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Human activities have been limited by coronavirus disease 2019 (COVID-19), and the normal conditions of our lifestyles have changed, particularly in terms of electricity usage. The aim of this study was to investigate the impact of COVID-19 on the power sector in the Lao PDR in 2020, as well as the challenge of using solar energy to supply power to the network using an optimal approach. The returns on investment of network extension and the purchase of solar energy were also evaluated. Furthermore, load conditions caused by the country's lockdown policy were analyzed. We analyzed the optimal sizing and location of solar energy using a particle swarm optimization method based on the main objective functions, with the system's power loss decreasing and its reliability improved. The results demonstrated that the suddenly reduced load from industry and commercial business did not have a large impact on its operations; however, revenue was reduced. The optimal method for connecting solar energy to a network can reduce power loss and improve system reliability. In addition, we discovered that the location and capacity of solar generation can reduce the investment costs of extensions for new lines, with the surplus power being exported. [ABSTRACT FROM AUTHOR]

Since the increases in electricity demand, environmental awareness, and power reliability requirements, solutions of single-objective optimal power flow (OPF) and multi-objective OPF (MOOPF) (two or three objectives) problems are inadequate for modern power system management and operation. Solutions to the many-objective OPF (more than three objectives) problems are necessary to meet modern power-system requirements, and an efficient optimization algorithm is needed to solve the problems. This paper presents a many-objective marine predators algorithm (MaMPA) for solving single-objective OPF (SOOPF), multi-objective OPF (MOOPF), and many-objective OPF (MaOPF) problems as this algorithm has been widely used to solve other different problems with many successes, except for MaOPF problems. The marine predators algorithm (MPA) itself cannot solve multi- or many-objective optimization problems, so the non-dominated sorting, crowding mechanism, and leader mechanism are applied to the MPA in this work. The considered objective functions include cost, emission, transmission loss, and voltage stability index (VSI), and the IEEE 30- and 118-bus systems are tested to evaluate the algorithm performance. The results of the SOOPF problem provided by MaMPA are found to be better than various algorithms in the literature where the provided cost of MaMPA is more than that of the compared algorithms for more than 1000 USD/h in the IEEE 118-bus system. The statistical results of MaMPA are investigated and express very high consistency with a very low standard deviation. The Pareto fronts and best-compromised solutions generated by MaMPA for MOOPF and MaOPF problems are compared with various algorithms based on the hypervolume indicator and show superiority over the compared algorithms, especially in the large system. The best-compromised solution of MaMPA for the MaOPF problem is found to be greater than the compared algorithms around 4.30 to 85.23% for the considered objectives in the IEEE 118-bus system. [ABSTRACT FROM AUTHOR]

The complexity and uncertainty of power sources connected to transmission networks need to be considered. Planners need information on the sustainability and economics of transmission network expansion planning (TNEP). This work presents a newly proposed method for TNEP that considers high-penetration solar energy by using the particle swarm optimization (PSO) algorithm. The power sources, thermal and hydropower plants, and conditions of load were set in the account, including an uncertain power source and solar energy (PV). The optimal sizing and locating of the PV to be connected to the network were determined by the PSO. The PV grid code was set in the account. The new line's investment cost and equipment was analyzed. The PV cost was considered based on the power loss, and the system's reliability was improved. The IEEE 118 bus test system and Lao PDR's system were requested to test the proposed practice. The results demonstrate that the proposed TNEP method is robust and feasible. The simulation results will be applied to guide the power system planning of Lao PDR. [ABSTRACT FROM AUTHOR]

Voltage stability is very important, as without it voltage collapse will occur. There is therefore a need to incorporate the consideration of voltage stability into the optimal power flow (OPF). This paper presents a comparison of the effectiveness of five voltage stability indices (VSIs) when incorporated in an OPF. The improved particle swarm optimization (IPSO) algorithm is used to solve the OPF problem. The five VSIs investigated are the L‐index, fast voltage stability index (FVSI), line stability index (Lmn), online voltage stability index (LVSI), and voltage collapse proximity indicator (VCPI). The effectiveness of each VSI is considered in the terms of the generation cost, emission, transmission loss, and voltage stability. The IEEE standard 14‐, 30‐, 57‐, and 118‐bus test systems are used for the comparison. It is found that considering each voltage stability index as part of the objective function for the OPF provided different values of generation cost, emission, transmission loss, and maximum loadability. The results suggest that FVSI and Lmn provide the minimum generation cost values for all test systems, while VCPI and LVSI provide lower emission values for most systems. VCPI gives the lowest transmission losses for all systems, and the L‐index provides the maximum loadability for most systems. Incorporation of these indices into the OPF can provide the optimal values in different terms, and the most suitable voltage stability index will depend on the situation and size of the system, which needs to be judiciously chosen. © 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]

Electric vehicles (EV) replacing the internal combustion engine vehicle may be the solution for the particulate matter (PM) 2.5 pollution issue. However, the uncontrolled charging of EVs would challenge the power system operation. Therefore, it is necessary to implement some level of control over the EV charging procedure, especially in the residential network. In this paper, an optimization of EVs charging scheduling considering energy arbitrage and the distribution network cost of an urban village environment is presented. The optimized strategy focuses on decreasing the loss of EV owners' energy arbitrage benefit, introduced as the penalty cost. Also, peak demand, power loss, and transformer aging are included in the estimation of the cost function for the distribution network. The optimization problem is solved using the genetic algorithm. As a case study, data from the urban village in Udon Thani, Thailand, are utilized to demonstrate the applicability of the proposed method. Simulation results show a reduction in the loss of energy arbitrage benefit, transformer peak load, power loss and the transformer loss of life. Therefore, the application of the optimized EV charging can prolong transformer lifetime benefiting both the EV owner and the distribution system operator. [ABSTRACT FROM AUTHOR]