CN116402223A - Cooperative scheduling method, system and equipment for power distribution network - Google Patents

Abstract

The invention discloses a power distribution network cooperative scheduling method, a system and equipment, and relates to the field of power distribution network scheduling optimization of a power system, wherein the method comprises the following steps: performing game balance analysis on the constructed multi-element emerging-load participation electric power market transaction model, wherein the multi-element emerging-load participation electric power market transaction model comprises a 5G base station aggregator bidding model, an EV aggregator bidding model and an electric power transaction center clearing model; converting objective functions of the multiple emerging loads participating in the electric power market transaction model according to game equilibrium analysis results, and respectively solving a 5G base station aggregator bidding model, an EV aggregator bidding model and an electric power transaction center clearing model; and constructing a multi-element emerging load aggregator market settlement model, including a 5G base station aggregator settlement model and an EV aggregator settlement model. The invention solves the bidding problem of participation of emerging load multi-main body in electric power market transaction and the market clearing problem of considering the safety and blocking of the distribution network.

Description

A distribution network coordinated dispatching method, system and device

Technical Field

The present invention relates to the technical field of power system distribution network dispatching optimization, and in particular to a distribution network coordinated dispatching method, system and equipment.

Background Art

With the accelerated construction of industries related to the "new infrastructure", emerging loads represented by 5G base stations and EVs are booming and becoming a new growth point for electric power, which has brought difficulties to the safe operation of the power system and the green development of the industry. To this end, it is urgent to start from the power demand side, give full play to the decisive role of power grid companies and the market in resource allocation, awaken the still dormant emerging load resources, promote the active participation and efficient use of power flexibility resources, and realize the transformation of the power system operation from the "source follows the load" mode to the "source-load interaction" mode.

At present, the bidding issues of multiple entities participating in electricity market transactions for emerging loads and the market clearing issues considering the safety and congestion of distribution networks need to be resolved.

Summary of the invention

1. Technical issues to be solved

In view of the deficiencies in the prior art, the present invention provides a distribution network coordinated dispatching method, system and device, which solves the bidding problem of multiple emerging load entities participating in power market transactions and the market clearing problem considering the safety and congestion of the distribution network.

(II) Technical solution

To achieve the above objectives, the present invention is implemented through the following technical solutions:

In a first aspect, a distribution network coordinated dispatching method is provided, comprising:

A game equilibrium analysis is conducted on the constructed model of multiple emerging loads participating in the power market transaction, wherein the model of multiple emerging loads participating in the power market transaction includes a 5G base station aggregator bidding model, an EV aggregator bidding model and a power trading center clearing model;

According to the results of game equilibrium analysis, the objective function of the multi-emerging loads participating in the power market transaction model is transformed, and the 5G base station aggregator bidding model, EV aggregator bidding model and power trading center clearing model are solved respectively;

Construct multiple emerging load aggregator market settlement models, including 5G base station aggregator settlement models and EV aggregator settlement models;

The 5G base station aggregator bidding model, EV aggregator bidding model, power trading center clearing model, 5G base station aggregator settlement model and EV aggregator settlement model are solved respectively, and the effectiveness of the scheduling strategy is verified by the solution results.

Preferably, the construction of the bidding model for the 5G base station aggregator is as follows:

When bidding, the goal is to minimize the electricity cost, that is, to maximize the total market revenue on the day before, and calculate the bidding of 5G base station aggregators:

表示第i个基站聚合商在时间t的报价；

表示节点n的出清电价；N_ic为5G基站聚合商i所管理的基站集群集合；

表示中压配电网节点n处5G基站集群储能资源的中标充放电功率，i≤n；

为中压配电网节点n处5G基站集群后备储能在时间t的电量储备；

为中压配电网节点n处5G基站集群的基础用电负荷；Δt为投标时间间隔，在日前市场中为1h；T为投标周期，在日前市场中为24h；Where: _fi5G is the bidding target of the i-th 5G base station aggregator ^;

represents the bid of the i-th base station aggregator at time t;

represents the clearing electricity price of node n; N _ic is the set of base station clusters managed by 5G base station aggregator i;

represents the winning bid charging and discharging power of the 5G base station cluster energy storage resource at the medium-voltage distribution network node n, i≤n;

The power reserve of the 5G base station cluster backup energy storage at the medium voltage distribution network node n at time t;

is the basic power load of the 5G base station cluster at node n of the medium-voltage distribution network; Δt is the bidding time interval, which is 1h in the day-ahead market; T is the bidding cycle, which is 24h in the day-ahead market;

5G base station aggregators must meet the quotation constraints and schedulable domain constraints when bidding:

Quote constraints:

为决策变量；Where: π ^max and π ^min are the maximum and minimum bids to protect healthy market competition; in the bidding model

is the decision variable;

5G base station cluster schedulable domain constraints:

为5G基站集群充放电标志，表示集群在每一时刻净功率只能处于一种状态，实际集群内部可以有充有放；η^5G,ch、η^5G,dis为集群充放电效率；

为基站集群可调度域参数。Where:

is the charge and discharge flag of the 5G base station cluster, indicating that the net power of the cluster can only be in one state at any moment, and there can be both charge and discharge inside the actual cluster; η ^5G,ch and η ^5G,dis are the cluster charge and discharge efficiencies;

is the schedulable domain parameter of the base station cluster.

Preferably, the construction of the EV aggregator bidding model is as follows:

The bidding is based on the distribution network node where the EV charging station is located. The day-ahead bidding target is represented by the distribution network node:

表示节点n处EV聚合商在时间t的报价；

表示中压配电网节点n处EV聚合商的中标充放电功率；

为节点n处充电站在时刻t可调度电量状态；Where: f _n ^ev is the bidding target of the EV aggregator at node n in the medium-voltage distribution network;

represents the bid of the EV aggregator at node n at time t;

represents the bidding charging and discharging power of the EV aggregator at the node n of the medium voltage distribution network;

is the dispatchable power state of the charging station at node n at time t;

EV aggregators must meet the bidding constraints and dispatchable domain constraints when bidding:

Quote constraints:

为EV聚合商j的报价，在投标模型中

为决策变量；Where:

is the bid of EV aggregator j, in the bidding model

is the decision variable;

EV charging station dispatchable domain constraints:

为EV集群充放电标志，表示集群在每一时刻净功率只能处于一种状态，集群内部可以有充有放；η^EV,ch、η^EV,dis为EV集群充放电效率；

为EV集群可调度域参数；Δτ为调度间隔。Where:

is the charge and discharge flag of the EV cluster, indicating that the net power of the cluster can only be in one state at each moment, and there can be both charging and discharging within the cluster; η ^EV,ch , η ^EV,dis are the charge and discharge efficiencies of the EV cluster;

is the schedulable domain parameter of the EV cluster; Δτ is the scheduling interval.

Preferably, the construction of the clearing model of the power trading center specifically includes:

For the clearing of the power trading center, its goal is to maximize the social welfare of the day-ahead market (i.e., consumer surplus). To facilitate the solution, it is changed to minimize

为发电商阶梯电价，g为阶梯标号；N_step为阶梯报价的阶梯集合，

为t时刻向上级电网购电每一级阶梯的出清功率；π^PV为光伏购电电价，N_pv为光伏所在节点数，

为节点n处光伏在t时刻的出清功率；N_i为基站聚合商集合；N_j为EV聚合商集合；需注意的是出清目标中

与

为报价，

为决策变量。Where: f ^ISO is the day-ahead clearing target, where the first item of the target represents the cost of purchasing electricity from the upper grid, the second item represents the cost of purchasing electricity from photovoltaic power generators, the third item represents the cost of purchasing electricity that all 5G base station aggregators are willing to pay, and the fourth item represents the charging cost that all EV aggregators are willing to pay;

is the power generation company's tiered electricity price, g is the tier number; N _step is the tier set of tiered quotations,

is the clearing power of each level of electricity purchased from the upper grid at time t; π ^PV is the photovoltaic power purchase price, N _pv is the number of photovoltaic nodes,

is the clearing power of the photovoltaic power plant at node n at time t; _Ni is the set of base station aggregators; _Nj is the set of EV aggregators; it should be noted that

and

For quotation,

is the decision variable.

When clearing, distribution network flow constraints, voltage safety constraints, line capacity constraints, upper power purchase constraints, emerging load constraints and photovoltaic output constraints are met:

Distribution network linearization power flow constraints:

为配电网节点n处在t时刻的基础有功负荷；Q_mn,t、Q_nk,t分别为支路nm、mk在时刻t的支路无功流动，

为配电网节点m处在t时刻的基础无功负荷，

为配电网节点n处光伏在t时刻的无功出力；

分别表示子节点n与父节点m在时刻t的电压平方值，R_mn、X_mn分别为支路mn的电阻值、电抗值；Wherein: the first to fourth, fifth to sixth, and seventh equations are active power balance constraints, reactive power balance constraints, and voltage balance constraints, respectively. For the convenience of expression, the active power balance constraints and reactive power balance constraints are expressed in a single form in the following text; N _M , _Ni , and N _pv are the set of medium voltage distribution network nodes, the set of base station aggregation and business nodes, and the set of photovoltaic nodes, respectively; P _mn,t , P _nk,t are the branch active power flows of branches mn and nk at time t, respectively; v(n) represents the set of all child nodes when node n is the parent node,

is the basic active load of the distribution network node n at time t; Q _mn,t and Q _nk,t are the reactive flows of branches nm and mk at time t, respectively.

is the basic reactive load of the distribution network node m at time t,

is the reactive power output of the photovoltaic power plant at the distribution network node n at time t;

They represent the square values of the voltages of the child node n and the parent node m at time t, respectively. R _mn and X _mn are the resistance and reactance of the branch mn, respectively.

Distribution network security constraints:

分别为节点电压平方的最大限值、最小限值。Where:

They are the maximum and minimum limits of the square of the node voltage respectively.

Distribution network congestion management:

为支路mn的最大负载容量；N_ML为中压配电网所有支路集合；Where:

is the maximum load capacity of branch mn; N _ML is the set of all branches of the medium voltage distribution network;

Constraints on power purchase from higher authorities:

Where: Node 1 represents the root node; v(1) represents the set of nodes connected to the root node; P _g ^G,max is the maximum power when the quotation is segmented g;

Emerging load power constraints:

Photovoltaic output constraints:

为节点n处光伏有功、无功最大调节量。Where:

is the maximum regulation of photovoltaic active and reactive power at node n.

Preferably, the game equilibrium analysis of the constructed multiple emerging loads participating in the power market transaction model specifically includes:

A Nash game is formed among the emerging load aggregators, and a Stackelberg game is formed between all emerging load aggregators and the power trading center. The Stackelberg game problem uses the KKT reconstruction method, the big M method, and the strong duality theorem to transform the BMINLP into a single-layer mixed integer linear programming model, and then solves it using a commercial solver. A generalized Nash game is formed between the single-layer bidding models of emerging load aggregators due to the coupling constraints of the distribution network.

Preferably, according to the game equilibrium analysis result, the objective function of the multi-element emerging load participation in the power market transaction model is transformed as follows:

Establishing KKT system:

The formula to be solved is as follows:

Where: f(x) is; _ha (x)=0 is an equality constraint; A is the number of equality constraints; _gb (x)≤0 is an inequality constraint; B is the number of inequality constraints;

The conversion KKT system is as follows:

Where: L(x,α,β) is the Lagrangian form; ⊥ is the complementary symbol, that is, the left and right equations have only one term equal to 0;

The complementary constraints in the KKT system are nonlinear constraints, which are linearized using the big M method. The big M method is transformed as follows:

Where: _db is an increasing Boolean variable; M is a large constant;

Transformation objective function:

The bidding objectives of 5G base station aggregators are ultimately transformed into:

为等式约束的对偶变量；

为不等式约束的对偶变量；Where:

is the dual variable of the equality constraint;

is the dual variable of the inequality constraint;

The EV aggregator bidding solution objective is ultimately translated into:

Preferably, the construction of the 5G base station aggregator settlement model specifically includes:

After clearing, the 5G base station aggregator will obtain the total charging and discharging power and clearing electricity price. The final energy consumption cost of the 5G base station aggregator will be settled by the following formula.

为聚合商i总用能成本。Where:

is the total energy cost of aggregator i.

In order to respond to system requirements, the energy storage charging and discharging power of each 5G base station needs to be managed. The output of each base station is optimized with the goal of minimizing the total output deviation of the 5G base station. Since linear power flow calculation is used, there is no unique solution to the objective function. In order to ensure the consistency of power supply reliability of different base stations, a base station power allocation algorithm based on the consistency of scheduling capacity and dispatchable capacity is proposed. The model is as follows:

为聚合商i内基站bs的计划充放电功率，N_bs为聚合商i内基站集合；

为保证弱一致性的辅助变量，

是一个保证弱一致性的偏差常数；第一项约束保证基站供电可靠性一致；其他约束为5G基站自身调控约束。Where: _fi ^plan is the optimization target of base station aggregator i,

is the planned charging and discharging power of base station bs in aggregator i, N _bs is the set of base stations in aggregator i;

To ensure weak consistency of auxiliary variables,

is a deviation constant that ensures weak consistency; the first constraint ensures the consistency of base station power supply reliability; the other constraints are the 5G base station's own control constraints.

Preferably, the construction of the EV aggregator settlement model specifically includes:

The formula for EV aggregator settlement after clearing is as follows:

为EV聚合商总用电成本；Where:

is the total electricity cost of the EV aggregator;

In order to respond to system requirements, the charging and discharging power of each EV is managed; the output of each EV is optimized with the goal of minimizing the total EV output deviation, and at the same time, a weak consistency constraint on the discharge charge is added to balance the battery loss of the interacting EVs; the model is as follows:

为EV聚合商j优化目标，

为聚合商j中车辆ev在t时刻的充放电计划，N_ev为聚合商j内EV集合；

为保证弱一致性的辅助变量，

是一个保证弱一致性的偏差常数；第一项约束保证车辆放电一致性；其他约束为EV自身调控约束；Where:

Optimizing the goal for EV aggregator j,

is the charging and discharging plan of vehicle ev in aggregator j at time t, N _ev is the set of EVs in aggregator j;

To ensure weak consistency of auxiliary variables,

is a deviation constant that ensures weak consistency; the first constraint ensures the consistency of vehicle discharge; the other constraints are EV self-regulation constraints;

The planned charging cost of each EV is the product of the clearing electricity price and the planned charging and discharging power:

为ev用电成本；η^agg为聚合商为盈利而设置的盈利系数，聚合商在投标时预测自身收益情况下发该系数，同时聚合商也通过调节该系数与预测出清电价的乘积吸引EV参与电网互动。Where:

is the electricity cost of EV; η ^agg is the profit coefficient set by the aggregator for profit. The aggregator issues this coefficient when bidding based on its own profit forecast. At the same time, the aggregator also attracts EV to participate in grid interaction by adjusting the product of this coefficient and the predicted clearing electricity price.

In a second aspect, a distribution network coordinated dispatching system is provided, comprising:

An analysis module is used to perform game equilibrium analysis on the constructed multiple emerging loads participating in the power market transaction model, wherein the multiple emerging loads participating in the power market transaction model includes a 5G base station aggregator bidding model, an EV aggregator bidding model and a power trading center clearing model;

A conversion module, used to convert the objective function of the multi-emerging load participation in the power market transaction model according to the game equilibrium analysis result, and solve the 5G base station aggregator bidding model, the EV aggregator bidding model and the power trading center clearing model respectively;

Settlement model building module, used to build multiple emerging load aggregator market settlement models, including 5G base station aggregator settlement model and EV aggregator settlement model;

The solution module is used to solve the 5G base station aggregator bidding model, the EV aggregator bidding model, the power trading center clearing model, the 5G base station aggregator settlement model and the EV aggregator settlement model respectively, and verify the effectiveness of the scheduling strategy through the solution results.

According to a third aspect, a computing device is provided, including:

One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include instructions for executing any of the methods described.

(III) Beneficial effects

(1) The present invention provides a distribution network coordinated dispatching method, system and device, which solves the bidding problem of multiple emerging load entities participating in power market transactions and the market clearing problem considering the safety and congestion of the distribution network.

(2) The present invention provides a distribution network coordinated dispatching method, system and device, which utilizes linearized power flow equations for voltage management and linear external approximate constraints for congestion management, thereby ensuring the safety of distribution network operation during power trading.

(3) The present invention provides a distribution network coordinated dispatching method, system and device, which utilizes a bidding model that minimizes the aggregated commercial electricity cost of emerging loads and a power market clearing model that maximizes social benefits, thereby improving the utilization rate of emerging load resources and reducing the energy cost of emerging loads without requiring distribution network operators to provide additional subsidies.

BRIEF DESCRIPTION OF THE DRAWINGS

Fig. 1 is a schematic flow chart of the method of the present invention;

FIG2 is a grid diagram of a method example provided by an embodiment of the present invention;

FIG3 is a voltage distribution diagram of a power distribution network after use provided by an embodiment of the present invention;

FIG4 is a diagram showing the load condition of the distribution network line after use provided by an embodiment of the present invention;

FIG5 is a diagram of a base station aggregator clearing structure provided by an embodiment of the present invention;

FIG. 6 is a diagram showing the clearing results of an EV aggregator provided in an embodiment of the present invention.

DETAILED DESCRIPTION

The following will be combined with the accompanying drawings of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work are within the scope of protection of the present invention.

Example

As shown in FIG1 , an embodiment of the present invention provides a distribution network coordinated scheduling method, including:

A game equilibrium analysis is conducted on the constructed model of multiple emerging loads participating in the power market transaction, wherein the model of multiple emerging loads participating in the power market transaction includes a 5G base station aggregator bidding model, an EV aggregator bidding model and a power trading center clearing model;

According to the results of game equilibrium analysis, the objective function of the multi-element emerging load participation in the power market transaction model is transformed, and the 5G base station aggregator bidding model, EV aggregator bidding model and power trading center clearing model are solved respectively;

Construct multiple emerging load aggregator market settlement models, including 5G base station aggregator settlement models and EV aggregator settlement models;

The 5G base station aggregator bidding model, EV aggregator bidding model, power trading center clearing model, 5G base station aggregator settlement model and EV aggregator settlement model are solved respectively, and the effectiveness of the scheduling strategy is verified by the solution results.

Please refer to Figure 2-6, the specific implementation steps are as follows:

(1) Construct a model for multiple emerging loads to participate in power market transactions, including a 5G base station aggregator bidding model, an EV aggregator bidding model, and a power trading center clearing model;

(2) Conduct game equilibrium analysis on the constructed transaction model;

(3) Transform the constructed transaction model into a model to facilitate solution;

(4) Construct a multi-faceted emerging load aggregator market settlement model, including a 5G base station aggregator settlement model and an EV aggregator settlement model;

(5) An actual radial grid is used to verify the effectiveness of the coordinated dispatching strategy of the distribution network considering the participation of multiple emerging load entities in power market transactions.

The implementation method of the present invention is described below:

Step 1: Construct a model for emerging loads with multiple entities participating in power market transactions. The main steps of this part are as follows:

(1) Build a bidding model for 5G base station aggregators.

When bidding, each emerging load aggregator hopes to minimize its own electricity cost, so the goal is to minimize the electricity cost, that is, to maximize the total market revenue on the day before, and to calculate the bidding of 5G base station aggregators. The goals for 5G base station aggregators are as follows:

表示第i个基站聚合商在时间t的报价；

表示节点n的出清电价；N_ic为5G基站聚合商i所管理的基站集群集合；

表示中压配电网节点n处5G基站集群储能资源的中标充放电功率，需要注意的是5G基站在国内主要由三大运营商建设，因此在投标时并不是以配电网节点为报价单位，即i≤n；

为中压配电网节点n处5G基站集群后备储能在时间t的电量储备；

为中压配电网节点n处5G基站集群的基础用电负荷；Δt为投标时间间隔，在日前市场中为1h；T为投标周期，在日前市场中为24h。Where: _fi5G is the bidding target of the i-th 5G base station aggregator ^;

represents the bid of the i-th base station aggregator at time t;

represents the clearing electricity price of node n; N _ic is the set of base station clusters managed by 5G base station aggregator i;

It represents the winning bid charging and discharging power of the 5G base station cluster energy storage resources at the medium-voltage distribution network node n. It should be noted that 5G base stations are mainly built by the three major operators in China, so the bidding is not based on the distribution network node, that is, i≤n;

The power reserve of the 5G base station cluster backup energy storage at the medium voltage distribution network node n at time t;

is the basic power load of the 5G base station cluster at node n of the medium-voltage distribution network; Δt is the bidding time interval, which is 1h in the day-ahead market; T is the bidding cycle, which is 24h in the day-ahead market.

5G base station aggregators should meet quotation constraints and schedulable domain constraints when bidding.

a) Quotation constraints:

为决策变量。Where: π ^max and π ^min are the maximum and minimum bids to protect healthy market competition; in the bidding model

is the decision variable.

b) Constraints on the schedulable domain of 5G base station clusters:

为5G基站集群充放电标志，表示集群在每一时刻净功率只能处于一种状态，实际集群内部可以有充有放；η^5G,ch、η^5G,dis为集群充放电效率；

为基站集群可调度域参数。Where:

is the charge and discharge flag of the 5G base station cluster, indicating that the net power of the cluster can only be in one state at any moment, and there can be both charge and discharge inside the actual cluster; η ^5G,ch and η ^5G,dis are the cluster charge and discharge efficiencies;

is the schedulable domain parameter of the base station cluster.

(2) Construct an EV aggregator bidding model.

Similar to 5G base station aggregators, EV aggregators bid the day-ahead with the goal of minimizing energy costs, but the difference is that EV energy costs are already included in the charging power. In addition, EV clusters are aggregated in the form of charging stations, so the bid is based on the distribution network node where the EV charging station is located. The day-ahead bidding target can be represented by the distribution network node:

为中压配电网节点n处EV聚合商的投标目标；

表示节点n处EV聚合商在时间t的报价；

表示中压配电网节点n处EV聚合商的中标充放电功率；

为节点n处充电站在时刻t可调度电量状态。Where:

is the bidding target of the EV aggregator at node n of the medium voltage distribution network;

represents the bid of the EV aggregator at node n at time t;

represents the bidding charging and discharging power of the EV aggregator at the node n of the medium voltage distribution network;

is the dispatchable power state of the charging station at node n at time t.

EV aggregators should meet the quotation constraints and dispatchable domain constraints when bidding.

a) Quotation constraints:

为EV聚合商j的报价，在投标模型中

为决策变量。Where:

is the bid of EV aggregator j, in the bidding model

is the decision variable.

b) EV charging station dispatchable domain constraints:

为EV集群充放电标志，表示集群在每一时刻净功率只能处于一种状态，集群内部可以有充有放；η^EV,ch、η^EV,dis为EV集群充放电效率；

为EV集群可调度域参数；Δτ为调度间隔。Where:

is the charge and discharge flag of the EV cluster, indicating that the net power of the cluster can only be in one state at each moment, and there can be both charging and discharging within the cluster; η ^EV,ch , η ^EV,dis are the charge and discharge efficiencies of the EV cluster;

is the schedulable domain parameter of the EV cluster; Δτ is the scheduling interval.

(3) Construct a clearing model for the power trading center.

For the clearing of the power trading center, its goal is to maximize the social welfare of the day-ahead market (i.e., consumer surplus). To facilitate the solution, it is changed to a minimization solution.

为发电商阶梯电价，g为阶梯标号；N_step为阶梯报价的阶梯集合，

为t时刻向上级电网购电每一级阶梯的出清功率；π^PV为光伏购电电价，N_pv为光伏所在节点数，

为节点n处光伏在t时刻的出清功率；N_i为基站聚合商集合；N_j为EV聚合商集合。需注意的是出清目标中

与

为报价，

为决策变量。Where: f ^ISO is the day-ahead clearing target, where the first item of the target represents the cost of purchasing electricity from the upper grid, the second item represents the cost of purchasing electricity from photovoltaic power generators, the third item represents the cost of purchasing electricity that all 5G base station aggregators are willing to pay, and the fourth item represents the charging cost that all EV aggregators are willing to pay;

is the power generation company's tiered electricity price, g is the tier number; N _step is the tier set of tiered quotations,

is the clearing power of each level of electricity purchased from the upper grid at time t; π ^PV is the photovoltaic power purchase price, N _pv is the number of photovoltaic nodes,

is the clearing power of the photovoltaic power plant at node n at time t; _Ni is the set of base station aggregators; _Nj is the set of EV aggregators.

and

For quotation,

is the decision variable.

When clearing, the distribution network flow constraints, voltage safety constraints, line capacity constraints, superior power purchase constraints, emerging load constraints and photovoltaic output constraints should be met.

a) Distribution network linearization power flow constraints:

In solving the electricity market transaction model, the power flow constraints based on the Distflow theory are a set of non-convex and nonlinear equations, which are not convenient for market clearing. Therefore, it is approximated by the linearized Distflow model:

为配电网节点n处在t时刻的基础有功负荷；Q_mn,t、Q_nk,t分别为支路nm、mk在时刻t的支路无功流动，

为配电网节点m处在t时刻的基础无功负荷，

为配电网节点n处光伏在t时刻的无功出力；

分别表示子节点n与父节点m在时刻t的电压平方值，R_mn、X_mn分别为支路mn的电阻值、电抗值。Wherein: the first to fourth, fifth to sixth, and seventh equations are active power balance constraints, reactive power balance constraints, and voltage balance constraints, respectively. For the convenience of expression, the active power balance constraints and reactive power balance constraints are expressed in a single form in the following text; N _M , _Ni , and N _pv are the set of medium voltage distribution network nodes, the set of base station aggregation and business nodes, and the set of photovoltaic nodes, respectively; P _mn,t , P _nk,t are the branch active power flows of branches mn and nk at time t, respectively; v(n) represents the set of all child nodes when node n is the parent node,

is the basic active load of the distribution network node n at time t; Q _mn,t and Q _nk,t are the reactive flows of branches nm and mk at time t, respectively.

is the basic reactive load of the distribution network node m at time t,

is the reactive power output of the photovoltaic power plant at the distribution network node n at time t;

They represent the square values of the voltages of the child node n and the parent node m at time t, respectively. R _mn and X _mn are the resistance and reactance of the branch mn, respectively.

b) Distribution network security constraints:

分别为节点电压平方的最大限值、最小限值。Where:

They are the maximum and minimum limits of the square of the node voltage respectively.

c) Distribution network congestion management:

为支路mn的最大负载容量；N_ML为中压配电网所有支路集合。线路容量约束虽然是凸二次约束，但由于其在出清问题中使用KKT条件将具有较强的非线性与非凸性，因此可采用线性外近似约束来进行阻塞管理，简化出清计算。Where:

is the maximum load capacity of branch mn; N _ML is the set of all branches in the medium voltage distribution network. Although the line capacity constraint is a convex quadratic constraint, it has strong nonlinearity and non-convexity when using KKT conditions in the clearing problem. Therefore, a linear external approximate constraint can be used to perform congestion management and simplify the clearing calculation.

d) Constraints on power purchase from higher authorities:

为报价分段g时的最大功率。Where: Node 1 represents the root node; v(1) represents the set of nodes connected to the root node;

The maximum power when the quotation is segmented g.

e) Emerging load power constraints:

f) Photovoltaic output constraints:

为节点n处光伏有功、无功最大调节量。Where:

is the maximum regulation of photovoltaic active and reactive power at node n.

Step 2: Conduct game equilibrium analysis on the transaction model constructed in step 1. The main steps of this part are as follows:

From the bidding and clearing model between emerging load aggregators and power trading centers, it can be seen that the emerging load aggregators (5G base station aggregator 1, ..., 5G base station aggregator k, ..., EV aggregator 1, ..., EV aggregator n) form a Nash game, and all emerging load aggregators and power trading centers form a Stackelberg game. The essence of the Stackelberg game problem is to solve a double-layer mixed integer nonlinear programming (BMINLP) model, which cannot be solved directly using a commercial solver. Therefore, the KKT reconstruction method, the big M method, and the strong duality theorem are used to transform the BMINLP into a single-layer mixed integer linear programming (MILP) model, and then the commercial solver is used to solve it. At this time, the single-layer bidding models of emerging load aggregators form a generalized Nash game due to the coupling constraints of the power distribution network. Since the equilibrium of the power retail market is universal, there is an equilibrium solution to the problem to be solved.

Step 3: Perform model conversion on the constructed transaction model. The main steps of this part are as follows:

(1) Establish a KKT system.

To facilitate the establishment of the KKT system, the basic form of KKT condition transformation is described below. The following equation is to be solved:

In the formula: f(x) is; _ha (x)=0 is an equality constraint; A is the number of equality constraints; _gb (x)≤0 is an inequality constraint; B is the number of inequality constraints.

The converted KKT system is as follows:

Where: L(x,α,β) is the Lagrangian form; ⊥ is the complementary symbol, that is, one and only one term of the two equations on the left and right is 0.

The complementary constraint in the KKT system is a nonlinear constraint, so it is linearized using the big M method. The big M method is transformed as follows:

Where: _db is an increasing Boolean variable; M is a large constant.

(2) Transform the objective function.

The bidding objectives of 5G base station aggregators are ultimately transformed into:

为等式约束的对偶变量；

为不等式约束的对偶变量。Where:

is the dual variable of the equality constraint;

is the dual variable of the inequality constraint.

The EV aggregator bidding solution objective is ultimately translated into:

Step 4: Construct a settlement model for multiple emerging load aggregators. The main steps of this part are as follows:

(1) Build a settlement model for 5G base station aggregators.

After clearing, the 5G base station aggregator will obtain the total charging and discharging power and clearing electricity price. The final energy consumption cost of the 5G base station aggregator will be settled by the following formula.

为聚合商i总用能成本。Where:

is the total energy cost of aggregator i.

In order to respond to system requirements, the energy storage charging and discharging power of each 5G base station needs to be managed. The output of each base station is optimized with the goal of minimizing the total output deviation of the 5G base station. Since linear power flow calculation is used, there is no unique solution to the objective function. In order to ensure the consistency of power supply reliability of different base stations, a base station power allocation algorithm based on the consistency of scheduling capacity and dispatchable capacity is proposed. The model is as follows:

为聚合商i内基站bs的计划充放电功率，N_bs为聚合商i内基站集合；

为保证弱一致性的辅助变量，

是一个保证弱一致性的偏差常数；第一项约束保证基站供电可靠性一致；其他约束为5G基站自身调控约束。Where: _fi ^plan is the optimization target of base station aggregator i,

is the planned charging and discharging power of base station bs in aggregator i, N _bs is the set of base stations in aggregator i;

To ensure weak consistency of auxiliary variables,

is a deviation constant that ensures weak consistency; the first constraint ensures the consistency of base station power supply reliability; the other constraints are the 5G base station's own control constraints.

(2) Build an EV aggregator settlement model.

Similar to 5G base station aggregators, EV aggregators settle in the following manner after clearance.

为EV聚合商总用电成本。Where:

is the total electricity cost of EV aggregator.

In order to respond to system requirements, the charging and discharging power of each EV is managed. The output of each EV is optimized with the goal of minimizing the total output deviation of EVs. At the same time, a weak consistency constraint on the discharge charge is added to balance the battery loss of the participating EVs. The model is as follows:

为EV聚合商j优化目标，

为聚合商j中车辆ev在t时刻的充放电计划，N_ev为聚合商j内EV集合；

为保证弱一致性的辅助变量，

是一个保证弱一致性的偏差常数；第一项约束保证车辆放电一致性；其他约束为EV自身调控约束。Where:

Optimizing the goal for EV aggregator j,

is the charging and discharging plan of vehicle ev in aggregator j at time t, N _ev is the set of EVs in aggregator j;

To ensure weak consistency of auxiliary variables,

is a deviation constant that ensures weak consistency; the first constraint ensures the consistency of vehicle discharge; the other constraints are EV's own regulation constraints.

The planned charging cost of each EV is the product of the clearing electricity price and the planned charging and discharging power:

为ev用电成本；η^agg为聚合商为盈利而设置的盈利系数，聚合商在投标时预测自身收益情况下发该系数，同时聚合商也通过调节该系数与预测出清电价的乘积吸引EV参与电网互动。Where:

is the electricity cost of EV; η ^agg is the profit coefficient set by the aggregator for profit. The aggregator issues this coefficient when bidding based on its own profit forecast. At the same time, the aggregator also attracts EV to participate in grid interaction by adjusting the product of this coefficient and the predicted clearing electricity price.

Step 5: Based on steps 1, 2, 3, and 4, an actual radial grid is used to verify the effectiveness of the coordinated dispatching strategy of the distribution network considering the participation of multiple emerging load entities in power market transactions. The main steps of this part are as follows:

(1) Calculate the bidding model for 5G base station aggregators and EV aggregators.

(2) Calculate the electricity market clearing model.

(3) Calculate the settlement model for 5G base station aggregators.

(4) Calculate the EV aggregator settlement model.

Table 1 Aggregator's expected electricity cost and actual electricity cost

An embodiment of the present invention provides a distribution network coordinated dispatching system, including:

An analysis module is used to perform game equilibrium analysis on the constructed multiple emerging loads participating in the power market transaction model, wherein the multiple emerging loads participating in the power market transaction model includes a 5G base station aggregator bidding model, an EV aggregator bidding model and a power trading center clearing model;

A conversion module, used to convert the objective function of the multi-emerging load participation in the power market transaction model according to the game equilibrium analysis result, and solve the 5G base station aggregator bidding model, the EV aggregator bidding model and the power trading center clearing model respectively;

Settlement model building module, used to build multiple emerging load aggregator market settlement models, including 5G base station aggregator settlement model and EV aggregator settlement model;

The solution module is used to solve the 5G base station aggregator bidding model, the EV aggregator bidding model, the power trading center clearing model, the 5G base station aggregator settlement model and the EV aggregator settlement model respectively, and verify the effectiveness of the scheduling strategy through the solution results.

The embodiments of the present application can be provided as methods or computer program products. Therefore, the present application can adopt the form of complete hardware embodiment, complete software embodiment, or the embodiment in combination with software and hardware. Moreover, the present application can adopt the form of computer program products implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code. The scheme in the embodiments of the present application can be implemented in various computer languages, for example, object-oriented programming language Java and literal scripting language JavaScript, etc.

The present application is described with reference to the flowchart and/or block diagram of the method, device (system) and computer program product according to the embodiment of the present application. It should be understood that each process and/or box in the flowchart and/or block diagram, and the combination of the process and/or box in the flowchart and/or block diagram can be realized by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device produce a device for realizing the function specified in one process or multiple processes in the flowchart and/or one box or multiple boxes in the block diagram.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction device that implements the functions specified in one or more processes in the flowchart and/or one or more boxes in the block diagram.

These computer program instructions may also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, whereby the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more processes in the flowchart and/or one or more boxes in the block diagram.

It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

Claims (10)

1. A distribution network coordinated dispatching method, characterized by comprising: A game equilibrium analysis is conducted on the constructed model of multiple emerging loads participating in the power market transaction, wherein the model of multiple emerging loads participating in the power market transaction includes a 5G base station aggregator bidding model, an EV aggregator bidding model and a power trading center clearing model; According to the results of game equilibrium analysis, the objective function of the multi-element emerging load participation in the power market transaction model is transformed, and the 5G base station aggregator bidding model, EV aggregator bidding model and power trading center clearing model are solved respectively; Construct multiple emerging load aggregator market settlement models, including 5G base station aggregator settlement models and EV aggregator settlement models; The 5G base station aggregator bidding model, EV aggregator bidding model, power trading center clearing model, 5G base station aggregator settlement model and EV aggregator settlement model are solved respectively, and the effectiveness of the scheduling strategy is verified through the solution results. 2. A distribution network coordinated dispatching method according to claim 1, characterized in that: the construction of the 5G base station aggregator bidding model is as follows: When bidding, the goal is to minimize the electricity cost, that is, to maximize the total market revenue on the day before, and calculate the bidding of 5G base station aggregators:

Where: _fi5G is the bidding target of the i-th 5G base station aggregator ^;

represents the bid of the i-th base station aggregator at time t;

represents the clearing electricity price of node n; N _ic is the set of base station clusters managed by 5G base station aggregator i;

represents the winning bid charging and discharging power of the 5G base station cluster energy storage resource at the medium-voltage distribution network node n, i≤n;

The power reserve of the 5G base station cluster backup energy storage at the medium voltage distribution network node n at time t;

is the basic power load of the 5G base station cluster at node n of the medium-voltage distribution network; Δt is the bidding time interval, which is 1h in the day-ahead market; T is the bidding cycle, which is 24h in the day-ahead market; 5G base station aggregators must meet the quotation constraints and schedulable domain constraints when bidding: Quote constraints:

Where: π ^max and π ^min are the maximum and minimum bids to protect healthy market competition; in the bidding model

is the decision variable; 5G base station cluster schedulable domain constraints:

Where:

is the charge and discharge flag of the 5G base station cluster, indicating that the net power of the cluster can only be in one state at any moment, and there can be both charge and discharge inside the actual cluster; η ^5G,ch and η ^5G,dis are the cluster charge and discharge efficiencies;

is the schedulable domain parameter of the base station cluster. 3. A distribution network coordinated dispatching method according to claim 2, characterized in that: the construction of the EV aggregator bidding model is as follows: The bidding is based on the distribution network node where the EV charging station is located. The day-ahead bidding target is represented by the distribution network node:

Where:

is the bidding target of the EV aggregator at node n of the medium voltage distribution network;

represents the bid of the EV aggregator at node n at time t;

represents the bidding charging and discharging power of the EV aggregator at the node n of the medium voltage distribution network;

is the dispatchable power state of the charging station at node n at time t; EV aggregators must meet the bidding constraints and dispatchable domain constraints when bidding: Quote constraints:

Where:

is the bid of EV aggregator j, in the bidding model

is the decision variable; EV charging station dispatchable domain constraints:

Where:

is the charge and discharge flag of the EV cluster, indicating that the net power of the cluster can only be in one state at each moment, and there can be both charging and discharging within the cluster; η ^EV,ch , η ^EV,dis are the charge and discharge efficiencies of the EV cluster;

is the schedulable domain parameter of the EV cluster; Δτ is the scheduling interval. 4. A distribution network coordinated dispatching method according to claim 3, characterized in that: the construction of the power trading center clearing model specifically includes: For the clearing of the power trading center, its goal is to maximize the social welfare of the day-ahead market (i.e., consumer surplus). To facilitate the solution, it is changed to minimize

Where: f ^ISO is the day-ahead clearing target, where the first item of the target represents the cost of purchasing electricity from the upper grid, the second item represents the cost of purchasing electricity from photovoltaic power generators, the third item represents the cost of purchasing electricity that all 5G base station aggregators are willing to pay, and the fourth item represents the charging cost that all EV aggregators are willing to pay;

is the power generation company's tiered electricity price, g is the tier number; N _step is the tier set of tiered quotations,

is the clearing power of each level of electricity purchased from the upper grid at time t; π ^PV is the photovoltaic power purchase price, N _pv is the number of photovoltaic nodes,

is the clearing power of the photovoltaic power plant at node n at time t; _Ni is the set of base station aggregators; _Nj is the set of EV aggregators; it should be noted that

and

For quotation,

is the decision variable. When clearing, distribution network flow constraints, voltage safety constraints, line capacity constraints, upper power purchase constraints, emerging load constraints and photovoltaic output constraints are met: Distribution network linearization power flow constraints:

Wherein: the first to fourth, fifth to sixth, and seventh equations are active power balance constraints, reactive power balance constraints, and voltage balance constraints, respectively. For the convenience of expression, the active power balance constraints and reactive power balance constraints are expressed in a single form in the following text; N _M , _Ni , and N _pv are the set of medium voltage distribution network nodes, the set of base station aggregation and business nodes, and the set of photovoltaic nodes, respectively; P _mn,t , P _nk,t are the branch active power flows of branches mn and nk at time t, respectively; v(n) represents the set of all child nodes when node n is the parent node,

is the basic active load of the distribution network node n at time t; Q _mn,t and Q _nk,t are the reactive flows of branches nm and mk at time t, respectively.

is the basic reactive load of the distribution network node m at time t,

is the reactive power output of the photovoltaic power plant at the distribution network node n at time t;

They represent the square values of the voltages of the child node n and the parent node m at time t, respectively. R _mn and X _mn are the resistance and reactance of the branch mn, respectively. Distribution network security constraints:

Where:

They are the maximum and minimum limits of the square of the node voltage respectively. Distribution network congestion management:

Where:

is the maximum load capacity of branch mn; N _ML is the set of all branches of the medium voltage distribution network; Constraints on power purchase from higher authorities:

Where: Node 1 represents the root node; v(1) represents the set of nodes connected to the root node;

The maximum power in the quoted segment g; Emerging load power constraints:

Photovoltaic output constraints:

Where:

is the maximum regulation of photovoltaic active and reactive power at node n. 5. A distribution network coordinated dispatching method according to claim 4, characterized in that: the game equilibrium analysis of the constructed multi-emerging load participation in the power market transaction model specifically includes: A Nash game is formed among the emerging load aggregators, and a Stackelberg game is formed between all emerging load aggregators and the power trading center. The Stackelberg game problem uses the KKT reconstruction method, the big M method, and the strong duality theorem to transform the BMINLP into a single-layer mixed integer linear programming model, and then solves it using a commercial solver. A generalized Nash game is formed between the single-layer bidding models of emerging load aggregators due to the coupling constraints of the distribution network. 6. A distribution network coordinated dispatching method according to claim 5, characterized in that: according to the game equilibrium analysis result, the objective function of the multi-element emerging load participating in the power market transaction model is transformed as follows: Establishing KKT system: The formula to be solved is as follows:

Where: f(x) is; _ha (x)=0 is an equality constraint; A is the number of equality constraints; _gb (x)≤0 is an inequality constraint; B is the number of inequality constraints; The converted KKT system is as follows:

Where: L(x,α,β) is the Lagrangian form; ⊥ is the complementary symbol, that is, the left and right equations have only one term equal to 0; The complementary constraints in the KKT system are nonlinear constraints, which are linearized using the big M method. The big M method is transformed as follows:

Where: _db is an increasing Boolean variable; M is a large constant; Transformation objective function: The bidding objectives of 5G base station aggregators are ultimately transformed into:

Where:

is the dual variable of the equality constraint;

is the dual variable of the inequality constraint; The EV aggregator bidding solution objective is ultimately translated into:

7. A distribution network coordinated dispatching method according to claim 6, characterized in that: the construction of the 5G base station aggregator settlement model specifically includes: After clearing, the 5G base station aggregator will obtain the total charging and discharging power and clearing electricity price. The final energy consumption cost of the 5G base station aggregator will be settled by the following formula.

Where:

is the total energy cost of aggregator i. In order to respond to system requirements, the energy storage charging and discharging power of each 5G base station needs to be managed. The output of each base station is optimized with the goal of minimizing the total output deviation of the 5G base station. Since linear power flow calculation is used, there is no unique solution to the objective function. In order to ensure the consistency of power supply reliability of different base stations, a base station power allocation algorithm based on the consistency of scheduling capacity and dispatchable capacity is proposed. The model is as follows:

Where: _fi ^plan is the optimization target of base station aggregator i,

is the planned charging and discharging power of base station bs in aggregator i, N _bs is the set of base stations in aggregator i;

To ensure weak consistency of auxiliary variables,

is a deviation constant that ensures weak consistency; the first constraint ensures the consistency of base station power supply reliability; the other constraints are the 5G base station's own control constraints. 8. A distribution network coordinated dispatching method according to claim 7, characterized in that: the construction of the EV aggregator settlement model specifically includes. The formula for EV aggregator settlement after clearing is as follows:

Where:

is the total electricity cost of the EV aggregator; In order to respond to system requirements, the charging and discharging power of each EV is managed; the output of each EV is optimized with the goal of minimizing the total EV output deviation, and at the same time, a weak consistency constraint on the discharge charge is added to balance the battery loss of the interacting EVs; the model is as follows:

Where: f _j ^plan is the optimization target of EV aggregator j,

is the charging and discharging plan of vehicle ev in aggregator j at time t, N _ev is the set of EVs in aggregator j;

To ensure weak consistency of auxiliary variables,

is a deviation constant that ensures weak consistency; the first constraint ensures the consistency of vehicle discharge; the other constraints are EV self-regulation constraints; The planned charging cost of each EV is the product of the clearing electricity price and the planned charging and discharging power:

Where:

is the electricity cost of EV; η ^agg is the profit coefficient set by the aggregator for profit. The aggregator issues this coefficient when bidding based on its own profit forecast. At the same time, the aggregator also attracts EV to participate in grid interaction by adjusting the product of this coefficient and the predicted clearing electricity price. 9. A distribution network coordinated dispatching system, comprising: An analysis module is used to perform game equilibrium analysis on the constructed multiple emerging loads participating in the power market transaction model, wherein the multiple emerging loads participating in the power market transaction model includes a 5G base station aggregator bidding model, an EV aggregator bidding model and a power trading center clearing model; A conversion module, used to convert the objective function of the multi-emerging load participation in the power market transaction model according to the game equilibrium analysis result, and solve the 5G base station aggregator bidding model, the EV aggregator bidding model and the power trading center clearing model respectively; Settlement model building module, used to build multiple emerging load aggregator market settlement models, including 5G base station aggregator settlement model and EV aggregator settlement model; The solution module is used to solve the 5G base station aggregator bidding model, the EV aggregator bidding model, the power trading center clearing model, the 5G base station aggregator settlement model and the EV aggregator settlement model respectively, and verify the effectiveness of the scheduling strategy through the solution results. 10. A computing device, comprising: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include instructions for executing any of the methods according to claims 1-8.

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