Bio: Khaled Sedraoui is an academic researcher from King Abdulaziz University. The author has contributed to research in topics: Computer science & Photovoltaic system. The author has an hindex of 9, co-authored 23 publications receiving 545 citations.
TL;DR: This paper formulates a multi-objective load scheduling problem to minimize two competing objectives: 1) potential serious peak-to-valley difference and 2) economic loss and proposes a weight aggregation (WA) strategy and implements a novel MOEA algorithm named WA-MOPSO by incorporating WA into MOPSO to solve the problem.
Abstract: In order to protect the environment and slow down global warming trend, many governments and environmentalists are keen at promoting the use of plug-in hybrid electric vehicles (PHEVs). As a result, more and more PHEVs have been put into use. However, load peak caused by their disordered charging can be detrimental to an entire power grid. Several methods have been proposed to establish ordered PHEV charging. While focusing on single-objective load scheduling, they fail to meet the real requirements that need one to conduct multiple objective optimization. This paper formulates a multi-objective load scheduling problem to minimize two competing objectives: 1) potential serious peak-to-valley difference and 2) economic loss. When we apply existing multi-objective evolutionary algorithms (MOEAs), i.e., multi-objective particle swarm optimization (MOPSO), Nondominated Sorting Genetic Algorithm II, MOEA based on decomposition, and multi-objective differential evolutionary algorithm to solve it, because its high dimension and special conditions we find that they fail to reach the Pareto Front or converge into a relatively small area only. Therefore, we propose a weight aggregation (WA) strategy and implement a novel MOEA algorithm named WA-MOPSO by incorporating WA into MOPSO to solve the problem. Its effectiveness and efficiency to generate a Pareto front of this problem are verified and compared with those of the state-of-the-art approaches. Furthermore, WA is also combined with other MOEAs to solve the defined scheduling problem.
TL;DR: In this paper, the optimal array and inverter size for a grid-connected PV system was analyzed for serving electricity in Makkah, Saudi Arabia, with an optimal inverter ratio of R = 1 with minimized CO2 emissions.
Abstract: Resource optimization is a major factor in the assessment of the effectiveness of renewable energy systems. Various methods have been utilized by different researchers in planning and sizing the grid-connected PV systems. This paper analyzes the optimal photovoltaic (PV) array and inverter sizes for a grid-connected PV system. Unmet load, excess electricity, fraction of renewable electricity, net present cost (NPC) and carbon dioxide (CO2) emissions percentage are considered in order to obtain optimal sizing of the grid-connected PV system. An optimum result, with unmet load and excess electricity of 0%, for serving electricity in Makkah, Saudi Arabia is achieved with the PV inverter size ratio of R = 1 with minimized CO2 emissions. However, inverter size can be downsized to 68% of the PV nominal power to reduce the inverter cost, and hence decrease the total NPC of the system.
TL;DR: In this paper, three new physical PV array arrangements are proposed to mitigate partial shading effects, based on maximizing the distance between adjacent PV modules within a PV array by appropriately arranging modules in different rows and columns without changing the electrical connections.
Abstract: Partial shading can dramatically reduce the power output of a PV array as well as complicate operation by causing multiple peaks to appear in the power-voltage (P-V) characteristic curve. The extent of these problems depends not only on the shading area but also and much more significantly on the shading pattern. In this paper three new physical PV array arrangements are proposed to mitigate partial shading effects. The arrangements are based on maximizing the distance between adjacent PV modules within a PV array by appropriately arranging modules in different rows and columns without changing the electrical connections. A systematic analysis is performed to assess the proposed PV array arrangements under different shading patterns and scenarios, and to compare performance with existing configurations. The new arrangements are shown to effectively (i) redistribute shading patterns over the entire PV array, (ii) minimize protection diodes power dissipation, (iii) eliminate multiple peaks, and (iv) maximize power output. The analysis considers shading scenarios related to cloud shape, size, transmissivity and passage over an array. The new configurations simplify operation and improve performance significantly compared to the reference Series-Parallel (SP) and Total Cross Tied (TCT) configurations: the characteristic P-V curves exhibit a single peak allowing tracking of the maximum power point with a simple controller removing the need for complex controller algorithms and costly hardware, and the power output gains range from 19 to 140% compared to SP, and 13 to 68% compared to TCT.
TL;DR: This paper discusses the recent progress of disassembly sequencing planning in four major aspects: product disassembly modeling methods, mathematical programming methods, artificial intelligence methods, and uncertainty handling.
Abstract: It is well-recognized that obsolete or discarded products can cause serious environmental pollution if they are poorly be handled. They contain reusable resource that can be recycled and used to generate desired economic benefits. Therefore, performing their efficient disassembly is highly important in green manufacturing and sustainable economic development. Their typical examples are electronic appliances and electromechanical/mechanical products. This paper presents a survey on the state of the art of disassembly sequence planning. It can help new researchers or decision makers to search for the right solution for optimal disassembly planning. It reviews the disassembly theory and methods that are applied for the processing, repair, and maintenance of obsolete/discarded products. This paper discusses the recent progress of disassembly sequencing planning in four major aspects: product disassembly modeling methods, mathematical programming methods, artificial intelligence methods, and uncertainty handling. This survey should stimulate readers to be engaged in the research, development and applications of disassembly and remanufacturing methodologies in the Industry 4.0 era.
TL;DR: In this paper, the effects of dust accumulation and weather conditions on PV panel power output were analyzed after one to four weeks of exposure, and the results revealed that local environmental conditions, i.e., dust, rain, and partial cloud, significantly reduce PV power output.
Abstract: Certain environmental conditions such as accumulation of dust and change in weather conditions affect the amount of solar radiation received by photovoltaic (PV) panel surfaces and thus have a significant effect on panel efficiency. This study conducted an experimental investigation in Surabaya, Indonesia, on the effect of these factors on output PV power reduction from the surface of a PV module. The module was exposed to outside weather conditions and connected to a measurement system developed using a rule-based model to identify different environmental conditions. The rule-based model, a clear sky solar irradiance model that included solar position, and a PV temperature model were then used to estimate the PV output power, and tests were also conducted using an ARM Cortex-M4 microcontroller STM32F407 as a standalone digital controller equipped with voltage, current, temperature, and humidity sensors to measure real time PV output power. In this system, humidity was monitored to identify dusty, cloudy, and rainy conditions. Validated test results demonstrate that the prediction error of PV power output based on the model is 3.6% compared to field measurements under clean surface conditions. The effects of dust accumulation and weather conditions on PV panel power output were then analyzed after one to four weeks of exposure. Results revealed that two weeks of dust accumulation caused a PV power output reduction of 10.8% in an average relative humidity of 52.24%. Results of the experiment under rainy conditions revealed a decrease in PV output power of more than 40% in average relative humidity of 76.32%, and a decrease in output power during cloudy conditions of more than 45% in an average relative humidity of 60.45% was observed. This study reveals that local environmental conditions, i.e., dust, rain, and partial cloud, significantly reduce PV power output.
TL;DR: A review of the state-of-the-art of researches which use HOMER for optimal planning of hybrid renewable energy systems is presented in this paper, where the authors present the most powerful tools for this purpose is Hybrid Optimization Model for Electric Renewables (HOMER) software that was developed by National Renewable Energy Laboratory (NREL).
Abstract: World energy consumption is rising due to population growth and increasing industrialization. Traditional energy resources cannot meet these requirements with notice to their challenges, e.g., greenhouse gas emission and high lifecycle costs. Renewable energy resources are the appropriate alternatives for traditional resources to meet the increasing energy consumption, especially in electricity sector. Integration of renewable energy resources with traditional fossil-based resources besides storages creates Hybrid Renewable Energy Systems (HRESs). To access minimum investment and operation costs and also meet the technical and emission constraints, optimal size of HRES׳s equipment should be determined. One of the most powerful tools for this purpose is Hybrid Optimization Model for Electric Renewables (HOMER) software that was developed by National Renewable Energy Laboratory (NREL), United States. This software has widely been used by many researchers around the world. In this paper a review of the state-of-the-art of researches, which use HOMER for optimal planning of HRES, is presented.
TL;DR: In this article, a large-scale assessment of the relevant energy literature was conducted to better understand energy-related interactions between SDGs, as well as their context-dependencies (relating to time, geography, governance, technology, and directionality).
Abstract: The United Nations' Sustainable Development Goals (SDGs) provide guide-posts to society as it attempts to respond to an array of pressing challenges One of these challenges is energy; thus, the SDGs have become paramount for energy policy-making Yet, while governments throughout the world have already declared the SDGs to be 'integrated and indivisible', there are still knowledge gaps surrounding how the interactions between the energy SDG targets and those of the non-energy-focused SDGs might play out in different contexts In this review, we report on a large-scale assessment of the relevant energy literature, which we conducted to better our understanding of key energy-related interactions between SDGs, as well as their context-dependencies (relating to time, geography, governance, technology, and directionality) By (i) evaluating the nature and strength of the interactions identified, (ii) indicating the robustness of the evidence base, the agreement of that evidence, and our confidence in it, and (iii) highlighting critical areas where better understanding is needed or context dependencies should be considered, our review points to potential ways forward for both the policy making and scientific communities First, we find that positive interactions between the SDGs outweigh the negative ones, both in number and magnitude Second, of relevance for the scientific community, in order to fill knowledge gaps in critical areas, there is an urgent need for interdisciplinary research geared toward developing new data, scientific tools, and fresh perspectives Third, of relevance for policy-making, wider efforts to promote policy coherence and integrated assessments are required to address potential policy spillovers across sectors, sustainability domains, and geographic and temporal boundaries The task of conducting comprehensive science-to-policy assessments covering all SDGs, such as for the UN's Global Sustainable Development Report, remains manageable pending the availability of systematic reviews focusing on a limited number of SDG dimensions in each case
TL;DR: This paper applies advanced battery modeling and multiobjective constrained nonlinear optimization techniques to derive suitable charging patterns for lithium-ion batteries and demonstrates that the proposed strategy can effectively offer feasible health-conscious charging with desirable tradeoffs among charging speed and energy conversion efficiency under different demand priorities.
Abstract: This paper applies advanced battery modeling and multiobjective constrained nonlinear optimization techniques to derive suitable charging patterns for lithium-ion batteries. Three important yet competing charging objectives, including battery health, charging time, and energy conversion efficiency, are taken into account simultaneously. These optimization objectives are first subject to a high-fidelity battery model that is synthesized from recently developed individual electrical, thermal, and aging models. The coupling relationship and multiple timescales among different model dynamics are identified. Furthermore, constraints are imposed explicitly on the current, voltage, state-of-charge, and temperature. Such a complex charging problem is solved by using an ensemble multiobjective biogeography-based optimization approach. As a result, two charging patterns, namely the constant current–constant voltage (CC–CV) and multistage CC–CV, are optimized to balance various combinations of charging objectives. Different tradeoffs and sensitive elements are compared and analyzed based on the Pareto frontiers. Illustrative results demonstrate that the proposed strategy can effectively offer feasible health-conscious charging with desirable tradeoffs among charging speed and energy conversion efficiency under different demand priorities.
TL;DR: This paper discretizes a novel and simple metaheuristic, named Jaya, resulting in DJaya, and improves it to solve FJRP for new job insertion arising from pump remanufacturing, and proposes five objective-oriented local search operators and four ensembles of them to improve the performance of DJaya.
Abstract: Rescheduling is a necessary procedure for a flexible job shop when newly arrived priority jobs must be inserted into an existing schedule. Instability measures the amount of change made to the existing schedule and is an important metrics to evaluate the quality of rescheduling solutions. This paper focuses on a flexible job-shop rescheduling problem (FJRP) for new job insertion. First, it formulates FJRP for new job insertion arising from pump remanufacturing. This paper deals with bi-objective FJRPs to minimize: 1) instability and 2) one of the following indices: a) makespan; b) total flow time; c) machine workload; and d) total machine workload. Next, it discretizes a novel and simple metaheuristic, named Jaya, resulting in DJaya and improves it to solve FJRP. Two simple heuristics are employed to initialize high-quality solutions. Finally, it proposes five objective-oriented local search operators and four ensembles of them to improve the performance of DJaya. Finally, it performs experiments on seven real-life cases with different scales from pump remanufacturing and compares DJaya with some state-of-the-art algorithms. The results show that DJaya is effective and efficient for solving the concerned FJRPs.
TL;DR: In this paper, the authors present a review of the effect of environmental conditions on photovoltaic (PV) module performance, in particular, the impact of dust fouling.
Abstract: The mitigation of environmental effects on clean-energy technology is an area of increasing interest. Photovoltaic (PV) modules have been widely used in small and large-scale applications for many years. However, they are not yet competitive with other electrical energy-generation technologies, especially in environments that suffer from dust, airborne particles, humidity and high ambient temperatures. This paper presents a review of the effect of climatic conditions on PV module performance, in particular, the effect of dust fouling. Research to date indicates that dust deposition has a considerable effect on PV module performance as it reduces the light transmissivity of the PV module surface cover. Studies on the ways in which dust is deposited on PV module surfaces are reviewed, as understanding this process is essential to develop effective mitigation approaches. Module performance is also adversely affected by high ambient temperature, humidity and lack of rainfall. The current review summarizes the past, current and promising future approaches towards mitigating environmental effects, in particular dust fouling. Electrostatic cleaning methods and micro/nanoscale surface functionalization methods both have the potential to counteract the negative effects of dust deposition, with the combination of the two methods showing special efficacy, particularly in arid regions.