Forecast-Based Energy Management for Domestic PV-Battery Systems: A U.K. Case Study

Ameena Sorour,Richard P Lewis,Ashraf A Fahmy,Mohammad Monfared,Meghdad Fazeli,Justin R Searle

doi:10.1109/access.2021.3072961

Abstract

This paper presents a predictive Energy Management System (EMS), aimed to improve the performance of a domestic PV-battery system and maximize self-consumption by minimizing energy exchange with the utility grid. The proposed algorithm facilitates a self-consumption approach, which reduces electricity bills, transmission losses, and the required central generation/storage systems. The proposed EMS uses a combination of Fuzzy Logic (FL) and a rule based-algorithm to optimally control the PV-battery system while considering the day-ahead energy forecast including forecast error and the battery State of Health (SOH). The FL maximizes the lifetime of the battery by using SOH and State of Charge (SOC) in decision making algorithm to charge/discharge the battery. The proposed Battery Management System (BMS) has been tested using Active Office Building (AOB) located in Swansea University, UK. Furthermore, it is compared with three recently published methods and with the current BMS utilized in the AOB to show the effectiveness of the proposed technique. The results show that the proposed BMS achieves a saving of 18% in the total energy cost over six months compared to a similar day-ahead forecast-based work. It also achieves a saving up to 95% compared to other methods (with a similar structure) but without a day-ahead forecast-based management. The proposed BMS enhances the battery’s lifetime by reducing the average SOC up to 47% compared to the previous methods through avoiding unnecessary charge and discharge cycles. The impact of the PV system size and the battery capacity on the net exchanged energy with the utility grid is also investigated in this study.

Highlights

The integration of Renewable Energy Sources (RESs) to utility grids, driven by environmental and socioeconomic factors, is increasing dramatically
The main drawback of this process is that during peak time, the extra PV output is fed into the grid rather than being used to charge the battery as the battery is already fully charged during off-peak time
This will result in higher operational costs as the battery is charged from the utility grid during the off-peak time eliminating the chance to be charged from the PV

Summary

INTRODUCTION

The integration of Renewable Energy Sources (RESs) to utility grids, driven by environmental and socioeconomic factors, is increasing dramatically. The authors in [13] and [14] proposed a price-based EMS algorithm to reduce the operation cost Their works did not consider the energy forecast and battery State of Heath (SOH) to optimize the battery utilization. Their work considered operation costs without utilizing an energy forecast, resulting in unnecessary charge/discharge cycles Another recent FL-based work in residential MG power management was reported in [2], aiming to balance different MG resources and reduce the FL controller rules. This paper proposes a comprehensive FL-based BMS that exploits the forecasted day-ahead solar generation and load demand to optimize the battery storage performance by storing only the required peak-time day-ahead mismatch energy.

SYSTEM CONFIGURATION

RESULTS AND DISCUSSIONS

CONCLUSION