Coupling source-network-load-storage regional energy system random opportunity collaborative optimization method

Abstract

The invention provides a source-network-load-storage coupled regional energy system random opportunity collaborative optimization method, and belongs to the technical field of regional energy utilization. The problem that when an existing regional energy system couples different distributed energy units, system economy and environmental benefits are not fully considered is solved. Comprising the following steps that a regional energy system is constructed, specifically, the regional energy system is a source-network-load-storage integrated regional energy system, and a source side comprises basic energy supply equipment and invested energy conversion equipment and is responsible for meeting the internal load requirement of a park; constructing a regional energy system distributed unit model; constructing a multi-objective random chance constraint collaborative optimization model, wherein the multi-objective random chance constraint collaborative optimization model constructs a multi-objective function with the optimal economic benefit and the optimal clean energy efficiency; solving a multi-target random opportunity constraint collaborative optimization model; the method is applied to regional energy system operation optimization.

Description

Technical Field

[0001] The invention provides a random chance collaborative optimization method for a regional energy system coupled with a source-grid-load-storage, belonging to the technical field of regional energy utilization. Background Art

[0002] With the development of urbanization and industrial transformation and upgrading, regional energy consumption demand continues to increase, and the environmental pollution caused by energy consumption is also becoming increasingly serious. How to effectively meet the energy load needs of regional users while alleviating environmental pollution has become a major challenge in today's society. Conventional energy systems have a single energy supply, which on the one hand leads to low energy utilization, and on the other hand cannot meet the various energy needs of today's regions. The regional energy system can integrate a variety of energy supply equipment such as "cold, hot and electric", which can effectively coordinate the contradictory relationship between the production and supply of various energy sources, and is an important means to solve the problem of regional energy utilization.

[0003] At present, although the existing literature has conducted in-depth discussions on regional energy systems from multiple perspectives such as planning and configuration, optimal scheduling and comprehensive evaluation, and has achieved remarkable results, there are still some research gaps. Specifically, existing research is still insufficient in the in-depth analysis of source-source synergy and multi-energy conversion processes within the regional energy system, especially the study of the differences in system economics and environmental benefits when different distributed energy units are coupled has not been fully carried out. Summary of the invention

[0004] In order to solve the above technical problems, the present invention proposes a random chance collaborative optimization method for a regional energy system coupled source-grid-load-storage, which can realize the collaborative optimization of basic energy supply units and energy conversion units, improve the overall energy utilization efficiency of the system, and enhance the comprehensive benefits of the regional energy system, thereby achieving more efficient, more economical and more environmentally friendly energy supply and management.

[0005] The technical solution adopted by the present invention is: a random chance collaborative optimization method for a regional energy system coupled with source-grid-load-storage, comprising the following steps:

[0006] S1: Building a regional energy system: The regional energy system is a regional energy system that integrates source-grid-load-storage. The source side includes basic energy supply equipment and investable energy conversion equipment, which is responsible for meeting the internal load demand of the park;

[0007] S2: Construct a distributed unit model for the regional energy system;

[0008] S3: Construct a multi-objective random chance constrained collaborative optimization model: The multi-objective random chance constrained collaborative optimization model includes a multi-objective function of optimizing the economic benefits of the regional energy system and optimizing the clean energy efficiency. The multi-objective random chance constrained collaborative optimization model is constrained by the power, supply and demand balance, equipment output, and equipment operating conditions of the distributed unit model of the regional energy system;

[0009] S4: Solution of multi-objective stochastic chance constrained collaborative optimization model.

[0010] Furthermore, the basic energy supply equipment is composed of a wind power plant and a photovoltaic power plant; the energy conversion equipment includes an electric conversion equipment and a gas conversion equipment, wherein the electric conversion equipment includes an electric-to-gas equipment, an electric refrigerator, and a ground source heat pump system; the gas conversion equipment includes a combined heat, cooling and power unit and a gas boiler, and the combined heat, cooling and power unit is composed of a gas turbine, a waste heat boiler, a heat exchange device and an absorption refrigerator.

[0011] Furthermore, the regional energy system distributed unit model constructed in step S2 includes: a wind power generation model, a photovoltaic power generation model, and a conversion-type distributed model, wherein the conversion-type distributed model includes: an energy storage device model, an electric conversion device model, and a gas conversion device model, and the energy storage device model includes an electric storage device model and a heat storage device model.

[0012] Furthermore, the expression of the multi-objective function is as follows:

[0013] ;

[0014] ;

[0015] Where: is the objective function 1, representing the net benefit of the regional energy system; is objective function 2, representing the clean energy consumption rate; and They are the benefits and costs of the district energy system; , , , and They are the electricity consumed by user energy supply, power storage equipment, electric refrigerators, ground source heat pump systems and P2G. is the power generation of the wind turbine, is the power generation of the combined heating, cooling and power unit.

[0016] Furthermore, in step S4, a two-stage solution algorithm is specifically used to solve the multi-objective random chance constrained collaborative optimization model. First, a multi-objective particle swarm optimization algorithm is used to solve and obtain the Pareto optimal solution. Then, an external archive is defined to access the Pareto optimal solution generated in each iteration. The external archive is screened again using a dilution sorting method based on crowding distance, and particles with large crowding distance are selected to remain in the external archive, so that the number of Pareto optimal solutions is 3-5. Finally, the Pareto optimal frontier is selected based on fuzzy set theory.

[0017] Further, wherein:

[0018] ;

[0019] ;

[0020] ;

[0021] ;

[0022] ;

[0023] ;

[0024] ;

[0025] Where: is the energy efficiency, is the energy demand of the user;

[0026] is the initial charge of the energy storage device, is the charge of the storage device, is the discharge capacity of the storage device, and are the charging and discharging efficiencies of the energy storage device, respectively;

[0027] is the cooling demand, is the cooling efficiency of the electric refrigerator;

[0028] Refers to the amount of electricity consumed by the ground source heat pump system in a specific period. Indicates the cooling capacity provided by the ground source heat pump system. is the coefficient of performance of the ground source heat pump system;

[0029] It refers to the amount of electricity consumed by the P2G device during period t. is the power of the P2G device; t is the operating time of the P2G device;

[0030] , and They are the prices of electricity, heating and cooling within the park; , and They are respectively the electricity, heating and cooling load demands satisfied by the units within the park;

[0031] is the operating cost of unit i, is the depreciation cost of unit i, Penalty cost for abandoned energy.

[0032] Further, the electric conversion equipment model includes a power-to-gas equipment model, an electric refrigerator model, and a ground source heat pump model;

[0033] The gas conversion equipment model includes a combined cooling, heating and power unit model and a gas boiler model.

[0034] Furthermore, in the integrated source-grid-load-storage regional energy system, the "source" includes the power supply side, heating, cooling side and gas supply side; the "grid" includes the transmission and distribution network, heating, cooling pipeline network and gas supply pipeline network; the "load" includes electricity, heat, cooling and gas loads; and the "storage" includes power storage equipment, heat storage equipment and gas storage equipment.

[0035] The beneficial effects of the present invention compared to the prior art are as follows: the present invention proposes a multi-objective random chance collaborative optimization model for regional energy systems, which couples the various links of "source, grid, load and storage", further refines the flexibility of the "source" end, and distinguishes it into basic energy supply equipment and energy conversion equipment. Through this method, the present invention can comprehensively evaluate the economic and environmental benefits of the regional energy system under the participation of different energy conversion units, and provide a theoretical basis and practical guidance for the optimal configuration and efficient operation of the regional energy system. This not only enriches the research field of regional energy systems, but also provides new perspectives and solutions for achieving sustainable energy utilization and regional energy transformation. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The present invention will be further described below in conjunction with the accompanying drawings:

[0037] Figure 1 This is the structural diagram of the regional energy system;

[0038] Figure 2 Flowchart for multi-objective stochastic chance constrained collaborative optimization. DETAILED DESCRIPTION

[0039] like Figure 1 and 2As shown, the present invention provides a random chance collaborative optimization method for a regional energy system coupled with source-grid-load-storage, aiming to construct a regional energy system with integrated coupling of "source-grid-load-storage", realize the collaborative optimization of basic energy supply equipment and energy conversion equipment, and improve the overall energy utilization efficiency of the system.

[0040] The implementation steps of the method of the present invention include:

[0041] S1: Building a regional energy system;

[0042] S2: Construct a distributed unit model for the regional energy system;

[0043] S3: Construct a multi-objective stochastic chance constrained collaborative optimization model;

[0044] S4: Design of two-stage solution algorithm for the model.

[0045] Specifically, the regional energy system constructed by the present invention is an integrated regional energy system of "source-grid-load-storage". "Source" includes the power supply side, heating (cooling) side and gas supply side; "grid" includes the transmission and distribution network, heating (cooling) pipeline network and gas pipeline network; "load" includes electricity, heat, cooling and gas loads; "storage" includes power storage equipment, heat storage equipment and gas storage equipment. Figure 1 The diagram is a diagram of the regional energy system structure. The "source" side includes basic energy supply equipment and investable energy conversion equipment, which are responsible for meeting the "load" demand within the park. The basic energy supply equipment consists of wind power plants and photovoltaic power plants; the energy conversion equipment is divided into electric conversion equipment and gas conversion equipment, among which the electric conversion equipment includes power to gas equipment (Power to gas, P2G), electric refrigerators, and ground source heat pump systems; the gas conversion equipment includes combined cooling, heating and power (Combined Cooling Heating and Power, CCHP) and gas boilers. CCHP consists of a gas turbine, a waste heat boiler, a heat exchange device, and an absorption refrigerator.

[0046] The steps for constructing the distributed unit model of the regional energy system are as follows:

[0047] S2.1: Construction of wind power generation model. The strength of wind speed determines the output power of wind turbines. Natural wind speed is highly random. The wind power generation model based on wind speed is as follows:

[0048] (1);

[0049] Where: is the output power of the wind turbine at time m; is the wind energy utilization coefficient; Indicates the air density; It is the vertical projection area of ​​the wind on the area swept by the wind turbine blades; is the rated power of the wind turbine; , and are the cut-in, rated and cut-out wind speeds of the wind turbine respectively, is the wind speed of the wind turbine.

[0050] S2.2: Photovoltaic power generation model construction. The intensity of light determines the output power of photovoltaic power generation. The probability density function of light radiation can usually be expressed by the beta distribution function:

[0051] (2);

[0052] Where: is the radiation intensity model, and are the shape parameters of the light radiation intensity, is the gamma function, is the solar radiation intensity.

[0053] The energy supply model of the photovoltaic generator set is as follows:

[0054] (3);

[0055] Where: is the output power of the photovoltaic generator set at time m, is the conversion efficiency, is the total area of ​​PV panels, is the solar radiation intensity at time m.

[0056] S2.3: Conversion-type distributed model construction. Conversion-type distributed model construction is a key tool for achieving optimal management of complex systems. It improves the efficiency, reliability and intelligence level of the overall system by effectively converting data and energy between different nodes or units. It specifically includes the following three parts.

[0057] (1) Construction of electric thermal energy storage equipment model. Electric thermal energy storage equipment includes power storage equipment and heat storage equipment. Electric thermal energy storage equipment absorbs clean energy waste during the off-peak period and releases it during the peak period to meet certain energy load requirements. The electric thermal energy storage equipment model refers to the expression of energy storage subsystem formula 6-6 in the document "Research on coordinated optimization scheduling and benefit balance of clean energy considering multi-energy complementarity".

[0058] (2) Construction of electrical conversion equipment model, including:

[0059] 1) P2G model construction. P2G performs water electrolysis to obtain hydrogen (H2) and oxygen (O2), and then reacts hydrogen with carbon dioxide (CO2) to generate methane (CH4) to synthesize storable natural gas. The model is expressed as follows:

[0060] (4);

[0061] Where: is the gas production power of P2G in period t, is the gas production efficiency, is the electric power entering P2G during period t.

[0062] 2) Construction of electric refrigerator model. The electric refrigerator consumes electricity to generate cold air to meet the cooling load demand of the system. The model is expressed as follows:

[0063] (5);

[0064] Where: is the output power of the electric refrigerator during period t, is the electric power entering the electric refrigerator during period t, is the cooling efficiency of the electric refrigerator.

[0065] 3) Construction of ground source heat pump model. The heat supply of the ground source heat pump system realizes the transfer from low-grade heat energy to high-grade heat energy by inputting a small amount of high-grade energy (such as electricity). The model is expressed as follows:

[0066] (6);

[0067] Where: is the heat supply of the ground source heat pump system during period t, is the electric power entering the ground source heat pump system during period t, is the heating efficiency of the ground source heat pump system.

[0068] (3) Construction of gas conversion equipment model, including:

[0069] 1) Construction of the combined cooling, heating and power unit model. The gas turbine, waste heat boiler, absorption chiller and heat exchanger together form CCHP. The gas turbine consumes gas to generate electricity. The heat generated can be recovered by the waste heat boiler, and the heat exchanger and absorption chiller can be used to provide heating and cooling respectively. The model is expressed as follows:

[0070] (7);

[0071] Where: is the power supply of CCHP during period t, is the amount of natural gas consumed by CCHP during period t, is the power generation efficiency of CCHP, is the waste heat power of CCHP power generation during period t, is the thermoelectric ratio, is the heat gained by the heat exchanger during period t, is the waste heat entering the heat exchange device during period t, is the heating efficiency of the heat exchanger, is the cooling energy obtained by the absorption refrigerator during period t, is the waste heat entering the absorption chiller during period t, is the refrigeration efficiency of the absorption chiller.

[0072] 2) Construction of gas boiler model. Gas boiler generates heat through gas to meet the heat load demand. The model is expressed as follows:

[0073] (8);

[0074] Where: is the heating capacity of the gas boiler during period t, is the amount of natural gas consumed by the gas boiler during period t, It is the heating efficiency of gas boiler.

[0075] The multi-objective random chance constrained collaborative optimization model provides a comprehensive, flexible and reliable decision support tool for the planning and operation of complex systems by comprehensively considering multiple objectives, uncertainties and synergies. The application of this model can bring significant economic and social benefits in industries such as energy, transportation, and manufacturing. Therefore, it is crucial to construct a multi-objective random chance constrained collaborative optimization model, which includes the following steps.

[0076] The calculation and optimization process of the regional energy system is mainly divided into two objective functions and a series of steps.

[0077] 1. Define the objective function:

[0078] Objective function 1: Maximizing Net Present Value (NPV). This involves calculating the economic benefits of users’ energy supply, electricity storage, electric refrigeration, ground source heat pump system, P2P (point-to-point energy trading) and other activities.

[0079] Objective function 2: Maximize clean energy consumption. This means using as much clean energy as possible in energy consumption in terms of electricity, heat, cooling, gas, etc.

[0080] 2. Determine the constraints, including:

[0081] (1) Electricity calculation:

[0082] Calculate the amount of clean energy consumed and the amount of electricity consumed, which may include electricity from renewable energy sources such as solar and wind power.

[0083] (2) Supply and demand balance constraints:

[0084] Ensure a balance between energy supply and demand, including the balance of electricity, heating, cooling and gas load requirements.

[0085] (3) Determine the output limit and time period of the equipment:

[0086] Consider the output cap and time period restrictions of wind power generation and photovoltaic units, as well as possible power abandonment.

[0087] (4) Determine the actual conditions:

[0088] Including the operating conditions of energy conversion equipment such as gas boilers and CCHP.

[0089] (5) Equipment constraints:

[0090] Determine the output range and constraints of electrical conversion equipment and gas conversion equipment.

[0091] (6) Electric conversion equipment constraints:

[0092] Consider the constraints of electrical conversion devices such as electrical refrigerators.

[0093] The purpose of the entire process is to optimize the performance of the regional energy system so that it is economically viable (by maximizing the net present value) while increasing the proportion of clean energy to achieve a more environmentally friendly and sustainable energy supply. This process requires comprehensive consideration of the performance, cost, supply and demand balance of various energy equipment, and the utilization efficiency of clean energy.

[0094] Specifically, the regional energy system usually pursues the maximization of economic benefits in the process of participating in the park transaction. However, due to the deepening of environmental problems and the difficulty of clean energy consumption, while pursuing economic benefits, it is also necessary to pay attention to environmental benefits and ensure a certain clean energy consumption. Therefore, the present invention constructs a multi-objective function of optimal economic benefits and optimal clean energy efficiency, and its expression is as follows:

[0095] (9);

[0096] (10);

[0097] Where: is the objective function 1, representing the net benefit of the regional energy system; is objective function 2, representing the clean energy consumption rate; and They are the benefits and costs of the district energy system; , , , and They are the electricity consumed by user energy supply, power storage equipment, electric refrigerators, ground source heat pump systems and P2G. is the power generation of the wind turbine, is the power generation of the combined heating, cooling and power unit.

[0098] in:

[0099] (11);

[0100] (12);

[0101] (13);

[0102] (14);

[0103] (15);

[0104] (16);

[0105] (17);

[0106] Where: is the energy efficiency, is the energy demand of the user;

[0107] is the initial charge of the energy storage device, is the charge of the storage device, is the discharge capacity of the storage device, and are the charging and discharging efficiencies of the energy storage device, respectively;

[0108] is the cooling demand (usually expressed in units of thermal energy, such as kilowatt-hours), is the cooling efficiency of the electric refrigerator;

[0109] Refers to the electrical energy consumed by the ground source heat pump system in a specific period, in kilowatt-hours (kW h). Indicates the cooling capacity provided by the ground source heat pump system, in kilowatts (kW). It is the coefficient of performance of the ground source heat pump system, which measures the cooling capacity that the system can provide per unit of electrical energy consumed;

[0110] It refers to the amount of electricity consumed by the P2G device during period t, in kilowatt-hours (kWh). is the power of the P2G device, in kilowatts (kW); t is the operating time of the P2G device, in hours (h);

[0111] , and They are the prices of electricity, heating and cooling within the park; , and They are respectively the electricity, heating and cooling load demands satisfied by the units within the park;

[0112] is the operating cost of unit i, is the depreciation cost of unit i, Penalty cost for abandoned energy.

[0113] in:

[0114] (18);

[0115] Where: is the unit operating cost of unit i; is the operating power of unit i during period t; is the total investment cost of unit i; is the remaining cost of unit i; is the service life of unit i; discarding energy for clean energy; Indicates the allowable energy abandonment factor; The penalty cost for unit energy abandonment.

[0116] The constraints of the model include:

[0117] (1) Supply and demand balance constraint, which is expressed as follows:

[0118] (19);

[0119] Where: is the confidence level of constraint function (19), , , , They are the electricity, heat, cold and gas load demands satisfied by the units within the park; , , , They are the electricity, heat, cooling and gas load demands satisfied by the external units respectively; , , , They are respectively the electricity, heating, cooling and gas load demands of the park.

[0120] (2) Output and balance constraints of wind turbines and photovoltaic generators, including:

[0121] 1) The expression of output constraint is as follows:

[0122] (20);

[0123] (twenty one);

[0124] Where: is the wind turbine output during period t, is the output of the photovoltaic generator set during period t, is the upper limit of wind turbine output, It is the upper limit of the output of photovoltaic generator sets.

[0125] 2) The expression of the equilibrium constraint is as follows:

[0126] (twenty two);

[0127] Where: is the user function in period t, is the amount of electricity consumed by the energy storage device during period t, is the amount of electricity consumed by the electric refrigerator during period t, is the amount of electricity consumed by the ground source heat pump system during period t, is the power consumed by P2G during period t, is the abandoned energy in period t, is the confidence level of constraint function (22).

[0128] (3) The expression of the electrical conversion device constraint is as follows:

[0129] (twenty three);

[0130] Where: is the lower limit of the output of the ground source heat pump system at time m, is the output of the ground source heat pump system at time m, It is the upper limit of the output of the ground source heat pump system at time m.

[0131] The constraints of P2G and electric refrigerator refer to formula (23).

[0132] (4) Gas conversion equipment constraints, including:

[0133] 1) Gas boiler output constraint, the expression is as follows:

[0134] (twenty four);

[0135] Where: is the lower limit of gas boiler output, The upper limit of the gas boiler output. The gas boiler supplies heat during period t.

[0136] 2) CCHP output constraints, including:

[0137] The gas turbine output constraint is expressed as follows:

[0138] (25);

[0139] Where: is the lower limit of gas turbine output, is the upper limit of gas turbine output, is the CCHP power supply during period t.

[0140] The output constraint of waste heat boiler is as follows:

[0141] (26);

[0142] Where: is the lower limit of the waste heat boiler output, is the upper limit of the waste heat boiler output, is the waste heat power of CCHP power generation during period t.

[0143] Heat exchangers and absorption refrigerators refer to formula (25).

[0144] (5) Constraints on energy storage devices, expressed as follows:

[0145] (27);

[0146] (28);

[0147] (29);

[0148] (30);

[0149] Where: represents the energy storage capacity of the power storage device at time m; and They are the upper and lower limits of the energy storage capacity of the power storage equipment respectively; Indicates the charging power of the power storage device at time m; Indicates the discharge power of the power storage device at time m; and They represent the charging and discharging power of the power storage device at time m respectively; , , and Respectively represent the upper and lower limits of charge and discharge of the power storage device; and They represent the energy storage capacity at the beginning and end of the operation of the electric storage device. The constraints of the thermal storage device refer to formulas (27)-(30).

[0150] The design of the two-stage solution algorithm of the model is to achieve the two-stage solution of the model through methods such as uncertainty processing solution algorithm, multi-objective particle swarm optimization algorithm, and Pareto optimal solution screening model.

[0151] S4.1: Uncertainty handling solution algorithm. First, assume that for The integer part of , resulting from samples; then, calculate the objective function ; Finally, output Middle The maximum value. is the sample value of the uncertainty factor, is the number of samples.

[0152] S4.2: Solving multi-objective function models, including:

[0153] 1. Multi-objective particle swarm optimization algorithm:

[0154] In order to avoid falling into local optimality and conflicts among objectives, the multi-objective particle swarm optimization algorithm is used to solve the problem based on its excellent convergence speed and the ability to explore a relatively complete Pareto frontier. The calculation process is as follows:

[0155] (1) Input system parameters and objective function, and randomly generate the initial particle swarm.

[0156] (2) Calculate the fitness of each particle and determine whether each particle is within the constraint range. If the constraint range is satisfied, initialize the position and velocity of the i-th particle in the nth iteration; if not, use the constraint condition as a penalty function, randomly generate new particles, and then initialize the position and velocity. The position and velocity model is as follows:

[0157] (31);

[0158] (32);

[0159] Where: is the position of the ith particle in the nth iteration, is the k-th component of the position vector of particle i at the n-th iteration; is the velocity of the ith particle in the nth iteration, is the k-th component of the flight velocity vector of particle i in the n-th iteration.

[0160] (3) By evaluating the fitness value of the objective function, the optimal individual position of each particle and the optimal population position are determined:

[0161] (33);

[0162] (34);

[0163] Where: is the optimal position of the ith particle in the nth iteration, is the k-th dimension component of the best position of the i-th particle in the n-th iteration; is the optimal position of the ith particle in the population in the nth iteration, It is the k-th dimension component of the best position vector experienced by the population in the n-th iteration.

[0164] (4) Update the speed, number, Pbest and Gbest of the particles in the next iteration:

[0165] (35);

[0166] Where: is the d-th dimension component of the flight velocity vector of the ith particle in the n+1th iteration; is the d-th dimension component of the position vector of the ith particle in the n+1th iteration; The d-th dimension component of the best position vector experienced by the i-th particle in the n-th iteration; It is the d-th dimension component of the best position vector experienced by the population in the n-th iteration. and It is a random number that follows a uniform distribution in the interval [0,1] to increase the randomness of the search; and is the learning factor, and its value range is [0,2]. It is used to balance the global optimization and local optimization capabilities among particles. It is the inertia weight, and its model is as follows:

[0167] (36);

[0168] Where: is the maximum number of iterations; is the current iteration number, is the maximum inertia weight, is the minimum inertia weight.

[0169] 2. Screening model for Pareto optimal solution:

[0170] The present invention defines an external archive to access the Pareto optimal solution generated in each iteration. However, as the number of iterations increases, the number of Pareto optimal solutions increases, and the size of the external archive continues to expand. In order to improve the running speed of the algorithm, ensure the diversity of the external archive, and avoid falling into the local optimum, the present invention uses a dilution sorting method based on crowding distance to screen the external archive again, and select particles with large crowding distance to remain in the external archive, so that the number of Pareto optimal solutions is 3-5, because too many solution sets are not conducive to the optimization of the solution, and too few solution sets will also ignore the optimal solution, which is defined as N P , represents the upper limit of the number of Pareto optimal solutions, and the model is as follows:

[0171] (37);

[0172] Where: represents the crowding distance of the ith particle; , are the minimum and maximum values ​​of the jth optimization objective function in the solution set, respectively. is the value of the i+1th particle of the jth optimization objective function.

[0173] 3. Pareto optimal frontier selection model based on fuzzy set theory:

[0174] The multi-objective particle swarm optimization algorithm obtains a set of Pareto optimal solutions, which cannot be determined by simply comparing the size. The present invention will select the Pareto optimal frontier based on fuzzy set theory, which can achieve the reasonable selection of the best point without knowing the decision maker's preferences and actual situation. The model is as follows:

[0175] (38);

[0176] Where: and are the j-th optimization objective function value and membership value respectively.

[0177] Finally, we conclude:

[0178] (39);

[0179] Where: For the comprehensive membership of each objective function, the maximum value is selected as the compromise solution.

[0180] This invention constructs an optimization model with the maximization of the net income of the regional energy system and the optimization of the clean energy efficiency as multi-objective functions, and deeply studies the important role of energy conversion equipment in the economy and environment of the regional energy system. The results of the example show that: (1) The electric conversion equipment is based on electricity and performs internal electric conversion in the system, which can effectively reduce the abandoned clean energy, reduce the penalty cost of abandoned clean energy, and meet various load requirements; (2) The participation of gas conversion equipment can effectively reduce the replenishment of the external backup system, and through the cascade utilization of energy, it can coordinately meet the electricity-heat-cooling load requirements and improve the multi-source coordination capability within the system. The participation of energy conversion equipment plays an important role in improving the economic and environmental benefits of the regional energy system, especially in achieving efficient consumption of clean energy and reducing the penalty cost of abandoned energy.

[0181] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A random chance collaborative optimization method for a regional energy system coupled with source-grid-load-storage, characterized by: The following steps are involved: S1: Building a regional energy system: The regional energy system is a regional energy system that integrates source-grid-load-storage. The source side includes basic energy supply equipment and investable energy conversion equipment, which is responsible for meeting the internal load demand of the park; S2: Construct a distributed unit model for the regional energy system; S3: Construct a multi-objective random chance constrained collaborative optimization model: The multi-objective random chance constrained collaborative optimization model includes a multi-objective function of optimizing the economic benefits of the regional energy system and optimizing the clean energy efficiency. The multi-objective random chance constrained collaborative optimization model is constrained by the power, supply and demand balance, equipment output, and equipment operating conditions of the distributed unit model of the regional energy system; S4: Solution of multi-objective stochastic chance constrained collaborative optimization model.

2. The random chance collaborative optimization method of a regional energy system coupled source-grid-load-storage according to claim 1 is characterized by: The basic energy supply equipment consists of a wind power plant and a photovoltaic power plant; the energy conversion equipment includes electric conversion equipment and gas conversion equipment, wherein the electric conversion equipment includes electric-to-gas equipment, an electric refrigerator, and a ground source heat pump system; the gas conversion equipment includes a combined heat, cooling and power unit and a gas boiler, and the combined heat, cooling and power unit consists of a gas turbine, a waste heat boiler, a heat exchange device and an absorption refrigerator.

3. The random chance collaborative optimization method of a regional energy system coupled source-grid-load-storage according to claim 2 is characterized by: The regional energy system distributed unit model constructed in step S2 includes: a wind power generation model, a photovoltaic power generation model, and a conversion-type distributed model, wherein the conversion-type distributed model includes: an energy storage device model, an electric conversion device model, and a gas conversion device model, and the energy storage device model includes an electric storage device model and a heat storage device model.

4. The random chance collaborative optimization method of a regional energy system coupled source-grid-load-storage according to claim 3 is characterized by: The expression of the multi-objective function is as follows: ； ； Where: is the objective function 1, representing the net benefit of the regional energy system; is objective function 2, representing the clean energy consumption rate; and are the district energy system benefits and costs respectively; , , , and They are the electricity consumed by user energy supply, power storage equipment, electric refrigerators, ground source heat pump systems and P2G. is the power generation of the wind turbine, is the power generation of the combined cooling, heating and power unit.

5. The random chance collaborative optimization method of a regional energy system coupled source-grid-load-storage according to claim 4 is characterized by: In the step S4, a two-stage solution algorithm is specifically used to solve the multi-objective random chance constrained collaborative optimization model. First, a multi-objective particle swarm optimization algorithm is used to solve and obtain the Pareto optimal solution. Then, an external archive is defined to access the Pareto optimal solution generated in each iteration. The external archive is screened again using a dilution sorting method based on crowding distance, and particles with large crowding distances are selected to remain in the external archive, so that the number of Pareto optimal solutions is 3-5. Finally, the Pareto optimal frontier is selected based on fuzzy set theory.

6. The random chance collaborative optimization method of a regional energy system coupled source-grid-load-storage according to claim 4 is characterized by: in: ； ； ； ； ； ； ； Where: is the energy efficiency, is the energy demand of the user; is the initial charge of the energy storage device, is the charge of the storage device, is the discharge capacity of the storage device, and are the charging and discharging efficiencies of the energy storage device, respectively; is the cooling demand, is the cooling efficiency of the electric refrigerator; Refers to the amount of electricity consumed by the ground source heat pump system in a specific period. Indicates the cooling capacity provided by the ground source heat pump system. is the coefficient of performance of the ground source heat pump system; It refers to the amount of electricity consumed by the P2G device during period t. is the power of the P2G device; t is the operation time of the P2G device; , and They are the prices of electricity, heating and cooling within the park; , and They are respectively the electricity, heating and cooling load demands satisfied by the units within the park; is the operating cost of unit i, is the depreciation cost of unit i, Penalty cost for abandoned energy.

7. The random chance collaborative optimization method of a regional energy system coupled source-grid-load-storage according to claim 3 is characterized by: The electric conversion equipment model includes an electric-to-gas equipment model, an electric refrigerator model, and a ground source heat pump model; The gas conversion equipment model includes a combined cooling, heating and power unit model and a gas boiler model.

8. A random chance collaborative optimization method for a regional energy system coupled source-grid-load-storage according to any one of claims 1 to 7, characterized in that: In the regional energy system with integrated source-grid-load-storage, "source" includes the power supply side, heating, cooling side and gas supply side; "grid" includes the transmission and distribution network, heating, cooling pipeline network and gas supply pipeline network; "load" includes electricity, heat, cooling and gas loads; "storage" includes power storage equipment, heat storage equipment and gas storage equipment.

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