Operation method of offshore island microgrid considering user comfort demand side management

Abstract

The present invention relates to an offshore island microgrid operation method that takes into account user comfort demand-side management, comprising the following steps: universal modeling of the distributed power source of the island microgrid, the distributed power source includes a wind turbine, a diesel generator and an energy storage system; fine modeling of various loads on the demand side, the loads include curtailable loads, shiftable loads and removable loads; using the k-means mean clustering algorithm to cluster the wind power generation on the island to generate typical scenarios; according to each distributed power source model and load model, the optimal operation strategy of the island microgrid under each typical scenario is obtained. The present invention improves the stability and reliability of the system and the comfort and satisfaction of electricity users by formulating a reasonable day-ahead scheduling plan.

Description

Technical Field

[0001] The invention relates to an offshore island microgrid operation method taking into account user comfort demand side management, and belongs to the technical field of power system operation analysis. Background Art

[0002] As the penetration rate of distributed renewable energy in the power grid continues to increase and people's demand for power supply quality continues to rise, microgrids, as one of the effective means of absorbing distributed energy, are gradually receiving attention for issues such as the economy and reliability of their operation.

[0003] Microgrids can maximize the utilization of distributed energy within the scope of dispatching capabilities. Its main working modes are divided into two types: grid-connected operation mode and off-grid operation mode. When a microgrid has a sudden failure, it is separated from the main grid and is in off-grid operation mode, which becomes an isolated microgrid. It mainly relies on the distributed power supply, energy storage system and backup diesel generators within the microgrid for short-term support. Since wind turbines and photovoltaics in distributed energy are extremely volatile and intermittent, it is very easy to cause an imbalance in the supply and demand of the isolated microgrid system, which poses a great inconvenience and threat to users in the isolated microgrid. Therefore, studying the reliability and economic operation and dispatching of isolated microgrids is of great significance to improving the power quality and comfort of users within the isolated microgrid and the stability of social order.

[0004] At present, there are several scheduling schemes that consider indicators such as satisfaction and comfort of load electricity consumption in isolated microgrids and the impact of the internal demand side of isolated microgrids: (1) While considering the economic benefits of the microgrid supply side, the electricity consumption satisfaction on the demand side is also taken into account. The proposed model can formulate response plans for users with different needs. (2) A multi-objective variable-time peak electricity price decision model that comprehensively considers the interests of wind power, power users and power supply companies is proposed, and a non-dominated sorting genetic algorithm is used to effectively balance the interests of all parties. (3) A scheduling model with the goal of maximizing the comprehensive benefits of the three parties of "source-grid-load" is constructed, and the coordinated integrated scheduling of the power generation and consumption sides is achieved by using the optimal electricity price and incentives.

[0005] However, most of the above modeling for user satisfaction classifies it as a general category, without further refining the demand response model of the specific load. Compared with other solutions, the present invention, in the context of offshore island microgrids, conducts refined demand-side response modeling for different types of users within the microgrid, and comprehensively considers the reliability and economy of the operation of the island microgrid and the electricity consumption experience of different types of users. At the same time, the k-means mean clustering algorithm is used to cluster the wind power generation scenarios on the island, and generate typical scenarios. Finally, the impact of the electricity consumption experience of different types of users on the reliable and economical operation of the island microgrid is verified under various typical wind power output scenarios. Summary of the invention

[0006] In order to overcome the above problems, the present invention provides an offshore island microgrid operation method taking into account user comfort demand-side management, which improves the stability and reliability of the system and the comfort and satisfaction of electricity users by formulating a reasonable day-ahead scheduling plan.

[0007] The technical solution of the present invention is as follows:

[0008] The method for operating an offshore island microgrid taking into account user comfort demand side management includes the following steps:

[0009] Generic modeling of distributed power sources of isolated microgrids, including wind turbines, diesel generators and energy storage systems;

[0010] Carry out detailed modeling of various types of loads on the demand side, including loads that can be cut, loads that can be translated, and loads that can be removed;

[0011] The k-means clustering algorithm is used to cluster the wind power generation scenarios on the island and generate typical scenarios;

[0012] Based on each distributed power model and load model, the optimal operation strategy of the isolated island microgrid under each typical scenario is obtained.

[0013] Furthermore, the load that can be cut includes air conditioning load, the load that can be shifted includes electric vehicle load and industrial load, and the load that can be cut is divided into three levels, the first level is the general load that can be cut, the second level is the load that participates in demand response, and the third level is the more important load;

[0014] When obtaining the optimal operation strategy of the isolated microgrid under different scenarios according to each distributed power model and load model, the objective function is to minimize the operation cost of the isolated microgrid. The objective function is as follows:

[0015]

[0016] Among them, C W is the operating cost of the isolated microgrid, C WT is the daily operation and maintenance cost of the fan, C ab is the cost of wind power abandonment, C DE is the fuel cost of the diesel generator during daily operation, C ESS is the operation and maintenance cost of the energy storage device during the charging and discharging process, C Air The economic compensation cost for air-conditioning users to shift the most suitable temperature, C EV Compensation costs for electric vehicle users, C load Compensation costs for industrial loads;

[0017] The power balance constraints within the microgrid must be met as follows:

[0018]

[0019] Among them, P demand,load (t) is the total power of all loads participating in demand response at time t, P WT (t) is the wind turbine power at time t, P DE (t) is the output power of the diesel generator at time t, P ESS,charge (t) is the energy storage charging power at time t, P ESS,discharge (t) is the energy storage discharge power at time t, is the removal amount of the kth load at time t, N Air is the number of types of air-conditioning users; is the output power of the jth type of air conditioner at time t, N k is the number of electric vehicle types, P EV,c,k (t) is the charging power of the k-th electric vehicle at time t, P EV,d,k (t) is the discharge power of the k-th electric vehicle at time t, P load (t) is the actual industrial load power.

[0020] Furthermore, the maintenance cost and output power model of wind turbines in different periods are as follows:

[0021]

[0022] Among them, ε WT is the maintenance cost coefficient of the fan; P WT (t) is the output power of the fan at time t; ε ab is the compensation coefficient of abandoned wind power; ab (t) is the wind power abandoned by the wind turbine at time t; T is the scheduling period.

[0023] Furthermore, the fuel cost and output power model of the diesel generator in each period is as follows:

[0024]

[0025] Among them, N DE is the total number; ε DE is the fuel cost per kW·h during operation;

[0026] The fuel cost and output power model of the diesel generator in each period should meet the following constraints:

[0027]

[0028] Among them, P DE,i is the output power of the i-th diesel generator, are the maximum and minimum power that the i-th diesel generator can output respectively; are the upper and lower limits of the climbing power of the i-th diesel generator respectively.

[0029] Furthermore, the energy storage system operation cost model is as follows:

[0030]

[0031] Among them, ε ESS P is the comprehensive cost coefficient of energy storage system operation and maintenance; ESS,charge (t), P ESS,discharge (t) are the charging and discharging powers of the energy storage system at time t;

[0032] The energy storage system operation cost model should satisfy the following constraints:

[0033]

[0034] Among them, SOC ESS (t) is the charge state of the energy storage system at time t; P ESS,c (t), P ESS,d (t) are the charging and discharging power of the energy storage system at time t; δ(t) is a 0-1 variable; η c , η d are the charging and discharging efficiency of the energy storage system respectively; c ESS is the energy storage capacity; They are the maximum and minimum state of charge of the energy storage facility, respectively; They are the maximum and minimum charging power allowed by the energy storage system respectively; They are respectively the maximum and minimum discharge power allowed by the energy storage system.

[0035] Furthermore, a variable frequency air conditioning model is used when finely modeling the air conditioning load, and a control method based on compressor frequency change is used to control the air conditioning. The air conditioning load model that is scheduled is as follows:

[0036]

[0037] Among them, f AC (T S ,T out ), P AC (T S ,T out ) are the outdoor temperatures T out When the indoor temperature is kept at the set value T sThe air conditioning frequency and power, R is the room thermal resistance, m, n are the constant coefficients of the linear function of the relationship between the output power of the variable frequency air conditioner and the frequency, a, b, c are the constant coefficients of the quadratic function of the relationship between the cooling capacity of the variable frequency air conditioner and the frequency;

[0038] The economic compensation cost model for air-conditioning users is as follows:

[0039]

[0040] Among them, c Air,j is the compensation coefficient for air-conditioning users; The power pre-set for the j-th air-conditioning user at time t; are the maximum and minimum power of air conditioner acceptable to the jth type of air conditioner user;

[0041] The comprehensive comfort level of all air-conditioning users is as follows:

[0042]

[0043] in, are the indoor temperature and the most suitable temperature of the j-th air-conditioning user at time t; are the highest and lowest indoor temperatures that the jth type of air conditioner user can bear respectively; is the maximum indoor temperature deviation acceptable to the j-th air-conditioning user at time t; The maximum indoor temperature acceptable to the jth type of air conditioner user.

[0044] Furthermore, the movable load includes electric vehicle load, and the electric vehicle load model that accepts scheduling is as follows:

[0045]

[0046] in, are the charging and discharging power of the k-th electric vehicle at time t; They are the maximum power limits for charging and discharging of the k-th electric vehicle at time t; is the charging and discharging state of the k-th electric vehicle, a variable between 0 and 1; are the state of charge of the k-th electric vehicle at time t, and its adjustable maximum and minimum states of charge; η c,EV , η d,EV are charge and discharge efficiency respectively; C EV,k is the capacity of the kth type battery;

[0047] The satisfaction of electric vehicle travel is as follows α k :

[0048]

[0049] Among them, t leave,k ,t arrive,k are the arrival and departure times of the kth type of electric vehicle, respectively; is the SOC value of the k-th electric vehicle at time t; is the maximum SOC value that the k-th electric vehicle can accept;

[0050] Electric vehicle charging and discharging times User satisfaction β m as follows:

[0051]

[0052] Among them, n change The number of charge and discharge times in a scheduling cycle;

[0053] Departure satisfaction is as follows γ k :

[0054]

[0055] in, is the SOC value of the k-th electric vehicle when it leaves;

[0056] Electric vehicle user comprehensive satisfaction F and electric vehicle user compensation cost C EV as follows:

[0057]

[0058]

[0059] Among them, ε1, ε2, and ε2 are the compensation coefficients of the three electric vehicle user satisfactions.

[0060] Furthermore, the movable load also includes industrial load, and the industrial user scheduling model is as follows:

[0061]

[0062] Among them, P in,load (t), P out,load (t) are the load power transferred out and in during period t; The maximum load power limit that can be transferred in and out during time period t, are the basic values ​​of the load transferred in and out during period t, and ρ(t) is the incentive price during period t;

[0063] The total power consumption constraint should also be met, which is as follows:

[0064]

[0065] Compensation cost of industrial load C load as follows:

[0066]

[0067] Among them, c basic is the basic compensation; load (t) They are the actual industrial load power at time t and the day-ahead predicted power respectively.

[0068] Furthermore, the removal compensation loss C cut as follows:

[0069]

[0070] in, is the compensation coefficient for the kth level of load removal.

[0071] The present invention has the following beneficial effects:

[0072] By formulating a reasonable day-ahead dispatch plan, the stability and reliability of the system as well as the comfort and satisfaction of electricity users can be improved. This method conducts refined modeling and differentiated demand response for different types of load users, proposes evaluation indicators for different users, and uses k-means clustering for typical scenarios. With the goal of maximizing user comfort and satisfaction and minimizing daily operating costs, a day-ahead dispatch plan is obtained. The comfort and satisfaction set by users are guaranteed, and the economic losses caused by fluctuations in new energy sources are reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] Figure 1 This is a structural diagram of an offshore island microgrid according to an embodiment of the present invention.

[0074] Figure 2 This is a diagram showing the relationship between indoor temperature and time under frequency determination according to an embodiment of the present invention.

[0075] Figure 3 This is an excitation response diagram of an embodiment of the present invention.

[0076] Figure 4 This is a wind power annual data diagram according to an embodiment of the present invention.

[0077] Figure 5 This is a full-year data chart of island load according to an embodiment of the present invention.

[0078] Figure 6 This is a k-means wind power scenario clustering diagram according to an embodiment of the present invention.

[0079] Figure 7 This is a diagram of day-ahead scheduling under typical scenario 1 of an embodiment of the present invention.

[0080] Figure 8 This is a diagram of day-ahead scheduling under typical scenario 2 of an embodiment of the present invention.

[0081] Fig. 9 This is a diagram of day-ahead scheduling under typical scenario 3 of an embodiment of the present invention.

[0082] Fig.10 This is a diagram of day-ahead scheduling under typical scenario 4 of an embodiment of the present invention. DETAILED DESCRIPTION

[0083] The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0084] Embodiment 1

[0085] The offshore island microgrid operation method taking into account user comfort demand side management includes the following steps:

[0086] Generic modeling of distributed power sources of isolated microgrids, including wind turbines, diesel generators and energy storage systems;

[0087] Carry out detailed modeling of various types of loads on the demand side, including loads that can be cut, loads that can be translated, and loads that can be removed;

[0088] The k-means clustering algorithm is used to cluster the wind power generation scenarios on the island and generate typical scenarios;

[0089] Based on each distributed power model and load model, the optimal operation strategy of the isolated island microgrid under each typical scenario is obtained.

[0090] Embodiment 2

[0091] The offshore island microgrid operation method for demand-side management taking into account user comfort is based on the first embodiment, wherein the load that can be cut includes air conditioning load, the load that can be shifted includes electric vehicle load and industrial load, and the load that can be cut is divided into three levels, the first level is general cut-off load, the second level is load participating in demand response, and the third level is relatively important load;

[0092] When obtaining the optimal operation strategy of the isolated microgrid under different scenarios according to each distributed power model and load model, the objective function is to minimize the operation cost of the isolated microgrid. The objective function is as follows:

[0093]

[0094] Among them, C W is the operating cost of the isolated microgrid, C WT is the daily operation and maintenance cost of the fan, C ab is the cost of wind power abandonment, C DEis the fuel cost of the diesel generator during daily operation, C ESS is the operation and maintenance cost of the energy storage device during the charging and discharging process, C Air The economic compensation cost for air-conditioning users to shift the most suitable temperature, C EV Compensation costs for electric vehicle users, C load Compensation costs for industrial loads;

[0095] The power balance constraints within the microgrid must be met as follows:

[0096]

[0097] Among them, P demand,load (t) is the total power of all loads participating in demand response at time t, P WT (t) is the wind turbine power at time t, P DE (t) is the output power of the diesel generator at time t, P ESS,charge (t) is the energy storage charging power at time t, P ESS,discharge (t) is the energy storage discharge power at time t, is the removal amount of the kth load at time t, N Air is the number of types of air-conditioning users; is the output power of the jth type of air conditioner at time t, N k is the number of electric vehicle types, P EV,c,k (t) is the charging power of the k-th electric vehicle at time t, P EV,d,k (t) is the discharge power of the k-th electric vehicle at time t, P load (t) is the actual industrial load power.

[0098] Embodiment 3

[0099] The offshore island microgrid operation method taking into account user comfort demand side management, based on the second embodiment, the maintenance cost and output power model of the wind turbine generator set in each period is as follows:

[0100]

[0101] Among them, ε WT is the maintenance cost coefficient of the fan; P WT (t) is the output power of the fan at time t; ε ab is the compensation coefficient of abandoned wind power; ab (t) is the wind power abandoned by the wind turbine at time t; T is the scheduling period.

[0102] Furthermore, the fuel cost and output power model of the diesel generator in each period is as follows:

[0103]

[0104] Among them, N DE is the total number; ε DE is the fuel cost per kW·h during operation;

[0105] The fuel cost and output power model of the diesel generator in each period should meet the following constraints:

[0106]

[0107] Among them, P DE,i is the output power of the i-th diesel generator, are the maximum and minimum power that the i-th diesel generator can output respectively; are the upper and lower limits of the climbing power of the i-th diesel generator respectively.

[0108] Furthermore, the energy storage system operation cost model is as follows:

[0109]

[0110] Among them, ε ESS P is the comprehensive cost coefficient of energy storage system operation and maintenance; ESS,charge (t), P ESS,discharge (t) are the charging and discharging powers of the energy storage system at time t;

[0111] The energy storage system operation cost model should satisfy the following constraints:

[0112]

[0113] Among them, SOC ESS (t) is the charge state of the energy storage system at time t; P ESS,c (t), P EsS,d (t) are the charging and discharging power of the energy storage system at time t; δ(t) is a 0-1 variable, that is, the value of δ(t) can only be 0 or 1, which is used to characterize the charging and discharging state of the energy storage system; η c , η d are the charging and discharging efficiency of the energy storage system respectively; c ESS is the energy storage capacity; They are the maximum and minimum state of charge of the energy storage facility, respectively; They are the maximum and minimum charging powers allowed by the energy storage system respectively; They are respectively the maximum and minimum discharge power allowed by the energy storage system.

[0114] Embodiment 4

[0115] The offshore island microgrid operation method taking into account user comfort demand side management, based on the second embodiment, adopts a variable frequency air conditioning model when finely modeling the air conditioning load, and adopts a control method based on compressor frequency change to control the air conditioning. The air conditioning load model that accepts scheduling is as follows:

[0116]

[0117] Among them, f AC (T S ,T out ), P AC (T S ,T out ) are the outdoor temperatures T out When the indoor temperature is kept at the set value T s The air conditioning frequency and power, R is the room thermal resistance, m, n are the constant coefficients of the linear function of the relationship between the output power of the variable frequency air conditioner and the frequency, a, b, c are the constant coefficients of the quadratic function of the relationship between the cooling capacity of the variable frequency air conditioner and the frequency;

[0118] The relationship between the output power and frequency of the variable frequency air conditioner can be fitted into a linear relationship, where m and n are the constant coefficients of the linear function. The cooling capacity and frequency of the variable frequency air conditioner can be fitted into a quadratic linear relationship, where a, b, and c are the constant coefficients of the quadratic function.

[0119] m, n, a, b, c are determined by the specific parameters of the air conditioner type.

[0120] The economic compensation cost model for air-conditioning users is as follows:

[0121]

[0122] Among them, c Air,j is the compensation coefficient for air-conditioning users; The power pre-set for the j-th air-conditioning user at time t; are the maximum and minimum power of air conditioner acceptable to the jth type of air conditioner user;

[0123] The comprehensive comfort level of all air-conditioning users is as follows:

[0124]

[0125] in, are the indoor temperature and the most suitable temperature of the j-th air-conditioning user at time t; are the highest and lowest indoor temperatures that the jth type of air conditioner user can bear respectively; is the maximum indoor temperature deviation acceptable to the j-th air-conditioning user at time t; The maximum indoor temperature acceptable to the jth type of air conditioner user.

[0126] Furthermore, the movable load includes electric vehicle load, and the electric vehicle load model that accepts scheduling is as follows:

[0127]

[0128] in, are the charging and discharging power of the k-th electric vehicle at time t; They are the maximum power limits for charging and discharging of the k-th electric vehicle at time t; is the charging and discharging state of the k-th electric vehicle, a variable between 0 and 1; are the state of charge of the k-th electric vehicle at time t, and its adjustable maximum and minimum states of charge; η c,EV , η d,EV are charge and discharge efficiency respectively; C EV,k is the capacity of the kth type battery;

[0129] The satisfaction of electric vehicle travel is as follows α k :

[0130]

[0131] Among them, t leave,k ,t arrive,k are the arrival and departure times of the kth type of electric vehicle, respectively; is the SOC value of the k-th electric vehicle at time t; is the maximum SOC value that the k-th electric vehicle can accept;

[0132] Electric vehicle charging and discharging times User satisfaction β m as follows:

[0133]

[0134] Among them, n change The number of charge and discharge times in a scheduling cycle;

[0135] Departure satisfaction is as follows γ k :

[0136]

[0137] in, is the SOC value of the k-th electric vehicle when it leaves;

[0138] Electric vehicle user comprehensive satisfaction F and electric vehicle user compensation cost C EV as follows:

[0139]

[0140] Among them, ε1, ε2, and ε2 are the compensation coefficients of the three electric vehicle user satisfactions.

[0141] Furthermore, the movable load also includes industrial load, and the industrial user scheduling model is as follows:

[0142]

[0143] Among them, P in,load (t), P out,load (t) are the load power transferred out and in during period t; The maximum load power limit that can be transferred in and out during time period t, are the basic values ​​of the load transferred in and out during period t, and ρ(t) is the incentive price during period t;

[0144] The total power consumption constraint should also be met, which is as follows:

[0145]

[0146] Compensation cost of industrial load C load as follows:

[0147]

[0148] Among them, c basic is the basic compensation; load (t) They are the actual industrial load power at time t and the day-ahead predicted power respectively.

[0149] Furthermore, the removal compensation loss C cut as follows:

[0150]

[0151] in, is the compensation coefficient for the kth level of load removal.

[0152] The method of the present invention is described below using a project in Fujian.

[0153] The structure diagram of the offshore island microgrid is as follows: Figure 1 As shown in Figure 2, the annual wind power data is as follows: Figure 4 As shown, the load data is Figure 5 As shown, the time interval is 1h. The project is located on an island with abundant wind power resources. When it is disconnected from the main grid, it will become an isolated island microgrid with more than 70% of internal renewable energy power generation.

[0154] The island is equipped with a 2MW / 4WMh energy storage system, equipped with two energy storage converters with a rated power of 630kW, and its SOC range is 0.1 to 0.95. In addition, the total capacity of diesel generators on the island is 300kW. The daily operation and maintenance coefficients of various power generation units are shown in Table 1.

[0155] Table 1 Daily operation and maintenance coefficient of power generation unit

[0156]

[0157] In this embodiment, the number of k-means clusters is determined in advance using the silhouette coefficient determination method, and then the k-means clustering method is used to cluster the annual data graph of wind power on the island. Figure 6 As shown, the k-means clustering method clusters wind power data into 8 scenarios. In this embodiment, 4 typical scenarios are preferably selected from the 8 clustering scenarios for example analysis.

[0158] Figure 7 to Figure 10 The four figures show the day-ahead dispatching output of the offshore island microgrid under four typical scenarios. It can be seen from the above four figures that: during the weak wind period (i.e. wind power), the diesel generator is basically fully powered all the time, and the load that can be removed increases significantly; and the EV releases power in some periods to ensure the normal operation of the island microgrid for a short time, throttling the energy storage system to cope with the subsequent possible lack of wind power and thus ensure the normal operation of important loads.

[0159] Table 2 shows the load shedding amount in the day-ahead dispatch of the isolated microgrid in a typical scenario. Due to the load shedding priority protection of users participating in demand response, the impact on users participating in demand response is minimized when the load is shelved.

[0160] Table 2 Load shedding under typical scenarios

[0161]

[0162] It can be seen from Table 2 that in most scenarios, the secondary loads participating in demand response will not be cut off, and only some secondary loads will be cut off in some extreme weather conditions, which can minimize the impact of load shedding on the electricity consumption experience of users participating in demand response.

[0163] Table 3 shows the comfort of air-conditioning users and the satisfaction of EV users in typical scenarios. The smaller the values ​​of comfort and satisfaction, the better the user's electricity experience. It can be seen from Table 3 that in the absence of wind power (scenario 1, 2), the comfort of air-conditioning users is reduced by an average of 153.42% compared with the comfort of sufficient wind power (scenario 3, 4). For EV users, the final SOC state of EV must be guaranteed in the absence of wind power, so EV discharge is less frequent; while in the case of sufficient wind power, in order to ensure the SOC of the energy storage system to cope with possible wind power shortages, the charging and discharging of EVs will be frequently triggered. The above reasons cause EV users' satisfaction in the case of sufficient wind power to be lower than that in the absence of wind power.

[0164] Table 3 User comfort and satisfaction in typical scenarios

[0165]

[0166] Table 4 shows the daily operation and maintenance cost of the isolated microgrid under typical scenarios. From Table 4, it can be seen that the load shedding caused by the lack of wind power will greatly increase the daily operation and maintenance cost of the isolated microgrid. The average operation cost in the case of sufficient wind power is 69.956% lower than that in the case of lack of wind power.

[0167] Table 4 Daily operation and maintenance costs of isolated microgrids under typical scenarios

[0168]

[0169] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure made using the contents of the present invention's specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. An offshore island microgrid operation method taking user comfort demand side management into account, characterized in that: The following steps are involved: Generic modeling of distributed power sources of an isolated microgrid, including wind turbines, diesel generators and energy storage systems; Carry out detailed modeling of various types of loads on the demand side, including loads that can be cut, loads that can be translated, and loads that can be removed; The k-means clustering algorithm is used to cluster the wind power generation scenarios on the island and generate typical scenarios; According to each distributed power model and load model, the optimal operation strategy of the isolated microgrid in each typical scenario is obtained; The load that can be cut includes air conditioning load, the load that can be shifted includes electric vehicle load and industrial load, and the load that can be cut is divided into three levels: the first level is the general load that can be cut, the second level is the load that participates in demand response, and the third level is the more important load; When obtaining the optimal operation strategy of the isolated microgrid under different scenarios according to each distributed power model and load model, the objective function is to minimize the operation cost of the isolated microgrid. The objective function is as follows: Among them, C W is the operating cost of the isolated microgrid, C WT is the daily operation and maintenance cost of the fan, C ab is the cost of wind power abandonment, C DE is the fuel cost of the diesel generator during daily operation, C ESS is the operation and maintenance cost of the energy storage device during the charging and discharging process, C Air The economic compensation cost for air-conditioning users to shift the most suitable temperature, C EV Compensation costs for electric vehicle users, C load Compensation costs for industrial loads; The power balance constraints within the microgrid must be met as follows: Among them, P demand,load (t) is the total power of all loads participating in demand response at time t, P WT (t) is the wind turbine power at time t, P DE (t) is the output power of the diesel generator at time t, P ESS,charge (t) is the energy storage charging power at time t, P ESS,discharge (t) is the energy storage discharge power at time t, is the removal amount of the kth load at time t, N Air is the number of types of air-conditioning users; is the output power of the jth type of air conditioner at time t, N k is the number of electric vehicle types, P EV,c,k (t) is the charging power of the k-th electric vehicle at time t, P EV,d,k (t) is the discharge power of the k-th electric vehicle at time t, P load (t) is the actual industrial load power; The maintenance cost and output power model of wind turbines in different periods are as follows: Among them, ε WT is the maintenance cost coefficient of the fan; P WT (t) is the output power of the fan at time t; ε ab is the compensation coefficient of abandoned wind power; ab (t) is the abandoned wind power of the wind turbine at time t; T is the dispatching period; The fuel cost and output power model of diesel generators in different periods are as follows: Among them, N DE is the total number; ε DE is the fuel cost per kW·h during operation; The fuel cost and output power model of the diesel generator in each period should meet the following constraints: Among them, P DE,i is the output power of the i-th diesel generator, are the maximum and minimum power that the i-th diesel generator can output respectively; are the upper and lower limits of the climbing power of the i-th diesel generator respectively; The operating cost model of the energy storage system is as follows: Among them, ε ESS P is the comprehensive cost coefficient of energy storage system operation and maintenance; ESS,charge (t), P ESS,discharge (t) are the charging and discharging powers of the energy storage system at time t; The energy storage system operation cost model should satisfy the following constraints: Among them, SOC ESS (t) is the charge state of the energy storage system at time t; P ESS,c (t), P ESS,d (t) are the charging and discharging power of the energy storage system at time t; δ(t) is a 0-1 variable; η c , η d are the charging and discharging efficiency of the energy storage system respectively; c EsS is the energy storage capacity; They are the maximum and minimum state of charge of the energy storage facility, respectively; They are the maximum and minimum charging powers allowed by the energy storage system respectively; They are the maximum and minimum discharge powers allowed by the energy storage system respectively; When fine-tuning the air conditioning load modeling, a variable frequency air conditioning model is used, and a control method based on compressor frequency change is used to control the air conditioning. The air conditioning load model that is scheduled is as follows: Among them, f AC (T S ,T out ), P AC (T S ,T out ) are the outdoor temperatures T out When the indoor temperature is kept at the set value T s The air conditioning frequency and power, R is the room thermal resistance, m, n are the constant coefficients of the linear function of the relationship between the output power of the variable frequency air conditioner and the frequency, a, b, c are the constant coefficients of the quadratic function of the relationship between the cooling capacity of the variable frequency air conditioner and the frequency; The economic compensation cost model for air-conditioning users is as follows: Among them, c Air,j is the compensation coefficient for air-conditioning users; The power pre-set for the j-th air-conditioning user at time t; are the maximum and minimum power of air conditioner acceptable to the jth type of air conditioner user; The comprehensive comfort level of all air-conditioning users is as follows: in, are the indoor temperature and the most suitable temperature of the j-th air-conditioning user at time t; are the highest and lowest indoor temperatures that the jth type of air conditioner user can bear respectively; is the maximum indoor temperature deviation acceptable to the j-th air-conditioning user at time t; The maximum indoor temperature acceptable to the jth type of air conditioner user; The electric vehicle load is refined and modeled to obtain the electric vehicle load model to be dispatched. The electric vehicle load model to be dispatched is as follows: in, are the charging and discharging power of the k-th electric vehicle at time t; They are the maximum power limits for charging and discharging of the k-th electric vehicle at time t; is the charging and discharging state of the k-th electric vehicle, a variable between 0 and 1; are the state of charge of the k-th electric vehicle at time t, and its adjustable maximum and minimum states of charge; η c,EV , η d,EV are charge and discharge efficiency respectively; C EV,k is the capacity of the kth type battery; The satisfaction of electric vehicle travel is as follows α k : Among them, t leave,k ,t arrive,k are the arrival and departure times of the kth type of electric vehicle, respectively; is the SOC value of the k-th electric vehicle at time t; is the maximum SOC value that the k-th electric vehicle can accept; Electric vehicle charging and discharging times User satisfaction β m as follows: Among them, n change It is the number of charge and discharge times in a scheduling cycle; Departure satisfaction is as follows γ k : in, is the SOC value of the k-th electric vehicle when it leaves; Electric vehicle user comprehensive satisfaction F and electric vehicle user compensation cost C EV as follows: Among them, ε1, ε2, and ε2 are the compensation coefficients of the three electric vehicle user satisfactions; The industrial load is refined to model the industrial user dispatching model, and the industrial user dispatching model is as follows: Among them, P in,load (t), P out,load (t) are the load power transferred out and in during period t; The maximum load power limit that can be transferred in and out during time period t, are the basic values ​​of the load transferred in and out during period t, and ρ(t) is the incentive price during period t; The total power consumption constraint should also be met, which is as follows: Compensation cost of industrial load C load as follows: Among them, c basic is the basic compensation; load (t) They are the actual industrial load power at time t and the day-ahead predicted power; Cut off compensation loss C cut as follows: in, is the compensation coefficient for the kth level of load removal.

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