Full-electric energy station operation optimization method and system considering multiple objectives

Abstract

The invention provides an all-electric energy station operation optimization method and system considering multiple objectives. The method comprises the following steps: acquiring equipment parameters and operation parameters of an all-electric energy station; based on the equipment parameters and the operation parameters, a comprehensive multi-objective hierarchical optimization model of the photovoltaic-hydrogen-natural gas comprehensive energy system model is solved by adopting a genetic-simulated annealing algorithm, and an optimal operation strategy of the full-electric energy station is obtained; according to the method, when the genetic-simulated annealing algorithm is adopted to solve the comprehensive multi-objective hierarchical optimization model, global search can be realized through group search and natural evolution operation, the capability of solving the complex nonlinear problem in the solving process can be improved, multi-objective optimization can still be implemented when the complex nonlinear problem exists, and the method is suitable for large-scale popularization and application. Therefore, the optimal operation strategy obtained by solving the model genetic-simulated annealing algorithm can significantly improve the flexible adjustment capability and the environmental change adaptation capability of the full-electric energy station.

Description

Technical Field

[0001] The patent application for this invention belongs to the technical field of energy system operation optimization, and specifically relates to an all-electric energy station operation optimization method and system considering multiple objectives. Background Art

[0002] With the construction of new power systems and new energy systems, the power industry has achieved a rapid energy transformation from thermal power generation to renewable energy green power installed capacity accounting for nearly 50% in recent years. This has provided unprecedented conditions and opportunities for the application of renewable energy in the construction industry and for vigorously promoting energy conservation and carbon reduction in the construction industry. Especially in the construction and operation of all-electric energy stations, renewable energy, such as photovoltaic and wind power generation, although the intermittent and instability of their power generation has brought challenges to the stable operation of the power grid, can be combined with energy storage technologies, such as hydrogen energy storage, pumped storage, compressed air energy storage, electrochemical energy storage, etc., to achieve more efficient energy consumption and utilization. Among them, hydrogen energy storage technology has attracted much attention in the industry due to its advantages of high energy density, high conversion efficiency and cross-seasonal storage capacity.

[0003] The operation optimization of traditional grid-connected systems is mostly linear optimization from a single aspect. When it is necessary to deal with complex nonlinear problems and implement multi-objective optimization, it is difficult to guarantee the processing capability and effect of nonlinear problems. Summary of the invention

[0004] In order to overcome the above-mentioned deficiencies of the prior art, the present invention patent application proposes a multi-objective all-electric energy station operation optimization method, including:

[0005] Obtain equipment parameters and operating parameters of the all-electric energy station;

[0006] Based on the equipment parameters and operating parameters, a genetic-simulated annealing algorithm is used to solve a comprehensive multi-objective hierarchical optimization model of the photovoltaic-hydrogen-natural gas integrated energy system model to obtain the optimal operating strategy of the all-electric energy station;

[0007] The comprehensive multi-objective hierarchical optimization model includes a comprehensive multi-objective hierarchical optimization function constructed with the goal of minimizing the average annual cost and carbon dioxide emissions of the all-electric energy station.

[0008] Preferably, the process of constructing the comprehensive multi-objective hierarchical optimization model of the photovoltaic-hydrogen-natural gas integrated energy system model includes:

[0009] Based on the sample equipment parameters, sample operating parameters and constraints of the all-electric energy station, a photovoltaic-hydrogen-natural gas integrated energy system model is constructed;

[0010] Taking the annual average cost and carbon dioxide emissions of the photovoltaic-hydrogen-natural gas integrated energy system model as the minimum, a comprehensive multi-objective hierarchical optimization function is constructed;

[0011] Based on the photovoltaic-hydrogen-natural gas integrated energy system model and the comprehensive multi-objective hierarchical optimization function, a comprehensive multi-objective hierarchical optimization model of the photovoltaic-hydrogen-natural gas integrated energy system model is constructed.

[0012] Preferably, the comprehensive multi-objective hierarchical optimization function includes a year-end cost minimization objective function and a carbon dioxide emission minimization objective function; wherein,

[0013] The year-end cost minimization objective function is constructed by minimizing the sum of various costs in the photovoltaic-hydrogen-natural gas integrated energy system model;

[0014] The carbon dioxide emission minimization objective function is constructed by minimizing carbon dioxide emissions in a photovoltaic-hydrogen-natural gas integrated energy system model.

[0015] Preferably, the expression of the year-end cost minimization objective function is as follows:

[0016] minATC=C inv +C om +C ng +C ct +C rep

[0017] Among them, ATC is the year-end cost, C inv is the investment cost, C om is the equipment operation and maintenance cost, C ng is the natural gas purchase cost, C ct is the carbon tax cost, C rep Equipment replacement cost.

[0018] Preferably, the expression of the minimum objective function of carbon dioxide emissions is as follows:

[0019] minCDE=F gt ×CVN×f ng

[0020] Where CDE is carbon dioxide emissions, f ng is the capital recovery factor, CVN is natural gas, F gt The consumption of natural gas.

[0021] Preferably, the photovoltaic-hydrogen-natural gas integrated energy system model includes at least one or more of the following: a photovoltaic physical model, an inverter physical model, a proton exchange membrane fuel cell physical model, a proton exchange membrane electrolyzer physical model and an energy efficiency model.

[0022] Preferably, based on the equipment parameters and operating parameters, a genetic-simulated annealing algorithm is used to solve a comprehensive multi-objective hierarchical optimization model of the photovoltaic-hydrogen-natural gas integrated energy system model to obtain the optimal operating strategy of the all-electric energy station, including:

[0023] Based on the equipment parameters and the operating parameters, the constructed comprehensive multi-objective hierarchical optimization function is used to obtain a determined comprehensive multi-objective hierarchical optimization function;

[0024] A genetic-simulated annealing algorithm is used to solve the determined comprehensive multi-objective hierarchical optimization function to obtain the optimal operation strategy of the all-electric energy station.

[0025] Preferably, the genetic-simulated annealing algorithm is used to solve the determined comprehensive multi-objective hierarchical optimization function to obtain the optimal operation strategy of the all-electric energy station, including:

[0026] Using a genetic algorithm, the determined comprehensive multi-objective hierarchical optimization function is initialized to obtain an initial operation strategy set as an initial solution population;

[0027] Calculating the fitness of the initial solution population, and performing selection, crossover and mutation operations according to the fitness to obtain the next generation population;

[0028] Using a simulated annealing algorithm, locally search and optimize the next generation population to obtain an operation strategy of the all-electric energy station corresponding to the next generation population;

[0029] Calculating the fitness of the next generation population, and performing iterative selection, crossover and mutation operations on the next generation population according to the fitness to generate a new next generation population, and calculating the operation strategy corresponding to the new next generation population;

[0030] When the iteration termination condition is met, the iteration ends, and the operation strategy corresponding to the last generation of population is used as the optimal operation strategy of the all-electric energy station;

[0031] The iteration termination condition includes that the current iteration number reaches a preset maximum iteration number, or the fitness difference between the new next generation population and the next generation population reaches a preset fitness threshold.

[0032] Preferably, the simulated annealing algorithm is used to perform local search and optimization on the next generation population to obtain the operation strategy of the all-electric energy station corresponding to the next generation population, including:

[0033] Setting a control parameter as a temperature value, and setting an initial solution and a maximum number of iterations of the next generation population;

[0034] Randomly perturb the current solution of the next generation population, iterate to obtain a new solution, judge whether the new solution is better than the current solution, and accept the new solution when the new solution is better than the current solution; otherwise, based on the mass difference between the new solution and the current solution and the temperature value, use the calculation formula of the probability of accepting the new solution in the Metropolis criterion to calculate the probability of accepting the new solution, and accept the new solution according to the probability of accepting the new solution;

[0035] Determine whether the current number of iterations meets the preset number of iterations. If not, continue to randomly perturb the current solution of the next generation population until the current number of iterations meets the preset number of iterations, and then determine whether the temperature value corresponding to the current solution meets the output condition;

[0036] Determine whether the temperature value corresponding to the current solution meets the output condition. If not, reduce the temperature value and reset the number of iterations, and continue to randomly perturb the current solution of the next generation population. If yes, take the current solution as the optimal solution corresponding to the multi-objective function, and obtain the operation strategy of the all-electric energy station according to the optimal solution;

[0037] The optimal solution includes: a solution with minimum annual average cost and a solution with minimum carbon dioxide emissions.

[0038] Preferably, the calculation formula for the probability of accepting a new solution is constructed based on the mass difference between the new solution and the current solution, and the temperature value.

[0039] Preferably, the probability of accepting a new solution is calculated as follows:

[0040] P(ΔE)=exp(-ΔE / (kT))

[0041] Among them, P(ΔE) is the probability of receiving a new solution, ΔE is the quality difference between the new solution and the current solution, T is the temperature value, and k is a constant.

[0042] Based on the same inventive concept, the patent application of the present invention also provides an all-electric energy station operation optimization system considering multiple objectives, including: a parameter acquisition module, an optimal operation strategy determination module;

[0043] The parameter acquisition module is used to obtain equipment parameters and operating parameters of the all-electric energy station;

[0044] The optimal operation strategy determination module is used to solve the comprehensive multi-objective hierarchical optimization model of the photovoltaic-hydrogen-natural gas integrated energy system model based on the equipment parameters and operation parameters, using a genetic-simulated annealing algorithm to obtain the optimal operation strategy of the all-electric energy station;

[0045] The comprehensive multi-objective hierarchical optimization model includes a comprehensive multi-objective hierarchical optimization function constructed with the goal of minimizing the average annual cost and carbon dioxide emissions of the all-electric energy station.

[0046] Preferably, it also includes: a comprehensive multi-objective hierarchical optimization model building module, which is used to:

[0047] Based on the sample equipment parameters, sample operating parameters and constraints of the all-electric energy station, a photovoltaic-hydrogen-natural gas integrated energy system model is constructed;

[0048] Taking the annual average cost and carbon dioxide emissions of the photovoltaic-hydrogen-natural gas integrated energy system model as the minimum, a comprehensive multi-objective hierarchical optimization function is constructed;

[0049] Based on the photovoltaic-hydrogen-natural gas integrated energy system model and the comprehensive multi-objective hierarchical optimization function, a comprehensive multi-objective hierarchical optimization model of the photovoltaic-hydrogen-natural gas integrated energy system model is constructed.

[0050] Preferably, the comprehensive multi-objective hierarchical optimization function includes a year-end cost minimization objective function and a carbon dioxide emission minimization objective function; wherein,

[0051] The year-end cost minimization objective function is constructed by minimizing the sum of various costs in the photovoltaic-hydrogen-natural gas integrated energy system model;

[0052] The carbon dioxide emission minimization objective function is constructed by minimizing carbon dioxide emissions in a photovoltaic-hydrogen-natural gas integrated energy system model.

[0053] Preferably, the expression of the year-end cost minimization objective function is as follows:

[0054] minATC=C inv +C om +C ng +C ct +C rep

[0055] Among them, ATC is the year-end cost, C inv is the investment cost, C om is the equipment operation and maintenance cost, C ng is the natural gas purchase cost, C ct is the carbon tax cost, C rep Equipment replacement cost.

[0056] Preferably, the expression of the minimum objective function of carbon dioxide emissions is as follows:

[0057] minCDE=F gt ×CVN×f ng

[0058] Where CDE is carbon dioxide emissions, f ngis the capital recovery factor, CVN is natural gas, F gt The consumption of natural gas.

[0059] Preferably, the photovoltaic-hydrogen-natural gas integrated energy system model includes at least one or more of the following: a photovoltaic physical model, an inverter physical model, a proton exchange membrane fuel cell physical model, a proton exchange membrane electrolyzer physical model and an energy efficiency model.

[0060] Preferably, the optimal operation strategy determination module includes:

[0061] A comprehensive multi-objective hierarchical optimization function determination submodule is used to obtain a determined comprehensive multi-objective hierarchical optimization function based on the equipment parameters and operating parameters using the constructed comprehensive multi-objective hierarchical optimization function;

[0062] The optimal operation strategy determination submodule is used to adopt a genetic-simulated annealing algorithm to solve the determined comprehensive multi-objective hierarchical optimization function to obtain the optimal operation strategy of the all-electric energy station.

[0063] Preferably, the optimal operation strategy determination submodule includes:

[0064] An initial solution population determination unit is used to calculate the fitness of the initial solution population and perform selection, crossover and mutation operations according to the fitness to obtain the next generation population;

[0065] A population local search optimization unit, used to use a simulated annealing algorithm to perform local search and optimization on the next generation population to obtain an operation strategy of the all-electric energy station corresponding to the next generation population;

[0066] A population iterative local optimization unit, used to calculate the fitness of the next generation population, and perform iterative selection, crossover and mutation operations on the next generation population according to the fitness to generate a new next generation population, and calculate the operation strategy corresponding to the new next generation population;

[0067] The optimal operation strategy determination unit is used to end the iteration when the iteration termination condition is met, and use the operation strategy corresponding to the last generation of population as the optimal operation strategy of the all-electric energy station;

[0068] The iteration termination condition includes that the current iteration number reaches a preset maximum iteration number, or the fitness difference between the new next generation population and the next generation population reaches a preset fitness threshold.

[0069] Preferably, the population local search optimization unit is specifically used for:

[0070] Setting a control parameter as a temperature value, and setting an initial solution and a maximum number of iterations of the next generation population;

[0071] Randomly perturb the current solution of the next generation population, iteratively obtain a new solution, determine whether the new solution is better than the current solution, and accept the new solution when the new solution is better than the current solution; otherwise, based on the mass difference between the new solution and the current solution and the temperature value, use the calculation formula of the probability of accepting the new solution in the Metropolis criterion to calculate the probability of accepting the new solution, and accept the new solution according to the probability of accepting the new solution;

[0072] Determine whether the current number of iterations meets the preset number of iterations. If not, continue to randomly perturb the current solution of the next generation population until the current number of iterations meets the preset number of iterations, and then determine whether the temperature value corresponding to the current solution meets the output condition;

[0073] Determine whether the temperature value corresponding to the current solution meets the output condition. If not, reduce the temperature value and reset the number of iterations, and continue to randomly perturb the current solution of the next generation population. If yes, take the current solution as the optimal solution corresponding to the multi-objective function, and obtain the operation strategy of the all-electric energy station according to the optimal solution;

[0074] The optimal solution includes: a solution with minimum annual average cost and a solution with minimum carbon dioxide emissions.

[0075] Preferably, the calculation formula for the probability of accepting a new solution is constructed based on the mass difference between the new solution and the current solution, and the temperature value.

[0076] Preferably, the probability of accepting a new solution is calculated as follows:

[0077] P(ΔE)=exp(-ΔE / (kT))

[0078] Among them, P(ΔE) is the probability of receiving a new solution, ΔE is the quality difference between the new solution and the current solution, T is the temperature value, and k is a constant.

[0079] Based on the same inventive concept, the patent application of the present invention also provides an electronic device, comprising: at least one processor and a memory; the memory and the processor are connected via a bus;

[0080] The memory is used to store one or more programs;

[0081] When the one or more programs are executed by the at least one processor, an all-electric energy station operation optimization method considering multiple objectives as described above is implemented.

[0082] Based on the same inventive concept, the patent application of the present invention also provides a readable storage medium on which an execution program is stored. When the execution program is executed, an all-electric energy station operation optimization method considering multiple objectives as described above is implemented.

[0083] Compared with the closest prior art, the patent application of this invention has the following beneficial effects:

[0084] The patent application of the present invention provides an all-electric energy station operation optimization method and system considering multiple objectives, including: obtaining equipment parameters and operating parameters of the all-electric energy station; based on the equipment parameters and operating parameters, using a genetic-simulated annealing algorithm to solve a comprehensive multi-objective hierarchical optimization model of a photovoltaic-hydrogen-natural gas integrated energy system model, and obtaining the optimal operation strategy of the all-electric energy station; wherein the comprehensive multi-objective hierarchical optimization model includes a comprehensive multi-objective hierarchical optimization function constructed with the goal of minimizing the average annual cost and carbon dioxide emissions of the all-electric energy station; the present invention uses a genetic-simulated annealing algorithm to solve the comprehensive multi-objective hierarchical optimization model, which can not only realize global search through group search and natural evolution operations, but also improve the ability to solve complex nonlinear problems in the solution process, ensuring that multi-objective optimization can still be implemented when complex nonlinear problems exist. Therefore, the optimal operation strategy obtained by solving the modular genetic-simulated annealing algorithm can significantly improve the flexible adjustment capability and the ability to adapt to environmental changes of the all-electric energy station. BRIEF DESCRIPTION OF THE DRAWINGS

[0085] Figure 1 A schematic flow chart of a multi-objective all-electric energy station operation optimization method provided for the patent application of the present invention;

[0086] Figure 2 A schematic diagram of the photovoltaic-hydrogen-natural gas integrated energy system structure provided for the patent application of this invention;

[0087] Figure 3 A schematic diagram of an energy system configuration optimization analysis framework provided for the patent application of the present invention;

[0088] Figure 4 A flow chart of the optimization process of the simulated annealing (SA) algorithm provided for the patent application of the present invention;

[0089] Figure 5 A schematic diagram of an all-electric energy station operation optimization system considering multiple objectives provided for the patent application of the present invention;

[0090] Figure 6 A schematic structural diagram of an electronic device provided for the patent application of the present invention. DETAILED DESCRIPTION

[0091] The specific implementation methods of the patent application of the present invention are further described in detail below in conjunction with the accompanying drawings.

[0092] Embodiment 1:

[0093] The present invention patent application provides a multi-objective all-electric energy station operation optimization method. Figure 1 As shown, including:

[0094] Step 1: Obtain equipment parameters and operating parameters of the all-electric energy station;

[0095] Step 2: Based on the equipment parameters and operating parameters, a genetic-simulated annealing algorithm is used to solve a comprehensive multi-objective hierarchical optimization model of the photovoltaic-hydrogen-natural gas integrated energy system model to obtain the optimal operating strategy of the all-electric energy station;

[0096] The comprehensive multi-objective hierarchical optimization model includes a comprehensive multi-objective hierarchical optimization function constructed with the goal of minimizing the average annual cost and carbon dioxide emissions of the all-electric energy station.

[0097] In one implementation, the process of constructing the comprehensive multi-objective hierarchical optimization model of the photovoltaic-hydrogen-natural gas integrated energy system model includes:

[0098] Based on the sample equipment parameters, sample operating parameters and constraints of the all-electric energy station, a photovoltaic-hydrogen-natural gas integrated energy system model is constructed;

[0099] Taking the annual average cost and carbon dioxide emissions of the photovoltaic-hydrogen-natural gas integrated energy system model as the minimum, a comprehensive multi-objective hierarchical optimization function is constructed;

[0100] Based on the photovoltaic-hydrogen-natural gas integrated energy system model and the comprehensive multi-objective hierarchical optimization function, a comprehensive multi-objective hierarchical optimization model of the photovoltaic-hydrogen-natural gas integrated energy system model is constructed.

[0101] For example, this application focuses on the two key factors of economy and environment, and then constructs a comprehensive multi-objective hierarchical optimization model; the comprehensive multi-objective hierarchical optimization model not only aims to quantitatively evaluate the annual total cost of the energy system, but also conducts an in-depth analysis of its carbon emissions. This application believes that economic benefits and environmental impacts are the core indicators for evaluating the success of any energy system. Therefore, in the design of the comprehensive multi-objective hierarchical optimization model, the annual total cost ATC (average total cost) and carbon dioxide emissions CDE (carbon dioxide emissions) of the energy system are specifically selected as the main evaluation targets. In this way, it is expected to provide a comprehensive and objective evaluation tool to help make more informed choices in energy strategy formulation;

[0102] This application focuses on the operation optimization of all-electric energy stations, such as Figure 2 The figure shows the schematic diagram of the photovoltaic-hydrogen-natural gas integrated energy system structure. Based on the construction of the photovoltaic-hydrogen-natural gas integrated energy system model, this application aims to solve the problems of power stability and efficiency of all-electric energy stations, reduce power losses, and optimize the operation performance of the power grid. The construction of a comprehensive multi-objective hierarchical optimization model not only improves the utilization efficiency of renewable energy in all-electric energy stations and reduces dependence on traditional fossil energy, but also enhances the environmental friendliness and economic benefits of the system.

[0103] This patent application firstly focuses on the economic and energy-saving evaluation indicators of the all-electric energy station, considers the characteristic constraints of the heat source tower system and equipment, and establishes a comprehensive photovoltaic-hydrogen-natural gas integrated energy system model. This model provides a detailed description of the actual operation of the all-electric energy station, which is the basis for the development of the optimization algorithm. Secondly, this application introduces a comprehensive multi-objective hierarchical optimization model for the operation optimization of the all-electric energy station. The comprehensive multi-objective hierarchical optimization model not only considers the multi-objective optimization of the integrated carbon emissions and total cost, but also considers the multiple objectives in the operation of the all-electric energy station, such as cost efficiency, energy utilization, environmental impact, etc., in order to achieve the comprehensive optimization of the operation efficiency and benefits of the all-electric energy station, and then can deeply explore the economic feasibility of the system with the optimization of carbon emissions as the primary task. Further consider the flexibility, reliability and dispatchability of the integrated distributed energy.

[0104] In one implementation, the comprehensive multi-objective hierarchical optimization function includes a year-end cost minimization objective function and a carbon dioxide emission minimization objective function; wherein,

[0105] The year-end cost minimization objective function is constructed by minimizing the sum of various costs in the photovoltaic-hydrogen-natural gas integrated energy system model;

[0106] For example, the annual total cost ATC consists of the following five categories: investment cost, equipment operation and maintenance cost, natural gas purchase cost, carbon emission tax cost, and equipment replacement cost;

[0107] The calculation formula for annual total cost ATC is as follows:

[0108] ATC=C inv +C om +C rep +C ng +C ct

[0109] Among them, ATC is the total annual cost, C inv is the investment cost, C om is the equipment operation and maintenance cost, C repis the equipment replacement cost, C ng is the natural gas purchase cost, C ct for the cost of carbon tax;

[0110] The investment cost is calculated as follows:

[0111]

[0112] Among them, C inv is the investment cost, C ep,i is the unit cost of investment cost, f e is the capital recovery factor, P rate,i is the rated power of the ith device, and n is the total number of devices.

[0113] The calculation formula for equipment operation and maintenance costs is as follows:

[0114]

[0115] Among them, C om is the equipment operation and maintenance cost, C om,i is the unit cost of equipment operation and maintenance cost;

[0116] The calculation formula for equipment replacement cost is as follows:

[0117]

[0118] Among them, C rep is the equipment replacement cost, C rep,i is the unit cost of equipment replacement cost;

[0119] C enc The calculation formula is as follows:

[0120]

[0121] Among them, C enc is the natural gas cost of the equipment, F ng,t is the amount of natural gas used at hour t, C ng is the unit cost of natural gas.

[0122] C env The calculation formula is as follows:

[0123] C env =CDE×C ct

[0124] Among them, C env is the annual emission cost of the equipment, CDE is the annual total emission, C ct is the unit cost of the emission tax.

[0125] f eqThe calculation formula is as follows:

[0126]

[0127] Among them, f eq is the capital regression coefficient, j is the interest rate, and m is the number of years.

[0128] The investment cost recovery factor converts the present value into an annual value. The capital recovery factor f eq , where j = 5%, n = 20;

[0129] The carbon dioxide emission minimization objective function is constructed by minimizing carbon dioxide emissions in a photovoltaic-hydrogen-natural gas integrated energy system model.

[0130] In one implementation, the expression of the year-end cost minimization objective function is as follows:

[0131] minATC=C inv +C om +C ng +C ct +C rep

[0132] Among them, ATC is the year-end cost, C inv is the investment cost, C om is the equipment operation and maintenance cost, C ng is the natural gas purchase cost, C ct is the carbon tax cost, C rep Equipment replacement cost.

[0133] In one implementation, the expression of the carbon dioxide emission minimum objective function is as follows:

[0134] minCDE=F gt ×CVN×f ng

[0135] Where CDE is carbon dioxide emissions, f ng is the capital recovery factor, CVN is natural gas, F gt The consumption of natural gas.

[0136] For example, the carbon dioxide emissions generated by the system come from the gas turbine, the carbon emission coefficient of natural gas is 0.202kg / kWh, and the calorific value of natural gas CVN is 37.62MJ / Nm 3 .

[0137] In one implementation, the photovoltaic-hydrogen-natural gas integrated energy system model includes at least one or more of the following: a photovoltaic physical model, an inverter physical model, a proton exchange membrane fuel cell physical model, a proton exchange membrane electrolyzer physical model, and an energy efficiency model.

[0138] For example, taking the photovoltaic power generation scenario as an example, hydrogen energy storage technology can effectively solve the volatility problem of photovoltaic power generation output and realize long-term and large-scale storage of green electricity from photovoltaic units. At the same time, considering the economy of the integrated energy system and the stability of energy supply, it is generally necessary to introduce natural gas power generation equipment such as gas turbines to further assist photovoltaic and hydrogen energy storage systems. In order to realize the diversified scenario application of the photovoltaic-hydrogen-natural gas multi-energy fusion complex system, it is necessary to establish a corresponding photovoltaic-hydrogen-natural gas integrated energy system model to assist the real physical system to realize the stable power supply of urban microgrids, ensure the power demand of the continuous production process of industrial parks, and provide independent and reliable power solutions for remote areas, while promoting the further development and application of green energy technologies.

[0139] In the existing research on integrated energy system models, different scholars have proposed their own methods and viewpoints. The literature Xiong, Y., Chen, L., Zheng, T., Si, Y. and Mei, S., 2021. Electricity-heat-hydrogen modeling of hydrogen storage system considering off-design characteristics. IEEE Access, 9, pp. 156768-156777. deeply analyzes the off-design characteristics of electrolysis and fuel cells, and based on this, proposes a multi-energy model that integrates electricity, heat energy and hydrogen energy. This model emphasizes the importance of considering off-design characteristics in energy management to improve the accuracy and economic benefits of the model. However, this study did not propose an effective solution to the complexity of processing off-design characteristics and the adaptability and scalability of the model in different application scenarios. The literature Marcos Tostado-Véliz, Paul Arévalo, Francisco Jurado, A comprehensive electrical-gas-hydrogen Microgrid model for energy management applications, Energy Conversion and Management, Volume 228, 2021, 113726, ISSN 0196-8904 proposes a comprehensive but highly operational multi-energy MG (Microgrid) model based on a detailed study of multi-energy microgrids. This model accurately expresses the interactions between electricity, natural gas, and hydrogen energy subsystems, and includes detailed models of gas-to-electricity or electricity-to-gas facilities, as well as charging stations serving electric vehicles, natural gas vehicles, and hydrogen vehicles. However, this study may simplify the energy conversion process between different energy sources, such as P2G (Power to Gas, renewable energy generation technology) or G2P (Gas to Power, gas-fired power generation) units, which may affect the accuracy of the model and the actual application effect. In addition, the modeling of complex interactions between different subsystems also requires further research and optimization. Wen Jingtao, Chen Geng, Wang Zihao, et al. A review of research on wind-solar hybrid power generation coupled with hydrogen energy storage system [J]. China Electric Power, 2022, 55(01): 75-83. Based on the temporal and spatial complementarity of wind and solar energy, a system integrating wind-solar hybrid power generation, water electrolysis hydrogen production, hydrogen storage and hydrogen fuel cells was constructed. This system effectively improves the utilization efficiency of wind and solar energy and reduces resource waste by utilizing renewable energy power generation and water electrolysis hydrogen production technology.However, the challenges faced by this research include the economic feasibility of the system, especially the issue of ensuring safety and stability in grid-connected systems. At the same time, the technical maturity and cost control of each component in the system are also issues that need to be focused on.

[0140] In order to realize a low-carbon, clean integrated energy system, combining renewable energy with hydrogen storage has become an effective means. This is because the output of photovoltaic power generation is intermittent and volatile, which poses a challenge to the safe and stable operation of the energy system. To ensure the safe and stable operation of the energy system, the use of energy storage systems to store excess electricity has become an important means to improve the utilization rate of renewable energy. In particular, the bidirectional power generation capacity and flexible regulation characteristics of energy storage systems greatly help to improve the acceptance of renewable energy in integrated energy systems.

[0141] Among the many energy storage technologies, hydrogen energy storage stands out due to its high energy density, high conversion efficiency and ability to store energy across seasons, making it an ideal choice for renewable energy storage. Therefore, the application of hydrogen energy storage helps to further promote the realization of low-carbon energy systems. However, considering the economy and the stability of energy supply, some energy systems still need to rely on traditional energy equipment such as gas turbines, diesel engines and gas boilers. Nevertheless, the integrated energy system consisting of new energy and hydrogen energy storage systems plays a vital role in achieving low-carbon energy transformation.

[0142] Based on the limitations inherent in the use of each single energy source mentioned above, the present invention proposes an innovative integrated energy system model that integrates photovoltaic, hydrogen and natural gas technologies. This integrated model aims to overcome the limitations of a single energy source and improve energy utilization and system stability by efficiently integrating different forms of energy. The system is mainly composed of a photovoltaic power generation system, a hydrogen energy storage system and a natural gas auxiliary system, specifically including photovoltaic panels, gas turbines, proton exchange membrane fuel cells, hydrogen storage tanks, proton exchange membrane electrolyzers, compressors and loads. The system is suitable for the storage of renewable energy. By combining a hybrid energy power generation system with photovoltaics and hydrogen energy storage, excess photovoltaic power generation is converted into hydrogen energy, thereby reducing power restrictions and improving grid performance.

[0143] Therefore, when constructing the photovoltaic physical model, the present invention uses a solar photovoltaic conversion system to convert sunlight into electrical energy through solar panels. The actual power generation of photovoltaic panels is affected by the intensity of solar radiation and the ambient temperature. When constructing the inverter physical model, when the photovoltaic power generation exceeds the grid-connected power, the power of the grid comes from photovoltaic energy, and the excess photovoltaic power is used for hydrogen production and compression. At this moment, the power of the inverter is equal to the total power supplied to the grid and the compressor. When the photovoltaic power generation is less than the grid-connected power, the power of the inverter is only equal to the photovoltaic power generation. When constructing the physical model of the proton exchange membrane fuel cell, electrical energy is generated through the chemical reaction of hydrogen and oxygen, and its only byproduct is water. This fuel cell has the characteristics of high efficiency, low temperature operation and fast start-up. When constructing the physical model of the proton exchange membrane electrolyzer, electrical energy is used to decompose water into hydrogen and oxygen. Compared with traditional alkaline water electrolysis equipment, the proton exchange membrane electrolyzer has higher efficiency, purer hydrogen output and faster response speed. Therefore, it is widely used in renewable energy storage and other scenarios.

[0144] When building an energy efficiency model, accurate load forecasting is crucial to consider in the design and operation of energy systems. If there is a large deviation between the predicted load demand and the actual value, it may lead to over-investment in equipment or waste of resources, thereby reducing the economic benefits of the system. In order to avoid this situation and ensure the efficient operation of the system, this patent application adopts a strategy to connect the electricity generated by photovoltaic power generation directly to the power grid. In order to meet the strict power quality requirements of the power grid, a constant output mode is adopted during the power supply period, which can ensure stable power supply under a wide range of load changes.

[0145] In addition, a mechanism is designed so that during the power supply period, the power generated by photovoltaics is stably transmitted to the grid at a certain ratio. Considering the rapid response capability of the hydrogen energy system to fluctuating power, when the power generated by photovoltaics exceeds the power required for grid connection, the excess power is transferred to the hydrogen energy system. Here, the excess electrical energy is used in the hydrogen production process, and the generated hydrogen is subsequently compressed by a compressor and stored in a dedicated hydrogen storage tank. This design ensures that the fluctuating photovoltaic power can be converted into stable power output by the hydrogen energy storage system and smoothly transmitted to the grid. Through this operation strategy, not only the stable power supply of the grid is ensured, but also the potential impact of the instability of photovoltaic power generation on the grid is effectively reduced.

[0146] The energy system is powered by photovoltaic power generation, and the electrical equipment used by the fuel cell and gas turbine are the electrolyzer, hydrogen compressor, and power supply to the grid;

[0147] The constraints of the power bus of the energy efficiency model are as follows:

[0148]

[0149] Among them, P pv,t is the power generation of the photovoltaic system at time t; P a,t is the total load power demand of the system at time t; η inv is the efficiency of the inverter, which is used to convert DC power into AC power; P el,t is the power consumption of the electrolyzer at time t, used for hydrogen production; P com,t P is the power consumption of the compressor at time t, used to compress hydrogen; waste,t P is the excess or wasted power at time t, i.e., the unused power; fc,t is the power generation of the fuel cell at time t; P gt,t is the power generation capacity of the gas turbine at time t.

[0150] When photovoltaic power generation exceeds the grid-connected power during the power supply period, the excess power is used for hydrogen production in the electrolyzer and hydrogen compression in the compressor; when photovoltaic power generation is lower than the grid-connected power, the fuel cell and gas turbine supplement the insufficient power; the electricity generated by photovoltaic power generation is used to produce hydrogen in the electrolyzer and compress hydrogen when the compressor is not supplied;

[0151] The present invention realizes the hybrid energy system combining photovoltaic and hydrogen energy storage systems to generate electricity by constructing a photovoltaic-hydrogen-natural gas integrated energy system model, converting excess photovoltaic fluctuation energy into hydrogen energy, thereby reducing power constraints and improving grid performance. In order to optimize the operation of the all-electric energy station, we also established a mathematical model for equipment output analysis. The photovoltaic-hydrogen-natural gas integrated energy system model provides a comprehensive and accurate framework for technology evaluation, allowing us to quantitatively evaluate its applicability and performance in different environments and application scenarios. This not only provides development suggestions and theoretical support for decision makers in the construction and planning of all-electric energy stations, but also promotes the widespread application of all-electric energy stations, thereby reducing energy waste and reducing environmental impact.

[0152] In the photovoltaic-hydrogen-natural gas integrated energy system, in order to ensure the economy of the entire system and the stability of continuous energy supply, it is very important to introduce natural gas to assist photovoltaic and hydrogen energy. This can not only provide a more stable energy support for the system, but also make use of the excellent peak-shaving performance of natural gas generator sets to absorb the instability of photovoltaic power generation, thereby ensuring the efficient, stable and economical operation of the entire integrated energy system.

[0153] In the context of global energy challenges and environmental issues, the intermittent and instability of renewable energy poses challenges to the stable operation of the entire power grid. Therefore, this patent constructs a photovoltaic-hydrogen-natural gas integrated energy system model to achieve hybrid energy power generation. This model cleverly converts excess photovoltaic power generation into hydrogen energy, aiming to reduce green power waste and enhance power grid stability. To ensure the economical and efficient operation of the system, this patent application introduces natural gas as an auxiliary energy source to absorb the fluctuations in photovoltaic power generation, thereby ensuring the stability of energy supply. This system not only provides solid support for the construction and planning of integrated energy systems in theory, but also promotes the practical application of all-electric energy stations, significantly reducing energy waste and reducing environmental impact.

[0154] The physical model of the all-electric energy station constructed by this patent application further provides a basis for the multi-objective optimization of the operation strategy, helping to more accurately evaluate the applicability of the all-electric energy station in different scenarios, thereby improving energy utilization efficiency and promoting clean energy transformation. At the same time, it assists decision makers in making more informed investment decisions, promotes the application of green energy technologies, and is conducive to the realization of sustainable development goals.

[0155] In step 1, the equipment parameters and operating parameters of the all-electric energy station under the required scenario application are obtained; the present invention provides basic data for obtaining the optimal operating strategy of the all-electric energy station under different scenarios by obtaining the equipment parameters and operating parameters under different application scenarios.

[0156] The above step 2 includes, based on the equipment parameters and operating parameters, using a genetic-simulated annealing algorithm to solve a comprehensive multi-objective hierarchical optimization model of the photovoltaic-hydrogen-natural gas integrated energy system model to obtain the optimal operating strategy of the all-electric energy station, including:

[0157] Based on the equipment parameters and the operating parameters, the constructed comprehensive multi-objective hierarchical optimization function is used to obtain a determined comprehensive multi-objective hierarchical optimization function;

[0158] A genetic-simulated annealing algorithm is used to solve the determined comprehensive multi-objective hierarchical optimization function to obtain the optimal operation strategy of the all-electric energy station.

[0159] This patent application introduces a method based on the operation optimization of an all-electric energy station that combines a GA algorithm (Genetic Algorithm) and a SA (Simulated Annealing algorithm). This method is different from traditional linear optimization techniques and is particularly suitable for dealing with nonlinear problems and implementing multi-objective optimization. By combining GA and SA, we can effectively solve complex optimization challenges that traditional methods are difficult to cope with. In addition, this patent also constructs a multi-objective hierarchical optimization model for system configuration based on the combination of GA and SA, which comprehensively considers multiple optimization objectives such as energy consumption and carbon emissions. Through this model, we can optimize the operation strategy of the all-electric energy station, reduce the load peak of the power system, and reduce the system load pressure. At the same time, this method can also use electricity more efficiently during heating and cooling, reduce load fluctuations in the power system, and thus reduce carbon emissions and energy consumption.

[0160] In one implementation, the genetic-simulated annealing algorithm is used to solve the determined comprehensive multi-objective hierarchical optimization function to obtain the optimal operation strategy of the all-electric energy station, including:

[0161] Using a genetic algorithm, the determined comprehensive multi-objective hierarchical optimization function is initialized to obtain an initial operation strategy set as an initial solution population;

[0162] Calculating the fitness of the initial solution population, and performing selection, crossover and mutation operations according to the fitness to obtain the next generation population;

[0163] Using a simulated annealing algorithm, locally search and optimize the next generation population to obtain an operation strategy of the all-electric energy station corresponding to the next generation population;

[0164] Calculating the fitness of the next generation population, and performing iterative selection, crossover and mutation operations on the next generation population according to the fitness to generate a new next generation population, and calculating the operation strategy corresponding to the new next generation population;

[0165] When the iteration termination condition is met, the iteration ends, and the operation strategy corresponding to the last generation of population is used as the optimal operation strategy of the all-electric energy station;

[0166] The iteration termination condition includes that the current iteration number reaches a preset maximum iteration number, or the fitness difference between the new next generation population and the next generation population reaches a preset fitness threshold.

[0167] For example, the optimization process combining the genetic algorithm GA and the push algorithm SA is as follows:

[0168] Initialization: Use GA to generate an initial solution population, each of which represents a possible system configuration.

[0169] Selection: Evaluate each individual according to the fitness function, select individuals with high fitness for crossover and mutation operations to generate the next generation of population.

[0170] Crossover and mutation: Increase the diversity of the population and avoid local optimal problems through crossover and mutation operations.

[0171] Local optimization: Apply the SA algorithm to individuals in each generation of the population to perform local search and optimization to improve the quality of the solution.

[0172] Termination condition: Repeat the above steps until the preset number of iterations is reached or the fitness function is no longer significantly improved; in view of the complexity of the energy system and the multi-objective optimization requirements, and in order to improve the effect of the operation optimization of the all-electric energy station, the present invention introduces a hybrid optimization method combining GA and SA for the operation optimization of the all-electric energy station. Among the existing optimization methods, GA and SA each have their own advantages. GA realizes global search through group search and natural evolution operations, while SA effectively avoids local optimal problems through the probability acceptance criterion. Compared with traditional linear optimization methods, simulated annealing algorithm SA and genetic algorithm GA have significant advantages. The SA algorithm can effectively handle complex nonlinear optimization problems and multiple optimization objectives, such as energy efficiency, cost and environmental impact. Through the simulated annealing process, the global optimal solution is gradually found, and it can perform well even in multi-peak and irregular search spaces. The GA algorithm can find high-quality solutions in complex nonlinear problems through evolutionary operations such as selection, crossover and mutation, and continuously improve during the iterative process. In addition, both SA and GA can effectively avoid local optimal problems. SA allows the acceptance of inferior solutions in the high temperature stage by introducing the probability acceptance criterion, thus avoiding falling into the local optimum; GA uses population diversity and group search mechanism to improve the ability to escape the local optimal solution. SA and GA also perform well in global search capabilities. SA can find the optimal solution in the entire solution space by performing global search through the annealing strategy; GA uses the characteristics of population evolution and diversity retention to search the solution space extensively and increase the possibility of finding the global optimal solution. Both also have the ability to adapt to dynamic changes. SA can dynamically adjust parameters during the iteration process to adapt to the dynamic changes of the system and improve the flexibility and adaptability of the algorithm; GA can cope with the changing conditions and constraints in complex systems through continuous evolution and fitness evaluation. Finally, the GA algorithm is easy to parallelize, suitable for parallel computing, and can significantly improve computing efficiency. Although SA itself is a sequential process, it can increase computing speed and efficiency by executing multiple independent annealing processes in parallel. Therefore, the optimization method combining GA and SA can play an important role in the operation optimization of all-electric energy stations and significantly improve the operating efficiency, stability and adaptability of the system.

[0173] In one implementation, the simulated annealing algorithm is used to perform local search and optimization on the next generation population to obtain the operation strategy of the all-electric energy station corresponding to the next generation population, including:

[0174] Setting a control parameter as a temperature value, and setting an initial solution and a maximum number of iterations of the next generation population;

[0175] Randomly perturb the current solution of the next generation population, iterate to obtain a new solution, judge whether the new solution is better than the current solution, and accept the new solution when the new solution is better than the current solution; otherwise, based on the mass difference between the new solution and the current solution and the temperature value, use the calculation formula of the probability of accepting the new solution in the Metropolis criterion to calculate the probability of accepting the new solution, and accept the new solution according to the probability of accepting the new solution;

[0176] Determine whether the current number of iterations meets the preset number of iterations. If not, continue to randomly perturb the current solution of the next generation population until the current number of iterations meets the preset number of iterations, and then determine whether the temperature value corresponding to the current solution meets the output condition;

[0177] Determine whether the temperature value corresponding to the current solution meets the output condition. If not, reduce the temperature value and reset the number of iterations, and continue to randomly perturb the current solution of the next generation population. If yes, take the current solution as the optimal solution corresponding to the multi-objective function, and obtain the operation strategy of the all-electric energy station according to the optimal solution;

[0178] The optimal solution includes: a solution with minimum annual average cost and a solution with minimum carbon dioxide emissions.

[0179] For example, simulated annealing (SA) is a probabilistic heuristic algorithm inspired by the metallurgical annealing process. In metallurgy, if the heating temperature is high enough to ensure that the metal atoms are in a random state, and the cooling process is slow enough to allow the thermal equilibrium to be achieved, then a perfect crystal with minimal defects can be formed, thus reaching the lowest energy state. In the search process of SA in the current work, the system can randomly adjust the resistance scheme from the function value f(x old )'s initial state randomly transitions to a function value of f(x new ) is a new state. In order to determine the probability of accepting or rejecting this move, the Metropolis criterion (Mahlers, 1995) is introduced into the algorithm. The mathematical expression of the Metropolis criterion is as follows:

[0180]

[0181] Where P is the acceptance probability of the new solution, T iis the annealing temperature of the i-th step, f(x new ) is the objective function value of the new solution, f(x old ) is the objective function value of the old solution.

[0182] The acceptance probability P of the new solution is within a specified range and needs to be selected based on the values ​​of the new and old solutions. It is used to decide whether to accept a solution with possibly poor performance in order to conduct a more extensive exploration in the search space.

[0183] For example, the constructed comprehensive multi-objective hierarchical optimization model is pre-analyzed. This step helps to identify and simplify the redundant parts in the model and eliminate unnecessary constraints. In this patent, the optimization problem of system configuration is precisely defined as a GA combined with SA method based on the operation optimization of all-electric energy stations. This patent selects MATLAB software as the main language platform to solve planning problems.

[0184] Initialization and exploration: At the beginning of the algorithm, a higher "temperature" and an initial solution are set. This stage allows the algorithm to explore a wider solution space to avoid falling into the local optimal solution.

[0185] Iterative optimization: The algorithm generates new solutions and uses the Metropolis criterion to decide whether to accept the new solution. Even if the new solution is not as good as the current solution, there is a certain probability that it will be accepted. This simulates the "cooling" effect of the annealing process and gradually reduces the probability of accepting a worse solution.

[0186] Convergence and output: As the "temperature" decreases and iterations proceed, the algorithm gradually focuses on a better solution. When the termination condition is met (such as reaching the minimum temperature or the maximum number of iterations), the algorithm stops and outputs the optimal solution currently found.

[0187] The "Metropolis criterion" comes from the Metropolis algorithm in statistical physics, which is called "Metropolis criterion" in Chinese. It is a probabilistic method for simulating the state transition of particle systems. In the annealing algorithm, the Metropolis criterion is used to decide whether the algorithm accepts a new solution. The core idea of ​​this criterion is to allow the algorithm to accept a new solution that is worse than the current solution with a certain probability, so as to avoid the algorithm falling into the local optimal solution too early, thereby increasing the possibility of finding the global optimal solution.

[0188] In the optimization process of the average annual cost model, "temperature" is a control parameter used to adjust the probability of accepting inferior solutions during the search process. The "optimal solution" refers to the solution that minimizes the average annual cost (ATC) of the system, taking into account investment costs, equipment operation and maintenance costs, natural gas purchase costs, carbon emission tax costs, and equipment replacement costs. This optimal solution represents the best solution for energy system design and operation under given economic and environmental constraints.

[0189] In the optimization of the CO2 emissions model, "temperature" also exists as a control parameter, which allows the algorithm to accept solutions that will increase emissions early in the search process. This helps to avoid the algorithm from being fixed on a local optimum early on. The "optimal solution" is the solution that minimizes CO2 emissions (CDE) while meeting other system performance requirements. This represents the best choice for the configuration of energy systems such as gas turbines after taking into account factors such as the carbon emission coefficient and calorific value of natural gas.

[0190] In one implementation, the calculation formula for the probability of accepting a new solution is constructed based on the mass difference and temperature value between the new solution and the current solution.

[0191] In one implementation, the probability of accepting a new solution is calculated as follows:

[0192] P(ΔE)=exp(-ΔE / (kT))

[0193] Among them, P(ΔE) is the probability of receiving a new solution, ΔE is the quality difference between the new solution and the current solution, T is the temperature value, and k is a constant.

[0194] The calculation formula for the probability of accepting a new solution shows that the worse the quality of the new solution (that is, the larger ΔE), the lower the probability of accepting the new solution. However, as the temperature T decreases, even a small quality difference ΔE may lead to a lower acceptance probability, which helps the algorithm eventually stabilize on a solution.

[0195] This invention mainly adopts the combination of GA and SA algorithms. By combining the global search capability of GA and the local optimization capability of SA, it explores the optimal system configuration and operating characteristics under the comprehensive optimization goals of minimizing the annual total cost of the power system and minimizing the annual carbon emissions. The advantage of the SA algorithm is that it can effectively handle complex optimization problems in the operation of all-electric energy stations, such as the comprehensive optimization of photovoltaic, hydrogen energy storage and natural gas systems. In addition, this method also clarifies the effective application of the operation regulation mechanism in the power generation system that integrates renewable energy technology and existing mainstream power generation technology.

[0196] This invention explores the coupling between renewable energy technology (photovoltaic power generation technology, hydrogen energy storage technology) and the power system, and reveals the operating rules of the power generation system under the condition of renewable energy instability. Photovoltaic power generation technology is introduced to determine the best use strategy of photovoltaic power generation technology under cost optimization, and the impact of photovoltaic power generation and energy storage on load fluctuations, carbon emissions and energy consumption in the power system is explored with the optimized proton exchange membrane fuel cell capacity as the upper limit. A hydrogen energy storage power system is introduced to explore the impact of hydrogen energy on system operation stability and energy supply complementarity, determine the optimal operation strategy of fuel consumption equipment, and obtain the optimal economic cost and carbon emission reduction of the system under clean energy integration.

[0197] The key to this application is to focus on evaluation indicators such as economy and energy saving, consider the constraints of the heat source tower system and the equipment's own characteristics of the all-electric energy station, first establish a physical model of the all-electric energy station to provide a basis for multi-objective optimization, and then establish an all-electric energy station operation optimization algorithm and calculation program under multiple objectives; the intended protection point of this application is to optimize the all-electric energy station operation strategy under multiple objectives based on the simulated annealing algorithm.

[0198] like Figure 3 The figure shows the energy system configuration optimization analysis framework. First, a comprehensive model of an all-electric energy station including photovoltaic, hydrogen energy storage and natural gas systems is constructed to ensure the adaptability and accuracy of the algorithm. The model comprehensively considers multiple optimization objectives of the energy system, including operating costs and carbon emissions. From the perspective of the economy and environment of the integrated energy system, the present invention elaborates on the construction of a multi-objective hierarchical optimization model. The content covers multi-objective functions based on annual total costs and carbon emissions, mathematical models of equipment output, and descriptions of electric and hydrogen energy systems in the system. Secondly, GA performs global search through selection, crossover and mutation operations. GA starts with an initial population, and each individual represents a possible system configuration. The fitness function is used to evaluate the pros and cons of each individual, and individuals with high fitness are selected for crossover operations to generate new individuals, thereby increasing the diversity of the population and the search space. In the new population generated by GA, simulated SA is applied to each individual for local optimization. As shown in Figure 4The figure shows the optimization process flow chart of the simulated annealing (SA) algorithm; SA overcomes the local optimal problem through random search and probability acceptance criteria, improves the search efficiency of the global optimal solution, and adjusts parameters in the iterative process to adapt to the dynamic changes of the energy system. By combining the global search capability of GA and the local optimization capability of SA, the hybrid optimization method significantly improves the search efficiency of the global optimal solution. GA explores the solution space globally to ensure that a high-quality initial solution is found; SA finely optimizes these initial solutions locally to improve the accuracy and quality of the solution. This patent application has significant advantages in improving energy efficiency, reducing operating costs and reducing carbon emissions, improving the operating efficiency and sustainability of all-electric energy stations, and providing innovative solutions for solving complex optimization problems of energy systems. In addition, this patent application proposes a comprehensive multi-objective hierarchical optimization model based on GA and SA algorithms, combining mathematical optimization algorithms with actual hybrid energy systems. By utilizing the global search capability of GA+SA and the efficiency of processing complex nonlinear problems, the flexible adjustment capability and ability to adapt to environmental changes of all-electric energy stations are significantly improved. Through the simulated annealing algorithm, this model can simulate and predict system performance more flexibly, especially in the face of variable and uncertain energy demand, which can effectively reduce the cost of trial and error and improve the system's regulation and response capabilities. This strategy helps to further promote the development and application of clean energy technologies and promote the use of sustainable energy, while also improving the system's adaptability and stability to complex operating conditions. In addition, the system improves the stability of the power system and simplifies the operation and control process by optimizing the model; this patent application is not limited to theoretical models and algorithms, but also includes the development of corresponding calculation programs. The program can actually execute these optimization algorithms to provide reliable decision support for the actual operation of all-electric energy stations.

[0199] Embodiment 2:

[0200] Based on the same inventive concept, the present invention patent application also provides an all-electric energy station operation optimization system considering multiple objectives such as Figure 5 As shown, it includes: a parameter acquisition module and an optimal operation strategy determination module;

[0201] The parameter acquisition module is used to obtain equipment parameters and operating parameters of the all-electric energy station;

[0202] The optimal operation strategy determination module is used to solve the comprehensive multi-objective hierarchical optimization model of the photovoltaic-hydrogen-natural gas integrated energy system model based on the equipment parameters and operation parameters, using a genetic-simulated annealing algorithm to obtain the optimal operation strategy of the all-electric energy station;

[0203] The comprehensive multi-objective hierarchical optimization model includes a comprehensive multi-objective hierarchical optimization function constructed with the goal of minimizing the average annual cost and carbon dioxide emissions of the all-electric energy station.

[0204] Preferably, it also includes: a comprehensive multi-objective hierarchical optimization model building module, which is used to:

[0205] Based on the sample equipment parameters, sample operating parameters and constraints of the all-electric energy station, a photovoltaic-hydrogen-natural gas integrated energy system model is constructed;

[0206] Taking the annual average cost and carbon dioxide emissions of the photovoltaic-hydrogen-natural gas integrated energy system model as the minimum, a comprehensive multi-objective hierarchical optimization function is constructed;

[0207] Based on the photovoltaic-hydrogen-natural gas integrated energy system model and the comprehensive multi-objective hierarchical optimization function, a comprehensive multi-objective hierarchical optimization model of the photovoltaic-hydrogen-natural gas integrated energy system model is constructed.

[0208] Preferably, the comprehensive multi-objective hierarchical optimization function includes a year-end cost minimization objective function and a carbon dioxide emission minimization objective function; wherein,

[0209] The year-end cost minimization objective function is constructed by minimizing the sum of various costs in the photovoltaic-hydrogen-natural gas integrated energy system model;

[0210] The carbon dioxide emission minimization objective function is constructed by minimizing carbon dioxide emissions in a photovoltaic-hydrogen-natural gas integrated energy system model.

[0211] Preferably, the expression of the year-end cost minimization objective function is as follows:

[0212] minATC=C inv +C om +C ng +C ct +C rep

[0213] Among them, ATC is the year-end cost, C inv is the investment cost, C om is the equipment operation and maintenance cost, C ng is the natural gas purchase cost, C ct is the carbon tax cost, C rep Equipment replacement cost.

[0214] Preferably, the expression of the minimum objective function of carbon dioxide emissions is as follows:

[0215] minCDE=F gt ×CVN×f ng

[0216] Where CDE is carbon dioxide emissions, f ngis the capital recovery factor, CVN is natural gas, F gt The consumption of natural gas.

[0217] Preferably, the photovoltaic-hydrogen-natural gas integrated energy system model includes at least one or more of the following: a photovoltaic physical model, an inverter physical model, a proton exchange membrane fuel cell physical model, a proton exchange membrane electrolyzer physical model and an energy efficiency model.

[0218] Preferably, the optimal operation strategy determination module includes:

[0219] A comprehensive multi-objective hierarchical optimization function determination submodule is used to obtain a determined comprehensive multi-objective hierarchical optimization function based on the equipment parameters and operating parameters using the constructed comprehensive multi-objective hierarchical optimization function;

[0220] The optimal operation strategy determination submodule is used to adopt a genetic-simulated annealing algorithm to solve the determined comprehensive multi-objective hierarchical optimization function to obtain the optimal operation strategy of the all-electric energy station.

[0221] Preferably, the optimal operation strategy determination submodule includes:

[0222] An initial solution population determination unit is used to calculate the fitness of the initial solution population and perform selection, crossover and mutation operations according to the fitness to obtain the next generation population;

[0223] A population local search optimization unit, used to use a simulated annealing algorithm to perform local search and optimization on the next generation population to obtain an operation strategy of the all-electric energy station corresponding to the next generation population;

[0224] A population iterative local optimization unit, used to calculate the fitness of the next generation population, and perform iterative selection, crossover and mutation operations on the next generation population according to the fitness to generate a new next generation population, and calculate the operation strategy corresponding to the new next generation population;

[0225] The optimal operation strategy determination unit is used to end the iteration when the iteration termination condition is met, and use the operation strategy corresponding to the last generation of population as the optimal operation strategy of the all-electric energy station;

[0226] The iteration termination condition includes that the current iteration number reaches a preset maximum iteration number, or the fitness difference between the new next generation population and the next generation population reaches a preset fitness threshold.

[0227] Preferably, the population local search optimization unit is specifically used for:

[0228] Setting a control parameter as a temperature value, and setting an initial solution and a maximum number of iterations of the next generation population;

[0229] Randomly perturb the current solution of the next generation population, iterate to obtain a new solution, judge whether the new solution is better than the current solution, and accept the new solution when the new solution is better than the current solution; otherwise, based on the mass difference between the new solution and the current solution and the temperature value, use the calculation formula of the probability of accepting the new solution in the Metropolis criterion to calculate the probability of accepting the new solution, and accept the new solution according to the probability of accepting the new solution;

[0230] Determine whether the current number of iterations meets the preset number of iterations. If not, continue to randomly perturb the current solution of the next generation population until the current number of iterations meets the preset number of iterations, and then determine whether the temperature value corresponding to the current solution meets the output condition;

[0231] Determine whether the temperature value corresponding to the current solution meets the output condition. If not, reduce the temperature value and reset the number of iterations, and continue to randomly perturb the current solution of the next generation population. If yes, take the current solution as the optimal solution corresponding to the multi-objective function, and obtain the operation strategy of the all-electric energy station according to the optimal solution;

[0232] The optimal solution includes: a solution with minimum annual average cost and a solution with minimum carbon dioxide emissions.

[0233] Preferably, the calculation formula for the probability of accepting a new solution is constructed based on the mass difference between the new solution and the current solution, and the temperature value.

[0234] Preferably, the probability of accepting a new solution is calculated as follows:

[0235] P(ΔE)=exp(-ΔE / (kT))

[0236] Among them, P(ΔE) is the probability of receiving a new solution, ΔE is the quality difference between the new solution and the current solution, T is the temperature value, and k is a constant.

[0237] Based on the same inventive concept, the patent application of the present invention also provides an electronic device, comprising: at least one processor and a memory; the memory and the processor are connected via a bus;

[0238] The memory is used to store one or more programs;

[0239] When the one or more programs are executed by the at least one processor, an all-electric energy station operation optimization method considering multiple objectives as described above is implemented.

[0240] Based on the same inventive concept, the patent application of the present invention also provides a readable storage medium on which an execution program is stored. When the execution program is executed, an all-electric energy station operation optimization method considering multiple objectives as described above is implemented.

[0241] Example 3

[0242] like Figure 6 As shown, the present invention also provides an electronic device, which may be a computer device, a single-chip device, an intelligent mobile device, etc. The electronic device in this embodiment may include a processor, a memory, a transceiver component, etc. The memory, the processor, and the transceiver component are connected via a bus; the memory may be used to store an execution program, and an exemplary execution program may include instructions; the processor is used to execute the instructions stored in the memory. The memory may also be used to store data, which may be called and / or modified when the instructions are executed.

[0243] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in a storage medium to implement the corresponding method flow or corresponding function, so as to realize the steps of a multi-objective all-electric energy station operation optimization method in the above-mentioned embodiment.

[0244] Example 4

[0245] Based on the same inventive concept, the present invention also provides a readable storage medium, specifically an electronic device readable storage medium (Memory), which is a memory device in an electronic device for storing programs and data. It can be understood that the storage medium here can include both built-in storage media in electronic devices and, of course, extended storage media supported by electronic devices. The storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by a processor are also stored in the storage space, and these instructions can be one or more execution programs (including program codes). It should be noted that the storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk storage. The processor loads and executes one or more instructions stored in the storage medium, which can implement the steps of a multi-objective all-electric energy station operation optimization method in the above embodiment.

[0246] It should be understood by those skilled in the art that the embodiments of the present invention patent application can be provided as methods, systems, or computer program products. Therefore, the present invention patent application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention patent application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0247] The present invention patent application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present invention patent application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0248] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0249] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0250] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the patent application of the present invention rather than to limit its protection scope. Although the patent application of the present invention is described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that after reading the patent application of the present invention, those skilled in the art can still make various changes, modifications or equivalent substitutions to the specific implementation methods of the application, but these changes, modifications or equivalent substitutions are all within the protection scope of the claims to be approved.

Claims

1. A multi-objective all-electric energy station operation optimization method, characterized in that: include: Obtain equipment parameters and operating parameters of the all-electric energy station; Based on the equipment parameters and operating parameters, a genetic-simulated annealing algorithm is used to solve a comprehensive multi-objective hierarchical optimization model of the photovoltaic-hydrogen-natural gas integrated energy system model to obtain the optimal operating strategy of the all-electric energy station; The comprehensive multi-objective hierarchical optimization model includes a comprehensive multi-objective hierarchical optimization function constructed with the goal of minimizing the average annual cost and carbon dioxide emissions of the all-electric energy station.

2. The method according to claim 1, characterized in that The construction process of the comprehensive multi-objective hierarchical optimization model of the photovoltaic-hydrogen-natural gas integrated energy system model includes: Based on the sample equipment parameters, sample operating parameters and constraints of the all-electric energy station, a photovoltaic-hydrogen-natural gas integrated energy system model is constructed; Taking the annual average cost and carbon dioxide emissions of the photovoltaic-hydrogen-natural gas integrated energy system model as the minimum, a comprehensive multi-objective hierarchical optimization function is constructed; Based on the photovoltaic-hydrogen-natural gas integrated energy system model and the comprehensive multi-objective hierarchical optimization function, a comprehensive multi-objective hierarchical optimization model of the photovoltaic-hydrogen-natural gas integrated energy system model is constructed.

3. The method according to claim 2, characterized in that The comprehensive multi-objective hierarchical optimization function includes the minimum year-end cost objective function and the minimum carbon dioxide emission objective function; wherein, The year-end cost minimization objective function is constructed by minimizing the sum of various costs in the photovoltaic-hydrogen-natural gas integrated energy system model; The carbon dioxide emission minimization objective function is constructed by minimizing carbon dioxide emissions in a photovoltaic-hydrogen-natural gas integrated energy system model.

4. The method according to claim 3, characterized in that The expression of the year-end cost minimization objective function is as follows: minATC=C inv +C om +C ng +C ct +C rep Among them, ATC is the year-end cost, C inv is the investment cost, C om is the equipment operation and maintenance cost, C ng is the natural gas purchase cost, C ct is the carbon tax cost, C rep Equipment replacement cost.

5. The method according to claim 3, characterized in that The expression of the minimum objective function of carbon dioxide emissions is as follows: minCDE=F gt ×CVN×f ng Where CDE is carbon dioxide emissions, f ng is the capital recovery factor, CVN is natural gas, F gt The consumption of natural gas.

6. The method according to claim 2, characterized in that The photovoltaic-hydrogen-natural gas integrated energy system model includes at least one or more of the following: a photovoltaic physical model, an inverter physical model, a proton exchange membrane fuel cell physical model, a proton exchange membrane electrolyzer physical model and an energy efficiency model.

7. The method according to claim 1, characterized in that Based on the equipment parameters and operation parameters, a genetic-simulated annealing algorithm is used to solve a comprehensive multi-objective hierarchical optimization model of the photovoltaic-hydrogen-natural gas integrated energy system model, and the optimal operation strategy of the all-electric energy station is obtained, including: Based on the equipment parameters and the operating parameters, the constructed comprehensive multi-objective hierarchical optimization function is used to obtain a determined comprehensive multi-objective hierarchical optimization function; A genetic-simulated annealing algorithm is used to solve the determined comprehensive multi-objective hierarchical optimization function to obtain the optimal operation strategy of the all-electric energy station.

8. The method according to claim 7, characterized in that The genetic-simulated annealing algorithm is used to solve the determined comprehensive multi-objective hierarchical optimization function to obtain the optimal operation strategy of the all-electric energy station, including: Using a genetic algorithm, the determined comprehensive multi-objective hierarchical optimization function is initialized to obtain an initial operation strategy set as an initial solution population; Calculating the fitness of the initial solution population, and performing selection, crossover and mutation operations according to the fitness to obtain the next generation population; Using a simulated annealing algorithm, locally search and optimize the next generation population to obtain an operation strategy of the all-electric energy station corresponding to the next generation population; Calculating the fitness of the next generation population, and performing iterative selection, crossover and mutation operations on the next generation population according to the fitness to generate a new next generation population, and calculating the operation strategy corresponding to the new next generation population; When the iteration termination condition is met, the iteration ends, and the operation strategy corresponding to the last generation of population is used as the optimal operation strategy of the all-electric energy station; The iteration termination condition includes that the current iteration number reaches a preset maximum iteration number, or the fitness difference between the new next generation population and the next generation population reaches a preset fitness threshold.

9. The method according to claim 8, characterized in that The simulated annealing algorithm is used to perform local search and optimization on the next generation population to obtain the operation strategy of the all-electric energy station corresponding to the next generation population, including: Setting a control parameter as a temperature value, and setting an initial solution and a maximum number of iterations of the next generation population; Randomly perturb the current solution of the next generation population, iteratively obtain a new solution, determine whether the new solution is better than the current solution, and accept the new solution when the new solution is better than the current solution; otherwise, based on the mass difference between the new solution and the current solution and the temperature value, use the calculation formula of the probability of accepting the new solution in the Metropolis criterion to calculate the probability of accepting the new solution, and accept the new solution according to the probability of accepting the new solution; Determine whether the current number of iterations meets the preset number of iterations. If not, continue to randomly perturb the current solution of the next generation population until the current number of iterations meets the preset number of iterations, and then determine whether the temperature value corresponding to the current solution meets the output condition; Determine whether the temperature value corresponding to the current solution meets the output condition. If not, reduce the temperature value and reset the number of iterations, and continue to randomly perturb the current solution of the next generation population. If yes, take the current solution as the optimal solution corresponding to the multi-objective function, and obtain the operation strategy of the all-electric energy station according to the optimal solution; The optimal solution includes: a solution with the minimum annual average cost and a solution with the minimum carbon dioxide emissions.

10. The method according to claim 9, characterized in that The calculation formula for the probability of accepting a new solution is generated based on the mass difference between the new solution and the current solution, and the temperature value.

11. The method according to claim 10, characterized in that The probability of accepting a new solution is calculated as follows: P(ΔE)=exp(-ΔE / (kT)) Among them, P(ΔE) is the probability of receiving a new solution, ΔE is the quality difference between the new solution and the current solution, T is the temperature value, and k is a constant.

12. An all-electric energy station operation optimization system considering multiple objectives, characterized in that: include: Parameter acquisition module, optimal operation strategy determination module; The parameter acquisition module is used to obtain equipment parameters and operating parameters of the all-electric energy station; The optimal operation strategy determination module is used to solve the comprehensive multi-objective hierarchical optimization model of the photovoltaic-hydrogen-natural gas integrated energy system model based on the equipment parameters and operation parameters, using a genetic-simulated annealing algorithm to obtain the optimal operation strategy of the all-electric energy station; The comprehensive multi-objective hierarchical optimization model includes a comprehensive multi-objective hierarchical optimization function constructed with the goal of minimizing the average annual cost and carbon dioxide emissions of the all-electric energy station.

13. The system of claim 12, wherein: Also includes: Comprehensive multi-objective hierarchical optimization model building module for: Based on the sample equipment parameters, sample operating parameters and constraints of the all-electric energy station, a photovoltaic-hydrogen-natural gas integrated energy system model is constructed; Taking the annual average cost and carbon dioxide emissions of the photovoltaic-hydrogen-natural gas integrated energy system model as the minimum, a comprehensive multi-objective hierarchical optimization function is constructed; Based on the photovoltaic-hydrogen-natural gas integrated energy system model and the comprehensive multi-objective hierarchical optimization function, a comprehensive multi-objective hierarchical optimization model of the photovoltaic-hydrogen-natural gas integrated energy system model is constructed.

14. The system of claim 13, wherein: The comprehensive multi-objective hierarchical optimization function includes the minimum year-end cost objective function and the minimum carbon dioxide emission objective function; wherein, The year-end cost minimization objective function is constructed by minimizing the sum of various costs in the photovoltaic-hydrogen-natural gas integrated energy system model; The carbon dioxide emission minimization objective function is constructed by minimizing carbon dioxide emissions in a photovoltaic-hydrogen-natural gas integrated energy system model.

15. The system of claim 14, wherein: The expression of the year-end cost minimization objective function is as follows: minATC=C inv +C om +C ng +C ct +C rep Among them, ATC is the year-end cost, C inv is the investment cost, C om is the equipment operation and maintenance cost, C ng is the natural gas purchase cost, C ct is the carbon tax cost, C rep Equipment replacement cost.

16. The system of claim 14, wherein: The expression of the minimum objective function of carbon dioxide emissions is as follows: minCDE=F gt ×CVN×f ng Where CDE is carbon dioxide emissions, f ng is the capital recovery factor, CVN is natural gas, F gt The consumption of natural gas.

17. The system of claim 13, wherein: The photovoltaic-hydrogen-natural gas integrated energy system model includes at least one or more of the following: a photovoltaic physical model, an inverter physical model, a proton exchange membrane fuel cell physical model, a proton exchange membrane electrolyzer physical model and an energy efficiency model.

18. The system of claim 12, wherein: The optimal operation strategy determination module includes: A comprehensive multi-objective hierarchical optimization function determination submodule is used to obtain a determined comprehensive multi-objective hierarchical optimization function based on the equipment parameters and operating parameters using the constructed comprehensive multi-objective hierarchical optimization function; The optimal operation strategy determination submodule is used to adopt a genetic-simulated annealing algorithm to solve the determined comprehensive multi-objective hierarchical optimization function to obtain the optimal operation strategy of the all-electric energy station.

19. The system of claim 18, wherein: The optimal operation strategy determination submodule includes: An initial solution population determination unit is used to calculate the fitness of the initial solution population and perform selection, crossover and mutation operations according to the fitness to obtain the next generation population; A population local search optimization unit, used to use a simulated annealing algorithm to perform local search and optimization on the next generation population to obtain an operation strategy of the all-electric energy station corresponding to the next generation population; A population iterative local optimization unit, used to calculate the fitness of the next generation population, and perform iterative selection, crossover and mutation operations on the next generation population according to the fitness to generate a new next generation population, and calculate the operation strategy corresponding to the new next generation population; The optimal operation strategy determination unit is used to end the iteration when the iteration termination condition is met, and use the operation strategy corresponding to the last generation of population as the optimal operation strategy of the all-electric energy station; The iteration termination condition includes that the current iteration number reaches a preset maximum iteration number, or the fitness difference between the new next generation population and the next generation population reaches a preset fitness threshold.

20. The system of claim 19, wherein: The population local search optimization unit is specifically used for: Setting a control parameter as a temperature value, and setting an initial solution and a maximum number of iterations of the next generation population; Randomly perturb the current solution of the next generation population, iteratively obtain a new solution, determine whether the new solution is better than the current solution, and accept the new solution when the new solution is better than the current solution; otherwise, based on the mass difference between the new solution and the current solution and the temperature value, use the calculation formula of the probability of accepting the new solution in the Metropolis criterion to calculate the probability of accepting the new solution, and accept the new solution according to the probability of accepting the new solution; Determine whether the current number of iterations meets the preset number of iterations. If not, continue to randomly perturb the current solution of the next generation population until the current number of iterations meets the preset number of iterations, and then determine whether the temperature value corresponding to the current solution meets the output condition; Determine whether the temperature value corresponding to the current solution meets the output condition. If not, reduce the temperature value and reset the number of iterations, and continue to randomly perturb the current solution of the next generation population. If yes, take the current solution as the optimal solution corresponding to the multi-objective function, and obtain the operation strategy of the all-electric energy station according to the optimal solution; The optimal solution includes: a solution with the minimum annual average cost and a solution with the minimum carbon dioxide emissions.

21. The system of claim 20, wherein: The calculation formula for the probability of accepting a new solution is constructed based on the mass difference between the new solution and the current solution, and the temperature value.

22. The system of claim 21, wherein: The probability of accepting a new solution is calculated as follows: P(ΔE)=exp(-ΔE / (kT)) Among them, P(ΔE) is the probability of receiving a new solution, ΔE is the quality difference between the new solution and the current solution, T is the temperature value, and k is a constant.

23. An electronic device, characterized in that: include: at least one processor and memory; The memory and the processor are connected via a bus; The memory is used to store one or more programs; When the one or more programs are executed by the at least one processor, an all-electric energy station operation optimization considering multiple objectives as described in any one of claims 1 to 11 is implemented.

24. A readable storage medium, characterized in that: An execution program is stored thereon, and when the execution program is executed, an all-electric energy station operation optimization method considering multiple objectives as described in any one of claims 1 to 11 is implemented.

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