In the contemporary era,the global expansion of electrical grids is propelled by various renewable energy sources(RESs).Efficient integration of stochastic RESs and optimal power flow(OPF)management are critical for n...In the contemporary era,the global expansion of electrical grids is propelled by various renewable energy sources(RESs).Efficient integration of stochastic RESs and optimal power flow(OPF)management are critical for network optimization.This study introduces an innovative solution,the Gaussian Bare-Bones Levy Cheetah Optimizer(GBBLCO),addressing OPF challenges in power generation systems with stochastic RESs.The primary objective is to minimize the total operating costs of RESs,considering four functions:overall operating costs,voltage deviation management,emissions reduction,voltage stability index(VSI)and power loss mitigation.Additionally,a carbon tax is included in the objective function to reduce carbon emissions.Thorough scrutiny,using modified IEEE 30-bus and IEEE 118-bus systems,validates GBBLCO’s superior performance in achieving optimal solutions.Simulation results demonstrate GBBLCO’s efficacy in six optimization scenarios:total cost with valve point effects,total cost with emission and carbon tax,total cost with prohibited operating zones,active power loss optimization,voltage deviation optimization and enhancing voltage stability index(VSI).GBBLCO outperforms conventional techniques in each scenario,showcasing rapid convergence and superior solution quality.Notably,GBBLCO navigates complexities introduced by valve point effects,adapts to environmental constraints,optimizes costs while considering prohibited operating zones,minimizes active power losses,and optimizes voltage deviation by enhancing the voltage stability index(VSI)effectively.This research significantly contributes to advancing OPF,emphasizing GBBLCO’s improved global search capabilities and ability to address challenges related to local minima.GBBLCO emerges as a versatile and robust optimization tool for diverse challenges in power systems,offering a promising solution for the evolving needs of renewable energy-integrated power grids.展开更多
The author puts forward the pattern of optimizing the structure of energy sources for generating power in the early stage of the 21st century in Fujian Province; analyzes imper’tant functions on speeding up nuclear p...The author puts forward the pattern of optimizing the structure of energy sources for generating power in the early stage of the 21st century in Fujian Province; analyzes imper’tant functions on speeding up nuclear power for adjusting the structure of energy sources and heightening economic benefits.and suggests that the first liquefied natural gas combined-cycle power plant will start to build at the end of this century and every effort is made so as to change the recent unreasonable structure of energy source step by step and form the optimized structure of energy sources for generating power, that includes hydropower, thermal power (coal, oil and natural gas), nuclear power, pumpedstorage power, and power from new energy sources. In order to reach the abovementioned significant target, the author discusses the technical and economic measures and the supporting policy to be taken at present and in future.展开更多
A distribution network plays an extremely important role in the safe and efficient operation of a power grid.As the core part of a power grid’s operation,a distribution network will have a significant impact on the s...A distribution network plays an extremely important role in the safe and efficient operation of a power grid.As the core part of a power grid’s operation,a distribution network will have a significant impact on the safety and reliability of residential electricity consumption.it is necessary to actively plan and modify the distribution network’s structure in the power grid,improve the quality of the distribution network,and optimize the planning of the distribution network,so that the network can be fully utilized to meet the needs of electricity consumption.In this paper,a distribution network grid planning algorithm based on the reliability of electricity consumption was completed using ant colony algorithm.For the distribution network structure planning of dual power sources,the parallel ant colony algorithm was used to prove that the premise of parallelism is the interactive process of ant colonies,and the dual power distribution network structure model is established based on the principle of the lowest cost.The artificial ants in the algorithm were compared with real ants in nature,and the basic steps and working principle of the ant colony optimization algorithm was studied with the help of the travelling salesman problem(TSP).Then,the limitations of the ant colony algorithm were analyzed,and an improvement strategy was proposed by using python for digital simulation.The results demonstrated the reliability of model-building and algorithm improvement.展开更多
To ensure the safety and reliability of the distribution network and adapt to the uncertain development of renewable energy sources and loads,a two-stage distributionally robust optimization model is proposed for the ...To ensure the safety and reliability of the distribution network and adapt to the uncertain development of renewable energy sources and loads,a two-stage distributionally robust optimization model is proposed for the active distribution network(ADN)optimization problem considering the uncertainties of the source and load in this paper.By establishing an ambiguity set to capture the uncertainties of the photovoltaic(PV)power,wind power and load,the piecewise-linear function and auxiliary parameters are introduced to help characterize the probability distribution of uncertain variables.The optimization goal of the model is to minimize the total expected cost under the worst-case distribution in the ambiguity set.The first-stage expected cost is obtained based on the predicted value of the uncertainty variable.The second-stage expected cost is based on the actual value of the uncertainty variable to solve the first-stage decision.The generalized linear decision rule approximates the two-stage optimization model,and the affine function is introduced to provide a closer approximation to the second-stage optimization model.Finally,the improved IEEE 33-node and IEEE 118-node systems are simulated and analyzed with deterministic methods,stochastic programming,and robust optimization methods to verify the feasibility and superiority of the proposed model and algorithm.展开更多
现有的数据中心节能降碳优化方法没有综合考虑碳足迹涉及的能源输入、生产耗能以及废余利用等环节的耦合性,难以实现系统性节能降碳。为此,提出了一种基于深度强化学习的优化算法DeepCCHP(deep combined cooling,heating and power gene...现有的数据中心节能降碳优化方法没有综合考虑碳足迹涉及的能源输入、生产耗能以及废余利用等环节的耦合性,难以实现系统性节能降碳。为此,提出了一种基于深度强化学习的优化算法DeepCCHP(deep combined cooling,heating and power generation),针对数据中心冷热电联产系统,联合控制供电子系统和制冷子系统,优化用电成本、碳排放量和能效。DeepCCHP结合长、短期时间序列网络和深度强化学习方法对联合优化问题进行求解,实现前摄式的联合控制发电设备和制冷设备。在基于Trnsys软件的仿真环境中,通过阿里巴巴数据中心集群数据的训练和验证。实验结果表明,与基准算法相比,DeepCCHP算法可以节省最高40%的成本和28%的碳排放量,且能够在能源成本、碳排放和能效三者之间取得更好的折中与平衡。展开更多
基金supported by the Deanship of Postgraduate Studies and Scientific Research at Majmaah University in Saudi Arabia under Project Number(ICR-2024-1002).
文摘In the contemporary era,the global expansion of electrical grids is propelled by various renewable energy sources(RESs).Efficient integration of stochastic RESs and optimal power flow(OPF)management are critical for network optimization.This study introduces an innovative solution,the Gaussian Bare-Bones Levy Cheetah Optimizer(GBBLCO),addressing OPF challenges in power generation systems with stochastic RESs.The primary objective is to minimize the total operating costs of RESs,considering four functions:overall operating costs,voltage deviation management,emissions reduction,voltage stability index(VSI)and power loss mitigation.Additionally,a carbon tax is included in the objective function to reduce carbon emissions.Thorough scrutiny,using modified IEEE 30-bus and IEEE 118-bus systems,validates GBBLCO’s superior performance in achieving optimal solutions.Simulation results demonstrate GBBLCO’s efficacy in six optimization scenarios:total cost with valve point effects,total cost with emission and carbon tax,total cost with prohibited operating zones,active power loss optimization,voltage deviation optimization and enhancing voltage stability index(VSI).GBBLCO outperforms conventional techniques in each scenario,showcasing rapid convergence and superior solution quality.Notably,GBBLCO navigates complexities introduced by valve point effects,adapts to environmental constraints,optimizes costs while considering prohibited operating zones,minimizes active power losses,and optimizes voltage deviation by enhancing the voltage stability index(VSI)effectively.This research significantly contributes to advancing OPF,emphasizing GBBLCO’s improved global search capabilities and ability to address challenges related to local minima.GBBLCO emerges as a versatile and robust optimization tool for diverse challenges in power systems,offering a promising solution for the evolving needs of renewable energy-integrated power grids.
文摘The author puts forward the pattern of optimizing the structure of energy sources for generating power in the early stage of the 21st century in Fujian Province; analyzes imper’tant functions on speeding up nuclear power for adjusting the structure of energy sources and heightening economic benefits.and suggests that the first liquefied natural gas combined-cycle power plant will start to build at the end of this century and every effort is made so as to change the recent unreasonable structure of energy source step by step and form the optimized structure of energy sources for generating power, that includes hydropower, thermal power (coal, oil and natural gas), nuclear power, pumpedstorage power, and power from new energy sources. In order to reach the abovementioned significant target, the author discusses the technical and economic measures and the supporting policy to be taken at present and in future.
文摘A distribution network plays an extremely important role in the safe and efficient operation of a power grid.As the core part of a power grid’s operation,a distribution network will have a significant impact on the safety and reliability of residential electricity consumption.it is necessary to actively plan and modify the distribution network’s structure in the power grid,improve the quality of the distribution network,and optimize the planning of the distribution network,so that the network can be fully utilized to meet the needs of electricity consumption.In this paper,a distribution network grid planning algorithm based on the reliability of electricity consumption was completed using ant colony algorithm.For the distribution network structure planning of dual power sources,the parallel ant colony algorithm was used to prove that the premise of parallelism is the interactive process of ant colonies,and the dual power distribution network structure model is established based on the principle of the lowest cost.The artificial ants in the algorithm were compared with real ants in nature,and the basic steps and working principle of the ant colony optimization algorithm was studied with the help of the travelling salesman problem(TSP).Then,the limitations of the ant colony algorithm were analyzed,and an improvement strategy was proposed by using python for digital simulation.The results demonstrated the reliability of model-building and algorithm improvement.
基金supported by Natural Science Foundation of Beijing Municipality(No.3161002)National Key R&D Program(No.2017YFB0903300).
文摘To ensure the safety and reliability of the distribution network and adapt to the uncertain development of renewable energy sources and loads,a two-stage distributionally robust optimization model is proposed for the active distribution network(ADN)optimization problem considering the uncertainties of the source and load in this paper.By establishing an ambiguity set to capture the uncertainties of the photovoltaic(PV)power,wind power and load,the piecewise-linear function and auxiliary parameters are introduced to help characterize the probability distribution of uncertain variables.The optimization goal of the model is to minimize the total expected cost under the worst-case distribution in the ambiguity set.The first-stage expected cost is obtained based on the predicted value of the uncertainty variable.The second-stage expected cost is based on the actual value of the uncertainty variable to solve the first-stage decision.The generalized linear decision rule approximates the two-stage optimization model,and the affine function is introduced to provide a closer approximation to the second-stage optimization model.Finally,the improved IEEE 33-node and IEEE 118-node systems are simulated and analyzed with deterministic methods,stochastic programming,and robust optimization methods to verify the feasibility and superiority of the proposed model and algorithm.
文摘现有的数据中心节能降碳优化方法没有综合考虑碳足迹涉及的能源输入、生产耗能以及废余利用等环节的耦合性,难以实现系统性节能降碳。为此,提出了一种基于深度强化学习的优化算法DeepCCHP(deep combined cooling,heating and power generation),针对数据中心冷热电联产系统,联合控制供电子系统和制冷子系统,优化用电成本、碳排放量和能效。DeepCCHP结合长、短期时间序列网络和深度强化学习方法对联合优化问题进行求解,实现前摄式的联合控制发电设备和制冷设备。在基于Trnsys软件的仿真环境中,通过阿里巴巴数据中心集群数据的训练和验证。实验结果表明,与基准算法相比,DeepCCHP算法可以节省最高40%的成本和28%的碳排放量,且能够在能源成本、碳排放和能效三者之间取得更好的折中与平衡。
