CN113361875B

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

Info

Publication number: CN113361875B
Application number: CN202110565983.3A
Authority: CN
Inventors: 徐艳春; 刘海权; 孙思涵; 汪平
Original assignee: China Three Gorges University CTGU
Current assignee: China Three Gorges University CTGU
Priority date: 2021-05-24
Filing date: 2021-05-24
Publication date: 2022-09-02
Anticipated expiration: 2041-05-24
Also published as: CN113361875A

Abstract

The optimization scheduling method of the multi-microgrid integrated energy system considering demand side response and shared energy storage comprises the steps of adding a shared energy storage system into the multi-microgrid integrated energy system containing electric energy interaction to form a multi-microgrid integrated energy system model containing shared energy storage and electric power interaction; 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; and 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. According to the invention, user demand side response is added to the multi-microgrid comprehensive energy system model containing shared energy storage and electric energy interaction, so that the effects of peak clipping and valley filling and cost saving of the microgrid and the user are achieved; and the win-win situation between the micro-grid and the user is realized.

Description

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

Technical Field

The invention relates to the technical field of optimization scheduling of multiple micro-grids, in particular to a multi-micro-grid comprehensive energy system optimization scheduling method considering demand side response and shared energy storage.

Background

In recent years, the problems of energy shortage, environmental pollution and the like are becoming more serious, and how to develop a clean and efficient energy supply mode and how to realize sustainable development of energy has attracted wide attention at home and abroad. The multi-microgrid comprehensive energy system realizes the cascade utilization of energy by playing different energy complementary characteristics, and is an important direction for the development of future energy. When the same power distribution area is connected with a plurality of micro-grids, a multi-micro-grid system can be formed. When each micro-grid in the multi-micro-grid system has power interaction, the planning operation of the micro-grid and the output of equipment in the multi-micro-grid system are greatly influenced. In the multi-microgrid integrated energy system, each microgrid belongs to different main bodies, and each microgrid user has an autonomous response behavior, so that a complex interest interaction relationship exists between the microgrid users, and great influence is brought to the operation regulation and control of the multi-microgrid integrated energy system.

In addition, the wind-solar output in the micro-grid accounts for more and more, and wind-solar power generation has the characteristics of intermittence, uncertainty and the like, so that the wind and light abandonment is serious. And the energy storage can carry out quick storage and release to the electric energy, can be for the too much electric energy of comprehensive energy system storage, reduces to abandon wind and abandon the light phenomenon. Therefore, under the condition of considering user autonomous behavior and complex benefit interaction, an optimal energy scheduling strategy is reasonably formulated for research according to the load characteristics and wind and light output of the micro-grid, so that the user benefit can be effectively improved, and the wind and light abandoning rate and the micro-grid operation cost can be reduced.

Disclosure of Invention

In order to enable the multi-microgrid integrated energy system to be more economical in the operation process, improve the wind and light consumption rate in the microgrid and enable users in the microgrid to be more economical and comfortable. The invention provides a multi-microgrid integrated energy system optimization scheduling method considering demand side response and shared energy storage, wherein user demand side response is added into a multi-microgrid integrated energy system model containing shared energy storage and electric energy interaction, so that the effects of peak clipping and valley filling and microgrid and user cost saving are achieved; and the win-win situation between the micro-grid and the user is realized.

The technical scheme adopted by the invention is as follows:

the optimization scheduling method of the multi-microgrid comprehensive energy system considering demand side response and shared energy storage comprises the following steps:

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;

step three: and 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 microgrid.

In the first step, the shared energy storage system means that a plurality of micro-grids use the same energy storage power station to store or release electric energy, and the energy storage power station is called as a shared energy storage system.

In the first step, the electric energy interaction refers to a behavior that each micro-grid transmits electric energy with other micro-grids according to the electric energy production and marketing condition of each micro-grid.

In the first step, the multi-microgrid integrated energy system is composed of a plurality of integrated energy systems, and the single integrated energy system coordinates and optimizes links of energy production, transmission, distribution, conversion, storage and the like through complementary characteristics of different energy sources to perform coupling conversion on different energy sources, so that the production and marketing of energy gradient utilization are realized for the integrated energy system.

In the first step, the devices included in a single microgrid mainly include: a Wind Turbine (WT), a Photovoltaic (PV), a Micro Gas Turbine (MGT), and a waste heat boiler (heat recovery boiler) to form a combined heat and power unit (CHP), a LiBr refrigerator (LBAC), and an electric refrigeration device; the energy storage stations in the microgrid include Thermal Energy Storage (TES), Cold Energy Storage (CES) and Gas Energy Storage (GES). Each microgrid can purchase electric energy and natural gas to external distribution network and natural gas net, and surplus heat energy can be sold to the heating network after combining the heat exchanger exchange. Energy between the energy source station and the energy storage station in a single microgrid is bidirectional interaction; through the inside equipment of microgrid to the energy of buying carry out the energy supply to user in the garden after the coupling conversion, each microgrid still can carry out the electric power interaction with sharing energy memory, and unnecessary electric energy can not give the distribution network by the way of reversing, can only absorb unnecessary electric energy or directly abandon the electricity through sharing energy storage power station.

In the first step, the shared energy storage system is built by paying money for each microgrid in the multi-microgrid integrated energy system, so that the microgrid does not need to consider paying service fee and electricity purchasing fee for the shared energy storage device, and only the construction cost of energy storage is considered.

In the second step, the autonomous response behavior of the user, namely the user demand side response behavior, means that the user adjusts different demands of different energy sources in the microgrid, so that the user autonomous response behavior of saving cost, peak clipping and valley filling is achieved. The autonomous response behavior of the user mainly considers four load types of movable electric load, movable gas load, flexible heat load and flexible cold load.

The movable electric loads are time-shiftable electric loads, and are divided into movable interruptible electric loads and movable non-interruptible electric loads. A movable interruptible electrical load refers to a time-translatable and interruptible electrical load in use; a movable uninterruptible electrical load refers to a time-shiftable uninterrupted electrical load that is used halfway through.

In the third step, the principal and subordinate game is a game type that one party acts first and the other party acts later; cooperative gaming is a type of game in which participants in the game maximize the league's revenue by enforcing their constraint agreements. The method comprises the steps that a master-slave-cooperative game two-stage optimization model is utilized to solve the optimal capacity of the whole optimization process and the shared energy storage device, in the first-stage optimization model, two main bodies of a microgrid and microgrid users are involved, firstly, the microgrid users jointly issue energy prices to the microgrid users, then the microgrid users carry out demand response according to the energy prices and feed back the result (load using condition) of the demand response to the microgrid operators, and the microgrid operators convert and output power to equipment in the microgrid according to the load using condition. Therefore, interactivity exists between the two main bodies in the first stage, a microgrid operator and users in the microgrid have different targets, and the two main bodies form master-slave competition. Therefore, the first-stage optimization model can introduce a master-slave game model to solve the first-stage optimization model. The purpose of the first-stage optimization model is to solve the energy conversion condition and the user load use condition of each microgrid under the condition that master-slave game balance is achieved between each microgrid and each microgrid user, and transmit the optimal electric energy production and marketing condition of each microgrid to the second-stage optimization model.

The micro-grid operators form a alliance micro-grid, energy prices are published to users in the alliance micro-grid, and then the users respond to demands according to the energy prices. In the process, the alliance microgrid operator is a pre-policy party, and the microgrid users are post-policy parties, 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 makes a demand response according to the energy price published by the leader, and the objective function is an equation (22). And feeding back the load use condition of the follower to the leader.

Step 2: the leader is according to the load use condition that the follower feedbacks, the objective function is equation (23). And adjusting and optimizing the output condition of the equipment in the microgrid.

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

In the third step, through the solution in the first stage, the daily electric energy production and sale conditions of each micro-grid and micro-grid users under the condition of 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 funding construction condition of each micro-grid on a shared energy storage device need to be considered in the second-stage optimization model, and because all micro-grids belong to different operators, the micro-grids have a cooperative relationship and a competitive relationship and also contain a complex benefit interaction relationship, a cooperative game is introduced to solve the second-stage optimization model.

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

The invention relates to an optimized dispatching method for a multi-microgrid comprehensive energy system considering demand side response and shared energy storage, which has the following technical effects:

1) according to the invention, the user demand side response is added in the multi-microgrid comprehensive energy system model containing the shared energy storage and electric energy interaction, so that the effects of peak clipping and valley filling and cost saving of the microgrid and the user are achieved, and the win-win situation between the microgrid and the user is realized.

2) The invention provides a master-slave-game two-stage optimization model, which is used for carrying out optimization scheduling on a multi-microgrid integrated energy system, under the optimization of the model, master-slave game balance is achieved between each microgrid system and microgrid users, win-win is realized, the multi-microgrid integrated energy system can be configured with the optimal capacity of a shared energy storage power station, the multi-microgrid integrated energy system can obtain more profits, and meanwhile, the integrated benefit of each microgrid user is also improved.

Drawings

FIG. 1 is a diagram of energy coupling for a single integrated energy system.

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

FIG. 3 is a diagram of a two-stage optimization model.

Fig. 4 is a flowchart of the overall optimization.

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

FIG. 6(a) is a graph of the electric energy price of each typical day microgrid;

FIG. 6(b) is a graph of gas energy prices for each typical day microgrid;

FIG. 6(c) is a graph of cold energy prices for each typical day microgrid;

fig. 6(d) is a graph of heat energy prices for each typical day microgrid.

Detailed Description

Preferred embodiments are described in detail below with reference to the accompanying drawings:

planning modeling of shared energy storage:

due to the fact that the energy storage construction cost is high, a single microgrid operator wants to configure an energy storage device with a large capacity, and high cost is brought to the microgrid operator, and the energy storage capacity configured by the single microgrid usually cannot well meet the requirement of wind and light electric energy storage in the microgrid. Therefore, the shared energy storage is built by combining a plurality of micro-grids, and each micro-grid does not need to consider paying service fee and electricity purchasing fee to the shared energy storage device, but only needs to consider the construction cost of the energy storage. The average annual investment and maintenance costs of an energy storage plant can be expressed as formula (1):

in the formula (1), mu _s The capacity cost of the energy storage power station is expressed in unit/(kWh); mu.s _p Represents 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.

According to the electric energy surplus condition of each micro-grid, the shared energy storage system has two working states of charging and discharging. 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)<And 0, indicating that the microgrid j lacks electric energy at the moment, and sending an energy release signal to the shared energy storage system. And the shared energy storage system performs supply and demand matching by collecting supply and demand signals of each microgrid. The total demand supply expression is shown in equation (2):

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

in formulae (2) and (3), D _sum Reporting the total energy demand to the shared energy storage system for all the micro-grids at the moment t; s _sum Reporting the total energy supply amount to the shared energy storage system for all the micro-grids at the moment t; if P _D (t)>And 0, indicating that the total discharge demand of all the micro-grids accessed to the shared energy storage power station at the moment t is discharge, and using an energy storage device to discharge by the shared energy storage system regulation and control center to meet the demand of the micro-grid group.

The state of charge and corresponding power constraints of the energy storage system are as shown in equation (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.

(II) a multi-microgrid comprehensive energy system model containing shared energy storage:

a single microgrid topology is shown in fig. 1. In fig. 1, the left side represents an external energy network, and the right side represents the energy type used by users in the microgrid. The single microgrid mainly comprises the following devices: a Wind Turbine (WT), a Photovoltaic (PV), a Micro Gas Turbine (MGT), and a waste heat boiler (waste heat boiler) to form a combined heat and power unit (CHP), a LiBr refrigerator (LBAC), and an electric refrigerating device. The energy storage stations in the microgrid include Thermal Energy Storage (TES), Cold Energy Storage (CES) and Gas Energy Storage (GES). Can know by figure 1, the load type of user has electric load, cold load, heat load and gas load in the microgrid, and the microgrid can purchase electric energy and natural gas to external distribution network and natural gas net, and unnecessary heat energy can be sold to the heat supply network after heat exchanger exchanges, and the energy is two-way interaction between single microgrid internal energy source station and the energy storage station, and the energy supply is carried out the user in the garden after the internal plant carries out coupling conversion to the energy of purchasing through microgrid.

The multi-microgrid comprehensive energy system comprising shared energy storage and power cooperative interaction is formed by 3 cold-hot electricity combined supply type microgrid and the shared energy storage, and the connection relationship among the microgrids is shown in fig. 2. As can be seen from fig. 2, each microgrid performs power cooperation interconnection through information interaction. In addition, each microgrid may also be in electrical interaction with a shared energy storage device. The invention sets that the redundant electric energy of each micro-grid can not be sent back to the power distribution network, and the redundant electric energy can be absorbed only by the shared energy storage power station or the electricity can be directly abandoned.

(III) a micro-grid load model and a user model:

the autonomous response behavior of the user, namely the user demand side response behavior, refers to the user autonomous response behavior that the user adjusts different demands of different energy sources in the microgrid, so that the purposes of saving cost and clipping peaks and filling valleys are achieved. The autonomous response behavior of the user mainly considers four load types of movable electric load, movable gas load, flexible heat load and flexible cold load.

The movable electrical loads refer to electrical loads which can be shifted in time, and are divided into movable interruptible electrical loads and movable non-interruptible electrical loads. A movable interruptible electrical load refers to an electrical load that can be time-shifted and interruptible in-transit; a movable uninterruptible electrical load refers to a time-shiftable uninterrupted electrical load that is used halfway through.

The movable uninterruptible electrical loads mainly comprise electrical equipment such as washing machines, dishwashers and the like. the expression of the movable uninterruptible electrical load at time t is given by equation (5):

in the formula (5), n represents the number of user households in the microgrid; n is a radical of an alkyl radical _p Representing a number of users participating in a response of the movable uninterruptible electrical load within the microgrid;

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

Can be represented by the formula (6):

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

The transferable interruptible electric load mainly considers the electric vehicle charging load in the microgrid, and the number of users with electric vehicles in one microgrid is assumed to be n _c With a charging duration of T _sp It is equivalent to the number of users as

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):

p in formula (7) _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 non-interruptible gas load mainly considers devices such as a gas wall-mounted furnace, a gas water heater and the like. the expression of the movable uninterruptible gas load at time t is shown in equation (8):

in the formula (8), n represents the number of user users in the microgrid; n is _p1 Representing a number of users participating in a response of the movable uninterruptible gas load within the microgrid;

represents n _p1 The load generated when the user uses the g-th equipment at the time t; g denotes 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 _g,e Representing the power used by the device g during the scheduling period.

The flexible heat load and the flexible cold load both belong to elastic loads, the flexible heat load mainly considers the hot water load, and the flexible cold load mainly considers the acceptable range of the indoor temperature of a user.

Supposing that the acceptance range of users in the microgrid to the temperature of hot water is 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 (8) to (9), 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; hh (t) represents a flexible thermal load value at time t.

The flexible cooling load takes into account the user's acceptable range of indoor cooling temperatures, given that the user's acceptable range of indoor temperatures is denoted as T _c,min ,T _c,max ]The minimum refrigeration load and the maximum refrigeration load at time t are expressed by equation (13) and equation (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; r _is Representing user house thermal resistance; t is _o And (t) represents the outdoor temperature of the user house at the time t. The flexible cooling load can be 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 the flexible cooling load at time t.

The actual load capacity 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 And (t) respectively represents the actual electric load, the actual gas load, the actual heat load and the actual cold load of the 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 And (t) respectively represents the basic electric load, the basic gas load, the basic heat load and the basic cold load at the moment t in the micro-grid j. L is a radical of an alcohol _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.

In order to make the user obtain more benefits as much as possible, a utility function is also added to the user objective function, and the utility function is often used in micro-economics to represent the degree of satisfaction that the consumer obtains from consuming a given commodity, and is often represented by a quadratic function. In addition, the user has an optimum amount of energy usage in each time period, which results in a loss of satisfaction when the user has a deviation from the optimum amount of energy usage. 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), α _j,e And beta _j,e Energy utilization preference system for users in microgrid jCounting; lambda [ alpha ] _j,e And theta _j,e Representing a satisfaction degree loss parameter of an energy source e in the microgrid j; l is a radical of an alcohol _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; where E ═ ele, gas, cold, heat }, indicates the type of energy purchased by the user.

The whole annual interest appeal of the user in the microgrid j can be expressed in the form of a formula (22):

in the formula (22), U _j,eu Representing the comprehensive benefit value of the user in the microgrid j all year round; 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.

(IV) two-stage optimization model:

the two-stage optimization model used is shown in fig. 3. The function and the specific solving method of each stage are explained below.

The primary and secondary game is a game type that one party acts first and the other party acts later; cooperative gaming is a type of game in which participants in the game maximize the league's revenue by enforcing their constraint agreements. The method comprises the steps that a master-slave-cooperative game two-stage optimization model is utilized to solve the optimal capacity of a whole optimization process and a shared energy storage device, in a first-stage optimization model, two main bodies, namely a micro-grid and micro-grid users, are involved, energy prices are issued to the micro-grid users by all micro-grid users in a combined mode, then all the micro-grid users carry out demand response according to the energy prices, the result of the demand response, namely the load use condition is fed back to the micro-grid operators, and all the micro-grid operators carry out conversion output on equipment in the micro-grid according to the load use condition. Therefore, interactivity exists between the two main bodies in the first stage, a microgrid operator and users in the microgrid have different targets, and the two main bodies form master-slave competition. Therefore, the first-stage optimization model can introduce a master-slave game model to solve the first-stage optimization model. The purpose of the first-stage optimization model is to solve the problem that the optimal power generation and marketing conditions of each microgrid are transmitted to the second-stage optimization model when the microgrid and each microgrid user achieve master-slave game balance.

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

in the formula (23), Pr represents the daily operation profit of the micro-grid; l is _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 a power distribution network and a natural gas network all day is represented by the expression (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 And (t) the price of the external energy e at the moment t is set by a power distribution network and a natural gas network.

In equation (24), Ceqi represents the daily maintenance cost of all devices in the microgrid, and the expression is shown in equation (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 denotes a set of all devices.

Therefore, all microgrid operators form the alliance microgrid, energy prices are published to users in the alliance microgrid at first, and then the users respond to demands according to the energy prices. In the process, the alliance microgrid operator is a pre-policy party, and the microgrid users are post-policy parties, 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 responds to the demand according to the energy price published by the leader, and the objective function is an equation (22). And feeding back the load use condition of the follower to the leader.

Through the first stage of solution, 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 all micro-grids belong to different operators, the micro-grids have both cooperative relationship and competitive relationship and also contain complex interest 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 network at time t; zeta _ele (t) represents the power price of the power distribution network at the time t; c _inv,y Representing the shared energy storage construction cost.

Therefore, the ultimate annual profit function for the alliance microgrid is as shown in equation (28):

in the formula (28), M represents the number of typical days; w represents typical days; e represents a set of energy types; l is _j,e,w,m (t) represents 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 equipment b at the time t in the microgrid j; v. of _b Represents the loss factor of device b; b represents a set of all devices; c _cost And the sum of annual electricity purchasing cost and annual shared energy storage construction cost of the alliance microgrid is represented.

Because the conditions of electric energy interaction and common investment construction sharing energy storage exist among the alliance micro-grids, the total extra profit of the alliance is distributed based on the total contribution degree of each micro-grid to the alliance micro-grid system, and the fairness of income distribution of each micro-grid in the alliance is ensured. The total extra profit of the alliance is the profit after the alliance of each microgrid minus the sum of 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 profit is the sum of profits of each micro-grid before alliance. The profit sharing model is shown in equations (30) - (32):

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.

In the two-stage optimization model, the first-stage optimization process also needs to satisfy a constraint condition of response, wherein an electric power balance constraint, a gas power balance constraint, a cold power balance constraint and a thermal power balance constraint are respectively shown as formulas (33) to (36):

in formula (33), P _pv (t) and P _wt (t) respectively representing the electric power generated by the photovoltaic and the electric power generated by the fan at the moment t; p _chp (t) Co-generation at time tThe electric power generated by the unit;

the step (b) represents that electric power entering the inner end of the transformer of the microgrid is purchased from an external power distribution network at the moment t; p is _ac (t) and ρ _ac Respectively representing the cold power and the energy efficiency ratio of the air conditioner in the microgrid at the moment t; l is _ele,R (t) represents the amount of electrical load consumed by a user in the microgrid at time t; p _ap And (t) represents the power discard amount at the moment t.

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

In the formula (34), G _pur (t) represents the amount of natural gas purchased by the microgrid from the outside; g _ges (t) represents gas power exchanged by the GES at time t; g _chp (t) representing the gas power absorbed by the cogeneration unit at the moment t; g _gb (t) represents the gas power consumed by the gas boiler at time t; l is _gas,R And (t) represents the air load amount consumed by the user in the microgrid at the moment t.

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

In the formula (35), C _lbac (t) represents the cooling power of the LBAC at time t; c _ces (t) represents the cold power of the CES exchange at time t; c _ac (t) represents the air conditioner refrigeration power at the time t; l is _cold,R And (t) represents the amount of cold load consumed by the user in the microgrid at the moment t.

In the formula (36), H _chp (t) represents the thermal power generated by the cogeneration unit at the moment t; h _gb (t) represents the thermal power generated by 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.g. of the type _lbac Representing the energy efficiency ratio of the LBAC; h _sell (t) represents the thermal power sold by the microgrid to the heat supply network at time t; l is _heat,R (t) represents time tThe amount of thermal load consumed by users within the microgrid.

In addition to the above power balance constraint, it should be considered that the micro grid and the distribution grid need to be converted by a Transformer (TR) when purchasing power, and the TR has a certain loss during conversion, so the equation constraint condition of equation (37) needs to be satisfied.

In the formula (37), the reaction mixture is,

representing the actual electric power purchased by the microgrid from an external power distribution network at the moment t;

representing the residual electric power quantity when the electric power purchased from the outside at the time t is transmitted to the micro-grid user side; e.g. of the type _tr Representing the conversion efficiency of the transformer.

In summary, the overall process of the optimization scheduling strategy of the multi-microgrid integrated energy system considering the integrated demand response and the shared energy storage provided by the invention can be represented as shown in fig. 4.

And (V) comparing and analyzing the strategy of the invention with other 3 strategy schemes:

the multi-microgrid integrated energy system used in the comparative analysis includes 3 cold-hot electricity cogeneration-type microgrid, hereinafter referred to as a microgrid a, a microgrid B, and a microgrid C. The method is characterized in that four typical days including spring, summer, autumn and winter are set, 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 among all micro-grids is 600kWh, the upper limit of electric energy purchase of all micro-grids to an external power distribution network is 1000kWh, and the upper limit of gas purchase of all micro-grids to an external natural gas grid is 3000 kWh. The price of selling heat energy to the outside world by the multi-microgrid system is 0.2 yuan/kWh. The present invention does not consider the cost of power path loss between the micro-grids, given the extremely small spacing between the micro-grids. The charge-discharge efficiency of the shared energy storage power station is 0.98, the upper limit and the lower limit of the charge state of the energy storage system are respectively 90% and 10% of the shared energy storage capacity, and the initial energy of the energy storage system is 90%. The shared energy storage construction cost refers to the price of 1100 yuan/(kWh) in a battery of an energy storage project in 2018, the power cost is 1000 yuan/kW, the operation and maintenance cost is 72 yuan/(kW), and the theoretical life cycle of an energy storage power station is 8 years.

The following 4 protocols were set up for comparative analysis validation:

1) scheme 1: the microgrid A, the microgrid B and the microgrid C are connected in an alliance mode, all the microgrids in the alliance are in power cooperation, power interaction between the alliance microgrid and the shared energy storage system is considered, and user demand side response behaviors are considered.

2) Scheme 2: alliances are carried out among the microgrid A, the microgrid B and the microgrid C, power cooperation is carried out in the microgrid, and the alliances and the microgrid consider power interaction with shared energy storage but do not consider user demand side response.

3) Scheme 3: the micro-grid A, the micro-grid B and the micro-grid C are mutually connected in a power cooperation mode, the power interaction condition between the micro-grid A and the micro-grid B and the power interaction condition between the micro-grid C and shared energy storage are not considered, each micro-grid carries an energy storage device, the numerical values are 100kWh, 100kWh and 150kWh, and the user side demand response behavior is still considered.

4) Scheme 4: the micro-grid A, the micro-grid B and the micro-grid C are not connected in a power cooperation mode, power connection with a shared energy storage system is not considered, the micro-grids respectively carry energy storage devices which are respectively 100kWh, 100kWh and 150kWh, and comprehensive demand response behaviors of all micro-grid users are still considered.

The prices in the schemes 2 to 4 are all consistent with the scheme 1, and the shared energy storage device parameters in the scheme 2 are consistent with the scheme 1. It can be seen from the above 4 comparison schemes that scheme 1 adopts the strategy provided by the present invention, scheme 2 only adopts the second-stage optimization model of the two-stage optimization model provided by the present invention, and scheme 4 only adopts the first-stage optimization model. Therefore, the comparison between the scheme 1 and the scheme 2 is mainly used for reflecting the influence of adding the user demand response on the whole optimization process; the scheme 1 is compared with the scheme 3 mainly to reflect the influence of adding shared energy storage on the whole optimization process; comparing the scheme 1 with the scheme 4, the influence of the second-stage optimization model on the whole optimization process is mainly reflected.

In MatlabR2018b, a chaos particle swarm algorithm is adopted to nest a gurobi solver and a cplex solver to solve, the population number of the chaos particle swarm is set to be 50, the iteration number is set to be 50, and the chaos coefficient is set to be 3.5.

By optimizing and calculating the scheme 1, the configuration result of the shared energy storage power station in the scheme 1 is as follows: the shared energy storage capacity is 1523kWh, and the maximum charge and discharge power is 527 kW.

Fig. 5 shows the result of optimizing the charging and discharging behavior of the shared energy storage system on each typical day, and if the value in fig. 5 is positive, it indicates that the energy storage power station is in a charging state; and if the numerical value is negative, the energy storage power station is in a discharging state. As can be seen from fig. 5, no matter on which typical day, the power interaction between the shared energy storage power station and the multi-microgrid integrated energy system reaches more than ten times. At 8-9 points, the maximum charging power 527kW of the shared energy storage power station in each typical day is achieved, and at 18-19 points in the spring typical day and 16-17 points in the summer typical day, the charging power of the shared energy storage power station is over 300 kW. In addition, the shared energy storage power stations reach the maximum discharge power at 1-2 points of typical spring days, typical summer days and typical autumn days, and the discharge power of the shared energy storage power stations reaches more than 300kW at 11-12 points and 13-14 points of typical spring days, 11-12 points, 20-21 points and 22-23 points of typical summer days, so that the shared energy storage device has at least one full charge and full discharge behavior

The scheme 1 solves the energy prices of the multi-microgrid integrated energy system in each typical day as shown in fig. 6(a), 6(b), 6(c) and 6(d), and it can be seen that the energy prices are all in a reasonable range and are acceptable for users.

And carrying out optimization calculation on the schemes 2-4 according to the price and the shared energy storage capacity of the scheme 1. The data of the total cost of the multiple micro-grids, the total profit of the multiple micro-grids, the wind-solar energy consumption ratio and the like under each scheme are counted and listed in a table 1, and the indexes of the user benefit, the energy purchasing cost and the like of each micro-grid in the scheme 1 and the scheme 2 are listed in a table 2.

TABLE 1 optimization results of the respective schemes

Table 2 optimization results of scheme 1 and scheme 2

As can be seen from comparison of the optimization results of the scheme 1 and the scheme 2 in the tables 1 and 2, for each microgrid user, after the autonomous response behavior of the user is added, the annual comprehensive benefit of each microgrid user is greatly improved, and the annual energy purchase cost of each microgrid user is improved and reduced by 90.1 ten thousand yuan. For a microgrid operator, after the autonomous response behavior of the user is added, although the annual income of the alliance (the total energy purchase cost of the alliance user) is reduced by 90.1 ten thousand yuan, the annual operation cost of the alliance microgrid system is reduced by 168.4 ten thousand yuan, so that the annual total profit of the alliance microgrid system is increased by 78.3 ten thousand yuan. In addition, the wind-solar consumption of the alliance micro-grid system is increased from 70.8% before the autonomous response behavior of the user is added to 82.6% after the autonomous response behavior of the user is added, and wind and light abandoning of the alliance micro-grid system is reduced. Therefore, after the user autonomous behavior is added, the benefits of the user and the microgrid are well improved through the solution of the master-slave game mechanism, and the multi-win situation between the alliance microgrid system and each microgrid user is realized. According to the optimization results of the scheme 1 and the scheme 3, after the shared energy storage system is added, the annual operation cost of the alliance micro-grid system is reduced by nearly 50 ten thousand yuan, and the annual wind-solar energy consumption ratio of the alliance micro-grid system is increased by 5.2%, because the electricity purchasing behavior of the alliance micro-grid system and an external power distribution network is reduced after the shared energy storage system is added, wind-light-electricity generated by the alliance micro-grid system is effectively adjusted through the shared energy storage system, and the wind-light-abandon is reduced. Compared with the optimization results of the scheme 1 and the scheme 4, after the second-stage optimization model in the two-stage optimization model is added, the annual operation cost of the alliance micro-grid system is reduced by 106.1 ten thousand yuan, and the annual wind-solar energy consumption ratio is improved by 10.3%. According to the optimization results of the scheme 3 and the scheme 4, after the alliance microgrid is added with power interaction, the annual operation cost of the alliance microgrid system is reduced by 56.2 ten thousand yuan, and the wind-solar energy consumption ratio is improved by 5.1%, because the economy of the operation of the multi-microgrid integrated energy system can be improved by adding power interconnection in the multi-microgrid integrated energy system.

In conclusion, after the multi-microgrid integrated energy system is added with user demand side response and a shared energy storage power station, the satisfaction degree of energy consumption of users in the microgrid can be effectively improved and the energy consumption cost of the users can be reduced through the solution of a master-slave-cooperation two-stage game model, the operation cost of each microgrid is reduced, the energy consumption pressure in the microgrid is relieved, and the wind and light abandoning rate is reduced.

Claims

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:

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

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):

and

and

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;

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:

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)