Scheduling method of low-voltage direct-current flexible micro-grid

Abstract

The invention discloses a scheduling method of a low-voltage direct-current flexible micro-grid, and relates to the technical field of power distribution of a power grid. Comprising the following steps: acquiring and analyzing a power grid operation simulation scheme and actual operation state data; evaluating the safety index grade of the power grid according to the current new energy intervention gradient grade; testing the power grid operation risk model framework in a simulated manner after evaluating the safety index level; acquiring risk rule information and current power grid operation data in a power grid test operation stage based on the power grid operation risk model framework; and determining a scheduling scheme of the low-voltage direct-current flexible micro-grid based on the risk rule information and the current grid operation data. According to the scheduling method of the low-voltage direct-current flexible micro-grid, related data and risk rule information can be truly measured, so that related personnel can conveniently carry out maintenance according to specified reasons, the influence after photovoltaic intervention in the power grid is reduced, and an optimized power distribution scheme is obtained through multiple risk measurement and calculation.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid distribution, and in particular to a dispatching method for a low-voltage direct current flexible microgrid. Background Art

[0002] As an important form of distributed energy system, low-voltage DC flexible microgrid has great development potential. With the popularization of renewable energy, the advancement of power electronics technology and the development of intelligent control technology, the application of low-voltage DC microgrid will become more and more extensive, especially in the fields of green buildings, smart cities, energy Internet, etc. Its main energy sources include solar photovoltaic systems, wind power generation systems, micro hydropower generation, fuel cells, etc.; the main distributed energy storage systems are lithium battery energy storage systems and lead-acid battery energy storage systems; power electronic conversion devices include DC-DC converters and DC-AC converters; adapted control systems, through intelligent controllers, microgrids can monitor the operating status of each component in real time, make real-time scheduling and optimization, and ensure the stability and reliability of power supply; adapted load management systems can be used to schedule loads according to real-time needs to ensure that the power grid can adapt to changing needs. Its electrical components and systems are usually in normal operating state, but they may also fail or operate abnormally.

[0003] Among them, power system failure refers to a state in which the equipment cannot work according to the expected indicators, that is, the equipment does not achieve the function it should achieve. The development of the power grid and the progress of society have put forward higher requirements for the operation of the power grid, and it is particularly important to strengthen the diagnosis and processing of power grid faults. However, as the scale of the power system becomes larger and the structure becomes more complex, faults are inevitable. And in the entire power production process, the most common, most dangerous and most serious fault is the short circuit fault. The causes of short circuit faults include aging or mechanical damage of insulation materials of electrical equipment, overvoltage caused by lightning strikes, tower collapse or wire breakage caused by natural disasters, short circuit caused by birds and animals jumping wires, and misoperation by operators, which have brought some new problems to the safe operation of the power system.

[0004] At the same time, with the development of computer technology, communication technology, network technology, etc., the use of more advanced intelligent technology to improve the performance of fault diagnosis systems has important research value and practical significance. There are few studies on the planning methods of photovoltaic access to distribution networks that take into account regional and climatic characteristics, and the uncertainty of photovoltaic output will have a greater impact on the planning of the power grid (low-voltage DC flexible microgrid), especially after large-scale photovoltaic access to the power grid in different regions, the degree of impact is also different. In view of this, the applicant has developed a scheduling method for low-voltage DC flexible microgrids. Summary of the invention

[0005] The present invention provides a scheduling method for a low-voltage DC flexible microgrid, which can truly measure relevant data and risk rule information so that relevant personnel can perform maintenance for designated reasons, thereby reducing the impact of photovoltaic intervention in the power grid, and obtaining an optimized distribution plan after multiple risk calculations.

[0006] The present invention is achieved through the following technical solutions:

[0007] The present invention provides a scheduling method for a low-voltage direct current flexible microgrid, comprising the following steps:

[0008] S1. Obtain and analyze the power grid operation simulation plan and actual operation status data;

[0009] S2. Evaluate the safety index level of the power grid according to the current gradient level of new energy intervention;

[0010] S3. After evaluating the safety index level, simulate the grid operation risk model framework;

[0011] S4. Acquire risk rule information and current grid operation data during the grid test operation phase based on the grid operation risk model framework;

[0012] S5. Determine the dispatching plan of the low-voltage DC flexible microgrid based on the risk rule information and the current grid operation data.

[0013] The scheduling method of the low-voltage DC flexible microgrid provided by the present invention first obtains and analyzes the grid operation simulation plan and the actual operation status data, and evaluates the safety index level of the grid according to the current new energy intervention gradient level. After evaluating the safety index level, the grid operation risk model framework is simulated and tested, and then the risk rule information and the current grid operation data are obtained in the grid operation risk model framework during the grid test operation phase. Finally, the scheduling plan of the low-voltage DC flexible microgrid is determined based on the risk rule information and the current grid operation data. The relevant data and risk rule information can be truly measured to facilitate relevant personnel to perform maintenance for designated reasons, so as to reduce the impact of photovoltaic intervention in the grid, and obtain an optimized distribution plan after multiple risk calculations.

[0014] In an optional implementation of the present application, in the power grid operation simulation scheme, the distribution operation center will take minimizing network losses as the optimization goal.

[0015] In an optional embodiment of the present application, the constraints in the power grid operation simulation scheme include the output of distributed power sources and energy storage systems in the power grid, node voltage constraints, power balance constraints, and power generation resource constraints.

[0016] In an optional implementation of the present application, step S3 also includes the step of optimizing the distribution network based on a power grid dispatch model and a power grid operation risk model framework.

[0017] In an optional implementation of the present application, the grid dispatching model is an integrated charging, swapping and storage microgrid optimization dispatching model based on cooperative game.

[0018] In an optional implementation of the present application, when optimizing the distribution network, a genetic algorithm is used to solve the power grid dispatching model.

[0019] In an optional implementation of the present application, the genetic algorithm is a non-dominated sorting genetic algorithm with an elite strategy.

[0020] In an optional implementation of the present application, the power grid dispatching model is solved in cooperation with a CPLEX solver.

[0021] In an optional implementation of the present application, in step S4, the actual operation status of the power grid is obtained when the power grid is offline to obtain current power grid operation data.

[0022] In an optional implementation of the present application, step S4 also includes the step of: adjusting the grid operation risk model framework in step S3 according to current grid operation data.

[0023] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0024] The scheduling method of the low-voltage DC flexible microgrid provided by the present invention first obtains and analyzes the grid operation simulation plan and the actual operation status data, and evaluates the safety index level of the grid according to the current new energy intervention gradient level. After evaluating the safety index level, the grid operation risk model framework is simulated and tested, and then the risk rule information and the current grid operation data are obtained in the grid operation risk model framework during the grid test operation phase. Finally, the scheduling plan of the low-voltage DC flexible microgrid is determined based on the risk rule information and the current grid operation data. The relevant data and risk rule information can be truly measured to facilitate relevant personnel to perform maintenance for designated reasons, so as to reduce the impact of photovoltaic intervention in the grid, and obtain an optimized distribution plan after multiple risk calculations. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope.

[0026] in:

[0027] Figure 1A schematic flow chart of a scheduling method for a low-voltage DC flexible microgrid provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0028] In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments.

[0029] Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the present application for which protection is sought, but merely represents selected embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0030] It should be noted that the embodiments and features in the embodiments in this application can be combined with each other without conflict. In the following description, a large number of specific details are set forth in order to provide a thorough understanding of the present invention. However, it is obvious to those of ordinary skill in the art that these specific details do not need to be adopted to implement the present invention. In other embodiments, in order to avoid confusing the present invention, known methods are not specifically described.

[0031] Example

[0032] Combination Figure 1 This embodiment provides a scheduling method for a low-voltage DC flexible microgrid, comprising the following steps:

[0033] S1. Obtain and analyze the power grid operation simulation plan and actual operation status data.

[0034] Specifically, in the power grid operation simulation scheme, the distribution operation center will take the minimum network loss as the optimization goal. At the same time, the constraints in the power grid operation simulation scheme include the output of distributed power sources and energy storage systems in the power grid, node voltage constraints, power balance constraints, and power generation resource constraints.

[0035] That is to say, during the simulation process, the distribution operation center will take minimizing network loss as the optimization goal, and optimize the output of controllable distributed power sources and energy storage equipment in the system. At the same time, while meeting the node voltage constraints, power balance constraints and power generation resource constraints, the network loss value of the entire distribution network system is minimized to ensure the economy, safety and stability of photovoltaics after being connected to the distribution network, which is conducive to promoting the implementation of photovoltaic policies.

[0036] S2. Evaluate the safety index level of the power grid based on the current gradient level of new energy intervention.

[0037] S3. After evaluating the safety index level, simulate the grid operation risk model framework.

[0038] Specifically, step S3 also includes the step of optimizing the distribution network based on the power grid dispatch model and the power grid operation risk model framework.

[0039] Among them, the power grid dispatching model is an integrated charging, swapping and storage microgrid optimization dispatching model based on cooperative game.

[0040] When optimizing the distribution network, a genetic algorithm is used to solve the power grid dispatching model.

[0041] In an embodiment, the genetic algorithm is a non-dominated sorting genetic algorithm with an elite strategy.

[0042] Furthermore, the power grid dispatching model is solved by using a CPLEX solver.

[0043] That is, this embodiment uses the non-dominated sorting genetic algorithm (NSGAII) with elite strategy combined with the solver CPLEX to solve the optimization scheduling model of the integrated microgrid of charging, swapping and storage based on cooperative game, converts complex business problems into mathematical programming, and quickly finds the optimal solution. It can distribute electric energy more evenly, reduce imbalance, reduce overload risks, and improve the stability and power quality of the system. It can more effectively manage power distribution, reduce line losses, improve power transmission efficiency, provide a more stable and reliable power supply, focus on giving full play to the regulation and control capabilities of flexible equipment, use flexible equipment such as VSC, and adjust current and voltage in real time to adapt to different loads and power demands, reduce power waste, and improve energy utilization. It helps to reduce power fluctuations and harmonics, improve power quality, and reduce power quality problems. It helps to solve power quality problems at the same time, thereby providing a more stable and high-quality power supply. A relatively short return on investment period can be achieved, which will encourage more people to adopt this transformation plan, thereby promoting the upgrading and sustainable development of the power system.

[0044] Specifically, the power grid dispatch model can be divided into several key components, including power generation dispatch, load dispatch, and transmission network dispatch. Each component has its corresponding mathematical modeling method.

[0045] Generation dispatch, determining the output power of individual generator sets to minimize generation costs and meet power demand and network constraints.

[0046] Load scheduling is the rational distribution of loads in the power grid to ensure that all users can get electricity while avoiding overloading of the power grid.

[0047] Transmission dispatching ensures that electricity can be transmitted from the power generation source to the load center and that the capacity of the transmission network is not overloaded by scheduling the flow of transmission lines.

[0048] Electricity market dispatch,In the electricity market, the dispatch model also needs to consider the market’s price mechanism, auction method, bidding strategy, etc.

[0049] For the economic dispatch model, the output power of the generator set is usually optimized under the conditions of meeting the power demand and various system constraints. The commonly used objective function is to minimize the total power generation cost, and its objective function is to minimize the total power generation cost (sum_{i=1}^{N}C_i(P_i)), where (C_i(P_i)) is the cost function of the generator set (i), (P_i) is the output power of the generator set, and (N) is the total number of generator sets. The constraints are that the sum of the power of all generator sets must be equal to the demand of the power grid, the output power of the generator set must be between its minimum and maximum values, the power generation capacity and demand of the system meet the safety constraints of the power grid, and the power flow of the transmission line must meet its carrying capacity.

[0050] The purpose of the safe dispatch model is to ensure that there are no problems such as system collapse, overload, frequency fluctuation, etc. during the operation of the power grid. At the same time, the safe dispatch model needs to take into account the physical constraints of the power system on the basis of economic dispatch to ensure the stability of the power system. Its objective function is usually the same as that of economic dispatch, but safety constraints need to be added on this basis. The safety constraints include:

[0051] Frequency constraints: the grid frequency must be kept within a certain range to avoid system collapse;

[0052] Voltage constraint: the voltage amplitude must be within the normal range;

[0053] Transmission capacity constraints, the power flow of each transmission line cannot exceed its maximum capacity.

[0054] The generator set regulation range ensures that the generator set operates within its regulation capacity.

[0055] An important concept in economic dispatch and load dispatch is load pricing (LMP, Locational Marginal Pricing). LMP refers to the calculation of the electricity price of each node based on the supply and demand relationship at different locations in the electricity market. The LMP model determines the electricity price of each node in the power grid by considering the power demand of the node, the cost of the generator set, and the limitations of the transmission network.

[0056] Genetic Algorithm (GA) is an optimization algorithm based on the principles of natural selection and genetics. It simulates natural selection, crossover, mutation and other operations in the biological evolution process to solve various optimization problems. The core of genetic algorithm is to find the optimal solution through iterative evolution. Coding is a very critical part of genetic algorithm, which involves how to express the solution of the problem in a form suitable for genetic operations.

[0057] Among them, the basic process of genetic algorithm includes the following steps:

[0058] Initialize the population: Randomly generate an initial population, where each individual (chromosome) represents a possible solution.

[0059] Fitness evaluation: Calculate the fitness value of each individual and evaluate its quality.

[0060] Selection operation: select some individuals to enter the next generation based on their fitness values.

[0061] Crossover operation: Cross the selected individuals to generate new individuals.

[0062] Mutation operation: mutate individuals to increase diversity.

[0063] Replacement operation: Replace the old generation with the new generation individuals and enter the next generation.

[0064] Termination condition: Determine whether the termination condition is met (such as the maximum number of iterations, or finding a good enough solution).

[0065] CPLEX is a high-performance optimization software developed by IBM, which is widely used to solve linear programming (LP), mixed integer programming (MIP), quadratic programming (QP) and other optimization problems. CPLEX provides a variety of algorithms, data structures and tools to help users solve complex optimization problems in business, engineering, economics and other fields.

[0066] The main features of CPLEX are:

[0067] Widely supported optimization problems:

[0068] Linear programming (LP): includes standard linear programming and linear programming problems with constraints.

[0069] Mixed Integer Programming (MIP): Solve optimization problems with mixed integer and continuous variables.

[0070] Quadratic Programming (QP): Optimization problems with quadratic objective functions.

[0071] Nonlinear Programming (NLP): Solve nonlinear constrained optimization problems (requires additional module support).

[0072] Efficient solver:

[0073] CPLEX provides a variety of optimization algorithms, such as the simplex method, interior point method, branch-and-bound method, branch-and-cut method, etc., which can efficiently solve large linear and integer programming problems.

[0074] For integer optimization problems, CPLEX uses efficient heuristic algorithms such as pruning strategies and cutting plane methods to speed up the solution process.

[0075] Easy to integrate:

[0076] CPLEX provides interfaces for multiple programming languages, including C, C++, Java, Python, MATLAB, and .NET, making it easy for users to integrate it into existing applications or models.

[0077] It also supports remote solving, and can perform remote calculations through the CPLEX optimization service interface.

[0078] Powerful problem modeling capabilities:

[0079] CPLEX provides compatibility with modeling languages ​​(such as AMPL and GAMS). Users can easily define optimization problems through these modeling languages ​​and submit them to CPLEX for solving.

[0080] CPLEX also includes an interactive optimization modeling and analysis environment for users to debug and view the solution process.

[0081] Result analysis and post-processing:

[0082] CPLEX can provide detailed solution logs, statistical information, and intermediate results during the optimization process. These features help users understand the solution process and analyze the optimization results.

[0083] A wide range of post-processing options are available to generate reports, graph results, and further analyze the solution.

[0084] Parallel computing and large-scale problem solving:

[0085] CPLEX supports multi-core processing and distributed computing, and can fully utilize modern hardware to accelerate the solution process.

[0086] It can efficiently handle large-scale problems, especially in big data and high-performance computing environments.

[0087] S4. Based on the grid operation risk model framework, risk rule information and current grid operation data are obtained during the grid test operation phase.

[0088] Specifically, in step S4, the actual operation state of the power grid is obtained when the power grid is offline to obtain current power grid operation data.

[0089] Furthermore, step S4 also includes the step of: adjusting the grid operation risk model framework in step S3 according to current grid operation data.

[0090] S5. Determine the dispatching plan of the low-voltage DC flexible microgrid based on the risk rule information and the current grid operation data.

[0091] In summary, the scheduling method for the low-voltage DC flexible microgrid provided in this embodiment first obtains and analyzes the grid operation simulation plan and the actual operation status data, and evaluates the safety index level of the grid according to the current new energy intervention gradient level. After evaluating the safety index level, the grid operation risk model framework is simulated and tested, and then the risk rule information and the current grid operation data are obtained in the grid operation risk model framework during the grid test operation phase. Finally, the scheduling plan of the low-voltage DC flexible microgrid is determined based on the risk rule information and the current grid operation data.

[0092] In summary, the scheduling method of the low-voltage DC flexible microgrid provided in this embodiment can truly measure the relevant data and risk rule information, so that relevant personnel can perform maintenance for designated reasons, reduce the impact of photovoltaic intervention in the power grid, and obtain an optimized distribution plan after multiple risk calculations. It solves the problem that there is little research on the planning method of photovoltaic access distribution network considering regional and climate characteristics in related technologies, and the uncertainty of photovoltaic output will have a greater impact on power grid planning.

[0093] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A dispatching method for a low voltage DC flexible microgrid, characterized in that: The following steps are involved: S1. Obtain and analyze the power grid operation simulation plan and actual operation status data; S2. Evaluate the safety index level of the power grid according to the current gradient level of new energy intervention; S3. After evaluating the safety index level, simulate the grid operation risk model framework; S4. Acquire risk rule information and current grid operation data during the grid test operation phase based on the grid operation risk model framework; S5. Determine the dispatching plan of the low-voltage DC flexible microgrid based on the risk rule information and the current grid operation data.

2. The dispatching method of the low voltage DC flexible microgrid according to claim 1 is characterized in that: In the power grid operation simulation scheme, the distribution operation center will take minimizing network losses as the optimization goal.

3. The dispatching method of the low voltage DC flexible microgrid according to claim 2 is characterized in that: The constraints in the power grid operation simulation scheme include the output of distributed power sources and energy storage systems in the power grid, node voltage constraints, power balance constraints, and power generation resource constraints.

4. The dispatching method of the low voltage DC flexible microgrid according to claim 1 is characterized in that: Step S3 also includes the step of optimizing the distribution network based on the power grid dispatch model and the power grid operation risk model framework.

5. The dispatching method of the low voltage DC flexible microgrid according to claim 4 is characterized in that: The grid dispatching model is a charging, swapping and storage integrated microgrid optimization dispatching model based on cooperative game.

6. The dispatching method of the low voltage DC flexible microgrid according to claim 5 is characterized in that: When optimizing the distribution network, a genetic algorithm is used to solve the power grid dispatching model.

7. The dispatching method of the low voltage DC flexible microgrid according to claim 6 is characterized in that: The genetic algorithm is a non-dominated sorting genetic algorithm with an elite strategy.

8. The dispatching method of the low voltage DC flexible microgrid according to claim 7, characterized in that: When solving the power grid dispatching model, the CPLEX solver is used for solving it.

9. The dispatching method of a low-voltage DC flexible microgrid according to any one of claims 1 to 8, characterized in that: In step S4, the actual operation state of the power grid is acquired in the offline state of the power grid to obtain the current power grid operation data.

10. The dispatching method of the low voltage DC flexible microgrid according to claim 9, characterized in that: Step S4 also includes the step of: adjusting the grid operation risk model framework in step S3 according to current grid operation data.

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