A Novel Political Optimizer Integrated with ThingSpeak Platform for Multi-Objective Energy Management in Microgrids

Microgrids (MGs) play a crucial role in the electrification of urban and rural areas due to the continuous growth of power demand. Introducing Renewable Energy Sources (RESs) into the electricity sector significantly helps decrease operational costs and emissions from conventional power generation, especially when installed in MGs near customers, reducing power transmission losses and costs. Incorporating RESs with different Distributed Generators (DGs) creates a critical need for an Energy Management System (EMS) to coordinate and distribute power among them. Additionally, sudden changes in load demand, RES outputs, and other system conditions require continuous and real-time monitoring of various system components and parameters. Therefore, this paper proposes a newly developed Political Optimizer (PO) integrated with Internet of Things (IoT) technology and the ThingSpeak platform to address and simulate a multi-objective energy management problem. The aim is to minimize total generation costs and emissions treatment expenses while maintaining supply-demand balance and providing customers with the required electricity consistently, reliably, and efficiently. The proposed MG comprises various DGs to dispatch power, such as Fuel Cells (FC), Gas Turbines (GT), RESs (Photovoltaics and Wind Turbines), Conventional Diesel Generators (CDG), and the main grid. The main grid operates in both active and passive modes of operation. The proposed PO, combined with IoT and ThingSpeak, successfully minimized generation costs and emissions treatment expenses. The results obtained demonstrated the effectiveness and efficiency of the proposed EMS, as it was compared with other algorithms and outperformed them, achieving the lowest costs in all test cases, with reductions reaching 10% in some cases.