A Study on the Effects of Solar Tracking Systems on the Performance of Photovoltaic Power Plants

The use of renewable energy resources to produce the electricity is a rising trend in various countries worldwide. This is especially because these energies do not produce the greenhouse gases;therefore, don’t become a destructive factor on the ozone layer and the environment. Besides, the fossil energies are always exposed to price changes and will eventually end, while the renewable energies don’t pose such problems and are freely acquired from the nature. Some of the most important of these reproducible energy resources are the energies of the wind and the solar energy. The generation capability of a photovoltaic power plant is largely dependent on the intensity of the sun radiation. On the other hand, the changing of position causes the sun to have a variable shining intensity in different seasons and different times of the year;therefore, some of the solar power plants possess solar tacking systems. This paper studies the different types of photovoltaic systems including fixed panel, photovoltaic farms equipped to the single axis and double axis tracking systems and their effects on the performance of the solar power plants. In order to determine the position of the sun in the sky, the zenith and azimuth angles of it are used to extract the relations between the different parameters of the sun radiation. The resulted parameters are used in the modeling of the single axis and double axis systems. In the paper it is utilized from the sun parameters in Qeshm Island of Iran in the summer of 2011.

Stochastic optimization for retailers with distributed wind generation considering demand response

In this paper, a multi-stage stochastic model is presented for a renewable distributed generation(RDG)-owning retailer to determine the trading strategies existing in a competitive electricity market. Uncertainties associated with wholesale electricity market price, clients' consumption and power output of wind resources are considered through auto regressive integrated moving average(ARIMA) approach. In the proposed method, three trading floors are addressed for the retailer to hedge against the uncertainties. In the first stage, the retailer participates in day-ahead market to supply the clients and in the second stage, intraday market is addressed to allow the retailer to modify the schedule of its clients' consumption/RDG production. Due to unfavorable uncertainties, especially in renewable power production, real-time market is considered in the third stage to diminish the uncertainty at power delivery time. Cost function of wind resources considering capital, operation and maintenance(O&M) cost is incorporated in the objective function to increase the applicability of the mechanism. The proposed approach is formulated for risk-averse and risk-taker retailer through conditional value at risk(CVaR) approach. In order to study the impact of retail strategies on consumption patternand consumers' electricity bills, time-of-use(TOU)demand response programs are discussed in this paper.Formulating the problem, the mixed integer non-linear programming(MILNP) problem is transformed into mixed integer linear programming(MILP) by jointly using decomposition and disjunctive constraints. Finally, a case study containing wind power resources, energy storage system and retailer is considered to analyze the proficiency of the proposed approach.