Park integrated energy system scheduling method and system

Abstract

The invention discloses a park integrated energy system scheduling method. The method comprises the steps of obtaining data information of a target park integrated energy system; solving power network carbon flow and node carbon potential distribution, and taking the carbon potential of a power node where the target park is located as an electric carbon factor of the park integrated energy system; an upper layer optimization model is constructed by taking economical efficiency, environmental protection performance and energy saving performance of the integrated energy system of the target park as optimization targets and taking energy flow balance, equipment operation and load demand side response of the target park as constraints; taking the optimal comprehensive efficiency of the power system as a target, and constructing a lower-layer optimization model on the basis of a direct-current power flow model construction constraint condition; and solving the constructed double-layer optimization model and completing the scheduling of the target integrated energy system. The invention also discloses a system for realizing the park integrated energy system scheduling method. According to the method, the optimal scheduling of the integrated energy system of the target park is realized, the reliability is higher, and the accuracy is better.

Description

Technical Field

[0001] The present invention belongs to the field of electrical automation, and particularly relates to a scheduling method and system for a park integrated energy system. Background Art

[0002] With the development of economic technology and the improvement of people's living standards, electric energy has become an essential secondary energy source in people's production and life, bringing endless convenience to people's production and life. Therefore, ensuring the stable and reliable supply of electric energy has become one of the most important tasks of the power system.

[0003] The park integrated energy system can couple multiple energy sources such as electricity, heat, and gas; through the coupling complementarity and coordinated optimization of multiple energy sources, on the premise of ensuring the energy supply of the park, it can improve the overall energy utilization efficiency of the park and reduce the carbon emissions of the park.

[0004] However, for the scheduling schemes of the park integrated energy system at the present stage, some schemes use energy efficiency as an index to measure the energy-saving performance of the system operation, only considering the reduction of the quantity of energy and ignoring the depreciation of energy quality; but there are differences in the quality of different forms of energy, so such schemes cannot truly improve the overall efficiency and environmental friendliness of the park. In addition, there are some schemes that use a constant carbon emission coefficient to calculate the carbon emissions and comprehensively consider other factors such as economy for the integrated scheduling of the park energy system; but such schemes use a constant carbon emission coefficient to calculate the carbon emissions, so there are also problems of poor reliability and poor accuracy. Summary of the Invention

[0005] One of the purposes of the present invention is to provide a scheduling method for a park integrated energy system with high reliability and good accuracy.

[0006] Another purpose of the present invention is to provide a system for implementing the scheduling method of the park integrated energy system.

[0007] The scheduling method for the park integrated energy system provided by the present invention includes the following steps:

[0008] S1. Obtain the data information of the target park integrated energy system;

[0009] S2. Based on the carbon flow theory of the power system, solve the carbon flow of the power network and the distribution of node carbon potential, and use the carbon potential of the power node where the target park is located as the electro-carbon factor of the park integrated energy system;

[0010] S3. Taking the economy, environmental protection, and energy conservation of the target park integrated energy system as optimization objectives, and taking the energy flow balance, equipment operation, and load demand response of the target park as constraints, construct an upper-layer optimization model;

[0011] S4. With the goal of optimizing the comprehensive efficiency of the power system, construct the constraint conditions based on the DC power flow model, and construct the lower-level optimization model;

[0012] S5. Solve the two-layer optimization model constructed in steps S3 and S4, and complete the scheduling of the target integrated energy system according to the solution results.

[0013] The steps of solving the carbon flow of the power grid and the distribution of node carbon potential based on the carbon flow theory of the power system in step S2, and taking the carbon potential of the power node where the target park is located as the electro-carbon factor of the park integrated energy system are specifically as follows:

[0014] Set the branch power flow distribution matrix as P B , and the unit injection distribution matrix as P G , then the node active power flux matrix P N is P N = diag(ζP Z ), where diag() is a diagonal matrix composed of the elements of the column vector, ζ is a row vector with all elements being 1, and P Z is an intermediate matrix and P Z = [P B P G T ;

[0015] Combined with the generator set carbon emission intensity vector E G , calculate the node electro-carbon factor vector λ N as

[0016]

[0017] The steps of taking the economy, environmental protection and energy conservation of the target park integrated energy system as the optimization objectives in step S3 are specifically as follows:

[0018] Adopt the following formula as the economic optimization objective:

[0019]

[0020] In the formula, f1 is the economic optimization objective value; is the power purchase from the superior power grid by the target park at time t; is the unit electricity price at time t; is the gas purchase power from the superior gas network by the target park at time t; is the unit gas price at time t; GCV is the calorific value of natural gas;

[0021] Adopt the following formula as the environmental protection optimization objective:

[0022]

[0023] In the formula, f2 is the optimized target value of environmental protection; is the dynamic electricity-carbon factor of the target park at time t; is the natural gas carbon emission factor of the target park at time t;

[0024] The following formula is used as the optimized target of energy conservation:

[0025]

[0026] In the formula, f3 is the optimized target value of energy conservation; E e is the energy quality coefficient of electricity, and E e = 1; is the electric power output by the target park at time t; E g is the energy quality coefficient of natural gas, and T0 is the environmental temperature, T b is the theoretical combustion temperature of natural gas; is the gas power output by the target park at time t; E h is the energy quality coefficient of heat, and T s is the water supply temperature of the thermal system, T r is the return water temperature of the thermal system; is the heat power output by the target park at time t.

[0027] Constrained by the energy flow balance, equipment operation and load demand response of the target park described in step S3, it specifically includes the following steps:

[0028] The following formula is used as the energy flow balance constraint:

[0029]

[0030] In the formula is the electrical load of the target park's integrated energy system at time t; η T is the efficiency of the transformer; is the electric power of the combined heat and power unit of the target park's integrated energy system at time t; is the electric power generated by the photovoltaic of the target park's integrated energy system at time t; is the electric power consumed by the electrical energy storage of the target park's integrated energy system at time t; is the electric power consumed by the electric boiler of the target park's integrated energy system at time t; is the electric power consumed by the heat pump of the target park's integrated energy system at time t; is the electric power released by the electrical energy storage of the target park's integrated energy system at time t; is the gas load of the target park's integrated energy system at time t; is the gas power consumed by the combined heat and power unit in the target park integrated energy system at time t; is the gas power consumed by the gas boiler in the target park integrated energy system at time t; is the heat load of the target park integrated energy system at time t; is the heat power generated by the combined heat and power unit in the target park integrated energy system at time t; is the heat power generated by the gas boiler in the target park integrated energy system at time t; is the heat power generated by the electric boiler in the target park integrated energy system at time t; is the heat power generated by the heat pump in the target park integrated energy system at time t;

[0031] The following formula is used as the power balance constraint, upper and lower limit constraint, and ramp constraint of the combined heat and power unit during operation

[0032]

[0033] In the formula, η e,CHP is the gas-electric conversion efficiency of the combined heat and power unit; η h,CHP is the gas-heat conversion efficiency of the combined heat and power unit; S CHP is the capacity of the combined heat and power unit; K min,CHP is the minimum output coefficient of the combined heat and power unit; K max,CHP is the maximum output coefficient of the combined heat and power unit; is the lower limit value of the ramp rate of the combined heat and power unit; is the output change of the combined heat and power unit at time t; is the upper limit value of the ramp rate of the combined heat and power unit;

[0034] The following formula is used as the power balance constraint and upper and lower limit constraint of the gas boiler, electric boiler, and heat pump:

[0035]

[0036]

[0037]

[0038]

[0039]

[0040]

[0041] In the formula, η h,GB is the gas-heat conversion efficiency of the gas boiler; S GB is the capacity of the gas boiler; K min,GBis the minimum output coefficient of the gas boiler; K max,GB is the maximum output coefficient of the gas boiler; η h,EB is the electro-thermal conversion efficiency of the electric boiler; S EB is the capacity of the electric boiler; K min,EB is the minimum output coefficient of the electric boiler; K max,EB is the maximum output coefficient of the electric boiler; η h,HP is the electro-thermal conversion efficiency of the heat pump; S HP is the capacity of the heat pump; K min,HP is the minimum output coefficient of the heat pump; K max,HP is the maximum output coefficient of the heat pump;

[0042] The following formula is used as the operation constraint of the electrical energy storage:

[0043]

[0044] In the formula is the stored electricity of the electrical energy storage at time t+1; η ES,l is the self-loss coefficient of the electrical energy storage; η ch is the charging efficiency of the electrical energy storage; η dch is the discharging efficiency of the electrical energy storage; x ch is the 0-1 variable of the charging state of the electrical energy storage, and if the electrical energy storage is in the charging state, then x ch =1, if the electrical energy storage is in the non-charging state, then x ch =0; P ch,max is the maximum charging power of the electrical energy storage; x dch is the 0-1 variable of the discharging state of the electrical energy storage, and if the electrical energy storage is in the discharging state, then x dch =1, if the electrical energy storage is in the non-discharging state, then x dch =0; SOC t is the state of charge of the electrical energy storage at time t; SOC min is the minimum state of charge of the electrical energy storage; SOC max is the maximum state of charge of the electrical energy storage; SOC s is the state of charge of the electrical energy storage at the start of the scheduling period; SOC e is the state of charge of the electrical energy storage at the end of the scheduling period;

[0045] The following formula is used as the load demand side response constraint:

[0046]

[0047] In the formula, τ is the demand response coefficient; is the electrical load of the target park integrated energy system at time t before the demand response.

[0048] The step of taking the optimal comprehensive efficiency of the power system as the goal in step S4 specifically includes the following steps:

[0049] The following formula is used as the optimization goal of the comprehensive efficiency of the power system:

[0050]

[0051] In the formula, C l is the optimization target value of the comprehensive efficiency of the power system; N is the number of power sources in the power system; is the output of the k-th power source at time t; c p,k is the unit power generation cost of the k-th power source; λ k is the electro-carbon factor of the k-th power source; p C is the carbon tax.

[0052] The step of constructing the constraint conditions based on the DC power flow model in step S4 specifically includes the following steps:

[0053] The following formula is used as the constraint condition:

[0054] P = Bθ

[0055] In the formula, P is the power node power deviation vector; B is the transfer admittance matrix; θ is the node voltage phase angle vector;

[0056] The following formula is used as the upper and lower limit constraints of the node voltage phase angle of the power system:

[0057] -π ≤ θ ≤ π

[0058] The following formula is used as the unit output constraint:

[0059]

[0060] In the formula, P min,k is the lower limit value of the output of the k-th power source in the power system; is the output value of the k-th power source in the power system; P max,k is the upper limit value of the output of the k-th power source in the power system; the power sources in the power system include coal-fired power units, gas-fired power units and hydraulic power units.

[0061] The step of solving the two-layer optimization model constructed in steps S3 and S4 in step S5 includes the following steps:

[0062] The NSGA-II algorithm is used to solve the upper-layer optimization model constructed in step S3, and the solver is used to solve the lower-layer optimization model constructed in step S4, and the multi-dimensional preference analysis linear programming is used to select the optimal scheduling plan for the target park's integrated energy system.

[0063] The step S5 specifically includes the following steps:

[0064] A. Input the parameter information of the target park and the parameter information of the power system;

[0065] B. Initialize the parameters of the NSGA-II algorithm, and generate an initial population and individuals; each individual represents a set of operation plans for the integrated energy system of the target park in the upper-layer optimization model;

[0066] C. The upper-layer optimization model transfers the current operation plan of the integrated energy system of the target park to the lower-layer optimization model;

[0067] D. According to the data information transferred by the upper-layer optimization model, use the CPLEX solver to solve the lower-layer optimization model, optimize the daily power output of the power sources in the power system and the power flow, obtain the carbon potential of the nodes where the target park is located, and transmit it to the upper-layer optimization model;

[0068] E. According to the data information transferred by the lower-layer optimization model, calculate the economic optimization objective values, environmental protection optimization objective values, and energy-saving optimization objective values of each individual in the NSGA-II algorithm to determine the non-dominated sorting levels of each individual;

[0069] F. Judge whether the objective values of the NSGA-II algorithm converge: if they converge, output the Pareto front solution set composed of the operation optimization plans of the integrated energy system of the target park; otherwise, generate a new population through genetic operations, and each individual in the new population represents a set of operation plans for the integrated energy system of the target park in the upper-layer optimization model, and return to step C for the next round of iteration;

[0070] G. According to the Pareto front solution set obtained in step F, use the multi-dimensional preference analysis linear programming method to select the optimal solution as the optimal scheduling plan for the integrated energy system of the target park.

[0071] The present invention also provides a system for implementing the scheduling method of the park integrated energy system, including a data acquisition module, a factor calculation module, an upper-layer optimization module, a lower-layer optimization module, and a system scheduling module; the data acquisition module, the factor calculation module, the upper-layer optimization module, the lower-layer optimization module, and the system scheduling module are connected in series in sequence; the data acquisition module is used to acquire the data information of the target park integrated energy system and upload the data information to the factor calculation module; the factor calculation module is used to solve the carbon flow of the power grid and the node carbon potential distribution based on the power system carbon flow theory according to the received data information, and use the carbon potential of the power node where the target park is located as the electro-carbon factor of the park integrated energy system, and upload the data information to the upper-layer optimization module; the upper-layer optimization module is used to construct an upper-layer optimization model with the economy, environmental protection, and energy conservation of the target park integrated energy system as the optimization objectives and the energy flow balance, equipment operation, and load demand-side response of the target park as the constraints according to the received data information, and upload the data information to the lower-layer optimization module; the lower-layer optimization module is used to construct a lower-layer optimization model with the optimal comprehensive efficiency of the power system as the objective and based on the DC power flow model to construct the constraint conditions according to the received data information, and upload the data information to the system scheduling module; the system scheduling module is used to solve the constructed double-layer optimization model according to the received data information and complete the scheduling of the target integrated energy system according to the solution result.

[0072] The scheduling method and system of the park integrated energy system provided by the present invention construct the upper-layer optimization objectives from multiple aspects of economy, environmental protection, and energy conservation, construct the lower-layer optimization objectives according to the DC power flow constraints of the power system, and solve the constructed double-layer optimization objectives, which not only realizes the optimal scheduling of the target park integrated energy system, but also has higher reliability and better accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] Figure 1 It is a schematic flow chart of the method of the present invention.

[0074] Figure 2 It is a schematic diagram of the power system topology of the embodiment of the method of the present invention.

[0075] Figure 3 It is a schematic diagram of the energy flow of the park integrated energy system in the embodiment of the method of the present invention.

[0076] Figure 4 It is a schematic diagram of the multi-energy load on the day to be optimized in the embodiment of the method of the present invention.

[0077] Figure 5 It is a schematic diagram of the Pareto frontier composed of the reasonable operation optimization scheme in the embodiment of the method of the present invention.

[0078] Figure 6Schematic diagram of the power balance of the operation plan 1 of the park integrated energy system in the method embodiment of the present invention.

[0079] Figure 7 Schematic diagram of the power balance of the operation plan 2 of the park integrated energy system in the method embodiment of the present invention.

[0080] Figure 8 Schematic diagram of the power balance of the operation plan 3 of the park integrated energy system in the method embodiment of the present invention.

[0081] Figure 9 Schematic diagram of the power balance of the operation plan 4 of the park integrated energy system in the method embodiment of the present invention.

[0082] Figure 10 Schematic diagram of the heat balance of the operation plan 1 of the park integrated energy system in the method embodiment of the present invention.

[0083] Figure 11 Schematic diagram of the heat balance of the operation plan 2 of the park integrated energy system in the method embodiment of the present invention.

[0084] Figure 12 Schematic diagram of the heat balance of the operation plan 3 of the park integrated energy system in the method embodiment of the present invention.

[0085] Figure 13 Schematic diagram of the heat balance of the operation plan 4 of the park integrated energy system in the method embodiment of the present invention.

[0086] Figure 14 Schematic diagram of the daily electricity carbon factor of the park integrated energy system of the operation plan 1 in the method embodiment of the present invention.

[0087] Figure 15 For the operation plans 1-4 in the method embodiment of the present invention Flow diagram.

[0088] Figure 16 Schematic diagram of the electrical load before and after demand response under the operation plans 1-4 in the method embodiment of the present invention.

[0089] Figure 17 Schematic diagram of the functional modules of the present invention system. Detailed implementation manners

[0090] As Figure 1 shown is the method flow diagram of the method of the present invention: The scheduling method of the park integrated energy system disclosed in the present invention includes the following steps:

[0091] S1. Obtain the data information of the target park integrated energy system;

[0092] S2. Based on the carbon flow theory of the power system, solve the carbon flow in the power network and the distribution of node carbon potential, and use the carbon potential of the power node where the target park is located as the electro-carbon factor of the park's integrated energy system; set the branch power flow distribution matrix as P B , the unit injection distribution matrix as P G , then the node active power flux matrix P N is P N = diag(ζP Z ), where diag() is a diagonal matrix composed of the elements of the column vector, ζ is a row vector with all elements being 1, and P Z is an intermediate matrix and P Z = [P B P G T ;

[0093] Combine the carbon emission intensity vector E of the generator set G , and calculate the node electro-carbon factor vector λ N as λ N The physical meaning of is the carbon emission value equivalent to the power generation side caused by consuming a unit of electricity at this node;

[0094] S3. Taking the economy, environmental protection and energy conservation of the target park's integrated energy system as the optimization objectives, and taking the energy flow balance, equipment operation and load demand side response of the target park as the constraints, construct the upper-layer optimization model;

[0095] Specifically, when implementing: use the following formula as the economic optimization objective:

[0096]

[0097] In the formula, f1 is the economic optimization objective value; is the power purchase power from the superior power grid by the target park at time t; is the unit electricity price at time t; is the gas purchase power from the superior gas network by the target park at time t; is the unit gas price at time t; GCV is the calorific value of natural gas;

[0098] Use the following formula as the environmental protection optimization objective:

[0099]

[0100] In the formula, f2 is the environmental protection optimization objective value; is the dynamic electro-carbon factor of the target park at time t; is the natural gas carbon emission factor of the target park at time t;

[0101] Use the following formula as the energy conservation optimization objective:​

[0102]

[0103] In the formula, f3 is the optimized target value of energy conservation; E e is the energy quality coefficient of electricity, and E e = 1; is the electric power output by the target park at time t; E g is the energy quality coefficient of natural gas, and T0 is the environmental temperature, T b is the theoretical combustion temperature of natural gas; is the gas power output by the target park at time t; E h is the energy quality coefficient of heat, and T s is the water supply temperature of the thermal system, T r is the return water temperature of the thermal system; is the heat power output by the target park at time t;

[0104] The following formula is used as the energy flow balance constraint:

[0105]

[0106] In the formula is the electric load of the integrated energy system of the target park at time t; η T is the efficiency of the transformer; is the electric power of the cogeneration unit of the integrated energy system of the target park at time t; is the electric power generated by the photovoltaic of the integrated energy system of the target park at time t; is the electric power consumed by the electric energy storage of the integrated energy system of the target park at time t; is the electric power consumed by the electric boiler of the integrated energy system of the target park at time t; is the electric power consumed by the heat pump of the integrated energy system of the target park at time t; is the electric power released by the electric energy storage of the integrated energy system of the target park at time t; is the gas load of the integrated energy system of the target park at time t; is the gas power consumed by the cogeneration unit of the integrated energy system of the target park at time t; is the gas power consumed by the gas boiler of the integrated energy system of the target park at time t; is the heat load of the integrated energy system of the target park at time t; is the heat power generated by the cogeneration unit of the integrated energy system of the target park at time t; is the heat power generated by the gas boiler of the integrated energy system of the target park at time t; is the thermal power generated by the electric boiler in the target park integrated energy system at time t; is the thermal power generated by the heat pump in the target park integrated energy system at time t;

[0107] The following formula is used as the power balance constraint, upper and lower limit constraint, and ramp constraint of the combined heat and power unit during operation

[0108]

[0109] In the formula, η e,CHP is the gas-electric conversion efficiency of the combined heat and power unit; η h,CHP is the gas-thermal conversion efficiency of the combined heat and power unit; S CHP is the capacity of the combined heat and power unit; K min,CHP is the minimum output coefficient of the combined heat and power unit; K max,CHP is the maximum output coefficient of the combined heat and power unit; is the lower limit value of the ramp rate of the combined heat and power unit; is the output change of the combined heat and power unit at time t; is the upper limit value of the ramp rate of the combined heat and power unit;

[0110] The following formula is used as the power balance constraint and upper and lower limit constraint of the gas boiler, electric boiler, and heat pump:

[0111]

[0112]

[0113]

[0114]

[0115]

[0116]

[0117] In the formula, η h,GB is the gas-thermal conversion efficiency of the gas boiler; S GB is the capacity of the gas boiler; K min,GB is the minimum output coefficient of the gas boiler; K max,GB is the maximum output coefficient of the gas boiler; η h,EB is the electric-thermal conversion efficiency of the electric boiler; S EB is the capacity of the electric boiler; K min,EB is the minimum output coefficient of the electric boiler; K max,EB is the maximum output coefficient of the electric boiler; η hHP is the electric-thermal conversion efficiency of the heat pump; S HP is the capacity of the heat pump; Kmin,HP is the minimum output coefficient of the electric heat pump; K max,HP is the maximum output coefficient of the electric heat pump;

[0118] The following formula is used as the operation constraint of the electrical energy storage:

[0119]

[0120] wherein is the stored electricity of the electrical energy storage at the moment t + 1; η ES,l is the self-loss coefficient of the electrical energy storage; η ch is the charging efficiency of the electrical energy storage; η dch is the discharging efficiency of the electrical energy storage; x ch is a 0-1 variable of the charging state of the electrical energy storage, and when the electrical energy storage is in the charging state, x ch = 1, and when the electrical energy storage is in the non-charging state, x ch = 0; P ch,max is the maximum charging power of the electrical energy storage; x dch is a 0-1 variable of the discharging state of the electrical energy storage, and when the electrical energy storage is in the discharging state, x dch = 1, and when the electrical energy storage is in the non-discharging state, x dch = 0; SOC t is the state of charge of the electrical energy storage at the moment t; SOC min is the minimum state of charge of the electrical energy storage; SOC max is the maximum state of charge of the electrical energy storage; SOC s is the state of charge of the electrical energy storage at the start of the scheduling period; SOC e is the state of charge of the electrical energy storage at the end of the scheduling period;

[0121] The following formula is used as the load demand side response constraint:

[0122]

[0123] wherein τ is the demand response coefficient; is the electrical load of the target park integrated energy system at the moment t before the demand response;

[0124] S4. Taking the optimal comprehensive efficiency of the power system as the goal, constructing constraint conditions based on the DC power flow model, and constructing the lower-layer optimization model;

[0125] The carbon emission flow of the power system is defined as a virtual network flow that exists attached to the power flow and is used to characterize the carbon emissions for maintaining the power flow of any branch in the power system, and it mainly depends on the active power flow in the network; therefore, the DC power flow model is used to solve the power flow of the power system to achieve the advantages of fast calculation speed and being convenient for linear programming solution;

[0126] The following formula is used as the optimization objective for the comprehensive efficiency of the power system:

[0127]

[0128] In the formula, C l is the optimization target value of the comprehensive efficiency of the power system; N is the number of power sources in the power system; is the output of the k-th power source at time t; c p,k is the unit power generation cost of the k-th power source; λ k is the electricity-carbon factor of the k-th power source; p C is the carbon tax;

[0129] The following formula is used as the constraint condition:

[0130] P = Bθ

[0131] In the formula, P is the power node power deviation vector; B is the transfer admittance matrix; θ is the node voltage phase angle vector;

[0132] The following formula is used as the upper and lower limit constraints of the node voltage phase angle of the power system:

[0133] -π ≤ θ ≤ π

[0134] The following formula is used as the generator output constraint:

[0135]

[0136] In the formula, P min,k is the lower limit value of the output of the k-th power source in the power system; is the output value of the k-th power source in the power system; P max,k is the upper limit value of the output of the k-th power source in the power system; The power sources in the power system include coal-fired power units, gas-fired power units and hydro power units;

[0137] S5. Solve the two-layer optimization model constructed in steps S3 and S4, and complete the scheduling of the target integrated energy system according to the solution results; specifically: use the NSGA-II algorithm to solve the upper-layer optimization model constructed in step S3, use the solver to solve the lower-layer optimization model constructed in step S4, and use multi-dimensional preference analysis linear programming to select the optimal scheduling plan for the target park integrated energy system;

[0138] Specifically, the following steps are included during implementation:

[0139] A. Input the parameter information of the target park and the parameter information of the power system;

[0140] B. Initialize the parameters of the NSGA-II algorithm, and generate the initial population and individuals; each individual represents a set of operation plans for the target park integrated energy system in the upper-layer optimization model;

[0141] C. The upper-layer optimization model transfers the current operation plan of the target park integrated energy system to the lower-layer optimization model;

[0142] D. According to the data information transferred by the upper-layer optimization model, use the CPLEX solver to solve the lower-layer optimization model, optimize the daily power output of the power sources in the power system and the power flow, obtain the carbon potential of the nodes where the target park is located, and transmit it to the upper-layer optimization model;

[0143] E. According to the data information transferred by the lower-layer optimization model, calculate the economic optimization objective values, environmental protection optimization objective values, and energy-saving optimization objective values of each individual in the NSGA-II algorithm to determine the non-dominated sorting levels of each individual;

[0144] F. Determine whether the objective values of the NSGA-II algorithm converge: if they converge, output the Pareto front solution set composed of the operation optimization plans of the target park integrated energy system; otherwise, generate a new population through genetic operations. Each individual in the new population represents a set of operation plans for the target park integrated energy system in the upper-layer optimization model, and return to step C for the next round of iteration;

[0145] G. According to the Pareto front solution set obtained in step F, use the multi-dimensional preference analysis linear programming method to select the optimal solution as the optimal scheduling plan for the target park integrated energy system.

[0146] The following further illustrates the method of the present invention in combination with specific embodiments:

[0147] Power system parameters: The power system is adapted from the IEEE 39 standard example, and the topology diagram is as Figure 2 shown. There are 10 MW of coal-fired power at nodes 1 and 13 respectively. There are 2 MW of photovoltaic power at nodes 2 and 25 respectively. There are 3 MW of wind turbines at nodes 9 and 22 respectively. There is 4 MW of hydropower at node 23. There is 5 MW of gas-fired power at node 27. The electricity-carbon factors of coal-fired power, gas-fired power, hydropower, wind turbines, and photovoltaic power are 0.9440, 0.4792, 0.0143, 0.0336, and 0.0545 kgCO2 / kW·h respectively, and the power generation costs are 0.24, 0.3, 0.07, 0.1, and 0.25 yuan / kW·h respectively. The carbon tax is 0.3 yuan / kgCO2.

[0148] Regional integrated energy system parameters: This article adapts the integrated energy system of a certain local example area, which is located at node 24 in the power system, and the energy flow structure is as Figure 3 shown. The multi-energy load is as Figure 4As shown in the figure. The energy input is from the upper-level power grid and the upper-level gas grid, and the outputs are electricity, gas, and heat loads. The energy equipment includes combined heat and power units, gas boilers, electric boilers, electric heat pumps, electric energy storage, and photovoltaics. Among them, the capacities of the combined heat and power units, gas boilers, and electric boilers are 3000 kW, the capacity of the electric heat pump is 500 kW, and the capacities of the electric energy storage and photovoltaics are 2000 kW. The gas-electric and gas-heat conversion efficiencies of the combined heat and power unit are 0.35 and 0.45 respectively. The gas-heat conversion efficiency of the gas boiler is 0.85. The electric-heat conversion efficiency of the electric boiler is 0.95. The electric-heat conversion efficiency of the electric heat pump is 4.5. The maximum charge and discharge power of the electric energy storage is 500 kW, the charge and discharge efficiency is 0.95, and the self-loss coefficient is 0.001. The carbon emission coefficient of natural gas is 2.1622 kgCO2 / m 3 .

[0149] Market environment: The electricity prices during peak (17:00 - 22:00), flat (6:00 - 11:00, 15:00 - 16:00, 23:00), and off-peak (0:00 - 5:00, 12:00 - 14:00) periods are 1.154, 0.738, and 0.323 yuan / kW·h respectively. The natural gas price is 4.5 yuan / m 3 .

[0150] Based on the low-carbon economic dispatch method of the park integrated energy system considering the dynamic electricity-carbon factor in this patent, with the daily operating cost, carbon emissions, efficiency (energy conservation) as the optimization objectives, the Pareto front composed of reasonable operation optimization schemes is as Figure 5 shown. As Figure 5 can be seen, there is a mutual exclusivity among economy, environmental protection, and energy conservation, and the three cannot meet the optimal simultaneously. In the Pareto curve, as the daily operating cost increases continuously, the carbon emissions decrease continuously, and the efficiency also decreases continuously. Based on the multi-dimensional preference analysis linear programming method, the optimal solution is selected from the Pareto front, and the scheme 1 in Figure 5 is obtained as the final operation optimization scheme of the park integrated energy system.

[0151] To analyze the influence of different optimization objectives on the operation scheme of the park integrated energy system, four schemes are selected for analysis in this paper: 1) Scheme 1 adopts the patent method, with the daily operating cost, carbon emissions, efficiency as the optimization objectives. 2) Scheme 2 only takes the daily operating cost as the optimization objective. 3) Scheme 3 only takes the carbon emissions as the optimization objective. 4) Scheme 4 only takes efficiency as the optimization objective. The operating indicators of the park integrated energy system under Schemes 1 - 4 are shown in Table 1:[[]]

[0152] Table 1 The objective functions of the operation schemes under different operating objectives

[0153]

[0154] Figure 6 , 7 , 8, and 9 show the power balance of the park's integrated energy system under Schemes 1, 2, 3, and 4, respectively. Figure 10 , 11 Figures 1, 2, and 3 respectively show the thermal balance of the park's integrated energy system under Schemes 1, 2, 3, and 4. The daily electricity price adopts a time-of-use electricity price, and the gas price is a fixed gas price. Scheme 2 aims at optimal economic efficiency. Whether to use electric heating or gas heating depends mainly on the current electricity price. Therefore, in Scheme 2, the proportion of heat produced by gas boilers is relatively high at low electricity price times such as early morning and noon. In the morning and afternoon, the proportion of heat produced by gas boilers is relatively high at flat electricity price times. In the evening, at peak electricity price times, even if the energy efficiency of the cogeneration unit is low, it is still used to generate heat and generate electricity to reduce the purchased power, thereby reducing the total operating cost. The electric energy storage is charged and discharged according to the electricity price, charging in the early morning and noon, and discharging in the morning and evening. The electric heat pump has high energy efficiency, and it outputs at maximum power at low and flat electricity price times, and at minimum power at peak electricity price times.

[0155] Scheme 3 aims at the best environmental performance. Whether to use electric heating or gas heating depends mainly on the current electricity-carbon factor. Figure 14 As shown in the figure, after optimizing the power flow, the electric carbon factor of node 24 generally shows a trend of high at night and low at noon. Therefore, the gas boiler mainly produces heat in the early morning, and the gas boiler mainly produces heat during the day and night. The electric heat pump outputs at maximum power at all times. The electric energy storage is charged at the low electric carbon factor at noon and discharged at the high electric carbon factor at night.

[0156] Option 4 aims to optimize energy saving. Figure 15 Demonstrates the integrated energy system of the park under Schemes 1-4 Flow chart. Under the four schemes, the park's integrated energy system outputs electricity, gas, and heat loads Equal, system Efficiency depends mainly on Among the three energy forms, electrical energy has the highest energy quality, while thermal energy has the lowest. Solution 4 is to reduce The heat output of the cogeneration unit is high during the day, and it operates at maximum power during most of the time. High efficiency, while converting gas into low-quality heat, it produces higher-quality electricity to suppress the depreciation of energy quality. In the early morning and at night, the electricity and heat loads are low, and increasing the power purchase power of the upper level combined with the heat production of gas boilers is more conducive to increasing the power and heat production of electricity and power by using cogeneration units. The efficiency is low, so gas boilers are mainly used to generate heat during the day and night. Gas boilers are not energy-efficient because they convert a large amount of high-quality electrical energy into heat, so they are used to generate heat at the minimum power at all times. Electric heat pumps, however, have extremely high energy efficiency. The efficiency is also high, and the maximum power is output at all times. The essence of electric energy storage is the translation of electric power. Due to its charging and discharging losses, the electric energy storage in Scheme 4 does not work.

[0157] Combination Figures 6 to 13 It can be seen that different planning objectives have a great impact on the formulation of energy equipment operation strategies for the park's integrated energy system. The best economy encourages the use of gas-to-heat or even electricity generation at high electricity prices, and gas-to-heat at low electricity prices. Electric energy storage transfers electricity from low electricity price times to high electricity price times. The best environmental protection encourages the use of gas-to-heat at times with high electricity carbon factors, and electricity-to-heat at times with low electricity carbon factors, reduces the use of inefficient equipment, and electric energy storage transfers electricity from low electricity carbon factor times to high times. The best energy-saving performance encourages the park's integrated energy system to use high-efficiency energy such as cogeneration units and gas boilers as much as possible. Efficiency equipment, reduce gas boilers and other low Efficient equipment use. Scheme 3 has the worst economy and energy saving performance among the four schemes because the gas boiler produces a lot of heat most of the time. Scheme 4 uses a cogeneration unit to produce heat most of the time, and due to its low energy efficiency, it has the worst environmental protection performance among the four schemes.

[0158] Scheme 1 is based on daily operating costs, carbon emissions, Efficiency is the optimization goal, and the economy, environmental protection, and energy saving of the park's comprehensive energy system are comprehensively considered, making all indicators better. Compared with Scheme 2, Scheme 1 reduces carbon emissions by 4.53%. The efficiency is increased by 0.50%. Compared with Scheme 3, the daily operating cost of Scheme 1 is reduced by 9.71%. The efficiency is increased by 4.24%. Compared with Scheme 4, Scheme 1 has a daily operating cost reduction of 5.07% and a carbon emission reduction of 5.07%. Figure 4 As shown in the figure, in Scheme 1, the gas boiler produces more heat when the electricity price and carbon factor are low at noon, the cogeneration unit produces more heat at the peak electricity price in the evening, and the gas boiler produces more heat at other times. Due to its high energy efficiency, the electric heat pump is at its maximum output at all times to promote energy conservation and emission reduction. The electric energy storage is charged in the early morning and noon when the electricity price and carbon factor are low, and discharged in the morning and evening when the electricity price and carbon factor are high.

[0159] Figure 16The electric load before and after demand response under Schemes 1-4 is shown. Scheme 2 aims at economic optimization, and the electric load shifts from high electricity price hours to low electricity price hours. The load shows a trend of shifting from morning, afternoon, and evening to early morning and noon. Scheme 3 aims at environmental optimization, and the electric load shifts from high carbon factor hours to low carbon factor hours. The load shows a trend of shifting from early morning and evening to daytime. Scheme 4 aims at energy optimization, and demand response does not affect the total electric energy. Input and output, so the electric load before and after the response is equal. Scheme 1 considers the above three objectives and transfers the electric load by comprehensively balancing the electricity price and the electric carbon factor. The load generally shows a trend of shifting from evening and morning to early morning, noon and afternoon. Therefore, the present invention can achieve the optimal scheduling of the park comprehensive energy system on the basis of considering economy, environmental protection and energy saving at the same time.

[0160] like Figure 17 The figure shows a schematic diagram of the functional modules of the system of the present invention: the system disclosed in the present invention for realizing the method for scheduling the integrated energy system of the park comprises a data acquisition module, a factor calculation module, an upper-layer optimization module, a lower-layer optimization module and a system scheduling module; the data acquisition module, the factor calculation module, the upper-layer optimization module, the lower-layer optimization module and the system scheduling module are connected in series in sequence; the data acquisition module is used to acquire data information of the integrated energy system of the target park, and upload the data information to the factor calculation module; the factor calculation module is used to solve the carbon flow of the power network and the carbon potential distribution of the node based on the carbon flow theory of the power system according to the received data information, and use the carbon potential of the power node where the target park is located as the electric carbon factor of the integrated energy system of the park, and The data information is uploaded to the upper-level optimization module; the upper-level optimization module is used to construct an upper-level optimization model based on the received data information, with the economy, environmental protection and energy saving of the comprehensive energy system of the target park as the optimization goals, and the energy flow balance, equipment operation and load demand side response of the target park as constraints, and upload the data information to the lower-level optimization module; the lower-level optimization module is used to construct constraints based on the DC power flow model based on the received data information, with the optimal comprehensive efficiency of the power system as the goal, to construct a lower-level optimization model, and upload the data information to the system scheduling module; the system scheduling module is used to solve the constructed two-layer optimization model based on the received data information, and complete the scheduling of the target comprehensive energy system according to the solution results.

Claims

1. A scheduling method for a comprehensive energy system in a park, comprising the following steps: S1. Obtain the data information of the target park comprehensive energy system; S2. Based on the carbon flow theory of the power system, solve the carbon flow of the power network and the carbon potential distribution of nodes, and use the carbon potential of the power node where the target park is located as the electro-carbon factor of the park comprehensive energy system; S3. With the economy, environmental protection, and energy conservation of the target park comprehensive energy system as the optimization objectives, and the energy flow balance, equipment operation, and load demand-side response of the target park as the constraints, construct an upper-layer optimization model; S4. With the optimal comprehensive efficiency of the power system as the objective, construct constraint conditions based on the DC power flow model, and construct a lower-layer optimization model; S5. Solve the two-layer optimization model constructed in steps S3 and S4, and complete the scheduling of the target comprehensive energy system according to the solution results.

2. The dispatching method of the integrated park energy system according to claim 1, characterized in that The step S2 of solving the carbon flow of the power network and the carbon potential distribution of nodes based on the carbon flow theory of the power system, and using the carbon potential of the power node where the target park is located as the electro-carbon factor of the park comprehensive energy system specifically includes the following steps: Set the branch power flow distribution matrix as P B , and the generator injection distribution matrix as P G . Then, the nodal active power flux matrix P N is P N = diag(ζP Z ), where diag() is a diagonal matrix formed by the elements of the column vector, ζ is a row vector with all elements equal to 1, and P Z is an intermediate matrix and P Z = [P B P G T ;​ Combined with the carbon emission intensity vector E of the generator set G , the node electro-carbon factor vector λ is calculated N as 3. The dispatching method for the integrated energy system of the park according to claim 2, wherein The step S3 of taking the economy, environmental protection, and energy conservation of the target park comprehensive energy system as the optimization objectives specifically includes the following steps: Use the following formula as the economic optimization objective: where f1 is the objective value of economic optimization; is the power purchase from the superior power grid by the target park at time t; is the unit electricity price at time t; is the gas purchase power from the superior gas grid by the target park at time t; is the unit gas price at time t; GCV is the calorific value of natural gas; Use the following formula as the environmental protection optimization objective: where f2 is the optimized target value of environmental protection; is the dynamic electricity-carbon factor of the target park at time t; is the natural gas carbon emission factor of the target park at time t; Use the following formula as the energy conservation optimization objective: where f3 is the optimization target value of energy conservation; E e is the energy quality coefficient of electricity, and E e = 1; is the electric power output by the target park at time t; E g is the energy quality coefficient of natural gas, and T0 is the environmental temperature, T b is the theoretical combustion temperature of natural gas; is the gas power output by the target park at time t; E h is the energy quality coefficient of heat, and T s is the water supply temperature of the thermal system, T r is the return water temperature of the thermal system; is the heat power output by the target park at time t.

4. The dispatching method for the park integrated energy system according to claim 3, characterized in that The step S3 of taking the energy flow balance, equipment operation, and load demand-side response of the target park as the constraints specifically includes the following steps: Use the following formula as the energy flow balance constraint: where is the electrical load of the target park integrated energy system at time t; η T is the efficiency of the transformer; is the electric power of the combined heat and power unit of the target park integrated energy system at time t; is the electric power generated by the photovoltaic of the target park integrated energy system at time t; is the electric power consumed by the electrical energy storage of the target park integrated energy system at time t; is the electric power consumed by the electric boiler of the target park integrated energy system at time t; is the electric power consumed by the heat pump of the target park integrated energy system at time t; is the electric power released by the electrical energy storage of the target park integrated energy system at time t; is the gas load of the target park integrated energy system at time t; is the gas power consumed by the combined heat and power unit of the target park integrated energy system at time t; is the gas power consumed by the gas boiler of the target park integrated energy system at time t; is the heat load of the target park integrated energy system at time t; is the heat power generated by the combined heat and power unit of the target park integrated energy system at time t; is the heat power generated by the gas boiler of the target park integrated energy system at time t; is the heat power generated by the electric boiler of the target park integrated energy system at time t; is the heat power generated by the heat pump of the target park integrated energy system at time t; Use the following formula as the power balance constraint, upper and lower limit constraints, and ramp constraint of the cogeneration unit during operation where η e,CHP is the gas - electricity conversion efficiency of the combined heat and power unit; η h,CHP is the gas - heat conversion efficiency of the combined heat and power unit; S CHP is the capacity of the combined heat and power unit; K min,CHP is the minimum output coefficient of the combined heat and power unit; K max,CHP is the maximum output coefficient of the combined heat and power unit; is the lower limit value of the ramp rate of the combined heat and power unit; is the output change of the combined heat and power unit at time t; is the upper limit value of the ramp rate of the combined heat and power unit; Use the following formula as the power balance constraint and upper and lower limit constraints of gas boilers, electric boilers, and heat pumps: where η h,GB is the gas-to-heat conversion efficiency of the gas boiler; S GB is the capacity of the gas boiler; K min,GB is the minimum output coefficient of the gas boiler; K max,GB is the maximum output coefficient of the gas boiler; η h,EB is the electro-thermal conversion efficiency of the electric boiler; S EB is the capacity of the electric boiler; K min,EB is the minimum output coefficient of the electric boiler; K max,EB is the maximum output coefficient of the electric boiler; η h,HP is the electro-thermal conversion efficiency of the heat pump; S HP is the capacity of the heat pump; K min,HP is the minimum output coefficient of the heat pump; K max,HP is the maximum output coefficient of the heat pump; Use the following formula as the operation constraint of the electrical energy storage: Where is the stored electricity of the electrical energy storage at the moment of t+1; η ES,l is the self-loss coefficient of the electrical energy storage; η ch is the charging efficiency of the electrical energy storage; η dch is the discharging efficiency of the electrical energy storage; x ch is the 0-1 variable of the charging state of the electrical energy storage, and if the electrical energy storage is in the charging state, then x ch =1, and if the electrical energy storage is in the non-charging state, then x ch =0; P ch,max is the maximum charging power of the electrical energy storage; x dch is the 0-1 variable of the discharging state of the electrical energy storage, and if the electrical energy storage is in the discharging state, then x dch =1, and if the electrical energy storage is in the non-discharging state, then x dch =0; SOC t is the state of charge of the electrical energy storage at the moment of t; SOC min is the minimum state of charge of the electrical energy storage; SOC max is the maximum state of charge of the electrical energy storage; SOC s is the state of charge of the electrical energy storage at the start of the scheduling period; SOC e is the state of charge of the electrical energy storage at the end of the scheduling period; Use the following formula as the load demand-side response constraint: where τ is the demand response coefficient; is the electrical load of the target park integrated energy system at time t before demand response.

5. The dispatching method for the integrated park energy system according to claim 4, characterized in that The step S4 of taking the optimal comprehensive efficiency of the power system as the objective specifically includes the following steps: Use the following formula as the power system comprehensive efficiency optimization objective: Where C l is the optimization target value of the comprehensive efficiency of the power system; N is the number of power sources in the power system; is the output of the k-th power source at time t; c p,k is the unit power generation cost of the k-th power source; λ k is the electricity-carbon factor of the k-th power source; p C is the carbon tax.

6. The dispatching method of the integrated energy system in the park according to claim 5, wherein The step S4 of constructing constraint conditions based on the DC power flow model specifically includes the following steps: Use the following formula as the constraint condition: P = Bθ In the formula, P is the power deviation vector of the power node; B is the transfer admittance matrix; θ is the node voltage phase angle vector; Use the following formula as the upper and lower limit constraints of the node voltage phase angle of the power system: -π ≤ θ ≤ π Use the following formula as the unit output constraint: where P min,k is the lower limit of the output of the k-th power source in the power system; is the output value of the k-th power source in the power system; P max,k is the upper limit of the output of the k-th power source in the power system; the power sources in the power system include coal-fired power units, gas-fired power units and hydraulic power units.

7. The dispatching method for the integrated energy system of the park according to claim 6, wherein The step S5 of solving the two-layer optimization model constructed in steps S3 and S4 includes the following steps: Use the NSGA-II algorithm to solve the upper-layer optimization model constructed in step S3, use the solver to solve the lower-layer optimization model constructed in step S4, and use multi-dimensional preference analysis linear programming to select the optimal scheduling plan for the target park comprehensive energy system.

8. The dispatching method of the integrated energy system of the park according to claim 7, characterized in that The step S5 specifically includes the following steps: A. Input the parameter information of the target park and the parameter information of the power system; B. Initialize the parameters of the NSGA-II algorithm, and generate an initial population and individuals; each individual represents a set of operation plans for the target park comprehensive energy system in the upper-layer optimization model; C. The upper-layer optimization model transfers the current operation plan of the target park integrated energy system to the lower-layer optimization model; D. According to the data information transferred by the upper-layer optimization model, the CPLEX solver is used to solve the lower-layer optimization model, optimize the daily power output of the power sources in the power system and the power flow, obtain the carbon potential of the nodes where the target park is located, and transmit it to the upper-layer optimization model; E. According to the data information transferred by the lower-layer optimization model, calculate the economic optimization objective value, environmental protection optimization objective value, and energy conservation optimization objective value of each individual in the NSGA-II algorithm to determine the non-dominated sorting level of each individual; F. Determine whether the objective value of the NSGA-II algorithm converges: If it converges, output the Pareto front solution set composed of the operation optimization plan of the target park integrated energy system; otherwise, generate a new population through genetic operations. Each individual in the new population represents a set of operation plans of the target park integrated energy system in the upper-layer optimization model, and return to step C for the next round of iteration; G. According to the Pareto front solution set obtained in step F, use the multi-dimensional preference analysis linear programming method to select the optimal solution as the optimal scheduling plan of the target park integrated energy system.

9. A system for implementing the method for dispatching a park integrated energy system according to any one of claims 1 to 8, characterized in that It includes a data acquisition module, a factor calculation module, an upper-layer optimization module, a lower-layer optimization module, and a system scheduling module; the data acquisition module, the factor calculation module, the upper-layer optimization module, the lower-layer optimization module, and the system scheduling module are connected in series in sequence; the data acquisition module is used to acquire the data information of the target park integrated energy system and upload the data information to the factor calculation module; the factor calculation module is used to solve the carbon flow of the power network and the node carbon potential distribution based on the carbon flow theory of the power system according to the received data information, use the carbon potential of the power node where the target park is located as the electro-carbon factor of the park integrated energy system, and upload the data information to the upper-layer optimization module; The upper-layer optimization module is used to construct an upper-layer optimization model with the economy, environmental protection, and energy conservation of the target park integrated energy system as the optimization objectives and the energy flow balance, equipment operation, and load demand side response of the target park as the constraints according to the received data information, and upload the data information to the lower-layer optimization module; The lower-layer optimization module is used to construct a lower-layer optimization model with the optimal comprehensive efficiency of the power system as the objective and based on the DC power flow model to construct the constraint conditions according to the received data information, and upload the data information to the system scheduling module; The system scheduling module is used to solve the constructed two-layer optimization model according to the received data information and complete the scheduling of the target integrated energy system according to the solution result.

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