Terms Of Energy Cost Research Articles

Gas Hydrates as High-Efficiency Storage System: Perspectives and Potentialities

The growing economic efforts and investment for the production of green hydrogen make the definition of new competitive and environmentally friendly storage methods. This article deals with the proposal of gas hydrate production with binary or ternary H2-based gaseous mixtures for hydrogen storage. In the text, the physical and chemical elements necessary to confirm the technical feasibility of the process are given. The proposed solution is also compared with the traditional ones in terms of energy costs, energy density, environmental sustainability, safety, ease of transport, future perspectives, and innovation level.

Utility Fairness for the Differentially Private Federated-Learning-Based Wireless IoT Networks

Federated learning (FL) allows predictive model training on the sensed data in a wireless Internet of Things (IoT) network evading data collection cost in terms of energy, time, and privacy. In this article, for an FL setting, we model the learning gain achieved by an IoT device against its participation cost as its utility. The local model quality and the associated cost differ from device to device due to the device heterogeneity, which could be time varying. We identify that this results in utility unfairness because the same global model is shared among the devices. In the vanilla FL setting, the master is unaware of devices’ local model computation and transmission costs, thus, it is unable to address the utility unfairness problem. In addition, a device may exploit this lack of knowledge at the master to intentionally reduce its expenditure and thereby boost its utility. We propose to control the quality of the global model shared with the devices, in each round, based on their contribution and expenditure. This is achieved by employing differential privacy (DP) to curtail global model divulgence based on the learning contribution. Furthermore, we devise adaptive computation and transmission policies for each device to control its expenditure in order to mitigate utility unfairness. Our results show that the proposed scheme reduces the standard deviation of the energy cost of devices by 99% in comparison to the benchmark scheme, while the standard deviation of the training loss of devices varies around 0.103.

Estimating energy costs and greenhouse gas emissions efficiency in the provision of domestic water: An empirical application for England and wales

• Specific efficiency sores for energy costs and greenhouse gas emissions were estimated. • English and Welsh water companies could save 35.6% of the current energy costs. • Efficiency on greenhouse gas emissions was estimated at 41.5% level. Reducing greenhouse gas (GHG) emissions and energy costs has been one of the main challenges faced by the water sector. This study provides a quantification of energy costs and GHG emissions efficiency of a sample of the English and the Welsh water companies over the period of 2013-2018. In doing so, the multi-directional data envelopment analysis (DEA) method was employed, which allows the quantification of the potential savings in energy costs and GHG emissions, i.e. defining the targets to be set by the water regulators. In the second stage of the analysis, bootstrap techniques were applied to identify environmental variables influencing the performance of water companies. The results indicate that the efficiency of the English and the Welsh water companies was low since the average efficiency scores for energy costs and GHG emissions were 0.644 and 0.415, respectively. It reveals that the water sector might save 35.6% and 58.6% of the current energy costs and GHG emissions. The study's findings demonstrated that the source of raw water, the treatment required to produce drinking water and population density were the environmental variables influencing the efficiency of these water companies in terms of energy costs and GHG emissions. The evidence provided by this study is of great interest to water regulators and water companies to implement policies and measures towards a low-carbon urban water cycle.

A Comparative Review on Energy Storage Systems and Their Application in Deregulated Systems

Electrical energy is critical to the advancement of both social and economic growth. Because of its importance, the electricity industry has historically been controlled and operated by governmental entities. The power market is being deregulated, and it has been modified throughout time. Both regulated and deregulated electricity markets have benefits and pitfalls in terms of energy costs, efficiency, and environmental repercussions. In regulated markets, policy-based strategies are often used to deal with the costs of fossil fuel resources and increase the feasibility of renewable energy sources. Renewables may be incorporated into deregulated markets by a mix of regulatory and market-based approaches, as described in this paper, to increase the systems economic stability. As the demand for energy has increased substantially in recent decades, particularly in developing nations, the quantity of greenhouse gas emissions has increased fast, as have fuel prices, which are the primary motivators for programmers to use renewable energy sources more effectively. Despite its obvious benefits, renewable energy has considerable drawbacks, such as irregularity in generation, because most renewable energy supplies are climate-dependent, demanding complex design, planning, and control optimization approaches. Several optimization solutions have been used in the renewable-integrated deregulated power system. Energy storage technology has risen in relevance as the usage of renewable energy has expanded, since these devices may absorb electricity generated by renewables during off-peak demand hours and feed it back into the grid during peak demand hours. Using renewable energy and storing it for future use instead of expanding fossil fuel power can assist in reducing greenhouse gas emissions. There is a desire to maximize the societal benefit of a deregulated system by better using existing power system capacity through the implementation of an energy storage system (ESS). As a result, good ESS device placement offers innovative control capabilities in steady-state power flow regulation as well as dynamic stability management. This paper examines numerous elements of renewable integrated deregulated power systems and gives a comprehensive overview of the most current research breakthroughs in this field. The main objectives of the reviews are the maximization of system profit, maximization of social welfare and minimization of system generation cost and loss by optimal placement of energy storage devices and renewable energy systems. This study will be very helpful for the power production companies who want to build new renewable-based power plant by sighted the present status of renewable energy sources along with the details of several EES systems. The incorporation of storage devices in the renewable-incorporated deregulated system will provide maximum social benefit by supplying additional power to the thermal power plant with minimum cost.

Remittances and multidimensional energy poverty: Evidence from a household survey in Bangladesh

This paper explores the impact of remittances on multidimensional energy poverty in Bangladesh by employing a nationally representative Household Income and Expenditure Survey (HIES)-2016 with a sample size of 46,080 households. Remittance is one of the sources of household income that can be utilized for energy services. Energy poverty is generally described and quantified at the household level, exposing potential household characteristics in terms of energy costs and benefits and access to energy services. Controlling for the relevant characteristics of investigated households, mixed-effect linear regression, two-stage least square, and generalized ordered logistic regression reveal that households with remittance income have a significantly lower level of multidimensional energy poverty in Bangladesh. The outcomes of this work advocate that an upsurge in remittance inflow engenders a strong contribution toward alleviating energy poverty in Bangladesh and likely in other developing nations. National programs should be established for devising policies to promote migrant workers, lowering energy costs at the household level, and investing remittance income into modern energy technologies to ensure and enhance access to clean energy sources.

Energy management system for distribution networks integrating photovoltaic and storage units

<span>The concept of the optimization energy management system, developed in this work, is to determine the optimal combination of energy from several generation sources and to schedule their commitment, while optimizing the cost of energy, power losses and voltage drops. In order to achieve these objectives, the non-dominated sorting genetic Algorithm II (NSGA-II) was modified and applied to an IEEE 33-bus test network containing 10 photovoltaic power plants and 4 battery energy storage systems placed at optimal points in the network. To evaluate the system performance, the resolution was performed under several test conditions. Optimal Pareto solutions were classified using three decision-making methods, namely analytic hierarchy process (AHP), TOPSIS and entropy-TOPSIS, compared to each other for more accurate results. The simulation results obtained by NSGA-II and classified using entropy-TOPSIS showed a significant and considerable reduction in terms of energy cost, power losses and voltage drops while successfully meeting all constraints. In addition, the diversity of the results proved once again the robustness and effectiveness of the algorithm. A graphical interface was also developed to display all the decisions made by the algorithm, and all other information such as the states of power systems, voltage profiles, alarms, and history.</span>

Economic performance index assessment of an industrial wastewater treatment plant in terms of the European Green Deal: effect of greenhouse gas emissions

Abstract This study aims to determine the effect of greenhouse gas (GHG) emissions on economic performance in terms of energy costs for an industrial wastewater treatment plant. Also, the mitigation of GHG emissions aimed at using process modification to obtain possible reductions in energy costs. Optimum energy consumptions were reported for the minimum GHG emission using the Data Envelopment Analysis (DEA) and Monte Carlo simulation model. In this paper, a new empirical approach has been developed depending on the GHG emissions for estimating the economic performance of the wastewater treatment plants. The results revealed that nitrous oxide (N2O) emissions led to the highest energy costs among direct emissions. In the second stage of the study, the effects of design conditions on GHG emissions and energy costs were investigated. If the aeration tank is operated at 24 h of hydraulic retention time (HRT) and 22 days of solid retention time (SRT), then, on average, 27, 27.9, and 30.7% of reduction in energy costs in terms of direct carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O) emissions, respectively, is observed in the plant. These reductions corresponded to approximately 17.33 €/kWh of cost-saving in this plant.

Edge Computing Parallel Approach for Efficient Energy Sharing in a Prosumer Community

The transition towards more sustainable energy management can be supported by the diffusion of energy communities, i.e., coalitions of prosumers that are willing to exchange the energy produced locally. The optimization of energy management requires the solution of a prosumer problem that can become impractical when the number of users increases. This paper presents a parallel approach, based on an edge computing architecture, which is suitable for large communities. The users are partitioned into groups whose proportions, in terms of producers and consumers, mirror the composition of the whole community. The prosumer problems for the different groups are first solved separately and in parallel by local edge nodes. Then, the solutions are combined by a central entity to redistribute the energy among the groups and minimize the exchange of energy with the external grid. A set of experiments show that the parallel approach, when compared with an approach that solves the optimization problem in a single stage, leads to a notable reduction of computing resources, and becomes feasible in large communities for which the single-stage approach is impossible. Moreover, the achieved solution is close to the optimal solution in terms of energy costs.

Optimal Demand-Side Management Using Flat Pricing Scheme in Smart Grid

This work proposes a framework to solve demand-side management (DSM) problem by systematically scheduling energy consumption using flat pricing scheme (FPS) in smart grid (SG). The framework includes microgrid with renewable energy sources (solar and wind), energy storage systems, electric vehicles (EVs), and building appliances like time flexible, power flexible, and base/critical appliances. For the proposed framework, we develop an ant colony optimization (ACO) algorithm, which efficiently schedules smart appliances, and EVs batteries charging/discharging with microgrid and without (W/O) microgrid under FPS to minimize energy cost, carbon emission, and peak to average ratio (PAR). An integrated technique of enhanced differential evolution (EDE) algorithm and artificial neural network (ANN) is devised to predict solar irradiance and wind speed for accurate microgrid energy estimation. To endorse the applicability of the proposed framework, simulations are conducted. Moreover, the proposed framework based on the ACO algorithm is compared to mixed-integer linear programming (MILP) and W/O scheduling energy management frameworks in terms of energy cost, carbon emission, and PAR. The developed ACO algorithm reduces energy cost, PAR, and carbon emission by 23.69%, 26.20%, and 15.35% in scenario I, and 25.09%, 31.45%, and 18.50% in scenario II, respectively, as compared to W/O scheduling case. The results affirm the applicability of the proposed framework in aspects of the desired objectives.

Energetic Cost of Measurements Using Quantum, Coherent, and Thermal Light.

Quantum measurements are basic operations that play a critical role in the study and application of quantum information. We study how the use of quantum, coherent, and classical thermal states of light in a circuit quantum electrodynamics setup impacts the performance of quantum measurements, by comparing their respective measurement backaction and measurement signal to noise ratio per photon. In the strong dispersive limit, we find that thermal light is capable of performing quantum measurements with comparable efficiency to coherent light, both being outperformed by single-photon light. We then analyze the thermodynamic cost of each measurement scheme. We show that single-photon light shows an advantage in terms of energy cost per information gain, reaching the fundamental thermodynamic cost.

Ore Processing Technologies Applied to Industrial Waste Decontamination: A Case Study

The correct management of industrial waste, as well as being an environmental obligation, can also be used as an opportunity to reduce costs in terms of energy and raw material consumption. A large amount of waste sand is generated in foundries with a high content of pollutants adhering to its surface structure. In this study, the material utilized consists of a silicic sand that comes from a casting process, with a thin layer of fixed carbon on the surface of the particles. The objective is to remove this contaminant, in order to have clean sands for use in alternative processes, such as in glass raw material, green concrete, or in the recirculation of these in the same process. The mechanical action that is best for eliminating surface attached contaminants is abrasion. In this regard, two specific devices, commonly used in ore processing operations, were utilized to apply energy in a material in order to reach abrasion by attrition, but with different kinetic approaches: stirring in a slurry media and using a light milling, in both cases reducing the grinding media in order to avoid material fracture. The test performance evaluation is mainly focused on the decontamination efficiency, the sand mass recovery ratio, and the energy consumption. The results show that in all cases, liberation is reachable in different levels at different residence times. We were able to decrease the LOI content from 4% to less than 1%, combined with a near 85% recovery rate of clean sand in the case of stirring. In the case of light milling, the results are even better: the final product reached near 0.5% of LOI content, despite mass flow recovery potentially being less than 80%. Finally, we discuss whether energy consumption is the factor which decides the best alternative. The energy consumed ratio when comparing light milling with stirring is near 9:1, which is a significant amount when taking into account the importance of reducing energy consumption in today’s industry due to its economic and environmental impact.

Energy-Efficient Data Transmission in Mobility-Aware Wireless Networks

Data transmission scheme is an effective mode to improve the energy-efficiency in packet delivery. In this paper, we investigate the energy-efficient data transmission over reliable links and unreliable links in mobility-aware wireless networks. The network topologies in mobility-aware wireless networks are changed from one time slot to another, and they could be described by a sequence of static graphs. We first model these network topologies in a time period as a virtual space-time graph. On the virtual space-time graph, energy-efficient data transmission problems over reliable links and unreliable links are defined. The aim of the two data transmission problems is to find a spatial-temporal path with the minimum energy cost. Next, we propose an Energy-Efficient Data Transmission algorithm over Reliable Links (EEDT-RL) to find the optimal space-time path. Based on EEDT-RL, we also develop a heuristic data transmission protocol over unreliable links (named EEDT-UL), in which the path reliability is taken into consideration. Simulation results show that our proposed algorithms perform well in terms of energy cost and transmission count compared with some existing algorithms.

Study on the Optimal Dispatching Strategy of a Combined Cooling, Heating and Electric Power System Based on Demand Response

This paper proposes a combined cooling, heating and electric power (CCHP) system based on demand side response. In order to improve the economy of the system, a two-stage optimal scheduling scheme is proposed with the goal of minimizing the total operating cost of the system and maximizing user satisfaction. The optimal operation of the system was divided into two optimization problems, including the demand side and the supply side. In the first stage, combined with user satisfaction, from the new point of view that users are prone to excessive behavior due to time-of-use electricity prices, the cooling, heating and power load curves are optimized. In the second stage, an economic dispatch model that includes operating costs in terms of energy, maintenance and environment is established. An improved artificial bee colony (IABC) algorithm is used to solve the optimal energy production scheme based on the demand curves optimized in the first stage. Case studies are conducted to verify the efficiency of the proposed method. Compared with the CCHP system that does not consider demand response, this method reduced operation cost on typical days in summer and winter by 5.20% and 5.76%, respectively.

Intelligent Sensing and Computing in Wireless Sensor Networks for Multiple Target Tracking

With sixth generation (6G) communication technologies, target sensing can be finished in milliseconds. The mobile tracking-oriented Internet of Things (MTT-IoT) as a kind of emerging application network can detect sensor nodes and track targets within their sensing ranges cooperatively. Nevertheless, huge data processing and low latency demands put tremendous pressure on the conventional architecture where sensing data is executed in the remote cloud and the short transmission distance of 6G channels presents new challenges into the design of network topology. To cope with the above difficulties, this paper proposes a new resource allocation scheme to perform delicate node scheduling and accurate tracking in multitarget tracking mobile networks. The dynamic tracking problem is formulated as an infinite horizon Markov Decision Process (MDP), where the state space that considers energy consumption, system responding delay, and target important degree is extended. A model-free reinforcement learning is applied to obtain satisfied tracking actions by frequent iterations, in which smart agents interact with the complicated environment directly. The performance of each episode is evaluated by the action-value function in search of the optimal reward. Simulation results demonstrate that the proposed scheme shows excellent tracking performance in terms of energy cost and tracking delay.

Effect of Haptic Training During Manual Wheelchair Propulsion on Shoulder Joint Reaction Moments.

BackgroundManual wheelchair propulsion remains a very ineffective means of locomotion in terms of energy cost and mechanical efficiency, as more than half of the forces applied to the pushrim do not contribute to move the wheelchair forward. Manual wheelchair propulsion training using the haptic biofeedback has shown an increase in mechanical efficiency at the handrim level. However, no information is available about the impact of this training on the load at the shoulders. We hypothesized that increasing propulsion mechanical efficiency by 10% during propulsion would not yield clinically significant augmentation of the load sustained at the shoulders.MethodsEighteen long-term manual wheelchair users with a spinal cord injury propelled a manual wheelchair over a wheelchair simulator offering the haptic biofeedback. Participants were asked to propel without the Haptic Biofeedback (HB) and, thereafter, they were subjected to five training blocks BL1–BL5 of 3 min in a random order with the haptic biofeedback targeting a 10% increase in force effectiveness. The training blocs such as BL1, BL2 BL3, BL4, and BL5 correspond, respectively, to a resistant moment of 5, 10, 15, 20, and 25%. Pushrim kinetics, shoulder joint moments, and forces during the propulsive cycle of wheelchair propulsion were assessed for each condition.ResultsThe tangential force component increases significantly by 74 and 87%, whereas value for the mechanical effective force increases by 9% between the pretraining and training blocks BL3. The haptic biofeedback resulted in a significant increase of the shoulder moments with 1–7 Nm.ConclusionIncreases in shoulder loads were found for the corresponding training blocks but even though the percentage of the increase seems high, the amplitude of the joint moment remains under the values of wheelchair propulsion found in the literature. The use of the HB simulator is considered here as a safe approach to increase mechanical effectiveness. However, the longitudinal impact of this enhancement remains unknown for the impact on the shoulder joint. Future studies will be focused on this impact in terms of shoulder risk injury during manual wheelchair propulsion.

Framework for Mapping and Optimizing the Solar Rooftop Potential of Buildings in Urban Systems

The accurate prediction of the solar energy that can be generated using the rooftops of buildings is an essential tool for many researchers, decision makers, and investors for creating sustainable cities and societies. This study is focused on the development of an automated method to extract the useable areas of rooftops and optimize the solar PV panel layout based on the given electricity loading of a building. In this context, the authors of this article developed two crucial methods. First, a special pixel-based rooftop recognition methodology was developed to analyze detailed and complex rooftop types while avoiding the challenges associated with the nature of the particular building rooftops. Second, a multi-objective enveloped min–max optimization algorithm was developed to maximize solar energy generation and minimize energy cost in terms of payback based on the marginal price signals. This optimization algorithm facilitates the optimal integration of three controlled variables—tilt angle, azimuth angle, and inter-row spacing—under a non-linear optimization space. The performance of proposed algorithms is demonstrated using three campus buildings at the University of Alberta, Edmonton, Alberta, Canada as case studies. It is shown that the proposed algorithms can be used to optimize PV panel distribution while effectively maintaining system constraints.

Comparative Analysis of Hybrid Renewable Energy Systems for Off-Grid Applications in Chad

In this study, a techno-economic feasibility analysis of hybrid renewable energy systems for four household categories in rural areas of Chad was studied based on the multi-criteria assessment technique. The problem of this study is to know the best optimal solution in the technical and economic feasibility study of the decentralized mini-grids for the rural electrification of isolated villages in Chad. The main objective of the work is to assess technically, economically and environmentally the feasibility of six scenarios of hybrid systems in five isolated sites in Chad. The performance analysis involved six scenarios of possible hybrid solutions while achieving a supply-demand balance for sustainable electrification of the remote villages, using the HOMER software. The results have shown that the optimum combination of the hybrid system was the photovoltaic/battery system with a Net Present Cost (NPC) of US $ 328,146 and it was found at Etena village. The photovoltaic/Wind/Diesel/Battery hybrid configuration was the least optimum system and it has appeared in Mandelia village. In terms of energy cost, the lowest Levelized Cost of Energy (LCOE) was estimated at US $ 0.236/kWh in a photovoltaic/Wind/Battery configuration at Koundoul site and the highest costs US $ 0.363/kWh in the photovoltaic/Battery configuration at the Linia site. It is established that hybrid solutions can be developed to make electricity available and accessible to the population of the remote rural areas in Chad. However, it is imperative that the local government must subsidize the diesel price to promote the adaptation of the abundant renewable solutions.

Deep Reinforcement Learning Empowered Edge Collaborative Caching Scheme for Internet of Vehicles

With the development of internet of vehicles, the traditional centralized content caching mode transmits content through the core network, which causes a large delay and cannot meet the demands for delay-sensitive services. To solve these problems, on basis of vehicle caching network, we propose an edge collaborative caching scheme. Road side unit (RSU) and mobile edge computing (MEC) are used to collect vehicle information, predict and cache popular content, thereby provide low-latency content delivery services. However, the storage capacity of a single RSU severely limits the edge caching performance and cannot handle intensive content requests at the same time. Through content sharing, collaborative caching can relieve the storage burden on caching servers. Therefore, we integrate RSU and collaborative caching to build a MEC-assisted vehicle edge collaborative caching (MVECC) scheme, so as to realize the collaborative caching among cloud, edge and vehicle. MVECC uses deep reinforcement learning to predict what needs to be cached on RSU, which enables RSUs to cache more popular content. In addition, MVECC also introduces a mobility-aware caching replacement scheme at the edge network to reduce redundant cache and improving cache efficiency, which allows RSU to dynamically replace the cached content in response to the mobility of vehicles. The simulation results show that the proposed MVECC scheme can improve cache performance in terms of energy cost and content hit rate.

Power Allocation and Energy Cost Minimization in Cloud Data Centers Microgrids: A Two-Stage Optimization Approach

Data centers are becoming an important asset in today&#x2019;s connected and intelligent world. Indeed, with the emergence of 5<i>G</i> networks, the growth of Internet of Things (IoT) and cloud computing, the data centers are increasingly becoming an essential component in delivering our daily services. On the other hand, cloud data centers, are outfitted with power-hungry servers and electrical installations that require enormous quantities of energy. In addition, operating cloud data centers in microgrids can be more reliable and sustainable than having them managed directly by the smart grid operator. However, microgrids have to deal with the high energy cost that their data centers may incur. In this context, managing the power allocated to the data centers and minimizing microgrids energy cost is a challenging problem. In this paper, we model a two-stage optimization approach in a microgrid-cloud architecture. In the first stage, we consider that the cloud data centers are run by multiple providers and try to get as much power as possible from the microgrid, to store this energy and to be able to execute the maximum of users applications. Such a power behavior requires an optimal power assignment from the microgrid to mitigate the risk of blackouts and power outages. Thus, the data center&#x2019;s huge power demand was modeled as a non-cooperative game. The microgrid controller calculates the optimal power that will be assigned to each data center according to its Power Usage Effectiveness (PUE), number of real-time applications and its network bandwidth usage. In the second stage, the microgrids try to minimize their energy cost by optimally purchasing power from the main grid and the other migrogrids, and by selling back their stored energy. We compare our approach with three existing power minimization approaches: (i) our previously presented scheme called "Basic Game Scheme" (<i>BGS</i>), (ii) traditional and (iii) price-based approaches. Simulations results show that our two stage optimization approach is up to 25% more effective in terms of energy cost. In addition, our scheme promotes <i>MGs</i> green energy usage which significantly reduces power load rate and CO2 emission with a percentage up to 52% comparing to "<i>BGS</i>" and price-based scheme.

태양광 발전량 증대를 위한 에너지 하베스팅 연구

In energy harvesting technology, the United States and Europe are leading the technology development, but since the technology is in the stage of introduction around the world, it is a national investment to secure excellent harvesting-related technology through concentration of capabilities of domestic companies and to preoccupy the global market. The power level that can be used through energy harvesting technology is limited to low power of several watts or less, but in terms of energy cost that can be provided per unit time and unit area provided by energy harvested over a long period of several years or more even compared to the batteries currently in use, it can have a significant alternative meaning. Therefore, in this paper, a voltage coupling method near the lowest voltage of inverter operation was developed and converted as a study to increase the amount of solar power generation in the low sunlight section at sunrise and sunset through voltage tightening amplification adjustment and energy storage technology. In addition, through the solar module voltage series/parallel variable connection conversion technology, the power generation time is extended even under the lowest inverter operating voltage to increase the amount of power generation.