according to the accuracy of tracking the daily plan. Therefore, a more comprehensive and reasonable distribution result can be obtained.

[0249] (3) To verify the economy of the scheduling method proposed in the present invention, in addition to the method proposed in the present invention, another 4 scheduling strategies are set to compare various economic indicators. Strategy 1: Without considering the green power market, after the energy storage completes the sharing task of the integrated new energy, it directly participates in the spot-peak shaving integrated market; Strategy 2: Without considering the green power market, after the energy storage completes the sharing task of the integrated new energy, it only participates in the electric energy spot market; Strategy 3: Without considering the green power market, after the energy storage completes the sharing task of the integrated new energy, it only participates in the peak shaving market and directly buys and sells electricity from the power grid, and the selling price is set at 80% of the main power grid purchase price; Strategy 4: Without considering the integration of energy storage and new energy, the energy storage independently participates in the spot market. The comparison results are shown in Table 4.

[0250] Table 4 Comparison of economic indicators under different scheduling methods

[0251]

[0252] As can be seen from Table 4, since energy storage can utilize the complementarity of peak-valley periods and electricity quantities between different markets, and make full use of its capacity and power resources, it can obtain excess profits in the spot-regulation-green power combined market. Compared with time-of-use electricity prices, electricity prices in the market environment will fluctuate according to the supply and demand situations on both the power generation and consumption sides. Especially in regions with a high proportion of new energy, the electricity price fluctuations are more intense, providing a greater arbitrage space for energy storage. Therefore, although the charge-discharge quantity of energy storage is more under Strategy 3, the daily profit of Strategy 2 has increased by 7.5% compared with Strategy 3, and the investment payback period has been shortened by 2.1 years. When energy storage participates in the deep regulation market, although the grid power purchase cost may be higher than that in the spot market, the regulated electricity quantity considering the regulation compensation cost has a certain price advantage. When energy storage participates in the spot-regulation combined market, it can obtain more low-cost electricity. When energy storage in Strategy 4 participates in the spot market independently, its daily profit is lower than that of Strategy 2 because all the capacity of energy storage is used to participate in the spot market, further increasing the impact on the market price and reducing the profit per unit electricity quantity of energy storage. Therefore, energy storage needs to develop various operation modes such as complementing with new energy and participating in the regulation market to reduce its dependence on the spread arbitrage profit model. Further comparing the method proposed in the present invention with Strategy 1, in the method proposed in the present invention, while energy storage and new energy participate in the green power market synergistically, part of the capacity is used to participate in the green power deviation balance, further reducing the impact of energy storage on the clearing electricity price in the spot market and realizing the dual values of new energy electric energy and environmental rights and interests, enabling energy storage to obtain the highest profit. Compared with the spot-regulation combined market operation mode of Strategy 1, after considering the green power market in the method proposed in the present invention, the investment payback period can be shortened by at least 1.1 years, and the internal rate of return can be increased by at least 1.1%, demonstrating the economy of the scheduling method proposed in the present invention.

[0253] Example 2:

[0254] See Figure 2, a collaborative scheduling system for a centralized energy storage power station to participate in the joint market, including an optimized scheduling module for the shared part of the energy storage capacity, an operation strategy adjustment module for the remaining energy storage capacity, and a revenue distribution module. The optimized scheduling module for the shared part of the energy storage capacity is used to form a joint system with new energy power stations for energy storage to participate in green power trading and spot trading, decompose the green power trading electricity based on the spot electricity price to obtain the green power trading electricity decomposition curve, and construct an optimized scheduling model for the joint system considering the green power trading electricity decomposition curve with the goal of maximizing the revenue of the joint system in the green power and spot markets, and optimize the scheduling of the shared part of the energy storage capacity; specifically, the optimized scheduling module for the shared part of the energy storage capacity includes a green power trading electricity decomposition curve acquisition module and an optimized scheduling module. The green power trading electricity decomposition curve acquisition module is used to construct a green power trading electricity decomposition model considering the uncertainty of the spot electricity price and solve the green power trading electricity decomposition model to obtain the green power trading electricity decomposition curve; the objective function and constraints of the green power trading electricity decomposition model refer to step S1 of Embodiment 1; the optimized scheduling module is used to construct an optimized scheduling model for the joint system considering the green power trading electricity decomposition curve and optimize the scheduling of the shared part of the energy storage capacity, and the objective function of the joint system optimized scheduling model refers to step S1 of Embodiment 1; the operation strategy adjustment module for the remaining energy storage capacity is used to independently participate in the power market competition with the remaining energy storage capacity, and construct a two-layer optimization model for the energy storage to participate in the spot-peak regulation-green power joint market. The two-layer optimization model includes an upper-layer optimization model and a lower-layer joint clearing model. The upper-layer optimization model is constructed with the goal of maximizing the revenue of the energy storage participating in peak regulation and the spot power market on the basis of the energy storage completing the call requirements of the new energy power stations in the joint system. Solve the upper-layer optimization model to obtain the energy storage operation strategy, and substitute the obtained energy storage operation strategy as the boundary condition into the lower-layer joint clearing model, and adjust the energy storage operation strategy based on the clearing price feedback by the lower-layer joint clearing model; the objective function and constraints of the upper-layer optimization model and the objective function and constraints of the lower-layer joint clearing model refer to step S2 of Embodiment 1; the revenue distribution module is used to calculate the contribution level of each subject in the joint system based on the energy storage operation strategy obtained by the operation strategy adjustment module for the remaining energy storage capacity, calculate the revenue distribution ratio according to the contribution level, and distribute the revenue to each subject in the joint system based on the revenue distribution ratio; specifically, the revenue distribution module includes a contribution level calculation module, a revenue distribution ratio calculation module, and a distribution module; the contribution level calculation module is used to calculate the energy storage contribution level, the photovoltaic contribution level, and the wind power contribution level, and the specific calculation formula refers to step S31 of Embodiment 1; the revenue distribution ratio calculation module is used to first calculate the revenue of each subject in the joint system after being corrected by the contribution level, and then calculate the cooperative revenue distribution ratio;For the specific calculation formula of the revenue after being corrected by the contribution level, please refer to step S32 of Embodiment 1. For the specific cooperation revenue distribution ratio of each entity in the joint system, please refer to step S33 of Embodiment 1. The distribution module is used to distribute the revenue to each entity in the joint system based on the cooperation revenue distribution ratio.

Claims

1. A coordinated dispatching method for a centralized energy storage power station to participate in a joint market, characterized in that: The collaborative scheduling method comprises: S1. Energy storage and new energy stations form a joint system to participate in green electricity trading and spot trading. Based on the spot electricity price, the green electricity trading volume is decomposed to obtain the green electricity trading volume decomposition curve. With the maximization of the joint system's revenue in the green electricity and spot markets as the optimization goal, a joint system optimization scheduling model taking into account the green electricity trading volume decomposition curve is constructed to optimize the scheduling of the shared capacity of energy storage; S2. Use the remaining capacity after energy storage sharing to independently participate in the electricity market competition, and build a two-layer optimization model for energy storage to participate in the spot-peak-shaving-green electricity joint market. The two-layer optimization model includes an upper optimization model and a lower joint clearing model. The upper optimization model is constructed on the basis of completing the call demand of new energy stations in the joint system, and participating in peak-shaving and maximizing the benefits of the spot electricity market. Solve the upper optimization model to obtain the energy storage operation strategy, substitute the obtained energy storage operation strategy into the lower joint clearing model as the boundary condition, and adjust the energy storage operation strategy based on the clearing price fed back by the lower joint clearing model. S3. Based on the energy storage operation strategy obtained in S2, the contribution level of each subject in the joint system is calculated, the profit distribution ratio is calculated according to the contribution level, and the profit distribution is performed on each subject in the joint system based on the profit distribution ratio; In S1, the method for obtaining the green electricity trading quantity decomposition curve is: A green electricity trading volume decomposition model considering the uncertainty of spot electricity prices is constructed, and the green electricity trading volume decomposition model is solved to obtain a green electricity trading volume decomposition curve; the objective function of the green electricity trading volume decomposition model includes: In the above formula, R fc is the expected revenue of the combined system in the green power and spot markets; N π is the number of day-ahead clearing price scenarios; T is the dispatch period; ρ k is the probability of occurrence of the day-ahead electricity price scenario k; β g The contract price for green power trading; is the green electricity amount decomposed into period t; are the predicted outputs of photovoltaic power and wind power in period t respectively; is the sum of the predicted outputs of photovoltaic and wind power in period t; represents the output of the joint system at time t in the day-ahead plan; They are respectively the charging power and discharging power of the energy storage at time t in the day-ahead plan; is the electricity price in the day-ahead spot market during period t under scenario k; Δt is the unit time; is the energy storage operation cost; ESS is the unit charging and discharging cost of energy storage; η dis , η ch are the charging efficiency and discharging efficiency of energy storage respectively; scenario k refers to the day-ahead clearing electricity price scenario; The constraints of the green power trading volume decomposition model include internal constraints of the joint system, charging and discharging power and state of charge constraints. The internal constraints of the joint system include: In the above formula, They are the lower and upper limits of green electricity decomposition in period t respectively; The planned value of green electricity in the dispatch period T is the sum of the green electricity at each moment in the dispatch period T; They are the charging powers that the photovoltaic power station and wind farm plan to charge the energy storage power station at time t.

2. A coordinated dispatching method for a centralized energy storage power station to participate in a joint market according to claim 1, characterized in that: The charging and discharging power and state of charge constraints include: In the above formula, is the maximum charge and discharge power of energy storage; It is a binary variable. When its value is 1, it means that the energy storage is in the charging state, and when its value is 0, it means that the energy storage is in the discharging state. max , SOC min E is the maximum and minimum charge state of energy storage respectively; rat Indicates the rated capacity of energy storage; E t 、E t-1 E is the amount of energy stored after the end of period t and period t-1; t0 is the initial storage capacity of energy storage; They are the maximum values ​​of energy storage charging power and discharging power respectively.

3. A coordinated dispatching method for a centralized energy storage power station to participate in a joint market according to claim 1, characterized in that: The objective function of the joint system optimization scheduling model includes: In the above formula, R all is the total revenue of the joint system within the scheduling period T; R sell is the electricity sales revenue of the joint system; C ab is the power abandonment loss of the combined system; C bias is the deviation cost of the joint system; The operating cost of energy storage; The electricity sales revenue of the combined system is R sell The calculation formula is: R sell =R g，t +R e，t ; In the above formula, R g，t is the green electricity trading income; β g The contract price for green power trading; Indicates the amount of green electricity decomposed into time period t; is the amount of green electricity actually delivered during period t; They are energy storage charging power and discharging power respectively; is the clearing electricity price in the day-ahead spot market during period t; is the clearing electricity price in the intraday market during period t; R e，t Income from spot trading; are the maximum outputs of wind farm and photovoltaic power station during period t, It is the sum of the maximum output of the wind farm and photovoltaic power station during period t; represents the actual total output of the combined system in period t; is the total power output of the joint system planned for the day ahead in period t; are the charging power of the photovoltaic power station and the wind farm to the energy storage power station in period t respectively; is the amount of power abandoned by the combined system during period t; The power loss of the combined system C ab The calculation formula is: In the above formula, μ TGC is the green certificate conversion coefficient, 1MWh of electricity corresponds to one green certificate; TGC is the green certificate price; The deviation cost C of the joint system bias The calculation formula is: In the above formula, ε is the allowable deviation margin; is the green power deviation assessment power in period t; g is the green electricity quantity deviation assessment coefficient; p is the spot deviation assessment coefficient; ΔP t + , ΔP t - They are respectively the positive deviation electricity and negative deviation electricity of the joint system exceeding the deviation assessment margin.

4. A coordinated dispatching method for a centralized energy storage power station to participate in a joint market according to claim 1, characterized in that: The objective function of the upper optimization model includes: In the above formula, represents the revenue of sharing part of the energy storage in the day-ahead spot-peak-shaving joint market; t0 is the opening period of the electricity spot market; T e is the day-ahead market trading cycle; s is the probability of the user participating in scenario s; The spot market clearing price for the user’s participation scenario s days ago; They are respectively the charging power of the energy storage as the electricity buyer and the discharging power as the electricity seller during period t; is the clearing price of the day-ahead peak-shaving market under scenario s; is the time-of-use electricity selling price of the power grid; β tr The price of electricity transmission and distribution for the power grid; Charging power bid for energy storage in the deep peaking market; The operating cost of energy storage; is the green electricity decomposition amount of the joint system in period t; The combined system output power after calling the energy storage; is the price of the green electricity trading contract signed by the joint system; scenario s refers to the bidding scenario of power users; The constraints of the upper optimization model include: In the above formula, E mk，t 、E mk，t-1 E is the capacity of energy storage to independently participate in the power market competition during period t and period t-1 respectively; t0 is the initial storage capacity of energy storage; The charging power of the energy storage used by the photovoltaic power plant and wind farm in the combined system during period t; The discharge power of the photovoltaic power station and wind farm in the combined system using the energy storage during period t; is the sum of the maximum output power of the photovoltaic power station and wind farm in the combined system predicted on the day before; γ re-ESS Reserve proportion for energy storage capacity; E is the sum of the rated powers of the photovoltaic power station and wind farm in the combined system; rat is the rated capacity of energy storage; soc max It is the maximum state of charge of energy storage; is the maximum charge and discharge power of energy storage; They are energy storage charging power and discharging power respectively; The objective function of the lower-level joint clearing model includes: In the above formula, F mk，s is the market operation cost, i.e. the value of excess electricity; t0 is the opening period of the daily energy spot market on that day; T e is the day-ahead market trading cycle; G em , U em PV em , W em They are the thermal power units, users, photovoltaic power stations and wind farms participating in the power market. dp A collection of thermal power units participating in the deep peak load regulation market; f 、h u They are the quotation segment numbers for thermal power units and power users respectively; are the price quote and output power of thermal power units respectively; are the bid and winning load of user u respectively; They are the electricity cost and output power of the photovoltaic station pv respectively; are the electricity cost and output power of wind turbine w respectively; They are respectively thermal power unit f and h df The peak-shaving price and output power reduction of the gear; The constraints of the lower-level joint clearing model include: market power balance constraints, generator unit operation constraints, and generator unit load power constraints.

5. The coordinated dispatching method for a centralized energy storage power station to participate in a joint market according to claim 1, characterized in that: S3 includes: S31. Calculate the contribution level of each subject in the joint system; the calculation formula for the energy storage contribution level is: θ ESS =w1I cost +w2I gen +w3I ESS +w4I gre +w5I corr ; ΔR ESS =F S -F S\{ESS} ; In the above formula, θ ESS is the energy storage contribution level; w1~w5 are the weights of each indicator; I cost ,I gen ,I ESS ,I gre ,I corr They are total energy storage cost, energy storage equivalent power generation, energy storage marginal contribution, green electricity trading deviation, and energy storage output accuracy; They are energy storage opportunity cost and energy storage operation cost respectively; is the clearing electricity price in the day-ahead spot market during period t; They are energy storage charging power and discharging power respectively; are the power abandonment of the combined system in period t with and without energy storage; ΔR ESS represents the marginal benefit of energy storage; S represents the combined system, ESS, PV, and W represent energy storage, photovoltaic power station, and wind farm respectively; F S represents the joint system benefit; F S\{ESS} represents the combined system benefits after removing energy storage; is the green power deviation of the combined system when there is no energy storage; ΔP t g is the green power deviation assessment power in period t; They are the average relative errors of the combined system output with and without energy storage, respectively; are the maximum tracking plan errors of the joint system with and without energy storage, respectively; The calculation formula for the photovoltaic contribution level is: In the above formula, θ PV is the photovoltaic contribution level; They are the average relative errors of the combined system output with and without photovoltaics, respectively; They are the maximum tracking plan errors of the combined system with and without photovoltaics, respectively; The calculation formula for wind power contribution level is: In the above formula, θ W is the contribution level of wind power; The average relative error of the combined system output with and without wind power; are the maximum tracking plan errors of the joint system with and without wind power, respectively; S32. Calculate the income of each entity in the joint system after the contribution level is corrected according to the following formula: In the above formula, is the benefit of subject i in the joint system S after the contribution level is corrected; θ i is the contribution level of subject i in the joint system S; R i，min is the minimum benefit of subject i participating in the cooperation in the joint system S; ΔR i is the maximum benefit of subject i participating in the cooperation in the joint system S; ΔF S is the cooperative benefit of the joint system S; S33. Calculate the proportion of cooperative benefits among the entities in the joint system according to the following formula: In the above formula, is the proportion of cooperative benefits in the joint system in the tth period; ρ m is the probability of scenario m, which is obtained by clustering the predicted wind and solar power output in the region where the joint system is located within the dispatch period, and K is the number of scenarios; is the benefit of subject i in the joint system after the contribution level is corrected in scenario m; R m，i，min is the minimum benefit of subject i participating in the cooperation in the joint system S under scenario m; ΔF m，S is the cooperation benefit of the joint system S under scenario m; S34. Based on the cooperation benefit distribution ratio calculated in S33, the benefits are distributed to each subject in the joint system.

6. A coordinated dispatching system for a centralized energy storage power station to participate in a joint market, characterized in that: The collaborative scheduling system comprises: The energy storage shared capacity optimization scheduling module is used to form a joint system with energy storage and new energy stations to participate in green electricity trading and spot trading. The green electricity trading volume is decomposed based on the spot electricity price to obtain the green electricity trading volume decomposition curve. With the maximization of the joint system's revenue in the green electricity and spot markets as the optimization goal, a joint system optimization scheduling model taking into account the green electricity trading volume decomposition curve is constructed to optimize the scheduling of the shared capacity of energy storage; The energy storage sharing capacity optimization scheduling module includes a green electricity transaction power decomposition curve acquisition module, which is used to build a green electricity transaction power decomposition model considering the uncertainty of spot electricity prices, and solve the green electricity transaction power decomposition model to obtain the green electricity transaction power decomposition curve; The objective function of the green electricity trading quantity decomposition model includes: In the above formula, R fc is the expected revenue of the combined system in the green power and spot markets; N π is the number of day-ahead clearing price scenarios; T is the dispatch period; ρ k is the probability of occurrence of the day-ahead electricity price scenario k; β g The contract price for green power trading; is the green electricity amount decomposed into period t; are the predicted outputs of photovoltaic power and wind power in period t respectively; is the sum of the predicted outputs of photovoltaic and wind power in period t; represents the output of the joint system at time t in the day-ahead plan; They are respectively the charging power and discharging power of the energy storage at time t in the day-ahead plan; is the electricity price in the day-ahead spot market during period t under scenario k; Δt is the unit time; is the energy storage operation cost; ESS is the unit charging and discharging cost of energy storage; η dis , η ch are the charging efficiency and discharging efficiency of energy storage respectively; scenario k refers to the day-ahead clearing electricity price scenario; The constraints of the green power trading volume decomposition model include internal constraints of the joint system, charging and discharging power and state of charge constraints. The internal constraints of the joint system include: In the above formula, They are the lower and upper limits of green electricity decomposition in period t respectively; The planned value of green electricity in the dispatch period T is the sum of the green electricity at each moment in the dispatch period T; are the charging powers that the photovoltaic power station and wind farm plan to charge the energy storage power station at time t; The energy storage surplus capacity operation strategy adjustment module is used to independently participate the energy storage surplus capacity in the power market competition and build a two-layer optimization model for energy storage to participate in the spot-peak-shaving-green electricity joint market. The two-layer optimization model includes an upper optimization model and a lower joint clearing model. The upper optimization model is constructed with the goal of participating in peak-shaving and spot power market profit maximization on the basis of energy storage completing the call demand of new energy stations in the joint system. The upper optimization model is solved to obtain the energy storage operation strategy, and the obtained energy storage operation strategy is substituted into the lower joint clearing model as a boundary condition. The energy storage operation strategy is adjusted based on the clearing price fed back by the lower joint clearing model. The profit distribution module is used to calculate the contribution level of each subject in the joint system based on the energy storage operation strategy obtained by the energy storage remaining capacity operation strategy adjustment module, calculate the profit distribution ratio according to the contribution level, and distribute the profit to each subject in the joint system based on the profit distribution ratio.

7. A coordinated dispatching system for a centralized energy storage power station participating in a joint market according to claim 6, characterized in that: The charging and discharging power and state of charge constraints include: In the above formula, is the maximum charge and discharge power of energy storage; is a binary variable. When its value is 1, it indicates that the energy storage is in a charging state. When its value is 0, it indicates that the energy storage is in a discharging state. max , SOC min E is the maximum and minimum charge state of energy storage respectively; rat Indicates the rated capacity of energy storage; E t 、E t-1 E is the amount of energy stored after the end of period t and period t-1; t0 is the initial storage capacity of energy storage; They are the maximum values ​​of energy storage charging power and discharging power respectively.

8. A coordinated dispatching system for a centralized energy storage power station participating in a joint market according to claim 6, characterized in that: The energy storage shared capacity optimization scheduling module also includes an optimization scheduling module, which is used to construct a joint system optimization scheduling model taking into account the green power transaction power decomposition curve to optimize the shared capacity of the energy storage; The objective function of the joint system optimization scheduling model includes: In the above formula, R all is the total revenue of the joint system within the scheduling period T; R sell is the electricity sales revenue of the joint system; C ab is the power abandonment loss of the combined system; C bias is the deviation cost of the joint system; The operating cost of energy storage; The electricity sales revenue of the combined system is R sell The calculation formula is: R sell =R g，t +R e，t ; In the above formula, R g，t is the green electricity trading income; β g The contract price for green power trading; Indicates the amount of green electricity decomposed into time period t; is the amount of green electricity actually delivered during period t; They are energy storage charging power and discharging power respectively; is the clearing electricity price in the day-ahead spot market during period t; is the clearing electricity price in the intraday market during period t; R e，t Income from spot trading; are the maximum outputs of wind farm and photovoltaic power station during period t, It is the sum of the maximum output of the wind farm and photovoltaic power station during period t; represents the actual total output of the combined system in period t; is the total power output of the joint system planned for the day ahead in period t; are the charging power of the photovoltaic power station and the wind farm to the energy storage power station in period t respectively; is the amount of power abandoned by the combined system during period t; The power loss of the combined system C ab The calculation formula is: In the above formula, μ TGC is the green certificate conversion coefficient, 1MWh of electricity corresponds to one green certificate; TGC is the green certificate price; The deviation cost C of the joint system bias The calculation formula is: In the above formula, ε is the allowable deviation margin; ΔP t g is the green power deviation assessment power in period t; g is the green electricity quantity deviation assessment coefficient; p is the spot deviation assessment coefficient; ΔP t + , ΔP t - They are respectively the positive deviation electricity and negative deviation electricity of the joint system exceeding the deviation assessment margin.

9. A coordinated dispatching system for a centralized energy storage power station participating in a joint market according to claim 6, characterized in that: The objective function of the upper optimization model includes: In the above formula, represents the revenue of sharing part of the energy storage in the day-ahead spot-peak-shaving joint market; t0 is the opening period of the electricity spot market; T e is the day-ahead market trading cycle; s is the probability of the user participating in scenario s; The spot market clearing price for the user’s participation scenario s days ago; They are respectively the charging power of the energy storage as the electricity buyer and the discharging power as the electricity seller during period t; is the clearing price of the day-ahead peak-shaving market under scenario s; is the time-of-use electricity selling price of the power grid; β tr The price of electricity transmission and distribution for the power grid; Charging power bid for energy storage in the deep peaking market; The operating cost of energy storage; is the green electricity decomposition amount of the joint system in period t; The combined system output power after calling the energy storage; is the price of the green electricity trading contract signed by the joint system; scenario s refers to the bidding scenario of power users; The constraints of the upper optimization model include: In the above formula, E mk，t 、E mk，t-1 E is the capacity of energy storage to independently participate in the power market competition during period t and period t-1 respectively; t0 is the initial storage capacity of energy storage; The charging power of the energy storage used by the photovoltaic power plant and wind farm in the combined system during period t; The discharge power of the photovoltaic power station and wind farm in the combined system using the energy storage during period t; is the sum of the maximum output power of the photovoltaic power station and wind farm in the combined system predicted on the day before; γ re-ESS Reserve proportion for energy storage capacity; E is the sum of the rated powers of the photovoltaic power station and wind farm in the combined system; rat is the rated capacity of energy storage; SOC max It is the maximum state of charge of energy storage; is the maximum charge and discharge power of energy storage; They are energy storage charging power and discharging power respectively; The objective function of the lower-level joint clearing model includes: In the above formula, F mk，s is the market operation cost, i.e. the value of excess electricity; t0 is the opening period of the daily energy spot market on that day; T e is the day-ahead market trading cycle; G em , U em PV em , W em They are the thermal power units, users, photovoltaic power stations and wind farms participating in the power market. dp A collection of thermal power units participating in the deep peak load regulation market; f 、h u They are the quotation segment numbers for thermal power units and power users respectively; are the price quote and output power of thermal power units respectively; are the bid and winning load of user u respectively; They are the electricity cost and output power of the photovoltaic station pv respectively; are the electricity cost and output power of wind turbine w respectively; They are respectively thermal power unit f and h df The peak-shaving price and output power reduction of the gear; The constraints of the lower-level joint clearing model include: market power balance constraints, thermal power unit operation constraints, and unit and load power constraints.

10. A coordinated dispatching system for a centralized energy storage power station participating in a joint market according to claim 6, characterized in that: The profit distribution module includes a contribution level calculation module, a profit distribution ratio calculation module, and a distribution module; The contribution level calculation module is used to calculate the energy storage contribution level according to the following formula: θ ESS =w1I cost +w2I gen +w3I ESS +w4I gre +w5I corr ; ΔR ESS =F S -F S\{ESS} ; In the above formula, θ ESS is the energy storage contribution level; w1~w5 are the weights of each indicator; I cost ,I gen ,I ESS ,I gre ,I corr They are total energy storage cost, energy storage equivalent power generation, energy storage marginal contribution, green electricity trading deviation, and output accuracy; They are energy storage opportunity cost and energy storage operation cost respectively; is the clearing electricity price in the day-ahead spot market during period t; They are energy storage charging power and discharging power respectively; are the power abandonment of the combined system in period t with and without energy storage; ΔR ESS represents the marginal benefit of energy storage; S represents the combined system, ESS, PV, and W represent energy storage, photovoltaic power station, and wind farm respectively; F s represents the joint system benefit; F s\{ESS} represents the combined system benefits after removing energy storage; is the green power deviation of the combined system when there is no energy storage; ΔP t g is the green power deviation assessment power in period t; They are the average relative errors of the combined system output with and without energy storage, respectively; are the maximum tracking plan errors of the joint system with and without energy storage, respectively; The photovoltaic contribution level is calculated according to the following formula: In the above formula, θ PV is the photovoltaic contribution level; They are the average relative errors of the combined system output with and without photovoltaics, respectively; They are the maximum tracking plan errors of the combined system with and without photovoltaics, respectively; The wind power contribution level is calculated according to the following formula: In the above formula, θ W is the contribution level of wind power; The average relative error of the combined system output with and without wind power; are the maximum tracking plan errors of the joint system with and without wind power, respectively; The profit distribution ratio calculation module is used to first calculate the profit of each subject in the joint system after the contribution level is corrected, and then calculate the cooperation profit distribution ratio. The calculation formula of the profit after the contribution level is corrected is: In the above formula, is the benefit of subject i in the joint system S after correction of contribution level; θ i is the contribution level of subject i in the joint system S; R i，min is the minimum benefit of subject i participating in the cooperation in the joint system S; ΔR i is the maximum benefit of subject i participating in the cooperation in the joint system S; ΔF S is the cooperative benefit of the joint system S; The calculation formula for the cooperative benefit distribution ratio of each subject in the joint system is: In the above formula, is the proportion of cooperative benefits in the joint system in the tth period; ρ m is the probability of scenario m, which is obtained by clustering the predicted wind and solar power output in the region where the joint system is located within the dispatch period, and K is the number of scenarios; is the benefit of subject i in the joint system after the contribution level is corrected in scenario m; R m，i，min is the minimum benefit of subject i participating in the cooperation in the joint system S under scenario m; ΔF m，S is the cooperation benefit of the joint system S under scenario m; The distribution module is used to distribute the benefits to each subject in the joint system based on the cooperation benefit distribution ratio.

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