Distribution network side power dispatching method based on power spot market collaboration

Abstract

The invention discloses a distribution network side electric power dispatching method based on electric power spot market collaboration, and relates to the technical field of electric power, and the distribution network side electric power dispatching method based on electric power spot market collaboration comprises the following steps: building a price signal coupling relation between an electric power market and a distribution network side market; establishing a cost optimization model of a distribution network side market subject in two-stage market transactions, and constructing a distribution network side market multi-type subject transaction clearing model in combination with a Nash bargaining protocol; the distribution network side market multi-type subject transaction clearing model is decomposed, and sub-problems generated after decomposition are optimized and solved; and simulating and deploying the optimal electric energy transaction strategy to the distribution network side market, and executing distribution network side market power dispatching based on the distribution network side market fluctuation condition. According to the invention, the income level of emerging market subjects such as a distribution network side micro-grid, novel energy storage and distributed photovoltaic can be effectively improved, so that the resource complementation advantage between local energy producers and local energy producers can be fully exerted.

Description

Technical Field

[0001] The present invention relates to the field of electric power technology, and in particular to a distribution network side electric power dispatching method based on electric power spot market collaboration. Background Art

[0002] In the construction of a new power system, a development strategy based on new energy is not only the core element for achieving the "dual carbon" goal, but also has far-reaching significance for building a modern energy system. Distribution-side energy systems such as microgrids, distributed photovoltaics, and energy storage power stations have made significant progress in improving the level of new energy consumption and ensuring high-quality energy supply. The rapid development of these technologies has also brought about a high level of attention to operating costs and revenue levels. With the deepening of power market reform, market transactions have shifted from the medium- and long-term power market to the spot trading market, which provides opportunities for emerging market players such as microgrids and distributed photovoltaics to actively participate in market transactions.

[0003] Power dispatch plays a vital role in this transformation, especially when a high proportion of renewable energy is connected to the grid. Power dispatch not only involves how to more efficiently utilize and allocate existing power resources, but also includes predicting power demand and adjusting power generation output to adapt to dynamic changes in market demand. On the distribution network side, local transactions of distributed generation can significantly improve the efficiency of the energy system, especially in the context of large-scale access to distributed photovoltaics. Power dispatch ensures the stability and economic operation of the power grid in this process, and makes the consumption of new energy more efficient and safe by adjusting power generation and load in real time.

[0004] 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 consumption time and space. Especially in the context of the trend of large-scale and continuous access of distributed photovoltaics, it can further increase the proportion of local consumption of new energy and improve the safe operation level of the power grid. It plays an important supporting role in building a market mechanism that adapts to the new power system.

[0005] At present, there have been many studies related to distributed transactions on the distribution network side at home and abroad, most of which focus on the mechanism and model design and optimized operation of the market. Game theory is an important branch of modern mathematics, and its goal is to study the optimal decision-making problem under conflict and confrontation conditions. Many studies have introduced game theory into distributed power transactions on the distribution network side, established relevant clearing optimization models around market trading models, and conducted beneficial explorations and attempts in the electric energy market transactions on the distribution network side. In the prior art, there is a distributed dispatching method for wind-storage microgrids that is suitable for spot market transactions. This method considers the distributed transactions of wind power, energy storage, and microgrids, but this method does not consider how each subject and the system as a whole can simultaneously obtain maximum benefits during the transaction process, and does not consider distributed photovoltaics, an important distributed energy source. In addition, there are the following defects:

[0006] (1) The market transaction model is relatively complex, which is not conducive to the spontaneous and flexible local transactions of the users in the distribution network market;

[0007] (2) The trading scope is limited to the distribution network market and is not connected to the electricity spot market;

[0008] (3) The types of market players are relatively single. With the emergence of new market players such as microgrids and energy storage power stations on the distribution network side, the distributed transaction model involving multiple types of market players is more realistic.

[0009] Currently, no effective solution has been proposed for the problems in the related technologies. Summary of the invention

[0010] 1. Technical issues to be solved

[0011] In view of the deficiencies in the prior art, the present invention provides a distribution network-side power dispatching method based on the coordination of the electricity spot market, which has the advantages of flexibility and economy of transactions by emerging market players in the distribution network-side market, and improves the level of safe operation of the power grid, thereby solving the problems existing in the distribution network-side market, such as complex trading modes, insufficient flexibility, limited trading scope, and relatively single market players.

[0012] (II) Technical solution

[0013] In order to realize the flexibility and economy of the above-mentioned emerging market players in the distribution network side market transactions and improve the safety operation level of the power grid, the specific technical solutions adopted by the present invention are as follows:

[0014] A distribution network side power dispatching method based on power spot market coordination, the distribution network side power dispatching method based on power spot market coordination comprises the following steps:

[0015] S1. Based on cooperative game rational trading behavior, establish a distribution network side market trading model based on a two-tier trading structure;

[0016] S2. Establish the price signal coupling between the power market and the distribution network market, and define the total transaction volume of the distribution network market players in the two-level market;

[0017] S3. Establish a cost optimization model for the transactions of the distribution network side market players in the two-level market, and build a transaction clearing model for multiple types of players in the distribution network side market in combination with the Nash bargaining protocol;

[0018] S4. Decompose the transaction clearing model of multiple types of entities in the distribution network market, and optimize and solve the sub-problems generated after the decomposition to obtain the optimal power trading strategy of multiple types of entities in the distribution network market;

[0019] S5. Simulate and deploy the optimal electricity trading strategy in the distribution network market, and execute power dispatch in the distribution network market based on the fluctuation of the distribution network market.

[0020] Preferably, the calculation formula for the total transaction volume of the distribution network side market entities in the two-level market is:

[0021] C trade,t =ΣQ L,t P L,t +Σ(Q D,t -Q L,t ) D,t +∑(Q R,t -Q D,t ) R,t +C DMO,t

[0022] In the formula, C trade,t represents the market transaction volume of the distribution network side market players in time period t, Q L,t P represents the amount of electricity in time period t signed by the market participants on the distribution network side in the medium and long-term electricity market; L,t It represents the electricity price in time period t signed by the market players on the distribution network side in the medium and long-term electricity market; Q D,t P represents the electricity consumption reported by the spot market in time period t; D,t P represents the node electricity price formed after the spot market is cleared in time period t; R,t represents the node electricity price formed after the real-time spot market transactions in time period t are cleared; Q R,t represents the amount of electricity traded through the power grid during period t; C DMO,t It represents the transaction volume of the distributed power market on the distribution network side by the market entities on the distribution network side; L represents the medium and long-term market; D represents the day-ahead market; and R represents the real-time market.

[0023] Preferably, establishing a cost optimization model for the transactions of the distribution network side market players in the two-level market, and building a transaction clearing model for multiple types of market players in the distribution network side market in combination with the Nash bargaining protocol includes the following steps:

[0024] S31. Establish a cost optimization model for microgrid transactions in the two-level market, a cost optimization model for energy storage power stations in the two-level market, and a cost optimization model for distributed photovoltaic transactions in the two-level market, respectively. The market entities on the distribution network side include at least energy storage power stations, distributed photovoltaics, and microgrids.

[0025] S32. Construct a Nash bargaining standard model based on the Nash bargaining protocol expressed in terms of operating costs;

[0026] S33. Substitute the cost optimization model of microgrid transactions in the two-level market, the cost optimization model of energy storage power stations in the two-level market, and the cost optimization model of distributed photovoltaic transactions in the two-level market into the Nash bargaining standard model to obtain the transaction clearing model of multiple types of entities in the distribution network side market.

[0027] Preferably, the cost optimization model of microgrid in two-level market transactions is expressed as:

[0028]

[0029] In the formula, C i,mg-grid represents the cost of microgrid i purchasing electricity from the grid; C i,gas represents the gas cost consumed by the CHP unit in microgrid i; C i,co2 I represents the CO2 treatment cost of the CHP unit in microgrid i; i,mg-grid I represents the revenue of microgrid i from selling electricity to the grid; i,grn represents the green electricity revenue of microgrid i; C i,j represents the transaction cost between microgrid i and energy storage power station j, C i,k represents the transaction cost between microgrid i and distributed photovoltaic k; T represents the cost calculation cycle; represents the load power of microgrid i; represents the day-ahead electricity purchase amount of microgrid i; represents the medium- and long-term contract electricity price of microgrid i; represents the day-ahead clearing electricity price of the load power of microgrid i; represents the gas consumption of CHP unit in microgrid i at time t; p gas represents the unit gas price of CHP unit in microgrid i at time t; p i,co2 represents the unit CO2 treatment cost of the CHP unit inside microgrid i at time t; represents the power sold by microgrid i to the grid at time t; p i,mg-grid represents the electricity price sold by microgrid i to the grid at time t; represents the photovoltaic power generation inside microgrid i at time t; p green It represents the photovoltaic power generation and unit green electricity revenue inside microgrid i at time t.

[0030] Preferably, the cost optimization model of energy storage power stations in two-level market transactions is expressed as:

[0031]

[0032]

[0033] In the formula, C j,lifeC represents the cost loss of energy storage station j during its life cycle due to charging and discharging; j,es-grid I represents the cost of energy storage j purchasing electricity from the grid; j,es-grid represents the revenue of energy storage power station j from selling electricity to the grid; C j,i represents the transaction cost between energy storage j and microgrid i; C j,k represents the transaction cost between energy storage j and distributed photovoltaic k; is the amount of electricity purchased by energy storage power station j at time t; represents the electricity sold by energy storage power station j at time t; p j,es represents the unit charge and discharge electricity cost of energy storage station j at time t; p j,es-grid represents the on-grid electricity price of energy storage power station j at time t; represents the electricity price purchased from the power grid by energy storage power station j at time t; represents the transaction power between microgrid i and energy storage j at time t; represents the amount of electricity purchased by energy storage j from distributed photovoltaic k at time t; represents the transaction price between energy storage j and microgrid i at time t; p j,k Represents the transaction electricity price between energy storage j and distributed photovoltaic k at time t.

[0034] Preferably, the cost optimization model of distributed photovoltaic transactions in the two-level market is expressed as:

[0035]

[0036] In the formula, C k,pv-grid represents the transaction cost between distributed photovoltaic k and the power grid; represents the transaction power between distributed photovoltaic k and the power grid at time t; represents the transaction electricity price between distributed photovoltaic k and the power grid at time t; represents the transaction power between distributed photovoltaic k and microgrid i at time t; p k,i represents the electricity price per unit of transaction electricity between distributed photovoltaic k and microgrid i; represents the transaction power between distributed photovoltaic power station k and energy storage power station j at time t; p k,j Represents the electricity price per unit of transaction electricity between distributed photovoltaic k and energy storage power station j.

[0037] Preferably, the transaction clearing model of multiple types of entities in the distribution network side market is decomposed, and the sub-problems generated after the decomposition are optimized and solved to obtain the optimal power trading strategy of multiple types of entities in the distribution network side market, including the following steps:

[0038] S41. Decompose the multi-type subject transaction clearing model in the distribution network side market into the total cost minimization sub-problem f 1And the payment benefit maximization subproblem f 2 ;

[0039] S42. Use sequential optimization technology to optimize and solve the two decomposed sub-problems to obtain the optimal electricity trading strategy for multiple types of entities in the distribution network market.

[0040] Preferably, the sequential optimization technology is used to optimize and solve the two decomposed sub-problems to obtain the optimal power trading strategy for multiple types of entities in the distribution network side market, including the following steps:

[0041] S421, combined sub-problem f 1 Corresponding constraints, using Cplex solver to solve subproblems f 1 Solve the problem and obtain the transaction volume of the market players on the distribution network side in the two-level market;

[0042] S422, substitute the transaction volume of the distribution network side market players in the two-level market into sub-problem f 2 In the combination of subproblems f 2 The corresponding constraints are solved by fmincon solver for the subproblem f 2 Solve the problem and obtain the transaction electricity price of the market players on the distribution network side in the two-level market.

[0043] Preferably, the subproblem f is solved using a Cplex solver. 1 The solution involves the following steps:

[0044] Extracting microgrid raw data and taking the microgrid raw data as input;

[0045] Set the optimization model parameters to the subproblem f 1 As the optimization objective function, the optimization models of the market entities on the distribution network side are established respectively;

[0046] Based on the Cplex solver format, the optimization model of the market players on the distribution network side is solved to obtain the electricity trading strategy, which is then sorted and summarized.

[0047] Preferably, the fmincon solver solves the subproblem f 2 The solution involves the following steps:

[0048] Set the maximum number of iterations and initialize the transaction electricity price of the distribution network side market players in the two-level market;

[0049] The convergence tolerance of transaction price and payment benefit function is set based on the transaction price of the market players on the distribution network side in the two-level market;

[0050] Subproblem f 1 The energy trading strategy of 2The amount of electricity input in the subproblem f is solved using the fmincon solver 2 Perform iterative solution;

[0051] Determine whether the error value between the target optimization result and the current optimization result meets the convergence tolerance of the transaction electricity price and the convergence tolerance of the payment benefit function; if the error value is within the tolerance range, stop the iteration and return the final optimization result; if it exceeds the tolerance range, repeat the iterative calculation until the convergence conditions are met.

[0052] (III) Beneficial effects

[0053] Compared with the prior art, the present invention provides a distribution network-side power dispatching method based on power spot market coordination, which has the following beneficial effects:

[0054] (1) The distribution network side power dispatching method based on the coordination of the power spot market provided by the present invention, by establishing a distribution network side market transaction model based on a two-tier transaction structure, can further expand the market-based transactions of various market entities, give full play to the market's role in allocating resources, and guide the reasonable distribution of electric energy in the time and space dimensions, thereby promoting the continuous deepening of power market reform.

[0055] (2) The present invention constructs a transaction clearing model for multiple types of entities in the distribution network market based on the cooperative game Nash bargaining theory, which can effectively improve the profit level of emerging market entities such as microgrids, new energy storage, and distributed photovoltaics on the distribution network side, thereby giving full play to the resource complementarity advantages between local energy producers and sellers and hedging the market risks caused by price fluctuations in the electricity market.

[0056] (3) In view of the problems existing in the distribution network side market, such as complex transaction mode, insufficient flexibility, limited transaction scope, and relatively single market players, the present invention establishes a distribution network side market transaction mode with a double-layer transaction structure connecting the power market and the distributed market based on Nash bargaining, so as to improve the flexibility and economy of transactions of emerging market players such as distributed photovoltaic and independent energy storage in the distribution network side market, thereby further promoting the consumption of new energy and improving the safe operation level of the power grid.

[0057] (4) The present invention simulates and deploys the optimal electricity trading strategy in the distribution network market and predicts the market fluctuation information on the distribution network side, helping users to better understand the potential fluctuations in electricity market prices and demand, adaptively adjust the electricity trading strategy to better adapt to changes in the electricity market, and establish an effective feedback loop to continuously adjust the prediction model according to the performance of the electricity trading strategy to optimize market response and trading efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0059] Figure 1 is a flow chart of a distribution network side power dispatching method based on power spot market collaboration according to an embodiment of the present invention;

[0060] Figure 2 is a schematic diagram of a specific implementation of a distribution network side power dispatching method based on power spot market collaboration according to an embodiment of the present invention;

[0061] Figure 3 It is a two-level market transaction structure diagram on the distribution network side in the distribution network side power dispatching method based on power spot market collaboration according to an embodiment of the present invention. DETAILED DESCRIPTION

[0062] To further illustrate each embodiment, the present invention provides drawings, which are part of the disclosure of the present invention and are mainly used to illustrate the embodiments. They can be used in conjunction with the relevant descriptions in the specification to explain the operating principles of the embodiments. With reference to these contents, ordinary technicians in this field should be able to understand other possible implementation methods and advantages of the present invention.

[0063] According to an embodiment of the present invention, a distribution network-side power dispatching method based on power spot market collaboration is provided.

[0064] The present invention is further described with reference to the accompanying drawings and specific embodiments. Figure 1-Figure 3 As shown, according to the distribution network side power dispatching method based on power spot market coordination according to an embodiment of the present invention, the distribution network side power dispatching method based on power spot market coordination includes the following steps:

[0065] S1. Based on cooperative game and rational trading behavior, establish a distribution network side market trading model based on a two-tier trading structure.

[0066] It should be noted that in the two-tier market, the upper market is the current medium- and long-term electricity and spot service market. The market players on the distribution network side sign medium- and long-term contract quantities independently according to the corresponding trading rules and participate in the spot market declaration. The lower market is the distributed power trading market on the distribution network side. After the spot day-ahead market is cleared, based on the signed medium- and long-term contracts and the spot clearing electricity prices, the distribution network side market trading organization (DMO) is responsible for organizing market players that can form nearby resource complementarity, further carry out internal power generation and consumption information declaration, and form the distribution network side market trading strategy after optimization and clearing, that is, the trading electricity price and quantity information between various entities.

[0067] S2. Establish a price signal coupling relationship between the electricity market and the distribution network market based on the CFD mechanism, and define the total transaction volume of the distribution network market players in the two-level market.

[0068] Among them, the calculation formula for the total transaction volume of market entities on the distribution network side in the two-level market is:

[0069] C trade,t =∑Q L,t P L,t +Σ(Q D,t -Q L,t ) D,t +Σ(Q R,t -Q D,t ) R,t +C DMO,t

[0070] In the formula, C trade,t represents the market transaction volume of the distribution network side market players in time period t, Q L,t P represents the amount of electricity in time period t signed by the market participants on the distribution network side in the medium and long-term electricity market; L,t It represents the electricity price in time period t signed by the market players on the distribution network side in the medium and long-term electricity market; Q D,t P represents the electricity consumption reported by the spot market in time period t; D,t P represents the node electricity price formed after the spot market is cleared in time period t; R,t represents the node electricity price formed after the real-time spot market transactions in time period t are cleared; Q R,t represents the amount of electricity traded through the power grid during period t; C DMO,t It represents the transaction volume of the distributed power market on the distribution network side by the market entities on the distribution network side; L represents the medium and long-term market; D represents the day-ahead market; and R represents the real-time market.

[0071] S3. Establish a cost optimization model for the market players on the distribution network side in the two-level market transactions, and build a transaction clearing model for multiple types of market players on the distribution network side in combination with the Nash bargaining protocol.

[0072] Among them, establishing a cost optimization model for the distribution network side market players in the two-level market transactions, and combining the Nash bargaining agreement to build a transaction clearing model for multiple types of players in the distribution network side market includes the following steps:

[0073] S31. Establish a cost optimization model for microgrid transactions in two-level markets, a cost optimization model for energy storage power stations in two-level markets, and a cost optimization model for distributed photovoltaic transactions in two-level markets, respectively. The market entities on the distribution network side include at least energy storage power stations, distributed photovoltaics, and microgrids.

[0074] Among them, the expression of the cost optimization model of microgrid in two-level market transactions is:

[0075]

[0076]

[0077] In the formula, C i,mg-grid represents the cost of microgrid i purchasing electricity from the grid; C i,gas represents the gas cost consumed by the CHP unit in microgrid i; C i,co2 I represents the CO2 treatment cost of the CHP unit in microgrid i; i,mg-grid I represents the revenue of microgrid i from selling electricity to the grid; i,grn represents the green electricity revenue of microgrid i; C i,j represents the transaction cost between microgrid i and energy storage power station j, C i,k represents the transaction cost between microgrid i and distributed photovoltaic k; T represents the cost calculation cycle; represents the load power of microgrid i; represents the day-ahead electricity purchase amount of microgrid i; represents the medium- and long-term contract electricity price of microgrid i; represents the day-ahead clearing electricity price of the load power of microgrid i; represents the gas consumption of CHP unit in microgrid i at time t; p gas represents the unit gas price of CHP unit in microgrid i at time t; p i,co2 represents the unit CO2 treatment cost of the CHP unit inside microgrid i at time t; represents the power sold by microgrid i to the grid at time t; p i,mg-grid represents the electricity price sold by microgrid i to the grid at time t; represents the photovoltaic power generation inside microgrid i at time t; p green It represents the photovoltaic power generation and unit green electricity revenue inside microgrid i at time t.

[0078] In order to achieve the above optimization goals, the following power balance constraints, power limit constraints, power direction constraints, and transaction price constraints must also be met:

[0079]

[0080] p i,k_min ≤p i,k ≤p i,k_max

[0081] Where P i,pv_cap represents the rated photovoltaic capacity within microgrid i; P i,c_max , P i,m_max , P i,D_maxThey represent the power limits of the CHP unit power generation inside microgrid i, the power sold by microgrid i to the grid, and the power purchased by microgrid i from the grid; represents the power generation of distributed photovoltaic k at time t; They represent the upper and lower limits of the electricity price that microgrid i purchases from energy storage power station j at time t; p i,k_max 、p i,k_min They represent the upper and lower limits of the electricity price that microgrid i purchases from distributed photovoltaic k.

[0082] Among them, the expression of the cost optimization model of energy storage power station in two-level market transactions is:

[0083]

[0084]

[0085] In the formula, C j,life C represents the cost loss of energy storage station j during its life cycle due to charging and discharging; j,es-grid I represents the cost of energy storage j purchasing electricity from the grid; j,es-grid represents the revenue of energy storage power station j from selling electricity to the grid; C j,i represents the transaction cost between energy storage j and microgrid i; C j,k represents the transaction cost between energy storage j and distributed photovoltaic k; is the amount of electricity purchased by energy storage power station j at time t; represents the electricity sold by energy storage power station j at time t; p j,es represents the unit charge and discharge electricity cost of energy storage station j at time t; p j,es-grid represents the on-grid electricity price of energy storage power station j at time t; represents the electricity price purchased from the power grid by energy storage power station j at time t; represents the transaction power between microgrid i and energy storage j at time t; represents the amount of electricity purchased by energy storage j from distributed photovoltaic k at time t; represents the transaction price between energy storage j and microgrid i at time t; p j,k Represents the transaction electricity price between energy storage j and distributed photovoltaic k at time t.

[0086] In order to achieve the above optimization goals, the following power limit constraints, power direction constraints, and transaction price constraints must also be met:

[0087]

[0088] p j,k_min ≤p j,k ≤p j,k_max

[0089] Where P j,ch_max , P j,disch_max They represent the maximum value of single charge and discharge power of energy storage station j; S j,max , S j,min They represent the upper and lower limits of the state of charge of the energy storage power station j respectively; They represent the upper and lower limits of the electricity price sold by energy storage j to microgrid i at time t; p j,k_max 、p j,k_min They represent the upper and lower limits of the electricity price that energy storage j purchases from distributed photovoltaic k.

[0090] Among them, the expression of the cost optimization model of distributed photovoltaic transactions in the two-level market is:

[0091]

[0092]

[0093] In the formula, C k,pv-grid represents the transaction cost between distributed photovoltaic k and the power grid; represents the transaction power between distributed photovoltaic k and the power grid at time t; represents the transaction electricity price between distributed photovoltaic k and the power grid at time t; represents the transaction power between distributed photovoltaic k and microgrid i at time t; p k,i represents the electricity price per unit of transaction electricity between distributed photovoltaic k and microgrid i; represents the transaction power between distributed photovoltaic power station k and energy storage power station j at time t; p k,j Represents the electricity price per unit of transaction electricity between distributed photovoltaic k and energy storage power station j.

[0094] In order to achieve the above optimization goals, the following power limit constraints, power direction constraints, and transaction price constraints must also be met:

[0095]

[0096] p k,j_min ≤p k,j ≤p k,j_max

[0097] p k,i_min ≤p k,i ≤p k,i_max

[0098] Where P k,dpv_cap represents the rated capacity of distributed photovoltaic k; p k,j_max 、p k,j_min They represent the upper and lower limits of the electricity price sold by distributed photovoltaic k to energy storage power station j; p k,i_max 、p k,i_minThey represent the upper and lower limits of the electricity price sold by distributed photovoltaic k to microgrid i.

[0099] S32. Based on the Nash bargaining protocol, a Nash bargaining standard model represented by operating costs is constructed, wherein the Nash bargaining standard model represented by operating costs is as follows:

[0100]

[0101] In the formula, N = {1, 2, ..., n}, which means a cooperative alliance composed of n entities participating in cooperative transactions; represents the optimal operating cost of the ith game subject when not participating in cooperation, also called the cooperation breakdown point; C i It represents the total operating cost of the ith game player after participating in the alliance cooperation bargaining.

[0102] S33. Substitute the cost optimization model of microgrid transactions in the two-level market, the cost optimization model of energy storage power stations in the two-level market, and the cost optimization model of distributed photovoltaic transactions in the two-level market into the Nash bargaining standard model to obtain the transaction clearing model of multiple types of entities in the distribution network side market.

[0103] Substituting the cost model of emerging market players such as microgrids, energy storage power stations, and distributed photovoltaics in the two-level market into the Nash bargaining standard model, we can obtain the transaction clearing model of multiple types of players in the distribution network side market:

[0104]

[0105] In the formula, They represent the optimal operating costs of the i-th microgrid, the j-th energy storage power station and the k-th distributed photovoltaic when only participating in electricity market transactions; C i,MG , C j,S , C k,PV They respectively represent the operating costs of the corresponding entities participating in the two-level markets.

[0106] S4. Decompose the transaction clearing model of multiple types of entities in the distribution network market, and optimize and solve the sub-problems generated after the decomposition to obtain the optimal electricity trading strategy for multiple types of entities in the distribution network market.

[0107] Among them, the transaction clearing model of multiple types of entities in the distribution network side market is decomposed, and the sub-problems generated after the decomposition are optimized and solved to obtain the optimal power trading strategy of multiple types of entities in the distribution network side market, including the following steps:

[0108] S41. Decompose the multi-type subject transaction clearing model in the distribution network side market into the total cost minimization sub-problem f 1 And the payment benefit maximization subproblem f 2 .

[0109] It should be noted that the method of model equivalent transformation decomposition is adopted to decompose the non-convex and nonlinear transaction clearing model into two sub-problems: total cost minimization sub-problem and payment benefit maximization sub-problem. The detailed steps are as follows:

[0110] By using the principle of mean inequality, the established multi-type subject transaction clearing model of the distribution network side market is further decomposed into the following two easy-to-solve sub-problems:

[0111]

[0112] In the formula, Respectively represent the sub-problems f 1 The optimized transaction power is substituted into the cost expression of the microgrid, energy storage power station and distributed photovoltaic cost model.

[0113] It should be noted that the model decomposition uses the important properties of the mean inequality, where the known mean inequality is:

[0114]

[0115] In the formula, R + represents the set of non-negative real numbers if and only if a 1 =a 2 =... =a m When the equality sign holds, in this inequality, when a 1 +a 2 +...+a m When a is a fixed value, 1 a 2 ...a m In a 1 =a 2 =... =a m The maximum value is obtained under the condition of .

[0116] S42. Use sequential optimization technology to optimize and solve the two decomposed sub-problems to obtain the optimal electricity trading strategy for multiple types of entities in the distribution network market.

[0117] Among them, the sequential optimization technology is used to optimize and solve the two sub-problems after decomposition, and the optimal power trading strategy for multiple types of entities in the distribution network market is obtained, which includes the following steps:

[0118] S421, combined sub-problem f 1 Corresponding constraints, using Cplex solver to solve subproblems f 1 By solving the problem, we can obtain the trading volume of the market entities on the distribution network side in the two-level market.

[0119] It should be noted that, combined with the sub-problem f 1Corresponding constraints, first use the Cplex solver to solve the subproblem f 1 Solve the problem and find out the transaction volume of emerging market players such as microgrids, energy storage power stations, and distributed photovoltaics in the two-level market {P DMO(i,j,k) ,P PMC(i,j,k)}.

[0120] It should be noted that the Cplex solver is used to solve the subproblem f 1 The detailed solution process is as follows:

[0121] ① Import the original data. The import includes the internal load of the microgrid Medium and long-term contract electricity Electricity market price (including medium and long-term electricity price) and day-ahead spot electricity price forecast ) Grid time-of-use electricity price Photovoltaic grid-connected electricity price Distributed photovoltaic power generation Photovoltaic power generation inside the microgrid And other data.

[0122] ② Set model parameters. Set the microgrid on-grid electricity price p i,mg-grid 、Energy storage grid electricity price j,es-grid 、Gas price p gas , unit carbon dioxide decomposition cost p i,co2 , Unit photovoltaic power generation green certificate income p green 、 Rated photovoltaic capacity P inside microgrid i i,pv_cap 、The upper limit of the power generation capacity of the CHP unit in the microgrid i is P i,c_max 、The upper limit of power sold by microgrid i to the grid P i,m_max 、The upper limit of power P that microgrid i can purchase from the grid i,D_max , the upper and lower limits of the electricity price that microgrid i purchases from energy storage power station j at time t The upper and lower limits of the electricity price that microgrid i purchases from distributed photovoltaic k (p i,k_max , p i,k_min ), the maximum value of a single charge and discharge power of the energy storage station j (P j,ch_max , P j,disch_max ), the upper and lower limits of the state of charge of the energy storage station j (S j,max , S j,min ), the upper and lower limits of the electricity price sold by energy storage j to microgrid i at time t The upper and lower limits of the electricity price purchased by energy storage j from distributed photovoltaic k (p j,k_max , p j,k_min ), the rated capacity P of distributed photovoltaic k k,dpv_cap , the upper and lower limits of the electricity price sold by distributed photovoltaic k to energy storage power station j (p k,j_max , p k,j_min), the upper and lower limits of the electricity price sold by distributed photovoltaic k to microgrid i (p k,i_max , p k,i_min ) and other optimization model related parameters.

[0123] ③ Establish an optimization model. On the MATLAB platform, program and model the microgrid, energy storage power station, and distributed photovoltaic entities, and use sub-problems f 1 As the final optimization objective function. Specifically: First, the day-ahead power purchase amount of microgrid i Gas consumption of CHP unit in microgrid i at time t The power sold by microgrid i to the grid at time t The amount of electricity purchased by energy storage station j from the grid at time t The amount of electricity sold by energy storage station j to the grid at time t The transaction amount between microgrid i and energy storage j at time t The amount of electricity purchased by energy storage j from distributed photovoltaic k at time t The transaction price between energy storage j and microgrid i at time t The transaction price p between energy storage j and distributed photovoltaic k at time t j,k , the transaction power between distributed photovoltaic k and the power grid at time t The transaction power between distributed photovoltaic k and microgrid i at time t The electricity price p per unit transaction volume between distributed photovoltaic k and microgrid i k,i , the transaction power between distributed photovoltaic k and energy storage power station j at time t The electricity price p per unit of transaction electricity between distributed photovoltaic k and energy storage power station j k,j , etc., and program the cost optimization model of each subject accordingly; finally, 1 The mathematical model is programmed and used as the final optimization objective function.

[0124] ④ Call Cplex to solve the built model. According to the format of Cplex solver, write relevant programs on the MATLAB platform and call Cplex solver to solve.

[0125] ⑤ Summarize transaction data. The energy transaction strategy {P DM(iO,j,k) ,P PM(iC,j,k)} to organize and summarize the results to facilitate the next step of solving the problem.

[0126] S422, substitute the transaction volume of the distribution network side market players in the two-level market into sub-problem f 2 In the combination of subproblems f 2 The corresponding constraints are solved by fmincon solver for the subproblem f 2Solve the problem and obtain the transaction electricity price of the market players on the distribution network side in the two-level market.

[0127] It should be noted that the required transaction power {P DMO(i,j,k) ,P PMC(i,j,k) Substitute into subproblem f 2 , combined with the sub-problem f 2 The corresponding constraints are solved using the fmincon solver. 2 Solve the problem and get the transaction electricity price of microgrid, energy storage power station, distributed photovoltaic and other emerging market players in the two-level market {p DM(Oi,j,k) ,p PM(Ci,j,k)}.

[0128] It should be noted that the fmincon solver is used to solve the subproblem f 2 The detailed solution process is as follows:

[0129] ①Set the maximum number of iterations Y max ; Initialize the transaction price between microgrid, energy storage power station and distributed photovoltaic p mg-pv 、p pv-es ; Set a reasonable transaction price convergence toleranceζ x and the payment benefit function convergence tolerance ζ f ; According to sub-problem f 2 The corresponding constraints set the coefficient matrix A and the boundary matrices b, lb, ub, where A and b correspond to the constraint condition AX≤b, and lb and ub correspond to the upper and lower limits of the optimization variable X, that is, lb≤X≤ub.

[0130] ②Receive sub-problem f 1 Optimization results of transaction power As a sub-problem f 2 The amount of electrical power input in .

[0131] ③ Use the solver to solve the subproblem f 2 Perform iterative solution and update the objective function value f 2 , Trading price variables p mg-pv 、p pv-es and the number of iterations Y.

[0132] ④ Determine whether the error value meets the variable convergence tolerance ζ x The sum function convergence tolerance ζ f If the error value is within the tolerance range, the iteration is stopped and the final optimization result is returned; if it exceeds the tolerance range, it returns to ③ and repeats the iterative calculation until the convergence condition or the maximum number of iterations is met.

[0133] It should be noted that the Cplex solver and the fmincon solver are two powerful tools commonly used for optimization problems. Cplex is a high-performance mathematical programming solver that can handle linear programming (LP), integer programming (IP), mixed integer linear programming (MILP), and quadratic programming (QP) problems. Cplex is known for its fast solution speed and strong stability, and is suitable for scenarios that need to handle large-scale and complex optimization problems.

[0134] The Fmincon decoder is specifically designed to solve constrained nonlinear optimization problems. It supports a variety of optimization algorithms, including interior point method, sequential quadratic programming (SQP) and trust region reflective method, and can handle optimization tasks with complex constraints.

[0135] It should be noted that the construction of the coordination mechanism enables the Cplex solver and the fmincon solver to exchange information in real time during the calculation process and jointly optimize the transaction volume and electricity price of the distribution network side market, thereby improving the market response efficiency and the adaptability of the optimization results. This mechanism not only improves the efficiency of the calculation process, but also enhances the application ability of the model under dynamic market conditions. The design of the coordination mechanism allows the two solvers to exchange information during the calculation process. For example, the Cplex solver solves the subproblem f 1 Then the solution and related parameters are directly passed to the fmincon solver for the subproblem f 2 The main process of solving includes:

[0136] Cplex solver for optimization calculation: Use Cplex solver to solve the subproblem f 1 In the process, the boundary nodes are optimized and calculated. Based on the high efficiency of the Cplex solver in dealing with linear and quadratic programming problems, the calculated nth iteration variables of the boundary nodes and the power flow data corresponding to the boundary nodes provide the necessary basic data for the subsequent steps.

[0137] Anderson acceleration: Anderson acceleration is used to improve the iteration results of the Cplex solver and increase the convergence speed of the algorithm. It is a common strategy for solving complex optimization problems and is suitable for accelerating nonlinear iterative processes. The corrected variables obtained through Anderson acceleration are used as input and passed to the next fmincon solver, ensuring the effective flow of information and the close connection between the iterative process.

[0138] Parallel optimization of fmincon solver: Considering the ability of fmincon in handling constrained optimization problems, especially its advantages in handling nonlinear constraints, the equivalent values ​​of the boundary nodes obtained by the fmincon solver after independent calculation are corrected again using Anderson acceleration, thereby improving the accuracy and efficiency of the calculation.

[0139] Parallel optimization control and information feedback: Check whether the fmincon solver has received the boundary information of the Cplex solver, which is crucial to the correctness and integrity of the overall optimization process. Only when all data have been received, the system will continue to move forward, which ensures the integrity of the data and the logical relationship between the steps. Combined with the change of the Lagrange multiplier vector, it is judged whether the solution results and the dynamic transfer process of related parameters are completed. If the preset convergence accuracy is not reached, the iteration continues; if it is satisfied, it ends, which ensures the clear goal and closed-loop control of the optimization process.

[0140] It should be noted that combining the changes in the Lagrange multiplier vector to determine whether the solution results and the dynamic transfer process of related parameters are completed is a very effective method, which has significant advantages in fields such as power system optimization.

[0141] By monitoring the changes in the Lagrange multiplier vector, it is possible to accurately determine whether the optimization process has achieved the predetermined goal, thereby avoiding premature cessation of iteration or excessive iteration. This method ensures that the algorithm converges to the true optimal solution. During the iteration process, if the change in the Lagrange multiplier does not reach the preset convergence accuracy, the algorithm parameters or decision variables can be dynamically adjusted, thereby enhancing the algorithm's adaptability to the complexity of the problem. Using the Lagrange multiplier as a feedback mechanism, the stability of the system can be effectively monitored and adjusted, especially in situations such as power systems that require high stability. This control mechanism ensures that the entire system operates in a safe and reliable state.

[0142] It should be noted that Anderson Acceleration is a technique used to accelerate the convergence of iterative methods, and is particularly suitable for solving point iteration problems of nonlinear equations. It improves the results of the current iteration by combining the information of the previous iterations, thereby accelerating the convergence speed of the entire solution process. Anderson Acceleration is an improvement on the traditional fixed-point iteration method. Its main advantage is that it can significantly reduce the number of iterations required to reach convergence. The working principle of Anderson Acceleration is as follows:

[0143] The core idea of ​​Anderson acceleration is to use the results and residuals of the previous iterations in each iteration to construct the value of the next iteration, which mainly includes:

[0144] Iterative update execution: Perform a standard iterative update, that is, calculate the new value according to the iterative function.

[0145] Residual calculation: Calculate the residual for the current iteration value, that is, the difference between the current value and the output of the iteration function.

[0146] Historical data collection: Collect and use the results of previous iterations and the corresponding residuals.

[0147] Weighted combination construction: Construct a weighted combination of the results of previous iterations and minimize the combined residual by adjusting the weights.

[0148] Result update: Use the result of weighted combination as the new iteration value to improve iteration efficiency and convergence speed.

[0149] Repeat iteration: Repeat the above steps until the iteration meets the termination condition, such as converging to a solution that is close enough.

[0150] It should be noted that the close coordination and real-time information exchange between the Cplex solver and the fmincon solver not only improves the computational efficiency and accuracy, but also makes the model more adaptable to rapidly changing market conditions. The implementation of this coordination mechanism enhances the overall performance of the power market optimization model, making its application in modern power systems more powerful and effective.

[0151] S5. Simulate and deploy the optimal electricity trading strategy in the distribution network market, and execute power dispatch in the distribution network market based on the fluctuation of the distribution network market.

[0152] It should be noted that the optimal power trading strategy of multiple types of entities in the distribution network side market obtained by optimization is simulated and deployed in the distribution network side market, and the fluctuation of the distribution network side market is predicted. At the same time, the optimal power trading strategy is adaptively adjusted to realize the power dispatch of the distribution network side market, including:

[0153] Input the optimal power trading strategies of various types of entities in the distribution network market into the simulation system to simulate and monitor the execution effect of the optimal power trading strategies;

[0154] The simulated monitoring data of the optimal electricity trading strategy is used as input, and the ARIMA-GARCH model is used to predict the time series changes of electricity price and demand, and to predict the market trading fluctuations on the distribution network side.

[0155] It should be noted that the ARIMA-GARCH model is a time series model that combines ARIMA (autoregressive integrated moving average) and GARCH (generalized autoregressive conditional heteroskedasticity), which is used to simultaneously simulate and predict time series data with volatility characteristics. This model is particularly suitable for areas such as energy markets, where price and demand data often show significant volatility and non-constant fluctuations.

[0156] The ARIMA model consists of:

[0157] AR (Autoregressive part): describes the relationship between the current value and its historical values.

[0158] I (difference part): used to make non-stationary series stationary.

[0159] MA (moving average part): describes the relationship between current error and historical error.

[0160] Combining ARIMA and GARCH can more accurately predict time series with volatility. The ARIMA model predicts the average behavior of the series, while the GARCH model predicts its volatility.

[0161] In the distribution network market, the ARIMA-GARCH model is very effective in predicting changes in electricity prices and demand, especially when the market exhibits high volatility. The ARIMA-GARCH model can provide dual predictions of the central trend and volatility of the time series, making the prediction of future values ​​more accurate. In the operation of the power market, the use of this model can help market participants better understand the potential fluctuations in prices and demand, thereby continuously optimizing trading strategies and risk management measures. Especially in an environment such as the power market, which is often affected by external factors and has large price fluctuations, the ARIMA-GARCH model provides a powerful tool to predict and respond to these fluctuations.

[0162] The following steps are mainly used to predict the time series changes of electricity prices and demand using the ARIMA-GARCH model and to predict the fluctuations of market transactions on the distribution network side:

[0163] Detection of heteroskedasticity effect: Detection of heteroskedasticity effect is performed to determine whether there is instability of conditional variance in time series data. If the time series shows stable conditional variance (i.e., there is no heteroskedasticity effect), there is no need to establish a GARCH model; when heteroskedasticity effect is detected, it is necessary to establish a GARCH model and perform parameter estimation.

[0164] Parameter estimation method: For time series with heteroskedastic effects, it is recommended to use maximum likelihood estimation for parameter estimation, because this method can more accurately utilize known joint distribution information. Although the least squares method is also a commonly used estimation method, maximum likelihood estimation provides better estimation accuracy when dealing with complex conditional variance structures.

[0165] Determination of the order of the model: The Akaike Information Criterion (AIC) is used to determine the optimal order (p, q) of the GARCH model, which helps to balance the complexity and goodness of fit of the model and ensure the accuracy and efficiency of the prediction. The Akaike Information Criterion (AIC) is a criterion for evaluating the relative merits of statistical models and is widely used in model selection, especially in time series analysis. The main purpose of AIC is to select a "best" model from a series of models. It penalizes the number of parameters to prevent overfitting. Models with lower AIC are generally considered to be better because they provide a better explanation of the data while maintaining the simplicity of the model.

[0166] Analysis of basic characteristics of time series: Preliminary analysis of the trend, volatility and seasonal characteristics of the original time series, and evaluation of the distribution of the time series through statistical indicators (mean, median, skewness, kurtosis, etc.), and use the autocorrelation function (ACF) and partial autocorrelation function (PACF) of the data to determine whether the series is a stationary process.

[0167] Model application and strategy adjustment: The volatility information obtained through ARIMA-GARCH model prediction is used to adaptively adjust the power trading strategy to better adapt to market changes. At the same time, an effective feedback loop is established to continuously adjust the prediction model and power dispatch strategy according to the performance of the strategy to optimize market response and trading efficiency.

[0168] To sum up, with the help of the above-mentioned technical scheme of the present invention, the distribution network side power dispatching method based on the coordination of the power spot market provided by the present invention, by establishing a distribution network side market transaction model based on a two-layer transaction structure, can further expand the market-oriented transactions of various market entities, give full play to the market's role in resource allocation, and guide the reasonable distribution of electric energy in the time and space dimensions, thereby helping to promote the continuous deepening of power market reform; the present invention can effectively improve the income level of emerging market entities such as distribution network side microgrids, new energy storage, and distributed photovoltaics by constructing a multi-type subject transaction clearing model for the distribution network side market based on the cooperative game Nash bargaining theory, so as to give full play to the resource complementarity advantages between local energy producers and sellers and hedge the market risks caused by price fluctuations in the power market; the present invention is aimed at In view of the problems existing in the distribution network market, such as complex transaction mode, insufficient flexibility, limited transaction scope, and relatively single market players, a distribution network market transaction mode with a double-layer transaction structure connecting the power market and the distributed market is established based on Nash bargaining to improve the flexibility and economy of emerging market players such as distributed photovoltaics and independent energy storage in the distribution network market, thereby further promoting the absorption of new energy and improving the safe operation level of the power grid; the present invention simulates and deploys the optimal electricity trading strategy to the distribution network market, and predicts the market fluctuation information on the distribution network side, so as to help users better understand the potential fluctuations in electricity market prices and demand, and adaptively adjust the electricity trading strategy to better adapt to changes in the electricity market. At the same time, an effective feedback loop is established to continuously adjust the prediction model according to the performance of the electricity trading strategy to optimize market response and transaction efficiency.

[0169] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A distribution network side power dispatching method based on power spot market collaboration, characterized in that: The distribution network side power dispatching method based on power spot market coordination includes the following steps: S1. Based on cooperative game rational trading behavior, establish a distribution network side market trading model based on a two-tier trading structure; S2. Establish the price signal coupling between the power market and the distribution network market, and define the total transaction volume of the distribution network market players in the two-level market; S3. Establish a cost optimization model for the transactions of the distribution network side market players in the two-level market, and build a transaction clearing model for multiple types of players in the distribution network side market in combination with the Nash bargaining protocol; S4. Decompose the transaction clearing model of multiple types of entities in the distribution network market, and optimize and solve the sub-problems generated after the decomposition to obtain the optimal power trading strategy of multiple types of entities in the distribution network market; S5. Simulate and deploy the optimal electricity trading strategy in the distribution network market, and execute power dispatch in the distribution network market based on the fluctuation of the distribution network market.

2. A distribution network side power dispatching method based on power spot market coordination according to claim 1, characterized in that: The calculation formula for the total transaction volume of the distribution network side market players in the two-level market is: C trade,t =ΣQ L,t P L,t +Σ(Q D,t -Q L,t )P D,t +Σ(Q R,t -Q D,t )P R,t +C DMO,t In the formula, C trade,t represents the market transaction volume of the distribution network side market players in time period t, Q L,t P represents the amount of electricity in time period t signed by the market participants on the distribution network side in the medium and long-term electricity market; L,t It represents the electricity price in time period t signed by the market players on the distribution network side in the medium and long-term electricity market; Q D,t P represents the electricity consumption reported by the spot market in time period t; D,t P represents the node electricity price formed after the spot market is cleared in time period t; R,t represents the node electricity price formed after the real-time spot market transactions in time period t are cleared; Q R,t represents the amount of electricity traded through the power grid during period t; C DMO,t It represents the transaction volume of the distributed power market on the distribution network side by the market entities on the distribution network side; L represents the medium and long-term market; D represents the day-ahead market; and R represents the real-time market.

3. A method for power dispatching on the distribution network side based on power spot market coordination according to claim 2, characterized in that: The establishment of a cost optimization model for transactions between distribution network side market entities and two-level markets, and the construction of a transaction clearing model for multiple types of entities in the distribution network side market in combination with the Nash bargaining protocol, include the following steps: S31. Establish a cost optimization model for microgrid transactions in two-level markets, a cost optimization model for energy storage power stations in two-level markets, and a cost optimization model for distributed photovoltaic transactions in two-level markets, respectively, wherein the market entities on the distribution network side include at least energy storage power stations, distributed photovoltaics, and microgrids; S32. Construct a Nash bargaining standard model based on the Nash bargaining protocol expressed in terms of operating costs; S33. Substitute the cost optimization model of microgrid transactions in the two-level market, the cost optimization model of energy storage power stations in the two-level market, and the cost optimization model of distributed photovoltaic transactions in the two-level market into the Nash bargaining standard model to obtain the transaction clearing model of multiple types of entities in the distribution network side market.

4. A distribution network side power dispatching method based on power spot market coordination according to claim 3, characterized in that: The expression of the cost optimization model of the microgrid in the two-level market transaction is: In the formula, C i,mg-grid represents the cost of microgrid i purchasing electricity from the grid; C i,gas represents the gas cost consumed by the CHP unit in microgrid i; C i,co2 represents the CO2 treatment cost of the CHP unit inside microgrid i; I i,mg-grid I represents the revenue of microgrid i from selling electricity to the grid; i,grn represents the green electricity revenue of microgrid i; C i,j represents the transaction cost between microgrid i and energy storage power station j, C i,k represents the transaction cost between microgrid i and distributed photovoltaic k; T represents the cost calculation cycle; represents the load power of microgrid i; represents the day-ahead electricity purchase amount of microgrid i; represents the medium- and long-term contract electricity price of microgrid i; represents the day-ahead clearing electricity price of the load power of microgrid i; represents the gas consumption of CHP unit in microgrid i at time t; p gas represents the unit gas price of CHP unit in microgrid i at time t; p i,co2 represents the unit CO2 treatment cost of the CHP unit inside microgrid i at time t; represents the power sold by microgrid i to the grid at time t; p i,mg-grid represents the electricity price sold by microgrid i to the grid at time t; represents the photovoltaic power generation inside microgrid i at time t; p green It represents the photovoltaic power generation and unit green electricity revenue inside microgrid i at time t.

5. A method for power dispatching on the distribution network side based on power spot market coordination according to claim 4, characterized in that: The expression of the cost optimization model of the energy storage power station in the two-level market transaction is: In the formula, C j,life C represents the cost loss of energy storage station j during its life cycle due to charging and discharging; j,es-grid I represents the cost of energy storage j purchasing electricity from the grid; j,es-grid represents the revenue of energy storage power station j from selling electricity to the grid; C j,i represents the transaction cost between energy storage j and microgrid i; C j,k represents the transaction cost between energy storage j and distributed photovoltaic k; is the amount of electricity purchased by energy storage power station j at time t; represents the electricity sold by energy storage power station j at time t; p j,es represents the unit charge and discharge electricity cost of energy storage station j at time t; p j,es-grid represents the on-grid electricity price of energy storage power station j at time t; represents the electricity price purchased from the power grid by energy storage power station j at time t; represents the transaction power between microgrid i and energy storage j at time t; represents the amount of electricity purchased by energy storage j from distributed photovoltaic k at time t; represents the transaction price between energy storage j and microgrid i at time t; p j,k Represents the transaction electricity price between energy storage j and distributed photovoltaic k at time t.

6. A method for power dispatching on the distribution network side based on power spot market coordination according to claim 5, characterized in that: The expression of the cost optimization model of distributed photovoltaic transactions in the two-level market is: In the formula, C k,pv-grid represents the transaction cost between distributed photovoltaic k and the power grid; represents the transaction power between distributed photovoltaic k and the power grid at time t; represents the transaction electricity price between distributed photovoltaic k and the power grid at time t; represents the transaction power between distributed photovoltaic k and microgrid i at time t; p k,i represents the electricity price per unit of transaction electricity between distributed photovoltaic k and microgrid i; represents the transaction power between distributed photovoltaic power station k and energy storage power station j at time t; p k, j represents the electricity price per unit of transaction electricity between distributed photovoltaic k and energy storage power station j.

7. A method for power dispatching on the distribution network side based on power spot market coordination according to claim 6, characterized in that: The method of decomposing the transaction clearing model of multiple types of entities in the distribution network side market and optimizing and solving the sub-problems generated after the decomposition to obtain the optimal power transaction strategy of multiple types of entities in the distribution network side market includes the following steps: S41. Decompose the transaction clearing model of multiple types of entities in the distribution network market into the total cost minimization sub-problem f1 and the payment benefit maximization sub-problem f2; S42. Use sequential optimization technology to optimize and solve the two sub-problems after decomposition, and obtain the optimal electricity trading strategy for multiple types of entities in the distribution network market.

8. A method for power dispatching at the distribution network side based on power spot market coordination according to claim 7, characterized in that: The method of optimizing and solving the two decomposed sub-problems by using sequential optimization technology to obtain the optimal power trading strategy for multiple types of entities in the distribution network side market includes the following steps: S421, combining the constraints corresponding to sub-problem f1, using Cplex solver to solve sub-problem f1, and obtaining the transaction power of the market entities on the distribution network side in the two-level market; S422. Substitute the transaction volume of the market entities on the distribution network side in the two-level market into sub-problem f2, and solve sub-problem f2 using the fmincon solver in combination with the constraints corresponding to sub-problem f2 to obtain the transaction power price of the market entities on the distribution network side in the two-level market.

9. A method for power dispatching at the distribution network side based on power spot market coordination according to claim 8, characterized in that: The method of solving the subproblem f1 by using the Cplex solver includes the following steps: Extracting microgrid raw data and taking the microgrid raw data as input; Set the optimization model parameters, and use sub-problem f1 as the optimization objective function to establish the optimization models of the distribution network side market entities; Based on the Cplex solver format, the optimization model of the market players on the distribution network side is solved to obtain the electricity trading strategy, which is then sorted and summarized.

10. A method for power dispatching at the distribution network side based on power spot market coordination according to claim 9, characterized in that: The fmincon solver solves the subproblem f2 by the following steps: Set the maximum number of iterations and initialize the transaction electricity price of the distribution network side market players in the two-level market; The convergence tolerance of transaction price and payment benefit function is set based on the transaction price of the market players on the distribution network side in the two-level market; The electric energy trading strategy of sub-problem f1 is used as the electric energy input in sub-problem f2, and sub-problem f2 is iteratively solved using the fmincon solver; Determine whether the error value between the target optimization result and the current optimization result meets the convergence tolerance of the transaction electricity price and the convergence tolerance of the payment benefit function; if the error value is within the tolerance range, stop the iteration and return the final optimization result; if it exceeds the tolerance range, repeat the iterative calculation until the convergence conditions are met.

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