Search Results (792)

The sustainable transition of power systems is currently hindered by fragmented carbon pricing systems and insufficient cross-market synergies. Considering this, we herein construct a system dynamics model of carbon tax regulation under conditions integrating electricity markets, carbon emission trading (CET) markets, and tradable green certificate (TGC) markets using Vensim PLE 7.3.5 software. We also propose a price-matching mechanism and implementation pathway for carbon taxation and CET to advance low-carbon sustainable development. The simulation results show that the introduction of a carbon tax at an initial rate of 50 CNY per ton significantly improves renewable energy investment returns. Moreover, effective coordination between the carbon tax and CET reduces carbon emissions from the power system, delivering benefits in terms of both environmental and socio-economic sustainability. We further identify a dynamic coordination scheme consisting of a carbon tax with an initial rate of 50 CNY per ton, which is appropriate when the CET prices stabilize at approximately 60 CNY per ton. An initial rate of 30 CNY per ton is more suitable when the CET prices rise above 100 CNY per ton. These findings verify the optimal matching rules for carbon tax intensity under different carbon allowance price levels, and they also provide quantitative policy tools and empirical support for the scenario-based regulation of carbon pricing systems to achieve sustainable energy transition goals. Full article

This study develops a bottom-up cost optimization model (DC-DECOM) to evaluate decarbonization pathways for Japan’s data center industry, targeting carbon neutrality of the information and communications technology (ICT) sector by 2040. The model represents Power Usage Effectiveness (PUE) as a dynamic function of ambient temperature and cooling technology, and integrates technology selection, regional energy supply, and carbon pricing within a single cost-minimization framework. Three scenarios are compared: a reference case (REF), a centralized carbon-neutral scenario (C-CN) that restricts new capacity to metropolitan areas, and a regional decentralization scenario (R-CN) that allows for nationwide siting. Input parameters are calibrated against data from the International Energy Agency (IEA), the Uptime Institute, Japan’s Ministry of Internal Affairs and Communications (MIC) White Papers, and the Japan Science and Technology Agency (JST). The R-CN scenario achieves the 2040 net-zero target at 18–23% lower total system cost than C-CN. The cost gap decomposes into four channels (cooling-energy reduction ∼35%, lower regional renewable procurement cost ∼30%, lower carbon cost ∼25%, and lower siting-related cost ∼10%). Sensitivity analysis identifies the carbon-price trajectory and the hardware-efficiency improvement rate as the most influential parameters; the R-CN advantage remains positive across all $\pm 1\sigma$ parameter variations and across two combined-scenario stress tests. Full article

The reduction of carbon dioxide related to the energy sector is one of the greatest challenges of this century. To ensure a proper transition towards a sustainable electric power system, innovative solutions are fundamental for the efficient integration of renewable energy sources. Hybrid Renewable Energy Systems (HRES) play a crucial role in this scenario; they can ensure a stable and reliable electricity supply thanks to the combination of different renewable technologies, particularly thanks to the integration of storage systems. However, the optimal sizing process of such systems is a complex challenge due to the multiple uncertainties that can be present, involving demand fluctuations and electricity zonal price variations. The aim of this work was to develop a Mixed-Integer Linear Programming (MILP) optimization approach for the robust sizing of a HRES under multiple sources of uncertainty. The developed hybrid model consists of a wind farm, a photovoltaic (PV) plant, a Battery Energy Storage System (BESS), and an industrial load with the entire infrastructure for connection to the national power grid. Additionally, the model includes the capability to manage the over-generation of renewable resources through curtailment mechanisms. The objective of the sizing tool is to minimize the Net Present Cost (NPC) of the plant, while ensuring the reliability of the system. The developed tool can represent a useful assistant for the evaluation of different possible configurations, helping the decision-making process during the design of a HRES. The results will show the best trade-off between economic and reliability aspects, highlighting the impact that the uncertainty has on the optimal size of the plant. In particular, the best configuration analyzed is able to reduce the NPC of more than 50% compared to a plant with a single renewable source. Full article

Against the backdrop of the global advancement of carbon neutrality goals and the energy transition in the building sector, zero-carbon buildings have emerged as pivotal enablers for achieving carbon neutrality in the construction industry. The rule-based scheduling of energy storage systems (ESS) is critical to enhancing energy efficiency and economic performance of buildings. This study takes the Jinan Zero-Carbon Operation Center Project in Shandong Province as the research object, developing a comprehensive technical framework covering the entire process from design to operation, and investigates the rule-based design and ESS scheduling strategies in response to Shandong’s newly implemented seasonal time-of-use (TOU) electricity pricing policy. First, core performance indicators are defined in accordance with national evaluation standards for zero-carbon buildings. Hourly building energy loads and photovoltaic (PV) generation profiles are simulated over a full year, which serves as the basis for determining the optimal PV installed capacity and ESS sizing. Second, an ESS scheduling strategy integrating PV generation forecasting and the seasonal TOU electricity price structure is formulated, with clear charging and discharging logic defined. Finally, the operational and economic performance of different scheduling modes are evaluated and compared through case studies. The results show that the annual PV generation ratio reaches 101.38%, with a self-consumption rate of 73% and a self-sufficiency rate of 72%, all meeting the core requirements for zero-carbon buildings. Compared with the conventional real-time scheduling mode (Mode 1), the proposed optimized mode (Mode 2) that incorporates TOU pricing and PV forecasting achieves an annual operational cost saving of 367,349 CNY, corresponding to a reduction of 47.02%. Distinct seasonal variations in core indicators are also observed: the PV generation ratio is lower in summer and winter but the self-consumption rate is higher, with the opposite trend in spring and autumn. The proposed technical framework and scheduling strategy provide practical guidance for the design and operational optimization of zero-carbon buildings and offer decision-making support for ESS operation under TOU electricity pricing policies. Full article

With the ongoing low-carbon transition of energy systems and the increasing penetration of distributed energy resources, the coordinated operation of heterogeneous prosumers has become essential for improving the economic and environmental performance of integrated energy systems. However, existing studies have not sufficiently addressed the joint coordination of electricity sharing, carbon emission trading, green certificate trading, and demand-side flexibility. To address this gap, this paper proposes a hybrid game-based optimal operation model for a multi-energy prosumer alliance coordinated by an Electricity Balance Service Provider (EBSP). The model is developed under coupled carbon emission trading (CET) and green certificate trading (GCT) markets. A piecewise linear dynamic pricing mechanism and a mutual recognition rule are introduced to describe the interaction between CET and GCT. Meanwhile, a price-based demand response model considering reducible and shiftable loads is incorporated to exploit load-side flexibility. On this basis, a Stackelberg-cooperative hybrid game is formulated to coordinate electricity pricing, integrated dispatch, electricity sharing, and benefit allocation between the EBSP and the prosumer alliance. The proposed model is solved using particle swarm optimization and the alternating direction method of multipliers. Case studies show that, compared with the corresponding benchmark scenarios, the proposed method reduces the alliance operating cost by 7.19%, the carbon trading cost by 41.35%, and total carbon emissions by 3.66%. It also decreases the peak-to-valley load difference ratio by 3.78 percentage points. These results demonstrate the effectiveness of the proposed method in improving economic performance, promoting low-carbon operation, and enhancing the peak-shaving and valley-filling capability of the prosumer alliance. Full article

As modern power systems undergo a paradigm shift toward decentralization, driven by substantial investments in Distributed Energy Resources (DERs), Virtual Power Plants (VPPs) have emerged as the primary mechanism for their effective technical and commercial integration. This paper provides a seminal and comprehensive literature review, dissecting the VPP ecosystem through operational, infrastructural, and coordination strategy perspectives. By categorizing VPPs into distinct technical and commercial frameworks, this study critically evaluates their role in optimizing smart grid components, including demand response, multifaceted market structures, cooperative game-theoretic behaviors, and multi-carrier energy systems. The analysis transcends basic infrastructure, focusing on the resolution of fundamental challenges: mitigating carbon emissions and energy costs, characterizing generation uncertainty and asynchrony, and maintaining the dynamic equilibrium between supply and demand. Furthermore, the review explores advanced strategies for incentivizing prosumer engagement, enhancing market pricing transparency, and ensuring transaction integrity within rigorous operational constraints. A significant methodological evolution is identified, highlighting the transition toward advanced mathematical frameworks and data-driven optimization techniques designed to enhance system resilience and operational stability under multifaceted uncertainties. The synthesis reveals that VPP-led sector coupling integrating electricity, thermal, and hydrogen vectors provides a robust pathway for minimizing grid imbalances and diminishing the overall carbon footprint. By evaluating the subject through a multidimensional lens (technical, economic, environmental, and regulatory) this study serves as a critical reference and strategic roadmap for researchers, planners, and policymakers aiming to navigate the complexities of future smart grids and build a sustainable energy ecosystem. Full article

The carbon allowance price series exhibits nonlinearity, non-stationarity, and high noise due to multiple factors. Accurate forecasting is crucial to the stability of the carbon market and to resource allocation. This paper proposes a forecasting framework using multi-scale decomposition and a TCN–LSTM hybrid model. First, the original carbon allowance price series is decomposed using CEEMDAN optimized by PSO. Then, VMD performs secondary decomposition of complex components based on sample entropy. Next, transfer entropy identifies causal relationships between each component and the original series, enabling reconstruction based on causality. Finally, a TCN–LSTM model uses reconstructed sequences to forecast carbon prices. The method achieves high-precision short-term forecasts using only the carbon allowance price series, avoiding reliance on external variables. Empirical results on the Hubei carbon market show an optimal lag of 3, with R² = 0.8873, outperforming the single LSTM and TCN models and achieving a lower RMSE. The forecast using January–March 2026 data shows stable carbon prices with slight fluctuations. This study provides a reliable method for data-constrained short-term carbon price forecasting, supporting decision-making and policy assessment. Full article

This study presents an integrated environmental assessment of plastic waste-to-hydrogen systems with varying CO₂ utilization ratios, combining process-level simulation with life-cycle assessment (LCA). The environmental impacts are evaluated across key categories, including global warming potential (GWP), fine particulate matter formation (PM), fossil resource scarcity (FRC), and water consumption (WC). The results reveal a non-linear relationship between CO₂ utilization and environmental impacts. As the CO₂ utilization ratio increases from the N₂ baseline to moderate levels (CO₂-40 to CO₂-50), environmental impacts decrease due to improved carbon utilization and reduced direct CO₂ emissions. However, further increases in CO₂ utilization lead to a reversal of this trend, with environmental burdens rising significantly due to increased energy and utility demand associated with intensified CO₂ recycling. Process contribution analysis shows that the dominant impact drivers shift from direct CO₂ emissions to utility-related contributions, particularly heat (steam) and electricity, at higher utilization levels. A trade-off analysis between direct CO₂ emissions and utility-related impacts identifies an optimal environmental operating range around CO₂-50. An integrated comparison with techno-economic performance, represented by the minimum hydrogen selling price (MHSP), reveals a divergence between environmental and economic optima. While environmental impacts are minimized at CO₂-40 to CO₂-50, the economic optimum occurs at higher utilization levels (CO₂-60 to CO₂-70). These results highlight that CO₂ utilization acts as a key design variable governing the trade-off between carbon efficiency and energy demand. An optimal compromise region is identified around CO₂-50 to CO₂-60, providing a balanced operating window for both environmental and economic performance. This study demonstrates that maximizing CO₂ utilization is not necessarily optimal from a system-level sustainability perspective and provides practical insights for the design and optimization of integrated plastic waste-to-hydrogen systems. Full article

At the Second Water Intake Pump Station of the Chenhang Reservoir in Shanghai, suboptimal pump scheduling resulted in electricity consumption cost attributable to pump-motor equipment accounting for an exceptionally large proportion of the total power expenditure. In response to the economical operation issues, a mathematical model of power consumption cost for the pump station was established by introducing time-of-use electricity pricing and constraint suppression terms. Taking the minimum cost as the research objective, the differential evolution (DE) algorithm was employed to establish a fitness function for electricity cost, aiming to find the most economical and reliable scheduling strategy. However, owing to its low computational speed and high complexity, machine learning was introduced to establish neural network surrogate models of the DE algorithm. By comparing three surrogate models, the Multilayer Perceptron (MLP) neural network model was adopted as the most appropriate surrogate model. It was optimized for robustness improvement and verified on site. The results demonstrate that implementing the surrogate model achieves over 25% savings in electricity cost per thousand cubic meters of water, while slashing the solution time by 88.53% compared to the standard DE algorithm. Furthermore, the overall power consumption is reduced by 2.20% under a cost-priority strategy and by 15.89% under a power-priority strategy, thereby directly mitigating the carbon footprint of the pump station. The proposed hybrid computational framework in this study bridges the gap between the computationally expensive heuristic optimization and the strict real-time control requirements in engineering, highlighting its significant contribution to the sustainable and low-carbon operation of water infrastructure. Full article

Achieving sustainability in the energy sector has become a critical global challenge under the increasing pressures of climate change. Amid increasingly severe global climate change, the Chinese government has attached great importance to carbon emission control and is actively advancing the construction of its carbon market. As a core element of carbon markets, the carbon price exerts a pivotal influence on the transition and low-carbon development of the coal-fired power industry. This paper undertakes an in-depth analysis of the low-carbon transition of China’s coal-fired power industry under the influence of the carbon price, aiming to provide a theoretical basis for policymakers in formulating relevant policies. It begins by reviewing the concept and characteristics of carbon markets, as well as the development status of both international and Chinese carbon markets. This is followed by an analysis of the current state of China’s coal-fired power industry, its transition needs, and the impacts of the carbon price on the industry. Subsequently, a self-developed comprehensive resource planning model is employed to simulate the effects of carbon price changes on the installed capacity development, structural adjustment, and carbon emissions from the power sector of China’s coal power enterprises. Finally, policy recommendations to facilitate the transition of the coal-fired power industry are proposed. The research findings indicate that changes in the carbon price have a significant impact on the coal-fired power industry, driving enterprises to reduce carbon emissions and improve energy efficiency. Furthermore, carbon market policies play a crucial role in promoting the low-carbon transition of the coal-fired power industry. Through policy adjustments and optimization, synergistic development between carbon market policies and the industry’s transition can be achieved. The findings provide important insights for enhancing environmental sustainability, economic efficiency, and system resilience in China’s power sector under carbon neutrality goals. Full article

Traditional fixed or linear carbon prices often fail to reflect the nonlinear incentives of real carbon markets. To address this, we propose a multi-objective optimal dispatch framework for integrated energy systems (IESs) incorporating a tiered carbon trading mechanism. The system—comprising photovoltaics, wind power, a gas turbine, energy storage (ESS), power-to-gas (P2G), and grid interaction—aims to minimize operating and carbon trading costs while maximizing renewable utilization. This is solved using an improved multi-objective particle swarm optimization (IMOPSO) algorithm. Simulations across five configurations reveal that tiered pricing nonlinearly penalizes high emissions, reshaping the Pareto front toward low-carbon outcomes. Consequently, the ESS evolves from a simple economic arbitrageur into a proactive “carbon buffer”, absorbing midday photovoltaic surpluses and substituting gas turbine output during evening peaks. Compared to a grid-only baseline, the optimized multi-energy configuration (gas turbine + ESS + P2G) reduced operating costs by 13.1% and carbon emissions by 9.9%, while increasing renewable utilization by 8.5%. Ultimately, this study demonstrates that a well-designed nonlinear carbon pricing mechanism is decisive for guiding the IES to achieve coordinated economic and low-carbon operation. Full article

Wind curtailment in Great Britain (GB) is increasing, leading to underutilisation of low-carbon energy and higher system costs. This paper develops a data-driven techno-economic framework for a hydrogen generation and storage system that converts curtailed wind energy into hydrogen. By modelling curtailment time series and electricity prices, and considering a proton exchange membrane (PEM) electrolyser-based power-to-gas system, The framework explicitly represents the operation and interaction of the PEM electrolyser, hydrogen compression, and high-pressure storage under time-varying curtailment and electricity price conditions using reconstructed GB curtailment time series. The levelised cost of hydrogen (LCOH), net present value (NPV), and delivered hydrogen volumes are evaluated. A new sizing metric, curtailment utilisation, is introduced to link curtailment availability with electrolyser and storage productivity. Using a GB curtailment dataset, two key relationships are identified. First, increasing access to low-cost curtailed energy reduces the LCOH until electrolyser utilisation saturates, beyond which additional energy purchases provide diminishing benefits. Second, hydrogen storage exhibits an economic optimum: Undersized tanks increase costs due to ramping and venting losses, whereas oversized tanks raise capital investment requirements and increase the LCOH. For the best-performing configuration, corresponding to 70.2 MWh of curtailed energy, a 2.3 MW electrolyser, and a 94 m³ high-pressure tank, the system achieves an LCOH of £3.51/kg H₂ (excluding downstream delivery) and an NPV of £2.17 M and meets 98.01% of the hydrogen demand. These results indicate that optimal system design requires not only appropriate component sizing but also explicit consideration of curtailment profiles and pricing structures. The proposed framework provides decision-grade guidance for developers and policymakers evaluating hydrogen production from wind curtailment. Future work will extend the model to hybridise with other energy storage system technologies, enable revenue stacking across multiple markets, address real-gas storage modelling, examine the sensitivity of stack degradation, and incorporate transport and delivery costs. These findings show that viable hydrogen production from curtailed wind depends on both low-cost electricity and coordinated electrolyser storage sizing under realistic curtailment conditions. The framework provides practical guidance for developers and policymakers. Full article

Electric vehicles (EVs) are essential for sustainable urban mobility, coordinating transportation demands with energy distribution networks. However, uncoordinated EV charging neglects trip chain continuity, inducing spatial–temporal congestion and overloading local charging capacities. Thus, effectively guiding EVs is a key problem in mitigating traffic emissions and preventing power grid-side stress. In this paper, a two-stage dynamic routing framework within a traffic–energy coordination architecture is proposed, integrating an AHP–Entropy–TOPSIS model for station selection and an Improved Ant Colony Optimization algorithm for trajectory execution. Using this framework, a series of macro–micro simulations on the Sioux Falls network was conducted alongside a congestion-driven dynamic pricing mechanism. The results indicate that the pricing strategy facilitates spatial load balancing through peak shaving at core nodes. Compared to conventional standard meta-heuristic baselines, this framework reduces average economic costs by 28.9% while ensuring battery safety and limiting indirect carbon emissions. The proposed framework provides a multi-objective navigation solution that prevents cross-layer decision fragmentation, supporting the sustainable development of smart city infrastructure. Full article

With the increasing integration of transportation and energy systems, highway energy replenishment facilities are gradually evolving into hybrid refueling stations that integrate photovoltaic generation, energy storage, battery charging, and hydrogen refueling. However, due to differences in resource conditions across stations, independently operated hybrid refueling stations find it difficult to simultaneously improve overall economic performance and renewable energy utilization. To address this issue, this paper investigates the coordinated operation and distributed optimization of highway hybrid refueling stations. First, an inter-station hydrogen–carbon–green certificate trading framework is established, and a trading model for a cluster of hybrid refueling stations is then developed on this basis. Then, the inter-station trading problem is decomposed into two subproblems: symmetric trading volume determination and asymmetric Nash bargaining-based price determination. These two subproblems are solved in a distributed manner using the alternating direction method of multipliers. In addition, a hydrogen transportation model is developed to translate trading decisions into feasible transportation arrangements under highway network and hydrogen tube trailer scheduling constraints. Finally, the case study demonstrates that the proposed model enables multi-resource sharing among hybrid refueling stations, reduces the overall system cost by 21.30%, and achieves a fairer distribution of benefits among stations. Full article

Remote communities in Canada face high electricity costs, energy insecurity, and significant greenhouse gas emissions due to heavy dependence on diesel generation. This study proposes and evaluates an AC-coupled hybrid distribution microgrid for remote off-grid communities, using Black Tickle, Newfoundland and Labrador as a representative case study. The system integrates two 200 kW wind turbines, a 200 kW diesel backup generator, a 16 MWh lithium-ion battery storage system, and a bidirectional converter, modeled and optimized in HOMER Pro 3.18.3 using local meteorological data, community load profiles, and a cycle-charging dispatch strategy. The optimized configuration achieves 86.7% wind penetration and 100% supply reliability with zero unmet load, yielding a total net present cost of USD 13.6 million and a levelized cost of energy of 0.999 USD/kWh over a 25-year horizon. Battery storage accounts for 73.5% of annualized costs, representing the primary economic challenge for wider deployment. Sensitivity analyses show that diesel price fluctuations exert approximately 4.1 times greater influence on system economics than equivalent carbon pricing changes, while the optimal configuration remains robust across all tested policy scenarios. These findings demonstrate that AC-coupled wind–diesel–battery microgrids offer a viable pathway for reducing fossil fuel dependence and supporting clean energy transition in remote, harsh-climate communities. Full article