Feasibility of Recharging Electric Vehicles With Photovoltaic Solar Panels

AbstractThere are many reasons for the development and the use of renewable energy sources, such as the public awareness in the fight against climate change, energy independence with the security of supply, national competitiveness, technological development and job creation in a sector that has a great future. In this line, and within the proposed electric vehicle sustainability, it is an alternative to achieve a reduction of pollutant emissions and to increase the efficiency of road transport.The article presents a study of the use of electric vehicles from different points of view. It has been compared combustion vehicles with the electric counterparts in terms of power and features appreciated by the user in the automobile market.The purpose of the study was to analyze the feasibility to recharge different electric vehicles by solar photovoltaic modules, so that energy generation would not contribute to any CO2 emissions, when the system would be installed and ready to supply these vehicles. The study also shows a comparative analysis of the cost of purchasing electricity to the distributor compared with the using of a photovoltaic system designed to recharge the vehicle, even it has also been calculated the depreciation.Finally, it has been analyzed comparatively the type of the solar photovoltaic system considered more economically viable for recharging a pure electric vehicle (EV) therefore it has been compared projects on houses and on a parking to recharge several vehicles

Classification, filtering, and identification of electrical customer load patterns through the use of self-organizing maps

Different methodologies are available for clustering purposes. The objective of this paper is to review the capacity of some of them and specifically to test the ability of self-organizing maps (SOMs) to filter, classify, and extract patterns from distributor, commercializer, or customer electrical demand databases. These market participants can achieve an interesting benefit through the knowledge of these patterns, for example, to evaluate the potential for distributed generation, energy efficiency, and demand-side response policies (market analysis). For simplicity, customer classification techniques usually used the historic load curves of each user. The first step in the methodology presented in this paper is anomalous data filtering: holidays, maintenance, and wrong measurements must be removed from the database. Subsequently, two different treatments (frequency and time domain) of demand data were tested to feed SOM maps and evaluate the advantages of each approach. Finally, the ability of SOM to classify new customers in different clusters is also examined. Both steps have been performed through a well-known technique: SOM maps. The results clearly show the suitability of this approach to improve data management and to easily find coherent clusters between electrical users, accounting for relevant information about weekend demand patterns.This work was supported by European Union Sixth Frame work Program under Project EU-DEEP SES6-CT-2003-503516.Paper no.TPWRS-00633-200

Reduction of Computational Burden and Accuracy Maximization in Short-Term Load Forecasting

Electrical energy is consumed at the same time as it is generated, since its storage is unfeasible. Therefore, short-term load forecasting is needed to manage energy operations. Due to better energy management, precise load forecasting indirectly saves money and CO2 emissions. In Europe, owing to directives and new technologies, prediction systems will be on a quarter-hour basis, which will reduce computation time and increase the computational burden. Therefore, a predictive system may not dispose of sufficient time to compute all future forecasts. Prediction systems perform calculations throughout the day, calculating the same forecasts repeatedly as the predicted time approaches. However, there are forecasts that are no more accurate than others that have already been made. If previous forecasts are used preferentially over these, then computational burden will be saved while accuracy increases. In this way, it will be possible to optimize the schedule of future quarter-hour systems and fulfill the execution time limits. This paper offers an algorithm to estimate which forecasts provide greater accuracy than previous ones, and then make a forecasting schedule. The algorithm has been applied to the forecasting system of the Spanish electricity operator, obtaining a calculation schedule that achieves better accuracy and involves less computational burden. This new algorithm could be applied to other forecasting systems in order to speed up computation times and to reduce forecasting errors

Reduction of Computational Burden and Accuracy Maximization in Short-Term Load Forecasting

Electrical energy is consumed at the same time as it is generated, since its storage is unfeasible. Therefore, short-term load forecasting is needed to manage energy operations. Due to better energy management, precise load forecasting indirectly saves money and CO2 emissions. In Europe, owing to directives and new technologies, prediction systems will be on a quarter-hour basis, which will reduce computation time and increase the computational burden. Therefore, a predictive system may not dispose of sufficient time to compute all future forecasts. Prediction systems perform calculations throughout the day, calculating the same forecasts repeatedly as the predicted time approaches. However, there are forecasts that are no more accurate than others that have already been made. If previous forecasts are used preferentially over these, then computational burden will be saved while accuracy increases. In this way, it will be possible to optimize the schedule of future quarter-hour systems and fulfill the execution time limits. This paper offers an algorithm to estimate which forecasts provide greater accuracy than previous ones, and then make a forecasting schedule. The algorithm has been applied to the forecasting system of the Spanish electricity operator, obtaining a calculation schedule that achieves better accuracy and involves less computational burden. This new algorithm could be applied to other forecasting systems in order to speed up computation times and to reduce forecasting errors

Automatic Selection of Temperature Variables for Short-Term Load Forecasting

Due to the infeasibility of large-scale electrical energy storage, electricity is generated and consumed simultaneously. Therefore, electricity entities need consumption forecasting systems to plan operations and manage supplies. In addition, accurate predictions allow renewable energies on electrical grids to be managed, thereby reducing greenhouse gas emissions. Temperature affects electricity consumption through air conditioning and heating equipment, although it is the consumer’s behavior that determines specifically to what extent. This work proposes an automatic method of processing and selecting variables, with a two-fold objective: improving both the accuracy and the interpretability of the overall forecasting system. The procedure has been tested by the predictive system of the Spanish electricity operator (Red Eléctrica de España) with regard to peninsular demand. During the test period, the forecasting error was consistently reduced for the forecasting horizon, with an improvement of 0.16% in MAPE and 59.71 MWh in RMSE. The new way of working with temperatures is interpretable, since they separate the effect of temperature according to location and time. It has been observed that heat has a greater influence than the cold. In addition, on hot days, the temperature of the second previous day has a greater influence than the previous one, while the opposite occurs on cold days

Development of new tools to promote a more effective consumer participation in short-term electricity markets

This paper summarizes the research work performed to show the capability of a combination of tools based on Self-Organizing Maps (SOM) and Physically Based Load Models (PBLM) to classify and extract pat-terns from distributor, aggregator and customer electrical demand databases (the objective known as data mining). This approach basically uses low cost information avail-able for almost all supply side agents: historic load curves of several kinds of customers. The first objective is to find a correlation between demand and the evolution of energy prices in short-term energy markets. A SOM was trained that should allow to select the most suitable customer clusters whose demand modification would benefit cus-tomer and supply-side agents through, for example, energy efficiency, distributed generation or demand response. After a previous evaluation through PBLM of different possible strategies to reduce demand during consumption peaks, a SOM was trained to detect opportunities among users with high reduction capabilities during periods when day-ahead prices are lower than shorter-term prices. The results obtained clearly show the suitability of SOM ap-proach to find easily coherent clusters between electrical users with high demand or available response capacity, and therefore a possible way to promote customer partici-pation in electrical energy markets is opened