This paper investigates the real-time scheduling of residential appliances in the home energy management system (HEMS), including electric water heater (EWH), air conditioner (AC), clothes dryer (CD), electric vehicle (EV), photovoltaic cell (PV) and battery. A demand response (DR) mechanism is proposed to stimulate the household to participate in the DR services. The conditional value-at-risk (CVaR) is applied to make a trade-off between the expected costs and the risk of exposure to uncertainties of the electricity price and other factors. Fuzzy logic controller is utilized to determine the output power of the battery, and two sets of rules are designed to adjust the charging and discharging operations of the battery according to the DR request from the grid. Stochastic aspects of the electricity price, outdoor temperature, hot water usage, PV generation, and loads are considered. Simulation results show that the proposed DR mechanism can decrease the cost of the household considerably, and the proposed real-time scheduling algorithm can optimize the operation of appliances close to the ideal deterministic results, even when the gap between the forecast electricity price and the real price is large.

This paper presents the results of three control strategies of managed energy services with home energy management system (HEMS)-integrated devices. The HEMS controls and monitors three types of managed devices: 1) heating; 2) task-specific; and 3) energy storage devices. Three approaches are proposed for the rolling optimization by the HEMS, namely, mixed integer linear programming (MILP), continuous relaxation (CR), and fuzzy logic controller (FLC). The CR approach is identified to reduce the computational complexity of the MILP by changing the MILP into an LP solution. Three types of FLC control approaches are proposed, namely, heat-related FLC, task-related FLC, and FLC for the battery. Each control strategy is evaluated against cost optimization, computational resource, and practical implementation. The findings in this paper show that all three algorithmic control strategies successfully perform cost optimization, even with inaccurate forecasting information.

Microgrid (MG) is one of the most effective solution to integrate distributed renewable energy into power system. However, modern MG has several technical challenges such as migration to multi vector energy system, increasing renewable energy penetration, and the uncertainties that arise from the large-scale incorporation of renewable energy. Recent development of multi-microgrid (MMG) could potentially mitigate these challenges. MMG has obvious advantages in improving the system stability, reliability, economy, and energy efficiency of power grid operation through autonomous management and coordinated control among networked MGs. In the meantime, uncertainties from many elements and operators in the MMG system also pose huge challenges in modeling. This paper will present and review typical architecture of MMG, including physical layer, information layer and application layer. Moreover, this paper will critically review and analyze challenges in uncertainty modelling and solution in MMGs. Finally, the paper will also discuss future research areas and development trends of MMG.