Adopting Scenario-Based approach to solve optimal reactive power Dispatch problem with integration of wind and solar energy using improved Marine predator algorithm

The penetration of renewable energy resources into electric power networks has been increased considerably to reduce the dependence of conventional energy resources, reducing the generation cost and greenhouse emissions. The wind and photovoltaic (PV) based systems are the most applied technologies in electrical systems compared to other technologies of renewable energy resources. However, there are some complications and challenges to incorporating these resources due to their stochastic nature, intermittency, and variability of output powers. Therefore, solving the optimal reactive power dispatch (ORPD) problem with considering the uncertainties of renewable energy resources is a challenging task. Application of the Marine Predators Algorithm (MPA) for solving complex multimodal and non-linear problems such as ORPD under system uncertainties may cause entrapment into local optima and suffer from stagnation. The aim of this paper is to solve the ORPD problem under deterministic and probabilistic states of the system using an improved marine predator algorithm (IMPA). The IMPA is based on enhancing the exploitation phase of the conventional MPA. The proposed enhancement is based on updating the locations of the populations in spiral orientation around the sorted populations in the first iteration process, while in the final stage, the locations of the populations are updated their locations in adaptive steps closed to the best population only. The scenario-based approach is utilized for uncertainties representation where a set of scenarios are generated with the combination of uncertainties the load demands and power of the renewable resources. The proposed algorithm is validated and tested on the IEEE 30-bus system as well as the captured results are compared with those outcomes from the state-of-the-art algorithms. A computational study shows the superiority of the proposed algorithm over the other reported algorithms

Enhancing distribution generator impact mitigation using an adaptive protection scheme based on modified pelican optimization algorithm and active database management system

Abstract This paper addresses the challenge of protecting electrical networks in the presence of distribution generators (DGs). The use of DGs affects fault currents, leading to miscoordination between protection relays and causing constraints on network reliability. To tackle this issue, the authors propose an adaptive protection scheme (APS) based on a modified pelican optimization algorithm (MPOA) and active database management system (ADBMS). The APS coordinates directional overcurrent relays and distance relays, while the MPOA simulates a pelican mating strategy and includes a modified internal equation. The proposed APS is further upgraded with ADBMS to save system resources by storing relay settings in the database and calling them when the state of DGs changes without running optimization algorithms. The effectiveness of the proposed APS is validated on the Institute of Electrical and Electronics Engineers (IEEE) eight‐bus test system and the IEEE 14‐bus distribution network. Results indicate that the APS can effectively protect electrical networks in the presence of DGs, while the ADBMS upgrade saves system resources

Optimizing the hybrid PVDG and DSTATCOM integration in electrical distribution systems based on a modified homonuclear molecules optimization algorithm

Abstract The increasing use of non‐linear loads in electrical distribution systems (EDS) led to a greater need for reactive power compensation, losses minimization, improved voltage and stability. This paper proposes the optimal integration of hybrid photovoltaic distributed generation (PVDG) and distribution static synchronous compensator (DSTATCOM) into IEEE 33 and 69‐bus EDS. A modified version of homonuclear molecules optimization (mHMO) is developed to determine the optimal allocation of the devices, while minimizing a multi‐objective function (MOF) based on total active power losses (TAPL), total voltage deviation (TVD), and investment cost of integrated devices (ICPVDG and ICDSTATCOM). The primary objective of the mHMO is to enhance the equilibrium between exploration and exploitation in the original HMO by implementing a fresh exploration stage. The effectiveness of mHMO was assessed using CEC17 benchmark functions. The findings demonstrate that mHMO achieved excellent results, including high‐quality solution and a favourable convergence rate. Additionally, results demonstrate that mHMO outperforms its basic version in reducing TAPL by 94.27% and 97.87% for the two EDS, while improving voltage profiles and reducing the cost of integrated devices. This study shows the potential of hybrid PVDG‐DSTATCOM in improving the performance of EDS and highlights the effectiveness of mHMO in optimizing their integration