Multi-Objective Optimal Energy Management of Nanogrid Using Improved Pelican Optimization Algorithm

The development of efficient energy management for nanogrid (NG) systems, while reducing both the carbon dioxide (CO2) emissions and power generation cost, is achievable through the effective utilization of available energy sources. This paper proposes a multi-objective optimal energy management strategy for grid-connected NG systems, which incorporates PV arrays and battery storage devices (BSDs), to reduce operating costs and CO2 emission simultaneously over a 24-hour scheduling period. This strategy, which is based on the improved pelican optimization algorithm (IPOA), involves the development of a multi-objective optimization (MOA) equation with several constraints, while taking into account the Malaysian grid purchasing and selling prices. An innovative IPOA-derived technique is developed to facilitate the NG’s optimal energy management operation in multi-objective situations. The proposed algorithm is tested on three distinct scenarios to affirm its efficacy. It is assumed that (a) power exchange between the NG and the main grid is limitless, (b) power interchange between the NG and main grid has a predetermined limit and (c) operating at the maximum capacity of PV array. In order to demonstrate the effectiveness of the proposed algorithm, The outcomes of the simulation are juxtaposed with results obtained from the initial Pelican Optimisation Algorithm (POA), the Bat Algorithm, and the Improved Differential Evolutionary (IDE) Algorithm. The simulation reveals that the suggested IPOA algorithm exhibited the most economical performance and the lowest CO2 emissions. Moreover, in the second scenario, operational costs decreased by 9.5%, and CO2 emissions were reduced by 15%. Conversely, in Scenario 3, there was a 2% decrease in cost and 23% reduction in CO2 emissions as against the first scenario.