Multi-objective optimization method for comprehensive energy power generation device

Abstract

The invention aims to provide a multi-objective optimization method for an integrated energy power generation device, and belongs to the field of distributed power generation. Setting a working current, an air excess ratio of the solid oxide fuel cell and a fuel utilization rate to obtain an initial constraint condition; the variables meeting the independent variable constraint conditions take the system efficiency and the return cycle of the comprehensive energy power generation device as optimization target values, and take the inlet and outlet temperature difference of the proton exchange membrane electrolytic cell, the air inlet flow of the solid oxide fuel cell and the inlet flow and temperature of the thermoelectric generator as constraint values. And performing non-dominated sorting on the variables in the initial population, and calculating the congestion degree. And obtaining a crossover and mutation solution through variation crossover according to the crowding degree of the individual. And obtaining a new population according to an elitism strategy. And outputting the Pareto optimal solution set until the number of iterations is reached. According to the method, multi-target optimization of the comprehensive energy power generation device can be realized, and the output Pareto optimal solution can provide theoretical and method support for actual operation of the comprehensive energy power generation device.

Description

TECHNICAL FIELD

[0001] The present application relates to a power generation device control method, in particular to a comprehensive energy power generation device optimization method. BACKGROUND

[0002] The comprehensive energy power generation device has the characteristics of small power generation capacity and short distance from the load end, and is modular and highly flexible. The comprehensive energy power generation device can include combined heat and power, fuel cells, solar power generation, wind power generation and other technologies, and becomes an important form of new energy development and utilization.

[0003] The comprehensive energy power generation device adopts a combination of various energy forms, including wind power, photovoltaic, hydrogen energy, hydrogen production and storage, and other energy forms. Different energy forms complement each other and have complementary advantages, so that the comprehensive energy power generation system has the advantages of being clean and efficient. However, the introduction of different energy types also increases the degree of freedom of the system, making the control of the system more difficult. In order to find the best operating parameters of the system, the system efficiency, payback period and other optimization objectives are considered, and a multi-objective optimization method is used to obtain the optimal solution of the optimization variables.

[0004] The multi-objective optimization algorithm can simultaneously consider and optimize multiple objectives and find a set of optimal solutions under specified constraints. These solutions trade off multiple indicators to provide more choices for system operation. SUMMARY

[0005] The purpose of the present application is to provide a comprehensive energy power generation device multi-objective optimization method that can optimize the operating parameters of the comprehensive energy power generation device, improve the efficiency of the comprehensive energy power generation device, and maximize the performance of the comprehensive energy power generation device.

[0006] The purpose of the present application is achieved as follows: The comprehensive energy power generation device multi-objective optimization method of the present application is characterized in that: the comprehensive energy power generation device includes a photovoltaic power generation subsystem, an energy storage subsystem, a hydrogen production subsystem, a hydrogen storage and release subsystem, a fuel cell power generation subsystem and a heat and power generation subsystem. The photovoltaic power generation subsystem, the energy storage subsystem, the fuel cell power generation subsystem and the heat and power generation subsystem mainly serve as power supply functions. The hydrogen production subsystem and the hydrogen storage and release subsystem are designed to provide fuel for the fuel cell power generation subsystem. The method includes the following steps: (1) According to the working condition, set the initial parameters of the comprehensive energy power generation device mechanism model, including: the working current of the comprehensive energy power generation device, the air excess ratio of the solid oxide fuel cell and the fuel utilization rate; (2) According to the parameter output of the comprehensive energy power generation device mechanism model, set the parameters of the multi-objective optimization method, and through the iterative update of the parameters of the multi-objective optimization method, obtain the parameter output of the target comprehensive energy power generation device mechanism model. (3) According to the initial parameters of the mechanism model of the comprehensive energy power generation device and the parameters of the multi-objective optimization method, the initial population of the optimization target is obtained; if the initial population of the optimization target does not satisfy the constraint condition of the independent variable of the mechanism model of the comprehensive energy power generation device, step (2) is returned to iterate again, and if the constraint condition of the independent variable of the mechanism model of the comprehensive energy power generation device is satisfied, the next step is performed; (4) The objective function of the initial population of the optimization target is calculated, and if the constraint condition is not satisfied, the initial population of the optimization target is reset in step (3); if the constraint condition is satisfied, the next step is performed; (5) The initial population of the optimization target satisfying the objective function is subjected to non-dominated sorting to obtain an optimal solution sequence; (6) The crowding degree of the individual in the optimal solution sequence is calculated, and the crossover and mutation calculation is performed according to the set mutation probability and crossover probability to obtain a crossover and mutation solution; (7) The elite strategy is adopted, the non-dominated sorting result in step (5) and the crossover and mutation solution obtained in step (6) are used to generate a new population from the reserved optimal solution sequence and the crossover and mutation solution; (8) It is judged whether the iteration number meets the requirement, if the iteration number does not meet the requirement, the new population returns to step (4) to optimize again, until the set iteration number is reached, and the Pareto optimal solution set is output.

[0007] The application can also include: 1. The parameters of the multi-objective optimization method in step (2) include: population size, crossover probability, mutation probability, and iteration number.

[0008] 2. The constraint condition of the independent variable of the mechanism model of the comprehensive energy power generation device in step (3) includes: the temperature difference limit range of the proton exchange membrane electrolytic cell inlet and outlet, the air excess ratio limit range of the solid oxide fuel cell, and the temperature limit range of the thermoelectric generator. The optimization target of the comprehensive energy power generation device is: the system efficiency and payback period of the comprehensive energy power generation device; the constraint value of the optimization target is: the temperature difference of the proton exchange membrane electrolytic cell inlet and outlet, the solid oxide fuel cell inlet flow, and the inlet flow and temperature of the thermoelectric generator.

[0009] 3. The Pareto optimal solution set in step (8) contains the working current of the comprehensive energy power generation device, the air excess ratio and fuel utilization rate of the solid oxide fuel cell under the optimal system efficiency and payback period of the comprehensive energy power generation device.

[0010] The advantage of the application is: The application adopts the fast non-dominated sorting genetic algorithm with an elitist strategy to optimize the performance of the comprehensive energy power generation device, and can realize system efficiency improvement and rapid payback. Through the multi-objective optimization method, not only the simulation calculation time can be shortened, but also multiple optimization objectives can be balanced, and the best operation parameter can be found.

[0011] The application takes the working current of the comprehensive energy power generation device, the air excess ratio of the solid oxide fuel cell and the fuel utilization rate as optimization variables, which not only includes the control of components, but also includes the efficiency of the fuel cell power generation subsystem and the whole thermoelectric power generation subsystem. The air excess ratio is an important operation parameter of the fuel cell power generation subsystem, which can make the solid oxide fuel cell work in a safe and efficient condition, and the fuel utilization rate measures the efficiency of the fuel cell power generation subsystem and the thermoelectric power generation subsystem. Therefore, the economy and control performance of the comprehensive energy power generation device are comprehensively considered.

[0012] In order to ensure the safe operation of the system, the application considers the temperature difference between the inlet and outlet of the proton exchange membrane electrolyzer, the inlet flow of the solid oxide fuel cell, the inlet flow and temperature of the thermoelectric generator and other constraint conditions. The solution set of the optimization variable is limited, so that the system under the optimal solution can work in a normal state. BRIEF DESCRIPTION OF DRAWINGS

[0013] Figure 1 Fig. 1 is a structural schematic diagram of the comprehensive energy power generation device; Figure 2 Fig. 2 is a flow chart of the application. DETAILED DESCRIPTION

[0014] The application will be described in more detail below with examples combined with the drawings: Combined Figures 1-2 , the application performs multi-objective optimization on the comprehensive energy power generation device, and the comprehensive energy power generation device includes: a photovoltaic power generation subsystem, an energy storage subsystem, a hydrogen production subsystem, a hydrogen storage and release subsystem, a fuel cell power generation subsystem and a thermoelectric power generation subsystem. The energy storage subsystem includes a lithium ion battery, a super capacitor or a hybrid energy storage system of lithium ion battery and super capacitor; the hydrogen production subsystem adopts a proton exchange membrane electrolyzer to produce hydrogen; the fuel cell power generation subsystem adopts a solid oxide fuel cell to generate electricity; and the thermoelectric power generation subsystem adopts a thermoelectric generator to generate electricity.

[0015] In the comprehensive energy power generation device, the photovoltaic power generation subsystem, the energy storage subsystem, the fuel cell power generation subsystem and the thermoelectric power generation subsystem mainly play the function of discharging; and the hydrogen production subsystem and the hydrogen storage and release subsystem aim to provide fuel for the fuel cell subsystem.

[0016] According to Figure 1The application discloses a comprehensive energy power generation device, and establishes a mechanism model of the comprehensive energy power generation device for multi-target optimization.

[0017] Combining Figure 2 With the mechanism model of the comprehensive energy power generation device, the multi-target optimization method of the comprehensive energy power generation device is optimized according to the process shown in the figure. Figure 2 The specific process comprises the following steps: (1) According to the working condition, the initial parameters of the mechanism model of the comprehensive energy power generation device are set, including the working current of the comprehensive energy power generation device, the air excess ratio of the solid oxide fuel cell and the fuel utilization rate.

[0018] (2) According to the parameter output of the mechanism model of the comprehensive energy power generation device, the parameters of the multi-target optimization method are set, and the parameter output of the target mechanism model of the comprehensive energy power generation device is obtained through continuous iteration and update of the parameters of the multi-target optimization method. The parameters of the multi-target optimization method include the population size, the crossover probability, the mutation probability and the iteration number.

[0019] (3) According to the set initial parameters of the mechanism model of the comprehensive energy power generation device and the parameters of the multi-target optimization method, the initial population of the optimization target is obtained. If the initial population of the optimization target does not satisfy the independent variable constraint condition of the mechanism model of the comprehensive energy power generation device, the step (2) is returned to iterate again, and if the initial population of the optimization target satisfies the independent variable constraint condition of the mechanism model of the comprehensive energy power generation device, the next optimization is performed.

[0020] The selected independent variable constraint condition of the mechanism model of the comprehensive energy power generation device includes the temperature difference limitation range of the proton exchange membrane electrolyzer, the air excess ratio limitation range of the solid oxide fuel cell and the temperature limitation range of the thermoelectric generator.

[0021] The selected optimization target of the comprehensive energy power generation device is the system efficiency and the payback period of the comprehensive energy power generation device.

[0022] The constraint value of the optimization target is the temperature difference of the proton exchange membrane electrolyzer, the inlet flow of the solid oxide fuel cell, the inlet flow and the temperature of the thermoelectric generator.

[0023] The objective function of the initial population of the optimization target is calculated, if the constraint condition is not satisfied, the initial population of the optimization target is reset in the step (3); if the constraint condition is satisfied, the next optimization is performed.

[0024] (5) The initial population of the optimization target satisfying the objective function is subjected to non-dominated sorting, and the optimal solution sequence is obtained.

[0025] (6) Calculate the crowding distance of the individual in the optimal solution sequence, and perform crossover and mutation calculation according to the set mutation probability and crossover probability to obtain the crossover and mutation solution.

[0026] (7) Adopt the elite strategy, and according to the non-dominated sorting result in step (5) and the crossover and mutation solution obtained in step (6), the optimal solution sequence and the crossover and mutation solution are reserved to generate a new population.

[0027] (8) Determine whether the iteration number meets the requirement, if the iteration number does not meet the requirement, the new population returns to step (4) to re-optimize, until the set iteration number is reached, and the Pareto optimal solution set is output. The Pareto optimal solution set contains the system efficiency of the optimal comprehensive energy power generation device and the working current of the comprehensive energy power generation device, the air excess ratio of the solid oxide fuel cell and the fuel utilization rate under the payback period.

[0028] From the above description, it can be known that the application provides a multi-objective optimization method of a comprehensive energy power generation device. The method optimizes the performance of the comprehensive energy power generation device, realizes the improvement of the system efficiency of the comprehensive energy power generation device and the rapid payback. Through the multi-objective optimization method, not only the calculation cost is shortened, but also the interaction between the multiple objectives is balanced, and the best operation parameter is sought. At the same time, the multi-objective optimization method takes the working current of the comprehensive energy power generation device, the air excess ratio of the solid oxide fuel cell and the fuel utilization rate as the optimization variables, not only contains the control of the components, but also considers the system efficiency and economy of the comprehensive energy power generation device as a whole. In addition, the proposed multi-objective optimization method considers the constraint conditions such as the temperature difference between the inlet and outlet of the proton exchange membrane electrolyzer, the inlet flow of the solid oxide fuel cell, the inlet flow and temperature of the thermoelectric generator, and further improves the stability and economy of the comprehensive energy power generation device under the premise of ensuring the system performance output.

Claims

1. A multi-objective optimization method for integrated energy power generation devices, characterized by: integrated... The energy generation device includes a photovoltaic power generation system, an energy storage subsystem, a hydrogen production subsystem, a hydrogen storage / discharge subsystem, a fuel cell power generation system, and a thermoelectric power generation system. The photovoltaic power generation system, energy storage subsystem, fuel cell power generation system, and thermoelectric power generation system mainly perform the discharge function; the hydrogen production subsystem and the hydrogen storage / discharge subsystem are designed to provide fuel for the fuel cell power generation system. Includes the following steps: (1) Based on the operating conditions, set the initial parameters of the mechanism model of the integrated energy power generation device, including: the operating current of the integrated energy power generation device, the air excess ratio of the solid oxide fuel cell and the fuel utilization rate; (2) Based on the parameter output of the mechanism model of the integrated energy power generation device, set the parameters of the multi-objective optimization method, and obtain the parameter output of the mechanism model of the integrated energy power generation device of the target through the parameter iterative update of the multi-objective optimization method; (3) Based on the initial parameters of the integrated energy power generation device mechanism model and the parameters of the multi-objective optimization method, the initial population of the optimization target is obtained; if the initial population of the optimization target does not meet the independent variable constraint conditions of the integrated energy power generation device mechanism model, then return to step (2) for another iteration; if it meets the independent variable constraint conditions of the integrated energy power generation device mechanism model, then proceed to the next step. (4) Calculate the objective function of the initial population of the optimization objective. If the constraints are not met, return to step (3) to reset the initial population of the optimization objective; if the constraints are met, proceed to the next step. (5) Perform non-dominated sorting on the initial population that satisfies the objective function to obtain the optimal solution sequence; (6) Calculate the crowding degree of individuals in the optimal solution sequence, and perform crossover and mutation calculations based on the set mutation probability and crossover probability to obtain the crossover and mutation solution; (7) Using an elite strategy, based on the non-dominated sorting results in step (5) and the crossover and mutation solutions obtained in step (6), the retained optimal solution sequence and the crossover and mutation solutions are used to generate a new population. (8) Determine whether the number of iterations meets the requirements. If the number of iterations does not meet the requirements, the new population returns to step (4) to perform optimization again until the set number of iterations is reached, and outputs the Pareto optimal solution set.

2. The multi-objective optimization method for an integrated energy power generation device according to claim 1, characterized in that: The parameters of the multi-objective optimization method described in step (2) include: population size, crossover probability, mutation probability, and number of iterations.

3. The multi-objective optimization method for an integrated energy power generation device according to claim 1, characterized in that: The independent variable constraints of the integrated energy power generation device mechanism model described in step (3) include: the temperature difference limit range between the inlet and outlet of the proton exchange membrane electrolyzer, the air excess ratio limit range of the solid oxide fuel cell, and the temperature limit range of the thermoelectric generator. The optimization objectives of the integrated energy power generation device are: system efficiency and payback period of the integrated energy power generation device; the optimization objectives are constrained by the following values: inlet and outlet temperature difference of the proton exchange membrane electrolyzer, inlet flow rate of the solid oxide fuel cell, and inlet flow rate and temperature of the thermoelectric generator.

4. The multi-objective optimization method for an integrated energy power generation device according to claim 1, characterized in that: The Pareto optimal solution set in step (8) includes the system efficiency of the optimal integrated energy power generation device and the operating current of the integrated energy power generation device under the payback period, as well as the air excess ratio and fuel utilization rate of the solid oxide fuel cell.

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