A trading method for a two-tier market structure with multi-subject end-to-end transactions on the distribution network side

Abstract

The present invention discloses a trading method for a two-tier market architecture for multi-subject end-to-end transactions on the distribution network side. The upper layer is a DLMP centralized pricing model trading layer, which calculates the marginal electricity price of different nodes in the distribution network based on the node load, providing a base price for the lower layer multi-subject distributed end-to-end trading market model; the lower layer multi-subject distributed end-to-end trading market model trading layer implements distributed transactions for new users within the node, determines the node load based on the transaction results, and returns them to the upper layer DLMP centralized pricing model for centralized clearing. The two-tier market architecture achieves the coupled coordination of centralized clearing and distributed end-to-end transactions; it provides a feasible market architecture for large-scale new users, and improves the economy and flexibility of distribution network operation and user-side resources.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network pricing, and in particular to a trading method of a double-layer market architecture for multi-subject end-to-end transactions on the distribution network side. Background Art

[0002] As climate change and environmental pollution increasingly impact human society, major countries around the world are transitioning their energy and power systems toward cleaner, lower-carbon ones. This structural shift in power systems, characterized by a high proportion of renewable energy, will reshape the global electricity market. In recent years, renewable energy has been connected to the grid on a large scale. The distributed energy market has numerous participants, resulting in high levels of uncertainty and randomness. Traditional electricity market mechanisms are ill-suited to the grid-connected operation and trading of large-scale distributed renewable energy. Traditional retailers' centralized customer management model struggles to meet the growing demands for computational complexity and information privacy, while also struggling to reconcile the diverging interests of various participants. Summary of the Invention

[0003] The purpose of the present invention is to provide a trading method for a two-tier market architecture with multi-subject end-to-end transactions on the distribution network side, which utilizes the advantages of end-to-end transactions, improves the existing DLMP pricing model, and adopts a lower-layer multi-subject distributed end-to-end transaction market model and an upper-layer DLMP centralized pricing model for collaborative optimization, providing a feasible market architecture for large-scale new users, and improving the economy and flexibility of distribution network operation and user-side resources.

[0004] To achieve the above objectives, the present invention provides a trading method for a two-tier market architecture with multi-party end-to-end transactions on the distribution network side, comprising the following steps:

[0005] S1. In a given distribution network system with known topology and line parameters, select distribution network nodes. Users within the distribution network nodes negotiate and interact with each other to conduct end-to-end transactions to share energy. A multi-agent distributed end-to-end transaction market model is established to form a distributed end-to-end transaction strategy at the distribution station level.

[0006] S2. Based on the transaction results of the established lower-level multi-agent distributed end-to-end transaction market model, the external transaction volume between the substation and the distribution network operator is used as the power injection of the node into the upper-level DLMP centralized pricing model. Using the expression of the line flow, the upper-level DLMP centralized pricing model that takes into account the injected power of the lower-level substation is constructed;

[0007] S3. Based on the objectives and constraints of the upper-layer DLMP centralized pricing model, a Lagrangian function containing its shadow price information is constructed to solve the marginal electricity price of the distribution network node;

[0008] S4. Solve the lower-layer multi-agent distributed end-to-end transaction market model, send the updated node power information to the upper-layer DLMP centralized pricing model, and optimize the transaction behavior between producers and consumers to minimize the total cost of producers and consumers in the autonomous area;

[0009] S5. The calculation results of the substation transaction affect the distribution network nodes connected to the autonomous substation, and the power injection and outflow of the nodes change, thereby updating the upper-layer DLMP centralized pricing model for the entire distribution network.

[0010] S6. Verify the effectiveness and analyze the results of the constructed distributed and centralized collaborative two-tier model, focusing on the cost-effectiveness of the two-tier model pricing mechanism and the location impact of the lower-tier substation.

[0011] Preferably, in said S1, the method for establishing the multi-agent distributed end-to-end transaction market model is specifically as follows:

[0012] Establish the cost-optimal objective function:

[0013]

[0014] Among them, m and n refer to the user producers and consumers within the autonomous area Ω, represents the electricity purchased by producer n at time t, C i (·) is the cost function of the prosumer, G(·) is the transaction revenue function of the energy sold by the distribution network, represents the energy of interaction;

[0015] Constraints that limit the underlying multi-agent distributed end-to-end transaction market model:

[0016] (1) Energy balance constraints for producers and consumers:

[0017]

[0018] Formula (2) shows that the buying and selling transactions between producers and consumers and the import and export interactions with the public power grid satisfy the energy balance. Refers to the total amount of electricity purchased by prosumer n, represents the total electricity sales of prosumer n, is the load demand of prosumer n;

[0019] (2) Constraints on the power purchase and sales relationship between producers and consumers:

[0020]

[0021]

[0022]

[0023] Formula (3) and formula (4) define the expressions of the sold and purchased electricity with producer n as the consumer. represents the purchasing energy between prosumers m and n. Equation (5) shows that the transaction between prosumers m and n is positive with purchase;

[0024] (3) Constraints on the total amount of electricity purchased and sold by producers and consumers:

[0025]

[0026]

[0027] Formula (6) limits the upper and lower limits of electricity purchase by producers and consumers, where It represents the minimum amount of electricity that the producer and consumer n can purchase. represents the maximum amount of electricity purchased by producer / consumer n; Formula (7) limits the upper and lower limits of the electricity sold by the producer / consumer, where represents the minimum electricity sales of prosumer n, represents the maximum electricity sales amount of prosumer n.

[0028] Preferably, in said S2, the power flow of the distribution network is calculated

[0029] In a distribution network with end-to-end transactions between autonomous substations, the import and export power interaction between substations affects the energy transmitted by the public distribution network. This requires the customization of new models to improve node power imbalance and power flow inaccuracy.

[0030] The power flow expression is as follows:

[0031]

[0032]

[0033] Among them, pf l ,qf l is the active and reactive power flow of line l, NB is the number of nodes, i is the node number, SFP p ,SFQ p They are the sensitivity matrices of active and reactive power amplitudes with the change of node active power injection, and SFP q ,SFQ q are the sensitivity matrices of active and reactive power amplitudes with the change of node reactive power injection, P and Q represent active and reactive power matrices, respectively. tran It is the transaction power between the substation and the distribution network node.

[0034] Preferably, in S2, the method for establishing the upper-layer DLMP centralized pricing model is specifically as follows:

[0035] Formulate the objective function

[0036] The objective function of the upper DLMP centralized pricing model is composed of generator cost and load cost. A flexible pricing mechanism model is established that considers reactive power flow, voltage variation and energy trading in the subordinate substations. The objective function of the upper DLMP centralized pricing model is expressed as

[0037]

[0038] Restriction constraints

[0039]

[0040]

[0041]

[0042]

[0043]

[0044]

[0045]

[0046] where λ p ,λ q , and is the dual variable of the corresponding constraint; SFV p SFV q are the sensitivity matrices of node voltage relative to node active and reactive power injection respectively; V1 is the reference node voltage, V i,max 、V i,min Indicates the maximum and minimum values ​​of the node voltage, PL max and PL min Respectively represent the upper and lower limits of the line active power flow; QL max and QL min Respectively represent the upper and lower limits of the line reactive power flow, Indicates the maximum and minimum values ​​of the generator's active output. Indicates the maximum and minimum values ​​of the generator's reactive output;

[0047] In the upper-level DLMP centralized pricing model, Equations (11) and (12) are the active and reactive power balances of the distribution network taking into account the interactive energy injection of autonomous substations, respectively; constraint (13) is the voltage limit, and constraints (14) and (15) are the active and reactive power flow capacity limits of the line, in which an internal approximation method is introduced to reconvert the power circle limit into a polygon; constraints (16) and (17) give the output boundaries of the active and reactive output of the generator; in order to ensure the decomposability of the DLMP formula, no node power balance constraints are introduced in the proposed upper-level DLMP centralized pricing model; the DLMP is calculated using the Lagrangian function of the model in the upper-level DLMP centralized pricing model.

[0048] Preferably, in S3, the specific process of solving the marginal electricity price of the distribution network node includes:

[0049] Constructing Lagrangian functions

[0050]

[0051] In formula (18), NL is the number of lines, the first-order partial derivatives of active load and reactive load represent the active and reactive DLMP, respectively. The DLMP of active power and reactive power is decomposed into three price components, including energy price, congestion caused by active power and reactive power, and voltage support price, which are expressed as and

[0052] The active and reactive power price components of distribution network node k are as follows:

[0053]

[0054]

[0055] Preferably, the S4 specifically includes:

[0056] Solve the underlying multi-agent distributed end-to-end transaction market model:

[0057] The lower-level multi-agent distributed end-to-end trading market model is an autonomous end-to-end trading problem. Producers and consumers optimize their inter-user transaction volume and the interactive power of the distribution network without considering the operation of the distribution network. The lower-level multi-agent distributed end-to-end trading market model is summarized as the problem of minimizing the purchase cost of electricity for all producers and consumers in a substation. Under the transaction model of negotiated power purchase and sale between users, the optimal decision of energy trading is calculated, namely the transaction volume between producers and consumers and the interactive energy bought and sold between the substation and the external distribution network operator.

[0058] Transmit transaction information:

[0059] By solving the end-to-end transaction problem of the multi-agent distributed end-to-end transaction market model, the updated node power information is sent to the upper-level model, and the transaction behavior between prosumers is optimized to minimize the total cost of prosumers in the autonomous substation. After receiving the interactive node power of the substation, the distribution network operator uses the upper-level DLMP centralized pricing model to calculate the marginal price of the distribution network node. Based on the power flow injection model, taking into account the node power injection after the substation's autonomous optimization, the distribution network node electricity price is customized.

[0060] Solve the upper-layer DLMP centralized pricing model:

[0061] The upper layer addresses the DLMP distribution network pricing problem, taking into account the price impact of power loss compensation and the correlation between active and reactive power in the distribution network. The node pricing strategy is optimized under the interaction of active and reactive power in the distribution system. Nodes connected to autonomous substations are affected by substation transactions, causing changes in their power injection and outflow. This allows for the calculation and update of the DLMP for the entire distribution network.

[0062] Iterate the interaction to an equilibrium state:

[0063] The trading decisions of each producer and consumer will affect the optimization results of all participants. Therefore, there is a state of equilibrium. Neither the upper nor the lower level can further optimize their own goals by unilaterally changing their decisions. Once the equilibrium state is reached, the final pricing decision will be determined, and a pricing scheme with coordinated coupling of centralized and distributed systems can be realized. When the node pricing is completed, the lower-level model will re-decide the buying and selling of energy from the distribution network operator to minimize the cost of the substation. Different power purchase and sales situations will affect the power injection of the distribution network substation nodes and continuously update their node marginal prices. That is, the lower-level distributed transaction results are transmitted to the distribution network nodes and quantified into node injection power. The upper-level centralized pricing is performed through the DLMP model to optimize the unit output and flow scheduling, and the node marginal price is solved, which further affects the trading behavior of the distribution network operators and autonomous substations. This price input to the lower-level model will affect the transaction results between the producer and consumer and the distribution network operator, and finally a balanced state is achieved in the interactive optimization of the upper and lower-level models.

[0064] Preferably, in S5, the process of updating and calculating the upper-layer DLMP centralized pricing model of the entire distribution network includes:

[0065] Evaluate the established two-layer model

[0066] When node pricing is completed, the lower-level multi-agent distributed end-to-end trading market model will re-decide the buying and selling of energy from the distribution network operator to minimize the cost of the substation. Different power purchase and sales situations will affect the power injection of the distribution network substation nodes, so its node marginal price will be continuously updated until a balance state is reached. Neither the upper nor the lower level can further optimize its own goals by unilaterally changing its decision. Once the balance state is reached, the final pricing decision will be determined, and a pricing scheme with centralized and distributed collaborative coupling can be realized.

[0067] The advantages and positive effects of the trading method of the dual-tier market architecture for multi-party end-to-end transactions on the distribution network side described in the present invention are:

[0068] 1. Based on the concept of end-to-end flexible trading and the DLMP model, this invention further innovates and proposes a trading method for a two-tier market architecture suitable for end-to-end trading among multiple entities on the distribution network side. It constructs a two-tier optimized trading model of distributed trading and centralized pricing, and collaboratively optimizes the end-to-end transactions of lower-tier producers and consumers with the pricing of upper-tier DLMP nodes to promote user benefits, improve social benefits, and obtain more reasonable electricity prices.

[0069] 2. Based on the improved DLMP model, the mutual influence of the correlation between active and reactive power on the active load and reactive power price, as well as the impact of the transaction power conducted by the autonomous substation, are considered to obtain a decomposable node price and a reasonable pricing method.

[0070] 3. After considering the end-to-end transactions in the substation area, the two-layer optimization model proposed in this invention meets the load demand of the user-side prosumers while reducing the cost of market transactions, enhancing the user's market participation, indirectly promoting the prosumers to assist in serving the power market, and improving the absorption capacity of new energy.

[0071] 4. The end-to-end transaction model can help adjust line power flows, making the active and reactive power loss price components in DLMP smaller, helping to alleviate line congestion and optimize power resource allocation.

[0072] 5. The location of distribution network nodes where autonomous substations are located will have a significant impact on DLMP, providing a reference for considering the location and operation decisions of substations in the distribution system. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] Figure 1 Schematic diagram of the market optimization principle of the two-tier model constructed for the present invention;

[0074] Figure 2 A market structure diagram of the two-tier model constructed for the present invention;

[0075] Figure 3 This is a schematic diagram of an end-to-end transaction at the substation level of the present invention;

[0076] Figure 4 It is a flowchart for solving the double-layer model of the present invention;

[0077] Figure 5 This is a topology diagram of the IEEE 33-node system according to an embodiment of the present invention;

[0078] Figure 6 A schematic diagram of the internal electricity purchases of each producer and consumer in the area over 24 hours;

[0079] Figure 7 This is a schematic diagram of active DLMP before and after considering end-to-end transactions in an embodiment of the present invention;

[0080] Figure 8 A schematic diagram of reactive DLMP before and after end-to-end transactions is considered in an embodiment of the present invention;

[0081] Figure 9 This is a schematic diagram of DLMP when autonomous substations are set at different nodes according to an embodiment of the present invention. DETAILED DESCRIPTION

[0082] The technical solution of the present invention is further described below with reference to the accompanying drawings and embodiments.

[0083] Example

[0084] like Figure 1-4 As shown, a trading method of a two-tier market architecture for multi-party end-to-end transactions on the distribution network side includes the following steps:

[0085] S1. In a given distribution network system with known topology and line parameters, reasonable distribution network nodes are selected. The distribution network node is usually a substation. Users within the distribution network node negotiate and interact with each other, conduct end-to-end transactions to share energy, establish a multi-agent distributed end-to-end transaction market model, and form a distributed end-to-end transaction strategy at the distribution substation level.

[0086] The specific method for establishing a multi-agent distributed end-to-end transaction market model is as follows:

[0087] Establish the optimal objective function:

[0088]

[0089] Among them, m and n refer to the user producers and consumers within the autonomous area Ω, represents the electricity purchased by producer n at time t, C i (·) is the cost function of the prosumer, G(·) is the transaction revenue function of the energy sold by the distribution network, Represents the energy of interaction.

[0090] Constraints that limit the underlying multi-agent distributed end-to-end transaction market model:

[0091] (1) Energy balance constraints for producers and consumers:

[0092]

[0093] Formula (2) (2) indicates that the buying and selling transactions between producers and consumers and the import and export interactions with the public power grid satisfy the energy balance. Refers to the total amount of electricity purchased by prosumer n, represents the total electricity sales of prosumer n, is the load demand of prosumer n.

[0094] (2) Constraints on the power purchase and sales relationship between producers and consumers:

[0095]

[0096]

[0097]

[0098] Formula (3) and formula (4) define the expressions of the sold and purchased electricity with producer n as the consumer. represents the purchasing energy between prosumers m and n. Equation (5) shows that the transaction between prosumers m and n is positive with purchase.

[0099] (3) Constraints on the total amount of electricity purchased and sold by producers and consumers:

[0100]

[0101]

[0102] Formula (6) limits the upper and lower limits of electricity purchase by producers and consumers, where It represents the minimum amount of electricity that the producer and consumer n can purchase. represents the maximum amount of electricity purchased by producer / consumer n; Formula (7) limits the upper and lower limits of the electricity sold by the producer / consumer, where represents the minimum electricity sales of prosumer n, represents the maximum electricity sales amount of prosumer n.

[0103] In the lower-level multi-agent distributed end-to-end transaction market model, the transaction indicators of producers and consumers are defined.

[0104] There are n,m,m1,…,m in the area k Multiple producers and consumers participate in end-to-end transactions. It refers to the electricity sold by prosumer n to prosumer m at time t, which is equal to the electricity purchased by prosumer m from prosumer n at time t, that is, Equal. Similarly, the transaction flows between other prosumers can be expressed accordingly. The total amount of electricity sold by prosumer n at time t is Its value is equal to the sum of the purchases of all prosumers who trade with n, that is,

[0105] S2. Based on the transaction results of the established lower-level multi-agent distributed end-to-end transaction market model, the external transaction volume between the substation and the distribution network operator is injected into the upper-level DLMP centralized pricing model as the power of the node. Using the expression of the line flow, an upper-level DLMP centralized pricing model that takes into account the injected power of the lower-level substation is constructed.

[0106] Calculate power flow on distribution lines

[0107] In a distribution network that considers end-to-end transactions between autonomous substations, the import and export interaction power of the substations affects the transmission energy of the public distribution network, which requires the customization of new models to improve node power imbalance and power flow inaccuracy.

[0108] The power flow expression is as follows:

[0109]

[0110]

[0111] Among them, pf l ,qf l is the active and reactive power flow of line l, NB is the number of nodes, i is the node number, SFP p ,SFQ p They are the sensitivity matrices of active and reactive power amplitudes with the change of node active power injection, and SFP q ,SFQ q are the sensitivity matrices of active and reactive power amplitudes with the change of node reactive power injection, P and Q represent active and reactive power matrices, respectively. tran It is the transaction power between the substation and the distribution network node.

[0112] The method for establishing the upper-layer DLMP centralized pricing model is as follows:

[0113] Formulate the objective function

[0114] The objective function of the upper DLMP centralized pricing model is composed of generator cost and load cost. A flexible pricing mechanism model is established that considers reactive power flow, voltage variation and energy trading in the subordinate substations. The objective function of the upper DLMP centralized pricing model is expressed as

[0115]

[0116] Restriction constraints

[0117]

[0118]

[0119]

[0120]

[0121]

[0122]

[0123]

[0124] where λ p ,λ q , and is the dual variable of the corresponding constraint; SFV p SFV q are the sensitivity matrices of node voltage relative to node active and reactive power injection respectively; V1 is the reference node voltage, V i,max 、V i,min Indicates the maximum and minimum values ​​of the node voltage, PL max and PL min Respectively represent the upper and lower limits of the line active power flow; QL max and QL min Respectively represent the upper and lower limits of the line reactive power flow, Indicates the maximum and minimum values ​​of the generator's active output. Indicates the maximum and minimum values ​​of the generator's reactive output.

[0125] In the upper-level DLMP centralized pricing model, Equations (11) and (12) are the active and reactive power balances of the distribution network taking into account the interactive energy injection of autonomous substations, respectively; constraint (13) is the voltage limit, and constraints (14) and (15) are the line active and reactive power flow capacity limits, in which an internal approximation method is introduced to reconvert the power circle limit into a polygon; constraints (16) and (17) give the output boundaries of the active and reactive outputs of the generator; in order to ensure the decomposability of the DLMP formula, no node power balance constraints are introduced in the proposed upper-level DLMP centralized pricing model; the DLMP is calculated using the Lagrangian function of the model in the upper-level DLMP centralized pricing model.

[0126] S3. According to the objectives and constraints of the upper-level DLMP centralized pricing model, a Lagrangian function containing its shadow price information is constructed to solve the marginal electricity price of the distribution network node.

[0127] The specific process of solving the marginal electricity price of distribution network nodes includes:

[0128] Constructing Lagrangian functions

[0129]

[0130] In formula (18), NL is the number of lines, the first-order partial derivatives of active load and reactive load represent the active and reactive DLMP, respectively. The DLMP of active power and reactive power is decomposed into three price components, including energy price, congestion caused by active power and reactive power, and voltage support price, which are expressed as and

[0131] The active and reactive power price components of distribution network node k are as follows:

[0132]

[0133]

[0134] S4. Solve the lower-level multi-agent distributed end-to-end trading market model, send the updated node power information to the upper-level DLMP centralized pricing model, and optimize the transaction behavior between producers and consumers to minimize the total cost of producers and consumers in the autonomous area.

[0135] Solve the underlying multi-agent distributed end-to-end transaction market model:

[0136] The lower-level multi-agent distributed end-to-end trading market model is an autonomous substation end-to-end trading problem. Producers and consumers optimize their inter-user transaction volume and the interactive power of the distribution network without considering the operation of the distribution network. The lower-level multi-agent distributed end-to-end trading market model is organized into a problem of minimizing the electricity purchase cost of all producers and consumers in a substation. Under the trading mode of negotiated buying and selling of electricity among users, the optimal decision of energy trading is calculated, that is, the transaction volume between producers and consumers and the interactive energy bought and sold between the substation and the external distribution network operator.

[0137] Transmit transaction information:

[0138] By solving the end-to-end transaction problem of the multi-agent distributed end-to-end trading market model, the updated node power information is sent to the upper-level model, and the transaction behavior between producers and consumers is optimized to minimize the total cost of autonomous producers and consumers. After receiving the interactive node power of the substation, the distribution network operator uses the upper-level DLMP centralized pricing model to calculate the marginal price of the distribution network node. Based on the power flow injection model, the node power injection after the autonomous optimization of the substation is taken into account to customize the distribution network node electricity price.

[0139] Solve the upper-layer DLMP centralized pricing model:

[0140] The upper layer is the DLMP distribution network pricing problem, which takes into account the price impact of distribution network power loss compensation and the correlation between active and reactive power, and rationally optimizes the node pricing strategy under the interaction of active and reactive power in the distribution system. The distribution network nodes connected to autonomous substations will be affected by the substation transaction situation, and the injection and outflow of their node power will change, thereby calculating and updating the DLMP of the entire distribution network.

[0141] Iterate the interaction to an equilibrium state:

[0142] The trading decisions of each producer and consumer will affect the optimization results of all participants. Therefore, there is a state of equilibrium. Neither the upper nor the lower level can further optimize their own goals by unilaterally changing their decisions. Once the equilibrium state is reached, the final pricing decision will be determined, and a pricing scheme with coordinated coupling of centralized and distributed systems can be realized. When the node pricing is completed, the lower-level model will re-decide the buying and selling of energy from the distribution network operator to minimize the cost of the substation. Different power purchase and sales situations will affect the power injection of the distribution network substation nodes and continuously update their node marginal prices. That is, the lower-level distributed transaction results are transmitted to the distribution network nodes and quantified into node injection power. The upper-level centralized pricing is performed through the DLMP model to optimize the unit output and flow scheduling, and the node marginal price is solved, which further affects the trading behavior of the distribution network operators and autonomous substations. This price input to the lower-level model will affect the transaction results between the producer and consumer and the distribution network operator, and finally a balanced state is achieved in the interactive optimization of the upper and lower-level models.

[0143] S5. The calculation results of the substation transactions affect the distribution network nodes connected to the autonomous substations, and the injection and outflow of power at the nodes change, thereby updating the upper-layer DLMP centralized pricing model for the entire distribution network.

[0144] The process of updating the upper-layer DLMP centralized pricing model for the entire distribution network includes:

[0145] Evaluate the established two-layer model

[0146] When node pricing is completed, the lower-level multi-agent distributed end-to-end trading market model will re-decide the buying and selling of energy from the distribution network operator to minimize the cost of the substation. Different power purchase and sales situations will affect the power injection of the distribution network substation nodes, so its node marginal price will be continuously updated until a balance state is reached. Neither the upper nor the lower level can further optimize its own goals by unilaterally changing its decision. Once the balance state is reached, the final pricing decision will be determined, and a pricing scheme with centralized and distributed collaborative coupling can be realized.

[0147] The solution process of the two-layer model taking into account multiple stakeholders is as follows:

[0148] (1): Execute initialization;

[0149] (2): Optimize the internal autonomous end-to-end transactions of each autonomous substation under the distribution network node and optimize the external energy transaction volume of the upload node;

[0150] (3): Using the results of step (2), calculate the distribution network line flow through equations (8) and (9);

[0151] (4): Optimize generators, static VAR compensators (SVCs) and capacitor compensators (CBs);

[0152] (5): Update the node DLMP using formula (19) and formula (20);

[0153] (6): If the DLMP and two consecutive iterations are less than the predefined allowable error ε, the output result is obtained; otherwise, return to step (2).

[0154] S6. Verify the effectiveness and analyze the results of the constructed distributed and centralized collaborative two-tier model. Simulation experiments were conducted on a laptop equipped with a 2.8GHz Intel Core i7-11700H CPU and 16GB RAM. An example simulation of an IEEE 33-node system was performed to analyze the cost-effectiveness of the two-tier model pricing mechanism and the impact of the location of the lower-tier substation. The process included:

[0155] Simulation experiment of IEEE 33-bus system

[0156] The proposed model is verified using an IEEE 33-bus system. Figure 5 The modified IEEE 33 system topology is shown. Figure 5 In the example, two generators are located at nodes 9 and 33, respectively. Since there is no reactive power market, the reactive power price is set to 0.1 of the active power price. The active power prices at PG1 and PG2 are 30 and 45.5 $ / MWh, respectively. Therefore, the reactive power prices at QG1 and QG2 are set to 3 and 4.55 $ / MVarh, respectively. The following three scenarios are considered:

[0157] Case 1: Comparison of the benefits of DLMP and market participants before and after following the proposed pricing model;

[0158] Case 2: Calculate the decomposed electricity price generated by P2P transactions in each autonomous substation in each DLMP;

[0159] Case 3: The energy exchange between the substation and the public grid affects the power flow distribution, node voltage, and system losses. Therefore, Case 3 discusses the impact of different autonomous substation locations.

[0160] All cases were conducted on a Windows 10 64-bit personal computer, and the validity of the model was verified using Matlab 2021b, Yalmip, and Gurobi:

[0161] Consider the before and after comparison of end-to-end transactions

[0162] The proposed peer-to-peer (P2P) transaction market mechanism is verified by application examples, and the benefits of market participants are explored and analyzed. Based on the distributed district-level producer-consumer peer-to-peer transaction model, three autonomous districts are set up in the IEEE33 node system, and each district has 10 power energy producers and consumers. Under the two-layer optimization model proposed in this application, the specific situation of the mutual power purchase transaction between producers and consumers in node 6 is as follows: Figure 6 As shown;

[0163] Figure 6 The internal end-to-end power purchases of each prosumer within a node during a 24-hour period are shown, illustrating the internal power purchase transactions of each prosumer at a specific time. At the same time, prosumers choose the power purchase and sales amount based on the transaction price and load demand. Correspondingly, the power sales of each prosumer within the node during the 24-hour period can also be calculated and obtained using the proposed model. In the end-to-end transaction area under 6 nodes, prosumers 1 and 10 both sell power from the 10th to the 16th hour. Prosumers 3, 6, and 9 all sell idle resources in the 22nd hour, while prosumer 2 sells power in the 9th hour. Prosumers 4, 5, 7, and 8 sell power in the 8th, 3rd, 1st, and 7th hours, respectively. Note that since the data setting sets the same bidding price for each prosumer, the power sales transaction volume shown in the results is 0.8MW.

[0164] The above diagrams and data show that energy producers and consumers in the autonomous substations do not have internally negotiated transaction volumes in every time period. It should be noted that the buying and selling prices within the nodes are set to be consistent, so the transaction results show that the buying and selling electricity values ​​are relatively consistent. Table 1 explains the external transaction situation between each substation and the distribution network within a 24-hour period.

[0165] Table 1 Transaction status of each substation and distribution network during 24 hours

[0166]

[0167]

[0168] Case 1: Using the DLMP pricing method, we calculate the marginal electricity price of distribution network nodes before and after setting up the autonomous end-to-end trading area to demonstrate the effectiveness and superiority of the proposed model. The DLMP results of each node in a single period are as follows: Figure 7As shown; compared with the DLMP model without setting the lower-level model, the node price calculated by this application shows an overall downward result, and the fluctuation trend is consistent with the traditional DLMP pricing method, which means that the energy interaction of the power flow has been basically satisfied in the autonomous substation. For the public distribution network, the node load is reduced, the node price is reduced, and the cost of market participants is reduced; at the same time, it can be seen that the autonomous substation tries to meet the self-sufficient transaction needs and reduce the impact on the flow of the main network, so the overall price fluctuation trend is consistent.

[0169] Figure 8 The figure shows the reactive power DLMP before and after considering end-to-end trading. The overall phase difference ratio of the reactive power DLMP is larger than that of the active power DLMP. This is because in the IEEE 33-bus system, the line resistance is greater than its reactance, resulting in a larger sensitivity factor related to reactive power changes and correspondingly larger fluctuations. However, its overall change trend is the same as that of the active power DLMP, which is consistent with the relative size of node electricity prices when autonomous substations are not set up.

[0170] The calculation example shows that the total cost for prosumers in the substation area is $224.17. If all the required electricity is purchased from the distribution network operator, the cost will be $230.88, which reduces the cost for prosumers and improves social benefits.

[0171] Based on the above analysis, the model described in this application can effectively utilize the autonomous functions of the substation to more reasonably price the distributed distribution network. Based on the reflection of the main grid price and flow information, it simplifies the distribution network resource allocation process and optimizes user benefits.

[0172] The node DLMP components in the end-to-end transaction mode are not set as shown in Table 2, and the node DLMP components in the end-to-end transaction mode are shown in Table 3.

[0173] Table 2 Node DLMP components without end-to-end transaction mode

[0174]

[0175]

[0176] Table 3. Node DLMP components considering end-to-end transaction mode

[0177]

[0178] The above table shows that the active and reactive power losses generated by end-to-end transactions are less expensive, which also helps to alleviate line congestion, assist in optimizing resource allocation, and improve network resilience.

[0179] Figure 9The results of different node substation locations are shown. The figure depicts the specific situation when the location of an autonomous substation changes. When the location of the autonomous substation changes, the node electricity price changes according to the DLMP clearing model; when the autonomous substation is located at bus nodes 1, 9 and 33, combined with the topology Figure 5 It can be seen that at this time the substation is set on the generator node, the energy price and voltage support price increase, and the electricity price is at a higher level; it is noted that the bidding price of the three generators is the lowest for the generator at node 33 and the highest for the generator at node 1, but combined with the output characteristics of the three generators, node 1 obviously has more power generation output, so each node should pay for energy, resulting in the autonomous substation at node 33 being higher than node 1 and higher than node 9; however, when the autonomous substation is located at nodes 6 and 25, congestion and voltage support prices are eliminated, the impact of the autonomous substation on the main grid is reduced, and sometimes it can even have a positive effect on reducing electricity prices; therefore, the impact of P2P autonomous substations on the power market of the distribution system should be seriously considered; different planning locations, operating decisions and load conditions will change the power flow and DLMP.

[0180] Therefore, the present invention adopts the trading method of the two-tier market architecture of multi-subject end-to-end transactions on the distribution network side, utilizes the advantages of end-to-end transactions, improves the existing DLMP pricing model, and adopts the coordinated optimization of the lower-layer multi-subject distributed end-to-end transaction market model and the upper-layer DLMP centralized pricing model to provide a feasible market architecture for large-scale new users, thereby improving the economy and flexibility of distribution network operation and user-side resources.

[0181] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit the same. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solutions of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A trading method for a two-tier market structure with multi-subject end-to-end transactions on the distribution network side, characterized in that: The following steps are involved: S1. In a given distribution network system with known topology and line parameters, select distribution network nodes. Users within the distribution network nodes negotiate and interact with each other to conduct end-to-end transactions to share energy. A multi-agent distributed end-to-end transaction market model is established to form a distributed end-to-end transaction strategy at the distribution station level. S2. Based on the transaction results of the established lower-level multi-agent distributed end-to-end transaction market model, the external transaction volume between the substation and the distribution network operator is used as the power injection of the node into the upper-level DLMP centralized pricing model. Using the expression of the line flow, the upper-level DLMP centralized pricing model that takes into account the injected power of the lower-level substation is constructed; S3. Based on the objectives and constraints of the upper-layer DLMP centralized pricing model, a Lagrangian function containing its shadow price information is constructed to solve the marginal electricity price of the distribution network node; S4. Solve the lower-layer multi-agent distributed end-to-end transaction market model, send the updated node power information to the upper-layer DLMP centralized pricing model, and optimize the transaction behavior between producers and consumers to minimize the total cost of producers and consumers in the autonomous area; S5. The calculation results of the substation transaction affect the distribution network nodes connected to the autonomous substation, and the power injection and outflow of the nodes change, thereby updating the upper-layer DLMP centralized pricing model for the entire distribution network. S6. Verify the effectiveness and analyze the results of the constructed distributed and centralized collaborative two-tier model, focusing on the cost-effectiveness of the two-tier model pricing mechanism and the location impact of the lower-tier substation.

2. The trading method of a two-tier market structure for multi-party end-to-end transactions on the distribution network side according to claim 1 is characterized in that: In S1, the method for establishing a multi-agent distributed end-to-end transaction market model is specifically as follows: Establish the cost-optimal objective function: Among them, m and n refer to the user producers and consumers within the autonomous area Ω, represents the electricity purchased by producer n at time t, C i (·) is the cost function of the prosumer, G(·) is the transaction revenue function of the energy sold by the distribution network, represents the energy of interaction; Constraints that limit the underlying multi-agent distributed end-to-end transaction market model: (1) Energy balance constraints for producers and consumers: Formula (2) shows that the buying and selling transactions between producers and consumers and the import and export interactions with the public power grid satisfy the energy balance. Refers to the total amount of electricity purchased by prosumer n, represents the total electricity sales of prosumer n, is the load demand of prosumer n; (2) Constraints on the power purchase and sales relationship between producers and consumers: Formula (3) and formula (4) define the expressions of the sold and purchased electricity with producer n as the consumer. represents the purchasing energy between prosumers m and n. Equation (5) shows that the transaction between prosumers m and n is positive with purchase; (3) Constraints on the total amount of electricity purchased and sold by producers and consumers: Formula (6) limits the upper and lower limits of electricity purchase by producers and consumers, where It represents the minimum amount of electricity that the producer and consumer n can purchase. represents the maximum amount of electricity purchased by producer / consumer n; Formula (7) limits the upper and lower limits of the electricity sold by the producer / consumer, where represents the minimum electricity sales of prosumer n, represents the maximum electricity sales amount of prosumer n.

3. The trading method of a two-tier market structure for multi-subject end-to-end transactions on the distribution network side according to claim 2 is characterized in that: In S2, the power flow of the distribution network is calculated. In a distribution network with end-to-end transactions between autonomous substations, the import and export power interaction between substations affects the energy transmitted by the public distribution network. This requires the customization of new models to improve node power imbalance and power flow inaccuracy. The power flow expression is as follows: Among them, pf l ,qf l is the active and reactive power flow of line l, NB is the number of nodes, i is the node number, SFP p ,SFQ p They are the sensitivity matrices of active and reactive power amplitudes with the change of node active power injection, and SFP q ,SFQ q are the sensitivity matrices of active and reactive power amplitudes with the change of node reactive power injection, P and Q represent active and reactive power matrices, respectively. tran It is the transaction power between the substation and the distribution network node.

4. The trading method of a two-tier market structure for multi-subject end-to-end transactions on the distribution network side according to claim 3 is characterized in that: In S2, the method for establishing the upper-layer DLMP centralized pricing model is specifically as follows: Formulate the objective function The objective function of the upper DLMP centralized pricing model is composed of generator cost and load cost. A flexible pricing mechanism model is established that considers reactive power flow, voltage variation and energy trading in the subordinate substations. The objective function of the upper DLMP centralized pricing model is expressed as Restriction constraints where λ p ,λ q , and is the dual variable of the corresponding constraint; SFV p SFV q are the sensitivity matrices of node voltage relative to node active and reactive power injection respectively; V1 is the reference node voltage, V i,max 、V i,min Indicates the maximum and minimum values ​​of the node voltage, PL max and PL min Respectively represent the upper and lower limits of the line active power flow; QL max and QL min Respectively represent the upper and lower limits of the line reactive power flow, Indicates the maximum and minimum values ​​of the generator's active output. Indicates the maximum and minimum values ​​of the generator's reactive output; In the upper-level DLMP centralized pricing model, Equations (11) and (12) are the active and reactive power balances of the distribution network taking into account the interactive energy injection of autonomous substations, respectively; constraint Equation (13) is the voltage limit, and constraints Equations (14) and (15) are the line active and reactive power flow capacity limits, in which an internal approximation method is introduced to reconvert the power circle limit into a polygon; constraints Equations (16) and (17) give the output boundaries of the active and reactive outputs of the generator; in order to ensure the decomposability of the DLMP formula, no node power balance constraints are introduced in the proposed upper-level DLMP centralized pricing model; the DLMP is calculated using the Lagrangian function of the model in the upper-level DLMP centralized pricing model.

5. The trading method of a two-tier market structure for multi-subject end-to-end transactions on the distribution network side according to claim 4 is characterized in that: In S3, the specific process of solving the marginal electricity price of the distribution network node includes: Constructing Lagrangian functions In formula (18), NL is the number of lines, the first-order partial derivatives of active load and reactive load represent the active and reactive DLMP, respectively. The DLMP of active power and reactive power is decomposed into three price components, including energy price, congestion caused by active power and reactive power, and voltage support price, which are expressed as and The active and reactive power price components of distribution network node k are as follows:

6. The trading method of a two-tier market structure for multi-agent end-to-end transactions on the distribution network side according to claim 5 is characterized in that: The S4 specifically includes: Solve the underlying multi-agent distributed end-to-end transaction market model: The lower-level multi-agent distributed end-to-end trading market model is an autonomous end-to-end trading problem. Producers and consumers optimize their inter-user transaction volume and the interactive power of the distribution network without considering the operation of the distribution network. The lower-level multi-agent distributed end-to-end trading market model is summarized as the problem of minimizing the purchase cost of electricity for all producers and consumers in a substation. Under the transaction model of negotiated power purchase and sale between users, the optimal decision of energy trading is calculated, namely the transaction volume between producers and consumers and the interactive energy bought and sold between the substation and the external distribution network operator. Transmit transaction information: By solving the end-to-end transaction problem of the multi-agent distributed end-to-end transaction market model, the updated node power information is sent to the upper-level model, and the transaction behavior between prosumers is optimized to minimize the total cost of prosumers in the autonomous substation. After receiving the interactive node power of the substation, the distribution network operator uses the upper-level DLMP centralized pricing model to calculate the marginal price of the distribution network node. Based on the power flow injection model, taking into account the node power injection after the substation's autonomous optimization, the distribution network node electricity price is customized. Solve the upper-layer DLMP centralized pricing model: The upper layer addresses the DLMP distribution network pricing problem, taking into account the price impact of power loss compensation and the correlation between active and reactive power in the distribution network. The node pricing strategy is optimized under the interaction of active and reactive power in the distribution system. Nodes connected to autonomous substations are affected by substation transactions, causing changes in their power injection and outflow. This allows for the calculation and update of the DLMP for the entire distribution network. Iterate the interaction to an equilibrium state: The trading decisions of each producer and consumer will affect the optimization results of all participants. Therefore, there is a state of equilibrium. Neither the upper nor the lower level can further optimize their own goals by unilaterally changing their decisions. Once the equilibrium state is reached, the final pricing decision will be determined, and a pricing scheme with coordinated coupling of centralized and distributed systems can be realized. When the node pricing is completed, the lower-level model will re-decide the buying and selling of energy from the distribution network operator to minimize the cost of the substation. Different power purchase and sales situations will affect the power injection of the distribution network substation nodes and continuously update their node marginal prices. That is, the lower-level distributed transaction results are transmitted to the distribution network nodes and quantified into node injection power. The upper-level centralized pricing is performed through the DLMP model to optimize the unit output and flow scheduling, and the node marginal price is solved, which further affects the trading behavior of the distribution network operators and autonomous substations. This price input to the lower-level model will affect the transaction results between the producer and consumer and the distribution network operator, and finally a balanced state is achieved in the interactive optimization of the upper and lower-level models.

7. The trading method of a two-tier market structure for multi-agent end-to-end transactions on the distribution network side according to claim 6 is characterized in that: In S5, the process of updating and calculating the upper-layer DLMP centralized pricing model of the entire distribution network includes: Evaluate the established two-layer model When node pricing is completed, the lower-level multi-agent distributed end-to-end trading market model will re-decide the buying and selling of energy from the distribution network operator to minimize the cost of the substation. Different power purchase and sales situations will affect the power injection of the distribution network substation nodes, so its node marginal price will be continuously updated until a balance state is reached. Neither the upper nor the lower level can further optimize its own goals by unilaterally changing its decision. Once the balance state is reached, the final pricing decision will be determined, and a pricing scheme with centralized and distributed collaborative coupling can be realized.

Images