As the electrification process advances,enormous power flexibility is becoming available on the demand side,which can be harnessed to facilitate power system decarbonisation.Hence,this paper studies the carbon-aware d...As the electrification process advances,enormous power flexibility is becoming available on the demand side,which can be harnessed to facilitate power system decarbonisation.Hence,this paper studies the carbon-aware demand response(C-DR)paradigm,where individual users aim to minimise their carbon footprints through the optimal scheduling of flexible load devices.The specific operational dynamics and constraints of deferrable loads and thermostatically controlled loads are considered,and the carbon emission flow method is employed to compute users'carbon footprints with nodal carbon intensities.Then,an optimal power dispatch model that integrates the C-DR mechanism is proposed for low-carbon power system operation,based on the carbon-aware optimal power flow method.Two solution algorithms,including a centralised Karush-Kuhn-Tucker refor-mulation algorithm and an iterative solution algorithm,are developed to solve the bi-level power dispatch optimisation model.Numerical simulations on the IEEE New England 39-bus system demonstrate the effectiveness of the proposed methods.展开更多
Traditional demand response(DR)programs for energy-intensive industries(EIIs)primarily rely on electricity price signals and often overlook carbon emission factors,limiting their effectiveness in supporting lowcarbon ...Traditional demand response(DR)programs for energy-intensive industries(EIIs)primarily rely on electricity price signals and often overlook carbon emission factors,limiting their effectiveness in supporting lowcarbon transitions.To address this challenge,this paper proposes an electricity–carbon integratedDR strategy based on a bi-level collaborative optimization framework that coordinates the interaction between the grid and EIIs.At the upper level,the grid operatorminimizes generation and curtailment costs by optimizing unit commitment while determining real-time electricity prices and dynamic carbon emission factors.At the lower level,EIIs respond to these dual signals by minimizing their combined electricity and carbon trading costs,considering their participation in medium-and long-term electricity markets,day-ahead spot markets,and carbon emissions trading schemes.The model accounts for direct and indirect carbon emissions,distributed photovoltaic(PV)generation,and battery energy storage systems.This interaction is structured as a Stackelberg game,where the grid acts as the leader and EIIs as followers,enabling dynamic feedback between pricing signals and load response.Simulation studies on an improved IEEE 30-bus system,with a cement plant as a representative user form EIIs,show that the proposed strategy reduces user-side carbon emissions by 7.95% and grid-side generation cost by 4.66%,though the user’s energy cost increases by 7.80% due to carbon trading.Theresults confirmthat the joint guidance of electricity and carbon prices effectively reshapes user load profiles,encourages peak shaving,and improves PV utilization.This coordinated approach not only achieves emission reduction and cost efficiency but also offers a theoretical and practical foundation for integrating carbon pricing into demand-side energy management in future low-carbon power systems.展开更多
The integration of deep learning into smart grid operations addresses critical challenges in dynamic load forecasting and optimal dispatch amid increasing renewable energy penetration.This study proposes a hybrid LSTM...The integration of deep learning into smart grid operations addresses critical challenges in dynamic load forecasting and optimal dispatch amid increasing renewable energy penetration.This study proposes a hybrid LSTM-Transformer architecture for multi-scale temporal-spatial load prediction,achieving 28%RMSE reduction on real-world datasets(CAISO,PJM),coupled with a deep reinforcement learning framework for multi-objective dispatch optimization that lowers operational costs by 12.4%while ensuring stability constraints.The synergy between adaptive forecasting models and scenario-based stochastic optimization demonstrates superior performance in handling renewable intermittency and demand volatility,validated through grid-scale case studies.Methodological innovations in federated feature extraction and carbon-aware scheduling further enhance scalability for distributed energy systems.These advancements provide actionable insights for grid operators transitioning to low-carbon paradigms,emphasizing computational efficiency and interoperability with legacy infrastructure.展开更多
With the deepening of global digital transformation,digital resources have become key elements in reshaping economic and social structures,but their material footprint and energy use intensify ecological pressure and ...With the deepening of global digital transformation,digital resources have become key elements in reshaping economic and social structures,but their material footprint and energy use intensify ecological pressure and ethical risks such as algorithmic opacity,data divides,and intergenerational injustice.This paper examines“green orientation and ethical reconstruction in digital resource governance”at theoretical,instrumental,and institutional levels.Theoretically,it uses the tension between Ecological Modernization Theory and the Jevons Paradox to argue that digital environmental governance must shift from growth logic to ecological rationality.Instrumentally,it analyzes Carbon-Aware Computing,Digital Product Passports(DPP),and ISO/IEC 30134 indicators as a lifecycle-based green governance toolkit for digital infrastructure.Institutionally,it compares the European Union’s rule-oriented governance with China’s engineering-oriented governance and incorporates frugal digital innovation in the Global South to outline diversified governance paths.The paper then proposes a digital ethics framework integrating environmental responsibility,social inclusion,and intergenerational justice,and argues that future digital governance should move beyond a single efficiency paradigm toward composite governance centered on ecological integrity and social justice.展开更多
基金Blockchain&Energy Research Consortium,Texas A&M University。
文摘As the electrification process advances,enormous power flexibility is becoming available on the demand side,which can be harnessed to facilitate power system decarbonisation.Hence,this paper studies the carbon-aware demand response(C-DR)paradigm,where individual users aim to minimise their carbon footprints through the optimal scheduling of flexible load devices.The specific operational dynamics and constraints of deferrable loads and thermostatically controlled loads are considered,and the carbon emission flow method is employed to compute users'carbon footprints with nodal carbon intensities.Then,an optimal power dispatch model that integrates the C-DR mechanism is proposed for low-carbon power system operation,based on the carbon-aware optimal power flow method.Two solution algorithms,including a centralised Karush-Kuhn-Tucker refor-mulation algorithm and an iterative solution algorithm,are developed to solve the bi-level power dispatch optimisation model.Numerical simulations on the IEEE New England 39-bus system demonstrate the effectiveness of the proposed methods.
基金supported by the Science and Technology Project of Yunnan Power Grid Co.,Ltd.under Grant No.YNKJXM20222410.
文摘Traditional demand response(DR)programs for energy-intensive industries(EIIs)primarily rely on electricity price signals and often overlook carbon emission factors,limiting their effectiveness in supporting lowcarbon transitions.To address this challenge,this paper proposes an electricity–carbon integratedDR strategy based on a bi-level collaborative optimization framework that coordinates the interaction between the grid and EIIs.At the upper level,the grid operatorminimizes generation and curtailment costs by optimizing unit commitment while determining real-time electricity prices and dynamic carbon emission factors.At the lower level,EIIs respond to these dual signals by minimizing their combined electricity and carbon trading costs,considering their participation in medium-and long-term electricity markets,day-ahead spot markets,and carbon emissions trading schemes.The model accounts for direct and indirect carbon emissions,distributed photovoltaic(PV)generation,and battery energy storage systems.This interaction is structured as a Stackelberg game,where the grid acts as the leader and EIIs as followers,enabling dynamic feedback between pricing signals and load response.Simulation studies on an improved IEEE 30-bus system,with a cement plant as a representative user form EIIs,show that the proposed strategy reduces user-side carbon emissions by 7.95% and grid-side generation cost by 4.66%,though the user’s energy cost increases by 7.80% due to carbon trading.Theresults confirmthat the joint guidance of electricity and carbon prices effectively reshapes user load profiles,encourages peak shaving,and improves PV utilization.This coordinated approach not only achieves emission reduction and cost efficiency but also offers a theoretical and practical foundation for integrating carbon pricing into demand-side energy management in future low-carbon power systems.
文摘The integration of deep learning into smart grid operations addresses critical challenges in dynamic load forecasting and optimal dispatch amid increasing renewable energy penetration.This study proposes a hybrid LSTM-Transformer architecture for multi-scale temporal-spatial load prediction,achieving 28%RMSE reduction on real-world datasets(CAISO,PJM),coupled with a deep reinforcement learning framework for multi-objective dispatch optimization that lowers operational costs by 12.4%while ensuring stability constraints.The synergy between adaptive forecasting models and scenario-based stochastic optimization demonstrates superior performance in handling renewable intermittency and demand volatility,validated through grid-scale case studies.Methodological innovations in federated feature extraction and carbon-aware scheduling further enhance scalability for distributed energy systems.These advancements provide actionable insights for grid operators transitioning to low-carbon paradigms,emphasizing computational efficiency and interoperability with legacy infrastructure.
文摘With the deepening of global digital transformation,digital resources have become key elements in reshaping economic and social structures,but their material footprint and energy use intensify ecological pressure and ethical risks such as algorithmic opacity,data divides,and intergenerational injustice.This paper examines“green orientation and ethical reconstruction in digital resource governance”at theoretical,instrumental,and institutional levels.Theoretically,it uses the tension between Ecological Modernization Theory and the Jevons Paradox to argue that digital environmental governance must shift from growth logic to ecological rationality.Instrumentally,it analyzes Carbon-Aware Computing,Digital Product Passports(DPP),and ISO/IEC 30134 indicators as a lifecycle-based green governance toolkit for digital infrastructure.Institutionally,it compares the European Union’s rule-oriented governance with China’s engineering-oriented governance and incorporates frugal digital innovation in the Global South to outline diversified governance paths.The paper then proposes a digital ethics framework integrating environmental responsibility,social inclusion,and intergenerational justice,and argues that future digital governance should move beyond a single efficiency paradigm toward composite governance centered on ecological integrity and social justice.
