Distributed storage for intermittent energy sources: Control design and performance limits

Abstract

One of the most important challenges in the integration of renewable energy sources into the power grid lies in their 'intermittent' nature. The power output of sources like wind and solar varies with time and location due to factors that cannot be controlled by the provider. Two strategies have been proposed to hedge against this variability: 1) use energy storage systems to effectively average the produced power over time; 2) exploit distributed generation to effectively average production over location. We introduce a network model to study the optimal use of storage and transmission resources in the presence of random energy sources. We propose a Linear-Quadratic based methodology to design control strategies, and we show that these strategies are asymptotically optimal for some simple network topologies. For these topologies, the dependence of optimal performance on storage and transmission capacity is explicitly quantified.

Optimal large-scale storage placement in single generator single load networks

“…From complementary slackness condition in (13d), it follows that tmax t=1 r * 1 (t) = 0. Combining equations (14) and (16) ≤ f.…”

“…Koutsopoulos et al [13] and Su et al [12] examine the operation of a single storage device, thus neglecting the network constraints. On the other hand, Kanoria et al in [14], Gayme et al in [15] and Chandy et al in [16] explicitly model the role of the networks in the operation of distributed storage resources. The engineering constraints are Emails: (cthrampo, boses, hassibi)@caltech.edu.…”

“…In [20] and [21], the authors consider this question from a purely economic point of view. Capacity provisioning of energy resources with network constraints of the physical system has been addressed, e.g., [12], [14]. Optimal placement and sizing storage units have been studied in [22], [23] using simulations.…”

“…Optimal placement and sizing storage units have been studied in [22], [23] using simulations. While [14], [22] rely on the DC-OPF model, [23] uses a convex relaxation of AC-OPF based on [24], [25].…”

A Generic Ontology-Based Information Model for Better Management of Microgrids

“…Due to the intermittent aspect [22] of the renewable energy sources and the exposure of the MG to predictable and non-predictable events (MG anomalies, storms, etc. ), an ontology modeling becomes essential since it can represent beliefs, goals, hypotheses, and predictions.…”