Microgrid economic optimization operation method considering endowment effect

Abstract

The invention discloses a microgrid economic optimization operation method considering endowment effects. The method comprises the following steps: establishing a microgrid economic operation model taking the lowest system operation cost as an optimization target; considering the decision-making influence when the user is used as a non-completely rational person, optimizing the demand response model based on the influence of psychological factors, and constructing a demand response model based on an endowment effect; and solving the micro-grid economic optimization operation model by adopting a parameter constraint-based Pareto optimization method. Compared with a traditional demand response model, the demand response model based on the endowment effect has the advantages in the aspects of peak load shifting, economic benefit improvement and the like.

Description

Technical Field

[0001] The present invention relates to the field of economic optimization operation of microgrids, and in particular to a method for economic optimization operation of microgrids taking endowment effect into consideration. Background Art

[0002] As an emerging energy model, microgrids can efficiently utilize distributed power sources, improve the system's self-sufficiency rate of electricity, and reduce the proportion of electricity purchases, so they have attracted widespread attention. As the proportion of renewable energy in the overall energy structure continues to grow, its inherent uncertainty and production capacity volatility have brought challenges to the stable operation and economic benefits of microgrid systems. Demand response strategy is a key measure to improve the microgrid's ability to absorb new energy.

[0003] In the prior art: Literature [1]: "Analysis of the impact of demand response on the reliability of power supply of distribution network" (Zhao Hongshan, Zhao Hangyu, Hou Jiequn, etc. Analysis of the impact of demand response on the reliability of power supply of distribution network [J]. Electric Power Automation Equipment, 2017, 37 (01): 8-14.) evaluates the change of load based on the elasticity matrix of electricity quantity and price, and constructs a model with peak-valley load difference as the target to optimize the time-of-use electricity price, so as to explore the impact mechanism of demand response on the reliability of power supply of distribution network. However, the economic operation objective function should not only consider the microgrid, but also the user.

[0004] Reference [2]: "Incentive-based demand response model for maximizing benefits of electricity retailers" (Chai Y, Xiang Y, Liu J, et al. Incentive-based demand response model for maximizing benefits of electricity retailers [J]. Journal of Modern Power Systems and Clean Energy, 2019, 7 (06): 1644-1650.) constructs an incentive-based demand response model that guides changes in consumer behavior by triggering incentive prices to increase revenue. However, the above studies all model user behavior under the assumption that users are completely rational people. This neglect of the complexity and diversity of user behavior will lead to a certain degree of deviation between the effect of demand response strategies in actual applications and expectations.

[0005] Reference [3]: "Day-ahead Optimal Dispatch of Household Microgrid Considering User Satisfaction" (Tang Wei, Gao Feng. Day-ahead Optimal Dispatch of Household Microgrid Considering User Satisfaction [J]. High Voltage Technology, 2017, 43(01): 140-148.) A day-ahead optimal dispatch model was established with the goal of maximizing user satisfaction. The model can provide corresponding optimal dispatch solutions according to different electricity demand. However, this study did not deeply consider the impact of behavioral economics on the enthusiasm of users to participate in demand response, and there is room for improvement. Summary of the invention

[0006] In order to solve the above technical problems, the present invention provides a microgrid economic optimization operation method taking into account the endowment effect. First, an economic operation model with the lowest system operation cost as the optimization goal is established; secondly, the performance of the current demand response model is optimized and its prediction accuracy is improved. By deeply analyzing the psychological factors of users in the process of participating in the demand response, a demand response model based on the endowment effect is constructed. The model emphasizes the importance of considering user satisfaction and operating costs when formulating microgrid operation strategies. Finally, the Pareto optimization method based on parameter constraints is used to solve the model. The demand response model based on the endowment effect established by the method of the present invention has advantages over the traditional demand response model in many aspects such as peak shaving and valley filling and improving economic benefits.

[0007] The technical solution adopted by the present invention is:

[0008] A method for economic optimization operation of a microgrid taking into account the endowment effect comprises the following steps:

[0009] Step 1: Establish a microgrid economic operation model with the lowest system operation cost as the optimization goal;

[0010] Step 2: Consider the impact of users’ decision-making when they are not completely rational people, optimize the demand response model based on the influence of psychological factors, and build a demand response model based on the endowment effect;

[0011] Step 3: Use the Pareto optimization method based on parameter constraints to solve the microgrid economic optimization operation model.

[0012] In step 1, the microgrid consists of wind power generation equipment, photovoltaic power generation equipment, energy storage equipment, gas turbines and users, etc. The control center is responsible for managing the output of distributed energy, purchasing and selling electricity, etc., and usually achieves the goals of peak load reduction and economic operation through scheduling and demand response.

[0013] In step 1, the microgrid economic operation model includes:

[0014] (1) Microgrid economic operation objective function:

[0015] The microgrid system consists of distributed power sources, energy storage, and users. In the day-ahead dispatch, its operation cycle is divided into 24 periods. Within the 24-hour dispatch cycle, the microgrid manages the user's variable load and energy storage system based on the output of the distributed power sources and the predicted value of the load;

[0016] The economic operation goal of the microgrid is to minimize the system operation cost. Its objective function F G for:

[0017] F G =C om +C d +C buy +C use +C GT +C char +U(△P);

[0018] Where: C om is the operation and maintenance cost of the microgrid system; C d is the demand response cost; C buy The cost of purchasing electricity from the large power grid for the microgrid system; C use The electricity purchase cost for microgrid users; C GT is the cost of gas turbine power generation; C char is the operation and maintenance cost of the energy storage equipment; U(△P) is the demand response utility function, where △P represents the total amount of demand response;

[0019]

[0020] Where: is the output of power generation equipment n, n∈{PV, WT, GT}; k om,n is the operation and maintenance coefficient of power generation equipment n; N means N equipment, and 23 means that 24 hours a day is divided into 24 equal parts from 0 to 23.

[0021]

[0022] Where: △P f , △P p , △P g are the demand response of users in peak, flat and valley periods respectively; μ f , μ p , μ g are the incentive coefficients for peak, flat and valley periods respectively;

[0023]

[0024] Where: Purchase electricity from the main power grid for the microgrid system; The price of electricity sold to the large power grid;

[0025]

[0026] Where: P t The electricity consumption of the user; is the time-of-use electricity price for users of the microgrid; m represents different microgrid users, and M represents the total number of microgrid users.

[0027]

[0028] Where: is the power generation of the gas turbine; k GT , k CO2 They are gas turbine fuel cost coefficient and CO2 treatment cost coefficient respectively;

[0029]

[0030] Where: are the discharge and charge amounts of the energy storage device respectively; k char The maintenance cost of the energy storage equipment.

[0031] (2) Constraints on economic operation of microgrids:

[0032] In order to ensure the stability and safety of the system in actual operation, it is necessary to consider constraints such as upper and lower limits of equipment operating power, power balance, and grid interaction restrictions.

[0033] 1) Power balance constraints:

[0034] The power emitted at each moment is equal to the user load and the power absorbed by the energy storage device;

[0035]

[0036] In the formula, Indicates the fan output power; Indicates photovoltaic output power; Indicates the discharge power of the energy storage device; Indicates the charging power of the energy storage device.

[0037] 2) Equipment operating power constraints:

[0038] The equipment in operation should meet the upper and lower power constraints according to its own operating characteristics;

[0039]

[0040] Where P WT,max is the maximum output power of the fan; P PV,max is the maximum photovoltaic output power; P buy,max is the maximum interaction power; P char,maxis the maximum charging power of the energy storage device; P dischar,max is the maximum discharge power of the energy storage device; P GT,max , P GT,min They are the upper and lower limits of gas turbine output respectively.

[0041] The following formula indicates that charging and discharging of the same energy storage device cannot occur at the same time:

[0042] P t m,char *P t m,dischar =0

[0043] 3) Demand response constraints:

[0044] Incentive demand response refers to a contract signed between the microgrid system and the user, which enables the user to cut off part of the load when the system has a peak shaving demand, and gives the user a certain amount of compensation; the constraints of incentive demand response are:

[0045] 0≤C d ≤C d,max

[0046] In the formula, C d,max Indicates the maximum value of the demand response incentive.

[0047] In step 2, the demand response model based on the endowment effect includes:

[0048] (1) Endowment effect:

[0049] In the era of new energy, the complexity of the power system is increasing, and deeper analysis is needed to solve related problems. The introduction of the endowment effect provides a unique perspective for the demand response model, enabling it to more comprehensively capture users' irrational evaluation and decision-making on energy resources. The endowment effect can be expressed by a utility function, as shown in the following formula:

[0050] U(△P)=α△P-β(△P) 2

[0051] In the formula, △P represents the change in electricity consumption, that is, the difference between current consumption and baseline consumption; U(△P) represents the change in utility caused by the change in electricity consumption; α and β are parameters related to the endowment effect, reflecting the relative importance of linear gain and quadratic loss, and reflecting the degree of the endowment effect; α△P represents the direct change in utility caused by an increase or decrease in electricity consumption; β(△P) 2 It represents the psychological loss caused by changes in consumption (whether increase or decrease) and reflects people's resistance to changes in consumption.

[0052] (2) User demand response model:

[0053] In order to describe the impact of electricity price changes on user electricity consumption, the price elasticity coefficient is introduced to model user demand response behavior. The price elasticity coefficient is a relational quantity used to measure the ratio of demand change to price change, including self-elasticity coefficient and mutual elasticity coefficient. The self-elasticity coefficient means that the load only responds to the electricity price in a certain period of time, and increases or reduces its own electricity consumption accordingly, and cannot be transferred to other periods. Its value is negative. The mutual elasticity coefficient means that users can transfer the load from peak electricity consumption to low electricity consumption as needed, and its value is positive. The expressions of the self-elasticity coefficient and the mutual elasticity coefficient are shown as follows:

[0054]

[0055] In the formula, ε ii represents the self-elastic coefficient; ε ij represents the mutual elastic coefficient; P 0 is the original power consumption before dispatch; L 0 is the original electricity price before dispatch; △P i is the change in electricity consumption in time period i; △L i , △L j are the changes in electricity prices in time period i and time period j respectively.

[0056] The user's electricity consumption behavior will not only be affected by the electricity price in the current period, but also by the electricity price transferred to the new period. According to the calculation formula of the self-elasticity coefficient and the mutual elasticity coefficient, the elasticity coefficient of each period is obtained as shown in the following formula:

[0057]

[0058] In the formula, ε ff , ε fp , ε fg They represent the elasticity coefficients of electrical equipment when it shifts from peak time to peak time, from peak time to normal time, and from peak time to valley time; ε pf , ε pp , ε pg They represent the elasticity coefficients of electrical equipment when it shifts from normal period to peak period, from normal period to normal period, and from normal period to valley period; ε gf , ε fp , ε gg They represent the elastic coefficients of electrical equipment when it moves from valley period to peak period, from valley period to normal period, and from valley period to valley period; △P f , △P p , △P g Respectively represent the change in electricity consumption before and after demand response during peak, flat and valley periods; △L f , △L p , △L gRespectively represent the change in electricity price before and after demand response during peak, flat and valley periods; L f , L p , L g Respectively represent the electricity prices in peak, flat and valley periods before demand response; P f , P p , P g They represent the electricity consumption in peak, flat and valley periods before demand response.

[0059] After receiving the electricity price information, users respond to demand and change their electricity consumption behavior. The electricity consumption in each period can be expressed as follows:

[0060] P fa =P f +△P f

[0061] P pa =P p +△P p

[0062] P ga =P g +△P g

[0063] Where P fa , P pa , P ga They are the electricity consumption after demand response during peak, flat and valley periods respectively.

[0064] Due to the influence of the endowment effect, users are not completely rational when participating in demand response, that is, users consider both economic and psychological factors to change their electricity consumption behavior. Therefore, the demand response amount of users in different periods will decrease to varying degrees. The specific expression is:

[0065]

[0066] In the formula, are the demand response quantities of the load at peak, flat and valley time periods, respectively; θ(t) is the endowment factor; △P f,max , △P p,max , △P g,max They represent the maximum value to which the load can respond.

[0067] Under the time-of-use electricity price system, users will adjust their electricity consumption behavior according to the changes in electricity prices to reduce electricity costs. Therefore, there will be a certain functional relationship between user electricity consumption and electricity prices, and electricity consumption will decrease as electricity prices rise. According to behavioral economics, users believe that they have the right to decide their daily energy consumption plans, and users regard their right to use electricity as an endowment.

[0068] In step 3, the microgrid economic operation model constructed by the present invention includes the objective function of the microgrid system and the objective function of the user, which is a dual-objective optimization task. In order to obtain the optimal solution through the traditional solver, the Pareto optimization technology of the ε-constraint method is used to convert the dual-objective optimization into a single-objective optimization. The core idea is to select one objective as the main optimization objective and convert other objectives into constraints. By adjusting the restriction level of these constraints, different solutions can be searched on the Pareto frontier;

[0069] The objective function F of the microgrid system is G As the single objective to be optimized, the user objective function is limited to εF U , as shown below:

[0070] F U =ω 1 α+ω 2 β≤εF U

[0071] Where: 1 ,ω 2 represents the weights of the two objective functions, and ε transforms the user objective function into a constraint condition; F U is the user objective function.

[0072] The objective functions of the microgrid system and the user can be changed in their feasible domains as objective functions and constraints, respectively, and the optimal solutions obtained under different conditions are the same. In the process of transforming the dual-objective optimization problem into an optimized single-objective problem, the constraints remain unchanged, ensuring that the conversion of the problem will not affect the feasibility of the solution and the realization of the optimization goal.

[0073] The initial ε value is different, and the F obtained after optimization G Value and F U The present invention treats the ε value itself as an optimization problem, and adopts a particle swarm algorithm to solve the problem of optimization result differences caused by different initial ε value settings, thereby obtaining a set of optimization solutions that comprehensively and balancedly consider all objectives.

[0074] Step 3 includes the following steps:

[0075] Step 3.1, Initialization: First, randomly generate a group of particles in the solution space, each particle represents a potential solution with a random position and speed. Then set the parameters of the particle swarm optimization algorithm, including the number of particles, the maximum number of iterations, the learning factor, and the speed limit range.

[0076] Step 3.2: Evaluate particle fitness. According to the objective function F G Calculate the fitness of each particle.

[0077] Step 3.3, update the optimal solution. According to the fitness evaluation results, update the individual optimal solution of each particle and the global optimal solution of the group, and then update the position of the particle based on the individual and global optimal solutions, that is, the limit value.

[0078] Step 3.4: Check the termination condition. When the number of iterations reaches the upper limit, terminate the iteration; otherwise, repeat steps 3.2-3.3.

[0079] Step 3.5: Based on the final global optimal solution, determine the optimal initial ε value setting.

[0080] After determining the optimal ε value, a series of solutions can be obtained, which are distributed on the Pareto frontier. Then, the fuzzy decision method is used to determine the optimal solution of the optimization. The specific steps are as follows:

[0081] Step 1: Calculation of membership function:

[0082] The linear membership function is given by To calculate the membership function of the objective function;

[0083]

[0084] q∈X={1,2,...,n q}

[0085] Where i represents the i-th objective function, which are F G and F U ; J i max , J i min are the maximum and minimum values ​​of the objective function respectively; parameter q is the index of the Pareto front member; X is the Pareto front set; n q is the number of Pareto front members; each member takes a different ε value; represents the linear membership function of individual member m on the Pareto frontier; J i,m represents the membership function of individual member m on the Pareto frontier; J i,q represents the membership function of the Pareto front members; represents the sum of the number of objective functions; G represents the objective function of the economic operation of the microgrid; U represents the user objective function; {1,2,...,n q} is the set of members of the Pareto front.

[0086] Step 2: Determine the minimum membership function:

[0087] The minimum membership function of individual member m on the Pareto front is searched by the following formula:

[0088]

[0089] In the formula, represents the minimum membership number of a Pareto front member; The linear membership function of the Pareto frontier members representing the economic operation of the microgrid; represents the linear membership function of the Pareto frontier members of the user objective function; represents all the Pareto front members;

[0090] Step 3: Determine the optimal compromise:

[0091] beg The maximum value of The position of the best compromise solution in set X is given;

[0092] Respectively represent the minimum membership number of each member.

[0093] At the above mentioned positions, the values ​​of functions and variables will represent the optimal values ​​for the problem being sought.

[0094] The present invention provides a microgrid economic optimization operation method taking into account the endowment effect, and the technical effects are as follows:

[0095] 1) Step 1 of the present invention establishes a microgrid economic operation system consisting of wind power generation equipment, photovoltaic power generation equipment, energy storage equipment, gas turbines and users, and proposes an objective function with the lowest economic operation cost as the goal, and considers dividing the microgrid operation cycle into 24 time periods in the day-ahead scheduling. Within the 24-hour scheduling cycle, the microgrid manages the user's variable load and energy storage system based on the output of distributed power sources and the predicted value of the load; and considers constraints such as the upper and lower limits of equipment operating power, power balance, and grid interaction restrictions.

[0096] 2) Step 2 of the present invention considers the impact of users’ decision-making when they are not completely rational people, optimizes the demand response model based on the impact of psychological factors, and constructs a demand response model based on the endowment effect, so that the demand response can more comprehensively capture users’ irrational evaluation and decision-making on energy resources.

[0097] 3) Step 3 of the present invention uses the Pareto optimization technology of the ε-constraint method to convert the dual-objective optimization into a single-objective optimization to solve the economic operation model, takes the objective function of the microgrid system as the main function, and converts the user objective function into a constraint condition, thereby ensuring the realization of the optimization goal; and treats the ε value itself as an optimization problem, and adopts the particle swarm algorithm to solve the problem of differences in optimization results caused by different initial ε value settings, thereby obtaining a comprehensive and balanced set of optimization solutions that consider all objectives. BRIEF DESCRIPTION OF THE DRAWINGS

[0098] The present invention will be further described below in conjunction with the accompanying drawings and embodiments:

[0099] Figure 1 The present invention provides a flow chart of a microgrid economic optimization operation method taking into account the endowment effect.

[0100] FIG2(a) is a load curve diagram before and after the demand response model established by the present invention and the output of each device Figure 1 ;

[0101] FIG2( b ) is a load curve diagram before and after the demand response of the demand response model established by the present invention and a second output diagram of each device. DETAILED DESCRIPTION

[0102] The present invention will be further described in detail below in conjunction with embodiments and drawings, but the embodiments of the present invention are not limited thereto.

[0103] Figure 1 This is a flow chart of a microgrid economic optimization operation method taking into account the endowment effect proposed in the present invention. First, an economic operation model with the lowest system operation cost as the optimization goal is established. The objective function considers the system operation cost, power constraints and demand response constraints. Secondly, consider optimizing the performance of the current demand response model and improving its prediction accuracy. By deeply analyzing the psychological factors of users in the process of participating in the demand response, a demand response model based on the endowment effect is constructed, in which the model emphasizes the importance of considering user satisfaction and operating costs when formulating microgrid operation strategies. Finally, the user objective function is converted into a constraint condition, and the objective function of the microgrid system is used as a single objective to be optimized. The Pareto optimization technology based on parameter constraints is used to solve the model proposed in the present invention. The demand response model based on the endowment effect proposed in the present invention has advantages over the traditional demand response model in many aspects such as peak shaving and valley filling and improving economic benefits.

[0104] Figure 2 (a) and Figure 2 (b) are load curves before and after the demand response of the demand response model established by the present invention and the output diagrams of each device. According to Figure 2 (a), in the time period of 2:00-9:00, the load after the demand response is higher than the load before the response, and in the time period of 9:00-16:00 and 17:00-22:00, the load after the response is lower than the load before the response. It can be seen that the demand response model proposed by the present invention can well achieve peak shaving and valley filling, and improve energy utilization efficiency.

[0105] According to Figure 2(b), during the period of 0:00-5:00, the wind power output increased slightly but was generally stable. Since there is no sunlight at night, the photovoltaic output is 0. During this period, the gas turbine output remains unchanged, indicating that the gas turbine serves as the base load power source during this period to ensure the stable operation of the system. However, the output of the large power grid has changed, and the difference in accurately meeting the load demand shows the regulatory role of the large power grid. During the period of 6:00-9:00, as the sunshine intensity increases, the photovoltaic output begins to increase, and at the same time, the increase in the output of the large power grid is also more significant. This is mainly because the load power consumption is large during this period, and the output of the equipment in the system cannot meet the load power demand, so the power purchase from the large power grid must be increased. During the period of 10:00-17:00, the power purchase from the large power grid is adjusted according to demand, especially during the period of high photovoltaic output, the power purchase is reduced. During the period of 18:00-23:00, the photovoltaic output is reduced to 0, and the output of the large power grid increases again, especially during the period of 20:00-21:00, to meet the peak power demand of households in the evening. The energy storage is charged during 1:00-3:00 and 15:00-16:00 when the electricity price is low, and discharged when the electricity price is high (9:00-12:00 and 19:00-20:00), so as to reduce the amount of electricity purchased by the microgrid when the electricity price is high and reduce the system cost.

[0106] Overall, Figure 2(a) and Figure 2(b) reflect the energy management strategy of the microgrid at different times of the day, especially how to combine renewable energy (wind power and photovoltaic power) with traditional energy (gas turbines and large power grids), while utilizing the role of energy storage systems to optimize operating efficiency, reduce energy costs, and improve economic performance and sustainability. Microgrids adjust their energy structure and use energy storage equipment to cope with fluctuations in energy supply and demand in different time periods, demonstrating their flexibility and high reliance on renewable energy, which is essential for promoting sustainable development and reducing environmental impact.

[0107] Table 1 Comparison of costs of multiple solutions

[0108]

[0109] Table 1 is a data comparison of the model proposed in this invention under different application strategies. The three schemes are scenario 1: all users adopt the traditional demand response model; scenario 2: 50% of users adopt the traditional demand response model, and 50% of users adopt the demand response model based on the endowment effect; scenario 3: all users adopt the demand response model based on the endowment effect. It can be seen from the analysis of Table 1 that in scenario 1, all users adopt the traditional demand response model, at which time the electricity cost is the lowest, and the demand response cost is relatively high. The comprehensive user satisfaction in this scenario is 1.03, which is lower than other scenarios; in scenario 2, 50% of users adopt the traditional demand response model, and the other 50% adopt the demand response model based on the endowment effect. This mixed use model method makes the electricity cost slightly increase, but the demand response cost decreases significantly. The total cost is reduced to 3678.4 yuan, and the comprehensive satisfaction is increased to 1.09; in scenario 3, all users adopt the demand response model based on the endowment effect, and their electricity cost increases slightly, but the demand response cost is reduced to the lowest, and the endowment effect cost is 66.1 yuan. The total cost was reduced to 3,628.6 yuan, and the overall satisfaction was the highest. This shows that the comprehensive adoption of the model based on the endowment effect can maximize the economic operation of the microgrid, reduce the total cost, and obtain the highest user satisfaction.

[0110] In summary, by introducing the demand response model based on the endowment effect, the demand response cost can be effectively reduced and the comprehensive satisfaction of users can be increased. As the adoption rate of the model increases, the total cost of the microgrid gradually decreases and user satisfaction increases accordingly. Therefore, the model proposed in this invention has shown obvious superiority in promoting the development of microgrids towards low carbon, high efficiency and high user satisfaction.

Claims

1. A method for economic optimization operation of a microgrid taking into account the endowment effect, characterized in that The following steps are involved: Step 1: Establish a microgrid economic operation model with the lowest system operation cost as the optimization goal; Step 2: Consider the impact of users’ decision-making when they are not completely rational people and build a demand response model based on the endowment effect; Step 3: Use the Pareto optimization method based on parameter constraints to solve the microgrid economic optimization operation model.

2. The method for economic optimization operation of a microgrid taking into account the endowment effect according to claim 1, characterized in that: In step 1, the microgrid economic operation model includes a microgrid economic operation objective function, which is as follows: The economic operation goal of the microgrid is to minimize the system operation cost. Its objective function F G for: F G =C om +C d +C buy +C use +C GT +C char +U(△P); Where: C om is the operation and maintenance cost of the microgrid system; C d is the demand response cost; C buy The cost of purchasing electricity from the large power grid for the microgrid system; C use The electricity purchase cost for microgrid users; C GT is the cost of gas turbine power generation; C char Operation and maintenance costs of energy storage equipment; U(△P) is the demand response utility function, where △P represents the total amount of demand response; Where: is the output of power generation equipment n, n∈{PV, WT, GT}; k om,n is the operation and maintenance coefficient of power generation equipment n; N represents N equipment; Where: △P f , △P p , △P g are the demand response of users in peak, flat and valley periods respectively; μ f , μ p , μ g are the incentive coefficients for peak, flat and valley periods respectively; Where: Purchase electricity from the main power grid for the microgrid system; The price of electricity sold to the large power grid; Where: P t The electricity consumption of the user; is the time-of-use electricity price for users of the microgrid; m represents different microgrid users, and M represents the total number of microgrid users; Where: is the power generation of the gas turbine; k GT , They are gas turbine fuel cost coefficient and CO2 treatment cost coefficient respectively; Where: are the discharge and charge amounts of the energy storage device respectively; k char The maintenance cost of the energy storage equipment.

3. The method for economic optimization operation of a microgrid taking into account the endowment effect according to claim 2, characterized in that: The microgrid economic operation model includes the microgrid economic operation constraints, which are as follows: 1) Power balance constraints: The power emitted at each moment is equal to the user load and the power absorbed by the energy storage device; In the formula, Indicates the fan output power; Indicates photovoltaic output power; Indicates the discharge power of the energy storage device; Indicates the charging power of the energy storage device; 2) Equipment operating power constraints: The equipment in operation should meet the upper and lower power constraints according to its own operating characteristics; Where P WT,max is the maximum output power of the fan; P PV,max is the maximum photovoltaic output power; P buy,max is the maximum interaction power; P char,max is the maximum charging power of the energy storage device; P dischar,max is the maximum discharge power of the energy storage device; P GT,max , P GT,min are the upper and lower limits of gas turbine output respectively; The following formula indicates that charging and discharging of the same energy storage device cannot occur at the same time: P t m,char *P t m,dischar =0 3) Demand response constraints: Incentive demand response refers to a contract signed between the microgrid system and the user, which enables the user to cut off part of the load when the system has a peak shaving demand, and gives the user a certain amount of compensation; the constraints of incentive demand response are: 0≤C d ≤C d,max In the formula, C d,max Indicates the maximum value of the demand response incentive.

4. The method for economic optimization operation of a microgrid taking into account the endowment effect according to claim 1, characterized in that: In step 2, the demand response model based on the endowment effect includes: (1) Endowment effect: The endowment effect is expressed through a utility function, as shown below: U(△P)=α△P-β(△P) 2 In the formula, △P represents the change in electricity consumption, that is, the difference between current consumption and baseline consumption; U(△P) represents the change in utility caused by the change in electricity consumption; α and β are parameters related to the endowment effect, reflecting the relative importance of linear gain and quadratic loss, and reflecting the degree of the endowment effect; α△P represents the direct change in utility caused by an increase or decrease in electricity consumption; β(△P) 2 represents the psychological loss caused by changes in consumption; (2) User demand response model: In order to describe the impact of electricity price changes on user electricity consumption, the price elasticity coefficient is introduced to model the user demand response behavior; the price elasticity coefficient includes two types: self-elasticity coefficient and mutual elasticity coefficient. The expressions of self-elasticity coefficient and mutual elasticity coefficient are shown as follows: In the formula, ε ii represents the self-elastic coefficient; ε ij represents the mutual elasticity coefficient; P0 is the original power consumption before dispatch; L0 is the original electricity price before dispatch; △P i is the change in electricity consumption in time period i; △L i , △L j are the changes in electricity prices in time periods i and j respectively; The user's electricity consumption behavior will not only be affected by the electricity price in the current period, but also by the electricity price transferred to the new period. According to the calculation formula of the self-elasticity coefficient and the mutual elasticity coefficient, the elasticity coefficient of each period is obtained as shown in the following formula: In the formula, ε ff , ε fp , ε fg They represent the elasticity coefficients of electrical equipment when it shifts from peak time to peak time, from peak time to normal time, and from peak time to valley time; ε pf , ε pp , ε pg They represent the elasticity coefficients of electrical equipment when it shifts from normal period to peak period, from normal period to normal period, and from normal period to valley period; ε gf , ε fp , ε gg They represent the elastic coefficients of electrical equipment when it moves from valley period to peak period, from valley period to normal period, and from valley period to valley period; △P f , △P p , △P g Respectively represent the change in electricity consumption before and after demand response during peak, flat and valley periods; △L f , △L p , △L g Respectively represent the change in electricity price before and after demand response during peak, flat and valley periods; L f , L p , L g Respectively represent the electricity prices in peak, flat and valley periods before demand response; P f , P p , P g Respectively represent the electricity consumption before demand response during peak, flat and valley periods; After receiving the electricity price information, the user responds to the demand and changes the electricity consumption behavior. The electricity consumption in each period is expressed as follows: P fa =P f +△P f P pa =P p +△P p P ga =P g +△P g Where P fa , P pa , P ga They are the electricity consumption after demand response during peak, flat and valley periods respectively; Due to the influence of the endowment effect, the demand response of users in different periods will decrease to varying degrees. The specific expression is: In the formula, are the demand response quantities of the load at peak, flat and valley time periods, respectively; θ(t) is the endowment factor; △P f,max , △P p,max , △P g,max They represent the maximum value to which the load can respond.

5. The method for economic optimization operation of a microgrid taking into account the endowment effect according to claim 1, characterized in that: In step 3, the constructed microgrid economic operation model includes the objective function of the microgrid system and the objective function of the user, which is a dual-objective optimization task; the Pareto optimization technology of the ε-constraint method is used to convert the dual-objective optimization into a single-objective optimization, and the specific method is to select one objective as the main optimization objective and convert other objectives into constraints; by adjusting the restriction level of these constraints, different solutions can be searched on the Pareto frontier.

6. The method for economic optimization operation of a microgrid taking into account the endowment effect according to claim 5, characterized in that: The objective function of the microgrid system F G As the single objective to be optimized, the user objective function is limited to εF U , as shown below: F U =ω1α+ω2β≤εF U Where: ω1 and ω2 represent the weights of the two objective functions, ε transforms the user objective function into a constraint condition; F U To transform the user objective function; The initial ε value is different, and the F obtained after optimization G Value and F U The values ​​of ε are also different; the ε value itself is treated as an optimization problem, and the particle swarm algorithm is used to solve the problem of different optimization results caused by different initial ε values, so as to obtain a set of optimization solutions that comprehensively and balancedly consider all objectives.

7. The method for economic optimization operation of a microgrid taking into account the endowment effect according to claim 6, characterized in that: Step 3 includes the following steps: Step 3.1, Initialization: First, randomly generate a group of particles in the solution space, each particle represents a potential solution with a random position and speed; then set the parameters of the particle swarm optimization algorithm, including the number of particles, the maximum number of iterations, the learning factor, and the speed limit; Step 3.2, evaluate the particle fitness; according to the objective function F G Calculate the fitness of each particle; Step 3.3, update the optimal solution; according to the fitness evaluation results, update the individual optimal solution of each particle and the global optimal solution of the group, and then update the position of the particle based on the individual and global optimal solutions, that is, the limit value; Step 3.4, check the termination condition; when the number of iterations reaches the upper limit, terminate the iteration; otherwise, repeat steps 3.2-3.3; Step 3.5: Based on the final global optimal solution, determine the optimal initial ε value setting; After determining the optimal ε value, a series of solutions are obtained, which are distributed on the Pareto frontier; then, the fuzzy decision method is used to determine the optimal solution of the optimization.

8. The method for economic optimization operation of a microgrid taking into account the endowment effect according to claim 7, characterized in that: The specific steps of using fuzzy decision-making method to determine the optimal solution are as follows: Step 1: Calculation of membership function: The linear membership function is given by To calculate the membership function of the objective function; q∈X={1,2,...,n q } Where i represents the i-th objective function, which are F G and F U ; are the maximum and minimum values ​​of the objective function respectively; parameter q is the index of the Pareto front member; X is the Pareto front set; n q is the number of Pareto front members; each member takes a different ε value; represents the linear membership function of individual member m on the Pareto frontier; J i,m represents the membership function of individual member m on the Pareto frontier; J i,q represents the membership function of the Pareto front members; represents the sum of the number of objective functions; G represents the objective function of the economic operation of the microgrid; U represents the user objective function; {1,2,...,n q } is the set of members of the Pareto front; Step 2: Determine the minimum membership function: The minimum membership function of individual member m on the Pareto front is searched by the following formula: In the formula, represents the minimum membership number of a Pareto front member; The linear membership function of the Pareto frontier members representing the economic operation of the microgrid; represents the linear membership function of the Pareto frontier members of the user objective function; represents all the Pareto front members; Step 3: Determine the optimal compromise: beg The maximum value of The position of the best compromise solution in set X is given; Respectively represent the minimum membership number of each member.

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