To achieve the goals of sustainable development of the energy system and the construction of a lowcarbon society,this study proposes a multi-energy storage collaborative optimization strategy for industrial park that ...To achieve the goals of sustainable development of the energy system and the construction of a lowcarbon society,this study proposes a multi-energy storage collaborative optimization strategy for industrial park that integrates the laddered carbon trading mechanism with demand response.Firstly,a dual dimensional DR model is constructed based on the characteristics of load elasticity.The alternativeDRenables flexible substitution of energy loads through complementary conversion of electricity/heat/cold multi-energy sources,while the price DR relies on timeof-use electricity price signals to guide load spatiotemporal migration;Secondly,the LCT mechanism is introduced to achieve optimal carbon emission costs through a tiered carbon quota allocation mechanism.On this basis,an optimization decision model is established with the core objective of maximizing the annual net profit of the park.The objective function takes into account energy sales revenue,generator unit costs,and investment and operation costs of multiple types of energy storage facilities.Themodel constraint system covers three key dimensions:dynamic operation constraints of power generation units,including unit output limits,ramping capability,and minimum start-stop time;the physical boundary of an electric/hot/cold multi-energy storage system involves energy storage capacity and charge/discharge efficiency;The multi-energy network coupling balance equation ensures that the energy conversion and transmission process satisfies the law of conservation of energy.Using CPLEX mathematical programming solver for simulation verification,construct an energy storage capacity configuration decision process that includes LCT-DR synergistic effect.The research results show that compared with the traditional single energy storage configuration mode,this strategy effectively enhances the economic feasibility and engineering practicality of industrial park operation by coordinating demand side resource scheduling and finely controlling carbon costs,while maintaining stable system operation.Its methodological framework provides a technical path that combines theoretical rigor and practical operability for the low-carbon transformation of regional integrated energy systems.展开更多
The demand response(DR)market,as a vital complement to the electricity spot market,plays a key role in evoking user-side regulation capability to mitigate system-level supply‒demand imbalances during extreme events.Wh...The demand response(DR)market,as a vital complement to the electricity spot market,plays a key role in evoking user-side regulation capability to mitigate system-level supply‒demand imbalances during extreme events.While the DR market offers the load aggregator(LA)additional profitable opportunities beyond the electricity spot market,it also introduces new trading risks due to the significant uncertainty in users’behaviors.Dispatching energy storage systems(ESSs)is an effective means to enhance the risk management capabilities of LAs;however,coordinating ESS operations with dual-market trading strategies remains an urgent challenge.To this end,this paper proposes a novel systematic risk-aware coordinated trading model for the LA in concurrently participating in the day-ahead electricity spot market and DR market,which incorporates the capacity allocation mechanism of ESS based on market clearing rules to jointly formulate bidding and pricing decisions for the dual market.First,the intrinsic coupling characteristics of the LA participating in the dual market are analyzed,and a joint optimization framework for formulating bidding and pricing strategies that integrates ESS facilities is proposed.Second,an uncertain user response model is developed based on price‒response mechanisms,and actual market settlement rules accounting for under-and over-responses are employed to calculate trading revenues,where possible revenue losses are quantified via conditional value at risk.Third,by imposing these terms and the capacity allocation mechanism of ESS,the risk-aware stochastic coordinated trading model of the LA is built,where the bidding and pricing strategies in the dual model that trade off risk and profit are derived.The simulation results of a case study validate the effectiveness of the proposed trading strategy in controlling trading risk and improving the trading income of the LA.展开更多
In contemporary power systems,delving into the flexible regulation potential of demand-side resources is of paramount significance for the efficient operation of power grids.This research puts forward an innovative mu...In contemporary power systems,delving into the flexible regulation potential of demand-side resources is of paramount significance for the efficient operation of power grids.This research puts forward an innovative multivariate flexible load aggregation control approach that takes dynamic demand response into full consideration.In the initial stage,using generalized time-domain aggregation modelling for a wide array of heterogeneous flexible loads,including temperature-controlled loads,electric vehicles,and energy storage devices,a novel calculation method for their maximum adjustable capacities is devised.Distinct from conventional methods,this newly developed approach enables more precise and adaptable quantification of the load-adjusting capabilities,thereby enhancing the accuracy and flexibility of demand-side resource management.Subsequently,an SSA-BiLSTM flexible load classification prediction model is established.This model represents an innovative application in the field,effectively combining the advantages of the Sparrow Search Algorithm(SSA)and the Bidirectional Long-Short-Term Memory(BiLSTM)neural network.Furthermore,a parallel Markov chain is introduced to evaluate the switching state transfer probability of flexible loads accurately.This integration allows for a more refined determination of the maximum response capacity range of the flexible load aggregator,significantly improving the precision of capacity assessment compared to existing methods.Finally,in consonance with the intra-day scheduling plan,a newly developed diffuse filling algorithm is implemented to control the activation times of flexible loads precisely,thus achieving real-time dynamic demand response.Through in-depth case analysis and comprehensive comparative studies,the effectiveness of the proposed method is convincingly validated.With its innovative techniques and enhanced performance,it is demonstrated that this method has the potential to substantially enhance the utilization efficiency of demand-side resources in power systems,providing a novel and effective solution for optimizing power grid operation and demand-side management.展开更多
The integration of substantial renewable energy and controllable resources disrupts the supply-demand balance in distribution grids.Secure operations are dependent on the participation of user-side resources in demand...The integration of substantial renewable energy and controllable resources disrupts the supply-demand balance in distribution grids.Secure operations are dependent on the participation of user-side resources in demand response at both the day-ahead and intraday levels.Current studies typically overlook the spatial--temporal variations and coordination between these timescales,leading to significant day-ahead optimization errors,high intraday costs,and slow convergence.To address these challenges,we developed a multiagent,multitimescale aggregated regulation method for spatial--temporal coordinated demand response of user-side resources.Firstly,we established a framework considering the spatial--temporal coordinated characteristics of user-side resources with the objective to min-imize the total regulation cost and weighted sum of distribution grid losses.The optimization problem was then solved for two different timescales:day-ahead and intraday.For the day-ahead timescale,we developed an improved particle swarm optimization(IPSO)algo-rithm that dynamically adjusts the number of particles based on intraday outcomes to optimize the regulation strategies.For the intraday timescale,we developed an improved alternating direction method of multipliers(IADMM)algorithm that distributes tasks across edge distribution stations,dynamically adjusting penalty factors by using historical day-ahead data to synchronize the regulations and enhance precision.The simulation results indicate that this method can fully achieve multitimescale spatial--temporal coordinated aggregated reg-ulation between day-ahead and intraday,effectively reduce the total regulation cost and distribution grid losses,and enhance smart grid resilience.展开更多
Addressing climate change and facilitating the large-scale integration of renewable energy sources(RESs)have driven the development of hydrogen-coupled integrated energy systems(HIES),which enhance energy sustainabili...Addressing climate change and facilitating the large-scale integration of renewable energy sources(RESs)have driven the development of hydrogen-coupled integrated energy systems(HIES),which enhance energy sustainability through coordinated electricity,thermal,natural gas,and hydrogen utilization.This study proposes a two-stage distributionally robust optimization(DRO)-based scheduling method to improve the economic efficiency and reduce carbon emissions of HIES.The framework incorporates a ladder-type carbon trading mechanism to regulate emissions and implements a demand response(DR)program to adjustflexible multi-energy loads,thereby prioritizing RES consumption.Uncertainties from RES generation and load demand are addressed through an ambiguity set,enabling robust decision-making.The column-and-constraint generation(C&CG)algorithm efficiently solves the two-stage DRO model.Case studies demonstrate that the proposed method reduces operational costs by 3.56%,increases photovoltaic consumption rates by 5.44%,and significantly lowers carbon emissions compared to conventional approaches.Furthermore,the DRO framework achieves a superior balance between conservativeness and robustness over conventional stochastic and robust optimization methods,highlighting its potential to advance cost-effective,low-carbon energy systems while ensuring grid stability under uncertainty.展开更多
The rapid advancement of artificial intelligence(AI)has significantly increased the computational load on data centers.AI-related computational activities consume considerable electricity and result in substantial car...The rapid advancement of artificial intelligence(AI)has significantly increased the computational load on data centers.AI-related computational activities consume considerable electricity and result in substantial carbon emissions.To mitigate these emissions,future data centers should be strategically planned and operated to fully utilize renewable energy resources while meeting growing computational demands.This paper aims to investigate how much carbon emission reduction can be achieved by using a carbonoriented demand response to guide the optimal planning and operation of data centers.A carbon-oriented data center planning model is proposed that considers the carbon-oriented demand response of the AI load.In the planning model,future operation simulations comprehensively coordinate the temporal‒spatial flexibility of computational loads and the quality of service(QoS).An empirical study based on the proposed models is conducted on real-world data from China.The results from the empirical analysis show that newly constructed data centers are recommended to be built in Gansu Province,Ningxia Hui Autonomous Region,Sichuan Province,Inner Mongolia Autonomous Region,and Qinghai Province,accounting for 57%of the total national increase in server capacity.33%of the computational load from Eastern China should be transferred to the West,which could reduce the overall load carbon emissions by 26%.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
BACKGROUND Sorafenib has been the conventional treatment for advanced hepatocellular carcinoma(HCC)since 2008.While radiological complete responses are extremely rare,improved supportive care and multidisciplinary app...BACKGROUND Sorafenib has been the conventional treatment for advanced hepatocellular carcinoma(HCC)since 2008.While radiological complete responses are extremely rare,improved supportive care and multidisciplinary approaches in clinical practice may explain the recent increase in case reports and retrospective series documenting such responses.CASE SUMMARY This case series describes 3 patients with advanced HCC who achieved durable complete responses using first-line sorafenib therapy,even in the presence of portal vein thrombosis or extrahepatic spread,and highlights the potential for sustained remission in selected patients.Dermatologic toxicity and non-viral etiology may correlate with favorable outcomes;however,reliable predictive biomarkers for sorafenib response are lacking.CONCLUSION Future research into the etiology and molecular differences in HCC is necessary to develop more personalized therapy options.展开更多
Foliar uptake of airborne metal(loid)s plays a crucial role in metal(loid)accumulation in plant organs and is influ-enced by the size and emission sources of aerosols.Given the high enrichment of toxic metal(loid)s in...Foliar uptake of airborne metal(loid)s plays a crucial role in metal(loid)accumulation in plant organs and is influ-enced by the size and emission sources of aerosols.Given the high enrichment of toxic metal(loid)s in submicron-scale particulates(PM1),this study established a PM1 exposure system to examine airborne metal(loid)accu-mulation and foliar physiological responses in Oryza sativa L.The results showed that the concentrations of Cu,Zn,As,Pb,and Cd in the leaves and grains were influenced not only by the airborne metal(loid)levels but also by the specific nature of the PM1 particles.The quantitative model for PM1-associated Pb entry into leaf tissue indicated that foliar Pb accumulation was primarily driven by particle adhesion,followed by hydrophilic pene-tration and trans-stomatal liquid film migration,accounting for 87%–89%of the total accumulation.The strong hygroscopicity and high Pb activity of PM1 emitted from waste incineration(WI)increased the Pb absorption coefficient via the hydrophilic and liquid film migration pathway.In contrast,the high hydrophobicity of PM1 from coal burning(CB)led to greater retention of Pb on leaf surfaces.Both foliar reactive oxygen metabolism and photosynthesis indices were sensitive to air pollution.Foliar metal(loid)accumulation and airborne PM1 concentration accounted for the variance in physiological responses in rice leaves.Our results also indicated that Pb was the key element in PM1 emissions from both coal burning(CB)and waste incineration(WI)responsible for significant physiological changes in rice leaves.展开更多
Post-translational modifications(PTMs)regulate the occurrence and development of cancer,and lactylation modification is a new form of PTMs.Recent studies have found that lactic acid modification can regulate the immun...Post-translational modifications(PTMs)regulate the occurrence and development of cancer,and lactylation modification is a new form of PTMs.Recent studies have found that lactic acid modification can regulate the immune tolerance of cancer cells.The classical theory holds that prostate apoptosis response-4(PAR-4)is a tumor suppressor protein.However,our recent research has found that PAR-4 has a biological function of promoting cancer in hepatocellular carcinoma(HCC),and our analysis shows that PAR-4 can be modified of lactic acid.These research evidences suggest that PAR-4 lactylation modification may drive immune tolerance in HCC.Therefore,inhibiting PAR-4 lactylation modification is very likely to increase the sensitivity of HCC to immunotherapy.展开更多
Owing to the inherent limitation of the internal pulse ionization chamber within the AlphaGUARD PQ2000 radon monitor,that is,its inability to discriminate the energy levels of α particles,the ingress of^(220)Rn from ...Owing to the inherent limitation of the internal pulse ionization chamber within the AlphaGUARD PQ2000 radon monitor,that is,its inability to discriminate the energy levels of α particles,the ingress of^(220)Rn from the surrounding environment,along with its decay progeny,poses a substantive challenge in accurately determining the^(222)Rn concentration in the measurement outcomes.Among these,the protracted influence primarily stems from the two enduring decay progenies,namely^(212)Pb with a half-life of 10.64 h and^(212)Bi with a half-life of 60.54 min.This study explored the influence of^(220)Rn progeny on the measurement results of an AlphaGUARD PQ2000 radon monitor by developing a theoretical calculation model.The response coefficient related to the residual^(220)Rn progeny within the AlphaGUARD PQ2000 radon monitor was experimentally validated.In addition,this study investigated the effects of temperature and wind speed on the sensitivity of the instrument to^(220)Rn gas.The research findings revealed commendable agreement between the experimentally measured response coefficients of the residual^(220)Rn progeny and the corresponding values derived from the theoretical model.Notably,both the response coefficients of the AlphaGUARD PQ2000 radon monitor to^(220)Rn gas and its internal residual^(220)Rn progeny increased with elevated temperatures and increased wind speeds,providing a reference for correcting the impact of^(220)Rn and its progeny on the measurement results of^(222)Rn concentration obtained using the AlphaGUARD PQ2000 radon monitor.展开更多
Buildings with indoor swimming pools are recognized as very high-energy consumers and present a great potential for electrical and thermal energy savings. A BEMS （building energy management system） could be conceive...Buildings with indoor swimming pools are recognized as very high-energy consumers and present a great potential for electrical and thermal energy savings. A BEMS （building energy management system） could be conceived in order to optimize the building energy demand and with smart grid interaction. This paper presents the condition and potential contract-based demand side response in indoor swimming pools context. The BEMS designed by the authors implements control strategies for HVAC （heating, ventilation and air conditioning） and pumping system in order to reduce the electricity demand during peak hours or in response to an emergency signal from the system operator in critical times. The control strategies for HVAC was carried out by Building Thermal Simulation and the used of a theoretical formula for pumping system, strategies can carry out a significant reduction in power demand both in HVAC and pumping systems.展开更多
The integrated energy system(IES)is an important energy supply method for mitigating the energy crisis.A station-and-network–coordinated planning method for the IES,which considers the integrated demand responses(IDR...The integrated energy system(IES)is an important energy supply method for mitigating the energy crisis.A station-and-network–coordinated planning method for the IES,which considers the integrated demand responses(IDRs)of flexible loads,electric vehicles,and energy storage is proposed in this work.First,based on load substitution at the user side,an energy-station model considering the IDR is established.Then,based on the characteristics of the energy network,a collaborative planning model is established for the energy station and energy network of the IES,considering the comprehensive system investment,operation and maintenance,and clean energy shortage penalty costs,to minimize the total cost.This can help optimize the locations of the power lines and natural gas pipelines and the capacities of the equipment in an energy station.Finally,simulations are performed to demonstrate that the proposed planning method can help delay or reduce the construction of new lines and energy-station equipment,thereby reducing the investment required and improving the planning economics of the IES.展开更多
To accommodate wind power as safely as possible and deal with the uncertainties of the output power of winddriven generators,a min-max-min two-stage robust optimization model is presented,considering the unit commitme...To accommodate wind power as safely as possible and deal with the uncertainties of the output power of winddriven generators,a min-max-min two-stage robust optimization model is presented,considering the unit commitment,source-network load collaboration,and control of the load demand response.After the constraint functions are linearized,the original problem is decomposed into the main problem and subproblem as a matrix using the strong dual method.The minimum-maximum of the original problem was continuously maximized using the iterative method,and the optimal solution was finally obtained.The constraint conditions expressed by the matrix may reduce the calculation time,and the upper and lower boundaries of the original problem may rapidly converge.The results of the example show that the injected nodes of the wind farms in the power grid should be selected appropriately;otherwise,it is easy to cause excessive accommodation of wind power at some nodes,leading to a surge in reserve costs and the load demand response is continuously optimized to reduce the inverse peak regulation characteristics of wind power.Thus,the most economical optimization scheme for the worst scenario of the output power of the generators is obtained,which proves the economy and reliability of the two-stage robust optimization method.展开更多
Electric system planning with high variable renewable energy(VRE)penetration levels has attracted great attention world-wide.Electricity production of VRE highly depends on the weather conditions and thus involves lar...Electric system planning with high variable renewable energy(VRE)penetration levels has attracted great attention world-wide.Electricity production of VRE highly depends on the weather conditions and thus involves large variability,uncertainty,and low-capacity credit.This gives rise to significant challenges for power system planning.Currently,many solutions are proposed to address the issue of operational flexibility inadequacy,including flexibility retrofit of thermal units,inter-regional transmission,electricity energy storage,and demand response(DR).Evidently,the performance and the cost of various solutions are different.It is relevant to explore the optimal portfolio to satisfy the flexibility requirement for a renewable dominated system and the role of each flexibility source.In this study,the value of diverse DR flexibilities was examined and a stochastic investment planning model considering DR is proposed.Two types of DRs,namely interrupted DR and transferred DR,were modeled.Chronological load and renewable generation curves with 8760 hours within a whole year were reduced to 4 weekly scenarios to accelerate the optimization.Clustered unit commitment constraints for accommodating variability of renewables were incorporated.Case studies based on IEEE RTS-96 system are reported to demonstrate the effectiveness of the proposed method and the DR potential to avoid energy storage investment.展开更多
This paper studies price-based residential demand response management(PB-RDRM)in smart grids,in which non-dispatchable and dispatchable loads(including general loads and plug-in electric vehicles(PEVs))are both involv...This paper studies price-based residential demand response management(PB-RDRM)in smart grids,in which non-dispatchable and dispatchable loads(including general loads and plug-in electric vehicles(PEVs))are both involved.The PB-RDRM is composed of a bi-level optimization problem,in which the upper-level dynamic retail pricing problem aims to maximize the profit of a utility company(UC)by selecting optimal retail prices(RPs),while the lower-level demand response(DR)problem expects to minimize the comprehensive cost of loads by coordinating their energy consumption behavior.The challenges here are mainly two-fold:1)the uncertainty of energy consumption and RPs;2)the flexible PEVs’temporally coupled constraints,which make it impossible to directly develop a model-based optimization algorithm to solve the PB-RDRM.To address these challenges,we first model the dynamic retail pricing problem as a Markovian decision process(MDP),and then employ a model-free reinforcement learning(RL)algorithm to learn the optimal dynamic RPs of UC according to the loads’responses.Our proposed RL-based DR algorithm is benchmarked against two model-based optimization approaches(i.e.,distributed dual decomposition-based(DDB)method and distributed primal-dual interior(PDI)-based method),which require exact load and electricity price models.The comparison results show that,compared with the benchmark solutions,our proposed algorithm can not only adaptively decide the RPs through on-line learning processes,but also achieve larger social welfare within an unknown electricity market environment.展开更多
With the increase in the power receiving proportion and an insufficient peak regulation capacity of the local units, the receiving-end power grid struggles to achieve peak regulation in valley time. To solve this prob...With the increase in the power receiving proportion and an insufficient peak regulation capacity of the local units, the receiving-end power grid struggles to achieve peak regulation in valley time. To solve this problem while considering the potential of the large-scale charge load of electric vehicles(EVs), an aggregator-based demand response(DR) mechanism for EVs that are participating in the peak regulation in valley time is proposed in this study. In this aggregator-based DR mechanism, the profits for the power grid’s operation and the participation willingness of the EV owners are considered. Based on the characteristics of the EV charging process and the day-ahead unit generation scheduling, a rolling unit commitment model with the DR is established to maximize the social welfare. In addition, to improve the efficiency of the optimization problem solving process and to achieve communication between the independent system operator(ISO) and the aggregators, the clustering algorithm is utilized to extract typical EV charging patterns. Finally, the feasibility and benefits of the aggregator-based DR mechanism for saving the costs and reducing the peak-valley difference of the receiving-end power grid are verified through case studies.展开更多
Recent seismic events have raised concerns over the safety and vulnerability of reinforced concrete moment resisting frame ＂RC-MRF＂ buildings. The seismic response of such buildings is greatly dependent on the compu...Recent seismic events have raised concerns over the safety and vulnerability of reinforced concrete moment resisting frame ＂RC-MRF＂ buildings. The seismic response of such buildings is greatly dependent on the computational tools used and the inherent assumptions in the modelling process. Thus, it is essential to investigate the sensitivity of the response demands to the corresponding modelling assumption. Many parameters and assumptions are justified to generate effective structural finite element（FE） models of buildings to simulate lateral behaviour and evaluate seismic design demands. As such, the present study focuses on the development of reliable FE models with various levels of refinement. The effects of the FE modelling assumptions on the seismic response demands on the design of buildings are investigated. the predictive ability of a FE model is tied to the accuracy of numerical analysis; a numerical analysis is performed for a series of symmetric buildings in active seismic zones. The results of the seismic response demands are presented in a comparative format to confirm drift and strength limits requirements. A proposed model is formulated based on a simplified modeling approach, where the most refined model is used to calibrate the simplified model.展开更多
To facilitate the coordinated and large-scale participation of residential flexible loads in demand response(DR),a load aggregator(LA)can integrate these loads for scheduling.In this study,a residential DR optimizatio...To facilitate the coordinated and large-scale participation of residential flexible loads in demand response(DR),a load aggregator(LA)can integrate these loads for scheduling.In this study,a residential DR optimization scheduling strategy was formulated considering the participation of flexible loads in DR.First,based on the operational characteristics of flexible loads such as electric vehicles,air conditioners,and dishwashers,their DR participation,the base to calculate the compensation price to users,was determined by considering these loads as virtual energy storage.It was quantified based on the state of virtual energy storage during each time slot.Second,flexible loads were clustered using the K-means algorithm,considering the typical operational and behavioral characteristics as the cluster centroid.Finally,the LA scheduling strategy was implemented by introducing a DR mechanism based on the directrix load.The simulation results demonstrate that the proposed DR approach can effectively reduce peak loads and fill valleys,thereby improving the load management performance.展开更多
In a multi-energy collaboration system, cooling, heating, electricity, and other energy components are coupled to complement each other. Through multi-energy coordination and cooperation, they can significantly improv...In a multi-energy collaboration system, cooling, heating, electricity, and other energy components are coupled to complement each other. Through multi-energy coordination and cooperation, they can significantly improve their individual operating efficiency and overall economic benefits. Demand response, as a multi-energy supply and demand balance method, can further improve system flexibility and economy. Therefore, a multi-energy cooperative system optimization model has been proposed, which is driven by price-based demand response to determine the impact of power-demand response on the optimal operating mode of a multi-energy cooperative system. The main components of the multi-energy collaborative system have been analyzed. The multi-energy coupling characteristics have been identified based on the energy hub model. Using market elasticity as a basis, a price-based demand response model has been built. The model has been optimized to minimize daily operating cost of the multi-energy collaborative system. Using data from an actual situation, the model has been verified, and we have shown that the adoption of price-based demand response measures can significantly improve the economy of multi-energy collaborative systems.展开更多
基金funded by Science and Technology Projects from State Grid Corporation of China,(Research on Adaptive Balance Optimization and Simulation Technology of Industrial community Energy System with High Proportion of Distributed Energy,No.:5100-202355752A-3-4-SY).
文摘To achieve the goals of sustainable development of the energy system and the construction of a lowcarbon society,this study proposes a multi-energy storage collaborative optimization strategy for industrial park that integrates the laddered carbon trading mechanism with demand response.Firstly,a dual dimensional DR model is constructed based on the characteristics of load elasticity.The alternativeDRenables flexible substitution of energy loads through complementary conversion of electricity/heat/cold multi-energy sources,while the price DR relies on timeof-use electricity price signals to guide load spatiotemporal migration;Secondly,the LCT mechanism is introduced to achieve optimal carbon emission costs through a tiered carbon quota allocation mechanism.On this basis,an optimization decision model is established with the core objective of maximizing the annual net profit of the park.The objective function takes into account energy sales revenue,generator unit costs,and investment and operation costs of multiple types of energy storage facilities.Themodel constraint system covers three key dimensions:dynamic operation constraints of power generation units,including unit output limits,ramping capability,and minimum start-stop time;the physical boundary of an electric/hot/cold multi-energy storage system involves energy storage capacity and charge/discharge efficiency;The multi-energy network coupling balance equation ensures that the energy conversion and transmission process satisfies the law of conservation of energy.Using CPLEX mathematical programming solver for simulation verification,construct an energy storage capacity configuration decision process that includes LCT-DR synergistic effect.The research results show that compared with the traditional single energy storage configuration mode,this strategy effectively enhances the economic feasibility and engineering practicality of industrial park operation by coordinating demand side resource scheduling and finely controlling carbon costs,while maintaining stable system operation.Its methodological framework provides a technical path that combines theoretical rigor and practical operability for the low-carbon transformation of regional integrated energy systems.
基金supported by National Natural Science Foundation of China(52407126).
文摘The demand response(DR)market,as a vital complement to the electricity spot market,plays a key role in evoking user-side regulation capability to mitigate system-level supply‒demand imbalances during extreme events.While the DR market offers the load aggregator(LA)additional profitable opportunities beyond the electricity spot market,it also introduces new trading risks due to the significant uncertainty in users’behaviors.Dispatching energy storage systems(ESSs)is an effective means to enhance the risk management capabilities of LAs;however,coordinating ESS operations with dual-market trading strategies remains an urgent challenge.To this end,this paper proposes a novel systematic risk-aware coordinated trading model for the LA in concurrently participating in the day-ahead electricity spot market and DR market,which incorporates the capacity allocation mechanism of ESS based on market clearing rules to jointly formulate bidding and pricing decisions for the dual market.First,the intrinsic coupling characteristics of the LA participating in the dual market are analyzed,and a joint optimization framework for formulating bidding and pricing strategies that integrates ESS facilities is proposed.Second,an uncertain user response model is developed based on price‒response mechanisms,and actual market settlement rules accounting for under-and over-responses are employed to calculate trading revenues,where possible revenue losses are quantified via conditional value at risk.Third,by imposing these terms and the capacity allocation mechanism of ESS,the risk-aware stochastic coordinated trading model of the LA is built,where the bidding and pricing strategies in the dual model that trade off risk and profit are derived.The simulation results of a case study validate the effectiveness of the proposed trading strategy in controlling trading risk and improving the trading income of the LA.
基金the Science and Technology Project of State Grid Shanxi Electric Power Co.,Ltd.,with the project number 52051L240001.
文摘In contemporary power systems,delving into the flexible regulation potential of demand-side resources is of paramount significance for the efficient operation of power grids.This research puts forward an innovative multivariate flexible load aggregation control approach that takes dynamic demand response into full consideration.In the initial stage,using generalized time-domain aggregation modelling for a wide array of heterogeneous flexible loads,including temperature-controlled loads,electric vehicles,and energy storage devices,a novel calculation method for their maximum adjustable capacities is devised.Distinct from conventional methods,this newly developed approach enables more precise and adaptable quantification of the load-adjusting capabilities,thereby enhancing the accuracy and flexibility of demand-side resource management.Subsequently,an SSA-BiLSTM flexible load classification prediction model is established.This model represents an innovative application in the field,effectively combining the advantages of the Sparrow Search Algorithm(SSA)and the Bidirectional Long-Short-Term Memory(BiLSTM)neural network.Furthermore,a parallel Markov chain is introduced to evaluate the switching state transfer probability of flexible loads accurately.This integration allows for a more refined determination of the maximum response capacity range of the flexible load aggregator,significantly improving the precision of capacity assessment compared to existing methods.Finally,in consonance with the intra-day scheduling plan,a newly developed diffuse filling algorithm is implemented to control the activation times of flexible loads precisely,thus achieving real-time dynamic demand response.Through in-depth case analysis and comprehensive comparative studies,the effectiveness of the proposed method is convincingly validated.With its innovative techniques and enhanced performance,it is demonstrated that this method has the potential to substantially enhance the utilization efficiency of demand-side resources in power systems,providing a novel and effective solution for optimizing power grid operation and demand-side management.
基金supported by Science and Technology Program of China Southern Power Grid Corporation under grant number 036000KK52222004(GDKJXM20222117)National Key R&D Program of China for International S&T Cooperation Projects(2019YFE0118700).
文摘The integration of substantial renewable energy and controllable resources disrupts the supply-demand balance in distribution grids.Secure operations are dependent on the participation of user-side resources in demand response at both the day-ahead and intraday levels.Current studies typically overlook the spatial--temporal variations and coordination between these timescales,leading to significant day-ahead optimization errors,high intraday costs,and slow convergence.To address these challenges,we developed a multiagent,multitimescale aggregated regulation method for spatial--temporal coordinated demand response of user-side resources.Firstly,we established a framework considering the spatial--temporal coordinated characteristics of user-side resources with the objective to min-imize the total regulation cost and weighted sum of distribution grid losses.The optimization problem was then solved for two different timescales:day-ahead and intraday.For the day-ahead timescale,we developed an improved particle swarm optimization(IPSO)algo-rithm that dynamically adjusts the number of particles based on intraday outcomes to optimize the regulation strategies.For the intraday timescale,we developed an improved alternating direction method of multipliers(IADMM)algorithm that distributes tasks across edge distribution stations,dynamically adjusting penalty factors by using historical day-ahead data to synchronize the regulations and enhance precision.The simulation results indicate that this method can fully achieve multitimescale spatial--temporal coordinated aggregated reg-ulation between day-ahead and intraday,effectively reduce the total regulation cost and distribution grid losses,and enhance smart grid resilience.
基金supported by National Key Research and Development Program(2024YFE0115600).
文摘Addressing climate change and facilitating the large-scale integration of renewable energy sources(RESs)have driven the development of hydrogen-coupled integrated energy systems(HIES),which enhance energy sustainability through coordinated electricity,thermal,natural gas,and hydrogen utilization.This study proposes a two-stage distributionally robust optimization(DRO)-based scheduling method to improve the economic efficiency and reduce carbon emissions of HIES.The framework incorporates a ladder-type carbon trading mechanism to regulate emissions and implements a demand response(DR)program to adjustflexible multi-energy loads,thereby prioritizing RES consumption.Uncertainties from RES generation and load demand are addressed through an ambiguity set,enabling robust decision-making.The column-and-constraint generation(C&CG)algorithm efficiently solves the two-stage DRO model.Case studies demonstrate that the proposed method reduces operational costs by 3.56%,increases photovoltaic consumption rates by 5.44%,and significantly lowers carbon emissions compared to conventional approaches.Furthermore,the DRO framework achieves a superior balance between conservativeness and robustness over conventional stochastic and robust optimization methods,highlighting its potential to advance cost-effective,low-carbon energy systems while ensuring grid stability under uncertainty.
基金supported by the Scientific&Technical Project of the State Grid(5700--202490228A--1--1-ZN).
文摘The rapid advancement of artificial intelligence(AI)has significantly increased the computational load on data centers.AI-related computational activities consume considerable electricity and result in substantial carbon emissions.To mitigate these emissions,future data centers should be strategically planned and operated to fully utilize renewable energy resources while meeting growing computational demands.This paper aims to investigate how much carbon emission reduction can be achieved by using a carbonoriented demand response to guide the optimal planning and operation of data centers.A carbon-oriented data center planning model is proposed that considers the carbon-oriented demand response of the AI load.In the planning model,future operation simulations comprehensively coordinate the temporal‒spatial flexibility of computational loads and the quality of service(QoS).An empirical study based on the proposed models is conducted on real-world data from China.The results from the empirical analysis show that newly constructed data centers are recommended to be built in Gansu Province,Ningxia Hui Autonomous Region,Sichuan Province,Inner Mongolia Autonomous Region,and Qinghai Province,accounting for 57%of the total national increase in server capacity.33%of the computational load from Eastern China should be transferred to the West,which could reduce the overall load carbon emissions by 26%.
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BACKGROUND Sorafenib has been the conventional treatment for advanced hepatocellular carcinoma(HCC)since 2008.While radiological complete responses are extremely rare,improved supportive care and multidisciplinary approaches in clinical practice may explain the recent increase in case reports and retrospective series documenting such responses.CASE SUMMARY This case series describes 3 patients with advanced HCC who achieved durable complete responses using first-line sorafenib therapy,even in the presence of portal vein thrombosis or extrahepatic spread,and highlights the potential for sustained remission in selected patients.Dermatologic toxicity and non-viral etiology may correlate with favorable outcomes;however,reliable predictive biomarkers for sorafenib response are lacking.CONCLUSION Future research into the etiology and molecular differences in HCC is necessary to develop more personalized therapy options.
基金supported by the National Natural Science Foundation of China(Nos.42077367 and 21677123).
文摘Foliar uptake of airborne metal(loid)s plays a crucial role in metal(loid)accumulation in plant organs and is influ-enced by the size and emission sources of aerosols.Given the high enrichment of toxic metal(loid)s in submicron-scale particulates(PM1),this study established a PM1 exposure system to examine airborne metal(loid)accu-mulation and foliar physiological responses in Oryza sativa L.The results showed that the concentrations of Cu,Zn,As,Pb,and Cd in the leaves and grains were influenced not only by the airborne metal(loid)levels but also by the specific nature of the PM1 particles.The quantitative model for PM1-associated Pb entry into leaf tissue indicated that foliar Pb accumulation was primarily driven by particle adhesion,followed by hydrophilic pene-tration and trans-stomatal liquid film migration,accounting for 87%–89%of the total accumulation.The strong hygroscopicity and high Pb activity of PM1 emitted from waste incineration(WI)increased the Pb absorption coefficient via the hydrophilic and liquid film migration pathway.In contrast,the high hydrophobicity of PM1 from coal burning(CB)led to greater retention of Pb on leaf surfaces.Both foliar reactive oxygen metabolism and photosynthesis indices were sensitive to air pollution.Foliar metal(loid)accumulation and airborne PM1 concentration accounted for the variance in physiological responses in rice leaves.Our results also indicated that Pb was the key element in PM1 emissions from both coal burning(CB)and waste incineration(WI)responsible for significant physiological changes in rice leaves.
基金supported by the National Natural Science Foundation of China(Nos.82573045,82460602,82560459)the Hainan Provincial Graduate Student Innovative Research Project(No.Qhys2024-440).
文摘Post-translational modifications(PTMs)regulate the occurrence and development of cancer,and lactylation modification is a new form of PTMs.Recent studies have found that lactic acid modification can regulate the immune tolerance of cancer cells.The classical theory holds that prostate apoptosis response-4(PAR-4)is a tumor suppressor protein.However,our recent research has found that PAR-4 has a biological function of promoting cancer in hepatocellular carcinoma(HCC),and our analysis shows that PAR-4 can be modified of lactic acid.These research evidences suggest that PAR-4 lactylation modification may drive immune tolerance in HCC.Therefore,inhibiting PAR-4 lactylation modification is very likely to increase the sensitivity of HCC to immunotherapy.
基金supported by the National Natural Science Foundation of China(No.12175102)Hunan Provincial Natural Science Foundation(No.2022JJ40346)the 2022 Hunan Provincial University Student Innovation and Entrepreneurship Training Program(No.S202210555144).
文摘Owing to the inherent limitation of the internal pulse ionization chamber within the AlphaGUARD PQ2000 radon monitor,that is,its inability to discriminate the energy levels of α particles,the ingress of^(220)Rn from the surrounding environment,along with its decay progeny,poses a substantive challenge in accurately determining the^(222)Rn concentration in the measurement outcomes.Among these,the protracted influence primarily stems from the two enduring decay progenies,namely^(212)Pb with a half-life of 10.64 h and^(212)Bi with a half-life of 60.54 min.This study explored the influence of^(220)Rn progeny on the measurement results of an AlphaGUARD PQ2000 radon monitor by developing a theoretical calculation model.The response coefficient related to the residual^(220)Rn progeny within the AlphaGUARD PQ2000 radon monitor was experimentally validated.In addition,this study investigated the effects of temperature and wind speed on the sensitivity of the instrument to^(220)Rn gas.The research findings revealed commendable agreement between the experimentally measured response coefficients of the residual^(220)Rn progeny and the corresponding values derived from the theoretical model.Notably,both the response coefficients of the AlphaGUARD PQ2000 radon monitor to^(220)Rn gas and its internal residual^(220)Rn progeny increased with elevated temperatures and increased wind speeds,providing a reference for correcting the impact of^(220)Rn and its progeny on the measurement results of^(222)Rn concentration obtained using the AlphaGUARD PQ2000 radon monitor.
文摘Buildings with indoor swimming pools are recognized as very high-energy consumers and present a great potential for electrical and thermal energy savings. A BEMS （building energy management system） could be conceived in order to optimize the building energy demand and with smart grid interaction. This paper presents the condition and potential contract-based demand side response in indoor swimming pools context. The BEMS designed by the authors implements control strategies for HVAC （heating, ventilation and air conditioning） and pumping system in order to reduce the electricity demand during peak hours or in response to an emergency signal from the system operator in critical times. The control strategies for HVAC was carried out by Building Thermal Simulation and the used of a theoretical formula for pumping system, strategies can carry out a significant reduction in power demand both in HVAC and pumping systems.
基金supported in part by the National Key R&D Program of China(2018YFB0905000)the Science and Technology Project of the State Grid Corporation of China(SGTJDK00DWJS1800232)
文摘The integrated energy system(IES)is an important energy supply method for mitigating the energy crisis.A station-and-network–coordinated planning method for the IES,which considers the integrated demand responses(IDRs)of flexible loads,electric vehicles,and energy storage is proposed in this work.First,based on load substitution at the user side,an energy-station model considering the IDR is established.Then,based on the characteristics of the energy network,a collaborative planning model is established for the energy station and energy network of the IES,considering the comprehensive system investment,operation and maintenance,and clean energy shortage penalty costs,to minimize the total cost.This can help optimize the locations of the power lines and natural gas pipelines and the capacities of the equipment in an energy station.Finally,simulations are performed to demonstrate that the proposed planning method can help delay or reduce the construction of new lines and energy-station equipment,thereby reducing the investment required and improving the planning economics of the IES.
基金supported by the Special Research Project on Power Planning of the Guangdong Power Grid Co.,Ltd.
文摘To accommodate wind power as safely as possible and deal with the uncertainties of the output power of winddriven generators,a min-max-min two-stage robust optimization model is presented,considering the unit commitment,source-network load collaboration,and control of the load demand response.After the constraint functions are linearized,the original problem is decomposed into the main problem and subproblem as a matrix using the strong dual method.The minimum-maximum of the original problem was continuously maximized using the iterative method,and the optimal solution was finally obtained.The constraint conditions expressed by the matrix may reduce the calculation time,and the upper and lower boundaries of the original problem may rapidly converge.The results of the example show that the injected nodes of the wind farms in the power grid should be selected appropriately;otherwise,it is easy to cause excessive accommodation of wind power at some nodes,leading to a surge in reserve costs and the load demand response is continuously optimized to reduce the inverse peak regulation characteristics of wind power.Thus,the most economical optimization scheme for the worst scenario of the output power of the generators is obtained,which proves the economy and reliability of the two-stage robust optimization method.
基金jointly supported by Youth Program of National Natural Science Foundation of China(No.51907100)Technical Program of Global Energy Interconnection Group Co.,Ltd(No.1100/2020-75001B)
文摘Electric system planning with high variable renewable energy(VRE)penetration levels has attracted great attention world-wide.Electricity production of VRE highly depends on the weather conditions and thus involves large variability,uncertainty,and low-capacity credit.This gives rise to significant challenges for power system planning.Currently,many solutions are proposed to address the issue of operational flexibility inadequacy,including flexibility retrofit of thermal units,inter-regional transmission,electricity energy storage,and demand response(DR).Evidently,the performance and the cost of various solutions are different.It is relevant to explore the optimal portfolio to satisfy the flexibility requirement for a renewable dominated system and the role of each flexibility source.In this study,the value of diverse DR flexibilities was examined and a stochastic investment planning model considering DR is proposed.Two types of DRs,namely interrupted DR and transferred DR,were modeled.Chronological load and renewable generation curves with 8760 hours within a whole year were reduced to 4 weekly scenarios to accelerate the optimization.Clustered unit commitment constraints for accommodating variability of renewables were incorporated.Case studies based on IEEE RTS-96 system are reported to demonstrate the effectiveness of the proposed method and the DR potential to avoid energy storage investment.
基金This work was supported in part by the National Natural Science Foundation of China(61922076,61725304,61873252,61991403,61991400)in part by the Australian Research Council Discovery Program(DP200101199).
文摘This paper studies price-based residential demand response management(PB-RDRM)in smart grids,in which non-dispatchable and dispatchable loads(including general loads and plug-in electric vehicles(PEVs))are both involved.The PB-RDRM is composed of a bi-level optimization problem,in which the upper-level dynamic retail pricing problem aims to maximize the profit of a utility company(UC)by selecting optimal retail prices(RPs),while the lower-level demand response(DR)problem expects to minimize the comprehensive cost of loads by coordinating their energy consumption behavior.The challenges here are mainly two-fold:1)the uncertainty of energy consumption and RPs;2)the flexible PEVs’temporally coupled constraints,which make it impossible to directly develop a model-based optimization algorithm to solve the PB-RDRM.To address these challenges,we first model the dynamic retail pricing problem as a Markovian decision process(MDP),and then employ a model-free reinforcement learning(RL)algorithm to learn the optimal dynamic RPs of UC according to the loads’responses.Our proposed RL-based DR algorithm is benchmarked against two model-based optimization approaches(i.e.,distributed dual decomposition-based(DDB)method and distributed primal-dual interior(PDI)-based method),which require exact load and electricity price models.The comparison results show that,compared with the benchmark solutions,our proposed algorithm can not only adaptively decide the RPs through on-line learning processes,but also achieve larger social welfare within an unknown electricity market environment.
基金supported by the Science and Technology Project from the State Grid Shanghai Municipal Electric Power Company of China (52094019006U)the Shanghai Rising-Star Program (18QB1400200)。
文摘With the increase in the power receiving proportion and an insufficient peak regulation capacity of the local units, the receiving-end power grid struggles to achieve peak regulation in valley time. To solve this problem while considering the potential of the large-scale charge load of electric vehicles(EVs), an aggregator-based demand response(DR) mechanism for EVs that are participating in the peak regulation in valley time is proposed in this study. In this aggregator-based DR mechanism, the profits for the power grid’s operation and the participation willingness of the EV owners are considered. Based on the characteristics of the EV charging process and the day-ahead unit generation scheduling, a rolling unit commitment model with the DR is established to maximize the social welfare. In addition, to improve the efficiency of the optimization problem solving process and to achieve communication between the independent system operator(ISO) and the aggregators, the clustering algorithm is utilized to extract typical EV charging patterns. Finally, the feasibility and benefits of the aggregator-based DR mechanism for saving the costs and reducing the peak-valley difference of the receiving-end power grid are verified through case studies.
基金Scientific Research Deanship,Taibah University Grant No.6363/436
文摘Recent seismic events have raised concerns over the safety and vulnerability of reinforced concrete moment resisting frame ＂RC-MRF＂ buildings. The seismic response of such buildings is greatly dependent on the computational tools used and the inherent assumptions in the modelling process. Thus, it is essential to investigate the sensitivity of the response demands to the corresponding modelling assumption. Many parameters and assumptions are justified to generate effective structural finite element（FE） models of buildings to simulate lateral behaviour and evaluate seismic design demands. As such, the present study focuses on the development of reliable FE models with various levels of refinement. The effects of the FE modelling assumptions on the seismic response demands on the design of buildings are investigated. the predictive ability of a FE model is tied to the accuracy of numerical analysis; a numerical analysis is performed for a series of symmetric buildings in active seismic zones. The results of the seismic response demands are presented in a comparative format to confirm drift and strength limits requirements. A proposed model is formulated based on a simplified modeling approach, where the most refined model is used to calibrate the simplified model.
基金supported by the Basic Science(Natural Science)Research Project of Jiangsu Higher Education Institutions(No.23KJB470020)the Natural Science Foundation of Jiangsu Province(Youth Fund)(No.BK20230384)。
文摘To facilitate the coordinated and large-scale participation of residential flexible loads in demand response(DR),a load aggregator(LA)can integrate these loads for scheduling.In this study,a residential DR optimization scheduling strategy was formulated considering the participation of flexible loads in DR.First,based on the operational characteristics of flexible loads such as electric vehicles,air conditioners,and dishwashers,their DR participation,the base to calculate the compensation price to users,was determined by considering these loads as virtual energy storage.It was quantified based on the state of virtual energy storage during each time slot.Second,flexible loads were clustered using the K-means algorithm,considering the typical operational and behavioral characteristics as the cluster centroid.Finally,the LA scheduling strategy was implemented by introducing a DR mechanism based on the directrix load.The simulation results demonstrate that the proposed DR approach can effectively reduce peak loads and fill valleys,thereby improving the load management performance.
基金supported by State Grid Corporation Technology Project (5400-201956447A-0-0-00)。
文摘In a multi-energy collaboration system, cooling, heating, electricity, and other energy components are coupled to complement each other. Through multi-energy coordination and cooperation, they can significantly improve their individual operating efficiency and overall economic benefits. Demand response, as a multi-energy supply and demand balance method, can further improve system flexibility and economy. Therefore, a multi-energy cooperative system optimization model has been proposed, which is driven by price-based demand response to determine the impact of power-demand response on the optimal operating mode of a multi-energy cooperative system. The main components of the multi-energy collaborative system have been analyzed. The multi-energy coupling characteristics have been identified based on the energy hub model. Using market elasticity as a basis, a price-based demand response model has been built. The model has been optimized to minimize daily operating cost of the multi-energy collaborative system. Using data from an actual situation, the model has been verified, and we have shown that the adoption of price-based demand response measures can significantly improve the economy of multi-energy collaborative systems.
