Design optimization of grid-connected PV-Hydrogen for energy prosumers considering sector-coupling paradigm: Case study of a university building in Algeria

Integrating sector coupling technologies into Hydrogen (H2) based hybrid renewable energy systems (HRES) is becoming a promising way to create energy prosumers, despite the very little research work being done in this largely unexplored field. In this paper, a sector coupling strategy (building and transportation) is developed and applied to a grid-connected PV/battery/H2 HRES, to maximise self-sufficiency for a University campus and to produce power and H2 for driving electric tram in Ouargla, Algeria. A multi-objective size optimization problem is solved as a single objective problem using the ε-constraint method, in which the cost of energy (COE) is defined as the main objective function to be minimized, while both loss of power supply probability (LPSP) and non-renewable usage (NRU) are defined as constraints. Particle swarm optimization and HOMER software are then employed for simulation and optimization purposes. Prior to the two scenarios investigated, a sensitivity study is performed to determine the effects of H2 demand by tram and NRU on the techno-economic feasibility of the proposed system, followed by a new reliability factor introduced in the optimization, namely loss of H2 supply probability (LHSP). The results of the first scenario show that by setting NRUmax = 100%, the system without H2 provides the best solution with COE of 0.016 $/kWh that reaches grid parity and has 13$/kWh. In the second scenario, it is also observed that an increased number of trams (i.e. increased H2 demands) causes a significant reduction in LHSP, COE, NRU and CO2 emissions. It is thus concluded that the grid/PV combination is the optimal choice for the studied system when considering economic aspects. However, taking into account the growing requirements of future energy systems, grid-connected PV with H2 will be the best solution

Deformation forecasting of a hydropower dam by hybridizing a long short-term memory deep learning network with the coronavirus optimization algorithm

The safety operation and management of hydropower dam play a critical role in social-economic development and ensure people’s safety in many countries; therefore, modeling and forecasting the hydropower dam’s deformations with high accuracy is crucial. This research aims to propose and validate a new model based on deep learning long short-term memory (LSTM) and the coronavirus optimization algorithm (CVOA), named CVOA-LSTM, for forecasting the defor mations of the hydropower dam. The second-largest hydropower dam of Viet nam, located in the Hoa Binh province, is focused. Herein, we used the LSTM to establish the deformation model, whereas the CVOA was utilized to opti mize the three parameters of the LSTM, the number of hidden layers, the learn ing rate, and the dropout. The efficacy of the proposed CVOA-LSTM model is assessed by comparing its forecasting performance with state-of-the-art bench marks, sequential minimal optimization for support vector regression, Gaussian process, M5’ model tree, multilayer perceptron neural network, reduced error pruning tree, random tree, random forest, and radial basis function neural net work. The result shows that the proposed CVOA-LSTM model has high fore casting capability (R2 = 0.874, root mean square error = 0.34, mean absolute error = 0.23) and outperforms the benchmarks. We conclude that CVOA-LSTM is a new tool that can be considered to forecast the hydropower dam’s deforma tions.Ministerio de Ciencia, Innovación y Universidades PID2020-117954RB-C2

An insight into the integration of distributed energy resources and energy storage systems with smart distribution networks using demand-side management

Demand-side management (DSM) is a significant component of the smart grid. DSM without sufficient generation capabilities cannot be realized; taking that concern into account, the integration of distributed energy resources (solar, wind, waste-to-energy, EV, or storage systems) has brought effective transformation and challenges to the smart grid. In this review article, it is noted that to overcome these issues, it is crucial to analyze demand-side management from the generation point of view in considering various operational constraints and objectives and identifying multiple factors that affect better planning, scheduling, and management. In this paper, gaps in the research and possible prospects are discussed briefly to provide a proper insight into the current implementation of DSM using distributed energy resources and storage. With the expectation of an increase in the adoption of various types of distributed generation, it is estimated that DSM operations can offer a valuable opportunity for customers and utility aggregators to become active participants in the scheduling, dispatch, and market-oriented trading of energy. This review of DSM will help develop better energy management strategies and reduce system uncertainties, variations, and constraints

Applied Metaheuristic Computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC

Entropy and Exergy in Renewable Energy

Lovelock identified Newcomen’s atmospheric steam engine as the start of Anthropocene with these words: “…there have been two previous decisive events in the history of our planet. The first was … when photosynthetic bacteria first appeared [conversing sunlight to usable energy]. The second was in 1712 when Newcomen created an efficient machine that converted the sunlight locked in coal directly into work.” This book is about the necessity of energy transition toward renewables that convert sunlight diurnally, thus a sustainable Anthropocene. Such an energy transition is equally momentous as that of the kick start of the second Industrial Revolution in 1712. Such an energy transition requires “it takes a village” collective effort of mankind; the book is a small part of the collective endeavor

Water Quality Assessments for Urban Water Environment

This special issue entitled “Water Quality Assessments for Urban Water Environment,” strives to highlights the status quo of water environment, opportunities and challenges for their sustainable management in lieu of rapid global changes (land us eland cover changes, climate change, population growth, change in socio-economic dimension, urbanization etc.), in the urban space particularly in developing nations around the world. It also highlights the effect of COVID19 pandemic on water resources and way forward to minimize the risk of spreading health risk associated with wastewater management. Considering the complex nature of the urban water security, it highlights the importance of emerging approaches like socio-hydrology, landscape ecology, regional-circular-ecological sphere etc., which presents a perfect combination of hard (infrastructure) and soft (numerical simulations, spatial technologies, participatory approaches, indigenous knowledge) measures, as the potential solutions to manage this precious water resource in coming future. Finally, what is the way forward to enhance science-policy interface in a better way to achieve global goals e.g., SDGs at local level in a timely manner. It provides valuable information about sustainable water resource management at the urban landscape, which is very much useful for policy-makers, decision-makers, local communities, and other relevant stakeholders

Green Technologies for Production Processes

This book focuses on original research works about Green Technologies for Production Processes, including discrete production processes and process production processes, from various aspects that tackle product, process, and system issues in production. The aim is to report the state-of-the-art on relevant research topics and highlight the barriers, challenges, and opportunities we are facing. This book includes 22 research papers and involves energy-saving and waste reduction in production processes, design and manufacturing of green products, low carbon manufacturing and remanufacturing, management and policy for sustainable production, technologies of mitigating CO2 emissions, and other green technologies

Data-Intensive Computing in Smart Microgrids

Microgrids have recently emerged as the building block of a smart grid, combining distributed renewable energy sources, energy storage devices, and load management in order to improve power system reliability, enhance sustainable development, and reduce carbon emissions. At the same time, rapid advancements in sensor and metering technologies, wireless and network communication, as well as cloud and fog computing are leading to the collection and accumulation of large amounts of data (e.g., device status data, energy generation data, consumption data). The application of big data analysis techniques (e.g., forecasting, classification, clustering) on such data can optimize the power generation and operation in real time by accurately predicting electricity demands, discovering electricity consumption patterns, and developing dynamic pricing mechanisms. An efficient and intelligent analysis of the data will enable smart microgrids to detect and recover from failures quickly, respond to electricity demand swiftly, supply more reliable and economical energy, and enable customers to have more control over their energy use. Overall, data-intensive analytics can provide effective and efficient decision support for all of the producers, operators, customers, and regulators in smart microgrids, in order to achieve holistic smart energy management, including energy generation, transmission, distribution, and demand-side management. This book contains an assortment of relevant novel research contributions that provide real-world applications of data-intensive analytics in smart grids and contribute to the dissemination of new ideas in this area

Planning a Renewable Power System in Texas as an Introduction to Smart Power Grid

Design electrical systems from six renewable energy sources: photovoltaic, wind energy, geothermal, concentrated solar energy, biomass energy, and hydropower in addition to a storage system in the state of Texas, This power system converts the electric system in Texas into a 100 % renewable energy power system. Optimization technique has applied to the results to make the system economical and reduce the wasting resources, this system is considered as decentralized as well which is a great advantage for achieving the smart grid technology compared with the conventional plants where the generation parts are deposed in a small part of the grid, this design makes each part of the grid have two roles as a generator as well as load. The storage system relies on the heat storage of traditional batteries and concentrated solar power plants. Hence this power system could reduce the greenhouse gases by more than 90 %, the annual electricity bill in Texas could be decreased by amount form 10-20 billion dollars yearly, and finally, achieve a higher level of security and reliability of the system by applying the smart grid concept

Applied Methuerstic computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC