A low-carbon economic optimal dispatching method for a wind-solar-fuel combined power supply system
Patent Information
- Application Number
- CN202611030713.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-07-12
- Publication Date
- 2026-09-29
AI Technical Summary
在风光出力不确定性建模方面，传统方法如蒙特卡罗模拟存在效率低、时序相关性差等缺点；而结合拉丁超立方抽样与k-means聚类的方法虽有所改进，但仍容易陷入局部最优，难以精确刻画风光出力的概率分布特性
1）基于LHS+K-medoids+后向削减法的精细化风光不确定性场景构建，显著提升典型场景的代表性和计算效率，本申请采用拉丁超立方抽样（LHS）生成风光出力的初始场景集，通过将风光出力概率分布划分为等概率区间并在每个区间内随机抽取样本点，保证样本点以较少数量覆盖整个概率分布空间，避免了大量冗余采样带来的计算负担。在此基础上，采用K-medoids聚类算法将相似场景归并为簇并以簇中心作为代表场景，再通过后向削减法逐步删除概率较小的冗余场景，最终保留预设数量的典型场景及其对应概率。
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Figure CN122844149A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to a method for optimizing power system dispatch, specifically a low-carbon and economical optimization dispatch method for a combined wind, solar, and thermal power supply system. Background Technology
[0002] With the acceleration of the global energy transition and the deepening of the "dual carbon" target, the proportion of clean energy, represented by wind power and photovoltaics, in the power system continues to increase. Green electricity direct connection projects must strictly adhere to the principle of "source determined by load," ensuring that the self-generated and self-consumed electricity from new energy sources is no less than 60% of the total available power generation and no less than 30% of the total electricity consumption. This policy guidance not only provides a clear path for enterprises to use green energy but also places higher demands on the dispatching capabilities of the joint power supply system.
[0003] However, wind and solar power generation are characterized by significant randomness, volatility, and intermittency. Their large-scale integration poses a severe challenge to the safe and stable operation of the power system. Especially in off-grid systems, a single wind and solar power or wind, solar, and energy storage configuration is insufficient to meet continuous power supply requirements. Thermal power still needs to be relied upon as a backup power source to compensate for the instability of wind and solar power output. Against this backdrop, a combined wind, solar, and thermal power supply system has become a feasible solution. It ensures system reliability through the flexible adjustment capability of thermal power while reducing overall carbon emissions by leveraging wind and solar energy.
[0004] Currently, some progress has been made in the research on the optimal scheduling of combined wind, solar, and thermal power systems, but the following prominent problems still exist: In terms of modeling the uncertainty of wind and solar power output, traditional methods such as Monte Carlo simulation have drawbacks such as low efficiency and poor temporal correlation. While the method combining Latin hypercube sampling and k-means clustering has made some improvements, it is still easy to get trapped in local optima and it is difficult to accurately characterize the probability distribution characteristics of wind and solar power output.
[0005] In terms of optimization algorithms, most studies adopt traditional multi-objective optimization methods such as genetic algorithm (NSGA-II) and particle swarm optimization (PSO). When dealing with large-scale optimization problems with high dimensions, multiple constraints, and nonlinearity, these algorithms often have problems such as slow convergence speed, uneven distribution of Pareto solution set, and premature convergence, making it difficult to effectively coordinate the conflict between economy and low carbon emissions.
[0006] In terms of scheduling model construction, existing studies mostly focus on single-objective optimization, or although they consider multiple objectives, they do not fully incorporate policy constraints (such as source-load matching ratio, self-consumption rate, etc.), especially in terms of insufficient characterization of low-carbon operation constraints of thermal power units. As a result, the system still prioritizes economic efficiency in actual operation, and the carbon emission control effect is limited. In addition, traditional models have a weak ability to suppress wind and solar curtailment, and fail to fully realize the low-carbon benefits of wind and solar resources.
[0007] Therefore, there is a need for a multi-objective optimization scheduling method that can simultaneously take into account policy compliance, economic operating costs, and low-carbon emission targets, and effectively handle the uncertainties of wind and solar power, in order to support the practical application and promotion of enterprise green power direct connection projects. Summary of the Invention
[0008] The purpose of this invention is to provide a more rationally designed, easy-to-implement, and convenient method for coordinating the economic, low-carbon, and reliable low-carbon economic scheduling of wind-solar-thermal power supply systems. This method involves constructing a refined set of uncertain wind and solar power scenarios, establishing a dual-objective optimization model that takes into account both carbon emissions and operating costs, and using an improved MOALA algorithm for efficient solution.
[0009] This invention discloses a low-carbon, economical, and optimized dispatching method for a combined wind-solar-thermal power supply system. The combined system includes a photovoltaic power generation system, a wind power generation system, and a thermal power plant energy storage power generation system. All of these power generation systems are existing technologies. The innovation of this invention lies in combining these power generation systems, and includes the following steps: S1. Construction of Uncertainty Scenarios for Wind-Solar-Coal Combined Power Supply System: Obtain generator output and load data of the wind-solar-thermal combined power supply system, generate an initial scenario set for wind and solar power output using Latin hypercube sampling, extract typical scenarios using K-medoids clustering algorithm and backward reduction method, construct wind-solar combined power supply scenarios using scenario analysis method, generate typical scenario sets using scenario generation and scenario reduction, and ensure the accuracy and stability of the optimized scheduling of the wind-solar-thermal combined power supply system by accurately simulating the uncertainty of wind and solar power output. S2. Based on the generated typical scenario set, a low-carbon economic dispatch model for the combined wind-solar-thermal power supply system is constructed: A low-carbon economic dispatch mathematical model is established with the dual objective functions of minimizing carbon emissions and minimizing the operating cost of the combined wind-solar-thermal power supply system. The model includes objective functions and constraints, including: system power balance constraints, thermal power unit constraints, system positive and negative spinning reserve constraints, and wind and solar power output constraints. Among them, thermal power unit constraints include thermal power unit output constraints and thermal power unit ramping constraints; wind and solar power output constraints include wind power output constraints and photovoltaic power output constraints. S3. Using the multi-objective lemming algorithm to solve the model: In the constructed low-carbon economic dispatch model of the wind-solar-thermal power supply system, the multi-objective lemming algorithm is introduced through non-dominated sorting and congestion calculation mechanism. By simulating the four behaviors of lemmings—migration, digging, foraging, and avoiding predators—global exploration and local optimization are achieved. Combined with the low-carbon economic dispatch model, the optimal scheme for low-carbon economic dispatch of each power source is obtained, thereby obtaining the optimal solution and realizing the low-carbon economic dispatch of the wind-solar-thermal power supply system. S4. Optimization Result Analysis and Scheduling Decision: Select a compromise solution from the Pareto optimal frontier to generate the day-ahead output plan for thermal power, wind power, and photovoltaic units, so as to achieve low-carbon, economical and stable operation of the system.
[0010] The objective function in step S2 is: 1) Lowest carbon emissions: In the formula, For system carbon emissions; The number of cycles; This refers to the number of thermal power units. for The output power of the thermal power unit at any given time; The carbon emission coefficient of thermal power units; for The time-of-use system purchases power from the external power grid; Carbon emission coefficient for purchased electricity; 2) Lowest system operating cost In the formula, F 2 represents system operating costs. a i , b i , c i For the first i Fuel cost coefficient for each thermal power unit; j For thermal power units, wind power units, and photovoltaic units; P j,t for t Time of the first j The output of the generating unit; For the unit j The operating and maintenance costs.
[0011] In step S2: 1) System power balance constraints In the formula, P i,t , P WT,t , P PV,t for t Power output of thermal power units, wind power units, and photovoltaic power units at all times, in MW; P Load,t for t System load value at any given time, in MW; 2) Constraints of thermal power units a. Output constraints of thermal power units In the formula, P i,t,min , P i,t,max for t Time of the first i The lower and upper limits of the output of thermal power units in Taiwan, in MW; b. Climbing constraints for thermal power units In the formula, P i,t-1 For thermal power units t-1 Always putting in effort, MW; R i,up , R i,down For the first i The rate at which the thermal power unit climbs uphill and slides downhill; 3) System positive and negative rotational spare constraints In the formula, u i,t The start-up and shutdown status of the thermal power unit at time t; , These are the positive and negative rotating reserve coefficients of the system, respectively. 4) Wind and solar power output constraints a. Wind power output constraints In the formula, P WT,t,min , P WT,t,max They are respectively t The minimum and maximum output of the wind turbine at all times; b. Photovoltaic output constraints In the formula, P PV,t,min , P PV,t,max They are respectively t The minimum and maximum output of the photovoltaic unit at all times.
[0012] Step S3 includes the following steps: S3.1: The initialization phase generates a solution space matrix defined by the objective function and constraints, suitable for handling uncertainties in wind and light conditions and high-dimensional search problems under constraints. In the formula, LBj For the first j The lower bound of the dimension, UB j For the first j The upper limit of the dimension, rand is a value randomly selected between [0,1]; S3.2: Long-distance migration behavior: When food becomes scarce due to overpopulation, lemmings will randomly undertake long-distance migrations. This phenomenon is modeled using Brownian motion to simulate the random fluctuations in solar and wind power output. A dynamic step size is used to achieve a global search capability for carbon emissions and system operating cost targets. The mathematical model is as follows: in, Z i ( t +1) is the first i A lemming t The position at +1 iteration; Z best This is the optimal solution for the current position. F Used to change the search direction; BM To represent the random number vector of Brownian motion, dynamics and a uniform step size are used to improve the global search capability; R This is a vector of size 1×Dim, whose elements are random numbers uniformly distributed in the interval [-1,1]. This vector controls the movement of the most powerful individual and random individuals at the current position in the population. BM , F and R The formula is: S3.3: Digging behavior: Lemmings randomly dig new burrows in their habitat based on the current burrow location and the location of random individuals within the population. They seek better solutions within a local space that satisfies system constraints, adapting to the source-load matching requirements of off-grid systems designed for self-use by enterprises. The mathematical model is as follows: In the formula, Z b For the search individual randomly selected from the population, b It is 1 and N Random integer index value between; L It is a random number related to the current iteration number, calculated using the following formula: S3.4: Foraging behavior: Lemmings move widely and freely within their burrows, using their keen sense of smell and hearing to locate food sources. The mathematical model for optimizing the local optimization efficiency of thermal power unit output and improving the renewable energy absorption rate using a spiral search mechanism is as follows: In the formula, spiral The spiral shape represents the random search during foraging. radius Let be the radius of the foraging range, which is the Euclidean distance between the current position and the optimal solution, expressed by the formula: S3.5: Avoiding predators: Burrows serve as refuges for lemmings. Whenever an enemy is detected, the lemmings use their superior running ability to return to their burrows and then use Levy's flight to escape local optima, thus mitigating scheduling risks caused by sudden load changes and deviations in weather forecasts. The mathematical model for this is as follows: In the formula, G is the lemming escape coefficient, representing its escape ability, which decreases as the number of iterations increases; T max This represents the maximum number of iterations. Levy The Levy flight function is used to simulate the deceptive movements of a lemming when it tries to escape. The Levy flight function is: In the formula, u and v A random value within the interval [0,1]; β It is a constant with a value of 1.5; S3.6: Calculate the fitness function for carbon emissions and system operating costs to obtain the Pareto solution; S3.7: Perform non-dominated sorting and crowding calculation to obtain the Pareto front; S4.8: Obtain the optimal result for a low-carbon economy.
[0013] Beneficial effects of this invention: 1) This application employs a refined approach to constructing uncertain wind and solar power scenarios based on LHS, K-medoids, and backward shaving, significantly improving the representativeness and computational efficiency of typical scenarios. The initial scenario set for wind and solar power output is generated using Latin hypercube sampling (LHS). By dividing the probability distribution of wind and solar power output into equal probability intervals and randomly sampling points within each interval, the application ensures that a small number of sample points cover the entire probability distribution space, avoiding the computational burden caused by a large amount of redundant sampling. Based on this, the K-medoids clustering algorithm is used to group similar scenarios into clusters, with the cluster center serving as the representative scenario. Then, the backward shaving method is used to gradually remove redundant scenarios with lower probabilities, ultimately retaining a predetermined number of typical scenarios and their corresponding probabilities.
[0014] 2) This application constructs a low-carbon economic dispatch model with the dual objective functions of minimizing carbon emissions and minimizing system operating costs. The carbon emission objective function covers two sources: carbon emissions from thermal power generation and carbon emissions from purchased electricity. The system operating cost objective function includes the fuel cost of thermal power units (in quadratic function form) and the operation and maintenance costs of various units. Through dual-objective optimization, the model can control operating costs while reducing system carbon emissions, avoiding a sharp increase in costs due to solely pursuing low carbon emissions or neglecting emission reduction targets due to solely pursuing economic efficiency. Practical examples show that when the cost weight is large, thermal power units operate stably but have high carbon emissions and experience wind and solar curtailment; when the carbon emission weight is large, thermal power units frequently start and stop for peak shaving, resulting in a significant increase in costs; while when the cost and carbon emissions are optimally balanced, wind and solar power consumption and cost control are taken into account, and system stability and environmental benefits are balanced. This dual-objective model provides decision-makers with Pareto optimal solutions under different preferences, allowing them to flexibly select compromise solutions according to actual policy requirements and business objectives.
[0015] 3) This paper proposes the MOALA multi-objective lemming algorithm, which achieves an efficient balance between global exploration and local optimization through the coordinated action of four behaviors. This application introduces non-dominated sorting and crowding calculation mechanisms into the lemming algorithm to construct a multi-objective lemming algorithm. It achieves efficient global exploration and local optimization by simulating four natural behaviors of lemmings: long-distance migration, burrowing, foraging, and predator avoidance. a. The long-distance migration behavior uses Brownian motion to simulate the random fluctuations in solar power output, utilizing dynamic step size to achieve a global search for carbon emissions and system operating costs. The random walk characteristic of Brownian motion allows individual lemmings to explore the solution space over a large area, avoiding premature entrapment in local optima. b. The burrowing behavior randomly digs new burrows in the habitat based on the current burrow location and the location of random individuals in the population, digging for better solutions within the local space that satisfies system constraints. This behavior introduces a sinusoidal random number L related to the iteration number, giving the search a strong ability to cause local disturbances in the early stages and gradually convergence in the later stages. c. The foraging behavior uses a spiral search mechanism to search for food sources around the burrows, specifically optimizing the output of thermal power units. Spiral search combines the periodic oscillation characteristics of sine and cosine functions, enabling the search entity to perform fine scanning around the optimal solution, thus improving the local optimization efficiency of renewable energy absorption rate; d. Avoiding predators involves using Lévy flight to escape local optima to cope with scheduling risks caused by load fluctuations and wind and solar forecast deviations. The long-tail jump characteristic of Lévy flight allows the search entity to occasionally make large jumps, escaping local extreme regions and enhancing the algorithm's global convergence ability; The synergistic effect of the four behaviors enables the MOALA algorithm to exhibit excellent search performance in high-dimensional, multi-constraint, and nonlinear joint scheduling problems of wind, solar and fire.
[0016] 4) Non-dominated sorting and crowding calculation ensure the diversity and uniformity of the Pareto solution set. This application introduces non-dominated sorting and crowding calculation mechanisms into the MOALA algorithm. Non-dominated sorting divides the population into multiple non-dominated levels according to the dominance relationship between individuals, retaining all non-dominated solutions to ensure the integrity of the Pareto front. Crowding calculation measures the density between individuals within the same non-dominated level, prioritizing the retention of individuals with high crowding to maintain the diversity of the solution set. The combination of the two results in a uniformly distributed and widely covered Pareto front, providing decision-makers with a rich variety of candidate scheduling schemes and avoiding the problem of the solution set being concentrated in a certain area in traditional multi-objective algorithms.
[0017] 5) Practical examples of this application verify significant carbon reduction, cost reduction, and renewable energy consumption effects. Simulations of the method of this invention applied to a real-world combined wind-solar-thermal power supply system show that: Carbon emissions have been effectively reduced: By coordinating the output of thermal power, wind power and photovoltaic power, the total carbon emissions of thermal power units and carbon emissions from purchased electricity have been reduced while meeting load demand. Operating costs have decreased significantly: the optimized power output distribution of thermal power units is more reasonable, avoiding frequent start-ups and shutdowns and long-term operation of high-energy-consuming units, and effectively controlling fuel costs and operation and maintenance costs; Wind and solar curtailment rates have been significantly reduced: By flexibly adjusting thermal power generation to make room for the consumption of new energy sources, the utilization rate of wind and solar resources has been greatly improved. The convergence speed and Pareto front quality are superior to NSGA-II: Compared with the traditional NSGA-II algorithm, the MOALA algorithm has a significant improvement in convergence speed, and the distribution range and uniformity of the Pareto front are better.
[0018] 6) This application constructs a representative typical wind and solar power output scenario by combining LHS and K-medoids clustering with the backward reduction method, effectively characterizing the statistical features of wind and solar uncertainty; the established dual-objective optimization model fully considers the self-consumption ratio and source-load matching requirements of the green electricity direct connection policy, and achieves synergistic optimization of carbon emissions and operating costs; the proposed MOALA algorithm is superior to the traditional NSGA-II algorithm in terms of convergence speed, solution set diversity and distribution uniformity, and is more suitable for solving high-dimensional, multi-constraint, and multi-objective optimization problems; actual examples show that the scheduling method of this application can effectively reduce system carbon emissions and operating costs, and the wind curtailment rate and solar curtailment rate also decrease significantly, significantly improving the renewable energy absorption capacity and system comprehensive performance. Attached Figure Description
[0019] Figure 1 The flowchart of this invention is as follows: Figure 2 This is a typical scenario diagram of wind power after simplification in this invention; Figure 3 This is a typical scenario diagram of photovoltaic reduction in this invention; Figure 4 This is the Pareto optimal frontier plot in this invention; Figure 5 This is a power output diagram of the combined wind, solar and thermal power supply system in this invention. Detailed Implementation
[0020] The present invention will be further described below with reference to the accompanying drawings.
[0021] This invention discloses a low-carbon, economical, and optimized dispatching method for a combined wind, solar, and thermal power supply system, comprising a photovoltaic power generation system, a wind power generation system, and a thermal power plant energy storage power generation system, and includes the following steps: S1. Construction of Uncertainty Scenarios for Wind-Solar Combined Power Supply: This involves acquiring generator output and load data for the wind-solar-thermal combined power supply system. Latin hypercube sampling is used to generate an initial scenario set for wind and solar power output. Typical scenarios are extracted using K-medoids clustering and backward reduction. Scenario analysis is employed to construct combined wind and solar power output scenarios. Scenario generation and reduction are then used to generate a typical scenario set. In the combined wind-solar-thermal power supply system, accurate simulation of wind and solar power output uncertainty ensures the accuracy and stability of the optimized scheduling of the system. Scenario generation refers to generating representative output scenarios using Latin hypercube sampling, a highly efficient statistical sampling technique that achieves uniform sampling in multidimensional space, ensuring that the interaction effects between variables are fully captured. Scenario reduction involves generating combined wind and solar power output scenarios that consider spatial correlations and obtaining a simplified typical scenario set through scenario reduction. This retains the main stochastic characteristics and related structures of the original wind and solar power output while improving the solution efficiency of subsequent power system optimization scheduling or planning models. S2. Based on the generated typical scenario set, a low-carbon economic dispatch model for the combined wind-solar-thermal power supply system is constructed: A low-carbon economic dispatch mathematical model is established with the dual objective functions of minimizing carbon emissions and minimizing the operating cost of the combined wind-solar-thermal power supply system. The model includes objective functions and constraints, including: system power balance constraints, thermal power unit constraints, system positive and negative spinning reserve constraints, and wind and solar power output constraints. Among them, thermal power unit constraints include thermal power unit output constraints and thermal power unit ramping constraints; wind and solar power output constraints include wind power output constraints and photovoltaic power output constraints. S3: Multi-objective lemming algorithm is used to solve the model: In the constructed low-carbon economic dispatch model of the wind-solar-thermal power supply system, the multi-objective lemming algorithm is introduced through non-dominated sorting and congestion calculation mechanism. By simulating the four behaviors of lemming migration, digging, foraging and avoiding predators, global exploration and local optimization are achieved. Combined with the low-carbon economic dispatch model, the optimal scheme for low-carbon economic dispatch of each power source is obtained, thereby obtaining the optimal solution and realizing the low-carbon economic dispatch of the wind-solar-thermal power supply system. S4: Optimization Result Analysis and Scheduling Decision: Selecting compromise solutions from the Pareto optimal frontier to generate day-ahead output plans for thermal power, wind power, and photovoltaic units, achieving low-carbon, economical, and stable system operation; compromise solutions refer to selecting one or more solutions from a set of non-dominated solutions, i.e., Pareto optimal solutions, in a multi-objective optimization problem that minimizes carbon emissions and reduces the operating cost of the combined wind, solar, and thermal power supply system. These solutions achieve a certain balance among multiple objectives to meet the collaborative optimization requirements in practical applications.
[0022] The objective function in step S2 is: 1) Lowest carbon emissions: In the formula, For system carbon emissions; The number of cycles; This refers to the number of thermal power units. for The output power of the thermal power unit at any given time; The carbon emission coefficient of thermal power units; for The time-of-use system purchases power from the external power grid; Carbon emission coefficient for purchased electricity; 2) Lowest system operating cost In the formula, F 2 represents system operating costs. a i , b i , c i For the first i Fuel cost coefficient for each thermal power unit; j For thermal power units, wind power units, and photovoltaic units; P j,t for t Time of the first j The output of the generating unit; For the unit j The operating and maintenance costs.
[0023] In step S2: 1) System power balance constraints In the formula, P i,t , P WT,t , P PV,t for t Power output of thermal power units, wind power units, and photovoltaic power units at all times, in MW; P Load,t for t System load value at any given time, in MW; 2) Constraints of thermal power units a. Output constraints of thermal power units In the formula, P i,t,min , P i,t,max for t Time of the first i The lower and upper limits of the output of thermal power units in Taiwan, in MW; b. Climbing constraints for thermal power units In the formula, P i,t-1 For thermal power units t-1 Always putting in effort, MW; R i,up , R i,down For the first i The rate at which the thermal power unit climbs uphill and slides downhill; 3) System positive and negative rotational spare constraints In the formula, u i,t The start-up and shutdown status of the thermal power unit at time t; , These are the positive and negative rotating reserve coefficients of the system, respectively. 4) Wind and solar power output constraints a. Wind power output constraints In the formula, P WT,t,min , P WT,t,max They are respectively t The minimum and maximum output of the wind turbine at all times; b. Photovoltaic output constraints In the formula, P PV,t,min , P PV,t,max They are respectively t The minimum and maximum output of the photovoltaic unit at all times.
[0024] Step S3 includes the following steps: S3.1: The initialization phase generates a solution space matrix defined by the objective function and constraints, suitable for handling uncertainties in wind and light conditions and high-dimensional search problems under constraints. In the formula, LB j For the first j The lower bound of the dimension, UB j For the first j The upper limit of the dimension, rand is a value randomly selected between [0,1]; S3.2: Long-distance migration behavior: When food becomes scarce due to overpopulation, lemmings will randomly undertake long-distance migrations. This phenomenon is modeled using Brownian motion to simulate the random fluctuations in solar and wind power output. A dynamic step size is used to achieve a global search capability for carbon emissions and system operating cost targets. The mathematical model is as follows: in, Z i ( t +1) is the first i A lemming t The position at +1 iteration; Z best This is the optimal solution for the current position. F Used to change the search direction; BM To represent the random number vector of Brownian motion, dynamics and a uniform step size are used to improve the global search capability; R This is a vector of size 1×Dim, whose elements are random numbers uniformly distributed in the interval [-1,1]. This vector controls the movement of the most powerful individual and random individuals at the current position in the population. BM , F and R The formula is: S3.3: Digging behavior: Lemmings randomly dig new burrows in their habitat based on the current burrow location and the location of random individuals within the population. They seek better solutions within a local space that satisfies system constraints, adapting to the source-load matching requirements of off-grid systems designed for self-use by enterprises. The mathematical model is as follows: In the formula, Z b For the search individual randomly selected from the population, b It is 1 and N Random integer index value between; L It is a random number related to the current iteration number, calculated using the following formula: S3.4: Foraging behavior: Lemmings move widely and freely within their burrows, using their keen sense of smell and hearing to locate food sources. The mathematical model for optimizing the local optimization efficiency of thermal power unit output and improving the renewable energy absorption rate using a spiral search mechanism is as follows: In the formula, spiral The spiral shape represents the random search during foraging. radius Let be the radius of the foraging range, which is the Euclidean distance between the current position and the optimal solution, expressed by the formula: S3.5: Avoiding predators: Burrows serve as refuges for lemmings. Whenever an enemy is detected, the lemmings use their superior running ability to return to their burrows and then use Levy's flight to escape local optima, thus mitigating scheduling risks caused by sudden load changes and deviations in weather forecasts. The mathematical model for this is as follows: In the formula, G is the lemming escape coefficient, representing its escape ability, which decreases as the number of iterations increases; T max This represents the maximum number of iterations. Levy The Levy flight function is used to simulate the deceptive movements of a lemming when it tries to escape. The Levy flight function is: In the formula, u and v A random value within the interval [0,1]; β It is a constant with a value of 1.5; S3.6: Calculate the fitness function for carbon emissions and system operating costs to obtain the Pareto solution; S3.7: Perform non-dominated sorting and crowding calculation to obtain the Pareto front; S4.8: Obtain the optimal result for a low-carbon economy.
[0025] like Figure 2 , Figure 3 As shown, this invention uses clustering and reduction algorithms to delete highly similar scenarios and retain representative ones, ultimately reducing the number to five typical scenarios. The probability of occurrence for wind power output scenarios after reduction is as follows: Scenario 1 25.0%, Scenario 2 23.5%, Scenario 3 18.0%, Scenario 4 17.0%, Scenario 5 16.5%; the probability of occurrence for photovoltaic output scenarios after reduction is as follows: Scenario 1 18.0%, Scenario 2 24.0%, Scenario 3 31.0%, Scenario 4 15.5%, Scenario 5 11.5%. Among these, wind power output scenario 1 and photovoltaic output scenario 3 have the highest probability of occurrence, therefore, they are selected for subsequent joint power generation system optimization scheduling.
[0026] like Figure 4As shown, the Pareto optimal front generated by the Multi-Objective Lemming Algorithm (MOALA) under the constraints of carbon emissions and system operating costs shows that when the cost weight is large, carbon emissions are high, thermal power units operate stably, and wind and solar power are limited by the peak-shaving capacity of thermal power units, resulting in wind and solar curtailment. The optimal solution shifts to the lower right, resulting in poor carbon reduction. When the carbon emission weight is large, thermal power units undertake peak-shaving tasks, frequently starting and stopping, significantly increasing costs. The optimal solution shifts to the upper left. When the trade-off between cost and carbon emissions is optimal, wind and solar power consumption and cost control are balanced, and system stability and environmental benefits are achieved.
[0027] like Figure 5 As shown in the figure, the characteristics of the coordinated operation of various units are illustrated. During the 0-5 hour load off-peak period, the thermal power units provide support to maintain output. As the load demand continues to rise, the output of wind and solar power increases significantly, the proportion of new energy consumption increases significantly, and the system output basically meets the load demand. This fully demonstrates the superiority of the MOALA algorithm in multi-energy coordinated operation, promoting the consumption of new energy while ensuring the safe and stable operation of the power system.
[0028] This invention demonstrates that the proposed scenario construction method can accurately characterize the fluctuations in wind and solar power output. Compared to the traditional NSGA-II algorithm, MOALA shows significant improvements in convergence speed, optimization ability, and Pareto front distribution. Carbon emissions and operating costs are effectively reduced, and both wind and solar curtailment rates are significantly lowered. By coordinating the output of thermal, wind, and solar power, this model can significantly improve the capacity for renewable energy absorption under the background of direct green electricity connection, and provides an efficient solution for the low-carbon economic dispatch of wind-solar-thermal combined power supply systems of relevant enterprises.
Claims
1. A low-carbon economic optimization dispatch method for a combined wind-solar-thermal power supply system, the combined wind-solar-thermal power supply system comprising a photovoltaic power generation system, a wind power generation system, and a thermal power plant energy storage power generation system, characterized in that, Includes the following steps: S1. Construction of Uncertainty Scenarios for Wind and Solar Power: Obtain the power output and load data of the generator units of the wind-solar-thermal combined power supply system, generate an initial scenario set of wind and solar power output using Latin hypercube sampling, extract typical scenarios using K-medoids clustering algorithm and backward reduction method, construct wind and solar combined power output scenarios using scenario analysis method, and generate typical scenario sets using scenario generation and scenario reduction. S2. Based on the generated typical scenario set, a low-carbon economic dispatch model for the combined wind-solar-thermal power supply system is constructed: A low-carbon economic dispatch mathematical model is established with the dual objective functions of minimizing carbon emissions and minimizing the operating cost of the combined wind-solar-thermal power supply system. The model includes objective functions and constraints, including: system power balance constraints, thermal power unit constraints, system positive and negative spinning reserve constraints, and wind and solar power output constraints. Among them, thermal power unit constraints include thermal power unit output constraints and thermal power unit ramping constraints; wind and solar power output constraints include wind power output constraints and photovoltaic power output constraints. S3. Using the multi-objective lemming algorithm to solve the model: In the constructed low-carbon economic dispatch model of the combined wind, solar and thermal power supply system, the multi-objective lemming algorithm is introduced through non-dominated sorting and congestion calculation mechanism. Global exploration and local optimization are achieved by simulating four behaviors of lemmings: migration, digging, foraging and avoiding predators. S4. Optimization Result Analysis and Scheduling Decision: Select a compromise solution from the Pareto optimal frontier to generate the day-ahead output plans for thermal power, wind power, and photovoltaic units.
2. The low-carbon economic optimization scheduling method for a combined wind, solar, and thermal power supply system as described in claim 1, characterized in that, The objective function in step S2 is: 1) Lowest carbon emissions: In the formula, For system carbon emissions; The number of cycles; This refers to the number of thermal power units. for The output power of the thermal power unit at any given time; The carbon emission coefficient of thermal power units; for The time-of-use system purchases power from the external power grid; Carbon emission coefficient for purchased electricity; 2) Lowest system operating cost In the formula, F 2 represents system operating costs. a i , b i , c i For the first i Fuel cost coefficient for each thermal power unit; j For thermal power units, wind power units, and photovoltaic units; P j,t for t Time of the first j The output of the generating unit; For the unit j The operating and maintenance costs.
3. The low-carbon economic optimization scheduling method for a combined wind, solar, and thermal power supply system as described in claim 1, characterized in that, In step S2: 1) System power balance constraints In the formula, P i,t , P WT,t , P PV,t for t Power output of thermal power units, wind power units, and photovoltaic power units at any given time, in MW; P Load,t for t System load value at any time, in MW; 2) Constraints of thermal power units a. Output constraints of thermal power units In the formula, P i,t,min , P i,t,max for t Time of the first i The lower and upper limits of the output of a thermal power unit in Taiwan, in MW; b. Climbing constraints for thermal power units In the formula, P i,t-1 For thermal power units t-1 Always putting in effort, MW; R i,up , R i,down For the first i The rate at which the thermal power unit climbs uphill and slides downhill; 3) System positive and negative rotational spare constraints In the formula, u i,t The start-up and shutdown status of the thermal power unit at time t; , These are the positive and negative rotating reserve coefficients of the system, respectively. 4) Wind and solar power output constraints a. Wind power output constraints In the formula, P WT,t,min , P WT,t,max They are respectively t The minimum and maximum output of the wind turbine at all times; b. Photovoltaic output constraints In the formula, P PV,t,min , P PV,t,max They are respectively t The minimum and maximum output of the photovoltaic unit at all times.
4. The low-carbon economic optimization scheduling method for a combined wind, solar, and thermal power supply system as described in claim 1, characterized in that, Step S3 includes the following steps: S3.1: The initialization phase generates a solution space matrix defined by the objective function and constraints, suitable for handling uncertainties in wind and light conditions and high-dimensional search problems under constraints. In the formula, LB j For the first j The lower bound of the dimension, UB j For the first j The upper limit of the dimension, rand is a value randomly selected between [0,1]; S3.2: Long-distance migration behavior: When food becomes scarce due to overpopulation, lemmings will randomly undertake long-distance migrations. This phenomenon is modeled using Brownian motion to simulate the random fluctuations in solar and wind power output. A dynamic step size is used to achieve a global search capability for carbon emissions and system operating cost targets. The mathematical model is as follows: in, Z i ( t +1) is the first i A lemming t The position at +1 iteration; Z best This is the optimal solution for the current position. F Used to change the search direction; BM To represent the random number vector of Brownian motion, dynamics and a uniform step size are used to improve the global search capability; R This is a vector of size 1×Dim, whose elements are random numbers uniformly distributed in the interval [-1,1]. This vector controls the movement of the most powerful individual and random individuals at the current position in the population. BM , F and R The formula is: ； S3.3: Digging behavior: Lemmings randomly dig new burrows in their habitat based on their current burrow location and the location of random individuals within the population. They seek better solutions within a local space that satisfies system constraints, adapting to the source-load matching requirements of off-grid systems designed for self-sufficiency. The mathematical model is as follows: In the formula, Z b For the search individual randomly selected from the population, b It is 1 and N Random integer index value between; L It is a random number related to the current iteration number, calculated using the following formula: S3.4: Foraging behavior: Lemmings move widely and freely within their burrows, using their keen sense of smell and hearing to locate food sources. A spiral search mechanism is used to optimize the output of thermal power units and improve the local optimization efficiency of new energy consumption. The mathematical model is as follows: In the formula, spiral The spiral shape represents the random search during foraging. radius Let be the radius of the foraging range, which is the Euclidean distance between the current position and the optimal solution, expressed by the formula: S3.5: Avoiding predators: Burrows serve as refuges for lemmings. Whenever an enemy is detected, the lemmings use their superior running ability to return to their burrows and then use Levy's flight to escape local optima, thus mitigating scheduling risks caused by sudden load changes and deviations in weather forecasts. The mathematical model for this is as follows: In the formula, G is the lemming escape coefficient, representing its escape ability, which decreases as the number of iterations increases; T max This represents the maximum number of iterations. Levy The Levy flight function is used to simulate the deceptive movements of a lemming when it tries to escape. The Levy flight function is: In the formula, u and v A random value within the interval [0,1]; β It is a constant with a value of 1.5; S3.6: Calculate the fitness function for carbon emissions and system operating costs to obtain the Pareto solution; S3.7: Perform non-dominated sorting and crowding calculation to obtain the Pareto front; S4.8: Obtain the optimal result for a low-carbon economy.