This paper proposes a new multi-objective method that efficiently solves the multi-objective optimal power flow (MOOPF) problem in power systems. The objective of solving the MOOPF problem is to concurrently optimize the fuel cost, emissions, and active power loss. The proposed multi-objective search group algorithm (MOSGA) is an effective method that combines the merits of the original search group algorithm with fast nondominated sorting, crowding distance, and archive selection strategies to acquire a nondominated set in a single run. The MOSGA is employed on IEEE 30-bus and 57-bus systems to validate its robustness and efficiency. It was found that implementing MOSGA to solve the MOOPF significantly enhanced the performance of power systems in terms of economic, environmental, and technical benefits. As for Case 6, the fuel cost, emissions, and active power loss were reduced by 16.5707%, 52.0605%, and 60.9443%, respectively. The simulation results were analyzed and compared with those of previously reported studies based on the best individual solutions, compromise solutions, and performance indicators. The comparative results confirmed the potential and advantage of MOSGA when solving the MOOPF problem efficiently and MOSGA had high-quality optimal solutions.

The aim of this research is to define the placement of distributed generations (DGs) in radial distribution networks (RDNs) using a meta-heuristic method called the Enhanced Search Group Algorithm (ESGA). This algorithm is an upgraded version of the conventional SGA that incorporates the Chaotic Local Search (CLS) approach to improve global exploration ability. The purposes of the optimal DG placement (ODGP) problem are to decrease active power losses, increase voltage stability, and boost the voltage profile of RDNs. The study applied the ESGA to optimize the placement and size of DGs considering two cases of power factors (unity and optimal) in 33, 69, and 118-bus RDNs. Based on the optimal results, employing optimal power factors in operating DGs pointedly enhances the performance of RDNs by decreasing power loss, reducing voltage deviation, and increasing voltage stability. The ESGA method outperforms other approaches regarding solution quality, indicating its effectiveness in resolving ODGP problems, particularly for large-scale and complex networks.

Producing energy from a variety of sources in a power system requires an optimal schedule to operate the power grids economically and efficiently. Nowadays, power grids might include thermal generators and renewable energy sources (RES). The integration of RES adds complexity to the optimal power flow problem due to intermittence and uncertainty. The study suggests a Multi-Objective Search Group Algorithm (MOSGA) to deal with multi-objective optimal power flow integrated with a stochastic wind and solar powers (MOOPF-WS) problem. Weibull and lognormal probability distribution functions (PDFs) are respectively adopted to describe uncertainties in wind speed and solar irradiance. The MOSGA incorporates crowding distance strategies, fast non-dominated sorting, and an archive selection mechanism to define and preserve the best non-dominated solutions. The total cost, real power loss, and emission were defined as the objectives for the MOOPF-WS problem. In the economic aspect, formulated cost modelling includes both overestimation and underestimation situations related to wind and solar power prediction. Further, uncertainty in load demand is represented by a normal PDF and is considered as a special study case due to its novelty. The effectiveness of MOSGA was validated on the IEEE 30-bus and 57-bus systems considering various combinations of objective functions as well as different loading scenarios. Its performance was comprehensively compared with the other three well-regarded multi-objective optimization algorithms including NSGA-II, MOALO, and MOGOA in terms of Spread metric, Hypervolume metric, and the best compromise solutions for all scenarios. The comparisons showed MOSGA was capable of obtaining well-distributed Pareto fronts and producing better quality solutions compared to the others in all tested scenarios. In addition, MOSGA also obtained better solution quality than significant research in the literature for all the comparable cases. These show the superiority of the MOSGA in dealing with the MOOPF-WS problem.

Peer-to-peer (P2P) electricity markets enable prosumers to minimize their costs, which has been extensively studied in recent research. However, there are several challenges with P2P trading when physical network constraints are also included. Moreover, most studies use fixed prices for grid power prices without considering dynamic grid pricing, and equity for all participants. This policy may negatively affect the long-term development of the market if prosumers with low demand are not treated fairly. An initial step towards addressing these problems is the design of a new decentralized P2P electricity market with two dynamic grid pricing schemes that are determined by consumer demand. Furthermore, we consider a decentralized system with physical constraints for optimizing power flow in networks without compromising privacy. We propose a dynamic congestion price to effectively address congestion and then prove the convergence and global optimality of the proposed method. Our experiments show that P2P energy trade decreases generation cost of main grid by 56.9% compared with previous works. Consumers reduce grid trading by 57.3% while the social welfare of consumers is barely affected by the increase of grid price.

Renewable energy sources (RESs) are a great source of power generation for microgrids with expeditious urbanization and increase in demand in the energy sector. One of the significant challenges in deploying RESs with microgrids is efficient energy management. Optimizing the power allocation among various available generation units to serve the load is the best way to achieve efficient energy management. This paper proposes a cost-effective multi-verse optimizer algorithm (CMVO) to solve this optimization problem. CMVO focuses on the optimal sharing of generated power in a microgrid between different available sources to reduce the generation cost. The proposed algorithm is analyzed for two different scale microgrids (IEEE 37-node test system and IEEE 141-node test system) using IEEE test feeder standards to assess its performance. The results show that CMVO outperforms multi-verse optimizer (MVO), particle swarm optimization (PSO), artificial hummingbird algorithm (AHA), and genetic algorithm (GA). The simulation results emphasize the cost reduction and execution time improvement in both IEEE test systems compared with other meta-heuristic algorithms.

Minimal power loss is highly desired for an efficient and economical operation in distributed systems. This paper presents an improved symbiotic organisms search (ISOS) for system reconfiguration (SR) and distributed generation placement (SR-DGP) simultaneously. The proposed ISOS combined the simple quadratic interpolation (SQI) strategy into SOS to improve the search process. The ISOS was adopted to define the optimal system topology, location, and capacity of distributed generators (DGs) to minimize power losses. The proposed ISOS was evaluated on the 33-node and 69-node systems. The proposed ISOS successfully reduced the power losses by 73.1206% and 84.2861% for the 33-bus and 69-bus. Moreover, ISOS was also compared with other approaches, where ISOS obtained better results than other approaches for all test systems. Hence, ISOS showed its effectiveness in dealing with the SR-DGP problem.