Modern electrical power systems are becoming increasingly complex and are expanding at an accelerating pace. The power system’s transmission lines are under more strain than ever before. As a result, the power system is experiencing a wide range of issues, including rising power losses, voltage instability, line overloads, and so on. Losses can be minimized and the voltage profile can be improved when energy resources are installed on appropriate buses to optimize real and reactive power. This is especially true in densely congested networks. Optimal power flow (OPF) is a basic tool for the secure and economic operation of power systems. It is a mathematical tool used to find the instantaneous optimal operation of a power system under constraints meeting operation feasibility and security. In this study, a new application algorithm named white shark optimizer (WSO) is proposed to solve the optimal power flow (OPF) problems based on a single objective and considering the minimization of the generation cost. The WSO is used to find the optimal solution for an upgraded power system that includes both traditional thermal power units (TPG) and renewable energy units, including wind (WPG) and solar photovoltaic generators (SPG). Although renewable energy sources such as wind and solar energy represent environmentally friendly sources in line with the United Nations sustainable development goals (UN SDG), they appear as a major challenge for power flow systems due to the problems of discontinuous energy production. For overcoming this problem, probability density functions of Weibull and Lognormal (PDF) have been used to aid in forecasting uncertain output powers from WPG and SPG, respectively. Testing on modified IEEE-30 buses’ systems is used to evaluate the proposed method’s performance. The results of the suggested WSO algorithm are compared to the results of the Northern Goshawk Optimizer (NGO) and two other optimization methods to investigate its effectiveness. The simulation results reveal that WSO is more effective at finding the best solution to the OPF problem when considering total power cost minimization and solution convergence. Moreover, the results of the proposed technique are compared to the other existing method described in the literature, with the results indicating that the suggested method can find better optimal solutions, employ less generated solutions, and save computation time.

Dynamic economic load dispatch (DELD), which determines the best generation scheduling for different power plants during the next 24 hours in order to meet the electricity demand as well as minimize the total energy cost, is a highly complex non-linear and non-convex problem. This study proposes an efficient modified teaching–learning–based optimization algorithm, called MTLBO, to effectively solve the DELD optimization problem in a power system containing thermal generations and wind power. The planning problem comprises the total fuel cost function with valve-point loading effect and the transmission power losses. Also, the uncertainties of wind power and load demand along with the various equality and inequality operational constraints such as power generation limits, ramp rate limits, prohibited operating zones and power balance are considered in the problem. Additionally, in the proposed MTLBO algorithm, the learning phase is properly integrated into the teaching phase to improve the convergence characteristic of the original TLBO. Moreover, in order to enhance the feature of local optima avoidance, interaction of up to five students are incorporated into the learning phase to improve the knowledge of each student. To exhibit the effectiveness of the proposed approach, the algorithm is applied into the 14 real test functions. In addition, several cases with 10 and 30 unit test systems are investigated over the planning period. Compared to the main TLBO algorithm which is mostly used optimization algorithms proposed in prior studies, the simulation results demonstrate the efficiency and superiority of the proposed optimization approach in terms of consistency, robustness, convergence rate and finding better plausible optimal solutions.

Saudi Arabia generates more than 98% of its electricity through hydrocarbon resources. To reduce the consumption of fossil fuel resources and protect the environment, the government of Saudi Arabia is planning to make renewable energy an essential part of its energy mix. In this study, due to the country’s abundant solar potential, solar energy has been selected as the energy source to generate renewable energy in Saudi Arabia. The two solar energy technologies, photovoltaic (PV) and solar thermal, have been analyzed in three different locations within the country. Multi-criteria decision-making (MCDM) techniques were used to rank the cities for each of the technologies. The SAW(Simple Additive Weighting)-AHP(Analytic Hierarchy Process) MCDM method based on climate, environmental, technical, economic, and social has been adopted to analyze the suitability of each technology for all locations. To assign weights to the criteria AHP method was used, while to rank the technologies, SAW was used. The results show that for the PV technology, Abha ranked 1st with a performance score of 91%, making it the most suitable location, followed by Jeddah with 83%. While for solar thermal technologies, Jeddah is the most suitable location, with a performance score of 96%, followed by Abha with 91%. The PV systems generated a maximum of 11,019 MWh in Abha, while the solar thermal produced maximum of 14,000 MWh in Jeddah. Overall, solar thermal technology outperformed PV technology in Saudi Arabia due to the country’s higher temperature. The analysis of photovoltaic and solar thermal technologies in this study provides valuable insight for the government of Saudi Arabia in identifying the best site for solar energy technologies in the country.

A key component of the design and operation of power transmission systems is the optimal power flow (OPF) problem. To solve this problem, several optimization algorithms have been developed. The primary objectives of the program are to minimize fuel costs, reduce emissions, improve voltage profiles, and reduce power losses. OPF is considered one of the most challenging optimization problems due to its nonconvexity and significant computational difficulty. Teaching–learning-based optimization (TLBO) is an optimization algorithm that can be used to solve engineering problems. Although the method has certain advantages, it does have one significant disadvantage: after several iterations, it becomes stuck in the local optimum. The purpose of this paper is to present a novel adaptive Gaussian TLBO (AGTLBO) that solves the problem and improves the performance of conventional TLBO. Validating the performance of the proposed algorithm is undertaken using test systems for IEEE standards 30-bus, 57-bus, and 118-bus. Twelve different scenarios have been tested to evaluate the algorithm. The results show that the proposed AGTLBO is evidently more efficient and effective when compared to other optimization algorithms published in the literature.