Optimal Corrective Dispatch of Uncertain Virtual Energy Storage Systems

Abstract

High penetrations of intermittent renewable energy resources in the power system require large balancing reserves for reliable operations. Aggregated and coordinated behind-the-meter loads can provide these fast reserves, but represent energy-constrained and uncertain reserves (in their energy state and capacity). To optimally dispatch uncertain, energy-constrained reserves, optimization-based techniques allow one to develop an appropriate trade-off between closed-loop performance and robustness of the dispatch. Therefore, this paper investigates the uncertainty associated with energy-constrained aggregations of flexible, behind-the-meter distributed energy resources (DERs). The uncertainty studied herein is associated with estimating the state of charge and the capacity of an aggregation of DERs (i.e., a virtual energy storage system or VESS). To that effect, a risk-based chance constrained control strategy is developed that optimizes the operational risk of unexpectedly saturating the VESS against deviating generators from their scheduled set-points. The controller coordinates energy-constrained VESSs to minimize unscheduled participation of and overcome ramprate limited generators for balancing variability from renewable generation, while taking into account grid conditions. To illustrate the effectiveness of the proposed method, simulation-based analysis is carried out on an augmented IEEE RTS-96 network with uncertain energy resources and temperature-based dynamic line ratings.

Hierarchical, Grid-Aware, and Economically Optimal Coordination of Distributed Energy Resources in Realistic Distribution Systems

“…The relation between the battery SoC and battery power is given by (31), where ∆t is the width of the discrete time steps. In this work, we employ the simplifying assumption that VBs have unity charge/discharge efficiencies, which avoids the technicalities around simultaneous charging and discharging, which is reasonable for VBs as explained in [45] and represents ongoing work [27,46].…”

“…where e is Euler's number. As indicated by the strict inequality, this approximation is, in fact, a tight inner approximation of f −1 sff (1 − α Y ), i.e., no less conservative, as detailed in [45]. Similarly, the deterministic, time-decoupled NLP optimization in (41)-(43) that must be solved for each time-step in the prediction horizon are also made robust against forecast errors by tightened voltage and line flow bounds to form the following decoupled robust NLPs for each timestep t ∈ T :…”

Stochastic multi-period optimal dispatch of energy storage in unbalanced distribution feeders

“…If γ << 1 then the solution is close to the deterministic solution, whereas for γ >> 1 we sacrifice performance for robustness. In-between these two extremes, the trade-off parameter γ represents a "price" on risk (i.e., cost of risk), which has been studied extensively in [23]. Simulation-based analysis can help inform grid operators on an appropriate value of γ for a specific system.…”

“…It is too strong an assumption to claim that forecast errors come from a Gaussian distribution, so instead, we employ the unimodal Chebyshev approximation above to generalize the result. Further details about the relative conservativeness of different distributions can be found in [23]. For the chance constraints, the acceptable voltage violation parameter α v is chosen to be 0.10.…”

Distributed Energy Scheduling Problem Based on Learning Algorithm

“…The figure shows the devices that make up the DE system in detail, as well as the energy transmission and conversion relationship between the devices. As can be seen from the above figure, the DE system mainly includes five parts: energy input, energy output, energy conversion, energy management, and distribution and energy storage [10]. The energy input part includes the input of renewable energy and the input of conventional energy.…”