Search

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.

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.