Power grid dispatching method, system and equipment based on renewable energy sources and storage medium

Abstract

The invention provides a power grid dispatching method, system and device based on renewable energy sources and a medium, and the method comprises the following steps: S1, obtaining the operation data of a target smart power grid, and building a microgrid energy optimization management framework model for achieving grid connection based on day-ahead dispatching according to the operation data; s2, based on the micro-grid energy optimization management framework model, establishing a smart grid energy scheduling optimization model; and S3, solving the intelligent power grid energy scheduling optimization model by adopting a multi-target wind-driven optimization algorithm to obtain an intelligent power grid energy scheduling optimal solution meeting the target. According to the method, firstly, a probability density function is used for uncertainty modeling of distributed power generation systems such as solar energy and wind energy, and then an optimization model integrating a multi-target wind-driven optimization algorithm and a fuzzy mechanism is developed based on a mixed demand response mechanism combining price and excitation demand response. Therefore, the optimal energy management problem based on the renewable energy power grid is effectively solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system optimization and control, and particularly relates to a power grid scheduling method, system, device and storage medium based on renewable energy. Background Technique

[0002] The continuously growing energy demand, increasingly serious environmental pollution and global warming worldwide are the main problems faced by environmentally sustainable and socially resilient cities. It is estimated that by 2040, the energy demand will increase by 30% compared with 2015. In addition, about 80% of the global electricity generation comes from traditional fossil fuels, which has a significant impact on pollution emissions and climate change. In addition, although cities account for only about 3% of the total land area, they account for 75% of the net power consumption and cause more than 80% of the pollution. Therefore, renewable energy sources (RES) are changing from alternative energy sources to the main energy sources to control the growing energy demand and increasing pollution emissions. Renewable energy sources such as wind energy and solar energy enter the power system, alleviating the energy crisis and reducing pollution emissions.

[0003] However, renewable energy sources RES are intermittent, with large fluctuations in power generation and are unreliable. In addition, renewable energy sources add more uncertainties to the power system, making multi-objective optimal scheduling challenging. Therefore, a model is needed to handle the uncertainties of renewable energy sources and solve the day-ahead scheduling problem for optimizing conflicting objectives.

[0004] Currently, various studies have been carried out to address the uncertainties in renewable energy sources and solve the day-ahead scheduling problem through multi-objective optimization in smart grids (SPG), covering different aspects and viewpoints such as operating costs, pollution emissions, reliability, power losses, user comfort, peak energy consumption, etc. Considering these objectives, current research on SPG mainly focuses on multi-objective optimization techniques for day-ahead scheduling. However, before performing day-ahead scheduling, a model is needed to accurately predict the power generation of renewable energy sources affected by fluctuations and uncertainties.

[0005] However, over time, probability and demand response (DR) methods that only focus on uncertainty and single-objective optimization have been unable to meet the requirements of the 21st century. Therefore, multi-objective optimization research needs to consider more factors and parameters. For this purpose, multi-objective wind-driven optimization (MOWDO), multi-objective particle swarm optimization (MOPSO), and multi-objective genetic algorithm (MOGA) have been used to solve multi-dimensional optimization problems, and the designed models optimize the economic, environmental, and renewable energy availability aspects of SPG. To address the uncertainty of load and renewable energy, an integrated method that combines DR and inclined block tariff (IBT) has been adopted, and an SPG optimization scheduling model has been created based on MOGA, considering DR and RESs while optimizing operating costs and pollutant emissions. In addition, a microgrid scheduling framework based on MOPSO has been developed for techno-economic optimization. MOPSO uses a weight factor to perform multi-objective optimization. Some people also use MOGA for effective energy management of the microgrid while optimizing energy consumption and battery degradation costs, etc.

[0006] The above studies comprehensively analyze the existing literature, conduct research from different perspectives, and gain a comprehensive insight into how to effectively achieve goals such as operating costs, pollution emissions, and user comfort. Few works can handle the uncertainty of renewable energy and focus on achieving operating cost optimization. In contrast, some studies use PDF and DR to solve RES uncertainty, with a focus on pollution emission optimization. However, the actual application scenarios need to take into account multiple aspects (operating costs, user comfort, pollution emissions, etc.). These goals are interdependent, so their synchronous optimization is crucial. In addition, the optimization results of existing models are not ideal. Existing studies have proposed several models to solve energy management problems through day-ahead scheduling. However, these models have the following defects:

[0007] 1) Price- and incentive-based disaster recovery plans offer higher flexibility, increasing the possibility of shifting most of the load to hours with relatively low prices. However, this increase in flexibility may lead to the generation of rebound peaks. Therefore, using simple DR, whether price-based DR or incentive-based DR, has the potential to generate rebound peaks, and this problem has not been addressed in the SPG energy optimization framework;

[0008] 2) The economic, environmental, and user comfort analysis within the SPG energy optimization framework, aiming to reduce pollution emissions, minimize operating costs, and reduce user discomfort, has not been previously proposed as a three-objective optimization problem;

[0009] 3) Previous research work has not taken any additional measures for the trade-off between DR and three-objective optimization (economic, environmental, and user comfort optimization). Summary of the Invention

[0010] The first object of the present invention is to provide a power grid scheduling method based on renewable energy for the problems mentioned above.

[0011] To achieve the above object, the present invention adopts the following technical solutions:

[0012] A power grid scheduling method based on renewable energy includes the following steps:

[0013] S1. Obtain the operation data of the target smart grid, and establish a microgrid energy optimization management framework model for grid connection based on the day-ahead scheduling according to the operation data;

[0014] S2. Based on the microgrid energy optimization management framework model, establish an energy scheduling optimization model for the smart grid;

[0015] S3. Use the multi-objective wind-driven optimization algorithm to solve the energy scheduling optimization model of the smart grid, and obtain the optimal solution of the energy scheduling of the smart grid that meets the objectives.

[0016] While adopting the above technical solutions, the present invention can also adopt or combine the following technical solutions:

[0017] As a preferred technical solution of the present invention: In step S1, the target smart grid includes a wind turbine generator and a solar photovoltaic generator.

[0018] As a preferred technical solution of the present invention: In step S1, the operation data of the target smart grid includes operation cost data, pollution emission data, user comfort data, wind speed data, solar radiation irradiance data, consumer demand data, and predicted renewable energy data.

[0019] As a preferred technical solution of the present invention: Step S2 further includes the following sub-steps:

[0020] S21. Based on the probability density function and the cumulative distribution function, establish a probability distribution model of the wind turbine generator according to the two-parameter Weibull distribution of the wind speed;

[0021] S22. Based on the probability density function and the cumulative distribution function, establish a probability distribution model of the solar photovoltaic generator according to the solar radiation irradiance;

[0022] S23. Based on the probability distribution model of the wind turbine generator and the probability distribution model of the solar photovoltaic generator, establish a probability prediction model of the renewable energy power generation system of the smart grid;

[0023] S24. Adopt a hybrid demand response based on price and incentive to establish a load model of the smart grid;

[0024] S25. Determine the constraint set of the target power grid. Based on the probability prediction model of the renewable energy power generation system and the load model of the smart grid, with the goals of minimizing the operating cost, minimizing the pollution emissions, and maximizing the user comfort, construct an energy scheduling optimization model for the smart grid.

[0025] As a preferred technical solution of the present invention: The mathematical formula of the probability distribution model of the wind turbine generator set is:

[0026]

[0027] In the formula, f bp (P w ) is the probability distribution model of the output power of the wind turbine generator set, E co is the cut-out wind speed, E ci is the cut-in wind speed, F E (E co ) is the cumulative distribution of the cut-out wind speed, F E (E ci ) is the cumulative distribution of the cut-in wind speed, E r is the rated wind speed of the wind turbine generator set, P r is the rated power of the wind turbine generator set, P w is the output power of the wind turbine generator set, E n is the average wind speed, F E (E r ) is the cumulative distribution of the rated wind speed.

[0028] As a preferred technical solution of the present invention: The formula of the probability distribution model of the solar photovoltaic generator set is:

[0029]

[0030] In the formula, f bp (P PV ) is the probability distribution model of the output power of the solar photovoltaic generator set, P PV is the output power of the solar photovoltaic generator set, A PV is the area of the solar panel, η PV is the solar cell efficiency, S i is the solar irradiance, and δ and λ are the shape parameter and the scale parameter respectively.

[0031] As a preferred technical solution of the present invention: The formula of the probability prediction model of the renewable energy power generation system is:

[0032] f bp (P CDG ) = f bp (P w ) * f bp (PPV )

[0033] In the formula, f bp (P CDG ) is the probability prediction model of the renewable energy power generation system; f bp (P w ) is the probability distribution model of the wind turbine generator set, and f bp (P PV ) is the probability distribution model of the solar photovoltaic generator set.

[0034] As a preferred technical solution of the present invention: The formula of the load model of the smart grid is:

[0035]

[0036] Wherein, is the net energy consumption threshold, is the first-level electricity price signal, is the second-level electricity price signal, Con(i,t) is the energy consumption reduction planned by each user, and γ is the increment.

[0037] As a preferred technical solution of the present invention: The smart grid energy scheduling optimization model includes a first objective function, a second objective function, and a third objective function, as shown in the following formula:

[0038] The first objective function is:

[0039]

[0040] In the formula, minf1(x) is the operating cost objective function, OCost(t) is the total operating cost, COCost(t) is the determined operating cost, UOCost(t) is the uncertain operating cost, and T is the time;

[0041] The second objective function is:

[0042]

[0043] In the formula, min f2(x) is the pollution emission objective function, Emission(t) is the total pollution emission, Emi DG (t) is the pollution emission generated by the distributed generator set, and Emi GD (t) is the pollution emission generated by the power grid during the power purchase period;

[0044] The third objective function is:

[0045]

[0046] In the formula, min f3(x) is the user comfort objective function, UCl (t) is for user comfort, is the preferred energy scheduling plan for consumers, is the optimal solution obtained, and L is the delay.

[0047] As a preferred technical solution of the present invention: Step S3 further includes the following sub-steps:

[0048] S31. Define the input data;

[0049] S32. Model the uncertainty of wind speed and solar energy;

[0050] S33. Initialize the algorithm;

[0051] S34. Calculate the intelligent grid energy scheduling optimization model and update the initial population;

[0052] S35. Use the fuzzy mechanism to determine the non-dominated solutions;

[0053] S36. Determine the best solution and store it in the repository;

[0054] S37. Iteratively determine and save the best position of each particle;

[0055] S38. Determine whether the iteration termination condition is reached. If so, output the optimal solution of the intelligent grid energy scheduling, otherwise return to step S36.

[0056] The second object of the present invention is to provide a power grid scheduling system based on renewable energy, including the following modules:

[0057] A data acquisition module, which is used to acquire the operation data of the target intelligent grid;

[0058] A framework model construction module, which is used to establish a microgrid energy optimization management framework model for grid connection based on the day-ahead scheduling according to the operation data;

[0059] An energy scheduling optimization model establishment module, which is used to establish an intelligent grid energy scheduling optimization model according to the microgrid energy optimization management framework model;

[0060] A calculation module, which uses a multi-objective wind-driven optimization algorithm to solve the intelligent grid energy scheduling optimization model and obtain the optimal solution of the intelligent grid energy scheduling that meets the objectives.

[0061] The third object of the present invention is to provide an electronic device, which includes a processor, a communication interface, a memory, and a communication bus. The processor, the communication interface, and the memory complete mutual communication through the communication bus. Its characteristics are as follows,

[0062] A memory for storing a computer program;

[0063] A processor for executing the computer program stored on the memory to implement the steps of the grid scheduling method based on renewable energy as described above.

[0064] Another object of the present invention is to provide a non-volatile storage medium storing an executable program, which when executed by a processor, implements the steps of the grid scheduling method based on renewable energy as described above.

[0065] Compared with the prior art, the present invention has the following beneficial effects:

[0066] The present invention first uses the probability density function (PDF) for uncertainty modeling of distributed generation systems (DG) such as solar and wind energy, so as to be able to consider objectives such as pollution emissions, operating costs, and user comfort at the same time; then, a hybrid demand response mechanism combining price-based demand response and incentive-based demand response can not only reduce the energy demand of users during peak hours, but also prevent the emergence of rebound peaks even during low-price periods; finally, an advanced optimization model integrating a multi-objective wind-driven optimization algorithm (MOWDO) and a fuzzy mechanism is developed to reduce pollution emissions, minimize operating costs, and reduce user discomfort as three objectives, ensuring a balanced trade-off between economic, environmental, and user-centered objectives by simultaneously optimizing these three objectives, thus effectively solving the optimal energy management problem of the grid based on renewable energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 It is a flowchart of the grid scheduling method based on renewable energy provided by the present invention.

[0068] Figure 2 It is a schematic diagram of the microgrid energy optimization management framework model.

[0069] Figure 3 It is a flowchart of the multi-objective wind-driven optimization algorithm.

[0070] In the figure: 1 - Fault knowledge graph data layer; 2 - Fault knowledge graph schema layer; 3 - Relationship subgraph extracted from the fault knowledge graph schema layer. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0071] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0072] As Figure 1 shown, a grid scheduling method based on renewable energy specifically includes the following steps:

[0073] S1. Obtain the operation data of the target smart grid, and establish a microgrid energy optimization management framework model for grid connection based on day-ahead scheduling according to the operation data;

[0074] Step S1 further includes the following sub-steps:

[0075] S11. Clean the operation data of the target smart grid obtained, and remove the interference of redundant data;

[0076] S12. Cluster the operation data of the obtained target smart grid according to the K-Mediods clustering analysis method to determine the initial clustering center;

[0077] S13. Calculate the Euclidean distance between the initial clustering center and each of the operation data, and complete the clustering division of each of the operation data according to the initial clustering center and the Euclidean distance to obtain a clustering result.

[0078] In step S1, the target smart grid includes a wind turbine generator and a solar photovoltaic generator.

[0079] In step S1, the operation data of the target smart grid includes operation cost data, pollution emission data, user comfort data, wind speed data, solar irradiance data, consumer demand data, and predicted renewable energy data.

[0080] In this embodiment, the proposed system model considers a grid-connected microgrid that performs day-ahead scheduling in a smart grid through a multi-objective optimization method. As Figure 2 shown, the microgrid includes an energy storage system (BESS), distributed generators (DG), and loads. The distributed generation system includes a solar photovoltaic power station (SPVES), a wind power station (WES), and a battery energy storage system (BESS). The loads include commercial, industrial, and household loads. The developed microgrid energy optimization management framework model aims to reduce the operation cost, reduce pollution emissions, and improve user comfort by considering demand response (DR) and distributed generators (DG) in the smart grid.

[0081] S2. Based on the microgrid energy optimization management framework model, establish a smart grid energy scheduling optimization model;

[0082] Step S2 further includes the following sub-steps:

[0083] S21. Based on the probability density function and the cumulative distribution function, establish a probability distribution model of the wind turbine generator according to the two-parameter Weibull distribution of the wind speed;

[0084] The mathematical formula of the probability distribution model of the wind turbine generator is:

[0085]

[0086] Wherein, f bp (P w ) is the probability distribution model of the output power of the wind turbine generator, E co is the cut-out wind speed, E ci is the cut-in wind speed, F E (E co ) is the cumulative distribution of the cut-out wind speed, P E (E ci ) is the cumulative distribution of the cut-in wind speed, E r is the rated wind speed of the wind turbine generator, P r is the rated power of the wind turbine generator, P w is the output power of the wind turbine generator, E n is the average wind speed, F E (E r ) is the cumulative distribution of the rated wind speed.

[0087] Wind energy systems (WESs) use wind turbines (WTs) to convert wind energy into electrical energy. The electrical energy of the WES (the output of the WES) depends on factors such as wind speed, wind availability, the power curve of the WT, the size and shape of the WT, etc. Since wind energy depends on weather, it is random and intermittent. Therefore, the Weibull distribution is used to model the wind speed behavior, where both the shape index and the scale parameter are 2.

[0088] S22. Based on the probability density function and the cumulative distribution function, establish the probability distribution model of the solar photovoltaic generator set according to the solar irradiance;

[0089] Solar photovoltaic systems (SPVESs) convert sunlight into electrical energy. The energy generated by the SPVES (the output) depends on solar radiation. Due to weather changes, the solar output is random and intermittent. The uncertainty of solar radiation is described by the probability density function (PDF) and the cumulative distribution function (CDF) models, as shown in the following equations respectively:

[0090]

[0091] Wherein, f bp (P PV ) is the probability distribution model of the output power of the solar photovoltaic generator set, P PV is the output power of the solar photovoltaic generator set, A PV is the area of the solar panel, η PV is the solar cell efficiency, S i is the solar irradiance, δ and λ are the shape parameter and the scale parameter respectively, is the solar irradiance Si The average value of is the solar irradiance S i The standard deviation of

[0092] Since the output power of the solar photovoltaic power generation unit satisfies P PV = A PV *η PV *S i The formula for the probability distribution model of the solar photovoltaic power generation unit is:

[0093]

[0094] In the formula, f bp (P PV ) is the probability distribution model of the output power of the solar photovoltaic power generation unit, P PV is the output power of the solar photovoltaic power generation unit, A PV is the area of the solar panel, η PV is the solar cell efficiency, S i is the solar irradiance, and δ and λ are the shape parameter and the scale parameter respectively.

[0095] S23. Based on the probability distribution model of the wind turbine generator set and the probability distribution model of the solar photovoltaic power generation unit, establish a probability prediction model for the renewable energy power generation system of the smart grid;

[0096] Since the total output power of the distributed power generation system is the sum of the output powers of the energy storage power generation unit and the wind power generation unit, the formula for the probability prediction model of the renewable energy power generation system is:

[0097] f bp (P CDG ) = f bp (P w ) * f bp (P PV )

[0098] Among them, f bp (P CDG ) is the probability prediction model of the renewable energy power generation system; f bp (P w ) is the probability distribution model of the wind turbine generator set, and f bp (P PV ) is the probability distribution model of the solar photovoltaic power generation unit.

[0099] S24. Adopt a hybrid demand response based on price and incentive to establish a load model for the smart grid;

[0100] The load model takes into account residential, industrial, and commercial consumers and uses demand response (DR) to simulate the load behavior of these consumers. Demand response (DR) is divided into two main categories: price-based demand response (PDR) and incentive-based demand response (IDR).

[0101] Among them, the mathematical expression of price-based demand response is as follows:

[0102]

[0103] In the formula, and are three defined tariffs, and T1, T2, and T3 are three different periods corresponding to low, high, or medium price levels respectively. The controller obtains DR signals, device operation modes, and power generation data, and formulates a load plan by shifting demand from peak hours to off-peak hours.

[0104] Secondly, incentive-based demand response IDR simulates the load behavior of residential, commercial, and industrial consumers. In this work, IDR proposes a constraint condition, that is, within any given hour, the energy minimization of each consumer should not exceed the maximum incentive provided in the DR. This behavioral model is shown in the following formula:

[0105]

[0106] In the formula, R, I, and C represent residential, industrial, and commercial consumers respectively. Con(i,t) represents the planned energy consumption reduction of each user, and (iCon max t) represents the maximum energy consumption reduction proposed by the user respectively. represents the incentive payment offer of each user, and C(i,t) represents the benefit obtained by the consumer due to the reduction of operating costs after participating in IDR.

[0107] Due to the higher flexibility of price and IDR programs and the greedy nature of consumers, everyone wants to reduce their electricity bills. Therefore, there is a great opportunity to shift a large amount of load to relatively low-price periods. As a result, a rebound will occur. To solve this dilemma, hybrid demand response (HDR) is proposed, which combines PDR and IDR. HDR provides differential electricity prices according to the level of net power consumption. Under the condition of HDR, consumers may shift the load to low-price periods in order to reduce their electricity bills without monitoring their net power consumption. Therefore, the net energy consumption in an hour may exceed the maximum power consumption, resulting in the user's cost exceeding the expectation. For this reason, HDR sets two price levels according to the power consumption and adjusts according to the change of the hourly price signal.

[0108] Therefore, the formula of the load model of the smart grid is as follows:

[0109]

[0110] In the formula, represents the net energy consumption threshold, represents the first-level electricity price signal, represents the second-level electricity price signal, and the increment is γ.

[0111] When the energy consumption is less than or equal to the net energy consumption threshold, consumers are charged according to the normal electricity price signal; otherwise, they are charged according to the incremental electricity price signal, that is ). Therefore, HDR not only reduces power demand during peak hours, but also prevents the rebound of power demand at low prices, thus promoting more stable and efficient energy use in the SPG system. HDR also helps to reduce the uncertainty of renewable energy generation, thereby improving the stability and efficiency of the SP.

[0112] S25. Determine the constraint set of the target power grid. According to the probabilistic prediction model and load model of the renewable energy generation system of the smart grid, an optimal energy scheduling model of the smart grid is constructed with the goals of minimizing operating costs, minimizing pollution emissions, and maximizing user comfort.

[0113] In this embodiment, the optimal energy scheduling model of the smart grid includes a first objective function, a second objective function, and a third objective function, corresponding to minimizing operating costs, minimizing pollution emissions, and maximizing user comfort, respectively.

[0114] Specifically:

[0115] The operating cost includes two parts: deterministic and uncertain costs. The first part, namely the deterministic operating cost, includes DG operation and startup costs, standby, demand response and power costs, power purchase / sale costs with the utility, and probabilities affected by solar parameters and wind power prices. The second part, the uncertain cost, includes DG operating costs, load shedding costs (VOLL), costs of participating in demand response, and expected energy that fails to be provided to consumers (EENS), representative operating costs.

[0116] The first objective function is:

[0117]

[0118] In the formula, min f1(x) is the operating cost objective function, OCost(t) is the total operating cost, COCost(t) is the deterministic operating cost, UOCost(t) is the uncertain operating cost, and T is time.

[0119] The second objective function is:

[0120]

[0121] In the formula, min f2(x) is the pollution emission target function, Emission(t) is the total pollution emission, and Emi DG (t) is the pollution emission generated by the distributed generation unit, and Emi GD (t) is the pollution emission generated by the power grid during the power purchase period;

[0122] The third target function is as follows:

[0123]

[0124] In the formula, min f3(x) is the user comfort target function, and UC l (t) is the user comfort, is the preferred energy scheduling plan for consumers, is the obtained optimal solution, and L is the delay.

[0125] Through the multi-objective optimization method, while meeting the following constraints, the developed model can reduce the operating cost, reduce the pollution emission, and improve the user comfort.

[0126] In the specific implementation process, the three target functions must meet the following constraints: the net power provided by the distributed power source and the power grid must be equal to the net load demand of the consumers, the constraints of the distributed generation (including the upper and lower limits), and the battery charging / discharging constraints.

[0127] S3. Use the multi-objective wind-driven optimization algorithm to solve the intelligent grid energy scheduling optimization model to obtain the optimal solution of the intelligent grid energy scheduling that meets the objectives.

[0128] In the implementation process, the multi-objective wind-driven optimization algorithm (MOWDO) is a recent advancement in the field of evolutionary algorithms, specifically designed to solve multi-objective optimization problems. Its unique ability to balance exploration and exploitation while maintaining diversity and adapting to conflicting objectives and constraints makes it a powerful tool for optimal energy management. Inspired by the dynamics of air masses flowing from high-pressure areas to low-pressure areas, MOWDO uses this metaphor to guide its optimization process. The core concept of the MOWDO algorithm is the movement of air masses, similar to particles in the search space. This population-based iterative metaheuristic algorithm mimics how the wind disperses and redistributes air masses. Each air mass represents a potential solution, with different positions and velocities. By iteratively updating these positions and velocities, the algorithm gradually converges to the optimal solution.

[0129] Newton's second law of motion provides the basis for the WDO algorithm, offering a framework for handling multi-dimensional optimization problems such as microgrid sizing, energy optimization, and load scheduling. The key parameters of the WDO algorithm include the coefficient of friction (α), acceleration due to gravity (g), universal gas constant (R), absolute temperature (T), and a constant 2RT. These parameters play a crucial role in adjusting the algorithm's performance.

[0130] MOWDO is highly effective in solving the optimal energy management problem through day-ahead scheduling, aiming to reduce operating costs, pollution emissions, and improve user comfort. This problem is modeled as a model with equality and inequality constraints, effectively addressing various aspects of energy management.

[0131] For ease of calculation, the following multi-objective function is defined based on three objective functions:

[0132] min F(X)=[f1(X), f2(X), f3(X)] T

[0133] where X is a vector of variables to be optimized.

[0134] The multi-objective function problem is also subject to equality constraints g i (X)=0 i=1,2,3...,i eq and inequality constraints h i (X)<0 i=1,2,3...,i ineq These constraints must be satisfied to obtain a feasible solution.

[0135] During the optimization process, the MOWDO algorithm explores the search space to identify solutions belonging to three categories: non-dominated solutions, dominated solutions, and the best. Non-dominated solutions, also known as Pareto optimal solutions, represent a set of trade-offs where no objective can be improved without worsening at least one other objective. Pareto optimal solutions are particularly valuable as they provide decision-makers with a variety of balanced options, each offering a different compromise between objectives. These solutions are then stored in a repository for further analysis and selection.

[0136] As Figure 3 shown, the specific steps of applying the MOWDO algorithm to this energy management problem in step S3 are as follows:

[0137] S31. Define the input data, such as DGs with SPVES and WES generation profiles, DR information including household, industrial, and commercial demand profiles, pricing signals, and objectives including operating costs, pollution emissions, and user comfort;

[0138] S32. Model the wind speed and solar uncertainties;

[0139] S33. Algorithm initialization, randomly initialize the air particle swarm;

[0140] S34. Calculate the intelligent power grid energy scheduling optimization model, that is, use multi-objective functions to update the initialized population;

[0141] S35. Use a fuzzy mechanism to determine non-dominated solutions, and the fuzzy decision function uses the membership function to determine the number of solutions;

[0142] S36. Determine the best solution and store it in the repository;

[0143] In this embodiment, in order to find the best solution, first, it is necessary to select the desired Pareto set solutions and put them into the warehouse; next, select a leader and use the roulette wheel selection method to select the best solution from the Pareto set solution warehouse; finally, update the speed and position of the solution according to the selected solution.

[0144] S37. Iteratively determine and save the best position of each particle, search for the best position, and compare the current position with the previously saved best position. If the current position is better, remove the best position from the warehouse and replace it with the current best position;

[0145] S38. Determine whether the iteration termination condition is reached. It is necessary to check whether the maximum number of iterations is reached or whether the optimal solution is found; once either condition is met, the optimization process will end; if neither condition is met, the current population will replace the previous population, and the algorithm will return to step S36.

[0146] The present invention also provides a power grid scheduling system based on renewable energy, including the following modules:

[0147] Data acquisition module, the data acquisition module is used to acquire the operation data of the target intelligent power grid;

[0148] Framework model construction module, the framework model construction module is used to establish a microgrid energy optimization management framework model for grid connection based on day-ahead scheduling according to the operation data;

[0149] Energy scheduling optimization model establishment module, the energy scheduling optimization model establishment module is used to establish an intelligent power grid energy scheduling optimization model according to the microgrid energy optimization management framework model;

[0150] Calculation module, the calculation module uses the multi-objective wind-driven optimization algorithm to solve the intelligent power grid energy scheduling optimization model and obtain the optimal solution of the intelligent power grid energy scheduling that meets the target.

[0151] The present invention also provides an electronic device, which includes a processor, a communication interface, a memory, and a communication bus. The processor, the communication interface, and the memory complete mutual communication through the communication bus.

[0152] The memory is used for storing a computer program.

[0153] The processor is used for executing the computer program stored on the memory to implement the steps of the grid scheduling method based on renewable energy as described above.

[0154] The present invention also provides a non-transitory readable storage medium, which is a non-volatile storage medium. The non-volatile storage medium stores an executable program. When the executable program is executed by a processor, it can implement the steps of the grid scheduling method based on renewable energy as described above.

[0155] So far, the technical solution of the present invention has been described in combination with the specific experimental process shown in the drawings. However, the protection scope of the present invention is not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

Claims

1. A power grid scheduling method based on renewable energy, characterized in that It includes the following steps: S1. Obtain the operation data of the target smart grid, and establish a microgrid energy optimization management framework model for grid connection based on day-ahead scheduling according to the operation data; S2. Based on the microgrid energy optimization management framework model, establish an intelligent grid energy scheduling optimization model; S3. Use a multi-objective wind-driven optimization algorithm to solve the intelligent grid energy scheduling optimization model, and obtain the optimal solution of the intelligent grid energy scheduling that meets the objectives.

2. The method according to claim 1, characterized in that: In step S1, the target smart grid includes a wind turbine generator and a solar photovoltaic generator.

3. The method according to claim 1, characterized in that: In step S1, the operation data of the target smart grid includes operation cost data, pollution emission data, user comfort data, wind speed data, solar radiation irradiance data, consumer demand data, and predicted renewable energy data.

4. The method according to claim 1, wherein: Step S2 further includes the following sub-steps: S21. Based on the probability density function and the cumulative distribution function, establish a probability distribution model of the wind turbine generator according to the two-parameter Weibull distribution of the wind speed; S22. Based on the probability density function and the cumulative distribution function, establish a probability distribution model of the solar photovoltaic generator according to the solar radiation irradiance; S23. Based on the probability distribution model of the wind turbine generator and the probability distribution model of the solar photovoltaic generator, establish a probability prediction model of the renewable energy power generation system of the intelligent grid; S24. Adopt a hybrid demand response based on price and incentive to establish a load model of the intelligent grid; S25. Determine the constraint set of the target grid, and construct an intelligent grid energy scheduling optimization model with the objectives of minimizing operation cost, minimizing pollution emissions, and maximizing user comfort according to the probability prediction model of the renewable energy power generation system of the intelligent grid and the load model.

5. The method according to claim 1 or 4, characterized in that: The mathematical formula of the probability distribution model of the wind turbine generator is: where f bp (P w ) is the probability distribution model of the output power of the wind turbine generator set, E co is the cut-out wind speed, E ci is the cut-in wind speed, F E (E co ) is the cumulative distribution of the cut-out wind speed, F E (E ci ) is the cumulative distribution of the cut-in wind speed, E r is the rated wind speed of the wind turbine generator set, P r is the rated power of the wind turbine generator set, P w is the output power of the wind turbine generator set, E n is the average wind speed, F E (E r ) is the cumulative distribution of the rated wind speed; The formula of the probability distribution model of the solar photovoltaic generator is: Where, f bp (P PV ) is the probability distribution model of the output power of the solar photovoltaic power generation unit, P PV is the output power of the solar photovoltaic power generation unit, A PV is the area of the solar panel, η PV is the solar cell efficiency, S i is the solar irradiance, and δ and λ are the shape parameter and the scale parameter respectively; The formula of the probability prediction model of the renewable energy power generation system is: f bp (P CDG ) = f bp (P w ) * f bp (P PV ) where, f bp (P CDG ) is the probability prediction model of the renewable energy power generation system; f bp (P w ) is the probability distribution model of the wind turbine generator set, and f bp (P PV ) is the probability distribution model of the solar photovoltaic generator set; The formula of the load model of the intelligent grid is: wherein is the net energy consumption threshold value, is the first-level electricity price signal, is the second-level electricity price signal, Con(i, t) is the planned energy consumption reduction of each user, and γ is the increment; The intelligent grid energy scheduling optimization model includes a first objective function, a second objective function, and a third objective function, as shown in the following formula: The first objective function is: In the formula, min f1(x) is the operation cost objective function, OCost(t) is the total operation cost, COCost(t) is the determined operation cost, UOCost(t) is the uncertain operation cost, and T is the time; The second objective function is: Where, min f2(x) is the pollution emission objective function, Emission(t) is the total pollution emission, Emi DG (t) is the pollution emission generated by the distributed generator set, Emi GD (t) is the pollution emission generated by the power grid during the power purchase period; The third objective function is: where min f3(x) is the user comfort objective function, and UC l (t) is the user comfort, is the energy scheduling plan preferred by consumers, is the optimal solution obtained, and L is the delay.

6. The method according to claim 1, characterized in that: Step S3 further includes the following sub-steps: S31. Define the input data; S32. Model the uncertainty of wind speed and solar energy; S33. Initialize the algorithm; S34. Calculate the intelligent grid energy scheduling optimization model and update the initial population; S35. Use a fuzzy mechanism to determine the non-dominated solutions; S36. Determine the best solution and store it in the repository; S37. Iteratively determine and save the best position of each particle; S38. Determine whether the iteration termination condition is reached. If so, output the optimal solution of the intelligent grid energy scheduling, otherwise return to step S36.

7. A power grid dispatching system based on renewable energy, characterized in that, It includes the following modules: A data acquisition module, which is used to acquire the operation data of the target smart grid; A framework model construction module, which is used to establish a microgrid energy optimization management framework model for grid connection based on day-ahead scheduling according to the operation data; An energy scheduling optimization model establishment module, which is used to establish a smart grid energy scheduling optimization model according to the microgrid energy optimization management framework model; A calculation module, which uses a multi-objective wind-driven optimization algorithm to solve the smart grid energy scheduling optimization model and obtain an optimal solution for the smart grid energy scheduling that meets the objectives.

8. An electronic device, which includes a processor, a communication interface, a memory, and a communication bus. The processor, the communication interface, and the memory complete mutual communication through the communication bus. It is characterized in that: A memory, which is used to store computer programs; A processor, which is used to execute the computer programs stored on the memory to implement the steps of the grid scheduling method based on renewable energy as described in any one of claims 1-6.

9. A non-volatile storage medium, characterized in that: The non-volatile storage medium stores an executable program, and when the executable program is executed by the processor, it implements the steps of the grid scheduling method based on renewable energy as described in any one of claims 1-6.

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