Virtual power plant intervention main-distribution two-level market distributed coordination optimization method based on reputation management and control mechanism

Abstract

The invention discloses a reputation management and control mechanism-based distributed coordination optimization method for a virtual power plant to intervene in a main-distribution two-level market. The main-distribution two-level market is decomposed into a main network wholesale market sub-problem and a plurality of power distribution system retail market sub-problems. In order to fully utilize the aggregated distributed power generation resources of the virtual power plant and flexibly adjust the load to reduce the operation cost of the strong-uncertainty power system, the organization and implementation process of the virtual power plant participating in the main-distribution two-level power market is perfected and perfected. Besides, in consideration of relatively large uncertainty of the actual power of the virtual power plant and relatively strong individual propriety of a virtual power plant agent, the virtual power plant is very easy to have default behaviors, a dynamic reputation index for evaluating the contract fulfillment completion degree of the virtual power plant is provided, and the contract fulfillment completion degree of the virtual power plant is evaluated. And the market default behavior of the virtual power plant is inhibited by setting a market access threshold and paying a defaulting fund.

Description

Technical Field

[0001] The present invention relates to electric energy storage technology and market coordination and optimization technology, and in particular to a distributed coordination and optimization method for virtual power plants intervening in a main and distribution two-level market based on a credit management and control mechanism. Background Art

[0002] Under the market-led mechanism, in order to eliminate the power imbalance between the main grid wholesale market and the distribution side retail market during operation, it is necessary to give full play to the coordination role of the main distribution system operators (main grid wholesale market operators / distribution side retail market operators) between different levels of markets, enhance the power flow and price interaction between the main and distribution markets, so as to further improve the safe and economic operation benefits of the power system.

[0003] It can be predicted that more and more distributed renewable energy generation devices will enter the low-voltage distribution system in the future. However, due to the large number and small capacity of distributed energy, it is not suitable to directly participate in market transactions. Distributed energy and flexibility resources in low-voltage distribution networks participate in the market through virtual power plants (VPPs). Considering the large volatility and uncertainty of the power generation of distributed power sources within VPPs, as well as the strong individual profit-seeking nature of market agents, market entities such as VPPs are prone to breach of contract, increasing the safety risk of power grid operation, and causing system operators to lose trust in VPPs, which ultimately affects the enthusiasm of VPPs to participate in market transactions.

[0004] In order to promote the integration of VPP into the operation of the power market, it is urgent to establish a market management model for VPP and other market players to evaluate the degree of contract performance, so as to ensure the safety of power grid operation and improve the enthusiasm of VPP to participate in the market. By introducing reputation evaluation indicators, clarifying the update rules of the reputation value of each trading entity, VPP is evaluated, recorded and updated, and users are encouraged to keep their promises voluntarily. Considering the large transaction capacity of the wholesale market on the main grid side, its market participants need to meet certain access capacity and grid connection conditions. The retail market on the distribution side is close to various distributed energy and flexibility resources, and has a small transaction capacity. For VPP, the entry threshold for participating in wholesale market transactions is significantly higher than that of the retail market. Therefore, the level of VPP agent reputation becomes the only critical criterion between its participation in the retail market on the distribution side or the wholesale market on the main grid side.

[0005] There is almost no coordination between the traditional main grid wholesale market operators and the distribution network retail market operators, and the fragmented market management model is no longer applicable to the situation where a large number of distributed power sources are connected to the distribution network. The reason is that, on the one hand, the main grid wholesale market operators must deal with the inevitable uncertainty, volatility and intermittency of large-scale renewable energy generation. In order to quickly adjust the balance between the power generation side and the demand side, the main grid wholesale market operators are required to have more sufficient flexibility resources and additional ancillary services. On the other hand, a large number of distributed energy sources such as controllable distributed generation, photovoltaic power generation, wind power and energy storage are connected to the distribution network, making the distribution network no longer a simple passive network, but a system with stronger flexibility resources. Distributed power sources connected to the distribution network can not only solve the problems of line congestion and voltage quality of the distribution network itself, but also provide flexibility and ancillary services to the main grid. In order to promote the development of the national unified power market, it is urgent to improve and perfect the coordination strategy between the main grid wholesale market and the distribution side retail market where VPP is involved, so as to make full use of the flexibility resources of the low-voltage distribution network to reduce the operating cost of the highly uncertain power system. Summary of the invention

[0006] Purpose of the invention: The purpose of the present invention is to solve the deficiencies existing in the prior art and to provide a distributed coordination optimization method for virtual power plants intervening in the main and distribution two-level markets based on a credit management mechanism; to ensure that each trading entity can actively perform the transaction contract, a credit evaluation index based on contract fulfillment is established to constrain the default behavior of market entities in the actual electricity delivery stage. The present invention mainly suppresses the default behavior of each entity by associating the credit value with the deposit to be paid and the market access threshold. The coordination between the main grid wholesale market and each distribution network retail market is conducive to clarifying the coupling relationship between supply, demand and price between the transmission and distribution networks, and solving the problem of transaction barriers caused by market liquidity between regions.

[0007] Technical solution: The present invention provides a distributed coordination optimization method for virtual power plants to intervene in the main distribution two-level market based on a reputation control mechanism. For each market entity in the virtual power plant VPP, the corresponding reputation value is first calculated according to the power deviation rate, and then the access of the virtual power plant to participate in the distribution side retail market and the main grid side wholesale market is judged according to the reputation value. For market entities whose actual reputation value is lower than the preset value, they are prohibited from participating in the main distribution two-level market; for market entities that are allowed to participate in the main distribution two-level market, the following optimization and coordination method is executed:

[0008] Step 1: Assume that the wholesale market operator on the main grid is the energy provider, and provides price signals for power generation and backup products. The retail market operator on the distribution side feeds back the power demand and backup service sharing value to the wholesale market operator on the main grid. The coordination optimization model of the main and distribution market operators is constructed as shown in equations (3)-(7):

[0009] Objective function:

[0010] constraint

[0011] g T (u T ,x T )≤0(5)

[0012]

[0013]

[0014] In the above formula, x TSO and are the power variables inside k on the main grid side and the distribution side respectively; is the power variable of the boundary node between the main grid side and the distribution side k; u TSO and are the price variables of the main grid and the distribution network k respectively; F, f and g are the objective function, equality constraint and inequality constraint respectively; superscript TSO and subscript D k They are the main grid and distribution network k, k=1,2,...,N D , N D The total number of distribution networks under the main grid;

[0015] Step 2: Decouple the objective function formula (3) according to the boundary information to obtain a main grid side wholesale market optimization clearing sub-problem and multiple distribution side retail market optimization clearing sub-problems; for the main grid side wholesale market optimization clearing sub-problem, construct an operator optimization model for the main grid side wholesale market, and for each distribution side retail market optimization clearing sub-problem, construct an operator optimization model for the distribution side retail market;

[0016] Step 2.1: The target of the operator optimization model of the distribution network retail market is the total electricity purchase cost of the kth distribution network retail market operator. Minimum, that is, the objective function is as follows:

[0017]

[0018] In the above formula, is the cost for the kth distribution-side retail market operator to purchase electricity and reserve from the main grid-side wholesale market operator; The cost of electricity purchased by the retail market operator from controllable distributed generation equipment (DG) on the distribution side; The cost of purchasing electricity from each virtual power plant VPP for the retail market operator on the distribution side; is an auxiliary function, which represents the impact of the optimization results of the wholesale market operator on the main grid side on the retail market operator on the distribution side;

[0019] Step 2.2: The target of the optimization model for operators in the wholesale market on the main grid side is the total purchase cost F of operators in the wholesale market on the main grid side. T Minimum, that is, the objective function is:

[0020]

[0021] In the above formula, is the cost of purchasing electricity and reserve for operators in the main grid wholesale market, κ auxT is the auxiliary objective function;

[0022] Step 2.3: Since the coordinated optimization of the two-level distribution market is achieved through interactive boundary information, the boundaries of the main and distribution markets are constrained to be consistent, that is, the power of the main grid side and each distribution side is equal at the boundary connection; for the distribution network, the main distribution boundary node is the balance node of the distribution network;

[0023]

[0024] In the formula, are the active power connected between the main grid side and the kth distribution side retail market operator respectively; is the injected active power of the kth distribution-side retail market operator at the balancing node.

[0025] Furthermore, when the virtual power plant participates in the retail market on the distribution side and the wholesale market on the main grid side, it includes the following four stages:

[0026] Phase 1: The virtual power plant agent reports the bidding price and output range of various market products to the wholesale market operator on the main grid side and the retail market operator on the distribution side respectively; and the virtual power plant agent determines its bidding method for participating in the power market based on the types and parameters of its internal distributed energy;

[0027] The main grid-side wholesale market operator and the distribution-side retail market operator shall collect the market entity parameters of all participating wholesale markets and retail markets according to the pre-announced transaction time, and determine the market product transaction price and transaction limit amount between the upper-level main grid-side wholesale market;

[0028] Stage 2, Day-ahead market stage:

[0029] Virtual power plant agents determine their market participation level based on their own reputation values; based on their day-ahead distributed energy forecast results, they submit day-ahead bidding plans and develop scheduling plans or price incentive methods for internal flexibility resources;

[0030] The main grid-side wholesale market operator and the distribution-side retail market operator collect information on all market participants, including virtual power plants, including the output range and cost characteristics of various units, clarify the product transaction prices, transaction demands and power limit quotas agreed upon between the main grid-side wholesale market operator and the distribution-side retail market operator, and conduct distributed coordinated optimization and clearing of the wholesale market and the retail market on the day-ahead according to the product demands at different levels and the system safety operation constraints. The distribution-side retail market operator reports the time-sharing interaction plans of various market products to the main grid-side wholesale market operator, and the main grid-side wholesale market operator issues the time-sharing prices of products to the distribution-side retail market operator;

[0031] Finally, the winning bid price and quantity of each virtual power plant, the dispatch plan of directly controlled equipment, and the product interaction plan between the distribution system and the wholesale market on the main grid side are determined. The wholesale market operator on the main grid side and the retail market operator on the distribution side will release the day-ahead market time-sharing clearing results to all market participants respectively.

[0032] Phase 3, real-time market phase:

[0033] The virtual power plant agent updates the real-time operation plan based on the real-time distributed energy forecast results; the main grid-side wholesale market operator and the distribution-side retail market operator update the real-time information of all market participants, perform real-time optimization and clearing, determine the real-time market product interaction plan and product price between the main grid-side wholesale market operator and the distribution-side retail market operator, and publish the real-time market clearing results to all market participants;

[0034] Phase 4, settlement phase: The wholesale market operator on the main grid side and the retail market operator on the distribution side settle with all market participants based on the day-ahead and real-time clearing results, update the virtual power plant node credit value and return the deposit or collect liquidated damages; the virtual power plant agent reviews the settlement results.

[0035] Furthermore, the specific process of reputation evaluation is as follows:

[0036] Step 1) Set the initial credit value of each virtual power plant to 100, and at the end of each round of transactions, the blockchain platform automatically updates the credit value based on the actual power delivery in this round of transactions;

[0037] Step 2), calculate the reputation value of each virtual power plant node based on the power deviation rate;

[0038] If the actual power curve delivered by the trading subject is not delivered according to the agreed power curve, the reputation value needs to be calculated based on the contract fulfillment degree; in order to encourage market players to adopt consistent trading strategies, the dynamic calculation of the reputation value is realized on the basis of the historical reputation value, and the weight of the historical data is adjusted to ensure that the reputation value of the market player depends on the latest data; the specific calculation method is as follows:

[0039]

[0040] Where: R i,t is the reputation value of market entity i in the transaction in period t; is the contract power traded by market entity i in period t; is the actual transaction power of market subject i in the tth period; λ is the reputation penalty coefficient;

[0041] Step 3) Each market player must pay a deposit before the start of trading in the tth period. If a breach of contract occurs, part of the deposit will be deducted as a penalty for breach of contract. The calculation method of the penalty for breach of contract is:

[0042]

[0043] Where: C i,t is the penalty that entity i needs to pay at the beginning of the transaction in period t; R i,t-1 is the credit value of subject i after the transaction in period t-1; R max is the maximum value of the reputation value; is the market node electricity price of entity i in period t;

[0044] It can be seen from equations (1) and (2) that the greater the power deviation actually delivered by the trading subject, the more the credit value will be deducted and the more liquidated damages will be paid. In addition, if the credit value of the trading subject is lower than the preset market access threshold, it will be prohibited from participating in the market. Therefore, each trading subject needs to actively perform the contract to improve its own credit value, avoid paying too much liquidated damages and obtain market access permission.

[0045] Furthermore, in the operator optimization model of the main grid wholesale market, the electricity purchase cost of the wholesale market players is The calculation method of is as follows,

[0046]

[0047] The superscript "E&R" stands for electric energy and reserve, and the subscript "g" stands for thermal power generating unit; TD k is the direction of power flow from the main grid to the distribution grid; u g,t and v g,t It is the start and stop state variable of the unit; and are the start-up and shutdown costs of unit g during period t; and P is the purchase cost function of the electricity and reserve obtained by the wholesale market operator on the main grid side from the generator set and the retail market operator on the kth distribution side; g,t Purchase power from generator g for the wholesale market operator on the main grid side; N is the amount of electricity that the wholesale market operator on the main grid side obtains from the kth retail market operator on the distribution side; G N is the total number of main grid generators; D is the number of retail market operators on the distribution side; T is the total number of time periods;

[0048] Auxiliary objective function κ auxT Characterize the impact of the optimization results of the retail market operator on the main grid side on the wholesale market operator to ensure the optimality of the global model. The calculation method is as follows:

[0049] κ auxT =(ζ D ) T x B (10)

[0050]

[0051] In the formula, the vector ζ BD The objective function and constraints of the distribution network are given by the boundary variables The sensitivity matrix of is the objective function of the k-th distribution side retail market operator’s distribution network; is the Lagrange multiplier vector of equation (4), is the Lagrange multiplier vector of inequality constraint (5);

[0052] Furthermore, the operator optimization model of the main network wholesale market needs to meet the following four constraints:

[0053] Constraint I: Main grid system power balance constraint

[0054]

[0055] In the formula, is the active power generated by the generator at the i-th node of the transmission network; is the active power of the virtual power plant; Active power output from the main network system RES; is the active load on the i-th node;

[0056] Constraint II, generator output active power constraint, climbing constraint

[0057]

[0058] In the formula, and are the active output of the generator at node i and its upper and lower limits; r i dn 、r i upThey are the upward and downward climbing rates of the unit respectively;

[0059] Constraint III: Line capacity constraints

[0060]

[0061] In the formula, f l max is the upper limit of power flow transmission of main network line l;

[0062] Constraint IV: Main network system spare capacity requirement constraint

[0063]

[0064] In the formula, is the positive and negative reserve capacity requirements of generator g; is the total uncertainty power of the main network.

[0065] The operator optimization model of the distribution network retail market adopts a radial network model based on MI-SOCP. In the distribution network, distributed energy DG mainly includes new energy represented by wind and solar power and flexible resources represented by micro diesel engines and micro gas turbines. The specific calculation method of the objective function formula (24) is as follows:

[0066] For the kth distribution-side retail market operator, the cost of purchasing electricity and reserve from the main grid-side wholesale market operator is It is equal to the product of price and quantity. Price refers to the node marginal price of the wholesale market of electricity energy of the wholesale market operator on the main grid side. Quantity refers to the power provided by the market subject of the wholesale market operator on the main grid side. The expression is as follows;

[0067]

[0068] In the formula, is the price of active power and reserve capacity provided by the wholesale market operator on the main grid side to the kth retail market operator on the distribution side during period t; is the active power and reserve capacity purchased by the kth distribution-side retail market operator from the main grid-side wholesale market operator in period t;

[0069] The cost of electricity purchased by distribution-side retail market operators from controllable distributed generation equipment DG Its expression is as follows:

[0070]

[0071] In the formula, is the bid price of the active power of DG at the kth distribution network node i in period t; is the active power output by the DG at the kth distribution network node i in period t; is the total number of nodes of the kth distribution-side retail market operator;

[0072] The cost of purchasing electricity from each virtual power plant VPP for the retail market operator on the distribution side Its expression is as follows:

[0073]

[0074] In the formula, The bid price for the virtual power plant at node i of the kth distribution-side retail market operator to provide active power and reserve capacity during period t; is the active power and reserve capacity purchased by the kth distribution-side retail market operator from the virtual power plant at node i during period t;

[0075] For helper functions The expression that characterizes the impact of the optimization results of the wholesale market operator on the main grid side on the retail market operator on the kth distribution side is:

[0076]

[0077] In the formula, λ T,k is the Lagrange multiplier vector of equation (8), is the power demand of the kth distribution-side retail market operator at the boundary node.

[0078] Furthermore, the operator optimization model for the distribution network retail market needs to meet the following five constraints:

[0079] Constraint i: Power balance constraint:

[0080]

[0081] In the formula, is the load demand of node k in the distribution network.

[0082] Constraint ii: Power constraints transmitted from the wholesale market operator on the main grid to the retail market operator on the distribution side:

[0083]

[0084] In the formula, The maximum power that the retail market operator on the distribution side can purchase from the wholesale market operator on the main grid side;

[0085] Constraint iii, output power constraint of distributed power generation DG within the virtual power plant, demand response constraint of the transferable load:

[0086]

[0087] In the formula, is the ramp value of DG power in period t; is the maximum power that can be removed by the k-th distribution-side retail market operator; T p is the set of all allowed response periods, and DG is removable during the allowed period;

[0088] Constraint iv. Internal branch power constraints of retail market operators on the distribution side:

[0089]

[0090] In the formula, is the injected power at the kth distribution-side retail market operator node i; is the upper limit of power flow transmission of branch l of distribution network k;

[0091] Constraint vi: Reserve capacity demand constraints for retail market operators on the distribution side:

[0092]

[0093] In the formula, is the shedding amount of the migratable load at the k-th distribution-side retail market operator node i; The increase or decrease of the power generated by the distributed generation; is the positive and negative reserve demand of the kth distribution side retail market operator.

[0094] Beneficial effects: The present invention utilizes the virtual power plant credibility mechanism to improve market efficiency and reduce systemic risks. The behavior of virtual power plants participating in the market is more effectively supervised and incentivized to avoid malicious defaults or over-reliance on uncontrollable resources. This mechanism not only improves the transparency of the power market, but also strengthens the trust of the wholesale market operators on the main grid side and the retail market operators on the distribution side in the dispatch of distributed resources, thereby improving the stability and reliability of the coordinated operation of the two-level market. The coordinated operation of the main and distribution two-level markets that takes into account the dynamic credibility of virtual power plants proposed in the present invention has significant advantages in the overall economic operation benefits of the power system, and is conducive to further promoting the construction and development of a unified national power market. BRIEF DESCRIPTION OF THE DRAWINGS

[0095] Figure 1 The overall flow chart of the distributed coordination optimization of the main and distribution two-level markets of the present invention;

[0096] Figure 2 It is a schematic diagram of the layout of each server in the two-level market of the present invention;

[0097] Figure 3 It is a schematic diagram of the process of different stages in the present invention;

[0098] Figure 4 It is a schematic diagram of the topological structure of the main distribution system of the present invention;

[0099] Figure 5 A schematic diagram of the power demand and the spare capacity that can be undertaken by two distribution-side retail market operators in the embodiment;

[0100] Figure 6 A schematic diagram of the bid winning power and actual power results of the virtual power plant in the embodiment;

[0101] Figure 7 Schematic diagram of the dynamic reputation value and default cost results of the virtual power plant in the embodiment. DETAILED DESCRIPTION

[0102] The technical solution of the present invention is described in detail below, but the protection scope of the present invention is not limited to the embodiments.

[0103] like Figure 1 As shown in the figure, take a transmission network with four distribution networks as an example. Figure 1 The algorithm flow chart of distributed coordination optimization of the main and distribution two-level markets is shown. The optimization solution of the retail market operators on the distribution side is first carried out, and then the optimization solution of the main grid wholesale market operators is carried out.

[0104] like Figure 2 As shown, in this embodiment, the distributed coordination optimization method for virtual power plants intervening in the main distribution two-level market based on the reputation control mechanism is as follows: for each virtual power plant, the corresponding reputation value is first calculated according to the power deviation rate, and then the access of the corresponding market entity to participate in the retail market on the distribution side and the wholesale market on the main grid side is judged according to the reputation value. For virtual power plants whose actual reputation value is lower than the preset value, they are prohibited from participating in the main distribution two-level market; for market entities that are admitted to participate in the main distribution two-level market, the following optimization coordination method is executed:

[0105] Step 1: Assume that the wholesale market operator on the main grid is the energy provider, and the price signals of power generation and backup products are issued. The retail market operator on the distribution side feeds back the power demand and backup service sharing value to the wholesale market operator on the main grid; the coordinated optimization model of the main and distribution market operators is constructed as shown in equations (3)-(7). Among them, equation (3) is the objective function of the coordinated optimization of the main and distribution market operators, equations (4) and (5) are the equality constraints and inequality constraints of the optimization model of the wholesale market operator on the main grid, respectively; equations (6) and (7) are the equality constraints and inequality constraints of the optimization model of the retail market operator on the distribution side, respectively.

[0106] Objective function:

[0107] constraint

[0108] g T (u T ,x T )≤0(5)

[0109]

[0110] In the above formula, x TSO and are the power variables inside k on the main grid side and the distribution side respectively; is the power variable of the boundary node between the main grid side and the distribution side k; u TSO and are the price variables of the main grid and the distribution network k respectively; F, f and g are the objective function, equality constraint and inequality constraint respectively; superscript TSO and subscript D k They are the main grid and distribution network k, k=1,2,...,N D , N D The total number of distribution networks under the main grid;

[0111] Step 2: Decouple the objective function formula (3) according to the boundary information to obtain a main grid side wholesale market optimization clearing sub-problem and multiple distribution side retail market optimization clearing sub-problems; for the main grid side wholesale market optimization clearing sub-problem, construct an operator optimization model for the main grid side wholesale market, and for each distribution side retail market optimization clearing sub-problem, construct an operator optimization model for the distribution side retail market;

[0112] Step 2.1: The target of the optimization model for operators in the distribution network retail market is the total electricity purchase cost of the kth distribution network retail market operator. Minimum, that is, the objective function is as follows:

[0113]

[0114] In the above formula, is the cost for the kth distribution network operator to purchase electricity and reserve from the main grid wholesale market operator; The cost of electricity purchased by the distribution network operator from controllable distributed generation equipment DG; The cost of purchasing electricity from each virtual power plant VPP for the distribution network operator; is an auxiliary function, which characterizes the impact of the optimization results of the wholesale market operator on the main grid side on the distribution network operator;

[0115] Step 2.2: The target of the optimization model for operators in the wholesale market on the main grid side is the total purchase cost F of operators in the wholesale market on the main grid side. T Minimum, that is, the objective function is:

[0116]

[0117] In the above formula, Cost of purchasing energy and reserves for wholesale market operators, κ auxT is an auxiliary objective function, which characterizes the impact of the optimization results of the retail market operator on the distribution side on the wholesale market operator on the main grid side;

[0118] Step 2.3: Since the coordinated optimization of the two-level distribution market is achieved through interactive boundary information, the boundaries of the main and distribution markets are constrained to be consistent, that is, the power of the main grid side and each distribution side is equal at the boundary connection; for the distribution network, the main distribution boundary node is the balance node of the distribution network;

[0119]

[0120] In the formula, are the active power connected between the main grid side and the kth distribution network operator; is the injected active power of the kth distribution network operator at the balancing node.

[0121] like Figure 3 As shown, in this embodiment, when the virtual power plant agent participates in the retail market on the distribution side and the wholesale market on the main grid side, it includes the following four stages:

[0122] Phase 1: The virtual power plant agent reports the bidding price and output range of various market products to the wholesale market operator on the main grid side and the retail market operator on the distribution side respectively; and the virtual power plant agent determines its bidding method for participating in the power market based on the types and parameters of its internal distributed energy;

[0123] The wholesale market operators on the main grid side and the retail market operators on the distribution side collect market subject parameters such as bid prices and bid powers of participating wholesale and retail markets respectively according to the pre-announced transaction time, and determine the market product transaction prices and transaction limit amounts between the upper-level wholesale market on the main grid side;

[0124] Stage 2, Day-ahead market stage:

[0125] Virtual power plant agents determine their market participation level based on their own reputation values; based on their day-ahead distributed energy forecast results, they submit day-ahead bidding plans and develop scheduling plans or price incentive methods for internal flexibility resources;

[0126] The main grid-side wholesale market operator and the distribution-side retail market operator collect information on all market participants including virtual power plants, clarify the product transaction prices, transaction demands and power limit quotas agreed upon between the main grid-side wholesale market operator and the distribution-side retail market operator, and conduct distributed coordinated optimization and clearing of the wholesale market and the retail market based on the product demands at different levels and system safety operation constraints. The distribution network operator reports the time-sharing interaction plans of various market products to the main grid-side wholesale market operator, and the main grid-side wholesale market operator issues the time-sharing prices of products to the distribution network operator.

[0127] Finally, the winning bid price and quantity of each virtual power plant are determined, and the wholesale market operator on the main grid side and the retail market operator on the distribution side respectively announce the day-ahead market time-sharing clearing results to all market participants;

[0128] Phase 3, real-time market phase:

[0129] Virtual power plant agents update real-time operation plans based on real-time distributed energy forecast results; the main grid-side wholesale market operator and the distribution-side retail market operator update the real-time information of all market participants, perform real-time optimization and clearing, determine the real-time market product interaction plan and product price between the main grid-side wholesale market operator and the distribution network operator, and publish the real-time market clearing results to all market participants;

[0130] Phase 4, settlement phase: The wholesale market operator on the main grid side and the retail market operator on the distribution side settle with all virtual power plant agents based on the day-ahead and real-time clearing results, update the virtual power plant node credit value and return the deposit or collect liquidated damages; the virtual power plant agents review the settlement results.

[0131] The specific process of reputation evaluation in this embodiment is as follows:

[0132] Step 1) Set the initial credit value of the virtual power plant to 100, and at the end of each round of trading, the blockchain platform automatically updates the credit value based on the actual power delivery in this round of trading;

[0133] Step 2) Calculate the reputation value of each virtual power plant VPP node based on the power deviation rate;

[0134] If the actual delivered power curve of the virtual power plant is not delivered according to the agreed power curve, the credit value is calculated dynamically; the specific calculation method is:

[0135]

[0136] Where: R i,t is the reputation value of virtual power plant i in the transaction in period t; is the contract power traded by virtual power plant i in period t; is the actual transaction power of virtual power plant i in period t; λ is the credit penalty coefficient;

[0137] Step 3) Each virtual power plant must pay a deposit before the start of trading in the tth period. If it defaults, part of the deposit will be deducted as a penalty. The calculation method of the penalty is:

[0138]

[0139] Where: C i,t is the penalty that virtual power plant i needs to pay at the beginning of trading in period t; R i,t-1 is the reputation value of virtual power plant i after the transaction in period t-1; R max is the maximum value of the reputation value; is the market node electricity price of virtual power plant i in period t.

[0140] In the operator optimization model of the main grid wholesale market in this embodiment, the electricity purchase cost provided by the wholesale market entity is The calculation method of is as follows,

[0141]

[0142] Where: E&R is the electric energy and reserve, g is the thermal power generating unit; TD k is the direction of power flow from the main grid to the distribution grid; u g,t and v g,t It is the start and stop state variable of the unit; and are the start-up and shutdown costs of unit g during period t; and P is the purchase cost function of the electricity and reserve obtained by the wholesale market operator on the main grid side from the generator set and the kth distribution network operator respectively; g,t Purchase power from generator g for the wholesale market operator on the main grid side; N is the amount of electricity obtained by the wholesale market operator on the main grid side from the kth distribution network operator; G N is the total number of main grid generators; D is the number of distribution network operators; T is the total number of time periods;

[0143] Auxiliary objective function κ auxT The calculation method is as follows:

[0144] κ auxT =(ζ D ) T x B (10)

[0145]

[0146] In the formula, the vector ζ BD The objective function and constraints of the distribution network are given by the boundary variables The sensitivity matrix of is the objective function of the kth distribution network operator; is the Lagrange multiplier vector of equation (4), is the Lagrange multiplier vector of the inequality constraint (5).

[0147] The operator optimization model of the main network wholesale market in this embodiment needs to meet the following four constraints:

[0148] Constraint I: Main grid system power balance constraint

[0149]

[0150] In the formula, is the active power generated by the generator at the i-th node of the transmission network; is the active power of the virtual power plant; Active power output from the main network system RES; is the active load on the i-th node;

[0151] Constraint II, generator output active power constraint, climbing constraint

[0152]

[0153]

[0154] In the formula, and are the active output of the generator at node i and its upper and lower limits; r i dn 、r i up They are the upward and downward climbing rates of the unit respectively;

[0155] Constraint III: Line capacity constraints

[0156]

[0157] In the formula, Inject power into the node of the main network i node; is the active power generated by the generator at the i-th node; is the active power of the virtual power plant; Active power output from the main network system RES; is the active load on the ith node; V is the set of all nodes in the main network; Ψ l is the power transmission distribution factor for the main grid, which is a known quantity; is the column vector consisting of the node injection power of all nodes in the main network; f l max is the upper limit of power flow transmission of main network line l;

[0158] Constraint IV: Main grid system reserve capacity demand constraint. Formulas (20)-(21) are the rated capacity constraints of the generators. Formulas (22)-(23) are the positive and negative spinning reserve capacity constraints of the system.

[0159]

[0160] In the formula, is the positive and negative reserve capacity requirements of generator g; is the maximum and minimum power generation of generator g; is the total uncertainty power of the main network.

[0161] The specific calculation method of the objective function formula (24) of this embodiment is as follows:

[0162] For the kth distribution-side retail market operator, the cost of purchasing electricity and reserve from the main grid-side wholesale market operator is It is equal to the product of price and quantity. Price refers to the node marginal price of the wholesale market of electric energy of the wholesale market operator on the main grid side. Quantity refers to the power provided by the market subject of the wholesale market operator on the main grid side. The expression is as follows;

[0163]

[0164] In the formula, is the price of active power and reserve capacity provided by the wholesale market operator on the main grid side to the kth retail market operator on the distribution side during period t; is the active power and reserve capacity purchased by the kth distribution-side retail market operator from the main grid-side wholesale market operator in period t;

[0165] The cost of electricity purchased by distribution-side retail market operators from controllable distributed generation equipment DG Its expression is as follows:

[0166]

[0167] In the formula, is the bid price of the active power of DG at the kth distribution network node i in period t; is the active power output by the DG at the kth distribution network node i in period t; is the total number of nodes of the kth distribution-side retail market operator;

[0168] The cost of purchasing electricity from each virtual power plant VPP for the retail market operator on the distribution side Its expression is as follows:

[0169]

[0170] In the formula, The bid price for the virtual power plant at node i of the kth distribution-side retail market operator to provide active power and reserve capacity during period t; is the active power and reserve capacity purchased by the kth distribution-side retail market operator from the virtual power plant at node i during period t;

[0171] For helper functions The expression that characterizes the impact of the optimization results of the wholesale market operator on the main grid side on the retail market operator on the kth distribution side is:

[0172]

[0173] In the formula, λ T,k is the Lagrange multiplier vector of equation (8), is the power demand of the kth distribution-side retail market operator at the boundary node.

[0174] The operator optimization model of the distribution network retail market in this embodiment needs to meet the following five constraints:

[0175] Constraint i: Power balance constraint:

[0176]

[0177] In the formula, is the load demand of node k in the distribution network;

[0178] Constraint ii: Power constraints transmitted from the wholesale market operator on the main grid to the retail market operator on the distribution side:

[0179]

[0180] In the formula, The maximum power that the retail market operator on the distribution side can purchase from the wholesale market operator on the main grid side;

[0181] Constraint iii, output power constraint of distributed power generation DG within the virtual power plant, demand response constraint of the transferable load:

[0182]

[0183] In the formula, is the ramp value of DG power in period t; is the maximum power that can be removed by the k-th distribution-side retail market operator; T pis the set of all allowed response time periods;

[0184] Constraint iv. Internal branch power constraints of retail market operators on the distribution side:

[0185]

[0186] In the formula, is the injected power at the kth distribution-side retail market operator node i; is the upper limit of power flow transmission of branch l of distribution network k;

[0187] Constraint vi: Reserve capacity demand constraints for retail market operators on the distribution side:

[0188]

[0189] In the formula, is the shedding amount of the migratable load at the k-th distribution-side retail market operator node i; The increase or decrease of the power generated by the distributed generation; is the positive and negative reserve demand of the kth distribution side retail market operator.

[0190] The settings of the distributed coordination optimization simulation example of the main and distribution two-level market in this embodiment are as follows: the main network adopts the IEEE39-node transmission system, 8 nodes are connected to the IEEE33-node distribution system, and 16 nodes are connected to the IEEE69-node distribution system. The topology is as follows: Figure 4 The market is cleared in 1h cycles, and the total number of clearing periods is 24.

[0191] Figure 5 The results of the power purchased by distribution network 1 and distribution network 2 from the transmission network are shown, as well as the reserve capacity that distribution network 1 and distribution network 2 can bear respectively. Figure 5 It can be seen that due to the existence of distributed energy generation inside distribution network 1 and distribution network 2, the demand for distribution network 1 and distribution network 2 to purchase electricity from the transmission network is not much; at the same time, since the virtual power plant can provide backup capacity, the lower-level distribution network can take the initiative to assume part of the backup capacity required by the main network.

[0192] Figure 5 It shows that the optimization method of the present invention can promote information interaction between markets at different levels, enhance the liquidity of electric energy and reserve in the main and distribution markets, and increase the enthusiasm of virtual power plants within the distribution network to participate in the market, thereby reducing the operating cost of the entire power system.

[0193] Figure 6The winning bid power and actual power of virtual power plants 1 to 5 are shown. The greater the difference between the actual power and the winning bid power, the worse the reputation of the virtual power plant, and the more liquidated damages it needs to pay. Figure 7 The dynamic reputation value and default cost of VPP1-6 are shown, combined with Figure 6 and Figure 7 It can be seen that compared with virtual power plant 1 and virtual power plant 5, the actual power of virtual power plant 2 is significantly different from the bid power, resulting in the fastest decline in its credibility value and the need to pay more default costs.

[0194] Figure 7 (a) is a diagram of the dynamic reputation value and default cost of VPP1-VPP3. Figure 7 (b) is a schematic diagram of the dynamic reputation value and default cost of VPP4-VPP6; Figure 7 The effectiveness of the market operator coordination optimization method considering the credit value of the virtual power plant proposed in the present invention is verified.

Claims

1. A distributed coordination optimization method for virtual power plants intervening in the main and distribution two-level markets based on a credit control mechanism, characterized in that: For each virtual power plant, the corresponding dynamic credit value is first calculated based on the power deviation rate, and then the access of the corresponding market players to the distribution side retail market and the main grid side wholesale market is determined based on the historical accumulated credit value. For virtual power plants whose actual credit value is lower than the preset value, they are prohibited from participating in the main distribution and two-level markets; for market players who are admitted to participate in the main distribution and two-level markets, the following optimization and coordination method is implemented: Step 1: Assume that the wholesale market operator on the main grid is the energy provider, and the price signals of power generation and backup products are issued. The retail market operator on the distribution side feeds back the power demand and backup service sharing value to the wholesale market operator on the main grid; based on the generalized master-slave decomposition and coordination method, the coordination optimization model of the main and distribution market operators is constructed as shown in equations (3)-(7). Among them, equation (3) is the objective function of the coordination optimization of the main and distribution market operators, equations (4) and (5) are the equality constraints and inequality constraints of the optimization model of the wholesale market operator on the main grid, respectively; equations (6) and (7) are the equality constraints and inequality constraints of the optimization model of the retail market operator on the distribution side, respectively; Objective function: constraint g T (u T ,x T )≤0(5) In the above formula, x TSO and are the power variables inside k on the main grid side and the distribution side respectively; is the power variable of the boundary node between the main grid side and the distribution side k; u TSO and are the price variables of the main grid and the distribution network k respectively; F, f and g are the objective function, equality constraint and inequality constraint respectively; superscript TSO and subscript D k They are the main grid and distribution network k, k=1,2,...,N D , N D The total number of distribution networks under the main grid; Step 2: Decouple the objective function formula (3) according to the boundary information to obtain a main grid side wholesale market optimization clearing sub-problem and multiple distribution side retail market optimization clearing sub-problems; for the main grid side wholesale market optimization clearing sub-problem, construct an operator optimization model for the main grid side wholesale market, and for each distribution side retail market optimization clearing sub-problem, construct an operator optimization model for the distribution side retail market; Step 2.1: The target of the operator optimization model of the distribution network retail market is the total electricity purchase cost of the kth distribution network retail market operator. Minimum, that is, the objective function is as follows: In the above formula, is the cost for the kth distribution network operator to purchase electricity and reserve from the main grid wholesale market operator; The cost of electricity purchased by the distribution network operator from controllable distributed generation equipment DG; The cost of purchasing electricity from each virtual power plant for the distribution network operator; is an auxiliary function, which characterizes the impact of the optimization results of the wholesale market operator on the main grid side on the distribution network operator; Step 2.2: The target of the optimization model for operators in the wholesale market on the main grid side is the total purchase cost F of operators in the wholesale market on the main grid side. T Minimum, that is, the objective function is: In the above formula, Cost of purchasing energy and reserves for wholesale market operators, κ auxT is an auxiliary objective function, which characterizes the impact of the optimization results of the retail market operator on the distribution side on the wholesale market operator on the main grid side; Step 2.3: Since the coordinated optimization of the two-level distribution market is achieved through interactive boundary information, the boundaries of the main and distribution markets are constrained to be consistent, that is, the power of the main grid side and each distribution side is equal at the boundary connection; for the distribution network, the main distribution boundary node is the balance node of the distribution network; In the formula, are the active power connected between the main grid side and the kth distribution network operator; is the injected active power of the kth distribution network operator at the balancing node.

2. The distributed coordination optimization method for virtual power plant intervention in the main and distribution two-level market based on the credit control mechanism according to claim 1 is characterized in that: When the virtual power plant agent participates in the retail market on the distribution side and the wholesale market on the main grid side, it includes the following four stages: Phase 1: The virtual power plant agent reports the bidding price and output range of various market products to the wholesale market operator on the main grid side and the retail market operator on the distribution side respectively; and the virtual power plant agent determines its bidding method for participating in the power market based on the types and parameters of its internal distributed energy; The wholesale market operators on the main grid side and the retail market operators on the distribution side collect market subject parameters such as bid prices and bid powers of participating wholesale and retail markets respectively according to the pre-announced transaction time, and determine the market product transaction prices and transaction limit amounts between the upper-level wholesale market on the main grid side; Stage 2, Day-ahead market stage: Virtual power plant agents determine their market participation level based on their own reputation values; based on their day-ahead distributed energy forecast results, they submit day-ahead bidding plans and develop scheduling plans or price incentive methods for internal flexibility resources; The main grid-side wholesale market operator and the distribution-side retail market operator collect information on all market participants including virtual power plants, clarify the product transaction prices, transaction demands and power limit quotas agreed upon between the main grid-side wholesale market operator and the distribution-side retail market operator, and conduct distributed coordinated optimization and clearing of the wholesale market and the retail market based on the product demands at different levels and system safety operation constraints. The distribution network operator reports the time-sharing interaction plans of various market products to the main grid-side wholesale market operator, and the main grid-side wholesale market operator issues the time-sharing prices of products to the distribution network operator. Finally, the winning bid price and quantity of each virtual power plant are determined, and the wholesale market operator on the main grid side and the retail market operator on the distribution side respectively announce the day-ahead market time-sharing clearing results to all market participants; Phase 3, real-time market phase: Virtual power plant agents update real-time operation plans based on real-time distributed energy forecast results; the main grid-side wholesale market operator and the distribution-side retail market operator update the real-time information of all market participants, perform real-time optimization and clearing, determine the real-time market product interaction plan and product price between the main grid-side wholesale market operator and the distribution network operator, and publish the real-time market clearing results to all market participants; Phase 4, settlement phase: The wholesale market operator on the main grid side and the retail market operator on the distribution side settle with all virtual power plant agents based on the day-ahead and real-time clearing results, update the virtual power plant node credit value and return the deposit or collect liquidated damages; the virtual power plant agents review the settlement results.

3. The distributed coordination optimization method for virtual power plant intervention in the main and distribution two-level market based on the credit control mechanism according to claim 1 or 2 is characterized in that: The specific process of reputation evaluation is as follows: Step 1) Set the initial credit value of the virtual power plant to 100, and at the end of each round of trading, the blockchain platform automatically updates the credit value based on the actual power delivery in this round of trading; Step 2) Calculate the reputation value of each virtual power plant VPP node based on the power deviation rate; If the actual delivered power curve of the virtual power plant is not delivered according to the agreed power curve, the credit value is calculated dynamically; the specific calculation method is: Where: R i,t is the reputation value of virtual power plant i in the transaction in period t; is the contract power traded by virtual power plant i in period t; is the actual transaction power of virtual power plant i in period t; λ is the credit penalty coefficient; Step 3) Each virtual power plant must pay a deposit before the start of trading in the tth period. If it defaults, part of the deposit will be deducted as a penalty. The calculation method of the penalty is: Where: C i,t is the penalty that virtual power plant i needs to pay at the beginning of trading in period t; R i,t-1 is the reputation value of virtual power plant i after the transaction in period t-1; R max is the maximum value of the reputation value; is the market node electricity price of virtual power plant i in period t.

4. The distributed coordination optimization method for virtual power plant intervention in the main and distribution two-level market based on the credit control mechanism according to claim 1 is characterized in that: In the operator optimization model of the main grid wholesale market, the electricity purchase cost of the wholesale market players is The calculation method of is as follows, Where: E&R is the electric energy and reserve, g is the thermal power generating unit; TD k is the direction of power flow from the main grid to the distribution grid; u g,t and v g,t It is the start and stop state variable of the unit; and are the start-up and shutdown costs of unit g during period t; and P is the purchase cost function of the electricity and reserve obtained by the wholesale market operator on the main grid side from the generator set and the kth distribution network operator respectively; g,t Purchase power from generator g for the wholesale market operator on the main grid side; N is the amount of electricity obtained by the wholesale market operator on the main grid side from the kth distribution network operator; G N is the total number of main grid generators; D is the number of distribution network operators; T is the total number of time periods; Auxiliary objective function κ auxT The calculation method is as follows: k auxT =(ζ D ) T x B (10) In the formula, the vector ζ BD The objective function and constraints of the distribution network are given by the boundary variables The sensitivity matrix of is the objective function of the kth distribution network operator; is the Lagrange multiplier vector of equation (4), is the Lagrange multiplier vector of the inequality constraint (5).

5. The distributed coordination optimization method for virtual power plant intervention in the main and distribution two-level market based on the credit control mechanism according to claim 1 or 4 is characterized in that: The operator optimization model of the main network wholesale market must meet the following four constraints: Constraint I: Main grid system power balance constraint In the formula, is the active power generated by the generator at the i-th node of the transmission network; is the active power of the virtual power plant; Active power output from the main network system RES; is the active load on the i-th node; Constraint II, generator output active power constraint, climbing constraint In the formula, and are the active output of the generator at node i and its upper and lower limits; r i dn 、r i up They are the upward and downward climbing rates of the unit respectively; Constraint III: Line capacity constraints In the formula, Inject power into the node of the main network i node; is the active power generated by the generator at the i-th node; is the active power of the virtual power plant; Active power output from the main network system RES; is the active load on the ith node; V is the set of all nodes in the main network; Ψ l is the power transmission distribution factor for the main grid, which is a known quantity; is the column vector consisting of the node injection power of all nodes in the main network; f l max is the upper limit of power flow transmission of main network line l; Constraint IV: Main grid system reserve capacity demand constraint. Formulas (20)-(21) are the rated capacity constraints of the generators. Formulas (22)-(23) are the positive and negative spinning reserve capacity constraints of the system. In the formula, is the positive and negative reserve capacity requirements of generator g; is the maximum and minimum power generation of generator g; is the total uncertainty power of the main network.

6. The distributed coordination optimization method for virtual power plant intervention in the main and distribution two-level market based on the credit control mechanism according to claim 1 is characterized by: The specific calculation method of the objective function formula (24) is as follows: For the kth distribution-side retail market operator, the cost of purchasing electricity and reserve from the main grid-side wholesale market operator is It is equal to the product of price and quantity. Price refers to the node marginal price of the wholesale market of electricity energy of the wholesale market operator on the main grid side. Quantity refers to the power provided by the market subject of the wholesale market operator on the main grid side. The expression is as follows; In the formula, is the price of active power and reserve capacity provided by the wholesale market operator on the main grid side to the kth retail market operator on the distribution side during period t; is the active power and reserve capacity purchased by the kth distribution-side retail market operator from the main grid-side wholesale market operator in period t; The cost of electricity purchased by distribution-side retail market operators from controllable distributed generation equipment DG Its expression is as follows: In the formula, is the bid price of the active power of DG at the kth distribution network node i in period t; is the active power output by the DG at the kth distribution network node i in period t; is the total number of nodes of the kth distribution-side retail market operator; The cost of purchasing electricity from each virtual power plant VPP for the retail market operator on the distribution side Its expression is as follows: In the formula, The bid price for the virtual power plant at node i of the kth distribution-side retail market operator to provide active power and reserve capacity during period t; is the active power and reserve capacity purchased by the kth distribution-side retail market operator from the virtual power plant at node i during period t; For helper functions The expression that characterizes the impact of the optimization results of the wholesale market operator on the main grid side on the retail market operator on the kth distribution side is: In the formula, λ T,k is the Lagrange multiplier vector of equation (8), is the power demand of the kth distribution-side retail market operator at the boundary node.

7. The distributed coordination optimization method for virtual power plant intervention in the main and distribution two-level market based on the credit control mechanism according to claim 1 or 6 is characterized in that: The operator optimization model for the distribution network retail market needs to meet the following five constraints: Constraint i: Power balance constraint: In the formula, is the load demand of node k in the distribution network; Constraint ii: Power constraints transmitted from the wholesale market operator on the main grid to the retail market operator on the distribution side: In the formula, The maximum power that the retail market operator on the distribution side can purchase from the wholesale market operator on the main grid side; Constraint iii, output power constraint of distributed power generation DG within the virtual power plant, demand response constraint of the transferable load: In the formula, is the ramp value of DG power in period t; is the maximum power that can be removed by the k-th distribution-side retail market operator; T p is the set of all allowed response time periods; Constraint iv. Internal branch power constraints of retail market operators on the distribution side: In the formula, is the injected power at the kth distribution-side retail market operator node i; is the upper limit of power flow transmission of branch l of distribution network k; Constraint vi: Reserve capacity demand constraints for retail market operators on the distribution side: In the formula, is the shedding amount of the migratable load at the k-th distribution-side retail market operator node i; The increase or decrease of the power generated by the distributed generation; is the positive and negative reserve demand of the kth distribution side retail market operator.

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