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

Charles D. Feinstein, Peter A. Morris and Stephen W. Chapel

Year: 1997
Volume: Volume 18
Number: Special Issue
DOI: 10.5547/ISSN0195-6574-EJ-Vol18-NoSI-5
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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.

Inter-temporal R&D and capital investment portfolios for the electricity industry’s low carbon future

Nidhi R. Santen, Mort D. Webster, David Popp, and Ignacio Pérez-Arriaga

Year: 2017
Volume: Volume 38
Number: Number 6
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A pressing question facing policy makers today in developing a long-term strategy to manage carbon emissions from the electric power sector is how to appropriately balance investment in R&D for driving innovation in emerging low-and zero-carbon technologies with investment in commercially available technologies for meeting existing energy needs. Likewise, policy makers need to determine how to allocate limited funding across multiple technologies. Unfortunately, existing modeling tools to study these questions lack a realistic representation of electric power system operations, the innovation process, or both. In this paper, we present a new modeling framework for long-term R&D and electricity generation capacity planning that combines an economic representation of endogenous non-linear technical change with a detailed representation of the power system. The model captures the complementary nature of technologies in the power sector; physical integration constraints of the system; and the opportunity to build new knowledge capital as a non-linear function of R&D and accumulated knowledge, reflective of the diminishing marginal returns to research inherent in the energy innovation process. Through a series of numerical experiments and sensitivity analyses - with and without carbon policy - we show how using frameworks that do not incorporate these features can over-or under-estimate the value of different emerging technologies, and potentially misrepresent the cost-effectiveness of R&D opportunities.

Simulating Annual Variation in Load, Wind, and Solar by Representative Hour Selection

Geoffrey J. Blanford, James H. Merrick, John E.T. Bistline, and David T. Young

Year: 2018
Volume: Volume 39
Number: Number 3
DOI: 10.5547/01956574.39.3.gbla
View Abstract

The spatial and temporal variability of renewable generation has important economic implications for electric sector investments and system operations. This study describes a method for selecting representative hours to preserve key distributional requirements for regional load, wind, and solar time series with a two-orders-of-magnitude reduction in dimensionality. We describe the implementation of this procedure in the US-REGEN model and compare impacts on energy system decisions with more common approaches. The results demonstrate how power sector modeling and capacity planning decisions are sensitive to the representation of intra-annual variation and how our proposed approach outperforms simple heuristic selection procedures with lower resolution. The representative hour approach preserves key properties of the joint underlying hourly distributions, whereas seasonal average approaches over-value wind and solar at higher penetration levels and under-value investment in dispatchable capacity by inaccurately capturing the corresponding residual load duration curves.

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