Power grid optimization operation method and system of wind-solar-storage complementary power generation system

Abstract

The invention discloses a power grid optimization operation method and system of a wind-solar-storage complementary power generation system. The method provided by the invention comprises the following steps of: constructing a power grid operation model of the wind-solar-storage complementary power generation system by taking the minimum daily operation cost of a power grid of the wind-solar-storage complementary power generation system as a target and combining the wind-solar-storage complementary power generation system and a power grid environment; and performing operation optimization on the objective function and the constraint condition formed by the power grid operation model of the wind-solar-energy-storage complementary power generation system by adopting an improved particle swarm algorithm to obtain an optimal power grid operation mode of the wind-solar-energy-storage complementary power generation system. Under the condition constraint of safe and reliable operation of each component of the system, electric energy interaction between the wind-solar-storage complementary power generation system and the power grid can be flexibly adjusted, so that accurate meeting of load requirements can be realized under different environment conditions and power utilization time periods, unnecessary electric energy waste and overhigh power purchase expenditure are effectively reduced, and the power utilization efficiency is improved. And the economical efficiency and the energy utilization efficiency of the system are improved.

Description

Technical Field

[0001] This invention belongs to the field of power system optimization and dispatching technology, specifically relating to a grid optimization operation method and system for a wind-solar-storage complementary power generation system. Background Technology

[0002] Currently, solar and wind power have been widely applied, and their technologies are relatively mature. Their utilization primarily involves generating electricity and transmitting it to the power grid. As the installed capacity of generators continues to increase, their proportion in the power grid is also gradually rising. However, both wind and solar power are affected to varying degrees by environmental climate, seasons, and geographical conditions. Coupled with their poor stability, wind power and photovoltaic power generation exhibit volatility and indirectness, reducing the stability of their power output and causing significant fluctuations in grid-connected power generation. Furthermore, grid-connected power generation from a single energy source cannot adequately meet the grid load demand, necessitating supplementary thermal power generation during peak electricity demand periods. Currently, there is some research both domestically and internationally on the operational optimization of various units in wind-solar-storage complementary power generation systems. The vast majority of this research is based on genetic algorithms or neural networks; research on the capacity configuration of system components or the variations in configuration schemes when using different energy storage devices is still relatively lacking. Summary of the Invention

[0003] To address the challenges of wind-solar-storage operation, this invention provides a grid optimization operation method, system, medium, and equipment for a wind-solar-storage complementary power generation system. This invention employs a particle swarm optimization algorithm to optimize the operation of the wind-solar-storage complementary power generation system containing energy storage batteries, thereby finding the optimal solution for system operating costs. This invention provides a grid optimization operation method for a wind-solar-storage complementary power generation system, comprising: Determine the energy conversion efficiency of the micro gas turbine in the wind-solar-storage complementary power generation system under different power conditions, and determine the operating cost of the micro gas turbine based on the energy conversion efficiency of the micro gas turbine; Determine the costs incurred by the interaction between the wind-solar-storage complementary power generation system and the power grid; Determine the maintenance costs of the micro-turbine, wind power, photovoltaic power, and energy storage batteries in a wind-solar-storage complementary power generation system; With the goal of minimizing the daily grid operating cost of the wind-solar-storage complementary power generation system, a grid operation model for the wind-solar-storage complementary power generation system is constructed by combining the system with the grid environment. An improved particle swarm optimization algorithm is used to optimize the objective function and constraints of the grid operation model of the wind-solar-storage complementary power generation system, thereby obtaining the optimal grid operation mode of the wind-solar-storage complementary power generation system.

[0004] The determination of the energy conversion efficiency of the micro gas turbine in the wind-solar-storage complementary power generation system under different power conditions, and the determination of the operating cost of the micro gas turbine based on the energy conversion efficiency, include: The energy conversion efficiency is determined based on the operating status of the micro-turbine. When the micro-turbine has not reached a stable output power, the energy conversion efficiency is calculated as follows:

[0005] in, P gt ( t ) indicates that the micro gas turbine is in t The amount of effort exerted at any given moment. η(t) Indicates energy conversion efficiency; When the output power of the micro-turbine reaches a stable level, the energy conversion efficiency is 0.27. The operating costs of a micro-turbine are calculated as follows:

[0006] in, C 1 represents the operating cost of the micro gas turbine. C Indicates the price of natural gas. H Indicates the calorific value of natural gas. P GT (t) This indicates the output of the micro-turbine at time t.

[0007] The specific calculation method for determining the cost incurred by the interaction between the wind-solar-storage complementary power generation system and the power grid is as follows:

[0008] in, C 2 represents the cost required to interact with the power grid. k m Indicates the electricity purchase price. P tm This indicates the amount of electricity purchased during interaction with the power grid. k s Indicates the electricity price. P ts This indicates the power sold during interaction with the power grid.

[0009] The determination of the maintenance costs of the micro-turbine, wind turbine, photovoltaic power generation device, and energy storage battery in the wind-solar-storage complementary power generation system is as follows:

[0010] in, C 3 represents the operating and maintenance costs of each piece of equipment in the power generation system. k gt This indicates the unit operating cost of a micro gas turbine.k wt This indicates the unit operating cost of a wind turbine. k pv This indicates the unit operating cost of a photovoltaic power generation device. k ESS This indicates the unit operating cost of energy storage batteries. P gt (t) This indicates the output of the micro gas turbine at time t. P wt (t) This indicates the power output of the wind turbine at time t. P pv (t) This indicates the power output of the photovoltaic power generation device at time t. P ESS ( t) represents the output of the energy storage battery at time t, and takes the absolute value.

[0011] The goal is to minimize the daily operating cost of a wind-solar-storage complementary power generation system. This involves constructing a grid operation model for the system, considering energy storage batteries, micro-turbines, and the grid environment. Specifically:

[0012] Where C1 represents the operating cost of the microturbine, C2 represents the cost incurred when interacting with the power grid, C3 represents the operation and maintenance cost of all equipment in the power generation system, C4 represents the total cost incurred when starting and stopping the microturbine, M represents the penalty factor, and delt NES delt GT delt pe delt GE These correspond to the deviation values ​​generated during power generation under the constraints of energy storage battery capacity, micro-turbine power ramp-up, electric power, and energy storage battery power ramp-up, respectively.

[0013] The constraints include balance constraints and heat exchange / energy storage constraints. The balance constraint is:

[0014] Among them, E grid E represents the power of the power grid. wt E represents the power of the wind turbine. pv E represents the electrical power generated by photovoltaic power generation. Gt Q represents the electrical power of the micro-turbine, and Q represents the electrical power consumed by the load. The energy storage constraint is:

[0015] Where, N ESSN represents the capacity of the energy storage battery at a certain moment. emax N represents the maximum charge capacity of the energy storage battery. emin This indicates the minimum charge capacity of the energy storage battery.

[0016] The improved particle swarm optimization algorithm is used to optimize the objective function and constraints of the grid operation model of the wind-solar-storage complementary power generation system. Specifically: S1, Initialize the particles in the population, assuming the initial velocity and position of each particle; S2, construct the fitness function and penalty term based on the objective function and constraints of the grid operation model of the wind-solar-storage complementary power generation system; S3, calculate the fitness value of each particle, and obtain the global optimal solution and the historical optimal solution by comparing them among all particles; S4, update the velocity and position of each particle. The velocity update is calculated using an asynchronous learning factor, and the position satisfies the constraints. S5. Repeat steps S2-S4 until the preset number of iterations is reached to obtain the operating mode with the lowest daily grid operating cost for the wind-solar-storage complementary power generation system.

[0017] The present invention also provides a grid optimization operation system for a wind-solar-storage complementary power generation system, comprising: The first module is used to determine the energy conversion efficiency of the micro gas turbine in the wind-solar-storage complementary power generation system under different power conditions, and to determine the operating cost of the micro gas turbine based on the energy conversion efficiency of the micro gas turbine. The second module is used to determine the costs incurred by the interaction between the wind-solar-storage complementary power generation system and the power grid; The third module is used to determine the maintenance costs of the micro-turbine, wind power, photovoltaic power, and energy storage battery in a wind-solar-storage complementary power generation system. The model building module is used to construct a grid operation model for the wind-solar-storage complementary power generation system with the goal of minimizing the daily grid operation cost of the system, taking into account the system and the grid environment. The optimization solution module is used to optimize the objective function and constraints of the grid operation model of the wind-solar-storage complementary power generation system using an improved particle swarm optimization algorithm, so as to obtain the optimal grid operation mode of the wind-solar-storage complementary power generation system.

[0018] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described grid optimization operation method for a wind-solar-storage complementary power generation system.

[0019] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the above-described method for optimizing the operation of a wind-solar-storage hybrid power generation system.

[0020] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention proposes a grid optimization operation method for wind-solar-storage complementary power generation systems. Based on the working principles and characteristics of wind power generation, photovoltaic power generation, energy storage batteries, and micro-turbines, this method constructs a grid operation model for the wind-solar-storage complementary power generation system. Then, an improved particle swarm optimization algorithm is used to optimize the objective function and constraints of these mathematical models, efficiently finding the optimal operation strategy. Under the constraint of ensuring the safe and reliable operation of each component of the system, this optimization method can flexibly adjust micro-turbine power generation and the power interaction with the grid, thereby accurately meeting load demand under different environmental conditions and electricity consumption periods. This method can significantly reduce the operating costs of wind-solar-storage complementary power generation systems. By using micro-turbine auxiliary power generation to replace high-priced electricity purchases during peak electricity consumption periods, and selling surplus electricity to the grid when wind and solar power are abundant, combined with the algorithm's optimized control of each link, unnecessary energy waste and excessive electricity purchase expenditures are effectively reduced, improving the system's economy and energy utilization efficiency. Attached Figure Description

[0021] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a flowchart of the power grid optimization operation method based on a wind-solar-storage complementary power generation system according to the present invention; Figure 2 The flowchart for the improved particle swarm optimization algorithm of this invention is shown below; Figure 3 Line graphs showing the power generation of wind power and photovoltaic power as examples; Figure 4 The following is a power curve of each device during the entire day operation of the micro-turbine, as shown in the example. Figure 5 The power output curves of each device during partial operation of the micro-turbine in this embodiment are shown. Figure 6 The following are the operating power curves of each device when the micro-turbine is not in operation, as shown in the example. Figure 7This is a schematic diagram of the grid optimization operation system structure based on a wind-solar-storage complementary power generation system according to a preferred embodiment of the present invention; Figure 8 This is a schematic diagram of the electronic device structure according to a preferred embodiment of the present invention. Detailed Implementation

[0023] To enable those skilled in the art to understand the features and effects of the present invention, the terms and expressions used in the specification and claims are explained and defined in general below. Unless otherwise specified, all technical and scientific terms used herein have the ordinary meaning understood by those skilled in the art regarding the present invention, and in case of conflict, the definitions in this specification shall prevail.

[0024] The theories or mechanisms described and disclosed herein, whether right or wrong, should not in any way limit the scope of the invention, that is, the contents of the invention can be implemented without being limited by any particular theory or mechanism.

[0025] In this document, all features defined by numerical ranges or percentage ranges, such as numerical values, quantities, contents, and concentrations, are for the sake of brevity and convenience only. Accordingly, descriptions of numerical ranges or percentage ranges should be considered as covering and specifically disclosing all possible sub-ranges and individual numerical values ​​(including integers and fractions) within those ranges.

[0026] In this article, unless otherwise specified, “contains,” “includes,” “containing,” “has,” or similar terms cover the meanings of “composed of” and “mainly composed of,” for example, “A contains a” covers the meanings of “A contains a and others” and “A contains only a.”

[0027] For the sake of brevity, not all possible combinations of the technical features in each implementation scheme or embodiment are described herein. Therefore, as long as there is no contradiction in the combination of these technical features, the technical features in each implementation scheme or embodiment can be combined arbitrarily, and all possible combinations should be considered within the scope of this specification.

[0028] To meet the electricity demand of the load, this invention constructs a wind-solar-storage complementary power generation system, comprising a wind turbine, a photovoltaic power generation device, an energy storage battery, and a micro-turbine. This system effectively reduces the amount of electricity purchased from the grid, maximizes the utilization of renewable and clean energy, and further reduces overall costs through optimized operating algorithms.

[0029] like Figure 1 As shown, the present invention provides a grid optimization operation method for a wind-solar-storage complementary power generation system, comprising: Determine the energy conversion efficiency of the micro gas turbine in the wind-solar-storage complementary power generation system under different power conditions, and determine the operating cost of the micro gas turbine based on the energy conversion efficiency of the micro gas turbine; Determine the costs incurred by the interaction between the wind-solar-storage complementary power generation system and the power grid; Determine the maintenance costs of the micro-turbine, wind power, photovoltaic power, and energy storage batteries in a wind-solar-storage complementary power generation system; With the goal of minimizing the daily grid operating cost of the wind-solar-storage complementary power generation system, a grid operation model for the wind-solar-storage complementary power generation system is constructed by combining the system with the grid environment. An improved particle swarm optimization algorithm is used to optimize the objective function and constraints of the grid operation model of the wind-solar-storage complementary power generation system, thereby obtaining the optimal grid operation mode of the wind-solar-storage complementary power generation system.

[0030] Because the energy conversion efficiency varies depending on the output power of the micro gas turbine, the power output of the micro gas turbine is differentiated based on actual conditions. Therefore, the energy conversion efficiency of the micro gas turbine in the wind-solar-storage complementary power generation system is determined at different power levels. Based on the energy conversion efficiency of the micro gas turbine, its operating cost is determined, specifically as follows: When the micro-turbine has not reached a stable output power, the energy conversion efficiency is calculated as follows:

[0031] in, P gt ( t ) indicates that the micro gas turbine is in t The amount of effort exerted at any given moment. η(t) Indicates energy conversion efficiency; When the output power of the micro-engine reaches a stable level, its energy conversion efficiency no longer fluctuates. At this point, the energy conversion efficiency... η (t) The value is 0.27; Therefore, the cost incurred during the operation of the micro-turbine is calculated as follows:

[0032] in, C The price of natural gas is 3 yuan / m³. 3 , H This indicates the calorific value of natural gas, taken as 9.7 kW•h / m³. 3 C1 represents the operating cost of the micro gas turbine.

[0033] In addition, the micro gas turbine incurs start-up and shutdown costs when it is shut down and restarted. Assuming that the cost of starting once is 6 yuan, the total cost of starting and stopping is C4.

[0034] In a wind-solar-storage complementary power generation system, if the electricity generated by the wind turbines and photovoltaic power generation is sufficient to meet the load demand and fully charge the energy storage batteries, any excess electricity is transferred to the grid. The grid purchases this excess electricity, thus generating revenue. However, when the system's own power generation is insufficient to meet the load demand, it needs to purchase electricity from the grid. Based on this, the costs incurred when interacting with the grid can be calculated. The specific calculation method for the costs incurred by the wind-solar-storage complementary power generation system in interacting with the grid is as follows:

[0035] in, C 2 represents the cost required to interact with the power grid. k m Indicates the electricity purchase price. P tm This indicates the amount of electricity purchased during interaction with the power grid. k s Indicates the electricity price. P ts This indicates the power sold during interaction with the power grid.

[0036] During the operation of a wind-solar-storage complementary power generation system, the microturbine, wind turbine, photovoltaic power generation device, and energy storage battery may all be in operation, thus incurring unavoidable wear and tear. The maintenance costs of the microturbine, wind turbine, photovoltaic power generation device, and energy storage battery in the wind-solar-storage complementary power generation system are determined as follows:

[0037] in, C 3 represents the operating and maintenance costs of each piece of equipment in the power generation system. k gt This indicates the unit operating cost of a micro gas turbine. k wt This indicates the unit operating cost of a wind turbine. k pv This indicates the unit operating cost of a photovoltaic power generation device. k ESS This indicates the unit operating cost of energy storage batteries. P gt (t) This indicates the output of the micro gas turbine at time t. P wt (t) This indicates the power output of the wind turbine at time t. P pv (t) represents the power output of the photovoltaic power generation device at time t. P ESS (t) This represents the output of the energy storage battery at time t, and is expressed in absolute value.

[0038] Based on the output of the aforementioned wind turbines and photovoltaic power generation devices, an optimization function is established to optimize the wind-solar hybrid power generation system. Considering the energy storage battery, micro-turbine, and grid environment, the system's operating cost must be minimized while ensuring the normal operation of all equipment. A grid operation model for the wind-solar-storage hybrid power generation system is constructed; specifically:

[0039] Where C1 represents the operating cost of the microturbine, C2 represents the cost of interacting with the grid, including selling excess power to the grid and purchasing power from the grid when power generation is insufficient, C3 represents the operation and maintenance costs of each piece of equipment in the power generation system, C4 represents the total cost incurred during the start-up and shutdown of the microturbine, and M represents the penalty factor, set to 10. 100 delt NES delt GT delt pe delt GE These correspond to the deviation values ​​of the power generation system under the constraints of energy storage battery capacity, micro-turbine power ramp-up, electric power, and energy storage battery power ramp-up, respectively. The sum of C1, C2, and C3 represents the cost incurred during the operation of the entire system.

[0040] During the operation of a wind-solar-storage complementary power generation system, when there is sufficient sunlight and strong wind, the power generated by the system will far exceed the load demand. At this time, the excess power absorbed by the wind-solar-storage complementary power generation system needs to be transmitted to the grid to avoid oversaturation of the power supply. However, in the case of continuous cloudy and rainy days with no wind, the wind-solar-storage complementary power generation system needs to absorb power from the grid to meet the basic power demand of the load.

[0041] Since the electricity price from the grid varies depending on the peak load, when the price is high during peak periods, the system's own micro-turbine auxiliary power generation can be used instead of purchasing electricity from the grid. In the operation of a wind-solar-storage complementary power generation system, wind and solar power generation are significantly affected by environmental factors. Therefore, it is necessary to adjust the power generation of the micro-turbine or control the energy exchange with the grid to meet the load's electricity demand. To ensure the safe and reliable operation of each unit in the entire wind-solar-storage complementary power generation system, corresponding constraints must be imposed on each component. These constraints include balance constraints and heat exchange / energy storage constraints. The balance constraints are:

[0042] Among them, E grid E represents the power of the power grid. wt E represents the power of the wind turbine. pv E represents the electrical power generated by photovoltaic power generation. Gt Q represents the electrical power of the micro-turbine, and Q represents the electrical power consumed by the load. Among these factors, energy storage batteries have limited capacity and low voltage resistance. Storing large amounts of electrical energy for extended periods will reduce battery life. Furthermore, the allowable state of charge (SOC) of the energy storage battery cannot be too low. Therefore, without considering self-discharge of the energy storage battery, the optimal state is for the battery capacity at the start of system operation to be equal to its capacity at the end of operation. Thus, the energy storage constraints are:

[0043] Where, N ESS N represents the capacity of the energy storage battery at a certain moment. emax N represents the maximum charge capacity of the energy storage battery. emin This indicates the minimum charge capacity of the energy storage battery.

[0044] During the operation of a wind-solar-storage complementary power generation system containing energy storage batteries, the power increase or decrease of some components during system connection or disconnection requires a certain buffer period and should not increase or decrease rapidly, otherwise it will damage the equipment. During the charging and discharging of the energy storage battery, to ensure normal battery operation, the charging and discharging currents cannot be too high; therefore, the charging and discharging power cannot be too large. The selected energy storage battery has a maximum allowable charging and discharging power of 50kW.

[0045] The micro-engine generates electricity by burning fuel to power a coil that rotates and cuts a magnetic field. However, excessive speed or high coil current can pose safety hazards and damage the micro-engine. The micro-engine used has a maximum climbing power of 40 kW.

[0046] like Figure 2 As shown, in actual operation, an improved particle swarm optimization algorithm is used to optimize the objective function and constraints of the grid operation model of the wind-solar-storage complementary power generation system. Specifically: S1, Initialize the particles in the population, assuming the initial velocity and position of each particle; S2, construct the fitness function and penalty term based on the objective function and constraints of the grid operation model of the wind-solar-storage complementary power generation system; S3, calculate the fitness value of each particle, and obtain the global optimal solution and the historical optimal solution by comparing them among all particles; S4, update the velocity and position of each particle. The velocity update is calculated using an asynchronous learning factor, and the position satisfies the constraints. S5. Repeat steps S2-S4 until the preset number of iterations is reached, and then obtain the operating mode with the lowest daily grid operating cost for the wind-solar-storage complementary power generation system.

[0047] By establishing a penalty term, the solutions encountered by particles in the particle swarm during the iteration process are compared with the constraints, reducing the fitness of infeasible solutions and increasing the selection and iteration of feasible solutions. The greater the deviation, the greater the probability of not being selected, and the particle will not search around this particle region.

[0048] In particle swarm optimization (PSO), learning factors are divided into two categories: self-awareness (c1) and social awareness (c2). When c1 is zero and c2 is non-zero, particles are relatively "selfless," but the population lacks diversity, and particles are prone to getting stuck in local optima during the optimization process, unable to escape the iterative search. When c1 is non-zero and c2 is zero, particles are relatively "self-centered," with no information sharing among them. Each particle searches for the optimal solution independently, ultimately reducing the algorithm's efficiency and increasing convergence time. When both c1 and c2 are non-zero, particles can balance both aspects, maintaining their self-awareness while sharing information with other particles, comprehensively covering the search range and reducing convergence time. Generally, c1 and c2 have equal values ​​and their sum does not exceed 4.

[0049] To accelerate the convergence speed of the particle swarm optimization, the learning factor is modified, and an asynchronous learning factor is adopted.

[0050] like Figure 7 As shown, another objective of this invention is to propose a grid optimization operation system for a wind-solar-storage complementary power generation system, comprising: The first module is used to determine the energy conversion efficiency of the micro gas turbine in the wind-solar-storage complementary power generation system under different power conditions, and to determine the operating cost of the micro gas turbine based on the energy conversion efficiency of the micro gas turbine. The second module is used to determine the costs incurred by the interaction between the wind-solar-storage complementary power generation system and the power grid; The third module is used to determine the maintenance costs of the micro-turbine, wind power, photovoltaic power, and energy storage battery in a wind-solar-storage complementary power generation system. The model building module is used to construct a grid operation model for the wind-solar-storage complementary power generation system with the goal of minimizing the daily grid operation cost of the system, taking into account the system and the grid environment. The optimization solution module is used to optimize the objective function and constraints of the grid operation model of the wind-solar-storage complementary power generation system using an improved particle swarm optimization algorithm, so as to obtain the optimal grid operation mode of the wind-solar-storage complementary power generation system.

[0051] like Figure 8As shown, a third objective of the present invention is to provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the grid optimization operation method for the wind-solar-storage complementary power generation system.

[0052] The grid optimization operation method for the wind-solar-storage complementary power generation system includes: Determine the energy conversion efficiency of the micro gas turbine in the wind-solar-storage complementary power generation system under different power conditions, and determine the operating cost of the micro gas turbine based on the energy conversion efficiency of the micro gas turbine; Determine the costs incurred by the interaction between the wind-solar-storage complementary power generation system and the power grid; Determine the maintenance costs of the micro-turbine, wind power, photovoltaic power, and energy storage batteries in a wind-solar-storage complementary power generation system; With the goal of minimizing the daily grid operating cost of the wind-solar-storage complementary power generation system, a grid operation model for the wind-solar-storage complementary power generation system is constructed by combining the system with the grid environment. An improved particle swarm optimization algorithm is used to optimize the objective function and constraints of the grid operation model of the wind-solar-storage complementary power generation system, thereby obtaining the optimal grid operation mode of the wind-solar-storage complementary power generation system.

[0053] A fourth objective of this invention is to provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the grid optimization operation method for the wind-solar-storage complementary power generation system.

[0054] The grid optimization operation method for the wind-solar-storage complementary power generation system includes: Determine the energy conversion efficiency of the micro gas turbine in the wind-solar-storage complementary power generation system under different power conditions, and determine the operating cost of the micro gas turbine based on the energy conversion efficiency of the micro gas turbine; Determine the costs incurred by the interaction between the wind-solar-storage complementary power generation system and the power grid; Determine the maintenance costs of the micro-turbine, wind power, photovoltaic power, and energy storage batteries in a wind-solar-storage complementary power generation system; With the goal of minimizing the daily grid operating cost of the wind-solar-storage complementary power generation system, a grid operation model for the wind-solar-storage complementary power generation system is constructed by combining the system with the grid environment. An improved particle swarm optimization algorithm is used to optimize the objective function and constraints of the grid operation model of the wind-solar-storage complementary power generation system, thereby obtaining the optimal grid operation mode of the wind-solar-storage complementary power generation system.

[0055] Example 1 Taking data collected from a wind-solar-storage hybrid power generation system within a day as an example, the power generation of photovoltaic and wind power collected at different times on a certain day, such as... Figure 3 As shown in Table 1, the system generates its own electrical energy and the electrical energy demanded by the loads within a day, as well as the difference between them.

[0056] Table 1 Power generation and load demand of wind-solar-storage complementary power generation system

[0057] The local power grid's purchase and sale prices for electricity vary depending on the time of day, as shown in Table 2 below: Table 2 Time-of-use electricity purchase and sales prices

[0058] The system utilizes wind power with a rated capacity of 150 kW, photovoltaic power with a rated capacity of 150 kW, and a micro-turbine with a rated capacity of 100 kW. Lead-acid batteries are selected as the energy storage system, with a rated capacity of 150 kWh, a maximum state of charge (SOC) of 95%, a minimum allowable SOC of 15%, and an efficiency of 95% during charging and discharging. The local natural gas price is 3 yuan per cubic meter, and the calorific value of the natural gas is taken as 9.7 kWh / m³. 3 .

[0059] Based on the objective function and constraints of the grid operation model of the wind-solar-storage complementary power generation system, a relevant program was written in MATLAB 2016a. In the program, we set the number of particles in the particle swarm to 500 and the number of iterations to 500. The improved algorithm was used to optimize the grid operation model of the wind-solar-storage complementary power generation system, and the result was obtained: Result=370.55; Result refers to the sum of the micro-turbine operating cost (including fuel cost and start-up and shutdown cost), the cost of system-grid interaction, and equipment operation and maintenance cost.

[0060] Ultimately, the optimal operating power of each device at different time periods was determined. Figure 4 As shown, Figure 4 In the text, GT stands for micro gas turbine, Grid stands for power grid, and ESS stands for energy storage battery. Figure 4 The grid power values ​​shown are mostly negative because the wind-solar-storage complementary power generation system is often in a state where power generation exceeds load consumption. Analysis of actual data shows that a portion of the generated electricity is supplied to the energy storage batteries. When the storage batteries reach saturation, the electricity is sold back to the grid, hence the negative grid input. Furthermore, the micro-turbine is mostly offline in the system, with zero power, indicating that no supplementary power generation from the micro-turbine is needed at this time.

[0061] Without establishing a wind-solar-storage complementary power generation system, the electricity cost to meet the system's load, calculated according to time-of-use pricing, would be 2334.014 yuan to purchase electricity from the grid. After establishing the system, calculations based on daily data show that the cost would be 370.55 yuan. Therefore, it is evident that establishing this system will significantly reduce electricity costs.

[0062] like Figure 5 As shown, if the working hours of the micro-gas engine are adjusted to 6:00 AM to 10:00 PM, the system's operating result under these circumstances is Result=452.7.

[0063] like Figure 6 As shown, if the micro-turbine is kept off the system, meaning it is disconnected from the system, then only wind power, photovoltaic power, and energy storage batteries exchange electricity with the grid. In this case, the algorithm's running time increases significantly. The final result is: Result=429.

[0064] We will compare the three micro-engines under different operating conditions:

[0065] Compared to purchasing electricity directly from the grid, the establishment of a wind-solar-storage complementary power generation system can indeed reduce costs. Furthermore, the addition of a micro gas turbine system during system operation can further reduce operating costs, indicating that the operating scheme designed in this paper can achieve optimization.

[0066] If the energy storage battery is kept in a non-operational state, i.e., disconnected from the system, only wind power, solar power, and the micro-turbine will be connected to the grid. Since the cost of generating electricity from the micro-turbine is higher than purchasing electricity from the grid, based solely on daily power data, if we disregard the presence of the energy storage battery and the micro-turbine, and sell the electricity generated by the wind turbine and solar power, purchasing from the grid when generation is insufficient, the system would generate revenue under this operating mode. However, this only applies when wind and solar power generation is sufficient. If wind and solar power output is insufficient, the micro-turbine may need to be activated. Therefore, it is necessary to consider weather conditions and more comprehensive data.

[0067] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0068] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0069] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0070] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0071] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A grid optimization operation method for a wind-solar-storage complementary power generation system, characterized in that, include: Determine the energy conversion efficiency of the micro gas turbine in the wind-solar-storage complementary power generation system under different power conditions, and determine the operating cost of the micro gas turbine based on the energy conversion efficiency of the micro gas turbine; Determine the costs incurred by the interaction between the wind-solar-storage complementary power generation system and the power grid; Determine the maintenance costs of the micro-turbine, wind power, photovoltaic power, and energy storage batteries in a wind-solar-storage complementary power generation system; With the goal of minimizing the daily grid operating cost of the wind-solar-storage complementary power generation system, a grid operation model for the wind-solar-storage complementary power generation system is constructed by combining the system with the grid environment. An improved particle swarm optimization algorithm is used to optimize the objective function and constraints of the grid operation model of the wind-solar-storage complementary power generation system, thereby obtaining the optimal grid operation mode of the wind-solar-storage complementary power generation system.

2. The grid optimization operation method for a wind-solar-storage complementary power generation system according to claim 1, characterized in that, The determination of the energy conversion efficiency of the micro gas turbine in the wind-solar-storage complementary power generation system under different power conditions, and the determination of the operating cost of the micro gas turbine based on the energy conversion efficiency, include: The energy conversion efficiency is determined based on the operating status of the micro-turbine. When the micro-turbine has not reached a stable output power, the energy conversion efficiency is calculated as follows: in, P gt ( t ) indicates that the micro gas turbine is in t The amount of effort exerted at any given moment. η(t) Indicates energy conversion efficiency; When the output power of the micro-turbine reaches a stable level, the energy conversion efficiency is 0.

27. The operating costs of a micro-turbine are calculated as follows: in, C 1 represents the operating cost of the micro gas turbine. C Indicates the price of natural gas. H Indicates the calorific value of natural gas. P GT (t) This indicates the output of the micro-turbine at time t.

3. The grid optimization operation method for a wind-solar-storage complementary power generation system according to claim 1, characterized in that, The specific calculation method for determining the cost incurred by the interaction between the wind-solar-storage complementary power generation system and the power grid is as follows: in, C 2 represents the cost required to interact with the power grid. k m Indicates the electricity purchase price. P tm This indicates the amount of electricity purchased during interaction with the power grid. k s Indicates the electricity price. P ts This indicates the power sold during interaction with the power grid.

4. The grid optimization operation method for a wind-solar-storage complementary power generation system according to claim 1, characterized in that, The determination of the maintenance costs of the micro-turbine, wind turbine, photovoltaic power generation device, and energy storage battery in the wind-solar-storage complementary power generation system is as follows: in, C 3 represents the operating and maintenance costs of each piece of equipment in the power generation system. k gt This indicates the unit operating cost of a micro gas turbine. k wt This indicates the unit operating cost of a wind turbine. k pv This indicates the unit operating cost of a photovoltaic power generation device. k ESS This indicates the unit operating cost of energy storage batteries. P gt (t) This indicates the output of the micro gas turbine at time t. P wt (t) This indicates the power output of the wind turbine at time t. P pv (t) This indicates the power output of the photovoltaic power generation device at time t. P ESS ( t) represents the output of the energy storage battery at time t, and takes the absolute value.

5. The grid optimization operation method for a wind-solar-storage complementary power generation system according to claim 1, characterized in that, The goal is to minimize the daily operating cost of a wind-solar-storage complementary power generation system. This involves constructing a grid operation model for the system, considering energy storage batteries, micro-turbines, and the grid environment. Specifically: Where C1 represents the operating cost of the microturbine, C2 represents the cost incurred when interacting with the power grid, C3 represents the operation and maintenance cost of all equipment in the power generation system, C4 represents the total cost incurred when starting and stopping the microturbine, M represents the penalty factor, and delt NES delt GT delt pe delt GE These correspond to the deviation values ​​generated during power generation under the constraints of energy storage battery capacity, micro-turbine power ramp-up, electric power, and energy storage battery power ramp-up, respectively.

6. The grid optimization operation method for a wind-solar-storage complementary power generation system according to claim 1, characterized in that, The constraints include balance constraints and heat exchange / energy storage constraints. The balance constraint is: Among them, E grid E represents the grid power. wt E represents the power of the wind turbine. pv E represents the electrical power generated by photovoltaic power generation. Gt Q represents the electrical power of the micro-turbine, and Q represents the electrical power consumed by the load. The energy storage constraint is: Where, N ESS N represents the capacity of the energy storage battery at a certain moment. emax N represents the maximum charge capacity of the energy storage battery. emin This indicates the minimum charge capacity of the energy storage battery.

7. The grid optimization operation method for a wind-solar-storage complementary power generation system according to claim 1, characterized in that, The improved particle swarm optimization algorithm is used to optimize the objective function and constraints of the grid operation model of the wind-solar-storage complementary power generation system. Specifically: S1, Initialize the particles in the population, assuming the initial velocity and position of each particle; S2, construct the fitness function and penalty term based on the objective function and constraints of the grid operation model of the wind-solar-storage complementary power generation system; S3, calculate the fitness value of each particle, and obtain the global optimal solution and the historical optimal solution by comparing them among all particles; S4, update the velocity and position of each particle. The velocity update is calculated using an asynchronous learning factor, and the position satisfies the constraints. S5. Repeat steps S2-S4 until the preset number of iterations is reached to obtain the operating mode with the lowest daily grid operating cost for the wind-solar-storage complementary power generation system.

8. A grid optimization operation system for a wind-solar-storage complementary power generation system, characterized in that, include: The first module is used to determine the energy conversion efficiency of the micro gas turbine in the wind-solar-storage complementary power generation system under different power conditions, and to determine the operating cost of the micro gas turbine based on the energy conversion efficiency of the micro gas turbine. The second module is used to determine the costs incurred by the interaction between the wind-solar-storage complementary power generation system and the power grid; The third module is used to determine the maintenance costs of the micro-turbine, wind power, photovoltaic power, and energy storage battery in a wind-solar-storage complementary power generation system. The model building module is used to construct a grid operation model for the wind-solar-storage complementary power generation system with the goal of minimizing the daily grid operation cost of the system, taking into account the system and the grid environment. The optimization solution module is used to optimize the objective function and constraints of the grid operation model of the wind-solar-storage complementary power generation system using an improved particle swarm optimization algorithm, so as to obtain the optimal grid operation mode of the wind-solar-storage complementary power generation system.

9. An electronic device, characterized in that, The system includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the grid optimization operation method for the wind-solar-storage complementary power generation system according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the grid optimization operation method for the wind-solar-storage complementary power generation system according to any one of claims 1-7.

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