Rolling Horizon Based Time-Triggered Distributed Control for AC/DC Home Area Power Network

Abstract

An energy-efficient smart home generally integrates renewable energy sources (RESs) with intelligent energy devices (IEDs) to make itself cost-efficient. However, the stochastic nature of the RESs and IEDs makes the power network uncertain, and it degrades the economic efficiency of the smart home. Hence a co-simulation based intelligent home energy management system (HEMS) is proposed in this article. It interconnects the rolling horizon-based model predictive energy scheduling mechanism with robust control strategies. It manages the load following energy supply entities (ESEs) optimally and efficiently. A time-ahead scheduler integrates an optimal algorithm to estimate the cost-optimal (e.g., minimizing total energy cost) decision signals for various ESEs by incorporating component based losses and efficiencies. Besides, a real-time distributed robust control strategy is established executing a coordinated energy sharing mechanism by incorporating an auxiliary power source. Time-triggered low-jitter wireless communication architecture is adopted to transfer the optimal decision signals from the central scheduler to the distributed device level local controller. A scenario based comparison of various ac/dc models of a home area power network evaluates the strength of the prospective HEMS.

Electric Vehicle Battery Storage Concentric Intelligent Home Energy Management System Using Real Life Data Sets

“…al [32] adopts a multi-objective method to make the EV charging and discharging more cost-effective by considering both the economy and user's preference. An RESs-integrated iEMS employed by Abdalla et al and Trinh et al [15,33] has adopted the moving sliding-window concept, which is called the receding horizon model predictive control or rolling horizon [6], to enhance the adaptability of scheduling algorithms. Additionally, Wu et al [34] introduce a unique machine learning-based energy management technique for a hybrid electric bus with an emphasis on thermal safety and battery deterioration.…”

“…These DGs, when representing a small sized power network (i.e., <20 kW), especially within a building, can be known as nanogrids (NG) [5]. The use of energy storage systems (ESSs), such as batteries, are a fairly easy solution to the aforesaid problem [6]. The ESS is utilized in this study to maximize the PV and EV serving capacity in a grid-connected NG.…”

Adaptive model predictive scheduling of flexible interconnected low-voltage distribution networks considering charging preferences of electric vehicles

“…Model predictive control (MPC) is a closed-loop control method that can reduce model and parameter uncertainties effectively, which is very suitable for scheduling of flexible resources and voltage/var optimization problems (Wang et al, 2014;Zhang et al, 2017;Qiu et al, 2018a;Qiu et al, 2018b;Zafar et al, 2018). MPC has been widely used in scheduling of combined heat and power systems (Yao et al, 2018), active distribution networks (Zhang et al, 2021), intelligent home energy management systems (Minhas and Frey, 2021) and microgrids (Rana et al, 2021). For the voltage/var optimization of distribution networks as an example, the circuit breakers, onload tap changers and capacitor banks are scheduled hourly in a rolling horizon based on predictive outputs of wind turbines and photovoltaic (PV) generators over a finite prediction horizon, e.g., 4 h, followed by a 15-min timescale scheduling of PV inverters and battery storage systems (Zafar et al, 2018) or furthermore a real-time inverter local droop control (Zhang et al, 2017;Qiu et al, 2018b).…”