Conventionally, base stations of off-grid telecommunication (telecom) towers are powered by diesel generator, which results in high operating cost and carbon emission. Hence, the photovoltaic (PV) wind battery based hybrid renewable energy system (HRES) is proposed for off-grid telecom towers in rural areas. However, owing to high capital cost and intermittent nature of renewable energy resources, optimal sizing of HRES is inevitable to have economical and reliable power supply. An index named excess energy generation (EEG) is developed to reduce the surplus renewable energy, which cannot be sold to grid in off-grid towers. Therefore, three objective functions are formulated viz., loss of power-supply probability, cost of electricity, and EEG, which are incommensurate and contradictory in nature. Thus, a discrete multiobjective grey wolf optimization (DMGWO) algorithm is developed for the optimal sizing of HRES. DMGWO utilizes the concept of internal archive to store nondominated solutions and enables fast convergence rate without premature termination as a result of inherent leader selection mechanism. A practical approach is adopted in optimal sizing by considering wind capacity as discrete variable, which is handled by the corrective algorithm embedded in DMGWO. The intermittency in source and variability in telecom load is incorporated using probability distribution functions. The Euclidean distance approach is adopted to find the solution (configuration of PV panels, wind turbines, and battery) from the optimal Pareto front obtained from DMGWO. Also, sensitivity analysis is carried out to observe the effect of the design variables on the objective functions.

The optimal configuration of energy sources in a nanogrid is important due to intermittency of renewable energy sources and variability in load. An oversized configuration gives the maximum power supply availability to load. However, this would result in higher cost of electricity and excess power generation. Since, dc nanogrids are seldom connected to central grid, the surplus power cannot be sold back. On the other end, economical configuration leads to undersized system, i.e., insufficient generation to meet the load demand. Hence, in general, a multiobjective Grey-Wolf optimization-based optimal sizing algorithm is proposed to get optimal configuration for the area of photovoltaic (PV) panel installation, rating of the wind energy conversion system and capacity of battery energy storage system. Furthermore, there is need for real-time energy management in dc nanogrid due to uncertainty in power generation and load forecasting. A virtual energy storage (VES)-based energy management is proposed in this article to enhance the availability of power supply. The VES concept models the high thermal inertia loads such as air-conditioner and refrigerator analogous to electrochemical battery. A priority-based strategy in VES algorithm turn- on and turn- off thermal loads during the power deficit and surplus renewable generation receptively. The VES-based energy management does not compromise the comfort of customers and does not change the usual number of switching in thermal loads.

Traditional buildings are responsible for carbonizing the environment by consuming a substantial amount of overall energy generation and are held accountable for violating the indexes of energy trilemma (ET). Optimally sizing the renewable energy sources (RES) with a battery system enhances the energy reliability and affordability that can address the challenges of ET indexes (ETI). To that end, an ETI-based multiobjective optimal sizing (MOOS) of PV-battery systems for a building situated in a tropical savanna climate (TSC) has been proposed. Objective functions based on ETI viz., loss of power supply probability (LPSP) that ensures energy security/reliability and levelized cost of energy (LCOE) that indicates energy affordability are formulated. An additional index named surplus energy generation (SEG) that minimizes the excess energy generated (EEG) from the PV which could not be sold back to the utility grid is also formulated. A multiobjective grey wolf optimization algorithm (MOGWO) for optimal sizing of PV-battery systems is utilized to resolve the challenges of the ETI. The Euclidean distance-based sorting approach is implemented to find the best optimal solution from the Pareto front of the MOGWO. Further, a techno-economic and reliability analysis of the optimized PV battery system is presented.

In this study, the current mode control strategy for a Cuk converter assisted self‐excited induction generator‐based wind energy conversion system (WECS) for DC nanogrid is proposed. The sliding mode controller (SMC) is a non‐linear controller and, is preferred because of good static and dynamic response for input ‐output disturbance rejections. However, the knowledge of all the state variables is required, which increases the number of sensors and control complexity. To reduce the number of sensors, reduced‐order SMC (ROSMC) can be implemented. Conventionally design of the voltage loop in ROSMC does not consider the uncertainty of the control parameter. However, the accuracy of the control signal generated by the ROSMC also depends on how accurately the reference current is generated. Therefore, a genetic algorithm tuned PI controller is proposed to generate the ripple‐free reference current. Artificial intelligence assisted ROSMC (AIROSMC) is proposed because of its advantages over SMC, which are a reduced number of sensors, improved dynamic response and easy implementation. The extensive simulation study is carried in MATLAB\Simulink for comparative analysis of fixed frequency SMC, two variable SMC, ROSMC, and AIROSMC. The harmonic spectrum analysis is carried out to justify the selection of DBR instead of the controlled rectifier.

This article proposes a unified controller for Cuk converter assisted variable speed wind turbine driven self-excited induction generator (SEIG) in dc nanogrid configuration for rural electrification. Cuk converter assisted SEIG based wind energy conversion system (WECS) is considered as a master source in the proposed nanogrid, which operates in both grid-connected and islanded mode of operation. In grid connected mode, the system operates in maximum power point tracking (MPPT) under varying wind velocity. A single sensor, modified perturb and observe (P&O) MPPT algorithm, which is independent of the generator parameter is developed and implemented through Cuk converter. During a fault, the WECS necessitates to operate in islanded mode with voltage regulation to supply the local loads continuously. The unified control strategy is developed for selecting the mode of operation, which are MPPT and voltage regulation in grid-connected and islanded mode, respectively. Harmonic spectrum analysis is carried to justify the selection of diode bridge rectifier over semicontrolled/controlled rectifier. The effectiveness and feasibility of the proposed system is verified by means of MATLAB/Simulink. To demonstrate the operational capability, a laboratory scaled prototype is fabricated and tested under various dynamic scenarios.