| All commercial electrical energy producers and consumers are connected to an electrical grid covering a pre-defined geographical area through transmission lines. The electrical grid facilitates buying and selling of electrical energy through a day-ahead bidding process, under a set of prior known rules and procedures between producers and consumers; it also maintains generation:load balance within +/-1.5% limits for stable operation of the grid.;A wind energy system is an arrangement of mechanical and electrical components which converts kinetic energy of the air moving over the earth's surface into electrical energy. Due to the intermittent nature of wind, wind energy sources are considered to be unreliable sources of electrical energy and therefore rather than participating in day-ahead markets most wind energy utilities enter into contracts with the local conventional suppliers. However, these contracts offer a low price compared to the electricity markets.;The main focus of this work was to provide a solution to important aspects of a wind energy facility required to participate in an electricity market, i.e., power production forecasting, uncertainty estimation and bidding strategies. This work took the history of wind-speed forecast and actual data provided by Environment Canada and calculated the forecast error distributions. A representative wind-speed realization was then modeled as the sum of a deterministic term and a stochastic term. The deterministic term was the forecast provided by Environment Canada, while the stochastic component, the error in the forecast, was modeled as a first-order gaussian markov process; it was demonstrated that wind-speed forecast error distributions are approximately normal, and their statistics (mean and standard deviation) were determined.;Wind-speed realizations were then input to a wind generator model developed in MATLABRTM/SimulinkRTM to get wind power realizations. The uncertainties in the wind speed-realizations were transferred to the wind power realizations as well. Monte Carlo Simulations were performed to assess the expected future power production for any delivery period and the likely range of wind power production using the wind-speed forecast error statistics. The statistics of wind power prediction obtained by performing Monte Carlo Simulation gave an idea of the risk involved in wind power production; then the question arose as to how much to bid into an electricity market to obtain a maximum profit. This dilemma was resolved in this work, by the development of an optimal bidding strategy.;It is well established that a combination of an uncertain production unit with a certain production unit reduces the overall uncertainty and risk. Therefore, the prospects of adding a natural gas microturbine or buying power from the grid were assessed to reduce uncertainty in the power production.;This work gave an insight into designing energy management and control software for a renewable energy system. With the conclusions drawn from the above outlined work, it was suggested that further research be done to validate the normality of wind-speed forecast error with more data; also it is strongly recommended that for performing Monte Carlo trials the wind power generator model in MATLABRTM/SimulinkRTM is not appropriate and should be replaced with a mathematical model to decrease the computational time. |
