Diesel generator sets (gen-sets) have traditionally been used for stand-by power generation. They are now often required to operate in parallel with the utility (peak shaving) or alone (peak lopping) to meet the peak demand at the consumer site. These peak shaving/lopping operations can reduce the system peak demand. As these operations are carried out only for a few hundred hours in a year, the gen-set capacity is unused during the remaining periods of the year. Developments in microcomputers and communication technology have enabled the customer-owned gen-sets to be remotely monitored and controlled from a central location. The central control of MW-size gen-sets can enhance their use in supporting the utility generation at critical times. It will be shown that central control provides an opportunity for more effective peak shaving. The paper also investigates the use of diesel gen-sets as stand-by reserve for isolated systems and presents a demonstration of central control in which a 100-kVA diesel gen-set is operated from a utility control centre.

Remotely connected wind farms can have a significant impact on voltages, load demand and power losses in radial distribution networks. Quantification of these effects is important in determining the true value of wind energy and requires detailed simulations of integrated network behaviour over an extended period of time. The steady state behaviour of commercial and residential radial distribution feeders with remotely connected wind turbines has been investigated using a recursive, loadflow-based simulation algorithm and time series data from a known system. The results of annual simulations reveal that large wind farm capacities can be freely operated in remote locations without violation of voltage limits and with a net reduction in feeder energy consumption. With automatic control of real and reactive power flows from the wind farm, connected capacity can be significantly expanded before operation becomes infeasible.

Renewable energy production in Northern Ireland has been dominated by six 5 MW wind farms commissioned under NI-NFFO1 (1994). Fully operational since 1996, these wind farms now supply 1.3% of annual electricity consumption. Their integrated operation holds important clues for wind power expansion into the small, isolated, local grid supply system and is the subject of investigation in this paper. Production data are statistically analysed for variability and predictability, and the potential for wind power expansion in the region is examined.

Guyanas dependence upon imported petroleum fuels can only be offset by the sustained exploitation of its indigenous resources. With its populated coastlands exposed to the northeast trade winds and a history of small-scale wind energy utilisation wind is one such potential energy source. In this study, the coastal wind regime is analysed and historical data from a coastal weather station are used to estimate the potential for wind generation. It is found that a hybrid Weibull probability density function best describes the annual wind speed frequency distribution at the reference height of 10.67 m. With an annual mean wind speed of 5.8 m⧸s, an energy pattern factor of 1.41, and an annual average power density of 159 W⧸m2, this distribution represents a class-3 wind resource, suitable for most wind turbine applications. Site analysis and observed trends in coastal wind availability suggest the strong likelihood of a greater wind resource in more open locations. In view of its apparent potential for wind farm operation, a comprehensive, wind resource assessment programme is recommended for the Guyana coastlands.

This paper presents results of a new application of the weighted least absolute value (WLAV) minimization criterion in a dynamic estimation algorithm to the problem of short-term load forecasting in electric power systems. The results are compared with those obtained using Kalman filtering and with an adaptive general exponential smoothing algorithm.

Short term load forecasting employs load models that express the effects of influential variables on system load. The model coefficients are found by fitting the load model to a data base of previous loads and observations of the variables, and then solving the resulting overdetermined system of equations. The coefficients thus obtained are critical to the forecasting process, as they directly affect its final predictive accuracy. This study compares two linear static parameter estimation techniques as they apply to the twenty-four hour off-line forecasting problem. Here a least squares and a least absolute value based linear programming algorithm will be used to simulate the forecast response of three twenty-four hour off-line load models. The three load models are (1) a multiple linear regression model, (2) a harmonic decomposition model and (3) a hybrid multiple linear regression/harmonic decomposition model. These models are simplistic in nature, and their primary purpose is to provide a basis for comparing the two parameter estimation techniques. The results obtained for each estimation algorithm via each load model, using the same data bases and forecasting periods, are presented and form the basis for comparisons presented in the paper.