Collaborative scheduling method and system for centralized energy storage power stations to participate in joint market

Abstract

The invention discloses a collaborative scheduling method and system for a centralized energy storage power station to participate in a joint market, and the method comprises the steps: enabling an energy storage station and a new energy station to form a joint system, taking the maximization of the income of the joint system in a green power and spot market as an optimization target, and constructing a joint system optimization scheduling model considering a green power transaction electric quantity decomposition curve; carrying out optimization scheduling on the capacity of the shared part of the energy storage; independently participating in power market competition by using the residual capacity after energy storage sharing, constructing a double-layer optimization model of participating in spot goods-peak regulation-green power combined market by energy storage, and solving the double-layer optimization model to obtain an energy storage operation strategy; and calculating the contribution degree level of each subject in the joint system, calculating a profit distribution proportion according to the contribution degree level, and further performing profit distribution on each subject in the joint system. According to the invention, flexible diversity of participation of centralized energy storage in multiple types of markets and multiple operation modes can be brought into full play, so that the energy storage utilization rate and the operation income are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of energy storage optimization scheduling, and specifically relates to a coordinated 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 smoothing wind and solar fluctuations and promoting the consumption of new energy has been paid attention to. New energy-energy storage complementary projects such as wind and solar storage, hydro-wind and solar storage have been built in many places, and the joint participation of energy storage and new energy in the power market has become a trend. 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 complementary participation of energy storage and new energy in the power market, existing research has not fully considered the flexibility and diversity of centralized energy storage in participating in multiple types of markets and multiple operating modes, and its profit potential has not been fully tapped. Therefore, in the case of frequent interactions between centralized energy storage and joint new energy power, and the coupling of multi-scale and heterogeneous power markets such as green power market and spot market, it is necessary to study a centralized energy storage collaborative dispatching method for simultaneous participation in multiple power markets and capacity sharing. At the same time, with regard to the problem of revenue distribution of new energy-energy storage joint operation, existing research has not fully considered the impact of green power trading, an emerging power trading variety, on the joint operation of new energy-energy storage. Summary of the invention

[0003] The purpose of the present invention is to address the above-mentioned problems existing in the prior art and provide a method and system for the coordinated dispatch of centralized energy storage power stations participating in the joint market, which divides energy storage into two forms, namely, a consortium and an independent body, and participates in the spot-peak-shaving-green electricity joint market with different strategies, thereby improving energy storage utilization and operating income. To achieve the above purpose, the technical solution of the present invention is as follows:

[0004] In a first aspect, the present invention proposes a coordinated dispatching method for a centralized energy storage power station to participate in a joint market, the coordinated dispatching method comprising:

[0005] 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;

[0006] 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.

[0007] 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.

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

[0009] 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:

[0010] ;

[0011] ; ;

[0012] ;

[0013] In the above formula, 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; For the day-ahead electricity price scenario The probability of occurrence; The contract price for green power trading; To decompose Green electricity consumption during the time period; , Photovoltaic and wind power The predicted output for the time period; For photovoltaic and wind power The sum of the predicted outputs for the time period; Indicates that the joint system plans on the day before Always contribute; , Day-ahead planning Charging power and discharging power at the moment; For the scene The day-ahead spot market Time-of-day electricity prices; is the unit time; The operating cost of energy storage; is the unit charging and discharging cost of energy storage; , They are the charging efficiency and discharging efficiency of energy storage; scenario It refers to the day-ahead clearing electricity price scenario;

[0014] The constraints of the green electricity trading volume decomposition model include:

[0015] Internal constraints of the joint system:

[0016] ; ; ; ;

[0017] ; ;

[0018] In the above formula, , They are The lower and upper limits of green electricity decomposition in each time period; To decompose into scheduling cycles The planned green power value within the period is decomposed into the scheduling period. The sum of green electricity at each moment; , The photovoltaic power station and wind farm are planned to be The charging power of the energy storage power station at all times;

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

[0020] ;

[0021] ; ;

[0022] ;

[0023] 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. When its value is 0, it means that the energy storage is in the discharging state. , They are the maximum and minimum states of charge for energy storage respectively; Indicates the rated capacity of energy storage; , For energy storage Time period, The amount of stored electricity after the period ends; is the initial storage capacity of energy storage; , They are the maximum values ​​of energy storage charging power and discharging power respectively.

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

[0025] ;

[0026] In the above formula, Scheduling cycle Total benefits of the internal joint system; Revenue from electricity sales for the combined system; The power abandonment loss of the combined system; is the deviation cost of the joint system; The operating cost of energy storage;

[0027] The electricity sales revenue of the combined system The calculation formula is:

[0028] ; ; ;

[0029] ;

[0030] ; ;

[0031] ;

[0032] In the above formula, Revenue from green electricity trading; The contract price for green power trading; Decomposition to Green electricity consumption during the time period; for The amount of green electricity actually delivered during the period; , They are energy storage charging power and discharging power respectively; Day-ahead spot market The clearing electricity price for the time period; For intraday market The clearing electricity price for the time period; Income from spot trading; , They are wind farms and photovoltaic power stations. The maximum output of the time period, Wind farms and photovoltaic power stations The sum of the maximum outputs during each period; Indicates that the joint system The actual total output during the period; For the joint system The total planned output for the day ahead in the time period; , They are photovoltaic power stations and wind farms. The charging power of the energy storage power station during the period; for The amount of power abandoned by the combined system during the time period;

[0033] The curtailment loss of the combined system The calculation formula is:

[0034] ;

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

[0036] Deviation costs of the combined system The calculation formula is:

[0037] ;

[0038] ;

[0039] ;

[0040] ;

[0041] In the above formula, is the allowable deviation margin; for Green electricity deviation assessment quantity in the time period; It is the green electricity quantity deviation assessment coefficient; is the spot deviation assessment coefficient; , They are respectively the positive deviation electricity and negative deviation electricity of the joint system exceeding the deviation assessment margin.

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

[0043] ;

[0044] In the above formula, Represents the benefits of sharing part of the energy storage in the day-ahead spot-peaking combined market; Opening of sessions for the electrical energy spot market; is the day-ahead market trading cycle; Provide user participation scenarios The probability of Provide user participation scenarios The day-ahead spot market clearing price; , Energy storage The time period is used as the charging power for the electricity buyer and the discharging power for the electricity seller; For the scene Next is the market clearing price for day-ahead peak load regulation; The time-of-use electricity sales price of the power grid; 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; For the joint system Green electricity breakdown by time period; The combined system output power after calling the energy storage; Green power trading contract price for the joint system; scenario It refers to the bidding scenario of power users;

[0045] The constraints of the upper optimization model include:

[0046] ; ;

[0047] ;

[0048] ;

[0049] ; ;

[0050] ;

[0051] In the above formula, , They are Time period, The capacity of time-slot energy storage to independently participate in electricity market competition; is the initial storage capacity of energy storage; For photovoltaic power plants and wind farms in combined systems The charging power of energy storage used in the time period; For photovoltaic power plants and wind farms in combined systems The discharge power of energy storage used in the time period; It is the sum of the maximum output power of the photovoltaic power plant and wind farm predicted on the day before in the combined system; Reserve proportion for energy storage capacity; It is the sum of the rated powers of the photovoltaic power plant and wind farm in the combined system; is the rated capacity of energy storage; It is the maximum charge state of energy storage;

[0052] The objective function of the lower-level joint clearing model includes:

[0053] ;

[0054] In the above formula, is the market operating cost, i.e. the value of excess electricity; Opening period for the daily energy spot market for that day; is the day-ahead market trading cycle; , , , They are the thermal power units, users, photovoltaic power stations and wind farms participating in the electricity market. A collection of thermal power units participating in the deep peak load regulation market; , 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; , For users quotations and winning bids; , Photovoltaic station The cost per kilowatt-hour and output power; , Wind turbines The cost per kilowatt-hour and output power; , Thermal power units No. The peak-shaving price and output power reduction of the gear;

[0055] The constraints of the lower-level joint clearing model include: market power balance constraints, generator unit operation constraints, and generator unit load power constraints.

[0056] S3 includes: S31, calculating the contribution level of each subject in the joint system; the calculation formula of the energy storage contribution level is:

[0057] ;

[0058] ;

[0059] ;

[0060] ; ;

[0061] ; ;

[0062] ;

[0063] In the above formula, The contribution level of energy storage; are the weights of each indicator respectively; , , , , 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; Day-ahead spot market The clearing electricity price for the time period; , They are energy storage charging power and discharging power respectively; , Combined system with and without energy storage The amount of electricity wasted during the time period; represents the marginal benefit of energy storage; represents a joint system, , , They represent energy storage, photovoltaic power stations, and wind farms respectively; represents the joint system benefit; represents the combined system benefits after removing energy storage; is the green power deviation of the combined system when there is no energy storage; for Green electricity deviation assessment quantity in the time period; , 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;

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

[0065] ;

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

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

[0068] ;

[0069] In the above formula, 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;

[0070] S32. Calculate the income of each entity in the joint system after the contribution level is corrected according to the following formula:

[0071] ;

[0072] In the above formula, For joint system Middle Subject Earnings adjusted for contribution level; For joint system Middle Subject The level of contribution; For joint system Middle Subject The minimum benefits of participating in the cooperation; For joint system Middle Subject The greatest benefits of participating in the collaboration; For joint system The benefits of cooperation;

[0073] S33. Calculate the proportion of cooperative benefits among the entities in the joint system according to the following formula:

[0074] ;

[0075] In the above formula, The first Proportion of cooperative income distribution in each period; For the scene Probability, scenario It is obtained by clustering the predicted wind and solar power output in the region where the joint system is located within the dispatch cycle. is the number of scenes; For the scene The main body in the joint system Earnings adjusted for contribution level; For the scene Lower Joint System Middle Subject The minimum benefits of participating in the cooperation; For the scene Lower Joint System The benefits of cooperation;

[0076] S34. Based on the cooperation benefit distribution ratio calculated in S33, the benefits are distributed to each subject in the joint system.

[0077] In a second aspect, the present invention proposes a coordinated dispatching system for a centralized energy storage power station to participate in a joint market, the coordinated dispatching system comprising:

[0078] 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;

[0079] 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.

[0080] 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.

[0081] The energy storage shared capacity optimization scheduling module includes a green electricity transaction power decomposition curve acquisition module, which is used to construct 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;

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

[0083] ;

[0084] ; ;

[0085] ;

[0086] In the above formula, 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; For the day-ahead electricity price scenario The probability of occurrence; The contract price for green power trading; To decompose Green electricity consumption during the time period; , Photovoltaic and wind power The predicted output for the time period; For photovoltaic and wind power The sum of the predicted outputs for the time period; Indicates that the joint system plans on the day before Always contribute; , Day-ahead planning Charging power and discharging power at the moment; For the scene The day-ahead spot market Time-of-day electricity prices; is the unit time; The operating cost of energy storage; is the unit charging and discharging cost of energy storage; , They are the charging efficiency and discharging efficiency of energy storage; scenario It refers to the day-ahead clearing electricity price scenario;

[0087] The constraints of the green electricity trading volume decomposition model include: Internal constraints of the joint system:

[0088] ; ; ; ;

[0089] ; ;

[0090] In the above formula, , They are The lower and upper limits of green electricity decomposition in each time period; To decompose into scheduling cycles The planned green power value within the period is decomposed into the scheduling period. The sum of green electricity at each moment; , The photovoltaic power station and wind farm are planned to be The charging power of the energy storage power station at all times;

[0091] Charge and discharge power and charge state constraints:

[0092] ;

[0093] ; ;

[0094] ;

[0095] 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. When its value is 0, it means that the energy storage is in the discharging state. , They are the maximum and minimum states of charge for energy storage respectively; Indicates the rated capacity of energy storage; , For energy storage Time period, The amount of stored electricity after the period ends; is the initial storage capacity of energy storage; , They are the maximum values ​​of energy storage charging power and discharging power respectively.

[0096] The energy storage shared part 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 electricity transaction power decomposition curve to optimize the shared part capacity of the energy storage;

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

[0098] ;

[0099] In the above formula, Scheduling cycle Total benefits of the internal joint system; Revenue from electricity sales for the combined system; The power abandonment loss of the combined system; is the deviation cost of the joint system; The operating cost of energy storage;

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

[0101] ; ; ;

[0102] ;

[0103] ; ;

[0104] ;

[0105] In the above formula, Revenue from green electricity trading; The contract price for green power trading; Decomposition to Green electricity consumption during the time period; for The amount of green electricity actually delivered during the period; , They are energy storage charging power and discharging power respectively; Day-ahead spot market The clearing electricity price for the time period; For intraday market The clearing electricity price for the time period; Income from spot trading; , They are wind farms and photovoltaic power stations. The maximum output of the time period, Wind farms and photovoltaic power stations The sum of the maximum outputs during each period; Indicates that the joint system The actual total output during the period; For the joint system The total planned output for the day ahead in the time period; , They are photovoltaic power stations and wind farms. The charging power of the energy storage power station during the period; for The amount of power abandoned by the combined system during 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 factor, 1MWh of electricity corresponds to one green certificate; is the green certificate price;

[0109] Deviation costs of the combined system The calculation formula is:

[0110] ;

[0111] ;

[0112] ;

[0113] ;

[0114] In the above formula, is the allowable deviation margin; for Green electricity deviation assessment quantity in the time period; It is the green electricity quantity deviation assessment coefficient; is the spot deviation assessment coefficient; , They are respectively the positive deviation electricity and negative deviation electricity of the joint system exceeding the deviation assessment margin.

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

[0116] ;

[0117] In the above formula, Represents the benefits of sharing part of the energy storage in the day-ahead spot-peaking combined market; Opening of sessions for the electrical energy spot market; is the day-ahead market trading cycle; Provide user participation scenarios The probability of Provide user participation scenarios The day-ahead spot market clearing price; , Energy storage The time period is used as the charging power for the electricity buyer and the discharging power for the electricity seller; For the scene Next is the market clearing price for day-ahead peak load regulation; The time-of-use electricity sales price of the power grid; 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; For the joint system Green electricity breakdown by time period; The combined system output power after calling the energy storage; Green power trading contract price for the joint system; scenario It refers to the bidding scenario of power users;

[0118] The constraints of the upper optimization model include:

[0119] ;

[0120] ;

[0121] ;

[0122] ;

[0123] ;

[0124] ;

[0125] ;

[0126] In the above formula, , They are Time period, The capacity of time-slot energy storage to independently participate in electricity market competition; is the initial storage capacity of energy storage; For photovoltaic power plants and wind farms in combined systems The charging power of energy storage used in the time period; For photovoltaic power plants and wind farms in combined systems The discharge power of energy storage used in the time period; It is the sum of the maximum output power of the photovoltaic power plant and wind farm predicted on the day before in the combined system; Reserve proportion for energy storage capacity; It is the sum of the rated powers of the photovoltaic power plant and wind farm in the combined system; is the rated capacity of energy storage; It is the maximum charge state of energy storage;

[0127] The objective function of the lower-level joint clearing model includes:

[0128] ;

[0129] In the above formula, is the market operating cost, i.e. the value of excess electricity; Opening period for the daily energy spot market for that day; is the day-ahead market trading cycle; , , , They are the thermal power units, users, photovoltaic power stations and wind farms participating in the electricity market. A collection of thermal power units participating in the deep peak load regulation market; , 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; , For users quotations and winning bids; , Photovoltaic station The cost per kilowatt-hour and output power; , Wind turbines The cost per kilowatt-hour and output power; , Thermal power units No. The peak-shaving price and output power reduction of the gear;

[0130] 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.

[0131] The profit distribution module includes a contribution level calculation module, a profit 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, The contribution level of energy storage; are the weights of each indicator respectively; , , , , 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; Day-ahead spot market The clearing electricity price for the time period; , They are energy storage charging power and discharging power respectively; , Combined system with and without energy storage The amount of electricity wasted during the time period; represents the marginal benefit of energy storage; represents a joint system, , , They represent energy storage, photovoltaic power stations, and wind farms respectively; represents the joint system benefit; represents the combined system benefits after removing energy storage; is the green power deviation of the combined system when there is no energy storage; for Green electricity deviation assessment quantity in the time period; , 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;

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

[0141] ;

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

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

[0144] ;

[0145] In the above formula, 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;

[0146] 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:

[0147] ;

[0148] In the above formula, For joint system Middle Subject Earnings adjusted for contribution level; For joint system Middle Subject The level of contribution; For joint system Middle Subject The minimum benefits of participating in the cooperation; For joint system Middle Subject The greatest benefits of participating in the collaboration; For joint system The benefits of cooperation;

[0149] The calculation formula for the cooperative benefit distribution ratio of each subject in the joint system is:

[0150] ;

[0151] In the above formula, The first Proportion of cooperative income distribution in each period; For the scene Probability, scenario It is obtained by clustering the predicted wind and solar power output in the region where the joint system is located within the dispatch cycle. is the number of scenes; For the scene The main body in the joint system Earnings adjusted for contribution level; For the scene Lower Joint System Middle Subject The minimum benefits of participating in the cooperation; For the scene Lower Joint System The benefits of cooperation;

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

[0153] Compared with the prior art, the present invention has the following beneficial effects:

[0154] 1. The collaborative dispatching method for a centralized energy storage power station participating in a joint market described in the present invention first divides the energy storage into two forms, a joint entity and an independent entity, to participate in the spot-peak-shaving-green electricity joint market with different strategies, and obtains an energy storage operation strategy for collaborative reuse of energy storage in multiple markets, and then distributes the benefits to the various entities of the joint system based on the contribution correction; on the one hand, the collaborative dispatching method forms a joint system with energy storage and new energy to participate in green electricity trading and spot trading, proposes a joint system optimization dispatching model taking into account the green electricity trading electricity decomposition curve, and optimizes the dispatching of the shared part of the energy storage capacity; on the other hand, the remaining capacity after energy storage sharing is used to independently participate in the power market competition, thereby giving full play to the flexibility and diversity of centralized energy storage in participating in multiple types of markets and multiple operation modes, thereby improving the energy storage utilization rate and energy storage operation income.

[0155] 2. The collaborative dispatching method for a centralized energy storage power station participating in a joint market described in the present invention quantitatively analyzes the contribution level of energy storage and new energy in collaboratively participating in medium- and long-term green electricity and spot transactions from the perspectives of cost and benefit, and then proposes a cooperative profit distribution method based on the correction of the contribution level. On the premise of ensuring fairness and rationality, it effectively guarantees that energy storage fully obtains the return in promoting the realization of green value of new energy, effectively enhances the enthusiasm of energy storage to collaborate with new energy, and thus improves overall benefits. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0157] Figure 2 This is a structural block diagram of the collaborative scheduling system described in Example 2. DETAILED DESCRIPTION

[0158] The present invention is further described in detail below in conjunction with specific implementations and drawings.

[0159] Embodiment 1:

[0160] See also Figure 1 , a coordinated dispatching method for a centralized energy storage power station to participate in a joint market, is performed in the following steps:

[0161] Step S1: Energy storage and new energy stations are formed as follows Figure 2The joint system shown in the figure participates in green electricity trading and spot trading, decomposes the green electricity trading electricity based on the spot electricity price, obtains the green electricity trading electricity decomposition curve, takes maximizing the revenue of the joint system in the green electricity and spot markets as the optimization goal, constructs a joint system optimization scheduling model taking into account the green electricity trading electricity decomposition curve, and optimizes the scheduling of the shared part of the energy storage capacity; the method for obtaining the green electricity trading electricity decomposition curve is: according to the historical electricity price data, Latin hypercube sampling and Kantorovich distance scenario reduction method are used to obtain multiple representative scenarios of the day-ahead clearing electricity price and their corresponding probabilities, construct a green electricity trading electricity decomposition model considering the uncertainty of the spot electricity price, and solve the green electricity trading electricity decomposition model to obtain the green electricity trading electricity decomposition curve; the objective function of the green electricity trading electricity decomposition model includes:

[0162] ;

[0163] ; ;

[0164] ;

[0165] In the above formula, 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, which is usually set to 24 hours; For the day-ahead electricity price scenario The probability of occurrence; The contract price for green power trading; To decompose Green electricity consumption during the time period; , Photovoltaic and wind power The predicted output for the time period; For photovoltaic and wind power The sum of the predicted outputs for the time period; Indicates that the joint system plans on the day before Always contribute; , Day-ahead planning Charging power and discharging power at the moment; For the scene The day-ahead spot market Time-of-day electricity prices; It is the unit time, usually set to one hour; The operating cost of energy storage; is the unit charging and discharging cost of energy storage; , They are the charging efficiency and discharging efficiency of energy storage; scenario Indicates the day-ahead clearing electricity price scenario ;

[0166] In order to avoid excessive speculation of new energy in the spot market and affect the smooth delivery of medium- and long-term green electricity transactions, it is stipulated that the sum of green electricity in each period cannot be lower than the daily electricity decomposed in the medium- and long-term green electricity transaction plan; green electricity transactions determine the settlement electricity according to the principle of taking the minimum value of the contract electricity, new energy grid-connected electricity, and green electricity user electricity consumption; when formulating the green electricity decomposition curve, it is necessary to take into account the electricity consumption of green electricity users, and the green electricity decomposed into each period should be within the range specified by the green electricity user, and should not exceed the wind and solar energy output of the period. Based on the above principles, the constraints of the green electricity transaction electricity decomposition model are set. The constraints of the green electricity transaction electricity decomposition model include: Joint system internal constraints:

[0167] ; ; ; ;

[0168] ; ;

[0169] In the above formula, , They are The lower and upper limits of green electricity decomposition in each time period; To decompose into scheduling cycles The planned green power value within the period is decomposed into the scheduling period. The sum of green electricity at each moment; , The photovoltaic power station and wind farm are planned to be The charging power of the energy storage power station at all times;

[0170] Charge and discharge power and charge state constraints:

[0171] ;

[0172] ; ;

[0173] ;

[0174] 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. When its value is 0, it means that the energy storage is in the discharging state. , They are the maximum and minimum states of charge for energy storage respectively; Indicates the rated capacity of energy storage; , For energy storage Time period, The amount of stored electricity after the period ends; is the initial storage capacity of energy storage; , They are the maximum values ​​of energy storage charging power and discharging power respectively;

[0175] In the joint system, due to the randomness of renewable energy power generation, the actual power of wind and solar power fluctuates greatly, which will cause a large deviation from the predicted value. When there is a difference between the power generation plan submitted by the renewable energy station and the actual output, it will affect its own power trading performance and the stable operation of the power system. Energy storage has the characteristics of bidirectional power flow. Energy storage power compensation can reduce the error assessment cost between the predicted output and the actual output of renewable energy. Based on the above characteristics, the objective function of the joint system optimization scheduling model is designed as: ; In the above formula, Scheduling cycle Total benefits of the internal joint system; Revenue from electricity sales for the combined system; The power abandonment loss of the combined system; is the deviation cost of the joint system; is the energy storage operation cost; the electricity sales revenue of the combined system The calculation formula is:

[0176] ; ; ;

[0177] ;

[0178] ; ;

[0179] ;

[0180] In the above formula, Revenue from green electricity trading; The contract price for green power trading; Decomposition to Green electricity consumption during the time period; for The amount of green electricity actually delivered during the period; , They are energy storage charging power and discharging power respectively; Day-ahead spot market The clearing electricity price for the time period; For intraday market The clearing electricity price for the time period; Income from spot trading; , They are wind farms and photovoltaic power stations. The maximum output of the time period, Wind farms and photovoltaic power stations The sum of the maximum outputs during each period; Indicates that the joint system The actual total output during the period; For the joint system The total planned output for the day ahead in the time period; , They are photovoltaic power stations and wind farms. The charging power of the energy storage power station during the period; for The amount of power abandoned by the combined system during the time period;

[0181] The curtailment loss of the combined system The calculation formula is:

[0182] ;

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

[0184] Deviation costs of the combined system The calculation formula is:

[0185] ; ;

[0186] ;

[0187] ;

[0188] In the above formula, is the allowable deviation margin; for Green electricity deviation assessment quantity in the time period; It is the green electricity quantity deviation assessment coefficient; is the spot deviation assessment coefficient; , They are respectively the positive deviation electric quantity and negative deviation electric quantity of the joint system exceeding the deviation assessment margin;

[0189] Step S2, using the remaining capacity after energy storage sharing to independently participate in the electricity market, participate in market competition as a price acceptor, 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, participating in peak-shaving and maximizing the benefits of the spot electricity market. Solving the upper optimization model to obtain the energy storage operation strategy, substituting the obtained energy storage operation strategy into the lower joint clearing model as a boundary condition, and adjusting the energy storage operation strategy based on the clearing price fed back by the lower joint clearing model; the objective function of the upper optimization model includes:

[0190] ;

[0191] In the above formula, Represents the benefits of sharing part of the energy storage in the day-ahead spot-peaking combined market; Opening of sessions for the electrical energy spot market; is the day-ahead market trading cycle; Provide user participation scenarios The probability of Provide user participation scenarios The day-ahead spot market clearing price; , Energy storage The time period is used as the charging power for the electricity buyer and the discharging power for the electricity seller; For the scene Next is the market clearing price for day-ahead peak load regulation; The time-of-use electricity sales price of the power grid; 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; For the joint system Green electricity breakdown by time period; The combined system output power after calling the energy storage; Green power trading contract price for the joint system; scenario Represents the bidding scenario of power users , is to generate multiple power user bidding scenarios through Latin hypercube sampling, which is used to describe the revenue risk brought by power user bidding quotations to energy storage;

[0192] In order to reserve some energy storage power and capacity for joint new energy station dispatch, the upper-level optimization model constraints are set as follows:

[0193] ; ;

[0194] ;

[0195] ;

[0196] ; ;

[0197] ;

[0198] In the above formula, , They are Time period, The capacity of time-slot energy storage to independently participate in electricity market competition; is the initial storage capacity of energy storage; For photovoltaic power plants and wind farms in combined systems The charging power of energy storage used in the time period; For photovoltaic power plants and wind farms in combined systems The discharge power of energy storage used in the time period; It is the sum of the maximum output power of the photovoltaic power plant and wind farm predicted on the day before in the combined system; Reserve proportion for energy storage capacity; It is the sum of the rated powers of the photovoltaic power plant and wind farm in the combined system; is the rated capacity of energy storage; It is the maximum charge state of energy storage;

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

[0200] ;

[0201] In the above formula, is the market operating cost, i.e. the value of excess electricity; Opening period for the daily energy spot market for that day; is the day-ahead market trading cycle; , , , They are the thermal power units, users, photovoltaic power stations and wind farms participating in the electricity market. A collection of thermal power units participating in the deep peak load regulation market; , They are the quotation segment numbers for thermal power units and power users respectively; , They are respectively the thermal power units in Segment Quotes Segment quotes and output power; , For users quotations and winning bids; , Photovoltaic station The cost per kilowatt-hour and output power; , Wind turbines The cost per kilowatt-hour and output power; , Thermal power units No. The peak-shaving price and output power reduction of the gear;

[0202] The constraints of the lower-level joint clearing model include: market power balance constraints:

[0203] ;

[0204] ;

[0205] In the above formula, , They are The dual multipliers of the time period corresponding constraints are the clearing prices of the spot market and the peak-shaving market respectively; , They are photovoltaic power stations and wind farms. Green electricity breakdown by time period; For electricity users exist The green electricity consumption agreed upon in the time period according to the green electricity decomposition curve; , They are The power limit of photovoltaic stations and wind farms during the period; For thermal power units that do not participate in the electricity market, this type of unit decides on its own The planned output for the period a day ago is ; for Net load during the non-market portion of the time period; For users In the The declared power of the segment;

[0206] Thermal power unit operation constraints:

[0207] For thermal power units participating in the spot market, The power generation of a time period is the sum of the power of all its winning power sections:

[0208] ;

[0209] For thermal power units participating in the deep peak load regulation market, The power generation in each period needs to comprehensively consider the winning bid power of the power market and the deep peak load regulation market:

[0210] ;

[0211] In the above formula, For thermal power units Technical output of the time period; Contribute to the conventional minimum technology; The basic peak load-shaving power of thermal power units that are obliged to provide basic peak load-shaving services;

[0212] ; ; ;

[0213] In the above formula, , are the minimum and maximum technical outputs of thermal power units, respectively. For thermal power units that do not participate in deep peak regulation, Minimum technical contribution for routine , for units participating in deep peak regulation, It is the maximum peak-shaving depth that can be achieved in the peak-shaving state; For thermal power units Time period, Technical output of the time period; , They are the up and down ramp rates of thermal power units respectively;

[0214] Unit load power constraints:

[0215] ; ;

[0216] ; ;

[0217] ;

[0218] In the above formula, For thermal power units in the spot market The declared power limit of the segment; ; ; For the peak load market, thermal power units The upper limit of power reduction reported in the file; For users In the The declared power limit of the segment;

[0219] Step S3: Based on the energy storage operation strategy obtained in step 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;

[0220] In a multi-subject cooperation model, unreasonable profit distribution methods among various subjects will make it difficult to effectively channel energy storage costs, resulting in insufficient enthusiasm for energy storage to cooperate with new energy sites to complete power transactions. Therefore, the collaborative scheduling method proposed in the present invention constructs an energy storage contribution index system from two levels: cost and benefit: the cost level includes energy storage opportunity cost and energy storage operation cost. In addition to calculating the energy storage operation cost, energy storage is assumed to be a price acceptor, and the revenue generated by peak-valley arbitrage of the energy storage capacity used by new energy in the real-time electricity market during the day is further calculated, and it is regarded as the opportunity cost of energy storage; the benefit level includes energy storage equivalent power generation, marginal profit contribution, green electricity transaction deviation improvement, and output accuracy improvement. Energy storage can store electricity that exceeds the power generation plan during wind and solar power abandonment periods, thereby avoiding the waste of clean energy and reducing the economic losses of wind and solar power sites. , this part of electricity can be regarded as the equivalent power generation of energy storage; marginal benefit refers to the difference between the alliance benefits when participants join the alliance and when they do not join the alliance. The marginal benefit contribution is the ratio of the marginal contribution value to the overall benefit of the alliance. The higher the marginal contribution, the more important the participant is in the alliance; on the one hand, the deviation of green electricity trading will reduce the economic benefits of new energy sites, and on the other hand, it will affect the performance evaluation of green electricity trading; in order to evaluate the effectiveness of energy storage tracking wind and solar power output plans, 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 accuracy of new energy output with and without the participation of energy storage is compared; the total cost of energy storage, equivalent power generation, improvement of green electricity deviation, improvement of output accuracy, and marginal benefit contribution are used as evaluation indicators of the contribution of energy storage, and the weights of each evaluation indicator are determined after normalizing each indicator;

[0221] Step S31, calculating the contribution level of each subject in the joint system; the calculation formula of the energy storage contribution level is:

[0222] ;

[0223] ; ;

[0224] ; ;

[0225] ; ;

[0226] ;

[0227] ;

[0228] ;

[0229] In the above formula, The contribution level of energy storage; are the weights of each indicator respectively; , , , , 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; Day-ahead spot market The clearing electricity price for the time period; , They are energy storage charging power and discharging power respectively; , Combined system with and without energy storage The amount of electricity wasted during the time period; represents the marginal benefit of energy storage; represents a joint system, , , They represent energy storage, photovoltaic power stations, and wind farms respectively; represents the joint system benefit; represents the combined system benefits after removing energy storage; is the green power deviation of the combined system when there is no energy storage; for Green electricity deviation assessment quantity in the time period; , 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; , They are wind farms and photovoltaic power stations. Maximum output during a period; , They are the predicted outputs of photovoltaic power stations and wind farms during the time periods;

[0230] The calculation formula for the photovoltaic contribution level is: ; In the above formula, 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 joint system with and without photovoltaics, respectively;

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

[0232] Step S32: In order to ensure the effective channeling of energy storage costs and stimulate the enthusiasm of energy storage to actively participate in the operation of the wind, photovoltaic and storage combined system, on the basis of comparing the economic benefits under the independent and joint operation modes of wind, photovoltaic and storage, the maximum and minimum cost method (MCRS method) in cooperative game theory is used to calculate the benefit contribution of energy storage and new energy, and the profit distribution is corrected in combination with the wind, photovoltaic and storage contribution index system. In order to achieve fair distribution, the marginal benefit of individuals is used as the distribution measurement standard, and the gain distribution is carried out according to the proportion of the difference between the maximum benefit and the minimum benefit of each stakeholder participating in the cooperation. The minimum benefit refers to the benefit obtained by the wind farm, photovoltaic power station and energy storage power station when they operate independently. In the independent operation mode, wind power and photovoltaic 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 electricity market and obtains benefits through low storage and high generation. Its net benefit is calculated by deducting the operating cost from the market benefit. The profit calculation formula of each subject in the joint system after the contribution level is corrected is:

[0233] ;

[0234] In the above formula, For joint system Middle Subject Earnings adjusted for contribution level; For joint system Middle Subject The level of contribution; For joint system Middle Subject The minimum benefits of participating in the cooperation; For joint system Middle Subject The greatest benefits of participating in the collaboration; For joint system The benefits of cooperation;

[0235] Step S33: To reduce the computational complexity, the K-means clustering method is used to cluster the wind and solar power forecast outputs in the region where the joint system is located during the scheduling period into The typical scenario is used to calculate the cooperative benefit distribution ratio of the joint system in combination with the scenario probability; the calculation formula for the cooperative benefit distribution ratio of each subject in the joint system is:

[0236] ;

[0237] In the above formula, The first Proportion of cooperative income distribution in each period; For the scene Probability, scenario It is obtained by clustering the predicted wind and solar power output in the region where the joint system is located within the dispatch cycle. is the number of scenes; For the scene The main body in the joint system Earnings adjusted for contribution level; For the scene Lower Joint System Middle Subject The minimum benefits of participating in the cooperation; For the scene Lower Joint System The benefits of cooperation;

[0238] Step S34: distribute the benefits to each subject in the joint system based on the cooperation benefit distribution ratio obtained in step S33.

[0239] The following simulation analysis is conducted on a certain region. The energy storage in the region signs a long-term contract with a wind farm and a photovoltaic power station at the same node to form a wind, solar and storage combined system. The specific operating parameters of the energy storage are shown in Table 1. The parameters such as the predicted output power of wind and solar power, the actual output power, and the power market clearing price are set as follows: wind turbines can generate electricity all day, with higher output power in the 18-24h period; photovoltaic power generation is distributed in the 7-18h period and reaches its peak at noon; the overall gap between the actual power of wind and solar power generation and the predicted power is not large; the market clearing price reaches its peak at 9am and 20pm, and reaches its trough at 14pm.

[0240] Table 1 Energy storage system equipment parameter settings

[0241]

[0242] (1) The simulation calculation results are as follows: For the decomposition curve of the day-ahead green electricity trading curve, since the green electricity price has been determined in the long term, within the limited green electricity decomposition area, the wind-solar-storage combined system will decompose green electricity into various low-price periods according to the predicted day-ahead electricity price, and the final green electricity 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 combined system tends to decompose more green electricity into the corresponding periods, and the decomposed green electricity reaches the upper limit set by green electricity users, while 8-11h and 17-21h are the opposite, and the decomposed green electricity in the corresponding periods drops to the lower limit set by green electricity 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 during the low-price period and resell it to the peak period. For the energy storage dispatch strategy, energy storage is only called to participate in green power trading and spot trading during the low and peak periods of clearing electricity prices, and no energy storage is called during 5-11h and 21-23h. In addition, although each new energy station has the motivation to decompose the green power trading volume into the low electricity price period, the total amount of green power decomposition in each period is relatively even. This is because the new energy station tends to decompose the green power trading volume within the output range to avoid the risk of green power trading deviation assessment, and the green power users also set the green power decomposition range. In the end, the green power trading volume is evenly distributed to each period, leaving enough bidding space for the day-ahead spot market. Since the behavior of energy storage in the spot and peak-shaving markets will affect the market clearing price, energy storage will disperse the bidding volume to each period. For example, during 20-21h, the discharge bidding volume of energy storage in the day-ahead spot market is lower than that of 19h during the highest electricity price period. This is because too much energy storage bidding will lower the clearing price and reduce revenue. At the same time, in order to obtain maximum benefits, energy storage will also disperse the charging bid volume to the two day-ahead markets. For example, in the 14h period, in order to avoid raising the spot electricity price through its charging behavior, energy storage will use part of its capacity to charge in the deep peak-shaving market while charging in the spot market, so as to obtain peak-shaving compensation while minimizing its electricity purchase costs.

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

[0244] Table 2 Comparison of revenues of different alliances

[0245]

[0246] Table 3 Comparison of benefits of each subject in the joint system under different allocation methods

[0247]

[0248] As can be seen from Table 2, compared with the scenario without energy storage, the net income of the wind, solar and storage combined system is still 12.33% higher than that of the wind and solar combined system, considering the operating cost of energy storage. As can be seen from Table 3, when allocating according to power generation, wind power has the largest actual power generation and obtains the most benefits. At the same time, since energy storage is not a power source, when the power generation is used as the basis for allocation, the proportion of energy storage allocation benefits is the lowest compared with other methods. This is because the marginal contribution of each subject is not considered when allocating benefits according to power. When the MCRS method is used, the benefits of wind, solar and storage after allocation are all higher than the benefits when they participate in the market independently, which meets the rational conditions for the formation of a joint system of wind, solar and storage, and because the marginal contribution value benefit distribution results are considered, it is fairer than the distribution according to power generation. The benefit distribution method proposed in S3 of the present invention further considers the contribution level of each subject to the alliance system. Energy storage participating in wind and solar power generation can improve its power deviation and improve the competitiveness of wind and solar in bilateral negotiated transactions such as green power transactions. Wind farms and photovoltaic power stations have made concessions to energy storage, so the energy storage benefits after contribution level correction have increased, which is 28.19% higher than the MCRS method. However, the actual power curve of the photovoltaic power station deviates greatly from the predicted power curve, and the actual power generation is lower than the day-ahead planned amount, and the deviation assessment power is large. Therefore, its incremental profit distribution ratio is the lowest after the contribution correction, and the incremental profit distribution ratio is reduced by 12.34% compared with the MCRS method. In summary, the profit distribution method proposed in S3 of the present invention adjusts the profit distribution of wind and solar new energy stations according to the accuracy of tracking the day-ahead plan while considering the contribution of energy storage to improve the overall economic efficiency of the combined system, so a more comprehensive and reasonable distribution result can be obtained.

[0249] (3) In order to verify the economic efficiency of the scheduling method proposed in the present invention, in addition to the method proposed in the present invention, four other scheduling strategies are set up to compare various economic indicators. Strategy 1: Without considering the green electricity market, energy storage directly participates in the spot-peak-shaving joint market after completing the sharing task of joint new energy; Strategy 2: Without considering the green electricity market, energy storage only participates in the electricity spot market after completing the sharing task of joint new energy; Strategy 3: Without considering the green electricity market, energy storage only participates in the peak-shaving market after completing the sharing task of joint new energy, and directly purchases and sells electricity from the power grid, and the electricity sales price is set to 80% of the main power grid purchase price; Strategy 4: Without considering the combination of energy storage and new energy, 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, energy storage can take advantage of the complementarity of peak and valley periods of electricity prices and electricity volume between different markets, and make full use of its capacity and power resources, thereby obtaining excess returns in the spot-peak-shaving-green electricity joint market. Compared with time-of-use electricity prices, electricity prices in the market environment will fluctuate according to the supply and demand conditions on both the power generation and power consumption sides. Especially in areas with a high proportion of new energy, electricity prices fluctuate more violently, which makes energy storage have a larger arbitrage space. Therefore, although the energy storage has more charging and discharging capacity under Strategy 3, the daily income of Strategy 2 is 7.5% higher than that of Strategy 3, and the investment payback period is shortened by 2.1 years. When energy storage participates in the deep peak-shaving market, although the grid's electricity purchase cost may be higher than the spot market, the peak-shaving electricity volume considering the peak-shaving compensation fee has a certain price advantage. When energy storage participates in the spot-peak-shaving joint market, it can obtain more cheap electricity. When the energy storage of Strategy 4 participates independently in the spot market, the daily income is lower than that of Strategy 2. This is because the entire capacity of energy storage is used to participate in the spot market, which further increases the impact on market prices and reduces the unit electricity income of energy storage. Therefore, energy storage needs to develop a variety of operating models such as complementing with new energy and participating in the peak-shaving market to reduce its dependence on the price difference arbitrage profit model. Further comparing the method proposed in the present invention with Strategy 1, in the method proposed in the present invention, energy storage and new energy cooperate to participate in the green electricity market, while using part of the capacity to participate in the green electricity deviation balance, further reducing the impact of energy storage on the spot market clearing electricity price, and realizing the dual value of new energy electricity and environmental rights, so that energy storage can obtain the highest profit. Compared with the spot-peak-shaving joint market operation mode of Strategy 1, the method proposed in the present invention considers the green electricity market, and 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%, which illustrates the economy of the scheduling method proposed in the present invention.

[0253] Embodiment 2:

[0254] See also Figure 2, a coordinated dispatching system for a centralized energy storage power station to participate in a joint market, including an energy storage shared capacity optimization dispatching module, an energy storage remaining capacity operation strategy adjustment module, and a revenue distribution module. The energy storage shared capacity optimization dispatching module is used to form a joint system with energy storage and new energy stations to participate in green electricity trading and spot trading, decompose the green electricity trading volume based on the spot electricity price, obtain the green electricity trading volume decomposition curve, take the maximization of the joint system's revenue in the green electricity and spot markets as the optimization goal, construct a joint system optimization dispatching model that takes into account the green electricity trading volume decomposition curve, and optimize the shared capacity of the energy storage; specifically, the energy storage shared capacity optimization dispatching module includes a green electricity trading volume decomposition Curve acquisition module, optimization scheduling module, the green electricity transaction electricity decomposition curve acquisition module is used to construct a green electricity transaction electricity decomposition model considering the uncertainty of spot electricity prices, and solve the green electricity transaction electricity decomposition model to obtain a green electricity transaction electricity decomposition curve; the objective function and constraint conditions of the green electricity transaction electricity decomposition model refer to step S1 of embodiment 1; the optimization scheduling module is used to construct a joint system optimization scheduling model taking into account the green electricity transaction electricity decomposition curve, and optimize the shared part of the energy storage capacity. The objective function of the joint system optimization scheduling model refers to step S1 of embodiment 1; the energy storage remaining capacity operation strategy adjustment module is used to independently allow the remaining energy storage capacity to participate in the power market competition, and construct 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 maximizing the benefits of the spot power market on the basis of energy storage completing the call demand of new energy sites in the joint system, solving the upper optimization model to obtain an energy storage operation strategy, substituting the obtained energy storage operation strategy into the lower joint clearing model as a boundary condition, and adjusting the energy storage operation strategy based on the clearing price fed back by the lower joint clearing model; the objective function and constraints of the upper optimization model and the objective function and constraints of the lower joint clearing model refer to step S2 of Example 1; the benefit distribution module uses Based on the energy storage operation strategy obtained in the energy storage remaining capacity operation strategy adjustment module, 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; specifically, 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, the photovoltaic contribution level, and the wind power contribution level. For the specific calculation formula, see step S31 of Example 1; 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 correction is specifically referred to in step S32 of embodiment 1, and the cooperation profit distribution ratio of each subject in the joint system is specifically referred to in step S33 of embodiment 1; the distribution module is used to distribute the profit to each subject in the joint system based on the cooperation profit 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.

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: In S1, the method for obtaining the green electricity trading quantity decomposition curve is: Construct a green electricity trading volume decomposition model considering the uncertainty of spot electricity prices, and solve the green electricity trading volume decomposition model to obtain the green electricity trading volume decomposition curve; The objective function of the green electricity trading quantity decomposition model includes: ； ； ； ； In the above formula, 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; For the day-ahead electricity price scenario The probability of occurrence; The contract price for green power trading; To decompose Green electricity consumption during the time period; , Photovoltaic and wind power The predicted output for the time period; For photovoltaic and wind power The sum of the predicted outputs for the time period; Indicates that the joint system plans on the day before Always contribute; , Day-ahead planning Charging power and discharging power at the moment; For the scene The next day spot market Time-of-day electricity prices; is the unit time; The operating cost of energy storage; is the unit charging and discharging cost of energy storage; , They are the charging efficiency and discharging efficiency of energy storage; scenario It refers to the day-ahead clearing electricity price scenario; The constraints of the green electricity trading volume decomposition model include: Internal constraints of the joint system: ； ； ； ； ； ； In the above formula, , They are The lower and upper limits of green electricity decomposition in each time period; To decompose into scheduling cycles The planned green power value within the period is decomposed into the scheduling period. The sum of green electricity at each moment; , The photovoltaic power station and wind farm are planned to be The charging power of the energy storage power station at all times; Charge and discharge power and charge state constraints: ； ； ； ； In the above formula, is the maximum charging and discharging 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. When its value is 0, it means that the energy storage is in the discharging state. , They are the maximum and minimum states of charge for energy storage respectively; Indicates the rated capacity of energy storage; , For energy storage Time period, The amount of stored electricity after the period ends; 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, Scheduling cycle Total benefits of the internal joint system; Revenue from electricity sales for the combined system; The power curtailment loss of the combined system; is the deviation cost of the joint system; The operating cost of energy storage; The electricity sales revenue of the combined system The calculation formula is: ； ； ； ； ； ； ； In the above formula, Revenue from green electricity trading; The contract price for green power trading; Decomposition to Green electricity consumption during the time period; for The amount of green electricity actually delivered during the period; , They are energy storage charging power and discharging power respectively; Day-ahead spot market The clearing electricity price for the time period; For intraday market The clearing electricity price for the time period; Income from spot trading; , They are wind farms and photovoltaic power stations. The maximum output of the time period, Wind farms and photovoltaic power stations The sum of the maximum outputs during each period; Indicates that the joint system The actual total output during the period; For the joint system The total planned output for the day ahead in the time period; , They are photovoltaic power stations and wind farms. The charging power of the energy storage power station during the period; for The amount of power abandoned by the combined system during the time period; The curtailment loss of the combined system The calculation formula is: ； In the above formula, is the green certificate conversion factor, 1MWh of electricity corresponds to one green certificate; is the green certificate price; Deviation costs of the combined system The calculation formula is: ； ； ； ； In the above formula, is the allowable deviation margin; for Green electricity deviation assessment quantity in the time period; It is the green electricity quantity deviation assessment coefficient; is the spot deviation assessment coefficient; , 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 benefits of sharing part of the energy storage in the day-ahead spot-peaking combined market; Opening of sessions for the electrical energy spot market; is the day-ahead market trading cycle; Provide user participation scenarios probability; Provide user participation scenarios The day-ahead spot market clearing price; , Energy storage The time period is used as the charging power for the electricity buyer and the discharging power for the electricity seller; For the scene Next is the market clearing price for day-ahead peak load regulation; The time-of-use electricity sales price of the power grid; 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; For the joint system Green electricity breakdown by time period; The combined system output power after calling the energy storage; Green power trading contract price for the joint system; scenario It refers to the bidding scenario of power users; The constraints of the upper optimization model include: ； ； ； ； ； ； ； In the above formula, , They are Time period, The capacity of time-slot energy storage to independently participate in electricity market competition; is the initial storage capacity of energy storage; For photovoltaic power plants and wind farms in combined systems The charging power of energy storage used in the time period; For photovoltaic power plants and wind farms in combined systems The discharge power of energy storage used in the time period; It is the sum of the maximum output power of the photovoltaic power plant and wind farm predicted on the day before in the combined system; Reserve proportion for energy storage capacity; It is the sum of the rated powers of the photovoltaic power plant and wind farm in the combined system; is the rated capacity of energy storage; It is the maximum charge state of energy storage; The objective function of the lower-level joint clearing model includes: ； In the above formula, is the market operating cost, i.e. the value of excess electricity; Opening period for the daily energy spot market for that day; is the day-ahead market trading cycle; , , , They are the thermal power units, users, photovoltaic power stations and wind farms participating in the electricity market. A collection of thermal power units participating in the deep peak load regulation market; , 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; , For users quotations and winning bids; , Photovoltaic station The cost per kilowatt-hour and output power; , Wind turbines The cost per kilowatt-hour and output power; , Thermal power units No. 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: ； ； ； ； ； ； ； ； In the above formula, The contribution level of energy storage; are the weights of each indicator respectively; , , , , 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; Day-ahead spot market The clearing electricity price for the time period; , They are energy storage charging power and discharging power respectively; , Combined system with and without energy storage The amount of electricity wasted during the time period; represents the marginal benefit of energy storage; represents a joint system, , , They represent energy storage, photovoltaic power stations, and wind farms respectively; represents the joint system benefit; represents the combined system benefits after removing energy storage; is the green power deviation of the combined system when there is no energy storage; for Green electricity deviation assessment quantity in the time period; , 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, 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, 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, For joint system Middle Subject Earnings adjusted for contribution level; For joint system Middle Subject The level of contribution; For joint system Middle Subject The minimum benefit of participating in the cooperation; For joint system Middle Subject The greatest benefits of participating in the collaboration; For joint system The benefits of cooperation; S33. Calculate the proportion of cooperative benefits among the entities in the joint system according to the following formula: ； In the above formula, The first Proportion of cooperative income distribution in each period; For the scene Probability, scenario It is obtained by clustering the predicted wind and solar power output in the region where the joint system is located within the dispatch cycle. is the number of scenes; For the scene The main body in the joint system Earnings adjusted for contribution level; For the scene Lower Joint System Middle Subject The minimum benefit of participating in the cooperation; For the scene Lower Joint System The benefits of cooperation; 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 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 energy storage shared capacity optimization scheduling module includes a green electricity transaction power decomposition curve acquisition module, which is used to construct 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, 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; For the day-ahead electricity price scenario The probability of occurrence; The contract price for green power trading; To decompose Green electricity consumption during the time period; , Photovoltaic and wind power The predicted output for the time period; For photovoltaic and wind power The sum of the predicted outputs for the time period; Indicates that the joint system plans on the day before Always contribute; , Day-ahead planning Charging power and discharging power at the moment; For the scene The day-ahead spot market Time-of-day electricity prices; is the unit time; The operating cost of energy storage; is the unit charging and discharging cost of energy storage; , They are the charging efficiency and discharging efficiency of energy storage; scenario It refers to the day-ahead clearing electricity price scenario; The constraints of the green electricity trading volume decomposition model include: Internal constraints of the joint system: ； ； ； ； ； ； In the above formula, , They are The lower and upper limits of green electricity decomposition in each time period; To decompose into scheduling cycles The planned green power value within the period is decomposed into the scheduling period. The sum of green electricity at each moment; , The photovoltaic power station and wind farm are planned to be The charging power of the energy storage power station at all times; Charge and discharge power and charge state constraints: ； ； ； ； In the above formula, is the maximum charging and discharging 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. When its value is 0, it means that the energy storage is in the discharging state. , They are the maximum and minimum states of charge for energy storage respectively; Indicates the rated capacity of energy storage; , For energy storage Time period, The amount of stored electricity after the period ends; 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 part 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 electricity transaction power decomposition curve to optimize the shared part capacity of the energy storage; The objective function of the joint system optimization scheduling model includes: ； In the above formula, Scheduling cycle Total benefits of the internal joint system; Revenue from electricity sales for the combined system; The power curtailment loss of the combined system; is the deviation cost of the joint system; The operating cost of energy storage; The electricity sales revenue of the combined system The calculation formula is: ； ； ； ； ； ； ； In the above formula, Revenue from green electricity trading; The contract price for green power trading; Decomposition to Green electricity consumption during the time period; for The amount of green electricity actually delivered during the period; , They are energy storage charging power and discharging power respectively; Day-ahead spot market The clearing electricity price for the time period; For intraday market The clearing electricity price for the time period; Income from spot trading; , They are wind farms and photovoltaic power stations. The maximum output of the time period, Wind farms and photovoltaic power stations The sum of the maximum outputs during each period; Indicates that the joint system The actual total output during the period; For the joint system The total planned output for the day ahead in the time period; , They are photovoltaic power stations and wind farms. The charging power of the energy storage power station during the period; for The amount of power abandoned by the combined system during the time period; The curtailment loss of the combined system The calculation formula is: ； In the above formula, is the green certificate conversion factor, 1MWh of electricity corresponds to one green certificate; is the green certificate price; Deviation costs of the combined system The calculation formula is: ； ； ； ； In the above formula, is the allowable deviation margin; for Green electricity deviation assessment quantity in the time period; It is the green electricity quantity deviation assessment coefficient; is the spot deviation assessment coefficient; , 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 benefits of sharing part of the energy storage in the day-ahead spot-peaking combined market; Opening of sessions for the electrical energy spot market; is the day-ahead market trading cycle; Provide user participation scenarios probability; Provide user participation scenarios The day-ahead spot market clearing price; , Energy storage The time period is used as the charging power for the electricity buyer and the discharging power for the electricity seller; For the scene Next is the market clearing price for day-ahead peak load regulation; The time-of-use electricity sales price of the power grid; 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; For the joint system Green electricity breakdown by time period; The combined system output power after calling the energy storage; Green power trading contract price for the joint system; scenario It refers to the bidding scenario of power users; The constraints of the upper optimization model include: ； ； ； ； ； ； ； In the above formula, , They are Time period, The capacity of time-slot energy storage to independently participate in electricity market competition; is the initial storage capacity of energy storage; For photovoltaic power plants and wind farms in combined systems The charging power of energy storage used in the time period; For photovoltaic power plants and wind farms in combined systems The discharge power of energy storage used in the time period; It is the sum of the maximum output power of the photovoltaic power plant and wind farm predicted on the day before in the combined system; Reserve proportion for energy storage capacity; It is the sum of the rated powers of the photovoltaic power plant and wind farm in the combined system; is the rated capacity of energy storage; It is the maximum charge state of energy storage; The objective function of the lower-level joint clearing model includes: ； In the above formula, is the market operating cost, i.e. the value of excess electricity; Opening period for the daily energy spot market for that day; is the day-ahead market trading cycle; , , , They are the thermal power units, users, photovoltaic power stations and wind farms participating in the electricity market. A collection of thermal power units participating in the deep peak load regulation market; , 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; , For users quotations and winning bids; , Photovoltaic station The cost per kilowatt-hour and output power; , Wind turbines The cost per kilowatt-hour and output power; , Thermal power units No. 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: ； ； ； ； ； ； ； ； In the above formula, The contribution level of energy storage; are the weights of each indicator respectively; , , , , 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; Day-ahead spot market The clearing electricity price for the time period; , They are energy storage charging power and discharging power respectively; , Combined system with and without energy storage The amount of electricity wasted during the time period; represents the marginal benefit of energy storage; represents a joint system, , , They represent energy storage, photovoltaic power stations, and wind farms respectively; represents the joint system benefit; represents the combined system benefits after removing energy storage; is the green power deviation of the combined system when there is no energy storage; for Green electricity deviation assessment quantity in the time period; , 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, 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, 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, For joint system Middle Subject Earnings adjusted for contribution level; For joint system Middle Subject The level of contribution; For joint system Middle Subject The minimum benefits of participating in the cooperation; For joint system Middle Subject The greatest benefits of participating in the collaboration; For joint system The benefits of cooperation; The calculation formula for the cooperative benefit distribution ratio of each subject in the joint system is: ； In the above formula, The first Proportion of cooperative income distribution in each period; For the scene Probability, scenario It is obtained by clustering the predicted wind and solar power output in the region where the joint system is located within the dispatch cycle. is the number of scenes; For the scene The main body in the joint system Earnings adjusted for contribution level; For the scene Lower Joint System Middle Subject The minimum benefit of participating in the cooperation; For the scene Lower Joint System The benefits of cooperation; 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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