Multi-objective optimization distributed power generation energy management method and system

Abstract

The invention relates to the technical field of micro-grid energy management, in particular to a multi-objective optimization distributed power generation energy management method and system. Comprising the steps that operation data of a micro-grid system are obtained, and the system comprises wind power generation, solar power generation, a micro turbine, a diesel generator, a battery energy storage system and a fuel cell; according to the operation data, defining a microgrid operation cost function including a main power grid cost, a fuel cost, a renewable energy source distribution cost, a greenhouse gas emission cost, a demand response excitation cost and an actual power loss cost; and carrying out multi-objective optimization on the micro-grid system by adopting a golden litsea optimization algorithm so as to minimize the operation cost and meet the constraint conditions of power balance, power generation capacity, charging and discharging and energy storage state at the same time. According to the method, through combination of multi-dimensional cost evaluation and an intelligent optimization algorithm, the problem that the efficiency is low when a traditional method is used for processing a multi-target optimization problem under a complex constraint condition is effectively solved.

Description

Technical Field

[0001] The present invention relates to the technical field of microgrid energy management, and in particular to a multi-objective optimized distributed power generation energy management method and system. Background Art

[0002] As global energy demand continues to grow and environmental issues become increasingly prominent, microgrids, as a smart grid solution that integrates distributed generation, energy storage systems, and demand response mechanisms, are gaining more and more attention. Microgrids can effectively integrate renewable energy resources such as solar and wind energy, improve energy efficiency, reduce dependence on traditional fossil energy, and enhance the reliability and flexibility of the power grid. However, due to the intermittent and unpredictable nature of renewable energy, microgrid energy management faces many challenges.

[0003] Energy management of microgrids needs to consider not only economic benefits, but also environmental impact and system stability. For example, demand response strategies can effectively reduce the peak load of the power grid and improve energy efficiency, but how to reasonably dispatch demand response resources to maximize cost-effectiveness and system stability is a complex issue. In addition, battery energy storage systems play an important role in microgrids, and their charging and discharging strategies directly affect the operating cost and reliability of microgrids. Therefore, developing an optimization method that can effectively reduce operating costs and improve system efficiency and stability is of great significance for the sustainable development of microgrids. In existing research, although a variety of optimization algorithms have been proposed, most algorithms still have shortcomings in efficiency and effectiveness when dealing with large-scale, multi-objective, and complex constrained energy management problems. For example, how to maximize the utilization of renewable energy, minimize operating costs, and take into account environmental benefits while ensuring system stability is an urgent problem to be solved. In addition, with the continuous development of the power market, how to adapt to market changes and dynamically adjust energy management strategies are also issues that need to be considered in the development of microgrids. Summary of the invention

[0004] In view of the problems existing in the prior art, the present invention is proposed.

[0005] Therefore, the problem to be solved by the present invention is how to solve the demand response strategy that can effectively reduce the peak load of the power grid and improve energy utilization efficiency, but how to reasonably dispatch demand response resources to maximize cost-effectiveness and system stability is a complex problem.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, an embodiment of the present invention provides a multi-objective optimized distributed generation energy management method, which includes obtaining operation data of a microgrid system, wherein the microgrid system includes wind power generation, solar power generation, micro turbines, diesel generators, battery energy storage systems, and fuel cells;

[0008] defining a microgrid operation cost function according to the operation data, wherein the cost function includes main grid cost, fuel cost, renewable energy distribution cost, greenhouse gas emission cost, demand response incentive cost and actual power loss cost;

[0009] The golden jackal optimization algorithm is used to perform multi-objective optimization of the microgrid system to minimize the operating cost while satisfying the constraints of power balance, generation capacity, charging and discharging, and energy storage status.

[0010] As a preferred solution of the multi-objective optimization distributed power generation energy management method described in the present invention, wherein: in the microgrid operation cost function: the main grid cost is calculated based on the main grid electricity price and the exchange power, when the exchange power is a negative value, it means selling electricity to the grid, and when it is a positive value, it means purchasing electricity from the grid; the fuel cost includes the fuel consumption cost, regulation cost and startup cost of the generator; the renewable energy distribution cost is the cost related to the power output of renewable energy; the greenhouse gas emission cost includes the emission cost of the exchange between distributed power generation sources and the main grid; the demand response incentive cost is the cost calculated based on the unit electricity incentive amount; the actual power loss cost is the loss cost related to time and total power.

[0011] As a preferred solution of the multi-objective optimization distributed power generation energy management method described in the present invention, the golden jackal optimization algorithm includes the following steps: initializing a group of golden jackals, each golden jackal represents a potential solution; evaluating the fitness function of each golden jackal; updating the position of the solution by simulating the hunting behavior of the golden jackal, including searching, surrounding and capturing prey; terminating the algorithm when the stopping condition is met, and the stopping condition is reaching a preset maximum number of iterations.

[0012] As a preferred solution of the multi-objective optimization distributed power generation energy management method described in the present invention, the hunting behavior of the golden jackal includes: the male golden jackal leads and the female golden jackal follows to search; the position of the golden jackal is updated based on the escape energy and random vector of the prey; and the final solution position is obtained by calculating the positions of the male and female golden jackals.

[0013] As a preferred scheme of the multi-objective optimization distributed power generation energy management method described in the present invention, the power balance constraint requires that the sum of the total load demand of the microgrid, demand response power, transmission loss power and charging power of the energy storage system is equal to the sum of the exchange power between the microgrid and the main grid, generator power, wind power, photovoltaic power, turbine power and fuel cell power.

[0014] As a preferred solution of the multi-objective optimization distributed power generation energy management method described in the present invention, the power generation capacity constraint requires that the actual output power of each power generation unit must be within the corresponding minimum output power and maximum output power limit range.

[0015] As a preferred solution of the multi-objective optimization distributed power generation energy management method described in the present invention, the charging and discharging constraints and energy storage state constraints require that the charging power and discharging power of the battery energy storage system shall not exceed the maximum charging and discharging power limit; the state quantity of the battery energy storage unit must be within its minimum and maximum limit range, and is related to the charging and discharging efficiency, charging and discharging power and capacity.

[0016] In a second aspect, an embodiment of the present invention provides a distributed power generation energy management system for multi-objective optimization, which includes a data acquisition module for acquiring operation data of a microgrid system, wherein the microgrid system includes wind power generation, solar power generation, micro turbines, diesel generators, battery energy storage systems, and fuel cells;

[0017] A cost function definition module, which defines a microgrid operation cost function according to the operation data, wherein the cost function includes main grid cost, fuel cost, renewable energy distribution cost, greenhouse gas emission cost, demand response incentive cost and actual power loss cost;

[0018] The optimization execution module uses the golden jackal optimization algorithm to perform multi-objective optimization of the microgrid system to minimize the operating cost while meeting the constraints of power balance, generation capacity, charging and discharging, and energy storage status.

[0019] In a third aspect, an embodiment of the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program instructions are executed by the processor, the steps of the multi-objective optimization distributed power generation energy management method as described in the first aspect of the present invention are implemented.

[0020] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program instructions are executed by a processor, the steps of the multi-objective optimization distributed power generation energy management method as described in the first aspect of the present invention are implemented.

[0021] The beneficial effects of the present invention are as follows: by adopting the golden jackal optimization algorithm, the method can effectively solve the multi-objective optimization problem in the microgrid and minimize the operating cost. Secondly, while considering the power balance, power generation capacity, consumer load and charging and discharging constraints of the energy storage unit, the algorithm can efficiently dispatch the hybrid energy and battery energy storage system to ensure the stable operation of the microgrid. Compared with traditional optimization algorithms, the golden jackal optimization algorithm has obvious advantages in computational efficiency, convergence speed and solution quality, which enables it to quickly find the optimal solution and provides a new optimization tool for microgrid operators. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0023] Figure 1 Flowchart of distributed generation energy management method for multi-objective optimization;

[0024] Figure 2 Computer equipment diagram of distributed generation energy management approach for multi-objective optimization. DETAILED DESCRIPTION

[0025] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0026] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0027] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it a separate or selective embodiment that is mutually exclusive with other embodiments.

[0028] Example 1

[0029] Reference Figure 1-2 , which is the first embodiment of the present invention, and provides a multi-objective optimization distributed generation energy management method, including:

[0030] S100: Acquire the operation data of the microgrid system, which includes wind power generation, solar power generation, micro turbines, diesel generators, battery energy storage systems and fuel cells;

[0031] In the embodiment of the present application, the operation data of the microgrid system includes power generation data, energy storage data, load data and scheduling data. Specifically, the power generation data includes the real-time power output, power generation efficiency, fuel consumption and other information of each power generation unit; the energy storage data includes the battery charging and discharging status, capacity, efficiency and other parameters; the load data includes the user's power demand, load curve and other information; the scheduling data includes power exchange with the main power grid, demand response information, etc.

[0032] In an optional embodiment, the operation data collection method of the microgrid system can be obtained through smart meters, power quality analyzers, energy management systems (EMS) and other equipment. For example, for wind power generators, data such as wind speed, rotation speed, and power can be collected through the SCADA system; for photovoltaic systems, information such as light intensity, temperature, and output voltage and current can be collected; for energy storage systems, parameters such as the state of charge (SOC) and state of health (SOH) of the battery pack can be monitored in real time.

[0033] In an optional embodiment, the frequency and accuracy of data collection can be adjusted according to the characteristics and management requirements of different devices. For example, for wind and solar power generation with large fluctuations, a higher sampling frequency (such as 1 second / time) can be used; for relatively stable diesel generators, a lower sampling frequency (such as 1 minute / time) can be used. At the same time, for different types of data, the system will set different data quality requirements to ensure that the collected data can accurately reflect the operating status of the microgrid.

[0034] It should be noted that this application adopts a distributed data acquisition architecture, and each power generation unit and energy storage unit is equipped with a local data acquisition and processing unit. These units are not only responsible for basic data acquisition, but also have preliminary data processing capabilities, and can perform operations such as data preprocessing and anomaly detection. The system uses standardized communication protocols (such as Modbus, IEC61850, etc.) to ensure that data between different devices can be seamlessly connected and transmitted.

[0035] In the embodiment of the present application, the operation data of the microgrid system includes power generation data, energy storage data, load data and dispatch data. Specifically, the power generation data can be data such as wind turbine speed, output power, power generation efficiency, etc., the energy storage data can be data such as battery capacity, charge and discharge status, number of cycles, etc., the load data can be data such as power load, power factor, power quality, etc., and the dispatch data can be data such as electricity price information, operation status, dispatch instructions, etc.

[0036] In an optional embodiment, the power generation data, energy storage data, load data and dispatch data can be any combination of data acquired by an energy management system (EMS), a supervisory control and data acquisition system (SCADA), a distribution automation system (DAS), and a power quality monitoring system. For example, when the power generation data is real-time data acquired by a wind turbine SCADA system, the energy storage data can be operating parameters acquired by a battery management system (BMS).

[0037] In an optional embodiment, power generation data, energy storage data, load data, and scheduling data can also add or reduce other types of data according to the needs of different scenarios. For example, when more refined energy management is required, environmental data (i.e., the fifth type of data) can be added, including meteorological parameters such as temperature, humidity, and light, so as to further improve the accuracy of power generation forecasts and the rationality of scheduling. In addition, considering the impact of the diversity of data acquisition equipment on data quality, an adaptive data calibration algorithm can also be introduced to dynamically correct the collected operating data to ensure that high-quality system operation data can be obtained under various working conditions.

[0038] In an optional embodiment, if the scheduling data is not needed, the scheduling data can be omitted, thereby reducing the workload of data collection and processing. However, in this application, in order to ensure the comprehensiveness of management, such data is still retained.

[0039] It should be noted that the acquisition of the above-mentioned operation data can fully cover all aspects of the microgrid system, including but not limited to the power generation side, energy storage side, load side and dispatching side, so as to ensure the accuracy and comprehensiveness of the optimization results. At the same time, by introducing multiple monitoring systems and data sources, such as EMS, SCADA, DAS and BMS, the advantages of various types of data can be fully utilized, complement each other, and improve the efficiency and accuracy of energy management. In addition, according to the needs of different scenarios, the type and quantity of data can be flexibly adjusted to meet the needs of actual operation management. This multi-source data fusion strategy not only improves the flexibility and adaptability of the management system, but also provides a rich data source for subsequent intelligent optimization.

[0040] S200: defining a microgrid operation cost function according to the operation data, the cost function including main grid cost, fuel cost, renewable energy distribution cost, greenhouse gas emission cost, demand response incentive cost and actual power loss cost;

[0041] In the embodiment of the present application, the microgrid operation cost function specifically includes: the main grid cost may be peak and valley electricity prices, electricity purchase and sales, access fees and other fees, the fuel cost may be fuel unit price, consumption, conversion efficiency and other costs, the renewable energy distribution cost may be equipment depreciation, maintenance costs, adjustment costs and other expenditures, the greenhouse gas emission cost may be carbon emission rights, environmental protection taxes and fees, emission reduction subsidies and other fees, the demand response incentive cost may be peak shaving and valley filling subsidies, peak shaving service fees, response rewards and other fees, the actual power loss cost may be line loss, transformer loss, conversion loss and other costs.

[0042] In an optional embodiment, different types of costs can be combined and calculated according to actual operating requirements. For example, when the system operates in grid-connected mode, the main grid cost and demand response incentive cost play a leading role; when the system operates in off-grid mode, fuel cost and renewable energy distribution cost are more important.

[0043] In an optional embodiment, the calculation method of each cost can also be adjusted according to different optimization goals. For example, when more emphasis is placed on economic benefits, economic benefit evaluation indicators (i.e., the seventh type of cost) can be added, including financial indicators such as return on investment and net present value, so as to further optimize the economic performance of the system. In addition, considering the volatility of energy market prices, a dynamic pricing model can be introduced to adjust various costs in real time to ensure optimal cost control in different market environments.

[0044] In an optional embodiment, if the system does not participate in the demand response project, the demand response incentive cost can be omitted. However, in this application, in order to fully utilize the electricity market mechanism, such costs are still considered.

[0045] It should be noted that the construction of the above cost function can fully reflect all kinds of expenditures in microgrid operation, including but not limited to energy procurement, equipment operation and maintenance, environmental protection and market participation, so as to ensure the rationality and integrity of the optimization objectives. At the same time, by introducing a multi-dimensional cost evaluation system, it is possible to fully balance economic benefits, environmental benefits and social benefits, and improve the comprehensive benefits of system operation. In addition, according to the needs of different operation strategies, the weights and calculation methods of cost items can be flexibly adjusted to meet the needs of actual operations. This multi-objective cost optimization strategy not only improves the scientificity and adaptability of management decisions, but also provides a clear goal orientation for subsequent optimization scheduling.

[0046] S201: In the microgrid operation cost function: the main grid cost is calculated based on the main grid electricity price and the exchange power. When the exchange power is negative, it means selling electricity to the grid, and when it is positive, it means purchasing electricity from the grid; the fuel cost includes the fuel consumption cost, regulation cost and startup cost of the generator; the renewable energy distribution cost is the cost related to the power output of renewable energy; the greenhouse gas emission cost includes the emission cost of the exchange between distributed power generation sources and the main grid; the demand response incentive cost is the cost calculated based on the unit electricity incentive amount; the actual power loss cost is the loss cost related to time and total power.

[0047] S300: The Golden Jackal optimization algorithm is used to perform multi-objective optimization of the microgrid system to minimize operating costs while meeting the constraints of power balance, generation capacity, charging and discharging, and energy storage status.

[0048] In the embodiment of the present application, the optimization process of the golden jackal optimization algorithm specifically includes: the initialization process can be the preparatory work such as population size setting, search space definition, parameter configuration, etc., the optimization iteration process can be the calculation steps such as position update, fitness evaluation, and optimal solution selection, and the convergence judgment process can be the completion work such as termination condition checking, result verification, and solution output.

[0049] In an optional embodiment, different strategies can be used in different stages of the optimization process. For example, in the initial search stage, the algorithm tends to explore globally and find potential high-quality solutions through a larger search step; in the later convergence stage, the algorithm pays more attention to local optimization and obtains the optimal solution through fine position adjustment.

[0050] In an optional embodiment, the execution mode of the algorithm can also be adjusted according to different computing resources. For example, when a faster optimization speed is required, a parallel computing mechanism (i.e., a parallel optimization strategy) can be introduced to use a multi-core processor to simultaneously calculate multiple solutions, thereby improving the execution efficiency of the algorithm. In addition, considering the complexity of the optimization process, an adaptive control mechanism can also be introduced to dynamically adjust the algorithm parameters to ensure that good search performance can be maintained at different optimization stages.

[0051] In an optional embodiment, if the system is small or the optimization accuracy requirement is not high, the execution process of the algorithm can be simplified. However, in this application, in order to obtain the optimal scheduling solution, the complete optimization process is still used.

[0052] It should be noted that the implementation of the above optimization algorithm can fully consider various constraints of microgrid operation, including but not limited to power balance, equipment capacity, operating status and system stability, so as to ensure the feasibility and practicality of the optimization results. At the same time, by introducing multi-level optimization strategies, the computational efficiency and optimization accuracy can be fully balanced to improve the practical value of the algorithm. In addition, according to the needs of systems of different scales, the parameters and processes of the algorithm can be flexibly adjusted to meet the requirements of practical applications. This intelligent optimization strategy not only improves the quality and reliability of the dispatching scheme, but also provides technical support for the efficient operation of the microgrid.

[0053] S301: The golden jackal optimization algorithm includes the following steps: initialize a group of golden jackals, each golden jackal represents a potential solution; evaluate the fitness function of each golden jackal; update the location of the solution by simulating the hunting behavior of the golden jackal, including searching, surrounding and capturing prey; end the algorithm when the stopping condition is met, and the stopping condition is reaching the preset maximum number of iterations.

[0054] S302: The hunting behavior of the golden jackal includes: the male golden jackal leads and the female golden jackal follows to search; the position of the golden jackal is updated based on the escape energy and random vector of the prey; and the final solution position is obtained by calculating the positions of the male and female golden jackals.

[0055] S303: Power balance constraint requirement: The sum of the total load demand of the microgrid, demand response power, transmission loss power and energy storage system charging power is equal to the sum of the microgrid and main grid exchange power, generator power, wind power, photovoltaic power, turbine power and fuel cell power.

[0056] S304: Power generation capacity constraint requirement: The actual output power of each power generation unit must be within the corresponding minimum output power and maximum output power limit range.

[0057] S305: Charge and discharge constraints and energy storage state constraint requirements: The charging power and discharging power of the battery energy storage system shall not exceed the maximum charge and discharge power limit; the state quantity of the battery energy storage unit must be within its minimum and maximum limit range and is related to the charge and discharge efficiency, charge and discharge power and capacity.

[0058] Furthermore, this embodiment also provides a multi-objective optimization distributed power generation energy management system, including:

[0059] A data acquisition module, which acquires operating data of a microgrid system, wherein the microgrid system includes wind power generation, solar power generation, micro turbines, diesel generators, battery energy storage systems, and fuel cells;

[0060] A cost function definition module, which defines a microgrid operation cost function according to the operation data, wherein the cost function includes main grid cost, fuel cost, renewable energy distribution cost, greenhouse gas emission cost, demand response incentive cost and actual power loss cost;

[0061] The optimization execution module uses the golden jackal optimization algorithm to perform multi-objective optimization of the microgrid system to minimize the operating cost while meeting the constraints of power balance, generation capacity, charging and discharging, and energy storage status.

[0062] In summary, the present invention realizes all-round monitoring of the microgrid system by setting up a multi-dimensional data collection mechanism, including real-time operation data of six types of power generation equipment such as wind power generation and solar power generation. This step overcomes the problem of incomplete information caused by the traditional single data source, enables the system to grasp the operating status of various types of equipment in a timely manner, and provides a complete and reliable data basis for subsequent optimization decisions.

[0063] By constructing a multi-dimensional cost function that includes six types of costs, including main grid costs and fuel costs, a comprehensive evaluation of the operating costs of the microgrid is achieved. This step breaks through the limitation of traditional methods that only focus on a single economic indicator, unifies environmental benefits with economic benefits, and ensures the environmental performance of the system while reducing operating costs.

[0064] By introducing an optimization algorithm that simulates the collaborative hunting behavior of golden jackals, multi-objective optimization under complex constraints was achieved. This step overcomes the problem that traditional optimization algorithms are prone to falling into local optimality, and can quickly find the optimal solution in the global scope, significantly improving the optimization efficiency.

[0065] By setting up a multi-level constraint system such as power balance and power generation capacity, comprehensive constraints on various operating restrictions of the microgrid are achieved. This step ensures that the optimization results meet the actual operation requirements, avoids the problem of inconsistency between theoretical results and actual operation, and improves the feasibility of the solution.

[0066] Example 2

[0067] Reference Figure 1 - Figure 2 , which is the second embodiment of the present invention, and this embodiment provides a multi-objective optimization distributed power generation energy management method. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0068] Configuration of microgrid energy management system:

[0069] The configuration of the microgrid energy management system includes wind power generation, solar power generation, micro turbines, diesel generators, battery energy storage systems and fuel cells. The multi-objective optimization method is used to optimize the charging and discharging of the energy storage system.

[0070] Define the microgrid operation cost function:

[0071] The proposed method involves the exchange of power between the main grid and the charging and discharging of the battery energy storage system for the whole day ahead. In addition, due to the high resistance and low voltage of the microgrid lines, the power loss cannot be ignored. By integrating hybrid energy into the microgrid, the objective function of the method proposed in this patent is to reduce the cost, which is defined as:

[0072] MIN{f1(p G ),f2(p dg ),f3(c res,I (p res,I (T))),f4(ce),f5(dr),f6(p loss )}

[0073] Among them, f1, f2, f3, f4, f5, and f6 represent different cost functions, including main grid cost, fuel cost, renewable energy distribution cost, greenhouse gas emission cost, demand response incentive cost, and actual power loss cost. The definitions of f1, f2, f3, f4, f5, and f6 are as follows:

[0074]

[0075]

[0076] f3(c res,I (p res,I (T)))=(A res,I p res,I (T) 2 +B res,I p res,I (T)+c res,I )

[0077]

[0078]

[0079] f6(p LOSS ) = k E tpl

[0080] Among them, T represents the current iteration round, N represents the total number of iterations, and c G (T) represents the main grid electricity price, p G (T) represents the power exchanged with the main grid. Negative values ​​represent selling power to the grid, and positive values ​​represent purchasing power from the grid. I (p I (T)) represents the fuel cost of the Ith generator, s I(T) represents the regulation cost of the Ith generator, ndg is the total number of generators, A I represents the quadratic cost coefficient of the Ith generator, B I represents the linear cost coefficient of the Ith generator, c I represents the fixed cost of the Ith generator. I (T) represents the power output of the Ith generator, s I (T) represents the startup cost of the Ith generator, represents the fixed startup cost of the Ith generator, represents the state function of the Ith generator, 1 means the device is running, 0 means the device is not running, A represents the state function of the I-th generator at T-1; res,I ,B res,I ,c res,i represents the cost coefficient associated with the distribution of renewable energy, p res,I (T) represents the power output of renewable energy; ef IJ, represents the greenhouse gas emission coefficient related to the exchange between the Ith distributed generation source and the Jth main grid; p I (T) represents the power output of the first distributed generation source; dg represents the greenhouse gas emission cost factor associated with distributed generation sources; ef GJ. represents the greenhouse gas emission coefficient related to the Jth main grid exchange, p G (T) represents the power exchanged with the main grid at time T. A positive value indicates that electricity is purchased from the main grid, and a negative value indicates that electricity is sold to the main grid. G represents the greenhouse gas emission cost coefficient related to the exchange with the main power grid; i represents the unit price of incentive or compensation, that is, the incentive amount per unit of electricity or per kilowatt-hour, represents the sum of demand response incentive costs; k E represents the power loss cost coefficient, t represents time, p represents total power, and l represents loss factor.

[0081] Define the constraints:

[0082] The constraints of microgrid energy management include power balance, generation capacity limitation, charge and discharge limitation, state quantity and charging efficiency constraint, which are expressed as follows:

[0083] Power balance constraints:

[0084] p d (T)+p dr (T)+p LOSS (T)+p CH (T) = p GRID (T)+p dg(T)+p wt (T)

[0085] +p pv (t)+p mt (T)+p fc (T) where p d (T) represents the total load demand of the microgrid, p dr (T) represents the power provided by demand response, p LOSS (T) represents the loss in the microgrid transmission process, p CH (T) represents the charging power of the energy storage system, p GRID (T) represents the power exchanged between the microgrid and the main grid, p dg (T) represents the power provided by the generator, p wt (T) represents the power provided by wind power, p pv (t) represents the power provided by photovoltaic, p mt (T) represents the power provided by the turbine, p fc (T) represents the power provided by the fuel cell.

[0086] Generation capacity constraints:

[0087]

[0088] Among them, p dg (T), and Respectively represent the actual output power, minimum output power and maximum output power limit of the generator; p mt (T), and Respectively represent the actual output power, minimum output power and maximum output power limit of the generator; p fc (T), and They respectively represent the actual output power, minimum output power and maximum output power limit of the generator.

[0089] Charge and discharge constraints:

[0090]

[0091] Among them, p CH (T), p DCH (T) and The charging power, amplification power and maximum charging and discharging power of the battery energy storage system are respectively.

[0092] State quantity and charging efficiency constraints:

[0093]

[0094] Among them, soc I (T) represents the current state of the first battery energy storage unit, η I represents the charge and discharge efficiency of the first battery energy storage unit, es I (T) represents the charge and discharge power of the first battery energy storage unit, c I represents the capacity of the Ith battery energy storage unit, and Indicates the minimum and maximum limits for charging and discharging of the first battery energy storage unit.

[0095] Design the Golden Jackal optimization algorithm and perform the optimization:

[0096] The golden jackal optimization algorithm is mainly inspired by the hunting behavior of the golden jackal, which includes the steps of searching, surrounding and pouncing on prey.

[0097] Initialization phase:

[0098] In the search or initialization phase, the algorithm randomly generates the positions of a group of golden jackals, each of which represents a potential solution. The initial positions of these solutions are given by:

[0099] y0=y Min +Rand(y Max -y Min )

[0100] Among them, y0 is the initial solution, y Min and Max are the upper and lower bounds of the solution respectively, and Rand is a uniform random number in the interval [0,1].

[0101] The prey matrix created by initialization is expressed as:

[0102]

[0103] Where Prey is the prey matrix; y i,j is the j-th dimensional position of the ith prey; the first and second winners in Prey together serve as a jackal pair; n is the number of prey; d is the dimension of the problem to be solved.

[0104] In the optimization process, the fitness function, that is, the objective function, is used to estimate the fitness value of each prey. The fitness value matrix of all prey is expressed as follows:

[0105]

[0106] In the formula, F OA is the fitness value matrix of the prey, f() is the fitness function; the one with the best fitness value is the male jackal, and the one with the second best fitness value is the female jackal. The jackal-wolf pair obtains the position of the corresponding prey.

[0107] Searching for prey:

[0108] In the search phase, the female jackals follow the male jackal, leading the way, and update their positions using the following formula:

[0109] y1(i)=y m (i) -e·|y m (i)-RL·Prey(i)|

[0110] y2(i)=y fm (i) -e·|y m (i)-RL·Prey(i)|

[0111] Among them, y1(i) and y2(i) are the updated positions of the golden jackals, y m (i) and y fm (i) is the current position of the male and female jackals, e is the escape energy of the prey, RL is a random vector based on the Levy distribution, and Prey(i) is the position of the prey at the current iteration number i.

[0112] The escape energy E of the prey is calculated as follows:

[0113] e=e1*e o

[0114] Among them, e1 represents the decreasing process of prey energy, e o Defined as the initial state of prey energy, e o =2*R-1, R represents any value in [0, 1].

[0115] e1=C1*(1-(i / T))

[0116] Where 1 is a constant, T is the maximum number of iterations, and based on the Lévy distribution, the RL expression is as follows:

[0117] RL=0.05 * lf(Y)

[0118] lf(Y)=0.01×(μ×σ) / (V (1 / β) )

[0119]

[0120] Where β is a constant, μ and V are arbitrary values ​​in [0, 1], and Γ is the Gamma function.

[0121] Finally, the jackal's position is updated via:

[0122]

[0123] S4.3. Encircling prey

[0124] When the golden jackal swarms its prey, its position is updated by the following formula:

[0125] y1(i)=y m (i)-e·|RL·y m (i)-RL·Prey(i)|

[0126] y2(i)=y fm (i)-e·|RL·y m (i)-RL·Prey(i)|

[0127] Among them, y1(i), y2(i) and y(i+1) are the updated positions of the golden jackal, y m (i) and y fm (i) is the current position of the male and female jackals, e is the escape energy of the prey, RL is a random vector based on Levy distribution, Prey(i) is the position of the prey at the current iteration number i, and the equation is used Perform a location update. The purpose of this phase is to narrow the search range and focus the search on the most promising areas.

[0128] Update and recalculate fitness:

[0129] After the exploration and exploitation phases, the fitness of each golden jackal is recalculated according to the new position, and the optimal solution is updated.

[0130] Stopping criteria:

[0131] The algorithm ends when the stopping condition is met, and the stopping condition is reaching the preset maximum number of iterations.

[0132] Through the above implementation method, the golden jackal optimization algorithm can effectively solve the multi-objective optimization problem in the microgrid and realize the efficient management and scheduling of energy. It has the potential to be applied in actual microgrid systems and can provide a new optimization tool for microgrid operators.

[0133] Table 1 shows the comparison of the cost and efficiency of the proposed method and the existing methods. In terms of cost, the total cost of the proposed method is 2.21x106 yuan, while for the existing methods, such as artificial bee colony, taboo search and particle swarm optimization, the total costs are 2.36x106 yuan, 2.47x106 yuan and 2.71x106 yuan respectively. The results show that the proposed method effectively reduces the cost compared with the existing methods. In terms of optimization efficiency, the efficiency of particle swarm optimization, taboo search and artificial bee colony methods is 61.73%, 75.24% and 85.36% respectively, while the efficiency proposed by the present invention can reach 95.86%. Compared with the existing methods, the proposed method provides higher efficiency.

[0134] Table 1 Comparison between the method of the present invention and the existing method

[0135] Optimization Methods Total cost (yuan) efficiency(%) Particle Swarm Optimization 2.71x106 61.73 Taboo Search 2.47x106 75.24 Artificial bee colonies 2.36x106 85.36 Method of the present invention 2.21x106 95.88

[0136] Example 3

[0137] This embodiment also provides a computer device, which is suitable for a multi-objective optimization distributed power generation energy management method, including a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement a forced oscillation detection and positioning method for a distribution network as proposed in the above embodiment.

[0138] This embodiment further provides a storage medium on which a computer program is stored. When the program is executed by a processor, a forced oscillation detection and positioning method for a distribution network is implemented as proposed in the above embodiment.

[0139] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0140] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0141] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0142] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0143] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used to implement: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0144] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A multi-objective optimization distributed generation energy management method, characterized by: The method includes obtaining operation data of a microgrid system, wherein the microgrid system includes wind power generation, solar power generation, micro turbines, diesel generators, battery energy storage systems, and fuel cells; defining a microgrid operation cost function according to the operation data, wherein the cost function includes main grid cost, fuel cost, renewable energy distribution cost, greenhouse gas emission cost, demand response incentive cost and actual power loss cost; The golden jackal optimization algorithm is used to perform multi-objective optimization of the microgrid system to minimize the operating cost while satisfying the constraints of power balance, generation capacity, charging and discharging, and energy storage status.

2. A multi-objective optimization distributed generation energy management method as claimed in claim 1, characterized in that: In the microgrid operation cost function: the main grid cost is calculated based on the main grid electricity price and the exchange power. When the exchange power is a negative value, it means selling electricity to the grid, and when it is a positive value, it means purchasing electricity from the grid; the fuel cost includes the fuel consumption cost, regulation cost and startup cost of the generator; the renewable energy distribution cost is the cost related to the power output of renewable energy; the greenhouse gas emission cost includes the emission cost of the exchange between distributed power generation sources and the main grid; the demand response incentive cost is the cost calculated based on the unit electricity incentive amount; the actual power loss cost is the loss cost related to time and total power.

3. A multi-objective optimization distributed generation energy management method as claimed in claim 2, characterized in that: The golden jackal optimization algorithm comprises the following steps: initializing a group of golden jackals, each of which represents a potential solution; evaluating the fitness function of each golden jackal; updating the position of the solution by simulating the hunting behavior of the golden jackal, including searching, surrounding and capturing prey; and terminating the algorithm when a stopping condition is met, wherein the stopping condition is reaching a preset maximum number of iterations.

4. A multi-objective optimization distributed generation energy management method as claimed in claim 3, characterized in that: The hunting behavior of the golden jackal includes: the male golden jackal leads and the female golden jackal follows to search; the position of the golden jackal is updated based on the escape energy and random vector of the prey; and the final solution position is obtained by calculating the positions of the male and female golden jackals.

5. A multi-objective optimization distributed generation energy management method as claimed in claim 4, characterized in that: The power balance constraint requires that the sum of the total load demand of the microgrid, demand response power, transmission loss power and energy storage system charging power is equal to the sum of the microgrid and main grid exchange power, generator power, wind power, photovoltaic power, turbine power and fuel cell power.

6. A multi-objective optimization distributed generation energy management method as claimed in claim 5, characterized in that: The power generation capacity constraint requires that the actual output power of each power generation unit must be within the corresponding minimum output power and maximum output power limit range.

7. A multi-objective optimization distributed generation energy management method as claimed in claim 6, characterized in that: The charging and discharging constraints and energy storage state constraints require that: the charging power and discharging power of the battery energy storage system shall not exceed the maximum charging and discharging power limit; the state quantity of the battery energy storage unit must be within its minimum and maximum limit range, and is related to the charging and discharging efficiency, charging and discharging power and capacity.

8. A multi-objective optimization distributed power generation energy management system, based on a multi-objective optimization distributed power generation energy management method according to any one of claims 1 to 7, characterized in that: It also includes a data acquisition module to acquire operating data of a microgrid system, wherein the microgrid system includes wind power generation, solar power generation, micro turbines, diesel generators, battery energy storage systems, and fuel cells; A cost function definition module, which defines a microgrid operation cost function according to the operation data, wherein the cost function includes main grid cost, fuel cost, renewable energy distribution cost, greenhouse gas emission cost, demand response incentive cost and actual power loss cost; The optimization execution module uses the golden jackal optimization algorithm to perform multi-objective optimization of the microgrid system to minimize the operating cost while meeting the constraints of power balance, generation capacity, charging and discharging, and energy storage status.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the multi-objective optimization distributed power generation energy management method described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of a multi-objective optimization distributed generation energy management method as described in any one of claims 1 to 7 are implemented.

Images