Long term load forecasting accuracy in electric utility integrated resource planning
| Autor: | Alan H. Sanstad, Peter H. Larsen, Charles Goldman, Juan Pablo Carvallo |
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| Rok vydání: | 2018 |
| Předmět: |
Consumption (economics)
business.industry 020209 energy 02 engineering and technology Energy consumption 010501 environmental sciences Management Monitoring Policy and Law Environmental economics 01 natural sciences Term (time) Electric utility General Energy Peak demand Risk analysis (business) Range (aeronautics) 0202 electrical engineering electronic engineering information engineering Economics Electricity business 0105 earth and related environmental sciences |
| Zdroj: | Energy Policy. 119:410-422 |
| ISSN: | 0301-4215 |
| Popis: | Forecasts of electricity consumption and peak demand over time horizons of one or two decades are a key element in electric utilities’ meeting their core objective and obligation to ensure reliable and affordable electricity supplies for their customers while complying with a range of energy and environmental regulations and policies. These forecasts are an important input to integrated resource planning (IRP) processes involving utilities, regulators, and other stake-holders. Despite their importance, however, there has been little analysis of long term utility load forecasting accuracy. We conduct a retrospective analysis of long term load forecasts on twelve Western U. S. electric utilities in the mid-2000s to find that most overestimated both energy consumption and peak demand growth. A key reason for this was the use of assumptions that led to an overestimation of economic growth. We find that the complexity of forecast methods and the accuracy of these forecasts are mildly correlated. In addition, sensitivity and risk analysis of load growth and its implications for capacity expansion were not well integrated with subsequent implementation. We review changes in the utilities load forecasting methods over the subsequent decade, and discuss the policy implications of long term load forecast inaccuracy and its underlying causes. |
| Databáze: | OpenAIRE |
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