nach oben

2024 | Buch

Kapitel lesen Erstes Kapitel lesen

Advances in Electrical Systems and Innovative Renewable Energy Techniques

The Proceedings of the International Conference on Electrical Systems and Automation (Volume 1)

herausgegeben von: Mohamed Bendaoud, Amine El Fathi, Farhad Ilahi Bakhsh, Siano Pierluigi

Verlag: Springer Nature Switzerland

Buchreihe : Advances in Science, Technology & Innovation

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

Einloggen, um Zugang zu erhalten

Über dieses Buch

This edited book on “Advances in Electrical Systems and Innovative Renewable Energy Techniques” is an outcome of the selected papers presented at the International Conference on Electrical Systems & Automation, (ICESA 2023) held from 29 to 30, May 2023 at the Faculty of Sciences and technologies, Al Hoceïma, Morocco.

This edited book is divided into 2 volumes. This volume will be divided into 3 parts, each devoted to distinct yet interconnected aspects of the subject matter.

The first part focuses on various advancements in renewable energy techniques. It explores topics ranging from biomass combustion characteristics and hydrogen production using photovoltaics to the assessment of wave energy potential and the performance evaluation of solar collectors. These research papers not only shed light on the current state-of-the-art technologies but also offer valuable insights into their implementation, efficiency, and potential impact on the energy landscape.

The second part focuses on interdisciplinary approaches between electrical and renewable energy systems and includes research chapters on photovoltaic (PV) energy, wind energy, and microgrid systems.

For PV systems, several topics and issues are addressed such as modeling of PV systems using single diode model and double diode model; analytical and numerical methods for extraction of PV parameters; extraction of maximum power from PV system using integral SMC strategy, sun-pointing orientation, SuDoKu, and ANN algorithms; and fault detection and classification based on metaheuristic technique, and feedforward neural network.

For the wind system, its modeling is first discussed, and then the control of the wind system using direct power, PI, fuzzy logic, sliding mode, and time delay strategies is analyzed.

In the third part, the chapters focus on efficient energy management, optimization of microgrids, and the use of advancedtechnologies to improve energy performance. Researchers present innovative solutions to address the challenges of energy efficiency, grid responsiveness, and the integration of new energy sources.

Inhaltsverzeichnis

Frontmatter

Advances in Innovative Renewable Energy Techniques

Frontmatter

Production of Hydrogen by Photovoltaic Using Seawater

Abstract

In this paper, we present an experimental validation of green hydrogen production using a seawater electrolyzer. Hydrogen is an important future energy source for the world. Also, the use of seawater as an electrolyte presents an alternative source of water in view of the water stress problem. In order to carry out this experiment and to validate the results, a flat electrolytic cell has been set up with the use of seawater as an electrolyte. The electrolyzer is powered by a photovoltaic system. This system is composed of a photovoltaic panel connected in cascade with a serial DC/DC converter (buck). This last is piloted by the MPPT control, based on a P&O algorithm in order to take advantage of the maximum power point generated by the photovoltaic panel. This experiment aims to see the effect of the electrolyzer on the seawater. For that, two phases of the electrolysis of the seawater have been made, and an analysis of each sample is interpreted, in order to validate that the production of hydrogen is valid by using the seawater as an electrolyte.

Imane Messaoudi, Sanae Dahbi, Abdelhafid Messaoudi, Jalal Blaacha, Saloua Yahyaoui, Abdelhak Aziz

A Numerical Evaluation of the Energy Potential of Waves Along Morocco’s Atlantic and Mediterranean Coast

Abstract

The global rise in energy consumption necessitates evaluating alternative energy sources, including marine energy. Modeling the transient nature of oceans remains a persistent challenge in harnessing marine energy. Using digital tools, we simulated various wave sizes to gain insights into wave behavior. Our study focuses on analyzing wave energy along Morocco's Atlantic coast. Using the ECMWF ERA dataset, we compared wave properties in five cities from 2005 to 2010: Tangier, Dakhla, Casablanca, El Jadida, and Essaouira. Results revealed significant wave power potential, particularly in Casablanca, El Jadida, and Essaouira. These Atlantic sites showed an average maximum power of up to 40 kW/m, with Essaouira having an annual average power of 13 kW/m. In summary, marine energy offers promising opportunities to meet global energy needs. Our study highlights the potential for wave power plants along Morocco's Atlantic coast, with specific locations showing strong wave power potential.

Soufiane EL Bouji, Noureddine Kamil, Zitouni Beidouri

Assessment of Bifacial Modules in an AgriVoltaic System Installed in Agadir, Morocco

Abstract

In this chapter, we realize a comparative study between the use of bifacial modules and conventional monofacial modules in APV systems. To this aim, we carried out simulations using SAM and PVsyst software to evaluate the energetic and economic performances of an APV system installed in AGADIR, Morocco. This PV system has an installed capacity of 2.1 kW for bifacial and monofacial modules mounted on a Fixed Tilted (Optimal) and Fixed Vertical and Horizontal Axis Tracking (HSAT) structure. The structures are raised to a sufficient height above a wheat field, for which the estimated albedo variation is a function of their growing. Simulation results indicate that the fixed-tilt bifacial module produces an energy gain of about 6 and a 2% reduction in LCOE compared to monofacial modules. Similarly, a bifacial module with HSAT produces the best results. This system increases energy production by up to 20% with an LCOE of about $23/MWh and a capacity factor of 28%. Given the results, ground albedo is a significant factor in the variation in energy production in the bifacial system, particularly in vertical systems. However, the variation of albedo in APV fields depends on the crop and the season.

Rania Benbba, Mohamed Akhsassi, Omar Ait Si Ahmed, Hasnae El Mouden, Ahmed Wifaya, Abdelkader Outzourhit

Numerical Analysis of a Parabolic Trough Collector Absorber with a Two Straight Tubes Exchanger

Abstract

In this chapter, a numerical study using Ansys Software is carried out to evaluate the performances of a Parabolic Trough Collector (PTC) receiver with Two Straight Tubes Exchanger (TSTE). Two configurations are analyzed: one with fluid circulation in both tubes in the same direction (TSTE-SD) and the other with fluid circulation in tubes in the opposite direction (TSTE-OD). Simulations are carried out for various mass flow rates and a constant inlet temperature of 26.85 °C. The efficiency reached a value of about 59% for the SD configuration and 58% for the OD configuration for a mass flow rate of 0.02 kg/s. The absorber temperature difference is decreased and the temperature distribution is improved using the TSTE-OD configuration compared with the TSTE-SD configuration. These results indicate that using the absorber with a TSTE is a promising way to enhance PTC system performance.

Oumachtaq Ayoub, Halimi Mohammed, Messaoudi Choukri, El Hassouani Youssef

Long-Term Investigation of Hybrid System for Building Integration: PTC-Based Heating System and Power Generation (CPV/T)

Abstract

In this chapter, a long-term investigation of a hybrid system for building an integration that incorporates a Concentrated Photovoltaic Thermal (CPV/T) solar collector is reported. The system comprises a Parabolic Trough Concentrator (PTC) with a concentrating photovoltaic/thermal collector. A simulation study of the proposed system layout, including all necessary components, was carried out using TRNSYS to evaluate the system’s feasibility and validity, particularly for low-temperature applications. The system tracks the sun from east to west using a single-axis tracking system under actual weather conditions. Simulation findings revealed that the solar fraction remains notably high throughout the year for the operational setup taken into account in this work. Thus, during summer, it covers nearly all of the needs. Nevertheless, a supplementary system is necessary during chilly periods. In summer, the tank temperature was about 92 °C, and the system sustained a heated slab temperature of about 28 °C. Throughout the year, the system’s electrical, thermal, and total efficiency reaches a maximum of about 9, 40 and 47%, respectively.

Halimi Mohammed, Oumachtaq Ayoub, Aumeur El Amrani, Abdel-illah Amrani, Ali Lamrani Alaoui, Messaoudi Choukri

Combustion Characteristics of Biomass Pellet Fuels in a Fixed-Bed Micro-Gasifier Cook Stove in Senegal

Abstract

Pellets from agricultural residues often represent a better alternative to domestic fossil fuels (charcoal, firewood, biomass), due to their accessibility but also their better density and higher calorific value. In this work, we present the results of the valorization of pellets made from the residues of the peanut shell, corn cob, palm nut shell, and typha. Performance tests and the measurement of the reaction gas were carried out on a new gasification stove prototype (pyro-gazo-UGB) that we designed. The tests at the level of the pyro-gazo-UGB have made it possible to highlight the importance of controlling the airflow and the importance of forcing the air into the bed of pellets in order to allow the progression of the flame in the gasifier stove. With the combustion of the pellets, we measured pyro-gasification reaction temperatures between 650 and 800 °C. The mass loss profile was monitored as well. The water boiling tests (WBT) then made it possible to evaluate the thermal performance of the pyro-gazo-UGB, with thermal efficiencies in the high-power phase around 15% and efficiencies of more than 40% in the low-power phase. The WBT also revealed a high thermal power, with flame powers of up to 20 kW. The measurements of the reaction gases revealed almost zero concentration of CO₂ produced and O₂ levels of around 21%, for a proportion of at least 78% (concentration of CO + H₂) for the synthetic gas produced.

C. Mbodji, D. Diouf, B. Piriou, A. Maïga, O. Diallo

Deep Learning Approach for Solar Irradiance Forecasting: A Moroccan Case Study

Abstract

Due to its influence on applications such as renewable energy generation, solar irradiance data, and meteorological parameters have risen in prominence. However, developing an accurate model for predicting solar irradiance based on multiple weather parameters remains a challenging issue. As a novelty, a multi-horizon forecasting scheme ranging from 1 to 3 days ahead is studied in the present work. A SeqtoSeq model architecture to forecast global horizontal irradiance based on Masen’s dataset. Univariate and multivariate SeqtoSeq models are implemented to forecast global horizontal irradiance (GHI) based on Masen’s dataset. Moreover, the performance of the proposed models was compared against other models such as Multi-Layer Perceptron (MLP), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM). The obtained results reveal that the proposed multivariate model outperforms all the other models and improves prediction in terms of Mean Absolute Error (MAE) by 42.49, 48.29, and 47.78% for 1 day ahead, 2 days ahead, and 3 days ahead, respectively.

Saad Benbrahim, Loubna Benabbou, Hanane Dagdougui, Ismail Belhaj, Hicham Bouzekri, Abdelaziz Berrado

PV Power Forecasting Using Deep Learning and Physical Models: Case Study of Morocco

Abstract

Of all renewable energy sources, photovoltaic technology is the most immediate way to convert solar radiation into electricity. Although the penetration of renewable energies has increased in recent years, the problem of intermittency still persists due to the nature of the solar resource. Accurate solar power forecasting is crucial for operations and maintenance (O &M) and day-to-day operations monitoring in solar plants. This chapter proposes a hybrid day-ahead forecasting approach that combines deep learning with Numerical Weather Prediction (NWP) and electrical models. The performance of this model is compared to two other models: a WRF-Solar + electric model and an LSTM + regression model. The models are evaluated using Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and Mean Absolute Error (MAE) metrics. The proposed LSTM + WRF-Solar + Two diode model demonstrates improved forecasting accuracy compared to the other two models, with an improvement of 8.79% compared to the LSTM + regression model and 3.3% compared to the WRF-Solar + Two diode model.

Samira Abousaid, Loubna Benabbou, Hanane Dagdougui, Ismail Belhaj, Hicham Bouzekri, Abdelaziz Berrado

Interdisciplinary Approaches in Renewable Energy Systems

Frontmatter

PV Modeling and Extracting the Single-Diode Model Parameters: A Review Study on Analytical and Numerical Methods

Abstract

The electrical modeling of photovoltaic modules is essential for installing, optimizing, and controlling photovoltaic systems. Several electric models exist, including the single-diode model (SDM), which has five unknown parameters. The challenge at hand pertains to determining these parameters, for which several methods are available. These methods are distinguished by their precision, complexity, and applicability. Generally, there are three categories: analytical (non-iterative), numerical (iterative), and metaheuristic methods. This work compares and tests the accuracy of eight analytical and numerical methods for determining the parameters of a single-diode model. The eight methods will validate on a PV module is Shell SQ160-C under standard test conditions (STC), and all give a realistic result for determining the unknown parameters and predicting the I-V curve.

Abdelfattah Elhammoudy, Mustapha Elyaqouti, Arjdal El Hanafi, Dris Ben Hmamou, Souad Lidaighbi, Driss Saadaoui, Imade Choulli

An Experimental Assessment of the Single- and Double-Diode Models: The Possibility of a Hybrid Approach

Abstract

The rise in CO₂ emissions due to increased productivity in industrial and agricultural sectors has led to negative impacts on the environment and human health. To combat this issue, green sources such as solar are being explored. Among various solar technologies, photovoltaic (PV) technology is eco-friendly and cost-effective. However, accurate mathematical modeling of the PV cell is required before integrating them into industrial or domestic applications. This work evaluates the performance of single- and double-diode configurations of PV cells using two parameter extraction methods under non-standard conditions. The study aims to prioritize the use of a particular configuration based on prevailing meteorological inputs to achieve high accuracy in PV cell modeling. Both models performed very well under the climate fluctuations with a remarkable performance of the single diode under the fluctuated profiles, especially the high variations. On the other hand, the double-diode model performs well with low variations. A hybrid algorithm is proposed to switch between the models based on the level of solar irradiance and temperature. This hybrid approach is validated by experiments conducted on a grid-connected PV system. The findings of this study could enhance the accuracy of PV cell modeling and provide insight for future research.

Yassine Chaibi, Abdelilah Et-taleby, Badr Elkari, Zakaria Chalh, Mohamed Benslimane

Intelligent PV Fault Detection and Categorization Based on Metaheuristic Algorithm and Feedforward Neural Network

Abstract

This study proposes a novel approach to identify and classify faults in photovoltaic systems. Specifically, a hybrid model is developed by integrating an artificial neural network with a differential evolution algorithm. The differential evolution algorithm is utilized to optimize the neural network's topology and enhance the accuracy of the fault detection and categorization system. The experimental results demonstrate the effectiveness of the proposed method in improving both prediction accuracy and training accuracy. Thus, this study contributes to the development of advanced techniques for monitoring and maintaining the reliability of photovoltaic systems.

Sebbane Saliha, El Akchioui Nabil, Fahim Mohamed

Detection and Classification of Faults in PV Systems Based on Thermal Imaging and Fuzzy Logic Algorithm

Abstract

Fault detection and classification systems in PV fields have become a top concern to ensure the functioning of PV panels. According to the literature, many studies have been conducted to address this issue. This work attempts to offer a defect detection and classification approach in PV panels using image processing of a thermal picture in this context. To detect and classify the degree and existence of the errors, the k-means and fuzzy logic methods were employed, respectively. The simulations indicate that using the suggested approach enables the accurate detection and classification of faults in solar panels.

Abdelilah Et-taleby, Yassine Chaibi, Badr Elkari, Mohamed Benslimane, Zakaria Chalh

Integral SMC Strategy for MPPT of the Solar PV System Under Varying Climatic Conditions

Abstract

The present work puts forward an integral sliding mode control (ISMC) approach so as to track a maximum power point (MPP) of a solar photovoltaic (PV) system that includes a PV array, a converter, and a resistant charge. The controller role consists of using the photovoltaic generation system to its required maximum power point by adjusting the boost converter duty cycle. Thus, the suggested control approach is modified by the introduction of an integral action in the sliding surface with the purpose of improving the transient response and reducing the steady-state error. The stability of the proposed ISMC is examined by means of the theory of Lyapunov and the control action didn’t present any chattering behavior. Numerical simulations are studied to prove the robustness of the designed concept, using a non-linear model. The results of simulation prove that the proposed controller (ISMC) offers a higher speed response and fewer stable-state error relative to the traditional SMC method.

I. Bekki, F. -E. Lamzouri, A. El Amrani, E.-M. Boufounas

Assessing MPPT Techniques for Nanosatellite EPS in Sun-Pointing Orientation: A Comparative Study

Abstract

This chapter compares different Maximum Power Point Tracking (MPPT) techniques for nanosatellites with body-mounted photovoltaic (PV) panels that are constrained by volume and mass and require special orientation scenarios to meet mission requirements. The nanosatellite’s PV panels are subject to irregular solar irradiance and temperature due to the specific orientation scenarios required for its mission. Consequently, an efficient strategy is necessary to extract the maximum power available from the PV panel. Since the Electrical Power Subsystem (EPS) is a critical subsystem responsible for generating power during the sunlight period, well-known MPPT techniques such as Perturb and Observe (P &O), Incremental Conductance (INC), and Fuzzy Logic Controller (FLC) were investigated under different scenarios to highlight optimal MPPT extractions for a 3U nanosatellite. The simulation was conducted using MATLAB/SIMULINK, and the findings indicated that P &O was the least effective, while FLC and INC performed better. The results imply that FLC and INC are more suitable for NanoSat applications than P &O, particularly when confronted with rapid changes in solar irradiance.

Amina Daghouri, Soumia El Hani, Nisrine Naseri, Imad Aboudrar

A Comparative Study of Various SuDoKu Algorithms for Improvement of Generated Power Under Partial Shading Conditions

Abstract

Photovoltaic arrays are susceptible to a phenomenon known as partial shadowing/mismatch conditions, which can result in considerable power losses and a shortened lifespan for the photovoltaic arrays. The partial shadowing conditions on the PV array are decreased by methods like SuDoKu-based reconfiguration, which may provide a solution to rearrange the connections between the PV modules in the PV array. SuDoKu-based reconfiguration must be modified to optimize output power because it can't always distribute partial shading. This paper presents various puzzle patterns, which are found in literature that are based on the SuDoKu pattern. By implementing the latest SuDoKu-based reconfiguration pattern, it is found that power generated by PV cells is increased while at the same instance, shadow was uniformly spread.

Vaishali Gautam, Mohd Faisal Jalil, Farhad Ilahi Bakhsh, Shahida Khatoon

Modeling and Control of a Standalone PMSG Wind Generation System to Extract the Maximum Power Based on Direct Power Control

Abstract

This chapter presents a control strategy for a standalone wind generation system based on a permanent magnet synchronous generator (PMSG), in order to extract the maximum electrical power under varying wind speed and apply the maximum power point tracking (MPPT) principle. This chapter is useful to simulate and model the wind energy conversion system (WECS) connected with DC load using MATLAB/Simulink environment. A direct power control (DPC) strategy is adopted for the generator side converter to rectify the PMSG voltage output. Also, a novel PI-control is presented to keep DC bus voltage constant. The results of the simulation presented show the effectiveness and the good reliability of the control strategy used for the power transfer.

Y. Moradi, F. E. Tahiri, M. A. Ouafi, K. Chikh

A Comparative Study of the ADRC and PI Controller of a Wind Turbine Driven by a PMSG

Abstract

The world is currently experiencing a huge shortage of energy sources, and global energy needs are constantly increasing due to the pace of industrialization and the improvement in the people’s living standards. Safer and less polluting renewable energies such as solar energy or wind power are the two most appropriate solutions now. The aim of this research is to study wind energy knowing its functionalities and characteristics, through an aerodynamic machine based on a permanent magnet synchronous generator (PMSG), which has advantages manifested in the absence of an excitation circuit and the lack of speed multiplier. This chapter involves modeling of the different components of the conversion chain, in order to extract the mechanical and electrical control equations. The control of the converters is ensured by the PI control, and also by another robust control named Active Disturbance Rejection Control (ADRC), taking into account obtaining maximum energy by the use of the Maximum Power Point Tracking (MPPT). The simulations are carried out in the MATLAB/SIMULINK environment with a comparison between the two control methods. According to the results obtained, the effectiveness of the ADRC control is shown.

Mohammed Latifi, Ilias Ouachtouk, Imad Aboudrar, Mourad Zegrari

The Efficiency of Fuzzy Logic Control on the Power Stabilization of Wind Turbine

Abstract

Wind turbines exhibit highly non-linear behavior, making it difficult to control their power output using traditional PID control methods. Fuzzy logic control has proven to be an effective solution for addressing this challenge. This work focuses on the application of fuzzy logic control for optimizing the power generated by wind turbines, specifically in high wind speed conditions where limiting the power output is necessary for the protection of the machine. The results of simulation studies comparing fuzzy logic control and conventional PID control are presented and analyzed, demonstrating the superiority of fuzzy logic control in ensuring the desired control performance while maintaining the safety of the wind turbine.

Lakhal Yassine, Baghli Fatima Zahra, Youssef Ait El Kadi, Benchagra Mohammed

Optimizing Wind Turbine Control with Sliding Mode and Time Delay Strategies

Abstract

For variable speed wind turbines, this article proposes a sliding mode control and time delay control approximation. In the first stage, the wind turbine system model is displayed. The sliding mode strategy will be used in the second part. A tremendous amount of energy may be captured with this method, which is distinguished by its excellent efficiency and necessitates the least amount of effort and resources. In order to strategically reduce the switching gain, sliding mode control (SMC) and time delay control (TDC) are combined during the sliding phase. This demonstrates the resilience, stability, and astounding efficiency displayed by the SMC law. In addition to guaranteeing precise and reliable system performance, SMC also optimizes energy usage and reduces unwanted perturbations, yielding a highly effective and cost-efficient control strategy. For each time delay, this approach directly detects unknown system dynamics and disturbances. Finally, we show how the sliding method is faster, more precise, and more accurate in terms of settling time, tracking accuracy, and energy usage.

Sanae El Bouassi, Zakaria Chalh, El Mehdi Mellouli

Sizing and Management of Electrical Grid

Frontmatter

Sizing and Simulation of an Alternative Microgrid System

Abstract

Renewable energies such as solar energy, wind energy, hydro energy, and geothermal energy have become increasingly important in large-scale energy development in recent years. In order to optimize the use of these energy sources, distributed generators such as microgrids are becoming more and more popular due to their efficiency and reliability. This chapter presents a study focused on the design and simulation of an AC-microgrid system consisting of a photovoltaic source, a battery bank, and the grid as a backup source, as well as the proposal for an energy management system. The objective is to ensure a stable energy flow between renewable energy sources and loads. A system sizing study was carried out using PVsyst software for the Oujda region in Morocco, and then a simulation of the system in the time domain was developed in the Matlab/Simulink environment to validate the microgrid's ability to provide stable electrical power to the loads. An energy management algorithm is used to manage power flows between the energy sources and the load unit to maintain an efficient, sustainable, and reliable power supply, increase battery life, and ensure their proper functioning in a safe area, taking into account different parameters such as the availability of photovoltaic energy and the state of charge of the battery bank. The contribution of this work is to demonstrate the design of an AC microgrid and to develop an energy management system that will be used in another study in a laboratory prototype at a reduced power scale.

Ayoub Rahmouni, Driss Yousfi, Mohammed Bachiri, Mohamed Bakhouya

Sizing Renewable Energy by Using Genetic Algorithm

Abstract

In this study, the levelized cost of energy (LCOE) is used as an objective function that will be minimized using the genetic algorithm (GA). The main objective is to assess the techno-economic viability of renewable energy (RE) systems less than 20 KW based on solar and wind energy and produce at least an annual output of 5000 KWh per year in 12 Moroccan locations that have a good potential of solar and wind energy. Moreover, this work discusses two scenarios. In the first scenario, the energy excess is not injected into the grid, whereas in the second scenario, all the energy excess is injected into the grid. The obtained results have demonstrated that a hybrid RE system consisting of both, wind and solar energy sources, is the most favorable type of system for the majority of the studied cities, with nine hybrid systems and three single-energy systems identified by the GA. The lowest LCOE value of 0.11 $/KWh was achieved in Dakhla using a single RE system based on wind with a nominal capacity of 8.7 KW. These findings indicate that the hybrid RE system design is a viable and cost-effective option for powering the cities in Morocco with RE, which can lead to significant environmental and economic benefits.

Mohammed Bouafia, Amine El Fathi, Mohamed Bendaoud, Azeddine El-Hammouchi, Nabil El Akchioui

Micro-Grid Design and Optimization Using COOT Optimization Algorithm

Abstract

This paper provides a brand-new metaheuristic method that draws inspiration from COOT bird behavior, which is applied to optimize the configuration of a micro-grid consisting of a Diesel Generator (DG), Photovoltaic (PV) panels, Wind Turbine (WT), and battery storage system. The optimized configuration aims to meet the energy needs of Dakhla City in Morocco, taking into account the prevailing weather conditions in the area. Two indices, Power Supply Loss Probability Factor (LPSP) and Cost Of Electricity (COE), are improved through this algorithm. The performance of COOT algorithm in finding the optimal solution is assessed through statistical analysis and compared to well-known optimizers such as Salp Swarming Algorithms (SSA) and Gray Wolf Optimizer (GWO). The results show that the COOT algorithm outperforms other optimizers in terms of system design.

Ali EL Marzougui, Saida Bahsine, Younes Chihab, Fatima Ait Nouh, Aziz Oukennou

Feasibility Study of the Design of a Floor Heating System for the Wet Rooms of a Hammam Using Solar Photovoltaic

Abstract

The objective of this chapter is to evaluate the energy potential, financial energy costs, and environmental impact reduction in the solar photovoltaic powered heating system. This study will compare traditional wood heating systems with a photovoltaic panel system for electric floor heating, which will be used to heat showers and heated rooms in traditional hammams. The electric floor heating has been chosen as a renovation solution that has never been used in Moroccan hammams and that fits into the context of the ecological hammam. This heating system presents an appropriate solution for heating wet spaces such as public baths. However, these systems are not yet widespread in Morocco. Therefore, hammams consume huge amounts of wood that threaten forest areas and green fields with the release of huge amounts of CO₂ and greenhouse gases that increase global warming. This system supplies the low temperature premises (3 showers) of the hammam in the climatic conditions of Marrakech, Morocco. In addition, the case study presented in this chapter aims to present an effective and excellent solution. Thus, the proposed solution aims to provide energy and financial savings on consumption as well as a reduction in CO₂ emissions. These savings will also contribute to the overall sustainability objectives by reducing the environmental impact of buildings and promoting more sustainable use of energy resources.

Yassine Anigrou, Mohammed Zouini, Mohamed El Khlifi

Grid-Tied Energy Management System for Hybrid Microgrid Using Advanced PSO Algorithm

Abstract

The Microgrid Energy System (MGES) is a power system known as a small-scale energy system, it is based generally on decentralized energy resources such as wind turbines (WT), photovoltaic solar panels (PV), battery energy storage systems (BESs), and loads demand (LD). MG could operate in either grid-connected or islanded mode, the power range is between a few KW to a few MW. This paper presents a Microgrid Energy Management System (M-EMS) for grid-tied mode by using photovoltaic panels (PV) and battery energy storage systems (BESs). The proposed MGES is focusing on optimization module. The optimization module is responsible for the daily programming between the production and load demand to achieve the availability of energy resources anytime needed during 24h. Thus, this article will handle the optimization for real-time energy management by using meta-heuristic algorithms, it is implemented by MATLAB to minimize the cost to the optimum value as well as maximize the energy distribution.

EL-Qasery Mouna, Abbou Ahmed, ID-Khajine Lahoucine

A Control Strategy for Energy Cost Reduction, Peak Shaving and Power Factor Correction Using Batteries

Abstract

Due to the continuous increase in demand and electricity prices, consumers are increasingly aware of the importance of solar panels and batteries in their homes. However, the good management of their energy remains a challenge that reduces the benefits of these installations. In this chapter, we seek to use batteries efficiently to reduce the cost and shave the peak of energy consumption while correcting the power factor. The latter is defined as the ratio of active (i.e., “useful”) power absorbed by the loads to the apparent power flowing in the circuit. First, we formulate our non-convex multi-objective optimization model. Then we solve it using the McCormick relaxation technique. Experimental validation is performed in Matlab. The simulation results show a reduction in both the cost (24%) and the peak as well as an improvement in the power factor.

Safaa Mimi, Yann Ben Maissa, Ahmed Tamtaoui

Robust Deterministic Optimization Approach for Optimal Reactive Energy Management in Electrical Transmission Network

Abstract

Reactive power management is inevitable in the optimal control of electrical power networks. It supports the network voltage which reduces active power loss in transmission lines by finding the optimal values of the control parameters. In this paper, the problem of optimal reactive power dispatch (ORPD) is studied using a deterministic optimization algorithm called Broyden Fletcher Goldfarb Shanno-based Augmented Lagrangian (BFGS-AL). The proposed approach is used to minimize real power losses and voltage deviation considering all network constraints for the IEEE 57-bus power grid. Simulation results validate the ability of BFGS-AL method to accelerate the convergence and provide optimal regulation of control variables, in comparison with other reported optimization methods.

El Hachmi Talbi, Mustapha El Moudden, Fadwa Baijou, Lhoussine Abaali

A Comparative Study of Metaheuristics Algorithms Applied for Optimal Reactive Power Dispatch Problem Considering Load Uncertainty

Abstract

The power grid is facing increased power losses and voltage instability due to the rising electricity demand. In addition, the unpredictability of energy demand caused by consumer behavior and seasonal changes creates difficulties in accurately forecasting and planning for the power system. This variability in load requirements can negatively impact decision-making in the power industry. However, incorporating this variability in demand can improve planning by allowing the power system to adapt to changing electrical demands. This chapter focuses on solving the optimal reactive power dispatch (ORPD) while considering the load uncertainty. The main objective of ORPD is to achieve minimal power losses by adjusting system control variables that involve both continuous and discrete control variables while satisfying the system's equality and inequality constraints. The Monte Carlo simulation (MCS) and scenario-based reduction (SBR) approaches are utilized for load uncertainty representation. The experimentation is carried out on IEEE-30 bus systems, and the results are evaluated in a comparative study between four metaheuristic optimization methods, namely black widow optimization (BWO) gray wolf optimization (GWO), particle swarm optimization (PSO), and harmony search (HS) algorithms. Through computational analysis, it is demonstrated that the GWO algorithm outperforms other reported algorithms.

Naima Agouzoul, Aziz Oukennou, Faissal Elmariami, Jamal Boukherouaa, Rabiaa Gadal, Ali Tarraq

Comparative Study of Machine Learning for Managing EV Energy Storage with Battery-Hydrogen Tank

Abstract

This study utilized machine learning methods to manage the battery storage system and hydrogen tank, while taking into account the motor speed, average speed (57.6 km/h), motor consumption, and State of Charge (SOC) of the vehicle battery. The study was conducted over six hours of vehicle motor operation and compared the performance of four machine learning classification methods (K-nearest neighbors (k-NN), AdaBoost, Gaussian Naive Bayes, and Random Forest) to determine which power source could best supply energy to the vehicle’s Electric Motor (EM). The results show that the AdaBoost method outperformed the other methods with an f1-score accuracy of 0.98 for the fuel cell (FC) state and 0.90 for the battery state in decision-making regarding the operation of the FC and the battery.

Ismail Elabbassi, Naima Elyanboiy, Mohamed Khala, Youssef El Hassouani, Omar Eloutassi, Choukri Messaoudi

Design and Simulation of an Intelligent Grid-Connected MPPT Inverter with Battery Storage Using ANN Algorithm

Abstract

When electrical demand is rapidly increasing, renewable energy sources are crucial for maintaining the electricity of the grid and powering disconnected loads. Photovoltaic array output is nonlinear and varies with sun irradiation and cell temperature. As a result, a Maximum Power Point Tracking (MPPT) approach is required to extract peak power from the solar array to optimize the produced energy. This research delves into the concept of MPPT technologies, which significantly improve the efficiency of a solar PV system. An MPPT controller based on an artificial neural network has been presented. The data for the ANN model are acquired using the perturbation and observation methods. The objective of the implementation of ANN is to extract the MPP regardless of irradiation variation. A boost converter is used to inject power from PV into the grid. An inverter (DC/AC) with filter LC is made a cascade with a boost converter to synchronize the frequency of the grid with the inverter with PID controller and SPWM technique. Lithium-ion batteries are the best solution utilized to stock energy. We control the charge and discharge of the battery by a PID controller, such as using a converter buck-boost. A simulation-based study of the system was provided utilizing the MATLAB/Simulink toolset.

Hajar Ahessab, Youness Hakam, Ahmed Gaga, Benachir EL Hadadi

Titel: Advances in Electrical Systems and Innovative Renewable Energy Techniques
herausgegeben von: Mohamed Bendaoud
Amine El Fathi
Farhad Ilahi Bakhsh
Siano Pierluigi
Verlag: Springer Nature Switzerland
Electronic ISBN: 978-3-031-49772-8
Print ISBN: 978-3-031-49771-1
DOI: https://doi.org/10.1007/978-3-031-49772-8