Electric heating micro-grid park planning method and system based on combination of weighting and fuzzy synthesis

Abstract

The invention discloses an electrothermal micro-grid park planning method and system based on combination of weighting and fuzzy synthesis, and the method comprises an electrothermal micro-grid park energy hub structure. Establishing a micro-grid park integrated energy system planning model, establishing a multi-level evaluation index system based on the micro-grid park integrated energy system planning model, obtaining and preprocessing each evaluation index in the multi-level evaluation index system, endowing each evaluation index with a comprehensive dynamic weight, and performing fuzzy comprehensive evaluation based on the comprehensive dynamic weight and fuzzy comprehensive evaluation. And obtaining a fuzzy evaluation result of the micro-grid park integrated energy system planning model. The method has the outstanding advantages of high practicability, high effectiveness, large application range and the like.

Description

Technical Field

[0001] The present invention relates to a method and system for planning an electro-thermal microgrid park based on combined weighting and fuzzy synthesis, belonging to the field of integrated energy system planning. Background Technique

[0002] With the increasing global attention to energy efficiency and sustainable development, electro-thermal microgrid parks, as an integrated energy system, have received more and more attention. Such a park system not only covers various energy supply and storage methods, but also needs to achieve an optimal balance between complex energy demands and supplies. To achieve the best economic and environmental benefits, the planning of electro-thermal microgrid parks must be based on scientific evaluation methods and optimization models.

[0003] In this context, the method of combined weighting and fuzzy comprehensive evaluation has played an important role in the planning of electro-thermal microgrid parks. These methods provide theoretical support and practical guidance for system optimization through multi-dimensional and comprehensive evaluation of planning schemes. Combining weighting and fuzzy comprehensive evaluation can handle complex factors involved in the planning process, including economic benefits, system energy efficiency, flexibility, and low-carbon operation, so as to achieve a comprehensive analysis and optimization of the overall park planning scheme.

[0004] However, although the existing evaluation models have made remarkable progress in optimizing the planning of electro-thermal microgrid parks, they still face some challenges in practical applications. Especially in the refined modeling of steam accumulators in energy systems, traditional methods often fail to accurately capture their complex dynamic characteristics. During the operation of the steam accumulator system, due to the coexistence of vapor and liquid phases inside, various mass and energy transfer and flow processes are triggered, which significantly increases the non-linearity of the model. This complexity makes the optimal scheduling of the electro-steam coupling system more difficult. In addition, there are significant heat losses during the production, storage, and transmission of steam, which not only require additional heating to supplement, but may also lead to an increase in the peak load of the power grid, further affecting the scheduling accuracy of the electro-steam coupling system. Therefore, when facing the heat transfer process of steam accumulators, traditional dynamic modeling methods may not be able to accurately describe their heat storage characteristics while keeping the computational complexity controllable.

[0005] CN112653121A discloses an evaluation method and device for the frequency modulation ability of a new energy microgrid participating in the power grid. This patent includes: obtaining the judgment matrix of the first-level indicators in the microgrid evaluation system, as well as the judgment matrix of the second-level indicators included in each first-level indicator. The judgment matrix is used to represent the importance degree between indicators; determining the weight vector of the corresponding indicators according to the judgment matrix; quantifying the indicator values of the second-level indicators; according to the quantified second-level indicator values and the weight vector of the second-level indicators, obtaining the evaluation value of the corresponding first-level indicator after weighting, and then combining with the weight vector of the first-level indicators to obtain the evaluation value of the frequency modulation ability of the new energy microgrid after weighting. Although this patent performs hierarchical processing in indicator evaluation through the judgment matrix and weight vector, it is still insufficient in the breadth and depth of multi-dimensional comprehensive evaluation. It fails to comprehensively consider the complex interaction of various factors, resulting in low effectiveness and a small scope of application of this patent.

[0006] CN110826228A discloses a method for evaluating the operation quality limit of a regional power grid. Starting from the telemetry and telecontrol data of the power system and the characteristics of the primary and secondary equipment of the power grid, this patent establishes economic operation indicators, voltage fluctuation indicators, fault frequency indicators, frequent operation indicators, and life loss indicators of the regional power grid to comprehensively evaluate the operation quality of the regional power grid; this patent analyzes various types of power data related to the operation quality of the regional power grid by constructing a steady-state simulation model PSSM of the regional power grid based on PandaPower, combining the optimal power flow method OPF and the principal component analysis method, and uses the limit evaluation method to realize the comprehensive evaluation of the operation quality of the regional power grid based on the current operation parameters of the system. Although this patent provides an evaluation of the power grid operation through the optimal power flow and principal component analysis methods, it lacks the comprehensive optimization ability for the overall system of the electro-thermal microgrid, especially in the scenario of electro-thermal coupling. Therefore, the practicality and effectiveness of this patent are not strong. Summary of the Invention

[0007] To solve the deficiencies in the prior art such as weak practicality, low effectiveness, and small scope of application, the present invention provides an electro-thermal microgrid park planning method and system based on combined weighting and fuzzy synthesis.

[0008] The present invention adopts the following technical solutions.

[0009] On the one hand, the present invention discloses an electro-thermal microgrid park planning method based on combined weighting and fuzzy synthesis, including:

[0010] Step 1: Based on the energy hub structure of the electro-thermal microgrid park, establish a comprehensive energy system planning model for the microgrid park;

[0011] Step 2: Establish a multi-level evaluation index system based on the comprehensive energy system planning model of the microgrid park;

[0012] Step 3: Obtain each evaluation index in the multi-level evaluation index system and perform preprocessing, and assign a comprehensive dynamic weight to each evaluation index;

[0013] Step 4: Based on the comprehensive dynamic weight and fuzzy comprehensive evaluation, obtain the fuzzy evaluation result of the integrated energy system planning model for the microgrid park.

[0014] Further preferably,

[0015] In step 1, the integrated energy system planning model for the microgrid park includes a comprehensive objective function and comprehensive operation constraints.

[0016] Further preferably,

[0017] The comprehensive objective function C is shown as follows:

[0018] C = min[C1 + C2 + C3];

[0019] Among them, C1 is the investment cost; C2 is the interaction cost between the microgrid and the power grid and the natural gas station; C3 is the maintenance cost; min[·] represents taking the minimum value within the brackets.

[0020] Further preferably,

[0021] Among them, the interaction cost C2 between the microgrid and the power grid and the natural gas station is shown as follows:

[0022]

[0023] Among them, t represents the time period to which the scheduling moment belongs; T is the total number of time periods in the daily scheduling cycle; is the electricity purchase price from the superior power grid; is the electricity selling price to the superior power grid; is the interaction power between the microgrid and the superior power grid at time t; is the gas purchase price of the natural gas station; is the gas selling price of the natural gas station; is the interaction power between the microgrid and the natural gas station at time t.

[0024] Further preferably,

[0025] The comprehensive operation constraints include installed capacity planning constraints, upper and lower limits of operation constraints for each equipment unit, power balance constraints, air source heat pump operation constraints, cold air conditioner operation constraints, electric boiler operation constraints, gas boiler operation constraints, photovoltaic generator output constraints and wind turbine output constraints, cold storage operation constraints, industrial steam boiler operation constraints.

[0026] Further preferably,

[0027] Among them, the operation constraints of the air source heat pump are as follows:

[0028]

[0029] In the formula, represents the thermal energy generated by the air-source heat pump; represents the cold energy generated by the air-source heat pump; COPh A represents the heating coefficient of the air-source heat pump; COPc A represents the refrigeration coefficient of the air-source heat pump; P t ASHP,H is the heating power of the heat pump in the t period; P t ASHP,C is the refrigeration power of the heat pump in the t period; P t ASHP represents the total energy generated by the air-source heat pump; is the heating operation state variable of the air-source heat pump unit in the t period; are respectively the refrigeration operation state variables of the air-source heat pump unit in the t period; when the air-source heat pump is heating when the air-source heat pump is refrigerating

[0030] Further preferably,

[0031] In step 2, the multi-level evaluation index system includes a composite recovery index, a system energy efficiency index, a system flexibility index, and a low-carbon operation index;

[0032] Among them, the composite recovery index is constructed based on the comprehensive objective function;

[0033] The system energy efficiency index is constructed based on the power consumption and natural gas consumption in the park;

[0034] The system flexibility index includes the steam equivalent energy storage utilization rate and the grid interaction fluctuation amount;

[0035] The low-carbon operation index is constructed based on the actual carbon emission values during the power generation of the grid coal-fired power unit and the operation of the gas boiler.

[0036] Further preferably,

[0037] Among them, the system energy efficiency index is shown as the following formula:

[0038]

[0039] Among them, P e (t) is the power consumption of the park in the t period; Q gas (t) is the natural gas consumption of the park at time t; λ gas represents the low calorific value of natural gas; ε is the power generation efficiency of the coal-fired unit; is the transmission process loss rate; is the conversion standard coal parameter of natural gas; The standard coal parameter for the conversion of electric energy.

[0040] Further preferably,

[0041] Among them, the grid interaction fluctuation amount is shown in the following formula:

[0042]

[0043] Among them, ΔP is the grid interaction fluctuation amount; P e (t) is the power consumption of the park in the t period; P sell (t) is the power sales volume of the park at time t.

[0044] Further preferably,

[0045] In step 3, the method for assigning comprehensive dynamic weights to each evaluation index is as follows:

[0046] Set that there are n dimensions for evaluating the importance of indicators, obtain the importance degree of each indicator under each evaluation dimension, and calculate the first weight of each indicator under each evaluation dimension;

[0047] Take the maximum first weight value of each indicator under each dimension as the reference weight value of the corresponding indicator;

[0048] Calculate the relative distance between each indicator and the reference weight value, and then obtain the comprehensive dynamic weight of each indicator.

[0049] Further preferably,

[0050] The calculation of the first weight of each indicator under each evaluation dimension is to sort the importance degree of each indicator under each evaluation dimension and calculate the first weight of the indicator according to the following formula:

[0051]

[0052] Among them, k is an integer and k ∈ [2, j]; j is an integer and j ∈ [2, m], where m is the total number of indicators; l is an integer and l ∈ [k, j]; s h is an integer and s h ∈ [1, n], where n is the total number of dimensions; is the weight corresponding to the indicator with the j-th largest importance degree under the s h -th evaluation dimension; r l sh is shown in the following formula:

[0053]

[0054] Among them is the importance degree of the indicator with the (l - 1)-th largest importance degree under the s h -th evaluation dimension; For the sth h importance degree of the l-th most important indicator under the evaluation dimension.

[0055] Further preferably,

[0056] The comprehensive dynamic weight ω of each indicator i is shown as follows:

[0057]

[0058] where i is an integer and i ∈ [1, m]; m is the total number of indicators; D i0 is the relative distance between the i-th indicator and the reference weight value.

[0059] Further preferably,

[0060] In step 4, based on the comprehensive dynamic weight and fuzzy comprehensive evaluation, the method for obtaining the fuzzy evaluation result of the microgrid park integrated energy system planning model is as follows:

[0061] Determine the fuzzy comprehensive evaluation set; the fuzzy comprehensive evaluation set includes an indicator set, a weight vector, and an evaluation set; where the evaluation set includes a first comment, a second comment, a third comment, and a fourth comment

[0062] The weight vector Ω is shown as follows:

[0063] Ω = [ω 1 , ω 2 ,..., ω m ;

[0064] ω 1 is the comprehensive dynamic weight of the first indicator; ω 2 is the comprehensive dynamic weight of the second indicator; ω m is the comprehensive dynamic weight of the m-th indicator;

[0065] Collect the observed sample values of each indicator in the indicator set and perform preprocessing; calculate the membership degree values of each indicator observation sample for each indicator based on the set membership function classification parameters, and then form a fuzzy evaluation matrix; the set membership function classification parameters include a first membership function parameter, a second membership function parameter, a third membership function parameter, and a fourth membership function parameter;

[0066] By multiplying the weight vector and the fuzzy evaluation matrix, the result of the fuzzy evaluation can be obtained.

[0067] Further preferably,

[0068] The fuzzy evaluation matrix R is shown as follows:

[0069]

[0070] where, r 11 is the membership degree value of the observation sample of the first index to the first index; r 14 is the membership degree value of the observation sample of the first index to the fourth index; r m1 is the membership degree value of the observation sample of the m-th index to the first index; r m4 is the membership degree value of the observation sample of the m-th index to the fourth index;

[0071] The membership degree value r of the observation sample of the i-th index to the j com -th index is as follows: ijcom As shown in the following formula:

[0072]

[0073] where, i = 1, 2,..., m; m is the total number of indexes; j com = 1, 2, 3, 4; q = 1,..., n sa ; n sa is the total number of observation samples of each index.

[0074] Further preferably,

[0075] It is calculated according to the following four formulas:

[0076] When j com = 1:

[0077] where, is the membership degree value of the q-th observation sample of the i-th index to the first membership degree; is the preprocessing result of the q-th observed value of the i-th index; a 1 is the first membership function parameter; a 2 is the second membership function parameter;

[0078] When j com = 2:

[0079] where, is the membership degree value of the q-th observation sample of the i-th index to the second membership degree; a 3 is the third membership function parameter;

[0080] When j com = 3:

[0081] where, is the membership degree value of the q-th observation sample of the i-th index to the third membership degree; a4 is the parameter of the fourth membership function;

[0082] When j com = 4:

[0083]

[0084] wherein, is the membership degree value of the q-th observation sample of the i-th index to the fourth comment.

[0085] On the other hand, the present invention discloses a combined weight and fuzzy comprehensive-based electric-heat microgrid park planning system for an electric-heat microgrid park planning method, including a system planning model establishment module, a multi-level evaluation index system construction module, a comprehensive dynamic weight assignment module, and a system planning model fuzzy evaluation module:

[0086] The system planning model establishment module establishes a comprehensive energy system planning model for the microgrid park based on the energy hub structure of the electric-heat microgrid park;

[0087] The multi-level evaluation index system construction module establishes a multi-level evaluation index system based on the comprehensive energy system planning model of the microgrid park;

[0088] The comprehensive dynamic weight assignment module obtains each evaluation index in the multi-level evaluation index system and performs preprocessing, and assigns a comprehensive dynamic weight to each evaluation index;

[0089] The system planning model fuzzy evaluation module obtains a fuzzy evaluation result of the comprehensive energy system planning model of the microgrid park based on the comprehensive dynamic weight and fuzzy comprehensive evaluation.

[0090] On the other hand, the present application discloses an electronic device, including a processor and a storage medium; characterized in that:

[0091] The storage medium is used to store instructions;

[0092] The processor is used to operate according to the instructions to execute the electric-heat microgrid park planning method based on the combination of weight assignment and fuzzy synthesis described above.

[0093] The present application also discloses a computer-readable storage medium, on which a computer program is stored, characterized in that the program, when executed by a processor, implements the electric-heat microgrid park planning method based on the combination of weight assignment and fuzzy synthesis.

[0094] The beneficial effects of the present invention are as follows. Compared with the prior art:

[0095] The beneficial effects of this patent lie in providing a comprehensive evaluation method and system for the planning scheme of an electro-thermal microgrid park. By combining weight assignment and fuzzy comprehensive evaluation, a comprehensive index system is established, covering multiple aspects such as economic benefits, system energy efficiency, flexibility, and low-carbon operation. This method not only improves the scientificity and accuracy of the evaluation but also provides an effective tool for decision-makers to optimize the planning scheme of the electro-thermal microgrid, thereby promoting sustainable energy management and environmental protection and achieving a win-win situation for economic and ecological benefits.

[0096] To solve these problems, the present invention proposes an electro-thermal microgrid park planning method based on the combination of weight assignment and fuzzy synthesis. By deeply analyzing the operating characteristics of the steam accumulator, the traditional modeling method is improved. This method not only improves the simulation accuracy of the dynamic heat storage process of the steam accumulator but also optimizes the scheduling strategy of the electric-steam coupling system. The method of combining weight assignment and fuzzy comprehensive evaluation can more accurately evaluate the overall planning effect of the electro-thermal microgrid park, thereby achieving more efficient energy management and system optimization. This innovation provides a solid foundation for the application and development of electro-thermal microgrid technology and promotes the sustainable development of the energy system.

[0097] In addition, the present invention also has prominent advantages such as strong practicability, high effectiveness, and a large scope of application. BRIEF DESCRIPTION OF THE DRAWINGS

[0098] Figure 1 It is a schematic flow chart of an electro-thermal microgrid park planning method based on the combination of weight assignment and fuzzy synthesis;

[0099] Figure 2 It is a schematic structural diagram of the energy hub of an electro-thermal microgrid park provided by an embodiment of the present invention;

[0100] Figure 3 It is a schematic flow chart of the comprehensive evaluation index system provided by an embodiment of the present invention;

[0101] Figure 4 It is a schematic diagram of the establishment of the evaluation index system provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0102] To make the objectives, technical solutions, and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. The embodiments described in this application are only a part of the embodiments of the present invention, not all of them. Based on the spirit of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.

[0103] This application discloses an electro-thermal microgrid park planning method based on the combination of weight assignment and fuzzy synthesis. Refer to the attached Figure 1, including:

[0104] Step 1: Based on the energy hub structure of the electro-thermal microgrid park, establish a comprehensive energy system planning model for the microgrid park;

[0105] The comprehensive energy system planning model for the microgrid park includes a comprehensive objective function and comprehensive operation constraints.

[0106] The comprehensive objective function C is shown as follows:

[0107] C = min[C1 + C2 + C3];

[0108] Among them, C1 is the investment cost; C2 is the interaction cost between the microgrid and the power grid and the gas station; C3 is the maintenance cost; min[·] represents taking the minimum value within the brackets.

[0109] Among them, the interaction cost C2 between the microgrid and the power grid and the gas station is shown as follows:

[0110]

[0111] Among them, t represents the time period to which the scheduling moment belongs; T is the total number of time periods in the daily scheduling cycle; is the electricity purchase price from the superior power grid; is the electricity selling price to the superior power grid; P t grid is the interaction power between the microgrid and the superior power grid at time t; is the gas purchase price of the gas station; is the gas selling price of the gas station; is the interaction power between the microgrid and the gas station at time t.

[0112] The comprehensive operation constraints include installed capacity planning constraints, upper and lower limits of operation constraints for each equipment unit, power balance constraints, air source heat pump operation constraints, cold air conditioner operation constraints, battery operation constraints, electric boiler operation constraints, gas boiler operation constraints, photovoltaic generator output constraints, wind turbine output constraints, cold storage operation constraints, and industrial steam boiler operation constraints.

[0113] Among them, the air source heat pump operation constraints are as follows:

[0114]

[0115] In the formula, represents the thermal energy generated by the air source heat pump; represents the cold energy generated by the air source heat pump; COPh A represents the heating coefficient of the air source heat pump; COPc A represents the refrigeration coefficient of the air source heat pump; P t ASHP,His the heating power of the heat pump during period t; P t ASHP,C is the cooling power of the heat pump during period t; P t ASHP represents the total energy produced by the air source heat pump; is the heating operation status variable of the air source heat pump unit during period t; are respectively the cooling operation status variables of the air source heat pump unit during period t; When the air source heat pump is heating When the air source heat pump is cooling

[0116] Step 2: Establish a multi-level evaluation index system based on the integrated energy system planning model of the microgrid park;

[0117] The multi-level evaluation index system includes a composite recovery index, a system energy efficiency index, a system flexibility index, and a low-carbon operation index;

[0118] Among them, the composite recovery index is constructed based on the comprehensive objective function;

[0119] The system energy efficiency index is constructed based on the power consumption and natural gas consumption of the park;

[0120] The system energy efficiency index is shown as follows:

[0121]

[0122] Among them, P e (t) is the power consumption of the park during period t; Q gas (t) is the natural gas consumption of the park at time t; λ gas represents the lower calorific value of natural gas; ε is the power generation efficiency of the coal-fired unit; is the power transmission process loss rate; is the converted standard coal parameter of natural gas; is the converted standard coal parameter of electric energy.

[0123] The system flexibility index includes the steam equivalent energy storage utilization rate and the grid interaction fluctuation amount;

[0124] Among them, the grid interaction fluctuation amount is shown as follows:

[0125]

[0126] Among them, ΔP is the grid interaction fluctuation amount; P e (t) is the power consumption of the park during period t; P sell (t) is the power sales volume of the park at time t.

[0127] The low-carbon operation index is constructed based on the actual carbon emission values during the power generation of the grid coal-fired unit and the operation of the gas boiler.

[0128] Step 3: Obtain each evaluation index in the multi-level evaluation index system and perform preprocessing, and assign a comprehensive dynamic weight to each evaluation index;

[0129] The method for assigning a comprehensive dynamic weight to each evaluation index is as follows:

[0130] Set that there are n dimensions for evaluating the importance of indicators, obtain the importance degree of each indicator under each evaluation dimension, and calculate the first weight of each indicator under each evaluation dimension;

[0131] The calculation of the first weight of each indicator under each evaluation dimension is to sort the importance degree of each indicator under each evaluation dimension, and calculate the first weight of the indicator according to the following formula:

[0132]

[0133] where k is an integer, and k ∈ [2, j]; j is an integer, and j ∈ [2, m], m is the total number of indicators; l is an integer, and l ∈ [k, j]; s h is an integer, and s h ∈ [1, n], n is the total number of dimensions; is the weight corresponding to the indicator with the j-th largest importance degree under the s h -th evaluation dimension; r l sh As shown in the following formula:

[0134]

[0135] where is the importance degree of the indicator with the (l - 1)-th largest importance degree under the s h -th evaluation dimension; ξ l sh is the importance degree of the indicator with the l-th largest importance degree under the s h -th evaluation dimension.

[0136] Take the maximum first weight value of each indicator under each dimension as the reference weight value of the corresponding indicator;

[0137] Calculate the relative distance between each indicator and the reference weight value, and then obtain the comprehensive dynamic weight of each indicator.

[0138] The comprehensive dynamic weight ω i of each indicator is as shown in the following formula:

[0139]

[0140] where i is an integer, and i ∈ [1, m]; m is the total number of indicators; D i0 is the relative distance between the i-th indicator and the reference weight value.

[0141] Step 4: Based on the comprehensive dynamic weight and fuzzy comprehensive evaluation, obtain the fuzzy evaluation result of the integrated energy system planning model for the microgrid park. The method for obtaining the fuzzy evaluation result of the integrated energy system planning model for the microgrid park based on the comprehensive dynamic weight and fuzzy comprehensive evaluation is as follows:

[0142] Determine the fuzzy comprehensive evaluation set; the fuzzy comprehensive evaluation set includes an index set, a weight vector, and an evaluation set; where the evaluation set includes a first comment, a second comment, a third comment, and a fourth comment

[0143] The weight vector Ω is shown as follows:

[0144] Ω = [ω 1 , ω 2 ,..., ω m ;

[0145] ω 1 is the comprehensive dynamic weight of the first index; ω 2 is the comprehensive dynamic weight of the second index; ω m is the comprehensive dynamic weight of the m-th index;

[0146] Collect the observed sample values of each index in the index set and perform preprocessing; calculate the membership degree values of the observed samples of each index to each index based on the set membership function classification parameters, and then form a fuzzy evaluation matrix; the set membership function classification parameters include a first membership function parameter, a second membership function parameter, a third membership function parameter, and a fourth membership function parameter;

[0147] The fuzzy evaluation matrix R is shown as follows:

[0148]

[0149] where r 11 is the membership degree value of the observed sample of the first index to the first index; r 14 is the membership degree value of the observed sample of the first index to the fourth index; r m1 is the membership degree value of the observed sample of the m-th index to the first index; r m4 is the membership degree value of the observed sample of the m-th index to the fourth index;

[0150] The membership degree value r com of the observed sample of the i-th index to the j ijcom -th index is shown as follows:

[0151]

[0152] where \(i = 1, 2, \ldots, m\); \(m\) is the total number of indicators; \(j\) com = 1, 2, 3, 4; \(q = 1, \ldots, n\) sa ; \(n\) sa is the total number of observed samples for each indicator. Calculate according to the following four formulas:

[0153] When \(j\) com = 1:

[0154] where is the membership degree value of the \(q\)th observed sample of the \(i\)th indicator to the first comment; is the preprocessing result of the \(q\)th observed value of the \(i\)th indicator; \(a\) 1 is the first membership function parameter; \(a\) 2 is the second membership function parameter;

[0155] When \(j\) com = 2:

[0156] where is the membership degree value of the \(q\)th observed sample of the \(i\)th indicator to the second comment; \(a\) 3 is the third membership function parameter;

[0157] When \(j\) com = 3:

[0158] where is the membership degree value of the \(q\)th observed sample of the \(i\)th indicator to the third comment; \(a\) 4 is the fourth membership function parameter;

[0159] When \(j\) com = 4:

[0160]

[0161] where is the membership degree value of the \(q\)th observed sample of the \(i\)th indicator to the fourth comment.

[0162] By multiplying the weight vector and the fuzzy evaluation matrix, the result of the fuzzy evaluation can be obtained.

[0163] Example 1

[0164] A method for planning an electric heating microgrid park based on combined weighting and fuzzy synthesis proposed by the present invention, please refer to Figure 1 , including the following steps:

[0165] Step 1: According to the energy hub structure of the electric-heat microgrid park, establish a comprehensive energy system planning model for the microgrid park, and conduct refined modeling on the equivalent energy storage of the steam accumulator in the microgrid park considering valley electricity heat storage.

[0166] Please refer to Figure 2 , in Step 1 of the present invention, in the energy hub structure of the electric-heat microgrid park, the power grid, battery, photovoltaic power generation, and wind turbine power generation can all supply power to the electric-heat microgrid park to meet the electricity demands of cold air conditioners, electric boilers, ASHP air source heat pump units, and other electrical loads in the electric-heat microgrid park. When there is surplus electricity from photovoltaic power generation and wind turbine power generation, the electricity is stored in the battery; the cold air conditioner converts electricity into cold energy; the electric boiler converts electricity into heat energy; the air source heat pump unit converts electricity into heat energy or cold energy; the natural gas station supplies natural gas to the electric-heat microgrid park, and the supplied natural gas is converted into heat energy by the gas boiler or, after passing through the gas turbine, is converted into heat energy by the waste heat boiler; the heat energy output by the electric boiler, air source heat pump unit, waste heat boiler, gas boiler, and accumulator is used for the heat load of the electric-heat microgrid park; when there is still surplus heat energy after the heat energy output by the electric boiler, air source heat pump unit, waste heat boiler, and gas boiler is used for the heat load, the surplus heat energy is stored in the accumulator; the cold energy output by the cold air conditioner, air source heat pump unit, and cold storage is used for the cold load of the electric-heat microgrid park; when there is still surplus cold energy after the cold energy output by the cold air conditioner and air source heat pump unit is used for the cold load, the surplus cold energy is stored in the cold storage.

[0167] The comprehensive energy system planning model for the microgrid park includes a comprehensive objective function, comprehensive operation constraints, and a steam equivalent energy storage model.

[0168] 1.1 The comprehensive objective function C is shown as follows:

[0169] C = min[C1 + C2 + C3];

[0170] Among them, C1 is the investment cost; C2 is the interaction cost between the microgrid and the power grid and natural gas station; C3 is the maintenance cost; the first three items constitute the cost model; min[·] represents taking the minimum value within the brackets.

[0171] Among them, k ∈ {PV, WT, EB, GB, ES, ASHP, HS, CS, SA, ESB, AIR}, where k is the equipment type; PV represents the photovoltaic unit, WT represents the wind turbine unit, EB represents the electric boiler unit, GB represents the gas boiler, ES represents the battery unit, ASHP represents the air source heat pump unit, HS represents the accumulator unit, CS represents the cold storage unit, SA represents the steam accumulator unit, ESB represents the industrial steam boiler unit, and AIR represents the cold air conditioner unit;

[0172]

[0173] Among them, the subscript t represents the time period to which the scheduling moment belongs; T is the total number of time periods in the daily scheduling cycle; r is the discount rate of each unit equipment; in the present invention, the discount rate of the equipment is uniformly set to 80%; Z is the life cycle of the equipment; in the present invention, the life cycle of each equipment is uniformly set to 20 years. is the unit investment planning cost of the k-th type of equipment, that is, the unit expected investment cost; M is the number of equipment types in the integrated energy system; is the planned installed capacity of the k-th type of equipment; P t PV is the output of the photovoltaic unit in the t-th time period; P t WT is the output of the wind turbine generator in the t-th time period; P t grid is the interactive power between the microgrid and the superior power grid in the t-th time period, which is negative when the microgrid buys electricity and positive when it sells electricity; is the interactive power between the microgrid and the superior natural gas station in the t-th time period, which is negative when the microgrid buys gas and positive when it sells gas; P t ASHP is the electric power consumed by the air source heat pump; is the electricity purchase price of the power grid; is the electricity selling price of the power grid; is the gas purchase price of the natural gas station; is the gas selling price of the natural gas station; is the charging power of the battery in the t-th time period; P t dis is the discharging power of the battery in the t-th time period; is the heat storage and heat release power of the thermal energy storage in the t-th time period; is the cold storage and cold release power of the cold storage in the t-th time period; P t EB is the power consumption of the electric boiler; F t GB is the natural gas consumption power of the gas boiler; P t ESB is the power consumption of the industrial steam boiler; P t AIR is the power consumption of the cold air conditioner; C SA is the operation and maintenance cost of the steam accumulator; are the unit operation power maintenance costs of the electric boiler, photovoltaic unit, wind turbine generator, battery, heat accumulator, cold storage, cold air conditioner, air source heat pump, gas boiler, industrial steam boiler, and steam accumulator respectively; C SA,on represents the operation and maintenance cost coefficient of the steam accumulator, 0.06 yuan / kWh; represents the steam filling flow rate of the steam accumulator at the t-th moment, and its unit is kg / h; represents the total steam discharge flow rate of the steam accumulator at time t, with the unit of kg / h; h SAcha represents the enthalpy value of the steam input into the steam accumulator; represents the steam flow rate discharged from the steam accumulator to the m-th textile production line at time t; represents the steam flow rate discharged from the steam accumulator to the n-th printing and dyeing production line at time t; represents the steam flow rate discharged from the steam accumulator to the l-th ironing process production line at time t; respectively represent the enthalpy values of the steam transported by the steam accumulator to the m-th textile production line, the n-th printing and dyeing production line, and the l-th ironing process production line at time t, with the unit of kJ / kg; Δt represents the time interval between two sampling points. Among them, the output of the wind power generation unit is the electric power generated by the wind power generation unit per unit time; the output of the photovoltaic unit is the electric power generated by the photovoltaic unit per unit time.

[0174] 1.2 Comprehensive operation constraints include installation capacity planning constraints, upper and lower limits of operation constraints for each equipment unit, power balance constraints, air source heat pump operation constraints, cold air conditioner operation constraints, electric boiler operation constraints, gas boiler operation constraints, photovoltaic generator set output constraints, wind turbine generator set output constraints, cold storage unit operation constraints, and industrial steam boiler operation constraints.

[0175] 1.2.1 The installation capacity planning constraints are as follows:

[0176]

[0177] where k ∈ {PV, WT, EB, GB, ES, ASHP, HS, CS, SA, ESB, AIR}, where PV represents the photovoltaic unit, WT represents the wind turbine unit, EB represents the electric boiler unit, GB represents the gas boiler unit, ES represents the battery unit, ASHP represents the air source heat pump unit, HS represents the heat accumulator unit, CS represents the cold storage unit, SA represents the steam accumulator unit, ESB represents the industrial steam boiler unit, and AIR represents the cold air conditioner unit; is the planned installation capacity of each unit; is the upper limit of the planned installation capacity of each unit.

[0178] 1.2.2 The upper and lower limits of operation constraints for each equipment unit are as follows:

[0179]

[0180] where P t PV is the output of the photovoltaic unit at time t; P t WT is the output of the wind turbine generator set at time t; E tis the electricity storage capacity of the battery at time t; P t EB is the power consumption of the electric boiler at time t; F t GB is the power of natural gas consumed by the gas boiler at time t; P t AIR is the electric power of the cold air conditioner at time t; P t ASHP is the power consumption of the ASHP unit at time t; is the cold storage capacity of the cold storage device at time t; is the cold storage capacity of the heat storage device at time t. P t ESB is the power consumption of the industrial steam boiler at time t; represents the steam energy stored in the steam accumulator at time t; are the original capacities of the photovoltaic power generation unit, wind power generation unit, electric boiler unit, gas boiler, battery, cold air conditioner, air source heat pump, cold storage unit, heat storage unit, industrial steam boiler, and steam accumulator unit, respectively; are the planned installed capacities of the photovoltaic power generation unit, wind power generation unit, electric boiler unit, gas boiler, battery, cold air conditioner, air source heat pump, cold storage unit, heat storage unit, industrial steam boiler, and steam accumulator unit, respectively.

[0181] The power balance constraints described in 1.2.3 include electric power balance constraints, thermal power balance constraints, cold electric power balance constraints, natural gas power balance constraints, and steam power balance constraints.

[0182] Among them, the electric power balance constraint is as follows:

[0183]

[0184] Among them, P t grid is the interactive power between the microgrid and the superior grid during time period t; P t dis is the discharge power of the battery during time period t; P t PV is the output of the photovoltaic unit at time t; P t WT is the output of the wind power generation unit at time t; P tLE is the power consumption of the basic electric load of the electro-thermal microgrid park during time period t; P t ch is the charging power of the battery during time period t; P t ASHP is the electric power consumed by the air source heat pump during time period t; P t EBis the power consumption of the electric boiler during period t; P t AIR is the power consumption of the cold air conditioner during period t. The basic electric load includes the lights in the electro-thermal microgrid park, etc.

[0185] The heat power balance constraint is as follows:

[0186]

[0187] Among them, P t LH is the heat power consumption of the basic heat load in the electro-thermal microgrid park during period t; represents the heat energy produced by the air source heat pump; Q t EB is the heating power of the electric boiler during period t; Q t GB is the heating power of the gas boiler unit during period t. The basic heat load includes the hot water supply in the electro-thermal microgrid park, etc.

[0188] The cold electric power balance constraint is as follows:

[0189]

[0190] Among them, P t LC is the cold power consumption of the basic cold load in the electro-thermal microgrid park during period t; is the cold storage power of the cold storage during period t; represents the cold energy produced by the air source heat pump; is the cold release power of the cold storage during period t; is the cooling power of the cold air conditioner at time t. The basic cold load includes the ice supply in the electro-thermal microgrid park, etc.

[0191] The natural gas power balance constraint is as follows:

[0192]

[0193] Among them, P t LG is the gas consumption power of the electro-thermal microgrid park during period t; is the interaction power between the microgrid and the superior natural gas station during period t; F t GB is the natural gas consumption power of the gas boiler.

[0194] The steam power balance constraint is as follows:

[0195]

[0196] Among them, P t LV is the steam consumption power of the electro-thermal microgrid park at time t; represents the steam charging flow rate of the steam accumulator at time t, with the unit of kg / h; h SAcha represents the steam enthalpy value input into the steam accumulator; Δt represents the time interval between two sampling points. represents the total steam discharging flow rate of the steam accumulator at time t, with the unit of kg / h; h SAdis represents the steam enthalpy value output from the steam accumulator; is the steam energy output by the industrial steam boiler at time t.

[0197] 1.2.4 The operating constraints of the ASHP air source heat pump are as follows:

[0198]

[0199] In the formula, represents the thermal energy produced by the air source heat pump, with the unit of J; represents the cooling energy produced by the air source heat pump, with the unit of J; COPh A represents the heating coefficient of the air source heat pump; COPc A represents the cooling coefficient of the air source heat pump; P t ASHP,H and P t ASHP,C are the heating and cooling powers of the heat pump at time t, respectively; P t ASHP represents the total energy produced by the air source heat pump, with the unit of J; are the heating and cooling operating state variables of the ASHP unit at time t, respectively. When heating When cooling

[0200] 1.2.5 The operating constraints of the cold air conditioner are as follows:

[0201]

[0202] In the formula, is the cooling power of the cold air conditioner at time t; η air is the air conditioner cooling efficiency; P t AIR is the power consumption of the cold air conditioner.

[0203] 1.2.6 The operating constraints of the battery are as follows:

[0204] E t =(1 - τ)E t-1 +(η esch P t ch -P t dis / η esdis )Δt

[0205]

[0206] E min ≤E t ≤E max

[0207] E t＝24 =E init

[0208]

[0209] Wherein, E t is the stored power of the battery at time t; τ is the sampling number corresponding to the last sampling of each day, and its preferred value is 24; E t is the stored power of the battery at time t-1; P t ch is the charging power of the battery during time period t; P t dis is the discharging power of the battery during time period t; η esch and η esdis are the charging and discharging efficiencies of the battery respectively; Δt represents the time interval between two sampling points. E min and E max are the upper and lower limits of the stored power of the battery respectively; are the charging and discharging state variables of the battery at time t. When charging When discharging is the minimum charging power of the battery; is the maximum charging power of the battery; is the minimum discharging power of the battery; is the maximum discharging power of the battery; E t＝24 is the stored power of the battery at the last sampling moment of a day; E init is the stored power of the battery at the first sampling moment of a day.

[0210] 1.2.7 The operating constraints of the electric boiler are as follows:

[0211]

[0212] Wherein, P t EB and Q t EB are the electricity consumption and heating power of the electric boiler at time t respectively; η EB is the heat conversion efficiency of the electric boiler.

[0213] 1.2.8 The operating constraints of the gas boiler are as follows:

[0214] Q tGB = η gb λ gas F t GB

[0215] Wherein, Q t GB is the heating power of the gas boiler unit during the t period, and η gb is the heat production efficiency of the gas boiler; λ gas represents the lower calorific value of natural gas, taking 9.97 kWh / m 3 ; F t GB represents the power of natural gas consumed by the gas boiler.

[0216] 1.2.9 The output constraints of the photovoltaic generator set and the wind turbine generator set are as follows:

[0217]

[0218] Wherein, P t pv is the output of the photovoltaic unit at time t, and P t f,pv is the predicted value of the output of the photovoltaic unit at time t; P t wt is the output of the wind turbine generator set at time t, and P t f,wt is the predicted value of the output of the wind turbine generator set at time t; This formula indicates that the wind-solar output needs to be within the range of (0.8, 1) of the rated output. The predicted value is the rated value.

[0219] 1.2.10 The operation constraints of the cold storage are as follows:

[0220]

[0221] C t＝24 = C init

[0222]

[0223] Wherein, represents the cold energy of the cold storage at time t; τ is the sampling number corresponding to the last sampling of each day, and its preferred value is 24; represents the cold energy of the cold storage at time t - 1; η cch and η cdis respectively represent the energy storage and energy release coefficients at time t. is the cold storage and cold release power of the cold storage during the t period; Δt represents the sampling point time interval, that is, the energy storage or energy release time; are respectively the charging and discharging state variables of the cold storage at time t. When charging When cooling down is the minimum charging power of the cold storage is the maximum charging power of the cold storage The minimum discharging power of the cold storage The maximum discharging power of the cold storage represents the minimum cold energy stored in the cold storage represents the maximum cold energy stored in the cold storage; C t＝24 is the cold storage capacity of the cold storage at the last sampling moment of a day; C init is the cold storage capacity of the cold storage at the first sampling moment of a day

[0224] 1.2.11 Operating constraints of industrial steam boilers

[0225]

[0226]

[0227] In the formula is the steam energy output by the industrial steam boiler at time t is the steam flow rate output by the industrial steam boiler at time t; h fg is the enthalpy of evaporation; η ESB represents the heat production efficiency of the industrial steam boiler; P t ESB is the electric power consumed by the industrial steam boiler at time t; Δt represents the time interval between two sampling points

[0228] 1.3 Equipment modeling

[0229] 1.3.1 Equivalent energy storage model of steam accumulator

[0230] The equivalent energy storage model of the steam accumulator includes the steam charging and discharging flow rate constraints, the steam accumulator capacity constraints, and the equivalent energy storage model

[0231] The steam charging and discharging flow rate constraints of the steam accumulator are as follows

[0232]

[0233] Among them respectively represent the flow rates of steam charged into and discharged from the steam accumulator at time t, kg / h indicates that the steam accumulator is in the state of charging steam at time t indicates that the steam accumulator is not charging steam at time t indicates that the steam accumulator is in the state of discharging steam at time t indicates that the steam accumulator is not discharging steam at time t respectively represent the maximum flow rates of steam charged into and discharged from the steam accumulator at time t, kg / h; the steam accumulator can charge and discharge steam simultaneously.

[0234] The steam energy stored in the steam accumulator at the t-th moment is related to the steam energy stored at the (t - 1)-th moment and the charging and discharging states of steam at the (t - 1)-th moment. At the same time, the energy stored inside the steam accumulator cannot exceed its upper and lower limit constraints. To sum up, the specific expression of the steam accumulator capacity constraint is as follows:

[0235]

[0236] Among them, respectively represent the enthalpy values of steam charged and discharged at the (t - 1)-th moment; respectively represent the flow rates of steam charged and discharged at the (t - 1)-th moment; represents the steam energy stored in the steam accumulator at time t; represents the steam energy stored in the steam accumulator at the (t - 1)-th moment; respectively represent the maximum and minimum values of the steam energy that the steam accumulator can store. Δt represents the time interval between two sampling points.

[0237] For general applicability between different optimization periods, the steam energy stored in the steam accumulator at the end of each optimization period is constrained by its initial state. To sum up, the expression of the equivalent energy storage model is as follows:

[0238]

[0239] Among them, represents the steam energy stored in the steam accumulator at the initial moment of the optimization period; represents the steam energy stored in the steam accumulator at the end of the optimization period.

[0240] According to the above constraints of the steam accumulator, its equivalent energy storage model is established as follows:

[0241]

[0242] Step 2: Establish a multi-level evaluation index system based on the integrated energy system planning model of the microgrid park;

[0243] Please refer to Figure 3 , after establishing the integrated energy system planning model of the microgrid park, analyze and establish an evaluation index system for the comprehensive evaluation of the planning scheme of the electric-heat microgrid park, including four levels of index systems: investment recovery, system energy efficiency, system flexibility, and low-carbon operation;

[0244] Please refer to Figure 4, in step 2 of the present invention, a multi-level evaluation index system is constructed based on the payback period, energy consumption, energy use efficiency, carbon emissions, etc. of the microgrid park.

[0245] Please refer to Figure 4 , the present invention analyzes and obtains the index layer, that is, the multi-level evaluation index system, from the optimal electro-thermal microgrid park planning at the target layer. The multi-level evaluation index system includes a composite recovery index, a system energy efficiency index, a system flexibility index, and a low-carbon operation index. Among them, the composite recovery index is set based on the payback period; the system energy efficiency index is set based on the primary energy consumption, and the system flexibility includes the energy storage utilization rate and the grid interaction fluctuation amount; the low-carbon operation index is set based on the carbon emissions.

[0246] 2.1 Composite recovery index P B As shown in the following formula:

[0247]

[0248] Among them, C is the calculation result of the comprehensive objective function; E s is the total investment of the annual net savings cost; r' is the annual interest rate.

[0249] 2.2 During the planning process of the electro-thermal microgrid park, the externally purchased energy mainly involves electricity and natural gas. Natural gas is regarded as primary energy, while electricity is secondary energy. The statistics of primary energy consumption are mainly to record the usage amount of primary energy. Therefore, it is necessary to convert secondary energy into standard coal to facilitate the comprehensive evaluation of the energy utilization efficiency and environmental impact of the park.

[0250] Those skilled in the art should know that converting energy into standard coal is for the unit unification to facilitate heat calculation.

[0251] The system energy efficiency index E is shown in the following formula:

[0252]

[0253] Among them, P e (t) is the power consumption of the park at time t, and its unit is kWh; Q gas (t) is the gas consumption of the park at time t; λ gas represents the lower calorific value of natural gas, taking 9.97 kWh / m 3 ; ε is the power generation efficiency of the coal-fired unit; is the transmission process loss rate; is the conversion standard coal parameter of natural gas, taking 1.19 kg / Nm 3 ; is the conversion standard coal parameter of electric energy, taking 0.33 kg / kWh.

[0254] 2.3 System flexibility indicators include energy storage utilization rate and grid interaction fluctuation volume.

[0255] 2.3.1 For the steam equivalent energy storage utilization rate, in the planning of the electro-thermal microgrid park, the steam equivalent energy storage utilization rate is defined as the sum of the ratio of the annual energy supply to the planned capacity of the energy storage device, including steam energy storage and thermal energy equipment. The higher this indicator, the better the utilization efficiency of the steam energy storage and the more reasonable the configuration. This indicates that the energy storage system in the park can effectively absorb, store, and release steam energy, thereby maximizing the utilization efficiency of renewable energy and the overall stability of the system.

[0256] The energy storage utilization rate is shown in the following formula:

[0257]

[0258] Among them, U is the steam equivalent energy storage utilization rate; represents the capacity of the steam storage device; Q dis (t) is the power of the steam storage device releasing steam.

[0259] 2.3.2 For the grid interaction fluctuation volume, during the planning process of the electro-thermal microgrid park, with the implementation of different schemes, the interaction volume between the electro-thermal microgrid and the external grid will change accordingly. The larger the interaction fluctuation volume, the more unstable this interaction becomes, thus exerting greater pressure on the grid regulation ability. To quantify this indicator, the mean square deviation is usually used for definition. The increase in the mean square deviation value indicates that there are large fluctuations between the output of renewable energy and the load in the park, thereby increasing the impact on the external grid and the regulation demand.

[0260] The grid interaction fluctuation volume is shown in the following formula:

[0261]

[0262] Among them, ΔP is the grid interaction fluctuation volume, P sell (t) is the electricity sales volume of the park at time t. T is the total number of time periods in the daily dispatch cycle, that is, taking one day as a cycle, the total number of time periods divided in one day.

[0263] 2.4 Since the carbon emissions generated by different energy equipment in the electro-thermal microgrid park during operation are different, in different planning schemes, the carbon emissions indicator can directly reflect the effect of the system in low-carbon operation. The higher the carbon emissions, the more negative the impact of the planning scheme on the environment.

[0264] The low-carbon operation indicator is shown in the following formula:

[0265]

[0266] E IES,a =Egria,a +E GB,a

[0267] Among them, E gria,a and E GB,a are the actual carbon emission values during the power generation of coal-fired power units in the power grid and the operation of gas boilers respectively; a 1 is the carbon conversion coefficient of the output of coal-fired units, that is, the carbon dioxide emissions corresponding to each kilowatt-hour of electric energy during the coal-fired power generation process in the power grid; Pe(t) is the power consumption of the park at time t, and its unit is kWh; H GB,a (t) is the natural gas consumption of the park at time t; a 2 is the carbon conversion coefficient of the output of gas units. That is, the carbon dioxide emissions corresponding to each kilowatt-hour of electric energy during the gas power generation process; ω is the coefficient of carbon absorption during the power-to-gas process;

[0268] Step 3: Refer to Figure 3 to obtain each evaluation index in the multi-level evaluation index system and perform preprocessing, and assign comprehensive dynamic weights to each evaluation index;

[0269] Evaluate the efficiency and benefits of the electric-heat microgrid park planning scheme through the indicators of the established index system, including index data preprocessing, determining the weights of the transmission grid efficiency and benefit indicators, and the comprehensive evaluation method of the transmission grid planning scheme based on fuzzy comprehensive evaluation;

[0270] 3.1 Preprocess the five types of index data obtained in step 2. The five types of index data include composite recovery index, system energy efficiency index, steam equivalent energy storage utilization rate, grid interaction fluctuation amount, and low-carbon operation index.

[0271] 3.1.1 Standardize the index types.

[0272] In the designed evaluation index system for the power grid planning scheme, the five types of index data can be divided into four types: positive type, negative type, intermediate type, and interval type:

[0273] Among them, for the positive type, the larger the index value, the more in line with the expectation.

[0274] For the negative type, the smaller the index value, the more in line with the expectation.

[0275] For the intermediate type, the closer the index value is to a certain specific value, the better the effect.

[0276] For the interval type, the index value should fall within a specific interval, and exceeding or being lower than this interval does not meet the expectation.

[0277] In view of the existence of these four types of indicators with different natures in the evaluation index system, it is necessary to unify them into the same nature. For this reason, the present invention converts all indicators into positive type indicators.

[0278] Let x represent a certain indicator data. From the meanings of each indicator, it can be obtained that:

[0279] x > 0, x ∈ D

[0280] where D is the set of indicator systems. This formula indicates that the calculation results of the above indicator values are all greater than 0.

[0281] By uniformly processing negative-type indicators, intermediate-type indicators, and interval-type indicators, they can be converted into positive-type indicators.

[0282] For positive-type indicators, make:

[0283] x * = x.

[0284] where x * is the converted indicator.

[0285] For negative-type indicators, let

[0286] x * = M - x

[0287] where M is the allowable maximum upper bound of indicator x; x * is the converted indicator.

[0288] For intermediate-type indicators, assume that its optimal expected value is x best , let

[0289] M = max{|x - x best |}

[0290] Convert the intermediate-type indicator x into a positive-type indicator through the following formula:

[0291]

[0292] where x * is the converted indicator.

[0293] For interval-type indicators, let

[0294]

[0295] where [q 1 , q 2 is the optimal expected interval of indicator x; M is the allowable or maximum upper bound of indicator x; m is the allowable or minimum lower bound of indicator x.

[0296] The fluctuations in energy prices have direct and indirect impacts on the cost structure and operation mode. Therefore, when making a plan, it is necessary to consider how to reduce the operation costs when energy prices fluctuate.

[0297] Through the above-mentioned unification process, negative-type indicators, intermediate-type indicators, and interval-type indicators have all been converted into positive-type indicators. In the planning of the electric heating microgrid park, the final comprehensive evaluation result is obtained by combining the calculations of individual indicators. Therefore, it can be concluded that the larger the value of the comprehensive quantification result, the better the park's planning scheme. This evaluation method ensures the comparability of different types of indicators in the comprehensive evaluation, thus enabling the final decision-making to more scientifically and effectively reflect the overall performance and sustainable development ability of the park.

[0298] 3.1.2 Dimensionless Processing of Indicators

[0299] The units and numerical magnitudes of each indicator are different. Therefore, when conducting a comprehensive evaluation, their influence weights will also vary. If the numerical ranges of two indicators differ significantly, then in the comprehensive evaluation, the indicator with a larger variation range will have a greater impact on the result, and thus its weight will increase accordingly. Therefore, it is necessary to perform dimensionless processing on the indicators. Assume that the indicator x has completed the unification process to obtain x * , and at this time, the main object of the dimensionless processing is the positive-type indicator. Let a certain indicator x i (i = 1,..., m) be a positive-type indicator, and its observed values be {x iq |i = 1,..., m; q = 1,..., n sa}; where m is the total number of indicators; n sa is the total number of observed samples for each indicator. After standardization, it is as follows:

[0300]

[0301] where, is the dimensionless result of the q-th observed value of the i-th indicator, is the mean of the q-th indicator observed values; s q (q = 1,..., n sa ) is the mean square deviation of the q-th indicator observed values;

[0302] For each extreme value, the processing method is:

[0303]

[0304] where, M q is the maximum value of the observed samples of the indicator x q , and m q is the minimum value.

[0305] The dimensionless sample values obtained through the extreme value processing method are restricted within the range of [0, 1]. Such a processing method meets the requirements for numerical values when conducting indicator evaluations.

[0306] 3.2 Assign comprehensive dynamic weights to each evaluation index.

[0307] 3.2.1 Determine the index order

[0308] Suppose there are n evaluation dimensions. If, in a certain evaluation dimension, the importance degree of index x in the microgrid park i is greater than that of x j , it is denoted as x i > x j . Preferably, in a certain evaluation dimension, the importance degree of each index is obtained based on an expert system. Generalized to m indexes, sorting according to the importance degree among the indexes, the following formula can be obtained:

[0309]

[0310] where s h is an integer, and s h ∈ [1, n]; represents the index with the greatest importance degree under the s h th evaluation dimension; represents the index with the second greatest importance degree under the s h th evaluation dimension; represents the index with the smallest importance degree under the s h th evaluation dimension. Preferably, in the present invention, m = 5, corresponding to 5 types of index data; the 5 types of index data include 5 indexes: composite recovery index, system energy efficiency index, steam equivalent energy storage utilization rate, grid interaction fluctuation amount, and low-carbon operation index.

[0311] 3.2.2 Calculate the first weight of the index according to the following formula:

[0312]

[0313] where k is an integer, and k ∈ [2, j]; j is an integer, and j ∈ [2, m], m is the total number of indexes; l is an integer, and l ∈ [k, j]; s h is an integer, and s h ∈ [1, n], n is the total number of dimensions; is the weight corresponding to the index with the jth greatest importance degree under the s h th evaluation dimension; r l sh is shown as the following formula:

[0314]

[0315] where is the importance degree of the index with the (l - 1)th greatest importance degree under the s h th evaluation dimension; is the importance degree of the index with the sth hThe importance degree of the l-th most important indicator under each evaluation dimension.

[0316] 3.2.3 Organize the indicators of each dimension into a weight matrix

[0317] Suppose m indicators are established for the evaluation object, and n dimensions are used to assign weights to each indicator through the above method. Then for the indicator x i (1 ≤ i ≤ m), the weights of the n dimensions can be represented by the following vector.

[0318] B i = [b i1 , b i2 ... b in ;

[0319] where i is an integer and i ∈ [1, m]; m is the total number of indicators; B i is the vector composed of the indicator values of the i-th indicator in each dimension; b i1 is the first weight of the i-th indicator under the first evaluation dimension; b i2 is the first weight of the i-th indicator under the second evaluation dimension; b in the first weight of the i-th indicator under the n-th evaluation dimension; where, when the i-th indicator corresponds to the j-th most important indicator after sorting the importance degrees of each indicator under the s h -th evaluation dimension,

[0320] For the m indicators in the indicator system, the weight data assigned to these indicators under n different dimensions can form a weight sequence matrix as follows:

[0321]

[0322] In this matrix, each element represents the weight assigned to the importance of the indicator under different dimensions. Specifically, b ish is the first weight of the i-th indicator under the s h -th dimension.

[0323] 3.2.4 Study the reference sequence, and take the maximum first weight value of each indicator under each dimension as the reference weight value of the corresponding indicator;

[0324] The reference sequence is a data sequence associated with the research object. It is used to evaluate the degree of relationship between the research object and this sequence, thereby helping to analyze the characteristics and laws of the research object. In the present invention, the maximum weight value of all dimensions is used as the common reference weight value.

[0325] b i0 = max{b i1 ,..., b in};

[0326] Among them, b i0 is the reference weight value corresponding to the i-th index; max{·} represents obtaining the maximum value in the brackets; b i1 is the first weight of the i-th index under the first evaluation dimension; b in is the first weight of the i-th index under the n-th evaluation dimension.

[0327] After calculating the common reference weight value, a reference sequence B 0 .

[0328] B 0 =(b 10 , b 20 ,..., b m0 ) T ;

[0329] Among them, (·) T represents the transpose of the vector in the brackets; b 10 is the reference weight value corresponding to the first index; b 20 is the reference weight value corresponding to the second index; b m0 is the reference weight value corresponding to the m-th index.

[0330] 3.2.5 Calculate the relative distance of each dimension based on the reference sequence of weight coefficients.

[0331] When calculating the combined weight coefficient of each index, the relative distance between the weight coefficient assigned by the evaluation expert and the reference sequence can be used to reflect the importance of the index in the expert group. If the weight evaluations of a certain index by each expert are relatively high, then the relative distance between the index and the reference sequence will be relatively small, resulting in a relatively large combined weight coefficient for it. The calculation formula for the relative distance is as follows.

[0332]

[0333] Among them, i is an integer, and i ∈ [1, m]; D i0 is the relative distance between the i-th index and the reference weight value; b i0 is the reference weight value corresponding to the i-th index; b ish is the first weight of the i-th index under the s h -th evaluation dimension;

[0334] 3.2.6 Calculate using the relative distance to obtain the combined weight of the n dimensions.

[0335]

[0336] 3.2.7 Normalize the combined weights to obtain the comprehensive dynamic weight ω of each index i As shown in the following formula:

[0337]

[0338] The value calculated by the above formula is the combined weight of the index, that is, the final weight value of the index.

[0339] Step 4: Based on the comprehensive dynamic weight and fuzzy comprehensive evaluation, obtain the fuzzy evaluation result of the microgrid park integrated energy system planning model. Please refer to Figure 3 , and the fuzzy comprehensive evaluation method includes:

[0340] In step 4 of the present invention, the efficiency and benefits of the electro-thermal microgrid park planning scheme are evaluated.

[0341] 4.1 Determine the fuzzy comprehensive evaluation set.

[0342] The fuzzy comprehensive evaluation set includes an index set, a weight vector, and an evaluation set;

[0343] Form an index set U with each index in the index system established for the evaluation object:

[0344] U = {x 1 , x 2 ,..., x m};

[0345] where x 1 is the first index; x 2 is the second index; x m is the mth index.

[0346] The weight of each index forms a weight vector Ω:

[0347] Ω = [ω 1 , ω 2 ,..., ω m ;

[0348] ω 1 is the comprehensive dynamic weight of the first index; ω 2 is the comprehensive dynamic weight of the second index; ω m is the comprehensive dynamic weight of the mth index.

[0349] Select an appropriate set of comments to construct the evaluation set:

[0350]

[0351] where n com represents the total number of comments; v 1is the first comment; v 2 is the second comment; v ncom is the n com th comment;

[0352] Preferably, when using the triangular membership function, n com = 4, that is, the evaluation set is shown as follows:

[0353] V = {v 1 , v 2 , v 3 , v 4};

[0354] wherein, v 1 is the first comment; v 2 is the second comment; v 1 is the third comment; v 2 is the fourth comment; the aforementioned four comments respectively correspond to very good, good, average, and poor.

[0355] 4.2 Obtain the fuzzy evaluation matrix.

[0356] Let x be the preprocessed evaluation index value, and the larger the value of x, the more it meets the expectation. In the triangular membership function, there are four membership function parameters, which are respectively: a 1 , a 2 , a 3 , a 4 , a 1 is the first membership function parameter; a 2 is the second membership function parameter; a 3 is the third membership function parameter; a 4 is the fourth membership function parameter; the values are respectively 1, 0.8, 0.5, 0.3; there are a total of 4 membership values, which are respectively: μ 1 , μ 2 , μ 3 , μ 4 . The method for calculating the membership degree of the index to each comment is as follows:

[0357]

[0358] wherein, is the membership degree value of the qth observation sample of the ith index to the first comment; is the preprocessing result of the qth observation value of the ith index; a 1 is the first membership function parameter; a 2 is the second membership function parameter;

[0359]

[0360] wherein, is the membership degree value of the q-th observed sample of the i-th index for the second comment; a 3 is the parameter of the third membership function;

[0361]

[0362] wherein, is the membership degree value of the q-th observed sample of the i-th index for the third comment; a 4 is the parameter of the fourth membership function;

[0363]

[0364] wherein, is the membership degree value of the q-th observed sample of the i-th index for the fourth comment. The membership degrees of all indices for the evaluation set will form a fuzzy evaluation matrix R.

[0365]

[0366] wherein, R is the fuzzy evaluation matrix of the electro-thermal microgrid park planning scheme; is the membership degree value of the observed sample of the i-th index for the j com -th comment; i = 1, 2,..., m; j com = 1, 2,…, n com .

[0367] The membership degree value of the observed sample of the i-th index for the j com -th comment is shown in the following formula:

[0368]

[0369] wherein, i = 1, 2,…, m; m is the total number of indices; j com = 1, 2, 3, 4; q = 1,..., n sa ; n sa is the total number of observed samples of each index.

[0370] 4.3 Calculate the fuzzy comprehensive evaluation result.

[0371] By multiplying the weight vector Ω with the fuzzy evaluation matrix R, the result of the fuzzy evaluation can be obtained.

[0372] X = ΩR;

[0373] The present application also discloses a planning system for an electro-thermal microgrid park based on a method for planning an electro-thermal microgrid park that combines weighting and fuzzy synthesis, including a system planning model establishment module, a multi-level evaluation index system construction module, a comprehensive dynamic weight assignment module, and a fuzzy evaluation module for the system planning model:

[0374] The system planning model establishment module establishes a comprehensive energy system planning model for the microgrid park based on the energy hub structure of the electro-thermal microgrid park;

[0375] The multi-level evaluation index system construction module establishes a multi-level evaluation index system based on the comprehensive energy system planning model of the microgrid park;

[0376] The comprehensive dynamic weight assignment module obtains each evaluation index in the multi-level evaluation index system and performs preprocessing, and assigns a comprehensive dynamic weight to each evaluation index;

[0377] The fuzzy evaluation module for the system planning model obtains a fuzzy evaluation result of the comprehensive energy system planning model of the microgrid park based on the comprehensive dynamic weight and fuzzy comprehensive evaluation.

[0378] The present disclosure may be a system, a method, and / or a computer program product. The computer program product may include a computer-readable storage medium having thereon computer-readable program instructions for causing a processor to implement various aspects of the present disclosure.

[0379] The computer-readable storage medium may be a tangible device that can retain and store instructions for use by an instruction execution device. The computer-readable storage medium may be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disc (DVD), a memory stick, a floppy disk, a mechanically encoded device such as a punched card or raised structures in grooves having instructions stored thereon, and any suitable combination of the foregoing. The computer-readable storage medium used herein is not construed as an instantaneous signal itself, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagated through a waveguide or other transmission medium (e.g., an optical pulse through an optical fiber cable), or an electrical signal transmitted through a wire.

[0380] The computer-readable program instructions described herein can be downloaded to various computing / processing devices from a computer-readable storage medium or downloaded to an external computer or external storage device through a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network may include copper transmission cables, optical fiber transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in each computing / processing device.

[0381] The computer program instructions for performing the operations of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state-setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages such as Smalltalk, C++, etc., and conventional procedural programming languages such as the "C" language or similar programming languages. The computer-readable program instructions may be executed entirely on the user's computer, partially on the user's computer, executed as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider). In some embodiments, by using the state information of the computer-readable program instructions to customize an electronic circuit, such as a programmable logic circuit, a field-programmable gate array (FPGA), or a programmable logic array (PLA), the electronic circuit can execute the computer-readable program instructions to implement various aspects of the present disclosure.

[0382] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications or equivalent replacements can still be made to the specific embodiments of the present invention, and any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention should be covered by the protection scope of the claims of the present invention.

Claims

1. A method for planning an electric heating microgrid park based on combining weighting and fuzzy synthesis, characterized by: Step 1: Based on the energy hub structure of the electric and thermal microgrid park, establish a comprehensive energy system planning model for the microgrid park; Step 2: Establish a multi-level evaluation index system based on the microgrid park integrated energy system planning model; Step 3: Obtain and preprocess each evaluation indicator in the multi-level evaluation indicator system, and assign a comprehensive dynamic weight to each evaluation indicator; Step 4: Based on the comprehensive dynamic weight and fuzzy comprehensive evaluation, the fuzzy evaluation results of the microgrid park integrated energy system planning model are obtained.

2. The electric heating microgrid park planning method according to claim 1 is characterized by: In step 1, the microgrid park integrated energy system planning model includes a comprehensive objective function and comprehensive operation constraints.

3. The electric heating microgrid park planning method according to claim 2 is characterized by: The comprehensive objective function C is shown as follows: C = min[C1+C2+C3]; Among them, C1 is the investment cost; C2 is the interaction cost between the microgrid and the power grid and natural gas station; C3 is the maintenance cost; min[·] means taking the minimum value in the brackets.

4. The electric heating microgrid park planning method according to claim 3 is characterized by: in, The interaction cost C2 between the microgrid, the grid and the natural gas station is as follows: Among them, t represents the time period to which the scheduling time belongs; T is the total number of time periods in the daily scheduling cycle; The price of electricity purchased from the upper power grid; P is the electricity price sold by the upper power grid; t grid is the interaction power between the microgrid and the upper grid during period t; The price of buying gas for natural gas stations; The gas selling price for natural gas stations; is the interaction power between the microgrid and the natural gas station during period t.

5. The electric heating microgrid park planning method according to claim 2 is characterized by: The comprehensive operation constraints include installation capacity planning constraints, upper and lower limit constraints of each equipment unit, power balance constraints, air source heat pump operation constraints, air conditioning operation constraints, electric boiler operation constraints, gas boiler operation constraints, photovoltaic generator set output constraints and wind generator set output constraints, cold storage device operation constraints, and industrial steam boiler operation constraints.

6. The electric heating microgrid park planning method according to claim 5 is characterized by: in, The operating constraints of air source heat pumps are as follows: In the formula, Represents the heat energy produced by the air source heat pump; Indicates the cooling energy produced by the air source heat pump; COPh A Indicates the heating coefficient of the air source heat pump; COPc A Indicates the cooling coefficient of the air source heat pump; P t ASHP,H P is the heating power of the heat pump during period t; t ASHP,C P is the cooling power of the heat pump during period t; t ASHP Represents the total energy produced by the air source heat pump; is the heating operation state variable of the air source heat pump unit during period t; are the cooling operation state variables of the air source heat pump unit during period t; when the air source heat pump is heating When the air source heat pump is cooling 7. The electric heating microgrid park planning method according to claim 1 is characterized by: In step 2, the multi-level evaluation index system includes a composite recycling index, a system energy efficiency index, a system flexibility index, and a low-carbon operation index; Among them, the composite recovery index is constructed based on the comprehensive objective function; The system energy efficiency index is constructed based on the park's electricity consumption and natural gas consumption; System flexibility indicators include steam equivalent energy storage utilization and grid interaction fluctuation; The low-carbon operation index is constructed based on the actual carbon emissions of coal-fired power units and gas-fired boilers in the power grid.

8. The electric heating microgrid park planning method according to claim 7 is characterized by: in, The system energy efficiency index is shown in the following formula: Where Pe(t) is the power consumption of the park during period t; Qgas(t) is the natural gas consumption of the park at time t; gas represents the lower calorific value of natural gas; ε is the power generation efficiency of coal-fired units; is the power transmission loss rate; is the standard coal conversion parameter of natural gas; It is the standard coal conversion parameter for electric energy.

9. The electric heating microgrid park planning method according to claim 7, characterized in that: in, The grid interaction fluctuation quantity is shown as follows: Among them, ΔP is the grid interaction fluctuation; P e (t) is the power consumption of the park during period t; P sell (t) is the electricity sales amount of the park at time t.

10. The electric heating microgrid park planning method according to claim 1, characterized in that: In step 3, the method of assigning comprehensive dynamic weights to each evaluation indicator is as follows: The evaluation of the importance of the indicator is set to have n dimensions, the importance of each indicator under each evaluation dimension is obtained, and the first weight of each indicator under each evaluation dimension is calculated; The maximum first weight value of each indicator in each dimension is used as the reference weight value of the corresponding indicator; Calculate the relative distance between each indicator and the reference weight value, and then obtain the comprehensive dynamic weight of each indicator.

11. The electric heating microgrid park planning method according to claim 10, characterized in that: The calculation of the first weight of each indicator under each evaluation dimension is to sort the importance of each indicator under each evaluation dimension and calculate the first weight of the indicator according to the following formula: Where k is an integer and k∈[2,j]; j is an integer and j∈[2,m], m is the total number of indicators; l is an integer and l∈[k,j]; s h is an integer, and s h ∈[1,n], n is the total number of dimensions; For the s h The weight corresponding to the jth most important indicator under the evaluation dimension; r l sh As shown below: in For the s h The importance of the indicator with the highest importance (l-1) under each evaluation dimension; For the s h The importance of the indicator with the highest importance under each evaluation dimension.

12. The electric heating microgrid park planning method according to claim 10, characterized in that: The comprehensive dynamic weight of each indicator ω i As shown below: Where i is an integer, and i∈[1,m]; m is the total number of indicators; D i0 The relative distance between the i-th indicator and the reference weight value.

13. The electric heating microgrid park planning method according to claim 1, characterized in that: In step 4, based on the comprehensive dynamic weight and fuzzy comprehensive evaluation, the method for obtaining the fuzzy evaluation result of the microgrid park integrated energy system planning model is as follows: Determine a fuzzy comprehensive evaluation set; the fuzzy comprehensive evaluation set includes an index set, a weight vector and an evaluation set; wherein the evaluation set includes a first comment, a second comment, a third comment and a fourth comment The weight vector Ω is as follows: Ω=[ω1,ω2,...,ω m ]; ω1 is the comprehensive dynamic weight of the first indicator; ω2 is the comprehensive dynamic weight of the second indicator; ω m is the comprehensive dynamic weight of the mth indicator; The observed sample values ​​of each indicator in the indicator set are collected and preprocessed; the comment membership value of each indicator observation sample to each indicator is calculated based on the set membership function classification parameters, and then a fuzzy evaluation matrix is ​​formed; the set membership function classification parameters include a first membership function parameter, a second membership function parameter, a third membership function parameter and a fourth membership function parameter; By multiplying the weight vector and the fuzzy evaluation matrix, the result of the fuzzy evaluation can be obtained.

14. The electric heating microgrid park planning method according to claim 13, characterized in that: The fuzzy evaluation matrix R is shown as follows: Among them, r 11 is the value of the comment membership of the first indicator for the first indicator's observation sample; r 14 The membership value of the comments of the first indicator's observation sample to the fourth indicator; r m1 is the membership value of the comment of the observed sample of the mth indicator to the first indicator; r m4 The membership value of the comments of the observed sample of the mth indicator to the fourth indicator; The observation sample of the i-th indicator has an effect on the j-th com The value of the comment membership of an indicator is r ijcom As shown below: Where i = 1, 2, ..., m; m is the total number of indicators; j com =1,2,3,4;q=1,...,n sa ;n sa is the total number of observation samples for each indicator.

15. The method for planning an electric heating microgrid park according to claim 13 or 14, characterized in that: Calculate according to the following four formulas: When com =1: in, Take the membership value of the first comment for the qth observation sample of the i-th indicator; is the preprocessing result of the qth observation value of the i-th indicator; a1 is the first membership function parameter; a2 is the second membership function parameter; When com =2: in, is the membership value of the qth observation sample of the i-th indicator to the second comment; a3 is the parameter of the third membership function; When com =3 o'clock: in, The membership value of the third comment for the qth observation sample of the i-th indicator; a4 is the fourth membership function parameter; When com =4: in, Take the membership value of the 4th comment for the qth observation sample of the ith indicator.

16. An electric heating microgrid park planning system using the electric heating microgrid park planning method according to any one of claims 1 to 15, characterized in that: It includes system planning model building module, multi-level evaluation index system building module, comprehensive dynamic weight assignment module and system planning model fuzzy evaluation module: The system planning model building module builds a microgrid park comprehensive energy system planning model based on the electric and thermal microgrid park energy hub structure; The multi-level evaluation index system construction module establishes a multi-level evaluation index system based on the microgrid park integrated energy system planning model; The comprehensive dynamic weight assignment module obtains and preprocesses each evaluation index in the multi-level evaluation index system, and assigns a comprehensive dynamic weight to each evaluation index; The system planning model fuzzy evaluation module obtains the fuzzy evaluation result of the microgrid park comprehensive energy system planning model based on the comprehensive dynamic weight and fuzzy comprehensive evaluation.

17. An electronic device, comprising a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is used to operate according to the instructions to execute the electric heating microgrid park planning method according to claims 1-15.

18. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the electric heating microgrid park planning method described in claims 1-15 is implemented.

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