This paper collects and synthesizes the technical requirements, implementation, and validation methods for quasi-steady agent-based simulations of interconnectionscale models with particular attention to the integrati...This paper collects and synthesizes the technical requirements, implementation, and validation methods for quasi-steady agent-based simulations of interconnectionscale models with particular attention to the integration of renewable generation and controllable loads. Approaches for modeling aggregated controllable loads are presented and placed in the same control and economic modeling framework as generation resources for interconnection planning studies. Model performance is examined with system parameters that are typical for an interconnection approximately the size of the Western Electricity Coordinating Council(WECC) and a control area about 1/100 the size of the system. These results are used to demonstrate and validate the methods presented.展开更多
The integration of renewable energy sources into electricity markets presents significant challenges due to the inherent variability and uncertainty of power generation from wind,solar,and other renewables.Accurate fo...The integration of renewable energy sources into electricity markets presents significant challenges due to the inherent variability and uncertainty of power generation from wind,solar,and other renewables.Accurate forecasting is crucial for ensuring grid stability,optimizing market operations,and minimizing economic risks.This paper introduces a hybrid forecasting framework incorporating fractional-order statistical models,fractal-based feature enginering,and deep learning architectures to improve renewable energy forecasting accuracy.Fractional autoregressive integrated moving average(FARIMA)and fractional exponential smoothing(FETS)models are explored for capturing long-memory dependencies in energy time-series data.Additionally,multifractal detrended fluctuation analysis(MFDFA)is used to analyze the intermittency of renewable energy generation.The hybrid approach further integrates wavelet transforms and convolutional long short-term memory(CNN-LSTM)networks to model shortand long-term dependencies effectively.Experimental results demonstrate that fractional and fractal-based hybrid forecasting techniques significantly outperform traditional models in terms of accuracy,reliability,and adaptability to energy market dynamics.This research provides insights for market participants,policymakers,and grid operators to develop more robust forecasting frameworks,ensuring a more sustainable and resilient electricity market.展开更多
An industrial park is one of the typical en ergy con sumption schemes in power systems owing to the heavy in dustrial loads and their abilities to resp ond to electricity price cha nges.Therefore,en ergy in tegrati on...An industrial park is one of the typical en ergy con sumption schemes in power systems owing to the heavy in dustrial loads and their abilities to resp ond to electricity price cha nges.Therefore,en ergy in tegrati on in the industrial sector is significant.Accordingly,the concept of industrial virtual power plant(IVPP)has been proposed to deal with such problems.This study demonstrates an IVPP model to man age resources in an eco-i ndustrial park,including en ergy storage systems,dema nd resp onse(DR)resources,and distributed energies.In addition,fuzzy theory is used to cha nge the deterministic system constraints to fuzzy parameters,considering the uncertainty of renewable energy,and fuzzy chance constraints are then set based on the credibility theory.By maximizi ng the daily ben efits of the IVPP owners in day-ahead markets,DR and energy storage systems can be scheduled economically.Therefore,the energy between the grid and IVPP can flow in both directions:the surplus renewable electricity of IVPP can be sold in the market;when the electricity gen erated in side IVPP is not enough for its use,IVPP can also purchase power through the market.Case studies based on three win d-level scenarios dem on strate the efficie nt syn ergies betwee n IVPP resources.The validatio n results indicate that IVPP can optimize the supply and demand resources in in dustrial parks,thereby decarbonizing the power systems.展开更多
With the continuous development of power electronic devices,intelligent control systems,and other technologies,the voltage level and transmission capacity of voltage source converter (VSC)-high-voltage direct current ...With the continuous development of power electronic devices,intelligent control systems,and other technologies,the voltage level and transmission capacity of voltage source converter (VSC)-high-voltage direct current (HVDC) technology will continue to increase,while the system losses and costs will gradually decrease.Therefore,it can be foreseen that VSC-HVDC transmission technology will be more widely applied in future large-scale renewable energy development projects.Adopting VSC-HVDC transmission technology can be used to overcome issues encountered by large-scale renewable energy transmission and integration projects,such as a weak local power grid,lack of support for synchronous power supply,and insufficient accommodation capacity.However,this solution also faces many technical challenges because of the differences between renewable energy and traditional synchronous power generation systems.Based on actual engineering practices that are used worldwide,this article analyzes the technical challenges encountered by integrating large-scale renewable energy systems that adopt the use of VSC-HVDC technology,while aiming to provide support for future research and engineering projects related to VSC-HVDC-based large-scale renewable energy integration projects.展开更多
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
This study presents a comprehensive impact analysis of the rotor angle stability of a proposed international connection between the Philippines and Sabah,Malaysia,as part of the Association of Southeast Asian Nations(...This study presents a comprehensive impact analysis of the rotor angle stability of a proposed international connection between the Philippines and Sabah,Malaysia,as part of the Association of Southeast Asian Nations(ASEAN)Power Grid.This study focuses on modeling and evaluating the dynamic performance of the interconnected system,considering the high penetration of renewable sources.Power flow,small signal stability,and transient stability analyses were conducted to assess the ability of the proposed linked power system models to withstand small and large disturbances,utilizing the Power Systems Analysis Toolbox(PSAT)software in MATLAB.All components used in the model are documented in the PSAT library.Currently,there is a lack of publicly available studies regarding the implementation of this specific system.Additionally,the study investigates the behavior of a system with a high penetration of renewable energy sources.Based on the findings,this study concludes that a system is generally stable when interconnection is realized,given its appropriate location and dynamic component parameters.Furthermore,the critical eigenvalues of the system also exhibited improvement as the renewable energy sources were augmented.展开更多
vip Editor in Chief:Professor Xiao-Ping Zhang, University of Birmingham The potential for renewable energy to make contributions to mitigating the impact of climate change is expected to increase significantly in th...vip Editor in Chief:Professor Xiao-Ping Zhang, University of Birmingham The potential for renewable energy to make contributions to mitigating the impact of climate change is expected to increase significantly in the longer term. Renewable energy generation technologies including onshore wind, offshore wind, wave,tidal, marine current, and ocean thermal energy generation as well as PV power generation, which are considered展开更多
Renewable power modules such as the thermoelectric generator and the PV panel are featured by low output voltage and low power.Aiming at maximum output power,a high energy efficiency module integrated converter(MIC),a...Renewable power modules such as the thermoelectric generator and the PV panel are featured by low output voltage and low power.Aiming at maximum output power,a high energy efficiency module integrated converter(MIC),as shown in Fig.1,and its control strategy for series connected distributed(SCD)renewable power systems,as shown in Fig.2,are proposed.The topology of the MIC is an improved one of the conventional H-bridge Buck-Boost converter.展开更多
The rapid evolution and expanding scale of AI(artificial intelligence)technologies exert unprecedented energy demands on global electrical grids.Powering computationally intensive tasks such as large-scale AI model tr...The rapid evolution and expanding scale of AI(artificial intelligence)technologies exert unprecedented energy demands on global electrical grids.Powering computationally intensive tasks such as large-scale AI model training and widespread real-time inference necessitates substantial electricity consumption,presenting a significant challenge to conventional power infrastructure.This paper examines the critical need for a fundamental shift towards smart energy grids in response to AI’s growing energy footprint.It delves into the symbiotic relationship wherein AI acts as a significant energy consumer while offering the intelligence required for dynamic load management,efficient integration of renewable energy sources,and optimized grid operations.We posit that advanced smart grids are indispensable for facilitating AI’s sustainable growth,underscoring this synergy as a pivotal advancement toward a resilient energy future.展开更多
Accurate energy demand forecasting is crucial in today’s rapidly electrifying world with decentralized systems and integrated renewables.Traditional models struggle with the dynamic complexities,but AI(artificial int...Accurate energy demand forecasting is crucial in today’s rapidly electrifying world with decentralized systems and integrated renewables.Traditional models struggle with the dynamic complexities,but AI(artificial intelligence),particularly ML(machine learning)and DL(deep learning),offers transformative solutions.This article explores how AI enhances forecasting accuracy,enables real-time adaptability,and supports strategic energy management.It examines the synergy between AI,IoT(Internet of Things)devices,and smart grids in generating predictive and prescriptive insights.Through case studies,we analyze the benefits and challenges of deploying AI in this domain,including data quality,model explainability,and infrastructure needs.Ultimately,AI emerges as a key enabler for the resilient,data-driven energy systems required to meet modern society’s evolving demands and achieve a sustainable future.展开更多
Significant advances in battery and fuel cell technologies over the past decade have catalyzed the transition toward electrified transportation systems and large-scale renewable energy integration.Concurrent with thes...Significant advances in battery and fuel cell technologies over the past decade have catalyzed the transition toward electrified transportation systems and large-scale renewable energy integration.Concurrent with these developments,the interdisciplinary role of mechanics has emerged as a critical research frontier.展开更多
Around the world,there has been a notable shift toward the use of renewable energy technology due to the growing demand for energy and the ongoing depletion of conventional resources,such as fossil fuels.Following thi...Around the world,there has been a notable shift toward the use of renewable energy technology due to the growing demand for energy and the ongoing depletion of conventional resources,such as fossil fuels.Following this worldwide trend,Brunei’s government has initiated several strategic programs aimed at encouraging the establishment of energy from renewable sources in the nation’s energy mix.These initiatives are designed not only to support environmental sustainability but also to make energy from renewable sources increasingly competitive in comparison to more conventional energy sources like gas and oil,which have historically dominated Brunei’s energy market.The optimization of a hybrid energy system that combines diesel generators,solar photovoltaic(PV)panels,and the national power grid is the focus of this study.The objective is to identify the most cost-effective and environmentally sustainable configuration that can reliably meet local energy demands.During optimization,several configuration was tried and tested,including only grid,PV and Grid and PV-generator.HOMER(Hybrid Optimization of Multiple Energy Resources)software,a popular simulation tool that makes it possible to simulate and analyze hybrid energy systems,is utilized in the optimization process.Inside the HOMER Pro optimization,various system configuration is taken into account for the optimization.While simulating,it takes into account different combinations of components such as solar panels,wind turbines and batteries.Later on,it is being ranked by different factors such as net present cost(NPC),Cost of Energy(COE),etc.A comprehensive techno-economic research is carried out to evaluate various system configurations,considering key performance indicators such as total energy generation cost,operational expenditure,and greenhouse gas emissions.The results provide valuable insights into how renewable-based hybrid systems can reduce environmental impact while maintaining economic viability,supporting Brunei’s broader goals of energy diversification and sustainability.The study also emphasizes how such hybrid systems could be scaled for off-grid and rural populations in Brunei,where a dependable electricity supply is still a problem.Furthermore,sensitivity analyses were performed to evaluate the effects of variations in solar irradiation,load demand,and fuel prices on the overall system performance.Policymakers and energy planners can use these insights to help them make data-driven decisions about future investments in infrastructure for renewable energy.展开更多
This paper presents a novel approach to dynamic pricing and distributed energy management in virtual power plant(VPP)networks using multi-agent reinforcement learning(MARL).As the energy landscape evolves towards grea...This paper presents a novel approach to dynamic pricing and distributed energy management in virtual power plant(VPP)networks using multi-agent reinforcement learning(MARL).As the energy landscape evolves towards greater decentralization and renewable integration,traditional optimization methods struggle to address the inherent complexities and uncertainties.Our proposed MARL framework enables adaptive,decentralized decision-making for both the distribution system operator and individual VPPs,optimizing economic efficiency while maintaining grid stability.We formulate the problem as a Markov decision process and develop a custom MARL algorithm that leverages actor-critic architectures and experience replay.Extensive simulations across diverse scenarios demonstrate that our approach consistently outperforms baseline methods,including Stackelberg game models and model predictive control,achieving an 18.73%reduction in costs and a 22.46%increase in VPP profits.The MARL framework shows particular strength in scenarios with high renewable energy penetration,where it improves system performance by 11.95%compared with traditional methods.Furthermore,our approach demonstrates superior adaptability to unexpected events and mis-predictions,highlighting its potential for real-world implementation.展开更多
The process of including renewable energy sources in power networks is moving quickly,so the need for innovative configuration solutions for grid-side ESS has grown.Among the new methods presented in this paper is GA-...The process of including renewable energy sources in power networks is moving quickly,so the need for innovative configuration solutions for grid-side ESS has grown.Among the new methods presented in this paper is GA-OCESE,which stands for Genetic Algorithm-based Optimization Configuration for Energy Storage in Electric Networks.This is one of the methods suggested in this study,which aims to enhance the sizing,positioning,and operational characteristics of structured ESS under dynamic grid conditions.Particularly,the aim is to maximize efficiency.A multiobjective genetic algorithm,the GA-OCESE framework,considers all these factors simultaneously.Besides considering cost-efficiency,response time,and energy use,the system also considers all these elements simultaneously.This enables it to effectively react to load uncertainty and variations in inputs connected to renewable sources.Results of an experimental assessment conducted on a standardized grid simulation platform indicate that by increasing energy use efficiency by 17.6%and reducing peak-load effects by 22.3%,GA-OCESE outperforms previous heuristic-based methods.This was found by contrasting the outcomes of the assessment with those of the evaluation.The results of the assessment helped to reveal this.The proposed approach will provide utility operators and energy planners with a decision-making tool that is both scalable and adaptable.This technology is particularly well-suited for smart grids,microgrid systems,and power infrastructures that heavily rely on renewable energy.Every technical component has been carefully recorded to ensure accuracy,reproducibility,and relevance across all power systems engineering software uses.This was done to ensure the program’s relevance.展开更多
Accurate Electric Load Forecasting(ELF)is crucial for optimizing production capacity,improving operational efficiency,and managing energy resources effectively.Moreover,precise ELF contributes to a smaller environment...Accurate Electric Load Forecasting(ELF)is crucial for optimizing production capacity,improving operational efficiency,and managing energy resources effectively.Moreover,precise ELF contributes to a smaller environmental footprint by reducing the risks of disruption,downtime,and waste.However,with increasingly complex energy consumption patterns driven by renewable energy integration and changing consumer behaviors,no single approach has emerged as universally effective.In response,this research presents a hybrid modeling framework that combines the strengths of Random Forest(RF)and Autoregressive Integrated Moving Average(ARIMA)models,enhanced with advanced feature selection—Minimum Redundancy Maximum Relevancy and Maximum Synergy(MRMRMS)method—to produce a sparse model.Additionally,the residual patterns are analyzed to enhance forecast accuracy.High-resolution weather data from Weather Underground and historical energy consumption data from PJM for Duke Energy Ohio and Kentucky(DEO&K)are used in this application.This methodology,termed SP-RF-ARIMA,is evaluated against existing approaches;it demonstrates more than 40%reduction in mean absolute error and root mean square error compared to the second-best method.展开更多
Structured microgrids(SμGs)and Flexible electronic large power transformers(FeLPTs)are emerging as two essential technologies for renewable energy integration,flexible power transmission,and active control.SμGs prov...Structured microgrids(SμGs)and Flexible electronic large power transformers(FeLPTs)are emerging as two essential technologies for renewable energy integration,flexible power transmission,and active control.SμGs provide the integration of renewable energy and storage to balance the energy demand and supply as needed for a given system design.FeLPT’s flexibility for processing,control,and re-configurability offers the capability for flexible transmission for effective flow control and enable SμGs connectivity while still keeping multiscale system level control.Early adaptors for combined heat and power have demonstrated significant economic benefits while reducing environmental foot prints.They bring tremendous benefits to utility companies also.With storage and active control capabilities,a 300-percent increase in bulk transmission and distribution lines are possible without having to increase capacity.SμGs and FeLPTs will also enable the utility industry to be better prepared for the emerging large increase in base load demand from electric transportation and data centers.This is a win-win-win situation for the consumer,the utilities(grid operators),and the environment.SμGs and FeLPTs provide value in power substation,energy surety,reliability,resiliency,and security.It is also shown that the initial cost associated with SμG and FeLPTs deployment can be easily offset with reduced operating cost,which in turn reduces the total life-cycle cost by 33%to 67%.展开更多
<span style="font-family:Verdana;">The ideal places and size of the distribution generators were determined by reducing the loss of power in the distribution networks. The ideal positioning of various ...<span style="font-family:Verdana;">The ideal places and size of the distribution generators were determined by reducing the loss of power in the distribution networks. The ideal positioning of various kinds of DGs has been suggested in the current job. In this job, the ideal power factor for DG supply has been acquired, both the active power as well as the reactive power. In the proposed approach</span><span style="font-family:Verdana;">,</span><span style="font-family:Verdana;"> different types of distribution generation (DG) supply both reactive and real power. For the optimal placement of DG sources</span><span style="font-family:Verdana;">,</span><span style="font-family:Verdana;"> particle swarm optimization technique</span><span style="font-family:Verdana;">s</span><span style="font-family:""><span style="font-family:Verdana;"> have been used in this job. Each of these innovations has its own strengths and drawbacks. Most of the methods that have been proposed so far to formulate DG’s optimum placement problem only consider Type-I DGs, Type-II and </span><span style="font-family:Verdana;">Type-III DGs </span></span><span style="font-family:Verdana;">that </span><span style="font-family:Verdana;">are considered for optimal position in the existing research.</span><span style="font-family:Verdana;"> In the reference, artificial bee colony algorithm was used to determine sites of DGs and condenser combinations and optimal size. The author used PSO method in the reference to determine the appropriate positioning of the DG’s and to maximize the savings of power loss and voltage profile in the distribution network.</span>展开更多
With the integration of renewable energy generations and other related changes,the smart grid,or the future electric power system,is confronted with new challenges and opportunities.Therefore,it is a promising subject...With the integration of renewable energy generations and other related changes,the smart grid,or the future electric power system,is confronted with new challenges and opportunities.Therefore,it is a promising subject in electrical engineering and much work has been done on it.This paper reviews the recent advances on the technologies of smart grid and renewable energy integration,from the aspects of modeling,simulation,protection and control,stability,operation,and planning.展开更多
A future smart grid must fulfill the vision of the Energy Internet in which millions of people produce their own energy from renewables in their homes, offices, and factories and share it with each other. Electric veh...A future smart grid must fulfill the vision of the Energy Internet in which millions of people produce their own energy from renewables in their homes, offices, and factories and share it with each other. Electric vehicles and local energy storage will be widely deployed. Internet technology will be utilized to transform the power grid into an energysharing inter-grid. To prepare for the future, a smart grid with intelligent periphery, or smart GRIP, is proposed. The building blocks of GRIP architecture are called clusters and include an energy-management system （EMS）-controlled transmission grid in the core and distribution grids, micro-grids, and smart buildings and homes on the periphery; all of which are hierarchically structured. The layered architecture of GRIP allows a seamless transition from the present to the future and plug-and-play interoperability. The basic functions of a cluster consist of （1） dispatch, （2） smoothing, and （3） mitigation. A risk-limiting dispatch methodology is presented; a new device, called the electric spring, is developed for smoothing out fluctuations in periphery clusters; and means to mitigate failures are discussed.展开更多
With the high penetration of renewable energy,new challenges,such as power fluctuation suppression and inertial support capability,have arisen in the power sector.Battery energy storage systems play an essential role ...With the high penetration of renewable energy,new challenges,such as power fluctuation suppression and inertial support capability,have arisen in the power sector.Battery energy storage systems play an essential role in renewable energy integration.In this paper,a distributed virtual synchronous generator(VSG)control method for a battery energy storage system(BESS)with a cascaded H-bridge converter in a grid-connected mode is proposed.The VSG is developed without communication dependence,and state-of-charge(SOC)balancing control is achieved using the distributed average algorithm.Owing to the low varying speed of SOC,the bandwidth of the distributed communication networks is extremely slow,which decreases the cost.Therefore,the proposed method can simultaneously provide inertial support and accurate SOC balancing.The stability is also proved using root locus analysis.Finally,simulations under different conditions are carried out to verify the effectiveness of the proposed method.展开更多
文摘This paper collects and synthesizes the technical requirements, implementation, and validation methods for quasi-steady agent-based simulations of interconnectionscale models with particular attention to the integration of renewable generation and controllable loads. Approaches for modeling aggregated controllable loads are presented and placed in the same control and economic modeling framework as generation resources for interconnection planning studies. Model performance is examined with system parameters that are typical for an interconnection approximately the size of the Western Electricity Coordinating Council(WECC) and a control area about 1/100 the size of the system. These results are used to demonstrate and validate the methods presented.
基金funded under research grant from the Research,Development,andInnovation Authority(RDIA),Saudi Arabia,grant No.13010-Tabuk-2023-UT-R-3-1-SE.
文摘The integration of renewable energy sources into electricity markets presents significant challenges due to the inherent variability and uncertainty of power generation from wind,solar,and other renewables.Accurate forecasting is crucial for ensuring grid stability,optimizing market operations,and minimizing economic risks.This paper introduces a hybrid forecasting framework incorporating fractional-order statistical models,fractal-based feature enginering,and deep learning architectures to improve renewable energy forecasting accuracy.Fractional autoregressive integrated moving average(FARIMA)and fractional exponential smoothing(FETS)models are explored for capturing long-memory dependencies in energy time-series data.Additionally,multifractal detrended fluctuation analysis(MFDFA)is used to analyze the intermittency of renewable energy generation.The hybrid approach further integrates wavelet transforms and convolutional long short-term memory(CNN-LSTM)networks to model shortand long-term dependencies effectively.Experimental results demonstrate that fractional and fractal-based hybrid forecasting techniques significantly outperform traditional models in terms of accuracy,reliability,and adaptability to energy market dynamics.This research provides insights for market participants,policymakers,and grid operators to develop more robust forecasting frameworks,ensuring a more sustainable and resilient electricity market.
基金Department of Science and Technology of Guangdong Province(Project 2019B0909011001).
文摘An industrial park is one of the typical en ergy con sumption schemes in power systems owing to the heavy in dustrial loads and their abilities to resp ond to electricity price cha nges.Therefore,en ergy in tegrati on in the industrial sector is significant.Accordingly,the concept of industrial virtual power plant(IVPP)has been proposed to deal with such problems.This study demonstrates an IVPP model to man age resources in an eco-i ndustrial park,including en ergy storage systems,dema nd resp onse(DR)resources,and distributed energies.In addition,fuzzy theory is used to cha nge the deterministic system constraints to fuzzy parameters,considering the uncertainty of renewable energy,and fuzzy chance constraints are then set based on the credibility theory.By maximizi ng the daily ben efits of the IVPP owners in day-ahead markets,DR and energy storage systems can be scheduled economically.Therefore,the energy between the grid and IVPP can flow in both directions:the surplus renewable electricity of IVPP can be sold in the market;when the electricity gen erated in side IVPP is not enough for its use,IVPP can also purchase power through the market.Case studies based on three win d-level scenarios dem on strate the efficie nt syn ergies betwee n IVPP resources.The validatio n results indicate that IVPP can optimize the supply and demand resources in in dustrial parks,thereby decarbonizing the power systems.
基金State Grid Corporation of China Science and Technology Project: Research on Power Transmission of Largescale Renewable Energy Base by VSC-LCC hybrid HVDC(No. NY71-19-037)
文摘With the continuous development of power electronic devices,intelligent control systems,and other technologies,the voltage level and transmission capacity of voltage source converter (VSC)-high-voltage direct current (HVDC) technology will continue to increase,while the system losses and costs will gradually decrease.Therefore,it can be foreseen that VSC-HVDC transmission technology will be more widely applied in future large-scale renewable energy development projects.Adopting VSC-HVDC transmission technology can be used to overcome issues encountered by large-scale renewable energy transmission and integration projects,such as a weak local power grid,lack of support for synchronous power supply,and insufficient accommodation capacity.However,this solution also faces many technical challenges because of the differences between renewable energy and traditional synchronous power generation systems.Based on actual engineering practices that are used worldwide,this article analyzes the technical challenges encountered by integrating large-scale renewable energy systems that adopt the use of VSC-HVDC technology,while aiming to provide support for future research and engineering projects related to VSC-HVDC-based large-scale renewable energy integration projects.
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This study presents a comprehensive impact analysis of the rotor angle stability of a proposed international connection between the Philippines and Sabah,Malaysia,as part of the Association of Southeast Asian Nations(ASEAN)Power Grid.This study focuses on modeling and evaluating the dynamic performance of the interconnected system,considering the high penetration of renewable sources.Power flow,small signal stability,and transient stability analyses were conducted to assess the ability of the proposed linked power system models to withstand small and large disturbances,utilizing the Power Systems Analysis Toolbox(PSAT)software in MATLAB.All components used in the model are documented in the PSAT library.Currently,there is a lack of publicly available studies regarding the implementation of this specific system.Additionally,the study investigates the behavior of a system with a high penetration of renewable energy sources.Based on the findings,this study concludes that a system is generally stable when interconnection is realized,given its appropriate location and dynamic component parameters.Furthermore,the critical eigenvalues of the system also exhibited improvement as the renewable energy sources were augmented.
文摘vip Editor in Chief:Professor Xiao-Ping Zhang, University of Birmingham The potential for renewable energy to make contributions to mitigating the impact of climate change is expected to increase significantly in the longer term. Renewable energy generation technologies including onshore wind, offshore wind, wave,tidal, marine current, and ocean thermal energy generation as well as PV power generation, which are considered
文摘Renewable power modules such as the thermoelectric generator and the PV panel are featured by low output voltage and low power.Aiming at maximum output power,a high energy efficiency module integrated converter(MIC),as shown in Fig.1,and its control strategy for series connected distributed(SCD)renewable power systems,as shown in Fig.2,are proposed.The topology of the MIC is an improved one of the conventional H-bridge Buck-Boost converter.
文摘The rapid evolution and expanding scale of AI(artificial intelligence)technologies exert unprecedented energy demands on global electrical grids.Powering computationally intensive tasks such as large-scale AI model training and widespread real-time inference necessitates substantial electricity consumption,presenting a significant challenge to conventional power infrastructure.This paper examines the critical need for a fundamental shift towards smart energy grids in response to AI’s growing energy footprint.It delves into the symbiotic relationship wherein AI acts as a significant energy consumer while offering the intelligence required for dynamic load management,efficient integration of renewable energy sources,and optimized grid operations.We posit that advanced smart grids are indispensable for facilitating AI’s sustainable growth,underscoring this synergy as a pivotal advancement toward a resilient energy future.
文摘Accurate energy demand forecasting is crucial in today’s rapidly electrifying world with decentralized systems and integrated renewables.Traditional models struggle with the dynamic complexities,but AI(artificial intelligence),particularly ML(machine learning)and DL(deep learning),offers transformative solutions.This article explores how AI enhances forecasting accuracy,enables real-time adaptability,and supports strategic energy management.It examines the synergy between AI,IoT(Internet of Things)devices,and smart grids in generating predictive and prescriptive insights.Through case studies,we analyze the benefits and challenges of deploying AI in this domain,including data quality,model explainability,and infrastructure needs.Ultimately,AI emerges as a key enabler for the resilient,data-driven energy systems required to meet modern society’s evolving demands and achieve a sustainable future.
文摘Significant advances in battery and fuel cell technologies over the past decade have catalyzed the transition toward electrified transportation systems and large-scale renewable energy integration.Concurrent with these developments,the interdisciplinary role of mechanics has emerged as a critical research frontier.
基金funded through Deanship of Scientific Research at Northern Border University,Arar,Saudi Arabia—project number“NBU-FFR-2025-3623-06”.
文摘Around the world,there has been a notable shift toward the use of renewable energy technology due to the growing demand for energy and the ongoing depletion of conventional resources,such as fossil fuels.Following this worldwide trend,Brunei’s government has initiated several strategic programs aimed at encouraging the establishment of energy from renewable sources in the nation’s energy mix.These initiatives are designed not only to support environmental sustainability but also to make energy from renewable sources increasingly competitive in comparison to more conventional energy sources like gas and oil,which have historically dominated Brunei’s energy market.The optimization of a hybrid energy system that combines diesel generators,solar photovoltaic(PV)panels,and the national power grid is the focus of this study.The objective is to identify the most cost-effective and environmentally sustainable configuration that can reliably meet local energy demands.During optimization,several configuration was tried and tested,including only grid,PV and Grid and PV-generator.HOMER(Hybrid Optimization of Multiple Energy Resources)software,a popular simulation tool that makes it possible to simulate and analyze hybrid energy systems,is utilized in the optimization process.Inside the HOMER Pro optimization,various system configuration is taken into account for the optimization.While simulating,it takes into account different combinations of components such as solar panels,wind turbines and batteries.Later on,it is being ranked by different factors such as net present cost(NPC),Cost of Energy(COE),etc.A comprehensive techno-economic research is carried out to evaluate various system configurations,considering key performance indicators such as total energy generation cost,operational expenditure,and greenhouse gas emissions.The results provide valuable insights into how renewable-based hybrid systems can reduce environmental impact while maintaining economic viability,supporting Brunei’s broader goals of energy diversification and sustainability.The study also emphasizes how such hybrid systems could be scaled for off-grid and rural populations in Brunei,where a dependable electricity supply is still a problem.Furthermore,sensitivity analyses were performed to evaluate the effects of variations in solar irradiation,load demand,and fuel prices on the overall system performance.Policymakers and energy planners can use these insights to help them make data-driven decisions about future investments in infrastructure for renewable energy.
基金supported by the Science and Technology Project of State Grid Sichuan Electric Power Company Chengdu Power Supply Company under Grant No.521904240005.
文摘This paper presents a novel approach to dynamic pricing and distributed energy management in virtual power plant(VPP)networks using multi-agent reinforcement learning(MARL).As the energy landscape evolves towards greater decentralization and renewable integration,traditional optimization methods struggle to address the inherent complexities and uncertainties.Our proposed MARL framework enables adaptive,decentralized decision-making for both the distribution system operator and individual VPPs,optimizing economic efficiency while maintaining grid stability.We formulate the problem as a Markov decision process and develop a custom MARL algorithm that leverages actor-critic architectures and experience replay.Extensive simulations across diverse scenarios demonstrate that our approach consistently outperforms baseline methods,including Stackelberg game models and model predictive control,achieving an 18.73%reduction in costs and a 22.46%increase in VPP profits.The MARL framework shows particular strength in scenarios with high renewable energy penetration,where it improves system performance by 11.95%compared with traditional methods.Furthermore,our approach demonstrates superior adaptability to unexpected events and mis-predictions,highlighting its potential for real-world implementation.
文摘The process of including renewable energy sources in power networks is moving quickly,so the need for innovative configuration solutions for grid-side ESS has grown.Among the new methods presented in this paper is GA-OCESE,which stands for Genetic Algorithm-based Optimization Configuration for Energy Storage in Electric Networks.This is one of the methods suggested in this study,which aims to enhance the sizing,positioning,and operational characteristics of structured ESS under dynamic grid conditions.Particularly,the aim is to maximize efficiency.A multiobjective genetic algorithm,the GA-OCESE framework,considers all these factors simultaneously.Besides considering cost-efficiency,response time,and energy use,the system also considers all these elements simultaneously.This enables it to effectively react to load uncertainty and variations in inputs connected to renewable sources.Results of an experimental assessment conducted on a standardized grid simulation platform indicate that by increasing energy use efficiency by 17.6%and reducing peak-load effects by 22.3%,GA-OCESE outperforms previous heuristic-based methods.This was found by contrasting the outcomes of the assessment with those of the evaluation.The results of the assessment helped to reveal this.The proposed approach will provide utility operators and energy planners with a decision-making tool that is both scalable and adaptable.This technology is particularly well-suited for smart grids,microgrid systems,and power infrastructures that heavily rely on renewable energy.Every technical component has been carefully recorded to ensure accuracy,reproducibility,and relevance across all power systems engineering software uses.This was done to ensure the program’s relevance.
基金supported by the Startup Grant(PG18929)awarded to F.Shokoohi.
文摘Accurate Electric Load Forecasting(ELF)is crucial for optimizing production capacity,improving operational efficiency,and managing energy resources effectively.Moreover,precise ELF contributes to a smaller environmental footprint by reducing the risks of disruption,downtime,and waste.However,with increasingly complex energy consumption patterns driven by renewable energy integration and changing consumer behaviors,no single approach has emerged as universally effective.In response,this research presents a hybrid modeling framework that combines the strengths of Random Forest(RF)and Autoregressive Integrated Moving Average(ARIMA)models,enhanced with advanced feature selection—Minimum Redundancy Maximum Relevancy and Maximum Synergy(MRMRMS)method—to produce a sparse model.Additionally,the residual patterns are analyzed to enhance forecast accuracy.High-resolution weather data from Weather Underground and historical energy consumption data from PJM for Duke Energy Ohio and Kentucky(DEO&K)are used in this application.This methodology,termed SP-RF-ARIMA,is evaluated against existing approaches;it demonstrates more than 40%reduction in mean absolute error and root mean square error compared to the second-best method.
文摘Structured microgrids(SμGs)and Flexible electronic large power transformers(FeLPTs)are emerging as two essential technologies for renewable energy integration,flexible power transmission,and active control.SμGs provide the integration of renewable energy and storage to balance the energy demand and supply as needed for a given system design.FeLPT’s flexibility for processing,control,and re-configurability offers the capability for flexible transmission for effective flow control and enable SμGs connectivity while still keeping multiscale system level control.Early adaptors for combined heat and power have demonstrated significant economic benefits while reducing environmental foot prints.They bring tremendous benefits to utility companies also.With storage and active control capabilities,a 300-percent increase in bulk transmission and distribution lines are possible without having to increase capacity.SμGs and FeLPTs will also enable the utility industry to be better prepared for the emerging large increase in base load demand from electric transportation and data centers.This is a win-win-win situation for the consumer,the utilities(grid operators),and the environment.SμGs and FeLPTs provide value in power substation,energy surety,reliability,resiliency,and security.It is also shown that the initial cost associated with SμG and FeLPTs deployment can be easily offset with reduced operating cost,which in turn reduces the total life-cycle cost by 33%to 67%.
文摘<span style="font-family:Verdana;">The ideal places and size of the distribution generators were determined by reducing the loss of power in the distribution networks. The ideal positioning of various kinds of DGs has been suggested in the current job. In this job, the ideal power factor for DG supply has been acquired, both the active power as well as the reactive power. In the proposed approach</span><span style="font-family:Verdana;">,</span><span style="font-family:Verdana;"> different types of distribution generation (DG) supply both reactive and real power. For the optimal placement of DG sources</span><span style="font-family:Verdana;">,</span><span style="font-family:Verdana;"> particle swarm optimization technique</span><span style="font-family:Verdana;">s</span><span style="font-family:""><span style="font-family:Verdana;"> have been used in this job. Each of these innovations has its own strengths and drawbacks. Most of the methods that have been proposed so far to formulate DG’s optimum placement problem only consider Type-I DGs, Type-II and </span><span style="font-family:Verdana;">Type-III DGs </span></span><span style="font-family:Verdana;">that </span><span style="font-family:Verdana;">are considered for optimal position in the existing research.</span><span style="font-family:Verdana;"> In the reference, artificial bee colony algorithm was used to determine sites of DGs and condenser combinations and optimal size. The author used PSO method in the reference to determine the appropriate positioning of the DG’s and to maximize the savings of power loss and voltage profile in the distribution network.</span>
基金supported by the National Natural Science Foundation of China(Grant Nos.51321005,51207076)the National High Technology Research and Development of China("863" Project)(Grant No.2012AA050204)
文摘With the integration of renewable energy generations and other related changes,the smart grid,or the future electric power system,is confronted with new challenges and opportunities.Therefore,it is a promising subject in electrical engineering and much work has been done on it.This paper reviews the recent advances on the technologies of smart grid and renewable energy integration,from the aspects of modeling,simulation,protection and control,stability,operation,and planning.
基金sponsored by National Key Basic Research Program of China (973 Program) (2012CB215102) for WuUS National Science Foundation Award (1135872) for VaraiyaHong Kong RGC Theme-based Research Project (T23-701/14-N) for Hui
文摘A future smart grid must fulfill the vision of the Energy Internet in which millions of people produce their own energy from renewables in their homes, offices, and factories and share it with each other. Electric vehicles and local energy storage will be widely deployed. Internet technology will be utilized to transform the power grid into an energysharing inter-grid. To prepare for the future, a smart grid with intelligent periphery, or smart GRIP, is proposed. The building blocks of GRIP architecture are called clusters and include an energy-management system （EMS）-controlled transmission grid in the core and distribution grids, micro-grids, and smart buildings and homes on the periphery; all of which are hierarchically structured. The layered architecture of GRIP allows a seamless transition from the present to the future and plug-and-play interoperability. The basic functions of a cluster consist of （1） dispatch, （2） smoothing, and （3） mitigation. A risk-limiting dispatch methodology is presented; a new device, called the electric spring, is developed for smoothing out fluctuations in periphery clusters; and means to mitigate failures are discussed.
基金This work was supported by National Natural Science Foundation of China under Grant U1909201,Distributed active learning theory and method for operational situation awareness of active distribution network.
文摘With the high penetration of renewable energy,new challenges,such as power fluctuation suppression and inertial support capability,have arisen in the power sector.Battery energy storage systems play an essential role in renewable energy integration.In this paper,a distributed virtual synchronous generator(VSG)control method for a battery energy storage system(BESS)with a cascaded H-bridge converter in a grid-connected mode is proposed.The VSG is developed without communication dependence,and state-of-charge(SOC)balancing control is achieved using the distributed average algorithm.Owing to the low varying speed of SOC,the bandwidth of the distributed communication networks is extremely slow,which decreases the cost.Therefore,the proposed method can simultaneously provide inertial support and accurate SOC balancing.The stability is also proved using root locus analysis.Finally,simulations under different conditions are carried out to verify the effectiveness of the proposed method.
