Smart community setups nowadays are subjected to complicated issues such as instability, intermittent integration of the load at the demand side, and lack of intelligent two-way communication process. These issues need to be addressed in terms of a balanced power demand dispatch (DD) in the realtime or day-ahead duplex signal regime under multi-microgrids. This paper offers an intelligent multi-agentbased approach that works between different levels of communication and their respective layers for a community-based system to optimize the power in community-based multi-microgrids model. This will further enhance user personal comfort. Constraints relative to cost minimization also have a relation with this model. A three-level structure with various layers of autonomous agents take intelligent decisions based on prioritized particle swarm optimization (P-PSO), prioritized plug and play (PPnP), and knapsack; considering DD as the main driver of the system to address objectives like price and power consumption uncertainties. Distinct smart home models, depending upon their living habits, are keenly observed providing their power infrastructure and personal comfort. Load appliances considered as load agents are individually contemplated for maximum proficiency. Furthermore, two-way communication between utility and consumers lowers down the risk of the inefficiency of the system.INDEX TERMS Demand dispatch, demand response, multi-agent system, multi-microgrid, python agent development, prioritized plug-and-play, prioritized particle swarm optimization, real-time pricing and usage.
Off-grid Photovoltaic (PV) system along with battery storage is very effective solution for electrification in remote areas. However, battery capacity selection is the most challenging task in system designing. In this study, an off-grid PV system along with battery storage is designed for the remote area of Karachi, Pakistan. The system is designed by considering the maximum energy requirement in summer season. The battery storage is selected to fulfill the energy demand during the night and cloudy seasons. On the basis of load, a total of 6 kW system is required to fulfill the energy demand. For such system, 925 Ah of battery is required to meet the energy requirement for a day in absence of solar irradiation. A regression-based correlation between battery capacity and energy demand is prepared for suitable battery sizing using Minitab. An economic analysis of the project is also carried out from which a net present value and simple payback are determined as USD 10,348 and 3 years, respectively. The environmental benefits are also been determined. It is found that the system will reduce around 7.32 tons of CO2 per annum which corresponds to the 183.69 tons of CO2 not produced in the entire project life.
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