A bi-level optimization method and system for regional integrated energy system

Abstract

The application provides a regional comprehensive energy system double-layer optimization method and system, the method comprising the following steps: calculating future meteorological data according to historical meteorological data and future prediction data; combining the future meteorological data to perform load simulation on a typical building to obtain a typical building annual dynamic load curve, and obtaining a regional typical daily dynamic load curve through accumulation and clustering; constructing a mathematical model of the regional comprehensive energy system; establishing an upper-layer capacity planning model and a lower-layer operation optimization model based on the mathematical model; determining power balance constraints by the regional typical daily dynamic load curve for the lower-layer operation optimization model; solving the model by using a non-dominated sorting genetic algorithm and an interval linear programming method; and finally determining an optimal scheme by using an entropy weight superior and inferior solution distance method. Based on the method, the application further provides a regional comprehensive energy system double-layer optimization system. The application considers the influence of future climate change and uncertainty on regional load prediction and regional comprehensive energy system planning, so that the planning result is more reasonable.

Description

Technical Field

[0001] This invention belongs to the field of integrated energy system optimization technology, and specifically relates to a two-layer optimization method and system for regional integrated energy systems. Background Technology

[0002] With socio-economic development, energy shortages are becoming increasingly severe in various countries. Regional integrated energy systems, with their advantages of cascaded energy utilization and multi-energy complementarity, have become an important development direction for energy conservation and emission reduction. A regional integrated energy system is an integrated energy production, supply, and sales system that couples multiple energy sources such as electricity, heat, cooling, and gas, as well as various energy equipment, coordinating and optimizing the production, transmission, distribution, conversion, storage, and consumption of energy. Because regional integrated energy systems involve the coupling of multiple energy sources and equipment, rational planning is crucial. Regional load forecasting is the first step in planning, and the impact of global warming on load forecasting should be taken seriously. At the same time, the optimization process of regional integrated energy systems involves multiple uncertainties, such as wind power output, photovoltaic power output, load forecasting, and energy price fluctuations; how these are handled will affect the accuracy of the results.

[0003] In existing research, due to a lack of historical data, regional load forecasting in the planning stage often relies on numerical simulation. However, these simulations are largely based on historical meteorological data and do not consider the impact of climate change on regional load forecasting and integrated regional energy planning. Furthermore, optimization objectives often prioritize economic efficiency, failing to reflect the combined economic, environmental, and energy impacts of optimization within the context of energy conservation and emission reduction. To address uncertainty, stochastic programming, fuzzy logic, and robust optimization methods are frequently employed; however, these methods require precise probability distributions and membership relationships, or yield relatively conservative results. Summary of the Invention

[0004] This invention proposes a two-level optimization method and system for regional integrated energy systems, assesses the impact of climate change on regional integrated energy system planning, and uses interval linear programming to handle the existing uncertainties. At the same time, it explores the impact of economic, environmental and energy aspects on optimization.

[0005] To achieve the above objectives, the present invention provides a two-layer optimization method for a regional integrated energy system, comprising the following steps:

[0006] Calculate future meteorological data based on historical meteorological data and future forecast data; combine the future meteorological data with building performance simulation software to simulate the load of typical buildings to obtain the annual dynamic load curve of typical buildings, and obtain the regional typical daily dynamic load curve by accumulation and clustering.

[0007] A mathematical model of a regional integrated energy system is constructed. The mathematical module includes a first type of model based on future meteorological data and a second type of model independent of meteorological data. An upper-level capacity planning model and a lower-level operation optimization model are established based on the mathematical model of the regional integrated energy system. The lower-level operation optimization model determines the power balance constraints using the typical daily dynamic load curve of the region.

[0008] A non-dominated sorting genetic algorithm is used to solve the upper-level capacity planning model to obtain the capacity configuration of each energy device. The capacity configuration is then passed to the lower-level operation optimization model. Considering the uncertainties in the optimization process, the lower-level operation optimization model is solved using interval linear programming to obtain the operating cost, which is then fed back to the upper-level capacity planning model. The Pareto front is obtained through iteration between the upper and lower levels. Finally, the optimal solution is determined using the entropy weight superiority distance method.

[0009] Furthermore, the method for calculating future meteorological data based on historical meteorological data and future forecast data is as follows:

[0010] s = s0 + Δs θ ;s=n θ s0; s = s0 + Δs θ +n θ ×(s0-<s0> θ )

[0011] Where s represents future hourly climate data; s0 represents historical hourly climate data; Δs m θ represents the predicted meteorological change value for the month; n θ θ is the monthly downscaling scaling factor; <s0> θ θ represents the average of historical meteorological data for the month of θ.

[0012] Furthermore, the first type of model established based on future meteorological data includes wind turbine models and photovoltaic unit models;

[0013] The wind turbine model is as follows:

[0014]

[0015] Among them, v input (t) is the rated access wind speed of the wind turbine; v rated (t) is the rated wind speed of the wind turbine; v output v(t) is the rated cut-out wind speed of the wind turbine; v(t) is the wind speed at time t under future weather conditions. This refers to the rated power of the fan;

[0016] The photovoltaic unit model is as follows:

[0017]

[0018] Among them, P PV (t) represents the power generation of the photovoltaic unit at time t; f PV The derating factor for the photovoltaic array; I(t) represents the rated capacity of the photovoltaic unit; I(t) represents the solar radiation intensity at time t under future weather conditions; I stc The rated solar radiation intensity of a photovoltaic system under standard test conditions; T stc The rated temperature of a photovoltaic cell under standard test conditions; k PV T is the power temperature coefficient of a photovoltaic cell; air (t) represents the ambient temperature at time t under future weather conditions.

[0019] Furthermore, the second type of models unrelated to meteorological data includes gas turbine generator models, waste heat boiler models, absorption chiller models, gas boiler models, heat pump models, electric chiller models, and energy storage equipment models;

[0020] The gas turbine generator model is as follows:

[0021]

[0022] Among them, P MT,e (t) represents the output power of the gas turbine at time t; V MT (t) represents the natural gas consumption of the gas turbine at time t; η MT,e LHV is the power generation efficiency of a gas turbine generator set. ng The lower heating value of natural gas is Δt; Δt is the dispatch time interval.

[0023] The waste heat boiler model is as follows:

[0024]

[0025] Among them, Q WHB (t) represents the heating power of the waste heat boiler; μ d μ is the dissipation factor of the gas turbine generator set. re η is the waste heat recovery coefficient. hex The heating efficiency of the waste heat boiler;

[0026] The absorption chiller model is as follows:

[0027]

[0028] in, η is the cooling capacity of the lithium bromide absorption chiller. AC The coefficient of performance (COP) of a lithium bromide absorption chiller;

[0029] The gas-fired boiler model is as follows:

[0030] Among them, Q GB (t) represents the heating power of the gas-fired boiler at time t; η GB Heating efficiency of gas-fired boilers; V GB (t) represents the natural gas consumption of the gas-fired boiler at time t;

[0031] The heat pump model is as follows:

[0032]

[0033]

[0034] In the formula, Q HP (t) represents the heating power of the heat pump at time t; P represents the cooling power of the heat pump at time t. HP (t) represents the electrical input power of the heat pump at time t; The heating efficiency of the heat pump; The cooling efficiency of the heat pump;

[0035] The electric chiller model is: Q EC (t)=COP EC ·P EC (t);

[0036] Among them, Q EC (t) represents the cooling power of the electric chiller; COP EC P is the coefficient of performance (COP) of the electric chiller. EC (t) represents the electrical input power of the electric chiller;

[0037] The energy storage device model is as follows:

[0038]

[0039] In the formula, SOC(t+1) is the state of charge at time t+1; SOC(t) is the state of charge at time t; η ES,ch For charging efficiency; η ES,dis For energy release efficiency; P ES,ch (t) represents the charging power at time t, P ES,dis (t) represents the energy released at time t; x ES,dis Let x be a binary variable representing the charging state; ES,ch For the energy release state, there are two variables; C m This refers to the rated capacity of the energy storage device.

[0040] Furthermore, the upper-level capacity planning model incorporates annual total cost, carbon trading cost, and Efficiency is the objective function, and equipment capacity constraints are used as the constraint conditions.

[0041] The total annual cost is: minC ACT =C inv +C rep +C b +C r +C om

[0042]

[0043]

[0044] C b =τ·(C inv +C rep )

[0045] Among them, C ACT C represents the total annual cost. inv Initial investment cost; C rep Cost of replacing energy storage equipment; C b For equipment transportation and installation costs; C r For the cost of purchasing and selling energy; C om For equipment maintenance costs; c i c is the unit price of power generation equipment i; j The unit price of heating equipment j; c l The unit price of refrigeration equipment l; c m Let m be the unit price of the energy storage device; Y is the discount rate; Y is the system design life; Y m N is the lifespan of energy storage device m; τ is the percentage of equipment installation and transportation costs in the initial investment and energy storage device replacement costs; N1 is the number of types of power generation equipment i; N2 is the number of types of heating equipment j; N3 is the number of types of cooling equipment l; N4 is the number of types of energy storage device m.

[0046] Carbon trading costs are calculated using a tiered reward and penalty carbon trading model, specifically as follows:

[0047]

[0048]

[0049]

[0050] Among them, C CO2 For tiered carbon trading costs; E L For carbon emission allowances; E P ξ represents actual carbon emissions; ξ represents the base price for carbon trading; σ represents the carbon trading price incentive coefficient; β represents the carbon trading price penalty coefficient; L represents the length of the carbon emission range; ω represents the carbon emission range. e Carbon emission allowance per unit of electricity; ω h Carbon emission allowance per unit of heat; unit: kg / kWh; Ψ is the conversion factor for converting electricity generation into heat supply; P Grid,buy (t) represents the power purchased from the external power grid at time t; T is the dispatch period; The carbon emission coefficient of a coal-fired power unit is ; Carbon emission coefficient for gas-fired boilers and CCHP units;

[0051] Efficiency is:

[0052]

[0053] In the formula, P out,e (t) represents the output power of electrical energy at time t; λ e P is the energy quality coefficient of electrical energy. out,c (t) represents the output power of the cold energy at time t; λ c P is the energy mass coefficient of cold energy. out,h (t) represents the output power of thermal energy at time t; λ h P is the energy mass coefficient of thermal energy. in,g (t) Input power of natural gas; λ g P is the energy quality coefficient of natural gas. in,e (t) represents the input power of electrical energy; P in,rn (t) represents the input power of the renewable energy source; λ rn Energy quality coefficient for renewable energy;

[0054] The equipment capacity constraint is:

[0055]

[0056]

[0057]

[0058] 0≤C m ≤C m,max

[0059] In the formula, The rated capacity of power generation equipment i; The rated capacity of heating equipment j; C is the rated capacity of the refrigeration equipment l; m This refers to the rated capacity of the energy storage device.

[0060] The upper limit of the rated capacity of power generation equipment i The upper limit of the rated capacity of heating equipment j, The upper limit of the rated capacity of refrigeration equipment l; C m,max This refers to the upper limit of the rated capacity of energy storage devices;

[0061] Furthermore, the lower-level operation optimization model takes operating cost as the objective function and uses power balance constraints, equipment output constraints, power grid and gas grid constraints, ramp rate constraints, and energy storage equipment constraints as constraints.

[0062] The objective function is: F1 = min{C r +C om }

[0063]

[0064]

[0065] In the formula, c e c is the unit electricity purchase price; g The gas purchase price; c s The electricity price; P Grid,sell (t) represents the power sold to the external power grid at time t; V gas.buy (t) represents the consumption of natural gas; c om,z The unit power maintenance cost of device z; P z (t) represents the power of device z at time t;

[0066] The power balance constraint is:

[0067]

[0068]

[0069]

[0070] In the formula, P EES,ch (t) represents the charging power of the energy storage device at time t, P EES,dis (t) represents the energy release power of the energy storage device at time t; P Load (t) represents the electrical load at time t under typical future weather conditions; Q TES,ch (t represents the charging power of the thermal energy storage device at time t; Q) TES,dis (t) represents the energy release power of the thermal energy storage device at time t; The heat load at time t under typical future weather conditions; The charging power of the cold energy storage device at time t, Let t be the energy release power of the cold energy storage device at time t; The cooling load at time t under typical future weather conditions;

[0071] The device processing constraints are:

[0072]

[0073]

[0074]

[0075] In the formula, P i (t) represents the output power of generator i at time t; Let j be the output power of the heating device at time t; Let be the output power of the cooling device l at time t; This represents the upper limit of the load range for generator i; This represents the lower limit of the load range for generator i; This represents the upper limit of the load range for heating equipment j; This represents the lower limit of the load range for heating equipment j. This represents the upper limit of the load range for the refrigeration equipment l; This is the lower limit of the load range for the refrigeration equipment l;

[0076] The constraints of the power grid and gas grid are as follows:

[0077]

[0078]

[0079] In the formula, P Grid (t) represents the electrical power exchanged between the external power grid and the system at time t; This represents the upper limit of the electrical power exchanged between the external power grid and the system. This represents the lower limit of the power exchanged between the external power grid and the system; P gas (t) represents the gas power supplied to the system by the external gas network at time t; The upper limit of the gas power supplied by the external gas network to the system. This represents the upper limit of the gas supply capacity from the external gas network to the system.

[0080] The slope rate constraint is:

[0081]

[0082]

[0083] In the formula, The uphill gradient of the gas turbine; The downhill ramp rate of the gas turbine; The ramp-up rate of the gas-fired boiler; The downhill slope rate of the gas-fired boiler;

[0084] The constraints of the energy storage device are:

[0085] 0≤x ES,dis +x ES,cha ≤1

[0086] SOC ES (0)=SOC ES (T)

[0087]

[0088] 0≤P ES,ch (t)≤r ch ·C m

[0089] 0≤P ES,dis (t)≤r dis ·C m

[0090] In the formula, SOC ES (0) represents the state of charge (SOC) of the energy storage device at the beginning of the scheduling cycle. ES (T) represents the state of charge of the energy storage device at the end of the scheduling cycle; This represents the upper limit of the state of charge of energy storage devices. This represents the lower limit of the state of charge (SOC) of the energy storage device; r ch The charging rate of the energy storage device; r dis This refers to the energy release rate of the energy storage device.

[0091] Furthermore, the process of using a non-dominated sorting genetic algorithm to solve the upper-level capacity planning model to obtain the capacity configuration of each energy system includes:

[0092] The parent population is initialized, and the capacity configuration results are passed to the lower-level operational optimization model. The lower-level model returns the operating cost to the upper level, and the annual total cost, carbon trading cost, and other costs are calculated. efficiency;

[0093] Non-dominated sorting is performed; offspring populations are generated through selection, crossover, and mutation; the capacity allocation results are then passed to the lower-level model, which returns the operating costs to the upper level to calculate the annual total cost and carbon trading costs. efficiency;

[0094] The parent and child generations are merged, and a fast non-dominated sorting and crowding calculation are performed; individuals are selected to generate the next parent population.

[0095] Determine if the termination condition has been met. If it has, output the Pareto front; otherwise, generate a new generation population in a loop.

[0096] Furthermore, the model for the interval linear programming is as follows:

[0097]

[0098] In the formula: f is the objective function, [f] is the interval value of the objective function, and [f] = [f] - ,f + ], f - f + Let f be the lower and upper bounds of the interval values ​​of f, respectively; U is the coefficient matrix of the objective function, and [U] is the interval value of matrix U, where [U] = ([u... pq ]) 1×h ,[u pq ]= 1×h indicates that matrix U is 1 row and h columns, u pq Let U be the value in the p-th row and q-th column of matrix U. Let X be the lower and upper bounds of the interval values ​​of upq; X is the decision variable matrix of the objective function, [X] is the interval value of matrix X, and [X] = ([x]). pq ]) h×1 , h×1 indicates that matrix X is h rows and 1 column, x pq Let X be the value in the p-th row and q-th column. For x pq The lower and upper bounds of the interval values; A is the coefficient matrix of the inequality constraint, [A] is the interval value of matrix A, [A] = ([a]). pq ]) g×h , g×h indicates that matrix A has g rows and h columns, a pq Let A be the value in the p-th row and q-th column of matrix A. For a pq The lower and upper bounds of the interval values; B is the constraint matrix, [B] is the interval value of matrix B, [B] = ([b pq ]) g×1 , g×1 represents matrix B as having g rows and 1 column, b pq Let be the value in the p-th row and q-th column of matrix B. For b pq The lower and upper limits of the interval values;

[0099] Decompose the interval linear programming problem into two sub-models and solve them separately:

[0100] First Sub-model

[0101]

[0102] In the formula, Let be the interval variable with positive coefficients in the objective function. Let k1 be the interval variable with negative coefficients in the objective function, and k1 be the number of interval variables with positive coefficients in the objective function.

[0103] The solution can be obtained by solving the first sub-model. The lower bound of the solution for the decision variables and upper limit value

[0104] Second Sub-model

[0105]

[0106] The solution can be obtained by solving the second sub-model. The upper limit of the solution for the decision variables and lower limit value Finally, the interval value of the objective function can be obtained. and the interval values ​​of decision variables

[0107] Furthermore, the process of determining the optimal solution using the entropy weighted superior-inferior solution distance method includes:

[0108] Decision matrices are established based on several options, and then regularized to obtain regularized decision matrices.

[0109] The weights of each objective are calculated using the entropy weight method, and the objectives are weighted accordingly; the positive and negative ideal solutions for each indicator are then determined.

[0110] The Euclidean distance is used to calculate the proximity of each scheme to the positive and negative ideal solutions to determine the degree of proximity of each scheme; the degree of proximity is ranked, and the scheme with the largest relative proximity is the optimal scheme.

[0111] This invention also proposes a two-layer optimization system for a regional integrated energy system, comprising: a calculation module, a model construction module, and a determination module;

[0112] The calculation module is used to calculate future meteorological data based on historical meteorological data and future forecast data; combined with the future meteorological data, the building performance simulation software is used to simulate the load of typical buildings to obtain the annual dynamic load curve of typical buildings, and the typical daily dynamic load curve of the region is obtained by accumulation and clustering.

[0113] The model construction module is used to construct a mathematical model of a regional integrated energy system. The mathematical module includes a first type of model based on future meteorological data and a second type of model unrelated to meteorological data. An upper-level capacity planning model and a lower-level operation optimization model are established based on the mathematical model of the regional integrated energy system. The lower-level operation optimization model determines the power balance constraints using the typical daily dynamic load curve of the region.

[0114] The determination module is used to solve the upper-level capacity planning model using a non-dominated sorting genetic algorithm to obtain the capacity configuration of each energy device; the capacity configuration is passed to the lower-level operation optimization model, and considering the uncertainties in the optimization process, the lower-level operation optimization model is solved using an interval linear programming method to obtain the operating cost, which is then fed back to the upper-level capacity planning model. The upper and lower levels iterate to obtain the Pareto front, and finally the optimal solution is determined using the entropy weight superiority distance method.

[0115] The effects described in the invention are merely those of the embodiments, and not all the effects of the invention. One of the above technical solutions has the following advantages or beneficial effects:

[0116] This invention also proposes a two-layer optimization method and system for a regional integrated energy system. The method includes: calculating future meteorological data based on historical and forecast data; using building performance simulation software to simulate the load of typical buildings using the future meteorological data to obtain annual dynamic load curves for typical buildings, and obtaining typical daily dynamic load curves for the region through accumulation and clustering; constructing a mathematical model of the regional integrated energy system; the mathematical module includes a first-class model based on future meteorological data and a second-class model independent of meteorological data; establishing an upper-level capacity planning model and a lower-level operation optimization model based on the mathematical model of the regional integrated energy system; the lower-level operation optimization model determines power balance constraints using the typical daily dynamic load curves for the region; using a non-dominated sorting genetic algorithm to solve the upper-level capacity planning model to obtain the capacity configuration of each energy device; passing the capacity configuration to the lower-level operation optimization model, considering the uncertainties in the optimization process, using interval linear programming to solve the lower-level operation optimization model to obtain the operating cost, and feeding it back to the upper-level capacity planning model; the upper and lower levels iterate to obtain the Pareto front; and finally, using the entropy-weighted superior-inferiority distance method to determine the optimal solution. Based on this two-layer optimization method for a regional integrated energy system, a two-layer optimization system for the regional integrated energy system is also proposed. This invention takes into account the impact of future climate change and uncertainties on regional load forecasting and regional integrated energy system planning, making the planning results more reasonable.

[0117] This invention employs a two-layer optimization method to simultaneously optimize the capacity and equipment output of the regional integrated energy system, with the optimization results taking into account the economic, environmental, and energy impacts.

[0118] This invention uses interval linear programming to handle uncertainty problems, reducing the dependence on precise data and making the optimization process of regional integrated energy systems simpler.

[0119] This invention uses the entropy weight superiority distance method to determine the final scheme, making the determination of the final scheme of the regional integrated energy system more objective. Attached Figure Description

[0120] like Figure 1 This is a flowchart of a two-layer optimization method for a regional integrated energy system according to Embodiment 1 of the present invention;

[0121] like Figure 2 This is a schematic diagram of the regional integrated energy system structure in Embodiment 1 of the present invention;

[0122] like Figure 3 This is a model solution method for a two-layer optimization method for a regional integrated energy system according to Embodiment 1 of the present invention;

[0123] like Figure 4 This is a schematic diagram of a two-layer optimization system for a regional integrated energy system according to Embodiment 2 of the present invention. Detailed Implementation

[0124] 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 some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0125] Example 1

[0126] Embodiment 1 of this invention proposes a two-level optimization method for regional integrated energy systems, assesses the impact of climate change on regional integrated energy system planning, uses interval linear programming to handle the existing uncertainties, and explores the impact of economic, environmental and energy aspects on optimization.

[0127] Embodiment 1 of this invention presents a flowchart of a two-layer optimization method for a regional integrated energy system;

[0128] In step S100, future meteorological data is calculated based on historical meteorological data and future forecast data; then, by combining historical meteorological data and future forecast data, a downscaling method is used to generate future meteorological data. The future forecast data is released by the Intergovernmental Panel on Climate Change (IPCC) of the United Nations, and different global climate models (GCMs) predict the future climate for different representative concentration pathways (RCPs), but its high spatial resolution prevents it from being directly used in building simulation software.

[0129] The scaling down method adopts the "deformation" method, which is achieved through three methods: displacement, linear stretching (scale factor), and a combination of displacement and linear stretching.

[0130] The following formula is:

[0131] s = s0 + Δs θ ;s=n θ s0; s = s0 + Δs θ +n θ ×(s0- <s0> θ )

[0132] Where s represents future hourly climate data; s0 represents historical hourly climate data; Δs m θ represents the predicted meteorological change value for the month; n θ θ is the monthly downscaling scaling factor; <s0> θ θ represents the average of historical meteorological data for the month of θ.

[0133] In step S101, the annual dynamic load curve of a typical building is obtained by combining future meteorological data with building performance simulation software. The typical daily dynamic load curve of the region is obtained by accumulation and clustering.

[0134] Based on the planning information of the planning area, relevant design regulations, and on-site investigations, typical building models of different types are established. Using building performance simulation software and incorporating future meteorological data, load simulations are performed on these typical buildings. A bottom-up approach is used to accumulate the annual regional building dynamic load, and clustering is employed to obtain typical daily dynamic load curves for summer, winter, and transitional seasons.

[0135] Typical building models represent the building types, geometric shapes, dimensions, scale, thermal parameters of the building envelope, operating time of air conditioning and heating systems, indoor temperature, lighting power density and switching time, per capita building area and occupancy rate, fresh air volume for personnel and operating time of fresh air units, power density and utilization rate of electrical equipment in the planning area.

[0136] In step S102, a mathematical model of the regional integrated energy system is constructed; the mathematical module includes a first type of model based on future meteorological data and a second type of model unrelated to meteorological data.

[0137] like Figure 2 This is a schematic diagram of the regional integrated energy system structure in Embodiment 1 of the present invention; the integrated energy system includes a wind turbine generator, a photovoltaic unit, a gas turbine generator, a waste heat boiler, an absorption chiller, a gas boiler, an electric chiller, a heat pump, and energy storage equipment; wherein the energy storage equipment includes electrical storage equipment, thermal storage equipment, and cold storage equipment.

[0138] The first category of models includes wind turbine models and photovoltaic unit models.

[0139] The wind turbine model is as follows:

[0140]

[0141] Among them, v input (t) is the rated access wind speed of the wind turbine, in m / s; v rated (t) is the rated wind speed of the wind turbine, in m / s; v output (t) is the rated cut-out wind speed of the wind turbine, in m / s; v(t) is the wind speed at time t under future weather conditions, in m / s; This refers to the rated power of the fan, measured in kW.

[0142] The photovoltaic unit model is as follows:

[0143]

[0144] Among them, P PV (t) represents the photovoltaic power generation at time t, in kW; f PV This refers to the derating factor for the photovoltaic array, expressed in kW. The rated capacity of the photovoltaic unit is given in kW; I(t) represents the solar radiation intensity at time t under future weather conditions, in kW / m². 2 ;;I stc The rated solar radiation intensity of a photovoltaic system under standard test conditions, expressed in kW / m². 2 ;T stc The rated temperature of a photovoltaic cell under standard test conditions, in °C; k PV T represents the power temperature coefficient of a photovoltaic cell, expressed in % / ℃. air (t) represents the ambient temperature at time t under future weather conditions, in °C.

[0145] The second category of models includes gas turbine generator models, waste heat boiler models, absorption chiller models, gas boiler models, heat pump models, electric chiller models, and energy storage equipment models.

[0146] The gas turbine generator model is as follows:

[0147]

[0148] Among them, P MT,e (t) represents the output power of the gas turbine at time t, in kW; V MT (t) represents the natural gas consumption of the gas turbine at time t, in m³. 3 η MT,e LHV is the power generation efficiency of a gas turbine generator set. ng The lower heating value of natural gas is expressed in W·h / m³. 3 Δt is the scheduling time interval, in hours (h).

[0149] The waste heat boiler model is as follows:

[0150]

[0151] Among them, Q WHB (t) represents the heating power of the waste heat boiler, in kW; μ d μ is the dissipation factor of the gas turbine generator set. re η is the waste heat recovery coefficient. hex The heating efficiency of the waste heat boiler;

[0152] The absorption chiller model is as follows:

[0153]

[0154] in, η represents the cooling capacity of a lithium bromide absorption chiller, measured in kW. AC The coefficient of performance (COP) of a lithium bromide absorption chiller;

[0155] The gas boiler model is as follows:

[0156]

[0157] Among them, Q GB (t) represents the heating power of the gas-fired boiler at time t, in kW; η GB Heating efficiency of gas-fired boilers; V GB (t) represents the natural gas consumption of the gas-fired boiler at time t, in m³. 3 ;

[0158] The heat pump model is as follows:

[0159]

[0160]

[0161] In the formula, Q HP (t) represents the heating power of the heat pump at time t, in kW; P represents the cooling power of the heat pump at time t. HP (t) represents the electrical input power of the heat pump at time t; The heating efficiency of the heat pump; The cooling efficiency of the heat pump is expressed in kW.

[0162] The electric chiller model is as follows:

[0163] Q EC (t)=COP EC ·P EC (t);

[0164] Among them, Q EC (t) represents the cooling power of the electric chiller at time t; COP EC P is the coefficient of performance (COP) of the electric chiller. EC (t) represents the electrical input power of the electric chiller at time t;

[0165] The energy storage device model is as follows:

[0166]

[0167] In the formula, SOC(t+1) is the state of charge at time t+1; SOC(t) is the state of charge at time t; η ES,ch For charging efficiency; η ES,dis For energy release efficiency; P ES,ch (t) represents the charging power at time t, P ES,dis (t) represents the energy released at time t; x ES,dis Let x be a binary variable representing the charging state; ES,cha For the energy release state, there are two variables; C m This refers to the rated capacity of the energy storage device.

[0168] In step S104, an upper-level capacity planning model is established based on the mathematical model of the regional integrated energy system;

[0169] Upper-level capacity planning models take into account annual total cost, carbon trading costs, and Efficiency is the objective function, and equipment capacity constraints are used as the constraint conditions.

[0170] The total annual cost is:

[0171] minC ACT =C inv +C rep +C b +C r +C om

[0172]

[0173]

[0174] C b =τ·(C inv +C rep )

[0175] Among them, C ACT C represents the total annual cost. inv C represents the initial investment cost; rep Cost of replacing energy storage equipment; C b For equipment transportation and installation costs; C r For the cost of purchasing and selling energy; C om For equipment maintenance costs; c i c is the unit price of power generation equipment i; j The unit price of heating equipment j; c l The unit price of refrigeration equipment l; c m Let m be the unit price of the energy storage device; Y is the discount rate; Y is the system design life; Y m N is the lifespan of energy storage device m; τ is the percentage of equipment installation and transportation costs in the initial investment and energy storage device replacement costs; N1 is the number of types of power generation equipment i; N2 is the number of types of heating equipment j; N3 is the number of types of cooling equipment l; N4 is the number of types of energy storage device m.

[0176] Carbon trading costs are calculated using a tiered reward and penalty carbon trading model, specifically as follows:

[0177]

[0178]

[0179]

[0180] Among them, C CO2 For tiered carbon trading costs; E L For carbon emission allowances; E P ξ represents actual carbon emissions; ξ represents the base price for carbon trading; σ represents the carbon trading price incentive coefficient; β represents the carbon trading price penalty coefficient; L represents the length of the carbon emission range; ω represents the carbon emission range. e Carbon emission allowance per unit of electricity; ω h Carbon emission allowance per unit of heat; unit: kg / kWh; Ψ is the conversion factor for converting electricity generation to heat supply, MJ / kWh; P Grid,buy (t) represents the power purchased from the external power grid at time t; T is the dispatch period; The carbon emission coefficient for coal-fired power units is expressed in kg / kWh. The carbon emission coefficient for gas-fired boilers and CCHP units is expressed in kg / kWh.

[0181] Efficiency is:

[0182]

[0183] In the formula, P out,e (t) represents the output power of electrical energy at time t; λ e P is the energy quality coefficient of electrical energy. out,c (t) represents the output power of the cold energy at time t; λ c P is the energy mass coefficient of cold energy. out,h (t) represents the output power of thermal energy at time t; λ h P is the energy mass coefficient of thermal energy. in,g (t) Input power of natural gas; λ g P is the energy quality coefficient of natural gas. in,e (t) represents the input power of electrical energy; P in,rn (t) represents the input power of the renewable energy source; λ rn The energy quality coefficient is the energy quality factor for renewable energy.

[0184] The equipment capacity constraint is:

[0185]

[0186]

[0187]

[0188] 0≤C m ≤C m,max

[0189] In the formula, The rated capacity of power generation equipment i; The rated capacity of heating equipment j; C is the rated capacity of the refrigeration equipment l; m This refers to the rated capacity of the energy storage device.

[0190] The upper limit of the rated capacity of power generation equipment i The upper limit of the rated capacity of heating equipment j, The upper limit of the rated capacity of refrigeration equipment l; C m,max This refers to the upper limit of the rated capacity of energy storage devices;

[0191] In step S105, a lower-level operation optimization model is established based on the mathematical model of the regional integrated energy system; the lower-level operation optimization model determines the power balance constraints using the typical daily dynamic load curve of the region.

[0192] The lower-level operation optimization model takes operating cost as the objective function and uses power balance constraints, equipment output constraints, power grid and gas grid constraints, ramp rate constraints, and energy storage equipment constraints as constraints.

[0193] The objective function is: F1 = min{C r +C om }

[0194]

[0195]

[0196] In the formula, c e c is the unit electricity purchase price; g The gas purchase price; c s The electricity price; P Grid,sell (t) represents the power sold to the external power grid at time t; V gas.buy (t) represents the consumption of natural gas; c om,z The unit power maintenance cost of device z; P z (t) represents the power of device z at time t;

[0197] The power balance constraint is:

[0198]

[0199]

[0200]

[0201] In the formula, P EES,ch (t) represents the charging power of the energy storage device at time t, P EES,dis (t) represents the energy release power of the energy storage device at time t; P Load (t) represents the electrical load at time t under typical future weather conditions; Q TES,cha (t) represents the charging power of the thermal energy storage device at time t; Q TES,dis (t) represents the energy release power of the thermal energy storage device at time t; The heat load at time t under typical future weather conditions; The charging power of the cold energy storage device at time t, Let t be the energy release power of the cold energy storage device at time t; The cooling load at time t under typical future weather conditions;

[0202] The equipment processing constraints are:

[0203]

[0204]

[0205]

[0206] In the formula, P i (t) represents the output power of generator i at time t; Let j be the output power of the heating device at time t; Let be the output power of the cooling device l at time t; This represents the upper limit of the load range for power generation equipment i; This represents the lower limit of the load range for generator i; This represents the upper limit of the load range for heating equipment j; This represents the lower limit of the load range for heating equipment j. This represents the upper limit of the load range for the refrigeration equipment l; This is the lower limit of the load range for the refrigeration equipment l;

[0207] The constraints of the power grid and gas grid are:

[0208]

[0209]

[0210] In the formula, P Grid (t) represents the electrical power exchanged between the external power grid and the system at time t; This represents the upper limit of the electrical power exchanged between the external power grid and the system. This represents the lower limit of the power exchanged between the external power grid and the system; P gas (t) represents the gas power supplied to the system by the external gas network at time t; The upper limit of the gas power supplied by the external gas network to the system. This represents the upper limit of the gas supply capacity from the external gas network to the system.

[0211] The gradient rate constraint is:

[0212]

[0213]

[0214] In the formula, The uphill gradient of the gas turbine; The downhill ramp rate of the gas turbine; The ramp-up rate of the gas-fired boiler; This refers to the downhill slope rate of the gas-fired boiler.

[0215] Constraints for energy storage devices are:

[0216] 0≤x ES,dis +x ES,ch ≤1

[0217] SOC ES (0)=SOC ES (T)

[0218]

[0219] 0≤P ES,ch (t)≤r ch ·C m

[0220] 0≤P ES,dis (t)≤r dis ·C m

[0221] In the formula, SOC ES (0) represents the state of charge (SOC) of the energy storage device at the beginning of the scheduling cycle. ES (T) represents the state of charge of the energy storage device at the end of the scheduling cycle; This represents the upper limit of the state of charge of energy storage devices. This represents the lower limit of the state of charge (SOC) of energy storage devices; r ch The charging rate of the energy storage device; r dis This refers to the energy release rate of the energy storage device.

[0222] In step S105, a non-dominated sorting genetic algorithm is used to solve the upper-level capacity planning model to obtain the capacity configuration of each energy device. The capacity configuration is then passed to the lower-level operation optimization model. Considering the uncertainties in the optimization process, the lower-level operation optimization model is solved using interval linear programming to obtain the operating cost, which is then fed back to the upper-level capacity planning model. The upper and lower levels iterate to obtain the Pareto front. Finally, the optimal solution is determined using the entropy weight superiority distance method.

[0223] like Figure 3 This is a model solution method for a two-layer optimization method for a regional integrated energy system according to Embodiment 1 of the present invention;

[0224] The parent population is initialized, and the capacity configuration results are passed to the lower-level operational optimization model. The lower-level model returns the operating cost to the upper level to calculate the annual total cost, carbon trading cost, and so on. efficiency;

[0225] Non-dominated sorting is performed; offspring populations are generated through selection, crossover, and mutation; the capacity allocation results are then passed to the lower-level model, which returns the operating costs to the upper level to calculate the annual total cost and carbon trading costs. efficiency;

[0226] The parent and child generations are merged, and a fast non-dominated sorting and crowding calculation are performed; individuals are selected to generate the next parent population.

[0227] Determine if the termination condition has been met. If it has, output the Pareto front; otherwise, generate a new generation population in a loop.

[0228] The general model for interval linear programming is:

[0229]

[0230] In the formula: f is the objective function, [f] is the interval value of the objective function, and [f] = [f] - ,f + ], f - f + Let f be the lower and upper bounds of the interval values ​​of f, respectively; U is the coefficient matrix of the objective function, and [U] is the interval value of matrix U, where [U] = ([u... pq ]) 1×h , 1×h indicates that matrix U is 1 row and h columns, u pq Let U be the value in the p-th row and q-th column of matrix U. For u pq The lower and upper bounds of the interval values; X is the decision variable matrix of the objective function, [X] is the interval value of matrix X, [X] = ([x]). pq ]) h×1 , h×1 indicates that matrix X is h rows and 1 column, x pq Let X be the value in the p-th row and q-th column. For x pq The lower and upper bounds of the interval values; A is the coefficient matrix of the inequality constraint, [A] is the interval value of matrix A, [A] = ([a]). pq ]) g×h , g×h indicates that matrix A has g rows and h columns, a pq Let A be the value in the p-th row and q-th column of matrix A. For a pq The lower and upper bounds of the interval values; B is the constraint matrix, [B] is the interval value of matrix B, [B] = ([b pq ]) g×1 , g×1 represents matrix B as having g rows and 1 column, b pq Let be the value in the p-th row and q-th column of matrix B. For b pq The lower and upper limits of the interval values;

[0231] The interval linear programming problem is decomposed into two sub-models and solved separately: The first sub-model is:

[0232]

[0233] In the formula, Let be the interval variable with positive coefficients in the objective function. Let k1 be the interval variable with negative coefficients in the objective function, and k1 be the number of interval variables with positive coefficients in the objective function.

[0234] The solution can be obtained by solving the first sub-model. The lower bound of the solution for the decision variables and upper limit value

[0235] Second Sub-model

[0236]

[0237] The solution can be obtained by solving the second sub-model. The upper limit of the solution for the decision variables and lower limit value Finally, the interval value of the objective function can be obtained. and the interval values ​​of decision variables

[0238]

[0239] In step S106, the optimal solution is determined using the entropy weight superiority distance method.

[0240] A decision matrix is ​​constructed based on the three objectives of ψ possible solutions, and then regularized to obtain the regularized decision matrix R = (r we ) ψ×3 r we It is the e-th objective of the w-th scheme (w = 1, 2, ..., ψ; e = 1, 2, 3);

[0241] The weights ω of each objective are calculated using the entropy weight method. e and for r we Weighted average yields y we =ω e r we ;

[0242] Determine the positive and negative ideal solutions for each indicator: for the maximization objective Efficiency, the ideal solution is Negative ideal solution is The positive and negative ideal solutions for minimizing the target annual total cost and carbon trading cost are opposite to these.

[0243] The distances between each solution and the positive and negative ideal solutions are calculated using Euclidean distance:

[0244]

[0245] Calculate the relative similarity of each option:

[0246]

[0247] The solutions are ranked according to their relative proximity, and the solution with the highest relative proximity is the optimal solution.

[0248] The two-level optimization method for regional integrated energy systems proposed in Embodiment 1 of this invention takes into account the impact of future climate change on regional load forecasting and regional integrated energy system planning, making the planning results more reasonable.

[0249] The regional integrated energy system dual-layer optimization method proposed in Embodiment 1 of the present invention adopts a dual-layer optimization method, which simultaneously optimizes the capacity and equipment operating output of the regional integrated energy system, and the optimization results take into account the impact of economy, environment and energy efficiency.

[0250] The two-level optimization method for regional integrated energy systems proposed in Embodiment 1 of this invention uses interval linear programming to handle uncertainty problems, reduces the dependence on accurate data, and makes the optimization process of regional integrated energy systems simpler.

[0251] The two-layer optimization method for regional integrated energy systems proposed in Embodiment 1 of this invention uses the entropy weight superiority distance method to determine the final scheme, making the determination of the final scheme of regional integrated energy systems more objective.

[0252] Example 2

[0253] Based on the two-layer optimization method for a regional integrated energy system proposed in Embodiment 1 of this invention, Embodiment 2 of this invention also proposes a two-layer optimization system for a regional integrated energy system, such as... Figure 4 This is a schematic diagram of a two-layer optimization system for a regional integrated energy system according to Embodiment 2 of the present invention. The system includes: a calculation module, a model construction module, and a determination module.

[0254] The calculation module is used to calculate future meteorological data based on historical meteorological data and future forecast data; combined with the future meteorological data, the building performance simulation software is used to simulate the load of typical buildings to obtain the annual dynamic load curve of typical buildings, and the typical daily dynamic load curve of the region is obtained by accumulation and clustering.

[0255] A model building module is used to construct a mathematical model of a regional integrated energy system. The mathematical module includes a first type of model based on future meteorological data and a second type of model that is independent of meteorological data. An upper-level capacity planning model and a lower-level operation optimization model are established based on the mathematical model of the regional integrated energy system. The lower-level operation optimization model determines the power balance constraints using the typical daily dynamic load curve of the region.

[0256] The determination module is used to solve the upper-level capacity planning model using a non-dominated sorting genetic algorithm to obtain the capacity configuration of each energy device; the capacity configuration is then passed to the lower-level operation optimization model. Considering the uncertainties in the optimization process, the lower-level operation optimization model is solved using an interval linear programming method to obtain the operating cost, which is then fed back to the upper-level capacity planning model. The upper and lower levels iterate to obtain the Pareto front, and finally the optimal solution is determined using the entropy weight superiority distance method.

[0257] In the computation module, downscaling methods are used to generate future meteorological data. The future forecast data is released by the Intergovernmental Panel on Climate Change (IPCC) of the United Nations. Different global climate models (GCMs) predict the future climate for different representative concentration pathways (RCPs), but the high spatial resolution means it cannot be directly used for building software simulations.

[0258] The scaling down method employs a "deformation" approach, which is achieved through three methods: displacement, linear stretching (scale factor), and a combination of displacement and linear stretching.

[0259] The following formula is: s=s0+Δs θ ;s=n θ s0;s=s0+Δs θ +n θ ×(s0- <s0> θ )

[0260] Where s represents future hourly climate data; s0 represents historical hourly climate data; Δs m θ represents the predicted meteorological change value for the month; n θ θ is the monthly downscaling scaling factor; <s0> θ θ represents the average of historical meteorological data for the month of θ.

[0261] By combining future meteorological data with building performance simulation software, typical daily dynamic load curves are obtained by simulating the loads of typical buildings. Based on planning information, relevant design regulations, and site surveys of the planning area, different types of typical building models are established. Using building performance simulation software, load simulations are performed on various types of typical buildings, and an annual regional building dynamic load is obtained through bottom-up accumulation. Clustering methods are then used to obtain typical daily dynamic load curves for summer, winter, and transitional seasons.

[0262] Typical building models represent the building types, geometric shapes, dimensions, scale, thermal parameters of the building envelope, operating time of air conditioning and heating systems, indoor temperature, lighting power density and switching time, per capita building area and occupancy rate, fresh air volume for personnel and operating time of fresh air units, power density and utilization rate of electrical equipment in the planning area.

[0263] During the execution of the model building module, the wind turbine model is as follows:

[0264]

[0265] Among them, v input (t) is the rated access wind speed of the wind turbine; v rated (t) is the rated wind speed of the wind turbine; v output v(t) is the rated cut-out wind speed of the wind turbine; v(t) is the wind speed at time t under future weather conditions. This refers to the rated power of the fan;

[0266] The photovoltaic unit model is as follows:

[0267]

[0268] Among them, P PV (t) represents the power generation of the photovoltaic unit at time t; f PV The derating factor for the photovoltaic array; I(t) represents the rated capacity of the photovoltaic unit; I(t) represents the solar radiation intensity at time t under future weather conditions; I stc The rated solar radiation intensity of a photovoltaic system under standard test conditions; T stc The rated temperature of a photovoltaic cell under standard test conditions; k PV T is the power temperature coefficient of a photovoltaic cell; air (t) represents the ambient temperature at time t under future weather conditions.

[0269] The second category of models, which are unrelated to meteorological data, includes gas turbine generator models, waste heat boiler models, absorption chiller models, gas boiler models, heat pump models, electric chiller models, and energy storage device models.

[0270] The gas turbine generator model is as follows:

[0271] Among them, P MT,e (t) represents the output power of the gas turbine at time t; V MT (t) represents the natural gas consumption of the gas turbine at time t; η MT,e LHV is the power generation efficiency of a gas turbine generator set. ng The lower heating value of natural gas is Δt; Δt is the dispatch time interval.

[0272] The waste heat boiler model is as follows:

[0273]

[0274] Among them, Q WHB (t) represents the heating power of the waste heat boiler; μ d μ is the dissipation factor of the gas turbine generator set. re η is the waste heat recovery coefficient. hex The heating efficiency of the waste heat boiler;

[0275] The absorption chiller model is as follows:

[0276]

[0277] in, η is the cooling capacity of the lithium bromide absorption chiller. AC The coefficient of performance (COP) of the lithium bromide absorption chiller; the gas boiler model is:

[0278]

[0279] Among them, Q GB (t) represents the heating power of the gas-fired boiler at time t; η GB Heating efficiency of gas-fired boilers; V GB (t) represents the natural gas consumption of the gas-fired boiler at time t;

[0280] The heat pump model is as follows:

[0281]

[0282]

[0283] In the formula, Q HP (t) represents the heating power of the heat pump at time t; P represents the cooling power of the heat pump at time t. HP (t) represents the electrical input power of the heat pump at time t; The heating efficiency of the heat pump; The cooling efficiency of the heat pump;

[0284] The electric chiller model is as follows:

[0285] Q EC (t)=COP EC ·P EC (t);

[0286] Among them, Q EC (t) represents the cooling power of the electric chiller at time t; COP EC P is the coefficient of performance (COP) of the electric chiller. EC (t) represents the electrical input power of the electric chiller at time t;

[0287] The energy storage device model is as follows:

[0288]

[0289] In the formula, SOC(t+1) is the state of charge at time t+1; SOC(t) is the state of charge at time t; η ES,ch For charging efficiency; η ES,dis For energy release efficiency; P ES,ch (t) represents the charging power at time t, P ES,dis (t) represents the energy released at time t; x ES,dis Let x be a binary variable representing the charging state; ES,cha For the energy release state, there are two variables; C m This refers to the rated capacity of the energy storage device.

[0290] Upper-level capacity planning models take into account annual total cost, carbon trading costs, and Efficiency is the objective function; equipment capacity constraints are used as the limiting conditions.

[0291] The total annual cost is:

[0292]

[0293]

[0294] C b =τ·(C inv +C rep )

[0295] Among them, C ACT C represents the total annual cost. inv C represents the initial investment cost; rep Cost of replacing energy storage equipment; C b For equipment transportation and installation costs; C r For the cost of purchasing and selling energy; C om For equipment maintenance costs; c i c is the unit price of power generation equipment i; j The unit price of heating equipment j; c l The unit price of refrigeration equipment l; c m Let m be the unit price of the energy storage device; Y is the discount rate; Y is the system design life; Y m N is the lifespan of energy storage device m; τ is the percentage of equipment installation and transportation costs in the initial investment and energy storage device replacement costs; N1 is the number of types of power generation equipment i; N2 is the number of types of heating equipment j; N3 is the number of types of cooling equipment l; N4 is the number of types of energy storage device m.

[0296] Carbon trading costs are calculated using a tiered reward and penalty carbon trading model, specifically as follows:

[0297]

[0298]

[0299]

[0300] Among them, C CO2 For tiered carbon trading costs; E L For carbon emission allowances; E P ξ represents actual carbon emissions; ξ represents the base price for carbon trading; σ represents the carbon trading price incentive coefficient; β represents the carbon trading price penalty coefficient; L represents the length of the carbon emission range; ω represents the carbon emission range. e Carbon emission allowance per unit of electricity; ω h Carbon emission allowance per unit of heat; unit: kg / kWh; Ψ is the conversion factor for converting electricity generation to heat supply, MJ / kWh; P Grid,buy (t) represents the power purchased from the external power grid at time t; T is the dispatch period; The carbon emission coefficient for coal-fired power units is expressed in kg / kWh. The carbon emission coefficient for gas-fired boilers and CCHP units is expressed in kg / kWh.

[0301] Efficiency is:

[0302]

[0303] In the formula, P out,e (t) represents the output power of electrical energy at time t; λ e P is the energy quality coefficient of electrical energy. out,c (t) represents the output power of the cold energy at time t; λ c P is the energy mass coefficient of cold energy. out,h (t) represents the output power of thermal energy at time t; λ h P is the energy mass coefficient of thermal energy. in,g (t) Input power of natural gas; λ g P is the energy quality coefficient of natural gas. in,e (t) represents the input power of electrical energy; P in,rn (t) represents the input power of the renewable energy source; λ rn The energy quality coefficient is the energy quality factor for renewable energy.

[0304] The equipment capacity constraint is:

[0305]

[0306]

[0307]

[0308] 0≤C m ≤C m,max

[0309] In the formula, The rated capacity of power generation equipment i; The rated capacity of heating equipment j; C is the rated capacity of the refrigeration equipment l; m This refers to the rated capacity of the energy storage device.

[0310] The upper limit of the rated capacity of power generation equipment i The upper limit of the rated capacity of heating equipment j, The upper limit of the rated capacity of refrigeration equipment l; C m,max This refers to the upper limit of the rated capacity of energy storage devices;

[0311] The lower-level operation optimization model takes operating cost as the objective function and uses power balance constraints, equipment output constraints, power grid and gas grid constraints, ramp rate constraints, and energy storage equipment constraints as constraints.

[0312] The objective function is: F1 = min{C r +C om }

[0313]

[0314]

[0315] In the formula, c e c is the unit electricity purchase price; g The gas purchase price; c s The electricity price; P Grid,sell (t) represents the power sold to the external power grid at time t; V gas.buy (t) represents the consumption of natural gas; c om,z The unit power maintenance cost of device z; P z (t) represents the power of device z at time t;

[0316] The power balance constraint is:

[0317]

[0318]

[0319]

[0320] In the formula, P EES,ch (t) represents the charging power of the energy storage device at time t, P EES,dis (t) represents the energy release power of the energy storage device at time t; P load (t) represents the electrical load at time t under typical future weather conditions; Q TES,ch (t) represents the charging power of the thermal energy storage device at time t; Q TES,dis (t) represents the energy release power of the thermal energy storage device at time t; The heat load at time t under typical future weather conditions; The charging power of the cold energy storage device at time t, Let t be the energy release power of the cold energy storage device at time t; The cooling load at time t under typical future weather conditions;

[0321] The equipment processing constraints are:

[0322]

[0323]

[0324]

[0325] In the formula, P i (t) represents the output power of generator i at time t; Let j be the output power of the heating device at time t; Let be the output power of the cooling device l at time t; This represents the upper limit of the load range for power generation equipment i; This represents the lower limit of the load range for generator i; This represents the upper limit of the load range for heating equipment j; This represents the lower limit of the load range for heating equipment j. This represents the upper limit of the load range for the refrigeration equipment l; This is the lower limit of the load range for the refrigeration equipment l;

[0326] The constraints of the power grid and gas grid are:

[0327]

[0328]

[0329] In the formula, P Grid (t) represents the electrical power exchanged between the external power grid and the system at time t; This represents the upper limit of the electrical power exchanged between the external power grid and the system. This represents the lower limit of the power exchanged between the external power grid and the system; P gas (t) represents the gas power supplied to the system by the external gas network at time t; The upper limit of the gas power supplied by the external gas network to the system. This represents the upper limit of the gas supply capacity from the external gas network to the system.

[0330] The slope rate constraint is:

[0331]

[0332]

[0333] In the formula, The uphill gradient of the gas turbine; The downhill ramp rate of the gas turbine; The ramp-up rate of the gas-fired boiler; This refers to the downhill slope rate of the gas-fired boiler.

[0334] The constraints of the energy storage device are:

[0335] 0≤x ES,dis +x ES,ch ≤1

[0336] SOC ES (0)=SOC ES (T)

[0337]

[0338] 0≤P ES,ch (t)≤r ch ·C m

[0339] 0≤P ES,dis (t)≤r dis ·C m

[0340] In the formula, SOC ES (0) represents the state of charge (SOC) of the energy storage device at the beginning of the scheduling cycle. ES (T) represents the state of charge of the energy storage device at the end of the scheduling cycle; This represents the upper limit of the state of charge of energy storage devices. This represents the lower limit of the state of charge (SOC) of energy storage devices; r ch The charging rate of the energy storage device; r dis This refers to the energy release rate of the energy storage device.

[0341] The process of determining the module execution includes: the process of using a non-dominated sorting genetic algorithm to solve the upper-level capacity planning model to obtain the capacity configuration of each energy system includes:

[0342] The parent population is initialized, and the capacity configuration results are passed to the lower-level operational optimization model. The lower-level model returns the operating cost to the upper level to calculate the annual total cost, carbon trading cost, and so on. efficiency;

[0343] Non-dominated sorting is performed; offspring populations are generated through selection, crossover, and mutation; the capacity allocation results are then passed to the lower-level model, which returns the operating costs to the upper level to calculate the annual total cost and carbon trading costs. efficiency;

[0344] The parent and child generations are merged, and a fast non-dominated sorting and crowding calculation are performed; individuals are selected to generate the next parent population.

[0345] Determine if the termination condition has been met. If it has, output the Pareto front; otherwise, generate a new generation population in a loop.

[0346] The model for interval linear programming is as follows:

[0347]

[0348] In the formula: f is the objective function, [f] is the interval value of the objective function, and [f] = [f] -, f + ], f - f + Let f be the lower and upper bounds of the interval values ​​of f, respectively; U is the coefficient matrix of the objective function, and [U] is the interval value of matrix U, where [U] = ([u... pq ]) 1×h ,[u pq ]= 1×h indicates that matrix U is 1 row and h columns, u pq Let U be the value in the p-th row and q-th column of matrix U. For u pq The lower and upper bounds of the interval values; X is the decision variable matrix of the objective function, [X] is the interval value of matrix X, [X] = ([x]). pq ]) h×1 , h×1 indicates that matrix X is h rows and 1 column, x pq Let X be the value in the p-th row and q-th column. For x pq The lower and upper bounds of the interval values; A is the coefficient matrix of the inequality constraint, [A] is the interval value of matrix A, [A] = ([a]). pq ]) g×h , g×h indicates that matrix A has g rows and h columns, a pq Let A be the value in the p-th row and q-th column of matrix A. For a pq The lower and upper bounds of the interval values; B is the constraint matrix, [B] is the interval value of matrix B, [B] = ([b pq ]) g×1 , g×1 represents matrix B as having g rows and 1 column, b pq Let be the value in the p-th row and q-th column of matrix B. For b pq The lower and upper limits of the interval values;

[0349] Decompose the interval linear programming problem into two sub-models and solve them separately:

[0350] First Sub-model

[0351]

[0352] In the formula, Let be the interval variable with positive coefficients in the objective function. Let k1 be the interval variable with negative coefficients in the objective function, and k1 be the number of interval variables with positive coefficients in the objective function.

[0353] The solution can be obtained by solving the first sub-model. The lower bound of the solution for the decision variables and upper limit value

[0354] Second Sub-model

[0355]

[0356] The solution can be obtained by solving the second sub-model. The upper limit of the solution for the decision variables and lower limit value Finally, the interval value of the objective function can be obtained. and the interval values ​​of decision variables

[0357] The process of determining the optimal solution using the entropy weighted superior-inferior solution distance method includes: establishing a decision matrix based on several options and regularizing it to obtain a regularized decision matrix; calculating the weights of each objective using the entropy weighted method and weighting each objective; and determining the positive and negative ideal solutions for each index: for the maximization objective... Efficiency: Euclidean distance is used to calculate the proximity of each scheme to the positive and negative ideal solutions to determine the degree of proximity of each scheme; the degree of proximity is ranked, and the scheme with the largest relative proximity is the optimal scheme.

[0358] The regional integrated energy system dual-layer optimization system provided in Embodiment 2 of this application modularizes the process of implementing the regional integrated energy system dual-layer optimization method proposed in Embodiment 1 of this application. For the description of the relevant parts, please refer to the detailed description of the corresponding parts in the regional integrated energy system dual-layer optimization method provided in Embodiment 1 of this application, and will not be repeated here.

[0359] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that the elements inherent in a process, method, article, or apparatus that includes a list of elements are included. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element. Additionally, portions of the technical solutions provided in the embodiments of this application that are consistent with the implementation principles of corresponding technical solutions in the prior art have not been described in detail to avoid excessive elaboration.

[0360] While specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art can make other modifications or variations based on the above description. It is neither necessary nor possible to exhaustively describe all embodiments here. Various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. A two-level optimization method for a regional integrated energy system, characterized in that, Includes the following steps: Calculate future meteorological data based on historical meteorological data and future forecast data; combine the future meteorological data with building performance simulation software to simulate the load of typical buildings to obtain the annual dynamic load curve of typical buildings, and obtain the regional typical daily dynamic load curve by accumulation and clustering. A mathematical model of a regional integrated energy system is constructed. The mathematical model includes a first type of model based on future meteorological data and a second type of model unrelated to meteorological data. An upper-level capacity planning model and a lower-level operation optimization model are established based on the mathematical model of the regional integrated energy system. The lower-level operation optimization model determines the power balance constraints using the typical daily dynamic load curve of the region. The upper-level capacity planning model uses the total annual cost, carbon trading cost, and efficiency as objective functions, and equipment capacity constraints as constraints. The lower-level operation optimization model takes operating cost as the objective function and uses power balance constraints, equipment output constraints, power grid and gas grid constraints, ramp rate constraints, and energy storage equipment constraints as constraints. A non-dominated sorting genetic algorithm is used to solve the upper-level capacity planning model to obtain the capacity configuration of each energy device. The capacity configuration is then passed to the lower-level operation optimization model. Considering the uncertainties in the optimization process, the lower-level operation optimization model is solved using interval linear programming to obtain the operating cost, which is then fed back to the upper-level capacity planning model. The Pareto front is obtained through iteration between the upper and lower levels. Finally, the optimal solution is determined using the entropy weight superiority distance method.

2. The two-layer optimization method for a regional integrated energy system according to claim 1, characterized in that, The method for calculating future meteorological data based on historical meteorological data and future forecast data is as follows: in, For future hourly climate data; Historical hourly climate data; for Monthly forecast meteorological changes; for Monthly scaling factor; for The average value of historical meteorological data for the month.

3. The two-layer optimization method for a regional integrated energy system according to claim 1, characterized in that, The first type of model based on future meteorological data includes wind turbine models and photovoltaic unit models; The wind turbine model is as follows: in, This is the rated access wind speed of the wind turbine unit; This is the rated wind speed of the wind turbine. This is the rated cut-out wind speed of the wind turbine unit; It is the wind speed at time t under future weather conditions; This refers to the rated power of the fan; The photovoltaic unit model is as follows: ； in, Let t be the power generation capacity of the photovoltaic unit; The derating factor for the photovoltaic array; This refers to the rated capacity of the photovoltaic unit. The solar radiation intensity at time t under future weather conditions; The rated solar radiation intensity of a photovoltaic system under standard test conditions; The rated temperature of a photovoltaic cell under standard test conditions; The power temperature coefficient of a photovoltaic cell; Let t be the ambient temperature at time t under future weather conditions.

4. The two-layer optimization method for a regional integrated energy system according to claim 3, characterized in that, The second type of models, which are unrelated to meteorological data, includes gas turbine generator models, waste heat boiler models, absorption chiller models, gas boiler models, heat pump models, electric chiller models, and energy storage equipment models. The gas turbine generator model is as follows: ； in, Let t be the output power of the gas turbine. Let be the natural gas consumption of the gas turbine at time t; The power generation efficiency of the gas turbine generator set; It is the lower heating value of natural gas; The scheduling time interval; The waste heat boiler model is as follows: in, This refers to the heating capacity of the waste heat boiler. The dissipation factor of the gas turbine generator set; The waste heat recovery coefficient; The heating efficiency of the waste heat boiler; The absorption chiller model is as follows: in, This refers to the cooling capacity of a lithium bromide absorption chiller. The coefficient of performance (COP) of a lithium bromide absorption chiller; The gas-fired boiler model is as follows: ； in, Let t be the heating power of the gas-fired boiler. Heating efficiency of gas-fired boilers; Let t be the natural gas consumption of the gas-fired boiler. The heat pump model is as follows: In the formula, Let t be the heating power of the heat pump at time t; Let t be the cooling power of the heat pump at time t; Let t be the electrical input power of the heat pump at time t; The heating efficiency of the heat pump; The cooling efficiency of the heat pump; The electric chiller model is as follows: ； in, Let t be the cooling power of the electric chiller; The coefficient of performance (COP) of the electric refrigeration unit; Let be the electrical input power of the electric chiller at time t; The energy storage device model is as follows: ； In the formula, SOC(t+1) is the state of charge at time t+1; SOC(t) is the state of charge at time t. For charging efficiency; For energy release efficiency; Let be the charging power at time t. Let be the energy released at time t; The charging state is a binary variable; For the energy release state, there are two variables; This refers to the rated capacity of the energy storage device.

5. The two-layer optimization method for a regional integrated energy system according to claim 4, characterized in that, The upper-level capacity planning model takes the annual total cost, carbon trading cost and efficiency as objective functions, and equipment capacity constraints as constraints. The total annual cost is: in, Total annual cost; Initial investment cost; Cost of replacing energy storage equipment; For equipment transportation and installation costs; For the cost of purchasing and selling energy; For equipment maintenance costs; For power generation equipment The unit price; For heating equipment The unit price; For refrigeration equipment The unit price; For energy storage devices The unit price; The discount rate; Design lifespan for the system; For energy storage devices Service life; The percentage of equipment installation and transportation costs in the initial investment and energy storage equipment replacement costs; N1 represents the power generation equipment. Type and quantity; N2 is heating equipment. Type and quantity; N3 is refrigeration equipment. Type and quantity; N4 is energy storage device The number of types; Carbon trading costs are calculated using a tiered reward and penalty carbon trading model, specifically as follows: in, For tiered carbon trading costs; For carbon emission quotas; For actual carbon emissions; The base price for carbon trading; This refers to the carbon trading price incentive coefficient. The penalty coefficient for carbon trading prices; L is the length of the carbon emission range; Carbon emission allowances per unit of electricity; Carbon emission allowance per unit of heat; A conversion factor for converting electricity generation into heat supply; Let t be the power purchased from the external power grid at time t; T is the dispatch period. The carbon emission coefficient for coal-fired power units; Carbon emission coefficients for gas-fired boilers and CCHP units; The efficiency is: In the formula, ; output power; for Energy mass coefficient; for Output power of cold energy; The energy mass coefficient of cold energy; for Thermal energy output power; The energy mass coefficient of thermal energy; Input power of natural gas; The energy quality coefficient of natural gas; The input power of electrical energy; Input power for renewable energy; The energy quality coefficient of renewable energy; The equipment capacity constraint is: In the formula, For power generation equipment Rated capacity; For heating equipment Rated capacity; For refrigeration equipment Rated capacity; This refers to the rated capacity of the energy storage device. For power generation equipment The upper limit of the rated capacity; For heating equipment The upper limit of the rated capacity; Refrigeration equipment The upper limit of the rated capacity; This represents the upper limit of the rated capacity of the energy storage device.

6. The two-layer optimization method for a regional integrated energy system according to claim 5, characterized in that, The lower-level operation optimization model takes operating cost as the objective function and uses power balance constraints, equipment output constraints, power grid and gas grid constraints, ramp rate constraints and energy storage equipment constraints as constraints. The objective function is: In the formula, The unit price for electricity purchase; The price for purchasing gas; The electricity price; Let t be the power output sold to the external power grid at time t; This refers to the consumption of natural gas. The unit power maintenance cost of device z; Let z be the power of the device at time t; The power balance constraint is: ； In the formula, The charging power of the energy storage device at time t, Let t be the energy release power of the energy storage device at time t; The electrical load at time t under typical future weather conditions; Let t be the charging power of the thermal energy storage device at time t; Let t be the energy release power of the thermal energy storage device at time t; The heat load at time t under typical future weather conditions; The charging power of the cold energy storage device at time t, Let t be the energy release power of the cold energy storage device at time t; The cooling load at time t under typical future weather conditions; The device processing constraints are: In the formula, For the power generation equipment at time t ; output power; Heating equipment at time t ; output power; Refrigeration equipment at time t ; output power; For power generation equipment The upper limit of the load range; For power generation equipment The lower limit of the load range; For heating equipment The upper limit of the load range; For heating equipment The lower limit of the load range; For refrigeration equipment The upper limit of the load range; For refrigeration equipment The lower limit of the load range; The constraints on the power grid and gas grid are as follows: In the formula, Let t be the electrical power exchanged between the external power grid and the system. This represents the upper limit of the electrical power exchanged between the external power grid and the system. This represents the lower limit of the electrical power exchanged between the external power grid and the system. Let t be the gas power supplied by the external gas network to the system; The upper limit of the gas power supplied by the external gas network to the system. This represents the lower limit of the gas supply capacity from the external gas network to the system. The slope rate constraint is: In the formula, The uphill gradient of the gas turbine; The downhill ramp rate of the gas turbine; The ramp-up rate of the gas-fired boiler; The downhill slope rate of the gas-fired boiler; The constraints of the energy storage device are: In the formula, This refers to the state of charge of the energy storage device at the beginning of the scheduling cycle. This refers to the state of charge of the energy storage device at the end of the scheduling cycle; This represents the upper limit of the state of charge of energy storage devices. This represents the lower limit of the state of charge of energy storage devices. The charging rate of energy storage devices; This refers to the energy release rate of the energy storage device.

7. The two-layer optimization method for a regional integrated energy system according to claim 1, characterized in that, The process of using a non-dominated sorting genetic algorithm to solve the upper-level capacity planning model to obtain the capacity configuration of each energy system includes: Initialize the parent population, pass the capacity configuration results to the lower-level operation optimization model, and the lower-level model returns the operating cost to the upper level to calculate the annual total cost, carbon trading cost and efficiency. Perform non-dominated sorting; select, crossover, and mutate to generate offspring populations, and pass the capacity allocation results to the lower-level model again. The lower-level model returns the operating cost to the upper level to calculate the annual total cost, carbon trading cost, and efficiency. The parent and child generations are merged, and a fast non-dominated sorting and crowding calculation are performed; individuals are selected to generate the next parent population. Determine if the termination condition has been met. If it has, output the Pareto front; otherwise, generate a new generation population in a loop.

8. The two-layer optimization method for a regional integrated energy system according to claim 1, characterized in that, The model for the interval linear programming is as follows: In the formula: Let be the objective function. Let the interval values ​​of the objective function be denoted by . , , They are respectively The lower and upper limits of the interval values; U is the coefficient matrix of the objective function. Let U be the interval values ​​of matrix U. , 1×h indicates that matrix U has 1 row and h columns. Let U be the value in the p-th row and q-th column of matrix U. , for The lower and upper limits of the interval values; X is the decision variable matrix of the objective function. Let X be the interval value of matrix X. , h×1 indicates that matrix X is in the h-row, 1-column configuration. Let X be the value in the p-th row and q-th column. , for The lower and upper bounds of the interval values; A is the coefficient matrix of the inequality constraints. Let A be the interval values ​​of matrix A. , , Let matrix A be row h, column h Let A be the value in the p-th row and q-th column of matrix A. , for The lower and upper limits of the interval values; B is the constraint matrix. Let B be the interval value of matrix B. , , The representative matrix B is Row 1, Column 1 Let be the value in the p-th row and q-th column of matrix B. , for The lower and upper limits of the interval values; Decompose the interval linear programming problem into two sub-models and solve them separately: First Sub-model In the formula, ( Let (i = 1, 2, ..., k1) be the interval variables with positive coefficients in the objective function. ( =k1+1,k1+2,…,h) represents the interval variables with negative coefficients in the objective function, and k1 represents the number of interval variables with positive coefficients in the objective function; The solution can be obtained by solving the first sub-model. The lower bound of the solution for the decision variables ( =1,2,…,k1) and upper limit value ( =k1+1,k1+2,…,h); Second Sub-model The solution can be obtained by solving the second sub-model. The upper limit of the solution for the decision variables ( =1,2,…,k1) and lower limit value ( =k1+1,k1+2,…,h); finally, the interval value of the objective function can be obtained. and the interval values ​​of decision variables .

9. The two-layer optimization method for a regional integrated energy system according to claim 1, characterized in that, The process of determining the optimal solution using the entropy-weighted superior-inferiority distance method includes: Decision matrices are established based on several options, and then regularized to obtain regularized decision matrices. The weights of each objective are calculated using the entropy weight method, and the objectives are weighted accordingly; the positive and negative ideal solutions for each indicator are determined. The Euclidean distance is used to calculate the proximity of each scheme to the positive and negative ideal solutions to determine the degree of proximity of each scheme; the degree of proximity is ranked, and the scheme with the largest relative proximity is the optimal scheme.

10. A two-level optimization system for a regional integrated energy system, used to execute the two-level optimization method for a regional integrated energy system as described in any one of claims 1 to 9, characterized in that, include: The module consists of a calculation module, a model building module, and a determination module. The calculation module is used to calculate future meteorological data based on historical meteorological data and future forecast data. By combining the aforementioned future meteorological data, building performance simulation software is used to simulate the load on typical buildings to obtain the annual dynamic load curve of typical buildings. Through accumulation and clustering, the typical daily dynamic load curve of the region is obtained. The model construction module is used to construct a mathematical model of a regional integrated energy system. The mathematical model includes a first type of model based on future meteorological data and a second type of model unrelated to meteorological data. An upper-level capacity planning model and a lower-level operation optimization model are established based on the mathematical model of the regional integrated energy system. The lower-level operation optimization model determines the power balance constraints using the typical daily dynamic load curve of the region. The determination module is used to solve the upper-level capacity planning model using a non-dominated sorting genetic algorithm to obtain the capacity configuration of each energy device; the capacity configuration is passed to the lower-level operation optimization model, and considering the uncertainties in the optimization process, the lower-level operation optimization model is solved using an interval linear programming method to obtain the operating cost, which is then fed back to the upper-level capacity planning model. The upper and lower levels iterate to obtain the Pareto front, and finally the optimal solution is determined using the entropy weight superiority distance method.

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