Bimal Kumar Dora

Renewable energy includes a wide variety of technologies that may provide electric energy without releasing greenhouse gases. However, due to the intermittent nature of renewable energy sources, relying on a single source cannot ensure a steady energy supply, making it essential to combine multiple renewable energies with thermal generators to meet the required energy demand. Furthermore, the economic feasibility of renewable energy can vary significantly across different geographical regions. These challenges can be addressed successfully through the global electricity grid concept. It enables the efficient transmission of clean energy over long distances, and it allows nations to capitalize on their unique renewable energy strengths, facilitating the seamless exchange of clean energy to meet global demand while optimizing the use of renewable resources worldwide. This paper examines global and regional initiatives aimed at fostering a sustainable energy future, highlighting the benefits and challenges associated with globally interconnected power grids and intercontinental transmission networks. Although the challenges and opportunities of the global electricity grid are well understood, the quantification of its costs, benefits, and environmental impacts remains in its infancy, leaving a significant gap in the current literature.

This work presents a new hybrid metaheuristic algorithm specifically developed to solve the Optimal Power Flow (OPF) problems. The OPF problem aims to minimize real power loss, quadratic fuel cost, and complex nonlinear fuel cost, while considering the impact of valve point loading (VPL) effects. This is done to maintain an economically efficient operational condition in a power system. The proposed hybrid algorithm combines the mutualism phase of the Symbiotic Organism Search Algorithm (SOSA) with the Hunter-Prey Optimization Algorithm (HPOA) in order to improve the local search capabilities of HPOA. HPOA manages the global search process, whereas SOSA and HPOA operate together during the mutualism phase and local search process, respectively. This combined technique effectively addresses complex nonlinear optimization problems. The proposed method is applied to determine the optimal settings to minimize real power loss, simple quadratic fuel cost and the fuel cost with VPL. The IEEE 30 bus system is used to evaluate the algorithm that is being considered. After that, its results are compared to those of other published algorithms.

The Prairie Dog Optimization Algorithm (PDOA) is a novel metaheuristic algorithm that takes its inspiration from foraging behavior, burrow building activities and communication alarm of Prairie Dog. PDOA has gained extensive popularity among the research community and is now utilized to tackle a variety of optimization challenges. However, the algorithm does not have a healthy equilibrium condition between its exploration and exploitation process. To overcome this problem a novel hybrid algorithm namely Nelder–Mead based Prairie Dog Optimization Algorithm (NMPDOA) is proposed with the help of Nelder–Mead algorithm (NMA). In this paper, NMPDOA is used for solving the multi-objective Optimal Power Flow (MO-OPF) problems. In order to prove the applicability of the suggested technique, it has been applied to 20 different benchmark functions. The suggested technique is tested in the IEEE 30 and IEEE118 bus standard test system and applied in Indian 62 bus system. To determine the effectiveness of the algorithm, the results of PDOA and NMPDOA are compared with other previously published results discussed in the literature. In addition, PDOA and NMPDOA are also applied in two MO-OPF problems. The statistical analysis and t-Test analysis confirm the effectiveness and consistency of NMPDOA for solving real world highly nonlinear and mixed integer optimization problems.

This paper proposes a hybrid metaheuristic algorithm for solving reactive power dispatch (RPD) problem. RPD is an optimization problem that minimizes the real power loss, total deviation in voltage and enhances the stability of voltage to secure and maintain an economical operating state in power system. In this paper, an enhanced local search capacity is achieved by incorporating the symbiotic organism search (SOS) into the Dwarf Mongoose optimization Algorithm (DMOA). The DMOA handles the exploration process in the proposed algorithm, while the mutualism phase and the DMOA’s local search process work together to solve the exploitation process. The suggested technique is used to find the best settings for minimizing actual power loss, total voltage variation, and L-Index. The MATLAB program has been developed for the objectives of the RPD problem and tested for IEEE 30 bus test system. The results of the proposed method are compared to those of various other algorithms including the original DMOA, and are shown to be superior. The efficiency and robustness of the hybrid algorithm is confirmed by the statistical analysis.

In the recent decade Generation Expansion Planning (GEP) has received a lot of attention among academics and energy policymakers, especially to reduce the greenhouse gas (GHG) emissions such as CO 2 , N 2 O and CH 4 . Every country strives to decarbonize its economy by enacting policies that improve the penetration of Renewable Energy Sources (RES) in its power production capacity. GEP is a significant challenge that incorporates technological, economic, financial, geographical, and environmental factors. GEP is most commonly used to tackle large-scale, dynamic, discrete, and non-linear constrained optimization problems. This research provides a multidimensional GEP architecture based on boosting RES integration with the help of Modified Remora Optimization Algorithm. The simulation results are reviewed and compared with the previously reported results in the results section. The proposed technique demonstrates the improved convergence characteristics and resilience when compared to the other compared results.