Modelled operation of the Shetland Islands power system comparing computational and human operators' load forecasts

Abstract

A load forecasting technique, based upon an autoregressive (AR) method is presented. Its use for short term load forecasting is assessed by direct comparison with real forecasts made by human operators of the Lerwick power station on the Shetland Islands. A substantial improvement in load prediction, as measured by a reduction of RMS error, is demonstrated. Shetland has a total installed capacity of about 68 MW, and an average load (1990) of around 20 MW. Although the operators could forecast the load for a few distinct hours better than the AR method, results from simulations of the scheduling and operation of the generating plant show that the AR forecasts provide increased overall system performance. A detailed model of the island power system, which includes plant scheduling, was run using the AR and Lerwick operators' forecasts as input to the scheduling routine. A reduction in plant cycling, underloading and fuel consumption was obtained using the AR forecasts rather than the operators' forecasts in simulations over a 28 day study period. It is concluded that the load forecasting method presented could be of benefit to the operators of such mesoscale power systems.