Browsing by Author "Canizares, C.A."

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Application of Public-Domain Market Information to Forecast Ontario's Wholesale Electricity Prices
(Institute of Electrical and Electronics Engineers (IEEE), 2006-11-30) Zareipour, H.; Canizares, C.A.; Bhattacharya, K.; Thomson, J.
This paper evaluates the usefulness of publicly available electricity market information in forecasting the hourly Ontario energy price (HOEP). In order to do so, relevant data from Ontario and its neighboring electricity markets, namely, New York, New England, and PJM electricity markets, are investigated, and a final set of explanatory variable candidates that are available before real-time are selected. Multivariate transfer function and dynamic regression models are employed to relate HOEP behavior to the selected explanatory variable candidates. Univariate ARIMA models are also developed for the HOEP. The HOEP models are developed on the basis of two forecasting horizons, i.e., 3 h and 24 h, and forecasting performance of the multivariate models is compared with that of the univariate models. The outcomes show that the market information publicly available before real-time can be used to improve HOEP forecast accuracy to some extent; however, unusually high or low prices remain unpredictable, and hence, the available data cannot lead to significantly more accurate forecasts. Nevertheless, the generated forecasts in this paper are significantly more accurate than currently available HOEP forecasts. To analyze the relatively low accuracy of the HOEP forecasts, comparisons are made with respect to ARIMA models developed for locational marginal prices (LMPs) of Ontario's three neighboring markets, and price volatility analyses are presented.
Reactive Power and Voltage Control in Distribution Systems With Limited Switching Operations
(Institute of Electrical and Electronics Engineers (IEEE), 2009-03-31) Liu, M.B.; Canizares, C.A.; Huang, W.
An algorithm based on a nonlinear interior-point method and discretization penalties is proposed in this paper for the solution of the mixed-integer nonlinear programming (MINLP) problem associated with reactive power and voltage control in distribution systems to minimize daily energy losses, with time-related constraints being considered. Some of these constraints represent limits on the number of switching operations of transformer load tap changers (LTCs) and capacitors, which are modeled as discrete control variables. The discrete variables are treated here as continuous variables during the solution process, thus transforming the MINLP problem into an NLP problem that can be more efficiently solved exploiting its highly sparse matrix structure; a strategy is developed to round these variables off to their nearest discrete values, so that daily switching operation limits are properly met. The proposed method is compared with respect to other well-known MINLP solution methods, namely, a genetic algorithm and the popular GAMS MINLP solvers BARON and DICOPT. The effectiveness of the proposed method is demonstrated in the well-known PG&E 69-bus distribution network and a real distribution system in the city of Guangzhou, China, where the proposed technique has been in operation since 2003.