Renewable Energy Systems(RES)provide a sustainable solution to climate warming and environmental pollution by enhancing stability and reliability through status acquisition and analysis on cloud platforms and intellig...Renewable Energy Systems(RES)provide a sustainable solution to climate warming and environmental pollution by enhancing stability and reliability through status acquisition and analysis on cloud platforms and intelligent processing on edge servers(ES).However,securely distributing encrypted data stored in the cloud to terminals that meet decryption requirements has become a prominent research topic.Additionally,managing attributes,including addition,deletion,and modification,is a crucial issue in the access control scheme for RES.To address these security concerns,a trust-based ciphertext-policy attribute-based encryption(CP-ABE)device access control scheme is proposed for RES(TB-CP-ABE).This scheme effectivelymanages the distribution and control of encrypted data on the cloud through robust attribute key management.By introducing trust management mechanisms and outsourced decryption technology,the ES system can effectively assess and manage the trust worthiness of terminal devices,ensuring that only trusted devices can participate in data exchange and access sensitive information.Besides,the ES system dynamically evaluates trust scores to set decryption trust thresholds,thereby regulating device data access permissions and enhancing the system’s security.To validate the security of the proposed TB-CP-ABE against chosen plaintext attacks,a comprehensive formal security analysis is conducted using the widely accepted random oraclemodel under the decisional q-Bilinear Diffie-Hellman Exponent(q-BDHE)assumption.Finally,comparative analysis with other schemes demonstrates that the TB-CP-ABE scheme cuts energy/communication costs by 43%,and scaleswell with rising terminals,maintaining average latency below 50ms,ensuring real-time service feasibility.The proposed scheme not only provides newinsights for the secure management of RES but also lays a foundation for future secure energy solutions.展开更多
Early and accurate fault detection and diagnosis for renewable energy systems can increase their safety and ensure the continuity of their service. This paper presents a comprehensive review of different fault detecti...Early and accurate fault detection and diagnosis for renewable energy systems can increase their safety and ensure the continuity of their service. This paper presents a comprehensive review of different fault detection and diagnosis methods for hybrid renewable energy systems consisting of a wind turbine power generator, a PV （photovoltaic） array, a PEM （polymer electrolyte membrane） fuel cell and a battery storage system. The need of batteries to store the generated power from the solar panel, wind turbine or PEM fuel cell is also emphasized. Finally, an overview of the current methods used in the diagnosing of the lead-acid battery degradation is given.展开更多
I.I NTRODUCTION W ITH the advent of low-carbon economy,there has been a growing interest in harnessing renewable energy resources particularly for electricity generation.Renewable energy resources are advocated for th...I.I NTRODUCTION W ITH the advent of low-carbon economy,there has been a growing interest in harnessing renewable energy resources particularly for electricity generation.Renewable energy resources are advocated for the economic and environ-展开更多
The main purpose of this article is to perform a technical-economic analysis of different microgeneration systems （photovoltaic and wind turbine systems） that can be installed in a single-family house. One of the st...The main purpose of this article is to perform a technical-economic analysis of different microgeneration systems （photovoltaic and wind turbine systems） that can be installed in a single-family house. One of the strategies for improving energy performance of buildings is the installation of microgeneration systems. This will enable Portugal to meet the goal regarding the share of renewable sources on gross final energy consumption imposed by the European Commission climate change and energy package for 2020. The analysis of four different micro-generation systems will take into account the local availability of renewable resources and market technologies and the new Portuguese law concerning microgeneration to promote renewable energy sources in households. The Portuguese microgeneration legal framework is also highlighted.展开更多
The lack of water in arid and semi-arid regions has often limited agricultural production. Indeed, even where water is available for irrigation, the lack of electricity, as well as the high costs of diesel, has create...The lack of water in arid and semi-arid regions has often limited agricultural production. Indeed, even where water is available for irrigation, the lack of electricity, as well as the high costs of diesel, has created constraints on small farmers. The purpose of this research is to review the renewable energy potential available in arid and semi-arid zones that can be used for irrigation as a substitute for fossil fuels. In this review, the solar thermal irrigation, solar photovoltaic (PV) irrigation, wind pumping and biomass pumping are discussed. The comparison of different hybrid pumping systems and analyses of renewable sources irrigation assessment in arid and semi-arid regions of Mozambique also are discussed. The results of this study showed that there are still certain technological limitations regarding the use of solar thermal energy for irrigation. As far as wind power is concerned, the analysis of the pumping water life cycle cost showed that the wind power water pumping system is more economical and viable compared to the diesel based system. However, the study concluded that photovoltaic solar energy has been shown to be more viable for pumping water for irrigation in arid and semi-arid regions.展开更多
Photovoltaic(PV)systems are environmentally friendly,generate green energy,and receive support from policies and organizations.However,weather fluctuations make large-scale PV power integration and management challeng...Photovoltaic(PV)systems are environmentally friendly,generate green energy,and receive support from policies and organizations.However,weather fluctuations make large-scale PV power integration and management challenging despite the economic benefits.Existing PV forecasting techniques(sequential and convolutional neural networks(CNN))are sensitive to environmental conditions,reducing energy distribution system performance.To handle these issues,this article proposes an efficient,weather-resilient convolutional-transformer-based network(CT-NET)for accurate and efficient PV power forecasting.The network consists of three main modules.First,the acquired PV generation data are forwarded to the pre-processing module for data refinement.Next,to carry out data encoding,a CNNbased multi-head attention(MHA)module is developed in which a single MHA is used to decode the encoded data.The encoder module is mainly composed of 1D convolutional and MHA layers,which extract local as well as contextual features,while the decoder part includes MHA and feedforward layers to generate the final prediction.Finally,the performance of the proposed network is evaluated using standard error metrics,including the mean squared error(MSE),root mean squared error(RMSE),and mean absolute percentage error(MAPE).An ablation study and comparative analysis with several competitive state-of-the-art approaches revealed a lower error rate in terms of MSE(0.0471),RMSE(0.2167),and MAPE(0.6135)over publicly available benchmark data.In addition,it is demonstrated that our proposed model is less complex,with the lowest number of parameters(0.0135 M),size(0.106 MB),and inference time(2 ms/step),suggesting that it is easy to integrate into the smart grid.展开更多
A distributed generation system(DG)has several benefits over a traditional centralized power system.However,the protection area in the case of the distributed generator requires special attention as it encounters stab...A distributed generation system(DG)has several benefits over a traditional centralized power system.However,the protection area in the case of the distributed generator requires special attention as it encounters stability loss,failure re-closure,fluctuations in voltage,etc.And thereby,it demands immediate attention in identifying the location&type of a fault without delay especially when occurred in a small,distributed generation system,as it would adversely affect the overall system and its operation.In the past,several methods were proposed for classification and localisation of a fault in a distributed generation system.Many of those methods were accurate in identifying location,but the accuracy in identifying the type of fault was not up to the acceptable mark.The proposed work here uses a shallow artificial neural network(sANN)model for identifying a particular type of fault that could happen in a specific distribution network when used in conjunction with distributed generators.Firstly,a distribution network consisting of two similar distributed generators(DG1 and DG2),one grid,and a 100 Km distribution line is modeled.Thereafter,different voltages and currents corresponding to various faults(line to line,line to ground)at different locations are tabulated,resulting in a matrix of 500×18 inputs.Secondly,the sANN is formulated for identifying the types of faults in the system in which the above-obtained data is used to train,validate,and test the neural network.The overall result shows an unprecedented almost zero percent error in identifying the type of the faults.展开更多
A modular system of cascaded converters based on model predictive control(MPC)is proposed to meet the application requirements ofmultiple voltage levels and electrical isolation in renewable energy generation systems....A modular system of cascaded converters based on model predictive control(MPC)is proposed to meet the application requirements ofmultiple voltage levels and electrical isolation in renewable energy generation systems.The system consists of a Buck/Boost+CLLLC cascaded converter as a submodule,which is combined in series and parallel on the input and output sides to achieve direct-current(DC)voltage transformation,bidirectional energy flow,and electrical isolation.The CLLLC converter operates in DC transformer mode in the submodule,while the Buck/Boost converter participates in voltage regulation.This article establishes a suitable mathematical model for the proposed system topology,and uses MPC to control the system based on this mathematical model.Module parameters are designed and calculated,and simulation is built in MATLAB/Simulink to complete the simulation comparison experiment between MPC and traditional proportional integral(PI)control.Finally,a physical experimental platform is built to complete the physical comparison experiment.The simulation and physical experimental results prove that the control accuracy and response speed ofMPC are better than traditional PI control strategy.展开更多
With the increasing penetration of renewable energy resources(RESs), the uncertainties of volatile renewable generations significantly affect the power system operation. Such uncertainties are usually modeled as stoch...With the increasing penetration of renewable energy resources(RESs), the uncertainties of volatile renewable generations significantly affect the power system operation. Such uncertainties are usually modeled as stochastic variables obeying specific distributions by neglecting the temporal correlations. Conventional approaches to hedge the negative effects caused by such uncertainties are thus hard to pursue a trade-off between computation efficiency and optimality. As an alternative, the theory of stochastic process can naturally model temporal correlation in closed forms. Attracted by this feature, our research group has been conducting thorough researches in the past decade to introduce stochastic processes within renewable power systems. This paper summarizes our works from the perspective of both the frequency domain and the time domain, provides the tools for the analysis and control of power systems under a unified framework of stochastic processes, and discusses the underlying reasons that stochastic process-based approaches can perform better than conventional approaches on both computational efficiency and optimality. These work may shed a new light on the research of analysis, control and operation of renewable power systems.Finally, this paper outlooks the theoretic developments of stochastic processes in future’s renewable power systems.展开更多
The development of distributed renewable energy,such as photovoltaic power and wind power generation,makes the energy system cleaner,and is of great significance in reducing carbon emissions.However,weather can affect...The development of distributed renewable energy,such as photovoltaic power and wind power generation,makes the energy system cleaner,and is of great significance in reducing carbon emissions.However,weather can affect distributed renewable energy power generation,and the uncertainty of output brings challenges to uncertainty planning for distributed renewable energy.Energy systems with high penetration of distributed renewable energy involve the high-dimensional,nonlinear dynamics of large-scale complex systems,and the optimal solution of the uncertainty model is a difficult problem.From the perspective of statistical machine learning,the theory of planning of distributed renewable energy systems under uncertainty is reviewed and some key technologies are put forward for applying advanced artificial intelligence to distributed renewable power uncertainty planning.展开更多
The growth of renewable energy reduces the moment of inertia for the synchronous AC grid,so the authors put forward two basic questions:1)What is the physics insight that a synchronous AC grid needs for mechanical ine...The growth of renewable energy reduces the moment of inertia for the synchronous AC grid,so the authors put forward two basic questions:1)What is the physics insight that a synchronous AC grid needs for mechanical inertia?2)How to provide inertial response for the power grid dominated with renewable energy?Based on Einstein’s special relativity and the Lorentz transformation,these papers illustrates that the nature of the inertia of the AC grid comes from the relativity of the electromagnetic field and motion,and from the strong coupling between them.According to their nature,the inertial response of the synchronous generator is self-proven.By contrast,the converter for the grid-connection of renewable energies used various algorithms in order to provide virtual inertia.But because algorithms do not rebuild the coupling between electromagnetic fields and motion,it is doubtful whether they can provide inertia and inertial responses.Therefore,the authors propose that there is a need to build extra electromagnetic fields and motion coupling for grids with high penetration rates of renewable energy.Therefore,a new grid-connection technology via Motor-Generator Pair(MGP)is discussed.The electromagnetic-motion coupling of the MGP is analyzed,and the results of simulation and experimental studies are also reported.展开更多
In this study,a comprehensive approach is presented for the sizing and management of hybrid renewable energy systems(HRESs)that incorporate a variety of energy sources,while emphasizing the role of artificial neural n...In this study,a comprehensive approach is presented for the sizing and management of hybrid renewable energy systems(HRESs)that incorporate a variety of energy sources,while emphasizing the role of artificial neural networks(ANNs)in system management.For optimal sizing of an HRES,the monthly average method wherein historical weather data are used to calculate the monthly averages of solar irradiance and wind speed,offering a well-balanced strategy for system sizing.This ensures that the HRES is appropriately scaled to meet the actual energy requirements of the specified location,avoiding the pitfalls of over-and under-sizing,and thereby enhancing the operational efficiency.Furthermore,the study details a cutting-edge strategy that employs ANNs for managing the inherent complexities of HRESs.It elaborates on the design,modeling,and control strategies for the HRES components by utilizing Matlab/Simulink for implementation.The findings demonstrate the proficiency of the ANN-based power manager in determining the operational modes guided by a specifically designed flowchart.By integrating ANN-driven energy management strategies into an HRES,the proposed approach marks a significant advancement in system adaptability,precision control,and efficiency,thereby maximizing the effective utilization of renewable resources.展开更多
The renewable hydrogen economy is recognized as an integral solution for decarbonizing energy sectors.However,high costs have hindered widespread deployment.One promising way of reducing the costs is optimization.Opti...The renewable hydrogen economy is recognized as an integral solution for decarbonizing energy sectors.However,high costs have hindered widespread deployment.One promising way of reducing the costs is optimization.Optimization generally involves finding the configuration of the renewable generation and hydrogen system components that maximizes return on investment.Previous studies have included many aspects into their optimizations,including technical parameters and different costs/socio-economic objective functions,however there is no clear best-practice framework for model development.To address these gaps,this critical review examines the latest development in renewable hydrogen microgrid models and summarizes the best modeling practice.The findings show that advances in machine learning integration are improving solar electricity generation forecasting,hydrogen system simulations,and load profile development,particularly in data-scarce regions.Additionally,it is important to account for electrolyzer and fuel cell dynamics,rather than utilizing fixed performance values.This review also demonstrates that typical meteorological year datasets are better for modeling solar irradiation than first-principle calculations.The practicability of socio-economic objective functions is also assessed,proposing that the more comprehensive Levelized Value Addition(LVA)is best suited for inclusion into models.Best practices for creating load profiles in regions like the Global South are discussed,along with an evaluation of AI-based and traditional optimization methods and software tools.Finally,a new evidence-based multi-criteria decision-making framework integrated with machine learning insights,is proposed to guide decision-makers in selecting optimal solutions based on multiple attributes,offering a more comprehensive and adaptive approach to renewable hydrogen system optimization.展开更多
Generally,the Battery Energy Storage Systems(BESS)may meet entire load requirements while the wind turbine and PV array are still not providing energy,which enhances the reliability of the power system's distribut...Generally,the Battery Energy Storage Systems(BESS)may meet entire load requirements while the wind turbine and PV array are still not providing energy,which enhances the reliability of the power system's distribution.The primary aim of the presented research is to produce a framework for enhancing power quality in hybrid devices attached to renewable sources via an optimised fractional order proportional integral(FOPI)Controller in Unified Power Quality Conditioner(UPQC).The source input is made up of RES.To address the PQ difficulties,the UPQC employs series and shunt filter management techniques.Utilizing suggested Adaptive Bald Eagle optimisation algorithm(ABE-OA),the variables are ideally optimised for greater control.To confirm the effectiveness of the proposed method in PQ enhancement,the effectiveness of the developed system is evaluated to that of previous techniques.The proposed method achieved better rise time of 2042.5 and settling min of 19,999.展开更多
This study proposes a novel and structured model to accelerate the deployment of wave energy systems by integrating them with offshore wind technologies in a hybrid configuration.The model employs a multi-step methodo...This study proposes a novel and structured model to accelerate the deployment of wave energy systems by integrating them with offshore wind technologies in a hybrid configuration.The model employs a multi-step methodology,comprising sub-location selection based on elimination criteria,site prioritization using a Multi-Criteria Stability Index(M-CSI),estimation of resource potential from meteorological data,technology matching,and hybrid system operation.A real-case validation is conducted in Turkey’s Black Sea region,identifying Kumkoy as the most suitable site.The wave energy potential in Kumkoy is estimated to be between 0.75 and 4.6875 kW/m,and the wind power potential reaches 1.102 MW.A 4 MW hybrid facility is designed to produce 12,193.506 MWh annually.The techno-economic analysis identifies three representative LCoE values for the hybrid system:168.51,231.5824,and 277.295 EUR/MWh,reflecting variations in system performance and economic parameters.Environmental impact is also assessed:if fossil fuels were used instead,the social cost of carbon emissions would be approximately $418,237.26 for natural gas and $699,977.28 for coal.By offering a holistic roadmap for site selection,technology integration,and economic evaluation,this model aims to overcome key barriers in wave and ocean energy development and support global renewable energy targets.展开更多
Hydrogen storage and ice storage are promising,environmentally friendly energy storage technologies.However,there are few investigations on the optimal configuration of hybrid renewable energy systems(HRES)for remote ...Hydrogen storage and ice storage are promising,environmentally friendly energy storage technologies.However,there are few investigations on the optimal configuration of hybrid renewable energy systems(HRES)for remote off-grid areas with localized scenarios.This paper proposes a new optimal configuration of an off-grid PV/wind/hydrogen/cooling system.Given three performance indices for evaluating HRES,i.e.,the levelized cost of energy(LCOE),the loss of power supply possibility(LPSP),and the power curtailment rate(PCR),we use theε-constraint method that formulates LCOE as the objective,while LPSP and PCR serve as constraints.Furthermore,to solve the optimal size of HRES,an improved salp swarm algorithm(ISSA)is proposed.The simulation results show that for an off-grid remote community,the LCOE,LPSP,and PCR of the optimal HRES configuration can achieve$0.31/kWh,5.00%,and 7.23%,respectively.The comparison of different systems illustrates that adding ice storage in the HRES with hydrogen storage will decrease the LCOE by 27.12%.In addition,compared with other heuristic algorithms,such as SSA,ISSA offers the configuration with the minimum LCOE.The hydrogen-ice storage system is economically significant to off-grid areas with cooling load demand,and the proposed ISSA has excellent accuracy.展开更多
The increasing penetration of renewables in power systems urgently entails the utilization of energy storage technologies.As the development of energy storage technologies depends highly on the profitability in electr...The increasing penetration of renewables in power systems urgently entails the utilization of energy storage technologies.As the development of energy storage technologies depends highly on the profitability in electricity markets,to evaluate the economic potentials for various types of energy storage technologies under the compre-hensive market environment is of great significance.To this end,this study aims at conducting a quantitative analysis on the economic potentials for typical energy storage technologies by establishing a joint clearing model for electric energy and ancillary service(AS)markets considering the operating features of energy storage systems(ESSs).Furthermore,a test system is adopted for numerical analysis that accurately represents for the real-world operation characteristics of power systems in China,with which the market prices,and operation schedules and profitability of ESSs are comparatively studied.The proposed methodology and results could provide benefi-cial references for the modifications on electricity markets and the development of ESSs towards the increasing penetration of renewables in power systems.展开更多
Photovoltaic(PV)systems are widely spread across MV and LV distribution systems and the penetration of PV generation is solidly growing.Because of the uncertain nature of the solar energy resource,PV power forecasting...Photovoltaic(PV)systems are widely spread across MV and LV distribution systems and the penetration of PV generation is solidly growing.Because of the uncertain nature of the solar energy resource,PV power forecasting models are crucial in any energy management system for smart distribution networks.Although point forecasts can suit many scopes,probabilistic forecasts add further flexibility to an energy management system and are recommended to enable a wider range of decision making and optimization strategies.This paper proposes methodology towards probabilistic PV power forecasting based on a Bayesian bootstrap quantile regression model,in which a Bayesian bootstrap is applied to estimate the parameters of a quantile regression model.A novel procedure is presented to optimize the extraction of the predictive quantiles from the bootstrapped estimation of the related coefficients,raising the predictive ability of the final forecasts.Numerical experiments based on actual data quantify an enhancement of the performance of up to 2.2%when compared to relevant benchmarks.展开更多
Low-carbon hydrogen is expected to play a key role in realizing net-zero and sustainable development plans.Nonetheless,there is a gap between the cost of producing low-carbon hydrogen and its potential users’willingn...Low-carbon hydrogen is expected to play a key role in realizing net-zero and sustainable development plans.Nonetheless,there is a gap between the cost of producing low-carbon hydrogen and its potential users’willingness to pay for such hydrogen.To implement support for the development of the industry,we propose using low-carbon hydrogen long-term agreements allocated through auction mechanisms.The objectives are 2-fold:(i)matching supply and demand volumes considering the time horizon and geographical delivery point specification and(ii)allocating the subsidy.This perspective article innovates by proposing a reference price indexed to liquid to natural-gas prices,which is the main product that low-carbon hydrogen aims to substitute.The premium and the production cost are defined through a double-sided auction.This aims to minimize the public policy funds required to incentivize the low-carbon hydrogen market while facilitating long-term agreements and mitigating price risks that may hinder investment.展开更多
INTRODUCTION The new Mind,Body&Spirits Inspired Dining Organic Restaurant,located at 301 S.Main Street in downtown Rochester,MI,opened in November of 2008.It is located on the southwest corner of S.Main Street and...INTRODUCTION The new Mind,Body&Spirits Inspired Dining Organic Restaurant,located at 301 S.Main Street in downtown Rochester,MI,opened in November of 2008.It is located on the southwest corner of S.Main Street and W.Third Street.It is housed in an existing two-story brick building with a full basement and has a New Greenhouse/Kitchen/Restrooms/Dining Deck Addition located in the former rear parking lot area to the west.展开更多
基金supported by the Science and Technology Project of the State Grid Corporation of China,Grant number 5700-202223189A-1-1-ZN.
文摘Renewable Energy Systems(RES)provide a sustainable solution to climate warming and environmental pollution by enhancing stability and reliability through status acquisition and analysis on cloud platforms and intelligent processing on edge servers(ES).However,securely distributing encrypted data stored in the cloud to terminals that meet decryption requirements has become a prominent research topic.Additionally,managing attributes,including addition,deletion,and modification,is a crucial issue in the access control scheme for RES.To address these security concerns,a trust-based ciphertext-policy attribute-based encryption(CP-ABE)device access control scheme is proposed for RES(TB-CP-ABE).This scheme effectivelymanages the distribution and control of encrypted data on the cloud through robust attribute key management.By introducing trust management mechanisms and outsourced decryption technology,the ES system can effectively assess and manage the trust worthiness of terminal devices,ensuring that only trusted devices can participate in data exchange and access sensitive information.Besides,the ES system dynamically evaluates trust scores to set decryption trust thresholds,thereby regulating device data access permissions and enhancing the system’s security.To validate the security of the proposed TB-CP-ABE against chosen plaintext attacks,a comprehensive formal security analysis is conducted using the widely accepted random oraclemodel under the decisional q-Bilinear Diffie-Hellman Exponent(q-BDHE)assumption.Finally,comparative analysis with other schemes demonstrates that the TB-CP-ABE scheme cuts energy/communication costs by 43%,and scaleswell with rising terminals,maintaining average latency below 50ms,ensuring real-time service feasibility.The proposed scheme not only provides newinsights for the secure management of RES but also lays a foundation for future secure energy solutions.
文摘Early and accurate fault detection and diagnosis for renewable energy systems can increase their safety and ensure the continuity of their service. This paper presents a comprehensive review of different fault detection and diagnosis methods for hybrid renewable energy systems consisting of a wind turbine power generator, a PV （photovoltaic） array, a PEM （polymer electrolyte membrane） fuel cell and a battery storage system. The need of batteries to store the generated power from the solar panel, wind turbine or PEM fuel cell is also emphasized. Finally, an overview of the current methods used in the diagnosing of the lead-acid battery degradation is given.
文摘I.I NTRODUCTION W ITH the advent of low-carbon economy,there has been a growing interest in harnessing renewable energy resources particularly for electricity generation.Renewable energy resources are advocated for the economic and environ-
文摘The main purpose of this article is to perform a technical-economic analysis of different microgeneration systems （photovoltaic and wind turbine systems） that can be installed in a single-family house. One of the strategies for improving energy performance of buildings is the installation of microgeneration systems. This will enable Portugal to meet the goal regarding the share of renewable sources on gross final energy consumption imposed by the European Commission climate change and energy package for 2020. The analysis of four different micro-generation systems will take into account the local availability of renewable resources and market technologies and the new Portuguese law concerning microgeneration to promote renewable energy sources in households. The Portuguese microgeneration legal framework is also highlighted.
文摘The lack of water in arid and semi-arid regions has often limited agricultural production. Indeed, even where water is available for irrigation, the lack of electricity, as well as the high costs of diesel, has created constraints on small farmers. The purpose of this research is to review the renewable energy potential available in arid and semi-arid zones that can be used for irrigation as a substitute for fossil fuels. In this review, the solar thermal irrigation, solar photovoltaic (PV) irrigation, wind pumping and biomass pumping are discussed. The comparison of different hybrid pumping systems and analyses of renewable sources irrigation assessment in arid and semi-arid regions of Mozambique also are discussed. The results of this study showed that there are still certain technological limitations regarding the use of solar thermal energy for irrigation. As far as wind power is concerned, the analysis of the pumping water life cycle cost showed that the wind power water pumping system is more economical and viable compared to the diesel based system. However, the study concluded that photovoltaic solar energy has been shown to be more viable for pumping water for irrigation in arid and semi-arid regions.
基金supported by the National Research Foundation of Korea (NRF)grant funded by the Korean government (MSIT) (No.2019M3F2A1073179).
文摘Photovoltaic(PV)systems are environmentally friendly,generate green energy,and receive support from policies and organizations.However,weather fluctuations make large-scale PV power integration and management challenging despite the economic benefits.Existing PV forecasting techniques(sequential and convolutional neural networks(CNN))are sensitive to environmental conditions,reducing energy distribution system performance.To handle these issues,this article proposes an efficient,weather-resilient convolutional-transformer-based network(CT-NET)for accurate and efficient PV power forecasting.The network consists of three main modules.First,the acquired PV generation data are forwarded to the pre-processing module for data refinement.Next,to carry out data encoding,a CNNbased multi-head attention(MHA)module is developed in which a single MHA is used to decode the encoded data.The encoder module is mainly composed of 1D convolutional and MHA layers,which extract local as well as contextual features,while the decoder part includes MHA and feedforward layers to generate the final prediction.Finally,the performance of the proposed network is evaluated using standard error metrics,including the mean squared error(MSE),root mean squared error(RMSE),and mean absolute percentage error(MAPE).An ablation study and comparative analysis with several competitive state-of-the-art approaches revealed a lower error rate in terms of MSE(0.0471),RMSE(0.2167),and MAPE(0.6135)over publicly available benchmark data.In addition,it is demonstrated that our proposed model is less complex,with the lowest number of parameters(0.0135 M),size(0.106 MB),and inference time(2 ms/step),suggesting that it is easy to integrate into the smart grid.
文摘A distributed generation system(DG)has several benefits over a traditional centralized power system.However,the protection area in the case of the distributed generator requires special attention as it encounters stability loss,failure re-closure,fluctuations in voltage,etc.And thereby,it demands immediate attention in identifying the location&type of a fault without delay especially when occurred in a small,distributed generation system,as it would adversely affect the overall system and its operation.In the past,several methods were proposed for classification and localisation of a fault in a distributed generation system.Many of those methods were accurate in identifying location,but the accuracy in identifying the type of fault was not up to the acceptable mark.The proposed work here uses a shallow artificial neural network(sANN)model for identifying a particular type of fault that could happen in a specific distribution network when used in conjunction with distributed generators.Firstly,a distribution network consisting of two similar distributed generators(DG1 and DG2),one grid,and a 100 Km distribution line is modeled.Thereafter,different voltages and currents corresponding to various faults(line to line,line to ground)at different locations are tabulated,resulting in a matrix of 500×18 inputs.Secondly,the sANN is formulated for identifying the types of faults in the system in which the above-obtained data is used to train,validate,and test the neural network.The overall result shows an unprecedented almost zero percent error in identifying the type of the faults.
基金supported by the National Key Research and Development Plan,Grant/Award Number:2018YFB1503005.
文摘A modular system of cascaded converters based on model predictive control(MPC)is proposed to meet the application requirements ofmultiple voltage levels and electrical isolation in renewable energy generation systems.The system consists of a Buck/Boost+CLLLC cascaded converter as a submodule,which is combined in series and parallel on the input and output sides to achieve direct-current(DC)voltage transformation,bidirectional energy flow,and electrical isolation.The CLLLC converter operates in DC transformer mode in the submodule,while the Buck/Boost converter participates in voltage regulation.This article establishes a suitable mathematical model for the proposed system topology,and uses MPC to control the system based on this mathematical model.Module parameters are designed and calculated,and simulation is built in MATLAB/Simulink to complete the simulation comparison experiment between MPC and traditional proportional integral(PI)control.Finally,a physical experimental platform is built to complete the physical comparison experiment.The simulation and physical experimental results prove that the control accuracy and response speed ofMPC are better than traditional PI control strategy.
基金supported by the National Key Research and Development Program of China(Grant No.2018YFB0905200)the National NaturalScience Foundation of China(Grant Nos.51577096,51677100&51761135015)
文摘With the increasing penetration of renewable energy resources(RESs), the uncertainties of volatile renewable generations significantly affect the power system operation. Such uncertainties are usually modeled as stochastic variables obeying specific distributions by neglecting the temporal correlations. Conventional approaches to hedge the negative effects caused by such uncertainties are thus hard to pursue a trade-off between computation efficiency and optimality. As an alternative, the theory of stochastic process can naturally model temporal correlation in closed forms. Attracted by this feature, our research group has been conducting thorough researches in the past decade to introduce stochastic processes within renewable power systems. This paper summarizes our works from the perspective of both the frequency domain and the time domain, provides the tools for the analysis and control of power systems under a unified framework of stochastic processes, and discusses the underlying reasons that stochastic process-based approaches can perform better than conventional approaches on both computational efficiency and optimality. These work may shed a new light on the research of analysis, control and operation of renewable power systems.Finally, this paper outlooks the theoretic developments of stochastic processes in future’s renewable power systems.
基金supported by the State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources under Grant No.LAPS21016the National Natural Science Foundation of China under Grant 52007193the 2115 Talent Development Program of China Agricultural University.
文摘The development of distributed renewable energy,such as photovoltaic power and wind power generation,makes the energy system cleaner,and is of great significance in reducing carbon emissions.However,weather can affect distributed renewable energy power generation,and the uncertainty of output brings challenges to uncertainty planning for distributed renewable energy.Energy systems with high penetration of distributed renewable energy involve the high-dimensional,nonlinear dynamics of large-scale complex systems,and the optimal solution of the uncertainty model is a difficult problem.From the perspective of statistical machine learning,the theory of planning of distributed renewable energy systems under uncertainty is reviewed and some key technologies are put forward for applying advanced artificial intelligence to distributed renewable power uncertainty planning.
文摘The growth of renewable energy reduces the moment of inertia for the synchronous AC grid,so the authors put forward two basic questions:1)What is the physics insight that a synchronous AC grid needs for mechanical inertia?2)How to provide inertial response for the power grid dominated with renewable energy?Based on Einstein’s special relativity and the Lorentz transformation,these papers illustrates that the nature of the inertia of the AC grid comes from the relativity of the electromagnetic field and motion,and from the strong coupling between them.According to their nature,the inertial response of the synchronous generator is self-proven.By contrast,the converter for the grid-connection of renewable energies used various algorithms in order to provide virtual inertia.But because algorithms do not rebuild the coupling between electromagnetic fields and motion,it is doubtful whether they can provide inertia and inertial responses.Therefore,the authors propose that there is a need to build extra electromagnetic fields and motion coupling for grids with high penetration rates of renewable energy.Therefore,a new grid-connection technology via Motor-Generator Pair(MGP)is discussed.The electromagnetic-motion coupling of the MGP is analyzed,and the results of simulation and experimental studies are also reported.
文摘In this study,a comprehensive approach is presented for the sizing and management of hybrid renewable energy systems(HRESs)that incorporate a variety of energy sources,while emphasizing the role of artificial neural networks(ANNs)in system management.For optimal sizing of an HRES,the monthly average method wherein historical weather data are used to calculate the monthly averages of solar irradiance and wind speed,offering a well-balanced strategy for system sizing.This ensures that the HRES is appropriately scaled to meet the actual energy requirements of the specified location,avoiding the pitfalls of over-and under-sizing,and thereby enhancing the operational efficiency.Furthermore,the study details a cutting-edge strategy that employs ANNs for managing the inherent complexities of HRESs.It elaborates on the design,modeling,and control strategies for the HRES components by utilizing Matlab/Simulink for implementation.The findings demonstrate the proficiency of the ANN-based power manager in determining the operational modes guided by a specifically designed flowchart.By integrating ANN-driven energy management strategies into an HRES,the proposed approach marks a significant advancement in system adaptability,precision control,and efficiency,thereby maximizing the effective utilization of renewable resources.
基金financial support offered to M.D.M.for his doctoral research by the UK Engineering and Physical Sciences Research Council and Loughborough University through the EPSRC Sustainable Hydrogen Centre for Doctoral Training funded by the UK Research and Innovation(UKRI)[grant number EP/S023909/1].
文摘The renewable hydrogen economy is recognized as an integral solution for decarbonizing energy sectors.However,high costs have hindered widespread deployment.One promising way of reducing the costs is optimization.Optimization generally involves finding the configuration of the renewable generation and hydrogen system components that maximizes return on investment.Previous studies have included many aspects into their optimizations,including technical parameters and different costs/socio-economic objective functions,however there is no clear best-practice framework for model development.To address these gaps,this critical review examines the latest development in renewable hydrogen microgrid models and summarizes the best modeling practice.The findings show that advances in machine learning integration are improving solar electricity generation forecasting,hydrogen system simulations,and load profile development,particularly in data-scarce regions.Additionally,it is important to account for electrolyzer and fuel cell dynamics,rather than utilizing fixed performance values.This review also demonstrates that typical meteorological year datasets are better for modeling solar irradiation than first-principle calculations.The practicability of socio-economic objective functions is also assessed,proposing that the more comprehensive Levelized Value Addition(LVA)is best suited for inclusion into models.Best practices for creating load profiles in regions like the Global South are discussed,along with an evaluation of AI-based and traditional optimization methods and software tools.Finally,a new evidence-based multi-criteria decision-making framework integrated with machine learning insights,is proposed to guide decision-makers in selecting optimal solutions based on multiple attributes,offering a more comprehensive and adaptive approach to renewable hydrogen system optimization.
文摘Generally,the Battery Energy Storage Systems(BESS)may meet entire load requirements while the wind turbine and PV array are still not providing energy,which enhances the reliability of the power system's distribution.The primary aim of the presented research is to produce a framework for enhancing power quality in hybrid devices attached to renewable sources via an optimised fractional order proportional integral(FOPI)Controller in Unified Power Quality Conditioner(UPQC).The source input is made up of RES.To address the PQ difficulties,the UPQC employs series and shunt filter management techniques.Utilizing suggested Adaptive Bald Eagle optimisation algorithm(ABE-OA),the variables are ideally optimised for greater control.To confirm the effectiveness of the proposed method in PQ enhancement,the effectiveness of the developed system is evaluated to that of previous techniques.The proposed method achieved better rise time of 2042.5 and settling min of 19,999.
基金the Malatya Turgut Ozal University Scientific Research Projects(BAP)Unit(Project No.24G35).
文摘This study proposes a novel and structured model to accelerate the deployment of wave energy systems by integrating them with offshore wind technologies in a hybrid configuration.The model employs a multi-step methodology,comprising sub-location selection based on elimination criteria,site prioritization using a Multi-Criteria Stability Index(M-CSI),estimation of resource potential from meteorological data,technology matching,and hybrid system operation.A real-case validation is conducted in Turkey’s Black Sea region,identifying Kumkoy as the most suitable site.The wave energy potential in Kumkoy is estimated to be between 0.75 and 4.6875 kW/m,and the wind power potential reaches 1.102 MW.A 4 MW hybrid facility is designed to produce 12,193.506 MWh annually.The techno-economic analysis identifies three representative LCoE values for the hybrid system:168.51,231.5824,and 277.295 EUR/MWh,reflecting variations in system performance and economic parameters.Environmental impact is also assessed:if fossil fuels were used instead,the social cost of carbon emissions would be approximately $418,237.26 for natural gas and $699,977.28 for coal.By offering a holistic roadmap for site selection,technology integration,and economic evaluation,this model aims to overcome key barriers in wave and ocean energy development and support global renewable energy targets.
基金supported by National Key R&D Program of China(No.2018YFB1500800)National Natural Science Foundation of China(No.51807134)Science and Technology Project of State Grid Corporation,and State Key Laboratory of Power System and Generation Equipment(SKLD21KM10).
文摘Hydrogen storage and ice storage are promising,environmentally friendly energy storage technologies.However,there are few investigations on the optimal configuration of hybrid renewable energy systems(HRES)for remote off-grid areas with localized scenarios.This paper proposes a new optimal configuration of an off-grid PV/wind/hydrogen/cooling system.Given three performance indices for evaluating HRES,i.e.,the levelized cost of energy(LCOE),the loss of power supply possibility(LPSP),and the power curtailment rate(PCR),we use theε-constraint method that formulates LCOE as the objective,while LPSP and PCR serve as constraints.Furthermore,to solve the optimal size of HRES,an improved salp swarm algorithm(ISSA)is proposed.The simulation results show that for an off-grid remote community,the LCOE,LPSP,and PCR of the optimal HRES configuration can achieve$0.31/kWh,5.00%,and 7.23%,respectively.The comparison of different systems illustrates that adding ice storage in the HRES with hydrogen storage will decrease the LCOE by 27.12%.In addition,compared with other heuristic algorithms,such as SSA,ISSA offers the configuration with the minimum LCOE.The hydrogen-ice storage system is economically significant to off-grid areas with cooling load demand,and the proposed ISSA has excellent accuracy.
基金Qinchuangyuan Cited High-level Innovation and Entrepreneurship Talents Project under(Grant No:2021QCYRC4-36)National Natural Science Fundation of China(Grant No.:72173095).
文摘The increasing penetration of renewables in power systems urgently entails the utilization of energy storage technologies.As the development of energy storage technologies depends highly on the profitability in electricity markets,to evaluate the economic potentials for various types of energy storage technologies under the compre-hensive market environment is of great significance.To this end,this study aims at conducting a quantitative analysis on the economic potentials for typical energy storage technologies by establishing a joint clearing model for electric energy and ancillary service(AS)markets considering the operating features of energy storage systems(ESSs).Furthermore,a test system is adopted for numerical analysis that accurately represents for the real-world operation characteristics of power systems in China,with which the market prices,and operation schedules and profitability of ESSs are comparatively studied.The proposed methodology and results could provide benefi-cial references for the modifications on electricity markets and the development of ESSs towards the increasing penetration of renewables in power systems.
基金supported by the Swiss Federal Office of Energy(SFOE)and by the Italian Ministry of Education,University and Research(MIUR),through the ERA-NET Smart Energy Systems RegSys joint call 2018 project“DiGRiFlex-Real time Distribution GRid control and Flexibility provision under uncertainties.”。
文摘Photovoltaic(PV)systems are widely spread across MV and LV distribution systems and the penetration of PV generation is solidly growing.Because of the uncertain nature of the solar energy resource,PV power forecasting models are crucial in any energy management system for smart distribution networks.Although point forecasts can suit many scopes,probabilistic forecasts add further flexibility to an energy management system and are recommended to enable a wider range of decision making and optimization strategies.This paper proposes methodology towards probabilistic PV power forecasting based on a Bayesian bootstrap quantile regression model,in which a Bayesian bootstrap is applied to estimate the parameters of a quantile regression model.A novel procedure is presented to optimize the extraction of the predictive quantiles from the bootstrapped estimation of the related coefficients,raising the predictive ability of the final forecasts.Numerical experiments based on actual data quantify an enhancement of the performance of up to 2.2%when compared to relevant benchmarks.
文摘Low-carbon hydrogen is expected to play a key role in realizing net-zero and sustainable development plans.Nonetheless,there is a gap between the cost of producing low-carbon hydrogen and its potential users’willingness to pay for such hydrogen.To implement support for the development of the industry,we propose using low-carbon hydrogen long-term agreements allocated through auction mechanisms.The objectives are 2-fold:(i)matching supply and demand volumes considering the time horizon and geographical delivery point specification and(ii)allocating the subsidy.This perspective article innovates by proposing a reference price indexed to liquid to natural-gas prices,which is the main product that low-carbon hydrogen aims to substitute.The premium and the production cost are defined through a double-sided auction.This aims to minimize the public policy funds required to incentivize the low-carbon hydrogen market while facilitating long-term agreements and mitigating price risks that may hinder investment.
文摘INTRODUCTION The new Mind,Body&Spirits Inspired Dining Organic Restaurant,located at 301 S.Main Street in downtown Rochester,MI,opened in November of 2008.It is located on the southwest corner of S.Main Street and W.Third Street.It is housed in an existing two-story brick building with a full basement and has a New Greenhouse/Kitchen/Restrooms/Dining Deck Addition located in the former rear parking lot area to the west.
