Multi-energy complementary optimization method, device and system for regional integrated energy system

Abstract

This invention discloses a multi-energy complementary optimization method, device, and system for a regional integrated energy system. The method includes acquiring an energy supply cost evaluation model, an energy supply stability evaluation model, and a primary energy utilization rate evaluation model; based on the acquired mathematical models of different energy supply devices and energy storage devices, as well as the energy supply cost evaluation model, energy supply stability evaluation model, and primary energy utilization rate evaluation model, an optimization model is constructed with the optimization objectives of lowest energy supply cost, best energy supply stability, and highest primary energy utilization rate, and with load balance, energy supply characteristics of each energy supply device, and energy state of energy storage devices as constraints; the optimization model is solved using a multi-objective adaptive optimization algorithm to obtain the optimal scheduling mode for each load, energy supply device, and energy storage device in the regional integrated energy system. This invention can realize source-grid-load-storage coordinated optimization in a regional integrated energy system and can improve the computational efficiency of the optimization process.

Description

Technical Field

[0001] This invention belongs to the field of regional integrated energy system energy supply structure planning, and specifically relates to a multi-energy complementary optimization method, device and system for regional integrated energy systems. Background Technology

[0002] Despite the rapid pace of renewable energy technology development, energy shortages persist in rural areas and on islands rich in renewable energy. Regional integrated energy systems, combining gas turbines, wind power, and photovoltaic power generation, integrate multiple energy supply methods (cooling, heating, and electricity) and complement each other, effectively achieving cascaded energy utilization. This is considered an effective way to solve the current energy supply problems in remote areas. However, the randomness of renewable energy generation and the volatility of loads make system optimization design for different application scenarios a challenge, significantly limiting the system's safety and reliability.

[0003] To address these issues, bio-inspired optimization methods have been extensively developed in recent years to improve the optimal operating strategies and energy supply efficiency of regional integrated energy systems. However, heuristic algorithms such as Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) suffer from problems such as coding difficulties, low computational efficiency, and a tendency to get trapped in local optima when solving multi-objective optimization problems. Therefore, there is an urgent need to propose new optimization methods to solve the collaborative optimization problem of regional integrated energy systems. Summary of the Invention

[0004] To address the aforementioned problems, this invention proposes a multi-energy complementary optimization method, device, and system for regional integrated energy systems, which can achieve coordinated optimization of source-grid-load-storage in regional integrated energy systems and improve the computational efficiency of the optimization process.

[0005] To achieve the above-mentioned technical objectives and effects, the present invention is implemented through the following technical solution:

[0006] In a first aspect, the present invention provides a multi-energy complementary optimization method for a regional integrated energy system, comprising:

[0007] Obtain an energy supply cost evaluation model for assessing the economics of a regional integrated energy system;

[0008] Obtain an energy supply stability evaluation model for assessing the energy supply stability of the regional integrated energy system;

[0009] Obtain a primary energy utilization rate evaluation model for evaluating the utilization rate of the regional integrated energy system;

[0010] Based on the mathematical models of different energy supply and storage devices, as well as the energy supply cost evaluation model, energy supply stability evaluation model and primary energy utilization rate evaluation model, an optimization model is constructed with the optimization objectives of the lowest energy supply cost, the best energy supply stability and the highest primary energy utilization rate, and with load balance, the energy supply characteristics of each energy supply device and the energy status of the energy storage device as constraints.

[0011] The optimization model is solved using a multi-objective adaptive optimization algorithm to obtain the optimal scheduling method for each load, energy supply equipment, and energy storage equipment in the regional integrated energy system.

[0012] Optionally, the mathematical models of the different energy supply devices and energy storage devices include:

[0013] The mathematical model of a combined cooling, heating and power system based on a gas turbine is expressed as follows:

[0014]

[0015]

[0016] In the formula, η CCHP (t) represents the power generation efficiency of the gas turbine at time t, P CCHP (t) represents the power generation of the gas turbine at time t; P CCHP,h (t) represents the waste heat output power of the gas turbine at time t, and η1 is the heat dissipation loss coefficient of the gas turbine;

[0017] The mathematical model of the supplementary heat gas boiler is expressed as follows:

[0018]

[0019] In the formula, P hg (t) represents the average thermal power over time Δt, η g V represents the heating efficiency of a gas-fired boiler. g To consume fuel volume, q g The calorific value of natural gas;

[0020] The mathematical model of an absorption chiller unit is expressed as follows:

[0021] P ca (t)=P ha (t)η a

[0022] In the formula, P ca (t) represents the cooling power at time t, P ha (t) represents the power consumption of the absorption chiller unit at time t, η a The refrigeration efficiency of the absorption chiller unit;

[0023] The mathematical model of the electric refrigeration unit is expressed as follows:

[0024] P ce (t)=P re (t)COP

[0025] In the formula, P ce (t) represents the cooling power at time t, P re (t) represents the power consumption of the electric chiller unit at time t, and COP is the coefficient of performance of the electric chiller unit.

[0026] The mathematical model of the compressed air energy storage system is expressed as follows:

[0027]

[0028] In the formula, P c (t) represents the input electrical power at time t during the compression process, q mc (t) represents the air mass flow rate during the compression process at time t, R g Let be the ideal gas constant. Let η be the air inlet temperature of the i-th stage compressor, κ be the polytropic coefficient of the compression process, and η be the inlet temperature of the i-th stage compressor. c,i Let π be the adiabatic efficiency of the i-th stage compressor, and π be the pressure ratio of a single-stage compressor.

[0029] The air outlet temperature of the i-th stage compressor is:

[0030]

[0031] The output electrical power during the expansion process at time t is:

[0032]

[0033]

[0034] In the formula, P e (t) represents the output electrical power at time t during the expansion process, q me (t) represents the air mass flow rate during the expansion process at time t. Let γ be the air inlet temperature of the j-th stage expander, γ be the polytropic coefficient of the expansion process, and η be the inequality. e,j Let be the adiabatic efficiency of the j-th stage expander, and μ be the expansion ratio of each stage expander;

[0035] The outlet temperature of the j-th stage expander is:

[0036]

[0037] The regenerative subsystem consists of a compression stage heat exchanger and an expansion stage heat exchanger. The compression stage heat exchanger transfers the heat of compression to a storage tank, while the expansion stage heat exchanger flexibly adjusts the temperature of the expanding air fluid according to load requirements. The mathematical model of the compression stage heat exchanger is as follows:

[0038]

[0039] In the formula, subscript 1 represents the hot fluid of the heat exchanger, 2 represents the cold fluid of the heat exchanger, c is the specific heat capacity of each fluid, and q m Let T be the mass flow rate of each fluid, ε be the heat exchanger efficiency, and T be the mass flow rate of each fluid. in T out These are the inflow and outflow temperatures of each fluid, (c p q) min This represents the minimum calorific value in both hot and cold fluids.

[0040] The compressed air energy storage system's compressed heat storage power at time t is:

[0041]

[0042] The expansion heating power of the compressed air energy storage system at time t is:

[0043]

[0044] At the same time, P he (t) represents the expansion heating power of the compressed air energy storage system at time t, q mc (t), q me (t) has only one greater than 0, T stor The gas storage temperature in the gas storage chamber. Let be the temperature of the cooling water in the i-th stage compression heat exchanger. Let c be the temperature of the hot water heated by the i-th stage expansion heat exchanger. p The specific heat capacity of air;

[0045] When the outlet temperature of the final turbine in the compressed air energy storage system is lower than the preset temperature, the energy system has the ability to output cold energy. The formula for calculating the cold energy is as follows:

[0046]

[0047] The high-pressure gas storage subsystem is part of the compressed air energy storage system. Air is compressed and enters this system, releasing gas upon energy release. It consists of a high-pressure gas storage chamber. The pressure within the chamber represents the energy storage state of the compressed air energy storage system. The expression for the energy storage state of the compressed air energy storage system is:

[0048]

[0049]

[0050] At the same time, q mc (t), q me (t) has only one greater than 0, p max p min These are the maximum and minimum gas storage pressures of the gas storage chamber, V. stor p is the volume of the high-pressure gas storage chamber. stor (t) represents the gas storage pressure in the gas storage chamber at time t;

[0051] The mathematical model of a high-temperature thermal storage tank is expressed as follows:

[0052]

[0053]

[0054] In the formula, SOE T (t) represents the energy storage state of the thermal storage unit. At the same time, P hc (t), P he (t) has only one greater than 0, Q T,max Q T,min These are the maximum and minimum heat storage capacity of the thermal storage tank, P. hg (t), P ha (t) represents the supplementary heating power of the gas boiler and the heat consumption power of the absorption chiller at time t, respectively. T (t) represents the amount of heat stored in the heat storage tank at time t;

[0055] The mathematical model of a photovoltaic power generation system is expressed as follows:

[0056]

[0057] In the formula, P N-PV G is the rated power output of the photovoltaic system under standard test conditions, and G is the measured solar radiation. ref It is a constant, with a value of 1kW / m. 2 K t Let T be a constant, T0 be the ambient temperature, and T be the temperature. ref The ambient temperature under standard test conditions;

[0058] The mathematical model of a wind power generation system is expressed as follows:

[0059]

[0060] In the formula, v in To cut off the wind speed, v r For the rated wind speed, v out To cut off the wind speed, P r v is the rated power, and v is the wind speed.

[0061] Optionally, the expression for the energy supply cost evaluation model is:

[0062]

[0063] C p (t)=C CCHP,p (t)+C CAES,p (t)+C gird,p (t)+C WT (t)+C PV (t)

[0064] C h (t)=C CCHP,h (t)+C TES (t)+C gas (t)

[0065] C c (t)=C cCAES (t)+C ca (t)+C ce (t)

[0066] In the formula, T is the evaluation period; C is the total energy purchase cost. p (t) represents the electricity cost at time t, C h (t) represents the cost of purchasing heat at time t, C c (t) represents the cost of purchasing the refrigeration unit at time t; C CCHP,p (t), C CAES,p (t), C gird,p (t) represents the user's cost of purchasing electricity from the gas turbine generator set, compressed air energy storage, and the main power grid at time t, respectively. C WT (t), C PV (t) represents the electricity supply costs of the wind turbine and photovoltaic generator at time t, respectively; C CCHP,h (t), C TES (t), C gas (t) represents the user's heat purchase costs from the gas turbine unit, thermal storage unit, and supplementary gas boiler at time t; C cCAES (t), C ca (t), C ce (t) represents the cost at time t for the user to obtain cooling from the compressed air energy storage system, absorption chiller, and electric chiller.

[0067] Optionally, the expression for the primary energy utilization rate evaluation model is:

[0068]

[0069] In the formula, This represents the total usable heat of the CCHP system over a year. This represents the total power generation of the CCHP system within one year. This represents the annual primary energy input consumed by the CCHP system.

[0070] Optionally, the expression for the power supply stability evaluation model is:

[0071]

[0072]

[0073] In the formula, P i P is the output power of the power supply device i. i,max N represents the maximum output power of power supply device i. b This refers to the total number of energy supply devices.

[0074] Optionally, the expression for the optimization model is:

[0075] Minimize F(χ)=[f1(χ),f2(χ),...,f k (χ)] T

[0076] Subject to g(χ=0)

[0077] and h(χ)≤0

[0078] In the formula, f k (χ) is the k-th objective function, g(χ) is the equality constraint, h(χ) is the inequality constraint, Minimize means to find the minimum, and Subject to means constraint.

[0079] Optionally, the constraints include:

[0080] Electrical balance constraints:

[0081] P CCHP (t)+P e (t)+P buy (t)+P WT (t)+P pv (t)=load e (t)+P c (t)+P re (t);

[0082] In the formula, P buy (t) represents the power purchased from the grid at time t, load e (t) represents the electrical load power at time t.

[0083] Thermal equilibrium constraint:

[0084] PCCHP,h (t)+P hg (t)+P hc (t)=load h (t)+P he (t)+P ha (t)

[0085] In the formula, load h (t) represents the heat load power at time t.

[0086] Cold equilibrium constraint

[0087] P ce (t)+P cCAES (t)+P ca (t)=load c (t)

[0088] In the formula, load c (t) represents the cooling load power at time t.

[0089] Gas turbine unit output constraints

[0090] P CCHP,min ≤P CCHP (t)≤P CCHP,max

[0091] In the formula, P CCHP,min P is the minimum output power of the gas turbine unit. CCHP,max This represents the maximum output power of the gas turbine unit.

[0092] Output constraints of compressed air energy storage systems

[0093] U c (t)P c min ≤P c (t)≤U c (t)P c max

[0094] U e (t)P e min ≤P e (t)≤U e (t)P e max

[0095] 0≤U c (t)+U e (t)≤1

[0096] Since a compressed air energy storage system can only be in a compressed or expanded state at any given time, it can be represented using a 0-1 variable, U. c(t) being 1 indicates the charging state, and 0 indicates the non-charging state. e (t) being 1 indicates a power supply state, while 0 indicates a non-power supply state. c min P c max P represents the upper and lower limits of the charging power of the compressed air energy storage system. e min P e max These represent the upper and lower limits of discharge power, respectively.

[0097] Energy state constraints of compressed air energy storage systems:

[0098]

[0099]

[0100] Energy state constraints of thermal storage units:

[0101]

[0102] Cooling unit constraints:

[0103]

[0104]

[0105] Optionally, the multi-objective adaptive optimization algorithm is a multi-objective adaptive differential evolution algorithm.

[0106] Secondly, the present invention provides a multi-energy complementary optimization device for a regional integrated energy system, comprising:

[0107] The first acquisition module is used to acquire an energy supply cost evaluation model for evaluating the economics of a regional integrated energy system.

[0108] The second acquisition module is used to acquire an energy supply stability evaluation model for evaluating the energy supply stability of the regional integrated energy system;

[0109] The third acquisition module is used to acquire a primary energy utilization rate evaluation model for evaluating the utilization rate of the regional integrated energy system;

[0110] The optimization modeling module is used to construct an optimization model based on the mathematical models of different energy supply devices and energy storage devices, as well as the energy supply cost evaluation model, energy supply stability evaluation model and primary energy utilization rate evaluation model. The optimization objectives are the lowest energy supply cost, the best energy supply stability and the highest primary energy utilization rate. The constraints are load balance, the energy supply characteristics of each energy supply device and the energy status of the energy storage device.

[0111] The solution module is used to solve the optimization model using a multi-objective adaptive optimization algorithm to obtain the optimal scheduling mode for each load, energy supply equipment and energy storage equipment in the regional integrated energy system.

[0112] Thirdly, the present invention provides a regional integrated energy system multi-energy complementary optimization system, including a storage medium and a processor;

[0113] The storage medium is used to store instructions;

[0114] The processor is configured to operate according to the instructions to perform the method according to any one of the first aspects.

[0115] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0116] This invention provides a multi-energy complementary optimization method, device, and system for regional integrated energy systems. Based on the mathematical models of different energy supply and storage devices, an optimization model is constructed. The optimization model is solved using a multi-objective adaptive optimization algorithm, which can obtain the optimal scheduling mode for each load, energy supply device, and energy storage device in the regional integrated energy system. It plays an important role in flexibly peak shaving and valley filling, ensuring the balance between power grid supply and demand, promoting the consumption of new energy sources, improving energy efficiency management, reducing energy costs, achieving urban energy conservation and emission reduction, and improving the resilience of urban energy supply systems. It has good application prospects. Attached Figure Description

[0117] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly described below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort, wherein:

[0118] Figure 1 This is a flowchart illustrating a multi-energy complementary optimization method for a regional integrated energy system according to an embodiment of the present invention.

[0119] Figure 2 This is a structural diagram of a regional integrated energy system according to an embodiment of the present invention;

[0120] Figure 3 This is a structural diagram of a compressed air energy storage system according to an embodiment of the present invention. Detailed Implementation

[0121] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0122] Furthermore, if the embodiments of this invention involve descriptions such as "first" or "second," these descriptions are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined with "first" or "second" may explicitly or implicitly include at least one of those features. Additionally, the technical solutions of the various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. If the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed by this invention.

[0123] Example 1

[0124] This invention provides a multi-energy complementary optimization method for a regional integrated energy system, comprising the following steps:

[0125] (1) Obtain an energy supply cost evaluation model for evaluating the economics of a regional integrated energy system;

[0126] (2) Obtain an energy supply stability evaluation model for evaluating the energy supply stability of the regional integrated energy system;

[0127] (3) Obtain a primary energy utilization rate evaluation model for evaluating the utilization rate of the regional integrated energy system;

[0128] (4) Based on the mathematical models of different energy supply devices and energy storage devices, as well as the energy supply cost evaluation model, energy supply stability evaluation model and primary energy utilization rate evaluation model, with the lowest energy supply cost, the best energy supply stability and the highest primary energy utilization rate as optimization objectives, and with load balance, energy supply characteristics of each energy supply device and energy status of energy storage devices as constraints, an optimization model is constructed.

[0129] (5) Solve the optimization model using a multi-objective adaptive optimization algorithm to obtain the optimal scheduling mode for each load, energy supply equipment and energy storage equipment in the regional integrated energy system.

[0130] In one specific embodiment of the present invention, such as Figure 2As shown, the regional integrated energy system includes: a wind power generation system, a photovoltaic power generation system, a gas turbine system, a compressed air energy storage system, an energy storage system, a thermal storage tank, a supplementary heating gas boiler, an absorption chiller unit, and an absorption chiller unit;

[0131] The mathematical models of the different energy supply and storage devices include: mathematical models of gas turbine systems, mathematical models of supplementary heating gas boilers, mathematical models of absorption chillers, mathematical models of electric chillers, mathematical models of compressed air energy storage systems, mathematical models of thermal storage tanks, mathematical models of photovoltaic power generation systems, mathematical models of wind power generation systems, mathematical models of energy assessment for energy storage systems, and mathematical models of energy assessment for thermal storage tanks.

[0132] Gas turbine-based combined cooling, heating and power system (by...) Figure 2 The mathematical model (consisting of multiple devices) is expressed as follows:

[0133]

[0134]

[0135] In the formula, η CCHP (t) represents the power generation efficiency of the gas turbine at time t, P CCHP (t) represents the power generation of the gas turbine at time t; P CCHP,h (t) represents the waste heat output power of the gas turbine at time t, and η1 is the heat dissipation loss coefficient of the gas turbine;

[0136] The mathematical model of the supplementary heat gas boiler is expressed as follows:

[0137]

[0138] In the formula, P hg (t) represents the average thermal power over time Δt, η g V represents the heating efficiency of a gas-fired boiler. g To consume fuel volume, q g The calorific value of natural gas;

[0139] The mathematical model of an absorption chiller unit is expressed as follows:

[0140] P ca (t)=P ha (t)η a

[0141] In the formula, P ca (t) represents the cooling power at time t, P ha (t) represents the power consumption of the absorption chiller unit at time t, η a The refrigeration efficiency of the absorption chiller unit;

[0142] The mathematical model of the electric refrigeration unit is expressed as follows:

[0143] P ce (t)=P re (t)COP

[0144] In the formula, P ce (t) represents the cooling power at time t, P re (t) represents the power consumption of the electric chiller unit at time t, and COP is the coefficient of performance of the electric chiller unit.

[0145] like Figure 3 The diagram shows the structure of a compressed air energy storage system. The system includes a compressor, heat exchanger, air tank, throttle valve, heat exchanger, and expander connected in sequence. The mathematical model of the compressed air energy storage system is expressed as follows:

[0146]

[0147] In the formula, P c (t) represents the input electrical power at time t during the compression process, q mc (t) represents the air mass flow rate during the compression process at time t, R g Let be the ideal gas constant. Let η be the air inlet temperature of the i-th stage compressor, κ be the polytropic coefficient of the compression process, and η be the inlet temperature of the i-th stage compressor. c,i Let π be the adiabatic efficiency of the i-th stage compressor, and π be the pressure ratio of a single-stage compressor.

[0148] The air outlet temperature of the i-th stage compressor is:

[0149]

[0150] The output electrical power during the expansion process at time t is:

[0151]

[0152]

[0153] In the formula, P e (t) represents the output electrical power at time t during the expansion process, q me (t) represents the air mass flow rate during the expansion process at time t. Let γ be the air inlet temperature of the j-th stage expander, γ be the polytropic coefficient of the expansion process, and η be the inequality. e,j Let be the adiabatic efficiency of the j-th stage expander, and μ be the expansion ratio of each stage expander;

[0154] The outlet temperature of the j-th stage expander is:

[0155]

[0156] The regenerative subsystem is a compressed air energy storage system, consisting of a compression stage heat exchanger and an expansion stage heat exchanger. The compression stage heat exchanger transfers the heat of compression to a storage tank, while the expansion stage heat exchanger flexibly adjusts the temperature of the expanding air fluid according to load requirements. The mathematical model of the compression stage heat exchanger is as follows:

[0157]

[0158] In the formula, subscript 1 represents the hot fluid of the heat exchanger, 2 represents the cold fluid of the heat exchanger, c is the specific heat capacity of each fluid, and q m Let T be the mass flow rate of each fluid, ε be the heat exchanger efficiency, and T be the mass flow rate of each fluid. in T out These are the inflow and outflow temperatures of each fluid, (c p q) min This represents the minimum calorific value in both hot and cold fluids.

[0159] The compressed air energy storage power (the heat exchanger is part of the compressed air energy storage system) at time t is:

[0160]

[0161] The expansion heating power of the compressed air energy storage system at time t is:

[0162]

[0163] At the same time, P he (t) represents the expansion heating power of the compressed air energy storage system at time t, q mc (t), q me (t) has only one greater than 0, T stor The gas storage temperature in the gas storage chamber. Let be the temperature of the cooling water in the i-th stage compression heat exchanger. Let c be the temperature of the hot water heated by the i-th stage expansion heat exchanger. p The specific heat capacity of air;

[0164] When the outlet temperature of the final turbine in the compressed air energy storage system is lower than the preset temperature, the energy system has the ability to output cold energy. The formula for calculating the cold energy is as follows:

[0165]

[0166] The high-pressure gas storage subsystem is part of the compressed air energy storage system. Air is compressed and enters this system, releasing gas upon energy release. It consists of a high-pressure gas storage chamber. The pressure within the chamber represents the energy storage state of the compressed air energy storage system. The expression for the energy storage state of the compressed air energy storage system is:

[0167]

[0168]

[0169] At the same time, q mc (t), q me (t) has only one greater than 0, p max p min These are the maximum and minimum gas storage pressures of the gas storage chamber, V. stor p is the volume of the high-pressure gas storage chamber. stor (t) represents the gas storage pressure in the gas storage chamber at time t;

[0170] The mathematical model of a high-temperature thermal storage tank is expressed as follows:

[0171]

[0172]

[0173] In the formula, SOE T (t) represents the energy storage state of the thermal storage unit. At the same time, P hc (t), P he (t) has only one greater than 0, Q T,max Q T,min These are the maximum and minimum heat storage capacity of the thermal storage tank, P. hg (t), P ha (t) represents the supplementary heating power of the gas boiler and the heat consumption power of the absorption chiller at time t, respectively. T (t) represents the amount of heat stored in the heat storage tank at time t;

[0174] The mathematical model of a photovoltaic power generation system is expressed as follows:

[0175]

[0176] In the formula, P N-PV G is the rated power output of the photovoltaic system under standard test conditions, and G is the measured solar radiation. ref It is a constant, with a value of 1kW / m. 2 K t Let T be a constant, T0 be the ambient temperature, and T be the temperature. ref The ambient temperature under standard test conditions;

[0177] The mathematical model of a wind power generation system is expressed as follows:

[0178]

[0179] In the formula, v in To cut off the wind speed, v r For the rated wind speed, v out To cut off the wind speed, P rv is the rated power, and v is the wind speed.

[0180] Optionally, the expression for the energy supply cost evaluation model is:

[0181]

[0182] C p (t)=C CCHP,p (t)+C CAES,p (t)+C gird,p (t)+C WT (t)+C PV (t)

[0183] C h (t)=C CCHP,h (t)+C TES (t)+C gas (t)

[0184] C c (t)=C cCAES (t)+C ca (t)+C ce (t)

[0185] In the formula, T is the evaluation period; C is the total energy purchase cost. p (t) represents the electricity cost at time t, C h (t) represents the cost of purchasing heat at time t, C c (t) represents the cost of purchasing the refrigeration unit at time t; C CCHP,p (t), C CAES,p (t), C gird,p (t) represents the user's cost of purchasing electricity from the gas turbine generator set, compressed air energy storage, and the main power grid at time t, respectively. C WT (t), C PV (t) represents the electricity supply costs of the wind turbine and photovoltaic generator at time t, respectively; C CCHP,h (t), C TES (t), C gas (t) represents the user's heat purchase costs from the gas turbine unit, thermal storage unit, and supplementary gas boiler at time t; C cCAES (t), C ca (t), C ce (t) represents the cost at time t for the user to obtain cooling from the compressed air energy storage system, absorption chiller, and electric chiller.

[0186] In one specific embodiment of the present invention, the expression of the primary energy utilization rate evaluation model is as follows:

[0187]

[0188] In the formula, This represents the total usable heat of the CCHP system over a year. This represents the total power generation of the CCHP system within one year. This represents the annual primary energy input consumed by the CCHP system.

[0189] In one specific embodiment of the present invention, the expression of the power supply stability evaluation model is:

[0190]

[0191]

[0192] In the formula, P i P is the output power of the power supply device i. i,max N represents the maximum output power of power supply device i. b This refers to the total number of energy supply devices.

[0193] In one specific embodiment of the present invention, the expression of the optimization model is:

[0194] Minimize F(χ)=[f1(χ),f2(χ),...,f k (χ)] T

[0195] Subject to g(χ)=0.

[0196] and h(χ)≤0

[0197] In the formula, f k (χ) is the k-th objective function, g(χ) is the equality constraint, h(χ) is the inequality constraint, Minimize means to find the minimum, and Subject to means constraint.

[0198] Optionally, the constraints include:

[0199] Electrical balance constraints:

[0200] P CCHP (t)+P e (t)+P buy (t)+P WT (t)+P pv (t)=load e (t)+P c (t)+P re (t);

[0201] In the formula, P buy (t) represents the power purchased from the grid at time t, load e(t) represents the electrical load power at time t.

[0202] Thermal equilibrium constraint:

[0203] P CCHP,h (t)+P hg (t)+P hc (t)=load h (t)+P he (t)+P ha (t)

[0204] In the formula, load h (t) represents the heat load power at time t.

[0205] Cold equilibrium constraint

[0206] P ce (t)+P cCAES (t)+P ca (t)=load c (t)

[0207] In the formula, load c (t) represents the cooling load power at time t.

[0208] Gas turbine unit output constraints

[0209] P CCHP,min ≤P CCHP (t)≤P CCHP,max

[0210] In the formula, P CCHP,min P is the minimum output power of the gas turbine unit. CCHP,max This represents the maximum output power of the gas turbine unit.

[0211] Output constraints of compressed air energy storage systems

[0212]

[0213]

[0214] 0≤U c (t)+U e (t)≤1

[0215] Since a compressed air energy storage system can only be in a compressed or expanded state at any given time, it can be represented using a 0-1 variable, U. c (t) being 1 indicates the charging state, and 0 indicates the non-charging state. e (t) being 1 indicates a power supply state, while 0 indicates a non-power supply state. c min P cmax P represents the upper and lower limits of the charging power of the compressed air energy storage system. e min P e max These represent the upper and lower limits of discharge power, respectively.

[0216] Energy state constraints of compressed air energy storage systems:

[0217]

[0218]

[0219] Energy state constraints of thermal storage units:

[0220]

[0221] Cooling unit constraints:

[0222]

[0223]

[0224] In one specific embodiment of the present invention, the multi-objective adaptive optimization algorithm is a multi-objective adaptive differential evolution algorithm (MOSaDE), and the solution of the optimization model specifically includes the following steps:

[0225] (1) Randomly generate NP populations in the search space.

[0226] Initialize the parameters, such as policy P k The probability of the crossover (CR∈[0,1])(CRmk) and the learning period (LP=50) are given by the probability of the crossover (CR∈[0,1])(CRmk). There are many differential mutation strategies for generating trial vectors. Commonly used differential mutation strategies include: DE / rand / 1 strategy, DE / rand / 2 strategy, DE / current to rand / 1 strategy, DE / best / 1 strategy, DE / best / 2 strategy, DE / rand tobest / 1 strategy, and DE / current to best / 1 strategy. In solving multi-objective optimization problems, rand / 1 / bin strategy and rand / 2 / bin strategy are commonly used.

[0227] The expression for the adaptive differential evolution algorithm is as follows:

[0228]

[0229] Where F is the difference weight (usually ∈ [0,2]), r iIt is a mutually exclusive integer randomly generated within the range [1, NP].

[0230] (2) Evaluate the optimized value of the objective function under each strategy (in the MOSaDE algorithm, the following criteria are followed: if individual A is better than individual B, then select individual A; if individual A is similar to individual B, then select the individual with lower crowding), and externally archive the results.

[0231] (3) Execute the following optimized loop:

[0232] (3.1) Calculate the strategy probability P k , which is the success rate of the trial vectors generated by each strategy during the learning period.

[0233] (3.2) Assign the test vector generation strategy and parameters to each target vector x i

[0234] a. Use random universal sampling (SUS) for each target vector x i Choose a strategy k;

[0235] b. Assign control parameters F and CR, where F is generated using a normal distribution with a linear mean decreasing from 1.0 to 0.05 and a standard deviation of 0.1, and CR is generated using a normal distribution of the mean and a standard deviation of 0.1.

[0236] (3.3) Generate a new population, and generate each trial vector u in the population according to the k, F and CR values ​​assigned in (2). k,i .

[0237] (3.4) Perform the following procedure for population selection:

[0238] FOR i = 1:NP

[0239] Calculate the test vector u k,i and compare it with the closest u in the solution space. k,i Target vector x ni Compare them.

[0240] IF x ni dominate u k,i

[0241] Ignore vector u k,i

[0242] ELSE IF u k,i dominate x ni

[0243] will u k,i Replace vector x ni

[0244] ELSE(u k,i With x ni (non-dominant relationship)

[0245] Choose the vector with lower crowding as the new target vector.

[0246] END FOR

[0247] (4) If the following conditions are met, the optimization will be skipped and the Pareto front will be displayed:

[0248] (4.1) If u exists k,i Compared to x ni In an even better scenario, the relevant parameters CR and the flag policy k are recorded as successful policies and externally archived.

[0249] (4.2) If the number of external files exceeds the maximum preset value, the suboptimal solution will be defined as the optimal strategy.

[0250] If the above conditions cannot be met, step 3 will be repeated.

[0251] Ultimately, the optimal strategy for multi-energy complementarity in a regional integrated energy system based on MOSaDE can be obtained through the above steps.

[0252] Example 2

[0253] Based on the same inventive concept as in Embodiment 1, this embodiment of the invention provides a multi-energy complementary optimization device for a regional integrated energy system, comprising:

[0254] The first acquisition module is used to acquire an energy supply cost evaluation model for evaluating the economics of a regional integrated energy system.

[0255] The second acquisition module is used to acquire an energy supply stability evaluation model for evaluating the energy supply stability of the regional integrated energy system;

[0256] The third acquisition module is used to acquire a primary energy utilization rate evaluation model for evaluating the utilization rate of the regional integrated energy system;

[0257] The optimization modeling module is used to construct an optimization model based on the mathematical models of different energy supply devices and energy storage devices, as well as the energy supply cost evaluation model, energy supply stability evaluation model and primary energy utilization rate evaluation model. The optimization objectives are the lowest energy supply cost, the best energy supply stability and the highest primary energy utilization rate. The constraints are load balance, the energy supply characteristics of each energy supply device and the energy status of the energy storage device.

[0258] The solution module is used to solve the optimization model using a multi-objective adaptive optimization algorithm to obtain the optimal scheduling mode for each load, energy supply equipment and energy storage equipment in the regional integrated energy system.

[0259] Example 3

[0260] Based on the same inventive concept as in Embodiment 1, this embodiment of the invention provides a regional integrated energy system multi-energy complementary optimization system, including a storage medium and a processor;

[0261] The storage medium is used to store instructions;

[0262] The processor is configured to operate according to the instructions to execute the method according to any one of Embodiment 1.

[0263] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0264] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0265] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0266] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0267] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims. All of these forms are within the protection scope of the present invention.

[0268] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1. A method for multi-energy complementary optimization of a regional integrated energy system, characterized in that, include: Obtain an energy supply cost evaluation model for assessing the economics of a regional integrated energy system; Obtain an energy supply stability evaluation model for assessing the energy supply stability of the regional integrated energy system; Obtain a primary energy utilization rate evaluation model for evaluating the utilization rate of the regional integrated energy system; Based on the mathematical models of different energy supply and storage devices, as well as the energy supply cost evaluation model, energy supply stability evaluation model and primary energy utilization rate evaluation model, an optimization model is constructed with the optimization objectives of the lowest energy supply cost, the best energy supply stability and the highest primary energy utilization rate, and with load balance, the energy supply characteristics of each energy supply device and the energy status of the energy storage device as constraints. The optimization model is solved using a multi-objective adaptive optimization algorithm to obtain the optimal scheduling method for each load, energy supply equipment and energy storage equipment in the regional integrated energy system. The mathematical models of the different energy supply and energy storage devices include: The mathematical model of a combined cooling, heating and power system based on a gas turbine is expressed as follows: ； ； wherein is the power generation efficiency of the gas turbine at the time t, is the power generation power of the gas turbine at the time t; is the waste heat output power of the gas turbine at the time t, is a heat dissipation loss coefficient of the gas turbine; The mathematical model of the supplementary heat gas boiler is expressed as follows: ； In the formula, for Average thermal power over time For the heating efficiency of gas-fired boilers, In order to consume fuel volume, The calorific value of natural gas; The mathematical model of an absorption chiller unit is expressed as follows: ； In the formula, for Cooling power at any moment for Power consumption of absorption chiller units The refrigeration efficiency of the absorption chiller unit; The mathematical model of the electric refrigeration unit is expressed as follows: ； In the formula, for Cooling power at any time for Power consumption of the refrigeration unit at all times. The coefficient of performance (COP) of the electric refrigeration unit; The mathematical model of the compressed air energy storage system is expressed as follows: ； In the formula, For compression process Input electrical power at any given time for Air mass flow rate during the compression process. Let be the ideal gas constant. For the first Air inlet temperature of the stage compressor This is a variable coefficient during the compression process. For the first Grade compressor adiabatic efficiency, The pressure ratio of a single-stage compressor; No. The air outlet temperature of the stage compressor is: ； The output electrical power during the expansion process is: ； ； In the formula, For expansion process Output power at any time for Air mass flow rate during the constant expansion process. For the first Air inlet temperature of the multistage expander The polytropic coefficient for the expansion process, For the first Insulation efficiency of multistage expanders These are the expansion ratios of each stage of the expander; No. The outlet temperature of the primary expander is: ； The regenerative subsystem consists of a compression stage heat exchanger and an expansion stage heat exchanger. The compression stage heat exchanger transfers the heat of compression to a storage tank, while the expansion stage heat exchanger flexibly adjusts the temperature of the expanding air fluid according to load requirements. The mathematical model of the compression stage heat exchanger is as follows: ； In the formula, subscript 1 represents the hot fluid in the heat exchanger, and subscript 2 represents the cold fluid in the heat exchanger. For the specific heat capacity of each fluid, For each fluid mass flow rate, For heat exchanger heat exchange efficiency, These are the inflow and outflow temperatures of each fluid. This represents the minimum calorific value in both hot and cold fluids. The compressed air energy storage system has the following compressed heat storage power: ； The expansion heating power of the compressed air energy storage system is: ； At the same time, for The expansion and heating power of the compressed air energy storage system at all times. , Only one is greater than 0. The gas storage temperature in the gas storage chamber. For the first The cooling water temperature of the multistage compression heat exchanger For the first The temperature of hot water heated by the multi-stage expansion heat exchanger The specific heat capacity of air; When the outlet temperature of the final turbine in the compressed air energy storage system is lower than the preset temperature, the energy system has the ability to output cold energy. The formula for calculating the cold energy is as follows: ； The high-pressure gas storage subsystem is part of the compressed air energy storage system. Air is compressed and enters this system, releasing gas upon energy release. It consists of a high-pressure gas storage chamber. The pressure within the chamber represents the energy storage state of the compressed air energy storage system. The expression for the energy storage state of the compressed air energy storage system is: ； ； At the same time, , Only one is greater than 0. , These are the maximum and minimum gas storage pressures of the gas storage chamber, respectively. This refers to the volume of the high-pressure gas storage chamber. Let t be the gas storage pressure in the gas storage chamber at time t; The mathematical model of a high-temperature thermal storage tank is expressed as follows: ； ； In the formula, Indicates the energy storage status of the thermal storage unit, at the same time, , Only one is greater than 0. , These are the maximum and minimum heat storage capacity of the thermal storage tank, respectively. , These represent the supplementary heating power of the gas-fired boiler and the heat consumption power of the absorption chiller unit at time t, respectively. Let be the amount of heat stored in the heat storage tank at time t; The mathematical model of a photovoltaic power generation system is expressed as follows: ； In the formula, It is the rated power output of the photovoltaic system under standard test conditions. For the measured solar radiation, It is a constant, with a value of 1kW / m. 2 , It is a constant. For ambient temperature, The ambient temperature under standard test conditions; The mathematical model of a wind power generation system is expressed as follows: ； In the formula, To cut into wind speed, Rated wind speed, To cut off the wind speed, Rated power, Wind speed; The expression for the energy supply cost evaluation model is as follows: ； ； ； ； In the formula, For the evaluation period; For total energy purchase cost, for Electricity purchase cost at all times for The cost of purchasing heating at any time, Cost of purchasing cooling equipment at time t; , , These represent the costs for the user to purchase electricity from the gas turbine generator set, compressed air energy storage, and the main power grid at time t, respectively. , They are respectively The cost of electricity supplied by wind turbines and photovoltaic generators at all times; , , At time t, the user's heat purchase cost from the gas turbine unit, the thermal storage unit, and the supplementary heat gas boiler are respectively: , , These represent the costs incurred by the user at time t for cooling from the compressed air energy storage system, absorption chiller, and electric chiller. The expression for the primary energy utilization rate evaluation model is as follows: ； In the formula, This represents the total usable heat of the CCHP system over a year. This represents the total power generation of the CCHP system within one year. This represents the annual primary energy input consumed by the CCHP system. The expression for the energy supply stability evaluation model is: ； ； In the formula, For power supply equipment 'output power' For power supply equipment Maximum output power This refers to the total number of power supply devices.

2. The multi-energy complementary optimization method for a regional integrated energy system according to claim 1, characterized in that: The expression for the optimization model is: ； In the formula, It is the first One objective function, It is an equality constraint. These are inequality constraints. It means finding the minimum. That is, constraints.

3. The multi-energy complementary optimization method for a regional integrated energy system according to claim 1, characterized in that: The constraints include: Electrical balance constraints: ； In the formula, for Power purchased from the grid at all times for Constant electrical load power; Thermal equilibrium constraint: ； In the formula, for Constant heat load power; Cold equilibrium constraint ； In the formula, for Real-time cooling load power; Gas turbine unit output constraints: ； In the formula, This represents the minimum output power of the gas turbine unit. This represents the maximum output power of the gas turbine unit. Output constraints of compressed air energy storage systems ； ； ； Since compressed air energy storage systems can only be in a compressed or expanded state at any given time, they can be represented using 0-1 variables. A value of 1 indicates a charging state, while a value of 0 indicates that the device is not charging. A value of 1 indicates that the device is powered, while a value of 0 indicates that it is not powered. , These represent the upper and lower limits of the charging power of the compressed air energy storage system. , These represent the upper and lower limits of discharge power, respectively. Energy state constraints of compressed air energy storage systems: ； ； Energy state constraints of thermal storage units: ； Cooling unit constraints: ； 。 4. The multi-energy complementary optimization method for a regional integrated energy system according to claim 1, characterized in that: The multi-objective adaptive optimization algorithm is a multi-objective adaptive differential evolution algorithm.

5. A multi-energy complementary optimization device for a regional integrated energy system, characterized in that, include: The first acquisition module is used to acquire an energy supply cost evaluation model for evaluating the economics of a regional integrated energy system. The second acquisition module is used to acquire an energy supply stability evaluation model for evaluating the energy supply stability of the regional integrated energy system; The third acquisition module is used to acquire a primary energy utilization rate evaluation model for evaluating the utilization rate of the regional integrated energy system; The optimization modeling module is used to construct an optimization model based on the mathematical models of different energy supply devices and energy storage devices, as well as the energy supply cost evaluation model, energy supply stability evaluation model and primary energy utilization rate evaluation model. The optimization objectives are the lowest energy supply cost, the best energy supply stability and the highest primary energy utilization rate. The constraints are load balance, the energy supply characteristics of each energy supply device and the energy status of the energy storage device. The solution module is used to solve the optimization model using a multi-objective adaptive optimization algorithm to obtain the optimal scheduling mode for each load, energy supply equipment and energy storage equipment in the regional integrated energy system; The mathematical models of the different energy supply and energy storage devices include: The mathematical model of a combined cooling, heating and power system based on a gas turbine is expressed as follows: ； ； In the formula, for The power generation efficiency of a gas turbine at all times for The power generation capacity of the gas turbine at any given time; for The waste heat output power of the gas turbine at all times. This is the heat dissipation loss coefficient of the gas turbine; The mathematical model of the supplementary heat gas boiler is expressed as follows: ； In the formula, for Average thermal power over time For the heating efficiency of gas-fired boilers, In order to consume fuel volume, The calorific value of natural gas; The mathematical model of an absorption chiller unit is expressed as follows: ； In the formula, for Cooling power at any moment for Power consumption of absorption chiller units The refrigeration efficiency of the absorption chiller unit; The mathematical model of the electric refrigeration unit is expressed as follows: ； In the formula, for Cooling power at any time for Power consumption of the refrigeration unit at all times. The coefficient of performance (COP) of the electric refrigeration unit; The mathematical model of the compressed air energy storage system is expressed as follows: ； In the formula, For compression process Input electrical power at any given time for Air mass flow rate during the compression process. Let be the ideal gas constant. For the first Air inlet temperature of the stage compressor This is a variable coefficient during the compression process. For the first Grade compressor adiabatic efficiency, The pressure ratio of a single-stage compressor; No. The air outlet temperature of the stage compressor is: ； The output electrical power during the expansion process is: ； ； In the formula, For expansion process Output power at any time for Air mass flow rate during the constant expansion process. For the first Air inlet temperature of the multistage expander The polytropic coefficient for the expansion process, For the first Insulation efficiency of multistage expanders These are the expansion ratios of each stage of the expander; No. The outlet temperature of the primary expander is: ； The regenerative subsystem consists of a compression stage heat exchanger and an expansion stage heat exchanger. The compression stage heat exchanger transfers the heat of compression to a storage tank, while the expansion stage heat exchanger flexibly adjusts the temperature of the expanding air fluid according to load requirements. The mathematical model of the compression stage heat exchanger is as follows: ； In the formula, subscript 1 represents the hot fluid in the heat exchanger, and subscript 2 represents the cold fluid in the heat exchanger. For the specific heat capacity of each fluid, For each fluid mass flow rate, For heat exchanger heat exchange efficiency, These are the inflow and outflow temperatures of each fluid. This represents the minimum calorific value in both hot and cold fluids. The compressed air energy storage system has the following compressed heat storage power: ； The expansion heating power of the compressed air energy storage system is: ； At the same time, for The expansion and heating power of the compressed air energy storage system at all times. , Only one is greater than 0. The gas storage temperature in the gas storage chamber. For the first The cooling water temperature of the multistage compression heat exchanger For the first The temperature of hot water heated by the multi-stage expansion heat exchanger The specific heat capacity of air; When the outlet temperature of the final turbine in the compressed air energy storage system is lower than the preset temperature, the energy system has the ability to output cold energy. The formula for calculating the cold energy is as follows: ； The high-pressure gas storage subsystem is part of the compressed air energy storage system. Air is compressed and enters this system, releasing gas upon energy release. It consists of a high-pressure gas storage chamber. The pressure within the chamber represents the energy storage state of the compressed air energy storage system. The expression for the energy storage state of the compressed air energy storage system is: ； ； At the same time, , Only one is greater than 0. , These are the maximum and minimum gas storage pressures of the gas storage chamber, respectively. This refers to the volume of the high-pressure gas storage chamber. Let t be the gas storage pressure in the gas storage chamber at time t; The mathematical model of a high-temperature thermal storage tank is expressed as follows: ； ； In the formula, Indicates the energy storage status of the thermal storage unit, at the same time, , Only one is greater than 0. , These are the maximum and minimum heat storage capacity of the thermal storage tank, respectively. , These represent the supplementary heating power of the gas-fired boiler and the heat consumption power of the absorption chiller unit at time t, respectively. Let be the amount of heat stored in the heat storage tank at time t; The mathematical model of a photovoltaic power generation system is expressed as follows: ； In the formula, It is the rated power output of the photovoltaic system under standard test conditions. For the measured solar radiation, It is a constant, with a value of 1kW / m. 2 , It is a constant. For ambient temperature, The ambient temperature under standard test conditions; The mathematical model of a wind power generation system is expressed as follows: ； In the formula, To cut into wind speed, Rated wind speed, To cut off the wind speed, Rated power, Wind speed; The expression for the energy supply cost evaluation model is as follows: ； ； ； ； In the formula, For the evaluation period; For total energy purchase cost, for Electricity purchase cost at all times for The cost of purchasing heating at any time, Cost of purchasing cooling equipment at time t; , , These represent the costs for the user to purchase electricity from the gas turbine generator set, compressed air energy storage, and the main power grid at time t, respectively. , They are respectively The cost of electricity supplied by wind turbines and photovoltaic generators at all times; , , At time t, the user's heat purchase cost from the gas turbine unit, the thermal storage unit, and the supplementary heat gas boiler are respectively: , , These represent the costs incurred by the user at time t for cooling from the compressed air energy storage system, absorption chiller, and electric chiller. The expression for the primary energy utilization rate evaluation model is as follows: ； In the formula, This represents the total usable heat of the CCHP system over a year. This represents the total power generation of the CCHP system within one year. This represents the annual primary energy input consumed by the CCHP system. The expression for the energy supply stability evaluation model is: ； ； In the formula, For power supply equipment 'output power' For power supply equipment Maximum output power This refers to the total number of power supply devices.

6. A multi-energy complementary optimization system for a regional integrated energy system, characterized in that, Including storage media and processor; The storage medium is used to store instructions; The processor is configured to operate according to the instructions to perform the method according to any one of claims 1-4.

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