Micro-grid optimization scheduling method

Abstract

The invention discloses a micro-grid optimization scheduling method, which comprises the steps of constructing a micro-grid system comprising an electric vehicle, wind power generation and a basic load, and respectively establishing a scheduling model based on electric vehicle charging time data and charging demand data and a wind power generation mathematical model comprising wind power randomness, wind power-electric vehicle CCHP coordinated energy scheduling is carried out by using a valley searching scheduling strategy, an interruptible scheduling strategy and a variable rate scheduling strategy, the utilization rate of random wind power is increased, the matching degree of supply and demand of electric energy in the microgrid is improved, and the satisfaction degree of an electric vehicle owner is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid dispatching, and in particular to a microgrid optimization dispatching method. Background Art

[0002] Under the triple pressure of energy demand, environmental protection and potential problems of increasingly complex large-scale centralized power supply, countries around the world have taken a series of practical measures. Among them, distributed generation (DG), as one of the technologies with the greatest development potential, helps to improve energy efficiency, promote the use of clean energy and renewable energy, improve energy supply structure and ensure energy supply security.

[0003] In recent years, in order to solve the problem of distributed power access, coordinate the contradiction between large power grid and distributed power, and fully tap the value and benefits that distributed power generation brings to the power grid and users, the concept of microgrid has emerged. Microgrid technology is regarded as an effective way to solve the current energy utilization problem and has become a hot research topic at home and abroad. Microgrid is a controllable system composed of multiple distributed power sources, energy storage devices, local cold and heat loads, and monitoring and protection devices. It has flexible operation mode and dispatchability. It can be connected to the large power grid through a single interface, or it can be stably and autonomously operated in an isolated island with energy storage units.

[0004] Distributed power sources are small modular, decentralized power generation units that are dispersed near power users, with power generation ranging from several kilowatts to tens of megawatts and using advanced information control technology. They are efficient, economical, reliable, and environmentally friendly. Data show that distributed power generation technology, especially power generation technology using renewable energy as primary energy, has developed rapidly. However, the disadvantages of distributed power sources themselves and the negative impact of access to traditional distribution networks have become important factors restricting their large-scale development: the cost of accessing distributed power sources is high and control is difficult; distributed power sources are an uncontrollable source relative to large power grids, so large power grids often deal with distributed power sources in a restricted and isolated manner to reduce their impact on large power grids, which greatly limits the full play of distributed energy efficiency.

[0005] Chinese patent document CN107671414A discloses a "microgrid optimization scheduling method". It includes establishing a microgrid model; constructing a first-stage optimization scheduling model based on the power purchase and sales status of the microgrid to the upstream power grid; using the microgrid operating cost and the amount of new energy discarded as the objective function, and using energy storage, new energy output, demand response and energy interaction between the microgrid and the power grid as constraints to construct a second-stage optimization scheduling model; solving the model to obtain the final microgrid optimization scheduling result. The present invention constructs a microgrid model composed of wind power, photovoltaics, energy storage, transferable loads and interruptible loads, and then establishes a fuzzy set based on KL divergence. Finally, a min-max-min three-layer two-stage distributed robust optimization model is proposed, and an algorithm based on C&CG is used to solve it. The above technical solution does not incorporate distributed power sources into the optimization scheduling, and it is difficult to meet the current microgrid optimization scheduling needs. Summary of the invention

[0006] The present invention mainly solves the technical problem that the original technical solution does not include distributed power sources in the optimization scheduling and is difficult to meet the current microgrid optimization scheduling needs. A microgrid optimization scheduling method is provided, a microgrid system containing electric vehicles, wind power generation and basic loads is constructed, a scheduling model based on electric vehicle charging time data and charging demand data and a wind power generation mathematical model containing wind power randomness are established respectively, and a valley-seeking scheduling strategy, an interruptible scheduling strategy and a variable rate scheduling strategy are used to perform wind power-electric vehicle-CCHP coordinated energy scheduling, increase the utilization rate of random wind power, improve the matching degree of power supply and demand within the microgrid, and improve the satisfaction of electric vehicle owners.

[0007] The above technical problems of the present invention are mainly solved by the following technical solutions: the present invention comprises the following steps: S1 builds a microgrid system containing electric vehicles, wind power generation and basic loads; S2 establishes a scheduling model based on electric vehicle charging time data and charging demand data; S3 establishes a mathematical model of wind power generation including the randomness of wind power; S4 conducts wind power-electric vehicle-CCHP coordinated energy dispatch; S5 uses the charging and discharging behavior of electric vehicles to increase the utilization rate of random wind power.

[0008] The typical structural block diagram of a combined heat and power microgrid including electric vehicles was constructed, and the mathematical model of each micro-source in the microgrid was established. On this basis, the objective function and constraints of the optimal dispatch of the combined heat and power microgrid including electric vehicles were established. The model not only considers the power generation characteristics of each unit in the microgrid, the power purchase cost, and the fuel cost, but also takes into account the constraints of the start and stop, ramp, and output of the micro-source, pollutant emissions, equipment depreciation, and the impact of time-of-use electricity prices. The economic dispatch model problem of the microgrid is a complex, nonlinear, multi-objective optimization problem. Its goal is to maximize the comprehensive benefits of the system by coordinating the output of controllable equipment (CCHP, GHP, EH or EC, etc.) in each period, the power of controllable loads (EVs), and the power exchange at the PCC under the premise of meeting various operating constraints and heat (cold) power load requirements. At the same time, it is necessary to consider the constraints of the microgrid energy dispatch model, which mainly include the start and stop, output, ramp of various micro-sources, and the system's power balance and heat (cold) balance constraints.

[0009] Preferably, the step S2 specifically includes: S2.1 obtains the electric vehicle charging time data and charging demand data set by the user; S2.2 obtaining the number of time periods of the electric vehicle connected to the microgrid and the number of time periods of the electric vehicle leaving the microgrid based on the charging time data of the electric vehicle; S2.3 obtaining the charging power of the electric vehicle based on the number of time periods of the electric vehicle connected to the microgrid and the number of time periods of the electric vehicle leaving the microgrid; S2.4 sets the charging state variables of the electric vehicle, and obtains the constraints and scheduling targets of the electric vehicle charging based on the electric vehicle charging time data and charging demand data; S2.5 builds a scheduling model based on the charging state variables, constraints and scheduling objectives of electric vehicles.

[0010] Preferably, the electric vehicle charging time data in step S2.1 includes: the time point when the electric vehicle is connected to the microgrid and the time point when the electric vehicle leaves the microgrid.

[0011] Preferably, the charging demand data in step S2.1 includes: the battery state of charge of the electric vehicle when it is connected to the microgrid, and the battery state of charge required for the electric vehicle to meet basic travel needs.

[0012] Preferably, the step S2.2 obtains the total load of the grid when the electric vehicle is charged disorderly based on the number of time periods for connecting to the microgrid, the number of time periods for leaving the microgrid and the charging demand data.

[0013] Preferably, the method for obtaining the number of time periods for accessing the microgrid in step S2.2 is as follows: in, represents the time point when the i-th electric vehicle is connected to the microgrid; ΔT represents the duration of a charging scheduling period; The number of time periods that the i-th electric vehicle leaves the microgrid is: in, represents the time point when the i-th electric vehicle is connected to the microgrid; ΔT represents the duration of a charging scheduling period.

[0014] Preferably, the step S2.3 obtains a charging urgency index based on the number of time periods for accessing the microgrid and the number of time periods for leaving the microgrid, and obtains the charging power of the electric vehicle based on the charging urgency index. The charging power of the electric vehicle is: in, Fast charging power for electric vehicles, Normal charging power for electric vehicles.

[0015] Preferably, the method for obtaining the charging urgency index is: in, represents the number of time periods remaining for the i-th electric vehicle to access the power grid; ΔT represents the duration of a charging scheduling time period; P EV Indicates the normal charging power of electric vehicles; η EV Indicates the charging efficiency of electric vehicles; E i It is represented by the charging amount required to meet the basic needs of the i-th electric vehicle.

[0016] Preferably, the scheduling in step S4 includes a valley-seeking scheduling strategy, an interruptible scheduling strategy and a variable rate scheduling strategy, which increases the utilization rate of random wind power, improves the matching degree of power supply and demand within the microgrid, and improves the satisfaction of electric vehicle owners.

[0017] The design of energy coordination and optimization algorithm for microgrid is based on the research of microgrid system model, operation characteristics and energy optimization method. From the perspective of energy coordination and economic optimization operation of microgrid, an improved particle swarm optimization algorithm, variable inertia weight particle swarm optimization algorithm, is proposed to solve the problem. This algorithm is conducive to expanding the search range in the early stage of optimization and strengthening the convergence ability of local areas in the later stage. In order to minimize the total cost in this project, CCHP power generation and heat production has a certain price advantage over purchasing electricity from the external power grid at high electricity prices. At low electricity prices, gas heat pumps, electric heating (cooling) equipment, wind power and external power grid energy supply meet the heat (cooling) and electricity needs; in order to minimize pollutant emissions, microgrids should maximize the use of their own micro-source energy supply and reduce the purchase of electricity from the external power grid; CCHP has a higher efficiency only when it generates electricity and generates heat; the intelligent scheduling charging strategy that coordinates scheduling with other micro-sources in the microgrid can not only achieve peak shaving and valley filling in the microgrid, but also bring certain benefits on the premise of meeting the daily driving needs of car owners.

[0018] The particle swarm algorithm is proposed to be used because it has the following advantages compared with other intelligent algorithms: the algorithm is simple, with fewer parameters, easy to implement, easy to understand and master, and can be widely used in engineering calculations and various related scientific calculations; the PSO algorithm has a fast convergence speed and consumes less computing resources; the final optimization result of the PSO algorithm has low dependence on the initial solution; the PSO algorithm does not rely on the gradient information of the optimization problem, and can solve both continuous problems and discrete problems and mixed integer programming problems. The main disadvantage of the PSO algorithm is that the basic PSO algorithm is prone to "premature" problems, which is the main reason for the low optimization accuracy. In order to balance the global and local search capabilities of the algorithm, we use a PSO algorithm with variable inertia weights for the microgrid optimization scheduling model. On the basic PSO algorithm, an inertia weight that decreases linearly with the number of iterations is used. This algorithm is conducive to expanding the search range in the early stage of optimization and strengthening the convergence ability of local areas in the later stage.

[0019] Preferably, the valley-finding scheduling strategy specifically includes: finding the valley sections of the daytime power supply curve and the evening power load curve respectively, and using the discharge or charging process of the electric vehicle to fill the valley. In this scheduling strategy, the charging and discharging process of the electric vehicle cannot be interrupted, and the charging and discharging rate is set to a fixed value. In order to further improve the utilization rate of excess wind power and the balance of power supply and demand within the microgrid, a variable rate scheduling strategy is proposed. This scheduling strategy is similar to the interruptible scheduling strategy and also adopts the concept of tangent. However, in this scheduling strategy, the charging / discharging rate of the electric vehicle can be changed according to demand. By effectively regulating the charging and discharging strategy of the electric vehicle, the potential of distributed wind power can be greatly exerted, its dynamic random output power fluctuations can be eliminated, and the utilization efficiency of renewable energy in the microgrid can be achieved.

[0020] The beneficial effects of the present invention are as follows: a microgrid system containing electric vehicles, wind power generation and basic loads is constructed, a scheduling model based on electric vehicle charging time data and charging demand data and a wind power generation mathematical model containing wind power randomness are established respectively, a valley-seeking scheduling strategy, an interruptible scheduling strategy and a variable rate scheduling strategy are used to perform wind power-electric vehicle-CCHP coordinated energy scheduling, thereby increasing the utilization rate of random wind power, improving the matching degree of power supply and demand within the microgrid and improving the satisfaction of electric vehicle owners. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is a flow chart of the present invention.

[0022] Figure 2 The present invention discloses a valley-seeking charging and discharging strategy diagram for an electric vehicle.

[0023] Figure 3 It is a schematic diagram of an interruptible charging and discharging strategy for an electric vehicle of the present invention.

[0024] Figure 4 This is a diagram of the experimental results of "synergistic efficiency enhancement" of electric vehicles and distributed wind power sources in a microgrid of the present invention. DETAILED DESCRIPTION

[0025] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the technical solution of the present invention is further described in detail below through embodiments and in combination with the accompanying drawings. It should be understood that the specific implementation method described here is only an optimal embodiment of the present invention, which is only used to explain the present invention and does not limit the scope of protection of the present invention. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0026] Before discussing the exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flow charts. Although the flow charts describe the operations (or steps) as sequential processes, many of the operations (or steps) therein can be implemented in parallel, concurrently, or simultaneously. In addition, the order of the operations can be rearranged. The process can be terminated when its operation is completed, but can also have additional steps not included in the drawings; the process can correspond to a method, function, procedure, subroutine, subprogram, etc.

[0027] First, a load forecasting method based on the granularity of microgrid users is established to break through the coarse-grained load forecasting model of traditional power grids and realize the refined load forecasting of microgrids; an adaptive load forecasting model is constructed to make it flexible and adaptive to various relevant influencing factors of microgrid operation; a verification and evaluation mechanism for load forecasting methods and models is established by estimating the upper limit of load forecasting accuracy; a reliable load forecasting correction algorithm is designed to intervene and adjust the forecasting results, so as to comprehensively improve the accuracy and reliability of microgrid load forecasting.

[0028] Second, formulate a global energy optimization planning strategy for the microgrid that meets the dual constraints of microgrid operation and microgrid users; systematically establish a microgrid energy planning optimization model to determine relevant optimization objectives and constraints; design a heuristic algorithm to solve the multi-dimensional constrained optimization planning problem and seek a quasi-optimal solution for energy optimization planning.

[0029] Finally, the system must establish a dynamic energy relationship model between controllable distributed power sources, energy storage and flexible loads on the user side of the microgrid; determine the scheduling cost function model of distributed power sources, energy storage and flexible loads; propose a strategy for real-time optimal scheduling of energy that meets the multiple constraints of microgrid energy planning and user equipment, determine the optimization objectives and constraints; seek the best method to effectively solve the problem, and design a scheduling algorithm.

[0030] Vigorously developing distributed generation technology can effectively alleviate the problems of energy depletion and environmental pollution. However, the intermittent, diverse and complex characteristics of DG have become important factors restricting its large-scale development. Microgrid combines multiple DGs and becomes a powerful supplement to centralized power supply, effectively improving power supply reliability and energy efficiency. In addition, the concept of low-carbon energy consumption has promoted the rapid development of electric vehicles. However, due to its dynamic characteristics, the integration of EVs into traditional power grids will bring certain technical challenges.

[0031] The technical solution of the present invention is further specifically described below through embodiments and in conjunction with the accompanying drawings.

[0032] Embodiment: A microgrid optimization scheduling method of this embodiment, such as Figure 1 As shown, the following steps are included: S1 builds a microgrid system containing electric vehicles, wind power generation and basic loads; S2 establishes a scheduling model based on electric vehicle charging time data and charging demand data, including: S2.1 obtains the electric vehicle charging time data and charging demand data set by the user. The electric vehicle charging time data includes: the time point when the electric vehicle is connected to the microgrid and the time point when the electric vehicle leaves the microgrid. The charging demand data includes: the battery state of charge when the electric vehicle is connected to the microgrid and the battery state of charge required for the electric vehicle to meet basic travel needs.

[0033] S2.2 Based on the electric vehicle charging time data, the number of time periods of the electric vehicle connected to the microgrid and the number of time periods of the electric vehicle leaving the microgrid are obtained. Based on the number of time periods of the electric vehicle connected to the microgrid, the number of time periods of the electric vehicle leaving the microgrid and the charging demand data, the total load of the power grid when the electric vehicle is charging in an unordered manner is obtained.

[0034] Method for obtaining the number of time periods connected to the microgrid: in, represents the time point when the i-th electric vehicle is connected to the microgrid; ΔT represents the duration of a charging scheduling period; The number of time periods that the i-th electric vehicle leaves the microgrid is: in, Indicates the time point when the i-th electric vehicle is connected to the microgrid; ΔT represents the duration of a charging scheduling time period. S2.3 Obtain the charging power of the electric vehicle based on the number of time periods connected to the microgrid and the number of time periods leaving the microgrid. Obtain the charging urgency index based on the number of time periods connected to the microgrid and the number of time periods leaving the microgrid. The method for obtaining the charging urgency index is: in, represents the number of time periods remaining for the i-th electric vehicle to access the power grid; ΔT represents the duration of a charging scheduling time period; P EV Indicates the normal charging power of electric vehicles; η EV Indicates the charging efficiency of electric vehicles; E i It is represented by the charging amount required to meet the basic needs of the i-th electric vehicle.

[0035] The charging power of the electric vehicle is obtained based on the charging urgency index. The charging power of the electric vehicle is: in, Fast charging power for electric vehicles, Normal charging power for electric vehicles.

[0036] S2.4 sets the charging state variables of the electric vehicle, and obtains the constraints and scheduling targets of the electric vehicle charging based on the electric vehicle charging time data and charging demand data; S2.5 builds a scheduling model based on the charging state variables, constraints and scheduling objectives of electric vehicles.

[0037] S3 establishes a mathematical model of wind power generation including the randomness of wind power; S4 conducts wind power-electric vehicle-CCHP coordinated energy dispatch. The dispatch includes valley-seeking dispatch strategy, interruptible dispatch strategy and variable rate dispatch strategy, which increases the utilization rate of random wind power, improves the matching degree of power supply and demand within the microgrid, and improves the satisfaction of electric vehicle owners.

[0038] The design of energy coordination and optimization algorithm for microgrid is based on the research of microgrid system model, operation characteristics and energy optimization method. From the perspective of energy coordination and economic optimization operation of microgrid, an improved particle swarm optimization algorithm, variable inertia weight particle swarm optimization algorithm, is proposed to solve the problem. This algorithm is conducive to expanding the search range in the early stage of optimization and strengthening the convergence ability of local areas in the later stage. In order to minimize the total cost in this project, CCHP power generation and heat production has a certain price advantage over purchasing electricity from the external power grid at high electricity prices. At low electricity prices, gas heat pumps, electric heating (cooling) equipment, wind power and external power grid energy supply meet the heat (cooling) and electricity needs; in order to minimize pollutant emissions, microgrids should maximize the use of their own micro-source energy supply and reduce the purchase of electricity from the external power grid; CCHP has a higher efficiency only when it generates electricity and generates heat; the intelligent scheduling charging strategy that coordinates scheduling with other micro-sources in the microgrid can not only achieve peak shaving and valley filling in the microgrid, but also bring certain benefits on the premise of meeting the daily driving needs of car owners.

[0039] The particle swarm algorithm is proposed to be used because it has the following advantages compared with other intelligent algorithms: the algorithm is simple, with fewer parameters, easy to implement, easy to understand and master, and can be widely used in engineering calculations and various related scientific calculations; the PSO algorithm has a fast convergence speed and consumes less computing resources; the final optimization result of the PSO algorithm has low dependence on the initial solution; the PSO algorithm does not rely on the gradient information of the optimization problem, and can solve both continuous problems and discrete problems and mixed integer programming problems. The main disadvantage of the PSO algorithm is that the basic PSO algorithm is prone to "premature" problems, which is the main reason for the low optimization accuracy. In order to balance the global and local search capabilities of the algorithm, we use a PSO algorithm with variable inertia weights for the microgrid optimization scheduling model. On the basic PSO algorithm, an inertia weight that decreases linearly with the number of iterations is used. This algorithm is conducive to expanding the search range in the early stage of optimization and strengthening the convergence ability of local areas in the later stage.

[0040] The valley-finding scheduling strategy specifically includes: finding the valley sections of the daytime power supply curve and the nighttime power load curve respectively, and using the discharge or charging process of electric vehicles to fill the valley. In this scheduling strategy, the charging and discharging process of electric vehicles cannot be interrupted, and the charging and discharging rate is set to a fixed value. In order to further improve the utilization rate of excess wind power and the balance of power supply and demand within the microgrid, a variable rate scheduling strategy is proposed. This scheduling strategy is similar to the interruptible scheduling strategy and also adopts the concept of tangent. However, in this scheduling strategy, the charging / discharging rate of electric vehicles can be changed according to demand. By effectively regulating the charging and discharging strategy of electric vehicles, the potential of distributed wind power can be greatly exerted, its dynamic random output power fluctuations can be eliminated, and the utilization efficiency of renewable energy in microgrids can be achieved.

[0041] S5 uses the charging and discharging behavior of electric vehicles to increase the utilization rate of random wind power.

[0042] The typical structure of a combined heat and power microgrid including electric vehicles was constructed and the mathematical model of each micro-source in the microgrid was established. On this basis, the objective function and constraints of the optimal dispatch of the combined heat and power microgrid including electric vehicles were established. The model not only considers the power generation characteristics of each unit in the microgrid, the power purchase cost, and the fuel cost, but also takes into account the constraints of the start and stop, ramp, and output of the micro-source, pollutant emissions, equipment depreciation, and the impact of time-of-use electricity prices. The economic dispatch model problem of the microgrid is a complex, nonlinear, multi-objective optimization problem. Its goal is to maximize the comprehensive benefits of the system by coordinating the output of controllable equipment (CCHP, GHP, EH or EC, etc.) in each period, the power of controllable loads (EVs), and the power exchange at the PCC under the premise of meeting various operating constraints and heat (cold) power load requirements. At the same time, it is necessary to consider the constraints of the microgrid energy dispatch model, which mainly include the start and stop, output, ramp of various micro-sources, and the system's power balance and heat (cold) balance constraints.

[0043] Design of energy coordination and optimization algorithm for microgrids. Based on the above-mentioned research on microgrid system model, operation characteristics and energy optimization methods, this topic is based on the perspective of energy coordination and economic optimization operation of microgrids. It is proposed to use an improved particle swarm optimization algorithm, variable inertia weight particle swarm optimization algorithm, to solve the problem. This algorithm is conducive to expanding the search range in the early stage of optimization and strengthening the convergence ability of local areas in the later stage. In order to minimize the total cost in this topic, CCHP power generation and heat production has a certain price advantage over purchasing electricity from the external power grid at high electricity prices. At low electricity prices, gas heat pumps, electric heating (cooling) equipment, wind power and external power grid energy supply meet the heat (cooling) and electricity needs; in order to minimize pollutant emissions, microgrids should maximize the use of their own micro-source energy supply and reduce the purchase of electricity from the external power grid; CCHP is only efficient when it generates both electricity and heat; the intelligent scheduling charging strategy that coordinates scheduling with other micro-sources in the microgrid can not only achieve peak shaving and valley filling in the microgrid, but also bring certain benefits on the premise of meeting the daily driving needs of car owners.

[0044] The particle swarm algorithm is proposed to be used because it has the following advantages compared with other intelligent algorithms: the algorithm is simple, with fewer parameters, easy to implement, easy to understand and master, and can be widely used in engineering calculations and various related scientific calculations; the PSO algorithm has a fast convergence speed and consumes less computing resources; the final optimization result of the PSO algorithm has low dependence on the initial solution; the PSO algorithm does not rely on the gradient information of the optimization problem, and can solve both continuous problems and discrete problems and mixed integer programming problems. The main disadvantage of the PSO algorithm is that the basic PSO algorithm is prone to "premature" problems, which is the main reason for the low optimization accuracy. In order to balance the global and local search capabilities of the algorithm, a PSO algorithm with variable inertia weights is used for the microgrid optimization scheduling model. On the basic PSO algorithm, an inertia weight that decreases linearly with the number of iterations is used. This algorithm is conducive to expanding the search range in the early stage of optimization and strengthening the convergence ability of local areas in the later stage.

[0045] Firstly, a mathematical model of the time and daily driving distance of electric vehicles connected to the grid is established according to the travel habits and driving characteristics of automobile users, and a mathematical model of wind power generation with wind power stochasticity is established by using Latin hypercube sampling technology and synchronous back-generation scenario reduction technology. On this basis, three scheduling strategies are proposed: valley-seeking, interruptible and variable rate scheduling strategies, whose goals are to increase the utilization rate of random wind power, improve the matching degree of power supply and demand within the microgrid, and improve the satisfaction of electric vehicle owners. In addition, in view of the bidirectional mobility of electric vehicle energy, an economic scheduling strategy and model of a combined heat and power microgrid including electric vehicles are proposed. The model not only considers the power generation characteristics of each unit in the microgrid, but also takes into account their constraints such as start-up and shutdown, ramp climbing, output and the influence of time-of-use electricity prices. The heat (cold) power loads of a specific microgrid on typical days in different seasons are selected, and the particle swarm optimization algorithm with variable inertia weights is used to give the optimal output combination of each micro-source under the two target values ​​of minimum total cost and minimum pollutant emission; in addition, the economic efficiency of different charging strategies for electric vehicles is analyzed in detail. According to the calculation results, the economic operation characteristics of the combined heat and power microgrid including electric vehicles under different target values ​​are summarized, which shows the rationality of the model and that the adoption of appropriate electric vehicle charging strategy can not only achieve peak shaving and valley filling of electrical load, but also bring considerable benefits to electric vehicle owners.

[0046] Three energy scheduling methods with different focuses: The first one: load control model based on DG. Users with their own power sources such as photovoltaic power generation can give priority to using their own power generation, without having to purchase electricity from the power grid and then pay for electricity. Due to the intermittent power generation and large fluctuations in energy supply of distributed power sources (DGs), excess power will cause energy waste, and insufficient power cannot meet load demand. Therefore, it is crucial to make full use of the power resources provided by DGs and establish a dynamic balance between power supply and user demand. Flexible load allows smart meters to independently choose power consumption time according to electricity prices in different time periods, control load consumption, and thus establish a load balancing mechanism. Depending on the different objects of action, there are different energy scheduling optimization goals: the user side focuses on saving electricity costs while meeting user needs, and the grid side seeks to reduce the peak-valley power difference.

[0047] The second type: RTP-based load dispatch model. By executing the DG-based load control model, DG energy can be used to the maximum extent without energy storage. However, when the overall power consumption is greater than the power supply, no matter how the flexible load power consumption period is arranged at some times, there will always be a situation where supply is lower than demand. In this case, the model can no longer continue to allocate the flexible load power consumption arrangement. However, since the home hybrid power supply system is connected to the large power grid, there are real-time changes in electricity price information. How to use RTP as an adjustment lever based on the real-time electricity price information of the power market to shift the working hours of part of the load to reduce the cost of purchasing electricity from the power grid is the key issue that needs to be solved here. It should be noted that the load dispatch strategy based on RTP does not simply migrate the load to the time when the electricity price is lower. Since the amount of electricity purchased at different times is a function related to time, and the real-time electricity price is related to time, the electricity demand is related to the real-time electricity price. Multiplying the real-time electricity price factor makes the entire target planning no longer a linear equation, nor is it just a single search for the overall balance of supply and demand. Instead, it uses price leverage to correct the supply and demand situation at low electricity prices while giving priority to meeting the supply and demand balance when the electricity price is higher. Giving priority to meeting the supply and demand matching at high electricity prices and using the excess electricity in other time periods to make up for the current demand can reduce the load demand for power supply from the power grid at high electricity prices, reduce the cost of electricity, and better reflect economic benefits. It can be seen that the load dispatch strategy based on RTP seeks the overall optimization of the product of the amount of electricity purchased and the electricity price.

[0048] The third type: energy optimization scheduling model based on energy storage equipment. With the goal of matching DG power generation, the load power consumption curve is adjusted through real-time electricity prices. On the basis of meeting the supply and demand balance as much as possible [i], the excess power is stored by energy storage devices such as batteries, and the insufficient power is discharged by lithium batteries for a short period of time. If it still cannot meet the user's power demand, it will purchase electricity from the smart grid at low electricity prices to form an energy scheduling plan. In this energy scheduling optimization process, electric vehicles are used as energy storage devices in the power grid system. The on-board energy storage device is used to exchange energy with the power grid. An optimization plan including energy storage factors is established to perform multi-objective dynamic planning scheduling of the microgrid. That is, after fully utilizing the power provided by DG power generation, a load migration strategy is adopted. Finally, a segmented judgment method is used to search for the optimal "DG-battery-load" energy optimization mode. Through the charging and discharging actions of the energy storage device, the DG power supply is dynamically adjusted to match the planned load power demand, thereby maximizing the DG utilization rate.

[0049] A person of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium, and the storage medium may include: a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk, etc.

[0050] The specific embodiments described herein are merely examples of the spirit of the present invention. The above embodiments only express several implementation methods of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that technicians in the technical field to which the present invention belongs can make various modifications or supplements to the described specific embodiments or replace them in a similar manner, but will not deviate from the spirit of the present invention or exceed the scope defined in the attached claims. For ordinary technicians in this field, multiple deformations and improvements can also be made without departing from the concept of the present invention. Therefore, the scope of protection of the patent of the present invention shall be based on the attached claims.

Claims

1. A microgrid optimization scheduling method, characterized in that: The following steps are involved: S1 builds a microgrid system containing electric vehicles, wind power generation and basic loads; S2 establishes a scheduling model based on electric vehicle charging time data and charging demand data; S3 establishes a mathematical model of wind power generation including the randomness of wind power; S4 conducts wind power-electric vehicle-CCHP coordinated energy dispatch; S5 uses the charging and discharging behavior of electric vehicles to increase the utilization rate of random wind power.

2. A microgrid optimization scheduling method according to claim 1, characterized in that: The step S2 specifically includes: S2.1 obtains the electric vehicle charging time data and charging demand data set by the user; S2.2 obtains the number of time periods during which the electric vehicle is connected to the microgrid and the number of time periods during which the electric vehicle is out of the microgrid based on the electric vehicle charging time data; S2.3 obtaining the charging power of the electric vehicle based on the number of time periods for connecting to the microgrid and the number of time periods for leaving the microgrid; S2.4 sets the charging state variables of the electric vehicle, and obtains the constraints and scheduling targets of the electric vehicle charging based on the electric vehicle charging time data and charging demand data; S2.5 builds a scheduling model based on the charging state variables, constraints and scheduling objectives of electric vehicles.

3. A microgrid optimization scheduling method according to claim 2, characterized in that: The electric vehicle charging time data in step S2.1 includes: the time point when the electric vehicle is connected to the microgrid and the time point when the electric vehicle leaves the microgrid.

4. A microgrid optimization scheduling method according to claim 2, characterized in that: The charging demand data in step S2.1 includes: the battery state of charge of the electric vehicle when it is connected to the microgrid, and the battery state of charge required for the electric vehicle to meet basic travel needs.

5. A microgrid optimization scheduling method according to claim 2, characterized in that: The step S2.2 obtains the total load of the power grid when the electric vehicle is charged in an unordered manner based on the number of time periods for connecting to the microgrid, the number of time periods for leaving the microgrid and the charging demand data.

6. A microgrid optimization scheduling method according to claim 2 or 5, characterized in that: The method for obtaining the number of time periods for accessing the microgrid in step S2.2 is as follows: in, represents the time point when the i-th electric vehicle is connected to the microgrid; ΔT represents the duration of a charging scheduling period; The number of time periods that the i-th electric vehicle leaves the microgrid is: in, represents the time point when the i-th electric vehicle is connected to the microgrid; ΔT represents the duration of a charging scheduling period.

7. A microgrid optimization scheduling method according to claim 2, characterized in that: The step S2.3 obtains a charging urgency index based on the number of time periods for accessing the microgrid and the number of time periods for leaving the microgrid, and obtains the charging power of the electric vehicle based on the charging urgency index. The charging power of the electric vehicle is: in, Fast charging power for electric vehicles, Normal charging power for electric vehicles.

8. A microgrid optimization scheduling method according to claim 7, characterized in that: The method for obtaining the charging urgency index is: in, represents the number of time periods remaining for the i-th electric vehicle to access the power grid; ΔT represents the duration of a charging scheduling time period; P EV Indicates the normal charging power of electric vehicles; η EV Indicates the charging efficiency of electric vehicles; E i It is represented by the charging amount required to meet the basic needs of the i-th electric vehicle.

9. A microgrid optimization scheduling method according to claim 1, characterized in that: The scheduling in step S4 includes valley-seeking scheduling strategy, interruptible scheduling strategy and variable rate scheduling strategy, which increases the utilization rate of random wind power, improves the matching degree of power supply and demand within the microgrid, and improves the satisfaction of electric vehicle owners.

10. A microgrid optimization scheduling method according to claim 9, characterized in that: The valley-finding scheduling strategy specifically includes: finding the valley sections of the daytime power supply curve and the evening power load curve respectively, and using the discharge or charging process of electric vehicles to fill the valleys. In this scheduling strategy, the charging and discharging process of electric vehicles cannot be interrupted, and the charging and discharging rate is set to a fixed value.

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