Distribution system optimization scheduling method and system considering peer-to-peer energy trading

Abstract

The present invention discloses a method and system for optimizing the scheduling of a distribution system considering point-to-point energy transactions, including: establishing DSO scheduling models in the case of successful and failed negotiations respectively; considering the transaction costs generated by the prosumer buying and selling electricity to the DSO and the P2P related transaction costs, establishing the prosumer scheduling models in the case of successful and failed negotiations respectively; constructing the Nash game model objective function between the DSO and the prosumer based on the Nash negotiation method; using a distributed algorithm to solve the Nash game model objective function, and obtaining the scheduling strategy of the DSO and the prosumer, so as to collaboratively complete the optimization scheduling of the entire distribution system. The present invention combines the scheduling of the DSO with the P2P transactions of the prosumer, and uses the scheduling process of the DSO to optimize the flow of the entire distribution system. At the same time, the P2P transaction mechanism is introduced to realize distributed and flexible energy transactions, so that the DSO and the prosumer can collaboratively realize the optimal scheduling result of the distribution system.

Description

Technical Field

[0001] The present invention relates to the technical field of power distribution system scheduling, and in particular to a method and system for optimizing scheduling of a power distribution system considering point-to-point energy trading. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] The penetration rate of distributed energy resources (distributed energy resources) in the distribution network continues to increase, prompting traditional electricity consumers to transform into "prosumers", that is, the combination of producers and consumers. Prosumers can sell excess electricity in the distribution network while using their own power generation. This promotes the consumption and utilization of renewable energy, but it also brings problems such as line congestion and voltage fluctuations. Due to the need for privacy protection and other reasons, it is difficult for distribution system operators (DSOs) to obtain the power generation data of these prosumers and sellers, let alone directly control these distributed energy resources; if prosumers are allowed to participate in the existing transmission market power transactions, a large number of prosumers with low installed capacity will affect market efficiency.

[0004] In this context, "interactive energy" was proposed, with the goal of dynamically adjusting the power balance of the power system through technical and economic means. For an interactive distribution system, it is required not only to achieve reliable regulation of the flow, but also to be able to build a local market to encourage producers and consumers to conduct flexible transactions and realize local energy consumption.

[0005] Existing research on interactive distribution system architecture is mainly divided into two categories: DSO-dominated and prosumer-dominated. The DSO-dominated type is mainly reflected in a centralized distribution system architecture, where the DSO regulates the resources of the entire distribution system and formulates reasonable incentives or market mechanisms to guide prosumers to participate in scheduling, such as dynamic electricity prices and node marginal electricity prices. Although this DSO-dominated method is relatively simple to implement and leaves a certain profit margin for the DSO, it hinders the electricity trading between prosumers, which may harm the interests of prosumers and dampen their trading enthusiasm. Therefore, researchers have proposed various prosumer-dominated methods, in which one or more prosumer aggregators optimize the distribution system containing prosumers, while considering the power flow and safety constraints of the distribution system, promoting the adjustment of prosumers' own operation plans and energy trading at the same time. This flexible model can improve the trading enthusiasm of prosumers themselves and increase their profits, which is mainly reflected in P2P transactions.

[0006] However, these prosumer-led approaches often have two problems: First, these approaches are difficult to fully consider the interests of DSOs, making it difficult for DSOs to obtain sufficient benefits to subsidize the operating expenses of the distribution system, such as network losses, and reserve to cope with power fluctuations caused by distributed energy. In addition, due to safety requirements, most existing distribution systems can only be controlled by DSOs, and prosumers find it difficult to obtain comprehensive system information, such as nodes and lines, and it is even more impossible to regulate the flow of the entire distribution system. Summary of the invention

[0007] In order to solve the above problems, the present invention proposes a distribution system optimization scheduling method and system considering peer-to-peer energy trading, combining the scheduling of DSO with the P2P trading of prosumers, and utilizing the scheduling process of DSO to optimize the power flow of the entire distribution system. At the same time, a P2P trading mechanism is introduced to realize distributed and flexible energy trading, thereby enabling DSO and prosumers to collaborate to achieve the optimal scheduling result of the distribution system.

[0008] In some embodiments, the following technical solutions are adopted:

[0009] A method for optimizing the dispatching of a power distribution system considering peer-to-peer energy trading, comprising:

[0010] Obtain distribution system operation data, consider the transmission market transaction costs under successful and failed negotiation scenarios, the transaction costs within the distribution market and between users and prosumers, the standby costs, and the P2P access fees collected, and establish DSO dispatch models under successful and failed negotiation scenarios respectively;

[0011] Considering the transaction costs of prosumers buying and selling electricity to DSOs and P2P-related transaction costs, prosumer dispatch models are established under the conditions of successful and failed negotiations.

[0012] Based on the Nash negotiation method, the objective function of the Nash game model between DSO and prosumers is constructed;

[0013] A distributed algorithm is used to solve the objective function of the Nash game model to obtain the scheduling strategy of the DSO and the prosumer, so as to collaboratively complete the optimal scheduling of the entire distribution system.

[0014] In other embodiments, the following technical solutions are adopted:

[0015] A distribution system optimization dispatching system considering peer-to-peer energy trading, comprising:

[0016] The DSO dispatch model module is used to obtain the distribution system operation data, consider the transaction costs of the transmission market under successful and failed negotiation conditions, the transaction costs of the distribution market and users and prosumers, the standby costs, and the P2P access fees collected, and establish the DSO dispatch model under successful and failed negotiation conditions respectively;

[0017] The prosumer dispatch model module is used to obtain the distribution system operation data, consider the transaction costs of prosumers buying and selling electricity to DSOs and P2P-related transaction costs, and establish prosumer dispatch models in the cases of successful and failed negotiations;

[0018] Nash negotiation module, used to construct the objective function of the Nash game model between DSO and prosumers based on the Nash negotiation method;

[0019] The model solving module is used to solve the objective function of the Nash game model using a distributed algorithm to obtain the scheduling strategy of the DSO and the prosumer to collaboratively complete the optimal scheduling of the entire distribution system.

[0020] Compared with the prior art, the present invention has the following beneficial effects:

[0021] (1) The present invention incorporates distributed energy transactions into the dispatching process to obtain sufficient information to assist the dispatching process while protecting the privacy of prosumers. Compared with the existing centralized dispatching method, this method allows prosumers to participate in the dispatching decision and more effectively regulate the flow of the distribution system within a day.

[0022] (2) The present invention constructs cooperative game problems between prosumers and between prosumers and DSOs. Through the Nash negotiation method, the benefit distribution of cooperative game problems in distribution systems involving prosumers is solved. Compared with the existing P2P transaction method, this method can guarantee the interests of prosumers and DSOs and achieve better benefit distribution.

[0023] (3) The present invention uses the alternating direction multiplier method to distribute the original problem and effectively protect the privacy information of each participant.

[0024] Other features and advantages of additional aspects of the present invention will be given in part in the following description, and in part will become obvious from the following description, or will be learned through the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 A schematic diagram of a market architecture related to a DSO and a prosumer in an embodiment of the present invention;

[0026] Figure 2 Schematic diagram of an IEEE-33 node system in an embodiment of the present invention;

[0027] Figure 3Schematic diagram of comparison of net load and spare capacity of prosumers in the entire power distribution system under two scenarios in an embodiment of the present invention;

[0028] Figure 4 It is a schematic diagram of macro power flow results in an embodiment of the present invention. DETAILED DESCRIPTION

[0029] It should be noted that the following detailed descriptions are illustrative and are intended to provide further explanation of the present application. Unless otherwise specified, all technical and scientific terms used in the present invention have the same meanings as those commonly understood by those skilled in the art to which the present application belongs.

[0030] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present application. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0031] Embodiment 1

[0032] In one or more embodiments, a method for optimizing the scheduling of a power distribution system considering peer-to-peer energy trading is disclosed, comprising the following process:

[0033] (1) Obtain distribution system operation data, consider the transmission market transaction costs, transaction costs within the distribution market and between users and prosumers, reserve costs, and P2P access fees under successful and failed negotiation scenarios, and establish DSO dispatch models under successful and failed negotiation scenarios;

[0034] (2) Considering the transaction costs of prosumers buying and selling electricity to DSOs and P2P-related transaction costs, a prosumer scheduling model is established under the conditions of successful and failed negotiations.

[0035] (3) Construct the objective function of the Nash game model between DSO and prosumers based on the Nash negotiation method;

[0036] (4) A distributed algorithm is used to solve the objective function of the Nash game model to obtain the scheduling strategy of the DSO and the prosumer, so as to collaboratively complete the optimal scheduling of the entire distribution system.

[0037] Specifically, the method of this embodiment takes into account the transaction interfaces between DSO and prosumers in four types of markets, namely, the transmission market (TM), the reserve market, the distribution market, and the P2P market. DSO needs to participate in the clearing of the transmission market and purchase reserve services in the reserve market to meet the power balance of the distribution system within a day. In addition, there are distribution markets and P2P markets within the distribution system as electricity trading platforms. The local market (DM) of the distribution system serves as a platform for direct transactions between prosumers and DSOs, and electricity settlement is carried out at a fixed price; in the P2P market, prosumers conduct direct electricity transactions with each other. The market architecture that DSO and prosumers need to participate in is as follows: Figure 1 shown.

[0038] Since the operation models and operation goals of the prosumers and DSOs are different, and there are even conflicts of interest, it is impossible to directly combine the two. Therefore, this embodiment models the problem as a cooperative game problem and uses the Nash negotiation method to solve it, while coordinating the conflicts of interest between the DSOs and prosumers, and obtaining the optimal scheduling plan for the entire system.

[0039] For a negotiation problem, it includes a series of negotiation success and showdown failure results, as well as the preferences of all participants for the negotiation results, which can be represented by a profit function. Therefore, the objective functions and constraints of DSO and prosumer under the conditions of negotiation success and negotiation failure are given below.

[0040] (1) The objective functions of the DSO scheduling model under successful and failed negotiation conditions include:

[0041]

[0042]

[0043] Among them, C DSO,Ag , C DSO,Dag are the costs of DSO in the case of successful and failed negotiations, are the transmission market transaction costs under successful and failed negotiation conditions respectively; are the transaction costs of the distribution market and users and prosumers in the case of successful and failed negotiations, respectively; are the standby costs in the case of successful negotiation and failed negotiation respectively; C DSO,NC The P2P Internet access fee is charged.

[0044] ① The transaction costs of the transmission market are as follows:

[0045]

[0046]

[0047] in, are the purchase and sale prices of electricity in the transmission market, P t DSO,TM,Dag , P t DSO,TM,Ag They are the electricity transaction volume in the transmission market when the negotiation is successful and when it fails (>0 represents electricity purchase). [·] + represents max(0,·).

[0048] ② The transaction costs for users and producers and consumers in the distribution market are as follows:

[0049]

[0050]

[0051] Among them, M is the set of all nodes in the distribution network, N is the set of producer and consumer nodes, The price at which DSO sells electricity to users in the local market. The price at which DSO purchases electricity from prosumers in the distribution market. The power injected into the prosumer node when the negotiation is successful and when the negotiation fails, respectively. The first item represents the cost of buying electricity from the prosumer, and the second item represents the revenue from selling electricity to the prosumer and the prosumer.

[0052] ③The standby cost is as follows:

[0053]

[0054]

[0055] in, For spare purchase cost, and Positive / negative spinning reserve for negotiation failure and success scenarios.

[0056] ④ The P2P Internet access fees collected are as follows:

[0057] The access fee that prosumer i needs to pay to the DSO for conducting P2P transactions with all other prosumers.

[0058]

[0059] Please note that the Internet access fee is charged to all participating P2P transaction parties.

[0060] The constraints of the DSO scheduling model are as follows:

[0061] 1) Power flow constraints

[0062]

[0063] This embodiment adopts the branch flow model shown in formula (10), where U, D represent the set of upstream and downstream nodes, P j,t and Q j,t is the active and reactive power injected into node j, P jk,t and Q jk,t is the active and reactive power flowing through line ij, v i,t is the square of the voltage amplitude at node i, l ij,t is the square of the current amplitude of line ij.

[0064] 2) Reserve capacity constraints

[0065]

[0066]

[0067] Among them, ε Res,Dag and ε Res,Ag are the proportion of DSO to arrange reserve according to the predicted value of distribution network load and the predicted value of net load of prosumers, P t Pred,Loads is the load forecast value of the entire distribution network excluding producers and consumers, and are the predicted net load values ​​of node i prosumer at time t when the negotiation fails and succeeds, respectively. Formula (11) is the reserve capacity requirement when the negotiation fails. Since all the power generation of the prosumer will be connected to the grid, and the DSO cannot know the net load of the prosumer in a day, it can only arrange the reserve according to the historical maximum value. Formula (12) is the reserve capacity requirement when the negotiation succeeds. Since the prosumer participates in the dispatching process of the entire distribution network, the reserve can be optimized according to the actual net load results of the planned nodes known in advance.

[0068] 3) Virtual power flow transactions / direction constraints

[0069] To prevent prosumers from buying electricity from DSOs while selling it to other prosumers, or from buying electricity from other prosumers in P2P transactions while selling it to DSOs, the following constraints are defined:

[0070]

[0071] in, is a 0-1 variable representing the local net load state of producer-consumer i. M is a sufficiently large positive number. When it is 1, it means that the net load of the prosumer is greater than 0, and the local power generation cannot meet the load demand, then the prosumer is allowed to purchase electricity from the DSO or other prosumers; vice versa. This constraint restricts the purchase and sale of electricity by prosumers in the distribution market and P2P transactions to be consistent, thereby avoiding the occurrence of reselling electricity.

[0072] 4) Negotiation conditions

[0073] C DSO,Ag ≤C DSO,Dag (14)

[0074] (2) The objective function of the prosumer scheduling model under successful and failed negotiation conditions specifically includes:

[0075]

[0076]

[0077] in, are the costs of producer-consumer i in the case of negotiation failure and negotiation success respectively. They represent the transaction costs of prosumer i in buying and selling electricity from DSO in the case of failed and successful negotiation, respectively. It is the transaction cost related to P2P.

[0078] ① In the distribution market, the cost of buying and selling electricity between prosumers and DSOs

[0079]

[0080]

[0081] in, The price at which DSO sells electricity to users in the distribution market. It is the price at which DSO purchases electricity from prosumers in the distribution market. It represents direct power transaction between prosumers and DSOs, and a value greater than 0 represents power purchase.

[0082] It should be noted that here λ t DM,Sell ,λ t DM,Buy The parameters of the DSO objective function are used, so the buying and selling relationship is exactly opposite to that of the producer and consumer.

[0083] ②P2P transaction related costs:

[0084]

[0085] in, represents the cost incurred by P2P transactions between prosumers i and j, where Payments made to prosumer j on behalf of prosumer i.

[0086] The constraints of the prosumer scheduling model are as follows:

[0087] 1) Restrictions on electricity consumption behavior of producers and consumers

[0088] Prosumers can flexibly adjust their electricity consumption behavior throughout the day:

[0089]

[0090]

[0091]

[0092]

[0093] in, They are the upper and lower limits of load adjustment for this period, is the lower limit of the total electricity demand of the prosumer in a day. For pure power generation nodes with no load and only distributed energy installed, the upper and lower limits can be regarded as 0. are the loads of the prosumer under the conditions of negotiation failure and negotiation success, respectively.

[0094] 2) Distributed energy generation constraints

[0095]

[0096]

[0097] in They represent the local distributed energy generation of the prosumer under the conditions of failed and successful negotiation, respectively. Represents the upper and lower limits of distributed energy generation. For nodes that do not install distributed energy and only participate in P2P transactions, it can be regarded as 0.

[0098] 3) P2P transaction constraints

[0099]

[0100]

[0101]

[0102]

[0103] in, is the electricity trading volume between prosumer i and prosumer j at time t. Represents prosumer i purchasing electricity from prosumer j. represents the transaction amount between prosumer i and prosumer j. Formula (29) constrains the electricity seller in the P2P transaction and cannot sell beyond the scope of its local net load.

[0104] 4) Negotiation conditions

[0105]

[0106] The benefits of a successful negotiation are greater than the benefits of a broken negotiation because both symbols are costs, so the size relationship is opposite to the benefits.

[0107] 5) Local power balancing

[0108] For each prosumer, its local power should be in a balanced state:

[0109]

[0110]

[0111] in, is the P2P transaction power value between prosumers i and j at time t (>0 represents power purchase); are the loads of the prosumer under the conditions of failed and successful negotiation, respectively; are the distributed energy generation power of the prosumer under the conditions of failed and successful negotiation respectively; They are the local market electricity purchase amount of the prosumer in the case of failed and successful negotiation respectively.

[0112] In the Nash negotiation model, each participant takes turns to bid until the negotiation succeeds or ends in failure. In this embodiment, after modeling each participant (i.e., the prosumer and the DSO) of the Nash negotiation model, the scheduling problem of the entire distribution system containing prosumers can be organized into the following form:

[0113] The objective function is

[0114]

[0115] The variable is: Pt DSO ,TM ,A g,

[0116] The constraints are: equations (10), (12)-(14), (21), (23), (25)-(30), (32).

[0117] A major drawback of the original problem is that it requires all the information of the producers and consumers, such as the generation plan and load of distributed energy, which is often difficult to obtain due to privacy concerns. Therefore, it is necessary to use a distributed algorithm to solve the problem. Here, the alternating direction multiplier method based on the consistency algorithm is used, and the problem after application is organized in matrix form as follows:

[0118]

[0119] stx0∈χ0 (35)

[0120] x i ∈χ i (36)

[0121]

[0122] Among them, x0 and f0(x0) are the decision variables and objective function of DSO, x i and f i (x i ) are the decision variables and objective functions of the producers and consumers, and It is a global backup of the decision variables of DSO and prosumer. i is the set to which the variable belongs, Equations (35) and (36) represent the local constraints of the DSO and the prosumer, and Equation (37) represents the coupling constraints between global variables. and is the Lagrange multiplier and ρ is the penalty parameter.

[0123] After completing the above steps, the original problem can be solved in a distributed manner. The solution steps are as follows:

[0124] 1) Initialize global variables Lagrange multiplier μ, converges residual ε, and starts k=1 iterations.

[0125] 2) When the conditions are met or condition , where x is the variable x0 and x i A general term for

[0126] 2.1) DSO updates local and global backup variables x0;

[0127] 2.2) Each producer and consumer updates its local and global backup variables x i ;

[0128] 2.3) The global coordinator updates the global variables

[0129] 2.4) Each participant updates its multiplier μ;

[0130] Let k = k + 1;

[0131] 3) If the above conditions are not met, the iteration will end. The result of the end of the iteration is that the solution is completed and the value of each variable is obtained.

[0132] The method of this embodiment is applied in Figure 2 The test was conducted on the IEEE-33 node system shown in the figure, which contains four prosumer nodes, equipped with distributed energy with an installed capacity of 600kVA and transferable loads. It should be noted that each prosumer node is actually a collection of all prosumers under the bus, such as all prosumers in a community. For simplicity, the basic power generation data and load data of distributed energy are all from existing literature data, but the distributed energy power generation of different prosumer nodes is superimposed with normal errors. The model used in this embodiment divides a day into two periods, 1.00-7.00 in the normal period and 8.00-24.00 in the peak period. In the normal period, the electricity price in the transmission market is 0.6￥ / kWh, and the electricity purchase price for users in the distribution market is 0.8￥ / kWh; in the peak period, the electricity price in the transmission market is 0.8￥ / kWh, and the electricity purchase price for users in the distribution market is 1￥ / kWh. The standby price and the electricity sales price for users in the distribution market are 0.3￥ / kWh and 0.4￥ / kWh in all periods. The example simulation is performed in MATLAB 2021a, modeled using Yalmip, and solved using Mosek.

[0133] (1) Scheduling results analysis

[0134] Since in the absence of negotiation, the prosumers will sell the excess electricity to the DSO through the local market according to the original power generation and load plan, and the DSO will maintain the original dispatch plan, the scenario without negotiation is the negotiation failure scenario. In order to compare the impact of negotiation on the dispatch process, the net load (i.e., load minus distributed energy generation power) and spare capacity of the prosumers in the entire distribution system under the two scenarios are compared. Figure 3 Given in.

[0135] from Figure 3It can be seen that compared with the case where negotiation is not considered, the net load of the proposed method is higher at 1.00-7.00, while the net load in other time periods is lower. This is because the electricity price in the transmission market is lower at 1.00-7.00, so the proposed method will transfer the net load from other time periods to this time period. In fact, since distributed energy generation cannot be transferred, the transfer of net load can be regarded as the transfer of the load of the producer and consumer. In addition, except for 1.00-7.00, the reserve capacity of the proposed method is much lower than that of the scenario where negotiation is not considered. This is because when negotiation is not considered, the DSO cannot know the specific distributed energy generation capacity of the producer and consumer, so it can only arrange the reserve according to the historical maximum net load. On the contrary, in the method proposed in this embodiment, the DSO and the producer and consumer will work together to optimize the entire distribution system, and the distributed energy will reduce the purchase of reserve capacity by obtaining a more accurate net load forecast value.

[0136] In addition, the various costs under the two scenarios can be calculated and given in the following Tables 1 and 2. For the sake of simplicity, in the two tables, the proposed method and the method without negotiation are referred to as successful negotiation and failed negotiation, respectively. In Table 1 below, TM cost, RM cost, DM cost, and NC cost represent the DSO transmission market cost, the reserve market cost, the local market and prosumer transaction cost, and the access fee cost collected in P2P transactions, respectively. In Table 2 below, the meaning of each cost is the same, except that the cost subject is the prosumer, so the sum of the local market cost and the access fee cost are opposite to the values ​​of the items in Table 1. In the scenario of failed negotiation, all costs of the prosumer are DM costs.

[0137] Table 1 DSO cost (￥)

[0138] Scenario Total Cost TM Cost DM Cost RM Cost NC Cost Negotiations failed 41947.39 42568.29 -5543.57 4922.67 0 Successful negotiation 38612.89 42067.72 -4914.90 1675.40 -215.34

[0139] Table 2 Prosumer costs

[0140]

[0141] As can be seen from Table 1, the TM cost and RM cost of the proposed method are lower than those in the negotiation failure scenario, while the DM cost is higher than those in the negotiation failure scenario. This is because the proposed method will use the Nash negotiation process to allow DSO and prosumers to cooperate in the optimal power flow, thereby reducing the cost of purchasing electricity in the external transmission market; at the same time, since DSO and prosumers are allowed to conduct P2P transactions together, DSO can indirectly obtain the net load information of prosumers through the information of the power purchased in the distribution market and the P2P transaction power, thereby reducing the backup cost. However, in the original negotiation failure scenario, prosumers can only buy and sell electricity from DSO, which allows DSO to resell the excess power generation of distributed energy to a certain extent and earn the difference, which also leads to the increase of DM cost of the proposed method. However, in order to compensate for the increase in DSO cost caused by P2P transactions, the proposed method adds the variable of access fee, allowing DSO and prosumers to adjust the distribution of benefits through negotiation. All the above factors make the DSO cost in the final successful negotiation scenario still slightly lower than the cost in the negotiation failure scenario.

[0142] As can be seen from Table 2, in the proposed method, the costs of all prosumers are reduced compared with the negotiation failure scenario. It should be noted that since prosumers cannot conduct P2P transactions in the negotiation failure scenario, all their costs are DM costs. As can be seen from the table, the DM costs of prosumers 1 and 3 are reduced compared with the negotiation failure scenario, and their P2P costs are positive (indicating that most of the electricity is purchased in P2P transactions), while the DM costs of prosumers 2 and 4 are increased, and their P2P costs are negative (indicating that most of the electricity is sold in P2P transactions). This is because in the negotiation failure scenario, prosumers can only trade with DSO in the distribution market. However, due to the limited ability of prosumers to store electricity, they will sell electricity to DSO when there is surplus electricity, and buy electricity from DSO when there is insufficient electricity. After the introduction of P2P transactions, prosumers will reduce the amount of electricity transactions in the distribution market, which will naturally increase the DM costs of prosumers with excess electricity in the original day and reduce the distribution market costs of prosumers with insufficient electricity. In summary, in the scenario of successful negotiation, even if the four prosumers are charged Internet access fees, P2P transactions have reduced their operating costs to a certain extent. This proves that the proposed method can enable DSOs and prosumers to operate in a more economical way while ensuring system security.

[0143] (2) P2P transaction analysis

[0144] In order to explore the impact of power generation and load on the power distribution market and P2P transaction volume from the perspective of the entire prosumer, the net load of all prosumers is calculated. Figure 4 At the same time, the distribution market transaction volume, P2P transaction volume, and the ratio of P2P transaction to total power transaction volume are defined as shown in equations (38)-(40), and all the calculated results are also Figure 4 Given in.

[0145]

[0146]

[0147]

[0148] Figure 4 Pros1-Pros4 represent prosumers 1-4 respectively. Before 7:00, there is no P2P transaction in the system, only the distribution market transaction. At this time, the distributed energy generation of the prosumer is less than the local load, so it is impossible to generate surplus electricity for trading, and can only be purchased from the distribution market. Starting from 8:00, the distributed energy generation of the prosumer increases, which makes the local prosumer with insufficient power conduct P2P electricity transactions with the prosumer with excess power.

[0149] Depend on Figure 4 It can be seen that the absolute value of the overall net load of the prosumers will affect the proportion of P2P transactions in the total virtual power. This is because at the moment when the overall net load is close to 0, such as 9:00 and 16:00, all prosumers can almost complete the power balance, so they will give priority to P2P transactions. At this time, the power generated by P2P transactions accounts for most of the power transactions, about 77% and 72%. On the contrary, the increase in the absolute value of the net load will lead to a decrease in the proportion of P2P transactions, such as 12:00 and 20:00, when the overall surplus power of the prosumers is insufficient / surplus, resulting in the increased power generation or load can only be traded through the distribution market.

[0150] (3) Testing of systems with a high proportion of producers and consumers

[0151] In order to test the effectiveness of the proposed method in a distribution system with a higher proportion of prosumers, the IEEE-33 node system with 15 prosumer nodes is tested. Among them, 10 nodes are prosumers with distributed energy and loads, 2 are prosumers with only distributed energy, and 2 are users with only transferable loads but can participate in P2P. After calculating the various costs of DSO in the agreement and disagreement situations, the results are given in Table 3.

[0152] Table 3 Various costs of DSO under agreement and disagreement conditions

[0153] Scenario All Cost TMCost DMCost RMCost NCCost Disagreement 37931.42 41440.36 -9990.83 6481.88 0 Agreement 33603.70 40231.74 -7149.27 1339.91 -818.72

[0154] Compared with the results in Table 1, in a system with a higher proportion of prosumers, the total cost of DSO will be greatly reduced, whether in the disagreement or successful negotiation scenario. In the negotiation failure scenario, although the backup cost increases due to the increase in distributed energy capacity, the increase in distributed energy generation of prosumers will lead to a decrease in the load demand of prosumers themselves, and DSO can resell the distributed energy generation of prosumers, which leads to a reduction in TM and DM costs, and further reduces the total cost. In the successful negotiation scenario, although the introduction of P2P transactions greatly increases the DM cost, the TM cost is reduced because prosumers can collaborate with DSO to optimize the flow; in addition, the reduction in NC income and RM costs also further makes the total cost lower than the negotiation failure scenario. In short, the proposed method is also applicable to systems with a high proportion of prosumers and can achieve more economical optimization results.

[0155] In the method of this embodiment, the producer and consumer can adjust their own operation plan to achieve the optimal power flow of the system, the DSO can obtain sufficient information to assist in scheduling decisions, and the interests of all participants are coordinated and allocated through the Nash negotiation method, and a distributed algorithm is further applied to ensure the privacy of all parties.

[0156] The method of this embodiment can effectively reduce the operating costs of DSOs and prosumers, and realize P2P flexible power trading between prosumers.

[0157] Embodiment 2

[0158] In one or more embodiments, a distribution system optimization scheduling system considering peer-to-peer energy trading is disclosed, including:

[0159] The DSO dispatch model module is used to obtain the distribution system operation data, consider the transaction costs of the transmission market under successful and failed negotiation conditions, the transaction costs of the distribution market and users and prosumers, the standby costs, and the P2P access fees collected, and establish the DSO dispatch model under successful and failed negotiation conditions respectively;

[0160] The prosumer dispatch model module is used to obtain the distribution system operation data, consider the transaction costs of prosumers buying and selling electricity to DSOs and P2P-related transaction costs, and establish prosumer dispatch models in the cases of successful and failed negotiations;

[0161] Nash negotiation module, used to construct the objective function of the Nash game model between DSO and prosumers based on the Nash negotiation method;

[0162] The model solving module is used to solve the objective function of the Nash game model using a distributed algorithm to obtain the scheduling strategy of the DSO and the prosumer to collaboratively complete the optimal scheduling of the entire distribution system.

[0163] It should be noted that the specific implementation methods of the above modules have been described in Example 1 and will not be described in detail here.

[0164] Although the above describes the specific implementation mode of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art on the basis of the technical solution of the present invention without creative work are still within the scope of protection of the present invention.

Claims

1. A method for optimizing the dispatching of a power distribution system considering peer-to-peer energy trading, characterized in that: include: Obtain distribution system operation data, consider the transmission market transaction costs under successful and failed negotiation scenarios, the transaction costs within the distribution market and between users and prosumers, the standby costs, and the P2P access fees collected, and establish DSO dispatch models under successful and failed negotiation scenarios respectively; Considering the transaction costs of prosumers buying and selling electricity to DSOs and P2P-related transaction costs, prosumer dispatch models are established under the conditions of successful and failed negotiations. Based on the Nash negotiation method, the objective function of the Nash game model between DSO and prosumers is constructed; A distributed algorithm is used to solve the objective function of the Nash game model to obtain the dispatching strategy of the DSO and the prosumer, so as to collaboratively complete the optimal dispatching of the entire distribution system; The constraints of the DSO dispatch model include: power flow constraints, reserve capacity constraints, and virtual power flow transaction / direction constraints; The virtual power flow transaction / direction constraint is: To prevent the prosumer from buying power from the DSO and selling power to other prosumers at the same time, or buying power from other prosumers in a P2P transaction and selling power to the DSO at the same time, the constraint is defined as follows: in, is a 0-1 variable representing the local net load state of producer-consumer i; M is a sufficiently large positive number; when When it is 1, it means that the net load of the prosumer is greater than 0, and the local power generation cannot meet the load demand, allowing the prosumer to purchase electricity from the DSO or other prosumers; The scheduling problem of the objective function of the Nash game model is transformed into the following form in matrix form: Among them, x0 and f0(x0) are the decision variables and objective function of DSO, x i and f i (x i ) are the decision variables and objective functions of the producers and consumers, and A global backup of DSO and prosumer decision variables, and is the Lagrange multiplier, ρ is the penalty parameter; The distributed algorithm is used to solve the objective function of the Nash game model to obtain the scheduling strategy of DSO and prosumer, which specifically includes: Initialize global variables, Lagrange multipliers, convergence residuals and number of iterations k = 1; When satisfied or The following operations are performed when conditions occur: DSO updates local and global backup variables x0; Each prosumer updates its local and global backup variables x i ; The global coordinator updates global variables and Each participant updates its Lagrange multiplier; Then proceed to the next iteration; If the above conditions are not met, the iteration ends and the value of each variable is obtained.

2. A method for optimizing the dispatching of a power distribution system considering peer-to-peer energy trading as claimed in claim 1, characterized in that: The DSO scheduling model established in the case of successful negotiation and failed negotiation specifically includes: Among them, C DSO,Ag , C DSO,Dag are the costs of DSO in the case of successful and failed negotiations, are the transmission market transaction costs under successful and failed negotiation conditions respectively; are the transaction costs of the distribution market and users and prosumers in the case of successful and failed negotiations, respectively; are the standby costs in the case of successful negotiation and failed negotiation respectively; C DSO,NC The P2P Internet access fee is charged.

3. A method for optimizing and dispatching a power distribution system considering peer-to-peer energy trading as claimed in claim 1, characterized in that: Establish a scheduling model for producers and consumers in the case of successful and failed negotiations, including: in, are the costs of producer-consumer i in the case of negotiation failure and negotiation success, respectively. They represent the transaction costs of prosumer i in buying and selling electricity from DSO in the case of failed and successful negotiation, respectively. It is the transaction cost related to P2P.

4. A method for optimizing and dispatching a power distribution system considering peer-to-peer energy trading as claimed in claim 1, characterized in that: The constraints of the prosumer scheduling model include: prosumer electricity consumption behavior constraints, distributed energy generation constraints and P2P transaction constraints.

5. A method for optimizing and dispatching a power distribution system considering peer-to-peer energy trading as claimed in claim 1, characterized in that: The condition for the DSO scheduling model or the prosumer scheduling model negotiation to be established is that the cost of successful negotiation is not greater than the cost of failed negotiation.

6. A method for optimizing and dispatching a power distribution system considering peer-to-peer energy trading as claimed in claim 1, characterized in that: Based on the Nash negotiation method, the objective function of the Nash game model between DSO and prosumers is constructed, including: Among them, C DSO,Ag , C DSO,Dag are the costs of DSO in the case of successful and failed negotiations, are the costs of producer-consumer i in the case of negotiation failure and negotiation success respectively.

7. A distribution system optimization scheduling system considering peer-to-peer energy trading, characterized in that: include: The DSO dispatch model module is used to obtain the distribution system operation data, consider the transaction costs of the transmission market under successful and failed negotiation conditions, the transaction costs of the distribution market and users and prosumers, the standby costs, and the P2P access fees collected, and establish the DSO dispatch model under successful and failed negotiation conditions respectively; The prosumer dispatch model module is used to obtain the distribution system operation data, consider the transaction costs of prosumers buying and selling electricity to DSOs and P2P-related transaction costs, and establish prosumer dispatch models in the cases of successful and failed negotiations; Nash negotiation module, used to construct the objective function of the Nash game model between DSO and prosumers based on the Nash negotiation method; A model solving module is used to solve the objective function of the Nash game model using a distributed algorithm to obtain the scheduling strategy of the DSO and the prosumer, so as to collaboratively complete the optimal scheduling of the entire distribution system; The constraints of the DSO dispatch model include: power flow constraints, reserve capacity constraints, and virtual power flow transaction / direction constraints; The virtual power flow transaction / direction constraint is: To prevent the prosumer from buying power from the DSO and selling power to other prosumers at the same time, or buying power from other prosumers in a P2P transaction and selling power to the DSO at the same time, the constraint is defined as follows: in, is a 0-1 variable representing the local net load state of producer-consumer i; M is a sufficiently large positive number; when When it is 1, it means that the net load of the prosumer is greater than 0, and the local power generation cannot meet the load demand, allowing the prosumer to purchase electricity from the DSO or other prosumers; The scheduling problem of the objective function of the Nash game model is transformed into the following form in matrix form: Among them, x0 and f0(x0) are the decision variables and objective function of DSO, x i and f i (x i ) are the decision variables and objective functions of the producers and consumers, and A global backup of DSO and prosumer decision variables, and is the Lagrange multiplier, ρ is the penalty parameter; The distributed algorithm is used to solve the objective function of the Nash game model to obtain the scheduling strategy of DSO and prosumer, which specifically includes: Initialize global variables, Lagrange multipliers, convergence residuals and number of iterations k = 1; When satisfied or The following operations are performed when conditions occur: DSO updates local and global backup variables x0; Each prosumer updates its local and global backup variables x i ; The global coordinator updates global variables and Each participant updates its Lagrange multiplier; Then proceed to the next iteration; If the above conditions are not met, the iteration ends and the value of each variable is obtained.

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