Reducing Grid Distortions Utilizing Energy Demand Prediction and Local Storages

Pascal A Schirmer,Iosif Mporas,Christian Geiger

doi:10.1109/access.2021.3053200

Abstract

Energy storage systems will play a key role in the establishment of future smart grids. Specifically, the integration of storages into the grid architecture serves several purposes, including the handling of the statistical variation of energy supply through increasing usage of renewable energy sources as well as the optimization of the daily energy usage through load scheduling. This article is focusing on the reduction of the grid distortions using non-linear convex optimization. In detail an analytic storage model is used in combination with a load forecasting technique based on socio-economic information of a community of households. It is shown that the proposed load forecasting technique leads to significantly reduced forecasting errors (relative reductions up-to 14.2%), while the proposed storage optimization based on non-linear convex optimizations leads to 12.9% reductions in terms of peak to average values for ideal storages and 9.9% for storages with consideration of losses respectively. Furthermore, it was shown that the largest improvements can be made when storages are utilized for a community of households with a storage size of 4.6-8.2 kWh per household showing the effectiveness of shared storages as well as load forecasting for a community of households.

Highlights

Global average temperatures are rising due to the increasing amount of greenhouse gas emissions causing natural disasters and having negative impact to nature and humans [1]
EXPERIMENTAL SETUP The architecture of the system model presented in Section II and the optimized load management including load prediction and storage optimization presented in Section III were evaluated using the dataset, parametrizations and experimental protocols presented below
In this article an storage optimization for a community of households was presented in order to reduce grid distortions

Summary

Introduction

Global average temperatures are rising due to the increasing amount of greenhouse gas emissions causing natural disasters and having negative impact to nature and humans [1]. As currently the largest share of CO2 emissions are caused by burning of fossil fuels, renewable energy sources are increasingly employed in order to reduce the carbon footprint of the primary energy sector [2]. To account for the increasing volatility of energy generation, combined with the volatility of energy demand, three major topics have been addressed in literature in order to reduce grid distortions and peak loads, namely load prediction, integration of storages and demand management [1], [4]. When considering storage optimizations or advanced demand management usually a load prediction architecture is combined with a shared storage unit in order to find optimal charging/discharging strategies under certain constraints.

Methods

Results

Discussion

Conclusion