CN113361875B - Optimization scheduling method for multi-microgrid comprehensive energy system considering demand side response and shared energy storage - Google Patents

Abstract

Considering demand-side response and shared energy storage, a multi-microgrid integrated energy system optimization scheduling method, adding the shared energy storage system to the multi-microgrid integrated energy system with electric energy interaction, constitutes a multi-microgrid integrated energy storage system with shared energy storage and electric power interaction. The grid integrated energy system model; the multi-microgrid integrated energy system model with shared energy storage and power interaction is added to the autonomous response behavior of users in the microgrid, and the user-side autonomous response behavior is considered to include shared energy storage and power interaction. The impact of the integrated energy system model of the power grid; a two-stage optimization model based on the master-slave-cooperative game is proposed to solve the entire optimization process and the optimal capacity of the shared energy storage device, and to distribute the profits of the alliance microgrid contribution. The invention adds user demand side response to the multi-microgrid integrated energy system model including shared energy storage and electric energy interaction, which has the effect of shaving peaks and filling valleys, saving the cost of microgrids and users; Win-win.

Description

Optimal scheduling method of multi-microgrid integrated energy system considering demand-side response and shared energy storage

technical field

The invention relates to the technical field of optimal scheduling of multiple microgrids, in particular to a method for optimal scheduling of a multi-microgrid integrated energy system that takes into account demand-side response and shared energy storage.

Background technique

In recent years, problems such as energy shortage and environmental pollution have become increasingly serious. How to develop a clean and efficient energy supply method and how to achieve sustainable energy development has attracted widespread attention at home and abroad. The multi-microgrid integrated energy system realizes the cascade utilization of energy by exerting different complementary characteristics of energy, which is an important direction of energy development in the future. When the same power distribution area is connected to multiple microgrids, a multi-microgrid system is formed. When each microgrid in the multi-microgrid system has power interaction, it will have a great impact on the planned operation of the microgrid and the output of the equipment in the multi-microgrid system. In the multi-microgrid integrated energy system, each microgrid belongs to different subjects and each microgrid user has an autonomous response behavior, and there is a complex interaction of interests between them, which brings about the operation and regulation of the multi-microgrid integrated energy system. big influence.

In addition, the proportion of wind and solar output in the microgrid is increasing. Due to the intermittent and uncertain characteristics of wind and solar power generation, the abandonment of wind and solar energy is serious. Energy storage can quickly store and release electrical energy, store excess electrical energy for the integrated energy system, and reduce the phenomenon of abandoning wind and light. Therefore, considering the user's autonomous behavior and complex interest interaction, the optimal energy dispatching strategy research can be formulated reasonably according to the load characteristics and wind and solar output of the microgrid, which can effectively improve the user's benefit, reduce the curtailment rate of wind and solar, and the operating cost of the microgrid.

SUMMARY OF THE INVENTION

In order to make the multi-microgrid integrated energy system more economical in the operation process, improve the wind and light consumption rate in the microgrid, and make the users in the microgrid more economical and comfortable energy consumption. The invention provides an optimal scheduling method for a multi-microgrid integrated energy system that takes into account demand-side response and shared energy storage. The user demand-side response is added to the multi-microgrid integrated energy system model including shared energy storage and electric energy interaction, and the To the effect of shaving peaks and filling valleys, saving the cost of microgrids and users; realizing a win-win situation between microgrids and users.

The technical scheme adopted in the present invention is:

The optimal dispatching method for a multi-microgrid integrated energy system considering demand-side response and shared energy storage includes the following steps:

Step 1: Add the shared energy storage system to the multi-microgrid integrated energy system with electric energy interaction to form a multi-microgrid integrated energy system model with shared energy storage and electric power interaction;

Step 2: Add the autonomous response behavior of users in the microgrid to the multi-microgrid integrated energy system model with shared energy storage and power interaction, and consider the user-side autonomous response behavior to the multi-microgrid integrated energy with shared energy storage and power interaction. The impact of the system model;

Step 3: Propose a two-stage optimization model based on the master-slave-cooperative game, solve the entire optimization process and the optimal capacity of the shared energy storage device, and distribute the profit of the alliance microgrid contribution.

In the first step, the shared energy storage system means that multiple microgrids use the same energy storage power station to store or release electrical energy, and this energy storage power station is called a shared energy storage system.

In the step 1, the power interaction refers to a behavior in which each microgrid performs power transmission with other microgrids according to its own power production and sales conditions.

In the first step, the multi-microgrid integrated energy system is composed of multiple integrated energy systems, and a single integrated energy system is used to produce, transmit, distribute, convert, and store energy through the complementary characteristics of different energy sources. After coordination and optimization of other links, different energy sources can be coupled and converted to realize the integrated energy system of production, sales and supply of energy cascade utilization.

In the first step, the equipment included in a single microgrid mainly includes: wind turbine (WT), photovoltaic (PV), micro gas turbine (MGT) and waste heat boiler (heat recoverysteam generator). combined heat and power unit (CHP), LiBr refrigerator (lithium bromide absorption chiller, LBAC), electric refrigeration equipment; energy storage stations in the microgrid include thermal energy storage (TES), cooling Energy storage (cold energy storage, CES) and gas energy storage (gas energy storage, GES). Each microgrid can purchase electricity and natural gas from the external distribution network and natural gas network, and the excess heat energy can be exchanged with heat exchangers and sold to the heating network. The energy between the energy station and the energy storage station in a single microgrid is two-way interaction; after the purchased energy is coupled and converted by the internal equipment of the microgrid, the energy is supplied to the users in the park, and each microgrid can also communicate with the shared energy storage device. Power interaction, excess power cannot be sent back to the distribution network, but can only be absorbed by shared energy storage power stations or directly abandoned.

In the first step, the shared energy storage system is jointly funded and constructed by each microgrid in the multi-microgrid integrated energy system, so each microgrid does not need to consider paying service fees and electricity purchase fees to the shared energy storage device, but only needs to consider energy storage. construction cost.

In the second step, the user's self-response behavior is the user's demand-side response behavior, which refers to a user's self-response behavior in which the user adjusts the different demands of different energy sources in the microgrid so as to save costs and reduce peaks and fill valleys. . The user's autonomous response behavior mainly considers four types of loads: movable electric load, movable air load, flexible heating load and flexible cooling load.

Movable electrical loads refer to electrical loads that can be translated in time. Movable electrical loads are further divided into movable and uninterrupted electrical loads and movable uninterrupted electrical loads. Movable and interruptible load refers to a load that can be shifted in time and can be interrupted in the middle of use; movable non-interruptible load refers to a load that can be shifted in time and cannot be interrupted in the middle of use.

In the third step, the master-slave game refers to a game type in which one party acts first and the other party acts later; the cooperative game is a game type in which the participants in the game make the highest profit from the alliance by enforcing their constraint agreement. The two-stage optimization model of master-slave-cooperative game is used to solve the entire optimization process and the optimal capacity of the shared energy storage device. The power grid jointly releases the energy price to each microgrid user, and then each microgrid user responds to the demand according to the energy price and feeds back the result of the demand response (load usage) to the microgrid operator where it is located, and each microgrid operator uses it according to the load. According to the situation, the equipment in the microgrid is converted into output. It can be seen from this that there is interaction between the two subjects in the first stage, and the microgrid operator and the users in the microgrid have different goals, which constitutes a master-slave competition between the two subjects. Therefore, the first-stage optimization model can be solved by introducing a master-slave game model. The purpose of the first-stage optimization model is to find out the energy conversion situation and user load usage of each microgrid in the case of reaching a master-slave game equilibrium with each microgrid user, and to transmit the optimal production and sales of electric energy of each microgrid. Optimize the model for the second stage.

Each microgrid operator forms an alliance microgrid and first announces energy prices to users in the alliance microgrid, and then users respond to demand based on energy prices. In this process, the alliance microgrid operators are the first decision-makers, and the microgrid users are the latter decision-makers. Therefore, the alliance microgrid operators are called leaders and microgrid users are called followers. The entire first-stage optimization model can be represented in two steps:

Step 1: The follower responds to the demand according to the energy price announced by the leader, and the objective function is formula (22). Feedback follower load usage to leader.

Step 2: The leader uses the load usage fed back by the followers, and the objective function is formula (23). Adjust and optimize the output of equipment in the microgrid.

The above steps are repeated until the alliance microgrid obtains the optimal daily profit, and the game equilibrium is considered to be reached.

In the third step, through the solution in the first stage, the daily electric energy production and sales situation under the condition that each microgrid and microgrid users reach the master-slave game equilibrium can be obtained. Since the capacity of the shared energy storage device needs to be solved in the second stage, it is necessary to analyze the annual electric energy production and sales of each microgrid. In addition, the second-stage optimization model needs to consider the power interaction between the microgrids and the investment and construction of the shared energy storage devices by each microgrid. Since each microgrid belongs to different operators, there are existing The cooperative relationship also has a competitive relationship, and at the same time contains a complex interaction of interests. Therefore, a cooperative game is introduced to solve the second-stage optimization model.

In the third step, the alliance microgrid refers to a large microgrid formed by a plurality of microgrid integrated energy systems through power interconnection.

The present invention is a multi-microgrid integrated energy system optimization scheduling method that takes into account demand-side response and shared energy storage, and has the following technical effects:

1) The present invention adds user demand side response to the multi-microgrid integrated energy system model including shared energy storage and electric energy interaction, which has the effect of shaving peaks and filling valleys, saving the cost of microgrids and users, and realizing microgrids and users. win-win between.

2) The present invention proposes a master-slave-game two-stage optimization model to optimize the scheduling of multi-microgrid integrated energy systems. Under the optimization of the model, a master-slave game equilibrium is achieved between each microgrid system and microgrid users. , to achieve a win-win situation, the multi-microgrid integrated energy system can configure the optimal capacity of the shared energy storage power station, so that the multi-microgrid integrated energy system can obtain more profits, and the comprehensive benefits of each microgrid user are also improved.

Description of drawings

Figure 1 shows the energy coupling relationship diagram of a single integrated energy system.

Figure 2 is a model diagram of a multi-microgrid integrated energy system with shared energy storage.

Figure 3 is a diagram of the two-stage optimization model.

Figure 4 is the overall optimization flow chart.

Figure 5 is a schematic diagram of the charging and discharging behavior of the shared energy storage system.

Figure 6(a) is the electricity price curve diagram of each typical day microgrid;

Figure 6(b) is the gas energy price curve of each typical daily microgrid;

Figure 6(c) is the price curve of cold energy for each typical day microgrid;

Figure 6(d) is a graph of the thermal energy price of each typical daily microgrid.

Detailed ways

The optimal dispatching method for a multi-microgrid integrated energy system considering demand-side response and shared energy storage includes the following steps:

Step 1: Add the shared energy storage system to the multi-microgrid integrated energy system with electric energy interaction to form a multi-microgrid integrated energy system model with shared energy storage and electric power interaction;

Step 2: Add the autonomous response behavior of users in the microgrid to the multi-microgrid integrated energy system model with shared energy storage and power interaction, and consider the user-side autonomous response behavior to the multi-microgrid integrated energy with shared energy storage and power interaction. The impact of the system model;

Step 3: Propose a two-stage optimization model based on the master-slave-cooperative game, solve the entire optimization process and the optimal capacity of the shared energy storage device, and distribute the profit of the alliance microgrid contribution.

Below in conjunction with accompanying drawing, preferred embodiment is described in detail:

(1) Planning and modeling of shared energy storage:

Due to the high cost of energy storage construction, if a single microgrid operator wants to configure a larger capacity energy storage device, it will bring higher costs to it, resulting in the energy storage capacity configured by a single microgrid usually cannot well meet the microgrid. Storage of solar energy in the interior. Therefore, the shared energy storage in the present invention is jointly funded and constructed by multiple microgrids, and each microgrid does not need to consider paying service fees and electricity purchase fees to the shared energy storage device, but only needs to consider the construction cost of the energy storage. The annual average investment cost and maintenance cost of the energy storage power station can be expressed as formula (1):

和

分别表示储能电站的最大容量和最大充放电功率；Y_s表示储能电站的预期使用年数；M_ess为储能电站的年维护成本。In formula (1), μ _s represents the capacity cost of the energy storage power station, and the unit is yuan/(kWh); μ _p represents the power cost of the energy storage power station, and the unit is yuan/kW.

and

respectively represent the maximum capacity and maximum charging and discharging power of the energy storage power station; Y _s represents the expected years of use of the energy storage power station; _Mess is the annual maintenance cost of the energy storage power station.

According to the remaining electric energy of each microgrid, the shared energy storage system has two working states: charging and discharging. If the excess power P _j,s (t)>0 of the microgrid j at time t, it means that the microgrid j has excess power at this time, and sends an energy storage signal to the shared energy storage system; if P _j,s (t)<0, It means that the microgrid j lacks electric energy at this time, and sends an energy discharge signal to the shared energy storage system. The shared energy storage system matches supply and demand by collecting the supply and demand signals of each microgrid. The expression of aggregate demand supply is shown in formula (2):

P _D (t)=|D _sum (t)|-|S _sum (t)| (3)

In equations (2) and (3), _Dsum is the total energy demand reported by all microgrids to the shared energy storage system at time t; _Ssum is the total energy supply reported by all microgrids to the shared energy storage system at time t ; If P _D (t)>0, it means that the total discharge demand of all microgrids connected to the shared energy storage power station at time t is discharge, and the shared energy storage system regulation center uses the energy storage device to discharge to meet the needs of the microgrid group.

The state of charge of the energy storage system and the corresponding power constraints are shown in equation (4):

和

分别表示共享储能t时刻的充电功率和放电功率；Soc_max和Soc_min分别表示储能系统荷电状态的上下限；

和

表示储能电站的充电状态变量和放电状态变量；

表示共享储能装置的最大充放电功率。In formula (4), Soc(t) represents the electric energy stored in the shared energy storage at time t; η ^abs and η ^relea represent the charging efficiency and the discharging efficiency, respectively;

and

respectively represent the charging power and discharging power of the shared energy storage at time t; Soc _max and Soc _min respectively represent the upper and lower limits of the state of charge of the energy storage system;

and

Represents the charge state variable and discharge state variable of the energy storage power station;

Indicates the maximum charge and discharge power of the shared energy storage device.

(2) Multi-microgrid integrated energy system model with shared energy storage:

A single microgrid topology is shown in Figure 1. In Figure 1, the left side represents the external energy network, and the right side is the user energy consumption type in the microgrid. The equipment included in a single microgrid mainly includes: wind turbine (WT), photovoltaic (PV), micro gas turbine (MGT), and heat recovery steam generator (heat recovery steam generator) combined to form a combined heat and power device ( combine heat and power unit, CHP), LiBr refrigerator (lithium bromide absorption chiller, LBAC), electric refrigeration equipment. The energy storage stations in the microgrid include thermal energy storage (TES), cold energy storage (CES) and gas energy storage (GES). It can be seen from Figure 1 that the load types of users in the microgrid include electrical load, cooling load, heat load and gas load. The microgrid can purchase electricity and natural gas from the external distribution network and natural gas network, and excess heat energy can be exchanged through heat exchangers. The back heating network is sold, and the energy between the energy station and the energy storage station in a single microgrid is two-way interaction, and the purchased energy is coupled and converted by the internal equipment of the microgrid to supply energy to users in the park.

A multi-microgrid integrated energy system with shared energy storage and electric power cooperation and interaction is composed of three combined cooling, heating and electrical microgrids and shared energy storage. The connection relationship between the microgrids is shown in Figure 2. It can be seen from Figure 2 that each microgrid performs power cooperation and interconnection through information exchange. In addition, each microgrid can also interact with the shared energy storage device for power. The present invention sets that the surplus electric energy of each microgrid cannot be sent back to the distribution network, and can only absorb the surplus electric energy through the shared energy storage power station or directly abandon the electricity.

(3) Microgrid load model and user model:

The user's self-response behavior is the user's demand-side response behavior, which refers to a user's self-response behavior that saves costs, cuts peaks and fills valleys by adjusting the different demands of different energy sources in the microgrid. The user's autonomous response behavior mainly considers four types of loads: movable electric load, movable air load, flexible heating load and flexible cooling load.

Movable electrical loads refer to electrical loads that can be shifted in time. Movable electrical loads are divided into movable and uninterruptable electrical loads and movable uninterrupted electrical loads. Movable and interruptible load refers to a load that can be shifted in time and can be interrupted in the middle of use; movable non-interruptible load refers to a load that can be shifted in time and cannot be interrupted in the middle of use.

Movable and uninterruptible electrical loads mainly include electrical equipment such as washing machines and dishwashers. The expression form of the movable and uninterruptible electrical load at time t is shown in Equation (5):

表示n_p户用户在t时刻使用第x种设备产生的负荷；X表示可用设备的集合。

可表示为式(6)所示：In formula (5), n represents the number of users in the microgrid; _np represents the number of users in the microgrid participating in the response of the movable and uninterrupted power load;

Represents the load generated by n _p users using the xth device at time t; X represents the set of available devices.

It can be expressed as formula (6):

In formula (6), n _p represents the number of users participating in the response of the movable and uninterruptible electrical load in the microgrid; P _x,e represents the power used by the equipment x in the scheduling period.

充电时长为1小时的电动汽车负荷。t时刻可转移可中断负荷模型可表示为式(7)所示：The transferable and interruptible power load mainly considers the electric vehicle charging load in the microgrid. Assuming that the number of users of electric vehicles in a microgrid is n _c , and the charging time is T _sp , it is equivalent to the number of users as

Electric vehicle load with a charging time of 1 hour. The transferable and interruptible load model at time t can be expressed as formula (7):

In formula (7), P _x,e represents the charging power of the charging vehicle in one scheduling cycle; n _c '(t) represents the number of electric vehicle households participating in the charging at time t

The transferable and uninterrupted gas load mainly considers gas wall-hung boilers, gas water heaters and other equipment. The expression form of the movable and uninterrupted gas load at time t is as shown in Equation (8):

表示n_p1户用户在t时刻使用第g种设备产生的负荷；G表示可用设备的集合。

可表示为式(9)所示：In formula (8), n represents the number of users in the microgrid; n _p1 represents the number of users in the microgrid participating in the response of the movable and uninterrupted gas load;

Represents the load generated by n _p1 users using the g-th device at time t; G represents the set of available devices.

It can be expressed as formula (9):

In formula (9), n _p1 represents the number of users in the microgrid participating in the response of the movable and uninterruptible power load; P _g,e represents the power used by the equipment g in the scheduling period.

Flexible heating load and flexible cooling load belong to elastic load. The flexible heating load mainly considers the hot water load, and the flexible cooling load mainly considers the user's acceptable range of indoor temperature.

Assuming that the user's acceptance range of hot water temperature in the microgrid is [T _h,min ,T _h,max ], H _h,min and H _h,max are used to represent the minimum hot water load power and the maximum hot water load power, respectively. The expressions are expressed as equations (10) and (11):

In equations (8)-(9), T _h,in represents the temperature of water added at time t; C _w and ρ _W represent the specific heat capacity of water and the density of water, respectively; V _c (t) represents the volume of cold water added at time t ; Δt represents the time step; H _h (t) represents the thermal load at time t. The flexible heat load satisfies the constraint condition (12):

H _h,min (t)≤H _h (t)≤H _h,max (t) (12)

In formula (12), H _h,min and H _h,max represent the minimum hot water load power and the maximum hot water load power respectively; Hh(t) represents the flexible heat load value at time t.

The flexible cooling load considers the user's acceptable range of indoor cooling temperature, assuming that the user's acceptable range of indoor temperature is expressed as [T _c,min ,T _c,max ], the minimum cooling load and the maximum cooling load at time t are respectively expressed For formula (13) and formula (14):

In equations (13)-(14), C _c,min (t) and C _c,max (t) represent the minimum cooling load and the maximum cooling load at time t, respectively; R _is the thermal resistance of the user's house; T _o (t) Indicates the outdoor temperature of the user's house at time t. The flexible cooling load can be expressed as:

C _c,min (t)≤C _c (t)≤C _c,max (t) (15)

In formula (15), C _c,min (t) and C _c,max (t) represent the minimum cooling load and the maximum cooling load at time t, respectively; C _c (t) represents the flexible cooling load at time t.

The actual load of the user at time t in the microgrid j can be expressed as equations (16)-(19):

L _j,e (t)=L _j,BE (t)+L _j,e,mob (t) (16)

L _j,g (t)=L _j,GE (t)+L _j,g,mob (t) (17)

L _j,h (t)=L _j,HE (t)+H _j,h (t) (18)

L _j,c (t)=L _j,CE (t)+C _j,c (t) (19)

In equations (16)-(19), L _j,e (t), L _j,g (t), L _j,h (t), and L _j,c (t) represent users in microgrid j at time t, respectively. The actual electrical load, actual gas load, actual heating load and actual cooling load. L _j,BE (t), L _{j, GE} (t), L _{j, HE} (t), L _{j, CE} (t) represent the basic electrical load, basic gas load, basic thermal load at time t in microgrid j, respectively load and basic cooling load. L _j,e,mob (t), L _j,g,mob (t), H _j,h (t), C _j,c (t) represent the movable electrical loads, movable and non-movable electrical loads in microgrid j, respectively. Interrupted air load, flexible heating load and flexible cooling load.

In order to allow users to obtain more benefits as much as possible, the utility function is also added to the user objective function. The utility function is commonly used in microeconomics to express the degree of satisfaction consumers obtain from consuming a given commodity. sub-function to represent. In addition, the user has the optimal energy consumption in each time period, and the satisfaction loss will occur when the user energy consumption deviates from the optimal energy consumption. The utility function and satisfaction loss of users in microgrid j are expressed as equations (20) and (21), respectively:

In equations (20)-(21), α _j,e and β _j,e both represent the energy preference coefficients of users in microgrid j; λ _j,e and θ _j,e represent the satisfaction of energy e in microgrid j degree loss parameters; L _j,e (t) and L _{j,e, B} (t) represent the actual load and baseline load of energy e of microgrid j at time t; where E={ele,gas,cold,heat} , which indicates the type of energy purchased by the user.

The overall interest demands of users in the microgrid j throughout the year can be expressed in the form of formula (22):

In formula (22), U _j,eu represents the annual comprehensive benefit value of users in microgrid j; γ _e (t) represents the price of energy e at time t, which is formulated and announced by the microgrid operator; d represents the number of days; t Indicates the time number.

(4) Two-stage optimization model:

The two-stage optimization model used is shown in Figure 3. The function of each stage and the specific solution method are described below.

The master-slave game refers to a type of game in which one party acts first and the other party acts later; the cooperative game is a type of game in which the participants in the game make the highest profit for the alliance by enforcing their binding agreements. The two-stage optimization model of master-slave-cooperative game is used to solve the entire optimization process and the optimal capacity of the shared energy storage device. The power grid jointly releases energy prices to each microgrid user, and then each microgrid user responds to the demand according to the energy price and feeds back the result of the demand response, that is, the load usage situation to the microgrid operator where it is located, and each microgrid operator uses the load according to the load. According to the situation, the equipment in the microgrid is converted into output. It can be seen from this that there is interaction between the two subjects in the first stage, and the microgrid operator and the users in the microgrid have different goals, which constitutes a master-slave competition between the two subjects. Therefore, the first-stage optimization model can be solved by introducing a master-slave game model. The purpose of the first-stage optimization model is to obtain the optimal production and sales of electric energy of each micro-grid to the second-stage optimization model when each micro-grid reaches a master-slave game equilibrium with each micro-grid user.

The first-stage objective function of each microgrid integrated energy system can be expressed as the profit maximization function of each microgrid, as shown in equation (23):

In formula (23), Pr represents the daily operating profit of the microgrid; L _j,e (t) represents the actual load of the energy e of the microgrid j at time t; γ _e (t) represents the price of energy e at time t; C _M represents the operating cost of microgrid operation, which can be expressed by Equation (24):

C _M = C _epe + C _eql (24)

In Equation (24), C _epe represents the sum of the cost of purchasing electric energy and natural gas from the distribution network and the natural gas network in the microgrid j throughout the day, and its expression is Equation (25):

表示t时刻微电网从外界购买能源e的功率值；ζ_e(t)表示t时刻外界能源e的价格，由配电网和天然气网制定。In formula (25), E'={ele, gas};

Represents the power value of energy e purchased by the microgrid from the outside world at time t; ζ _e (t) represents the price of external energy e at time t, which is determined by the distribution network and the natural gas network.

In Equation (24), Ceqi represents the daily maintenance cost of all equipment in the microgrid, and its expression is shown in Equation (26):

表示t时刻设备b的输出功率；v_b表示设备b的损耗系数；B表示所有设备的集合。In formula (24),

represents the output power of device b at time t; v _b represents the loss coefficient of device b; B represents the set of all devices.

It can be seen from the above that each microgrid operator forms an alliance microgrid and first announces the energy price to the users in the alliance microgrid, and then the users respond to demand according to the energy price. In this process, the alliance microgrid operators are the first decision-makers, and the microgrid users are the latter decision-makers. Therefore, the alliance microgrid operators are called leaders and microgrid users are called followers. The entire first-stage optimization model can be represented in two steps:

Step 1: The follower responds to the demand according to the energy price announced by the leader, and the objective function is formula (22). Feedback follower load usage to leader.

Step 2: The leader uses the load usage fed back by the followers, and the objective function is formula (23). Adjust and optimize the output of equipment in the microgrid.

The above steps are repeated until the alliance microgrid obtains the optimal daily profit, and the game equilibrium is considered to be reached.

Through the solution of the first stage, the daily electric energy production and sales can be obtained when each microgrid and microgrid users reach the master-slave game equilibrium. Since the capacity of the shared energy storage device needs to be solved in the second stage, it is necessary to analyze the annual electric energy production and sales of each microgrid. In addition, the second-stage optimization model needs to consider the power interaction between the microgrids and the investment and construction of the shared energy storage devices by each microgrid. Since each microgrid belongs to different operators, there are existing The cooperative relationship also has a competitive relationship, and at the same time contains a complex interaction of interests. Therefore, a cooperative game is introduced to solve the second-stage optimization model. The second-stage objective function can be expressed as formula (27):

In formula (27), C _cost represents the sum of the annual minimum electricity purchase cost of the alliance microgrid and the annual construction cost of shared energy storage; M represents the typical number of days; W represents the typical number of days; J represents the number of microgrids in the alliance microgrid. P _j,w,m (t) represents the electric power purchased by the alliance microgrid from the distribution network at time t; ζ _ele (t) represents the electricity price of the distribution network at time t; C _inv,y represents the construction cost of shared energy storage.

Therefore, the final annual profit function of the alliance microgrid is shown in formula (28):

表示微电网j在第m种典型日第w天t时刻从外界购气量；ζ_gas(t)表示外界天然气价格；

表示微电网j内t时刻设备b的输出功率；v_b表示设备b的损耗系数；B表示所有设备的集合；C_cost表示联盟微电网全年购电成本与共享储能年建设成本之和。In formula (28), M represents the number of typical days; W represents the number of typical days; E represents the set of energy types; L _j,e,w,m (t) represents the wth day t of the mth typical day for microgrid j The actual load of energy e at time; γ _e (t) represents the price of energy e at time t;

represents the amount of gas purchased by microgrid j from the outside world at time t on the wth day of the mth typical day; ζ _gas (t) represents the price of outside natural gas;

represents the output power of device b at time t in microgrid j; v _b represents the loss coefficient of device b; B represents the set of all devices; C _cost represents the sum of the annual power purchase cost of the alliance microgrid and the annual construction cost of shared energy storage.

Due to the interaction of electric energy and the joint investment and construction of shared energy storage between the alliance microgrids, the total additional profit of the alliance is distributed based on the total contribution of each microgrid to the alliance microgrid system to ensure the distribution of the profits of each microgrid within the alliance. of fairness. The total additional profit of the alliance is the sum of the profit after the alliance of each microgrid minus the profit before the alliance of each microgrid, as shown in formula (29):

为各微电网未联盟前的利润之和。利润分配模型如式(30)-(32)所示：In formula (29), U _ext is the extra profit obtained by the alliance; Pr' is the profit after the alliance of each microgrid,

It is the sum of the profits of each microgrid before the alliance. The profit distribution model is shown in equations (30)-(32):

A _j =Cv _j ·U _ext (30)

In equations (30)-(32), A _j represents the extra profit shared by microgrid j; C _j represents the total contribution of microgrid j; Es _j (t) represents the transmission of microgrid j to other microgrids at time t. The sum of the electric energy of the grid; P _j,s (t) represents the power obtained by the microgrid j from the shared energy storage at time t; Cv _j represents the contribution of the microgrid j in the alliance.

In the two-stage optimization model, the first-stage optimization process also needs to meet the constraints of the response, in which the electric power balance constraint, the gas power balance constraint, the cold power balance constraint, and the thermal power balance constraint are as shown in equations (33)-(36), respectively. Show:

表示t时刻从外界配电网购买进入微电网变压器内端的电功率；P_ac(t)和ρ_ac分别表示微电网内空调在t时刻发出的冷功率和能效比；L_ele,R(t)表示t时刻微电网内用户消耗的电负荷量；P_ap(t)表示t时刻弃电量。In formula (33), P _pv (t) and P _wt (t) represent the electrical power generated by the photovoltaic and the fan at time t, respectively; P _chp (t) represents the electrical power generated by the cogeneration unit at time t;

represents the electric power purchased from the external distribution network and enters the inner end of the microgrid transformer at time t; P _ac (t) and ρ _ac represent the cooling power and energy efficiency ratio issued by the air conditioner in the microgrid at time t, respectively; L _ele,R (t) represents The electricity load consumed by users in the microgrid at time t; P _ap (t) represents the power abandoned at time t.

G _pur (t)+G _ges (t)=G _chp (t)+G _gb (t)+L _gas,R (t) (34)

In formula (34), G _pur (t) represents the amount of natural gas purchased by the microgrid from the outside world; G _ges (t) represents the gas power exchanged by GES at time t; G _chp (t) represents the gas absorbed by the cogeneration unit at time t. power; G _gb (t) represents the gas power consumed by the gas boiler at time t; L _gas,R (t) represents the gas load consumed by users in the microgrid at time t.

C _lbac (t)+C _ac (t)+C _ces (t)=L _cold,R (t) (35)

In formula (35), C _lbac (t) represents the cooling power of the LBAC at time t; C _ces (t) represents the cooling power exchanged by the CES at time t; C _ac (t) represents the cooling power of the air conditioner at time t; L _cold,R ( t) represents the cooling load consumed by users in the microgrid at time t.

In formula (36), H _chp (t) represents the thermal power from the cogeneration unit at time t; H _gb (t) represents the thermal power from the gas boiler at time t; C _lbac (t) represents the cooling power of the LBAC at time t ; H _tes (t) represents the exchange power of TES at time t; e _lbac represents the energy efficiency ratio of LBAC; H _sell (t) represents the thermal power sold by the microgrid to the heat grid at time t; L _heat,R (t) represents time t The amount of thermal load consumed by users within the microgrid.

In addition to the above power balance constraints, it should also be considered that the microgrid and the distribution network need to be converted by a transformer (TR) when purchasing electric energy. formula constraints.

表示t时刻微网从外界配电网购买的实际电功率；

表示t时刻从外界购买的电功率传输到微电网用户端时所剩电功率量；e_tr表示变压器的转换效率。In formula (37),

represents the actual electric power purchased by the microgrid from the external distribution network at time t;

Represents the amount of remaining electrical power when the electrical power purchased from the outside world is transmitted to the microgrid user at time t; e _tr represents the conversion efficiency of the transformer.

To sum up, the overall process of the optimal dispatching strategy for the multi-microgrid integrated energy system under the consideration of comprehensive demand response and shared energy storage proposed by the present invention can be represented as shown in FIG. 4 .

(5), the strategy of the present invention is compared and analyzed with other 3 kinds of strategies:

The multi-microgrid integrated energy system used in the comparative analysis includes three microgrids of combined cooling, heating and electrical supply type, which are denoted as microgrid A, microgrid B, and microgrid C below. The present invention sets four typical days of spring, summer, autumn and winter, the number of days corresponding to each typical day is 90 days, and the scheduling time of each typical day is 24 hours. The upper limit of electric energy interaction between microgrids is 600kWh, the upper limit of electric energy purchased by each microgrid from the external distribution network is 1000kWh, and the upper limit of gas purchases from the external natural gas network is 3000kWh. The price of heat energy sold by the multi-microgrid system to the outside world is 0.2 yuan/kWh. Assuming that the interval between the microgrids is extremely small, the present invention does not consider the cost of power line loss between the microgrids. The charge and discharge efficiency of the shared energy storage power station is taken as 0.98, the upper and lower limits of the state of charge of the energy storage system are taken as 90% and 10% of the shared energy storage capacity respectively, and the initial energy of the energy storage system is taken as 90%. The construction cost of shared energy storage refers to the winning price of a battery of an energy storage project in 2018 of 1100 yuan/(kWh), the power cost of 1000 yuan/kW, the operation and maintenance cost of 72 yuan/(kW), and the theoretical life cycle of the energy storage power station is 8 years.

The following 4 schemes were established for comparative analysis and verification:

1) Scheme 1: Alliance between microgrid A, microgrid B, and microgrid C, and power cooperation among the microgrids in the alliance, while considering the power interaction between the alliance microgrid and the shared energy storage system, and considering users Demand-side response behavior.

2) Scheme 2: Alliance between microgrid A, microgrid B, and microgrid C, and power cooperation within the microgrid. The alliance microgrid considers the power interaction with the shared energy storage, but does not consider the user demand side response .

3) Option 3: Power cooperation and interconnection between microgrid A, microgrid B, and microgrid C, regardless of the power interaction with the shared energy storage, each microgrid carries energy storage devices with values of 100kWh and 100kWh , 150kWh, the user-side demand response behavior is still considered.

4) Scheme 4: Microgrid A, microgrid B, and microgrid C do not carry out power cooperation and interconnection, and do not consider power interconnection with the shared energy storage system. Each microgrid carries energy storage devices, which are 100kWh, 100kWh, and 150kWh respectively. , still considering the comprehensive demand response behavior of each microgrid user.

The prices in options 2-4 are the same as those in option 1, and the parameters of the shared energy storage device in option 2 are the same as those in option 1. It can be seen from the above four comparison schemes that scheme 1 adopts the strategy proposed by the present invention, scheme 2 only adopts the second-stage optimization model in the two-stage optimization model proposed by the present invention, and scheme 4 only adopts the first-stage optimization model. Therefore, the comparison between Scheme 1 and Scheme 2 is mainly to reflect the impact of adding user demand response to the entire optimization process; the comparison between Scheme 1 and Scheme 3 is mainly to reflect the impact of adding shared energy storage on the entire optimization process; The comparison mainly reflects the influence on the entire optimization process after adding the second-stage optimization model of the present invention.

In MatlabR2018b, the chaotic particle swarm algorithm is used to nest gurobi and cplex solvers for solving, and the number of chaotic particle swarm population is 50, the number of iterations is 50, and the chaotic coefficient is 3.5.

Through the optimization calculation of scheme 1, the configuration result of the shared energy storage power station in scheme 1 is obtained: the shared energy storage capacity is 1523kWh, and the maximum charging and discharging power is 527kW.

Figure 5 shows the optimization results of the charging and discharging behavior of the shared energy storage system in each typical day. If the value in Figure 5 is positive, it means that the energy storage power station is in the charging state; if the value is negative, it means that the energy storage power station is in the discharging state. It can be seen from Figure 5 that no matter on a typical day, the power interaction between the shared energy storage power station and the multi-microgrid integrated energy system has reached more than ten times. At 8-9 o'clock, the maximum charging power of each typical day-to-day shared energy storage power station reached 527kW, and at 18-19 o'clock on a typical day in spring and 16-17 o'clock on a typical day in summer, the charging power of the shared energy storage power station reached 300 kW. above. In addition, on typical days in spring, typical days in summer, and typical days in autumn at 1-2 o'clock, the shared energy storage power station has reached the maximum discharge power. In typical days in spring, 11-12 o'clock and 13-14 o'clock, typical days in summer are 11-12 o'clock. At 12 o'clock, 20-21 o'clock, and 22-23 o'clock, the discharge power of the shared energy storage power station all reached more than 300kW. It can be seen that the shared energy storage device has at least one full charge and full discharge behavior.

Scheme 1 solves the energy price of each typical daily multi-microgrid integrated energy system as shown in Figure 6(a), Figure 6(b), Figure 6(c), and Figure 6(d). It can be seen that various energy sources The prices are all within a reasonable range and are acceptable energy prices for users.

Option 2-4 are optimized according to the price of option 1 and the shared energy storage capacity. The total cost of multi-microgrids, the total profit of multi-microgrids, and the wind-solar consumption ratio under each scheme are listed in Table 1, and the user benefits and energy purchase costs of each microgrid in Schemes 1 and 2 are listed in Table 1. Table 2.

Table 1 Optimization results of each scheme

Table 2 Optimization results of scheme 1 and scheme 2

From the comparison of the optimization results of Scheme 1 and Scheme 2 in Table 1 and Table 2, it can be seen that for each microgrid user, after adding the user's autonomous response behavior, the annual comprehensive benefit of each microgrid user has been greatly improved, and each microgrid user has been greatly improved. The annual cost of purchasing energy for grid users has improved by 901,000 yuan. For microgrid operators, after joining the user's self-response behavior, although the annual income of the alliance (the total cost of purchasing energy of the alliance users) has dropped by 901,000 yuan, the annual operating cost of the alliance's microgrid system has dropped by 1.684 million yuan, making the alliance The total annual profit of the microgrid system increased by 783,000 yuan. In addition, the wind power consumption ratio of the alliance microgrid system increased from 70.8% before adding the user's self-response behavior to 82.6% after adding the user's self-response behavior, reducing the abandonment of wind and light in the alliance microgrid system. It can be seen that after adding the user's autonomous behavior, the interests of the user and the microgrid are well improved through the solution of the master-slave game mechanism, and the multi-win between the alliance microgrid system and each microgrid user is realized. From the optimization results of scheme 1 and scheme 3, after adding the shared energy storage system, the annual operating cost of the alliance microgrid system is reduced by nearly 500,000 yuan, and the annual wind power consumption ratio of the alliance microgrid system increases by 5.2%. This is because after joining the shared energy storage system, the power purchase behavior of the alliance microgrid system and the external distribution network is reduced, and the wind and photovoltaic power generated by the alliance microgrid system can be effectively adjusted through the shared energy storage system, thereby reducing the abandonment of wind and solar energy. It can be seen from the comparison of the optimization results of scheme 1 and scheme 4 that after adding the second-stage optimization model in the two-stage optimization model of the present invention, the annual operating cost of the alliance microgrid system is reduced by 1.061 million yuan, and the annual wind-solar consumption ratio is increased by 10.3 %. From the optimization results of Scheme 3 and Scheme 4, it can be seen that after the alliance microgrid joins the power interaction, the annual operating cost of the alliance microgrid system drops by 562,000 yuan, and the wind power consumption ratio increases by 5.1%. Adding power interconnection to the integrated energy system can improve the economics of the operation of the multi-microgrid integrated energy system.

In summary, after adding user demand-side response and shared energy storage power station to the multi-microgrid integrated energy system, the solution of the master-slave-cooperation two-stage game model can effectively improve the energy consumption satisfaction of users in the microgrid and reduce energy consumption. The energy cost of users, reduce the operating cost of each microgrid, ease the energy pressure in the microgrid, and reduce the rate of curtailment of wind and light.

Claims (7)

1. The optimization scheduling method of the multi-microgrid comprehensive energy system considering demand side response and shared energy storage is characterized by comprising the following steps of:

the method comprises the following steps: adding the shared energy storage system into a multi-microgrid integrated energy system containing electric energy interaction to form a multi-microgrid integrated energy system model containing shared energy storage and electric energy interaction;

step two: adding an autonomous response behavior of a user in the microgrid on a multi-microgrid integrated energy system model with shared energy storage and power interaction, and considering the influence of the autonomous response behavior of the user side on the multi-microgrid integrated energy system model with shared energy storage and power interaction;

in the second step, the autonomous response behavior of the user considers four load types of movable electric load, movable gas load, flexible heat load and flexible cold load:

the movable electric load refers to an electric load capable of translating in time, and the movable electric load comprises a movable interruptible electric load and a movable non-interruptible electric load; a movable interruptible electrical load refers to an electrical load that can be time-shifted and interruptible in-transit; a movable uninterruptible electrical load means a time-translatable uninterruptible electrical load midway through use;

movable interruptible electrical loads include washing machine, dishwasher electrical appliances; the expression of the movable uninterruptible electrical load at time t is given by equation (5):

in the formula (5), L _EDR，s (t) represents the amount of movable uninterruptible electrical load at time t; n represents the number of user households in the microgrid; n is _p Representing a number of users of the microgrid participating in a response of the movable uninterruptible electrical load;

represents n _p The load generated when the user uses the x-th equipment at the time t; x represents a set of available devices;

can be expressed as shown in formula (6):

in the formula (6), n _p Representing a number of users participating in a response of the movable uninterruptible electrical load within the microgrid; p _x,e Represents the power used by device x during the scheduling period;

the movable interruptible electric load considers the electric vehicle charging load in the micro-grid, and the number of users with electric vehicles in one micro-grid is set to be n _c With a charging duration of T _sp The number of the users is equivalent to

The charging time is 1 hour of electric automobile load; the transferable interruptible load model at time t can be expressed as shown in equation (7):

l in the formula (7) _EDR，d (t) represents the amount of movable interruptible electrical load at time t; p _x,e Representing the charging power of the charging automobile in a scheduling period; n is _c ' (t) represents the number of electric vehicle users participating in charging at time t;

the transferable uninterrupted gas load considers gas wall-hanging furnaces and gas water heater equipment; the expression form of the movable uninterruptible gas load at time t is shown in equation (8):

in formula (8), L _g，mob (t) represents the movable uninterruptible gas load at time t; n represents the number of user households in the microgrid; n is _p1 Representing the number of users participating in the response of the movable uninterruptible gas load in the microgrid;

represents n _p1 The user uses the load generated by the g-th equipment at the time t; g represents a set of available devices;

can be expressed as shown in formula (9):

in the formula (9), n _p1 Representing a number of users participating in a response of the movable uninterruptible electrical load within the microgrid; p is _g,e Represents the power used by the device g during the scheduling period;

the flexible heat load considers the hot water load, and the acceptance range of users in the microgrid to the hot water temperature is set as T _h,min ,T _h,max ]By H _h,min And H _h,max The minimum hot water load power and the maximum hot water load power are expressed by the following expressions (10) and (11), respectively:

in formulae (10) to (11), T _h,in Represents the temperature of the added water at the moment t; c _w And ρ _W Respectively representing the specific heat capacity of water and the density of water; v _c (t) represents the volume of cold water added at time t; Δ t represents a time step; h _h (t) represents the thermal load at time t; the flexible thermal load satisfies the constraint equation (12):

H _h,min (t)≤H _h (t)≤H _h,max (t) (12)

in the formula (12), H _h,min And H _h,max Respectively representing the minimum hot water load power and the maximum hot water load power; h _h (t) represents a flexible thermal load value at time t;

the flexible cold load considers the acceptable range of the indoor cooling temperature of the user, and the acceptable range of the indoor temperature of the user is expressed as [ T _c,min ,T _c,max ]The minimum refrigeration load and the maximum refrigeration load at time t are expressed by expressions (13) and (14), respectively:

in formulae (13) to (14), C _c,min (t) and C _c,max (t) represents a minimum refrigeration load and a maximum refrigeration load at time t, respectively; t is _c,min And T _c,max Respectively representing the upper limit and the lower limit of the acceptable range of the indoor temperature for the user; r _is Representing a user house thermal resistance; t is _o (t) represents the outdoor temperature of the user's house at time t; the flexible cold load is expressed as:

C _c,min (t)≤C _c (t)≤C _c,max (t) (15)

in the formula (15), C _c,min (t) and C _c,max (t) represents a minimum refrigeration load and a maximum refrigeration load at time t, respectively; c _c (t) represents flexible cooling load at time t;

step three: providing a two-stage optimization model based on a master-slave cooperation game, solving the optimal capacity of the whole optimization process and the shared energy storage device, and distributing the profit of the contribution degree of the alliance micro-grid;

in the third step, a two-stage optimization model of a master-slave cooperation game is utilized to solve the optimal capacity of the whole optimization process and the shared energy storage device, the first-stage optimization model can introduce the master-slave game model to solve the optimal capacity, the first-stage optimization model solves the energy conversion condition and the user load use condition of each micro-grid under the condition that the master-slave game balance between each micro-grid and each micro-grid user is achieved, and the optimal electric energy production and sale condition of each micro-grid is transmitted to the second-stage optimization model;

the first-stage objective function of each microgrid integrated energy system is expressed as a profit maximization function of each microgrid, and the formula (23) shows:

in the formula (23), Pr represents the daily operating profit of the micro-grid; l is a radical of an alcohol _j,e (t) represents the actual load capacity of the energy source e of the microgrid j at the moment t; gamma ray _e (t) represents the price of energy e at time t; c _M Represents the operating cost of the micro-grid, and can be represented by the formula (24):

C _M ＝C _epe +C _eql (24)

in the formula (24), C _epe The total cost of the micro-grid j for purchasing electric energy and natural gas from the power distribution network and the natural gas network all day is represented by the expressionFormula (25):

in formula (25), E' ═ ele, gas };

representing the power value of the energy e purchased by the micro-grid from the outside at the moment t; zeta _e (t) the price of the external energy e at the moment t is formulated by a power distribution network and a natural gas network;

in formula (24), C _eqi The daily maintenance cost of all the devices in the microgrid is represented by the expression (26):

in the formula (24), the reaction mixture is,

represents the output power of the device b at time t; v. of _b Represents the loss factor of device b; b represents a set of all devices;

according to the method, each microgrid operator forms the alliance microgrid, energy prices are published to users in the alliance microgrid, and then the users respond to demands according to the energy prices; in the process, the alliance microgrid operator is a pre-decision maker, and the microgrid users are post-decision makers, so that the alliance microgrid operator is called a leader, and the microgrid users are called followers; the entire first stage optimization model can be represented in two steps:

step 1: the follower carries out demand response according to the energy price published by the leader, and the objective function is an equation (22); feeding the load use condition of the follower back to the leader;

step 2: the leader uses the situation according to the load that the follower feedbacks, the objective function is the equation (23); adjusting and optimizing the output condition of equipment in the microgrid;

repeating the steps until the alliance micro-grid obtains the optimal daily profit, and considering that the game balance is achieved;

through the solution of the first stage, the daily electric energy production and marketing condition under the condition that each micro-grid and micro-grid user achieve master-slave game balance can be obtained; in the second stage, the capacity of the shared energy storage device needs to be solved, so that the annual electric energy production and sale conditions of each microgrid need to be analyzed; in addition, electric energy interaction among all micro-grids and the capital construction condition of each micro-grid on a shared energy storage device need to be considered in the second-stage optimization model, and because each micro-grid belongs to different operators, the micro-grids have both cooperative relationship and competitive relationship and also contain complex benefit interaction relationship, cooperative game is introduced to solve the second-stage optimization model; the second stage objective function can be expressed as equation (27):

in the formula (27), C _cost Representing the sum of the lowest electricity purchasing cost of the alliance microgrid all the year around and the construction cost of the shared energy storage year; m represents the typical number of days; w represents typical days; j represents the number of micro grids in the alliance micro grid; p _j,w,m (t) represents the electric power purchased by the alliance microgrid to the power distribution grid at time t; zeta _ele (t) represents the power price of the power distribution network at the moment t; c _inv,y Representing shared energy storage construction costs.

2. The optimized dispatching method for the multi-microgrid integrated energy system considering demand side response and shared energy storage as claimed in claim 1, characterized in that: in the first step, a plurality of micro-grids use the same energy storage power station to store or release electric energy, the energy storage power station is called a shared energy storage system, and the annual average investment cost and maintenance cost of the energy storage power station are expressed as formula (1):

in the formula (1), C _inv,y Representing the annual average investment cost of the energy storage power station; mu.s _s The capacity cost of the energy storage power station is expressed by the unit: yuan/(kWh); mu.s _p Representing the power cost of the energy storage power station in units of: yuan/kW;

and

respectively representing the maximum capacity and the maximum charge-discharge power of the energy storage power station; y is _s Representing the expected years of use of the energy storage power plant; m _ess Which is the annual maintenance cost of energy storage power stations.

3. The optimized dispatching method for the multi-microgrid integrated energy system considering demand side response and shared energy storage as claimed in claim 2, characterized in that: in the first step, the shared energy storage system has two working states of charging and discharging according to the electric energy surplus condition of each microgrid:

if the excessive power P of the microgrid j at the moment t _j,s (t)>0, indicating that the microgrid j has residual electric energy at the moment and sending an energy storage signal to the shared energy storage system; if P _j,s (t)<0, indicating that the microgrid j lacks electric energy at the moment, and sending an energy release signal to the shared energy storage system; the shared energy storage system performs supply and demand matching by collecting supply and demand signals of each microgrid; the total demand supply expression and the total charge-discharge demand expression sharing the stored energy are respectively shown as formula (2) and formula (3):

P _D (t)＝|D _sum (t)|-|S _sum (t)| (3)

in formulae (2) and (3), D _sum Reporting to a shared energy storage system for all micro-grids at time tTotal energy demand of (a); s _sum Reporting the total energy supply amount to the shared energy storage system at the moment t for all the micro-grids; p _j,s (t) represents the excess power of the microgrid j at the moment t; p _D (t) represents the total charge and discharge demand for shared storage if P _D (t)>0, the total discharge requirement of all the micro-grids accessed to the shared energy storage power station at the time t is discharge, and the shared energy storage system regulation and control center uses the energy storage power station to discharge to meet the requirement of the micro-grid group; if P _D And (t) < 0, which indicates that the total discharging requirement of all the micro-grids accessed to the shared energy storage power station at the time t is charging, and the shared energy storage system regulation and control center uses the energy storage power station for charging to meet the requirement of the micro-grid group.

4. The optimal scheduling method for the multi-microgrid integrated energy system considering demand side response and shared energy storage according to claim 3, characterized in that:

in the first step, the state of charge and the corresponding power of the energy storage system should satisfy a certain constraint condition, as shown in formula (4):

in the formula (4), soc (t) represents the electric energy stored at the time of shared energy storage t; eta ^abs And η ^relea Respectively representing charge efficiency and discharge efficiency;

and

respectively representing the charging power and the discharging power at the moment of sharing the stored energy t; soc _max And Soc _min Respectively representing the upper limit and the lower limit of the state of charge of the energy storage system;

and

representing a charging state variable and a discharging state variable of the energy storage power station;

representing the maximum charge and discharge power of the shared energy storage device.

5. The optimized dispatching method for the multi-microgrid integrated energy system considering demand side response and shared energy storage as claimed in claim 1, characterized in that:

in the second step, the actual load amount of the user t in the microgrid j at the moment can be expressed by the following formulas (16) to (19):

L _j,e (t)＝L _j,BE (t)+L _j,e,mob (t) (16)

L _j,g (t)＝L _j,GE (t)+L _j,g,mob (t) (17)

L _j,h (t)＝L _j,HE (t)+H _j,h (t) (18)

L _j,c (t)＝L _j,CE (t)+C _j,c (t) (19)

in formulae (16) to (19), L _j,e (t)、L _j,g (t)、L _j,h (t)、L _j,c (t) respectively representing the actual electric load, the actual gas load, the actual heat load and the actual cold load of a user in the microgrid j at the moment t; l is _j,BE (t)、L _j,GE (t)、L _j,HE (t)、L _j,CE (t) respectively representing a basic electric load, a basic gas load, a basic heat load and a basic cold load at the moment t in the microgrid j; l is _j,e,mob (t)、L _j,g,mob (t)、H _j,h (t)、C _j,c (t) represents a movable electrical load, a movable non-interruptible gas load, a flexible thermal load and a flexible cold load within the microgrid j, respectively.

6. The optimal scheduling method for the multi-microgrid integrated energy system considering demand side response and shared energy storage according to claim 5, characterized in that:

in the second step, the utility function is also added into the user objective function, the utility function is represented by a quadratic function, the user has the optimal energy consumption in each time period, and the satisfaction loss is generated when the user energy consumption deviates from the optimal energy consumption; the utility function and the satisfaction loss of the user in the microgrid j are respectively expressed by the following formulas (20) and (21):

in formulae (20) to (21), U _j,ut (t) represents a utility function of the user at time t; u shape _j,loss (t) represents a loss of user satisfaction at time t; alpha (alpha) ("alpha") _j,e And beta _j,e All represent the energy utilization preference coefficient of the user in the microgrid j; lambda _j,e And theta _j,e Representing a satisfaction degree loss parameter of an energy source e in the microgrid j; l is _j,e (t) and L _j,e，B (t) represents the actual load capacity and the baseline load of the energy source e of the microgrid j at the moment t; wherein E ═ { ele, gas, cold, heat }, represents the user's type of energy purchased;

the whole interest demand of the user in the microgrid j all year round can be expressed in the form of formula (22):

in the formula (22), U _j,eu (t) represents the annual comprehensive benefit value of the users in the microgrid j; u shape _j,ut (t) represents the utility function of the user at time t; u shape _j,loss (t) represents a loss of user satisfaction at time t; gamma ray _e (t) the price of the energy e at the time t is formulated and published by the microgrid operator; d represents the number of days; t represents the number of times.

7. The optimized dispatching method for the multi-microgrid integrated energy system considering demand side response and shared energy storage as claimed in claim 1, characterized in that: in the third step, the final annual profit function of the micro-grid of the alliance is shown as the formula (28):

in the formula (28), Pr' represents an annual profit function; m represents the typical number of days; w represents typical days; e represents a set of energy types; l is a radical of an alcohol _j,e,w,m (t) representing the actual load capacity of the energy source e of the microgrid j at the time t on the w-th day of the mth typical day; gamma ray _e (t) represents the price of energy e at time t;

indicating that the micro-grid j purchases gas from the outside at the time of w day t on the mth typical day; zeta _gas (t) represents the external natural gas price;

representing the output power of the device b at the moment t in the microgrid j; v. of _b Represents the loss factor of device b; b represents a set of all devices; c _cost Representing the sum of annual electricity purchasing cost and shared energy storage annual construction cost of the alliance microgrid;

the total extra profit of the alliance is the profit after the alliance of each microgrid is deducted by the sum of the profits before the alliance of each microgrid, and the formula (29) shows that:

in formula (29), U _ext Additional profits obtained for the federation; pr' is the profit after each microgrid alliance,

the sum of profits before each microgrid is not united; the profit sharing model is given by the formula (30) - ((32) Shown in the specification:

A _j ＝Cv _j ·U _ext (30)

in the formulae (30) to (32), A _j Represents the extra profit allocated by the microgrid j; c _j Representing the total contribution of the microgrid j; es _j (t) represents the sum of the electric energy transmitted by the microgrid j to other microgrids at the moment t; p is _j,s (t) represents the power obtained by the microgrid j from the shared energy storage at the moment t; cv _j Representing the contribution of the microgrid j in the alliance.

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