Distribution network user side day market transaction method based on improved MCRS method

Abstract

The invention discloses a distribution network user side intraday market transaction method based on an improved MCRS method, and relates to the technical field of electricity market transactions, and the method comprises the steps: obtaining the features of a day-ahead clearing plan according to different types of subjects of a distribution network user side, and building a day-ahead market transaction data set; based on the day-ahead market transaction data set, an objective function is established with the purpose of minimizing the intra-day operation cost of different types of subjects on the distribution network user side, and an intra-day cost model is constructed in combination with power balance constraints and power limit constraints to obtain intra-day optimization execution features; according to the day-ahead clearing plan features and the intra-day optimization execution features, the MCRS method is utilized to calculate the operation cost deviations of different types of subjects on the distribution network user side, correction is carried out through the cost correction value, and the transaction cost of the cooperative operation subject is obtained. According to the invention, the economical efficiency and fairness of different types of emerging market subjects in market transactions on the distribution network side are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power market trading, and in particular to a distribution network user-side intraday market trading method based on an improved MCRS method. Background Art

[0002] Building a new power system with new energy as the main body is a key measure to build a modern energy system. As an important part of the construction of a new power system, energy systems on the distribution network side, such as microgrids, distributed photovoltaics, and energy storage power stations, can effectively improve the level of new energy consumption and ensure high-quality energy supply. They have achieved rapid development in recent years. Carrying out local trading of distributed power generation on the distribution network side can give full play to the complementary advantages of different types of energy systems in terms of energy use time and space. Especially in the context of the trend of large-scale and continuous access to distributed photovoltaics, it can further increase the proportion of local consumption of new energy and improve the safe operation level of the power grid, which plays an important supporting role in building a market mechanism that adapts to the new power system.

[0003] At present, there have been many studies on distributed transactions on the distribution network side at home and abroad, but there is currently no clear mechanism for the intraday operation and profit assessment and distribution of the distribution network user side market including various emerging market players. The rolling optimization algorithm is a better algorithm for dealing with the uncertainty of real-time operation prediction, which can reduce the impact of prediction errors in the intraday operation stage on decision-making; the MCRS method can consider the contribution of various entities to the cooperative alliance and achieve a more fair and reasonable profit distribution, which is suitable for the profit distribution of large-scale systems. For example, Chinese patent 202211113178.8 discloses a distributed trading method for wind-storage microgrids that is suitable for spot market transactions. It establishes a distributed trading method that considers wind power, energy storage, and microgrids; however, this method does not consider how each entity and the system as a whole can simultaneously obtain maximum benefits during the transaction process, nor does it consider the distribution and assessment of intraday execution deviations.

[0004] The existing intraday market trading methods on the distribution network user side have the following technical drawbacks: First, from the perspective of operation optimization, in the intraday market on the distribution network user side, renewable energy can accurately predict its available power generation capacity only within a relatively short time range before real-time operation. Therefore, an optimization algorithm with real-time optimization characteristics is required for decision-making optimization. However, commonly used optimization algorithms such as robust optimization and stochastic optimization for dealing with prediction uncertainties cannot track the operation of the system in real time. Although the MPC optimization algorithm has real-time optimization characteristics, its model is relatively complex. Second, after the day-ahead market on the distribution network user side is optimized and cleared, there are inevitable operation deviations during the intraday market execution phase. Currently, the power market mostly distributes deviations through assessment, rarely considering the contribution of each operation entity, and this consideration is even scarcer in the distribution network user side market. In addition, traditional allocation methods such as the Shapley value are only applicable to situations with a small number of cooperative entities. When the number of cooperative entities is large, a combinatorial explosion problem will occur. The existing MCRS method considering contribution degree can achieve relatively fair benefit distribution, but it does not comprehensively consider the deviation assessment on the distribution network user side, and the allocation result cannot reflect the actual contribution degree of each cooperative operation entity participating in the distribution network user side market.

[0005] Regarding the problems in the related technology, no effective solution has been proposed yet. Summary of the Invention

[0006] Regarding the problems in the related technology, the present invention proposes an intraday market trading method for the distribution network user side based on the improved MCRS method, which has the advantages of adopting a rolling optimization algorithm to handle the uncertainty of power prediction deviation, applying the MCRS method to allocate the execution deviation of the intraday market on the distribution network side, and correcting the allocation value of the MCRS method based on the deviation assessment with positive and negative characteristics, improving the economy and fairness of emerging market entities such as distributed photovoltaics and independent energy storage in the distribution network side market transactions, further promoting the consumption of new energy, and improving the safe operation level of the power grid. Furthermore, it solves the problems of execution deviation caused by power prediction deviation in the intraday market on the distribution network side, deviation assessment and deviation allocation methods that do not consider the actual contribution degree, and the problem of combinatorial explosion that occurs in traditional allocation methods considering contribution degree when there are many cooperative entities.

[0007] Therefore, the specific technical solution adopted by the present invention is as follows:

[0008] An intraday market trading method for the distribution network user side based on the improved MCRS method, the intraday market trading method for the distribution network user side based on the improved MCRS method includes:

[0009] S1. According to different types of entities on the distribution network user side, obtain the characteristics of the day-ahead clearing plan and establish a day-ahead market trading data set;

[0010] S2. Based on the day-ahead market transaction dataset, establish an objective function aiming to minimize the intraday operating costs of different types of entities on the distribution network user side, and construct an intraday cost model by combining power balance constraints and power limit constraints to obtain the intraday optimization execution characteristics;

[0011] S3. According to the day-ahead clearing plan characteristics and the intraday optimization execution characteristics, use the MCRS method to calculate the operating cost deviations of different types of entities on the distribution network user side respectively, and correct them through cost correction values to obtain the transaction costs of the cooperative operating entities.

[0012] Furthermore, the day-ahead clearing plan characteristics include the day-ahead optimal operating cost of the microgrid, the day-ahead optimal operating cost of the independent energy storage, the day-ahead optimal operating cost of the distributed photovoltaic, the day-ahead optimal operating cost of the adjustable power load, and the day-ahead planned cost;

[0013] The day-ahead market transaction data includes the day-ahead optimization clearing plan characteristics, the day-ahead market transaction data of the microgrid, the day-ahead market transaction data of the independent energy storage, the day-ahead market transaction data of the distributed photovoltaic, and the day-ahead market transaction data of the adjustable power load.

[0014] Furthermore, based on the day-ahead market transaction dataset, establish an objective function aiming to minimize the intraday operating costs of different types of entities on the distribution network user side, and construct an intraday cost model by combining power balance constraints and power limit constraints. The obtained intraday optimization execution characteristics include:

[0015] S21. According to the day-ahead market transaction dataset, calculate the intraday operating cost of the microgrid, the intraday operating cost of the independent energy storage, the intraday operating cost of the distributed photovoltaic, and the intraday operating cost of the adjustable power load, and sum them up to obtain the intraday optimization execution cost;

[0016] S22. Establish an objective function aiming to minimize the intraday optimization execution cost, and establish an intraday cost model for the distribution network user side by combining power balance constraints and power limit constraints;

[0017] S23. Use the rolling optimization mode to solve the intraday cost model of the distribution network user side to obtain the intraday optimization execution characteristics.

[0018] Furthermore, the expression of the objective function is:

[0019] f = min(C i,MG + C j,S + C k,PV + C m,L );

[0020] In the formula, C i,MG represents the intraday stage operating cost of the microgrid i; C j,S represents the intraday stage operating cost of the independent energy storage j; C k,PVrepresents the operating cost of distributed PV k during the intraday stage; C m,L represents the operating cost of adjustable power load m during the intraday stage.

[0021] Furthermore, the intraday optimization execution features include the intraday optimal operating cost of the microgrid, the intraday optimal operating cost of the independent energy storage, the intraday optimal operating cost of the distributed PV, the intraday optimal operating cost of the adjustable power load, and the intraday optimization execution cost;

[0022] The intraday optimization execution cost is equal to the sum of the intraday optimal operating cost of the microgrid, the intraday optimal operating cost of the independent energy storage, the intraday optimal operating cost of the distributed PV, and the intraday optimal operating cost of the adjustable power load.

[0023] Furthermore, the expression for the intraday stage operating cost of the adjustable power load is:

[0024]

[0025]

[0026] In the formula, C m,load-grid represents the transaction cost between the adjustable power load m and the power grid; C m,i , C m,j , C m,k respectively represent the transaction costs between the adjustable power load m and the microgrid i, the independent energy storage j, and the distributed PV k; C m,adj represents the load regulation cost of the adjustable power load m; represents the intraday transaction power between the adjustable power load m and the independent energy storage j at time t; represents the load regulation power of the adjustable power load m at time t; represents the intraday transaction power between the adjustable power load m and the distributed PV k at time t.

[0027] Furthermore, according to the day-ahead clearing plan features and the intraday optimization execution features, using the MCRS method, the operating cost deviations of different types of entities on the distribution network user side are calculated respectively, and corrected through the cost correction value, and the transaction costs of the cooperative operation entities are obtained, including:

[0028] S31. Based on the day-ahead clearing plan features and the intraday optimization execution features, use the MCRS method to calculate the distribution values of the operating cost deviations of different types of entities on the distribution network user side respectively;

[0029] S32. According to the distribution values of the operating cost deviations of different types of entities on the distribution network user side, solve the sharing values of the assessment total costs of different types of entities, and correct the assessment costs of different types of entities to obtain the cost correction value of the cooperative operation entities on the distribution network user side;

[0030] S33. Calculate the transaction cost of the cooperative operation entity on the distribution network side based on the operation cost deviation allocation values of different types of entities on the user side of the distribution network and in combination with the cost correction value of the cooperative operation entity on the user side of the distribution network.

[0031] Further, based on the characteristics of the day-ahead clearing plan and the characteristics of intraday optimization execution, use the MCRS method to calculate the operation cost deviation allocation values of different types of entities on the user side of the distribution network, including:

[0032] S311. Calculate the operation cost deviation using the MCRS method according to the characteristics of the day-ahead clearing plan and the characteristics of intraday optimization execution.

[0033] S312. Calculate the operation cost deviation allocation value of the microgrid based on the intraday operation cost of the microgrid and the day-ahead optimal operation cost of the microgrid, in combination with the operation cost deviation.

[0034] S313. Determine the operation cost deviation allocation value of the independent energy storage based on the intraday operation cost of the independent energy storage and the day-ahead optimal operation cost of the independent energy storage, in combination with the operation cost deviation.

[0035] S314. Analyze the operation cost deviation allocation value of the distributed photovoltaic based on the intraday operation cost of the distributed photovoltaic and the day-ahead optimal operation cost of the distributed photovoltaic, in combination with the operation cost deviation.

[0036] S315. Solve the operation cost deviation allocation value of the adjustable power load based on the intraday operation cost of the adjustable power load and the day-ahead optimal operation cost of the adjustable power load, in combination with the operation cost deviation.

[0037] Further, the operation cost deviation allocation values of different types of entities on the user side of the distribution network include the microgrid allocation cost deviation allocation value, the independent energy storage operation cost deviation allocation value, the distributed photovoltaic operation cost deviation allocation value, and the adjustable power load operation cost deviation allocation value.

[0038] The expression for the operation cost deviation of different types of entities on the user side of the distribution network is:

[0039]

[0040] In the formula, C i,MG_mc-dev represents the operation cost deviation allocation value of microgrid i; C j,S_mc-dev represents the operation cost deviation allocation value of independent energy storage j; C k,PV_mc-dev represents the operation cost deviation allocation value of distributed photovoltaic k; C m,L_mc-dev represents the operation cost deviation allocation value of adjustable power load m; ∑ΔC represents the operation cost deviation; C ALL represents the intraday optimization execution cost; C ALL 0 represents the day-ahead clearing plan cost; C ALL\i,MGRepresents the optimal operating cost of the microgrid in the i-th day; C ALL\j,S Represents the optimal operating cost of the independent energy storage in the j-th day; C ALL\k,PV Represents the optimal operating cost of the distributed photovoltaic in the k-th day; C ALL\m,L Represents the optimal operating cost of the adjustable power load in the m-th day; C i,MG_0 Represents the optimal operating cost of the microgrid in the stage before the i-th day; C j,S_0 Represents the optimal operating cost of the independent energy storage in the stage before the j-th day; C k,PV_0 Represents the optimal operating cost of the distributed photovoltaic in the stage before the k-th day; C m,L_0 Represents the optimal operating cost of the adjustable power load in the stage before the m-th day; I represents the total number of microgrids; J represents the total number of independent energy storages; K represents the total number of distributed photovoltaics; M represents the total number of adjustable power loads.

[0041] Furthermore, according to the distribution values of the operating cost deviations of different types of entities on the distribution network user side, solve the sharing values of the total assessment costs of different types of entities, and correct the assessment costs of different types of entities to obtain the cost correction values of the cooperative operating entities on the distribution network user side, including:

[0042] S321. Based on the positive incentive and negative penalty mechanisms, calculate the assessment costs of the microgrid, independent energy storage, distributed photovoltaic, and adjustable power load respectively;

[0043] S322. According to the total assessment cost, use the distribution values of the operating cost deviations of different types of entities on the distribution network user side to solve the sharing values of the total assessment costs of the microgrid, independent energy storage, distributed photovoltaic, and adjustable power load respectively;

[0044] S323. Use the sharing values of the total assessment costs to correct the assessment costs of the microgrid, independent energy storage, distributed photovoltaic, and adjustable power load to obtain the cost correction values of the cooperative operating entities on the distribution network user side.

[0045] Furthermore, the expression for the assessment cost of the microgrid is:

[0046] C i,MG_ex = |(C i,MG - C i,MG_0 )| × (α load - α pv ) ;

[0047] The expression for the assessment cost of the independent energy storage is:

[0048] C j,S_ex = |(C j,S - C j,S_0 )| × (-α S ) ;

[0049] The expression for the assessment cost of the distributed photovoltaic is:

[0050] C k,PV_ex = |(C k,PV - C k,PV_0 )| × (-α dpv );

[0051] The assessment cost expression for the adjustable power load is:

[0052] C m,L_ex = |(C m,L - C m,L_0 )| × (α L_dev + α L_ad );

[0053] In the formula, C i,MG_ex represents the assessment cost of microgrid i; α load represents the deviation ratio between the actual value and the day-ahead predicted value of the load inside the microgrid; α pv represents the deviation ratio between the actual value and the day-ahead predicted value of the photovoltaic power generation inside the microgrid; C j,S_ex represents the assessment cost of the independent energy storage j; α S represents the deviation ratio between the actual charge-discharge power of the energy storage power station and the day-ahead planned charge-discharge power; C k,PV_ex represents the assessment cost of the distributed photovoltaic k; α dpv represents the deviation ratio between the actual value and the day-ahead predicted value of the distributed photovoltaic power generation; C m,L_ex represents the assessment cost of the adjustable power load m; α L_dev represents the deviation ratio between the actual power and the day-ahead predicted power of the constant load in the adjustable power load; α L_ad represents the deviation ratio between the actual value and the day-ahead predicted value of the load regulation power in the adjustable power load.

[0054] Furthermore, the expression for the sharing value of the total assessment cost of the microgrid is:

[0055]

[0056] The expression for the sharing value of the total assessment cost of the independent energy storage is:

[0057]

[0058] The expression for the sharing value of the total assessment cost of the distributed photovoltaic is:

[0059]

[0060] The expression for the sharing value of the total assessment cost of the adjustable power load is:

[0061]

[0062] In the formula, CALL_ex Represents the total assessment cost; C i,MG_ex_res Represents the share value of the total assessment cost for microgrid i; C j,S_ex_res Represents the share value of the total assessment cost for independent energy storage j; C k,PV_ex_res Represents the share value of the total assessment cost for distributed photovoltaic k; C m,L_ex_res Represents the share value of the total assessment cost for adjustable power load m.

[0063] Furthermore, the expression for the cost correction value of the cooperative operation entity on the distribution network user side is:

[0064] ΔC i,MG_mc_dve = C i,MG_ex - C i,MG_ex_rse ;

[0065] ΔC j,S_mc_dev = C j,S_ex - C j,S_ex_res ;

[0066] ΔC k,PV_mc_dev = C k,PV_ex - C k,PV_ex_res ;

[0067] ΔC m,L_mc_dev = C m,L_ex - C m,L_ex_res ;

[0068] In the formula, ΔC i,MG_mc-dev Represents the cost correction value of microgrid i; ΔC j,S_mc-dev Represents the cost correction value of independent energy storage j; ΔC k,PV_mc-dev Represents the cost correction value of distributed photovoltaic k; ΔC m,L_mc-dev Represents the cost correction value of adjustable power load m.

[0069] Furthermore, the transaction costs of the cooperative operation entity on the distribution network side include microgrid transaction costs, independent energy storage transaction costs, distributed photovoltaic transaction costs, and adjustable power load transaction costs;

[0070] Based on the operation cost deviation allocation values of different types of entities on the distribution network user side and combined with the cost correction values of the cooperative operation entity on the distribution network user side, the calculation of the transaction costs of the cooperative operation entity on the distribution network side includes:

[0071] S331. According to the day-ahead optimal operation cost of the microgrid, combined with the operation cost deviation allocation value and cost correction value of the microgrid, calculate the microgrid transaction cost;

[0072] S332. Based on the day-ahead optimal operation cost of the independent energy storage, combined with the operation cost deviation allocation value and cost correction value of the independent energy storage, calculate the independent energy storage transaction cost;

[0073] S333. Calculate the distributed PV trading cost based on the day-ahead optimal operating cost of the distributed PV, in combination with the operating cost deviation allocation value and the cost correction value of the distributed PV.

[0074] S334. Calculate the adjustable power load trading cost based on the day-ahead optimal operating cost of the adjustable power load, in combination with the operating cost deviation allocation value and the cost correction value of the adjustable power load.

[0075] Furthermore, the expression for the microgrid trading cost is:

[0076] C i,MG_end = C i,MG_0 + C i,MG_mc_dev + ΔC i,MG_mc_dev ;

[0077] The expression for the independent energy storage trading cost is:

[0078] C j,S_end = C j,S_0 + C j,S_mc_dev + ΔC j,S_mc_dev ;

[0079] The expression for the distributed PV trading cost is:

[0080] C k,PV_end = C k,PV_0 + C k,PV_mc_dev + ΔC k,PV_mc_dev ;

[0081] The expression for the adjustable power load trading cost is:

[0082] C m,L_end = C m,L_0 + C m,L_mc_dev + ΔC m,L_mc_dev ;

[0083] In the formula, C i,MG_end represents the microgrid trading cost; C j,S_end represents the independent energy storage trading cost; C k,PV_end represents the distributed PV trading cost; C m,L_end represents the adjustable power load trading cost.

[0084] The beneficial effects of the present invention are:

[0085] (1) The present invention provides a method for intraday market trading on the distribution network user side considering the contribution degree of the main body. Aiming at the trading deviation problem caused by prediction errors during the intraday execution stage of the distribution network user side market, the rolling optimization algorithm is used to handle the uncertainty brought by the prediction errors, thereby improving the economy and reliability of the intraday operation of the distribution network users. The present invention solves the problems that the optimization algorithms such as robust optimization and stochastic optimization commonly used in the prior art to cope with prediction uncertainty cannot track the operation of the system in real time, and although the MPC optimization algorithm has the characteristics of real-time optimization, the model is relatively complex. At the same time, from the aspect of operation optimization, in the intraday market on the distribution network user side, the available power generation capacity can be accurately predicted within a short time range before the real-time operation of renewable energy, realizing real-time decision optimization.

[0086] (2) The present invention applies the MCRS method to allocate the execution deviation of the intraday market on the distribution network user side, which can effectively solve the problem of combinatorial explosion existing in the traditional cooperative income distribution method in a large-scale system, and avoid the inevitable operation deviation during the intraday market execution stage after the optimal clearing of the day-ahead market on the distribution network user side. In the existing power market, the deviation is mostly allocated through assessment, and the contribution of each operating entity in the distribution network user side market cannot be considered.

[0087] (3) While considering the contribution degree of each cooperative operating entity to the trading deviation, the present invention conducts deviation assessment with positive and negative characteristics for different types of operating entities, which can achieve more reasonable allocation, thereby improving the economy and fairness of emerging market entities such as distributed photovoltaics and independent energy storage in the distribution network side market transactions, further promoting the consumption of new energy, improving the safe operation level of the power grid, and being able to comprehensively consider the deviation assessment on the distribution network user side while achieving relatively fair benefit distribution. The allocation result can reflect the actual contribution degree of each cooperative operating entity participating in the distribution network user side market. BRIEF DESCRIPTION OF THE DRAWINGS

[0088] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0089] Figure 1 is a flowchart of a method for intraday market trading on the distribution network user side based on the improved MCRS method according to an embodiment of the present invention;

[0090] Figure 2 is a specific implementation diagram of a method for intraday market trading on the distribution network user side based on the improved MCRS method according to an embodiment of the present invention;

[0091] Figure 3 It is the real-time rolling optimization schematic diagram in a method for intraday market trading on the distribution network user side based on the improved MCRS method according to an embodiment of the present invention. Specific implementation manners

[0092] To further illustrate each embodiment, the present invention provides accompanying drawings, which are a part of the disclosure of the present invention. They are mainly used to illustrate the embodiments and can be combined with the relevant descriptions in the specification to explain the operation principle of the embodiments. With reference to these contents, those of ordinary skill in the art should be able to understand other possible implementation manners and the advantages of the present invention. The components in the figures are not drawn to scale, and similar component symbols are usually used to represent similar components.

[0093] According to an embodiment of the present invention, a method for intraday market trading on the distribution network user side based on the improved MCRS method is provided.

[0094] Now, the present invention will be further described in combination with the accompanying drawings and specific implementation manners. As Figure 1 shown, according to an embodiment of the present invention, a method for intraday market trading on the distribution network user side based on the improved MCRS method is provided. The method for intraday market trading on the distribution network user side based on the improved MCRS method includes the following steps:

[0095] S1. According to different types of entities on the distribution network user side, obtain the characteristics of the day-ahead clearing plan and establish a day-ahead market trading data set;

[0096] Specifically, according to different types of entities on the distribution network user side, obtain the characteristics of the day-ahead clearing plan, and establish a day-ahead market trading data set for the distribution network user side by integrating the day-ahead market trading data of different types of entities on the distribution network user side;

[0097] S2. Based on the day-ahead market trading data set, establish an objective function with the goal of minimizing the intraday operating costs of different types of entities on the distribution network user side, and combine the power balance constraint and the power limit constraint to construct an intraday cost model to obtain the intraday optimization execution characteristics;

[0098] Specifically, based on the day-ahead market trading data set for the distribution network user side, establish an objective function with the goal of minimizing the intraday stage operating costs of the microgrid, independent energy storage, distributed photovoltaic, and adjustable power load, and combine the power balance constraint and the power limit constraint to construct an intraday cost model for the distribution network user side to obtain the intraday optimization execution characteristics;

[0099] S3. According to the characteristics of the day-ahead clearing plan and the intraday optimization execution characteristics, use the MCRS method to calculate the operating cost deviations of different types of entities on the distribution network user side respectively, and correct them through the cost correction value to obtain the trading costs of the cooperative operating entities.

[0100] Specifically, according to the characteristics of the day-ahead clearing plan and the characteristics of intraday optimization execution, the MCRS method is used to calculate the operation cost deviations of different types of entities on the distribution network user side respectively, and the operation cost deviations are corrected through the cost correction values of the cooperative operation entities on the distribution network user side to obtain the transaction costs of the cooperative operation entities on the distribution network side.

[0101] In one embodiment, different types of entities on the distribution network user side include microgrids, independent energy storage, distributed photovoltaics, and adjustable power loads;

[0102] The characteristics of the day-ahead clearing plan include the optimal operation cost of the microgrid in the day-ahead stage, the optimal operation cost of the independent energy storage in the day-ahead stage, the optimal operation cost of the distributed photovoltaics in the day-ahead stage, the optimal operation cost of the adjustable power load in the day-ahead stage, and the day-ahead plan cost;

[0103] The day-ahead market transaction data on the distribution network user side includes the characteristics of the day-ahead optimized clearing plan, the day-ahead market transaction data of the microgrid, the day-ahead market transaction data of the independent energy storage, the day-ahead market transaction data of the distributed photovoltaics, and the day-ahead market transaction data of the adjustable power load;

[0104] Among them, the day-ahead plan cost is equal to the sum of the optimal operation cost of the microgrid in the day-ahead stage, the optimal operation cost of the independent energy storage in the day-ahead stage, the optimal operation cost of the distributed photovoltaics in the day-ahead stage, and the optimal operation cost of the adjustable power load in the day-ahead stage.

[0105] Specifically, in step ①, obtain the day-ahead market transaction data on the distribution network user side. The detailed steps are as follows: Based on the day-ahead optimization model of multiple types of entities on the distribution network user side using the cooperative game Nash bargaining theory, formulate the optimal power trading strategy (characteristics of the day-ahead clearing plan) of the distribution network users including four types of entities: distributed photovoltaics, microgrids, independent energy storage, and adjustable power loads in the power market and the distribution network user side market, and execute it as the power plan in the intraday stage.

[0106] Specifically, the characteristics of the day-ahead clearing plan include the respective optimal operation costs C i,MG_0 、C j,S_0 、C k,PV_0 、C m,L_0 of the microgrid, independent energy storage, distributed photovoltaics, and adjustable power load in the day-ahead stage, as well as the cooperative optimal total operation cost C ALL_0 , and each optimal operation cost is obtained from the day-ahead optimization model of multiple types of entities on the distribution network user side based on the cooperative game Nash bargaining theory in the day-ahead stage, and is used as a known parameter in the characteristics of the day-ahead clearing plan.

[0107] Specifically, the day-ahead market transaction data of the microgrid includes the day-ahead power purchase quantity of the microgrid i The day-ahead clearing electricity price in the power market The gas consumption of the CHP unit inside the microgrid i at time t The power sold by microgrid i to the power grid at time t The electricity selling price p of microgrid i selling electricity to the power grid at time t i,mg-grid ; The power generation of the photovoltaic in microgrid i at time t

[0108] Specifically, the day-ahead market trading data of the independent energy storage includes the electricity purchase quantity of the independent energy storage (the energy storage power station in this embodiment) j at time t The electricity selling quantity of the independent energy storage j at time t The trading electricity quantity between microgrid i and independent energy storage j at time t Indicates the electricity quantity purchased by the independent energy storage j from the distributed photovoltaic k at time t The trading electricity price between the independent energy storage j and microgrid i at time t Indicates the trading electricity price p between the independent energy storage j and the distributed photovoltaic k at time t j,k ; The trading electricity quantity between the distributed photovoltaic k and the power grid at time t

[0109] Specifically, the day-ahead market trading data of the distributed photovoltaic includes the trading electricity price between the distributed photovoltaic k and the power grid at time t The trading electricity quantity between the distributed photovoltaic k and microgrid i at time t The electricity price per unit trading electricity quantity between the distributed photovoltaic k and microgrid i k,i ; The trading electricity quantity between the distributed photovoltaic k and the independent energy storage j at time t The electricity price per unit trading electricity quantity between the distributed photovoltaic k and the independent energy storage j k,j ; The electricity purchase quantity of the adjustable power load m from the power grid at time t Electricity price

[0110] Specifically, the day-ahead market trading data of the adjustable power load includes the trading electricity quantity between the adjustable power load m and microgrid i Electricity price The trading electricity quantity between the adjustable power load m and the independent energy storage j Electricity price The trading electricity quantity between the adjustable power load m and the distributed photovoltaic k Electricity price

[0111] In addition, it should be noted that the day-ahead optimization model for multiple types of entities on the distribution network user side based on the cooperative game Nash bargaining theory proposed in the present invention is a prior art, and the core content of this technology is the day-ahead cooperative game optimization operation of the distribution network user side market including distributed photovoltaics, independent energy storage, and microgrids. The trading data of the distribution network side day-ahead market in step ① of this invention patent is calculated through this technology.

[0112] In addition, it should be noted that MCRS (Minimum Cost Remainin Saving) represents the minimum remaining saving cost; MPC (Model Predictive Control) represents model predictive control; CHP (combined heat and power) represents combined heat and power, specifically referring to the combined heat and power unit within the microgrid; green power revenue represents the green power revenue obtained from green energy with green certificates. Taking photovoltaic power generation as an example, the calculation formula is the total photovoltaic power generation multiplied by the unit green certificate revenue.

[0113] In one embodiment, based on the day-ahead market trading dataset of the distribution network user side, a target function is established with the goal of minimizing the intraday operating costs of the microgrid, independent energy storage, distributed photovoltaic, and adjustable power load, and combined with power balance constraints and power limit constraints, a distribution network user-side intraday cost model is constructed. The steps to obtain the intraday optimization execution characteristics are as follows:

[0114] S21. Calculate the intraday operating cost of the microgrid, the intraday operating cost of the independent energy storage, the intraday operating cost of the distributed photovoltaic, and the intraday operating cost of the adjustable power load according to the day-ahead market trading dataset of the distribution network user side;

[0115] Specifically, calculating the intraday operating cost of the microgrid, the intraday operating cost of the independent energy storage, the intraday operating cost of the distributed photovoltaic, and the intraday operating cost of the adjustable power load according to the day-ahead market trading dataset of the distribution network user side includes the following steps:

[0116] S211. Analyze the intraday operating cost of the microgrid based on the power purchase cost, gas cost, carbon emission cost, power sales revenue, green power revenue, and combined with the transaction costs between the microgrid and other entities;

[0117] S212. Solve the intraday operating cost of the independent energy storage according to the life cycle cost loss, power purchase cost, power sales revenue, and independent energy storage, and combined with the transaction costs between the independent energy storage and other entities;

[0118] S213. Calculate the intraday operating cost of the distributed photovoltaic based on the transaction costs between the distributed photovoltaic and the power grid, and combined with the transaction costs between the distributed photovoltaic and the microgrid, independent energy storage, and adjustable load;

[0119] S214. Determine the intraday operating cost of the adjustable power load according to the transaction costs between the adjustable power load and the power grid, load regulation cost, and combined with the transaction costs between the adjustable power load and the microgrid, independent energy storage, and distributed photovoltaic.

[0120] S22. Establish an objective function aiming to minimize the intraday optimization execution cost, and establish an intraday cost model for the user side of the distribution network in combination with power balance constraints and power limit constraints;

[0121] Specifically, S221. Calculate the intraday optimization execution cost based on the intraday stage operation cost of the microgrid, the intraday stage operation cost of the independent energy storage, the intraday stage operation cost of the distributed photovoltaic, and the intraday stage operation cost of the adjustable power load, and establish an objective function aiming to minimize the intraday optimization execution cost;

[0122] Specifically, in step ②, establish a real-time optimization model for the intraday market on the user side of the distribution network. The detailed steps are as follows: The real-time optimization goal of the intraday market on the distribution network side is to minimize the total operation cost of the four types of entities, namely distributed photovoltaic, microgrid, independent energy storage, and adjustable power load, through the regulation of independent energy storage and adjustable power load on the basis of considering the uncertainty of prediction deviation. The expression of its objective function is:

[0123] f = min(C i,MG + C j,S + C k,PV + C m,L );

[0124] In the formula, C i,MG represents the intraday stage operation cost of the microgrid i; C j,S represents the intraday stage operation cost of the independent energy storage j (the energy storage power station in this embodiment); C k,PV represents the intraday stage operation cost of the distributed photovoltaic k; C m,L represents the intraday stage operation cost of the adjustable power load m.

[0125] Specifically, the intraday optimization execution cost is equal to the sum of the intraday stage optimal operation cost of the microgrid, the intraday stage optimal operation cost of the independent energy storage, the intraday stage optimal operation cost of the distributed photovoltaic, and the intraday stage optimal operation cost of the adjustable power load.

[0126] Specifically, the expression of the intraday stage operation cost of the microgrid i is:

[0127]

[0128] In the formula, C i,mg-grid represents the power purchase cost of the microgrid i from the power grid; C i,gas represents the gas cost consumed by the CHP unit inside the microgrid i; C i,co2 represents the carbon dioxide treatment cost of the CHP unit inside the microgrid i; I i,mg-grid represents the power selling revenue of the microgrid i to the power grid; I i,grn represents the green power revenue of the microgrid i; C i,jDenotes the transaction cost between microgrid i and independent energy storage j, C i,k Denotes the transaction cost between microgrid i and distributed photovoltaic k; C i,m Denotes the transaction cost between microgrid i and adjustable power load m; the intraday transaction power between microgrid i and independent energy storage j at time t Is a variable to be optimized, and the rest are known variables in the day-ahead plan.

[0129] Specifically, the expression for the intraday stage operating cost of independent energy storage j is:

[0130]

[0131] In the formula, C j,life Denotes the cost loss during the life cycle of independent energy storage j due to charging and discharging; C j,es-grid Denotes the cost of independent energy storage j purchasing electricity from the grid; I j,es-grid Denotes the revenue of independent energy storage j selling electricity to the grid; C j,i 、C j,k 、C j,m Denote the transaction costs between independent energy storage j and microgrid i, distributed photovoltaic k, and adjustable load m respectively; the intraday transaction power between independent energy storage j and the grid, microgrid i, distributed photovoltaic k, and adjustable power load m at time t Are variables to be optimized, and the rest are known variables in the day-ahead plan.

[0132] Specifically, the expression for the intraday stage operating cost of distributed photovoltaic k is:

[0133]

[0134] In the formula, C k,pv-grid Denotes the transaction cost between distributed photovoltaic k and the grid; C k,i 、C k,j 、C k,m Denote the transaction costs between distributed photovoltaic k and microgrid i, independent energy storage j, and adjustable power load m respectively; the intraday transaction power between distributed photovoltaic k and independent energy storage j at time t And the intraday transaction power between adjustable power load m Are variables to be optimized, and the rest are known variables in the day-ahead plan.

[0135] Specifically, the expression for the intraday stage operating cost of adjustable power load is:

[0136]

[0137]

[0138] In the formula, C m,load-grid represents the transaction cost between the adjustable power load m and the power grid; C m,i , C m,j , C m,k respectively represent the transaction costs between the adjustable power load m and the microgrid i, the independent energy storage j, and the distributed photovoltaic k; C m,adj represents the load regulation cost of the adjustable power load m; represents the intra-day transaction power between the adjustable power load m and the independent energy storage j at time t; represents the load regulation power of the adjustable power load m at time t; represents the intra-day transaction power between the adjustable power load m and the distributed photovoltaic k at time t.

[0139] Specifically, in S222, according to the objective function, combined with the power balance constraint and the power limit constraint, an intra-day cost model for the distribution network user side is established;

[0140] Specifically, in order to achieve the above optimization goal, the following power balance constraint and power limit constraint considering the prediction error also need to be satisfied:

[0141]

[0142]

[0143] In the formula, represents the power sold by the microgrid i to the adjustable load m at time t; represents the power purchased by the microgrid i from the distributed photovoltaic k at time t; represents the power purchased by the adjustable load m from the power market at time t; is the internal load power of the microgrid i at time t; is the power generation of the distributed photovoltaic k at time t; is the total load power of the adjustable power load m at time t; is the basic load power of the adjustable power load m at time t; is the load power regulation amount of the adjustable power load m at time t; P j,ch_max , P j,disch_max are respectively the maximum charge and discharge powers of the energy storage power station j at time t; S j,max , S j,min are respectively the maximum and minimum allowable values of the SOC of the energy storage power station j at time t; P k_max is the maximum power generation of the distributed photovoltaic k at time t; are respectively the maximum limit and the minimum limit of the total load power of the adjustable power load m at time t.

[0144] S23. Using the rolling optimization mode, solve the intra-day cost model of the distribution network user side to obtain the intra-day optimization execution characteristics;

[0145] Among them, the intra-day optimization execution characteristics include the optimal operating cost of the microgrid in the intra-day stage, the optimal operating cost of the independent energy storage in the intra-day stage, the optimal operating cost of the distributed photovoltaic in the intra-day stage, the optimal operating cost of the adjustable power load in the intra-day stage, and the intra-day optimization execution cost.

[0146] Specifically, using the above-mentioned intra-day cost model of the distribution network user side, adopting the rolling optimization mode, and performing optimization and solution according to the mode of predicting once every 15 minutes within 24 hours of a day, sampling point data every 15 minutes within the next 4 hours for each prediction, calculating once every 15 minutes, and scheduling once every 15 minutes, the optimal power scheduling strategy (intra-day optimization execution characteristics) of the cooperation entities on the distribution network user side within the day can be obtained.

[0147] In one embodiment, according to the day-ahead clearing plan characteristics and the intra-day optimization execution characteristics, using the MCRS method, calculate the operation cost deviation of different types of entities on the distribution network user side respectively, and correct it through the cost correction value of the cooperation operation entities on the distribution network user side, and the trading cost of the cooperation operation entities on the distribution network side is obtained through the following steps:

[0148] S31. Based on the day-ahead clearing plan characteristics and the intra-day optimization execution characteristics, using the MCRS method, calculate the distribution values of the operation cost deviations of different types of entities on the distribution network user side respectively;

[0149] S32. According to the distribution values of the operation cost deviations of different types of entities on the distribution network user side, solve the sharing values of the total assessment costs of different types of entities, and correct the assessment costs of different types of entities to obtain the cost correction value of the cooperation operation entities on the distribution network user side;

[0150] Specifically, according to the distribution values of the operation cost deviations of different types of entities on the distribution network user side, solve the sharing values of the total assessment costs of the microgrid, independent energy storage, distributed photovoltaic, and adjustable power load, and correct the assessment costs of the microgrid, independent energy storage, distributed photovoltaic, and adjustable power load to obtain the cost correction value of the cooperation operation entities on the distribution network user side;

[0151] S33. Based on the distribution values of the operation cost deviations of different types of entities on the distribution network user side, combined with the cost correction value of the cooperation operation entities on the distribution network user side, calculate the trading cost of the cooperation operation entities on the distribution network side.

[0152] In one embodiment, based on the day-ahead clearing plan characteristics and the intra-day optimization execution characteristics, using the MCRS method, calculating the distribution values of the operation cost deviations of different types of entities on the distribution network user side respectively includes the following steps:

[0153] S311. Calculate the operation cost deviation using the MCRS method based on the characteristics of the day-ahead clearing plan and the intraday optimization execution characteristics.

[0154] S312. Calculate the distribution value of the microgrid operation cost deviation based on the intraday stage operation cost of the microgrid and the optimal operation cost of the microgrid in the day-ahead stage, combined with the operation cost deviation.

[0155] S313. Determine the distribution value of the independent energy storage operation cost deviation based on the intraday stage operation cost of the independent energy storage and the optimal operation cost of the independent energy storage in the day-ahead stage, combined with the operation cost deviation.

[0156] S314. Analyze the distribution value of the distributed photovoltaic operation cost deviation based on the intraday stage operation cost of the distributed photovoltaic and the optimal operation cost of the distributed photovoltaic in the day-ahead stage, combined with the operation cost deviation.

[0157] S315. Solve the distribution value of the adjustable power load operation cost deviation based on the intraday stage operation cost of the adjustable power load and the optimal operation cost of the adjustable power load in the day-ahead stage, combined with the operation cost deviation.

[0158] Specifically, in step ③, based on the MCRS method, calculate the intraday execution deviation distribution value on the user side of the distribution network. The detailed steps are as follows:

[0159] Among the data of the distribution network side day-ahead market received in step ①, it includes the optimal operation costs C i,MG_0 、C j,S_0 、C k,PV_0 、C m,L_0 of the microgrid, independent energy storage, distributed photovoltaic, and adjustable power load in the day-ahead stage, respectively, and the cooperative optimal total operation cost (day-ahead clearing plan cost) C ALL_0 .

[0160] In step ②, by solving the intraday market optimization model on the user side of the distribution network, the optimal operation costs C ALL\i,MG 、C ALL\j,S 、C ALL\k,PV 、C ALL\m,L of the microgrid, independent energy storage, distributed photovoltaic, and adjustable power load in the intraday stage, respectively, and the cooperative optimal total operation cost (intraday optimization execution cost) C ALL can be obtained.

[0161] Specifically, there is an operation cost deviation value C ALL - C ALL_0 between the intraday execution cost and the day-ahead clearing plan cost. To consider the contribution degrees of each operation entity, the present invention uses the MCRS distribution method to allocate this cost deviation. The calculation formula for the MCRS deviation distribution value corresponding to the cooperative operation entity n on the user side of the distribution network can be described as follows:

[0162]

[0163] x n,min = u(n);

[0164] x n,max = u(N) - u(N\{n});

[0165] Wherein, Δx n represents the profit distribution value obtained by the operating entity n; x n,max represents the maximum profit obtained by the operating entity n; x o,max represents the maximum profit obtained by any operating entity o in the coalition N; x o,min represents the minimum profit obtained by any operating entity o in the coalition N; u(N) represents the total profit of the coalition N in cooperative operation; u(n) represents the independent operation profit of the operating entity n; u(N\{n} represents the total cooperative profit obtained after removing the operating entity n from the coalition N.

[0166] Specifically, the operating cost deviation distribution values of different types of entities on the distribution network user side include the microgrid distribution cost deviation distribution value, the independent energy storage operating cost deviation distribution value, the distributed photovoltaic operating cost deviation distribution value, and the adjustable power load operating cost deviation distribution value.

[0167] Specifically, according to the calculation formula of the above MCRS method, substituting the corresponding cost parameters, the respective operating cost deviation distribution values C i,MG_mc-dev 、C j,S_mc-dev 、C k,PV_mc-dev 、C m,L_mc-dev after the MCRS method distribution for the microgrid i, independent energy storage j, distributed photovoltaic k, adjustable power load m, etc. within the day stage can be obtained. The specific calculation formula is as follows:

[0168]

[0169] Wherein, C i,MG_mc-dev represents the operating cost deviation distribution value of the microgrid i; C j,S_mc-dev represents the operating cost deviation distribution value of the independent energy storage j; C k,PV_mc-dev represents the operating cost deviation distribution value of the distributed photovoltaic k; C m,L_mc-dev represents the operating cost deviation distribution value of the adjustable power load m; ∑ΔC represents the operating cost deviation; C ALL represents the in-day optimization execution cost; C ALL_0 represents the day-ahead clearing plan cost; C ALL\i,MG represents the optimal in-day operating cost of the microgrid i; C ALL\j,S represents the optimal in-day operating cost of the independent energy storage j; C ALL\k,PV represents the optimal in-day operating cost of the distributed photovoltaic k;ALL\m,L Denote the stage optimal operation cost of the adjustable power load within m days; C i,MG_0 Denote the stage optimal operation cost of the microgrid i days ago; C j,S_0 Denote the stage optimal operation cost of the independent energy storage j days ago; C k,PV_0 Denote the stage optimal operation cost of the distributed photovoltaic k days ago; C m,L_0 Denote the stage optimal operation cost of the adjustable power load within m days; I represents the total number of microgrids; J represents the total number of independent energy storages; K represents the total number of distributed photovoltaics; M represents the total number of adjustable power loads.

[0170] In one embodiment, according to the operation cost deviation allocation values of different types of entities on the distribution network user side, solve the total assessment cost sharing values of the microgrid, independent energy storage, distributed photovoltaic, and adjustable power load, and correct the assessment costs of the microgrid, independent energy storage, distributed photovoltaic, and adjustable power load to obtain the cost correction values of the cooperative operation entities on the distribution network user side, including the following steps:

[0171] S321. Based on the positive incentive and negative penalty mechanisms, calculate the assessment costs of the microgrid, independent energy storage, distributed photovoltaic, and adjustable power load respectively;

[0172] Specifically, in step ④, the intra-day deviation cost assessment of different types of cooperative operation entities on the distribution network side, and the assessment result is the correction of the calculation result of the MCRS method in step ③. The detailed steps are as follows: The assessment of each operation entity participating in the distribution network user side market in the present invention reflects the contribution level to cooperative operation, that is, positive incentives are given to those beneficial to cooperative operation, and corresponding penalties are given to those not conducive to cooperative operation. The assessment costs C i,MG_ex , C j,S_ex , C k,PV_ex , C m,L_ex of the microgrid i, independent energy storage j, distributed photovoltaic k, and adjustable power load m can be described as follows:

[0173] The expression of the assessment cost of the microgrid is:

[0174] C i,MG_ex = |(C i,MG - C i,MG_0 )| × (α load - α pv );

[0175] The expression of the assessment cost of the independent energy storage is:

[0176] C j,S_ex = |(C j,S - C j,S_0 )| × (-α S );

[0177] The expression of the assessment cost of the distributed photovoltaic is:

[0178] C k,PV_ex = |(C k,PV - C k,PV_0 )| × (-α dpv );

[0179] The assessment cost expression for the adjustable power load is:

[0180] C m,L_ex = |(C m,L - C m,L_0 )| × (α L_dev + α L_ad );

[0181] In the formula, C i,MG_ex represents the assessment cost of microgrid i; α load represents the deviation ratio between the actual value and the day-ahead predicted value of the load inside the microgrid; α pv represents the deviation ratio between the actual value and the day-ahead predicted value of the photovoltaic power generation inside the microgrid; C j,S_ex represents the assessment cost of independent energy storage j; α S represents the deviation ratio between the actual charge-discharge power of the energy storage power station and the day-ahead planned charge-discharge power; C k,PV_ex represents the assessment cost of distributed photovoltaic k; α dpv represents the deviation ratio between the actual value and the day-ahead predicted value of the distributed photovoltaic power generation; C m,L_ex represents the assessment cost of adjustable power load m; α L_dev represents the deviation ratio between the actual power and the day-ahead predicted power of the constant load in the adjustable power load; α L_ad represents the deviation ratio between the actual value and the day-ahead predicted value of the load regulation power in the adjustable power load.

[0182] S322. According to the total assessment cost, using the deviation distribution values of the operating costs of different types of entities on the distribution network user side, respectively solve the sharing values of the total assessment costs of the microgrid, independent energy storage, distributed photovoltaic, and adjustable power load;

[0183] Specifically, since the total assessment cost C ALL_ex does not include the total operating cost deviation value C ALL - C ALL_0 , it is necessary for each operating entity to jointly bear the total assessment cost. Since the contribution degrees of each operating entity are different, the deviation costs allocated by the MCRS method are also different. In order to achieve a more fair distribution, the deviation cost distribution values C i,MG_mc-dev , C j,S_mc-dev , C k,PV_mc-dev , C m,L_mc-dev obtained in step 3 are approximately used as the total assessment cost CALL_ex The sharing ratio. Then, for the microgrid i, the independent energy storage j, the distributed photovoltaic k, and the adjustable power load m, the share values of the assessment total cost C ALL_ex are as follows:

[0184] The expression for the share value of the assessment total cost of the microgrid is:

[0185]

[0186] The expression for the share value of the assessment total cost of the independent energy storage is:

[0187]

[0188] The expression for the share value of the assessment total cost of the distributed photovoltaic is:

[0189]

[0190] The expression for the share value of the assessment total cost of the adjustable power load is:

[0191]

[0192] In the formula, C ALL_ex represents the assessment total cost; C i,MG_ex_res represents the share value of the assessment total cost of the microgrid i; C j,S_ex_res represents the share value of the assessment total cost of the independent energy storage j; C k,PV_ex_res represents the share value of the assessment total cost of the distributed photovoltaic k; C m,L_ex_res represents the share value of the assessment total cost of the adjustable power load m.

[0193] S323. Using the share values of the assessment total cost, correct the assessment costs of the microgrid, independent energy storage, distributed photovoltaic, and adjustable power load to obtain the cost correction values of the cooperative operation entities on the user side of the distribution network.

[0194] Specifically, based on the above assessment costs and share values of the assessment total cost, for the respective operation cost deviations C i,MG_mc-dev 、C j,S_mc-dev 、C k,PV_mc-dev 、C m,L_mc-dev of the microgrid i, independent energy storage j, distributed photovoltaic k, and adjustable power load m after being allocated by the MCRS method, the cost correction values ΔC i,MG_mc-dev 、ΔC j,S_mc-dev 、ΔC k,PV_mc-dev 、ΔC m,L_mc-dev can be expressed as:

[0195] ΔC i,MG_mc_dve =C i,MG_ex -C i,MG_ex_rse ;

[0196] ΔCj,S_mc_dev = C j,S_ex - C j,S_ex_res ;

[0197] ΔC k,PV_mc_dev = C k,PV_ex - C k,PV_ex_res ;

[0198] ΔC m,L_mc_dev = C m,L_ex - C m,L_ex_res ;

[0199] Wherein, ΔC i,MG_mc-dev represents the cost correction value of microgrid i; ΔC j,S_mc-dev represents the cost correction value of independent energy storage j; ΔC k,PV_mc-dev represents the cost correction value of distributed PV k; ΔC m,L_mc-dev represents the cost correction value of adjustable power load m.

[0200] In one embodiment, the trading costs of the cooperative operation entities on the distribution network side include the trading costs of microgrids, independent energy storage, distributed PV, and adjustable power loads;

[0201] Based on the operating cost deviation allocation values of different types of entities on the distribution network user side and combined with the cost correction values of the cooperative operation entities on the distribution network user side, calculating the trading costs of the cooperative operation entities on the distribution network side includes the following steps:

[0202] S331. According to the optimal operating cost of the microgrid in the day-ahead stage, combined with the operating cost deviation allocation value of the microgrid and the cost correction value of the microgrid, calculate the trading cost of the microgrid;

[0203] S332. Based on the optimal operating cost of the independent energy storage in the day-ahead stage, combined with the operating cost deviation allocation value of the independent energy storage and the cost correction value of the independent energy storage, calculate the trading cost of the independent energy storage;

[0204] S333. According to the optimal operating cost of the distributed PV in the day-ahead stage, combined with the operating cost deviation allocation value of the distributed PV and the cost correction value of the distributed PV, calculate the trading cost of the distributed PV;

[0205] S334. According to the optimal operating cost of the adjustable power load in the day-ahead stage, combined with the operating cost deviation allocation value of the adjustable power load and the cost correction value of the adjustable power load, calculate the trading cost of the adjustable power load.

[0206] Specifically, combining the operating cost deviation allocation values obtained by using the MCRS method in steps ③ and ④ and the correction of the MCRS allocation value (operating cost deviation allocation value), for the final settlement costs C i,MG_end 、Cj,S_end , C k,PV_end , C m,L_end The calculation model of

[0207] The expression of the microgrid transaction cost is:

[0208] C i,MG_end = C i,MG_0 + C i,MG_mc_dev + ΔC i,MG_mc_dev ;

[0209] The expression of the independent energy storage transaction cost is:

[0210] C j,S_end = C j,S_0 + C j,S_mc_dev + ΔC j,S_mc_dev ;

[0211] The expression of the distributed PV transaction cost is:

[0212] C k,PV_end = C k,PV_0 + C k,PV_mc_dev + ΔC k,PV_mc_dev ;

[0213] The expression of the adjustable power load transaction cost is:

[0214] C m,L_end = C m,L_0 + C m,L_mc_dev + ΔC m,L_mc_dev ;

[0215] In the formula, C i,MG_end represents the microgrid transaction cost; C j,S_end represents the independent energy storage transaction cost; C k,PV_end represents the distributed PV transaction cost; C m,L_end represents the adjustable power load transaction cost.

[0216] In summary, by means of the above technical solutions of the present invention, a method for intraday market trading on the distribution network user side considering the contribution degree of the main body is provided. Aiming at the trading deviation problem caused by prediction errors during the intraday execution stage of the distribution network user side market, the uncertainty brought by the prediction errors is processed through a rolling optimization algorithm, thereby improving the economy and reliability of the intraday operation of the distribution network users; the present invention solves the problems that the optimization algorithms such as robust optimization and stochastic optimization commonly used in the prior art to cope with prediction uncertainty cannot track the operation of the system in real time, and although the MPC optimization algorithm has the characteristics of real-time optimization, the model is relatively complex; at the same time, from the perspective of operation optimization, in the intraday market on the distribution network user side, the present invention can accurately predict its available power generation capacity within a short time range before the real-time operation of renewable energy, realizing real-time decision optimization. The present invention applies the MCRS method to allocate the execution deviation of the intraday market on the distribution network user side, which can effectively solve the problem of combinatorial explosion existing in the traditional cooperative income distribution method in a large-scale system, and avoid the inevitable operation deviation during the intraday market execution stage after the optimal clearing of the day-ahead market on the distribution network user side. Most of the existing power markets allocate deviations through assessment and cannot consider the contribution of each operating entity in the distribution network user side market. While considering the contribution degree of each cooperative operating entity to the trading deviation, the present invention conducts deviation assessment with positive and negative characteristics on different types of operating entities, which can achieve a more reasonable allocation, thereby improving the economy and fairness of emerging market entities such as distributed photovoltaics and independent energy storage in the distribution network side market trading, further promoting the consumption of new energy, improving the safe operation level of the power grid, and being able to achieve a relatively fair interest distribution while comprehensively considering the deviation assessment on the distribution network user side. The allocation result can reflect the actual contribution degree of each cooperative operating entity participating in the distribution network user side market.

[0217] The foregoing is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for intraday market trading on the distribution network user side based on an improved MCRS method, characterized in that The method includes: S1. Obtain the characteristics of the day-ahead clearing plan according to different types of entities on the distribution network user side, and establish a day-ahead market trading data set; S2. Based on the day-ahead market trading data set, establish an objective function with the goal of minimizing the intraday operating costs of different types of entities on the distribution network user side, and combine the power balance constraint and the power limit constraint to construct an intraday cost model, and obtain the intraday optimization execution characteristics; S3. According to the day-ahead clearing plan characteristics and the intraday optimization execution characteristics, use the MCRS method to calculate the operating cost deviations of different types of entities on the distribution network user side respectively, and correct them through cost correction values to obtain the trading costs of the cooperative operating entities.

2. The method for intraday market trading on the distribution network user side based on the improved MCRS method according to claim 1, wherein The day-ahead clearing plan characteristics include the day-ahead optimal operating cost of the microgrid, the day-ahead optimal operating cost of the independent energy storage, the day-ahead optimal operating cost of the distributed photovoltaic, the day-ahead optimal operating cost of the adjustable power load, and the day-ahead plan cost; The day-ahead market trading data includes the day-ahead optimization clearing plan characteristics, the day-ahead market trading data of the microgrid, the day-ahead market trading data of the independent energy storage, the day-ahead market trading data of the distributed photovoltaic, and the day-ahead market trading data of the adjustable power load.

3. The intraday market trading method for the distribution network user side based on the improved MCRS method according to claim 1, wherein Based on the day-ahead market trading data set, establishing an objective function with the goal of minimizing the intraday operating costs of different types of entities on the distribution network user side, and combining the power balance constraint and the power limit constraint to construct an intraday cost model, and the obtained intraday optimization execution characteristics include: S21. According to the day-ahead market trading data set, calculate the intraday operating cost of the microgrid, the intraday operating cost of the independent energy storage, the intraday operating cost of the distributed photovoltaic, and the intraday operating cost of the adjustable power load, and sum them up to obtain the intraday optimization execution cost; S22. Establish an objective function with the goal of minimizing the intraday optimization execution cost, and combine the power balance constraint and the power limit constraint to establish an intraday cost model for the distribution network user side; S23. Use the rolling optimization mode to solve the intraday cost model of the distribution network user side to obtain the intraday optimization execution characteristics.

4. A method for intraday market trading on the distribution network user side based on the improved MCRS method according to claim 3, characterized in that, The expression of the objective function is: f = min(C i,MG + C j,S + C k,PV + C m,L ); Where, C i,MG represents the intraday stage operating cost of microgrid i; C j,S represents the intraday stage operating cost of independent energy storage j; C k,PV represents the intraday stage operating cost of distributed photovoltaic k; C m,L represents the intraday stage operating cost of adjustable power load m.

5. The intraday market trading method for the distribution network user side based on the improved MCRS method according to claim 3, wherein The intraday optimization execution characteristics include the intraday optimal operating cost of the microgrid, the intraday optimal operating cost of the independent energy storage, the intraday optimal operating cost of the distributed photovoltaic, the intraday optimal operating cost of the adjustable power load, and the intraday optimization execution cost; The intraday optimization execution cost is equal to the sum of the intraday optimal operating cost of the microgrid, the intraday optimal operating cost of the independent energy storage, the intraday optimal operating cost of the distributed photovoltaic, and the intraday optimal operating cost of the adjustable power load.

6. The intraday market trading method for distribution network users on the user side based on the improved MCRS method according to claim 3, wherein The expression of the intraday stage operating cost of the adjustable power load is: where, C m,load-grid represents the transaction cost between the adjustable power load m and the power grid; C m,i , C m,j , C m,k respectively represent the transaction costs between the adjustable power load m and the microgrid i, the independent energy storage j, and the distributed photovoltaic k; C m,adj represents the load regulation cost of the adjustable power load m; represents the intraday transaction power between the adjustable power load m and the independent energy storage j at time t; represents the load regulation power of the adjustable power load m at time t; represents the intraday transaction power between the adjustable power load m and the distributed photovoltaic k at time t.

7. A method for intraday market trading on the distribution network user side based on the improved MCRS method according to claim 1, characterized in that, According to the day-ahead clearing plan characteristics and the intraday optimization execution characteristics, using the MCRS method to calculate the operating cost deviations of different types of entities on the distribution network user side respectively, and correcting them through cost correction values to obtain the trading costs of the cooperative operating entities, including: S31. Based on the day-ahead clearing plan characteristics and the intraday optimization execution characteristics, use the MCRS method to calculate the distribution values of the operating cost deviations of different types of entities on the distribution network user side; S32. Solve the assessment total cost sharing values of different types of entities according to the operation cost deviation allocation values of different types of entities on the distribution network user side, and correct the assessment costs of different types of entities to obtain the cost correction values of the cooperative operation entities on the distribution network user side; S33. Calculate the transaction costs of the cooperative operation entities on the distribution network side based on the operation cost deviation allocation values of different types of entities on the distribution network user side and in combination with the cost correction values of the cooperative operation entities on the distribution network user side.

8. A method for intraday market trading on the distribution network user side based on the improved MCRS method according to claim 7, characterized in that, The calculation of the operation cost deviation allocation values of different types of entities on the distribution network user side by using the MCRS method based on the characteristics of the day-ahead clearing plan and the characteristics of the intraday optimized execution includes: S311. Calculate the operation cost deviation by using the MCRS method according to the characteristics of the day-ahead clearing plan and the characteristics of the intraday optimized execution; S312. Calculate the operation cost deviation allocation value of the microgrid based on the intraday operation cost of the microgrid and the day-ahead optimal operation cost of the microgrid and in combination with the operation cost deviation; S313. Determine the operation cost deviation allocation value of the independent energy storage according to the intraday operation cost of the independent energy storage and the day-ahead optimal operation cost of the independent energy storage and in combination with the operation cost deviation; S314. Analyze the operation cost deviation allocation value of the distributed photovoltaic based on the intraday operation cost of the distributed photovoltaic and the day-ahead optimal operation cost of the distributed photovoltaic and in combination with the operation cost deviation; S315. Solve the operation cost deviation allocation value of the adjustable power load according to the intraday operation cost of the adjustable power load and the day-ahead optimal operation cost of the adjustable power load and in combination with the operation cost deviation.

9. A method for intraday market trading on the distribution network user side based on the improved MCRS method according to claim 8, characterized in that, The operation cost deviation allocation values of different types of entities on the distribution network user side include the microgrid allocation cost deviation allocation value, the independent energy storage operation cost deviation allocation value, the distributed photovoltaic operation cost deviation allocation value, and the adjustable power load operation cost deviation allocation value; The expression of the operation cost deviation of different types of entities on the distribution network user side is: where, C i,MG_mc-dev represents the operating cost deviation allocation value of microgrid i; C j,S_mc-dev represents the operating cost deviation allocation value of independent energy storage j; C k,PV_mc-dev represents the operating cost deviation allocation value of distributed photovoltaic k; C m,L_mc-dev represents the operating cost deviation allocation value of adjustable power load m; ∑ΔC represents the operating cost deviation; C ALL represents the intraday optimization execution cost; C ALL_0 represents the day-ahead clearing plan cost; C ALL\i,MG represents the intraday-stage optimal operating cost of microgrid i; C ALL\j,S represents the intraday-stage optimal operating cost of independent energy storage j; C ALL\k,PV represents the intraday-stage optimal operating cost of distributed photovoltaic k; C ALL\m,L represents the intraday-stage optimal operating cost of adjustable power load m; C i,MG_0 represents the day-ahead-stage optimal operating cost of microgrid i; C j,S_0 represents the day-ahead-stage optimal operating cost of independent energy storage j; C k,PV_0 represents the day-ahead-stage optimal operating cost of distributed photovoltaic k; C m,L_0 represents the day-ahead-stage optimal operating cost of adjustable power load m; I represents the total number of microgrids; J represents the total number of independent energy storages; K represents the total number of distributed photovoltaics; M represents the total number of adjustable power loads.

10. A method for intraday market trading on the distribution network user side based on the improved MCRS method according to claim 7, characterized in that, The solution of the assessment total cost sharing values of different types of entities according to the operation cost deviation allocation values of different types of entities on the distribution network user side, and the correction of the assessment costs of different types of entities to obtain the cost correction values of the cooperative operation entities on the distribution network user side includes: S321. Calculate the assessment costs of the microgrid, the independent energy storage, the distributed photovoltaic, and the adjustable power load respectively based on the positive incentive and negative punishment mechanisms; S322. Solve the assessment total cost sharing values of the microgrid, the independent energy storage, the distributed photovoltaic, and the adjustable power load respectively according to the assessment total cost by using the operation cost deviation allocation values of different types of entities on the distribution network user side; S323. Use the assessment total cost sharing values to correct the assessment costs of the microgrid, the independent energy storage, the distributed photovoltaic, and the adjustable power load to obtain the cost correction values of the cooperative operation entities on the distribution network user side.

11. A method for intraday market trading on the distribution network user side based on the improved MCRS method according to claim 10, characterized in that, The expression of the assessment cost of the microgrid is: C i,MG_ex = |(C i,MG - C i,MG_0 )| × (α load - α pv ); The expression of the assessment cost of the independent energy storage is: C j,S_ex = |(C j,S - C j,S_0 )| × (-α S ); The expression of the assessment cost of the distributed photovoltaic is: C k,PV_ex = |(C k,PV - C k,PV_0 )| × (-α dpv ); The expression of the assessment cost of the adjustable power load is: C m,L_ex = |(C m,L - C m,L_0 )| × (α L_dev + α L_ad ); Where, C i,MG_ex represents the assessment cost of microgrid i; α load represents the deviation ratio of the actual value to the day-ahead predicted value of the internal load of the microgrid; α pv represents the deviation ratio of the actual value to the day-ahead predicted value of the photovoltaic power generation in the microgrid; C j,S_ex represents the assessment cost of independent energy storage j; α S represents the deviation ratio of the actual charge-discharge power of the energy storage power station to the day-ahead planned charge-discharge power; C k,PV_ex represents the assessment cost of distributed photovoltaic k; α dpv represents the deviation ratio of the actual value to the day-ahead predicted value of the distributed photovoltaic power generation; C m,L_ex represents the assessment cost of adjustable power load m; α L_dev represents the deviation ratio of the actual power to the day-ahead predicted power of the constant load in the adjustable power load; α L_ad represents the deviation ratio of the actual value to the day-ahead predicted value of the load regulation power in the adjustable power load.

12. A method for intraday market trading on the distribution network user side based on the improved MCRS method according to claim 10, characterized in that, The expression of the assessment total cost sharing value of the microgrid is: The expression of the assessment total cost sharing value of the independent energy storage is: The expression of the assessment total cost sharing value of the distributed photovoltaic is: The expression of the assessment total cost sharing value of the adjustable power load is: Where, C ALL_ex represents the total assessment cost; C i,MG_ex_res represents the share value of the total assessment cost of microgrid i; C j,S_ex_res represents the share value of the total assessment cost of independent energy storage j; C k,PV_ex_res represents the share value of the total assessment cost of distributed photovoltaic k; C m,L_ex_res represents the share value of the total assessment cost of adjustable power load m.

13. A method for intraday market trading on the distribution network user side based on the improved MCRS method according to claim 10, characterized in that, The expression for the cost correction value of the cooperative operation entity on the distribution network user side is as follows: ΔC i,MG_mc_dve = C i,MG_ex - C i,MG_ex_rse ; ΔC j,S_mc_dev = C j,S_ex - C j,S_ex_res ; ΔC k,PV_mc_dev = C k,PV_ex - C k,PV_ex_res ; ΔC m,L_mc_dev = C m,L_ex - C m,L_ex_res ; where, ΔC i,MG_mc-dev represents the cost correction value of microgrid i; ΔC j,S_mc-dev represents the cost correction value of independent energy storage j; ΔC k,PV_mc-dev represents the cost correction value of distributed photovoltaic k; ΔC m,L_mc-dev represents the cost correction value of adjustable power load m.

14. A method for intraday market trading on the distribution network user side based on the improved MCRS method according to claim 7, characterized in that The transaction costs of the cooperative operation entity on the distribution network side include the transaction costs of the microgrid, the transaction costs of the independent energy storage, the transaction costs of the distributed photovoltaic, and the transaction costs of the adjustable power load; Calculating the transaction costs of the cooperative operation entity on the distribution network side based on the operation cost deviation allocation value of different types of entities on the distribution network user side and combining the cost correction value of the cooperative operation entity on the distribution network user side includes: S331. Calculate the transaction costs of the microgrid according to the day-ahead optimal operation cost of the microgrid, combining the operation cost deviation allocation value and the cost correction value of the microgrid; S332. Calculate the transaction costs of the independent energy storage based on the day-ahead optimal operation cost of the independent energy storage, combining the operation cost deviation allocation value and the cost correction value of the independent energy storage; S333. Calculate the transaction costs of the distributed photovoltaic according to the day-ahead optimal operation cost of the distributed photovoltaic, combining the operation cost deviation allocation value and the cost correction value of the distributed photovoltaic; S334. Calculate the transaction costs of the adjustable power load based on the day-ahead optimal operation cost of the adjustable power load, combining the operation cost deviation allocation value and the cost correction value of the adjustable power load.

15. A method for intra-day market trading on the distribution network user side based on the improved MCRS method according to claim 14, characterized in that, The expression for the transaction costs of the microgrid is: C i,MG_end = C i,MG_0 + C i,MG_mc_dev + ΔC i,MG_mc_dev ; The expression for the transaction costs of the independent energy storage is: C j,S_end = C j,S_0 + C j,S_mc_dev + ΔC j,S_mc_dev ; The expression for the transaction costs of the distributed photovoltaic is: C k,PV_end = C k,PV_0 + C k,PV_mc_dev + ΔC k,PV_mc_dev ; The expression for the transaction costs of the adjustable power load is: C m,L_end = C m,L_0 + C m,L_mc_dev + ΔC m,L_mc_dev ; Where, C i,MG_end represents the microgrid trading cost; C j,S_end represents the independent energy storage trading cost; C k,PV_end represents the distributed photovoltaic trading cost; C m,L_end represents the adjustable power load trading cost.

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