Abstract
Increased uptake of variable renewable generation and further electrification of energy demand necessitate efficient coordination of flexible demand resources to make most efficient use of power system assets. Flexible electrical loads are typically small, numerous, heterogeneous and owned by self-interested agents. Considering the multi-temporal nature of flexibility and the uncertainty involved, scheduling them is a complex task. This paper proposes a forecast-mediated real-time market-based control approach (F-MBC) for cost minimizing coordination of uninterruptible time-shiftable (i.e. deferrable) loads. F-MBC is scalable, privacy preserving, and usable by device agents with small computational power. Moreover, F-MBC is proven to overcome the challenge of mutually conflicting decisions from equivalent devices. Simulations in a simplified but challenging case study show that F-MBC produces near-optimal behaviour over multiple time-steps.
Highlights
Power systems have seen an increasing penetration of distributed energy resources (DERs), such as distributed generators, flexible demand, and small-scale renewable generation
We propose the forecastmediated market-based control approach (F-Market-based control (MBC))
forecast-mediated real-time market-based control approach (F-MBC) relies on decentralized bid formulation and centralized oneshot market clearing to coordinate among these devices
Summary
Power systems have seen an increasing penetration of distributed energy resources (DERs), such as distributed generators, flexible demand, and small-scale renewable generation. This trend has significant impacts on the network, leading to congestion, reduced network utilization, and even instability or system inoperability at the distribution level [1]. Optimal coordination among DERs is a complex multidimensional problem, especially in settings with small, numerous, heterogeneous DERs owned by self-interested agents. A suitable coordination approach for such a setting is required to be simple and usable by agents with small computational power [2], scalable for
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