Data-Driven Mitigation of Energy Scheduling Inaccuracy in Renewable-Penetrated Grids: Summerside Electric Use Case

Mostafa Farrokhabadi

doi:10.3390/en12122228

Abstract

This paper presents findings on mitigating the negative impact of renewable energy resources variability on the energy scheduling problem, in particular for island grids and microgrids. The methods and findings presented in this paper are twofold. First, data obtained from the City of Summerside in the province of Prince Edward Island, Canada, is leveraged to demonstrate the effectiveness of state-of-the-art time series predictors in mitigating energy scheduling inaccuracy. Second, the outcome of the time series prediction analysis is used to propose a novel data-driven battery energy storage system (BESS) sizing study for energy scheduling purposes. The proposed probabilistic method accounts for intra-interval variations of generation and demand, thus mitigating the trade-off between time resolution of the problem formulation and the solution accuracy. In addition, as part of the sizing study, a BESS management strategy is proposed to minimize energy scheduling inaccuracies, and is then used to obtain the optimal BESS size. Finally, the paper presents quantitative analyses of the impact of both the energy predictors and the BESS on the supplied energy cost using the actual data of the Summerside Electric grid. The paper reveals the significant potential for reducing energy cost in renewable-penetrated grids and microgrids through state-of-the-art predictors combined with applications of properly-sized energy storage systems.

Highlights

Driven by the need for cheap, sustainable, and clean sources of energy, renewable energy resources (RES), in particular wind and solar energies are being deployed increasingly across many countries [1].High penetration of wind and solar energies poses some operational challenges, affecting system stability, reliability, and economics [2]
These challenges can be categorized into two main groups: (i) those associated with the decrease of system inertia [3]; and (ii) those associated with the intrinsic variability and uncertainty of RES [4]
While there may be slight variations in the wind output profile depending on the season of the year, i.e., winter or summer, the results presented here are applicable to any period; the proposed techniques do not depend on the choice of the test set, and the 30-day period in winter is represented without the loss of generality

Summary

Introduction

High penetration of wind and solar energies poses some operational challenges, affecting system stability, reliability, and economics [2] These challenges can be categorized into two main groups: (i) those associated with the decrease of system inertia [3]; and (ii) those associated with the intrinsic variability and uncertainty of RES [4]. Island grids and microgrids, where RES penetration tends to be high, are more prone to negative impacts of RES variability and low system inertia [5]. In view of these challenges, numerous techniques have been proposed in the literature to alleviate the impact of high RES penetration. These include methods pertaining to enhancing the prediction accuracy of RES [6,7,8] and techniques pertaining to the use of energy storage systems (ESS) [9,10], in particular battery energy storage systems (BESS) [11]

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Conclusion