Pricing Multi-Interval Dispatch under Uncertainty Part I: Dispatch-Following Incentives

TL;DR

This paper introduces a new pricing mechanism called TLMP for multi-interval power dispatch under uncertainty, ensuring truthful bidding and eliminating uplift payments, with theoretical validation and broader network analysis.

Contribution

It proposes TLMP, a nonuniform pricing scheme that guarantees dispatch-following incentives and truthful bidding under uncertainty, extending standard LMP to include ramping costs.

Findings

TLMP eliminates out-of-the-market uplifts.

TLMP guarantees truthful bidding incentives.

Extended analysis to general network settings.

Abstract

Pricing multi-interval economic dispatch of electric power under operational uncertainty is considered in this two-part paper. Part I investigates dispatch-following incentives of profit-maximizing generators and shows that, under mild conditions, no uniform-pricing scheme for the rolling-window economic dispatch provides dispatch-following incentives that avoid discriminative out-of-the-market uplifts. A nonuniform pricing mechanism, referred to as the temporal locational marginal pricing (TLMP), is proposed. As an extension of the standard locational marginal pricing (LMP), TLMP takes into account both generation and ramping-induced opportunity costs. It eliminates the need for the out-of-the-market uplifts and guarantees full dispatch-following incentives regardless of the accuracy of the demand forecasts used in the dispatch. It is also shown that, under TLMP, a price-taking market participant has incentives to bid truthfully with its marginal cost of generation. Part II of the paper extends the theoretical results developed in Part I to more general network settings. It investigates a broader set of performance measures, including the incentives of the truthful revelation of ramping limits, revenue adequacy of the operator, consumer payments, generator profits, and price volatility under the rolling-window dispatch model with demand forecast errors.