Energy Consumption Index Research Articles

Three-Dimensional Characterization of Potatoes Under Different Drying Methods: Quality Optimization for Hybrid Drying Approach.

The quality evaluation of processed potatoes is vital in the food industry. In this study, the effect of three different drying methods on the post-processing quality of potatoes utilizing 4, 8, 12, and 16 h of freeze drying (FD), infrared drying (ID), and oven drying (OD) was investigated. The impact of the drying methods on the potato's microstructure was analyzed and quantified using 3D X-ray micro-computed tomography images. A new Hybrid Quality Score Evaluator (HQSE) was introduced and used to assess the Quality Index (QI) and Specific Energy Consumption Index (SECI) across various drying methods and durations. Mathematical models were developed to predict the optimal drying method. FD showed significantly higher (p < 0.05) colour retention, rehydration ratio, and total porosity, with minimal shrinkage, although it had higher energy consumption. ID had the shortest drying time, followed by OD and FD. The optimization showed that for FD, the optimal time of 5.78 h increased QI by 9.7% and SECI by 30.6%. The mathematical models could accurately predict the QI and SECI under different drying methods, balancing quality preservation with energy efficiency. The findings suggest that a hybrid drying system could optimize potato quality and energy consumption.

Multi-objective optimal configuration of CCHP system containing hybrid electric-hydrogen energy storage system

In order to cope with the increasing energy demand and achieve the “double carbon “goal of China’s 14th Five-Year Plan,” combined with hydrogen energy storage technology, it has the characteristics of zero pollution, high efficiency and rich source. In the context of reducing energy consumption and the vigorous development of hydrogen energy storage technology, a multi-objective optimization configuration model with economy, energy consumption index and carbon emission index is proposed, which takes into account the working characteristics of the hydrogen energy storage system, and the exothermic heat release from the electrolysis tanks and fuel cells when they are working to provide the loads with an additional heat source of the Combined Cooling, Heating and Power (CCHP) system, to reduce energy consumption and carbon emission. Finally, taking a region as an example, a multi-objective optimization algorithm based on decomposition is used to solve the model, so as to obtain a series of alternatives with better optimization effect. At the same time, the two-way projection method based on interval intuitionistic fuzzy information is used to make decisions, and the scheme that optimizes the economy, energy consumption index and carbon emission index is obtained, which verifies the feasibility of the system proposed in this paper.

A Study of Heavy Road Freight Transport in Poland in the Context of the Pursuit of Sustainable Road Transport

The efficiency of road freight transport determines—to a significant degree—the total environmental footprint and the amount of greenhouse gases and other pollutants released into the atmosphere by inland transport. The rate of empty or partially empty vehicles is one of the key metrics for improvement of the environmental performance of freight road transportation. This paper presents the characteristics of road freight transport in Poland on the basis of data collected by weigh-in-motion stations. Data aggregation for environmental analysis represents a novel aspect of the work. Indicators describing the degree of loading, the share of empty vehicles in traffic, and the share of vehicles of maximum permissible total weight in traffic were determined for a representative group of heavy goods vehicles. Based on the representative load factor value (LFA), a classification of the road section into four groups was proposed. The results obtained show a clear differentiation of the values of the indicators analyzed for individual groups and their variability in the three-year period covered by the analysis. The aggregation method presented can be used to identify the nature of the distribution of the weight categories of the heavy goods vehicles and provide input information for targeted analyses relating to sustainable road transport management and environmental protection. Finally, the grouped LFA values were linked with indicators of energy consumption (ECI) and on-road emissions (EI).

MMD-TSC: An Adaptive Multi-Objective Traffic Signal Control for Energy Saving with Traffic Efficiency

Reducing traffic energy consumption is crucial for smart cities, and vehicle carbon emissions are a key energy indicator. Traffic signal control (TSC) is a useful method because it can affect the energy consumption of vehicles on the road by controlling the stop-and-go of vehicles at traffic intersections. However, setting traffic signals to reduce energy consumption will affect traffic efficiency and this is not in line with traffic management objectives. Current studies adopt multi-objective optimization methods with high traffic efficiency and low carbon emissions to solve this problem. However, most methods use static weights, which cannot adapt to complex and dynamic traffic states, resulting in non-optimal performance. Current energy indicators for urban transportation often fail to consider passenger fairness. This fairness is significant because the purpose of urban transportation is to serve people’s mobility needs not vehicles. Therefore, this paper proposes Multi-objective Adaptive Meta-DQN TSC (MMD-TSC), which introduces a dynamic weight adaptation mechanism to simultaneously optimize traffic efficiency and energy saving, and incorporates the per capita carbon emissions as the energy indicator. Firstly, this paper integrates traffic state data such as vehicle positions, velocities, vehicle types, and the number of passengers and incorporates fairness into the energy indicators, using per capita carbon emissions as the target for reducing energy consumption. Then, it proposes MMD-TSC with dynamic weights between energy consumption and traffic efficiency as reward functions. The MMD-TSC model includes two agents, the TSC agent and the weight agent, which are responsible for traffic signal adjustment and weight calculation, respectively. The weights are calculated by a function of traffic states. Finally, the paper describes the design of the MMD-TSC model learning algorithm and uses a SUMO (Simulation of Urban Mobility) v.1.20.0 for traffic simulation. The results show that in non-highly congested traffic states, the MMD-TSC model has higher traffic efficiency and lower energy consumption compared to static multi-objective TSC models and single-objective TSC models, and can adaptively achieve traffic management objectives. Compared with using vehicle average carbon emissions as the energy consumption indicator, using per capita carbon emissions achieves Pareto improvements in traffic efficiency and energy consumption indicators. The energy utilization efficiency of the MMD-TSC model is improved by 35% compared to the fixed-time TSC.

BIM and orthogonal test methods to optimize the energy consumption of green buildings

The construction industry’s rapid growth significantly impacts energy consumption and environmental health. It is crucial to develop optimization strategies to enhance green building energy efficiency and encompass comprehensive analysis methods. This study aims to introduce and validate a novel framework for optimizing energy efficiency design in green buildings by integrating Building Information Modeling (BIM) technology, Life Cycle Cost (LCC) analysis, and orthogonal testing methods, focusing on enhancing energy efficiency and reducing life cycle costs. The optimization parameters for the building envelope are identified by analyzing energy consumption components and key green building factors. The orthogonal testing method was applied to streamline design options. Building Energy Consumption Simulation (BECS) software and LCC analysis tools were employed to calculate each optimized option’s total annual energy consumption and the current life cycle costs. Using the efficiency coefficient method, each optimization scheme’s energy consumption and economic indicators were thoroughly analyzed. The framework’s validity and applicability were confirmed through an empirical analysis of a campus green building case in Fujian Province, demonstrating that the optimized framework could reduce energy consumption by 4.85 kWh/m2 per year and lower costs by 38.89 Yuan/m2 compared to the reference building. The case study highlights the framework’s significant benefits in enhancing environmental performance and economic gains. The results provide critical parameter selection and offer scientific and technological support for the design of building energy efficiency, promoting optimization techniques and sustainable development within the construction industry.

Grouping-maintenance of multi-components production system under ecological background

Purpose: The purpose of this paper is to propose an opportunistic group maintenance model for a multi-component series system considering reducing CO2 emission and increasing system efficiency, which establishes a maintenance policy to minimize the cost rate of the system life cycle.Design/methodology/approach: Structural dependence and economic dependence between the components in a multi-component series system are analyzed to make a condition-based maintenance policy. To minimize the cost rate of the system life cycle, clustering theory and two decision variables, which include the preventative maintenance cycle multiplier and basic preventive maintenance interval of each component, is utilized to make an opportunistic policy for system optimal maintenance under the background of increasing energy efficiency and reducing CO2 emissions.Findings: It can be concluded that the government imposes fines on excessive CO2 emissions and energy consumption indicators, which can influence the maintenance decisions of enterprises, promote enterprises to shorten the cycle of preventive maintenance, and avoid excessive CO2 emissions of various components and exceed the indicators as much as possible, so as to enable enterprises to actively save energy, reduce emissions and control carbon emissions. Furthermore, when the single preparation cost of repair maintenance increases, enterprises need to shorten the maintenance period to avoid frequent component failures. As the cost of a single prep for preventive maintenance rises, organizations need to extend maintenance cycles and avoid frequent parts downtime - minimizing their own repair costs.Practical implications: Considering the high maintenance cost and low energy efficiency of multi-component systems, this model assists production managers to have better maintenance of these systems.Originality/value: 1.Model innovation: comprehensive consideration of two variables in the selection of decision variables for preventive maintenance or opportunistic maintenance; The selection and synthesis of ecological factors when making ecologically conscious maintenance decisions for multi-component systems make up for the gaps of single variables and one-sided indicators in previous studies and models. 2. Methodological innovation: In the identification of opportunity maintenance, the idea of clustering is first combined; In the simulation analysis, genetic algorithm is used to obtain the optimal parameters quickly and accurately. A blend of management, statistics, biology and computer science.

Power Dispatching Strategy Considering the Health Status of Multi-Energy Conversion Equipment in Highway Power Supply Systems

In order to extend the service life of a highway power supply system and the level of new energy consumption, a power dispatching strategy considering the health status of multi-energy conversion equipment is proposed in this paper. Firstly, the energy and load forms of the highway power supply system are introduced, and the structure of the multi-energy conversion equipment, the topological structures of the DC–DC and DC–AC modules, and the operating characteristics are analyzed. Secondly, the module temperatures and output voltages are used as main parameters to establish the health indexes of DC–DC and DC–AC modules, and then the health index of the multi-energy conversion equipment is further calculated. Thirdly, the new energy consumption index is defined, and a multi-objective optimization model for power dispatching of highway power supply systems is established with the goal of improving the health index of multi-energy conversion equipment and the new energy consumption index. The case study shows that the power dispatching strategy in this paper can better control the temperature of each module, improve the health status of multi-energy conversion equipment, and have a high level of new energy consumption.

Research on energy consumption evaluation and energy-saving design of cranes in service based on structure-mechanism coupling

Aiming at the problem that the life cycle energy consumption index of hoisting machinery is difficult to be objectively quantified, a crane energy consumption evaluation calculation method based on the working cycle of the service period is proposed, the typical service working mode of the crane is analyzed, and the working cycle process of the mechanism is clarified. On this basis, the stress cycle characteristics of the crane structure during the service period are determined based on the coupling relationship between the mechanism and the structure, which lays the foundation for accurately evaluating the life index of the crane. According to the relationship between the load rate and effective power of each mechanism during the service period of the crane, the calculation method for the evaluation and calculation of the structure energy consumption during the service period of the crane is studied. Finally, the crane is designed based on the concept of absolute service safety, and an optimal design model of the main girder structure of the crane with the goal of the lowest energy consumption in service is established. Using the research method of this paper to study a certain type of bridge crane, the energy consumption of the hoisting machinery during its service period is not only related to the accumulation of the operating characteristics of the mechanism but also related to the characteristic parameters such as the structure self-weight. The comprehensive evaluation of energy consumption provides an effective quantitative method and a reliable green design theory for crane design.

Performance study of an electrified temperature vacuum swing adsorption cycle for post combustion carbon capture

The performance of a fully electrified temperature vacuum swing adsorption (E-TVSA) carbon capture process is investigated and compared with the performance of a VSA cycle as the reference case. A five-step process including (I) adsorption, (II) co-current heating, (III) counter-current evacuation, (IV) counter-current pressurizing, and (V) co-current closed loop cooling steps was devised and simulated. The adsorption time, electric heating power, and evacuation pressure were chosen as the independent variables for evaluating their impact on key performing indicators (KPI) of purity, recovery, productivity and energy consumption. In total 2717 cases were simulated to the cyclic steady state condition to provide 44 contours of the KPIs. From these contours it can be concluded that in E-TVSA carbon capture using 13X zeolite, a temperature rise of almost 50 degrees is necessary during the heating step, and the evacuation step should be continued to reach a pressure between 2.5 and 3 kPa. Under these conditions, a recovery and purity of more than 98 % were achieved with a production rate above 1.1 kg CO2/kg ads day and a specific energy consumption of 1.74 MJ/kg CO2 (0.61 MJ/kg CO2 for evacuation and 1.13 MJ/kg CO2 for heating). When comparing E-TVSA and VSA, it is noteworthy that the production rate and specific energy consumption of E-TVSA are in the same order as that of VSA. However, E-TVSA surpasses VSA in terms of purity and recovery rates. Further analysis of the transformation efficiency from electricity to heat revealed the transformation efficiency must be at least 50 % to keep the energy consumption below 3 MJ/kg CO2.

Information flow dynamics between cryptocurrency returns and electricity consumption: A comparative analysis of Bitcoin and Ethereum

Understanding energy consumption associated with cryptocurrency mining gained increasing attention, with the literature focusing mainly on Bitcoin. This study uses data from the two energy consumption indices, to estimate static and dynamic transfer entropies. The results provide a nuanced understanding of the bidirectional relationships and their implications. The dominant direction of information flow for Bitcoin is from electricity consumption to returns, while for Ethereum, it is from returns to electricity consumption, suggesting that Ethereum's returns significantly impact electricity consumption patterns. Results highlight the need for policies that integrate energy forecasting and environmental sustainability considerations and has significant implications for policymaking.

The Potential Risks of Mining and Investment in Digital Currencies based on Financial Technology Applications

This study aimed to explore the potential risks associated with mining and investing in digital currencies through financial technology (fintech) applications. The research approach employed a combination of quantitative and qualitative methods, utilizing an inductive-deductive framework. The researcher conducted online surveys, and interviews with professors and experts, and analyzed the collected data using Microsoft Excel and NVIVO software. The study also utilized illustrative graphs of digital currency markets, computing power charts, and energy consumption indexes related to digital currency mining. The findings revealed various potential risks of mining and investing in digital currencies, impacting individuals, businesses, and ecosystems. These risks encompass environmental concerns, excessive energy consumption, security vulnerabilities, and financial losses. Additionally, investing in digital currencies through fintech applications can lead to inadequate investor protections, market volatility, regulatory challenges, fraudulent activities, lack of transparency, and insufficient investor understanding. The value of this study lies in its ability to analyze the potential risks associated with mining and investing in digital currencies, offering valuable insights for individuals, businesses, and policymakers. It can aid stakeholders in making informed decisions, developing risk management strategies, and enhancing their overall understanding of the potential implications on individuals, businesses, and the ecosystem.

Modeling and control strategy optimization of battery pack thermal management system considering aging and temperature inconsistency for fast charging

Maintaining the battery pack’s temperature in the desired range is crucial for fulfilling the thermal management requirements of a battery pack during fast charging. Furthermore, the temperature difference, temperature gradient, aging loss and energy consumption of the battery pack should be balanced to optimize its performance. This paper establishes the liquid cooling thermal management system model for an electric vehicle’s battery pack, which accurately characterizes the temperature distribution and electrical and aging characteristics of the battery pack. The discrepancies between the experimental and simulated results of battery’s fast charging-cooling suggest that maximum absolute temperature error is 2 °C, and the maximum relative voltage error is 1.5 %. Based on the non-dominated sorting genetic algorithm II algorithm, an optimization model with aging and temperature gradient of cells as objective functions is established. Three different thermal management strategies are created according to the optimization results. The comparison of these thermal management methods with the constant temperature cooling strategy demonstrates that the minimum temperature gradient strategy enhances the temperature consistency of the battery pack and reduces the maximum temperature gradient by 27 %. The minimum aging thermal management strategy depicts an aging loss of 0.091 %, reducing the charging time by 272 s and the maximum temperature gradient by 2.0 °C. The balanced thermal management strategy enables the battery pack to balance the temperature gradient and aging loss by optimizing the charging time, battery pack temperature difference, energy consumption and other indicators. The weight of each indicator is determined by its information entropy, which can be replaced according to the diverse needs to achieve different balanced thermal management strategies, providing a reference for the actual application of thermal management strategies.

The Impact of Wall Structure with Photovoltaic Function on Prefabricated Buildings

The wall structure is the main load-bearing component of the prefabricated building, which plays an important role in the stability and aesthetics of the building. With the deepening of the application of new energy in the construction field, the application of photovoltaic wall with photovoltaic function is becoming more and more extensive. However, there is still controversy about whether the photovoltaic function can meet the requirements of the building. On this basis, this paper discusses the wall structure with photovoltaic function in depth, and analyzes its energy consumption, heat preservation, ventilation and cost indicators. The results show that the wall structure of photovoltaic function can improve the aesthetics of prefabricated buildings, meet customer demand rate of more than 75%, save energy consumption by 25%~30%, increase indoor temperature by 1~3 degrees, and ventilate at 1.2 levels. It can be inferred that the load-bearing wall of photovoltaic function is suitable for prefabricated buildings and has the potential and role of vigorous development.

МЕТОДИКА ВЫБОРА РЕСУРСОСБЕРЕГАЮЩИХ ОРГАНИЗАЦИОННО-ТЕХНОЛОГИЧЕСКИХ РЕШЕНИЙ НА ЭТАПЕ ЗАВЕРШЕНИЯ ЖИЗНЕННОГО ЦИКЛА ЗДАНИЙ

The article discusses the main aspects of resource and energy conservation at the stage of completion of the life cycle of buildings. It is shown that the stage of completion of the life cycle is extremely important for buildings and other capital construction projects and requires a systematic approach to the choice of organizational and technological solutions for the liquidation or reconstruction of the building. The concept of the final stage of the life cycle of buildings has been clarified. The importance of making resource-saving organizational and technical decisions taken at the stage of completion of the life cycle of buildings is shown. Indicators have been developed for making appropriate organizational and technical decisions. The methodology of choosing organizational and technological solutions at the stage of completion of the life cycle of buildings, taking into account resource conservation, is presented. The methodology includes several stages, the results of which are a star-shaped infographic model of the values of organizational and technological solutions, which is created at the stage of completion of the life cycle of buildings. The methodology takes into account the indicators of resource and energy consumption for the implementation of various organizational and technological solutions. Calculation formulas for use in the infographic model are proposed: material consumption, labor costs of workers, financial costs labor costs of construction machines and mechanisms energy intensity of construction reconstruction. The proposed model is used to evaluate and select options for the feasibility of reorganizing buildings at the end of their life cycle, taking into account resource and energy conservation.

Sensitivity analysis on heat pump - mechanical vapor recompression desalination system by recovering waste heat of offshore power converter station

Abstract Offshore wind power converter stations produce massive low-temperature waste heat, which can hardly be used constrained by their offshore location. Therefore, the recovery of the waste heat has been raising widespread concern. Meanwhile, a large amount of fresh water is needed for its cooling system. So, a novel system combining high temperature heat pump and a mechanical vapor recompression system (HP-MVR) was proposed, and sensitivity analysis was performed to optimize it. The heat pump was used to absorb the waste heat and to produce high temperature water. The mechanical vapor recompression system was adopted to produce fresh water and to recover the condensation heat from the steam. In order to determine the impact parameters on the two crucial performance indicators of freshwater production and unit energy consumption, this article introduces a sensitivity analysis method, focusing on analyzing the sensitivity of the three operating parameters of heat pump condensation temperature, saturated water vapor inlet temperature entering the compressor, and freshwater condensation temperature to these two performance indicators. The results show that the sensitivity coefficients of heat pump condensation temperature, saturated water vapor inlet temperature entering the compressor, and freshwater condensation temperature are 1.13, -0.26, and 1.56. So, the freshwater condensation temperature has the most significant effect on freshwater output. Their sensitivity coefficients to unit energy consumption are 1.02, 1.41, and -0.64. The saturated water vapor inlet temperature entering the compressor has the most significant impact on the required power consumption per unit of freshwater. It will give some guidance for the application of low-temperature waste heat in seawater desalination and the reduction of operating costs.

Энергосбережение и оценка энергоэффективности угледобывающего предприятия

The article is devoted to consideration of issues related to assessing the energy efficiency of a coal mining enterprise. Coal mining enterprises are characterized by a large volume of energy intensive ore mining operations. The article presents possible ways of energy saving in the mining industry. During the research, a system of energy efficiency indicators for coal mining enterprises is presented. Materials and methods. The article proposes a system of indicators for coal enterprises based on existing methods and approaches to assessing energy efficiency. Energy consumption dependence graphs, benchmarking, and pinch analysis are considered as a starting point. Results. The study presents a system of energy efficiency indicators for coal mining enterprises. The proposed system has several levels. The existing methods and approaches for assessing this important indicator for industrial enterprises in the coal industry as a whole are considered. The author’s approach to formalizing the system of energy efficiency indicators for coal mining enterprises is proposed. The significance of energy audit as a key assessment measure is indicated and its stages are highlighted. The influencing factors determining energy costs and their reduction for a coal mining enterprise are systematized, barriers preventing the practical implementation of energy saving measures are analyzed. Discussion. The proposed metrics system provides a comprehensive framework for assessing the energy efficiency of coal mining enterprises at various levels. Its flexibility allows it to be applied to the analysis of individual equipment units, production processes, and overall enterprise performance. Its multi-level approach provides a detailed understanding of energy consumption, which allows for informed decision-making to optimize operations. At the equipment level, the metrics system evaluates the energy consumption and efficiency of individual machines and devices. At the production process level, the metrics system analyzes the energy consumption and efficiency of various stages of coal mining. At the enterprise level, the metrics system provides a comprehensive assessment of overall energy efficiency. It takes into account energy consumption at all levels and allows enterprises to compare their performance with each other and with industry benchmarks. Conclusion. Energy efficiency assessment of a coal mining enterprise allows to understand how and where energy is used, where its use can be improved, and how to reduce its costs. To conduct an energy efficiency assessment, the article proposes a system of indicators, highlighting the features of an energy audit. The main advantage of the proposed system of indicators is that it covers a wide range of energy consumption indicators, which are carefully selected and located at different levels of detail. In general, a balanced approach to the level of detail and generalization in the system of indicators provides flexibility in adapting indicators to specific research questions and helps to interpret the results in the context of structural differences between energy systems. Resume. Overall, the proposed metrics system offers a comprehensive approach to analyzing the energy efficiency of coal mining enterprises, covering all levels of operations. Its use allows enterprises to improve energy efficiency, reduce operating costs, and contribute to a more sustainable coal mining industry. Suggestions for practical applications and directions for future research. The use and implementation of the developed system at a coal mining company allows it to improve energy efficiency, reduce operating costs and contribute to a more sustainable coal mining industry.

Multi-UAV Cooperative Coverage Search for Various Regions Based on Differential Evolution Algorithm.

In recent years, remotely controlling an unmanned aerial vehicle (UAV) to perform coverage search missions has become increasingly popular due to the advantages of the UAV, such as small size, high maneuverability, and low cost. However, due to the distance limitations of the remote control and endurance of a UAV, a single UAV cannot effectively perform a search mission in various and complex regions. Thus, using a group of UAVs to deal with coverage search missions has become a research hotspot in the last decade. In this paper, a differential evolution (DE)-based multi-UAV cooperative coverage algorithm is proposed to deal with the coverage tasks in different regions. In the proposed algorithm, named DECSMU, the entire coverage process is divided into many coverage stages. Before each coverage stage, every UAV automatically plans its flight path based on DE. To obtain a promising flight trajectory for a UAV, a dynamic reward function is designed to evaluate the quality of the planned path in terms of the coverage rate and the energy consumption of the UAV. In each coverage stage, an information interaction between different UAVs is carried out through a communication network, and a distributed model predictive control is used to realize the collaborative coverage of multiple UAVs. The experimental results show that the strategy can achieve high coverage and a low energy consumption index under the constraints of collision avoidance. The favorable performance in DECSMU on different regions also demonstrate that it has outstanding stability and generality.

Water-energy-food-greenhouse gas nexus: An approach to solutions for water scarcity in agriculture of a semi-arid region

CONTEXTAgricultural systems involve intricate interdependencies among water, energy, and food. Increasing understanding of these linkages, along with implications for greenhouse gas (GHG) emissions and developing new assessment approaches are critical for achieving key sustainability goals. OBJECTIVES1) Evaluation of the impacts of tillage, irrigation and residue management practices on water and energy consumption, and GHG emission in the cultivation of irrigated wheat and rapeseed, 2) Developing a novel Water-Energy-Food-Greenhouse gas (WEFG) nexus index to provide a holistic assessment of the linkages among water, energy, food, and GHG emissions in agriculture, and 3) Assessing the sustainability of irrigated wheat and rapeseed cultivation under different methods of tillage, irrigation and residue management applying the WEFG nexus index. METHODSThis study formulated a new WEFG nexus index applying eight indicators of water and energy consumption, CO2-eq (CO2 equivalent) emission, water and energy mass productivity, water, energy and CO2-eq economic productivity to evaluate the sustainability of wheat and rapeseed cultivation under two field management practices: 1) furrow irrigation with conventional tillage (FICT), and 2) center pivot irrigation with no-tillage (CPNT), within a semi-arid region in northeastern Iran. Irrigation in both systems was done by applying a deficit irrigation approach. RESULTS AND CONCLUSIONSCPNT resulted on average in 46% and 53% energy consumption reductions from water and diesel usage, respectively, compared to FICT. The mean CO2-eq emission under CPNT was 26% lower than that recorded under FICT. Furthermore, the WEFG nexus index score for wheat and rapeseed under CPNT was 0.91 and 0.73, respectively, out of 1, compared to 0.18 and 0.12 for FICT. These scores suggest that the CPNT approach is a more appropriate strategy than FICT, as it effectively reduced water and energy consumption while aligning better with long-term environmental and economic aims. SIGNIFICANCEThe study applies more accurate GHG emission-based indicators to introduce a new WEFG nexus index, and it uses these for evaluation of different field management at the farm level to reduce the uncertainties in the large-scale studies. The proposed methodology for assessing multiple aspects of the WEFG nexus can change previous perceptions about agricultural management in other regions confronting multiple resources and environmental crises.

Multi-objective optimization method for medium and long-term power supply and demand balance considering the spatiotemporal correlation of source and load

The medium and long-term supply-demand imbalance of the power system in the context of the new power system is becoming more and more prominent due to the fluctuation and intermittency brought about by the high proportion of new energy sources connected to the grid. In this regard, a multi-objective power supply-demand balance optimization method considering the spatiotemporal correlation of source and load is proposed in this work. First, the autocorrelation and inter-correlation characteristics of source and load are analyzed. On this basis, a multi-dimensional scenario set construction method considering the spatiotemporal correlation of source and load is proposed. Then, the planning capacity of each regional power source and the system operation under each scenario are taken as the optimization variables. Renewable energy electricity curtailment, equivalent annual total cost, and inter-region transmission electricity are taken as the optimization objectives. Various constraints such as power source planning and operation, power balance, inter-region power transmission, and renewable energy power curtailment rate are considered comprehensively. The optimization method for the medium and long-term power supply and demand balance is proposed. Finally, the method is applied to Hunan Province, China to guide power planning. The results show that compared with traditional multi-dimensional correlation scene construction methods, the average probability density functions error of wind turbine output, photovoltaic output, and load constructed in this work decrease by 44.08 %, 73.64 %, and 57.54 %, respectively. It takes into account the regional, temporal, temporal autocorrelation, and inter-correlation of the source and load, and has similar characteristics to historical data. Compared with traditional planning that only considers economy, the optimization plan for power supply and demand balance in this work reduces electricity curtailment and inter-region transmission by 97.04 % and 72.71 %, respectively, balancing renewable energy consumption, economy, and regional independent balancing indicators.

The impact of renewable energy on inflation in G7 economies: Evidence from artificial neural networks and machine learning methods

This paper examines the impact of cleaner energy adoption (i.e., renewable energy consumption and generation) on inflation rates in G7 economies from 1997 to 2021. The Principal Component Analysis is used to construct the renewable energy consumption and generation indices. Then, the paper runs various artificial neural networks and machine learning methods to test the validity of the cleaner energy-led inflationary economy hypothesis. It is observed that renewable energy consumption and production significantly predict inflation rates along with macroeconomic variables. The effects of renewable energy consumption and production on inflation rates are positive. Related policy implications are also discussed.