Impact analysis of renewable energy resources and electric vehicles in hybrid power systems - Kumar, Anil; Chanana, Saurabh; Kumar, Amit (Computers & electrical engineering, 2025)
•This article proposes a new optimization technique named Electric Eel Foraging Optimization (EEFO) for the optimal tuning of a proposed FOI-PDF controller in load frequency control (LFC).•The proposed power system is a two-area multi-source interconnected power system (MSIPS) consisting of thermal, hydro, and gas-based power plants. Furthermore, LFC analysis has also been done for the renewable energy sources (RESs) and electric vehicles (EVs) integrated power systems and also examine the effect of communication time (CT) delay.•The result analysis shows that the proposed EEFO technique and FOI-PDF controller outperform previous published work.•The proposed methodology has been validated by an empirical inquiry carried out in real time using the OPAL-RT platform. This study concentrates on improving load frequency control (LFC) methods for integrated power networks, particularly addressing the fluctuating attributes of energy from renewable sources and electric vehicles. A modified fractional order controller (i.e., fractional order integral-proportional derivative with filter (FOI-PDF)) has been built for the system being studied. Additionally, a new optimization named the Electric Eel Foraging Optimisation (EEFO) has been introduced for improving the settings of various controller parameters. The proposed system under analysis is mathematically modelled and examined to include hydro power plants (HPPs), thermal power plants (TPPs), and gas power plants (GPPs) in each of the two interconnected hybrid power systems. Furthermore, to accommodate case studies, both control areas connect intermittent power from wind power plants (WPPs) & solar power plants (SPPs) along with electric vehicles (EVs) and also examine the effect of communication time (CT) delay. The proposed EEFO optimisation technique surpasses earlier meta-heuristic optimization techniques (MOTs) like (Whale Optimisation Algorithm (WOA), Sine Cosine Algorithm (SCA), Quadratic Interpolation Optimisation (QIV), Arithmetic Optimisation Algorithm (AOA), and Ant Lion Optimisation (ALO)) in terms of convergence curve and the objective function of integral time absolute error (ITAE) value. The ITAE value of EEFO is 88.74%, 88.99%, 5.54%, 90.51%, and 90.27% lower than the values of WOA, SCA, QIV, AOA, and ALO, respectively. A thorough evaluation of several scenarios, including step, multistep, and random disturbances, has been carried out to assess the effectiveness of the suggested control method in contrast to current controllers. In the case of step load disturbances (SLDs), the settling time of the EEFO-based FOI-PDF is 46.05% faster than the recently developed fractional order integral derivative-tilt (FID-T) controller in ΔF1, 19.65% faster in ΔF2, and 63% faster in ΔPtie, respectively. The comprehensive data investigations indicate that the anticipated hybrid power system is the subject of a dynamic performance study that is both superior and enhanced. Additionally, the stability study, encompassing Bode plots and eigenvalues along with sensitivity analysis, has been conducted. The proposed methodology has been validated by an empirical inquiry carried out in real real-time simulator using the OPAL-RT platform.