A Comparative Study of Optimal Energy Management Strategies for Energy Storage with Stochastic Loads

Abstract

This paper aims to present the significance of predicting stochastic loads to improve the performance of a low voltage (LV) network with an energy storage system (ESS) by employing several optimal energy controllers. Considering the highly stochastic behaviour that rubber tyre gantry (RTG) cranes demand, this study develops and compares optimal energy controllers based on a model predictive controller (MPC) with a rolling point forecast model and a stochastic model predictive controller (SMPC) based on a stochastic prediction demand model as potentially suitable approaches to minimise the impact of the demand uncertainty. The proposed MPC and SMPC control models are compared to an optimal energy controller with perfect and fixed load forecast profiles and a standard set-point controller. The results show that the optimal controllers, which utilise a load forecast, improve peak reduction and cost savings of the storage device compared to the traditional control algorithm. Further improvements are presented for the receding horizon controllers, MPC and SMPC, which better handle the volatility of the crane demand. Furthermore, a computational cost analysis for optimal controllers is presented to evaluate the complexity for a practical implementation of the predictive optimal control systems.

Review of Low Voltage Load Forecasting: Methods, Applications, and Recommendations

“…Without these investigations and comparisons, it is difficult to assess the value or importance of the forecast's quality. A few examples, including [199], [8] have shown that improved forecast accuracy can improve the performance of the application. Further [125] shows that using prediction intervals improves peak load reduction compared to some basic point forecast methods.…”

Optimal Controllers and Configurations of 100% PV and Energy Storage Systems for a Microgrid: The Case Study of a Small Town in Jordan

“…In addition, this work aims to develop and present an optimal energy controller for the ESS to improve the power network performance compared to the common energy controller, the set-point algorithm [25]. The optimal energy controller aims to find the optimal ESS operation condition (charging and discharging).…”

“…The main components of the proposed system in this paper, as presented in Figure 2, will be discussed in the following subsections. In addition, this work aims to develop and present an optimal energy contro the ESS to improve the power network performance compared to the common controller, the set-point algorithm [25]. The optimal energy controller aims to find timal ESS operation condition (charging and discharging).…”

“…This increases the complexity of designing and de ing 100% renewable energy resources with ESS. In this paper, electrical demand d a town in Jordan were collected from the Electrical Distribution Co (EDCO) over In addition, this work aims to develop and present an optimal energy controller for the ESS to improve the power network performance compared to the common energy controller, the set-point algorithm [25]. The optimal energy controller aims to find the optimal ESS operation condition (charging and discharging).…”

“…In addition, this control pro for the BESS is called the set-point control model. With regard to the ESS (lithium-ion battery) specification and re-electrification performance [25,28], the inverter efficiency is set to 85% and the charge/discharge efficiency is 90%. Then, the minimum and maximum State of Charge (SoC) is set to be equal to 20% and 100%, respectively [25,28].…”