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The paper presents a modified hybrid particle swarm optimization with bat algorithm parameter inspired acceleration coefficients (MHPSO-BAAC) without and with the constriction factor to find the optimal solution of the economic dispatch problems (EDPs) incorporating conventional as well as hybrid and renewable energy sources (RESs) based plants. The algorithm is designed by modifying the recently presented hybrid PSO and BA (HPSOBA) algorithm applied for the achievement of the optimal solution of the EDPs. The modified algorithm is implemented to solve EDPs of all RESs-based power systems for three scenarios, without constraints, with time-varying demand, and with the consideration of regional load sharing dispatch (RLSD). The performance of the algorithm is also verified through the implementation of various combinations of hybrid as well as thermal power plants (TPPs). The case of TPPs consists of three different scenarios: 1) a small-scale system with constraints like ramp-rate limits (RRLs), prohibited operating zones (POZs), and power losses; 2) a medium-scale power system with consideration of emission-economic dispatch (EED); 3) a large-scale power system with valve-point loading (VPL) effect. The results of the designed MHPSO-BAAC algorithm are compared with the various metaheuristic algorithms available in the literature and the comparative analysis shows the superior performance of the developed algorithm in terms of fuel cost reduction, fast convergence, and computational time.INDEX TERMS Emission-economic dispatch (EED), HPSO-BAAC, regional load sharing dispatch (RLSD), optimal economic dispatch (OED), prohibited operating zones (POZs), ramp-rate limits (RRLs), renewable energy sources (RESs), thermal power plants (TPPs), valve point loading (VPL).

The Smart Grid (SG) is the technological development that incorporates digital technologies and advanced communication methods to determine and respond to variations in electricity consumption in order to revolutionize power distribution, transmission, and generation. In the conventional electrical grid, customers remain unaware to their energy usage patterns that not only results in energy loss but also money. The consumers' usage and consumption standards need to be regulated in order to improve energy efficiency (EE). SG utilizes demand side management (DSM) for energy savings by use of various approaches like financial incentives, subsidized tariffs, and awareness to alter consumers' energy demand. In smart environments (SE), Internet-of-Things (IoT) is evolving as a significant partner for resource and energy management. DSM in SG must take advantage of smart energy management system (SEMS) developed on smart meters (SMs) and modern technologies like IoT. Using SMs with IoT based technologies makes SEMS more effective in the SG. This paper offers design, deployment, implementation, and performance evaluation of an IoT based SEMS, including SMs as well as IoT middleware module and its related benefits. The proposed SEMS operates online and offers real-time (RT) load profiles (LPs) to customers and suppliers remotely. The customers' LPs allow suppliers to disseminate and regulate their incentives as well as incite the customers to alter their energy consumption. Furthermore, these LPs serves as an input for developing numerous DSM approaches. The proposed solution is installed and evaluated at 4 different locations of Stylo Pvt. Ltd. Pakistan, which can communicate commands and observe the efficiency of electricity supplied by the utility. Moreover, the RT impact of using separate SM for automated control of heating, ventilation and air conditioning (HVAC) system is shown in terms of power consumption. The RT case study presents the efficacy of the proposed IoT-based-SEMS.INDEX TERMS Demand Side Management (DSM), Energy Management System (EMS), Internet of Things (IoT), Smart Meter (SM), Smart Grid (SG)

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