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While traditional methods for modelling the thermal and electrical behaviour of photovoltaic (PV) modules rely on analytical and empirical techniques, machine learning is gaining interest as a way to reduce the time, expertise, and tools required by designers or experts while maintaining high accuracy and reliability. This research presents a data-driven machine learning tool based on artificial neural networks (ANNs) that can forecast yearly PV electricity directly at the optimal PV inclination angle without geographic restrictions and is valid for a wide range of electrical characteristics of PV modules. Additionally, empirical correlations were developed to easily determine the optimal PV inclination angle worldwide. The ANN algorithm, developed in Matlab, systematically and quantitatively summarizes the behaviour of eight PV modules in 48 worldwide climatic conditions. The algorithm’s applicability and robustness were proven by considering two different PV modules in the same 48 locations. Yearly climatic variables and electrical/thermal PV module parameters serve as input training data. The yearly PV electricity is derived using dynamic simulations in the TRNSYS environment, which is a simulation program primarily and extensively used in the fields of renewable energy engineering and building simulation for passive as well as active solar design. Multiple performance metrics validate that the ANN-based machine learning tool demonstrates high reliability and accuracy in the PV energy production forecasting for all weather conditions and PV module characteristics. In particular, by using 20 neurons, the highest value of R-square of 0.9797 and the lowest values of the root mean square error and coefficient of variance of 14.67 kWh and 3.8%, respectively, were obtained in the training phase. This high accuracy was confirmed in the ANN validation phase considering other PV modules. An R-square of 0.9218 and values of the root mean square error and coefficient of variance of 31.95 kWh and 7.8%, respectively, were obtained.The results demonstrate the algorithm’s vast potential to enhance the worldwide diffusion and economic growth of solar energy, aligned with the seventh sustainable development goal.

Abstract In the active distribution networks (ADNs), the emerging trend of high penetration of sustainable energy sources such as wind turbines, photovoltaics, and the forthcoming integration of energy storage devices and additional dynamic loads like plug‐in‐electric vehicles (PEVs) influences to change the nature of electric distribution networks. Consequently, it deteriorates power quality and reliability in terms of unacceptable voltage rise‐drop, harmonic distortion, voltage unbalance, and excessive power loss. In this context of transition into the active distribution network, the probabilistic optimal power flow (P‐OPF) in the ADN is considered an important tool that helps the distribution grid operators for defining the optimal settings of the system's control variables. However, the optimal solution of P‐OPF becomes highly complex while considering the multiple variables, time‐varying characteristics and operational uncertainties associated with sustainable resources, consumer behaviour and electricity market prices. This review aims at promoting the research and discussion of P‐OPF studies in the context of ADNs characteristics and challenges. First, we perform a data‐driven and visualized scientometric analysis of 1988 quality documents retrieved from the Scopus during the last ten years using VOSviewer software. Second, an in‐depth investigation of the recent and emerging techniques for solving the P‐OPF problem is presented. Third, a set of recommendations and highlights of future challenges for P‐OPF in ADNs are offered. The review findings showed high interest among the scientific community in sustainable energy source integration, given the recent rise in publications and citations. Also, there is still a lack of innovative optimization techniques that can adequately handle the current issues associated with P‐OPF and objectives with fair computational efficiency and accuracy. Finally, this paper offers a guide for P‐OPF researchers to navigate the recent studies considering the sustainable sources, spot the new research frontiers, and identify the most impactful countries and publishers.

A hybrid microgrid is an energy system composed of multiple power sources such as photovoltaic panels, wind turbines, fossil-fuel generators, converters, battery storage systems, and an energy management system that guarantees stability and balance of the entire system. According to the system operation and the meteorological conditions, the microgrid can be linked to the electrical grid or work as a standalone system. The battery storage system is the key element of the microgrid and is integrated to maintain the load demand and better energy management using various controllers. In this paper, we presented an overview of energy management and control of the hybrid microgrid by proposing the implementation of the most cited control methods such as artificial neural network, fuzzy logic, sliding mode controller, and proportional integral derivative. Further, we addressed the solar power maximization using maximum power point tracking based on a developed artificial neural controller and compared it with the other controllers. Finally, implementing the proposed four controllers for the developed hybrid microgrid offers to find the best control strategy with multiple aspects and implications. Matlab/Simulink software was used to design a hybrid microgrid with two buses, AC Bus and DC Bus, to supply the power and fulfill the investigated load demands. The simulation is carried out for complicated system operation scenarios with multiple changes in system variables such as weather conditions and load demand to test the robustness of each controller. The simulation results and comparison show the performance of each control method in terms of voltage control, frequency stabilization and energy saving.

This paper proposes a novel optimization method, namely, hybrid PSOS-CGSA for state estimation in three-phase unbalanced DG-integrated distribution systems. The distribution system state estimation (DSSE) is formulated as a nonlinear optimization problem with constraints where loads and DG outputs are considered as the control variables, while real-time measurements are treated as dependent variables. The proposed DSSE model estimates the loads and DG outputs at each bus by minimizing the difference between the measure and calculated values of the variables monitored in real-time. A novel hybrid algorithm of particle swarm optimization with sigmoid-based acceleration coefficients and chaotic gravitational search algorithm (PSOS-CGSA) is proposed and applied for the DG-integrated DSSE. The feasibility of the proposed approach is verified on the IEEE 13-bus test system, the IEEE 37-bus test system, and the IEEE 123-bus test system. These simulations show that the proposed DSSE model provides reliable and accurate state estimation of DG-integrated distribution systems with a very limited number of real-time measurements at the source substation. The results obtained by proposed hybrid PSOS-CGSA are evaluated by comparing with other methods under the same test conditions, and the obtained results demonstrate the merits of the proposed scheme.

Integrating renewable energy sources (RESs) into modern electric power systems offers various techno-economic benefits. However, the inconsistent power profile of RES influences the power flow of the entire distribution network, so it is crucial to optimize the power flow in order to achieve stable and reliable operation. Therefore, this paper proposes a newly developed circle search algorithm (CSA) for the optimal solution of the probabilistic optimal power flow (OPF). Our research began with the development and evaluation of the proposed CSA. Firstly, we solved the OPF problem to achieve minimum generation fuel costs; this used the classical OPF. Then, the newly developed CSA method was used to deal with the probabilistic power flow problem effectively. The impact of the intermittency of solar and wind energy sources on the total generation costs was investigated. Variations in the system’s demands are also considered in the probabilistic OPF problem scenarios. The proposed method was verified by applying it to the IEEE 57-bus and the 118-bus test systems. This study’s main contributions are to test the newly developed CSA on the OPF problem to consider stochastic models of the RESs, providing probabilistic modes to represent the RESs. The robustness and efficiency of the proposed CSA in solving the probabilistic OPF problem are evaluated by comparing it with other methods, such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and the hybrid machine learning and transient search algorithm (ML-TSO) under the same parameters. The comparative results showed that the proposed CSA is robust and applicable; as evidence, an observable decrease was obtained in the costs of the conventional generators’ operation, due to the penetration of renewable energy sources into the studied networks.

This paper proposes a novel hybrid optimization technique based on a machine learning (ML) approach and transient search optimization (TSO) to solve the optimal power flow problem. First, the study aims at developing and evaluating the proposed hybrid ML-TSO algorithm. To do so, the optimization technique is implemented to solve the classical optimal power flow problem (OPF), with an objective function formulated to minimize the total generation costs. Second, the hybrid ML-TSO is adapted to solve the probabilistic OPF problem by studying the impact of the unavoidable uncertainty of renewable energy sources (solar photovoltaic and wind turbines) and time-varying load profiles on the generation costs. The evaluation of the proposed solution method is examined and validated on IEEE 57-bus and 118-bus standard systems. The simulation results and comparisons confirmed the robustness and applicability of the proposed hybrid ML-TSO algorithm in solving the classical and probabilistic OPF problems. Meanwhile, a significant reduction in the generation costs is attained upon the integration of the solar and wind sources into the investigated power systems.