A distributed optimization scheduling method and system for distribution network under multi-stakeholder game

Abstract

The present invention discloses a distributed optimization scheduling method and system for distribution network under multi-stakeholder game, which relates to the technical field of distribution network optimization scheduling, including: generating a transaction layer scheduling layer collaborative interactive energy management framework based on the characteristics of highly coupled active distribution network transactions and operation information accessed by multiple stakeholders; based on the transaction layer scheduling layer collaborative interactive energy management framework, introducing random programming theory to deal with the uncertainty of renewable energy output and load, generating a microgrid operation model with the goal of maximizing microgrid benefits, and converting the microgrid operation model into a multi-master and multi-slave game model under a certain environment based on Stackelberg game theory; obtaining electric energy exchange Yi Information uses a distributed solution algorithm to calculate the transaction price and power consumption between multiple microgrid stakeholders. It integrates transaction game strategy security verification and the safety and economy of distribution network operation. Based on the forecast information of renewable energy output and load, combined with the power trading decision obtained by solving the multi-master and multi-slave game model under a deterministic environment, it generates a distributed optimization scheduling model for the distribution network under the multi-stakeholder game. The distribution network transaction costs and operating network losses, as well as the transaction price and power consumption between multiple microgrid stakeholders, are input into the distributed optimization scheduling model under the multi-stakeholder game. The model is solved based on the N-ADMM solution strategy and the optimized scheduling result is output.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network optimization and scheduling, and specifically to a method and system for distributed optimization and scheduling of distribution networks under multi-stakeholder game. Background Art

[0002] To address the "dual carbon" goals, a large number of renewable distributed energy sources, such as wind power and photovoltaics, are being connected to the power grid. Their intermittent and volatile nature poses significant challenges to the economic and stable operation of the power system. Microgrid technology is a key means of accommodating local renewable energy. Microgrids, as independent stakeholders independent of the distribution network's control, participate in the electricity market, engaging in a flexible game of negotiation among multiple stakeholders to maximize their own interests. Each stakeholder independently chooses the transaction partners and transaction methods, often failing to directly consider the internal physical network constraints of each participant. This results in each stakeholder failing to fully leverage their support for the distribution network. Consequently, traditional distribution network operation and control methods are becoming unsustainable. Furthermore, the significant uncertainty inherent in the distribution network, where multiple microgrids are connected, further exacerbates the complexity of this issue. Summary of the Invention

[0003] To address the deficiencies mentioned in the above background technology, the present invention aims to provide a method and system for distributed optimization scheduling of distribution networks under multi-stakeholder game, so as to achieve coordinated interaction between the transaction layer and the scheduling layer.

[0004] In a first aspect, the purpose of the present invention can be achieved by the following technical solution: a distributed optimization scheduling method for distribution network under multi-stakeholder game, the method comprising the following steps:

[0005] The highly coupled nature of active distribution network transactions and operational information accessed by multiple stakeholders is captured to generate a collaborative and interactive energy management framework at the transaction and scheduling layers. Based on this collaborative and interactive energy management framework, stochastic programming theory is introduced to address the uncertainty of renewable energy output and load, generating a microgrid operation model with the goal of maximizing microgrid profits.

[0006] Based on Stackelberg game theory, the microgrid operation model is converted into a multi-master, multi-slave game model in a deterministic environment. Electricity trading information is obtained and a distributed solution algorithm is used to calculate the transaction price and power consumption between multiple microgrid stakeholders. A distributed optimization scheduling model for the distribution network under the multi-stakeholder game is generated by integrating the safety verification of the trading game strategy and the safety and economy of the distribution network operation. Based on the forecast information of renewable energy output and load, and combined with the power trading decisions obtained from the multi-master, multi-slave game model in a deterministic environment, a comprehensive trading strategy is used to generate a distributed optimization scheduling model for the distribution network under the multi-stakeholder game.

[0007] The transaction costs and operating network losses of the distribution network, as well as the transaction prices and electricity quantities among multiple microgrid stakeholders, are obtained. These costs and operating network losses, as well as the transaction prices and electricity quantities among multiple microgrid stakeholders are input into the distributed optimization scheduling model of the distribution network under the multi-stakeholder game. The model is solved based on the N-ADMM solution strategy, and the optimized scheduling results are output.

[0008] In conjunction with the first aspect, in certain implementations of the first aspect, the method further includes: within the active distribution network transaction and operation information, taking into account the benefit pursuit and operation characteristics of multiple stakeholders in the active distribution network, constructing an energy management framework for coordinated interaction between the transaction layer and the scheduling layer, including the transaction layer and the scheduling layer;

[0009] At the dispatching level, the distribution network makes the transaction decision of microgrid i Generate virtual load equivalently and establish transaction adjustment variables of microgrid i , and then build a scheduling model based on physical constraints, and solve the scheduling decision and ,like , it means that the transaction decision meets the physical network constraints; otherwise, Return to Microgrid.

[0010] In conjunction with the first aspect, in certain implementations of the first aspect, the method further includes: the process of generating the microgrid operation model:

[0011] The general objective function of stochastic programming SP is as follows:

[0012]

[0013] Where y is the vector of all decision variables and state variables, ξ is the vector of uncertain variables, and f(y,ξ) is the objective function E ξ (·) is the expectation of the function (.);

[0014] The probability density of PV is expressed by Beta distribution, while the probability density of WT is generally described by Weibull distribution. Let the probability density function of PV active power be f PV (P PV ), let the probability density function corresponding to WT be f WT (P WT ), for any scenario n, the active power output interval is described as [P n PV,min ,P n PV,max ], then the mean and probability of the scene are obtained by the following formula:

[0015]

[0016]

[0017] The same method is used to obtain the average active power output by WT under typical scenario m: and probability ;

[0018] The scene construction method is as follows:

[0019] The independent scenario set of WT and PV active power is established through equations (2)-(3):

[0020]

[0021] The set of joint scenarios of WT and PV active power is obtained through Cartesian product:

[0022]

[0023] The occurrence probability of scene s in the joint scene set is:

[0024]

[0025] Through the above steps, we can get the joint scene set ,Then, the SP-based MG operation model is described as:

[0026]

[0027] Where s is any scene, π s is the probability of occurrence of the corresponding scenario, ξ s is the actual value of the random variable in this scenario.

[0028] In conjunction with the first aspect, in certain implementations of the first aspect, the method further includes: a process of converting the microgrid operation model into a multi-master multi-slave game model based on Stackelberg game theory:

[0029] The maximum profit purchased by each microgrid (MG) includes power consumption and energy sales revenue, while reducing the operating cost composed of MT operating cost. For any MGi, the energy trading revenue is:

[0030]

[0031] Where, represents the electricity price at which MG sells and purchases electricity from DS; represents the transaction electricity price between MGs during period t; It is represented by the amount of electricity purchased and sold by MGi to the DSO in period t; represents the amount of electricity purchased and sold by MGi to other MGs in period t;

[0032] The energy consumption benefits of MGi are:

[0033]

[0034] Where, The parameter represents the satisfaction of MGi in period t; represents the active load of node i in period t;

[0035] Operating costs include MT costs, and the mathematical formula is:

[0036]

[0037] Where a i,n ,b i,n ,c i,n represents the cost curve coefficient of MTn in MGi; represents the active power output of MTn in MGi during period t;

[0038] According to the above analysis, the objective function is expressed as:

[0039]

[0040] The operation constraints of MG operation include power flow constraints, MT operation constraints and safety constraints.

[0041] In conjunction with the first aspect, in certain implementations of the first aspect, the method further includes: constructing the multi-master and multi-slave game model as follows:

[0042] The game participants are all MGOs, and the set is represented by N = {MG1,MG2,…,MG N}, the game strategy set is expressed as exist

[0043]

[0044] Where, It is represented by the electric energy purchased and sold by MGi to MGj during period t;

[0045] The profit pool includes the profit generated by the MGO when trading energy with other MGOs. express;

[0046] By solving the established MG operation model, the surplus or deficit of MG can be obtained. The surplus or deficit of MG is highly coupled with the power generation of RES, the decision of controllable equipment, the load demand and the power transaction price between MGs. In order to determine the power transaction price, the game participant set is formed as follows: MGs with surplus power are regarded as sellers, MGs with power deficit are regarded as buyers, and other MGs are regarded as bystanders. On this basis, assuming that there are N1 sellers and N2 buyers, the seller set is formed.

[0047] Buyer Collection At the same time, N1+N2≤N;

[0048] Using the constructed MG operation model and participant set, the game model of MMGs’ P2P transactions is expressed as:

[0049]

[0050] Where x i , x -i Denotes the decision variable vector of MGi and other MGs, and the solution x={x1,…,x N} is the optimal decision for MG operation and transaction, which is obtained by solving the game model. Formula (23-1) converts x -i The objective function of profit maximization is defined as x -i ={x1,...,x i-1 ,x i+1 ,...,x N}, Equation (23-2) describes the constraints of MG operation, Θ i (x -i ) is the feasible domain of the variable.

[0051] In conjunction with the first aspect, in certain implementations of the first aspect, the method further includes: a process for calculating the distribution network transaction costs and operating network losses, and the transaction electricity prices and electricity quantities between multiple microgrid stakeholders:

[0052] Initialization: Initialize the electricity price of MG power transactions during the entire scheduling period and iteration index k;

[0053] Solve the operation problem of each MG: Solve the constructed MG operation model, then each MG obtains its transaction power as or

[0054] Forming a gaming alliance;

[0055] Electricity price update: electricity prices are updated based on supply and demand;

[0056] Power update: Power updates are performed according to the proportion of power transaction demand;

[0057] Convergence test: If the electricity price residual between two iterations is less than the iteration criterion, terminate; otherwise, set k = k + 1 and return to re-solve the MG operation problems.

[0058] In conjunction with the first aspect, in certain implementations of the first aspect, the method further includes: minimizing an objective function of the distribution network transaction cost, operating network loss, and transaction adjustment, where the objective function is as follows:

[0059]

[0060] Where: and The electricity purchase and sales prices published by the distribution network; and The power purchased and sold by the distribution network from the i-th MG; ΔE i,t is the transaction adjustment amount; γ and α are the transaction adjustment loss cost coefficient and network loss coefficient; r ij is the resistance of branch ij; l ij,t is the square of the current in branch ij.

[0061] In conjunction with the first aspect, in certain implementations of the first aspect, the method further includes: the objective function of the distributed optimization scheduling model of the distribution network under the multi-stakeholder game is as follows:

[0062]

[0063] Where: s is any scene; π s The probability of the scenario occurring; ξ s is the true value of the uncertain variable in the scenario;

[0064] The constraints include: power flow balance constraints, joint regulation of on-load tap-changing transformer constraints, capacitor bank switching constraints, photovoltaic capacity limitation constraints and distribution network operation safety constraints.

[0065] In conjunction with the first aspect, in certain implementations of the first aspect, the method further includes: solving based on the N-ADMM solution strategy and outputting an optimized scheduling result:

[0066] Update the decision variable x a,k :The clusters of the distribution network are decoupled by Lagrangian functions, and the decision variables of each area can be obtained by solving the following equation:

[0067]

[0068] Update auxiliary variables , based on the results, update the auxiliary variables according to the following formula:

[0069]

[0070] Update the dual variables Based on Nesterov's accelerated gradient method, the update rule of the dual variable is improved. The information weight of the k-1 iteration in the traditional ADMM is increased from 1 to 1+k / (k+3), and the dual variable information of the k-2 iteration is introduced. The details are as follows:

[0071]

[0072] in,

[0073]

[0074] Where ρ is the penalty function;

[0075] Convergence judgment: If the convergence condition is met, the result is output and the algorithm ends; if the convergence condition is not met, the number of iterations k = k + 1 is updated, and the decision variable x is updated again a,k ; The convergence condition of the algorithm is:

[0076] max{r k ,s k}≤τ(60)

[0077] Where τ is a predefined small positive number; r k and s k are the original residual and the dual residual respectively, and the expressions are:

[0078]

[0079] In a second aspect, in order to achieve the above-mentioned purpose, the present invention discloses a distributed optimization dispatching system for distribution networks under a multi-stakeholder game, comprising:

[0080] The model generation module is used to receive the highly coupled characteristics of active distribution network transactions and operation information accessed by multiple stakeholders and generate a collaborative interactive energy management framework at the transaction and scheduling layers. Based on this collaborative interactive energy management framework at the transaction and scheduling layers, stochastic programming theory is introduced to deal with the uncertainty of renewable energy output and load, and a microgrid operation model is generated with the goal of maximizing microgrid benefits.

[0081] The model processing module is used to convert the microgrid operation model into a multi-master, multi-slave game model in a deterministic environment based on Stackelberg game theory; obtain power transaction information and use a distributed solution algorithm to calculate the transaction electricity price and power among multiple microgrid stakeholders; comprehensively verify the safety of the transaction game strategy and the safety and economy of the distribution network operation, based on the forecast information of renewable energy output and load, and the power transaction decision obtained by solving the multi-master, multi-slave game model in a deterministic environment, to generate a distributed optimization scheduling model for the distribution network under the multi-stakeholder game;

[0082] The optimization scheduling module is used to obtain the transaction costs and operating network losses of the distribution network, as well as the transaction prices and electricity quantities among multiple microgrid stakeholders. The distribution network transaction costs and operating network losses, as well as the transaction prices and electricity quantities among multiple microgrid stakeholders are input into the distributed optimization scheduling model of the distribution network under the multi-stakeholder game, and the solution is obtained based on the N-ADMM solution strategy to output the optimized scheduling results.

[0083] Beneficial effects of the present invention:

[0084] In view of the fact that traditional transaction game strategies are difficult to directly consider physical network constraints, the present invention constructs a distributed optimization scheduling model for active distribution networks under multi-stakeholder games based on the coordinated interaction of the transaction layer and the scheduling layer. A highly reliable distributed algorithm has been developed, which does not require a centralized central coordination center. Through simple boundary information exchange between different regions, each region can solve the model locally and obtain the optimal solution through alternating iterations. At the same time, in the process of iterative solution, the historical information of auxiliary variables and dual variables can be fully utilized to speed up the solution of the model. The present invention can achieve the organic coordination of multi-stakeholder transactions and the optimized operation of the distribution network, improve the economy and safety of the real-time operation of the distribution network, promote the active consumption of renewable energy, and promote the zero-carbonization process of electricity growth. BRIEF DESCRIPTION OF THE DRAWINGS

[0085] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, those skilled in the art can derive other drawings based on these drawings without inventive effort.

[0086] Figure 1 It is a schematic flow chart of the method of the present invention;

[0087] Figure 2 This is a schematic diagram of the energy management framework of the present invention where the transaction layer and scheduling layer coordinate and interact;

[0088] Figure 3 This is a schematic diagram of distributed optimization scheduling of active distribution network under multi-stakeholder game of the present invention;

[0089] Figure 4 Schematic diagram of the system structure of the present invention;

[0090] Figure 5 It is the day-ahead prediction graph of WT, PV and PD of 4 MGs of the present invention;

[0091] Figure 6 It is the electricity purchase price PBDS and electricity selling price PSDS diagram of the MG0 in the transaction between the present invention and the DSO;

[0092] Figure 7 It is the transaction price graph of MGs in the P2P mode of the present invention;

[0093] Figure 8 It is a scheduling decision diagram of MT and active load of a single MG of the present invention;

[0094] Figure 9 This is a diagram of the reactive power provided by the PV of the present invention to a single MG;

[0095] Figure 10 FIG. 4 is a diagram of the voltage level of a single MG in this embodiment. DETAILED DESCRIPTION

[0096] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0097] Example 1:

[0098] The following is an introduction to the relevant terms involved in the embodiments of this application:

[0099] Distribution network: A distribution network receives electricity from the transmission grid or regional power plants and distributes it locally or step-by-step according to voltage to various users through distribution facilities. It is composed of overhead lines, cables, towers, distribution transformers, disconnectors, VAR compensators, and other ancillary facilities, and plays a key role in distributing electricity within the power grid.

[0100] like Figure 1 As shown, a distributed optimization scheduling method for distribution network under multi-stakeholder game includes the following steps:

[0101] S101: Receive the highly coupled characteristics of active distribution network transactions and operation information for multi-stakeholder access, and generate a collaborative interactive energy management framework at the transaction and scheduling layers. Based on this collaborative interactive energy management framework at the transaction and scheduling layers, introduce stochastic programming theory to deal with the uncertainty of renewable energy output and load, and generate a microgrid operation model with the goal of maximizing microgrid benefits.

[0102] In the active distribution network transaction and operation information, considering the benefit pursuit and operation characteristics of multiple stakeholders in the active distribution network, a coordinated and interactive energy management framework is constructed, including the transaction layer and the dispatching layer;

[0103] 1) At the transaction level, microgrid operators have independent pricing and quantity rights when trading with multiple microgrids. They often determine the transaction price within a range that is higher than the distribution network's purchase price but lower than the distribution network's sales price to improve their own efficiency. That is, based on the power deficit or surplus, microgrids, with the goal of maximizing their own interests, engage in trading games among multiple microgrids to determine the transaction power and power prices. At the same time, a microgrid i submits the power trading decision at time t to the distribution network based on the power deficit / surplus after trading with other microgrids.

[0104] 2) At the dispatching level, the distribution network makes the transaction decision of microgrid i Generate virtual load equivalently and establish transaction adjustment variables of microgrid i , and then build a scheduling model based on physical constraints, and solve the scheduling decision and ,like , it means that the transaction decision meets the physical network constraints; otherwise, Return to Microgrid.

[0105] 3) Under the above energy management framework, the trading layer satisfies the pursuit of maximizing the interests of various stakeholders, and the dispatching layer ensures that trading decisions meet the constraints of the physical network. Through the interaction between the trading layer and the dispatching layer, the coordination and unification of multi-stakeholder transactions and the safe and economical operation of the active distribution network are promoted.

[0106] Generally speaking, optimization methods for uncertainty include stochastic programming (SP), robust optimization (RO), and distributionally robust optimization (DRO). However, RO and DRO problems form max-min-max formulas, making it difficult to prove the unique existence of a game equilibrium using such complex formulas. However, the SP model used here is linear, which does not change the properties of the game model, thus guaranteeing the game equilibrium.

[0107] The process of generating a microgrid operation model by using stochastic programming theory to deal with the uncertainty of renewable energy output and load:

[0108] The general objective function of stochastic programming SP is as follows:

[0109]

[0110] Where y is the vector of all decision variables and state variables, ξ is the vector of uncertain variables, and f(y,ξ) is the objective function E ξ (·) is the expectation of the function (.);

[0111] The probability density of PV is expressed by Beta distribution, while the probability density of WT is generally described by Weibull distribution. Let the probability density function of PV active power be f PV (P PV ), let the probability density function corresponding to WT be f WT (P WT ), for any scenario n, the active power output interval is described as , then the mean and probability of the scene are obtained by the following formula:

[0112]

[0113]

[0114] The same method is used to obtain the average active power output by WT under typical scenario m: and probability ;

[0115] The scene construction method is as follows:

[0116] The independent scenario set of WT and PV active power is established through equations (2)-(3):

[0117]

[0118] The set of joint scenarios of WT and PV active power is obtained through Cartesian product:

[0119]

[0120] The occurrence probability of scene s in the joint scene set is:

[0121]

[0122] Through the above steps, we can get the joint scene set ,Then, the SP-based MG operation model is described as:

[0123]

[0124] Where s is any scene, π s is the probability of occurrence of the corresponding scenario, ξ sis the actual value of the random variable in this scenario.

[0125] The process of converting the microgrid operation model into a multi-master multi-slave game model based on Stackelberg game theory:

[0126] The maximum profit purchased by each microgrid (MG) includes power consumption and energy sales revenue, while reducing the operating cost composed of MT operating cost. For any MGi, the energy trading revenue is:

[0127]

[0128] Where, represents the electricity price at which MG sells and purchases electricity from DS; represents the transaction electricity price between MGs during period t; It is represented by the amount of electricity purchased and sold by MGi to the DSO in period t; represents the amount of electricity purchased and sold by MGi to other MGs in period t;

[0129] The energy consumption benefits of MGi are:

[0130]

[0131] Where, The parameter represents the satisfaction of MGi in period t; represents the active load of node i in period t;

[0132] Operating costs include MT costs, and the mathematical formula is:

[0133]

[0134] Where a i,n ,b i,n ,c i,n represents the cost curve coefficient of MTn in MGi; represents the active power output of MTn in MGi during period t;

[0135] According to the above analysis, the objective function is expressed as:

[0136]

[0137] ② Operation constraints: The operation constraints of MG operation include power flow constraints, MT operation constraints and safety constraints. In order to illustrate the power flow characteristics of MGs, the distribution flow model is inverted as shown below:

[0138]

[0139] Where r ij , xij represents the resistance / reactance of branch ij; represents the PV active and reactive power of node j; represents the active power of WT of node j in period t; represents the active and reactive loads of node j in period t; v i,t ,l ij,t Represents the node voltage and the square of the current.

[0140] The operating constraints of PV are as follows:

[0141]

[0142] Where, represents the PV capacity of node i in period t.

[0143] Within each MG operation, MGOs can trade with other MGOs or DSOs to increase profits and reduce costs. At the same time, the trading flow on the grid should be limited to avoid overcapacity.

[0144]

[0145]

[0146] Where, Indicates the upper bound of active power in the transaction.

[0147] At the same time, the operational safety of MGs must also be ensured, and its safety constraints are constructed as follows:

[0148]

[0149] Where, Indicates the upper / lower limit of the allowed voltage; Indicates the upper and lower limit values ​​of the allowed current.

[0150] 2) Establish a game-based P2P transaction model for MMGs. Through the bargaining P2P model, MGs can determine the electricity price. Obviously, if , buyers tend to trade with other MGOs rather than with DSOs to reduce the cost of electricity purchase. When the seller trades with other MGOs, the electricity sales revenue will increase compared to selling to DSO. There is a relationship between it and the DS transaction price, and the relationship is as follows.

[0151]

[0152] During the actual operation of MGs, some MGs may have excess power while others may be short on power. Each MG buyer prefers to purchase electricity from other MGs at a lower price than the DSO, while each MG seller prefers to sell electricity to other MGs to generate more sales revenue. This creates a competitive relationship between MG sellers and MG buyers. The energy trading problem between MGOs is a typical non-cooperative game problem, and the game model is constructed as follows:

[0153] The game participants are all MGOs, and the set is represented by N = {MG1,MG2,…,MG N}, the game strategy set is expressed as exist

[0154]

[0155] Where, It is represented by the electric energy purchased and sold by MGi to MGj during period t.

[0156] The profit pool includes the profit generated by the MGO when trading energy with other MGOs. express.

[0157] By solving the established MG operation model, the surplus or deficit of MG can be obtained, and the surplus or deficit is highly coupled with the RES power generation, controllable device decision-making, load demand, and the power transaction price between MGs. To determine the power transaction price, the game participant set is formed as follows: MGs with surplus power are regarded as sellers, MGs with power deficit are regarded as buyers, and other MGs are regarded as bystanders. On this basis, assuming there are N1 sellers and N2 buyers, the seller set is formed. Buyer Collection , and N1+N2≤N.

[0158] Using the constructed MG operation model and participant set, the game model of P2P transactions of MMGs can be expressed as:

[0159]

[0160] stx i ∈Θ i (x -i )(23-2)

[0161] Where x i , x -i Represents the decision variable vector of MGi and other MGs. Solution x={x1,…,x N} is the optimal decision of MG operation and transaction, which can be obtained by solving the game model.-i The objective function of profit maximization (i.e., the decision of other MGs) is defined as x -i ={x1,...,x i-1 ,x i+1 ,...,x N Equation (23-2) describes the constraints of MG operation, Θ i (x -i ) is the feasible domain of the variable.

[0162] S102: Based on Stackelberg game theory, the microgrid operation model is converted into a multi-master, multi-slave game model under a deterministic environment. Electricity transaction information is obtained and a distributed solution algorithm is used to calculate the transaction price and power consumption between multiple microgrid stakeholders. The distributed optimization scheduling model of the distribution network under the multi-stakeholder game is generated by integrating the safety verification of the transaction game strategy and the safety and economy of the distribution network operation, based on the forecast information of renewable energy output and load, and the power transaction decision obtained by solving the multi-master, multi-slave game model under the deterministic environment.

[0163] Because each MGO is a stakeholder, its information about power generation, consumption, renewable capacity, installed equipment, and network parameters is private. Therefore, traditional centralized algorithms are inapplicable due to their inability to protect privacy. This paper proposes a distributed algorithm that requires minimal information (energy transaction prices and quantities). This approach describes the relationship between power supply and demand, and its process is as follows:

[0164] (1) Initialization: Initialize the electricity price ρ of MG power transactions during the entire scheduling period MG , and iteration index k.

[0165] (2) Solving the operation problem of each MG: Solving the constructed MG operation model, each MG can obtain its transaction power as or .

[0166] (3) Forming a game alliance: Game participants form a temporary alliance in the form of a buyer and seller alliance, and the game is played between these two alliances. The membership of these two alliances is not fixed in different iterations. If the energy transaction price rises high enough in the next iteration, some buyers may also become sellers in the next iteration. In addition, if there is an oversupply of electricity between MGs, then the seller is the leader and the buyer is the follower; if there is an undersupply of electricity between MGs, then the buyer is the leader and the seller is the follower. During the game, each leader will exchange information with the followers.

[0167] (4) Electricity price update: Based on the supply and demand relationship, the electricity price is updated using the following formula:

[0168]

[0169] (5) Power update: According to the proportion of power transaction demand, the power is updated using the following formula:

[0170]

[0171] (6) Convergence test: If the electricity price residual between two iterations is less than the iteration criterion, terminate; otherwise, set k = k + 1 and return to step 2.

[0172]

[0173] S103: Obtain the transaction costs and operating network losses of the distribution network, as well as the transaction prices and electricity quantities among multiple microgrid stakeholders. Input the transaction costs and operating network losses of the distribution network, as well as the transaction prices and electricity quantities among multiple microgrid stakeholders into the distributed optimization scheduling model of the distribution network under the multi-stakeholder game, solve it based on the N-ADMM solution strategy, and output the optimized scheduling result.

[0174] Taking into account the transaction cost of the distribution network, the operating network loss, and the transaction adjustment between the microgrid and the ADN, with the goal of minimizing them, the active / reactive decision variables of the controllable equipment in the ADN and the transaction adjustment ΔE are adjusted. i,t The constraints of the ADN distributed optimization scheduling model are constructed by analyzing the power flow balance, equipment operation constraints, distribution network operation safety, and sub-region boundary consistency of the distribution network. Based on the forecast information of renewable energy output and load, the stochastic programming theory is introduced to establish a distributed optimization scheduling model for the distribution network under the multi-stakeholder game.

[0175] Taking into account the minimization objective function of distribution network transaction costs, operating network losses and transaction adjustments, the objective function is as follows:

[0176]

[0177] Where: and The electricity purchase and sales prices published by the distribution network; and The power purchased and sold by the distribution network from the i-th MG; ΔE i,t is the transaction adjustment amount; γ and α are the transaction adjustment loss cost coefficient and network loss coefficient; r ij is the resistance of branch ij; l ij,t is the square of the current in branch ij.

[0178] The power flow balance constraints are as follows:

[0179]

[0180]

[0181]

[0182] Where: P ij,t and Q ij,t are the active power and reactive power flowing through branch ij respectively; x ij is the reactance of branch ij; v i,t is the square of the node voltage.

[0183] The combined regulation of the on-load tap-changing transformer (OLTC) constraints is as follows:

[0184]

[0185] Where: v 1,t and v base,t is the square of the primary voltage and secondary voltage of OLTC; r min is the square of the voltage corresponding to the lowest tap position of the OLTC; r s is the difference between the two taps of OLTC; is the auxiliary variable of OLTC; T t OLTC It is the tap position of OLTC; Adjust the maximum gear position for OLTC; The maximum number of times the OLTC gear can be adjusted during the T period.

[0186] The capacitor bank (CB) switching constraints are as follows:

[0187]

[0188]

[0189] Where: Q tap is the compensation reactive power of each group of CB; To determine the number of CB groups put into operation; is the auxiliary variable of CB; The upper limit of the number of capacitor bank switching groups; The maximum number of switching times allowed within the T period.

[0190] Photovoltaic (PV) capacity limit constraints are as follows:

[0191]

[0192] Where: is the capacity of distributed photovoltaic connected to node i.

[0193] The distribution network operation security constraints are as follows:

[0194]

[0195]

[0196]

[0197] Where: are the upper and lower limits of the voltage at node i; are the lower and upper limits of the branch current.

[0198] The size of photovoltaic output is related to the light intensity. The photovoltaic output size is as follows:

[0199]

[0200] Where: P PVn is the rated output of the photovoltaic panel; S is the light radiation; S stc is the light radiation under standard test conditions; R c The light radiation point.

[0201] The probability density of PV is usually expressed by Beta distribution, and the probability density function is as follows:

[0202]

[0203] Where: Γ(·) is the gamma function; s and s max are the actual illumination radiation intensity and the maximum illumination radiation intensity; α and β are shape parameters.

[0204] For any scenario, the probability of PV is as follows:

[0205]

[0206] The random probability model is as follows:

[0207]

[0208] Where: y is the decision variable and state variable; ξ is the uncertain variable; E ξ (·) is the expectation of the function; f(y,ξ) is the objective function.

[0209] Using forecast information on renewable energy output and load, based on stochastic programming theory, and using probability density to characterize the uncertainty of photovoltaics, we can then transform the uncertainty model into a deterministic model. Therefore, the objective function of the distributed optimization scheduling model for distribution networks under multi-stakeholder game is rewritten as follows:

[0210]

[0211] Where: s is any scene; π s The probability of the scenario occurring; ξ s is the true value of the uncertain variable in the scenario.

[0212] Regarding step (6), Nesterov's accelerated gradient is used to improve the dual variable update rule of ADMM, and the N-ADMM solution strategy is proposed. By improving the mining and utilization of historical iteration information, the distributed solution speed of the distribution network model is improved. The specific steps are as follows:

[0213] (1) Update the decision variable x a,k Since the clusters of the distribution network are decoupled through the Lagrangian function, the decision variables of each region can be obtained by solving the following equation.

[0214]

[0215] (2) Update auxiliary variables Based on the result obtained in step 1, update the auxiliary variables according to the following formula:

[0216]

[0217] (3) Update the dual variables Based on Nesterov's accelerated gradient method, the update rule of the dual variable is improved. The information weight of the k-1 iteration in the traditional ADMM is increased from 1 to 1+k / (k+3), and the dual variable information of the k-2 iteration is introduced, thereby speeding up the solution. The details are as follows:

[0218]

[0219] in,

[0220]

[0221] Where ρ is the penalty function.

[0222] (4) Determine convergence. If the convergence condition is met, the result is output and the algorithm ends; if the convergence condition is not met, the number of iterations k = k + 1 is updated and the algorithm goes to step 1. The convergence condition of the algorithm is:

[0223] max{r k ,s k}≤τ(60)

[0224] Among them, τ is a predefined small positive number, representing the tolerance of the algorithm; r k and s kare the original residual and the dual residual respectively, and the specific expressions are:

[0225]

[0226] Example 2: The second aspect, as Figure 4 As shown, in order to achieve the above-mentioned purpose, the present invention discloses a distributed optimization dispatching system for distribution network under multi-stakeholder game, comprising:

[0227] The model generation module 11 is used to receive the characteristics of the highly coupled active distribution network transactions and operation information accessed by multiple stakeholders, and generate a collaborative interactive energy management framework at the transaction layer and the scheduling layer. Based on the collaborative interactive energy management framework at the transaction layer and the scheduling layer, stochastic programming theory is introduced to deal with the uncertainty of renewable energy output and load, and a microgrid operation model is generated with the goal of maximizing microgrid benefits.

[0228] The model processing module 12 is used to convert the microgrid operation model into a multi-master multi-slave game model under a deterministic environment based on Stackelberg game theory; obtain electric energy transaction information and use a distributed solution algorithm to calculate the transaction electricity price and power among multiple microgrid stakeholders; comprehensively verify the safety of the transaction game strategy and the safety and economy of the distribution network operation, based on the forecast information of renewable energy output and load, and combined with the power transaction decision obtained by solving the multi-master multi-slave game model under the deterministic environment, to generate a distributed optimization scheduling model for the distribution network under the multi-stakeholder game;

[0229] The optimization scheduling module 13 is used to obtain the transaction costs and operating network losses of the distribution network, as well as the transaction prices and electricity quantities between multiple microgrid stakeholders, and input the distribution network transaction costs and operating network losses, as well as the transaction prices and electricity quantities between multiple microgrid stakeholders into the distributed optimization scheduling model of the distribution network under the multi-stakeholder game, solve it based on the N-ADMM solution strategy, and output the optimized scheduling results.

[0230] The actual verification case of this patent will be given below.

[0231] The effectiveness of the proposed DS-NGS and distributed algorithm was verified through simulations of a 4-microgrid test system. All programming and computations were performed using MATLAB 2019b software and the YALMIP platform. The model was solved using the commercial solver GUROBI.

[0232] A. Test System Description

[0233] This paper builds a 4MGs test system based on the IEEE 123 bus test system to verify the effectiveness of the proposed P2P transaction model and solution. In addition, the four MGs are connected to each other in a point-to-point manner through six connection lines, similar to the literature. Each MG is configured with PV, WT, and MT. The day-ahead forecast of WT, PV active power, and active load is shown in the figure. The power sales and purchase prices of the DSO are shown in the figure. Figure 6 shown.

[0234] Table 1 Cost parameters of PD and MT

[0235]

[0236] Table 2 Installation parameters of MT, PV, and WT

[0237]

[0238]

[0239] B. P2P transaction test between MGs

[0240] According to the constructed model and solution, the results of energy transactions are as follows Figure 7-8 As shown in Figure 2, WTs and PVs produce clean and renewable electricity, so they are not restricted or curtailed throughout the dispatch timeframe. It can be seen that transactions between MGs are intended to reduce their electricity purchase costs and increase their electricity sales revenue, resulting in the MGs' electricity trading price being lower than the DSO's electricity sales price and higher than the DSO's electricity purchase price.

[0241] The scheduling decisions of MT and active load of 4 MGs are as follows: Figure 8 As shown in the figure, the MT provides flexible active power to balance power consumption and demand. It's worth noting that the decision to supply power to the MT or purchase power from the DSO is driven by cost. Furthermore, load consumption may be curtailed at any given time, with the amount of curtailment determined by comparing the cost of purchased power with the cost of load reduction.

[0242] contrast Figure 7 and Figure 8 As can be seen, the trading and dispatching decisions are highly consistent. For MG1 and MG2, the consumption parameters of the MTs installed in these two MGs are smaller than those of the MTs installed in MG3 and MG4. Therefore, the MT installed in MG1 provides the most electricity, while the MT installed in MG4 provides the least. Furthermore, since MG4's PV and WT active power are high, while its active load is low, MG4 primarily sells electricity to other MGs and DS, without requiring MT power generation. Conversely, for MG3, the active power supply from MTs and PV cannot sustain the active load consumption, requiring MG3 to purchase electricity from other MGs and DS.

[0243] As can be seen, among the four MGs, when renewable energy supply is less than their active power supply, the MT will provide active power. Furthermore, during this period, none of the MGs will sell power to the other four MGs; instead, they will purchase power from the DS. The decision to purchase power from the DS or use the MT is determined by the corresponding costs.

[0244] from Figure 9 It can be seen that all PVs absorb reactive power from the system. This is because WT and PV provide sufficient active power supply to the microgrid, thereby increasing the voltage level. In order to reduce the voltage deviation, all PVs are controlled to absorb reactive power. The voltage level corresponding to MG is as follows Figure 10 shown.

[0245] As can be seen, the voltage level is maintained during safe operation, and the voltage deviation is also small. This demonstrates the effectiveness of the proposed method in maintaining safe operation of MGs. Furthermore, the transaction results for two scenarios, trading only with the DS and trading with both the DS and MGs, are shown in Table III.

[0246] Table 3. MGS benefits under two scenarios

[0247]

[0248] The total cost of all MGs transactions decreased from 138.5225 to 103.0761, a decrease of 25.58%. In addition, for MG 1 and MG 3, which have more PV and WT installed, the cost reduction is very obvious, which means that the proposed method shows better performance when the demand for energy trading is high.

[0249] The above shows and describes the basic principles, main features and advantages of the present disclosure. Those skilled in the art should understand that the present disclosure is not limited to the above embodiments. The above embodiments and descriptions are merely illustrative of the principles of the present disclosure. Various changes and improvements may be made to the present disclosure without departing from the spirit and scope of the present disclosure, and such changes and improvements shall fall within the scope of the present disclosure.

Claims

1. A distributed optimization scheduling method for distribution network under multi-stakeholder game, characterized by: The method comprises the following steps: Receive the highly coupled characteristics of active distribution network transactions and operation information accessed by multiple stakeholders, and generate a collaborative interactive energy management framework at the transaction layer and scheduling layer; Based on the collaborative interactive energy management framework of the transaction layer and the scheduling layer, stochastic programming theory is introduced to deal with the uncertainty of renewable energy output and load, and a microgrid operation model is generated with the goal of maximizing microgrid benefits. Based on Stackelberg game theory, the microgrid operation model is converted into a multi-master and multi-slave game model under a deterministic environment. Electricity transaction information is obtained and a distributed solution algorithm is used to calculate the transaction electricity price and power among multiple microgrid stakeholders. Comprehensive transaction game strategy security verification and distribution network operation safety and economy, based on renewable energy output and load forecast information, combined with the power transaction decision obtained by solving the multi-master and multi-slave game model under a deterministic environment, generate a distributed optimization scheduling model for the distribution network under the multi-stakeholder game; The process of converting the microgrid operation model into a multi-master multi-slave game model based on Stackelberg game theory is as follows: The maximum profit of each microgrid MG purchase includes power consumption and energy sales revenue, while reducing the operating cost composed of MT operating cost. For any MGi, the energy trading revenue is: Where, represents the electricity price at which MG sells and purchases electricity from DS; represents the transaction electricity price between MGs during period t; It is represented by the amount of electricity purchased and sold by MGi to the DSO in period t; represents the amount of electricity purchased and sold by MGi to other MGs in period t; The energy consumption benefit of MGi is: Where, The parameter represents the satisfaction of MGi in period t; represents the active load of node i in period t; Operating costs include MT costs, and the mathematical formula is: Where a i,n ,b i,n ,c i,n represents the cost curve coefficient of MTn in MGi; represents the active power output of MTn in MGi during period t; According to the above analysis, the objective function is expressed as: The operational constraints of MG operation include power flow constraints, MT operation constraints, and safety constraints; The multi-master and multi-slave game model is constructed as follows: The game participants are all MGOs, and the set is represented by N = {MG1,MG2,…,MG N }, the game strategy set is expressed as exist Where, It is represented by the electric energy purchased and sold by MGi to MGj during period t; The profit pool includes the profit generated by the MGO when trading energy with other MGOs. express; By solving the established MG operation model, the surplus or deficit of MG can be obtained. The surplus or deficit of MG is highly coupled with the power generation of RES, the decision of controllable equipment, the load demand and the power transaction price between MGs. In order to determine the power transaction price, the game participant set is formed as follows: MGs with surplus power are regarded as sellers, MGs with power deficit are regarded as buyers, and other MGs are regarded as bystanders. On this basis, assuming that there are N1 sellers and N2 buyers, the seller set is formed. Buyer Collection At the same time, N1+N2≤N; Using the constructed MG operation model and participant set, the game model of MMGs’ P2P transactions is expressed as: s.t. x i ∈Θ i (x -i ) (23-2) Where x i , x -i Denotes the decision variable vector of MGi and other MGs, and the solution x={x1,…,x N } is the optimal decision for MG operation and transaction, which is obtained by solving the game model. Formula (23-1) converts x -i The objective function of profit maximization is defined as x -i ={x1,...,x i-1 ,x i+1 ,...,x N }, Equation (23-2) describes the constraints of MG operation, Θ i (x -i ) is the feasible domain of the variable; The transaction costs and operating network losses of the distribution network, as well as the transaction prices and electricity quantities among multiple microgrid stakeholders, are obtained. These costs and operating network losses, as well as the transaction prices and electricity quantities among multiple microgrid stakeholders are input into the distributed optimization scheduling model of the distribution network under the multi-stakeholder game. The model is solved based on the N-ADMM solution strategy, and the optimized scheduling results are output.

2. The method for distributed optimization scheduling of distribution network under multi-stakeholder game according to claim 1 is characterized in that: In the active distribution network transaction and operation information, the efficiency pursuit and operation characteristics of multiple stakeholders in the active distribution network are taken into consideration to build an energy management framework for coordinated interaction between the transaction layer and the scheduling layer, including the transaction layer and the scheduling layer; At the dispatching level, the distribution network makes the transaction decision of microgrid i Generate virtual load equivalently and establish transaction adjustment variables of microgrid i Then build a scheduling model based on physical constraints and solve the scheduling decision and like It means that the transaction decision meets the physical network constraints; otherwise, Return to Microgrid.

3. The method for distributed optimization scheduling of distribution network under multi-stakeholder game according to claim 1, characterized in that: The process of generating the microgrid operation model: The general objective function of stochastic programming SP is as follows: Where y is the vector of all decision variables and state variables, ξ is the vector of uncertain variables, and f(y,ξ) is the objective function E ξ (·) is the expectation of the function (·); The probability density of PV is expressed by Beta distribution, while the probability density of WT is generally described by Weibull distribution. Let the probability density function of PV active power be f PV (P PV ), let the probability density function corresponding to WT be f WT (P WT ), for any scenario n, the active power output interval is described as The mean and probability of the scenario are obtained by the following formula: The same method is used to obtain the average active power output by WT under typical scenario m: and probability The scene construction method is as follows: The independent scenario set of WT and PV active power is established through equations (2)-(3): The set of joint scenarios of WT and PV active power is obtained through Cartesian product: The occurrence probability of scene s in the joint scene set is: Through the above steps, we can get the joint scene set Then, the SP-based MG operation model is described as: Where s is any scene, π s is the probability of occurrence of the corresponding scenario, ξ s is the actual value of the random variable in this scenario.

4. The method for distributed optimization scheduling of distribution network under multi-stakeholder game according to claim 1, characterized in that: The calculation process of the distribution network transaction costs and operating network losses as well as the transaction prices and electricity volumes between multiple microgrid stakeholders is as follows: Initialization: Initialize the electricity price ρ of MG power transaction during the entire scheduling period MG , and iteration index k; Solve the operation problem of each MG: Solve the constructed MG operation model, then each MG obtains its transaction power as or Forming a gaming alliance; Electricity price update: electricity prices are updated based on supply and demand; Power update: Power updates are performed according to the proportion of power transaction demand; Convergence test: If the electricity price residual between two iterations is less than the iteration criterion, terminate; otherwise, set k = k + 1 and return to re-solve the MG operation problems.

5. The method for distributed optimization scheduling of distribution network under multi-stakeholder game according to claim 1, characterized in that: The objective function for minimizing the distribution network transaction cost, operation network loss, and transaction adjustment is as follows: Where: and The electricity purchase and sales prices published by the distribution network; and The power purchased and sold by the distribution network from the i-th MG; ΔE i,t is the transaction adjustment amount; γ and α are the transaction adjustment loss cost coefficient and network loss coefficient; r ij is the resistance of branch ij; l ij,t is the square of the current in branch ij.

6. The method for distributed optimization scheduling of distribution network under multi-stakeholder game according to claim 1, characterized in that: The objective function of the distributed optimization scheduling model of the distribution network under the multi-stakeholder game is as follows: Where: s is any scene; π s The probability of the scenario occurring; ξ s is the true value of the uncertain variable in the scenario; The constraints include: power flow balance constraints, joint regulation of on-load tap-changing transformer constraints, capacitor bank switching constraints, photovoltaic capacity limitation constraints and distribution network operation safety constraints.

7. The method for distributed optimization scheduling of distribution network under multi-stakeholder game according to claim 1, characterized in that: The process of solving the problem based on the N-ADMM solution strategy and outputting the optimized scheduling result is as follows: Update the decision variable x a,k :The clusters of the distribution network are decoupled by Lagrangian functions, and the decision variables of each area can be obtained by solving the following equation: Update auxiliary variables Based on the results obtained, the auxiliary variables are updated according to the following formula: Update the dual variables Based on Nesterov's accelerated gradient method, the update rule of the dual variable is improved. The information weight of the k-1 iteration in the traditional ADMM is increased from 1 to 1+k / (k+3), and the dual variable information of the k-2 iteration is introduced. The details are as follows: in, Where ρ is the penalty function; Convergence judgment: If the convergence condition is met, the result is output and the algorithm ends; if the convergence condition is not met, the number of iterations k = k + 1 is updated, and the decision variable x is updated again a,k ; The convergence condition of the algorithm is: max{r k ,s k }≤τ (60) Where τ is a predefined small positive number; r k and s k are the original residual and the dual residual respectively, and the expressions are:

8. A distributed optimization dispatching system for a distribution network under a multi-stakeholder game, which adopts a distributed optimization dispatching method for a distribution network under a multi-stakeholder game according to any one of claims 1 to 7, characterized in that: include: The model generation module is used to receive the characteristics of the high coupling of active distribution network transactions and operation information accessed by multiple stakeholders, and generate a collaborative interactive energy management framework at the transaction layer and scheduling layer; Based on the collaborative interactive energy management framework of the transaction layer and the scheduling layer, stochastic programming theory is introduced to deal with the uncertainty of renewable energy output and load, and a microgrid operation model is generated with the goal of maximizing microgrid benefits. The model processing module is used to convert the microgrid operation model into a multi-master and multi-slave game model under a deterministic environment based on Stackelberg game theory; obtain power transaction information and use a distributed solution algorithm to calculate the transaction electricity price and power among multiple microgrid stakeholders; Comprehensive transaction game strategy security verification and distribution network operation safety and economy, based on renewable energy output and load forecast information, combined with the power transaction decision obtained by solving the multi-master and multi-slave game model under a deterministic environment, generate a distributed optimization scheduling model for the distribution network under the multi-stakeholder game; The optimization scheduling module is used to obtain the transaction costs and operating network losses of the distribution network, as well as the transaction prices and electricity quantities among multiple microgrid stakeholders. The distribution network transaction costs and operating network losses, as well as the transaction prices and electricity quantities among multiple microgrid stakeholders are input into the distributed optimization scheduling model of the distribution network under the multi-stakeholder game, and the solution is obtained based on the N-ADMM solution strategy to output the optimized scheduling results.

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