Energy internet two-way auction optimization regulation method based on multi-period network flow

Abstract

This invention relates to the field of energy internet optimization technology, specifically to a two-way auction optimization and control method for the energy internet based on multi-time-period network flow. The method includes: acquiring market participant declaration information and distribution network information; establishing an objective optimization function based on maximizing social welfare; transforming the optimization problem into a minimum-cost maximum-flow problem and constructing its network flow model; solving the minimum-cost maximum-flow problem to clear the market, obtaining one-to-one matching transactions between buyers and sellers, thus more clearly and transparently depicting the market equilibrium process dynamically; introducing a reduction trading mechanism to ensure incentive compatibility; eliminating invalid transactions based on the market marginal price; and calculating the full-cost electricity price based on the market marginal price and the distribution costs between the transactions. This ultimately forms a two-way auction optimization and control method for the energy internet that balances efficiency and fairness.

Description

Technical Field

[0001] This invention relates to the field of energy internet optimization technology, and more specifically, to a bidirectional auction optimization and control method for energy internet based on multi-time period network flow. Background Technology

[0002] The energy internet connects countless energy supplies and demands through the power grid and the internet, realizing the interconnection of everything. It inherently possesses the ability to optimize resource allocation, but this only completes the technical construction. The application of technology requires the guarantee of mechanisms; therefore, it is urgent to build an energy ecosystem trading mechanism that meets incentive compatibility requirements.

[0003] In fact, two-way auction mechanisms are a relatively common market mechanism in economics. Their characteristics enable efficient resource allocation and ensure the effectiveness of coordinated allocation. However, previous research has largely sought to comprehensively and meticulously characterize the distribution network based on existing electricity market mechanisms using digital technology. While existing nodal pricing-based electricity market mechanisms maximize social welfare through centralized optimization models, they lack effective profit-sharing methods and neglect the reasonable allocation of distribution costs. Their application in distribution network market transactions presents the following problems: the total network cost, including distribution costs, is not effectively allocated. Providing accurate price signals to market participants is crucial for market mechanism design, and market transactions that ignore "transaction costs" inevitably lead to "free-riding" and market distortions. Especially with the current calls for "remote electricity sales," the reasonable allocation of distribution costs must be clarified. Based on these considerations, a two-way auction optimization and control method for the energy internet based on multi-period network flows is urgently needed to address these issues. Summary of the Invention

[0004] The purpose of this invention is to provide an energy internet bidirectional auction optimization and control method based on multi-time period network flow. By constructing an energy ecosystem trading mechanism that differs from the traditional node electricity price mechanism, the optimization problem is transformed into a network flow model for solution, making the market equilibrium process clearer and more transparent. Ultimately, it achieves reasonable allocation of transaction costs, effective distribution of social welfare, and incentives for market participants to make genuine bids based on the scarcity of transmission capacity, thereby solving the problems pointed out in the background art.

[0005] The embodiments of the present invention are achieved through the following technical solution: a two-way auction optimization and control method for the energy internet based on multi-time period network flow, comprising the following steps:

[0006] Step 1: Obtain market participant declaration information and distribution network information, establish an objective optimization function based on maximizing social welfare, and determine the constraints;

[0007] Step 2: Transform the optimization problem into a minimum cost maximum flow problem and construct its network flow model;

[0008] Step 3: By finding the minimum cost augmenting path in the residual network and determining the maximum flow on the path, solve the minimum cost maximum flow problem to clear the market and obtain a one-to-one matching transaction pair between the electricity buyer and seller.

[0009] Step 4: Eliminate invalid trades based on market marginal prices;

[0010] Step 5: Calculate the full-cost electricity price based on the market marginal price and the distribution cost between the transaction pairs.

[0011] According to a preferred embodiment, the market participants in step 1 include electricity purchasers, electricity sellers, and energy storage providers, and the expression of the objective optimization function based on maximizing social welfare is as follows:

[0012]

[0013] In the above formula, Indicates the electricity purchaser. Indicates the electricity seller. Indicates energy storage, Indicates time period, Indicates the electricity seller Application period Price and volume information Indicates the electricity purchaser With electricity seller The transaction pairs formed The cost of power distribution between them Indicates the electricity purchaser Application period Price and volume information Indicates time period Power purchaser and electricity sellers Intermittent transaction volume Indicates energy storage The declared discharge cost, Indicates the electricity purchaser With energy storage The transaction pairs formed The cost of power distribution between them Indicates time period Power purchaser and energy storage Intermittent transaction volume Indicates energy storage With electricity seller The transaction pairs formed The cost of power distribution between them Indicates energy storage The declared charging cost, Indicates time period Electricity seller and energy storage Intermittent transaction volume.

[0014] According to a preferred embodiment, the constraints determined in step 1 include: cumulative transaction volume constraints between the power purchaser and seller, line power flow constraints, maximum charging and discharging power constraints of energy storage, and state of charge constraints of energy storage.

[0015] According to a preferred embodiment, the constraint conditions include:

[0016] The cumulative transaction volume constraint for both the electricity purchaser and seller is:

[0017]

[0018]

[0019] In the above formula, This indicates the electricity volume purchased by the node. Indicates the electricity volume applied for at each node;

[0020] The power flow constraints of the line are:

[0021]

[0022]

[0023] In the above formula, Indicates the line During the period Maximum transmission capacity Indicates the line During the period The actual current size, Indicates the electricity seller For the line The power flow transfer allocation factor, Indicates the electricity purchaser For the line The power flow transfer allocation factor, Indicates energy storage For the line The power flow transfer allocation factor;

[0024] The maximum charge and discharge power constraint for energy storage is:

[0025]

[0026]

[0027] In the above formula, Indicates energy storage Maximum charging power, Indicates energy storage Maximum discharge power;

[0028] The state of charge constraint for energy storage is:

[0029]

[0030] In the above formula, Indicates energy storage The initial state of charge, Indicates energy storage The minimum state of charge, Indicates energy storage Maximum state of charge, Indicates energy storage capacity, Indicates time period Electricity seller and energy storage Intermittent transaction volume Indicates time period Electricity seller and energy storage Intermittent transaction volume.

[0031] According to a preferred embodiment, the minimum cost maximum flow problem in step 2 is expressed as follows:

[0032]

[0033] The corresponding constraints include edge capacity constraints and flow conservation constraints. The edge capacity constraints are as follows:

[0034]

[0035] The flow conservation constraint is:

[0036]

[0037] In the above formula, Represents a set of nodes. , Indicates the number of time periods. Indicates time period Node set, , Indicates the actual number of nodes in the distribution network. Indicates the source point, Indicates the remittance point. Represents a set of virtual nodes. , express The cost per unit of traffic on the edge. express Minimum capacity of edges, express Maximum capacity of edges express The actual trend on the side.

[0038] According to a preferred embodiment, step 3 specifically includes:

[0039] 3.1. The network flow model is split into two sub-networks containing only unidirectional edges: the forward flow network and the reverse flow network.

[0040] 3.2 Determine the cost matrices for the forward and reverse power flow networks;

[0041] 3.3 Determine the source point based on the cost matrix Exchange Point Minimum cost path between ;

[0042] 3.4 Determine the obtained path Maximum flow on;

[0043] 3.5. Based on the sum of the flows of corresponding edges in the forward and reverse power flow networks, the flow of the edges is corrected.

[0044] 3.6 Repeat steps 3.3 to 3.5 until there are no augmenting paths in the current residual network, and obtain the minimum cost maximum flow of the network flow.

[0045] According to a preferred embodiment, the cost matrix of the forward power flow network is:

[0046]

[0047] In the above formula, The cost matrix represents the cost of a forward-flowing network. Represents any edge of a forward-flowing network Traffic;

[0048] The cost matrix of the reverse power flow network is:

[0049]

[0050] In the above formula, This represents the cost matrix of a reverse power flow network. Represents any edge of a reverse power flow network Traffic.

[0051] According to a preferred embodiment, the obtained path is determined in step 3.4. The expression for the maximum flow on is as follows:

[0052]

[0053]

[0054] In the above formula, Representing a path Up to the actual trend Direction and Path Edges with the same direction, Representing a path Up to the actual trend Direction and Path Sides in opposite directions, Indicates the obtained path Maximum flow on, express The available capacity of the edge, express Maximum capacity of the edges.

[0055] According to a preferred embodiment, step 4 specifically includes:

[0056] 4.1 Calculate the marginal market price based on the average of the minimum purchase price and the maximum sales price in the transaction pairs. The expression is as follows:

[0057]

[0058]

[0059]

[0060] In the above formula, Indicates the transaction pair The corresponding electricity purchase price for the electricity buyer, Indicates the transaction pair The corresponding electricity sales price from the electricity seller, This indicates the trading volume of each traded pair obtained after the clearing process. Indicates the electricity purchaser Quotation, Electricity seller Quotation;

[0061] 4.2. Eliminate invalid transaction pairs based on the market marginal price. Invalid transaction pairs include: transaction pairs formed between marginal market participants and other market participants, and transaction pairs where the electricity purchase price is lower than the market marginal price. Or the electricity price is greater than the marginal market price. The transaction pairs, wherein the marginal market members are the market members who declare the minimum purchase price and the maximum sales price of electricity.

[0062] According to a preferred embodiment, the expression for calculating the full-cost electricity price in step 5 is as follows:

[0063]

[0064]

[0065] In the above formula, Indicates the transaction pair China Power Purchase The actual full cost electricity price paid. Indicates the transaction pair China Electricity Sales The full-cost electricity price that represents the actual revenue.

[0066] The technical solution of the present invention has at least the following advantages and beneficial effects: (1) The method provided by the present invention constructs a corresponding network flow model based on the information declared by market members and the relevant information of the distribution network, clears the market by solving the minimum cost maximum flow problem, forms a transaction pair that is matched one by one between the power purchaser and the power seller, and then introduces a reduction transaction mechanism to ensure the incentive compatibility of the mechanism, and can achieve accurate identification of the value creation and cost allocation of market members; (2) Market members allocate the corresponding power distribution cost according to their actual utilization of the power distribution equipment, and the social welfare created by any transaction pair is completely allocated by the power purchaser and the power seller. Attached Figure Description

[0067] Figure 1 This is a flowchart illustrating the energy internet bidirectional auction optimization and control method provided in Embodiment 1 of the present invention.

[0068] Figure 2 This is a flowchart provided for Embodiment 1 of the present invention;

[0069] Figure 3 This is a diagram of a single-time network flow model provided in an embodiment of the present invention;

[0070] Figure 4 This is a diagram illustrating a multi-time-period network flow model provided in an embodiment of the present invention.

[0071] Figure 5 Forward and reverse power flow network diagrams provided in embodiments of the present invention; Detailed Implementation

[0072] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0073] Example 1

[0074] See Figure 1 As shown, Figure 1 This is a flowchart illustrating the energy internet two-way auction optimization and control method provided in an embodiment of the present invention.

[0075] This invention provides a method for optimizing and controlling bidirectional auctions in the energy internet based on multi-time period network flows, comprising the following steps:

[0076] Step 1, Reference Figure 2 As shown, market participants submit sealed bids, declare quantity and price information, and obtain market participant declaration information and distribution network related information. In this embodiment, market participants specifically include electricity purchasers, electricity sellers, and energy storage. Market participants can choose to purchase electricity from other market participants as electricity purchasers or sell electricity to other market participants as electricity sellers at any time period based on the forecast results.

[0077] In one embodiment of this example, the market participant declaration information includes the energy-price curves declared by both the power purchaser and seller in segments, as well as the charging and discharging costs declared by the energy storage provider; the distribution network related information includes the distribution network topology, line transmission capacity constraints, and distribution costs.

[0078] Furthermore, a bidirectional auction-based optimal scheduling problem for the energy internet, based on multi-time-period network flows, is constructed, including:

[0079] The objective optimization function based on maximizing social welfare is established as follows:

[0080]

[0081] In the above formula, Indicates the electricity purchaser. Indicates the electricity seller. Indicates energy storage, Indicates time period, Indicates the electricity seller Application period Price and volume information Indicates the electricity purchaser With electricity seller The transaction pairs formed The cost of power distribution between them Indicates the electricity purchaser Application period Price and volume information Indicates time period Power purchaser and electricity sellers Intermittent transaction volume Indicates energy storage The declared discharge cost, Indicates the electricity purchaser With energy storage The transaction pairs formed The cost of power distribution between them Indicates time period Power purchaser and energy storage Intermittent transaction volume Indicates energy storage With electricity seller The transaction pairs formed The cost of power distribution between them Indicates energy storage The declared charging cost, Indicates time period Electricity seller and energy storage Intermittent transaction volume.

[0082] The constraints are defined, including the cumulative transaction volume constraints between the power purchaser and seller, line power flow constraints, the maximum charging and discharging power constraints of energy storage, and the state of charge constraints of energy storage.

[0083] Among the constraints:

[0084] If the total transaction volume of an electricity purchaser does not exceed the electricity volume it has declared, it is represented as follows:

[0085]

[0086] If the cumulative transaction volume of an electricity seller does not exceed the electricity volume it has declared, it is represented as follows:

[0087]

[0088] In the above formula, This indicates the electricity volume purchased by the node. Indicates the electricity volume applied for at each node;

[0089] The line power flow does not exceed the maximum transmission capacity, expressed as:

[0090]

[0091]

[0092] In the above formula, Indicates the line During the period Maximum transmission capacity Indicates the line During the period The actual current size, Indicates the electricity seller For the line The power flow transfer allocation factor, Indicates the electricity purchaser For the line The power flow transfer allocation factor, Indicates energy storage For the line The power flow transfer allocation factor;

[0093] The maximum discharge power constraint for energy storage is expressed as:

[0094]

[0095] The maximum charging power constraint for energy storage is expressed as:

[0096]

[0097] In the above formula, Indicates energy storage Maximum charging power, Indicates energy storage Maximum discharge power;

[0098] Ensuring that the state of charge of energy storage remains within a defined range is expressed as:

[0099]

[0100] In the above formula, Indicates energy storage The initial state of charge, Indicates energy storage The minimum state of charge, Indicates energy storage Maximum state of charge, Indicates energy storage capacity, Indicates time period Electricity seller and energy storage Intermittent transaction volume Indicates time period Electricity seller and energy storage Intermittent transaction volume.

[0101] Step 2: Transform the above optimization problem into a minimum cost maximum flow problem and construct its network flow model. The details are as follows:

[0102] The minimum-cost maximum flow problem is represented as:

[0103]

[0104] The corresponding constraints include edge capacity constraints and flow conservation constraints. The edge capacity constraints are as follows:

[0105]

[0106] The flow conservation constraint is:

[0107]

[0108] In the above formula, Represents a set of nodes. , Indicates the number of time periods. Indicates time period Node set, , Indicates the actual number of nodes in the distribution network. Indicates the source point, Indicates the remittance point. Represents a set of virtual nodes. , express The cost per unit of traffic on the edge. express Minimum capacity of edges, express Maximum capacity of edges express The actual flow of the edge, the flow conservation constraint, means that for any node in the network flow that is not a source or sink, the sum of all flows to its adjacent nodes is 0.

[0109] It should be noted that the edges in the network flow model correspond to the actual power distribution lines. It is a bidirectional edge, and its maximum capacity is the maximum transmission capacity of the line. Minimum capacity is The cost is the power distribution cost. ; Pointing from the electricity purchase node to the virtual node The maximum capacity of the unidirectional edge is the electricity purchased by the node. The minimum capacity is 0, and the cost is the negative of the node's quoted price. ; by virtual nodes A one-way edge pointing to a power sales node, with a maximum capacity equal to the amount of electricity the node can sell. Minimum capacity is 0, cost is the node quote. The sub-network flow model at different time periods is connected through energy storage nodes. The unidirectional edge formed by the interconnection of energy storage nodes at different time periods always points from the previous time period to the next time period, and the maximum capacity is the energy storage capacity. Minimum capacity is 0, cost is energy storage The sum of charging and discharging costs .

[0110] This method enables accurate identification of value creation and cost allocation among market participants. Market participants allocate corresponding power distribution costs based on their actual utilization of power distribution equipment, and the social welfare created by any transaction pair is entirely distributed between the power purchaser and seller.

[0111] refer to Figure 3 As shown in the figure, the network flow model corresponds to a single-time optimization problem of a 7-node distribution network system. Corresponding to the actual nodes of the distribution network, , For virtual nodes, For electricity sales nodes, This refers to the power purchase node. Bidirectional edges between actual nodes in the distribution network correspond to actual distribution lines, while power purchase / sales nodes are connected to virtual nodes to form unidirectional edges. The capacity and cost of the edges are shown in the figure. Considering the symmetry of the power system, the absolute values ​​of the maximum and minimum capacities of bidirectional edges are the same, and only their absolute values ​​are shown in the figure; the minimum capacity of unidirectional edges is 0, and is omitted from the figure without being separately marked.

[0112] refer to Figure 4 As shown in the figure, the network flow model corresponds to a two-time-period joint optimization problem of a 7-node distribution network system. Corresponding to the actual nodes of the distribution network, , For virtual nodes, For electricity sales nodes, For electricity purchase nodes, This refers to energy storage nodes. Bidirectional edges between actual nodes in the distribution network correspond to actual distribution lines, while power purchase / sales nodes are connected to virtual nodes to form unidirectional edges. Energy storage nodes at different times are connected to form unidirectional edges. The capacity and cost of each edge are shown in the figure. Considering the symmetry of the power system, the absolute values ​​of the maximum and minimum capacities of bidirectional edges are the same; only their absolute values ​​are shown in the figure. The minimum capacity of a unidirectional edge is 0, and is omitted from the figure and not separately marked.

[0113] This method, by prioritizing the allocation of line transmission capacity as a scarce resource to the trading pair that creates the greatest social welfare, can incentivize both buyers and sellers to make rational bids.

[0114] Step 3: By finding the minimum-cost augmenting path in the residual network and determining the maximum flow on the path, the minimum-cost maximum flow problem is solved to clear the market and obtain one-to-one matching transactions between the electricity buyer and seller. Specifically, this includes:

[0115] 3.1. The network flow model is split into two sub-networks containing only unidirectional edges: a forward flow network and a reverse flow network. See details. Figure 5 As shown;

[0116] 3.2 Determine the cost matrices for the forward power flow network and the reverse power flow network, wherein the cost matrix for the forward power flow network is:

[0117]

[0118] In the above formula, The cost matrix represents the cost of a forward-flowing network. Represents any edge of a forward-flowing network Traffic;

[0119] The cost matrix of the reverse power flow network is:

[0120]

[0121] In the above formula, This represents the cost matrix of a reverse power flow network. Represents any edge of a reverse power flow network Traffic;

[0122] It should be noted that in the residual network, any one-way edge of the subnetwork corresponds to two virtual arcs of opposite directions, and the capacity and cost attributes of the arcs are determined by the above formula.

[0123] 3.3 Determine the source point based on the cost matrix Exchange Point Minimum cost path between That is, to find the minimum cost augmenting path in the residual network;

[0124] 3.4 Determine the obtained path The maximum flow on is expressed as follows:

[0125]

[0126]

[0127] In the above formula, Representing a path Up to the actual trend Direction and Path Edges with the same direction, Representing a path Up to the actual trend Direction and Path Sides in opposite directions, Indicates the obtained path Maximum flow on, express The available capacity of the edge, express Maximum capacity of edges;

[0128] 3.5. Based on the sum of the flows of corresponding edges in the forward and reverse flow networks, the flow of the edges is corrected, that is, the flow of the edges on the minimum cost maximum flow path is updated, and a new residual network is formed, as shown in the following expression:

[0129]

[0130]

[0131] In the above formula, This represents the path in a positive current network. Same edges, This represents the path in a positive current network. Opposite edge, Represents the path in a reverse power flow network Same edges, Represents the path in a reverse power flow network Opposite edges;

[0132] 3.6. Determine if the network has an augmenting path. If so, proceed to step 3.3. If not, repeat steps 3.3 to 3.5 until the current residual network has no augmenting path, obtaining the minimum cost maximum flow of the network flow. Otherwise, further determine... Is it equal to 0, when hour, Then proceed with the next steps.

[0133] This method forms market clearing by solving the minimum-cost maximum flow problem, which can more clearly and transparently depict the market equilibrium process dynamically.

[0134] Step 4: Eliminate invalid trades based on market marginal prices. This includes:

[0135] 4.1 Calculate the marginal market price based on the average of the minimum purchase price and the maximum sales price in the transaction pairs. The expression is as follows:

[0136]

[0137]

[0138]

[0139] In the above formula, Indicates the transaction pair The corresponding electricity purchase price for the electricity buyer, Indicates the transaction pair The corresponding electricity sales price from the electricity seller, This indicates the trading volume of each traded pair obtained after the clearing process. Indicates the electricity purchaser Quotation, Electricity seller Quotation;

[0140] 4.2. Invalid transaction pairs are eliminated based on the marginal market price to ensure the incentive compatibility of the trading mechanism. Invalid transaction pairs include: transaction pairs formed between marginal market participants and other market participants, and transaction pairs where the electricity purchase price is lower than the marginal market price. Or the electricity price is greater than the marginal market price. The transaction pairs, wherein the marginal market members are the market members who declare the minimum purchase price and the maximum sales price of electricity.

[0141] Solve the minimum cost maximum flow problem again, that is, repeat step 3 to complete the formal market clearing.

[0142] The method provided by this invention constructs a corresponding network flow model based on the information declared by market participants and relevant information of the distribution network. It clears the market by solving the minimum cost maximum flow problem, forming a one-to-one matching transaction pair between the power purchaser and the power seller. Then, it introduces a reduction trading mechanism to ensure the incentive compatibility of the mechanism.

[0143] Step 5: Calculate the full-cost electricity price based on the marginal market price and the distribution cost between the transaction pairs. The expression for calculating the full-cost electricity price is as follows:

[0144]

[0145]

[0146] In the above formula, Indicates the transaction pair China Power Purchase The actual full cost electricity price paid. Indicates the transaction pair China Electricity Sales The full-cost electricity price that represents the actual revenue.

[0147] In summary, the energy internet two-way auction optimization and control method provided by this invention can balance efficiency and fairness.

[0148] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for optimizing regulation and control of a power internet based on multi-period network flow bidirectional auction, characterized in that, Includes the following steps: Step 1: Obtain market participant declaration information and distribution network information, establish an objective optimization function based on maximizing social welfare, and determine the constraints; Step 2: Transform the optimization problem into a minimum cost maximum flow problem and construct its network flow model; Step 3: By finding the minimum cost augmenting path in the residual network and determining the maximum flow on the path, solve the minimum cost maximum flow problem to clear the market and obtain a one-to-one matching transaction pair between the electricity buyer and seller. Step 4: Eliminate invalid trades based on market marginal prices; Step 5: Calculate the full-cost electricity price based on the marginal market price and the distribution cost between the transaction pairs; Step 3 specifically includes: 3.

1. The network flow model is split into two sub-networks containing only unidirectional edges: the forward flow network and the reverse flow network. 3.2 Determine the cost matrices for the forward and reverse power flow networks; 3.3 Determine the source point based on the cost matrix Exchange Point Minimum cost path between ; 3.4 Determine the obtained path Maximum flow on; 3.

5. Based on the sum of the flows of corresponding edges in the forward and reverse power flow networks, the flow of the edges is corrected. 3.6 Repeat steps 3.3 to 3.5 until there are no augmenting paths in the current residual network, and obtain the minimum cost maximum flow of the network flow; The cost matrix of the forward power flow network is as follows: In the above formula, The cost matrix represents the cost of a forward-flowing network. Represents any edge of a forward-flowing network Traffic, express The cost per unit of traffic on the edge. express Maximum capacity of edges; The cost matrix of the reverse power flow network is: In the above formula, This represents the cost matrix of a reverse power flow network. Represents any edge of a reverse power flow network Traffic.

2. The energy internet bidirectional auction optimization and control method based on multi-time period network flow as described in claim 1, characterized in that, The market participants mentioned in step 1 include electricity purchasers, electricity sellers, and energy storage providers. The expression for the objective optimization function based on maximizing social welfare is as follows: In the above formula, Indicates the electricity purchaser. Indicates the electricity seller. Indicates energy storage, Indicates time period, Indicates the electricity seller Application period Price and volume information Indicates the electricity purchaser With electricity seller The transaction pairs formed The cost of power distribution between them Indicates the electricity purchaser Application period Price and volume information Indicates time period Power purchaser and electricity sellers Intermittent transaction volume Indicates energy storage The declared discharge cost, Indicates the electricity purchaser With energy storage The transaction pairs formed The cost of power distribution between them Indicates time period Power purchaser and energy storage Intermittent transaction volume Indicates energy storage With electricity seller The transaction pairs formed The cost of power distribution between them Indicates energy storage The declared charging cost, Indicates time period Electricity seller and energy storage Intermittent transaction volume.

3. The energy internet bidirectional auction optimization and control method based on multi-time period network flow as described in claim 2, characterized in that, The constraints determined in step 1 include: the cumulative transaction volume constraints between the power purchaser and seller, the power flow constraints of the transmission lines, the maximum charging and discharging power constraints of the energy storage, and the state of charge constraints of the energy storage.

4. The energy internet bidirectional auction optimization and control method based on multi-time period network flow as described in claim 3, characterized in that, Among the constraints: The cumulative transaction volume constraint for both the electricity purchaser and seller is: In the above formula, This indicates the electricity volume purchased by the node. Indicates the electricity volume applied for at each node; The power flow constraints of the line are: In the above formula, Indicates the line During the period Maximum transmission capacity Indicates the line During the period The actual current size, Indicates the electricity seller For the line The power flow transfer allocation factor, Indicates the electricity purchaser For the line The power flow transfer allocation factor, Indicates energy storage For the line The power flow transfer allocation factor; The maximum charge and discharge power constraint for energy storage is: In the above formula, Indicates energy storage Maximum charging power, Indicates energy storage Maximum discharge power; The state of charge constraint for energy storage is: In the above formula, Indicates energy storage The initial state of charge, Indicates energy storage The minimum state of charge, Indicates energy storage Maximum state of charge, Indicates energy storage capacity, Indicates time period Electricity seller and energy storage Intermittent transaction volume Indicates time period Power purchaser and energy storage Intermittent transaction volume.

5. The energy internet bidirectional auction optimization and control method based on multi-time period network flow as described in claim 1, characterized in that, The expression for the minimum cost maximum flow problem in step 2 is as follows: The corresponding constraints include edge capacity constraints and flow conservation constraints. The edge capacity constraints are as follows: The flow conservation constraint is: In the above formula, Represents a set of nodes. , Indicates the number of time periods. Indicates time period Node set, , Indicates the actual number of nodes in the distribution network. Indicates the source point, Indicates the remittance point. Represents a set of virtual nodes. , express Minimum capacity of edges, express The actual trend on the side.

6. The energy internet bidirectional auction optimization control method based on multi-time period network flow as described in claim 1, characterized in that, In step 3.4, the obtained path is determined. The expression for the maximum flow on is as follows: In the above formula, Representing a path Up to the actual trend Direction and Path Edges with the same direction, Representing a path Up to the actual trend Direction and Path Sides in opposite directions, Indicates the obtained path Maximum flow on, express The available capacity of the edge, express Maximum capacity of the edges.

7. The energy internet bidirectional auction optimization control method based on multi-time period network flow as described in any one of claims 1 to 6, characterized in that, Step 4 specifically includes: 4.1 Calculate the marginal market price based on the average of the minimum purchase price and the maximum sales price in the transaction pairs. The expression is as follows: In the above formula, Indicates the transaction pair The corresponding electricity purchase price for the electricity buyer, Indicates the transaction pair The corresponding electricity sales price from the electricity seller, This indicates the trading volume of each traded pair obtained after the clearing process. Indicates the electricity purchaser Quotation, Electricity seller Quotation, Indicates the electricity purchaser With electricity seller The transaction pairs formed The cost of power distribution between areas; 4.

2. Eliminate invalid transaction pairs based on the market marginal price. Invalid transaction pairs include: transaction pairs formed between marginal market participants and other market participants, and transaction pairs where the electricity purchase price is lower than the market marginal price. Or the electricity price is greater than the marginal market price. The transaction pairs, wherein the marginal market members are the market members who declare the minimum purchase price and the maximum sales price of electricity.

8. The energy internet bidirectional auction optimization control method based on multi-time period network flow as described in claim 7, characterized in that, The expression for calculating the full-cost electricity price in step 5 is as follows: In the above formula, Indicates the transaction pair China Power Purchase The actual full cost electricity price paid. Indicates the transaction pair China Electricity Sales The full-cost electricity price that represents the actual revenue.

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