Search Results (154)

Under the construction background of carbon peak and carbon neutrality, high-energy-consuming enterprises, represented by the electrolytic aluminum industry, have become important carriers for energy conservation and emission reduction. These enterprises are characterized by significant energy consumption and high carbon emissions, greatly impacting the economic and environmental benefits of regional power grids. Existing research often focuses on grid revenue, leaving high-energy-consuming enterprises in a passive regulatory position. To address this, this paper constructs an economic dispatch strategy for power grids that considers waste heat utilization in high-energy-consuming enterprises. A typical representative, electrolytic aluminum load and its waste heat utilization model, for the entire production process of high-energy-consuming loads, is established. Using a tiered carbon trading calculation formula, a low-carbon production scheme for high-energy-consuming enterprises is developed. On the grid side, considering local load levels, the uncertainty of wind power output, and the energy demands of aluminum production, a robust day-ahead economic dispatch model is established. Case analysis based on the modified IEEE-30 node system demonstrates that the proposed method balances economic efficiency and low-carbon performance while reducing the conservatism of traditional optimization approaches. Full article

This paper addresses the economic–environmental dispatch (EED) problem in DC power grids integrating thermoelectric and photovoltaic generation. A multi-objective optimization model is developed to minimize both fuel costs and CO₂ emissions while considering power balance, voltage constraints, generation limits, and thermal line capacities. To overcome the non-convexity introduced by quadratic voltage products in the power flow equations, a convex reformulation is proposed using second-order cone programming (SOCP) with auxiliary variables. This reformulation ensures global optimality and enhances computational efficiency. Two test systems are used for validation: a 6-node DC grid and an 11-node grid incorporating hourly photovoltaic generation. Comparative analyses show that the convex model achieves objective values with errors below 0.01% compared to the original non-convex formulation. For the 11-node system, the integration of photovoltaic generation led to a 24.34% reduction in operating costs (from USD 10.45 million to USD 7.91 million) and a 27.27% decrease in CO₂ emissions (from 9.14 million kg to 6.64 million kg) over a 24 h period. These results confirm the effectiveness of the proposed SOCP-based methodology and demonstrate the environmental and economic benefits of renewable integration in DC networks. Full article

Addressing the imperative for energy transition amid depleting fossil fuels, distributed generation (DG) is increasingly integrated into distribution networks (DNs). This integration necessitates low-carbon dispatching solutions that reconcile economic and environmental objectives. To bridge the gap between conventional “electricity perspective” optimization and emerging “carbon perspective” requirements, this research integrated Carbon Emission Flow (CEF) theory to analyze spatiotemporal carbon flow characteristics within DN. Recognizing the limitations of the single-objective approach in balancing multifaceted demands, a multi-objective optimization model was formulated. This model could capture the spatiotemporal dynamics of nodal carbon intensity for low-carbon dispatching while comprehensively incorporating diverse operational economic costs to achieve collaborative low-carbon and economic dispatch in DG-embedded DN. To efficiently solve this complex constrained model, a novel Q-learning enhanced Moth Flame Optimization (QMFO) algorithm was proposed. QMFO synergized the global search capability of the Moth Flame Optimization (MFO) algorithm with the adaptive decision-making of Q-learning, embedding an adaptive exploration strategy to significantly enhance solution efficiency and accuracy for multi-objective problems. Validated on a 16-node three-feeder system, the method co-optimizes switch configurations and DG outputs, achieving dual objectives of loss reduction and carbon emission mitigation while preserving radial topology feasibility. Full article

This paper proposes a ladder carbon trading-based low-carbon economic dispatch model for integrated energy systems (IESs), incorporating flexible load optimization and hybrid energy storage systems consisting of battery and thermal energy storage. First, a ladder-type carbon trading mechanism is introduced, in which the carbon trading cost increases progressively with emission levels, thereby providing stronger incentives for emission reduction. Second, flexible loads are categorized and modeled as shiftable, transferable, and reducible types, each with distinct operational constraints and compensation mechanisms. Third, both battery and thermal energy storage systems are considered to improve system flexibility by storing excess energy and supplying it when needed. Finally, a unified optimization framework is developed to coordinate the dispatch of renewable generation, gas turbines, waste heat recovery units, and multi-energy storage devices while integrating flexible load flexibility. The objective is to minimize the total system cost, which includes energy procurement, carbon trading expenditures, and demand response compensation. Three comparative case studies are conducted to evaluate system performance under different operational configurations: the proposed comprehensive model, a carbon trading-only approach, and a conventional baseline scenario. Results demonstrate that the proposed framework effectively balances economic and environmental objectives through coordinated demand-side management, hybrid storage utilization, and the ladder-type carbon trading market mechanism. It reshapes the system load profile via peak shaving and valley filling, improves renewable energy integration, and enhances overall system efficiency. Full article

Under the “dual carbon” goals, the low-carbon economic dispatch of integrated energy systems (IES) faces multiple challenges, including suboptimal economic efficiency, excessive carbon emissions, and limited renewable energy integration. While traditional green certificate trading (GCT) enhances renewable energy adoption, its emission reduction effect remains inadequate. Conversely, standalone carbon emission trading (CET) effectively curbs emissions but often at the expense of increased operational costs, making it difficult to achieve both economic and environmental objectives simultaneously. To address these limitations, this study proposes an innovative green certificate trading–tiered carbon emission trading (GCT–CET) synergistic mechanism integrated with demand-side flexible load optimization, developing a low-carbon dispatch model designed to minimize total system costs. Simulation experiments conducted with the CPLEX solver demonstrate that, compared to individual GCT or CET implementations, the proposed coordinated mechanism effectively combines renewable energy incentives (through GCT) with stringent emission control (via stepped CET), resulting in a 47.8% reduction in carbon emissions and a 5.4% decrease in total costs. Furthermore, the participation of flexible loads enhances supply–demand balancing, presenting a transformative solution for achieving high-efficiency and low-carbon operation in IES. Full article

This study proposes an improved multi-objective black-winged kite algorithm (MOBKA-QL) integrating Q-learning with adaptive mutation strategies for optimizing multi-objective scheduling in integrated energy systems (IES). The algorithm dynamically selects mutation strategies through Q-learning to enhance solution diversity and accelerate convergence. First, an optimal scheduling model is established, incorporating a carbon capture system (CCS), power-to-gas (P2G), solar thermal, wind power, and energy storage to minimize economic costs and carbon emissions while maximizing energy efficiency. Second, the heat-to-power ratio of the cogeneration system is dynamically adjusted according to load demand, enabling flexible control of combined heat and power (CHP) output. The integration of CCS+P2G further reduces carbon emissions and wind curtailment, with the produced methane utilized in boilers and cogeneration systems. Hydrogen fuel cells (HFCs) are employed to mitigate cascading energy losses. Using forecasted load and renewable energy data from a specific region, dispatch experiments demonstrate that the proposed system reduces economic costs and CO₂ emissions by 14.63% and 13.9%, respectively, while improving energy efficiency by 28.84%. Additionally, the adjustable heat-to-power ratio of CHP yields synergistic economic, energy, and environmental benefits. Full article

Thermal Energy Storage (TES) technologies improve solar power dispatchability by addressing the important challenge of energy intermittency. Sensible heat energy storage technology using materials based on Ordinary Portland Cement (OPC) is the simplest and most economical. However, the operation of these materials is limited to temperatures below 400 °C due to the structural degradation of OPC at this temperature. This paper investigates the design and development of inorganic polymers based on Construction and Demolition Waste (CDW) as a sustainable, low-cost, and environmentally friendly alternative to OPC-based materials for high-temperature sensible TES applications. Based on the ternary systems Na₂O-SiO₂-Al₂O₃ and K₂O-SiO₂-Al₂O₃, representative compositions of CDW-based inorganic polymers were theoretically designed and evaluated using the thermochemical software FactSage 7.0. The experimental verification of the theoretically designed inorganic polymers confirmed that they can withstand temperatures higher than 500 and up to 700 °C. The optimized materials developed compressive strength around 20 MPa, which was improved with temperatures up to 500 °C and then decreased. Moreover, they presented thermal capacities from 600 to 1090 J kg⁻¹ °C ⁻¹, thermal diffusivity in the range of 4.7–5.6 × 10⁻⁷ m² s⁻¹, and thermal conductivity from 0.6 to 1 W m⁻¹ °C⁻¹. These properties render the developed inorganic polymers significant candidates for TES applications. Full article

To achieve low-carbon economic dispatch and collaborative optimization of carbon capture efficiency in power systems, this paper proposes a flexible carbon capture power plant and generalized energy storage collaborative operation model under a dynamic carbon quota mechanism. First, adjustable carbon capture devices are integrated into high-emission thermal power units to construct carbon–electricity coupled operation modules, enabling a dynamic reduction of carbon emission intensity and enhancing low-carbon performance. Second, a time-varying carbon quota allocation mechanism and a dynamic correction model for carbon emission factors are designed to improve the regulation capability of carbon capture units during peak demand periods. Furthermore, pumped storage systems and price-guided demand response are integrated to form a generalized energy storage system, establishing a “source–load–storage” coordinated peak-shaving framework that alleviates the regulation burden on carbon capture units. Finally, a multi-timescale optimization scheduling model is developed and solved using the GUROBI algorithm to ensure the economic efficiency and operational synergy of system resources. Simulation results demonstrate that, compared with the traditional static quota mode, the proposed dynamic carbon quota mechanism reduces wind curtailment cost by 9.6%, the loss of load cost by 48.8%, and carbon emission cost by 15%. Moreover, the inclusion of generalized energy storage—including pumped storage and demand response—further decreases coal consumption cost by 9% and carbon emission cost by 17%, validating the effectiveness of the proposed approach in achieving both economic and environmental benefits. Full article

To facilitate power system decarbonization, optimizing clean energy integration has emerged as a critical pathway for establishing sustainable power infrastructure. This study addresses the multi-timescale operational challenges inherent in power networks with high renewable penetration, proposing a novel stochastic dynamic programming framework that synergizes intraday microgrid dispatch with a multi-phase carbon cost calculation mechanism. A probabilistic carbon flux quantification model is developed, incorporating source–load carbon flow tracing and nonconvex carbon pricing dynamics to enhance environmental–economic co-optimization constraints. The spatiotemporally coupled multi-microgrid (MMG) coordination paradigm is reformulated as a continuous state-action Markov game process governed by stochastic differential Stackelberg game principles. A communication mechanism-enabled multi-agent twin-delayed deep deterministic policy gradient (CMMA-TD3) algorithm is implemented to achieve Pareto-optimal solutions through cyber–physical collaboration. Results of the measurements in the MMG containing three microgrids show that the proposed approach reduces operation costs by 61.59% and carbon emissions by 27.95% compared to the least effective benchmark solution. Full article

The global energy transition is accelerating, bringing new challenges to power systems. A high penetration of renewable energy increases grid volatility. Virtual power plants (VPPs) address this by dynamically responding to market signals. They integrate renewables, energy storage, and flexible loads. Additionally, they participate in multi-tier markets, including energy, ancillary services, and capacity trading. This study proposes a load factor-based VPP pre-dispatch model for optimal resource allocation. It incorporates the coupling effects of electricity–carbon markets. A Nash negotiation strategy is developed for multi-VPP cooperation. The model uses an accelerated adaptive alternating-direction multiplier method (AA-ADMM) for efficient demand response. The approach balances computational efficiency with privacy protection. Revenue is allocated fairly based on individual contributions. The study uses data from a VPP dispatch center in Shanxi Province. Shanxi has abundant wind and solar resources, necessitating advanced scheduling methods. Cooperative operation boosts profits for three VPPs by CNY 1101, 260, and 823, respectively. The alliance’s total profit rises by CNY 2184. Carbon emissions drop by 31.3% to 8.113 tons, with a CNY 926 gain over independent operation. Post-cooperation, VPP1 and VPP2 see slight emission increases, while VPP3 achieves major reductions. This leads to significant low-carbon benefits. This method proves effective in cutting costs and emissions. It also balances economic and environmental gains while ensuring fair profit distribution. Full article

This paper examines how the slope of the merit-order curve and the share of non-zero-dollar dispatched energy affect photovoltaic (PV) price cannibalization and the declining market value of all generation types. Using historical merit-order data from Alberta, Canada—during its coal-to-gas transition—we simulated the introduction of zero-marginal-cost PV offers. The increased PV penetration rapidly suppresses midday electricity prices, forming a “duck curve” that challenges solar project economics. Emission reductions improve with rising carbon prices, indicating environmental benefits despite declining market revenues. Years with steeper merit-order slopes and lower non-zero-dollar dispatch shares show intensified price cannibalization and a reduced PV market value. The integration of battery storage alongside PV significantly flattened daily price profiles—raising the trough prices during charging and lowering the highest prices during discharging. While this reduces price volatility, it also diminishes the market value of all generation types, as batteries discharge at zero marginal cost during high-price hours. Battery arbitrage remains limited in low- and moderate-price regimes but becomes more profitable under high-price regimes. Overall, these dynamics underscore the challenges of integrating large-scale PV in energy-only markets, where price cannibalization erodes long-term investment signals for clean energy technologies. These insights inform sustainable energy policy design aimed at supporting decarbonization, and investment viability in liberalized electricity markets. Full article

Within the framework of “dual carbon”, intending to enhance the use of green energies and minimize the emissions of carbon from energy systems, this study suggests a cost-effective low-carbon scheduling model that accounts for stepwise carbon trading for an integrated electricity, heat, gas, cooling, and hydrogen energy system. Firstly, given the clean and low-carbon attributes of hydrogen energy, a refined two-step operational framework for electricity-to-gas conversion is proposed. Building upon this foundation, a hydrogen fuel cell is integrated to formulate a multi-energy complementary coupling network. Second, a phased carbon trading approach is established to further explore the mechanism’s carbon footprint potential. And then, an environmentally conscious and economically viable power dispatch model is developed to minimize total operating costs while maintaining ecological sustainability. This objective optimization framework is effectively implemented and solved using the CPLEX solver. Through a comparative analysis involving multiple case studies, the findings demonstrate that integrating electric-hydrogen coupling with phased carbon trading effectively enhances wind and solar energy utilization rates. This approach concurrently reduces the system’s carbon emissions by 34.4% and lowers operating costs by 58.6%. Full article

Dynamic line rating (DLR) technology dynamically adjusts the current-carrying capacity of transmission lines based on real-time environmental parameters and plays a critical role in maximizing line utilization, alleviating power flow congestion, and enhancing the security and economic efficiency of power systems. However, the strong coupling between the dynamic capacity and environmental conditions increases the system’s sensitivity to multiple uncertainties and causes complications in the overload risk assessment. Furthermore, conventional evaluation methods struggle to meet the minute-level risk refresh requirements in ultrashort-term forecasting scenarios. To address these challenges, in this study, an analytical overload risk assessment framework is proposed based on the second-order reliability method (SORM). By transforming multidimensional probabilistic integrals into analytical computations and establishing a multiscenario stochastic analysis model, the framework comprehensively accounts for uncertainties such as component random failures, wind power fluctuations, and load variations and enables the accurate evaluation of the overload probabilities under complex environmental conditions with DLR implementation. The results from this study provide a robust theoretical foundation for secure power system dispatch and optimization using multiscenario coupled modeling. The effectiveness of the proposed methodology is validated using case studies on a constructed test system. Full article

Gravity energy storage, a technology based on gravitational potential energy conversion, offers advantages including long lifespan, environmental friendliness, and low maintenance costs, demonstrating broad application prospects in renewable energy integration and grid peak regulation. This paper reviews the technical principles, characteristics, and application progress of liquid gravity energy storage (LGES), like pumped hydro storage (PHS) and solid gravity energy storage (SGES) systems—tower-based (T-SGES), shaft-type (S-SGES), rail-mounted (R-SGES), and mountain gravity energy storage (M-SGES). PHS, the most mature technology, is widely deployed for large-scale energy storage but faces significant geographical constraints. T-SGES and R-SGES exhibit higher flexibility for diverse terrains, while S-SGES leverage abandoned mines for resource reuse. Despite advantages such as high round-trip efficiency and extended lifecycle, challenges remain in efficiency optimization, high initial investments, and land utilization. Future development of gravity energy storage will require technological innovation, intelligent dispatch systems, and policy support to enhance economic viability and accelerate commercialization. Full article

Shared autonomous vehicles (SAVs) are emerging as a potential tool for sustainable transportation, yet their impact on travel behavior and environmental outcomes remains uncertain. This review evaluates the sustainability implications of SAV adoption, including its potential to reduce emissions through optimized fleet operations, enhance social equity by improving mobility access, and increase economic efficiency through resource-sharing models. This systematic literature review examines 107 articles from English and Chinese databases, focusing on SAVs’ effects on total travel demand, mode choice, and in-vehicle time use. Findings indicate that SAVs could increase vehicle miles traveled due to unoccupied relocation and new demand from previously underserved demographics, though advanced booking and dispatch systems may mitigate this increase. The study identifies 59 factors influencing SAV adoption, categorized as user-centric, contextual, and psycho-attitudinal. Analysis of in-vehicle time use shows varied activities, from productivity to leisure, with contradictory findings in the value of travel time (VOT) compared to conventional vehicles: while some studies report up to 34% lower VOT for SAVs due to multitasking opportunities, others find up to 29% higher VOT. Privacy and personal space emerge as important factors, with users showing a high willingness to pay to avoid additional passengers. The review highlights underexplored variables and methodological limitations in current research, including psychological influences and mode substitution dynamics. These insights inform policymakers and urban planners on how to integrate SAVs into sustainable transportation systems by mitigating their environmental impact, promoting equitable access, and ensuring alignment with smart urban planning strategies. Full article