Micro energy network multi-objective optimization scheduling model

Abstract

The present application relates to micro energy network multi-objective optimization scheduling model, including: for micro energy network, establish multi-objective optimization scheduling model;Improved weighted fuzzy method is constructed;Specifically, including: constructing membership function, each sub objective function is fuzzy;According to the actual operation of micro energy network, determine the weight coefficient of each sub objective function at different time;The multi-objective optimization scheduling model is converted into single objective optimization scheduling model;Solve through traditional nonlinear optimization theory.The present application, improved weighted fuzzy method, the membership function and the weight setting of multi-objective function are optimized, the comprehensive coordination between multi-objective is improved, and the robustness of the algorithm is also improved, and the optimization scheduling model achieves the purpose of optimizing the output of each device in the operation process of micro energy network.

Description

Technical Field

[0001] This invention relates to the field of microgrids and their optimal scheduling technology, specifically to a multi-objective optimal scheduling model for microgrids. More particularly, it relates to a multi-objective optimal scheduling model for microgrids based on an improved weighted fuzzy method. Background Technology

[0002] As a terminal-type micro-integrated energy system, micro-energy grids fulfill the significance and purpose of developing integrated energy systems. They are small in scale, easy to implement, and have great promotional value, making them one of the important technological development directions for the energy industry in the 21st century.

[0003] To better achieve coordinated development of energy, environment, and economic benefits, numerous studies on multi-objective optimal scheduling of microgrids have been conducted both domestically and internationally in recent years. Current research mainly considers factors such as economic cost, environmental factors, energy consumption, energy utilization rate, and user satisfaction to establish multi-objective optimal scheduling models for microgrids. Microgrids contain numerous devices and energy sources, each with its own characteristics; therefore, the accuracy of the model and its ability to truly reflect the actual operation of the microgrid will significantly impact its optimal scheduling and operation.

[0004] The solutions to multi-objective optimization scheduling models for microgrids mainly fall into two categories:

[0005] One approach is to first transform the multi-objective optimization problem into a single-objective optimization problem, and then use a single-objective optimization algorithm to solve it.

[0006] Another type uses intelligent algorithms to directly obtain the optimal solution set, such as particle swarm optimization, tabu search, and ant colony optimization.

[0007] Transformation methods that can be used to convert multi-objective optimization problems into single-objective optimization problems mainly include fuzzy methods, hierarchical sequence methods, and interactive programming methods. Among them, the weighted fuzzy method considers the priority order of each objective, and changes in weights do not affect the form of the equivalent model, nor do they increase computation time and workload, making it widely applicable. However, the traditional membership function in the weighted fuzzy method is not continuously differentiable, which reduces the robustness of the algorithm. Furthermore, the selection of weights is often based on subjectivity, lacking objectivity. Additionally, the same weight setting method is often used at different times when selecting the weights of each sub-objective, lacking flexibility and comprehensive coordination between the sub-objectives.

[0008] The information disclosed in this background section is intended only to enhance the understanding of the general background of the invention and should not be construed as an admission or in any way implying that the information constitutes prior art known to those skilled in the art. Summary of the Invention

[0009] To address the shortcomings of existing technologies, the present invention aims to provide a multi-objective optimization scheduling model for microgrids. It improves the weighted fuzzy method, optimizes the membership function and the weight settings of the multi-objective function, enhances the comprehensive coordination among multiple objectives, and improves the robustness of the algorithm. The optimization scheduling model achieves the goal of optimizing the output of each device during the operation of the microgrid.

[0010] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0011] A multi-objective optimization scheduling model for microgrids, characterized by comprising:

[0012] For microgrids, a multi-objective optimization scheduling model is established;

[0013] An improved weighted fuzzy method is constructed, which specifically includes: constructing a membership function and fuzzifying each sub-objective function; and determining the weight coefficients of each sub-objective function at different times based on the actual operation of the microgrid.

[0014] The multi-objective optimization scheduling model is transformed into a single-objective optimization scheduling model by improving the weighted fuzzy method.

[0015] The solution is obtained using traditional nonlinear optimization theory.

[0016] Based on the above technical solutions, when establishing a multi-objective optimization scheduling model, daily operating cost, carbon emission cost and primary energy consumption cost are taken as three sub-objectives, and the three sub-objectives are minimized under the premise of satisfying the constraints of cooling, heating and power loads and equipment in the microgrid.

[0017] The objective function F in the scheduling model is shown in equation (1):

[0018]

[0019] In the formula, For the daily operating cost among the three sub-objectives, The carbon emission costs among the three sub-goals, The primary energy consumption cost is one of the three sub-objectives.

[0020] The objective function F is used to represent minimizing the three sub-objectives during a 24-hour operation.

[0021] The constraints in the scheduling model are as follows:

[0022] The output power constraints of the electric boiler, micro gas turbine, electric chiller, and energy storage system in the microgrid are set as shown in equation (8):

[0023]

[0024] In the formula, Pmt,e (t) represents the output electrical power of the micro gas turbine at time t, P mt,max P mt,min Indicates the upper and lower limits of the output electrical power of the micro gas turbine; Q eb,h (t) represents the thermal power output of the electric boiler at time t, Q. eb,max Q eb,min Indicates the upper and lower limits of the output thermal power of the electric boiler; Q ec,c (t) represents the output cooling power of the electric refrigeration equipment at time t, Q. ec,max Q ec,min Indicates the upper and lower limits of the output cooling power of the electric refrigeration equipment; P es,c (t) represents the output electrical power of the energy storage device at time t, P es.c.max P es.c.min The upper and lower limits of the output power of the energy storage device are represented by SOC(t); SOC(t) represents the state of charge of the energy storage device at time t. max SOC min Indicates the upper and lower limits of the state of charge of the energy storage system;

[0025] The balance constraints of cooling, heating, and electrical energy within the microgrid are set as shown in equation (9):

[0026]

[0027] In the formula, P grid (t), P gt (t), P mt,e (t), P es,c (t) represent the electrical power output by the power grid, photovoltaic system, gas turbine, and energy storage device at time t, respectively; P ec (t), P eb (t) represent the electrical power consumed by the electric boiler and the electric refrigeration equipment at time t, respectively; L e (t) represents the electrical load at time t; Q eb,h (t), Q mt,left (t) represents the thermal power and waste heat power output by the electric boiler and the micro gas turbine at time t, respectively; L h (t) represents the heat load at time t; Q ec,c (t) represents the cooling power output of the electric chiller at time t; L c (t) represents the cooling load at time t.

[0028] Based on the above technical solution, the daily operating cost is determined by adding the gas purchase cost D1(t), the electricity purchase cost D2(t), and the operation and maintenance cost of each piece of equipment D3(t), as expressed by the formula:

[0029] f1(t)=D1(t)+D2(t)+D3(t) (2)

[0030] The expressions for gas purchase cost D1(t), electricity purchase cost D2(t), and equipment operation and maintenance cost D3(t) are as follows:

[0031] D1(t)=C grid ·P grid (t) (3)

[0032] D2(t)=C gas ·V(t) (4)

[0033]

[0034] In the formula, C grid C gas C tou,i C an,i P represents the grid electricity price, natural gas price, investment cost per unit capacity of micro-source i, and maintenance cost per unit capacity of micro-source i, respectively; grid P(t) and V(t) represent the electricity and gas purchased by the microgrid at time t; i (t) represents the output power of the i-th device in the microgrid at time t; C q This represents the cost of a single start-stop cycle for a micro gas turbine; n1 represents the number of start-stop cycles for the micro gas turbine; 8760 represents the number of hours included in a year of 365 days.

[0035] The carbon emission cost is set to only include the penalty cost of CO2 gas emissions, because the pollutant emissions from purchasing electricity and gas are mainly CO2. The formula is expressed as follows:

[0036]

[0037] In the formula, C CO2 δ represents the penalty cost incurred for emitting a unit mass of CO2; g δ mt P represents the carbon dioxide emission coefficients corresponding to unit electrical energy and micro-turbine output, respectively; grid (t) represents the power output of the power grid at time t; P mt (t) represents the output electrical power of the micro gas turbine at time t;

[0038] The formula for the cost of primary energy consumption is expressed as follows:

[0039] f3(t)=λ1·P grid (t)+λ2·V(t) (7)

[0040] In the formula, λ1 and λ2 represent the energy consumption cost calculated per unit of electricity and natural gas, respectively; P grid V(t) represents the power output of the power grid at time t; V(t) represents the natural gas consumption at time t.

[0041] Based on the above technical solution, the multi-objective optimization scheduling model is simplified as shown in equation (10):

[0042]

[0043] Equation (10) is used to express the goal of minimizing the three sub-objectives under the conditions of satisfying the balance of cold, heat and electricity energy in the micro-energy grid and the output constraints of each device;

[0044] In the formula, x is the optimization variable, that is, the output power of each device in the microgrid; For the daily operating cost among the three sub-objectives, The carbon emission costs among the three sub-goals, H(x,t) represents the primary energy consumption cost among the three sub-objectives; H(x,t) represents the balance constraint of cold, heat and electricity energy in the microgrid, which is an equality constraint; G(x,t) represents the output constraint of each device in the microgrid, which is an inequality constraint.

[0045] Based on the above technical solutions, and according to the principle of the weighted fuzzy method, equation (10) is equivalent to the single-objective optimization model shown in equation (11):

[0046]

[0047] In the formula, w i (t) represents the weight of the i-th sub-target at time t, satisfying... f i (x,t) represents the value of the i-th sub-objective function when the optimization variable at time t takes the value x; u(f i (x,t) is f i The membership function of (x,t) is also called the satisfaction function; H(x,t) represents the balance constraint of cold, heat and electricity energy in the microgrid, which is an equality constraint; G(x,t) represents the output constraint of each device in the microgrid, which is an inequality constraint.

[0048] For the membership function u(f) i (x,t) is constructed as follows:

[0049] To enhance the robustness of the algorithm, the membership function is constructed as shown in equation (12) to be continuously differentiable on its interval, which increases the robustness of the algorithm to a certain extent.

[0050]

[0051] In the formula, f i (x,t) represents the value of the i-th sub-objective function when the optimization variable at time t takes the value x; f i,min (x,t) represents the optimal value, i.e., the minimum value, of the i-th sub-objective function when the optimization variable at time t is x; βi u(f) represents the elasticity of the i-th sub-objective function; i (x,t) represents the satisfaction level of the i-th sub-objective function at time t, when its value is less than f. i When (x,t), u(f) i (x,t))=1, indicating the most satisfactory value for this selection; when the value is within f i (x,t) and (1+β)f i When (x,t) is between, u(f) i (x,t) varies between 1 and 0; when the value is greater than (1+β)f i When (x,t), u(f) i (x,t))=0 indicates that the value taken is the least satisfactory;

[0052] When determining the weight coefficients of each sub-objective, different weights are set for the three sub-objectives at different times, and the specific values ​​of the weights are calculated using the improved analytic hierarchy process. The specific steps are as follows:

[0053] (1) Assume that the weights of the hourly operating cost f1(t), hourly carbon emission cost f2(t), and hourly primary energy consumption cost f3(t) of the microgrid are w1(t), w2(t), and w3(t), respectively;

[0054] Setting an hourly pollutant emission limit (g) for microgrid capacity max ;

[0055] When the minimum hourly pollutant emission exceeds g max If the pollutant emissions in the microgrid exceed the standard at this time, then the weights should be set based on carbon emission cost being the most important, primary energy consumption cost being the second most important, and operating cost being the least important.

[0056] When the minimum hourly pollutant emission is less than or equal to g max When the pollutant emissions in the microgrid meet the standards, then: considering the economic efficiency of the microgrid system operation, the weights are set based on operating costs being the most important, primary energy consumption costs being the second most important, and carbon emission costs being the least important.

[0057] The weighted sorting is shown in equation (14):

[0058]

[0059] In the formula, w i (t) represents the weight corresponding to the i-th sub-objective at time t; f 2,min (x,t) represents the optimal, i.e., minimum, carbon emission cost when the optimization variable at time t is x; g max Indicates the hourly pollutant emission limit; C CO2This represents the penalty cost incurred for emitting a unit mass of CO2;

[0060] (2) After the priority order of the weights of each objective function is determined, the improved analytic hierarchy process is used to establish a judgment matrix;

[0061] (3) Based on the judgment matrix, the comparison matrix is ​​obtained, and then the transmission matrix, the optimal transmission matrix, and the quasi-optimal consistent transmission matrix are obtained. Finally, the weight values ​​are obtained by normalizing the output.

[0062] Based on the above technical solution, the steps for solving the multi-objective optimization scheduling model of the microgrid are as follows:

[0063] Based on the constraints of the microgrid, calculate the optimal solutions of each sub-objective function at different times;

[0064] Substitute the optimal solutions of each sub-objective function at different times into the constructed membership function;

[0065] Calculate whether the optimal solution for carbon emission cost at each time point reaches the upper limit. If it does, set the weights of each sub-objective function according to w2(t)≥w3(t)≥w1(t). If it does not reach the upper limit, set the weights of each sub-objective function according to w1(t)≥w3(t)≥w2(t).

[0066] The weights of each sub-objective function at each time step are calculated using an improved analytic hierarchy process.

[0067] The multi-objective optimization problem of micro-energy network shown in Equation (10) is transformed into a single-objective optimization problem shown in Equation (11);

[0068] Equation (11) is solved using traditional nonlinear optimization theory.

[0069] The multi-objective optimization scheduling model for microgrids described in this invention has the following beneficial effects:

[0070] 1. During the model solution process, the membership function and the weight settings of the multi-objective function were optimized, which can improve the comprehensive coordination between multiple objectives and the robustness of the algorithm to a certain extent, optimize the output of each device in the micro-energy network, and achieve coordinated development of energy, environment and economic benefits.

[0071] 2. The improved weighted fuzzy method using the optimized membership function and multi-objective function weight settings described in this invention is not only applicable to solving multi-objective optimization problems in microgrids, but also applicable to solving multi-objective optimization problems in other scenarios.

[0072] 3. This invention employs an improved weighted fuzzy method to solve the multi-objective optimization scheduling model of micro-energy networks. It can not only reflect the importance of different objectives through the improved weighting coefficients, but also reflect the relationship between the optimal solution of the single-objective problem and the satisfactory solution of the multi-objective problem. The combination of the two methods is more flexible and applicable than the single maximum fuzzy satisfaction method or weighted method.

[0073] 4. Based on the aforementioned microgrid multi-objective optimization scheduling model, this invention takes the daily operating cost, carbon emission cost, and primary energy consumption cost of the microgrid as objectives, considers the output constraints of each device, and comprehensively considers the coordination between the three objectives, transforming the multi-objective optimization problem into a single-objective optimization problem for solution, thereby achieving optimized scheduling of the output of each device. Attached Figure Description

[0074] The present invention includes the following figures:

[0075] The accompanying drawings are provided to better understand the invention and are not intended to unduly limit the scope of the invention. Wherein:

[0076] Figure 1 This is a schematic diagram of the traditional membership function and the improved membership function.

[0077] Figure 2 This is a flowchart for solving the multi-objective optimization scheduling model of the microgrid described in this invention.

[0078] Figure 3 This shows the typical summer load and photovoltaic power generation curves.

[0079] Figure 4 This is a comparison chart of the satisfaction levels of each objective function in Case 4.

[0080] Figure 5 This is a comparison chart of the satisfaction levels of each objective function under Case 5.

[0081] Figure 6 This is a comparison chart of the satisfaction levels of each objective function in Case 6. Detailed Implementation

[0082] The present invention will be further described in detail below with reference to the accompanying drawings. This detailed description is an illustration in conjunction with exemplary embodiments of the invention, including various details of the embodiments to aid understanding, and should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the invention. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0083] This invention presents a multi-objective optimization scheduling model for microgrids, including:

[0084] For microgrids, a multi-objective optimization scheduling model is established;

[0085] An improved weighted fuzzy method is constructed, which specifically includes: constructing a membership function and fuzzifying each sub-objective function; and determining the weight coefficients of each sub-objective function at different times based on the actual operation of the microgrid.

[0086] The multi-objective optimization scheduling model is transformed into a single-objective optimization scheduling model by improving the weighted fuzzy method.

[0087] The solution is obtained using traditional nonlinear optimization theory.

[0088] Based on the above technical solutions, when establishing a multi-objective optimization scheduling model, daily operating cost, carbon emission cost and primary energy consumption cost are taken as three sub-objectives, and the three sub-objectives are minimized under the premise of satisfying the constraints of cooling, heating and power loads and equipment in the microgrid.

[0089] The objective function F in the scheduling model is shown in equation (1):

[0090]

[0091] In the formula, For the daily operating cost among the three sub-objectives, The carbon emission costs among the three sub-goals, The primary energy consumption cost is one of the three sub-objectives.

[0092] The objective function F is used to represent minimizing the three sub-objectives during a 24-hour operation.

[0093] The objective function F in the scheduling model is a multi-objective programming problem, in which the sub-objectives are generally contradictory. Therefore, it is often required that each sub-objective be optimized as much as possible.

[0094] Based on the above technical solution, the daily operating cost is determined by adding the gas purchase cost D1(t), the electricity purchase cost D2(t), and the operation and maintenance cost of each piece of equipment D3(t), as expressed by the formula:

[0095] f1(t)=D1(t)+D2(t)+D3(t) (2)

[0096] The expressions for gas purchase cost D1(t), electricity purchase cost D2(t), and equipment operation and maintenance cost D3(t) are as follows:

[0097] D1(t)=C grid ·P grid (t) (3)

[0098] D2(t)=Cgas ·V(t) (4)

[0099]

[0100] In the formula, C grid C gas C tou,i C an,i P represents the grid electricity price, natural gas price, investment cost per unit capacity of micro-source i, and maintenance cost per unit capacity of micro-source i, respectively; grid P(t) and V(t) represent the electricity and gas purchased by the microgrid at time t; i (t) represents the output power of the i-th device in the microgrid at time t; C q 8760 represents the cost of a single start-stop cycle for a micro gas turbine; n1 represents the number of start-stop cycles for the micro gas turbine; and 8760 represents the number of hours included in a year of 365 days.

[0101] Based on the above technical solution, the carbon emission cost is set to only include the penalty cost of CO2 gas emissions, because the pollutant emissions caused by purchasing electricity and gas are mainly CO2. The formula is expressed as follows:

[0102]

[0103] In the formula, C CO2 δ represents the penalty cost incurred for emitting a unit mass of CO2; g δ mt P represents the carbon dioxide emission coefficients corresponding to unit electrical energy and micro-turbine output, respectively; grid (t) represents the power output of the power grid at time t; P mt (t) represents the output electrical power of the micro gas turbine at time t.

[0104] Based on the above technical solution, the formula for primary energy consumption cost is defined as follows:

[0105] f3(t)=λ1·P grid (t)+λ2·V(t) (7)

[0106] In the formula, λ1 and λ2 represent the energy consumption cost calculated per unit of electricity and natural gas, respectively; P grid V(t) represents the power output of the power grid at time t; V(t) represents the natural gas consumption at time t.

[0107] Based on the above technical solution, the constraints in the scheduling model are as follows:

[0108] The output power constraints of the electric boiler, micro gas turbine, electric chiller, and energy storage system in the microgrid are set as shown in equation (8):

[0109]

[0110] In the formula, P mt,e (t) represents the output electrical power of the micro gas turbine at time t, P mt,max P mt,min Indicates the upper and lower limits of the output electrical power of the micro gas turbine; Q eb,h (t) represents the thermal power output of the electric boiler at time t, Q. eb,max Q eb,min Indicates the upper and lower limits of the output thermal power of the electric boiler; Q ec,c (t) represents the output cooling power of the electric refrigeration equipment at time t, Q. ec,max Q ec,min Indicates the upper and lower limits of the output cooling power of the electric refrigeration equipment; P es,c (t) represents the output electrical power of the energy storage device at time t, P es.c.max P es.c.min The upper and lower limits of the output power of the energy storage device are represented by SOC(t); SOC(t) represents the state of charge of the energy storage device at time t. max SOC min Indicates the upper and lower limits of the state of charge of the energy storage system;

[0111] The balance constraints of cooling, heating, and electrical energy within the microgrid are set as shown in equation (9):

[0112]

[0113] In the formula, P grid (t), P gt (t), P mt,e (t), P es,c (t) represent the electrical power output by the power grid, photovoltaic system, gas turbine, and energy storage device at time t, respectively; P ec (t), P eb (t) represent the electrical power consumed by the electric boiler and the electric refrigeration equipment at time t, respectively; L e (t) represents the electrical load at time t; Q eb,h (t), Q mt,left (t) represents the thermal power and waste heat power output by the electric boiler and the micro gas turbine at time t, respectively; L h (t) represents the heat load at time t; Q ec,c (t) represents the cooling power output of the electric chiller at time t; L c (t) represents the cooling load at time t.

[0114] Based on the above technical solution, the multi-objective optimization scheduling model is simplified as shown in equation (10):

[0115]

[0116] Equation (10) is used to express the goal of minimizing the three sub-objectives under the conditions of satisfying the balance of cold, heat and electricity energy in the micro-energy grid and the output constraints of each device;

[0117] In the formula, x is the optimization variable, that is, the output power of each device in the microgrid; For the daily operating cost among the three sub-objectives, The carbon emission costs among the three sub-goals, H(x,t) represents the primary energy consumption cost among the three sub-objectives; H(x,t) represents the balance constraint of cold, heat and electricity energy in the microgrid, which is an equality constraint; G(x,t) represents the output constraint of each device in the microgrid, which is an inequality constraint.

[0118] The above outlines the content for establishing a multi-objective optimization scheduling model for microgrids.

[0119] Based on the above technical solutions, and according to the principle of the weighted fuzzy method, equation (10) is equivalent to the single-objective optimization model shown in equation (11):

[0120]

[0121] In the formula, w i (t) represents the weight of the i-th sub-target at time t, satisfying... f i (x,t) represents the value of the i-th sub-objective function when the optimization variable at time t takes the value x; u(f i (x,t) is f i The membership function of (x,t) is also called the satisfaction function; H(x,t) represents the balance constraint of cold, heat and electricity energy in the microgrid, which is an equality constraint; G(x,t) represents the output constraint of each device in the microgrid, which is an inequality constraint.

[0122] The purpose of the membership functions of each sub-objective in constructive formula (9) is to fuzzify the multi-objective programming, and to fuzzify the membership function u(f i (x,t) is constructed as follows:

[0123] To enhance the robustness of the algorithm, the membership function is constructed as shown in equation (12) to be continuously differentiable on its interval, which increases the robustness of the algorithm to a certain extent.

[0124]

[0125] In the formula, f i (x,t) represents the value of the i-th sub-objective function when the optimization variable at time t takes the value x; f i,min (x,t) represents the optimal value, i.e., the minimum value, of the i-th sub-objective function when the optimization variable at time t is x; β iu(f) represents the elasticity of the i-th sub-objective function; i (x,t) represents the satisfaction level of the i-th sub-objective function at time t, when its value is less than f. i When (x,t), u(f) i (x,t))=1, indicating the most satisfactory value for this selection; when the value is within f i (x,t) and (1+β)f i When (x,t) is between, u(f) i (x,t) varies between 1 and 0; when the value is greater than (1+β)f i When (x,t), u(f) i (x,t))=0 indicates that the value taken is the least satisfactory;

[0126] Equation (12) is an optimization of the traditional "decreasing half-trapezoidal" membership function. The traditional "decreasing half-trapezoidal" membership function is not continuously differentiable, as shown in Equation (13):

[0127]

[0128] A schematic diagram of the traditional membership function and the improved membership function is shown below. Figure 1 As shown;

[0129] When determining the weight coefficients of each sub-objective, different weights are set for the three sub-objectives at different times, and the specific values ​​of the weights are calculated using the improved analytic hierarchy process. The specific steps are as follows:

[0130] (1) Assume that the weights of the hourly operating cost f1(t), hourly carbon emission cost f2(t), and hourly primary energy consumption cost f3(t) of the microgrid are w1(t), w2(t), and w3(t), respectively;

[0131] Setting an hourly pollutant emission limit (g) for microgrid capacity max ;

[0132] When the minimum hourly pollutant emission exceeds g max If the pollutant emissions in the microgrid exceed the standard at this time, then the weights should be set based on carbon emission cost being the most important, primary energy consumption cost being the second most important, and operating cost being the least important.

[0133] When the minimum hourly pollutant emission is less than or equal to g max When the pollutant emissions in the microgrid meet the standards, then: considering the economic efficiency of the microgrid system operation, the weights are set based on operating costs being the most important, primary energy consumption costs being the second most important, and carbon emission costs being the least important.

[0134] The weighted sorting is shown in equation (14):

[0135]

[0136] In the formula, w i (t) represents the weight corresponding to the i-th sub-objective at time t; f 2,min (x,t) represents the optimal, i.e., minimum, carbon emission cost when the optimization variable at time t is x; g max Indicates the hourly pollutant emission limit; C CO2 This represents the penalty cost incurred for emitting a unit mass of CO2;

[0137] (2) After the priority order of the weights of each objective function is determined, the improved analytic hierarchy process is used to establish a judgment matrix;

[0138] (3) Based on the judgment matrix, the comparison matrix is ​​obtained, and then the transmission matrix, the optimal transmission matrix, and the quasi-optimal consistent transmission matrix are obtained. Finally, the weight values ​​are obtained by normalizing the output.

[0139] By using the above-mentioned improved weighted fuzzy method, the multi-objective optimization scheduling model of micro energy network shown in Equation (10) is transformed into a single-objective optimization model shown in Equation (11). The constraints of the single-objective optimization model include: the output power constraints of electric boiler, micro gas turbine, electric chiller and energy storage system shown in Equation (8), the balance constraints of cold, heat and electricity energy in the network shown in Equation (9), and the membership function shown in Equation (12).

[0140] Based on the above technical solutions, such as Figure 2 As shown, the steps for solving the multi-objective optimization scheduling model of the microgrid are as follows:

[0141] Based on the constraints of the microgrid, calculate the optimal solutions of each sub-objective function at different times;

[0142] Substitute the optimal solutions of each sub-objective function at different times into the constructed membership function;

[0143] Calculate whether the optimal solution for carbon emission cost at each time point reaches the upper limit. If it does, set the weights of each sub-objective function according to w2(t)≥w3(t)≥w1(t). If it does not reach the upper limit, set the weights of each sub-objective function according to w1(t)≥w3(t)≥w2(t).

[0144] The weights of each sub-objective function at each time step are calculated using an improved analytic hierarchy process.

[0145] The multi-objective optimization problem of micro-energy network shown in Equation (10) is transformed into a single-objective optimization problem shown in Equation (11);

[0146] Equation (11) is solved using traditional nonlinear optimization theory.

[0147] The following are specific examples.

[0148] In this specific embodiment, the microgrid architecture includes a power grid, photovoltaic panels, a micro gas turbine, an electric boiler, an electric chiller, and an energy storage system, which supply energy to the electrical, thermal, and cooling loads, respectively. The electricity generated by the power grid, photovoltaic panels, and micro gas turbine supplies the electrical load, the electric boiler, and the electric chiller, respectively. The electric chiller converts electrical energy into cooling energy to meet the cooling load demand; the thermal energy converted from electrical energy by the electric boiler and some waste heat generated by the micro gas turbine together meet the thermal load demand. The energy storage system plays a role in peak shaving and valley filling, enabling better comprehensive cascade utilization of energy. In this specific embodiment, the meanings of the parameters are as described above.

[0149] 1. Establish the objective function of the multi-objective optimization scheduling model for microgrids, with the daily operating cost, carbon emission cost, and primary energy consumption cost as objectives.

[0150] The objective function F of the multi-objective optimization scheduling model for microgrids is shown in equation (1):

[0151]

[0152] The daily operating cost is calculated by adding the gas purchase cost D1(t), the electricity purchase cost D2(t), and the operation and maintenance cost of each piece of equipment D3(t), as shown in the formula:

[0153] f1(t)=D1(t)+D2(t)+D3(t) (2)

[0154] The expressions for gas purchase cost D1(t), electricity purchase cost D2(t), and equipment operation and maintenance cost D3(t) are as follows:

[0155] D1(t)=C grid ·P grid (t) (3)

[0156] D2(t)=C gas ·V(t) (4)

[0157]

[0158] The carbon emission cost is set to include only the penalty cost of CO2 emissions, expressed by the following formula:

[0159]

[0160] The formula for the cost of primary energy consumption is expressed as follows:

[0161] f3(t)=λ1·P grid (t)+λ2·V(t) (7).

[0162] 2. Consider the processing constraints of each device in the microgrid and the energy balance constraints of cooling, heating and electricity, and set the constraint conditions.

[0163] The output power constraints of the electric boiler, micro gas turbine, electric chiller, and energy storage system in the micro energy grid, as well as the balance constraints of the heating, cooling, and electrical energy within the grid, are set as shown in equations (8) and (9), respectively:

[0164]

[0165] .

[0166] 3. First, calculate the optimal solution of each sub-objective function, and substitute the optimal solution of each sub-objective function into the improved membership function (12) to calculate the satisfaction.

[0167] .

[0168] 4. Assume the weights of the hourly operating cost f1(t), hourly carbon emission cost f2(t), and hourly primary energy consumption cost f3(t) of the microgrid are w1(t), w2(t), and w3(t), respectively. Considering the increasing emphasis on environmental issues and the pressure to control pollution in recent years, an hourly pollutant emission cap g is set for the microgrid capacity. max When the minimum hourly pollutant emission exceeds g max When the pollutant emissions within the microgrid exceed the standard, the weighting should be based on carbon emission cost as the most important factor, followed by primary energy consumption cost, and then operating cost as the least important factor; when the minimum hourly pollutant emission is less than or equal to g... max When the pollutant emissions within the microgrid meet the standards, considering the economic efficiency of the microgrid system, the weights should be set based on operating costs as the most important factor, followed by primary energy consumption costs, and then carbon emission costs as the least important factor.

[0169] The weighted sorting is shown in equation (14):

[0170] .

[0171] After determining the priority order of the weights of each objective function, an improved analytic hierarchy process (AHP) is used to establish a judgment matrix. Based on the obtained comparison matrix, the transmission matrix, the optimal transmission matrix, and the quasi-optimal consistent transmission matrix are obtained. Finally, the weight values ​​are obtained after normalization.

[0172] 5. The multi-objective optimization problem (10) of the micro-energy network is transformed into a single-objective optimization problem (11), which can be solved using traditional nonlinear optimization theory.

[0173] This specific embodiment predicts load and photovoltaic output based on actual operating data of a certain region. The typical summer load and photovoltaic output curves are shown below. Figure 3 As shown in Table 1, the equipment capacity and parameter settings of the microgrid system are shown in Table 2, and the relevant parameters for carbon emissions and energy consumption are shown in Table 2.

[0174] Table 1 Microgrid Parameters

[0175]

[0176] Table 2 Carbon Emission Related Parameters

[0177]

[0178] The following sections compare and analyze the single-objective optimization results and multi-objective optimization results of microgrids. Based on the varying degrees of emphasis placed on each objective function in the microgrid, several case studies are presented.

[0179] Case 1 aims to minimize daily operating costs over 24 hours.

[0180] Case 2 aims to minimize 24-hour carbon emission costs;

[0181] Case 3 aims to minimize the cost of energy consumption over 24 hours.

[0182] Case 4: The weights of each objective function per hour are set as w2(t)≥w3(t)≥w1(t);

[0183] Case 5: The weights of each objective function per hour are set as w1(t)≥w3(t)≥w2(t);

[0184] Case 6: According to the method proposed in this invention, the weights of each objective function are set according to equation (9);

[0185] Table 3 shows the optimization results of each objective function for the microgrid under different cases.

[0186] Table 3 Comparison of Microgrid Operation Data under Different Cases

[0187]

[0188] As shown in Table 3, in Cases 1-3, pursuing the benefit of a single objective can lead to other objectives exceeding their optimal values ​​significantly. For example, in Case 1, when daily operating cost is the objective, carbon emission cost exceeds its optimal value by 33.7%, and daily primary energy consumption cost exceeds its optimal value by 19.9%. However, in Cases 4-6, multi-objective optimal scheduling can effectively reduce the percentage by which each objective exceeds its optimal value. For example, in Case 4, prioritizing carbon emission cost reduces the percentage by which carbon emission cost exceeds the minimum value from a high of 33.7% to 3.9%, and the percentage by which energy consumption cost exceeds the minimum value from a high of 19.9% ​​to 3.8%. Although the daily operating cost increases, leading to an increase in the total operating cost of the microgrid compared to Case 1, the carbon emission cost and energy consumption cost are significantly reduced, indicating that multi-objective optimal scheduling effectively balances economic and environmental benefits. In Case 6, the improved fuzzy method proposed in this invention sets different weights for each sub-objective function at different times. Although the cost is higher than that of Case 5, it takes into account the coordination between the three objectives at different times and minimizes the system operating cost as much as possible while meeting the carbon emission requirements.

[0189] To further illustrate the effects of this invention, the satisfaction levels of each sub-objective function in Cases 4-6 were calculated as follows: Figures 4-6 As shown. Higher satisfaction levels indicate that the objective function is closer to its optimal value. (From...) Figure 4 Therefore, in Case 4, by assigning weights to carbon emission costs, energy consumption costs, and operating costs from highest to lowest, the overall satisfaction with carbon emission costs was higher, while the satisfaction with operating costs was lower. Figure 5 Therefore, in Case 5, with the weighting order reversed from Case 4, the satisfaction with operating costs was higher, while the satisfaction with carbon emission costs was lower. This indicates a certain contradiction between economic benefits and environmental benefits. Figure 6 Therefore, in Case 6, the improved weighted fuzzy method proposed in this invention sets the weights of operating cost, energy consumption cost, and carbon emission cost from largest to smallest between 12:00 and 19:00, while setting the weights of carbon emission cost, energy consumption cost, and operating cost from largest to smallest during other time periods. Under the premise of ensuring that pollutant emissions do not exceed the standard, the system's economic efficiency is guaranteed as much as possible. The resulting overall satisfaction with multi-objective optimization scheduling is higher than that with multi-objective optimization scheduling in Cases 4 and 5.

[0190] The contents not described in detail in this specification are existing technologies known to those skilled in the art.

[0191] The above description is only a preferred embodiment of the present invention. The scope of protection of the present invention is not limited to the above embodiments. Any equivalent modifications or changes made by those skilled in the art based on the content disclosed in the present invention should be included in the scope of protection set forth in the claims.

[0192] Those skilled in the art will understand that various modifications and variations can be made to this invention without departing from its spirit and scope. Therefore, if any modification or variation falls within the scope of the appended claims and their equivalents, the invention is considered to cover such modifications and variations. Content not described in detail in this specification is prior art known to those skilled in the art.

Claims

1. A multi-objective optimization scheduling model for microgrids, characterized in that, include: For microgrids, a multi-objective optimization scheduling model is established. When establishing the multi-objective optimization scheduling model, the daily operating cost, carbon emission cost and primary energy consumption cost are taken as three sub-objectives, and the three sub-objectives are minimized under the premise of satisfying the cooling, heating and power loads and the constraints of each device in the microgrid. An improved weighted fuzzy method is constructed, which specifically includes: constructing a membership function and fuzzifying each sub-objective function; and determining the weight coefficients of each sub-objective function at different times based on the actual operation of the microgrid. The multi-objective optimization scheduling model is transformed into a single-objective optimization scheduling model by improving the weighted fuzzy method; the single-objective optimization model is shown in equation (11): ； In the formula, w i ( t () represents the weight of the i-th sub-objective at time t, satisfying , f i ( x,t Let ) be the value of the optimization variable at time t. x The value of the i-th sub-objective function; u ( f i ( x,t ))yes f i ( x,t The membership function of () is also called the satisfaction function; H (x,t) represents the balance constraint of cold, heat and electricity energy in the microgrid, which is an equality constraint; G (x,t) represents the output constraints of each device in the microgrid, which are inequality constraints; membership function u ( f i ( x,t The structure is as follows: To enhance the robustness of the algorithm, the membership function is constructed as shown in equation (12) to be continuously differentiable on its interval, which increases the robustness of the algorithm to a certain extent. ； In the formula, f i ( x,t )express t The value of the time-optimization variable is x The value of the i-th sub-objective function; f i,min ( x,t )express t The optimal value, i.e. the minimum value, of the i-th sub-objective function when the value of the optimization variable is x at any given time. β i This indicates the elasticity of the i-th sub-objective function; u ( f i ( x,t )) represents the satisfaction level of the i-th sub-objective function at time t, when its value is less than f i ( x,t )hour, u ( f i ( x,t ))=1 indicates that the user is most satisfied with the current value; when the value is in f i ( x,t ) and (1+ β ) f i ( x,t When between ) u ( f i ( x,t The value varies between 1 and 0; when the value is greater than (1+ β ) f i ( x,t )hour, u ( f i ( x,t ))=0 indicates the least satisfactory value received; When determining the weight coefficients of each sub-objective, different weights are set for the three sub-objectives at different times, and the specific values ​​of the weights are calculated using the improved analytic hierarchy process. The specific steps are as follows: (1) Assume the hourly operating cost of the microgrid f 1( t ), hourly carbon emission cost f 2( t ), hourly primary energy consumption cost f 3( t The weights corresponding to ) are respectively w 1 ( t ), w 2 ( t ), w 3 ( t ); Setting hourly pollutant emission limits for microgrid capacity g max ; When the minimum hourly pollutant emission exceeds g max If the pollutant emissions in the microgrid exceed the standard at this time, then the weights should be set based on carbon emission cost being the most important, primary energy consumption cost being the second most important, and operating cost being the least important. When the minimum hourly pollutant emission is less than or equal to g max When the pollutant emissions in the microgrid meet the standards, then: considering the economic efficiency of the microgrid system operation, the weights are set based on operating costs being the most important, primary energy consumption costs being the second most important, and carbon emission costs being the least important. The weighted sorting is shown in equation (14): ； In the formula, w i ( t () represents the weight corresponding to the i-th sub-objective at time t; f 2,min ( x,t )express t The optimal value, or minimum value, of carbon emission cost when the value of the optimization variable is x at any given time. g max Indicates the hourly pollutant emission limit ；C CO2 This represents the penalty cost incurred for emitting a unit mass of CO2; (2) After the priority order of the weights of each objective function is determined, the improved analytic hierarchy process is used to establish a judgment matrix; (3) Based on the judgment matrix, the comparison matrix is ​​obtained, and then the transmission matrix, the optimal transmission matrix, and the quasi-optimal consistent transmission matrix are obtained. Finally, the weight values ​​are obtained by normalizing the output. The solution is obtained using traditional nonlinear optimization theory.

2. The microgrid multi-objective optimization scheduling model as described in claim 1, characterized in that, The objective function F in the scheduling model is shown in equation (1): ； In the formula, For the daily operating cost among the three sub-objectives, The carbon emission costs among the three sub-goals, The primary energy consumption cost is one of the three sub-objectives. The objective function F is used to represent minimizing the three sub-objectives during a 24-hour operation. The constraints in the scheduling model are as follows: The output power constraints of the electric boiler, micro gas turbine, electric chiller, and energy storage system in the micro-energy grid are set as shown in equation (8): ； In the formula, P mt,e ( t () represents the output electrical power of the micro gas turbine at time t. P mt,max 、P mt,min Indicates the upper and lower limits of the output electrical power of the micro gas turbine; Q eb,h ( t () represents the output thermal power of the electric boiler at time t. Q eb,max 、Q eb,min Indicates the upper and lower limits of the output thermal power of the electric boiler; Q ec,c ( t () represents the output cooling power of the electric refrigeration equipment at time t. Q ec,max 、Q ec,min Indicates the upper and lower limits of the output cooling power of the electric refrigeration equipment; P es,c ( t () represents the output electrical power of the energy storage device at time t. P es.c.max 、P es.c.min Indicates the upper and lower limits of the output power of the energy storage device; SOC ( t () represents the state of charge of the energy storage device at time t. SOC max SOC min Indicates the upper and lower limits of the state of charge of the energy storage system; The balance constraints of cooling, heating, and electrical energy within the microgrid are set as shown in equation (9): ； In the formula, P grid ( t ), P gt ( t ), P mt,e ( t ), P es,c ( t () represent the electrical power output of the power grid, photovoltaic system, gas turbine, and energy storage device at time t, respectively; P ec (t), P eb (t) represent the electrical power consumed by the electric boiler and the electric refrigeration equipment at time t, respectively; L e ( t () represents the electrical load at time t; Q eb,h (t), Q mt,left (t) represents the thermal power and waste heat power output by the electric boiler and the micro gas turbine at time t, respectively; L h (t) represents the heat load at time t; Q ec,c (t) represents the cooling power output of the electric chiller at time t; L c (t) This represents the cooling load at time t.

3. The microgrid multi-objective optimization scheduling model as described in claim 2, characterized in that, The daily operating cost is set by the gas purchase cost. D 1 (t), electricity purchase cost D 2 (t), operating and maintenance costs of each piece of equipment D 3 ( t The sum of these two components gives the formula: ； Gas purchase cost D 1 (t), electricity purchase cost D 2 (t), operating and maintenance costs of each piece of equipment D 3 ( t The expression for ) is: ； ； ； In the formula, C grid , C gas , C tou,i , C an,i These represent the grid electricity price, natural gas price, and micro-source price, respectively. i Unit capacity investment cost, micro-source i Unit capacity maintenance cost; P grid ( t ), V ( t )express t The electricity and gas purchased by Moment Micro Energy Network; P i ( t ) represents the first digit in the microgrid at time t. i The output power of each device; C q This indicates the cost of a single start-stop cycle for a micro gas turbine. n 1 This indicates the number of times the micro gas turbine starts and stops; 8760 indicates the number of hours contained in a year of 365 days. The carbon emission cost is set to only include the penalty cost of CO2 gas emissions, because the pollutant emissions from purchasing electricity and gas are mainly CO2. The formula is expressed as follows: ； In the formula, C CO2 This represents the penalty cost incurred for emitting a unit mass of CO2. These represent the carbon dioxide emission coefficients corresponding to unit electrical energy and micro-turbine output, respectively; P grid ( t () represents the electrical power output of the power grid at time t; P mt ( t () represents the output electrical power of the micro gas turbine at time t; The formula for the cost of primary energy consumption is expressed as follows: ； In the formula, λ 1 , λ 2 These represent the energy consumption costs calculated per unit of electricity and natural gas, respectively. P grid ( t () represents the electrical power output of the power grid at time t; V ( t () represents the amount of natural gas consumed at time t.

4. The microgrid multi-objective optimization scheduling model as described in claim 2, characterized in that, The multi-objective optimization scheduling model is simplified as shown in equation (10): ； Equation (10) is used to express the goal of minimizing the three sub-objectives under the conditions of satisfying the balance of cold, heat and electricity energy in the micro-energy grid and the output constraints of each device; In the formula, x To optimize the variables, namely the output power of each device in the microgrid; For the daily operating cost among the three sub-objectives, The carbon emission costs among the three sub-goals, The primary energy consumption cost is one of the three sub-objectives. H (x,t) represents the balance constraint of cold, heat and electricity energy in the microgrid, which is an equality constraint; G (x,t) represents the output constraints of each device in the microgrid, which are inequality constraints; According to the principle of weighted fuzzy method, equation (10) is equivalent to equation (11).

5. The microgrid multi-objective optimization scheduling model as described in claim 4, characterized in that, The steps for solving the multi-objective optimization scheduling model of the microgrid are as follows: Based on the constraints of the microgrid, calculate the optimal solutions of each sub-objective function at different times; Substitute the optimal solutions of each sub-objective function at different times into the constructed membership function; Calculate whether the optimal carbon emission cost solution at each time point reaches the upper limit. If it does, then proceed according to... Set the weights for each sub-objective function; if the upper limit is not reached, then... Set the weights for each sub-objective function; The weights of each sub-objective function at each time step are calculated using an improved analytic hierarchy process. The multi-objective optimization problem of micro-energy network shown in Equation (10) is transformed into a single-objective optimization problem shown in Equation (11); Equation (11) is solved using traditional nonlinear optimization theory.

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