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We propose a multistage multiscale linear stochastic model to optimize electricity generation, storage, and transmission investments over a long planning horizon. The multiscale structure captures ‘large-scale’ uncertainties, such as investment and fuel-cost changes and long-run demand-growth rates, and ‘small-scale’ uncertainties, such as hour-to-hour demand and renewable-availability uncertainty. The model also includes a detailed treatment of operating periods so that the effect of dispatch decisions on long-term investments are captured. The proposed model can be large in size. The progressive hedging algorithm is applied to decompose the model by scenario, greatly reducing computation times. We also derive bounds on the optimal objective-function value, to assess solution quality. We use a case study based on the state of Texas to demonstrate the model and show the benefits of its detailed representation of the operating periods in making investment decisions.
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Value of model enhancements: quantifying the benefit of improved transmission planning models
A framework to quantify the value of model enhancements (VOMEs) in transmission planning models is proposed and applied to a case study of the large‐scale, long‐term planning of the Western Electricity Coordinating Council (WECC) system. The VOME, which is closely related to the concept of the value of information from decision analysis, quantifies the probability‐weighted improvement in the system performance resulting from changes in decisions that result from model enhancements. The WECC case study shows that it is practical to quantify VOME and illustrates the type of insights that can be obtained. The values of four types of model enhancements are compared. The results show major benefits from considering long‐run uncertainty using multiple scenarios of technology, policy, and economics; these benefits are as much as 14% of total benefits of new transmission built in the first ten years. However, less benefit is obtained from more temporal granularity, more complex network representations, and inclusion of unit commitment constraints and costs. This framework can be applied to quantify the VOMEs in any planning context, such as integrated resource planning.
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- Award ID(s):
- 1736414
- PAR ID:
- 10570708
- Publisher / Repository:
- DOI PREFIX: 10.1049
- Date Published:
- Journal Name:
- IET Generation, Transmission & Distribution
- Volume:
- 13
- Issue:
- 13
- ISSN:
- 1751-8687
- Format(s):
- Medium: X Size: p. 2836-2845
- Size(s):
- p. 2836-2845
- Sponsoring Org:
- National Science Foundation
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