Multi-objective planning method and system for integrated energy system and storage medium
By constructing lower-level and upper-level models and combining them with optimization algorithms, the problem of insufficient planning caused by differences in energy quality in integrated energy systems was solved, achieving energy quantity and quality matching and system optimization, and improving the reliability and accuracy of planning.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- STATE GRID HUNAN ELECTRIC POWER COMPANY LIMITED
- Filing Date
- 2023-11-03
- Publication Date
- 2026-05-29
Smart Images
Figure CN117575204B_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of electrical automation, and specifically relates to a multi-objective planning method, system and storage medium for integrated energy systems. Background Technology
[0002] Currently, increasingly prominent social, environmental, and resource issues have led to a growing demand for efficient and green energy across society. Integrated energy systems can meet diverse load demands while maximizing energy conservation and emission reduction through the complementarity of various energy sources; therefore, integrated energy systems have received widespread attention from all sectors of society in recent years.
[0003] Integrated energy systems involve multiple energy forms such as wind, solar, electricity, gas, and heat. The overall efficiency of the system is maximized through the complementarity and scheduling of these energy forms. Currently, most traditional integrated energy system planning schemes are based on energy-level system planning models, simply focusing on energy quantity matching. However, integrated energy systems involve multiple energy forms, including wind, solar, electricity, gas, and heat, and the quality of these different energy sources varies significantly. For example, in terms of work done, electricity can be fully converted into work, while heat can only be partially converted; therefore, electricity is generally considered to have higher quality than heat. However, current integrated planning methods only consider energy quantity matching, neglecting the differences in energy quality between different forms and the degree of energy quantity / quality matching between the supply and demand sides of the integrated energy system. This results in poor reliability, accuracy, and energy efficiency in the final planning. Summary of the Invention
[0004] One of the objectives of this invention is to provide a multi-objective planning method for integrated energy systems that is highly reliable, accurate, and objectively scientific.
[0005] The second objective of this invention is to provide a system for implementing the multi-objective planning method for the integrated energy system.
[0006] A third objective of this invention is to provide a storage medium that incorporates the aforementioned multi-objective planning method for integrated energy systems.
[0007] The multi-objective planning method for integrated energy systems provided by this invention includes the following steps:
[0008] To obtain operational and planning data information for integrated energy systems;
[0009] Based on the acquired data, an optimization model for the operation of the lower-level integrated energy system is constructed, with annual operating cost as the objective function and power balance and equipment operation rules as constraints.
[0010] Based on the acquired data, an upper-level integrated energy system planning model is constructed with equivalent annual total cost, energy efficiency, and energy level balance coefficient as optimization objectives and equipment capacity as constraints.
[0011] The operation optimization model of the lower-level integrated energy system and the planning model of the upper-level integrated energy system are solved to obtain the final multi-objective planning result of the integrated energy system.
[0012] The aforementioned method, based on the acquired data, constructs a lower-level integrated energy system operation optimization model with annual operating cost as the objective function and power balance and equipment operation rules as constraints. This includes the following steps:
[0013] With the goal of minimizing annual operating costs, and constrained by the power balance of electricity, gas, and heat in the integrated energy system, the energy purchase of the integrated energy system, the upper and lower limits of energy equipment in the integrated energy system, and the operating rules of electric energy storage, a lower-level integrated energy system operation optimization model is constructed.
[0014] Based on the acquired data, and with annual operating cost as the objective function and power balance and equipment operation rules as constraints, a lower-level integrated energy system operation optimization model is constructed, which specifically includes the following steps:
[0015] The following formula is used as the objective function:
[0016] minC opr =C opr,e +C opr,g
[0017] In the formula C opr The annual operating cost of the integrated energy system; C opr,e The cost of purchasing electricity from the upper-level power grid for the integrated energy system and Where 'sea' represents the type of typical day, including summer, winter, and transitional seasons, and 'c' represents the type of day. e,t Let be the unit electricity price at time t. For a typical daytime (t) of the integrated energy system (d) of the sea type, the amount of electricity purchased from the upper-level power grid is d. sea C represents the number of typical days for sea-related diseases in a year. opr,g The cost of purchasing gas from the upstream gas grid for the integrated energy system and Where c g,t Let be the unit gas price at time t. This refers to the amount of gas purchased by the integrated energy system from the higher-level gas network at time t, which is typical for sea-type days.
[0018] The following formula is used as the constraint condition:
[0019] Electricity, gas, and heat power balance constraints of integrated energy systems:
[0020]
[0021] In the formula Let t be the electrical power supplied by the upstream power grid at time t; Let t be the electrical power supplied by the photovoltaic system at time t; The electrical power supplied by the cogeneration unit at time t; Let be the discharge power of the stored energy at time t; Let t be the electrical power consumed by the electric boiler. Let t be the electrical power consumed by the heat pump at time t; Let be the electrical power consumed by the load at time t; Let t be the charging power of the stored energy. The gas power supplied by the upstream gas network at time t; Let t be the gas power consumed by the cogeneration unit; Let t be the gas power consumed by the gas boiler; Let be the gas power consumed by the load at time t; The thermal power supplied by the cogeneration unit at time t; The heat power supplied by the gas-fired boiler at time t; The thermal power supplied by the thermal power boiler at time t; Let t be the heat power supplied by the heat pump at time t; Let be the heat power consumed by the load at time t;
[0022] Energy purchase constraints for integrated energy systems:
[0023]
[0024] In the formula This serves as a lower limit constraint on the amount of electricity the system purchases from the power grid. This sets an upper limit constraint on the amount of electricity the system can purchase from the power grid. This serves as a lower limit constraint on the system's gas purchase capacity from the gas network. This sets an upper limit constraint on the system's gas purchase capacity from the gas network.
[0025] Power requirements constraints for the input and output of energy equipment:
[0026] Power constraints for heat production by electric boilers, gas boilers, and heat pumps:
[0027]
[0028] In the formula, Ω1 represents the type of energy equipment and Ω1∈[EB,GB,HP], Ω1=EB indicates that the energy equipment is an electric boiler, Ω1=GB indicates that the energy equipment is a gas boiler, and Ω1=HP indicates that the energy equipment is a heat pump; k represents the type of energy equipment and takes a value of 1~3; Let K be the thermal power output of energy device Ω1 of type k at time t; Ω1k.min K represents the minimum output coefficient of type k energy equipment Ω1; Ω1k.max S is the maximum output coefficient of type k energy equipment Ω1; Ω1k The planned capacity for type k energy equipment Ω1;
[0029] Output upper and lower limits and ramping constraints for combined heat and power units:
[0030]
[0031] In the formula, Ω2 represents the type of energy equipment and Ω2∈CHP, Ω2=CHP indicates that the energy equipment is a combined heat and power unit; S Ω2k The planned capacity of type k energy equipment Ω2; K Ω2k.min Ω2 is the minimum output coefficient of type k energy equipment; Let Ω2 be the electrical power output of the type k energy device at time t; K represents the thermal power output of energy device Ω2 of type k at time t; Ω2k.max The maximum output coefficient of type k energy equipment Ω2; The lower limit of the ramp rate for type k energy equipment Ω2; ΔP t Ω2k Let Ω2 be the change in output power of type k energy device at time t; This represents the upper limit of the ramp rate for type k energy equipment Ω2;
[0032] Energy storage operation constraints:
[0033]
[0034] In the formula, Ω3 represents the type of energy device and Ω3∈[FES,EES,SES], Ω3=FES indicates that the energy device is a flywheel energy storage device, Ω3=EES indicates that the energy device is an electrochemical energy storage device, and Ω3=SES indicates that the energy device is a superconducting energy storage device; Let Ω3 be the energy stored in type k energy device at time t+1; η represents the stored energy of type k energy device Ω3 at time t; l Ω3k The loss efficiency of type k energy equipment Ω3; The charging power of type k energy device Ω3 at time t; The charging efficiency of the K-type energy device Ω3; Let Ω3 be the discharge power of the type k energy device at time t; The discharge efficiency of type k energy device Ω3; Let be the charging state variable (0-1) of type k energy device Ω3, and This indicates that the type K energy device Ω3 is in a charging state. This indicates that the type K energy device Ω3 is not being charged; This is the upper limit of the charging power for the k-type energy device Ω3; Let be the discharge state (0-1) variable of type k energy device Ω3, and This indicates that the type K energy device Ω3 is in a discharging state. This indicates that the type K energy device Ω3 is in a non-discharged state; This is the upper limit of the discharge power of the k-type energy device Ω3; Let Ω3, a type k energy device, be in its state of charge at time t. This is the lower limit value of the charged state of Ω3 for type k energy equipment; This represents the upper limit of the state of charge of type k energy device Ω3; The state of charge of type k energy device Ω3 at the start of dispatch; This represents the state of charge of type k energy device Ω3 at the end of the dispatch process.
[0035] The process of constructing an upper-level integrated energy system planning model based on the acquired data, with equivalent annual total cost, energy efficiency, and energy level balance coefficient as optimization objectives and equipment capacity as a constraint, includes the following steps:
[0036] With the primary objective of minimizing the equivalent annual total cost, the secondary objective of maximizing the energy efficiency of the integrated energy system, and the tertiary objective of minimizing the energy level balance coefficient of the integrated energy system, and with equipment capacity as a constraint, a planning model for the upper-level integrated energy system is constructed.
[0037] Based on the acquired data, and with equivalent annual total cost, energy efficiency, and energy level balance coefficient as optimization objectives and equipment capacity as constraints, a higher-level integrated energy system planning model is constructed. This process includes the following steps:
[0038] First objective: Minimize the equivalent annual total cost.
[0039] min C all =C inv +C main +C opt +C CO2
[0040] In the formula C all C is the equivalent annual total cost; inv The equivalent annual investment cost of the equipment and Ω represents the type of energy equipment, and Ω = 1 to 6, corresponding to the energy equipment types of photovoltaic, combined heat and power units, gas boilers, electric boilers, heat pumps, and electric energy storage, respectively; C inv,Ωk S represents the unit capacity investment cost of type k energy equipment Ω; ΩkThe planned capacity of type k energy equipment Ω; r is the discount rate; N Ωk The service life of type K energy equipment Ω; C main The equivalent annual maintenance cost of the equipment and C main,Ωk The unit capacity maintenance cost of type k energy equipment Ω; C opt The equivalent annual operating cost of the integrated energy system and "Sea" indicates the type of typical day, including summer, winter, and transitional seasons; "t" indicates the time of day. This represents the amount of electricity supplied by the upstream power grid to the substation at time t on a typical day for sea-type applications. This represents the amount of natural gas supplied by the upstream gas network to the gas station at time t on a typical day for sea-type gas systems. sea The number of days corresponding to a typical day in a year; The equivalent annual environmental cost of the integrated energy system and δ rc η is the carbon dioxide emission coefficient for raw coal. e-sc η is the conversion coefficient between electricity and standard coal. rc-sc δ is the raw coal to standard coal conversion coefficient. g This represents the carbon dioxide emission coefficient for natural gas. For carbon tax;
[0041] Second objective: Maximize the energy efficiency of the integrated energy system.
[0042]
[0043] In the formula η EX For the energy efficiency of integrated energy systems; E n,out The output energy of the integrated energy system; E n,in As the input energy for the integrated energy system;
[0044] Third objective: Minimize the energy level balance coefficient of the integrated energy system.
[0045]
[0046] In the formula η EL E represents the energy level balance coefficient of the integrated energy system. x,out For the output of integrated energy systems E x,in Input for integrated energy systems
[0047] Constraints:
[0048] Equipment capacity constraints:
[0049] 0≤S Ωk ≤S max,Ωk
[0050] In the formula S max,Ωk The upper limit of the planned capacity for type k energy equipment Ω;
[0051] Equipment model constraints:
[0052] Solving the constructed lower-level integrated energy system operation optimization model and upper-level integrated energy system planning model to obtain the final multi-objective planning scheme for the integrated energy system that considers the energy quantity and quality matching on both the supply and demand sides includes the following steps:
[0053] The operational optimization model of the constructed lower-level integrated energy system is solved using the CPLEX solver.
[0054] For the constructed upper-level integrated energy system planning model, the Pareto front is obtained by solving the problem using the NSGA-II algorithm;
[0055] The optimal programming solution is selected by using the multidimensional preference linear programming method, and the final multi-objective programming scheme of the integrated energy system is obtained.
[0056] The process of solving the constructed lower-level integrated energy system operation optimization model and upper-level integrated energy system planning model to obtain the final multi-objective planning scheme for the integrated energy system includes the following steps:
[0057] Data input: Input the acquired operational and planning data into the model;
[0058] Initialization: Initialize NSGA-II parameters, generate an initial population and individuals, each individual representing a set of planning schemes in the upper-level model; the planning schemes correspond to the selection and capacity of energy equipment;
[0059] Lower-level integrated energy system operation optimization: Based on the equipment type and capacity transferred from the upper-level integrated energy system planning model, constraints are established, and the CPLEX solver is used to solve the lower-level integrated energy system operation optimization model to optimize the output of energy equipment under typical days;
[0060] Upper-level integrated energy system planning optimization: Based on the energy equipment output transmitted by the lower-level integrated energy system operation optimization model, calculate the target value of each NSGA-II individual and determine the non-dominated ranking level of each individual; the target value includes the equivalent annual total cost, energy efficiency and energy level balance coefficient;
[0061] Iterative optimization: Determine whether the NSGA-II objective value has converged: If it has converged, output the Pareto front solution set formed by the planning scheme; otherwise, generate a new population through genetic operations and return to the "lower-level integrated energy system operation optimization" step for continued iteration; the new population corresponds to a new set of equipment selection and capacity schemes.
[0062] Planning scheme determination: The optimal solution is selected from the Pareto front using the multidimensional preference linear programming method, which serves as the final multi-objective planning scheme for the integrated energy system.
[0063] This invention also provides a system for implementing the multi-objective planning method for the integrated energy system, comprising a data acquisition module, a lower-level model construction module, an upper-level model construction module, and a computational planning module; the output of the data acquisition module is simultaneously connected to the inputs of both the lower-level and upper-level model construction modules; the outputs of both modules are simultaneously connected to the computational planning module; the data acquisition module acquires operational and planning data information of the integrated energy system and uploads the data to both the lower-level and upper-level model construction modules; the lower-level model construction module, based on the acquired data information, calculates the target function using annual operating cost. With power balance and equipment operation rules as constraints, a lower-level integrated energy system operation optimization model is constructed, and the data is uploaded to the calculation and planning module. The upper-level model construction module is used to construct an upper-level integrated energy system planning model based on the acquired data, with equivalent annual total cost, energy efficiency, and energy level balance coefficient as optimization objectives and equipment capacity as constraints, and the data is uploaded to the calculation and planning module. The calculation and planning module is used to solve the constructed lower-level integrated energy system operation optimization model and upper-level integrated energy system planning model based on the received data, and obtain the final multi-objective planning scheme for the integrated energy system that considers the energy quantity and quality matching on both the supply and demand sides.
[0064] The present invention also provides a storage medium on which a computer program is stored; when the computer program is executed by a processor, it implements the multi-objective planning method for the integrated energy system.
[0065] The multi-objective planning method and system for integrated energy systems provided by this invention considers the differences in energy quality and takes into account economic efficiency, the degree of matching between the quantity and quality of energy on both the supply and demand sides, and the requirements of system operation. Therefore, this invention can not only complete the multi-objective planning of integrated energy systems under the premise of considering the matching of energy quantity and quality, reduce the loss of effective energy, and achieve the improvement of system energy quality and efficiency, but also has high reliability, good accuracy and is objective and scientific. Attached Figure Description
[0066] Figure 1 This is a schematic diagram of the method flow of the present invention.
[0067] Figure 2 This is a schematic diagram of the integrated energy system structure in an embodiment of the method of the present invention.
[0068] Figure 3 This is a schematic diagram of the Pareto front constructed by a reasonable planning scheme for an embodiment of the method of the present invention.
[0069] Figure 4 Energy flow and energy distribution of integrated energy systems under different planning schemes in embodiments of the method of the present invention. Flow diagram; Figure 4 (a) is a schematic diagram of energy flow in Scheme I. Figure 4 (b) is Scheme I Flow diagram, Figure 4 (c) is a schematic diagram of energy flow in Scheme II. Figure 4 (d) is Scheme II Flow diagram, Figure 4 (e) is a schematic diagram of energy flow in Scheme III. Figure 4 (f) is Scheme III Flow diagram.
[0070] Figure 5 This is a schematic diagram of the functional modules of the system based on this invention. Detailed Implementation
[0071] like Figure 1 The diagram shown is a flowchart of the method of the present invention: The multi-objective planning method for integrated energy systems provided by the present invention includes the following steps:
[0072] To obtain operational and planning data information for integrated energy systems;
[0073] Based on the acquired data, an optimization model for the operation of the lower-level integrated energy system is constructed, with annual operating cost as the objective function and power balance and equipment operation rules as constraints. This includes the following steps:
[0074] With the goal of minimizing annual operating costs, and constrained by the power balance of electricity, gas, and heat in the integrated energy system, the energy purchase of the integrated energy system, the upper and lower limits of energy equipment in the integrated energy system, and the operating rules of electric energy storage, a lower-level integrated energy system operation optimization model is constructed.
[0075] In practice, the following steps shall be taken:
[0076] The following formula is used as the objective function:
[0077] minC opr =C opr,e +C opr,g
[0078] In the formula C opr The annual operating cost of the integrated energy system; C opr,e The cost of purchasing electricity from the upper-level power grid for the integrated energy system and Where 'sea' represents the type of typical day, including summer, winter, and transitional seasons, and 'c' represents the type of day. e,t Let be the unit electricity price at time t. For a typical daytime (t) of the integrated energy system (d) of the sea type, the amount of electricity purchased from the upper-level power grid is d. sea C represents the number of typical days for sea-related diseases in a year. opr,g The cost of purchasing gas from the upstream gas grid for the integrated energy system and Where c g,t Let be the unit gas price at time t. This refers to the amount of gas purchased by the integrated energy system from the higher-level gas network at time t, which is typical for sea-type days.
[0079] The following formula is used as the constraint condition:
[0080] Electricity, gas, and heat power balance constraints of integrated energy systems:
[0081]
[0082] In the formula Let t be the electrical power supplied by the upstream power grid at time t; Let t be the electrical power supplied by the photovoltaic system at time t; The electrical power supplied by the cogeneration unit at time t; Let be the discharge power of the stored energy at time t; Let t be the electrical power consumed by the electric boiler. Let t be the electrical power consumed by the heat pump at time t; Let be the electrical power consumed by the load at time t; Let t be the charging power of the stored energy. The gas power supplied by the upstream gas network at time t; Let t be the gas power consumed by the cogeneration unit; Let t be the gas power consumed by the gas boiler; Let be the gas power consumed by the load at time t; The thermal power supplied by the cogeneration unit at time t; The heat power supplied by the gas-fired boiler at time t; The thermal power supplied by the thermal power boiler at time t; The heat power supplied by the heat pump at time t; Let be the heat power consumed by the load at time t;
[0083] Energy purchase constraints for integrated energy systems:
[0084]
[0085] In the formula This serves as a lower limit constraint on the amount of electricity the system purchases from the power grid. This sets an upper limit constraint on the amount of electricity the system can purchase from the power grid. This serves as a lower limit constraint on the system's gas purchase capacity from the gas network. This sets an upper limit constraint on the system's gas purchase capacity from the gas network.
[0086] Power requirements constraints for the input and output of energy equipment:
[0087] Power constraints for heat production by electric boilers, gas boilers, and heat pumps:
[0088]
[0089] In the formula, Ω1 represents the type of energy equipment and Ω1∈[EB,GB,HP], Ω1=EB indicates that the energy equipment is an electric boiler, Ω1=GB indicates that the energy equipment is a gas boiler, and Ω1=HP indicates that the energy equipment is a heat pump; k represents the type of energy equipment and takes a value of 1~3; Let K be the thermal power output of energy device Ω1 of type k at time t; Ω1k.min K represents the minimum output coefficient of type k energy equipment Ω1; Ω1k.max S is the maximum output coefficient of type k energy equipment Ω1; Ω1k The planned capacity for type k energy equipment Ω1;
[0090] Output upper and lower limits and ramping constraints for combined heat and power units:
[0091]
[0092] In the formula, Ω2 represents the type of energy equipment and Ω2∈CHP, Ω2=CHP indicates that the energy equipment is a combined heat and power unit; S Ω2k The planned capacity of type k energy equipment Ω2; K Ω2k.min Ω2 is the minimum output coefficient of type k energy equipment; Let Ω2 be the electrical power output of the type k energy device at time t; K represents the thermal power output of energy device Ω2 of type k at time t; Ω2k.max The maximum output coefficient of type k energy equipment Ω2; The lower limit of the ramp rate for type k energy equipment Ω2; ΔP t Ω2k Let Ω2 be the change in output power of type k energy device at time t; This represents the upper limit of the ramp rate for type k energy equipment Ω2;
[0093] Energy storage operation constraints:
[0094]
[0095] In the formula, Ω3 represents the type of energy device and Ω3∈[FES,EES,SES], Ω3=FES indicates that the energy device is a flywheel energy storage device, Ω3=EES indicates that the energy device is an electrochemical energy storage device, and Ω3=SES indicates that the energy device is a superconducting energy storage device; Let Ω3 be the energy stored in type k energy device at time t+1; Let Ω3 be the energy stored in type k energy device at time t; The loss efficiency of type k energy device Ω3; The charging power of type k energy device Ω3 at time t; The charging efficiency of type K energy device Ω3; Let Ω3 be the discharge power of the type k energy device at time t; The discharge efficiency of type k energy device Ω3; Let be the charging state variable (0-1) of type k energy device Ω3, and This indicates that the type K energy device Ω3 is in a charging state. This indicates that the type K energy device Ω3 is not being charged; This is the upper limit of the charging power for the k-type energy device Ω3; Let be the discharge state (0-1) variable of type k energy device Ω3, and This indicates that the type K energy device Ω3 is in a discharging state. This indicates that the type K energy device Ω3 is in a non-discharged state; This is the upper limit of the discharge power of the k-type energy device Ω3; Let Ω3, a type k energy device, be in its state of charge at time t. This is the lower limit value of the charged state of Ω3 for type k energy equipment; This represents the upper limit of the state of charge of type k energy device Ω3; The state of charge of type k energy device Ω3 at the start of dispatch; The state of charge of type k energy device Ω3 at the end of the dispatch;
[0096] Based on the acquired data, and with equivalent annual total cost, energy efficiency, and energy level balance coefficient as optimization objectives, and equipment capacity as a constraint, a comprehensive upper-level energy system planning model is constructed, including the following steps:
[0097] With the primary objective of minimizing the equivalent annual total cost, the secondary objective of maximizing the energy efficiency of the integrated energy system, and the tertiary objective of minimizing the energy level balance coefficient of the integrated energy system, and with equipment capacity as a constraint, an upper-level integrated energy system planning model is constructed.
[0098] In practice, the following steps shall be taken:
[0099] First objective: Minimize the equivalent annual total cost.
[0100] min C all =C inv +C main +C opt +CCO2
[0101] In the formula C all C is the equivalent annual total cost; inv The equivalent annual investment cost of the equipment and Ω represents the type of energy equipment, and Ω = 1 to 6, corresponding to the energy equipment types of photovoltaic, combined heat and power units, gas boilers, electric boilers, heat pumps, and electric energy storage, respectively; C inv,Ωk S represents the unit capacity investment cost of type k energy equipment Ω; Ωk The planned capacity of type k energy equipment Ω; r is the discount rate; N Ωk The service life of type K energy equipment Ω; C main The equivalent annual maintenance cost of the equipment and C main,Ωk The unit capacity maintenance cost of type k energy equipment Ω; C opt The equivalent annual operating cost of the integrated energy system and "Sea" indicates the type of typical day, including summer, winter, and transitional seasons; "t" indicates the time of day. This represents the amount of electricity supplied by the upstream power grid to the substation at time t on a typical day for sea-type applications. This represents the amount of natural gas supplied by the upstream gas network to the gas station at time t on a typical day for sea-type gas systems. sea The number of days corresponding to a typical day in a year; The equivalent annual environmental cost of the integrated energy system and δ rc η is the carbon dioxide emission coefficient for raw coal. e-sc η is the conversion coefficient between electricity and standard coal. rc-sc δ is the raw coal to standard coal conversion coefficient. g This represents the carbon dioxide emission coefficient for natural gas. For carbon tax;
[0102] Second objective: Maximize the energy efficiency of the integrated energy system.
[0103]
[0104] In the formula η EX For the energy efficiency of integrated energy systems; E n,out The output energy of the integrated energy system; E n,in As the input energy for the integrated energy system;
[0105] Third objective: Minimize the energy level balance coefficient of the integrated energy system.
[0106]
[0107] In the formula η ELE represents the energy level balance coefficient of the integrated energy system. x,out For the output of integrated energy systems E x,in Input for integrated energy systems
[0108] Constraints:
[0109] Equipment capacity constraints:
[0110] 0≤S Ωk ≤S max,Ωk
[0111] In the formula S max,Ωk The upper limit of the planned capacity for type k energy equipment Ω;
[0112] Equipment model constraints:
[0113] The operation optimization model of the lower-level integrated energy system and the planning model of the upper-level integrated energy system are solved to obtain the final multi-objective planning scheme considering the integrated energy system; the steps include:
[0114] The operational optimization model of the constructed lower-level integrated energy system is solved using the CPLEX solver.
[0115] For the constructed upper-level integrated energy system planning model, the Pareto front is obtained by solving the problem using the NSGA-II algorithm;
[0116] The optimal planning solution is selected by using the multidimensional preference linear programming method, and the final multi-objective planning scheme of the integrated energy system is obtained.
[0117] In practice, the following steps shall be taken:
[0118] Data input: Input the acquired operational and planning data into the model;
[0119] Initialization: Initialize NSGA-II parameters, generate an initial population and individuals, each individual representing a set of planning schemes in the upper-level model; the planning schemes correspond to the selection and capacity of energy equipment;
[0120] Lower-level integrated energy system operation optimization: Based on the equipment models and capacities passed from the upper-level integrated energy system planning model, constraints are established, and the CPLEX solver is used to solve the lower-level integrated energy system operation optimization model to optimize the output of energy equipment under typical days;
[0121] Upper-level integrated energy system planning optimization: Based on the energy equipment output transmitted by the lower-level integrated energy system operation optimization model, calculate the target value of each NSGA-II individual and determine the non-dominated ranking level of each individual; the target value includes the equivalent annual total cost, energy efficiency and energy level balance coefficient;
[0122] Iterative optimization: Determine whether the NSGA-II objective value has converged: If it has converged, output the Pareto front solution set formed by the planning scheme; otherwise, generate a new population through genetic operations and return to the "lower-level integrated energy system operation optimization" step for continued iteration; the new population corresponds to a new set of equipment selection and capacity schemes.
[0123] Planning scheme determination: The optimal solution is selected from the Pareto front using the multidimensional preference linear programming method, which serves as the final multi-objective planning scheme for the integrated energy system.
[0124] The method of the present invention will be further described below with reference to an embodiment:
[0125] Implementation examples in Figure 2 In the integrated energy system shown, GB, CHP, EB, HP, PV, and ES represent gas-fired boiler, combined heat and power unit, electric boiler, heat pump, photovoltaic, and electric energy storage, respectively. The numbers following these symbols indicate the model. FES, EES, and SES represent flywheel energy storage, electrochemical energy storage, and superconducting energy storage, respectively. The Pareto front, constructed using a reasonable planning scheme, is obtained based on the NSGA-II algorithm, as shown below. Figure 3 As shown in the figure, LINMAP represents the multidimensional preference linear programming method;
[0126] Depend on Figure 3 It can be seen that as energy efficiency improves, the energy level balance coefficient of the simulation system gradually decreases, while the equivalent annual total cost first decreases and then increases. This indicates that the equivalent annual total cost, energy efficiency, and energy level balance coefficient are mutually exclusive and correlated, and the planning scheme cannot achieve the optimal balance coefficient for all three objectives simultaneously. Figure 3 As shown, based on the multidimensional preference linear programming method, Scheme I was selected from the Pareto front as the final planning scheme for the example. The specific planning results are shown in Scheme I in Table 1, where EES represents electrochemical energy storage.
[0127] Table 1. Schematic diagram of system planning results for the three schemes.
[0128]
[0129] Further analysis of the characteristics of different optimal solutions in the Pareto front, selecting Figure 3Schemes II and III are compared with Scheme I. Compared with Scheme I, Scheme II has a lower energy level balance coefficient, but a higher equivalent annual total cost and lower energy efficiency. Compared with Scheme I, Scheme III has higher energy efficiency, but a higher equivalent annual total cost and energy level balance coefficient. The planning results for each scheme are shown in Table 1.
[0130] The annual equivalent investment cost, annual maintenance cost, annual energy purchase cost, and annual environmental cost of the three schemes are shown in Table 2.
[0131] Table 2. Equivalent Annual Total Cost of the Three Schemes
[0132]
[0133] Considering that the capacity of heat-generating equipment accounts for a relatively high proportion of the total equipment capacity, the investment and maintenance costs of the scheme mainly depend on the configuration of the heat-generating equipment. The unit capacity investment and maintenance costs of combined heat and power (CHP) units are higher, while those of gas-fired boilers and electric boilers are lower. Compared to other schemes, Scheme II configures a larger capacity CHP unit, therefore Scheme II has the highest investment and maintenance costs. In Scheme III, the capacity of both CHP units and gas-fired boilers is relatively small, with heating almost entirely handled by a single electric boiler; therefore, Scheme III has the lowest investment and maintenance costs among the three schemes. The increased capacity of the CHP units increases the amount of electricity supplied to the load, thereby reducing the amount of electricity purchased from the upstream grid by the system in the simulation. Although the amount of gas purchased from the upstream gas grid increases, the gas price per unit of energy is lower than the electricity price; therefore, the configuration of CHP units helps reduce the energy purchase cost of the simulation system. Scheme II has the lowest operating cost due to the large number of CHP units. Conversely, increasing the capacity of the electric boiler increases the amount of electricity the system purchases from the grid, thus increasing the system's energy purchase cost. Therefore, Scheme III, due to its large-capacity electric boiler, has the highest operating cost. The CO2 emissions of the system are calculated by inputting the electrical energy and natural gas consumption of the system. Considering that the electricity purchased from the grid comes from coal-fired power generation, the CO2 emissions per unit of electricity generated are higher than those of a combined heat and power (CHP) unit. The configuration of a CHP unit reduces environmental costs by decreasing the amount of electricity purchased. Conversely, the configuration of an electric boiler increases environmental costs. Therefore, Scheme II, which primarily uses CHP units for heating, has the lowest environmental cost, while Scheme III, which primarily uses electric boilers for heating, has the highest environmental cost.
[0134] In summary, while the configuration of combined heat and power (CHP) units reduces the economic efficiency of equipment investment and maintenance in the calculated system, it improves the economic efficiency of system operation and environmental protection. Conversely, while the configuration of electric boilers improves the economic efficiency of equipment investment and maintenance, it reduces the economic efficiency of system operation and environmental protection. Considering all costs, Scheme II has the lowest equivalent annual total cost due to the large-scale configuration of CHP units, while Scheme III has the highest equivalent annual total cost due to the large-scale configuration of electric boilers.
[0135] Figure 4 The annual energy flow of the example system under optimal operating conditions is shown under three schemes. The flow conditions are shown in the diagram, where GB, CHP, EB, HP, PV, and ES represent gas-fired boilers, combined heat and power units, electric boilers, heat pumps, photovoltaic systems, and electric energy storage, respectively. Figure 4 The energy efficiency and energy level balance coefficient of the simulation system under different schemes can be calculated. Since the differences among the three schemes mainly lie in the configuration of the heat-generating equipment, the energy efficiency and energy level balance coefficient of the simulation system primarily depend on the output of the heat-generating equipment. Figure 3 It can be seen that Scheme III has the highest energy efficiency among the three schemes, while Scheme I has the lowest. This is because Scheme III relies almost entirely on high-efficiency electric boilers for heating, resulting in higher energy efficiency for the system in this scheme. Scheme II, on the other hand, uses a large-capacity, low-efficiency combined heat and power (CHP) unit, leading to lower energy efficiency for the system in this scheme. In summary, the use of electric boilers improves system energy efficiency, while the use of CHP units reduces it.
[0136] Depend on Figure 3 It can be seen that among the three schemes, Scheme II has the smallest energy level balance coefficient, which is negative. This is because large-capacity cogeneration units, while consuming natural gas to produce low-quality heat, generate higher-quality electricity, which helps to suppress the degradation of energy quality during the conversion process. Since the multi-energy load is the same under different planning schemes, the output energy and... Since the energy levels are the same, the energy level balance coefficient of the example system mainly depends on the energy level on the supply side. In Scheme II, the proportion of natural gas energy input is relatively high, and the energy level on the supply side of the example system is relatively low. Therefore, this scheme has the smallest energy level balance coefficient. The solar energy input from photovoltaics comes from nature and is theoretically inexhaustible. It can be considered as a natural resource under natural environmental conditions. The value is 0, meaning the input system corresponding to photovoltaic power is... The value is 0, while the electrical energy output by the photovoltaic system to the load is 0. The value is not zero. Therefore, the configuration of new energy equipment helps to reduce the energy level on the input side of the integrated energy system, thereby improving the matching degree of energy quality on both the supply and demand sides of the system. For example... Figure 3It can be shown that the configuration of new energy sources leads to a negative energy level balance coefficient. Among the three schemes, Scheme III has the largest energy level balance coefficient. This is because Scheme III has a larger proportion of input electrical energy, resulting in a higher energy level on the supply side. The large-capacity electric boiler converts a large amount of high-quality electrical energy into low-quality heat, leading to a significant reduction in energy level. In summary, the configuration of new energy sources and combined heat and power (CHP) units is beneficial to reducing the energy level balance coefficient of the integrated energy system, while the configuration of electric boilers increases the system's energy level balance coefficient.
[0137] In summary, while configuring a higher-capacity electric boiler improves the energy efficiency of the simulation system, it also increases the system's energy balance coefficient and equivalent annual total cost. Conversely, configuring a higher-capacity cogeneration unit reduces the system's energy efficiency but decreases the energy balance coefficient and equivalent annual total cost. Scheme I, selected based on multidimensional preference linear programming, features relatively similar capacities for each heat-generating device. Compared to Schemes II and III, Scheme I balances system economy, CO2 emissions, and the quality and quantity matching of energy supply and demand, resulting in a more balanced set of indicators. Therefore, Scheme I can serve as a reasonable planning scheme for equipment selection and capacity determination in the simulation system.
[0138] like Figure 5 The diagram shows the functional modules of the system of the present invention: The system disclosed in this invention for implementing the multi-objective planning method for the integrated energy system includes a data acquisition module, a lower-level model construction module, an upper-level model construction module, and a computational planning module; the output of the data acquisition module is simultaneously connected to the inputs of both the lower-level and upper-level model construction modules; the outputs of both modules are simultaneously connected to the computational planning module; the data acquisition module is used to acquire operational and planning data information of the integrated energy system and upload the data to both the lower-level and upper-level model construction modules; the lower-level model construction module is used to... The system uses information to construct a lower-level integrated energy system operation optimization model with annual operating cost as the objective function and power balance and equipment operation rules as constraints. This data is then uploaded to the calculation and planning module. The upper-level model construction module, based on the acquired data, constructs an upper-level integrated energy system planning model with equivalent annual total cost, energy efficiency, and energy level balance coefficient as optimization objectives and equipment capacity as constraints. This data is also uploaded to the calculation and planning module. The calculation and planning module then solves the constructed lower-level integrated energy system operation optimization model and the upper-level integrated energy system planning model based on the received data, obtaining the final multi-objective planning scheme for the integrated energy system.
Claims
1. A multi-objective programming method for an integrated energy system, comprising the following steps: To obtain operational and planning data information for integrated energy systems; Based on the acquired data, an optimization model for the operation of the lower-level integrated energy system is constructed, with annual operating cost as the objective function and power balance and equipment operation rules as constraints. This includes the following steps: With the goal of minimizing annual operating costs, and constrained by the power balance of electricity, gas, and heat in the integrated energy system, the energy purchase of the integrated energy system, the upper and lower limits of energy equipment in the integrated energy system, and the operating rules of electric energy storage, a lower-level integrated energy system operation optimization model is constructed. Specifically, the steps include the following: The following formula is used as the objective function: In the formula The annual operating cost of the integrated energy system; The cost of purchasing electricity from the upper-level power grid for the integrated energy system and ,in Indicates the type of typical day. Let be the unit electricity price at time t. This refers to the electricity purchased by the integrated energy system from the upper-level power grid at time t, which is typical for sea-type solar energy systems. This represents the number of typical days for sea-related events in a year. The cost of purchasing gas from the upstream gas grid for the integrated energy system and ,in Let be the unit gas price at time t. This refers to the amount of gas purchased by the integrated energy system from the higher-level gas network at time t, which is typical for sea-type days. The following formula is used as the constraint condition: Electricity, gas, and heat power balance constraints of integrated energy systems: In the formula Let t be the electrical power supplied by the upstream power grid at time t; Let t be the electrical power supplied by the photovoltaic system at time t; The electrical power supplied by the cogeneration unit at time t; Let be the discharge power of the stored energy at time t; Let t be the electrical power consumed by the electric boiler. Let t be the electrical power consumed by the heat pump at time t; Let be the electrical power consumed by the load at time t; Let t be the charging power of the stored energy. The gas power supplied by the upstream gas network at time t; Let t be the gas power consumed by the cogeneration unit; Let t be the gas power consumed by the gas boiler; Let be the gas power consumed by the load at time t; The thermal power supplied by the cogeneration unit at time t; The heat power supplied by the gas-fired boiler at time t; The thermal power supplied by the thermal power boiler at time t; Let t be the heat power supplied by the heat pump at time t; Let be the heat power consumed by the load at time t; Energy purchase constraints for integrated energy systems: In the formula This serves as a lower limit constraint on the amount of electricity the system purchases from the power grid. This sets an upper limit constraint on the amount of electricity the system can purchase from the power grid. This serves as a lower limit constraint on the system's gas purchase capacity from the gas network. This sets an upper limit constraint on the system's gas purchase capacity from the gas network. Power requirements constraints for the input and output of energy equipment: Power constraints for heat production by electric boilers, gas boilers, and heat pumps: In the formula Indicate the type of energy equipment and , This indicates that the energy equipment is an electric boiler. This indicates that the energy equipment is a gas-fired boiler. This indicates that the energy device is a heat pump; k represents the type of energy device and takes a value from 1 to 3. For K-type energy equipment The thermal power output at time t; For K-type energy equipment The minimum output coefficient; For K-type energy equipment Maximum output coefficient; For K-type energy equipment The planned capacity; Output upper and lower limits and ramping constraints for combined heat and power units: In the formula Indicate the type of energy equipment and , This indicates that the energy equipment is a combined heat and power (CHP) unit; For K-type energy equipment The planned capacity; For K-type energy equipment The minimum output coefficient; For K-type energy equipment The electrical power output at time t; For K-type energy equipment The thermal power output at time t; For K-type energy equipment Maximum output coefficient; For K-type energy equipment The lower limit of the slope rate; For K-type energy equipment The change in output power at time t; For K-type energy equipment The upper limit of the slope rate; Energy storage operation constraints: In the formula Indicate the type of energy equipment and , This indicates that the energy device is a flywheel energy storage device. This indicates that the energy device is an electrochemical energy storage device. This indicates that the energy device is a superconducting energy storage device; For K-type energy equipment The amount of electricity stored at time t+1; For K-type energy equipment The amount of electricity stored at time t; For K-type energy equipment The loss of efficiency; For K-type energy equipment The charging power at time t; For K-type energy equipment The charging efficiency; For K-type energy equipment The discharge power at time t; For K-type energy equipment The discharge efficiency; For K-type energy equipment The charging state is a 0-1 variable, and Indicates type K energy equipment In charging state. Indicates type K energy equipment Not charging; For K-type energy equipment The upper limit of charging power; For K-type energy equipment The discharge state is a 0-1 variable, and Indicates type K energy equipment It is in a discharge state. Indicates type K energy equipment It is in an undischarged state; For K-type energy equipment The upper limit of discharge power; For K-type energy equipment The state of charge at time t; For K-type energy equipment The limit value of the charged state; For K-type energy equipment The upper limit of the state of charge; For K-type energy equipment The state of charge at the start of the scheduling; For K-type energy equipment The state of charge at the end of the scheduling process; Based on the acquired data, an upper-level integrated energy system planning model is constructed with equivalent annual total cost, energy efficiency, and energy level balance coefficient as optimization objectives and equipment capacity as constraints. The operation optimization model of the lower-level integrated energy system and the planning model of the upper-level integrated energy system are solved to obtain the final multi-objective planning scheme of the integrated energy system that considers the matching of energy quantity and quality on both the supply and demand sides.
2. The multi-objective programming method for integrated energy systems according to claim 1, characterized in that... The process of constructing an upper-level integrated energy system planning model based on the acquired data, with equivalent annual total cost, energy efficiency, and energy level balance coefficient as optimization objectives and equipment capacity as a constraint, includes the following steps: With the primary objective of minimizing the equivalent annual total cost, the secondary objective of maximizing the energy efficiency of the integrated energy system, and the tertiary objective of minimizing the energy level balance coefficient of the integrated energy system, and with equipment capacity as a constraint, a planning model for the upper-level integrated energy system is constructed.
3. The multi-objective programming method for integrated energy systems according to claim 2, characterized in that... Based on the acquired data, and with equivalent annual total cost, energy efficiency, and energy level balance coefficient as optimization objectives and equipment capacity as constraints, a higher-level integrated energy system planning model is constructed. This process includes the following steps: First objective: Minimize the equivalent annual total cost. In the formula This represents the equivalent annual total cost; The equivalent annual investment cost of the equipment and , Indicate the type of energy equipment and These correspond to the energy equipment types as follows: photovoltaic, combined heat and power units, gas boilers, electric boilers, heat pumps, and electric energy storage. For K-type energy equipment The unit capacity investment cost; For K-type energy equipment The planned capacity; The discount rate; For K-type energy equipment Service life; The equivalent annual maintenance cost of the equipment and , For K-type energy equipment The unit capacity maintenance cost; The equivalent annual operating cost of the integrated energy system and , Indicates the type of typical day. Indicates a time of day. express The amount of electricity supplied by the upstream power grid to the substation at time t on a typical day. express The amount of natural gas supplied to the gas station by the upstream gas network at time t on a typical day. The number of days corresponding to a typical day in a year; The equivalent annual environmental cost of the integrated energy system and , This represents the carbon dioxide emission coefficient for raw coal. The conversion coefficient between electricity and standard coal is given. The conversion coefficient between raw coal and standard coal is given. This represents the carbon dioxide emission coefficient for natural gas. For carbon tax; Second objective: Maximize the energy efficiency of the integrated energy system. In the formula For the energy efficiency of integrated energy systems; The output energy of the integrated energy system; As the input energy for the integrated energy system; Third objective: Minimize the energy level balance coefficient of the integrated energy system. In the formula The energy level balance coefficient of the integrated energy system; For the output of the integrated energy system; For the input of the integrated energy system; Constraints: Equipment capacity constraints: In the formula For K-type energy equipment The planned capacity limit; Equipment model constraints: .
4. The multi-objective programming method for integrated energy systems according to claim 3, characterized in that... Solving the constructed lower-level integrated energy system operation optimization model and upper-level integrated energy system planning model to obtain the final multi-objective planning scheme for the integrated energy system that considers the energy quantity and quality matching on both the supply and demand sides includes the following steps: The operational optimization model of the constructed lower-level integrated energy system is solved using the CPLEX solver. For the constructed upper-level integrated energy system planning model, the Pareto front is obtained by solving the problem using the NSGA-II algorithm; The optimal planning solution is selected by using the multidimensional preference linear programming method, resulting in a multi-objective planning scheme for the integrated energy system that considers the matching of energy quantity and quality on both the supply and demand sides.
5. The multi-objective programming method for integrated energy systems according to claim 4, characterized in that... The aforementioned steps involve solving the constructed lower-level integrated energy system operation optimization model and upper-level integrated energy system planning model to obtain the final multi-objective planning scheme for the integrated energy system that considers the energy quantity and quality matching on both the supply and demand sides. Specifically, these steps include the following: Data input: Input the acquired operational and planning data into the model; Initialization: Initialize NSGA-II parameters, generate an initial population and individuals, each individual representing a set of planning schemes in the upper-level model; the planning schemes correspond to the selection and capacity of energy equipment; Lower-level integrated energy system operation optimization: Based on the equipment type and capacity transferred from the upper-level integrated energy system planning model, constraints are established, and the CPLEX solver is used to solve the lower-level integrated energy system operation optimization model to optimize the output of energy equipment under typical days; Upper-level integrated energy system planning optimization: Based on the energy equipment output transmitted by the lower-level integrated energy system operation optimization model, calculate the target value of each NSGA-II individual and determine the non-dominated ranking level of each individual; the target value includes the equivalent annual total cost, energy efficiency and energy level balance coefficient; Iterative optimization: Determine whether the NSGA-II objective value has converged: If it has converged, output the Pareto front solution set formed by the planning scheme; otherwise, generate a new population through genetic operations and return to the "lower-level integrated energy system operation optimization" step for continued iteration; the new population corresponds to a new set of equipment selection and capacity schemes. The planning scheme was determined by selecting the optimal solution from the Pareto front using multidimensional preference linear programming, which is then used as the final multi-objective planning scheme for the integrated energy system that considers the matching of energy quantity and quality on both the supply and demand sides.
6. A system for implementing the multi-objective programming method for integrated energy systems as described in any one of claims 1 to 5, characterized in that... It includes a data acquisition module, a lower-level model construction module, an upper-level model construction module, and a calculation and planning module; the output of the data acquisition module is connected to the input of both the lower-level and upper-level model construction modules; the outputs of both the lower-level and upper-level model construction modules are connected to the calculation and planning module; the data acquisition module is used to acquire the operation and planning data information of the integrated energy system and upload the data to the lower-level and upper-level model construction modules. The lower-level model building module is used to construct a lower-level integrated energy system operation optimization model based on the acquired data information, with annual operating cost as the objective function and power balance and equipment operation rules as constraints, and upload the data to the calculation and planning module. The upper-level model building module is used to construct an upper-level integrated energy system planning model based on the acquired data information, with the equivalent annual total cost, energy efficiency and energy level balance coefficient as optimization objectives and equipment capacity as constraints, and upload the data to the calculation and planning module. The computational planning module is used to solve the constructed lower-level integrated energy system operation optimization model and upper-level integrated energy system planning model based on the received data, and obtain the final integrated energy system multi-objective planning scheme.
7. A storage medium storing a computer program thereon; when the computer program is executed by a processor, it implements the multi-objective planning method for integrated energy systems as described in any one of claims 1 to 5.