Local Energy Consumption Research Articles

Computing Offloading Based on TD3 Algorithm in Cache-Assisted Vehicular NOMA-MEC Networks.

In this paper, in order to reduce the energy consumption and time of data transmission, the non-orthogonal multiple access (NOMA) and mobile edge caching technologies are jointly considered in mobile edge computing (MEC) networks. As for the cache-assisted vehicular NOMA-MEC networks, a problem of minimizing the energy consumed by vehicles (mobile devices, MDs) is formulated under time and resource constraints, which jointly optimize the computing resource allocation, subchannel selection, device association, offloading and caching decisions. To solve the formulated problem, we develop an effective joint computation offloading and task-caching algorithm based on the twin-delayed deep deterministic policy gradient (TD3) algorithm. Such a TD3-based offloading (TD3O) algorithm includes a designed action transformation (AT) algorithm used for transforming continuous action space into a discrete one. In addition, to solve the formulated problem in a non-iterative manner, an effective heuristic algorithm (HA) is also designed. As for the designed algorithms, we provide some detailed analyses of computation complexity and convergence, and give some meaningful insights through simulation. Simulation results show that the TD3O algorithm could achieve lower local energy consumption than several benchmark algorithms, and HA could achieve lower consumption than the completely offloading algorithm and local execution algorithm.

Energy budget of cold and hot gas–solid fluidized beds through computational fluid dynamics-discrete element method simulations

Direct energy budget is carried out for both cold and hot flow in gas–solid fluidization systems. First, the energy paths are proposed from thermodynamic viewpoints. Energy consumption means total power input to the specific system, and it can be decomposed into energy retention and energy dissipation. Energy retention is the variation of accumulated mechanical energy in the system, and energy dissipation is the energy converted to heat by irreversible processes. Then based on the computational fluid dynamics-discrete element method framework, different energy terms are quantified from the specific flow elements of fluid cells and particles as well as their interactions with the wall. In order to clarify the energy budget, it is important to identify which system is studied: the particle-fluid system or the particle sub-system. For the cold flow, the total energy consumption of the particle sub-system can well indicate the onset of bubbling and turbulent, while the variation of local energy consumption terms can reflect the evolution of heterogeneous structures. For the hot flow, different heat transfer mechanisms are analyzed and the solver is modified to reproduce the experimental results. The impact of the heat transfer mechanisms and heat production on energy consumption is also investigated. The proposed budget method has proven to be energy-conservative and easy to conduct, and it is hopeful to be applied to other multiphase flow systems.

An energy community as a platform for local sector coupling: From complex modelling to simulation and implementation

This paper presents the reference architecture of a model for sustainable local planning and sector coupling based on an energy-community framework. The proposed approach includes four building blocks: (1) the data-acquisition building block - local energy consumption, (2) the building block for modelling and calculations - local energy conversions, (3) the building block for connecting the infrastructure status and needs with development plans of the energy-network operators and aggregators, and (4) the decision-support building block for local environmental, spatial and energy planning. The proposed approach was tested in a real industrial environment and a local community. Initial simulation results confirmed that excess heat from the energy-intensive company has the potential to replace the natural gas that is currently used for the production of heat in a local district-heating system. The natural gas savings in the district-heating system would be 3,299,676 Nm3, as the excess heat could cover as much as 68.4 % of the required heat for the local community. It has also been proven that complex modelling and simulation of a future sustainable solution can contribute to its optimization, understanding, and acceptance by all stakeholders and thereby increase the potential of the project's implementation.

Grid-Aware Energy Management System for Distribution Grids Based on a Co-Simulation Approach

The energy sector is undergoing a profound transformation to tackle climate change issues rapidly and efficiently. The development of decentralized renewable energy and the electrification of the transport sector are two major aspects affecting electrical grids. This paper presents a novel energy management strategy that supervises controllable loads like electric vehicles and electric water heaters in a distribution grid to reduce energy transmission costs and promote the local consumption of renewable energy. The proposed strategy uses day-ahead forecasts of energy production and consumption to schedule future load profiles. It also considers potential grid constraints using a real-time co-simulation approach. The estimated constraints are mitigated by modifying the controllable loads' dispatch or adjusting their power set-points. The system's performance is finally evaluated on a real distribution grid model during days that present the most voltage constraints in the year.

Parametric study on utilization of PV-energy in residential microgrids with hot water heating

The seamless coexistence of distributed photovoltaics (PV) with the utility network may be maintained by focusing on higher energy autonomy of micro-grids.This may be approached by seeking any opportunities to increase local energy consumption, especially when legal regulations are cutting-down the benefits for energy overproduction.The microgrids combining PV with electric storage and heat pumps largely fail at satisfying local energy needs autonomously in winter periods and create excessive load of power network. Although the all-electric microgrid is a long term target, any improvements to the existing residential infrastructure with mixed-supply sources are worth revisiting as low-cost steps towards more conscious energy utilization.The concept of using Domestic Hot Water (DHW) heating to dump locally the excess of PV-energy is affordable method to improve energy autonomy of residential microgrids with minimal interference to their existing configuration and the advantage of no additional energy demand.The use of PV for DHW heating was studied in various configurations, but in contrast to former works, this paper presents the parametric analysis focusing on increasing the local consumption (goal A), minimizing the interference with utility grid (goal B) and optimal adjustment to net-metering accounting rules (goal C).The case study is the residential-type micro-grid in climate of Central Europe. The study uses both simulated (energy consumption profiles) and recorded (solar irradiance) data. The simulation is based on microgrid proportions and provides the results beyond reach of single experiments and conclusions applicable to similar cases, regardless the absolute size.

Information Gap Decision Theory-Based Stochastic Optimization for Smart Microgrids with Multiple Transformers

Multi-microgrid collaborative scheduling can promote the local consumption of renewable energy in the smart grid and reduce the operating costs of the power grid park. At the same time, the access of the distributed energy storage (ES) system provides an opportunity to further enhance the park’s peak shaving and valley filling capacity, thereby reducing costs. However, the uncertainty of photovoltaic (PV) power generation and load demand seriously affects the profit maximization of the microgrid in the park. To address this challenge, this paper proposes a stochastic optimal scheduling strategy for industrial park smart microgrids with multiple transformers based on the information gap decision theory (IGDT). We first introduce a revenue maximization model for industrial parks, incorporating a two-part tariff system and distributed ES. Subsequently, we employ an envelope constraint model to accurately represent the uncertainty associated with PV generation and load demand. By integrating these components, we establish the IGDT stochastic optimization scheduling model for industrial parks with multiple transformers. Finally, we simulate and analyze the performance of the proposed IGDT model under various cost deviation factors during typical spring and summer days. The simulation results demonstrate the effectiveness of the proposed control strategy in mitigating the impact of PV generation and load uncertainty on industrial parks. The IGDT-based scheduling approach provides an efficient solution for maximizing revenue and enhancing the operational stability of industrial park microgrids.

Managing Complex Knowledge in Sustainable Planning: A Semantic-Based Model for Multiagent Water-Related Concepts

The concepts of green infrastructures, nature-based solutions and ecosystem services are today considered an integral part of the broader theme of the urban bioregion, with an intrinsic character of complexity. It is certainly difficult to structure bioregional processes in a balanced and sustainable way, able to keep local energy production and consumption cycles closed. It is a complex issue of knowledge bases, and problems are increased by the participatory dimension of environmental planning. In fact, when rational planning models have failed in the face of prominent individual needs and environmental complexity, a path has emerged towards the inclusion of multiple citizens’ and stakeholders’ knowledge. The cognitive structure of the plans has thus changed from systems of exclusively expert, formal knowledge to systems of diffused, multi-agent knowledge. This has involved richness but also significant problems in understanding and managing knowledge bases. In this complexity, there are some common peculiarities when it comes to socio-environmental systems. A common feature of the reference domains of ecosystem services, nature-based solutions and green infrastructures is the water resource. A management model of hydrological data, which are structurally relevant and cross-sectoral in environmental planning actions, could represent a flagship initiative. The used approach could be conveyed to more complex and extensive areas of the environmental domain in a perspective of sustainable planning. The present paper is part of a research work oriented toward handling complex environmental subjects, such as green infrastructures, nature-based solutions or ecosystem services, with a knowledge modelling approach. This approach is based on semantic extensions, elaborated form the concept of semantic web, to allow shared interpretations of knowledge coming from different languages and scientific domains. It is also based on using applied ontologies, elaborated from the concept of ontology-based classification, to support a structured organization of knowledge contents. The main research objective is therefore to investigate about a knowledge management system with semantic extensions, populated with hydrological knowledge contents, as well as to propose a preliminary functional architecture. A simple ontology of data is extracted, aiming at clarifying and improving inter-domain communication, so as to enhance a common semantic understanding in a complex environmental system.

Does human capital matter for energy consumption in China? Evidence from 30 Chinese provinces.

The impact of human capital on energy consumption is an important economic and environmental issue, especially in a country like China with a strong energy demand and continuously improving education levels. This study uses panel data from 30 provinces in China from 2000 to 2020 to explore the impact of human capital on energy consumption. Empirical research shows that human capital can not only reduce local energy consumption but also reduce energy consumption in surrounding areas, through structural, technological, and knowledge spillover effects. The impact of human capital on energy consumption exhibits a U-shaped curve, decreasing first and then increasing, when economic growth reaches a threshold level. As industrial structure and urbanization develop, the impact of human capital on energy consumption increases. This study inform policy by demonstrating the energy conservation and emission reduction effects of human capital and provide insights for investors and policymakers.

A Secure Localization Scheme for UASNs Based on Anchor Node Self-Adaptive Adjustment

The UASNs are widely used in underwater communications and monitoring, and many applications require accurate information regarding the position of nodes. However, intentional attacks against devices or information transmission may exist in the network, and the localization process is periodic, so it is necessary to quickly address attacks and optimize the network structure. This paper proposed an anchor node self-adaptive adjustment localization scheme (ASAL), in which the anchor node can adjust the state and depth of its participation. Two filters were used to adjust the states of referable nodes. The first filter was based on the distance difference of reverse information transmission after direct localization based on anchor nodes. The second was based on the error of the anchor node’s reverse localization after network localization was completed. In addition, a depth-adjustment mechanism of anchor nodes was proposed to optimize the network structure, the virtual force vector was introduced to describe the cost of depth adjustment, and the whale optimization algorithm was used to converge to the depth with the minimum total cost. The simulation results showed that the scheme can ensure localization accuracy and coverage in attack scenarios and reduce localization energy consumption.

A pricing game strategy with virtual prosumer guidance in community grid

AbstractThe peer‐to‐peer (P2P) energy trading model is considered to be one of the appropriate models for managing local energy trading in community grids. But free pricing mechanisms can lead to energy wastage and long response times. In order to facilitate the local consumption of renewable energy within the community, this paper proposes a pricing game strategy with virtual prosumer (VP) guidance on a P2P energy market model in community grid. To prevent market disruption caused by free pricing strategies in the P2P market and ensure the overall social benefit of the community, a VP controlled by the community microgrid manager is introduced to regulate the energy trading of the entire community. A novel and efficient prosumer trading model is proposed where the VP changes its pricing strategy to guide other prosumers to achieve Nash equilibrium and improve the internal consumption rate of new energy in the community. The proposed strategy's rationality and effectiveness are verified through case studies.

Exploring influential factors of CO2 emissions in China's cities using machine learning techniques.

The precise and exhaustive discernment of factors influencing CO2 emissions underpins the advancement toward sustainable, low-carbon development. Although numerous studies have probed the correlation between predetermined proxy variables and carbon emissions, methodological constraints have often led to an inability to effectively discern carbon emission determinants among numerous potential variables or unravel complex, non-linear relationships, and interaction effects. To redress these research gaps, this research utilized machine learning models to correlate urban CO2 emissions with socioeconomic indicators. The model outputs were then visualized and interpreted using explainable methods. The findings indicated that the model successfully identified a comprehensive array of dominant influences on urban CO2 emissions, principally associated with local fiscal policies, land use, energy consumption, industrial development, and urban transportation. The findings further revealed a complex non-linear association between these factors and urban CO2 emissions; however, the majority of these variables displayed a prevalent propensity to intensify carbon emissions in correspondence with an increase in sample value. Additionally, these factors exhibited a complex interactive influence on urban CO2 emissions, with distinct pairings producing a suppressive effect exclusively at specific combination of sample values. Consequently, this research posited that a robust correlation between urban socioeconomic development and CO2 emissions in China remains to be established. Given the varied impacts of these influencing factors across different cities, a differentiated approach to development should be adopted when charting low-carbon trajectories.

Research on the Relationship between FDI, Energy Consumption Intensity and Economic Growth Based on Spatial Effect

In order to reveal the driving factors of China ’ s economic growth, based on the panel data of 28 provinces from 2010 to 2019, the spatial Durbin model is used to study the relationship between foreign direct investment, energy consumption intensity and economic growth. The results show that energy consumption intensity has a negative impact on the economic growth of the region, and has a positive spillover effect on other adjacent regions. Foreign direct investment has a positive impact on local energy consumption, and has a positive spillover effect on other neighboring regions.

Impact of the clean energy structure of building operation on the co-benefits of CO2 and air pollutant emission reductions in Chinese provinces

Energy consumption in the building sector is an essential source of CO2 and air pollution. Identifying the impact of changes in the energy structure on emission trends is the basis of scientific control measures. However, the co-benefits of CO2 and air pollutant emission reductions generated by the change in energy structure for different building types in China are still unknown. Using the cases of 30 provincial regions in China, based on detailed local electricity, heat, and household energy consumption data and emission factors, we established provincial CO2 and air pollutant emissions from building operations during 2016–2020. The impact of changes in the energy structure on the co-benefits of air pollutants emission reduction was investigated. Results showed that in 2020, building operation energy consumption in China was 610 Mtce. CO2 emissions were 2.53 Gt, and emissions of PM2.5, SO2, and NOX were 0.18, 0.38, and 0.48 Mt, respectively. From 2016 to 2020, urban public buildings contributed the highest to CO2 and air pollutant emissions. The CO2, PM2.5, SO2, and NOX indirect emissions of electricity increased by 52.3, 46.0, 45.2, and 52.5%, respectively. In 2020, the co-benefits of PM2.5 and SO2 emission reduction from the clean energy structure of building operations were higher than that of NOX emission reduction, and the value of comprehensive co-benefits of air pollutants was 1.5. This work fills the gap in the evaluation of the co-benefits of air pollutants with low-carbon energy for different building types, and the crucial sub-sectors in each region with high co-benefits have been picked out.

Gradient and Channel Aware Dynamic Scheduling for Over-the-Air Computation in Federated Edge Learning Systems

To satisfy the expected plethora of computation-heavy applications, federated edge learning (FEEL) is a new paradigm featuring distributed learning to carry the capacities of low-latency and privacy-preserving. To further improve the efficiency of wireless data aggregation and model learning, over-the-air computation (AirComp) is emerging as a promising solution by using the superposition characteristics of wireless channels. However, the fading and noise of wireless channels can cause aggregate distortions in AirComp enabled federated learning. In addition, the quality of collected data and energy consumption of edge devices may also impact the accuracy and efficiency of model aggregation as well as convergence. To solve these problems, this work proposes a dynamic device scheduling mechanism, which can select qualified edge devices to transmit their local models with a proper power control policy so as to participate the model training at the server in federated learning via AirComp. In this mechanism, the data importance is measured by the gradient of local model parameter, channel condition and energy consumption of the device jointly. In particular, to fully use distributed datasets and accelerate the convergence rate of federated learning, the local updates of unselected devices are also retained and accumulated for future potential transmission, instead of being discarded directly. Furthermore, the Lyapunov drift-plus-penalty optimization problem is formulated for searching the optimal device selection strategy. Simulation results validate that the proposed scheduling mechanism can achieve higher test accuracy and faster convergence rate, and is robust against different channel conditions.

Analysis of financial support efficiency and influencing factors of listed seed companies from the perspective of energy consumption and carbon emissions.

The seed industry has a prominent role in strengthening ecological stability and national food security as it provides the basic ground for agriculture sector. In the current research, the effectiveness of financial support provided to listed seed enterprises and its influencing factors from the perspectives of energy consumption and carbon emissions are examined using a three-stage DEA-Tobit model. The dataset for the underlined study variables mainly comes from the financial data published by 32 listed seed enterprises and the China Energy Statistical Yearbook from 2016 to 2021. In order to make the results more accurate, the influence of external environmental factors such as economic development level, total energy consumption, and total carbon emission on listed seed enterprises are excluded. The results revealed that the mean value of financial support efficiency of listed seed enterprises increased significantly after excluding the influence of external environmental and random factors. External environmental factors such as regional energy consumption and carbon dioxide emission played an important role in the process of the financial system supporting the development of listed seed enterprises. The development of some listed seed enterprises with high financial support efficiency came at the cost of high local carbon dioxide emission and high energy consumption. Internal factors such as operating profit, equity concentration, financial structure, and enterprise size are the key intra-firm factors that affect the efficiency of financial support for listed seed enterprises. Thus, it is suggested that enterprises must pay attention to the environmental performance to reach a win-win situation in reducing and improving the energy consumption and financial performance, respectively. Similarly, the improvement of energy use efficiency through endogenous and external innovation should be prioritized to achieve sustainable economic development.

A Novel DC Transmission and Distribution System with 100% New Energy Consumption

With the continuous development of new energy technology and the continuous improvement of people’s green and low-carbon awareness, distributed photovoltaic, wind power, and other new energy are more and more widely used. On the other hand, the development of new industries, such as electric vehicle charging stations and data centers, has increased the DC load significantly, showing the characteristics of the coexistence of AC and DC user loads. Therefore, it is of great practical significance to study how to effectively utilize new energy and realize the direct power supply of AC and DC loads to achieve the dual carbon goal. Combined with the development trend of new energy and DC transmission, this paper proposes a ±10 kV DC transmission and distribution architecture with 100% new energy consumption, which can realize the complete local consumption of new energy and reliable power supply for diversified AC and DC loads, maximizing the reduction of power loss caused by AC-DC conversion. This paper discusses the key devices and key technologies in the above architecture and analyzes the advantages and disadvantages and application scope of various methods in the current research results.

Research on DC Power Supply for Electrolytic Water to Hydrogen Based on Renewable Energy

Hydrogen production from electrolytic water based on Renewable Energy has been found as a vital method for the local consumption of new energy and the utilization of hydrogen energy. In this paper, the hydrogen production power supply matching the working characteristics of electrolytic water production was investigated. Through the analysis of the correlation between the electrolysis current and temperature of the proton exchange membrane electrolyzer and the electrolyzer port voltage, energy efficiency, and hydrogen production speed, it was concluded that the hydrogen production power supply should be characterized by low output current ripple, high output current, and wide range voltage output. To meet the requirements of the system of hydrogen production from electrolytic water based on new energy, a hydrogen production power supply scheme was proposed based on Y which is the type three, is the phase staggered parallel LLC topology. In the proposed scheme, the cavity with three is the phase staggered parallel output is resonated to meet the operating characteristics (high current and low ripple) of the electrolyzer, and pulse frequency control is adopted to achieve resonant soft in the switching operation and increase conversion efficiency. Lastly, a simulation model and a 6kW experimental prototype were built to verify the rationality and feasibility of the proposed scheme.

A Novel Day Optimal Scheduling Strategy for Integrated Energy System including Electric Vehicle and Multisource Energy Storage

Aiming at problems such as high operating costs, the low utilization efficiency of renewable energy, and the increase in the peak-valley difference in user load caused by disordered charging of electric vehicles (EVs), the operation mode of “setting electricity by heat” or “setting heat by electricity” is adopted in the integrated energy system of intelligent residential areas on the distribution side. In this paper, a method is proposed to use multiple energy storage, demand response technology, and make EV orderly charge and discharge plan to consume renewable resources and realize optimal economic scheduling in the integrated energy system of combined heat and power (CHP). Firstly, the demand response model of electricity load adjusting according to renewable energy output and time-of-use electricity prices has been established. Secondly, considering the comfort of the space thermal load, the orderly charge and discharge plan of electric energy storage (ESS), thermal energy storage (HSS), and EV and aiming at the minimum operating cost of the integrated energy system, the optimal output operation of each equipment was scheduled. Finally, through simulation analysis and comparative experiments, it is verified that the proposed method can promote the local consumption of renewable energy, realize the decoupling of thermoelectric constraints, and reduce the operating cost of the integrated energy system.

A Fuzzy Logic-Based Energy-Adaptive Localization Scheme by Fusing WiFi and PDR

Fusing WiFi fingerprint localization and pedestrian dead reckoning (PDR) on smartphones is attractive because of their obvious complementarity in localization accuracy and energy consumption. Although fusion localization algorithms tend to improve localization accuracy, extra hardware and software involved will result in extra computations, such that energy consumption is inevitably increased. Thus, in this study, we propose a novel fusion localization scheme based on fuzzy logic, which aims to achieve ideal localization accuracy by consuming as little energy as possible. Specifically, energy-efficient inertial measurement unit (IMU) sensors are routinely called to provide the displacement of a smartphone user in the manner of PDR, whereas a fuzzy inference system is employed to adaptively schedule energy-hungry WiFi scans to fulfill WiFi fingerprint localization according to a coarse metric for fusion localization errors and the remaining energy of the smartphone, so as to achieve a trade-off between localization accuracy and energy consumption. Moreover, in order to mitigate the effect of drift errors induced by PDR, straight trajectories of the user are further identified using a series of WiFi localization results to calibrate heading estimates of PDR. Extensive experimental results demonstrate that the proposed scheme achieves the same accuracy as the complementary filter, but consumes 38.02% energy than the complementary filter, confirming that the proposed scheme can effectively balance the localization accuracy and energy consumption.

ОСОБЕННОСТИ ЦИФРОВОЙ ЭПОХИ

The development of digitalization causes irreversible changes in society and the environment. Issues of human dignity, quality of life, biodiversity conservation, planetary and local ecology, energy and resource consumption, and human interaction with humanoid machines are on the agenda. Some of them require immediate solutions, for example, the complete recycling of electronic waste, while others require many years of international discussion, for example, changing the nature of intellectual work. Although the development of digital technologies is dynamic, it cannot be definitively assessed and it is already obvious that this process is unmanageable and focused solely on economic goals. Unfortunately, social and environmental goals in today’s model of digitalization are secondary, since economic interests prevail in modern society. In the digital format, human dignity must remain a central, unchanging guideline, since without borders and frameworks, global digital transformation will create an even greater threat to the existence of mankind. The article presents specific proposals for achieving social goals and meaningful recommendations on various aspects of digitalization. The focus is on the problems of preserving human dignity and preventing environmental disasters.