Storage Scheduling with Stochastic Uncertainties: Feasibility and Cost of Imbalances

Abstract

Dispatchability of renewable energy sources and inflexible loads can be achieved using a volatility-compensating energy storage. However, as the future power outputs of the inflexible devices are uncertain, the computation of a dispatch schedule for such aggregated systems is non-trivial. In the present paper, we propose a novel scheduling method that enforces the feasibility of the dispatch schedule with a pre-determined probability based on a description of the operation of the system as a two-stage decision process. Thereby, a crucial point is the use of probabilistic forecasts, in terms of cumulative density function, of the inflexible energy consumption/production profile. Then, for the sake of comparison, we introduce a second scheduling method based on state-of-the-art scenario optimization, where, unlike the proposed method, the focus is on the minimization of the expected final cost. We draw upon simulations based on real consumption and production data to compare the methods and illustrate our findings.

Reliable Dispatch of Renewable Generation via Charging of Time-Varying PEV Populations

“…. From (6), it follows that both uncertainties, i.e. P(k) and E v (k a v ), affect the SOC of the "time varying-battery", which is therefore a random variable itself: E(k) with realization e(k).…”

“…P(k) and E v (k a v ), affect the SOC of the "time varying-battery", which is therefore a random variable itself: E(k) with realization e(k). We describe the dynamics of E(k) using a stochastic model similar to the one in [6], where the conversion losses of the "time-varying battery" are approximated to their expected value. However, such a model is extended in the following to include the additional uncertainty E v (k a v ).…”

“…However, such a model is extended in the following to include the additional uncertainty E v (k a v ). 3) Stochastic Model of Stored Energy: Similar to [6], we divide deterministic and stochastic variables as follows…”

“…[5]. In [6], it is shown that chance constrained optimization, based on probabilistic forecasts, might outperform scenario-based schemes.…”

“…In the present paper we aim at dispatchability of a charging station for PEVs, combining chance constraint optimization and device aggregation. Our major contribution is the extension of the scheduling and control scheme described in [6], [10], which cannot be applied as-it-is to systems including more than a single static energy storage. The novel method described in this paper is instead able to cope with a dynamic aggregation of energy storage and direct controllable loads, considering multiple sources of uncertainty (e.g.…”