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There is a growing need for electricity-system flexibility to maintain real-time balance between energy supply and demand. In this paper, we explore optimal and incentive-compatible scheduling of generators for this purpose. Specifically, we examine a setting wherein each generator has a different operating cost if it is committed in advance (e.g., day- or hour-ahead) as opposed to being reserved as flexible real-time supply. We model an optimal division of generators between advanced commitment and real-time flexible reserves to minimize the expected cost of serving an uncertain demand. Next, we propose an incentive-compatible remuneration scheme with two key properties. First, the remuneration scheme incentivizes generators to reveal their true costs. Second, the scheme aligns generators’ incentives with the market operator’s optimal division of generators between advanced commitment and real-time reserve. We use a simple example to illustrate the market operator’s decision and the remuneration scheme. JEL Classification: C61, D47, D82, L94, Q4 

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.