CN112886567A - Master-slave game-based demand side resource flexibility optimal scheduling method and system - Google Patents

Abstract

The invention discloses a demand side resource flexibility optimal scheduling method and system based on a master-slave game. The optimized scheduling method comprises the following steps: analyzing the type of the generalized demand side resource, and establishing a flexible optimization scheduling framework of the large-scale demand side resource; establishing a general model of the generalized demand side resources, and providing a large-scale demand side resource polymerization flexible modeling method based on external approximation; taking three typical generalized demand side resources of a distributed power supply, a load and an energy storage as examples, establishing an economic model of various demand side resources; a demand side resource aggregator is used as a leader, a distributed power source owner, a power user and an energy storage owner are used as slaves, a demand side resource flexibility optimization scheduling method based on a master-slave game is provided, and an iterative game balanced distributed solving method is designed. The invention also discloses an optimized scheduling system, and the demand side resource flexibility optimized scheduling method provided by the invention can effectively aggregate the flexible adjustment capability of large-scale demand side resources and has certain practical application value.

Description

A method and system for optimal scheduling of demand-side resource flexibility based on master-slave game

technical field

The invention relates to the field of energy, in particular to a demand-side resource flexibility optimization scheduling method and system based on a master-slave game.

Background technique

Vigorously developing new energy sources such as wind and light is an inevitable choice to deal with energy and environmental issues. The output of new energy power generation represented by wind and light has significant intermittency, volatility and uncertainty, which puts forward higher requirements for the flexibility of power system operation. Exploiting the flexible adjustment capability of demand-side resources can effectively promote the consumption and utilization of new energy and alleviate the peak-to-valley difference in the net load of the system. However, demand-side resources have the characteristics of many types, small capacity, and large scale. With the increasing scale of demand-side resources, the optimal scheduling of demand-side resources faces the following challenges: First, the existing demand-side resource modeling methods are suitable for demand-side resources. In small-scale scenarios, in the case of large-scale access of demand-side resources, there are significant problems such as increased optimization variables, complex calculations, and difficult solutions; second, the existing optimal scheduling methods cannot well solve the collaborative optimization between different types of demand-side resources and data privacy issues. Therefore, it is necessary to carry out relevant research on the optimal scheduling method of demand-side resource flexibility based on master-slave game, to provide support for promoting the consumption and utilization of renewable energy and increasing the income of demand-side resource owners.

SUMMARY OF THE INVENTION

The purpose of the present invention is to provide a demand-side resource flexibility optimization scheduling method and system that can consider multiple operating subjects.

For achieving the above object, the present invention provides the following scheme:

A demand-side resource flexibility optimization scheduling method based on a master-slave game, the optimal scheduling method comprising:

Analyze the types of generalized demand-side resources;

Establish a flexible and optimal scheduling framework for large-scale demand-side resources;

Establish a general model of generalized demand-side resources;

A flexible modeling method for large-scale demand-side resource aggregation based on external approximation is proposed;

Establish economic models of various demand-side resources;

A demand-side resource flexibility optimization scheduling method based on master-slave game is proposed;

Design an iterative game equilibrium distributed solution method.

Optionally, the types of the generalized demand-side resources specifically include:

1) Distributed power supply. Such generalized demand-side resources can generally be divided into two categories: dispatchable distributed power sources (such as fuel cells, micro gas turbines, etc.) and non-dispatchable distributed power sources (such as wind power, photovoltaic power generation, etc.). It should be noted that unschedulable here means that its output cannot be completely controlled. If necessary, the safety of the system can still be ensured by abandoning the wind and light.

2) Load resources. There are a large number of power loads on the demand side that can cooperate with the grid friendly, and have great potential for regulation. Common loads can be divided into fixed loads and translational loads. In the smart grid environment, grid companies or demand-side resource aggregators can guide users to actively participate in grid optimization through incentives or price signals.

3) Energy storage or energy storage-like resources. Such resources usually include static energy storage and energy storage-like resources (such as electric vehicles), which have dual attributes of power supply and load.

Optionally, the flexible optimization scheduling framework for large-scale demand-side resources specifically includes:

Demand-side resources have the characteristics of various types, small capacity and large scale, which can provide flexibility and adjustment capability for the power grid. In order to fully explore the flexibility potential of large-scale heterogeneous demand-side resources, an optimal scheduling framework for demand-side resource flexibility based on master-slave game is constructed. Among them, the demand-side resource aggregator, as the leader of the game framework, responds to the grid regulation needs by aggregating the flexibility of large-scale differentiated demand-side resources. At the same time, based on the price signal of the power grid, it guides the behavior of various demand-side resources by formulating internal prices; taking distributed power, load, and energy storage as examples, all kinds of demand-side resources serve as followers of the game framework. Schedule your own generation or consumption schedule in response to price signals from aggregators.

Optionally, the general model of the generalized demand-side resources specifically includes:

Diesel generator model:

分别为柴油发电机g的出力上下限，

和

分别为柴油发电机g的向上和向下爬坡速率；P_g,t和P_g,t-1分别为柴油发电机g在时段t和t+1的有功出力，Δt为优化时间间隔；Among them, T is the optimization time period, and G is the set of diesel generators;

are the upper and lower output limits of the diesel generator g, respectively,

and

are the upward and downward climbing rates of diesel generator g, respectively; P _g,t and P _g,t-1 are the active power output of diesel generator g in time periods t and t+1, respectively, and Δt is the optimization time interval;

Photovoltaic model:

为光伏机组j在时段t的最大出力，P_j,t为光伏机组j在时段t的实际出力；Among them, J is the set of photovoltaic units, T is the optimization time period,

is the maximum output of photovoltaic unit j in time period t, and P _j,t is the actual output of photovoltaic unit j in time period t;

Load model:

For electrical loads, it is assumed to consist of fixed and translatable loads. For the former, it can be considered as inflexible. For the latter, its flexibility model is as follows:

分别为用户i在时段t的总负荷、固定负荷和可平移负荷；

和

分别为用户i在时段t的可平移负荷上下限，t_i,max和t_i,min分别为用户i的可平移负荷调节时间范围的上下限，Q_i为用户i的可平移负荷总量；Among them, I is the set of power users, T is the optimization time period; L _i,t ,

are the total load, fixed load and translatable load of user i in time period t, respectively;

and

are the upper and lower limits of the translatable load of user i in time period t, respectively, t _i,max and t _{i ,min} are the upper and lower limits of the adjustment time range of user i's translatable load, respectively, and Qi is the total amount of user i's translatable load;

Energy storage model:

和

分别表示储能设备k在时刻t的电功率和剩余电量，

表示储能设备k在时刻t+1的剩余电量；

和

分别表示储能设备k的充放电功率上下限，

和

分别表示储能设备k的容量上下限；T为优化时间周期，Δt为优化时间间隔；Among them, K is the set of energy storage devices;

and

represent the electric power and remaining power of the energy storage device k at time t, respectively,

represents the remaining power of the energy storage device k at time t+1;

and

respectively represent the upper and lower limits of the charging and discharging power of the energy storage device k,

and

respectively represent the upper and lower limits of the capacity of the energy storage device k; T is the optimization time period, and Δt is the optimization time interval;

Optionally, the external approximation-based scaled demand-side resource aggregation flexibility modeling method specifically includes:

For demand-side resources, it has the characteristics of small capacity and large scale, and is affected by factors such as physical characteristics, natural conditions, and human habits. At the same time, with the continuous increase of the scale of demand-side resources, the above-mentioned flexibility modeling methods based on a single type of demand-side resources face the problem of "dimension disaster" during optimization. Therefore, an external approximation method is used to quantify the aggregation flexibility of large-scale demand-side resources, thereby reducing the computational complexity.

For diesel generators, the aggregation flexibility is:

分别为柴油发电机在时段t和时段t-1的聚合出力；

和

分别表示柴油发电机聚合功率的上下限；

和

分别表示柴油发电机的聚合向上/向下爬坡速率；T为优化时间周期，Δt为优化时间间隔；G为柴油发电机的集合，

分别为柴油发电机g的出力上下限，

和

分别为柴油发电机g的向上和向下爬坡速率；in,

are the aggregated output of diesel generators in time period t and time period t-1, respectively;

and

Respectively represent the upper and lower limits of the aggregate power of diesel generators;

and

respectively represent the aggregate up/down ramp rate of diesel generators; T is the optimization time period, Δt is the optimization time interval; G is the set of diesel generators,

are the upper and lower output limits of the diesel generator g, respectively,

and

are the upward and downward climbing rates of the diesel generator g, respectively;

Likewise, the aggregate flexibility of PV, load, and storage is:

为光伏在时段t的聚合输出功率上限，

光伏机组在时段t的聚合出力，T为优化时间周期；

和

分别表示聚合可平移负荷在时段t的上下限；

为用户在时段t的聚合可平移负荷，

和

分别为用户在时段t的聚合可平移负荷上下限，t_i,max和t_i,min分别为用户可平移负荷调节时间范围的上下限，Q^agg为用户的聚合可平移负荷总量；

和

分别表示储能设备在时刻t的聚合电功率和剩余电量，

表示储能设备在时刻t+1的聚合剩余电量；

和

分别表示储能设备的聚合充放电功率上下限，

和

分别表示储能设备的聚合容量上下限；Δt为优化时间间隔；in,

is the upper limit of aggregate output power of photovoltaics in time period t,

The aggregate output of photovoltaic units in time period t, T is the optimization time period;

and

respectively represent the upper and lower limits of aggregate shiftable load in time period t;

is the aggregated shiftable load of the user at time period t,

and

are the upper and lower limits of the user’s aggregated shiftable load in time period t, respectively, t _i,max and t _i,min are the upper and lower limits of the user’s shiftable load adjustment time range, respectively, and Q ^agg is the total amount of the user’s aggregated shiftable load;

and

represent the aggregated electric power and remaining power of the energy storage device at time t, respectively,

Represents the aggregate remaining power of the energy storage device at time t+1;

and

respectively represent the upper and lower limits of aggregated charge and discharge power of energy storage devices,

and

respectively represent the upper and lower limits of the aggregate capacity of the energy storage device; Δt is the optimization time interval;

Optionally, the establishment of economic models of various demand-side resources specifically includes:

Grid price model:

For the demand-side resource aggregator, it acts as the receiver of the grid price signal, and then optimizes the power generation or electricity consumption behavior of various demand-side resources. The price model of the grid is as follows:

为电网的售电价格，

为电网的购电价格；

分别为时段1、时段2和时段T的售电价格，

分别为时段1、时段2和时段T的购电价格；需要说明的是，在所提优化调度模型中，假定电网的购售电价格为给定值；in,

the price of electricity sold to the grid,

the electricity purchase price for the grid;

are the electricity sales prices in time period 1, time period 2 and time period T, respectively,

are the electricity purchase prices of time period 1, time period 2 and time period T respectively; it should be noted that in the proposed optimal dispatch model, it is assumed that the electricity purchase and sale price of the power grid is a given value;

Demand-side resource aggregator cost model:

Demand-side resource aggregators guide various demand-side resource behaviors by setting internal prices. The specific internal price model is as follows:

和

分别表示需求侧资源聚合者制定的内部售电和购电价格；

分别为时段1、时段2和时段T需求侧资源聚合者制定的售电价格，

分别为时段1、时段2和时段T需求侧资源聚合者制定的购电价格；需要说明的是，内部售电和购电价格需满足如下约束：in,

and

Respectively represent the internal electricity sales and electricity purchase prices set by demand-side resource aggregators;

are the electricity sales prices set by demand-side resource aggregators for time period 1, time period 2, and time period T, respectively,

They are the power purchase prices set by the demand-side resource aggregators in time period 1, time period 2, and time period T respectively; it should be noted that the internal power sales and power purchase prices must meet the following constraints:

和

分别为时段t需求侧资源聚合者制定的售电和购电价格，T为优化时间周期；in,

and

are the electricity sales and electricity purchase prices set by demand-side resource aggregators in time period t, respectively, and T is the optimization time period;

For demand-side resource aggregators, on the one hand, they can respond to the power regulation demand of the power grid through the flexibility of aggregating various demand-side resources, and smooth the net load fluctuation caused by a high proportion of photovoltaic access; on the other hand, demand-side resources need to be realized. The aggregation cost is minimal. Therefore, its cost model is:

F _AGG =ωC _AGG +(1-ω)f _AGG

C _AGG = C _grid + C _DG + C _load + C _ES

和

为时段t电网的售电和购电价格，

和

为时段t聚合者内部的售电和购电价格；

分别为时段t的固定负荷、实际可平移负荷、柴油发电机出力、光伏出力及储能出力，T为优化时间周期；Among them, C _AGG represents the operating cost of the aggregator, f _AGG represents the aggregated power fluctuation of demand-side resources, and ω weight coefficient; C _grid , C _DG , C _load and C _ES represent the aggregator and the grid, the owner of distributed power, the The interaction cost of power users and energy storage owners; NL _t is the net load of the aggregate demand-side resource period t, and L _ave is the average value of the net load in the optimization period;

and

is the electricity sale and purchase price of the power grid in period t,

and

is the price of electricity sales and electricity purchases within the aggregator in time period t;

are the fixed load, actual translatable load, diesel generator output, photovoltaic output and energy storage output in period t, and T is the optimization time period;

Distributed power owner benefit model:

为柴油发电机在时段t的有功出力，

为时段t的光伏出力；

为时段t向聚合者的售电电价；a_G、b_G、c_G分别为柴油发电机的燃料成本系数；Among them, F _DG represents the utility function of the DG owner, the first item of the utility function represents the electricity sales revenue of the DG owner to the aggregator, and the second item is the power generation cost of the controllable DG;

is the active power output of the diesel generator in time period t,

is the photovoltaic output of time period t;

is the electricity selling price to the aggregator in period t; a _G , b _G , and c _G are the fuel cost coefficients of diesel generators respectively;

User benefit model:

和

分别为时段t用户的聚合固定负荷和聚合可平移负荷，

为时段t向聚合者的购电电价；κ为偏好系数；Among them, F _user represents the revenue function of the power user; the first term represents the user's utility function, and the second term represents the user's electricity purchase cost;

and

are the aggregated fixed load and aggregated shiftable load of users in period t, respectively,

is the electricity purchase price from the aggregator in period t; κ is the preference coefficient;

Energy storage owner benefit model:

为时段t储能的出力，

和

为储能所有者在时段t向聚合者的售电和购电价格；

为储能损耗系数。Among them, F _ES represents the revenue function of the energy storage owner; the first term represents the revenue of energy storage, and the second term represents the loss cost of energy storage;

is the output of energy storage for period t,

and

is the price of electricity sold and purchased by the energy storage owner to the aggregator at time t;

is the energy storage loss factor.

Optionally, the proposed method for optimal scheduling of demand-side resource flexibility based on master-slave game specifically includes:

Build a master-slave game model for demand-side resource flexibility scheduling:

分别为分布式电源所有者、电力负荷以及储能所有者的策略；

和

为需求侧资源聚合者的策略；F_DG、F_user、F_ES和F_AGG分别表示分布式电源所有者、电力负荷、储能所有者以及需求侧资源聚合者的支付函数；Among them, N represents the set of game followers, M is the game leader;

Strategies for distributed power owners, power loads, and energy storage owners, respectively;

and

is the strategy of the demand-side resource aggregator; F _DG , F _user , F _ES and F _AGG represent the payment functions of the distributed power generation owner, power load, energy storage owner and demand-side resource aggregator respectively;

In the above game model, when the strategy of each game participant satisfies the following conditions:

是所述主从博弈模型的均衡。policy set

is the equilibrium of the master-slave game model.

Optionally, the design is based on an iterative game equilibrium distributed solution method, which specifically includes:

For the constructed master-slave game model, the general solution idea is to transform the subordinate optimization problem into an equivalent KKT condition as an additional constraint for the leader optimization problem. However, due to the existence of data privacy between different game subjects, the existing solution methods cannot protect the privacy information of game subordinates. Therefore, an iterative-based distributed solution algorithm is designed: first, the demand-side resource aggregator formulates the initial internal electricity purchase and sale price based on the electricity purchase and sale price information of the power grid and publishes it to each subordinate; then, each game subordinate according to the aggregation The internal electricity purchase and sale prices published by the producers, obtain their own power generation or electricity consumption strategies by solving their respective benefit maximization problems, and feed them back to the demand-side resource aggregators; the demand-side resource aggregators adjust the price signal according to the feedback information. Repeat this until the convergence conditions are met.

In order to achieve the above object, the present invention also provides the following scheme:

A demand-side resource flexibility optimization scheduling system based on master-slave game, the optimal scheduling system includes:

The data acquisition module is used to collect the load, photovoltaic output and electricity price data required for the flexible optimal scheduling of demand-side resources;

A flexible optimization scheduling framework establishment module is used to analyze the types of generalized demand-side resources and establish a flexible and optimal scheduling framework for large-scale demand-side resources;

A general model building module for demand-side resources, which is used to build general models of generalized demand-side resources such as distributed power, load, and energy storage;

Demand-side resource aggregation flexibility model building module, used to establish the aggregation flexibility model of large-scale distributed power, load, and energy storage;

The demand-side resource economic model building module is used to build the cost model of demand-side resource aggregators, and build the income model of distributed power generation owners, power users, and energy storage owners;

The master-slave game model building module is used to establish a master-slave game model between demand-side resource aggregators and distributed power generation owners, power users and energy storage owners;

The distributed solving module is used to solve the equilibrium solution of the constructed master-slave game model.

According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects: The present invention discloses a demand-side resource flexibility optimization scheduling method and system based on a master-slave game. By analyzing the types of generalized demand-side resources, a Flexibility optimization scheduling framework for large-scale demand-side resources; establishes a general model of generalized demand-side resources, proposes a modeling method for demand-side resource aggregation flexibility based on external approximation, and quantifies the aggregation flexibility of large-scale demand-side resources ; established economic models of various demand-side resources, proposed a flexible optimal scheduling method of demand-side resources based on master-slave game, and designed an iterative game equilibrium distributed solution method, which overcomes the difficulty of traditional demand-side resource optimal scheduling. The issue of protecting the data privacy of different subjects. The flexible optimal scheduling method for demand-side resources proposed by the present invention can effectively aggregate the flexible adjustment capability of large-scale demand-side resources, and has certain practical application value.

Description of drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings required in the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some of the present invention. In the embodiments, for those of ordinary skill in the art, other drawings can also be obtained according to these drawings without creative labor.

Fig. 1 is the flow chart of the demand side resource flexibility optimization scheduling method based on master-slave game provided by the present invention;

FIG. 2 is a block diagram of the composition of a demand-side resource flexibility optimization scheduling system based on a master-slave game provided by the present invention.

Detailed ways

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

The purpose of the present invention is to provide a demand-side resource flexibility optimization scheduling method and system based on master-slave game.

In order to make the above objects, features and advantages of the present invention more clearly understood, the present invention will be described in further detail below with reference to the accompanying drawings and specific embodiments.

As shown in Figure 1, the present invention provides demand-side resource flexibility optimization scheduling based on master-slave game, and the optimization scheduling method includes:

Step 100: Collect load, photovoltaic output, and electricity price data required for optimal scheduling of demand-side resource flexibility;

Step 200: analyze the types of generalized demand-side resources, and establish a flexible optimal scheduling framework for large-scale demand-side resources;

Step 300: Build a general model of generalized demand-side resources such as distributed power, load, and energy storage;

Step 400: establish an aggregation flexibility model of large-scale distributed power, load, and energy storage;

Step 500: constructing a cost model for demand-side resource aggregators, and constructing a revenue model for distributed power generation owners, power users, and energy storage owners;

Step 600: Establish a master-slave game model between demand-side resource aggregators and distributed power generation owners, power users and energy storage owners;

Step 700: Solve the equilibrium solution of the constructed master-slave game model.

The step 200: analyze the types of generalized demand-side resources, and establish a flexible optimal scheduling framework for large-scale demand-side resources, which specifically includes:

1) Distributed power supply. Such generalized demand-side resources can generally be divided into two categories: dispatchable distributed power sources (such as fuel cells, micro gas turbines, etc.) and non-dispatchable distributed power sources (such as wind power, photovoltaic power generation, etc.). It should be noted that unschedulable here means that its output cannot be completely controlled. If necessary, the safety of the system can still be ensured by abandoning the wind and light.

2) Load resources. There are a large number of power loads on the demand side that can cooperate with the grid friendly, and have great potential for regulation. Common loads can be divided into fixed loads and translational loads. In the smart grid environment, grid companies or demand-side resource aggregators can guide users to actively participate in grid optimization through incentives or price signals.

3) Energy storage or energy storage-like resources. Such resources usually include static energy storage and energy storage-like resources (such as electric vehicles), which have dual attributes of power supply and load.

Demand-side resources have the characteristics of various types, small capacity and large scale, which can provide flexibility and adjustment capability for the power grid. In order to fully explore the flexibility potential of large-scale heterogeneous demand-side resources, an optimal scheduling framework for demand-side resource flexibility based on master-slave game is constructed. Among them, the demand-side resource aggregator, as the leader of the game framework, responds to the grid regulation needs by aggregating the flexibility of large-scale differentiated demand-side resources. At the same time, based on the price signal of the power grid, it guides the behavior of various demand-side resources by formulating internal prices; taking distributed power, load, and energy storage as examples, all kinds of demand-side resources serve as followers of the game framework. Schedule your own generation or consumption schedule in response to price signals from aggregators.

Step 300: Build a general model of generalized demand-side resources such as distributed power sources, loads, and energy storage, which specifically includes:

Diesel generator model:

分别为柴油发电机g的出力上下限，

和

分别为柴油发电机g的向上和向下爬坡速率；P_g,t和P_g,t-1分别为柴油发电机g在时段t和t+1的有功出力，Δt为优化时间间隔；Among them, T is the optimization time period, and G is the set of diesel generators;

are the upper and lower output limits of the diesel generator g, respectively,

and

are the upward and downward climbing rates of diesel generator g, respectively; P _g,t and P _g,t-1 are the active power output of diesel generator g in time periods t and t+1, respectively, and Δt is the optimization time interval;

Photovoltaic model:

为光伏机组j在时段t的最大出力，P_j,t为光伏机组j在时段t的实际出力；Among them, J is the set of photovoltaic units, T is the optimization time period,

is the maximum output of photovoltaic unit j in time period t, and P _j,t is the actual output of photovoltaic unit j in time period t;

Load model:

For electrical loads, it is assumed to consist of fixed and translatable loads. For the former, it can be considered as inflexible. For the latter, its flexibility model is as follows:

分别为用户i在时段t的总负荷、固定负荷和可平移负荷；

和

分别为用户i在时段t的可平移负荷上下限，t_i,max和t_i,min分别为用户i的可平移负荷调节时间范围的上下限，Q_i为用户i的可平移负荷总量；Among them, I is the set of power users, T is the optimization time period; L _i,t ,

are the total load, fixed load and translatable load of user i in time period t, respectively;

and

are the upper and lower limits of the translatable load of user i in time period t, respectively, t _i,max and t _{i ,min} are the upper and lower limits of the adjustment time range of user i's translatable load, respectively, and Qi is the total amount of user i's translatable load;

Energy storage model:

和

分别表示储能设备k在时刻t的电功率和剩余电量，

表示储能设备k在时刻t+1的剩余电量；

和

分别表示储能设备k的充放电功率上下限，

和

分别表示储能设备k的容量上下限；T为优化时间周期，Δt为优化时间间隔；Among them, K is the set of energy storage devices;

and

represent the electric power and remaining power of the energy storage device k at time t, respectively,

represents the remaining power of the energy storage device k at time t+1;

and

respectively represent the upper and lower limits of the charging and discharging power of the energy storage device k,

and

respectively represent the upper and lower limits of the capacity of the energy storage device k; T is the optimization time period, and Δt is the optimization time interval;

The step 400: establish a scaled distributed power supply, load, and energy storage aggregation flexibility model, which specifically includes:

For demand-side resources, it has the characteristics of small capacity and large scale, and is affected by factors such as physical characteristics, natural conditions, and human habits. At the same time, with the continuous increase of the scale of demand-side resources, the above-mentioned flexibility modeling methods based on a single type of demand-side resources face the problem of "dimension disaster" during optimization. Therefore, an external approximation method is used to quantify the aggregation flexibility of large-scale demand-side resources, thereby reducing the computational complexity.

For diesel generators, the aggregation flexibility is:

分别为柴油发电机在时段t和时段t-1的聚合出力；

和

分别表示柴油发电机聚合功率的上下限；

和

分别表示柴油发电机的聚合向上/向下爬坡速率；T为优化时间周期，Δt为优化时间间隔；G为柴油发电机的集合，

分别为柴油发电机g的出力上下限，

和

分别为柴油发电机g的向上和向下爬坡速率；in,

are the aggregated output of diesel generators in time period t and time period t-1, respectively;

and

Respectively represent the upper and lower limits of the aggregate power of diesel generators;

and

respectively represent the aggregate up/down ramp rate of diesel generators; T is the optimization time period, Δt is the optimization time interval; G is the set of diesel generators,

are the upper and lower output limits of the diesel generator g, respectively,

and

are the upward and downward climbing rates of the diesel generator g, respectively;

Likewise, the aggregate flexibility of PV, load, and storage is:

为光伏在时段t的聚合输出功率上限，

光伏机组在时段t的聚合出力，T为优化时间周期；

和

分别表示聚合可平移负荷在时段t的上下限；

为用户在时段t的聚合可平移负荷，

和

分别为用户在时段t的聚合可平移负荷上下限，t_i,max和t_i,min分别为用户可平移负荷调节时间范围的上下限，Q^agg为用户的聚合可平移负荷总量；

和

分别表示储能设备在时刻t的聚合电功率和剩余电量，

表示储能设备在时刻t+1的聚合剩余电量；

和

分别表示储能设备的聚合充放电功率上下限，

和

分别表示储能设备的聚合容量上下限；Δt为优化时间间隔；in,

is the upper limit of aggregate output power of photovoltaics in time period t,

The aggregate output of photovoltaic units in time period t, T is the optimization time period;

and

respectively represent the upper and lower limits of aggregate shiftable load in time period t;

is the aggregated shiftable load of the user at time period t,

and

are the upper and lower limits of the user’s aggregated shiftable load in time period t, respectively, t _i,max and t _i,min are the upper and lower limits of the user’s shiftable load adjustment time range, respectively, and Q ^agg is the total amount of the user’s aggregated shiftable load;

and

represent the aggregated electric power and remaining power of the energy storage device at time t, respectively,

Represents the aggregate remaining power of the energy storage device at time t+1;

and

respectively represent the upper and lower limits of aggregated charge and discharge power of energy storage devices,

and

respectively represent the upper and lower limits of the aggregate capacity of the energy storage device; Δt is the optimization time interval;

The step 500: constructing a cost model of demand-side resource aggregators, and constructing a revenue model for distributed power generation owners, power users, and energy storage owners, specifically including:

Grid price model:

For the demand-side resource aggregator, it acts as the receiver of the grid price signal, and then optimizes the power generation or electricity consumption behavior of various demand-side resources. The price model of the grid is as follows:

为电网的售电价格，

为电网的购电价格；

分别为时段1、时段2和时段T的售电价格，

分别为时段1、时段2和时段T的购电价格；需要说明的是，在所提优化调度模型中，假定电网的购售电价格为给定值；in,

the price of electricity sold to the grid,

the electricity purchase price for the grid;

are the electricity sales prices in time period 1, time period 2 and time period T, respectively,

are the electricity purchase prices of time period 1, time period 2 and time period T respectively; it should be noted that in the proposed optimal dispatch model, it is assumed that the electricity purchase and sale price of the power grid is a given value;

Demand-side resource aggregator cost model:

Demand-side resource aggregators guide various demand-side resource behaviors by setting internal prices. The specific internal price model is as follows:

和

分别表示需求侧资源聚合者制定的内部售电和购电价格；

分别为时段1、时段2和时段T需求侧资源聚合者制定的售电价格，

分别为时段1、时段2和时段T需求侧资源聚合者制定的购电价格；需要说明的是，内部售电和购电价格需满足如下约束：in,

and

Respectively represent the internal electricity sales and electricity purchase prices set by demand-side resource aggregators;

are the electricity sales prices set by demand-side resource aggregators for time period 1, time period 2, and time period T, respectively,

They are the power purchase prices set by the demand-side resource aggregators in time period 1, time period 2, and time period T respectively; it should be noted that the internal power sales and power purchase prices must meet the following constraints:

和

分别为时段t需求侧资源聚合者制定的售电和购电价格，T为优化时间周期；in,

and

are the electricity sales and electricity purchase prices set by demand-side resource aggregators in time period t, respectively, and T is the optimization time period;

For demand-side resource aggregators, on the one hand, they can respond to the power regulation demand of the power grid through the flexibility of aggregating various demand-side resources, and smooth the net load fluctuation caused by a high proportion of photovoltaic access; on the other hand, demand-side resources need to be realized. The aggregation cost is minimal. Therefore, its cost model is:

F _AGG =ωC _AGG +(1-ω)f _AGG

C _AGG = C _grid + C _DG + C _load + C _ES

和

为时段t电网的售电和购电价格，

和

为时段t聚合者内部的售电和购电价格；

分别为时段t的固定负荷、实际可平移负荷、柴油发电机出力、光伏出力及储能出力，T为优化时间周期；Among them, C _AGG represents the operating cost of the aggregator, f _AGG represents the aggregated power fluctuation of demand-side resources, and ω weight coefficient; C _grid , C _DG , C _load and C _ES represent the aggregator and the grid, the owner of distributed power, the The interaction cost of power users and energy storage owners; NL _t is the net load of the aggregate demand-side resource period t, and L _ave is the average value of the net load in the optimization period;

and

is the electricity sale and purchase price of the power grid in period t,

and

is the price of electricity sales and electricity purchases within the aggregator in time period t;

are the fixed load, actual translatable load, diesel generator output, photovoltaic output and energy storage output in period t, and T is the optimization time period;

Distributed power owner benefit model:

为柴油发电机在时段t的有功出力，

为时段t的光伏出力；

为时段t向聚合者的售电电价；a_G、b_G、c_G分别为柴油发电机的燃料成本系数；Among them, F _DG represents the utility function of the DG owner, the first item of the utility function represents the electricity sales revenue of the DG owner to the aggregator, and the second item is the power generation cost of the controllable DG;

is the active power output of the diesel generator in time period t,

is the photovoltaic output of time period t;

is the electricity selling price to the aggregator in period t; a _G , b _G , and c _G are the fuel cost coefficients of diesel generators respectively;

User benefit model:

和

分别为时段t用户的聚合固定负荷和聚合可平移负荷，

为时段t向聚合者的购电电价；κ为偏好系数；Among them, F _user represents the revenue function of the power user; the first term represents the user's utility function, and the second term represents the user's electricity purchase cost;

and

are the aggregated fixed load and aggregated shiftable load of users in period t, respectively,

is the electricity purchase price from the aggregator in period t; κ is the preference coefficient;

Energy storage owner benefit model:

为时段t储能的出力，

和

为储能所有者在时段t向聚合者的售电和购电价格；

为储能损耗系数；Among them, F _ES represents the revenue function of the energy storage owner; the first term represents the revenue of energy storage, and the second term represents the loss cost of energy storage;

is the output of energy storage for period t,

and

is the price of electricity sold and purchased by the energy storage owner to the aggregator at time t;

is the energy storage loss coefficient;

Step 600: Establish a master-slave game model between demand-side resource aggregators and distributed power supply owners, power users and energy storage owners, specifically including:

Build a master-slave game model for demand-side resource flexibility scheduling:

分别为分布式电源所有者、电力负荷以及储能所有者的策略；

和

为需求侧资源聚合者的策略；F_DG、F_user、F_ES和F_AGG分别表示分布式电源所有者、电力负荷、储能所有者以及需求侧资源聚合者的支付函数；Among them, N represents the set of game followers, M is the game leader;

Strategies for distributed power owners, power loads, and energy storage owners, respectively;

and

is the strategy of the demand-side resource aggregator; F _DG , F _user , F _ES and F _AGG represent the payment functions of the distributed power generation owner, power load, energy storage owner and demand-side resource aggregator respectively;

In the above game model, when the strategy of each game participant satisfies the following conditions:

是所述主从博弈模型的均衡。policy set

is the equilibrium of the master-slave game model.

Step 700: Solving the equilibrium solution of the constructed master-slave game model, specifically including:

For the constructed master-slave game model, the general solution idea is to transform the subordinate optimization problem into an equivalent KKT condition as an additional constraint for the leader optimization problem. However, due to the existence of data privacy between different game subjects, the existing solution methods cannot protect the privacy information of game subordinates. Therefore, an iterative-based distributed solution algorithm is designed: first, the demand-side resource aggregator formulates the initial internal electricity purchase and sale price based on the electricity purchase and sale price information of the power grid and publishes it to each subordinate; then, each game subordinate according to the aggregation The internal electricity purchase and sale prices published by the producers, obtain their own power generation or electricity consumption strategies by solving their respective benefit maximization problems, and feed them back to the demand-side resource aggregators; the demand-side resource aggregators adjust the price signal according to the feedback information. Repeat this until the convergence conditions are met.

As shown in FIG. 2, the present invention also provides a demand-side resource flexibility optimization scheduling system based on master-slave game, and the optimal scheduling system includes:

The data acquisition module 1 is used to collect the load, photovoltaic output and electricity price data required for optimal scheduling of demand-side resource flexibility;

Flexibility optimization scheduling framework establishment module 2 is used to analyze the types of generalized demand side resources and establish a flexible optimization scheduling framework for large-scale demand side resources;

Demand-side resource general model building module 3, used to build a general model of generalized demand-side resources such as distributed power, load, and energy storage;

Demand-side resource aggregation flexibility model building module 4, which is used to establish a large-scale distributed power source, load, and energy storage aggregation flexibility model;

The demand-side resource economic model building module 5 is used to construct a cost model for demand-side resource aggregators, and to construct a revenue model for distributed power generation owners, power users, and energy storage owners;

The master-slave game model building module 6 is used to establish a master-slave game model between demand-side resource aggregators and distributed power generation owners, power users and energy storage owners;

The distributed solving module 7 is used to solve the equilibrium solution of the constructed master-slave game model.

Beneficial effects of the present invention:

By using the demand-side resource flexibility optimization scheduling method of the present invention, the optimized result can be applied to the operation of the actual large-scale demand-side resource access power distribution system. The basic data on which the optimal scheduling method in the present invention is based includes photovoltaic output, electricity load and electricity price data, and the decision-making subjects participating in the optimal scheduling include demand-side resource aggregators, distributed power source owners, power users and energy storage owners. The actual situation of the development of the power distribution system; considering that the number of demand-side resources such as distributed power sources and translatable loads (such as electric vehicles) is increasing, the proposed modeling method of demand-side resource aggregation flexibility based on external approximation can be used to alleviate The "curse of dimensionality" brought about by large-scale computing. The proposed optimal scheduling framework based on master-slave game can be used to describe the collaborative optimization problem among multiple decision-making agents; the proposed iterative game equilibrium solution method can overcome the privacy data leakage problem in the traditional centralized solution process. By using the optimal scheduling method of the present invention, the optimization results are applied to the flexible optimal scheduling of large-scale demand-side resources, and the power generation or consumption of various demand-side resources can be reasonably arranged on the premise of ensuring that the power grid regulation needs are met. The plan is conducive to promoting the consumption and utilization of new energy and ensuring the benefits of all demand-side resource owners.

The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments, and the same and similar parts between the various embodiments can be referred to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant part can be referred to the description of the method.

In this paper, specific examples are used to illustrate the principles and implementations of the present invention. The descriptions of the above embodiments are only used to help understand the methods and core ideas of the present invention; meanwhile, for those skilled in the art, according to the present invention There will be changes in the specific implementation and application scope. In conclusion, the contents of this specification should not be construed as limiting the present invention.

Claims (10)

1. a demand-side resource flexibility optimization scheduling method based on master-slave game, it is characterized in that, described optimization scheduling method comprises: Analyze the types of generalized demand-side resources; Establish a flexible and optimal scheduling framework for large-scale demand-side resources; Establish a general model of generalized demand-side resources; A flexible modeling method for large-scale demand-side resource aggregation based on external approximation is proposed; Establish economic models of various demand-side resources; A demand-side resource flexibility optimization scheduling method based on master-slave game is proposed; Design an iterative game equilibrium distributed solution method. 2. A kind of demand side resource flexibility optimization scheduling method based on master-slave game according to claim 1, is characterized in that, the type of described generalized demand side resource specifically comprises: 1) Distributed power supply; such generalized demand-side resources are divided into two categories: schedulable distributed power supply and unschedulable distributed power supply; 2) Load resources; there are power loads on the demand side that can cooperate friendly with the power grid, and the loads are divided into fixed loads and translatable loads; in the smart grid environment, power grid companies or demand-side resource aggregators guide users to participate through incentives or price signals Grid optimization operation; 3) Energy storage or energy storage-like resources; this type of resources includes static energy storage and energy storage-like resources, with dual attributes of power supply and load. 3. The master-slave game-based optimal scheduling method for demand-side resource flexibility according to claim 2, wherein the schedulable distributed power sources include fuel cells and micro gas turbines; the unschedulable distributed power sources Including wind power and photovoltaic power generation; the non-dispatchable means that its output cannot be fully controlled, and the safety of the system is ensured by abandoning wind and light. 4. The master-slave game-based resource flexibility optimization scheduling method for demand side resources according to claim 1, wherein the flexibility optimization scheduling framework for large-scale demand side resources specifically includes: In order to fully explore the flexibility potential of large-scale heterogeneous demand-side resources, build a demand-side resource flexibility optimization scheduling framework based on master-slave game. Flexibility of resources, responding to grid regulation needs, based on grid price signals, and guiding the behavior of various demand-side resources by formulating internal prices; distributed power, load and energy storage various demand-side resources as followers of the game framework , which responds to price signals from aggregators by scheduling its own generation or consumption. 5. The method for optimal scheduling of demand-side resources based on master-slave game according to claim 1, wherein the general model of the generalized demand-side resources specifically comprises: Diesel generator model:

Among them, T is the optimization time period, and G is the set of diesel generators;

are the upper and lower output limits of the diesel generator g, respectively,

and

are the upward and downward climbing rates of diesel generator g, respectively; P _g,t and P _g,t-1 are the active power output of diesel generator g in time periods t and t+1, respectively, and Δt is the optimization time interval; Photovoltaic model:

Among them, J is the set of photovoltaic units, T is the optimization time period,

is the maximum output of photovoltaic unit j in time period t, and P _j,t is the actual output of photovoltaic unit j in time period t; Load model: For the electrical load, it is assumed that it consists of a fixed load and a translational load; for the former, it is considered to have no flexibility; for the latter, the flexibility model is as follows:

Among them, I is the set of power users, T is the optimization time period; L _i,t ,

are the total load, fixed load and translatable load of user i in time period t, respectively;

and

are the upper and lower limits of the translatable load of user i in time period t, respectively, t _i,max and t _{i ,min} are the upper and lower limits of the adjustment time range of user i's translatable load, respectively, and Qi is the total amount of user i's translatable load; Energy storage model:

Among them, K is the set of energy storage devices;

and

represent the electric power and remaining power of the energy storage device k at time t, respectively,

represents the remaining power of the energy storage device k at time t+1;

and

respectively represent the upper and lower limits of the charging and discharging power of the energy storage device k,

and

respectively represent the upper and lower limits of the capacity of the energy storage device k; T is the optimization time period, and Δt is the optimization time interval. 6. A master-slave game-based optimal scheduling method for demand-side resource flexibility according to claim 1, wherein the external approximation-based scaled demand-side resource aggregation flexibility modeling method specifically comprises: For demand-side resources, it is affected by physical characteristics, natural conditions and human habits; with the continuous increase in the scale of demand-side resources, the above-mentioned flexibility modeling methods based on a single type of demand-side resources face the problem of dimensionality disaster when optimizing. ; Using an external approximation method to quantify the aggregation flexibility of large-scale demand-side resources, thereby reducing the complexity of the operation; For diesel generators, the aggregation flexibility is:

in,

are the aggregated output of diesel generators in time period t and time period t-1, respectively;

and

Respectively represent the upper and lower limits of the aggregate power of diesel generators;

and

respectively represent the aggregate up/down ramp rate of diesel generators; T is the optimization time period, Δt is the optimization time interval; G is the set of diesel generators,

are the upper and lower output limits of diesel generator g, respectively,

and

are the upward and downward climbing rates of the diesel generator g, respectively; Likewise, the aggregate flexibility of PV, load, and storage is:

in,

is the upper limit of aggregate output power of photovoltaics in time period t,

The aggregate output of photovoltaic units in time period t, T is the optimization time period;

and

respectively represent the upper and lower limits of aggregate shiftable load in time period t;

is the aggregated shiftable load of the user at time period t,

and

are the upper and lower limits of the user's aggregated shiftable load in time period t, respectively, t _i,max and t _i,min are the upper and lower limits of the user's shiftable load adjustment time range, respectively, and Q ^agg is the total amount of the user's aggregated shiftable load;

and

represent the aggregated electric power and remaining power of the energy storage device at time t, respectively,

Represents the aggregate remaining power of the energy storage device at time t+1;

and

respectively represent the upper and lower limits of aggregated charge and discharge power of energy storage devices,

and

respectively represent the upper and lower limits of the aggregate capacity of the energy storage device; Δt is the optimization time interval. 7. A master-slave game-based optimal scheduling method for demand-side resource flexibility, wherein the economic model for establishing various demand-side resources specifically includes: Grid price model: For the demand-side resource aggregator, it acts as the receiver of the grid price signal, and then optimizes the power generation or electricity consumption behavior of various demand-side resources; the price model of the grid is as follows:

in,

the price of electricity sold to the grid,

the electricity purchase price for the grid;

are the electricity sales prices in time period 1, time period 2 and time period T, respectively,

are the electricity purchase prices of time period 1, time period 2 and time period T respectively; it should be noted that in the proposed optimal dispatch model, it is assumed that the electricity purchase and sale price of the power grid is a given value; Demand-side resource aggregator cost model: Demand-side resource aggregators guide various demand-side resource behaviors by setting internal prices. The specific internal price model is as follows:

in,

and

Respectively represent the internal electricity sales and electricity purchase prices set by demand-side resource aggregators;

are the electricity sales prices set by demand-side resource aggregators for time period 1, time period 2, and time period T, respectively,

They are the power purchase prices set by the demand-side resource aggregators in time period 1, time period 2, and time period T respectively; it should be noted that the internal power sales and power purchase prices must meet the following constraints:

in,

and

are the electricity sales and electricity purchase prices set by demand-side resource aggregators in time period t, respectively, and T is the optimization time period; For demand-side resource aggregators, on the one hand, it responds to the power regulation needs of the power grid through the flexibility of aggregating various demand-side resources, and smoothes the net load fluctuation caused by a high proportion of photovoltaic access; on the other hand, it realizes the aggregation of demand-side resources. The cost is minimal; therefore, its cost model is: F _AGG =ωC _AGG +(1-ω)f _AGG C _AGG = C _grid + C _DG + C _load + C _ES

Among them, C _AGG represents the operating cost of the aggregator, f _AGG represents the aggregated power fluctuation of demand-side resources, and ω weight coefficient; C _grid , C _DG , C _load and C _ES represent the aggregator and the grid, the owner of distributed power, the The interaction cost of power users and energy storage owners; NL _t is the net load of the aggregate demand-side resource period t, and L _ave is the average value of the net load in the optimization period;

and

is the electricity sale and purchase price of the power grid in period t,

and

is the price of electricity sales and electricity purchases within the aggregator in time period t;

are the fixed load, actual translatable load, diesel generator output, photovoltaic output and energy storage output in period t, and T is the optimization time period; Distributed power owner benefit model:

Among them, F _DG represents the utility function of the DG owner, the first item of the utility function represents the electricity sales revenue of the DG owner to the aggregator, and the second item is the power generation cost of the controllable DG;

is the active power output of the diesel generator in time period t,

is the photovoltaic output of time period t;

is the electricity selling price to the aggregator in period t; a _G , b _G , and c _G are the fuel cost coefficients of diesel generators respectively; User benefit model:

Among them, F _user represents the revenue function of the power user; the first term represents the user's utility function, and the second term represents the user's electricity purchase cost;

and

are the aggregated fixed load and aggregated shiftable load of users in period t, respectively,

is the electricity purchase price from the aggregator in period t; κ is the preference coefficient; Energy storage owner revenue model:

Among them, F _ES represents the revenue function of the energy storage owner; the first term represents the revenue of energy storage, and the second term represents the loss cost of energy storage;

is the output of energy storage in period t,

and

is the price of electricity sold and purchased by the energy storage owner to the aggregator at time t;

is the energy storage loss factor. 8. The master-slave game-based demand-side resource flexibility optimization scheduling method according to claim 1, wherein the proposed demand-side resource flexibility optimization scheduling method based on the master-slave game specifically includes: Build a master-slave game model for demand-side resource flexibility scheduling:

Among them, N represents the set of game followers, M is the game leader;

Strategies for distributed power owners, power loads, and energy storage owners, respectively;

and

is the strategy of the demand-side resource aggregator; F _DG , F _user , F _ES and F _AGG represent the payment functions of the distributed power generation owner, power load, energy storage owner and demand-side resource aggregator respectively; In the above game model, when the strategy of each game participant satisfies the following conditions:

policy set

is the equilibrium of the master-slave game model. 9. A master-slave game-based optimal scheduling method for demand-side resource flexibility according to claim 1, wherein the design is based on an iterative game equilibrium distributed solution method, which specifically includes: For the constructed master-slave game model, the solution idea is to convert the subordinate optimization problem into an equivalent KKT condition as an additional constraint for the leader optimization problem; however, due to the existence of data privacy between different game subjects, the existing solution methods cannot protect Game the private information of slaves; design an iterative distributed solution algorithm: first, the demand-side resource aggregator formulates the initial internal purchase and sale price based on the electricity purchase and sale price information of the power grid and publishes it to each slave; then, each Game slaves obtain their own power generation or electricity consumption strategies by solving their own benefit maximization problems according to the internal electricity purchase and sale prices released by the aggregators, and feed them back to the demand-side resource aggregators; the demand-side resource aggregators adjust according to the feedback information A price signal; this is repeated until the convergence conditions are met. 10. A demand-side resource flexibility optimization scheduling system based on a master-slave game, wherein the optimal scheduling system comprises: The data acquisition module is used to collect the load, photovoltaic output and electricity price data required for the flexible optimal scheduling of demand-side resources; A flexible optimization scheduling framework establishment module is used to analyze the types of generalized demand-side resources and establish a flexible and optimal scheduling framework for large-scale demand-side resources; A general model building module for demand-side resources, which is used to build a general model for generalized demand-side resources of distributed power, load, and energy storage; Demand-side resource aggregation flexibility model building module, used to establish the aggregation flexibility model of large-scale distributed power, load, and energy storage; The demand-side resource economic model building module is used to build the cost model of demand-side resource aggregators, and build the income model of distributed power generation owners, power users, and energy storage owners; The master-slave game model building module is used to establish a master-slave game model between demand-side resource aggregators and distributed power generation owners, power users and energy storage owners; The distributed solving module is used to solve the equilibrium solution of the constructed master-slave game model.

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