Search Results (307)

The study presents a scalable decision-support framework to assess energy-sharing strategies within mixed-use urban districts, with a focus on planning, sustainability, and policy relevance. Two renewable energy-sharing mechanisms—energy sharing (ES) and net metering (NM)—are compared through a techno-economic analysis applied to a real neighborhood in Montréal, Canada. The workflow integrates irradiance-aware PV simulation, archetype-based urban building modeling, and financial sensitivity analysis adaptable to local regulatory conditions. Key performance indicators (KPIs)—including Self-Consumption Ratio (SCR), Self-Sufficiency Ratio (SSR), and peak load reduction—are used to evaluate technical performance. Results show that ES outperforms NM, achieving higher SCR (77% vs. 66%) and SSR (40% vs. 35%), and seasonal analysis reveals that peak shaving reaches 30.3% during summer afternoons, while PV impact is limited to 15.6% in winter mornings and negligible during winter evenings. Although both mechanisms are currently unprofitable under existing Québec tariffs, scenario analysis reveals that a 50% CAPEX subsidy or a 0.12 CAD/kWh feed-in tariff could make the system viable. The novelty of this study lies in the development of a replicable, archetype-driven, and policy-oriented simulation framework that enables the evaluation of renewable energy communities in mixed-use and data-scarce urban environments, contributing new insights into the Canadian energy transition context. Full article

The article proposes a novel modulation scheme with a low peak-to-average ratio (PAPR), referred to as offset rotation interpolation modulation (ORIM), which is particularly suitable for low-power consumption and enhanced coverage scenarios in the sixth generation (6G) of wireless communication. ORIM comprises three modulation schemes: I-QPSK, I-BPSK, and I-π/2 BPSK. They are derived from cyclic offsetting, phase rotation, and interpolation, and applied to QPSK, BPSK, and π/2 BPSK, respectively. Simulation results in discrete Fourier transform-spread-orthogonal frequency division multiplexing (DFT-s-OFDM) systems demonstrate that ORIM achieves a lower peak-to-average power ratio (PAPR) than the π/2-BPSK scheme specified in the 5G New Radio (NR) protocol, without incurring any performance degradation in terms of block error rate (BLER). Moreover, with the addition of frequency domain spectrum shaping (FDSS), I-π/2 BPSK demonstrates superior performance over π/2-BPSK in both PAPR and BLER metrics under the TDL-A channel conditions. In addition, the complexity of modulation at the transmitting end or demodulation at the receiving end of ORIM is of the same order of magnitude as that of π/2 BPSK, thereby achieving a certain level of overall performance improvement. Full article

In the process of responding to global climate change, carbon tariffs have attracted much attention as a new type of trade protection and environmental governance means. The European Union is a pioneer in global carbon tariff policies. Currently, there is no research system to assess the impact of the Carbon Border Adjustment Mechanism on China’s economic, energy and environmental development. Based on the dynamic computable general equilibrium model, this paper assesses the long-term impact of Carbon Border Adjustment Mechanism on China’s economic growth, power supply and demand, and environmental benefits. The research findings are as follows: (1) The implementation of Carbon Border Adjustment Mechanism has reduced China’s total GDP, especially when the free quota was completely abolished, which is when the decline was the greatest; The output of high energy-consuming industries such as steel and aluminum will also decrease simultaneously. (2) The implementation of Carbon Border Adjustment Mechanism has significantly increased the proportion of photovoltaic power generation, while reducing the electricity consumption of the manufacturing industry, accelerating the green transformation of China’s power generation structure. (3) Carbon Border Adjustment Mechanism has enabled China to reach its carbon peak earlier and lower the peak value, but the marginal cost of emission reduction is higher than that of existing carbon reduction measures. This research is of great significance for addressing the challenges of Carbon Border Adjustment Mechanism and promoting the low-carbon transformation of the economy. Full article

Addressing the carbon footprint in pig production is a fundamental technical basis for achieving carbon neutrality and peak carbon emissions. Only by systematically studying the carbon footprint can the goals of carbon neutrality and peak carbon emissions be effectively realized. This study aims to reduce the carbon footprint through optimized feeding strategies based on minimizing carbon emissions. To this end, this study conducted a full-lifecycle monitoring of the carbon footprint during pig growth from December 2024 to May 2025, optimizing feeding strategies using a real-time pig weight estimation model driven by deep learning to reduce resource consumption and the carbon footprint. We introduce EcoSegLite, a lightweight deep learning model designed for non-contact real-time pig weight estimation. By incorporating ShuffleNetV2, Linear Deformable Convolution (LDConv), and ACmix modules, it achieves high precision in resource-constrained environments with only 1.6 M parameters, attaining a 96.7% mAP50. Based on full-lifecycle weight monitoring of 63 pigs at the Pianguan farm from December 2024 to May 2025, the EcoSegLite model was integrated with a life cycle assessment (LCA) framework to optimize feeding management. This approach achieved a 7.8% reduction in feed intake, an 11.9% reduction in manure output, and a 5.1% reduction in carbon footprint. The resulting growth curves further validated the effectiveness of the optimized feeding strategy, while the reduction in feed and manure also potentially reduced water consumption and nitrogen runoff. This study offers a data-driven solution that enhances resource efficiency and reduces environmental impact, paving new pathways for precision agriculture and sustainable livestock production. Full article

Reducing energy consumption in belt conveyor systems is critical to improving the overall energy efficiency of lignite mining operations. This study presents a theoretical and empirical analysis of energy use in overburden and coal conveyors, with a focus on balancing the relationship between belt speed and load. Building on the theory of conveyor motion resistance, the energy consumption index (WskZE)—previously introduced by the authors—is revisited as a function of two key variables: belt speed (v) and real-time material flow rate (Q_r). Empirical validation was conducted using operational data from variable-speed conveyors in the Konin lignite mine and compared to similar-length conveyors in the Bełchatów mine. Energy consumption measurements allowed for the analysis of energy consumption for two different scenarios: (i) in the Bełchatów mine the belt speed was constant and the excavator capacity was variable and (ii) in the Konin mine the excavator capacity was kept constant and the conveyor belt speed was varied. The results confirm that WskZE is linearly dependent on belt speed and inversely proportional to throughput, as predicted by theoretical models. However, findings also show that lowering belt speed—while effective in reducing energy use—results in a higher proportion of power being consumed to move the belt and heavy idlers, especially when these components are sized for peak loads. This study suggests a revised conveyor design philosophy (a new paradigm) that emphasizes maximizing the mass ratio of transported material to moving components. Additionally, it recommends integrating real-time monitoring of energy performance indicators into mine control systems to enable energy-aware operational decisions. Full article

With the rapid development of energy transformation, the proportion of new energy is increasing, and the efficient trading mechanism of multi-microgrids can realize energy sharing to improve the consumption rate of new energy. A distributed multi-microgrid cooperative energy sharing strategy is proposed based on Nash bargaining. Firstly, by comprehensively considering the adjustable heat-to-electrical ratio, ladder-type positive and negative carbon trading, peak–valley electricity price and demand response, a multi-microgrid system with wind–solar-storage-load and combined heat and power is constructed. Then, a multi-microgrid cooperative game optimization framework is established based on Nash bargaining, and the complex nonlinear problem is decomposed into two stages to be solved. In the first stage, the cost minimization problem of multi-microgrids is solved based on the alternating direction multiplier method to maximize consumption rate and protect privacy. In the second stage, through the established contribution quantification model, Nash bargaining theory is used to fairly distribute the benefits of cooperation. The simulation results of three typical microgrids verify that the proposed strategy has good convergence properties and computational efficiency. Compared with the independent operation, the proposed strategy reduces the cost by 41% and the carbon emission by 18,490 kg, thus realizing low-carbon operation and optimal economic dispatch. Meanwhile, the power supply pressure of the main grid is reduced through energy interaction, thus improving the utilization rate of renewable energy. Full article

In line with the goals of “peak carbon emissions and carbon neutrality”, this study aims to develop a market-coordinated operation mechanism to promote renewable energy adoption and consumption, addressing the challenges of integrating medium- and long-term trading with spot markets in power systems with high renewable energy penetration. A three-stage joint operation framework is proposed. First, a medium- and long-term trading game model is established, considering multiple energy types to optimize the benefits of market participants. Second, machine learning algorithms are employed to predict renewable energy output, and a contract decomposition mechanism is developed to ensure a smooth transition from medium- and long-term contracts to real-time market operations. Finally, a day-ahead market-clearing strategy and an incentive-compatible settlement mechanism, incorporating the constraints from contract decomposition, are proposed to link the two markets effectively. Simulation results demonstrate that the proposed mechanism effectively enhances resource allocation and stabilizes market operations, leading to significant revenue improvements across various generation units and increased renewable energy utilization. Specifically, thermal power units achieve a 19.12% increase in revenue, while wind and photovoltaic units show more substantial gains of 38.76% and 47.52%, respectively. Concurrently, the mechanism drives a 10.61% increase in renewable energy absorption capacity and yields a 13.47% improvement in Tradable Green Certificate (TGC) utilization efficiency, confirming its overall effectiveness. This research shows that coordinated optimization between medium- and long-term/spot markets, combined with a well-designed settlement mechanism, significantly strengthens the market competitiveness of renewable energy, providing theoretical support for the market-based operation of the new power system. Full article

Cities serve as the primary arenas for achieving the strategic objectives of “carbon peak and carbon neutrality”. This study employed the LMDI method to systematically analyze the evolution trend of energy-related carbon emissions in Hong Kong and their influencing factors from 1980 to 2023. The main findings are as follows: (1) Hong Kong’s energy consumption structure remains dominated by coal and oil. Influenced by energy prices, significant shifts in this structure occurred across different periods. Imported electricity from mainland China, in particular, has exerted a promoting effect on the optimization of its energy consumption mix. (2) Economic output and population concentration are the primary drivers of increased carbon emissions. However, the contribution of economic growth to carbon emissions has gradually weakened in recent years due to a lack of new growth drivers. (3) Energy consumption intensity, energy consumption structure, and carbon intensity are the primary influencing factors in curbing carbon emissions. Among these, the carbon reduction impact of energy consumption intensity is the most significant. Hong Kong should continue to adopt a robust strategy for controlling total energy consumption to effectively mitigate carbon emissions. Additionally, it should remain vigilant regarding the potential implications of future energy price fluctuations. It is also essential to sustain cross-border energy cooperation, primarily based on electricity imports from the Pearl River Delta, while simultaneously expanding international and domestic supply channels for natural gas. Full article

As part of the carbon peak and neutrality drive, an influx of renewable energy into the grid is imminent. However, the unpredictability of renewables like wind and solar can lead to significant curtailment if the power system relies solely on traditional generators. This paper presents a demand response mechanism to enhance renewable energy uptake by defining an optimal load curve for each node, considering the generator’s dynamic impact, system operations, and renewable energy projections. Once the ideal load curve is published, consumers, influenced by incentives, voluntarily align their consumption, steering the actual load to resemble the proposed curve. This strategy not only guides flexible generation resources to better utilize renewables but also minimizes the communication and control expenses associated with large-scale customer demand response. Additionally, a new evaluation metric for user response is proposed to ensure equitable incentive distribution. The model has been shown to lower both consumer power costs and system generation expenses, achieving a 22% reduction in renewable energy wastage. Full article

Against the backdrop of the increasing penetration rate of new energy year by year, power systems face a continuously growing demand for flexibility. Under the structure of such a new power system, it is essential not only to introduce diverse flexible power sources but also to explore the flexible regulation capabilities of existing conventional power sources. To fully utilize the flexibility of thermal power units (TPUs), this study proposes a real-time optimal scheduling strategy for a wind–thermal energy-storage integrated system with an adaptive time division and variable objectives. Based on the evaluation results of the real-time flexible supply–demand relationship within a regional power grid, the operation modes of TPUs are categorized into three types: economic mode, peak shaving mode, and coordination mode. For each operation mode, corresponding optimization objectives are defined, and an energy storage control strategy is developed to assist in the peak shaving of TPUs. While effectively harnessing the flexibility of TPUs, the proposed method reduces both the frequency and capacity of TPUs entering deep peak shaving. Using data from a province in Northwest China as a case study, simulation calculations and analyses demonstrate that the proposed method increases renewable energy consumption by 314.37 MWh while decreasing system economic benefits by CNY 129,000. Compared with traditional scheduling methods for TPUs to accommodate renewable energy, the system benefit increases by CNY 297,000, and an additional 13.53 MWh of peak wind power is accommodated. These results confirm that the proposed scheduling strategy can significantly enhance the system’s ability to integrate new energy while maintaining its economic efficiency. Full article

This paper addresses the critical need for detailed electricity and peak power demand maps for urban public transportation vehicles. Current approaches often rely on overly general assumptions, leading to considerable errors in specific applications or, conversely, overly specific measurements that limit generalisability. We aim to present a comprehensive data-driven methodology for analysing energy consumption within a large urban agglomeration. The method leverages a unique and extensive set of real-world performance data, collected over two years from onboard recorders on all public bus lines in the Capital City of Warsaw. This large dataset enables a robust probabilistic analysis, ensuring high accuracy of the results. For this study, three representative bus lines were selected. The approach involves isolating inter-stop trips, for which instantaneous power waveforms and energy consumption are determined using classical mathematical models of vehicle drive systems. The extracted data for these sections is then characterised using probability distributions. This methodology provides accurate calculation results for specific operating conditions and allows for generalisation with additional factors like air conditioning or heating. The direct result of this paper is a detailed urban map of energy demand and peak power for public transport vehicles. Such a map is invaluable for planning new traffic routes, verifying existing ones regarding energy consumption, and providing a reliable input source for strategic charger deployment analysis along the route. Full article

Nowadays, when battery-powered electric vehicles (EVs) travel along motorways, their drivers decide where to recharge their cars’ batteries with no or scarce information on the occupancy status of the next charging stations. While this may still be acceptable in most countries, due to the limited number of EVs on motorways, long queues may build-up in the coming years with increased electric mobility, unless smart allocation strategies are designed and implemented. For instance, as we shall investigate in this manuscript, a centralised coordination of the charging strategies of individual EVs has the potential to significantly reduce the queuing time at charging stations. In particular, in this paper we explain how the charging problem on motorways can be modelled as an optimisation problem, we propose some strategies based on dynamic optimisation to solve it, and we explain how this may be implemented in practice using a centralised charge manager that exchanges information with the EVs and solves the optimisation problems. Finally, we compare in a realistic scenario the current decentralised recharging strategies with a centralised one, and we show that, under simplifying assumptions, queueing times can be reduced by more than 50%. Such a significant reduction allows one to greatly improve vehicular flows and general journey durations without requiring building new infrastructure. Reducing queuing times has a positive impact on traffic congestion and emissions, and the more geographically balanced energy demand of the proposed methodology mitigates energy consumption peaks. Full article

Under carbon emission constraints, to promote low-carbon transformation and achieve the aim of carbon peaking and carbon neutrality in the energy sector, this paper constructs an operational optimization model for the coordinated operation of a virtual power plant cluster (VPPC). Considering the resource characteristics of different virtual power plants (VPPs) within a cooperative alliance, we propose a multi-VPP interaction and sharing architecture accounting for electricity–carbon interaction. An optimization model for VPPC is developed based on the asymmetric Nash bargaining theory. Finally, the proposed model is solved using an alternating-direction method of multipliers (ADMM) algorithm featuring an improved penalty factor. The research results show that P2P trading within the VPPC achieves resource optimization and allocation at a larger scale. The proposed distributed ADMM solution algorithm requires only the exchange of traded electricity volume and price among VPPs, thus preserving user privacy. Compared with independent operation, the total operation cost of the VPPC is reduced by 20.37%, and the overall proportion of new energy consumption is increased by 16.83%. The operation costs of the three VPPs are reduced by 1.12%, 20.51%, and 6.42%, respectively, while their carbon emissions are decreased by 4.47%, 5.80%, and 5.47%, respectively. In addition, the bargaining index incorporated in the proposed (point-to-point) P2P trading mechanism motivates each VPP to enhance its contribution to the alliance to achieve higher bargaining power, thereby improving the resource allocation efficiency of the entire alliance. The ADMM algorithm based on the improved penalty factor demonstrates good computational performance and achieves a solution speed increase of 15.8% compared to the unimproved version. Full article

This paper shows that using renewable energy sources in the power supply of transportation infrastructure is gradually becoming a new trend. Renewable energy systems are already valuable for railway and automotive infrastructure in various countries; however, this use is limited. This paper examines the improvement of control in a grid-connected, hybrid renewable energy system to meet the needs of a railway transportation infrastructure object by utilizing an additional diesel generator in autonomous mode. The aim is to reduce the depth of battery discharge and limit energy consumption from the grid during peak demand hours, considering the wide fluctuations in power consumption of the object and deviations in renewable energy generation relative to the forecast. Additionally, the task of ensuring long-term autonomous operation of the system is addressed. A control system is proposed based on the deviation of the battery’s state of charge relative to a set schedule, which is determined according to the forecast using an additional variable that sets the power consumption limit. This ensures the minimum possible depth of discharge and peak consumption, taking into account the generation of renewable energy sources, with a power-increase factor ranging from 1 to 1.5 relative to the calculated value. In autonomous mode, the task of minimizing energy consumption by the diesel generator is addressed. Solutions have been developed to implement control in grid and autonomous modes with the corresponding calculation algorithm. The system is not sensitive to the load schedule, and the battery’s depth of discharge limitations are maintained even when renewable energy generation is below the forecast by up to 20%. When generating renewable energy sources below the average monthly value in summer, it is possible to maintain a DoD of no less than 60%. Full article

The rapid growth of urban metro systems requires novel strategies to guarantee operational dependability and energy efficiency. This article describes a new way to use deep reinforcement learning (DRL) to help metro networks with predictive maintenance that adapts to changing conditions and energy optimization. We used real-world transit data from the General Transit Feed Specification (GTFS) to model the maintenance scheduling and energy management problem as a Markov Decision Process. This included important operational metrics like peak-hour demand, train arrival times, and station stop densities. A custom reinforcement learning environment mimics the changing conditions of metro operations. Deep Q-Networks (DQNs) and Proximal Policy Optimization (PPO) sophisticated deep reinforcement learning techniques were used to identify the optimal policies for decreasing energy consumption and downtime. The PPO hyperparameters were additionally optimized using Bayesian optimization by implementing Optuna, which produces a far greater performance than baseline DQNs and basic PPO. Comparative tests showed that our improved DRL-based method improves the accuracy of predictive maintenance and the efficiency of energy use, which lowers operational costs and raises the dependability of the service. These results show that advanced learning and optimization techniques could be added to public transportation systems in cities. This could lead to more sustainable and smart transportation management in big cities. Full article