Makbul Ramli - Profile on Academia.edu (original) (raw)

Papers by Makbul Ramli

IEEE Access

The uncertainty in wind speed and load demand fluctuations has made the deployment of renewable e... more The uncertainty in wind speed and load demand fluctuations has made the deployment of renewable energy (RE) challenging. However, in islanded microgrids, the implementation of hybrid energy storage systems (HESS) can improve the reliability of power supply and enable further utilization of surplus energy. In this study, we configure an energy management system (EMS) for an optimally constructed system that includes wind turbines (WTs), an electric storage system (i.e., lithium-ion battery), a hydrogen storage system (i.e., PEM type), and diesel generator (DG), based on model predictive control (MPC), to meet specified technical and economic benchmarks for a standalone microgrid (SMG). MPC is intended to maintain the state of charge (SOC) and level of hydrogen (LOH) within their technical limits to prevent degradation and extend the lifetimes of HESS by minimizing the objective function. Additionally, disturbances due to wind power and load demand variations are captured to determine the optimal instantaneous powers of hydrogen and DG sources exposed to certain weighting factors within opted constraints. To perform the simulations, MPC toolbox in MATLAB Simulink environment is used. We consider four cases under various weather conditions to validate the robustness of MPC on the designed EMS for the SMG with shared system element powers for 24 hours using real data of wind velocity and load demand. From the simulation results, we observe minimum and maximum SOC of 20% and 88.52%, and LOH of 10% and 90.06% for the entire studied periods respectively. The results of the designed EMS show that MPC has ensured the HRES bounds to prevent degradation, overcome power interruptions due to weather intermittencies, and reduce grid-integration establishment charges. Moreover, the utilization of the entire renewable energy produced by wind turbines is achieved. Likewise, the load demand is met completely by using this technique and excellent performance of MPC under uncertainty is achieved. INDEX TERMS hybrid energy storage system, standalone microgrid, model predictive control, degradation, control oriented, energy management system, optimal design.

Frontiers in Energy Research, Mar 18, 2022

The output voltage of a photovoltaic (PV) system relies on temperature and solar irradiance; ther... more The output voltage of a photovoltaic (PV) system relies on temperature and solar irradiance; therefore, the PV system and a load cannot be connected directly. To control the output voltage, a DC-DC boost converter is required. However, regulating this converter is a very complicated problem due to its non-linear time-variant and non-minimum phase circuit. Furthermore, the problem becomes more challenging due to uncertainty about the output voltage of the PV system and variation in the load, which is a non-linear disturbance. In this study, an observer-based backstepping sliding mode control (OBSMC) is proposed to regulate the output voltage of a DC-DC boost converter. The input voltage of the converter can be a DC energy source such as PV-based microgrid systems. An adaptive scheme and sliding mode controller constructed from a dynamic model of the converter is used to design an observer. This observer estimates unmeasured system states such as inductor current, capacitor voltage, uncertainty output voltages of the PV cell, and variation of loads such that the system does not need any sensors. In addition, the backstepping technique has been combined with the SMC to make the controller more stable and robust. In addition, the Lyapunov direct method is employed to ensure the stability of the proposed method. By employing the proposed configuration, the control performance was improved. To verify the effectiveness of the proposed controller, a numerical simulation was conducted. The simulation results show that the proposed method is always able to accurately follow the desired voltage with more robustness, fewer steady-state errors, smaller overshoot, faster recovery time, and faster transient response time. In addition, the proposed method consistently produces the least value of integral absolute error.

Sustainability, Jul 14, 2019

Substituting a single large power grid into various manageable microgrids is the emerging form fo... more Substituting a single large power grid into various manageable microgrids is the emerging form for maintaining power systems. A microgrid is usually comprised of small units of renewable energy sources, battery storage, combined heat and power (CHP) plants and most importantly, an energy management system (EMS). An EMS is responsible for the core functioning of a microgrid, which includes establishing continuous and reliable communication among all distributed generation (DG) units and ensuring well-coordinated activities. This research focuses on improving the performance of EMS. The problem at hand is the optimal scheduling of the generation units and battery storage in a microgrid. Therefore, EMS should ensure that the power is shared among different sources following an imposed scenario to meet the load requirements, while the operational costs of the microgrid are kept as low as possible. This problem is formulated as an optimization problem. To solve this problem, this research proposes an enhanced version of the most valuable player algorithm (MVPA) which is a new metaheuristic optimization algorithm, inspired by actual sporting events. The obtained results are compared with numerous well-known optimization algorithms to validate the efficiency of the proposed EMS.

Electronics, Jun 30, 2022

Solar energy is a promising renewable energy source that can fulfill the world's current and futu... more Solar energy is a promising renewable energy source that can fulfill the world's current and future energy needs. The angle at which a photovoltaic (PV) panel faces the horizon determines the incidence of solar radiation. The incident solar radiation on PV panels could be optimized by adjusting their tilt angles and increasing the power output of the PV array. In this study, solar energy model-based research was conducted in the Saudi Arabian cities of Dhahran and Makkah. This study investigated the performance of a 1 kW monocrystalline silicon PV array in these cities. Analyzing the optimal tilt angle for efficiency and performance improvement of the PV panel is challenging. The optimal tilt angle is determined by combining the data of the Sun's diffuse, direct radiation and the global horizontal Sun radiation. This research examined the four empirical models by applying the electric charged particle optimization (ECPO) algorithm to estimate the solar radiation on sloped surfaces. The model's results were compared to the global horizontal solar radiation based on the daily mean solar radiation value in these cities. The Hay-Davies-Klucher-Reindel model presented the maximum amount of tilted surface solar radiation in the year and at different periods. In contrast, the Badescu model exhibited the weakest results of all the isotropic and anisotropic models. Finally, using the ECPO algorithm, all models indicated that tilted surfaces (I T ) received more solar radiation than horizontal surfaces (I g ).

Sustainability

In this paper, a small-scale PV/Wind/Diesel Hybrid Microgrid System (HMS) for the city of Yanbu, ... more In this paper, a small-scale PV/Wind/Diesel Hybrid Microgrid System (HMS) for the city of Yanbu, Saudi Arabia is optimally designed, considering the uncertainties of renewable energy resources and battery degradation. The optimization problem is formulated as a multi-objective one with two objective functions: the Loss of Power Supply Probability (LPSP) and the Cost of Electricity (COE). An Improved Decomposition Multi-Objective Evolutionary Algorithm (IMOEAD) is proposed and applied to solve this problem. In this approach, different decomposition schemes are combined effectively to achieve better results than the classical MOEA/D approach. Twelve case studies are investigated based on different scenarios and different numbers of houses (5 and 10 houses). Each time, the suggested approach produced a set of solutions that formed a Pareto front (PF). Considering a variety of parameters, the optimal compromise option can be selected by the designer from the PF.

Electronics

Maximum power point tracking (MPPT) controllers have already achieved remarkable efficiencies. Fo... more Maximum power point tracking (MPPT) controllers have already achieved remarkable efficiencies. For smaller photovoltaic (PV) systems, any improvement will not really be worth mentioning as an achievement. However, for large solar farms, even a fractional improvement will eventually create a significant impact. This paper presents an MPPT control scheme using global sliding mode control (GSMC) with adaptive gain scheduling. In the two-loop controller, the first loop determines the maximum power point (MPP) reference using online calculations, while the GSMC with adaptive gain scheduling in the second loop adjusts the boost converter’s pulse width modulation (PWM) to force the PV system to operate at the MPP with improved performance. The adaptive gain scheduling regulates the gain of the switching control to maintain the controller performance over a wide range of operating conditions, while GSMC guarantees the system robustness throughout the control process by eliminating the reach...

Sustainability

In this paper, the Wind Farm Layout Optimization/Expansion (WFLO/E) problem is formulated in a mu... more In this paper, the Wind Farm Layout Optimization/Expansion (WFLO/E) problem is formulated in a multi-objective optimization way with specific constraints. Furthermore, a new approach is proposed and tested for the variable reduction technique in the WFLO/E problem. To solve this problem, a new method based on the hybridization of the Multi-Objective Evolutionary Algorithm Based on An Enhanced Inverted Generational Distance Metric (MOEA/IGD-NS) and the Two-Archive Algorithm 2 (Two Arch2) is developed. This approach is named (MOEA/IGD-NS/TA2). The performance of the proposed approach is tested against six case studies. For each case study, a set of solutions represented by the Pareto Front (PF) is obtained and analyzed. It can be concluded from the obtained results that the designer/planner has the freedom to select several configurations based on their experience and economic and technical constraints.

Frontiers in Energy Research

It is necessary to predict solar photovoltaic (PV) output and load profile to guarantee the secur... more It is necessary to predict solar photovoltaic (PV) output and load profile to guarantee the security, stability, and reliability of hybrid solar power systems. Severe frequency fluctuations in hybrid solar systems are expected due to the intermittent nature of the solar photovoltaic (PV) output and the unexpected variation in load. This paper proposes designing a PID controller along with the integration of a battery energy storage system (BESS) and plug-in hybrid electric vehicle (PHEV) for frequency damping in the hybrid solar power system. The solar PV output is predicted with high accuracy using artificial neural networks (ANN) given that solar irradiance and cell temperature are inputs to the model. The variation in load is also forecasted considering the factors affecting the load using ANN. Optimum values of the PID controller have been found using genetic algorithm, particle swarm optimization, artificial bee colony, and firefly algorithm considering integral absolute error ...

IEEE Access

Power systems have been evolving dynamically due to the integration of renewable energy sources, ... more Power systems have been evolving dynamically due to the integration of renewable energy sources, making it more challenging for power grids to control the frequency and tie-line power variations. In this context, this paper proposes an efficient automatic load frequency control of hybrid power system based on deep reinforcement learning. By incorporating intermittent renewable energy sources, variable loads and electric vehicles, the complexity of the interconnected power system is escalated for a more realistic approach. The proposed method tunes the proportional-integral-derivative (PID) controller parameters using an improved twin delayed deep deterministic policy gradient (TD3) based reinforcement learning agent, where a non-negative fully connected layer is added with absolute function to avoid negative gain values. Multi deep reinforcement learning agents are trained to obtain the optimal controller gains for the given two-area interconnected system, and each agent uses the local area control error information to minimize the deviations in frequency and tie-line power. The integral absolute error of area control error is used as a reward function to derive the controller gains. The proposed approach is tested under random load-generation disturbances along with nonlinear generation behaviors. The simulation results demonstrate the superiority of the proposed approach compared to other techniques presented in the literature and show that it can effectively cope with nonlinearities caused by load-generation variations. INDEX TERMS Load frequency control, deep reinforcement learning, twin delayed deep deterministic policy gradient (TD3), hybrid power system.

Energies, 2021

This paper presents an optimal design for a nanogrid/microgrid for desert camps in the city of Ha... more This paper presents an optimal design for a nanogrid/microgrid for desert camps in the city of Hafr Al-Batin in Saudi Arabia. The camps were designed to operate as separate nanogrids or to operate as an interconnected microgrid. The hybrid nanogrid/microgrid considered in this paper consists of a solar system, storage batteries, diesel generators, inverter, and load components. To offer the designer/operator various choices, the problem was formulated as a multi-objective optimization problem considering two objective functions, namely: the cost of electricity (COE) and the loss of power supply probability (LPSP). Furthermore, various component models were implemented, which offer a variety of equipment compilation possibilities. The formulated problem was then solved using the multi-objective evolutionary algorithm, based on both dominance and decomposition (MOEA/DD). Two cases were investigated corresponding to the two proposed modes of operation, i.e., nanogrid operation mode and...

Frontiers in Energy Research, 2021

A key factor in the performance of PV panels is the tilt angle, adjustable via various tracking s... more A key factor in the performance of PV panels is the tilt angle, adjustable via various tracking systems. Fixed tilt angle PV panels miss out on most of the solar radiation each day whereas continuous tracking systems are not always cost-efficient, rather impractical in some cases. Therefore, adjusting the tilt angle using a limited number of periods per year can be a good, compromised solution. In this paper, a new approach is proposed to maximize the impact of solar radiation on PV panels by adjusting their tilt angles. Based on a limited number of periods or intervals per year, the optimal duration (number of days) of each period or interval along with the optimum tilt angle corresponding to each interval are determined by solving two interlinked optimization problems. These two problems are solved using the Most Valuable Player Algorithm (MVPA) combined with the Particle Swarm Optimization (PSO) algorithm. The case study for Yanbu, a western coastal city of Saudi Arabia has been ...

Energy Reports, 2021

Designing a nanogrid involves intricate considerations. Its primary system components, including ... more Designing a nanogrid involves intricate considerations. Its primary system components, including PV systems, inverter type and control, batteries, and diesel generator, always offer a trade-off among conflicting design objectives -the cost of electricity and reliability, for example. This research proposes a synergistic Parallel Multiobjective PSO-based approach (PMOPSO), a merger of four optimization methods to optimally design a hybrid photovoltaic/diesel/battery nanogrid. The merged approaches are the Speed-Constrained Multiobjective Particle Swarm Optimization (SMPSO), MultiObjective Particle Swarm Optimization Algorithm Based on Decomposition (MPSO-D), Novel multiobjective particle swarm optimization (NMPSO), and Competitive Mechanism-Based Multiobjective Particle Swarm Optimizer (CMPSO). The developed approach allows the designer/operator to test multiple component models based on cost and reliability and choose the design that gives the best-suited solution. The four combined algorithms are run in parallel, and the obtained solutions are aggregated together in an archive pool where only non-dominated solutions are kept. A desert camp in the sub-urban area of Hafr Al-Batin city, situated in the Western region of Saudi Arabia, is used as a test case. The approach obtains a well-spread and large Pareto Front (PF), offering many options (solutions) to the designer/operator in a single run. The results achieved a superior set of solutions than those obtained by using each of the four combined PSO-based algorithms individually. Therefore, the developed technique provides improved and viable design solutions for a hybrid nanogrid.

IEEE Access, 2020

Wind farms are developed and implemented in many places around the globe. Designing a wind farm i... more Wind farms are developed and implemented in many places around the globe. Designing a wind farm is becoming more and more complex especially with the recent trend towards large farms. Finding the optimal locations of wind turbines inside a wind farm to reduce energy cost is a highly challenging task, as it requires the handling of conflicting criteria and depending on the number of turbines considered it can turn to a large scale-optimization problem. Therefore, the aim of this paper is to place efficiently wind turbines inside a given area considering all constraints. This problem formulated as an optimization problem is referred to as the wind farm layout optimization (WFLO) problem. This real-world problem is nonlinear and difficult to solve using classical optimization algorithms and it has to take into consideration wind scenarios, power curve and wake effects. For this purpose, a binary version of the most valuable player algorithm (MVPA) called BMVPA is developed and implemented. Furthermore, ten scenarios were investigated using different wind speeds, terrain sizes with and without obstacles. For the same terrain but including obstacles, it was found that the energy cost increased due to the presence of obstacles that could limit the search space and consequently reduces the number of available options. The empirical results obtained using BMVPA were compared with those obtained using other well-known algorithms like the binary particle swarm optimization and genetic algorithm. BMVPA showed better results in solving the WFLO problem than the comparative algorithms. The optimum design of the wind farm obtained will allow an efficient and economic exploitation of wind resource. Wind farm, layout design, wind energy, optimization.

IEEE Access, 2020

Since the last decade, power systems have been evolving dynamically due to smart grid technologie... more Since the last decade, power systems have been evolving dynamically due to smart grid technologies. In this context, energy management and optimal scheduling of different resources are very important. The main objective of this paper is to study the optimal scheduling of distributed energy resources (OSDER) problem. This problem is a challenging, complex and very large-scale mixed-integer non-linear programming (MINLP) problem. Its complexity escalates with incorporation of uncertain and intermittent renewable sources, electric vehicles, variable loads and markets which makes it hard to be solved using traditional optimization algorithms and solvers. However, it can be handled efficiently and without approximation or modification of the original formulation using modern optimization algorithms such as metaheuristics. In this paper, an improved version of the variable neighborhood search (IVNS) algorithm is proposed to solve the OSDER problem. The proposed algorithm was tested on two large-scale centralized day-ahead energy resource scenarios. In the first scenario, the 12.66 kV, 33-bus test system with a total of 49,920 design variables is used whilst in the second scenario, the 30 kV, 180-bus test system is used with a total of 154,800 design variables. The optimization results using the proposed algorithm were compared with five existing optimization algorithms, i.e., chaotic biogeography-based optimization (CBBO), cross-entropy method and evolutionary PSO (CEEPSO), chaotic differential evolution with PSO (Chaotic-DEEPSO), Levy differential evolution with PSO (Levy-DEEPSO), and the variable neighborhood search (VNS). For the first test system, the IVNS has achieved a score of -5598.89 while for the second test system it has achieved a score of -3180.15. A comparative study of the results has shown that the proposed IVNS algorithm performs better than the remaining algorithms for both cases. INDEX TERMS Distributed energy resources, large-scale optimization, smart grids, variable neighborhood search. Real part of the admittance of a line C DG (I ,t) Costs of generation of distributed unit (DGU) I in period t C Discharge(E,t) Costs of discharging of energy storage unit (ESU) E in period t C Discharge(V ,t) Costs of discharging of electric vehicle (EV) V in period t The associate editor coordinating the review of this manuscript and approving it for publication was Giacomo Verticale . Costs of curtailment of DGU I in period t C LoadDR (L,t) Costs of load reduction (DR) of load L in period t C NSD (L,t) Costs of non-supplied demand (NSD) of load L in period t C Supplier (S,t) Costs of external supplier S in period t E BatCap(V ) Battery energy capacity of EV V E MinCharge(V ,t) Minimum stored energy to be guaranteed for the EV V at the end of period t E Stored

Sustainability, 2019

Journal of Renewable and Sustainable Energy, 2017

This paper analyzes the electricity production potential and economic viability of grid-connected... more This paper analyzes the electricity production potential and economic viability of grid-connected wind/photovoltaic (PV) energy systems at two coastal cities, Yanbu and Dhahran in Saudi Arabia. First, wind energy is assessed based on the hourly wind speed observation data recorded over the entire year 2013 in the selected locations. Electricity generation potential is estimated using two wind turbines: Vestas V82 and V90 models. The results indicate that both locations have sufficient wind resources for wind turbine operation. Strong wind resources are more common at Dhahran than at Yanbu with wind speeds above 3.5 m/s, accounting for 60.12% of the wind data at Dhahran, which is higher than 51.2% of Yanbu. Grid-connected hybrid systems using Vestas V90 wind turbines had the highest net present cost (NPC) compared with other configurations. The inclusion of battery storage units slightly increases the NPC. Surprisingly, systems with the highest NPC produced the least electricity. In ...

IET Renewable Power Generation, 2018

The amount of solar energy incidence on a photovoltaic (PV) panel depends on the PV tilt angles w... more The amount of solar energy incidence on a photovoltaic (PV) panel depends on the PV tilt angles with respect to the horizon. It is thus crucial to investigate the optimum tilt angles to maximise the efficiency of PV panels and at the same time to increase the performance of solar energy systems. The objective of this study is to estimate the optimum tilt angle for PV panels in order to collect the maximum solar radiation for the city of Dhahran in Saudi Arabia. A newly developed optimisation algorithm called the vortex search algorithm is used to estimate the solar radiation on the tilted surface. Moreover, one year can be divided into different periods in the proposed approach, and the optimum angle can be obtained for each one of these periods separately. The horizontal solar data (i.e. direct, diffuse and global solar radiation) is used to estimate the optimum tilt angle. The results demonstrate that the solar radiation estimated using the optimum tilt angle is maximised compared with the one estimated on a horizontal surface.

Sustainable Cities and Society, 2018

1. Optimization approaches for hybrid distributed generation systems was reviewed. 2. AI techniqu... more 1. Optimization approaches for hybrid distributed generation systems was reviewed. 2. AI techniques are dominating the techniques used for optimization of DEG systems. 3. The objective functions are maximum reliability and optimum operation schedule. 4. Developments are undertaken to improve the operational efficiency in implementation.

Analyzing the potential and progress of distributed generation applications in Saudi Arabia: The case of solar and wind resources

Renewable and Sustainable Energy Reviews, 2017

In this paper, the potential of solar and wind energy-based distributed generation (DG) in Saudi ... more In this paper, the potential of solar and wind energy-based distributed generation (DG) in Saudi Arabia is simultaneously analyzed with the aim of maximizing the utilization of available resources. It begins with an analysis of DG application potential for wind and solar energy resources in various regions of Saudi Arabia. The progress of DG applications in terms of research, planning, and exploitation of wind and solar energy resources is then presented. An assessment of the DG contribution to the energy sector of Saudi Arabia has been conducted and the barriers and challenges for the implementation of DG systems in the country are discussed with suggested measures to overcome the challenges. The main findings are that with the huge potential of wind and solar resources for DG applications the country has targeted 50GW of wind and solar capacity by the year 2040. The Saudi government is expected to provide full support in the form of financial incentives for solar and wind energy projects in order to boost renewable energy development.

IEEE Access

Frontiers in Energy Research, Mar 18, 2022

Sustainability, Jul 14, 2019

Electronics, Jun 30, 2022

Sustainability

Electronics

Sustainability

Frontiers in Energy Research

IEEE Access

Energies, 2021

Frontiers in Energy Research, 2021

Energy Reports, 2021

IEEE Access, 2020

Sustainability, 2019

Journal of Renewable and Sustainable Energy, 2017

IET Renewable Power Generation, 2018

Sustainable Cities and Society, 2018

Analyzing the potential and progress of distributed generation applications in Saudi Arabia: The case of solar and wind resources

Renewable and Sustainable Energy Reviews, 2017