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The efficiency and dynamics of hybrid electric vehicles are inherently linked to effective energy management strategies. However, complexity is heightened due to uncertainty and variations in real driving conditions. This article introduces an innovative strategy for extended-range electric vehicles, grounded in the optimization of driving cycles, prediction of driving conditions, and predictive control through neural networks. First, the challenges of the energy management system are addressed by merging deep reinforcement learning with strongly convex objective optimization, giving rise to a pioneering method called DQL-AMSGrad. Subsequently, the DQL algorithm has been implemented, allowing temporal difference-based updates to adjust Q values to maximize the expected cumulative reward. The loss function is calculated as the mean squared error between the current estimate and the calculated target. The AMSGrad optimization method has been applied to efficiently adjust the weights of the artificial neural network. Hyperparameters such as the learning rate and discount factor have been tuned using data collected during real-world driving tests. This strategy tackles the "curse of dimensionality" and demonstrates a 30% improvement in adaptability to changing environmental conditions. With a 20%-faster convergence speed and a 15%-superior effectiveness in updating neural network weights compared to conventional approaches, it also highlights an 18% reduction in fuel consumption in a case study with the Nissan Xtrail e-POWER system, validating its practical applicability. [ABSTRACT FROM AUTHOR]

This study presents a 100% renewable and diversified system taking advantage of the available energy potential of renewable energies in Mexico with a view to a planned energy transition in cooperation with the environment. The processes of change that are experienced worldwide in favor of the planet make us reflect and propose alternatives that break traditional schemes in the production of energy (for which reason Mexico cannot deviate from its current model). It is here that this research becomes a transcendental and important reference for decision-making and the transformation of the energy sector in Mexico. The current electrical system relies on fossil fuels that need to be replaced by renewable energy sources (and it is necessary to satisfy growing demands in the long term). The methodological process is carried out with the use of the 100% renewable energy market design tool EnergyPLAN, which puts the concept of intelligent energy into practice by 2050. Finally, after analyzing the results, it is concluded that a good energy mix for 2050 is 30% solar photovoltaic, 25% wind, 14.5% hydraulic, 13.8% CSP plants, and 16.7% other technologies. Surpluses may be sold to the United States and Central America through interconnection points. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) and renewable energy resources (RERs) are widely integrated into electrical systems to reduce dependency on fossil fuels and emissions. The energy management of microgrids (MGs) is a challenging task due to uncertainty about EVs and RERs. In this regard, an improved version of the RUNge Kutta optimizer (RUN) was developed to solve the energy management of MGs and assign the optimal charging powers of the EVs for reducing the operating cost. The improved RUN optimizer is based on two improved strategies: Weibull flight distribution (WFD) and a fitness–distance balance selection (FDB) strategy, which are applied to the conventional RUN optimizer to improve its performance and searching ability. In this paper, the energy management of MGs is solved both at a deterministic level (i.e., without considering the uncertainties of the system) and while considering the uncertainties of the system, with and without a smart charging strategy for EVs. The studied MG consists of two diesel generators, two wind turbines (WTs), three fuel cells (FCs), an electrical vehicle charging station and interconnected loads. The obtained results reveal that the proposed algorithm is efficient for solving the EM of the MG compared to the other algorithms. In addition, the operating cost is reduced with the optimal charging strategy. [ABSTRACT FROM AUTHOR]

As the world's population continues to grow and the demand for energy increases, there is an urgent need for sustainable and efficient energy systems. Renewable energy sources, such as wind and solar power, have the potential to play a significant role in meeting this demand, but their intermittency can make integration into existing energy systems a challenge. Moreover, the development of sustainable energy systems has become even more critical in recent years, due to a confluence of events, including the decline in fuel prices, geopolitical conflicts, and the recent COVID-19 pandemic. The decrease in fuel prices has led to a decline in investment in renewable energy and has slowed the transition to sustainable energy systems. Additionally, geopolitical conflicts and pandemics have highlighted the need for resilient and self-sufficient energy systems that can operate independently of external factors. Also, energy storage technologies play a critical role in achieving this goal by providing reliable backup power and enabling microgrids to operate independently of the larger power grid. As such, developing efficient and effective energy storage technologies is essential for creating sustainable energy systems that can meet the demands of modern society while mitigating the impact of external factors. In this regard, this work provides an overview of microgrids' latest energy storage technologies, including their applications, types, integration strategies, optimization algorithms, software, and uncertainty analysis. Energy storage technologies have a wide range of applications in microgrids, including providing backup power and balancing the supply and demand of energy. Different energy storage techniques have been discussed, including batteries, flywheels, supercapacitors, pumped hydro energy storage, and others. Moreover, integration strategies of energy storage in microgrids, models, assessment indices, and optimization algorithms used in the design of energy storage systems are presented in detail. The capabilities of software used in energy storage sizing are explored. Further, uncertainty analysis in modeling energy storage devices is presented and discussed. This state-of-the-art technology has been prepared to demonstrate the effectiveness of energy storage technologies in microgrids, providing valuable insights for future developments in the field. [ABSTRACT FROM AUTHOR]

This paper establishes an accurate and reliable study for estimating the lithium-ion battery's State of Charge (SoC). An accurate state space model is used to determine the parameters of the battery's nonlinear model. African Vultures Optimizers (AVOA) are used to solve the issue of identifying the battery parameters to accurately estimate SoC. A hybrid approach consists of the Coulomb Counting Method (CCM) with an Adaptive Unscented Kalman Filter (AUKF) to estimate the SoC of the battery. At different temperatures, four approaches are applied to the battery, varying between including load and battery fading or not. Numerical simulations are applied to a 2.6 Ahr Panasonic Li-ion battery to demonstrate the hybrid method's effectiveness for the State of Charge estimate. In comparison to existing hybrid approaches, the suggested method is very accurate. Compared to other strategies, the proposed hybrid method achieves the least error of different methods. [ABSTRACT FROM AUTHOR]

Nowadays, energy decarbonization due to integrating renewable energy sources presents important challenges to overcome. The intermittent nature of photovoltaic systems reduces power quality by producing voltage variations and frequency deviations in electrical system networks, especially in weak and isolated distribution systems in developing countries. This paper presents a power smoothing method for improving the low-pass filter and moving average for grid-connected photovoltaic systems. This novel method includes state-of-charge monitoring control of the supercapacitor's energy storage system to reduce the fluctuations of photovoltaic power at the point of common coupling. A case study for a microgrid in a high-altitude city in Ecuador is presented with exhaustive laboratory tests using real data. This research aims to improve energy power quality in electrical distribution systems to cope with the growth of renewable penetration. The results demonstrate significant power quality and stability improvements achieved through the proposed method. For instance, the power smoothing method effectively reduced power fluctuations by 16.7% with the low-pass filter, 14.05% with the ramp-rate filter, and 9.7% with the moving average filter. [ABSTRACT FROM AUTHOR]

Since renewable energy sources (RESs) have an intermittent nature, conventional secondary frequency control, i.e., load frequency control (LFC), cannot mitigate the effects of variations in system frequency. Thus, this paper proposes incorporating ultralocal model (ULM) control into LFC to enhance microgrid (µG) frequency stability. ULM controllers are regarded as model-free controllers that yield high rejection rates for disturbances caused by load/RES uncertainties. Typically, ULM parameters are set using trial-and-error methods, which makes it difficult to determine the optimal values that will provide the best system performance and stability. To address this issue, the African vultures optimization algorithm (AVOA) was applied to fine-tune the ULM parameters, thereby stabilizing the system frequency despite different disturbances. The proposed LFC controller was compared with the traditional secondary controller based on an integral controller to prove its superior performance. For several contingencies, the simulation results demonstrated that the proposed controller based on the optimal ULM coupled with LFC could significantly promote RESs into the µG. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Worldwide, due to the abrupt growth of population, the load demand has been rising dramatically in the last few years. This led to an increase in branch overloads, voltage deviations, and power losses. These problems may result in line outages or the occurrence of blackouts. Flexible AC transmission system (FACTS) devices can be installed in the power system to ensure increased power flow capability and flexible voltage control to address these issues. In this paper, one of the most used FACTS is utilised. It is called Static VAR Compensator (SVC). This controller is one of the most commonly used shunt FACTS controllers due to its low cost in comparison to others, ease of operation, and integration into the power grid. Two Optimization algorithms are combined to form a hybrid optimization approach: Cuckoo Search (CS) and Antlion Optimization (ALO). This hybrid approach employs the exploration of ALO to adjust the optimum allocation and size for SVCs in the power system. This study proposes the IEEE 57 bus scheme as a fairly large structure, with the 50 and 41 branch outages considered the worst-case scenarios for line outages in this system. The simulation results show that the proposed methodology balances exploring the research space and exploiting the best existing solutions compared to some of the other introduced approaches in the literature. [ABSTRACT FROM AUTHOR]

Bioenergy is one of the most important renewable-energy sources worldwide, accounting for more than two-thirds of the renewable-energy mix. Biomass accounted for 13–14% of the primary energy consumption in 2018, and by 2050, it is expected to account for 50% of the global primary energy consumption. This article studies the biomass potential in Iraq. The potential of this country to be one of the leading producers of bioenergy is discussed, remarking on the importance of agricultural crop waste. Nowadays, Iraq generates a great quantity of biomass every year. Unfortunately, instead of contributing to the energy industry and economic progress, these wastes are burned directly, potentially causing a slew of environmental issues. Based on earlier studies, the theoretical energy potential of Iraq agricultural wastes is assessed. It is concluded that 10 million tons of dry agricultural leftovers can create 115 PJ of energy per year. According to the findings of this study, 10 million heads of cattle in Iraq could generate 72 million m3 of biogas per day, with a total potential power of 946 TJ per year from animal wastes, mainly cattle dung. On the other hand, bioenergy potential is heavily reliant on the geographical distribution, availability, and accessibility of real waste. Wasit, Qadisiyah, and Mosul are the most feasible locations for this agricultural waste potential. This might lead to the development of a long-term economic plan for the successful and sustainable utilization of important accessible waste for bioenergy generation. [ABSTRACT FROM AUTHOR]

Feeding biomass-fueled gas engines (BFGEs) with olive tree pruning residues offers new opportunities to decrease fossil fuel use in road vehicles and electricity generation. BFGEs, coupled to radial distribution systems (RDSs), provide renewable energy and power that can feed electric vehicle (EV) charging stations. However, the combined impact of BFGEs and EVs on RDSs must be assessed to assure the technical constraint fulfilment. Because of the stochastic nature of source/load, it was decided that a probabilistic approach was the most viable option for this assessment. Consequently, this research developed an analytical technique to evaluate the technical constraint fulfilment in RDSs with this combined interaction. The proposed analytical technique (PAT) involved the calculation of cumulants and the linearization of load-flow equations, along with the application of the cumulant method, and Cornish-Fisher expansion. The uncertainties related to biomass stock and its heating value (HV) were important factors that were assessed for the first time. Application of the PAT in a Spanish RDS with BFGEs and EVs confirmed the feasibility of the proposal and its additional benefits. Specifically, BFGEs were found to clearly contribute to the voltage constraint fulfilment. The computational cost of the PAT was lower than that associated with Monte-Carlo simulations (MCSs). [ABSTRACT FROM AUTHOR]

In this work, a neural super-twisting algorithm is applied to the design of a controller for a flywheel energy storage system (FESS) emulator. Emulation of the FESS is achieved through driving a Permanent Magnet Synchronous Machine (PMSM) coupled to a shaft to shaft DC-motor. The emulation of the FESS is carried out by controlling the velocity of the PMSM in the energy storage stag and then by controlling the DC-motor velocity in the energy feedback stage, where the plant's states of both electrical machines are identified via a neural network. For the neural identification, a Recurrent Wavelet First-Order Neural Network (RWFONN) is proposed. For the design of the velocity controller, a super-twisting algorithm is applied by using a sliding surface as the argument; the latter is designed based on the states of the RWFONN, in combination with the block control linearization technique to the control of the angular velocity from both machines in their respective operation stage. The RWFONN is trained online using the filtered error algorithm. Closed-loop stability analysis is included when assuming boundedness of the synaptic weights. The results obtained from Matlab/Simulink validate the performance of the proposal in the control of an FESS. [ABSTRACT FROM AUTHOR]

In recent years, multilevel inverters (MLIs) have emerged to be the most empowered power transformation technology for numerous operations such as renewable energy resources (RERs), flexible AC transmission systems (FACTS), electric motor drives, etc. MLI has gained popularity in medium- to high-power operations because of numerous merits such as minimum harmonic contents, less dissipation of power from power electronic switches, and less electromagnetic interference (EMI) at the receiving end. The MLI possesses many essential advantages in comparison to a conventional two-level inverter, such as voltage profile enhancement, increased efficiency of the overall system, the capability of high-quality output generation with the reduced switching frequency, decreased total harmonic distortions (THD) without reducing the power of the inverter and use of very low ratings of the device. Although classical MLIs find their use in various vital key areas, newer MLI configurations have an expanding concern to the limited count of power electronic devices, gate drivers, and isolated DC sources. In this review article, an attempt has been made to focus on various aspects of MLIs such as different configurations, modulation techniques, the concept of new reduced switch count MLI topologies, applications regarding interface with renewable energy, motor drives, and FACTS controller. Further, deep insights for future prospective towards hassle-free addition of MLI technology towards more enhanced application for various fields of the power system have also been discussed. This article is believed to be extremely helpful for academics, researchers, and industrialists working in the direction of MLI technology. [ABSTRACT FROM AUTHOR]

This paper presents a novel minimum seeking algorithm referred to as the Hunger Games Search (HGS) algorithm. The HGS is used to obtain optimal values in the model describing proton exchange membrane fuel cells (PEMFCs). The PEMFC model has many parameters that are linked in a nonlinear manner, as well as a set of constraints. The HGS was used with the aforementioned model to test its performance against nonlinear models. The main aim of the optimization problem was to obtain accurate values of PEMFC parameters. The proposed heuristic algorithm was used with two commercial PEMFCs: the Ballard Mark V and the BCS 500 W. The simulation results obtained using the HGS-based model were compared to the experimental results. The effectiveness of the proposed model was verified under various temperature and partial pressure conditions. The numerical output results of the HGS-based fuel cell model were compared with other optimization algorithm-based models with respect to their efficiency. Moreover, the parametric t-test and other statistical analysis methods were employed to check the robustness of the proposed algorithm under various independent runs. Using the proposed HGS-based PEMFC model, a model with very high precision could be obtained, affecting the operation and control of the fuel cells in the simulation analyses. [ABSTRACT FROM AUTHOR]

This work presents a comprehensive analysis of two cubic techniques for Power Flow (PF) studies. In this regard, the families of Weerakoon-like and Darvishi-like techniques are considered. Several theoretical findings are presented and posteriorly confirmed by multiple numerical results. Based on the obtained results, the Weerakoon's technique is considered more reliable than the Newton-Raphson and Darvishi's methods. As counterpart, it presents a high computational burden. Regarding this point, the Darvishi's technique has turned out to be quite efficient and fully competitive with the Newton's scheme. [ABSTRACT FROM AUTHOR]

The optimal reactive power dispatch (ORPD) problem is an important issue to assign the most efficient and secure operating point of the electrical system. The ORPD became a strenuous task, especially with the high penetration of renewable energy resources due to the intermittent and stochastic nature of wind speed and solar irradiance. In this paper, the ORPD is solved using a new natural inspired algorithm called the marine predators' algorithm (MPA) considering the uncertainties of the load demand and the output powers of wind and solar generation systems. The scenario-based method is applied to handle the uncertainties of the system by generating deterministic scenarios from the probability density functions of the system parameters. The proposed algorithm is applied to solve the ORPD of the IEEE-30 bus system to minimize the power loss and the system voltage devotions. The result verifies that the proposed method is an efficient method for solving the ORPD compared with the state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

The increase of renewable energy generation to change the productivity of a country and electrify isolated sectors are some of the priorities that several governments have imposed in the medium term. Research centers are looking for new technologies to optimize the use of renewable energies and incorporate them into hybrid generation systems. In the present work, the modeling of a solar thermal energy generation plant is being carried out. The climatic data used belong to two coastal cities and one island of Ecuador. The contribution of this work is to simulate a complete model of SCF and PCS, in which the variables of outlet temperature and oil flow are involved at the same time. Previously investigations use only outlet temperature for evaluating power plants. The model of the solar thermal plant is composed of a field of solar collectors, a storage tank, and an energy conversion system. As a result, we obtain a model of a thermosolar plant that will allow us to make decisions when considering the incorporation of micronetworks in systems isolated from the electrical network. The use of thermosolar technology allows the reduction in the risk of spills by the transport of fossil fuels in ships. The study of the CO2 emission factor in Ecuador from 2011 to 2018 is also carried out. [ABSTRACT FROM AUTHOR]

This paper presents the theoretical model and the simulation of a cutting edge hybrid power system composed of an externally-fired gas turbine (EFGT) coupled with an organic Rankine cycle (ORC) as a bottoming unit for the maximization of electrical power. The power plant is fed with different biomass sources from olive industry wastes (pruning, dry pomace, stones, leaves and twigs), providing more than 550 kW of electric power and a net electrical efficiency of 26.0%. These wastes were burnt directly at atmospheric pressure in an EFGT, producing 400 kW of electric power and exhaust gases at 300 °C. Ten suitable ORC working fluids have been studied to maximize the electric power generation: cyclohexane, isohexane, pentane, isopentane, neopentane, R113, R245fa, R365mfc, R1233zd and methanol. The best fluid was R1233zd, reaching 152.4 kW and 22.1% of ORC thermal efficiency; as drawback, however, R1233zd was not suitable for Combined Heat and Power CHP applications due its lower condensation temperature. Thus, despite R113 gave minor electricity production (137.5 kW) this allowed to generate additional thermal power (506.8 kW) in the way of hot water at 45 °C. [ABSTRACT FROM AUTHOR]

The optimal power flow (OPF) problem is a non-linear and non-smooth optimization problem. OPF problem is a complicated optimization problem, especially when considering the system constraints. This paper proposes a new enhanced version for the grey wolf optimization technique called Developed Grey Wolf Optimizer (DGWO) to solve the optimal power flow (OPF) problem by an efficient way. Although the GWO is an efficient technique, it may be prone to stagnate at local optima for some cases due to the insufficient diversity of wolves, hence the DGWO algorithm is proposed for improving the search capabilities of this optimizer. The DGWO is based on enhancing the exploration process by applying a random mutation to increase the diversity of population, while an exploitation process is enhanced by updating the position of populations in spiral path around the best solution. An adaptive operator is employed in DGWO to find a balance between the exploration and exploitation phases during the iterative process. The considered objective functions are quadratic fuel cost minimization, piecewise quadratic cost minimization, and quadratic fuel cost minimization considering the valve point effect. The DGWO is validated using the standard IEEE 30-bus test system. The obtained results showed the effectiveness and superiority of DGWO for solving the OPF problem compared with the other well-known meta-heuristic techniques. [ABSTRACT FROM AUTHOR]