The Experts below are selected from a list of 159 Experts worldwide ranked by ideXlab platform
Mingtang Tsai - One of the best experts on this subject based on the ideXlab platform.
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particle swarm optimisation aided least square support vector machine for load forecast with spikes
Iet Generation Transmission & Distribution, 2016Co-Authors: Wheimin Lin, Renfu Yang, Mingtang TsaiAbstract:This study developed a load forecasting system for Electric Market participants. Combining the least-square support vector machine (LSSVM) and particle swarm optimisation (PSO), a LSSVM_PSO was proposed for the solving process. The loads, temperature, and relative humidity of the Taipower system were collected in the Excel Database. Data mining techniques is used to discover meaningful patterns, with the PSO applied to adjust learning rates. The forecasting error can be reduced during the training process to improve both the accuracy and reliability, where even the spikes were nicely followed. The support vector regression, LSSVM, radial basis function neural network and the proposed LSSVM_PSO were all developed and compared to check the convergence and performance. Simulation results demonstrated the effectiveness of the proposed method in a price volatile environment.
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an enhanced radial basis function network for short term Electricity price forecasting
Applied Energy, 2010Co-Authors: Mingtang TsaiAbstract:This paper proposed a price forecasting system for Electric Market participants to reduce the risk of price volatility. Combining the Radial Basis Function Network (RBFN) and Orthogonal Experimental Design (OED), an Enhanced Radial Basis Function Network (ERBFN) has been proposed for the solving process. The Locational Marginal Price (LMP), system load, transmission flow and temperature of the PJM system were collected and the data clusters were embedded in the Excel Database according to the year, season, workday and weekend. With the OED applied to learning rates in the ERBFN, the forecasting error can be reduced during the training process to improve both accuracy and reliability. This would mean that even the "spikes" could be tracked closely. The Back-propagation Neural Network (BPN), Probability Neural Network (PNN), other algorithms, and the proposed ERBFN were all developed and compared to check the performance. Simulation results demonstrated the effectiveness of the proposed ERBFN to provide quality information in a price volatile environment.
Lian Li - One of the best experts on this subject based on the ideXlab platform.
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A hybrid application algorithm based on the support vector machine and artificial intelligence: An example of Electric load forecasting
Applied Mathematical Modelling, 2014Co-Authors: Yanhua Chen, Chaoqun Liu, Caihong Li, Yi Yang, Lian LiAbstract:Abstract Accurate Electric load forecasting could prove to be a very useful tool for all Market participants in Electricity Markets. Because it can not only help power producers and consumers make their plans but also can maximize their profits. In this paper, a new combined forecasting method (ESPLSSVM) based on empirical mode decomposition, seasonal adjustment, particle swarm optimization (PSO) and least squares support vector machine (LSSVM) model is proposed. In the Electric Market, noise signals usually affect the forecasting accuracy, which were caused by different erratic factors. First of all, {ESPLSSVM} uses an empirical mode decomposition-based signal filtering method to reduce the influence of noise signals. Secondly, {ESPLSSVM} eliminates the seasonal components from the de-noised resulting series and then it models the resultant series using the {LSSVM} which is optimized by {PSO} (PLSSVM). Finally, by multiplying the seasonal indexes by the {PLSSVM} forecasts, {ESPLSSVM} acquires the final forecasting result. The effectiveness of the presented method is examined by comparing with different methods including basic {LSSVM} (LSSVM), empirical mode decomposition-based signal filtering method processed by {LSSVM} (ELSSVM) and seasonal adjustment processed by {LSSVM} (SLSSVM). Case studies show {ESPLSSVM} performed better than the other three load forecasting approaches.
Meiyan Wang - One of the best experts on this subject based on the ideXlab platform.
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vsc mtdc system integrating offshore wind farms based optimal distribution method for financial improvement on wind producers
IEEE Transactions on Industry Applications, 2019Co-Authors: Kaiqi Sun, Wei-jen Lee, Zhuodi Wang, Weiyu Bao, Zhijie Liu, Meiyan WangAbstract:As the typical clean and renewable energy, wind energy has witnessed a continuous annual increase in the last few decades. Due to the random and intermittent characteristics, the wind producers in the Electric Market meet serious financial losses caused by the deviation between the actual power output and forecasting result. It is difficult to increase the accuracy of forecasting in a short time. How to improve the financial income has become a major task to wind producers and academia. Being the backbone network of the offshore wind farms (OWFs), voltage source converter-based multi-terminal HVdc system (VSC-MTdc) has been regarded as one of the effective solutions to transport wind power. In this paper, an optimal distribution method is proposed for VSC-MTdc system integrating OWFs to reduce the financial loss due to wind power output deviation. The proposed method could be divided into two optimizing functions. The first optimizing function of the proposed method is to analyze the onshore external system according to the historical system operation data and adjust the droop coefficient with the analytic hierarchy process. The second optimizing function of the proposed method is to do further adjusting with the regulating price. With the case study, the proposed optimal distribution method has proved that it can bring more benefit to wind producers.
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vsc mtdc system integrating offshore wind farms based optimal distribution method for financial improvement on wind producers
IEEE Industry Applications Society Annual Meeting, 2018Co-Authors: Kaiqi Sun, Wei-jen Lee, Zhuodi Wang, Weiyu Bao, Zhijie Liu, Meiyan WangAbstract:As the typical clean and renewable energy, wind energy has witness a continuous annual increase in last few decades. Due to the random and intermittent characteristics, the wind producers in the Electric Market meet serious financial losses caused by deviation between the actual power output and forecasting result. Increasing the accurate of forecasting is difficult to achieve in a short time, how to improve the financial income has become a major task to wind producers and academia. As the backbone network of the offshore wind farms, VSC-MTDC has been regarded as one of the effective solutions to transport wind power. In this paper, an optimal distribution method is proposed for VSC-MTDC system integrating offshore wind farms to reduce the financial loss due to deviation between the actual wind power output and forecasting result. The proposed method could be divided into two parts. The first part of the proposed method is to analyze the onshore external system according to the historical system operation data and adjust the droop coefficient with analytic hierarchy process. The second part of the proposed method is to do the further adjusting according to the regulating price. With the simulation results, the proposed optimal distribution method is proved that it could bring more benefit to wind producers without more additional investment.
Jiang Chuanwen - One of the best experts on this subject based on the ideXlab platform.
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a review on the economic dispatch and risk management considering wind power in the power Market
Renewable & Sustainable Energy Reviews, 2009Co-Authors: Ren Boqiang, Jiang ChuanwenAbstract:With the rapid development of world economy, wind power has been given more and more consideration owing to its energy saving and environmental protection. But due to intermittency and unpredictability nature of wind power generation, many new problems come into being when infusing wind power into power network with conventional generators. Aiming at these difficulties, this paper presents a review on the historical research production of this theme. The models of economic dispatch schedule of wind power considering dissimilar actual condition, different optimized algorithms and risk management in the Electric Market are discussed and the future trend is prospected in this paper.
Luis M Romeo - One of the best experts on this subject based on the ideXlab platform.
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avoidance of partial load operation at coal fired power plants by storing nuclear power through power to gas
International Journal of Hydrogen Energy, 2019Co-Authors: Manuel Bailera, Pilar Lisbona, Luis M RomeoAbstract:Abstract The need of fast regulation of Electricity production leads to a number of inconveniences occurred to the Electric generation system and the Electric Market, especially to the nuclear power. A new concept to control nuclear power production is posed in order to allow the regulation of the Electricity sent to the grid. This concept proposes the joint operation of a nuclear power plant, a coal power plant with postcombustion capture and a methanation plant. The cost effectiveness of this technology and its capability to reduce the CO2 emissions -consumed in the methanation process- are assessed through the design and economic and environmental analysis of a hybrid facility. Mainly due to the increase of the operating hours of the coal-fired power plant, the environmental feasibility of the initial proposal seems to be limited. However, given that benefits are expected in the medium and long-term (2020–2030) for the Power to Gas facility, a future alternative use is proposed. The target of this new alternative configuration will be the storage of CO2 together with the storage of renewable energy.