Multiple timescale dispatch and scheduling for stochastic reliability in smart grids with wind generation integration

Abstract

Integrating volatile renewable energy resources into the bulk power grid is challenging, due to the reliability requirement that the load and generation in the system remain balanced all the time. In this study, we tackle this challenge for smart grid with integrated wind generation, by leveraging multi-timescale dispatch and scheduling. Specifically, we consider smart grids with two classes of energy userstraditional energy users and opportunistic energy users (e.g., smart meters or smart appliances), and investigate pricing and dispatch at two timescales, via day-ahead scheduling and realtime scheduling. In day-ahead scheduling, with the statistical information on wind generation and energy demands, we characterize the optimal procurement of the energy supply and the day-ahead retail price for the traditional energy users; in real-time scheduling, with the realization of wind generation and the load of traditional energy users, we optimize realtime prices to manage the opportunistic energy users so as to achieve system-wide reliability. More specifically, when the opportunistic users are non-persistent, we obtain closed-form solutions to the two-level scheduling problem. For the persistent case, we treat the scheduling problem as a multi-timescale Markov decision process. We show that it can be recast, explicitly, as a classic Markov decision process with continuous state and action spaces, the solution to which can be found via standard techniques.

Design and Implementation of an IoT Access Point for Smart Home

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A Markov Decision Model for Cooperative Virtual Power Plants Market Participation

“…To our knowledge, in most existing models, controllable resources utilization is planned only after market participation, to meet furniture contracts priory made [13], [14]; moreover, a number of them deal with the case where a unique stakeholder owns the whole VPP production means, and use controllable resources as backup means. Or, in a configuration with substantial storage amounts to control, controllable units cannot be considered only as adjustment variables; indeed, their respective utilities pass by their own market participation.…”

Robust strategy synthesis for probabilistic systems applied to risk-limiting renewable-energy pricing

“…That effort focuses on modeling the uncertainty in energy availability on the supply side, but it does not consider the problem of controlling user demand through economic incentives. The effectiveness of demand response in balancing supply and demand in power grids was studied in [8], and a stochastic framework to optimize operator profits was presented in [9]. We closely follow the optimization setup presented in these works, but we also constrain the operator risk and the user QoS at synthesis time.…”