To facilitate the possible technology and demand changes in a renewable-energy dominated future energy system, an integrated approach that involves Renewable Energy Sources (RES)-based generation, cutting-edge communication strategies, and advanced Demand Side Management (DSM) is essential. A Home Energy Management System (HEMS) with integrated Demand Response (DR) programs is able to perform optimal coordination and scheduling of various smart appliances. This paper develops an advanced DSM framework for microgrids, which encompasses modeling of a microgrid, inclusion of a smart HEMS comprising of smart load monitoring and an intelligent load controller, and finally, incorporation of a DR strategy to reduce peak demand and energy costs. Effectiveness of the proposed framework is assessed through a case study analysis, by investigation of DR opportunities and identification of energy savings for the developed model on a typical summer day in Western Australia. From the case study analysis, it is evident that a maximum amount of 2.95 kWh energy can be shifted to low demand periods, which provides a total daily energy savings of 3%. The total energy cost per day is AU$2.50 and AU$3.49 for a house with and without HEMS, respectively. Finally, maximum possible peak shaving, maximum shiftable energy, and maximum standby power losses and energy cost savings with or without HEMS have been calculated to identify the energy saving opportunities of the proposed strategy for a microgrid of 100 houses with solar, wind, and a back-up diesel generator in the generation side.

Sector coupling is one of the emerging topics in recent energy and climate change policy discussions. It can play a significant role in creating the pathway of a renewable-based energy system in the European energy sector. The North Sea region is very likely to play a key role in the transition to a sustainable energy system. Although different energy modelling approaches allow a versatile use, they lead to the problem of an unclear understanding of specific aspects of sector coupling, and the relevance of existing tools and techniques to model and analyze such a system. This paper is aimed at providing a comprehensive understanding of sector coupling and its incorporation in energy system models. Following a thorough literature review on sector coupling and energy system modelling, the paper outlines an approach to select an appropriate tool based on the specific rationales of the research. The paper also presents the open energy modelling framework, ‘Oemof’, as an open model tool to address the complex challenges of energy systems. The conclusions from the literature review provide a detailed understanding of the concept of sector coupling and indicate that it can be advantageous from the viewpoints of decarbonization, flexibility, network optimization, and system efficiency. To solve the coupling barriers, diversified techno-socio-economic circumstances should be taken into account through the use of model collaboration. It is also demonstrated how a list of appropriate tools for model collaboration can be picked up methodologically from an available wide range of models. Finally, ‘Oemof’ is hypothesized as a progressive tool to design a sector-coupled and renewable-based energy system in the North Sea region.

Local supply-demand matching in power grids by means of advanced information and communication technology (ICT) is emerging due to the increasing integration of distributed energy resources (DER). Although advantages of the local matching mechanism have been proved by either research works or demonstrations, there are some difficulties on being proactive to handle uncertainty from renewable energy sources (RES) and new types of load consumption. This paper aims to enhance the matching mechanism using multi-agent systems (MAS) and artificial neural network (ANN) to investigate and determine DER's flexibility to compensate that uncertainty. Under a more general platform for smart grid functions, this paper presents a model to achieve a match between the forecasted supply and demand. Short-term forecasting based on an ANN model is used to predict the stochastic behavior of weather data. The model considers various scenarios and the potential from household demand side management. The results from the performed simulations indicate feasible DER's flexibility for power matching, which can be further adapted for different scenarios to serve local or more grid-related optimization objectives.

Demand Response (DR) applications via Home Energy Management System (HEMS) at the building level in a strategically integrated RES-based electricity grid can help reduce the building peak demand as well as energy consumption and the power inefficiency in the grid respectively. Distributed Energy Generation (DEG) based on Renewable Energy Sources (RES) are seen as a reliable alternative and more efficient grid optimization to the traditional fossil energy sources-based grid. But a proper combination of demand response and the DEG can significantly make a revolutionary change in the electricity grid. This paper focuses on the impact of integrated control of Solar PV and the Wind Turbine on improving the supply-side and coordinated control strategy for automated demand response by ensuring efficient use of home appliances including Electric Vehicle (EV), air cooling, water heating etc. based on HEMS in order to reduce the peak demand and overall power inefficiency in the grid. For that purpose and analysis, a highly accurate aggregated model of Solar PV, Wind turbine and the home appliances has been developed in the Simulink (MATLAB) and an iterative algorithm based on Load Shifting technique has been established for HEMS controller with embedded MATLAB code. The obtained simulation result illustrates the effectiveness of the proposed system.

Electric Vehicles (EV) have become popular due to increasing oil prices and environmental issues. Vehicle-to-Grid (V2G) system allows bidirectional electricity flow from vehicle's battery to power grid. In this paper, Plug-in Hybrid Electric Vehicle (PHEV) has been considered as an option for energy storage in a smart system where renewable energy sources (RES) are included along with main grid. Smart charging system will ensure that an electric vehicle owner will charge the battery during off peak hours and discharge remaining energy back to the grid during peak demand hours. Development of such charging station in a smart grid network is capable of solving the problem of peak demand crisis. PHEV and grid energy management model has been analyzed using HOMER. A Graphical User Interface (GUI) is built where an electric vehicle owner will get the precise time period for charging and discharging their vehicles and the cost of energy through smart metering.