System energy management optimization method and system based on improved genetic algorithm

Abstract

The invention relates to a system energy management optimization method and system based on an improved genetic algorithm, and the method comprises the following steps: 1, taking a hydrogen-containing carrier energy storage region integrated energy system as a research object, taking the local consumption maximization of renewable energy power generation as a target function, building a comprehensive energy system management optimization model, and carrying out the optimization of the management of the comprehensive energy system; day-ahead coordination and optimization control of the energy system are carried out; and step 2, solving the integrated energy system management optimization model by using a hybrid genetic algorithm, and further completing energy optimization management of the integrated energy system. According to the invention, the problems of volatility and randomness of wind power and photovoltaic power generation can be solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of regional integrated energy management optimization including hydrogen storage under renewable energy access conditions, and relates to a system energy management optimization method and system, in particular to a system energy management optimization method and system based on an improved genetic algorithm. Background Art

[0002] With the highly developed economy of human society and the continuous progress of science and technology, the reserves of fossil fuels can no longer meet the needs of human development. The large-scale use of fossil fuels has made the global environmental pollution problem increasingly serious. Wind power and photovoltaic power generation are currently the most widely used and most mature forms of clean energy. However, wind and light resources are random, that is, wind power and photovoltaic power generation have their own volatility, which leads to large-scale grid connection of wind power and photovoltaic power generation, which will have different degrees of impact on the power grid. It also indirectly leads to an increase in the ratio of wind and light abandonment. In order to further develop the application of renewable energy, it is crucial to solve the problem that wind power and photovoltaic power cannot be smoothly connected to the Internet. At present, people have realized that the effective solution is to increase the energy storage link.

[0003] Hydrogen energy storage is a new type of power system energy storage mode that has emerged in recent years. Hydrogen energy has the advantages of being clean and green, having high energy density, and being easy to store and transport. For example, compared with traditional chemical energy storage, the energy storage system with hydrogen energy as the core has a large energy storage capacity and a long operating life. It is a scenario for building a buffer zone between power generation and the power grid.

[0004] At present, many scholars at home and abroad have used different algorithms to study the energy management and capacity optimization of wind hybrid integrated energy systems, and have achieved relevant results, including but not limited to:

[0005] (1) Based on the differential evolution particle swarm optimization algorithm, an improved intelligent algorithm is used to study the capacity optimization problem of energy storage system. (2) Supercapacitors and batteries are used as hybrid energy storage systems, and the capacity of the energy storage system is optimized using genetic algorithms. (3) An improved particle swarm optimization algorithm is proposed and applied to the daily operation optimization model of microgrids for simulation. (4) Pumped storage power stations are used as energy storage systems for wind-wind complementary power generation systems, and capacity optimization is studied based on the improved particle swarm optimization algorithm. Genetic algorithms (GAs), as a search algorithm that simulates natural selection and genetic mechanisms, are widely used in the optimization problems of integrated energy systems. By simulating the evolutionary process of "survival of the fittest", genetic algorithms can search for optimal solutions globally, which provides an effective tool for solving complex energy optimization problems. Most of the traditional optimization methods used require a good set of initial values ​​of design variables and then converge quickly to produce better results. However, most optimization algorithms face the same difficulties, such as a long trial-and-error process or slow convergence when finding a better set of initial design variables. The set of initial design variables is generally determined by engineering intuition, and different sets of initial design variables usually give different optimal results.

[0006] Therefore, how to choose a better initial value of the design variable is a key step in the traditional method. As for using genetic algorithms, it has the advantage of working in a random population. Although genetic algorithms can find solutions in the entire domain, it is not easy to solve constraint problems, especially for precise constraint problems.

[0007] Therefore, in order to solve the above problems, the present invention proposes a system energy management optimization method based on an improved genetic algorithm.

[0008] After searching, no public documents of the prior art identical or similar to the present invention were found. Summary of the invention

[0009] The purpose of the present invention is to overcome the shortcomings of the prior art and propose a system energy management optimization method and system based on an improved genetic algorithm, which can solve the volatility and randomness problems of wind power and photovoltaic power generation, while taking into account the economic benefits of hydrogen energy storage, and realizing the optimization of the regional integrated energy management system.

[0010] The present invention solves the practical problem by adopting the following technical solutions:

[0011] A system energy management optimization method based on an improved genetic algorithm comprises the following steps:

[0012] Step 1: Taking the integrated energy system of hydrogen-containing carrier energy storage area as the research object and maximizing the local consumption of renewable energy power generation as the objective function, an integrated energy system management optimization model is established to conduct day-ahead coordination and optimization control of the energy system;

[0013] Step 2: Use the hybrid genetic algorithm to solve the integrated energy system management optimization model, and then complete the energy optimization management of the integrated energy system.

[0014] Moreover, the integrated energy system management optimization model of step 1 is:

[0015]

[0016] Where Δt is the length of the unit cycle; T is the total number of time cycles corresponding to the time cycle; t is the number of cycles; N is the number of PV power stations in the region; i is the PV power station number; where P i,t is the active power of the ith photovoltaic power station in period t; c v is the power generation cost per unit of electricity of the photovoltaic power station; M is the number of wind farms in the region; J is the number of wind farms; where P j,t is the active power of the jth wind farm in period t; c w is the unit electricity generation cost of the wind farm; K is the number of fuel cells in the region; k is the fuel cell number;, P k,t is the active power of the kth fuel cell in the tth cycle; c k is the unit electricity cost of fuel cell power generation; F is the number of hydrogen production devices in the region; f is the number of hydrogen production devices; where P f,t is the active power of the fth hydrogen production device in period t; c f is the electricity cost per unit of hydrogen produced by the hydrogen production device; D is the number of electric heating furnaces in the area; d is the number of electric boilers; where Q d,t d is the active power of the electric boiler in period t; c d is the unit heat consumption cost of electric boiler; Y is the number of small hydropower stations in the region; y is the number of small hydropower stations; where P y,t y is the active power of the small hydropower station in period t; c y is the power generation cost of small hydropower units; L is the number of extranet links in the region; l is the number of extranet contact wires; where P l+,t Inject active power into this area for the external network connection line 1; c l+ is the electricity price per unit of electricity sold by the external power grid; where P l-,t The active power absorbed by an external network connection line in this area; c l- It is the purchase price of electricity per unit of electricity from the external power grid.

[0017] Moreover, the constraints of the integrated energy system management optimization model in step 1 include:

[0018] (1) Power balance constraints:

[0019] The most basic principle of power generation is to ensure that the power supply meets the power supply demand, so the system power balance constraint of the renewable energy system can be expressed as:

[0020]

[0021] In the formula, H is the number of energy storage power sources in the area; h is the number of energy storage power sources; in the formula, P h,t is the active power of the hth energy storage power source in time period t; L is the number of extranet links in the region; l is the number of extranet contact lines; P l,t is the active power exchanged between the first extranet connection line and the region; D is the number of electric heating furnaces in the region; d is the number of electric boilers in the region; Q d,t is the active 74 power source d-electric boiler; G is the number of conventional load zones in the area; C is the number of conventional load divisions in the area; P g,t is the active power of the g-th conventional load area; C is the number of interruptible loads in this area; c is the number of interruptible loads in this area; where P c,t c- is the active power of the interruptible load.

[0022] (2) Output constraints of photovoltaic power generation:

[0023] The actual power of the photovoltaic generator set is set to be less than the theoretical power as a constraint:

[0024] P i,t ≤P′ i,t

[0025] In the formula, P′ i,t is the theoretical power of the ith photovoltaic power station in the tth period; where P i,t is the active power of the i-th PV power station in the t-th period.

[0026] (3) Output constraints of hydrogen fuel cells:

[0027] P k,t ≤P k,rate

[0028] P is the rated power of the kth fuel-fired controllable power source; where P k,t is the active power of the kth fuel-type controllable power source in the tth cycle.

[0029] (4) Charge and discharge constraints of energy storage power supply:

[0030] P h,rate ≤P h,t ≤P h,rate

[0031] Wherein, h rate P is the rated power of the hth energy storage power source; h,t is the active power of the hth energy storage power source in period t;

[0032] The charging state of the energy storage power supply should meet the following constraints:

[0033]

[0034] SOC h,min ≤SOC h,t ≤SOC h,max

[0035] In the formula, SOC h,t The t-th SOC is the charging state of the h-th energy storage power supply in the t-th period; h rate E is the rated energy of the h-th energy storage power supply; where E h,t is the energy state of the h-storage power source at the t period; where SOC h,max is the upper limit of the charging state of the h-th energy storage power source; wherein, the minimum state of charge is the lower limit of the charging state of the h-th energy storage power source.

[0036] (5) Load constraints of electrolytic hydrogen production device Output constraints of photovoltaic power generation:

[0037] P f,t ≤P f,rate

[0038] P is the rated power of the tth hydrogen production device; where P f,t is the active power of the fth hydrogen production device in the tth cycle;

[0039] The energy of hydrogen output from the electrolyzer is expressed by the following mathematical model:

[0040]

[0041] In the formula P is the efficiency coefficient of the electrolytic cell; abc KW is a constant related to the efficiency of the electrolytic cell; where E f,t is the amount of hydrogen produced by the electrolyzer device in the tth cycle; S is the rated capacity of the electrolyzer module.

[0042] (6) Interruptible load constraints:

[0043] First, the interruptible load should meet the requirement that the active power is less than or equal to the rated power:

[0044] P c,t ≤P c,rate

[0045] Where P c,tc is the active power of the interruptible load in period t; c rate P is the rated power of the interruptible load;

[0046] Secondly, avoid interrupting the load when it exceeds the maximum power and maximum allowable duration of the load when it is cut off, otherwise it will cause unnecessary losses:

[0047] ΔP c,t ≤ΔP c,max

[0048] In the formula, ΔP c,t c- is the active power removed by the interruptible load during the period t; ΔP c,max c- the maximum permissible cutting load power of the interruptible load;

[0049] (7) Constraints on thermal and electrical balance indicators of photovoltaic power generation:

[0050]

[0051] Q is the heat load demand in period t; loss Q is the heat network loss power in period t; Q hst is the heat absorption and release power of the heat storage facility in period t (heat release is positive, heat absorption is negative); Q d,t d-the heating power of the electric boiler in period t;

[0052] Moreover, the specific method of step 2 is:

[0053] First, the traditional genetic algorithm is used to initialize various electrical parameters, read relevant electrical parameters, calculate the regional cumulative electricity cost and the profit of electricity purchase and sale, and use the genetic algorithm to iterate to ensure that the optimal solution can be found. Then, it is determined whether the maximum number of iterations has been reached. After reaching the maximum number of iterations, the objective function value and fitness are evaluated, and the genetic algorithm is used for selection, crossover and mutation to generate a new generation. Then, the fitness of the new generation results is evaluated and the objective function value is compared to find a set of initial design variables, and then the traditional algorithm is used to output the optimal number of power stations and the optimal location of the external network connection of each power station, so that the optimization of energy management is completed.

[0054] A system energy management optimization system based on an improved genetic algorithm, comprising:

[0055] The module for establishing the integrated energy system management optimization model takes the integrated energy system of the hydrogen carrier energy storage area as the research object, takes the maximization of local consumption of renewable energy power generation as the objective function, establishes the integrated energy system management optimization model, and conducts day-ahead coordination and optimization control of the energy system;

[0056] The solution module uses a hybrid genetic algorithm to solve the integrated energy system management optimization model, and then completes the energy optimization management of the integrated energy system.

[0057] Moreover, the integrated energy system management optimization model in the integrated energy system management optimization model building module is:

[0058]

[0059] Where Δt is the length of the unit cycle; T is the total number of time cycles corresponding to the time cycle; t is the number of cycles; N is the number of PV power stations in the region; i is the PV power station number; where P i,t is the active power of the ith photovoltaic power station in period t; c v is the power generation cost per unit of electricity of the photovoltaic power station; M is the number of wind farms in the region; J is the number of wind farms; where P j,t is the active power of the jth wind farm in period t; c w is the unit electricity generation cost of the wind farm; K is the number of fuel cells in the region; k is the fuel cell number;, P k,t is the active power of the kth fuel cell in the tth cycle; c k is the unit electricity cost of fuel cell power generation; F is the number of hydrogen production devices in the region; f is the number of hydrogen production devices; where P f,t is the active power of the fth hydrogen production device in period t; c f is the electricity cost per unit of hydrogen produced by the hydrogen production device; D is the number of electric heating furnaces in the area; d is the number of electric boilers; where Q d,t d is the active power of the electric boiler in period t; c d is the unit heat consumption cost of electric boiler; Y is the number of small hydropower stations in the region; y is the number of small hydropower stations; where P y,t y is the active power of the small hydropower station in period t; c y is the power generation cost of small hydropower units; L is the number of extranet links in the region; l is the number of extranet contact wires; where P l+,t Inject active power into this area for the external network connection line 1; c l+ is the electricity price per unit of electricity sold by the external power grid; where P l-,t The active power absorbed by an external network connection line in this area; c l- It is the purchase price of electricity per unit of electricity from the external power grid.

[0060] Advantages and beneficial effects of the present invention:

[0061] 1. The present invention proposes a hybrid optimization method that combines a genetic algorithm with a traditional optimization method. A hybrid genetic algorithm (HGA) is used to provide a set of initial design variables, which avoids the problem of a long trial and error process in the traditional optimization method to find a better set of initial design variables; then, the traditional algorithm is used to determine the optimal result. This hybrid algorithm can be called a hybrid genetic algorithm (HGA), which is more effective than the traditional algorithm.

[0062] 2. In terms of capacity optimization and configuration, traditional methods do not consider the economic benefits brought by hydrogen transportation, and there is little research on the coordination and optimization control of the system. This invention takes the integrated energy system of the hydrogen carrier energy storage area as the research object, establishes an integrated energy system management optimization model, considers photovoltaic power generation, wind power generation, hydrogen production, hydrogen fuel cells, battery energy storage devices, electric boilers and distributed power sources and loads as various types of interruptible loads, and the optimization goal is to minimize the regional comprehensive utilization cost.

[0063] 3. The present invention adopts a hybrid genetic algorithm to solve the model and discusses the optimal absorption scheme of each distributed power source in the system under a given capacity configuration. The background technology mentioned that traditional genetic algorithms have difficulties in solving constraint problems, especially precise constraint problems. The present invention uses a hybrid genetic algorithm. After the genetic algorithm generates the initial design variables, the traditional algorithm is used to determine the optimal result, which can more effectively handle precise constraint problems. The present invention combines the advantages of genetic algorithms in global search and the advantages of traditional algorithms in handling constraints, thereby improving the accuracy and efficiency of model solving.

[0064] 4. The present invention proposes a system energy management optimization method based on an improved genetic algorithm, which takes the minimization of the comprehensive electricity cost in the region as the goal within a certain period of time, taking into account a variety of distributed power sources and load types such as wind power generation, photovoltaic power generation, hydrogen power generation, hydrogen energy storage, battery energy storage, electric boilers, and interruptible loads. This comprehensive consideration method can more comprehensively evaluate and optimize the comprehensive energy utilization cost in the region. Through this comprehensive method, the present invention can achieve the optimal allocation and utilization of energy, reduce dependence on fossil fuels, reduce environmental pollution, and at the same time improve the utilization rate of renewable energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 A flow chart of the present invention for solving a model using a genetic algorithm;

[0066] Figure 2 A diagram of energy management strategy for a small hydropower station in an embodiment of the present invention;

[0067] Figure 3 is an energy management strategy diagram of a battery energy storage system in an embodiment of the present invention;

[0068] Figure 4 is an energy management strategy diagram of an electric heating system in an embodiment of the present invention;

[0069] Figure 5 An energy management strategy diagram of a public power grid in an embodiment of the present invention;

[0070] Figure 6 is an energy management strategy diagram of a hydrogen production device in an embodiment of the present invention;

[0071] Figure 7 FIG. 4 is a diagram of an energy management strategy for a fuel cell in an embodiment of the present invention. DETAILED DESCRIPTION

[0072] The embodiments of the present invention are further described in detail below with reference to the accompanying drawings:

[0073] A system energy management optimization method based on improved genetic algorithm, such as Figure 1 As shown, the following steps are included:

[0074] Step 1: Taking the integrated energy system of hydrogen-containing carrier energy storage area as the research object and maximizing the local consumption of renewable energy power generation as the objective function, an integrated energy system management optimization model is established to conduct day-ahead coordination and optimization control of the energy system;

[0075] The integrated energy system management optimization model of step 1 is:

[0076]

[0077] Where Δt is the length of the unit cycle; T is the total number of time cycles corresponding to the time cycle; t is the number of cycles; N is the number of PV power stations in the region; i is the PV power station number; where P i,t is the active power of the ith photovoltaic power station in period t; c v is the power generation cost per unit of electricity of the photovoltaic power station; M is the number of wind farms in the region; J is the number of wind farms; where P j,t is the active power of the jth wind farm in period t; c w is the unit electricity generation cost of the wind farm; K is the number of fuel cells in the region; k is the fuel cell number;, P k,t is the active power of the kth fuel cell in the tth cycle; c k is the unit electricity cost of fuel cell power generation; F is the number of hydrogen production devices in the region; f is the number of hydrogen production devices; where P f,t is the active power of the fth hydrogen production device in period t; c f is the electricity cost per unit of hydrogen produced by the hydrogen production device; D is the number of electric heating furnaces in the area; d is the number of electric boilers; where Q d,t d is the active power of the electric boiler in period t; cd is the unit heat consumption cost of electric boiler; Y is the number of small hydropower stations in the region; y is the number of small hydropower stations; where P y,t y is the active power of the small hydropower station in period t; c y is the power generation cost of small hydropower units; L is the number of extranet links in the region; l is the number of extranet contact wires; where P l+,t Inject active power into this area for the external network connection line 1; c l+ is the electricity price per unit of electricity sold by the external power grid; where P l-,t The active power absorbed by an external network connection line in this area; c l- It is the purchase price of electricity per unit of electricity from the external power grid.

[0078] The constraints of the integrated energy system management optimization model in step 1 include:

[0079] (1) Power balance constraints:

[0080] The most basic principle of power generation is to ensure that the power supply meets the power supply demand, so the system power balance constraint of the renewable energy system can be expressed as:

[0081]

[0082] In the formula, H is the number of energy storage power sources in the area; h is the number of energy storage power sources; in the formula, P h,t is the active power of the hth energy storage power source in the t period (discharging is positive, charging is negative); L is the number of external network links in the area; l is the number of external network contact lines; where P l,t is the active power exchanged between the first extranet connection line and the region (injection is positive, absorption is negative); D is the number of electric heating furnaces in the region; d is the number of electric boilers in the region; Q d,t is the active 74 power source d-electric boiler; G is the number of conventional load zones in the region; C is the number of conventional load divisions in the region;, P g,t is the active power of the g-th conventional load area; C is the number of interruptible loads in this area; c is the number of interruptible loads in this area; where P c,t c- is the active power of the interruptible load.

[0083] (2) Output constraints of photovoltaic power generation:

[0084] The output power characteristics of photovoltaic generators are related to the type of photovoltaic generator and the light intensity. The light intensity will change with the rise and fall of the sun every day and the influence of weather factors. Light intensity is the decisive factor in the productivity of photovoltaic generator sets, which is severely constrained by the environment and has strong randomness.

[0085] Therefore, the actual power of the photovoltaic generator set is set to be less than the theoretical power as a constraint:

[0086] P i,t ≤P′ i,t

[0087] In the formula, P′ i,t is the theoretical power of the ith photovoltaic power station in the tth period; where P i,t is the active power of the i-th PV power station in the t-th period.

[0088] (3) Output constraints of hydrogen fuel cells:

[0089] Hydrogen fuel cells can convert clean energy hydrogen into electrical energy. The output power of hydrogen fuel cells must be within the rated power range to ensure stable operation. Its output constraint can be expressed as:

[0090] P k,t ≤P k,rate

[0091] P is the rated power of the kth fuel-fired controllable power source; where P k,t is the active power of the kth fuel-type controllable power source in the tth cycle.

[0092] (4) Charge and discharge constraints of energy storage power supply:

[0093] P h,rate ≤P h,t ≤P h,rate

[0094] Wherein, h rate P is the rated power of the hth energy storage power source; h,t is the active power of the hth energy storage power source in period t (discharging is positive and charging is negative).

[0095] At the same time, in order to protect the energy storage power supply, it is not advisable to release all the energy stored in the energy storage power supply, and the energy storage power supply also has an upper and lower limit of energy storage, so the charging state of the energy storage power supply should meet the following constraints:

[0096]

[0097] SOC h,min ≤SOC h,t ≤SOC h,max

[0098] In the formula, SOC h,t The t-th SOC is the charging state of the h-th energy storage power supply in the t-th period; h rate E is the rated energy of the h-th energy storage power supply; where E h,t is the energy state of the h-storage power source at the t period; where SOC h,maxis the upper limit of the charging state of the h-th energy storage power source; wherein, the minimum state of charge is the lower limit of the charging state of the h-th energy storage power source.

[0099] (5) Load constraints of electrolytic hydrogen production device Output constraints of photovoltaic power generation:

[0100] In order to avoid over-power operation of the electrolytic hydrogen production device, the power constraint of the hydrogen production device can be set:

[0101] P f,t ≤P f,rate

[0102] P is the rated power of the tth hydrogen production device; where P f,t is the active power of the fth hydrogen production device in the tth cycle.

[0103] Hydrogen production by electrolyzer is the basis of all electricity-to-gas conversion processes. The following is an electrolyzer model based on proton exchange membrane technology. In the actual electrolysis process, the conversion efficiency of the electrolyzer is determined by the performance parameters of the electrolysis equipment and the input power of the electrical energy, rather than being fixed. In order to simulate this process, the energy of hydrogen output by the electrolyzer is expressed by the following mathematical model:

[0104]

[0105] In the formula P is the efficiency coefficient of the electrolytic cell; abc KW is a constant related to the efficiency of the electrolytic cell; where E f,t is the amount of hydrogen produced by the electrolyzer device in the tth cycle; S is the rated capacity of the electrolyzer module.

[0106] (6) Interruptible load constraints: Interruptible loads are flexible control objects of the regional power grid. They can be cut off in the event of power shortage or failure to relieve the pressure on the power grid system.

[0107] First, the interruptible load should meet the requirement that the active power is less than or equal to the rated power:

[0108] P c,t ≤P c,rate

[0109] Where P c,t c is the active power of the interruptible load in period t; c rate P is the rated power of the interruptible load;

[0110] Secondly, avoid interrupting the load when it exceeds the maximum power and maximum allowable duration of the load when it is cut off, otherwise it will cause unnecessary losses:

[0111] ΔP c,t ≤ΔP c,max

[0112] In the formula, ΔP c,t c- is the active power removed by the interruptible load during the period t; ΔP c,max c- the maximum permissible cutting load power of the interruptible load;

[0113] (7) Constraints on thermal and electrical balance indicators of photovoltaic power generation:

[0114] The most basic heating principle is to ensure that the heating meets the heating demand. Therefore, the system thermal power balance constraint of the renewable energy system can be expressed as:

[0115]

[0116] Q is the heat load demand in period t; loss Q is the heat network loss power in period t; Q hst is the heat absorption and release power of the heat storage facility in period t (heat release is positive, heat absorption is negative); Q d,t d-the heating power of the electric boiler in period t;

[0117] Step 2: Use the hybrid genetic algorithm to solve the integrated energy system management optimization model, and then complete the energy optimization management of the integrated energy system.

[0118] The specific method of step 2 is:

[0119] First, the traditional genetic algorithm is used to initialize various electrical parameters, read relevant electrical parameters, calculate the regional cumulative electricity cost and the profit of electricity purchase and sale, and use the genetic algorithm to iterate to ensure that the optimal solution can be found. Then, it is determined whether the maximum number of iterations has been reached. After reaching the maximum number of iterations, the objective function value and fitness are evaluated, and the genetic algorithm is used for selection, crossover and mutation to generate a new generation. Then, the fitness of the new generation results is evaluated and the objective function value is compared to find a set of initial design variables, and then the traditional algorithm is used to output the optimal number of power stations and the optimal location of the external network connection of each power station, so that the optimization of energy management is completed.

[0120] A system energy management optimization system based on an improved genetic algorithm, comprising:

[0121] The module for establishing the integrated energy system management optimization model takes the integrated energy system of the hydrogen carrier energy storage area as the research object, takes the maximization of local consumption of renewable energy power generation as the objective function, establishes the integrated energy system management optimization model, and conducts day-ahead coordination and optimization control of the energy system;

[0122] The solution module uses a hybrid genetic algorithm to solve the integrated energy system management optimization model, and then completes the energy optimization management of the integrated energy system.

[0123] The integrated energy system management optimization model in the integrated energy system management optimization model building module is:

[0124]

[0125] Where Δt is the length of the unit cycle; T is the total number of time cycles corresponding to the time cycle; t is the number of cycles; N is the number of PV power stations in the region; i is the PV power station number; where P i,t is the active power of the ith photovoltaic power station in period t; c v is the power generation cost per unit of electricity of the photovoltaic power station; M is the number of wind farms in the region; J is the number of wind farms; where P j,t is the active power of the jth wind farm in period t; c w is the unit electricity generation cost of the wind farm; K is the number of fuel cells in the region; k is the fuel cell number;, P k,t is the active power of the kth fuel cell in the tth cycle; c k is the unit electricity cost of fuel cell power generation; F is the number of hydrogen production devices in the region; f is the number of hydrogen production devices; where P f,t is the active power of the fth hydrogen production device in period t; c f is the electricity cost per unit of hydrogen produced by the hydrogen production device; D is the number of electric heating furnaces in the area; d is the number of electric boilers; where Q d,t d is the active power of the electric boiler in period t; c d is the unit heat consumption cost of electric boiler; Y is the number of small hydropower stations in the region; y is the number of small hydropower stations; where P y,t y is the active power of the small hydropower station in period t; c y is the power generation cost of small hydropower units; L is the number of extranet links in the region; l is the number of extranet contact wires; where P l+,t Inject active power into this area for the external network connection line 1; c l+ is the electricity price per unit of electricity sold by the external power grid; where P l-,t The active power absorbed by an external network connection line in this area; c l- It is the purchase price of electricity per unit of electricity from the external power grid.

[0126] Embodiment 1:

[0127] Taking a regional integrated energy system as an example, it is assumed that the system includes 20MW photovoltaic power generation installed capacity and 20MW wind power installed capacity. The installed capacity of small hydropower is 10MW, and the maximum output in winter is 50% of the rated capacity. The lithium battery energy storage system is 2MW / 2MWh, and the charging and discharging efficiency is calculated to be 80%; the thermal storage electric boiler system is 10MW; the hydrogen fuel cell power generation system is 2MW, and the power generation efficiency is 60%. Considering the utilization of waste heat, the hydrogen energy utilization efficiency is 90%. The rated power of the hydrogen production system is 500kW, and the capacity of the hydrogen storage system is designed according to the fuel cell with a rated power of 2h. The energy conversion efficiency of the hydrogen production system is calculated to be 75%. Table I gives the power load, heat load, photovoltaic and wind power data from 00:00 to 24:00 on a typical day. Among them, the power load includes the power load of the electric boiler, the hydrogen production load and the general power load. In the initial state, the battery energy storage system is 20% of the rated capacity; the hydrogen system is initially 80% of the rated capacity. Under standard conditions, the mass of 1m3 of hydrogen is 0.0899kg, and the calorific value of hydrogen is 28667kCAR / kg, totaling 33kWh.

[0128] Figure 2-Figure 7 It is the energy management strategy given by the algorithm proposed in this invention.

[0129] Tables 1 and 2 below give the power supply characteristics and electricity prices in a certain area. Applying the energy management method of the present invention to solve the problem, the following results are obtained: Figures 2 to 7 The optimal energy management strategy shown; Figures 2 to 7 It can express which stage has more energy supply and which stage has less energy supply, that is, the energy management strategy.

[0130] Table 1: Power characteristics in typical daily scenarios

[0131]

[0132] The purchase and sale price of the external public power grid adopts the time-of-use electricity price, which gives full play to the leverage effect to encourage users to consciously adjust their production plans, participate in demand-side responses such as peak shaving and valley filling, and balance energy consumption, as shown in Table 2.

[0133] Table 2: Time-of-use on-grid electricity purchase and sales prices

[0134]

[0135] The working principle of the present invention is:

[0136] The present invention aims at a series of adverse effects such as the increasingly serious global environmental pollution problem caused by the large-scale use of fossil fuels, the randomness of wind and light resources, the volatility of wind power and photovoltaic power generation, the varying degrees of impact on the power grid after large-scale grid connection of wind power and photovoltaic power generation, and the increase in the ratio of wind and light abandonment. The present invention is based on genetic algorithms and considers the economic benefits brought by hydrogen transportation in terms of capacity optimization configuration. Taking the integrated energy system of hydrogen-containing carrier energy storage area as the research object, an integrated energy system management optimization model is established, considering photovoltaic power generation, wind power generation, hydrogen production, hydrogen fuel cells, battery energy storage devices, electric boilers and distributed power sources and loads as various types of interruptible loads. The optimization goal is to minimize the regional comprehensive utilization cost. The model is solved to find the optimal absorption plan for each distributed power source in the system under a given capacity configuration.

[0137] In the present invention, a hybrid genetic algorithm is used to study the optimal adjustment of different flexible resources in the wind-solar-hydrogen storage integrated energy system. By adding a hydrogen storage system and a traditional battery energy storage system, resources are flexibly adjusted to maximize the consumption of local renewable energy power generation, reduce the consumption of fossil energy and the power supply of the external power grid; and under the premise of ensuring regional energy consumption, large power grids are used as much as possible to minimize the regional comprehensive energy utilization cost.

[0138] The results show that this strategy can fully tap the consumption space of renewable energy power generation and maximize the local consumption of renewable energy power generation, which has certain engineering feasibility. Subsequently, considering the volatility and uncertainty of renewable energy such as off-grid power and photovoltaic in the off-grid regional integrated energy system, the energy management optimization decision of photovoltaic integrated energy system is further studied.

[0139] It should be emphasized that the embodiments of the present invention are illustrative rather than restrictive. Therefore, the present invention includes but is not limited to the embodiments described in the specific implementation modes. Any other implementation modes derived by those skilled in the art based on the technical solutions of the present invention also fall within the scope of protection of the present invention.

Claims

1. A system energy management optimization method based on an improved genetic algorithm, characterized in that: The following steps are involved: Step 1: Taking the integrated energy system of hydrogen-containing carrier energy storage area as the research object and maximizing the local consumption of renewable energy power generation as the objective function, an integrated energy system management optimization model is established to conduct day-ahead coordination and optimization control of the energy system; Step 2: Use the hybrid genetic algorithm to solve the integrated energy system management optimization model, and then complete the energy optimization management of the integrated energy system.

2. The system energy management optimization method based on improved genetic algorithm according to claim 1, characterized in that: The integrated energy system management optimization model of step 1 is: Where Δt is the length of the unit cycle; T is the total number of time cycles corresponding to the time cycle; t is the number of cycles; N is the number of PV power stations in the region; i is the PV power station number; where P i,t is the active power of the ith photovoltaic power station in period t; c v is the power generation cost per unit of electricity of the photovoltaic power station; M is the number of wind farms in the region; J is the number of wind farms; where P j,t is the active power of the jth wind farm in period t; c w is the unit electricity generation cost of the wind farm; K is the number of fuel cells in the region; k is the fuel cell number;, P k,t is the active power of the kth fuel cell in the tth cycle; c k is the unit electricity cost of fuel cell power generation; F is the number of hydrogen production devices in the region; f is the number of hydrogen production devices; where P f,t is the active power of the fth hydrogen production device in period t; c f is the electricity cost per unit of hydrogen produced by the hydrogen production device; D is the number of electric heating furnaces in the area; d is the number of electric boilers; where Q d,t d is the active power of the electric boiler in period t; c d is the unit heat consumption cost of electric boiler; Y is the number of small hydropower stations in the region; y is the number of small hydropower stations; where P y,t y is the active power of the small hydropower station in period t; c y is the power generation cost of small hydropower units; L is the number of extranet links in the region; l is the number of extranet contact wires; where P l+,t Inject active power into this area for the external network connection line 1; c l+ is the electricity price per unit of electricity sold by the external power grid; where P l-,t The active power absorbed by an external network connection line in this area; c l- It is the purchase price of electricity per unit of electricity from the external power grid.

3. The system energy management optimization method based on improved genetic algorithm according to claim 1, characterized in that: The constraints of the integrated energy system management optimization model in step 1 include: (1) Power balance constraints: The most basic principle of power generation is to ensure that the power supply meets the power supply demand, so the system power balance constraint of the renewable energy system can be expressed as: In the formula, H is the number of energy storage power sources in the area; h is the number of energy storage power sources; in the formula, P h,t is the active power of the hth energy storage power source in time period t; L is the number of extranet links in the region; l is the number of extranet contact lines; P l,t is the active power exchanged between the first extranet connection line and the region; D is the number of electric heating furnaces in the region; d is the number of electric boilers in the region; Q d,t is the active 74 power source d-electric boiler; G is the number of conventional load zones in the area; C is the number of conventional load divisions in the area; P g,t is the active power of the g-th conventional load area; C is the number of interruptible loads in this area; c is the number of interruptible loads in this area; where P c,t c-active power of interruptible load; (2) Output constraints of photovoltaic power generation: The actual power of the photovoltaic generator set is set to be less than the theoretical power as a constraint: P i,t ≤P′ i,t In the formula, P′ i,t is the theoretical power of the ith photovoltaic power station in the tth period; where P i,t is the active power of the i-th PV power station in the t-th period; (3) Output constraints of hydrogen fuel cells: P k,t ≤P k,rate P is the rated power of the kth fuel-fired controllable power source; where P k,t is the active power of the kth fuel-type controllable power source in the tth cycle; (4) Charge and discharge constraints of energy storage power supply: P h,rate ≤P h,t ≤P h,rate Wherein, h rate P is the rated power of the hth energy storage power source; h,t is the active power of the hth energy storage power source in period t; The charging state of the energy storage power supply should meet the following constraints: SOC h,min ≤SOC h,t ≤SOC h,max In the formula, SOC h,t The t-th SOC is the charging state of the h-th energy storage power supply in the t-th period; h rate E is the rated energy of the h-th energy storage power supply; where E h,t is the energy state of the h-storage power source at the t period; where SOC h,max is the upper limit of the charging state of the h-th energy storage power source; wherein, the minimum state of charge is the lower limit of the charging state of the h-th energy storage power source; (5) Load constraints of electrolytic hydrogen production device Output constraints of photovoltaic power generation: P f,t ≤P f,rate P is the rated power of the tth hydrogen production device; where P f,t is the active power of the fth hydrogen production device in the tth cycle; The energy of hydrogen output from the electrolyzer is expressed by the following mathematical model: In the formula P is the efficiency coefficient of the electrolytic cell; abc KW is a constant related to the efficiency of the electrolytic cell; where E f,t is the amount of hydrogen produced by the electrolyzer device in the tth cycle; S is the rated capacity of the electrolyzer module; (6) Interruptible load constraints: First, the interruptible load should meet the requirement that the active power is less than or equal to the rated power: P c,t ≤P c,rate Where P c,t c is the active power of the interruptible load in period t; c rate P is the rated power of the interruptible load; Secondly, avoid interrupting the load when it exceeds the maximum power and maximum allowable duration of the load when it is cut off, otherwise it will cause unnecessary losses: ΔP c,t ≤ΔP c,max In the formula, ΔP c,t c- is the active power removed by the interruptible load during the period t; ΔP c,max c- the maximum permissible cutting load power of the interruptible load; (7) Constraints on thermal and electrical balance indicators of photovoltaic power generation: Q is the heat load demand in period t; loss Q is the heat network loss power in period t; Q hst is the heat absorption and release power of the heat storage facility in period t (heat release is positive, heat absorption is negative); Q d,t d- is the heating power of the electric boiler in period t.

4. The system energy management optimization method based on improved genetic algorithm according to claim 1, characterized in that: The specific method of step 2 is: First, the traditional genetic algorithm is used to initialize various electrical parameters, read relevant electrical parameters, calculate the cumulative electricity cost of the region and the profit of purchasing and selling electricity, and use the genetic algorithm to iterate to ensure that the optimal solution can be found; then it is determined whether the maximum number of iterations has been reached. After the maximum number of iterations has been reached, the objective function value and fitness are evaluated, and the genetic algorithm is used for selection, crossover and mutation to generate a new generation. Then, the fitness of the new generation results is evaluated and the objective function value is compared to find an initial set of design variables, and then the traditional algorithm is used to output the optimal number of power stations and the optimal location of the external network connection of each power station. At this point, the optimization of energy management is completed.

5. A system energy management optimization system based on an improved genetic algorithm, characterized in that: include: The module for establishing the integrated energy system management optimization model takes the integrated energy system of the hydrogen carrier energy storage area as the research object, takes the maximization of local consumption of renewable energy power generation as the objective function, establishes the integrated energy system management optimization model, and conducts day-ahead coordination and optimization control of the energy system; The solution module uses a hybrid genetic algorithm to solve the integrated energy system management optimization model, and then completes the energy optimization management of the integrated energy system.

6. The system energy management optimization system based on improved genetic algorithm according to claim 5, characterized in that: The integrated energy system management optimization model in the integrated energy system management optimization model building module is: Where Δt is the length of the unit cycle; T is the total number of time cycles corresponding to the time cycle; t is the number of cycles; N is the number of PV power stations in the region; i is the PV power station number; where P i,t is the active power of the ith photovoltaic power station in period t; c v is the power generation cost per unit of electricity of the photovoltaic power station; M is the number of wind farms in the region; J is the number of wind farms; where P j,t is the active power of the jth wind farm in period t; c w is the unit electricity generation cost of the wind farm; K is the number of fuel cells in the region; k is the fuel cell number;, P k,t is the active power of the kth fuel cell in the tth cycle; c k is the unit electricity cost of fuel cell power generation; F is the number of hydrogen production devices in the region; f is the number of hydrogen production devices; where P f,t is the active power of the fth hydrogen production device in period t; c f is the electricity cost per unit of hydrogen produced by the hydrogen production device; D is the number of electric heating furnaces in the area; d is the number of electric boilers; where Q d,t d is the active power of the electric boiler in period t; c d is the unit heat consumption cost of electric boiler; Y is the number of small hydropower stations in the region; y is the number of small hydropower stations; where P y,t y is the active power of the small hydropower station in period t; c y is the power generation cost of small hydropower units; L is the number of extranet links in the region; l is the number of extranet contact wires; where P l+,t Inject active power into this area for the external network connection line 1; c l+ is the electricity price per unit of electricity sold by the external power grid; where P l-,t The active power absorbed by an external network connection line in this area; c l- It is the purchase price of electricity per unit of electricity from the external power grid.

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