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Capacity Planning Under Uncertainty: Developing Local Area Strategies for Integrating Distributed Resources

This paper presents a methodology that helps DR planners evaluate strategic investment policies under uncertainty. Application of the methodology will not only lower utilities' costs, but also help them prepare for the future with contingency plans and a deeper understanding of the opportunities and risks they face. The formulation responds to the need to evaluate future options as uncertainty unfolds over time. For such problems, the joint consideration of dynamics and uncertainty makes the problem much too large for conventional probabilistic analysis methods and places it beyond the scope of conventional deterministic engineering analyses. The problem is formulated as a dynamic optimization problem under uncertainty. A practical solution technique for solving the problem based on a compact specification of the system state is introduced. An example, taken from actual practice, is presented. The potentially large economic value of DR investments in providing managerial flexibility is quantified. We demonstrate that the optimal level of DR investment found by our approach is superior to the level of DR investment specified by existing methodologies. Although the concepts are presented in the context of electric utility distributed resources planning, they are more widely applicable to other strategic investment problems.

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Energy Specializations: Electricity – Local Distribution; Electricity – Distributed Generation

JEL Codes: D81: Criteria for Decision-Making under Risk and Uncertainty, Q41: Energy: Demand and Supply; Prices, Q42: Alternative Energy Sources, Q54: Climate; Natural Disasters and Their Management; Global Warming, G31: Capital Budgeting; Fixed Investment and Inventory Studies; Capacity, Q21: Renewable Resources and Conservation: Demand and Supply; Prices

Keywords: Electric utilities, capacity planning, distributed generation, uncertainty

DOI: 10.5547/ISSN0195-6574-EJ-Vol18-NoSI-5

Published in Volume 18, Special Issue of the bi-monthly journal of the IAEE's Energy Economics Education Foundation.


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