Residential Community Load Management Based on Optimal Design of Standalone HRES with Model Predictive Control

Microgrids being an important entity in the distribution system, and to get their full advantages by incorporating maximum distributed generation, standalone hybrid renewable energy systems (HRESs), being environmentally-safe and economically-efficient, are considered as the promising solution to electrify remote areas where the grid power is not available. In this work, a techno-economic investigation with an optimal design of HRES is presented to fulfill the domestic electricity need for a residential area of the Sherani district in the Province of Baluchistan, Pakistan. Nine case studies based on PV/wind/diesel/battery are analyzed based on net present cost (NPC), cost of energy (COE), and emission to decide the feasible solution. HOMER tool is utilized to accomplish modeling and simulation for economic analysis and optimal sizing. Simulation results demonstrated that HRES with PV-wind-battery is the most viable option for the specified area, and the optimal sizing of components are also obtained with 28,620 NPC and 0.311/kWh COE which shows 81.65 % reduction in cost and 100 % preserving in toxic emission while fulfilling 100 % energy demand with 67.3 % of excess energy. Furthermore, MATLAB/Simulink modeling for the optimally designed system is built for technical analysis while its effectiveness is proved by keeping dc and ac buses voltage constant, safe operating range of battery state of charge (SOC) with active power balance between HRES components, as well as efficient ac voltage quality, regardless of generation disturbances and load fluctuations. The output signal has total harmonic distortion (THD) of 0.30 % as compared to 5.44 % with the conventional control scheme. The novelty lies in the sequential application of both HOMER and MATLAB simulations of the proposed HRES model and validation of the proposition for the studied area; by using and implementing model predictive control (MPC) of a reconfigurable inverter.