• Energy Research

In this research work, the best operating conditions of microbial electrolysis cells (MECs) were identified using artificial intelligence and modern optimization. MECs are innovative materials that can be used for simultaneous wastewater treatment and bio-hydrogen production. The main objective is the maximization of bio-hydrogen production during the wastewater treatment process by MECs. The suggested strategy contains two main stages: modelling and optimal parameter identification. Firstly, using adaptive neuro-Fuzzy inference system (ANFIS) modelling, an accurate model of the MES was created. Secondly, the optimal parameters of the operating conditions were determined using the jellyfish optimizer (JO). Three operating variables were studied: incubation temperature (°C), initial potential of hydrogen (pH), and influent chemical oxygen demand (COD) concentration (%). Using some measured data points, the ANFIS model was built for simulating the output of MFC considering the operating parameters. Afterward, a jellyfish optimizer was applied to determine the optimal temperature, initial pH, and influent COD concentration values. To demonstrate the accuracy of the proposed strategy, a comparison with previous approaches was conducted. For the modelling stage, compared with the response surface methodology (RSM), the coefficient of determination increased from 0.8953 using RSM to 0.963 using ANFIS, by around 7.56%. In addition, the RMSE decreased from 0.1924 (using RSM) to 0.0302 using ANFIS, whereas for the optimal parameter identification stage, the optimal values were 30.2 °C, 6.53, and 59.98 (%), respectively, for the incubation temperature, the initial potential of hydrogen (pH), and the influent COD concentration. Under this condition, the maximum rate of the hydrogen production is 1.252 m3H2/m3d. Therefore, the proposed strategy successfully increased the hydrogen production from 1.1747 m3H2/m3d to 1.253 m3H2/m3d by around 6.7% compared to RSM.

This paper proposes a controller to track the maximum power point (MPP) of a photovoltaic (PV) system using a fractional-order proportional integral derivative (FOPID) controller. The employed MPPT is operated based on a dp/dv feedback approach. The designed FOPID-MPPT method includes a differentiator of order (μ) and integrator of order (λ), meaning it is an extension of the conventional PID controller. FOPID has more flexibility and achieves dynamical tuning, which leads to an efficient control system. The contribution of our paper lies is optimizing FOPID-MPPT parameters using Aquila optimizer (AO). The obtained results with the proposed AO-based FOPID-MPPT are contrasted with those acquired with moth flame optimizer (MFO). The performance of our FOPID-MPPT controller with the conventional technique perturb and observe (P&O) and the classical PID controller is analyzed. In addition, a robustness test is used to assess the performance of the FOPID-MPPT controller under load variations, providing valuable insights into its practical applicability and robustness. The simulation results clearly prove the superiority and high performance of the proposed control system to track the MPP of PV systems.

Due to the unpredictable nature of renewable sources such as sun and wind, the integration of such sources to a grid is complicated. However, a hybrid renewable energy system (HRES) can solve this problem. Constructing a reliable HRES in remote areas is essential. Therefore, this paper proposes a new methodology incorporating a crow search algorithm (CSA) for optimizing the scale of an HRES installed in a remote area. The constructed system comprises photovoltaic (PV) panels, wind turbines (WTs), batteries, and diesel generators (DGs). The target is to achieve the most economical and efficient use of renewable energy sources (RESs). The CSA is used as it is simple in implementation, it only requires a few parameters, and it has a high flexibility. The designed system is constructed to serve an electrical load installed in the northeastern region of the Kingdom of Saudi Arabia. The load data are provided by the Saudi Electricity Company, including those of the Aljouf region (Sakaka, Alqurayyat, Tabarjal, Dumat Aljandal, and its villages) and the northern border region (Arar, Tarif, Rafha, and its affiliated villages). The temperature, irradiance, and wind speed of the Aljouf region (latitude 29.764° and longitude 40.01°) are collected from the National Aeronautics and Space Administration (NASA) from 1 January to 31 December 2020. Three design factors are considered: the PV number, the WT number, and the number of days of battery autonomy (AD). We compared our results to the reported approaches of an elephant herding optimizer (EHO), a grasshopper optimization algorithm (GOA), a Harris hawks optimizer (HHO), a seagull optimization algorithm (SOA), and a spotted hyena optimizer (SHO). Moreover, the loss of power supply probability (LPSP) is calculated to assess the constructed system’s reliability. The proposed COA succeeded in achieving the best fitness values of 0.03883 USD/kWh, 0.03863 USD/kWh, and 0.04585 USD/kWh for PV/WT/battery, PV/battery, and WT/battery systems, respectively. The obtained results confirmed the superiority of the proposed approach in providing the best configuration of an HRES compared to the others.

In recent years, the solar energy has become one of the most important alternative sources of electric energy, so it is important to operate photovoltaic (PV) panel at the optimal point to obtain the possible maximum efficiency. This paper presents a new optimization approach to maximize the electrical power of a PV panel. The technique which is based on objective function represents the output power of the PV panel and constraints, equality and inequality. First the dummy variables that have effect on the output power are classified into two categories: dependent and independent. The proposed approach is a multistage one as the genetic algorithm, GA, is used to obtain the best initial population at optimal solution and this initial population is fed to Lagrange multiplier algorithm (LM), then a comparison between the two algorithms, GA and LM, is performed. The proposed technique is applied to solar radiation measured at Helwan city at latitude 29.87°, Egypt. The results showed that the proposed technique is applicable.

An optimized energy management strategy (EMS) based on a recent coyote optimization algorithm (COA) applied to a hybrid electric power system is proposed in this paper. The proposed hybrid system comprises fuel cell (FC), battery storage bank (BSB) and supercapacitors (SCs). The FC has been selected to be the chief power source to meet the load demand at steady state. Whereas BSB is used as the chief energy buffer and to help the FC during deficit periods and SCs are employed to meet the transient maximum power. The performance of the hybrid electric power system mostly depends on how to distribute the demanded load through different kinds of power sources. Therefore, optimized EMS is highly required to do this job. The key objective of the proposed EMS is to reduce hydrogen consumption by the hybrid system and increase the durability of power sources. To investigate the superiority and validity of COA, a comparison with other approaches is carried based on minimum hydrogen consumption and high energy efficiency. Such methods include external energy maximization strategy (EEMS), particle swarm optimizer (PSO), genetic algorithm (GA), grey wolf optimizer (GWO), grasshopper optimization algorithm (GOA), multi-verse optimizer (MVO), salp swarm algorithm (SSA) and sunflower optimization (SFO). The obtained results confirmed the superiority of the proposed COA. Using COA reduced hydrogen consumption by 38.8% compared to the EEMS method. Based on the minimum hydrogen consumption, the strategies are ranked from the best as following; COA, GWO, SSA, GOA, MVO, GA, PSO, and EEMS.

This paper identifies the best energy management strategy of hybrid photovoltaic–diesel battery-based water desalination systems in isolated regions using technical, economic and techno–economic criteria. The employed procedures include Criteria Importance Through Intercriteria Correlation (CRITIC) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) as tools for the solution. Twelve alternatives, containing three–four energy management strategies; four energy management strategies, load following (LF), cycle charging (CC), combined LF–CC, and predictive strategy; and three different sizes of brackish water reverse osmosis (BWRO) water desalination units, BWRO-150, BWRO-250, and BWRO-500, are investigated with capacity of 150, 250, and 500 m3/day, respectively. Eight attributes comprising different technical and economic metrics are considered during the evaluation procedure. HOMER Pro® software is utilized to perform the simulation and optimization. The main findings confirmed that the best energy management strategies are predictive strategies and the reverse osmosis (RO) unit’s optimal size is RO-250. For such an option, the annual operating cost and initial costs are $4590 and $78,435, respectively, whereas the cost of energy is $0.156/kWh. The excess energy and unmet loads are 27,532 kWh and 20.3 kWh, respectively. The breakeven grid extension distance and the amount of CO2 are 6.02 km and 14,289 kg per year, respectively. Compared with CC–RO-150, the amount of CO2 has been sharply decreased by 61.2%.

Fuel cell hybrid electric vehicles (FCEVs) are mainly electrified by the fuel cell (FC) system. As a supplementary power source, a battery or supercapacitor (SC) is employed (besides the FC) to enhance the power response due to the slow dynamics of the FC. Indeed, the performance of the hybrid power system mainly depends on the required power distribution manner among the sources, which is managed by the energy management strategy (EMS). This paper considers an FCEV based on the proton exchange membrane FC (PEMFC)/battery/SC. The energy management strategy is designed to ensure optimum power distribution between the sources considering hydrogen consumption. Its main objective is to meet the electric motor’s required power with economic hydrogen consumption and better electrical efficiency. The proposed EMS combines the external energy maximization strategy (EEMS) and the bald eagle search algorithm (BES). Simulation tests for the Extra-Urban Driving Cycle (EUDC) and New European Driving Cycle (NEDC) profiles were performed. The test is supposed to be performed in typical conditions t = 25 °C on a flat road without no wind effect. In addition, this strategy was compared with the state machine control strategy, classic PI, and equivalent consumption minimization strategy. In terms of optimization, the proposed approach was compared with the original EEMS, particle swarm optimization (PSO)-based EEMS, and equilibrium optimizer (EO)-based EEMS. The results confirm the ability of the proposed strategy to reduce fuel consumption and enhance system efficiency. This strategy provides 26.36% for NEDC and 11.35% for EUDC fuel-saving and efficiency enhancement by 6.74% for NEDC and 36.19% for EUDC.

The appropriate planning of electric power systems has a significant effect on the economic situation of countries. For the protection and reliability of the power system, the optimal reactive power dispatch (ORPD) problem is an essential issue. The ORPD is a non-linear, non-convex, and continuous or non-continuous optimization problem. Therefore, introducing a reliable optimizer is a challenging task to solve this optimization problem. This study proposes a robust and flexible optimization algorithm with the minimum adjustable parameters named Improved Marine Predators Algorithm and Particle Swarm Optimization (IMPAPSO) algorithm, for dealing with the non-linearity of ORPD. The IMPAPSO is evaluated using various test cases, including IEEE 30 bus, IEEE 57 bus, and IEEE 118 bus systems. An effectiveness of the proposed optimization algorithm was verified through a rigorous comparative study with other optimization methods. There was a noticeable enhancement in the electric power networks behavior when using the IMPAPSO method. Moreover, the IMPAPSO high convergence speed was an observed feature in a comparison with its peers.