The hybrid series-parallel microgrid attracts more attention by combining the advantages of both the series-stacked voltage and parallel-expanded capacity.Low-voltage distributed generations(DGs)are connected in serie...The hybrid series-parallel microgrid attracts more attention by combining the advantages of both the series-stacked voltage and parallel-expanded capacity.Low-voltage distributed generations(DGs)are connected in series to form the intra-string,and then multiple strings are interconnected in parallel.For the existing control strategies,both intra-string and inter-string depend on the centralized or distributed control with high communication reliance.It has limited scalability and redundancy under abnormal conditions.Alternatively,in this study,an intra-string distributed and inter-string decentralized control framework is proposed.Within the string,a few DGs close to the AC bus are the leaders to get the string power information and the rest DGs are the followers to acquire the synchronization information through the droop-based distributed consistency.Specifically,the output of the entire string has the active power−angular frequency(ω-P)droop characteristic,and the decentralized control among strings can be autonomously guaranteed.Moreover,the secondary control is designed to realize multi-mode objectives,including on/off-grid mode switching,grid-connected power interactive management,and off-grid voltage quality regulation.As a result,the proposed method has the ability of plug-and-play capabilities,single-point failure redundancy,and seamless mode-switching.Experimental results are provided to verify the effectiveness of the proposed practical solution.展开更多
In practical microgrids,current saturation of inverters and power interaction coupling of different forms of DERs complicate the system's transient behaviors.Existing methods of online transient stability predicti...In practical microgrids,current saturation of inverters and power interaction coupling of different forms of DERs complicate the system's transient behaviors.Existing methods of online transient stability prediction(TSP)are suitable for power systems consisting of homogeneous distributed energy resources(DERs),thus showing limited accuracy for stability prediction of microgrids.This paper develops a deep-learning-based TSP method for accurate online prediction of microgrids consisting of diverse forms of DERs under current saturation.First,a general key input feature selection method for microgrid TSP is systematically designed to ensure prediction accuracy.It is derived from a comprehensive mechanism analysis of the influence of DER's intrinsic and interaction characteristics under current saturation.Besides,impacts of load fluctuation and fault change are also considered to improve robust prediction performance.Second,to further improve prediction accuracy,an online TSP model based on deep learning is developed by effectively using the powerful nonlinear mapping capability of the deep belief network(DBN).Then,by combining feature selection method and deep-learning-based TSP model,an online TSP method is derived.Test results show the proposed method greatly improves accuracy of microgrid TSP under complex operating conditions.Furthermore,the method effectively avoids feature redundancy and the curse of dimensionality.Numbers of input features are independent of the scale of microgrids.展开更多
This paper proposes a novel framework based on the Stackelberg game and deep reinforcement learning for multi-microgrids(MGs)in achieving peer-to-peer(P2P)energy trading.A multi-leaders,multi-followers Stackelberg gam...This paper proposes a novel framework based on the Stackelberg game and deep reinforcement learning for multi-microgrids(MGs)in achieving peer-to-peer(P2P)energy trading.A multi-leaders,multi-followers Stackelberg game is utilized to model the P2P energy trading process.Stackelberg equilibrium(SE)is regarded as a P2P optimal trading strategy.A two-stage privacy protection solution technique combining data-driven and model-driven is developed to obtain the SE.Specifically,energy storage scheduling problem in MGs is formulated as a Markov decision process with discrete periods,and a multi-action single-observation deep deterministic policy gradient(MASO-DDPG)algorithm is proposed to tackle optimal scheduling of energy storage in the first stage.According to optimal scheduling of energy storage,the closed-form expression for SE based on model-driven is derived,and distributed SE solution technique(DSET)is developed to obtain SE in the second stage.Case studies involving a 4-Microgrid demonstrate the P2P electricity price obtained by the two-stage method,as a novel pricing mechanism,can reasonably regulate microgrid operation mode and improve microgrid income participating in the P2P market,which verifies effectiveness and superiority of the proposed P2P energy trading model and two-stage solution method.展开更多
To address the issue of transient low-voltage instability in AC-DC hybrid power systems following large disturbances,conventional voltage assessment and control strategies typically adopt a sequential“assess-then-act...To address the issue of transient low-voltage instability in AC-DC hybrid power systems following large disturbances,conventional voltage assessment and control strategies typically adopt a sequential“assess-then-act”paradigm,which struggles to simultaneously meet the requirements for both high accuracy and rapid response.This paper proposes a transient voltage assessment and control method based on a hybrid neural network incorporated with an improved snow ablation optimization(ISAO)algorithm.The core innovation of the proposed method lies in constructing an intelligent“physics-informed and neural network-integrated”framework,which achieves the integration of stability assessment and control strategy generation.Firstly,to construct a highly correlated input set,response characteristics reflecting the system’s voltage stable/unstable states are screened.Simultaneously,the transient voltage severity index(TVSI)is introduced as a comprehensive metric to quantify the system’s post-disturbance transient voltage performance.Furthermore,the load bus voltage sensitivity index(LVSI)is defined as the ratio of the voltage change magnitude at a load node(or bus)to the change in the system-level TVSI,thereby pinpointing the response characteristics of critical load nodes.Secondly,both the transient voltage stability assessment result and its corresponding under-voltage load shedding(UVLS)control amount are jointly utilized as the outputs of the response-driven model.Subsequently,the snow ablation optimization(SAO)algorithm is enhanced using a good point set strategy and a Gaussian mutation strategy.This improved algorithm is then employed to optimize the key hyperparameters of the hybrid neural network.Finally,the superiority of the proposed method is validated on a modified CEPRI-36 system and an actual power grid case.Comparisons with various artificial intelligence methods demonstrate its significant advantages in model speed and accuracy.Additionally,when compared to traditional emergency control schemes and UVLS strategies,the proposed method exhibits exceptional rapidness and real-time capability in control decision-making.展开更多
In recent years,the hybrid AC-DC microgrid has been well accepted as it combines the advantages of both AC and DC systems.As the microgrid contains both DC sub-grids and AC sub-grids,interlinking DC-AC converters are ...In recent years,the hybrid AC-DC microgrid has been well accepted as it combines the advantages of both AC and DC systems.As the microgrid contains both DC sub-grids and AC sub-grids,interlinking DC-AC converters are essential.Meanwhile,considering the nonlinear AC loads may deteriorate the voltage quality of the AC bus,embedding an ancillary harmonic compensation function to the interlinking converters is promising.However,the conventional harmonic control methods used for active power filters(APFs)may not be suitable for the interlinking converters due to the main purpose of it is to exchange real and reactive power between the DC and AC sub-grids.The switching frequency is preferred to be lower than the APFs when the capacity of the microgrid is large.At low switching frequency,harmonic compensation performance or even the system stability may be affected.In this paper,a harmonic compensation approach suitable for hybrid AC-DC interlinking converters at low switching frequency is proposed.Through feeding the PWM reference signal with the harmonic compensation component directly to avoid the multi-loop control path of the fundamental component,the proposed method can achieve the effective harmonics compensation without being limited by the closed-loop control bandwidth.The proposed method,modeling approaches,stability analysis,as well as detailed virtual impedance design are presented.Experimental verification is also provided.展开更多
在两级式AC-DC变换器中,前级功率因数校正(power factor correction,PFC)固有的瞬时功率波动特性会造成母线电压存在二倍频纹波,影响后级CLLLC谐振变换器的输出电压质量。针对以上问题,该文提出了基于二阶广义积分(second order general...在两级式AC-DC变换器中,前级功率因数校正(power factor correction,PFC)固有的瞬时功率波动特性会造成母线电压存在二倍频纹波,影响后级CLLLC谐振变换器的输出电压质量。针对以上问题,该文提出了基于二阶广义积分(second order generalized integral,SOGI)的可变增益母线电压纹波前馈控制方法。采用SOGI提取母线电压纹波信息,基于品质因数Q与电压增益的关系和母线电压纹波对归一化频率的影响,解析了母线电压纹波对CLLLC谐振变换器输出电压的影响机理,得到Q值与前馈增益系数Ka的关系,采用仿真寻优加数据拟合的方法得到前馈可变增益系数曲线。仿真和实验结果表明,相比于无前馈控制,所提控制方法对CLLLC谐振变换器的输出电压纹波具有较好的抑制效果,输出电压纹波降低了72%,验证了所提算法的有效性。展开更多
The existing power management schemes for interlinked AC-DC microgrids have several operational drawbacks.Some of the existing control schemes are designed with the main objective of sharing power among the interlinke...The existing power management schemes for interlinked AC-DC microgrids have several operational drawbacks.Some of the existing control schemes are designed with the main objective of sharing power among the interlinked microgrids based on their loading conditions,while other schemes regulate the voltage of the interlinked microgrids without considering the specific loading conditions.However,the existing schemes cannot achieve both objectives efficiently.To address these issues,an autonomous power management scheme is proposed,which explicitly considers the specific loading condition of the DC microgrid before importing power from the interlinked AC microgrid.This strategy enables voltage regulation in the DC microgrid,and also reduces the number of converters in operation.The proposed scheme is fully autonomous while it retains the plug-nplay features for generators and tie-converters.The performance of the proposed control scheme has been validated under different operating scenarios.The results demonstrate the effectiveness of the proposed scheme in managing the power deficit in the DC microgrid efficiently and autonomously while maintaining the better voltage regulation in the DC microgrid.展开更多
In order to realize the modular design of the microgrid,this paper proposed a new modular topology for the AC-DC mixed microgrid.In that topology,the AC microgrid unit and the DC microgrid unit were packaged together ...In order to realize the modular design of the microgrid,this paper proposed a new modular topology for the AC-DC mixed microgrid.In that topology,the AC microgrid unit and the DC microgrid unit were packaged together by the back-to-back converter.The battery-supercapacitor hybrid energy storage system was connected to the DC bus of back-to-back converter.By the reasonable design on the battery-supercapacitor hybrid system,the energy storage system could supply the rapid power and energy support for the microgrid spontaneously.The mathematical model and the control algorithm of that microgrid topology were studied.By the simulation analysis,it can be concluded that AC-DC mixed modular microgrid topology could operate steadily on both the grid-connected mode and the isolated mode.Furthermore,we can conclude by the simulation that the designed modular microgrid could operate uninterrupted when the microgrid topology switched from the grid-connected mode to the isolated mode.The seamless switching became the natural property for the modular microgrid.As a result,the modular microgrid topology can be considered as a usual power/load module to realize the friendly power interaction with the power grid.展开更多
This paper presents a novel machine learning(ML)enhanced energy management framework for residential microgrids.It dynamically integrates solar photovoltaics(PV),wind turbines,lithium-ion battery energy storage system...This paper presents a novel machine learning(ML)enhanced energy management framework for residential microgrids.It dynamically integrates solar photovoltaics(PV),wind turbines,lithium-ion battery energy storage systems(BESS),and bidirectional electric vehicle(EV)charging.The proposed architecture addresses the limitations of traditional rule-based controls by incorporating ConvLSTM for real-time forecasting,a Twin Delayed Deep Deterministic Policy Gradient(TD3)reinforcement learning agent for optimal BESS scheduling,and federated learning for EV charging prediction—ensuring both privacy and efficiency.Simulated in a high-fidelity MATLAB/Simulink environment,the system achieves 98.7%solar/wind forecast accuracy and 98.2% Maximum Power Point Tracking(MPPT)tracking efficiency,while reducing torque oscillations by 41% and peak demand by 22%.Compared to baseline methods,the solution improves voltage and frequency stability(maintaining 400V±2%,50Hz±0.015Hz)and achieves a 70% reduction in battery State of Charge(SOC)management error.The EV scheduler,informed by data from over 500 households,reduces charging costs by 31% with rapid failover to critical loads during outages.The architecture is validated using ISO 8528-8 transient tests,demonstrating 99.98% uptime.These results confirm the feasibility of transitioningmicrogrids fromreactive systems to adaptive,cognitive infrastructures capable of self-optimization under highly variable renewable generation and EV behaviors.展开更多
Most developing countries continue to face challenges in accessing sustainable energy.This study investigates a solar panel and battery-powered system for an urban off-grid microgrid in Nigeria,where demand-sideflexib...Most developing countries continue to face challenges in accessing sustainable energy.This study investigates a solar panel and battery-powered system for an urban off-grid microgrid in Nigeria,where demand-sideflexibility and strategic interactions between households and utilities can optimize system sizing.A nonlinear programming model is built using bilevel problem formulation that incorporates both the households’willingness to reduce their energy consumption and the utility’s agreement to provide price rebates.The results show that,for an energy community of 10 households with annual energy demand of 7.8 MWh,an oversized solar-storage system is required(12 kWp of photovoltaic solar panels and 26 kWh of battery storage).The calculated average cost of 0.31€/kWh is three times higher than the current tariff,making it unaffordable for most Nigerian households.To address this,the utility company could implement Demand Response programs with direct load control that delay the use of certain appliances,such as fans,irons and air conditioners.If these measures reduce total demand by 5%,both the required system size and overall costs could decrease significantly,by approximately one-third.This adjustment leads to a reduced tariffof 0.20€/kWh.When Demand Response is imple-mented through negotiation between the utility and households,the amount of load-shaving achieved is lower.This is because house-holds experience discomfort from curtailment and are generally less willing to provideflexibility.However,negotiation allows for greaterflexibility than direct control,due to dynamic interactions and more active consumer participation in the energy transition.Nonetheless,tariffs remain higher than current market prices.Off-grid contracts could become competitive iffinancial support is pro-vided,such as low-interest loans and capital grants covering up to 75%of the upfront cost.展开更多
基金supported by the Smart Grid-National Science and Technology Major Project(2025ZD0804500)the National Natural Science Foundation of China under Grant 52307232the Hunan Provincial Natural Science Foundation of China under Grant 2024JJ4055.
文摘The hybrid series-parallel microgrid attracts more attention by combining the advantages of both the series-stacked voltage and parallel-expanded capacity.Low-voltage distributed generations(DGs)are connected in series to form the intra-string,and then multiple strings are interconnected in parallel.For the existing control strategies,both intra-string and inter-string depend on the centralized or distributed control with high communication reliance.It has limited scalability and redundancy under abnormal conditions.Alternatively,in this study,an intra-string distributed and inter-string decentralized control framework is proposed.Within the string,a few DGs close to the AC bus are the leaders to get the string power information and the rest DGs are the followers to acquire the synchronization information through the droop-based distributed consistency.Specifically,the output of the entire string has the active power−angular frequency(ω-P)droop characteristic,and the decentralized control among strings can be autonomously guaranteed.Moreover,the secondary control is designed to realize multi-mode objectives,including on/off-grid mode switching,grid-connected power interactive management,and off-grid voltage quality regulation.As a result,the proposed method has the ability of plug-and-play capabilities,single-point failure redundancy,and seamless mode-switching.Experimental results are provided to verify the effectiveness of the proposed practical solution.
基金supported in part by the National Key RD Program of China under Grant 2023YFB4204400,and in part by the National Natural Science Foundation of China under Grant 52125705.
文摘In practical microgrids,current saturation of inverters and power interaction coupling of different forms of DERs complicate the system's transient behaviors.Existing methods of online transient stability prediction(TSP)are suitable for power systems consisting of homogeneous distributed energy resources(DERs),thus showing limited accuracy for stability prediction of microgrids.This paper develops a deep-learning-based TSP method for accurate online prediction of microgrids consisting of diverse forms of DERs under current saturation.First,a general key input feature selection method for microgrid TSP is systematically designed to ensure prediction accuracy.It is derived from a comprehensive mechanism analysis of the influence of DER's intrinsic and interaction characteristics under current saturation.Besides,impacts of load fluctuation and fault change are also considered to improve robust prediction performance.Second,to further improve prediction accuracy,an online TSP model based on deep learning is developed by effectively using the powerful nonlinear mapping capability of the deep belief network(DBN).Then,by combining feature selection method and deep-learning-based TSP model,an online TSP method is derived.Test results show the proposed method greatly improves accuracy of microgrid TSP under complex operating conditions.Furthermore,the method effectively avoids feature redundancy and the curse of dimensionality.Numbers of input features are independent of the scale of microgrids.
基金supported in part by the Fundamental Research Funds for the Central Universities(No.2020YJS162).
文摘This paper proposes a novel framework based on the Stackelberg game and deep reinforcement learning for multi-microgrids(MGs)in achieving peer-to-peer(P2P)energy trading.A multi-leaders,multi-followers Stackelberg game is utilized to model the P2P energy trading process.Stackelberg equilibrium(SE)is regarded as a P2P optimal trading strategy.A two-stage privacy protection solution technique combining data-driven and model-driven is developed to obtain the SE.Specifically,energy storage scheduling problem in MGs is formulated as a Markov decision process with discrete periods,and a multi-action single-observation deep deterministic policy gradient(MASO-DDPG)algorithm is proposed to tackle optimal scheduling of energy storage in the first stage.According to optimal scheduling of energy storage,the closed-form expression for SE based on model-driven is derived,and distributed SE solution technique(DSET)is developed to obtain SE in the second stage.Case studies involving a 4-Microgrid demonstrate the P2P electricity price obtained by the two-stage method,as a novel pricing mechanism,can reasonably regulate microgrid operation mode and improve microgrid income participating in the P2P market,which verifies effectiveness and superiority of the proposed P2P energy trading model and two-stage solution method.
基金supported by the State Grid Shanxi Electric Power Company science and technology project“Research on Key Technologies for Voltage Stability Analysis and Control of UHV Transmission Sending-End Grid with Large-Scale Integration of Wind-Solar-Storage Systems”(520530240026).
文摘To address the issue of transient low-voltage instability in AC-DC hybrid power systems following large disturbances,conventional voltage assessment and control strategies typically adopt a sequential“assess-then-act”paradigm,which struggles to simultaneously meet the requirements for both high accuracy and rapid response.This paper proposes a transient voltage assessment and control method based on a hybrid neural network incorporated with an improved snow ablation optimization(ISAO)algorithm.The core innovation of the proposed method lies in constructing an intelligent“physics-informed and neural network-integrated”framework,which achieves the integration of stability assessment and control strategy generation.Firstly,to construct a highly correlated input set,response characteristics reflecting the system’s voltage stable/unstable states are screened.Simultaneously,the transient voltage severity index(TVSI)is introduced as a comprehensive metric to quantify the system’s post-disturbance transient voltage performance.Furthermore,the load bus voltage sensitivity index(LVSI)is defined as the ratio of the voltage change magnitude at a load node(or bus)to the change in the system-level TVSI,thereby pinpointing the response characteristics of critical load nodes.Secondly,both the transient voltage stability assessment result and its corresponding under-voltage load shedding(UVLS)control amount are jointly utilized as the outputs of the response-driven model.Subsequently,the snow ablation optimization(SAO)algorithm is enhanced using a good point set strategy and a Gaussian mutation strategy.This improved algorithm is then employed to optimize the key hyperparameters of the hybrid neural network.Finally,the superiority of the proposed method is validated on a modified CEPRI-36 system and an actual power grid case.Comparisons with various artificial intelligence methods demonstrate its significant advantages in model speed and accuracy.Additionally,when compared to traditional emergency control schemes and UVLS strategies,the proposed method exhibits exceptional rapidness and real-time capability in control decision-making.
文摘In recent years,the hybrid AC-DC microgrid has been well accepted as it combines the advantages of both AC and DC systems.As the microgrid contains both DC sub-grids and AC sub-grids,interlinking DC-AC converters are essential.Meanwhile,considering the nonlinear AC loads may deteriorate the voltage quality of the AC bus,embedding an ancillary harmonic compensation function to the interlinking converters is promising.However,the conventional harmonic control methods used for active power filters(APFs)may not be suitable for the interlinking converters due to the main purpose of it is to exchange real and reactive power between the DC and AC sub-grids.The switching frequency is preferred to be lower than the APFs when the capacity of the microgrid is large.At low switching frequency,harmonic compensation performance or even the system stability may be affected.In this paper,a harmonic compensation approach suitable for hybrid AC-DC interlinking converters at low switching frequency is proposed.Through feeding the PWM reference signal with the harmonic compensation component directly to avoid the multi-loop control path of the fundamental component,the proposed method can achieve the effective harmonics compensation without being limited by the closed-loop control bandwidth.The proposed method,modeling approaches,stability analysis,as well as detailed virtual impedance design are presented.Experimental verification is also provided.
文摘The existing power management schemes for interlinked AC-DC microgrids have several operational drawbacks.Some of the existing control schemes are designed with the main objective of sharing power among the interlinked microgrids based on their loading conditions,while other schemes regulate the voltage of the interlinked microgrids without considering the specific loading conditions.However,the existing schemes cannot achieve both objectives efficiently.To address these issues,an autonomous power management scheme is proposed,which explicitly considers the specific loading condition of the DC microgrid before importing power from the interlinked AC microgrid.This strategy enables voltage regulation in the DC microgrid,and also reduces the number of converters in operation.The proposed scheme is fully autonomous while it retains the plug-nplay features for generators and tie-converters.The performance of the proposed control scheme has been validated under different operating scenarios.The results demonstrate the effectiveness of the proposed scheme in managing the power deficit in the DC microgrid efficiently and autonomously while maintaining the better voltage regulation in the DC microgrid.
文摘In order to realize the modular design of the microgrid,this paper proposed a new modular topology for the AC-DC mixed microgrid.In that topology,the AC microgrid unit and the DC microgrid unit were packaged together by the back-to-back converter.The battery-supercapacitor hybrid energy storage system was connected to the DC bus of back-to-back converter.By the reasonable design on the battery-supercapacitor hybrid system,the energy storage system could supply the rapid power and energy support for the microgrid spontaneously.The mathematical model and the control algorithm of that microgrid topology were studied.By the simulation analysis,it can be concluded that AC-DC mixed modular microgrid topology could operate steadily on both the grid-connected mode and the isolated mode.Furthermore,we can conclude by the simulation that the designed modular microgrid could operate uninterrupted when the microgrid topology switched from the grid-connected mode to the isolated mode.The seamless switching became the natural property for the modular microgrid.As a result,the modular microgrid topology can be considered as a usual power/load module to realize the friendly power interaction with the power grid.
基金supported by a grant fromtheUniversity of Tabuk,SaudiArabia(GrantNo.UT-2024-CIT-0527)Additional funding was provided by the Saudi Arabian Ministry of Education through the Scientific Research Support Program.
文摘This paper presents a novel machine learning(ML)enhanced energy management framework for residential microgrids.It dynamically integrates solar photovoltaics(PV),wind turbines,lithium-ion battery energy storage systems(BESS),and bidirectional electric vehicle(EV)charging.The proposed architecture addresses the limitations of traditional rule-based controls by incorporating ConvLSTM for real-time forecasting,a Twin Delayed Deep Deterministic Policy Gradient(TD3)reinforcement learning agent for optimal BESS scheduling,and federated learning for EV charging prediction—ensuring both privacy and efficiency.Simulated in a high-fidelity MATLAB/Simulink environment,the system achieves 98.7%solar/wind forecast accuracy and 98.2% Maximum Power Point Tracking(MPPT)tracking efficiency,while reducing torque oscillations by 41% and peak demand by 22%.Compared to baseline methods,the solution improves voltage and frequency stability(maintaining 400V±2%,50Hz±0.015Hz)and achieves a 70% reduction in battery State of Charge(SOC)management error.The EV scheduler,informed by data from over 500 households,reduces charging costs by 31% with rapid failover to critical loads during outages.The architecture is validated using ISO 8528-8 transient tests,demonstrating 99.98% uptime.These results confirm the feasibility of transitioningmicrogrids fromreactive systems to adaptive,cognitive infrastructures capable of self-optimization under highly variable renewable generation and EV behaviors.
基金support from Nantes Universite through the project AAP II GENOME(Ges-tion des Energies Nouvelles et Optimisation Electrique)and LEAP-RE MiDiNa project,grant N°NR-23-LERE-0002-01.
文摘Most developing countries continue to face challenges in accessing sustainable energy.This study investigates a solar panel and battery-powered system for an urban off-grid microgrid in Nigeria,where demand-sideflexibility and strategic interactions between households and utilities can optimize system sizing.A nonlinear programming model is built using bilevel problem formulation that incorporates both the households’willingness to reduce their energy consumption and the utility’s agreement to provide price rebates.The results show that,for an energy community of 10 households with annual energy demand of 7.8 MWh,an oversized solar-storage system is required(12 kWp of photovoltaic solar panels and 26 kWh of battery storage).The calculated average cost of 0.31€/kWh is three times higher than the current tariff,making it unaffordable for most Nigerian households.To address this,the utility company could implement Demand Response programs with direct load control that delay the use of certain appliances,such as fans,irons and air conditioners.If these measures reduce total demand by 5%,both the required system size and overall costs could decrease significantly,by approximately one-third.This adjustment leads to a reduced tariffof 0.20€/kWh.When Demand Response is imple-mented through negotiation between the utility and households,the amount of load-shaving achieved is lower.This is because house-holds experience discomfort from curtailment and are generally less willing to provideflexibility.However,negotiation allows for greaterflexibility than direct control,due to dynamic interactions and more active consumer participation in the energy transition.Nonetheless,tariffs remain higher than current market prices.Off-grid contracts could become competitive iffinancial support is pro-vided,such as low-interest loans and capital grants covering up to 75%of the upfront cost.
