A method and system for optimizing configuration of a renewable micro-energy network

Abstract

The present invention provides a method for optimizing the configuration of a renewable micro-energy network, comprising: obtaining configuration parameters and cost parameters of candidate energy equipment, electric heating and cooling load curves of typical days in each season, and typical wind and solar output scenarios in each season; inputting the configuration parameters and cost parameters of the candidate energy equipment, electric heating and cooling load curves of typical days in each season, and typical wind and solar output scenarios in each season into an optimization configuration model to obtain a Pareto solution set of the capacity of the candidate energy equipment; screening the Pareto solution set of the capacity of the candidate energy equipment based on fuzzy theory to obtain an optimal configuration scheme for the candidate energy equipment; the optimization configuration model comprises: an upper-layer optimization configuration model and a lower-layer operation optimization scheduling model. The present invention sets the optimization configuration model in consideration of the operation effect, and the obtained configuration scheme can simultaneously achieve optimal operation effect and optimal equipment configuration, which is more reasonable than traditional optimization configuration results.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system optimization configuration, and in particular relates to a method and system for optimizing configuration of a renewable micro-energy network. Background Art

[0002] The increasingly diverse nature of suburban energy demand is placing higher demands on the quality of energy services. However, existing suburban energy supplies are limited to electricity and fail to effectively utilize the abundant local renewable resources. Furthermore, the increasingly acute conflict between humanity and nature requires that the planning and design of suburban energy systems must focus not solely on economic factors. A multi-energy complementary system that integrates electricity, cooling, and heating supplies, utilizing different energy sources in a cascaded manner, breaks down the traditional barriers of independent energy supply and is an effective way to promote low-carbon emissions reduction and improve energy efficiency in suburban areas. Therefore, the rational allocation of numerous renewable energy sources and the construction of a green suburban micro-energy grid system are of great significance for accelerating suburban modernization.

[0003] Currently, there are many plans and designs for hybrid renewable energy systems, but the following problems still exist: first, they cannot fully utilize local renewable resources; second, they do not consider the impact of system operation mode on planning results; third, they cannot evaluate the effect after optimization configuration. Therefore, how to solve the above problems existing in the existing technology is a problem that technicians in this field need to solve. Summary of the Invention

[0004] To overcome the above-mentioned deficiencies of the prior art, the present invention provides a method for optimizing the configuration of a renewable micro-energy network, comprising:

[0005] Obtain the configuration parameters and cost parameters of the energy equipment to be selected, the electric heating and cooling load curves for typical days in each season, and the typical wind and solar output scenarios in each season;

[0006] Input the configuration parameters and cost parameters of the candidate energy equipment, the electric heating and cooling load curves of typical days in each season, and the typical wind and solar output scenarios in each season into a pre-set optimization configuration model to obtain a Pareto solution set of the capacity of the candidate energy equipment;

[0007] Screening the Pareto solution set of the capacity of the candidate energy equipment based on fuzzy theory to obtain the optimal configuration plan of the candidate energy equipment;

[0008] The optimization configuration model includes: an upper-level optimization configuration model and a lower-level operation optimization scheduling model, wherein the upper-level optimization configuration model is optimally constructed based on the quantity configuration of the selected energy equipment, and the lower-level operation optimization scheduling model is optimally constructed based on the equipment output of the selected energy equipment.

[0009] Preferably, the energy equipment to be selected includes: photovoltaic equipment, wind power equipment, biogas cogeneration equipment, biogas boilers, ground source heat pumps, electric refrigerators, absorption refrigerators, power storage devices, cold storage devices and heat storage devices;

[0010] The configuration parameters of the energy equipment to be selected include: the rated power, output power range, maximum ramping coefficient, dissipation coefficient, and waste heat recovery coefficient of the biogas cogeneration equipment; the rated power, output power range, maximum ramping coefficient, and thermal efficiency of the biogas boiler; the rated power, output power range, maximum ramping coefficient, electric-to-heat conversion efficiency, and heat exchange efficiency of the heat exchange pump; the output power range and energy efficiency ratio of the electric refrigerator and absorption refrigerator; the charging and discharging efficiency, maximum charging and discharging coefficient, dissipation coefficient, and energy storage coefficient of the electric storage device, cold storage device, and heat storage device;

[0011] The cost parameters of the energy equipment to be selected include: purchase cost, installation cost and maintenance cost.

[0012] Preferably, the setting of the optimization configuration model includes:

[0013] Based on the pre-set multi-dimensional evaluation indicators of renewable micro-energy networks, the upper-level optimization configuration objective function is constructed with the equipment capacity of the selected energy equipment as the decision variable and the annual operating cost, pollution emissions and energy utilization efficiency as the minimum goals. The upper-level optimization configuration model is set with the installation capacity of the selected energy equipment as the upper-level optimization configuration constraint condition.

[0014] Based on the multidimensional evaluation indicators of renewable micro-energy networks, the output of the selected energy equipment is used as the decision variable, and the procurement cost, maintenance cost and curtailment cost of the selected energy equipment are minimized as the goal to construct the lower-level operation optimization scheduling objective function; the bus transmission power constraint, the interactive power constraint with the external interconnection line, the energy coupling device constraint and the energy storage device constraint are used as constraint conditions to set the lower-level operation optimization scheduling model.

[0015] Preferably, the upper layer optimization configuration objective function is as follows:

[0016] min f1=(C,F,1 / E)

[0017] Among them, C is the annual operating cost, F is the pollution emissions, and E is the energy utilization efficiency.

[0018] Preferably, the calculation formula for the annual operating cost is as follows:

[0019]

[0020] Among them, C EI is the installation cost of the selected energy equipment, C EP is the purchase cost of the selected energy equipment, C EMis the maintenance cost of the selected energy equipment, N i is the installed capacity of the i-th type of energy equipment to be selected; k is the benchmark discount rate, y is the useful life of the energy equipment to be selected, c i is the unit price of the equipment for the i-th type of selected energy, is the unit power purchase price at time t on day d, is the grid tie line interaction power at time t on day d, is the price of electricity per unit power at time t on day d, c bio is the biogas production cost per cubic meter, is the biogas consumption of the micro gas turbine at time t on day d, is the biogas consumption of the biogas boiler at time t on the dth day, is the unit power maintenance cost of the equipment of the i-th type of selected energy source, The output power of the equipment of the i-th type of candidate energy at time t on day d, where D is the number of days in a year and T is the number of hours per day.

[0021] Preferably, the pollution emission calculation formula is as follows:

[0022]

[0023] in, is the grid tie line interaction power at time t on day d, η gen is the power generation efficiency of the power plant, η loss is the transmission line loss rate of the power plant, is the emission coefficient of the jth type of pollutant generated by coal-fired power generation, is the biogas consumption of the micro gas turbine at time t on day d, is the biogas consumption of the biogas boiler at time t on the dth day, q is the standard biogas lower calorific value, is the emission coefficient of the jth type of pollutant produced by biogas combustion, D is the number of days in a year, and T is the number of hours per day.

[0024] Preferably, the calculation formula for energy utilization efficiency is as follows:

[0025]

[0026] Among them, Q out is the system output energy, Q in is the system input, λ e is the electric energy conversion coefficient, is the grid tie line interaction power at time t on day d, is the rated output power of the photovoltaic power generation unit at time t on day d, The rated output power of the wind power generation unit at time t on day d is: g Biogas energy conversion coefficient, is the biogas consumption of the micro gas turbine at time t on day d, is the biogas consumption of the biogas boiler at time t on the dth day, q is the lower calorific value of the standard biogas, λ p It is the cooling energy conversion system. Input ground source heat energy to the ground source heat exchange pump at time t on day d, is the user's electric load power at time t on the dth day, λ h is the thermal energy conversion coefficient, is the heat load power of the user at time t on day d, λ c is the cooling energy conversion coefficient, is the cooling load power of the user at time t on day d.

[0027] Preferably, the installation capacity constraints of the selected energy equipment are as follows:

[0028]

[0029] in, is the lower limit of the installed capacity of type i equipment, The upper limit of the installed capacity for type i equipment, N i Install capacity for equipment of type i.

[0030] Preferably, the lower layer operation optimization scheduling objective function is as follows:

[0031]

[0032] Among them, C EP is the purchase cost of the selected energy equipment, C EM is the maintenance cost of the selected energy equipment, C AE Cost of curtailing solar and wind power, is the abandoned optical power, is the wind power curtailment, c PV is the unit power abandonment cost, c WT is the cost of wind curtailment per unit power, and T is the number of hours per day.

[0033] Preferably, the bus transmission power constraint is as follows:

[0034]

[0035] in, is the actual photovoltaic output power at time t, is the actual wind power output power at time t, is the actual output power of the cogeneration system at time t, is the grid tie line interaction power at time t, is the user's electric load power at time t, is the power consumed by the ground source heat pump at time t, is the power consumed by the electric refrigerator at time t, is the power consumption of the storage device at time t, is the heating power of the cogeneration system at time t, is the heating power of the biogas boiler at time t, is the heating power of the ground source heat pump at time t, is the heat load power of the user at time t, is the heat power absorbed by the absorption refrigerator at time t, is the thermal energy storage output at time t, is the cooling power of the electric refrigerator at time t, is the cooling power of the absorption chiller at time t, is the cold storage energy output of the ground source heat pump at time t, The user's cooling load power, It is the cold energy storage output at time t.

[0036] Preferably, the interaction power constraint with the external tie line is as follows:

[0037]

[0038] in, is the grid tie line interaction power at time t, It is the upper limit of the power for interaction with the external grid tie line.

[0039] Preferably, the energy coupling device constraint is as follows:

[0040]

[0041]

[0042] in, is the output of the i-th type of equipment at time t, is the lower limit of the output of the i-th type of equipment at time t, is the output upper limit of the i-th type equipment at time t, is the start and stop status of the i-th type of equipment at time t, is the output of the i-th type of equipment at time t-1, ΔS i It is the output ramp limit of the i-th category equipment.

[0043] Preferably, the configuration parameters and cost parameters of the candidate energy equipment, the electric heating and cooling load curves of typical days in each season, and the typical wind and solar output scenarios in each season are input into a pre-set optimization configuration model to obtain a Pareto solution set of the capacity of the candidate energy equipment, including:

[0044] Inputting the configuration parameters and cost parameters of the candidate energy equipment into the upper-level optimization configuration model, solving the upper-level optimization configuration model using a non-dominated sorting genetic algorithm to obtain the candidate energy equipment configuration corresponding to each generation of parent chromosomes;

[0045] Input the candidate energy equipment configuration corresponding to each generation of parent chromosomes, the electric heating and cooling load curves of typical days in each season, and the typical wind and solar output scenarios in each season into the lower-level operation optimization scheduling model to obtain the equipment output under the candidate energy equipment configuration corresponding to each generation of parent chromosomes;

[0046] Based on the equipment output under the candidate energy equipment configuration corresponding to each generation of parent chromosomes, the population of the non-dominated sorting genetic algorithm is screened to obtain a new generation of population, until the number of iterations exceeds the pre-set iteration upper limit of the non-dominated sorting genetic algorithm, and the Pareto solution set of the candidate energy equipment capacity corresponding to the last generation of population is obtained.

[0047] Preferably, based on the device output under the selected energy device configuration corresponding to each generation of parent chromosomes, the population of the non-dominated sorting genetic algorithm is screened to obtain a new generation of population, including:

[0048] Based on the equipment output under the candidate energy equipment configuration corresponding to each generation of parent chromosomes, the objective function value of the upper-level optimization configuration model corresponding to each generation of candidate energy equipment configuration is calculated;

[0049] Based on the objective function value of the upper-level optimization configuration model corresponding to each generation of candidate energy equipment configuration, the population of the non-dominated sorting genetic algorithm is screened to obtain a new generation of population.

[0050] Preferably, the Pareto solution set of the capacity of the candidate energy equipment is screened based on fuzzy theory to obtain the optimal configuration scheme of the candidate energy equipment, including:

[0051] Based on the Pareto solution set of the capacity of the selected energy equipment, the corresponding membership functions of the upper-level optimization configuration target annual operating cost, pollution emissions and energy utilization efficiency are calculated respectively;

[0052] Performing a weighted operation on the membership functions corresponding to the annual operating cost, pollution emissions, and energy efficiency to obtain a comprehensive membership function;

[0053] Based on the Pareto solution set of the capacity of the energy equipment to be selected, the comprehensive membership corresponding to each solution is calculated and the solution corresponding to the maximum comprehensive membership is determined as the optimal configuration solution for the energy equipment to be selected.

[0054] Preferably, after obtaining the optimal configuration scheme of the candidate energy equipment, the method further includes: evaluating the optimized configuration result using the multi-dimensional evaluation index of the renewable micro energy network;

[0055] The multi-dimensional evaluation indicators of the renewable micro-energy network include: cost saving rate indicator, pollution emission reduction rate indicator and energy efficiency improvement rate indicator.

[0056] Preferably, the cost saving rate is calculated as follows:

[0057]

[0058] Among them, R CS is the cost saving rate, C is the annual operating cost of the renewable micro-energy grid system, and C SP is the annual operating cost of the energy distribution system.

[0059] Preferably, the pollution reduction rate is calculated as follows:

[0060]

[0061] Among them, R PR is the pollution reduction rate, F is the pollution emission of the renewable micro-energy grid system, and F SP It is the pollution emission of energy production system.

[0062] Preferably, the energy efficiency improvement rate is calculated as follows:

[0063]

[0064] Among them, R EI is the energy efficiency improvement rate, E is the energy utilization efficiency of the renewable micro-energy grid system, and E SP Energy utilization efficiency of energy distribution system.

[0065] Based on the same concept, the present invention also provides an optimization configuration system for a renewable micro-energy network, comprising:

[0066] The data acquisition module is used to obtain the configuration parameters and cost parameters of the selected energy equipment, the electric heating and cooling load curves of typical days in each season, and the typical wind and solar output scenarios in each season;

[0067] a calculation module for inputting configuration parameters and cost parameters of the candidate energy equipment, the electric heating and cooling load curves of typical days in each season, and the typical wind and solar output scenarios in each season into a pre-set optimization configuration model to obtain a Pareto solution set of the capacity of the candidate energy equipment;

[0068] An optimization module, configured to screen the Pareto solution set of the capacity of the candidate energy equipment based on fuzzy theory to obtain an optimal configuration scheme for the candidate energy equipment;

[0069] The optimization configuration model includes: an upper-level optimization configuration model and a lower-level operation optimization scheduling model, wherein the upper-level optimization configuration model is optimally constructed based on the quantity configuration of the selected energy equipment, and the lower-level operation optimization scheduling model is optimally constructed based on the equipment output of the selected energy equipment.

[0070] Compared with the closest prior art, the present invention has the following beneficial effects:

[0071] The present invention provides a method for optimizing the configuration of a renewable micro-energy network, comprising: obtaining configuration parameters and cost parameters of candidate energy equipment, electric heating and cooling load curves of typical days in each season, and typical wind and light output scenarios in each season; inputting the configuration parameters and cost parameters of the candidate energy equipment, electric heating and cooling load curves of typical days in each season, and typical wind and light output scenarios in each season into a pre-set optimization configuration model to obtain a Pareto solution set of the capacity of the candidate energy equipment; screening the Pareto solution set of the capacity of the candidate energy equipment based on fuzzy theory to obtain an optimal configuration scheme for the candidate energy equipment; the optimization configuration model comprises: an upper-layer optimization configuration model and a lower-layer operation optimization scheduling model, wherein the upper-layer optimization configuration model is constructed based on the number of candidate energy equipment, and the lower-layer operation optimization scheduling model is constructed based on the equipment output of the candidate energy equipment. The present invention sets the optimization configuration model in consideration of the operation effect, and the obtained configuration scheme can simultaneously achieve optimal operation effect and optimal equipment configuration, which is more reasonable than traditional optimization configuration results.

[0072] The optimization configuration model of the present invention integrates multiple renewable energy sources such as wind, light, biomass, and geothermal energy, which helps to maximize the utilization of renewable clean energy in suburban areas.

[0073] At the same time, the present invention sets evaluation indicators for the renewable micro-energy network system and evaluates the optimization configuration results, which can clearly evaluate the effect of the optimization configuration. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] Figure 1 A schematic diagram of an optimization configuration method for a renewable micro-energy network provided by the present invention;

[0075] Figure 2 A schematic diagram of an optimized configuration system for a renewable micro-energy network provided by the present invention;

[0076] Figure 3 This is a schematic diagram of the renewable micro-energy network architecture provided in an embodiment of the present invention;

[0077] Figure 4 A schematic diagram of the calculation steps for the double-layer optimization configuration of the renewable micro-energy network provided in an embodiment of the present invention;

[0078] Figure 5Schematic diagram of a typical load curve and wind / solar output scenario provided in an embodiment of the present invention;

[0079] Figure 6 A schematic diagram of a Pareto non-inferior solution set provided in an embodiment of the present invention;

[0080] Figure 7 Schematic diagram of comparison of multiple evaluation indicators provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0081] The specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0082] Example 1:

[0083] The embodiment of the present invention discloses a method for optimizing the configuration of a renewable micro energy network. Figure 1 As shown, including:

[0084] S1 Configuration parameters and cost parameters of the selected energy equipment, electric heating and cooling load curves on typical days in each season, and typical wind and solar output scenarios in each season;

[0085] S2 Input the configuration parameters and cost parameters of the candidate energy equipment, the electric heating and cooling load curves of typical days in each season, and the typical wind and solar output scenarios in each season into a pre-set optimization configuration model to obtain a Pareto solution set of the capacity of the candidate energy equipment;

[0086] S3 screens the Pareto solution set of the capacity of the candidate energy equipment based on fuzzy theory to obtain the optimal configuration plan of the candidate energy equipment.

[0087] S2 Input the configuration parameters and cost parameters of the candidate energy equipment, the electric heating and cooling load curves of typical days in each season, and the typical wind and solar output scenarios in each season into a pre-set optimization configuration model to obtain the Pareto solution set of the capacity of the candidate energy equipment, specifically including:

[0088] S2-1 Constructing mathematical models of energy equipment to be selected

[0089] S2-1-1 Mathematical Model of Biogas Combined Heat and Power System

[0090] Currently, suburban areas have abundant biomass energy reserves generated by agriculture, forestry, and animal husbandry. However, due to its relatively scattered distribution and the fact that most of it is only used for heating, it is seriously wasted. Therefore, this invention adopts a moderately centralized processing method, using biomass methane as the gas source, and selects a combined heating and power (CHP) system consisting of a micro turbine (MT) and a flue gas waste heat recovery device as the main energy supply equipment for the suburban micro-energy network. Its output model can be described as follows:

[0091]

[0092] Where: are the power generation efficiency, output thermal power, heat loss coefficient, and waste heat recovery rate of CHP at time t respectively; are the output power, biogas consumption, available waste heat and rated power of MT at time t respectively; q is the lower calorific value of standard biogas, which is 5.98kW·h / m3 in the present invention.

[0093] S2-1-2 Mathematical Model of Biogas Boiler

[0094] A biogas boiler (BB) is a gas-heat coupling device that uses biogas as fuel. It can convert biomass energy into thermal energy. It is an auxiliary heat source device in a micro-energy grid and has the advantages of low cost, low pollution, and high thermal efficiency. Its mathematical model can be expressed as:

[0095]

[0096] Where: are the biogas input volume and output thermal power of the biogas boiler at time t, η BB It is the heating efficiency of biogas boiler.

[0097] S2-1-3 Mathematical Model of Ground Source Heat Pump

[0098] As an emerging energy conversion method, heat pump technology has an energy efficiency ratio (coefficient of performance, COP) that is usually greater than 4, which is much higher than most existing energy supply equipment. It can work in cooling or refrigeration state according to user needs. It has the advantages of energy saving, environmental protection, safety, reliability and easy maintenance. Therefore, it has attracted much attention from the world and has a very broad development prospect. In addition, as a typical multi-energy coupling device, the introduction of heat pump will also promote the rational allocation of energy on the supply side of the multi-energy complementary system, thereby improving the level of comprehensive energy utilization. The present invention selects ground source heat pump (GSHP) as the micro-energy network energy supply equipment. The device consists of a ground source heat exchange pump and a heat pump host. Its energy consumption and output model can be expressed as:

[0099]

[0100] Where: They are the ground source heat energy output by the ground source heat exchange pump at time t, the heat (cold) power output by the GSHP and its operating energy efficiency; are the power of ground source heat pump, ground source heat exchange pump and heat pump host at time t respectively; η CP ,η HP They are the GSHP rated heating (cooling) power, the ground source heat pump heat transfer efficiency and the heat pump main unit electric-to-heat conversion efficiency.

[0101] S2-1-4 source heat pump energy storage equipment mathematical model

[0102] Energy storage devices are one of the important components of the multi-energy complementary system under the background of energy internet, and play an important role in alleviating the contradiction between energy supply and demand. They can store the remaining energy and fill the vacant demand according to the output status of energy equipment and load size at different times, thereby changing the spatiotemporal distribution of multi-energy flow and contributing to the coordinated optimization operation of the multi-energy complementary system. Considering that the proportion of renewable energy in micro-energy grids is high, the output of units has obvious fluctuations, and the daily load demand in suburban areas varies greatly, the present invention configures multiple energy storage devices including batteries (electrical storage, ES), heating storage (heating storage, HS) and cooling storage (cooling storage, CS) in the model, in order to improve the economic and environmental benefits of the energy supply system. Since energy storage devices of different energy types have similar operating characteristics, the present invention only takes batteries as an example and gives their output and energy storage mathematical models as shown in equations (5) and (6) respectively:

[0103]

[0104]

[0105] Where: are the battery's energy storage, input power, and output power at time t respectively; is the self-discharge rate; are charge and discharge efficiency respectively; is a 0-1 variable indicating the charge or discharge status, and

[0106] S2-2 Constructing multi-dimensional evaluation indicators for renewable micro-energy networks

[0107] S2-2-1 Cost Saving Rate Index

[0108] Whether it is an SP system or a micro energy grid system, its annual operating cost C is composed of the equipment installation cost C EI , Energy procurement cost C EP and equipment maintenance costs C EM Together they consist of:

[0109]

[0110] Where: N i is the installed capacity of the i-th type of equipment; k and y represent the base discount rate and the equipment life cycle, which are set to 7% and 20 years respectively in the present invention; P grid is the interactive power of the grid tie line, a positive value indicates the purchase of electric energy, and a negative value indicates the sale of electric energy; c i 、 are the unit price and unit power maintenance cost of the i-th type equipment respectively; c buy 、c sell 、c bio are the electricity purchase and sales prices per unit power and the biogas production cost per cubic meter; S i is the output power of the i-th type of equipment; D and T are the number of days in a year and the number of hours per day, respectively. In this paper, 365 days and 24 hours are used. This paper formulates the cost saving rate (CSR) index of the micro-energy network with reference to the SP system to measure the economic benefits brought by the multi-energy complementary system. The CSR is specifically defined as the ratio of the annual operating cost saved by the micro-energy network relative to the SP system to the annual operating cost of the SP system, that is:

[0111]

[0112] S2-2-2 Pollution reduction rate index

[0113] The pollutants generated during energy conversion mainly include greenhouse gas CO2 and acid gases SO2 and NO x , which is mainly caused by external power plant power generation pollution F grid and biomass gas combustion pollution Fbio For SP system and micro energy grid, the pollutant emission F SP and F RMEN It can be expressed as:

[0114]

[0115] Where: η gen ,η loss represent the power plant generation efficiency and transmission line loss rate respectively; are the emission coefficients of the jth type of pollutant generated by coal-fired power generation and biogas combustion, respectively. Based on this, the pollution reduction rate (PRR) of the multi-energy complementary system is defined as shown in Equation (10). This indicator can be used to effectively evaluate the environmental protection performance of the cogeneration system.

[0116]

[0117] S2-2-3 Energy efficiency improvement rate index

[0118] Due to the quality differences between different types of energy, this paper starts from the second law of thermodynamics and considers the comprehensive energy utilization level and renewable energy consumption potential. It defines energy utilization efficiency E as the system output energy Q out With input energy Q in For the SP system and micro energy grid, the input and output energy are shown in formula (11):

[0119]

[0120] Where: P load , Q load,h , Q load,c They are the electricity, heating and cooling load powers of suburban users respectively; are the rated output power of photovoltaic and wind power generation units respectively; λ e ,λ h ,λ c are the conversion coefficients of electricity, heat and cold energy respectively, g ,λ p are the conversion coefficients for biogas and geothermal energy, respectively.

[0121] According to the above formula, the energy utilization efficiency E of SP and micro energy grid can be obtained respectively: SP and E RMEN , and the energy efficiency improvement rate (EIR) of renewable micro-energy grid is defined as:

[0122]

[0123] S2-3 Construction of a two-layer optimization configuration model for renewable micro-energy networks

[0124] S2-3-1 Constructing the objective function

[0125] In combination with the above evaluation indicators, the present invention selects three different objectives, namely annual operating cost, pollution emission and energy efficiency, as the upper optimization configuration objective function f1.

[0126] min f1=(C RMEN , F RMEN , 1 / E RMEN ) (13)

[0127] The lower-level intraday optimization scheduling mainly considers the energy procurement cost C EP , Equipment maintenance cost C EM and the cost of curtailed solar and wind power, C AE , and define the daily comprehensive operating cost f2 as the objective function. Among them, C EP and C EM See formula (7), C AE The calculation method is shown in formula (14):

[0128]

[0129] Where: are the abandoned solar power and abandoned wind power respectively; c PV 、c WT They are the unit power cost of curtailed solar power and wind power, which are taken as 0.15 and 0.12 RMB / kW·h respectively in the present invention.

[0130] S2-3-2 Construction Constraints

[0131] S2-3-2-1 Constraints for building the upper-level model: Equipment installation capacity constraints

[0132] The present invention limits the installed capacity of the equipment so that it is not redundant under the maximum load value. The constraints are as follows:

[0133]

[0134] Where: The upper and lower limits of the installed capacity of the i-th type of equipment are respectively. Among them, CHP, BB, and GSHP are configured by number, while the remaining equipment is set in kW.

[0135] S2-3-2-2 Constraints for constructing the upper model:

[0136] 1) Transmission power constraints

[0137] The renewable micro-energy grid system must meet the electricity, heating, and cooling energy needs of users in the area at any time. Therefore, the following bus power transmission constraints exist:

[0138]

[0139] Where: are the actual output power of photovoltaic cell (PV), wind turbine generator (WT) and electric cooler (EC) at time t respectively; are the GSHP heating power, absorption cooler (AC) heat absorption power and thermal energy storage output at time t respectively; are the cooling power of EC, AC, and GSHP at time t, as well as the cold storage output. In addition, the interconnection line with the external power grid must also meet the following constraints:

[0140]

[0141] Where: It is the upper limit of the power for interaction with the external power grid.

[0142] 2) Energy coupling device constraints

[0143] The energy coupling devices CHP, BB, GSHP, PV, WT, EC, and AC in this article must all meet the device output constraints:

[0144]

[0145] Where: are the output upper limit, output lower limit and start / stop status of the i-th type equipment at time t. In addition, CHP, BB and GSHP also have unit climbing constraints:

[0146]

[0147] Where: ΔS i It is the output ramp limit of the i-th category equipment.

[0148] 3) Energy storage equipment constraints

[0149] For various types of energy storage devices, taking batteries as an example, their operating constraints are:

[0150]

[0151] Where: are the maximum charge and discharge power of the battery respectively; are the upper and lower limits of energy storage capacity respectively.

[0152] S3 uses fuzzy theory to screen the Pareto solution set of the capacity of the candidate energy equipment to obtain the optimal configuration plan of the candidate energy equipment, specifically:

[0153] Suppose that in the domain U, μ A is a function that maps any u∈U to [0,1], that is, μ A :U→[0,1],u→μ A (u), then μ A is the membership function on U, μ A (u) is the membership degree of u relative to the fuzzy set A. Let A={μ A (u)|u∈U}, then A is a fuzzy set on U. The fuzzy set is completely determined by its membership function. According to fuzzy theory, the membership function μ(x) can be used to fuzzify each objective function, and μ(x)∈[0,1]. In this paper, the membership function is:

[0154]

[0155] Where: μ i (x) is the membership function, f i (x) is the objective function; and The maximum and minimum values ​​of each objective function are obtained under single-objective optimization; after obtaining the membership function of each objective function, the single-objective optimization function is obtained by weighted addition of each membership function:

[0156]

[0157] Where: w i is the weight coefficient of each objective function, and w i ≥0,∑w i =1.

[0158] The present invention uses the non-dominated sorting genetic algorithm (NSGA-II) and IBM commercial software Cplex to solve the above model. The upper-level decision variable is equipment capacity, and the objective function is annual operating cost, pollution emissions, and energy utilization efficiency. The lower-level decision variable is equipment output, and the objective function is annual comprehensive operating cost. The optimal configuration scheme of the renewable micro-energy network and its evaluation indicators are output. The model establishment and solution process can be summarized as follows:

[0159] Step 1. Build a renewable micro-energy grid model, inputting NSGA-II algorithm parameters, basic configuration parameters of the selected units, various cost parameters, typical solar heating and cooling load curves for each season, and typical wind and solar output scenarios;

[0160] Step 2. Solve the multi-objective, two-level optimization configuration model, using NSGA-II for the upper-level model and IBM's commercial software Cplex for the lower-level model. The specific solution process is as follows: First, the upper-level model randomly generates decision variables and brings them into the lower-level model. The Cplex solver is called to optimize the equipment output. Based on the lower-level optimization results, the upper-level objective function is calculated and passed to the upper-level model. NSGA-II continues to solve the problem, and this process repeats until the upper-level iteration count exceeds the iteration limit. Finally, the multi-objective optimization configuration Pareto solution set is output.

[0161] Step 3. Based on fuzzy theory, make decisions on the Pareto frontier solution set obtained from the multi-objective optimization configuration model, and output the compromise optimal configuration scheme of the renewable micro-energy network and its multi-dimensional evaluation indicators.

[0162] The specific steps of the two-layer optimization configuration calculation of renewable micro-energy grid are as follows: Figure 4 shown.

[0163] Example 2:

[0164] The present invention is further described below through a calculation example.

[0165] Taking a multi-energy complementary system demonstration project in Jiangxi Province as an example, the proposed method is used to optimize the design of the system. The schematic diagram of the renewable micro-energy network architecture is shown in the figure. Figure 3 According to local climate conditions, the whole year is divided into summer (June to August), winter (December to February) and transition season. The typical solar heating and cooling load curves and typical wind and solar output scenarios of each season are shown as follows: Figure 5 As shown in Table 1, the peak energy consumption in the three seasons is 1382.7kW·h, 1159.3kW·h and 976.4kW·h respectively. The purchase price adopts the local peak-valley time-of-use electricity price, as shown in Table 1. The electricity sales price adopts the benchmark electricity price of new energy on-grid electricity of 0.4593￥ / kW·h. The unit biogas production cost is 0.58￥ / m3. The configurable equipment parameters are shown in Table 2. In the NSGA-II algorithm, the population size is 150, the number of iterations is 100, and the crossover rate and mutation rate are 0.9 and 0.1 respectively.

[0166] Table 1 Time-of-use electricity price list

[0167]

[0168] Table 2 Parameters of energy equipment to be selected

[0169]

[0170]

[0171] The Pareto non-inferior solution set obtained by the NSGA-II optimization algorithm in the grid-connected mode is as follows: Figure 6 As shown in the figure, it can be seen that the proposed method can effectively capture the Pareto frontier of multi-objective planning and design problems for investors to make trade-offs. At the same time, it can be seen that there is an obvious conflict between the economic benefit indicators and environmental protection performance of the micro-energy grid.

[0172] Based on the principle of optimality for each sub-goal, three representative scenarios, Schemes 1, 2, and 3, were selected from the solution set. The optimized configuration results are shown in Table 3. As can be seen, Schemes 1 and 2 have the lowest annual operating costs and the lowest total pollution emissions, respectively. However, Scheme 1's environmental performance and Scheme 2's economic efficiency are also the worst of all the scenarios. This is because Scheme 1 uses fewer expensive renewable energy power generation equipment and energy storage devices, significantly reducing equipment purchase costs. However, this also results in a decrease in the proportion of electricity generated by non-polluting wind and solar energy, shifting to a reliance on biomass energy for power supply, which in turn increases pollutant emissions. Scheme 2 reduces the energy output of biogas combined heat and power units and deploys a large number of clean energy equipment, including WT, PV, and GSHP. The number of energy storage devices also increases, resulting in improved environmental performance at the expense of economic costs. Compared to the previous two scenarios, Scheme 3 utilizes more CHP units, which enable energy cascade utilization, and saves operating costs by selling electricity. It also achieves good energy efficiency. However, the high biogas consumption also causes some damage to the local ecological environment.

[0173] Table 3 Typical solution optimization configuration results

[0174]

[0175]

[0176] To comprehensively consider the investment costs, environmental performance, and operational energy efficiency of renewable micro-energy grids, this paper, based on fuzzy theory, selected the compromise optimization configuration schemes with the highest overall satisfaction, namely, Schemes 4 and 5, under both grid-connected and off-grid operating conditions. These schemes were then compared with the SP system to measure its comprehensive energy supply potential. The optimized configuration results and operating parameters of these systems are shown in Table 4. As can be seen, the SP system offers advantages in terms of energy equipment procurement and operation and maintenance costs due to its simple energy supply structure and low unit equipment price. However, it does not fully utilize local renewable resources and does not achieve cascaded energy utilization, resulting in significantly lower environmental performance and energy utilization efficiency.

[0177] In comparison, Schemes 4 and 5 offer significant advantages in various operational indicators. The grid-connected scheme primarily relies on local renewable resources for energy consumption, with minimal purchased electricity. It also incorporates a certain amount of energy storage to ensure flexible system operation. Regarding energy output, Scheme 4 utilizes relatively inexpensive WT and CHP units as power generation equipment, meeting the load demands of suburban users while also supplying clean electricity to the external grid, resulting in favorable economic returns. Compared to the grid-connected approach, the coupling between multiple energy flows in the off-grid system is more pronounced, so its configuration incorporates additional energy storage to ensure optimal energy distribution across different time periods. This significantly increases operating costs. Furthermore, Scheme 5 reduces installed wind and solar power capacity to reduce total curtailment and relies on GSHP to increase renewable energy consumption in summer and winter, thereby improving system energy efficiency. To maintain system power balance, particularly the balance between electrical and thermal power during the cooling season, Scheme 5 also utilizes absorption chillers more frequently than electric chillers as cooling sources. Since the off-grid system only needs to meet its own energy needs, its biomass gas consumption is low and its environmental benefits are better than those of the grid-connected system.

[0178] Table 4 Comparison of optimization configuration results

[0179]

[0180]

[0181] By referring to the SP system, the system operation indicators of schemes 1 to 5 can be obtained respectively. The results are as follows: Figure 7 As shown in the figure, the proposed scheme significantly improves operational indicators compared to traditional energy supply methods, particularly in terms of low carbon and environmental protection. This demonstrates the significant advantages of multi-energy coupling systems in green suburban energy supply. Furthermore, the fuzzy optimal solution effectively balances multiple indicators, fully tapping the energy supply potential of micro-energy grids and avoiding the blindness of previous single-target planning.

[0182] Example 3

[0183] An optimal configuration system for renewable micro-energy grid, such as Figure 2 As shown, including:

[0184] The data acquisition module is used to obtain the configuration parameters and cost parameters of the selected energy equipment, the electric heating and cooling load curves of typical days in each season, and the typical wind and solar output scenarios in each season;

[0185] a calculation module for inputting configuration parameters and cost parameters of the candidate energy equipment, the electric heating and cooling load curves of typical days in each season, and the typical wind and solar output scenarios in each season into a pre-set optimization configuration model to obtain a Pareto solution set of the capacity of the candidate energy equipment;

[0186] An optimization module, configured to screen the Pareto solution set of the capacity of the candidate energy equipment based on fuzzy theory to obtain an optimal configuration scheme for the candidate energy equipment;

[0187] The optimization configuration model includes: an upper-level optimization configuration model and a lower-level operation optimization scheduling model, wherein the upper-level optimization configuration model is optimally constructed based on the quantity configuration of the selected energy equipment, and the lower-level operation optimization scheduling model is constructed based on the equipment output of the selected energy equipment.

[0188] The energy equipment to be selected includes: photovoltaic equipment, wind power equipment, biogas cogeneration equipment, biogas boilers, ground source heat pumps, electric refrigerators, absorption refrigerators, electricity storage devices, cold storage devices and heat storage devices;

[0189] The configuration parameters of the energy equipment to be selected include: the rated power, output power range, maximum ramping coefficient, dissipation coefficient, and waste heat recovery coefficient of the biogas cogeneration equipment; the rated power, output power range, maximum ramping coefficient, and thermal efficiency of the biogas boiler; the rated power, output power range, maximum ramping coefficient, electric-to-heat conversion efficiency, and heat exchange efficiency of the heat exchange pump; the output power range and energy efficiency ratio of the electric refrigerator and absorption refrigerator; the charging and discharging efficiency, maximum charging and discharging coefficient, dissipation coefficient, and energy storage coefficient of the electric storage device, cold storage device, and heat storage device;

[0190] The cost parameters of the energy equipment to be selected include: purchase cost, installation cost and maintenance cost.

[0191] The system further includes an optimization configuration model setting module, wherein the optimization configuration model setting module includes:

[0192] The upper-level optimization configuration model setting module is used to construct the upper-level optimization configuration objective function based on the pre-set multi-dimensional evaluation indicators of the renewable micro-energy network, with the equipment capacity of the selected energy equipment as the decision variable, and with the minimum annual operating cost, pollution emissions and energy utilization efficiency as the goal; the upper-level optimization configuration model is set with the installation capacity of the selected energy equipment as the upper-level optimization configuration constraint condition;

[0193] The lower-layer operation optimization scheduling model setting module is used to construct the lower-layer operation optimization scheduling objective function based on the multi-dimensional evaluation index of the renewable micro-energy network, with the equipment output of the selected energy equipment as the decision variable, and with the goal of minimizing the procurement cost, maintenance cost and abandoned solar and wind costs of the selected energy equipment; and set the lower-layer operation optimization scheduling model with the bus transmission power constraint, the interactive power constraint with the external interconnection line, the energy coupling device constraint and the energy storage device constraint as the constraint conditions.

[0194] The upper-level optimization configuration objective function is as follows:

[0195] min f1=(C,F,1 / E)

[0196] Among them, C is the annual operating cost, F is the pollution emissions, and E is the energy utilization efficiency.

[0197] Preferably, the calculation formula for the annual operating cost is as follows:

[0198]

[0199] Among them, C EI is the installation cost of the selected energy equipment, C EP is the purchase cost of the selected energy equipment, C EM is the maintenance cost of the selected energy equipment, N i is the installed capacity of the i-th type of energy equipment to be selected; k is the benchmark discount rate, y is the useful life of the energy equipment to be selected, c i is the unit price of the equipment for the i-th type of selected energy, is the unit power purchase price at time t on day d, is the grid tie line interaction power at time t on day d, is the price of electricity per unit power at time t on day d, c bio is the biogas production cost per cubic meter, is the biogas consumption of the micro gas turbine at time t on day d, is the biogas consumption of the biogas boiler at time t on the dth day, is the unit power maintenance cost of the equipment of the i-th type of selected energy source, The output power of the equipment of the i-th type of candidate energy at time t on day d, where D is the number of days in a year and T is the number of hours per day.

[0200] The calculation formula for pollution emissions is as follows:

[0201]

[0202] in, is the grid tie line interaction power at time t on day d, η gen is the power generation efficiency of the power plant, η loss is the transmission line loss rate of the power plant, is the emission coefficient of the jth type of pollutant generated by coal-fired power generation, is the biogas consumption of the micro gas turbine at time t on day d, is the biogas consumption of the biogas boiler at time t on the dth day, q is the standard biogas lower calorific value, is the emission coefficient of the jth type of pollutant produced by biogas combustion, D is the number of days in a year, and T is the number of hours per day.

[0203] The calculation formula for energy utilization efficiency is as follows:

[0204]

[0205] Among them, Q out is the system output energy, Q in is the system input, λ e is the electric energy conversion coefficient, is the grid tie line interaction power at time t on day d, is the rated output power of the photovoltaic power generation unit at time t on day d, The rated output power of the wind power generation unit at time t on day d is: g Biogas energy conversion coefficient, is the biogas consumption of the micro gas turbine at time t on day d, is the biogas consumption of the biogas boiler at time t on the dth day, q is the lower calorific value of the standard biogas, λ p It is the cooling energy conversion system. Input ground source heat energy to the ground source heat exchange pump at time t on day d, is the user's electric load power at time t on the dth day, λ h is the thermal energy conversion coefficient, is the heat load power of the user at time t on day d, λ c is the cooling energy conversion coefficient, is the cooling load power of suburban users at time t on day d.

[0206] The installation capacity constraints of the selected energy equipment are as follows:

[0207]

[0208] in, is the lower limit of the installed capacity of type i equipment, The upper limit of the installed capacity for type i equipment, N i Install capacity for equipment of type i.

[0209] The lower-level operation optimization scheduling objective function is as follows:

[0210]

[0211] Among them, C EP is the purchase cost of the selected energy equipment, C EM is the maintenance cost of the selected energy equipment, C AE Cost of curtailing solar and wind power, is the abandoned optical power, is the wind power curtailment, c PV is the unit power abandonment cost, c WT is the cost of wind curtailment per unit power, and T is the number of hours per day.

[0212] The bus transfer power constraints are as follows:

[0213]

[0214] in, is the actual photovoltaic output power at time t, is the actual wind power output power at time t, is the actual output power of the cogeneration system at time t, is the grid tie line interaction power at time t, is the user's electric load power at time t, is the power consumed by the ground source heat pump at time t, is the power consumed by the electric refrigerator at time t, is the power consumption of the storage device at time t, is the heating power of the cogeneration system at time t, is the heating power of the biogas boiler at time t, is the heating power of the ground source heat pump at time t, is the heat load power of the user at time t, is the heat power absorbed by the absorption refrigerator at time t, is the thermal energy storage output at time t, is the cooling power of the electric refrigerator at time t, is the cooling power of the absorption chiller at time t, is the cold storage energy output of the ground source heat pump at time t, The user's cooling load power, It is the cold energy storage output at time t.

[0215] The interaction power constraints with external tie lines are as follows:

[0216]

[0217] in, is the grid tie line interaction power at time t, It is the upper limit of the power for interaction with the external grid tie line.

[0218] The energy coupling device constraints are as follows:

[0219]

[0220]

[0221] in, is the output of the i-th type of equipment at time t, is the lower limit of the output of the i-th type of equipment at time t, is the output upper limit of the i-th type equipment at time t, is the start and stop status of the i-th type of equipment at time t, is the output of the i-th type of equipment at time t-1, ΔS i It is the output ramp limit of the i-th category equipment.

[0222] The calculation module includes:

[0223] Calculation module 1, for inputting the configuration parameters and cost parameters of the candidate energy equipment into the upper-level optimization configuration model, and solving the upper-level optimization configuration model using a non-dominated sorting genetic algorithm to obtain the candidate energy equipment configuration corresponding to each generation of parent chromosomes;

[0224] Calculation module 2 is used to input the candidate energy device configuration corresponding to each generation of parent chromosomes, the electric heating and cooling load curves of typical days in each season, and the typical wind and solar output scenarios in each season into the lower-level operation optimization scheduling model to obtain the device output under the candidate energy device configuration corresponding to each generation of parent chromosomes;

[0225] Calculation module 3 is used to screen the population of the non-dominated sorting genetic algorithm based on the equipment output under the configuration of the candidate energy equipment corresponding to each generation of parent chromosomes to obtain a new generation of population, until the number of iterations exceeds the pre-set iteration upper limit of the non-dominated sorting genetic algorithm, and obtain the Pareto solution set of the capacity of the candidate energy equipment corresponding to the last generation of population.

[0226] Computing module 3, including:

[0227] An objective function calculation module is used to calculate the objective function value of the upper-level optimization configuration model corresponding to each generation of candidate energy device configuration based on the device output under the candidate energy device configuration corresponding to each generation of parent chromosomes;

[0228] The new population generation module is used to screen the population of the non-dominated sorting genetic algorithm based on the objective function value of the upper-level optimization configuration model corresponding to each generation of candidate energy equipment configuration to obtain a new generation of population.

[0229] Optimization modules, including:

[0230] A membership function calculation module is used to calculate the membership functions corresponding to the annual operating cost, pollution emissions and energy utilization efficiency of the upper-level optimization configuration target based on the Pareto solution set of the capacity of the selected energy equipment;

[0231] A comprehensive membership function calculation module is used to perform weighted operations on the membership functions corresponding to the annual operating costs, pollution emissions, and energy efficiency to obtain a comprehensive membership function;

[0232] The configuration generation module is used to calculate the comprehensive membership corresponding to each solution based on the Pareto solution set of the capacity of the candidate energy equipment and determine the solution corresponding to the maximum comprehensive membership as the optimal configuration scheme for the candidate energy equipment.

[0233] The system also includes an evaluation module, wherein the evaluation module includes: a cost saving rate evaluation module, a pollution reduction rate evaluation module and an energy efficiency improvement rate evaluation module;

[0234] Cost saving rate evaluation module, used to evaluate the cost saving of renewable micro-energy grid with optimal configuration scheme;

[0235] The pollution reduction rate evaluation module is used to evaluate the pollution reduction rate of the renewable micro-energy grid with the optimal configuration scheme;

[0236] The energy efficiency improvement rate evaluation module is used to evaluate the energy efficiency improvement rate of the renewable micro-energy network with the optimal configuration solution.

[0237] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0238] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0239] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0240] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0241] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application and are not intended to limit its scope of protection. Although the present application has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that after reading this application, those skilled in the art may still make various changes, modifications or equivalent substitutions to the specific implementation methods of the application, but these changes, modifications or equivalent substitutions are all within the scope of protection of the pending claims of the application.

Claims

1. A method for optimizing the configuration of a renewable micro-energy network, characterized in that: include: Obtain the configuration parameters and cost parameters of the energy equipment to be selected, the electric heating and cooling load curves for typical days in each season, and the typical wind and solar output scenarios in each season; Input the configuration parameters and cost parameters of the candidate energy equipment, the electric heating and cooling load curves of typical days in each season, and the typical wind and solar output scenarios in each season into a pre-set optimization configuration model to obtain a Pareto solution set of the capacity of the candidate energy equipment; Screening the Pareto solution set of the capacity of the candidate energy equipment based on fuzzy theory to obtain the optimal configuration plan of the candidate energy equipment; The optimization configuration model includes: an upper-layer optimization configuration model and a lower-layer operation optimization scheduling model, wherein the upper-layer optimization configuration model is constructed based on the optimal configuration of the number of energy devices to be selected, and the lower-layer operation optimization scheduling model is constructed based on the optimal output of the energy devices to be selected; The setting of the optimization configuration model includes: Based on the pre-set multi-dimensional evaluation indicators of renewable micro-energy networks, the upper-level optimization configuration objective function is constructed with the equipment capacity of the selected energy equipment as the decision variable and the annual operating cost, pollution emissions and energy utilization efficiency as the minimum goals. The upper-level optimization configuration model is set with the installation capacity of the selected energy equipment as the upper-level optimization configuration constraint condition. Based on the multi-dimensional evaluation index of the renewable micro-energy network, the output of the selected energy equipment is used as the decision variable, and the procurement cost, maintenance cost and curtailment cost of the selected energy equipment are minimized as the goal to construct the lower-level operation optimization scheduling objective function; the bus transmission power constraint, the interaction power constraint with the external tie line, the energy coupling device constraint and the energy storage device constraint are used as the constraint conditions to set the lower-level operation optimization scheduling model; The upper layer optimization configuration objective function is as follows: in, The annual operating costs, is the pollution emission, for energy efficiency; The calculation formula for the pollution emissions is as follows: in, is the grid tie line interaction power at time t on day d, is the power generation efficiency of the power plant, is the transmission line loss rate of the power plant, is the emission coefficient of the jth type of pollutant generated by coal-fired power generation, is the biogas consumption of the micro gas turbine at time t on day d, is the biogas consumption of the biogas boiler at time t on the dth day, q is the standard biogas lower calorific value, is the emission coefficient of the jth type of pollutant generated by biogas combustion, D is the number of days in a year, and T is the number of hours per day; The calculation formula of the energy utilization efficiency is as follows: in, Output energy to the system, Input energy to the system, is the electric energy conversion coefficient, is the grid tie line interaction power at time t on day d, is the rated output power of the photovoltaic power generation unit at time t on day d, The rated output power of the wind power generation unit at time t on day d is: Biogas energy conversion coefficient, is the biogas consumption of the micro gas turbine at time t on day d, is the biogas consumption of the biogas boiler at time t on the dth day, q is the standard biogas lower calorific value, is the geothermal energy conversion coefficient, Input ground source heat energy to the ground source heat exchange pump at time t on day d, is the user's electric load power at time t on day d, is the thermal energy conversion coefficient, is the heat load power of the user at time t on day d, is the cooling energy conversion coefficient, is the cooling load power of the user at time t on day d.

2. The method according to claim 1, wherein The energy equipment to be selected includes: photovoltaic equipment, wind power equipment, biogas cogeneration equipment, biogas boilers, ground source heat pumps, electric refrigerators, absorption refrigerators, power storage devices, cold storage devices and heat storage devices; The configuration parameters of the energy equipment to be selected include: the rated power, output power range, maximum ramping coefficient, dissipation coefficient, and waste heat recovery coefficient of the biogas cogeneration equipment; the rated power, output power range, maximum ramping coefficient, and thermal efficiency of the biogas boiler; the rated power, output power range, maximum ramping coefficient, electric-to-heat conversion efficiency, and heat exchange efficiency of the heat exchange pump; the output power range and energy efficiency ratio of the electric refrigerator and absorption refrigerator; the charging and discharging efficiency, maximum charging and discharging coefficient, dissipation coefficient, and energy storage coefficient of the electric storage device, cold storage device, and heat storage device; The cost parameters of the energy equipment to be selected include: purchase cost, installation cost and maintenance cost.

3. The method according to claim 1, wherein The calculation formula for the annual operating costs is as follows: in, C EI is the installation cost of the selected energy equipment, C EP is the purchase cost of the selected energy equipment, C EM is the maintenance cost of the selected energy equipment, is the installed capacity of the i-th type of energy equipment to be selected; k is the benchmark discount rate, y is the useful life of the energy equipment to be selected, is the unit price of the equipment for the i-th type of selected energy, is the unit power purchase price at time t on day d, is the grid tie line interaction power at time t on day d, is the price of electricity per unit power at time t on day d, is the biogas production cost per cubic meter, is the biogas consumption of the micro gas turbine at time t on day d, is the biogas consumption of the biogas boiler at time t on the dth day, is the unit power maintenance cost of the equipment of the i-th type of selected energy source, The output power of the equipment of the i-th type of candidate energy at time t on day d, where D is the number of days in a year and T is the number of hours per day.

4. The method according to claim 1, wherein The installation capacity constraints of the selected energy equipment are as follows: in, is the lower limit of the installed capacity of type i equipment, The upper limit of installed capacity for category i equipment, Install capacity for equipment of type i.

5. The method according to claim 1, wherein The lower-level operation optimization scheduling objective function is as follows: in, C EP is the purchase cost of the selected energy equipment, C EM is the maintenance cost of the selected energy equipment, C AE Cost of curtailing solar and wind power, is the abandoned optical power, is the wind power curtailment, is the unit power abandonment cost, is the cost of wind curtailment per unit power, and T is the number of hours per day.

6. The method according to claim 1, wherein The bus transfer power constraints are as follows: in, is the actual photovoltaic output power at time t, is the actual wind power output power at time t, is the actual output power of the cogeneration system at time t, is the grid tie line interaction power at time t, is the user's electric load power at time t, is the power consumed by the ground source heat pump at time t, is the power consumed by the electric refrigerator at time t, is the power consumption of the storage device at time t, is the heating power of the cogeneration system at time t, is the heating power of the biogas boiler at time t, is the heating power of the ground source heat pump at time t, is the heat load power of the user at time t, is the heat power absorbed by the absorption refrigerator at time t, is the thermal energy storage output at time t, is the cooling power of the electric refrigerator at time t, is the cooling power of the absorption chiller at time t, is the cold storage energy output of the ground source heat pump at time t, The user's cooling load power, It is the cold energy storage output at time t.

7. The method according to claim 1, wherein The interaction power constraint with the external tie line is as follows: in, is the grid tie line interaction power at time t, It is the upper limit of the power for interaction with the external grid tie line.

8. The method according to claim 1, wherein The energy coupling device constraint is as follows: in, is the output of the i-th type of equipment at time t, is the lower limit of the output of the i-th type of equipment at time t, is the output upper limit of the i-th type equipment at time t, is the start and stop status of the i-th type of equipment at time t, is the output of the i-th type of equipment at time t-1, It is the output ramp limit of the i-th category equipment.

9. The method according to claim 1, wherein The configuration parameters and cost parameters of the candidate energy equipment, the electric heating and cooling load curves of typical days in each season, and the typical wind and solar output scenarios in each season are input into a pre-set optimization configuration model to obtain a Pareto solution set of the capacity of the candidate energy equipment, including: Inputting the configuration parameters and cost parameters of the candidate energy equipment into the upper-level optimization configuration model, solving the upper-level optimization configuration model using a non-dominated sorting genetic algorithm to obtain the candidate energy equipment configuration corresponding to each generation of parent chromosomes; Input the candidate energy equipment configuration corresponding to each generation of parent chromosomes, the electric heating and cooling load curves of typical days in each season, and the typical wind and solar output scenarios in each season into the lower-level operation optimization scheduling model to obtain the equipment output under the candidate energy equipment configuration corresponding to each generation of parent chromosomes; Based on the equipment output under the candidate energy equipment configuration corresponding to each generation of parent chromosomes, the population of the non-dominated sorting genetic algorithm is screened to obtain a new generation of population, until the number of iterations exceeds the pre-set iteration upper limit of the non-dominated sorting genetic algorithm, and the Pareto solution set of the candidate energy equipment capacity corresponding to the last generation of population is obtained.

10. The method according to claim 9, wherein The device output under the selected energy device configuration corresponding to each generation of parent chromosomes is used to screen the population of the non-dominated sorting genetic algorithm to obtain a new generation of population, including: Based on the equipment output under the candidate energy equipment configuration corresponding to each generation of parent chromosomes, the objective function value of the upper-level optimization configuration model corresponding to each generation of candidate energy equipment configuration is calculated; Based on the objective function value of the upper-level optimization configuration model corresponding to each generation of candidate energy equipment configuration, the population of the non-dominated sorting genetic algorithm is screened to obtain a new generation of population.

11. The method according to claim 1, wherein The method of screening the Pareto solution set of the capacity of the candidate energy equipment based on fuzzy theory to obtain the optimal configuration scheme of the candidate energy equipment includes: Based on the Pareto solution set of the capacity of the selected energy equipment, the corresponding membership functions of the upper-level optimization configuration target annual operating cost, pollution emissions and energy utilization efficiency are calculated respectively; Performing a weighted operation on the membership functions corresponding to the annual operating cost, pollution emissions, and energy efficiency to obtain a comprehensive membership function; Based on the Pareto solution set of the capacity of the energy equipment to be selected, the comprehensive membership corresponding to each solution is calculated and the solution corresponding to the maximum comprehensive membership is determined as the optimal configuration solution for the energy equipment to be selected.

12. The method according to claim 1, wherein After obtaining the optimal configuration scheme of the selected energy equipment, the method further includes: evaluating the optimized configuration result using the multi-dimensional evaluation index of the renewable micro-energy network; The multi-dimensional evaluation indicators of the renewable micro-energy network include: cost saving rate indicator, pollution emission reduction rate indicator and energy efficiency improvement rate indicator.

13. The method according to claim 12, wherein: The cost saving rate is calculated as follows: in, is the cost saving rate, is the annual operating cost of the renewable micro-energy grid system, is the annual operating cost of the energy distribution system.

14. The method according to claim 12, wherein: The pollution reduction rate is calculated as follows: in, is the pollution reduction rate, is the pollution emission of the renewable micro-energy grid system, It is the pollution emission of energy production system.

15. The method according to claim 12, wherein The energy efficiency improvement rate is calculated as follows: in, is the energy efficiency improvement rate, For the energy utilization efficiency of renewable micro-energy grid system, Energy utilization efficiency of energy distribution system.

16. A renewable micro-energy network optimization configuration system, used to implement the method according to claim 1, characterized in that: include: The data acquisition module is used to obtain the configuration parameters and cost parameters of the selected energy equipment, the electric heating and cooling load curves of typical days in each season, and the typical wind and solar output scenarios in each season; a calculation module for inputting configuration parameters and cost parameters of the candidate energy equipment, the electric heating and cooling load curves of typical days in each season, and the typical wind and solar output scenarios in each season into a pre-set optimization configuration model to obtain a Pareto solution set of the capacity of the candidate energy equipment; An optimization module, configured to screen the Pareto solution set of the capacity of the candidate energy equipment based on fuzzy theory to obtain an optimal configuration scheme for the candidate energy equipment; The optimization configuration model includes: an upper-level optimization configuration model and a lower-level operation optimization scheduling model, wherein the upper-level optimization configuration model is optimally constructed based on the quantity configuration of the selected energy equipment, and the lower-level operation optimization scheduling model is optimally constructed based on the equipment output of the selected energy equipment.

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