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Optimal Sizing and Techno-Economic Analysis of Grid-Independent Hybrid Energy System for Sustained Rural Electrification in Developing Countries: A Case Study in Bangladesh
The absence of electricity is among the gravest problems preventing a nation’s development. Hybrid renewable energy systems (HRES) play a vital role to reducing this issue. The major goal of this study is to use the non-dominated sorting genetic algorithm (NSGA)-II and hybrid optimization of multiple energy resources (HOMER) Pro Software to reduce the net present cost (NPC), cost of energy (COE), and CO2 emissions of proposed power system. Five cases have been considered to understand the optimal HRES system for Kutubdia Island in Bangladesh and analyzed the technical viability and economic potential of this system. To demonstrate the efficacy of the suggested strategy, the best case outcomes from the two approaches are compared. The study’s optimal solution is also subjected to a sensitivity analysis to take into account fluctuations in the annual wind speed, solar radiation, and fuel costs. According to the data, the optimized PV/Wind/Battery/DG system (USD 711,943) has a lower NPC than the other cases. The NPC obtained by the NSGA-II technique is 2.69% lower than that of the HOMER-based system.
Journal Article
Doing ‘business as usual’ comes with a cost
Salinization of agricultural lands is a major threat to agriculture. Many different factors affect and determine plant salt tolerance. Nonetheless, there is a consensus on the relevance of maintaining an optimal cytosolic potassium : sodium ion (K⁺ : Na⁺) ratio for salinity tolerance in plants. This ratio depends on the operation of plasma membrane and tonoplast transporters. In the present review we focus on some aspects related to the energetic cost of maintaining that K⁺ : Na⁺ ratio. One of the factors that affect the cost of the first step of K⁺ acquisition – root K⁺ uptake through High Affinity K⁺ transporter and Arabidopsis K⁺ transport system 1 transport systems – is the value of the plasma membrane potential of root cells, a parameter that may differ amongst plant species. In addition to its role in nutrition, cytosolic K⁺ also is important for signalling, and K⁺ efflux through gated outward-rectifying K⁺ and nonselective cation channels can be regarded as a switch to redirect energy towards defence reactions. In maintaining cytosolic K⁺, the great buffer capacity of the vacuole should be considered. The possible role of high-affinity K⁺ transporters (HKT)2s in mediating K⁺ uptake under saline conditions and the importance of cycling of K⁺ throughout the plant also are discussed.
Journal Article
Cost Estimation of Polymeric Adsorbents
One of the most promising techniques of recent research is adsorption. This technique attracts great attention in environmental technology, especially in the decontamination of water and wastewaters. A “hidden” point of the above is the cost of adsorbents. As can be easily observed in the literature, there is not any mention about the synthesis cost of adsorbents. What are the basic criteria with which an industry can select an adsorbent? What is the synthesis (recipe) cost? What is the energy demand to synthesize an efficient material? All of these are questions which have not been answered, until now. The reason for this is that the estimation of adsorbents’ cost is relatively difficult, because too many cost factors are involved (labor cost, raw materials cost, energy cost, tax cost, etc.). In this work, the first estimation cost of adsorbents is presented, taking into consideration all of the major factors which influence the final value. To be more comparable, the adsorbents used are from a list of polymeric materials which are already synthesized and tested in our laboratory. All of them are polymeric materials with chitosan as a substrate, which is efficiently used for the removal of heavy metal ions.
Journal Article
Performance Assessment and Comparative Analysis of Photovoltaic-Battery System Scheduling in an Existing Zero-Energy House Based on Reinforcement Learning Control
The development of distributed renewable energy resources and smart energy management are efficient approaches to decarbonizing building energy systems. Reinforcement learning (RL) is a data-driven control algorithm that trains a large amount of data to learn control policy. However, this learning process generally presents low learning efficiency using real-world stochastic data. To address this challenge, this study proposes a model-based RL approach to optimize the operation of existing zero-energy houses considering PV generation consumption and energy costs. The model-based approach takes advantage of the inner understanding of the system dynamics; this knowledge improves the learning efficiency. A reward function is designed considering the physical constraints of battery storage, photovoltaic (PV) production feed-in profit, and energy cost. Measured data of a zero-energy house are used to train and test the proposed RL agent control, including Q-learning, deep Q network (DQN), and deep deterministic policy gradient (DDPG) agents. The results show that the proposed RL agents can achieve fast convergence during the training process. In comparison with the rule-based strategy, test cases verify the cost-effectiveness performances of proposed RL approaches in scheduling operations of the hybrid energy system under different scenarios. The comparative analysis of test periods shows that the DQN agent presents better energy cost-saving performances than Q-learning while the Q-learning agent presents more flexible action control of the battery with the fluctuation of real-time electricity prices. The DDPG algorithm can achieve the highest PV self-consumption ratio, 49.4%, and the self-sufficiency ratio reaches 36.7%. The DDPG algorithm outperforms rule-based operation by 7.2% for energy cost during test periods.
Journal Article
Minimum-cost control of complex networks
Finding the solution for driving a complex network at the minimum energy cost with a given number of controllers, known as the minimum-cost control problem, is critically important but remains largely open. We propose a projected gradient method to tackle this problem, which works efficiently in both synthetic and real-life networks. The study is then extended to the case where each controller can only be connected to a single network node to have the lowest connection complexity. We obtain the interesting insight that such connections basically avoid high-degree nodes of the network, which is in resonance with recent observations on controllability of complex networks. Our results provide the first technical path to enabling minimum-cost control of complex networks, and contribute new insights to locating the key nodes from a minimum-cost control perspective.
Journal Article
Optimizing energy cost in the residential sector through home energy management systems in a smart grid environment
Worldwide energy demand is increasing exponentially, presenting significant challenges for existing power generation systems to meet this demand. Enhancing energy efficiency has become critical for reducing consumption and addressing the ongoing environmental crisis. Consequently, there is a need for smart control systems that optimize system costs and improve efficiency. Because of the introduction of smart grids, customers can now participate in demand-side management and integrate renewable energy sources (RESs). Electricity consumption during peak hours often leads to increased grid demand and higher costs. However, the integration of RESs enables consumers to operate appliances during peak hours, thereby reducing reliance on grid power. Therefore, residential load management seeks to reduce power peaks and electrical energy costs. In home energy management systems (HEMS), appliance scheduling is crucial because it continually monitors appliance usage, ensuring that energy supply and demand are balanced. This research aims to optimize power usage by reducing peak loads and electricity costs through the integration of RESs, such as solar or photovoltaic (PV) systems, while considering grid limitations, PV capacity, appliance ON/OFF schedules, and time-of-use tariffs. A genetic algorithm (GA) based optimization technique was employed to evaluate the performance of a HEMS and validated with particle swarm optimization (PSO) technique under identical initial conditions for each appliance and their corresponding energy pricing over different periods. The results show that GA achieved a 48% cost reduction compared to PSO, with significant peak load reduction and improved energy optimization when integrated with PV systems. GA also demonstrated better appliance scheduling, with appliances in the “ON” state for 82% of the time, compared to 52% with PSO.
Journal Article
Life cycle cost analysis (LCCA) of construction projects: sustainability perspective
Construction industry projects play a significant role in the sustainable economic growth of all other industries. To achieve a sustainable economy, the future associated costs act as a barrier that must be addressed in the initial stages of a construction project. To evaluate the future costs, Life Cycle Cost Analysis (LCCA) is found to be an effective technique that determines the present worth of future costs. This study focuses on reviewing the conducted research in the field of optimising cost during the project life cycle via LCCA to sustain economic sustainability and associating the environmental and social cost factors to enhance sustainability. A systematic literature review strategy is developed to extract relevant literature from Scopus, Web of Science, Science Direct, Emerald and American Society of Civil Engineering from the year 2009 to 2020. Adopting the PRISMA statement, a total of 83 articles are reviewed systematically in detail. Many construction sections are explored with the impact of LCCA on them. The LCCA impact the performance of construction projects during certain practices such as structural designing, energy cost optimisation, building envelope efficiency in energy demand and utilisation optimisation and earthquake engineering. Moreover, this study highlights the influence of LCCA in optimising the environmental impact of a new or existing construction project to avail economic sustainability along with the social and environmental. A conceptual framework has been proposed that shows the influence of LCCA on the construction industry, which directly impacts economic sustainability and indirectly environmental sustainability.
Journal Article
Optimal planning and performance estimation of renewable energy model for isolated hilly Indian area
Indian hilly areas are blessed with a lot of potentials of renewable energy sources, yet their residents are facing the problem of power unavailability. This makes their life even more difficult in these isolated areas and creates a hindrance in their holistic development. In this paper, an optimized solar photo voltaic (SPV)/wind energy conversion(WEC)/biogas generator (BGG)/biomass generator (BMG)/micro hydropower system (MHPS)/battery based integrated renewable energy model(IREM) is suggested for satisfying the energy need of different load sectors of the Mori Block, Uttarakashi district, Uttarakhand State, India. An extensive investigation has been done for accessing the demand and potential of locally available renewable energy sources. Genetic Algorithm (GA) technique has been utilized for optimizing various energy cost and emission parameters such as; cost of energy (CE), cost of generation (CG), cost of total energy (COTE), and CO2 emission. The suggested energy model is not only ensures the availability of reliable and economic power supply but also significantly limits the environmental degradation caused by the diesel generator operation in terms of CO2 emission. Further, the suggested optimization technique is also compared with other techniques for establishing its effectiveness in the field of isolated hilly areas electrification and holistic development.
Journal Article
Innovation and Spillover Effects of Energy Demand Shocks in Belt and Road Economies
The induced innovation hypothesis, initially proposed by Sir John Hicks, posits that as the cost of energy rises compared to other input factors, firms are motivated to engage in innovative practices to counteract the increased expenses related to energy consumption. This innovation can manifest through the development and implementation of technologies, processes, or methodologies that enhance energy efficiency or diminish overall energy dependency. In this study, we empirically examine and validate this hypothesis. By theoretically modeling how innovation responds to elevated energy costs, we exploit China’s substantial surge in energy demand as an external shock to global demand, to empirically test the predictions associated with our theoretical framework. We test these predictions using firm level data in Belt and Road Initiative (BRI) countries. Our findings strongly support the induced innovation hypothesis, revealing that, on average, a 1 percent rise in the relative cost of energy corresponds to a 2.1 to 5.1 percent increase in the likelihood of innovation in energy-exporting countries and a 0.5 to 3.6 percent increase in non-energy-exporting countries. These results are robust to various methodological variations and data restriction exercises.
JEL Classification: D22, D24, O13, O14
Journal Article