With an increase in the electrification of end-use sectors,various resources on the demand side provide great flexibility potential for system operation,which also leads to problems such as the strong randomness of po...With an increase in the electrification of end-use sectors,various resources on the demand side provide great flexibility potential for system operation,which also leads to problems such as the strong randomness of power consumption behavior,the low utilization rate of flexible resources,and difficulties in cost recovery.With the core idea of“access over ownership”,the concept of the sharing economy has gained substantial popularity in the local energy market in recent years.Thus,we provide an overview of the potential market design for the sharing economy in local energy markets(LEMs)and conduct a detailed review of research related to local energy sharing,enabling technologies,and potential practices.This paper can provide a useful reference and insights for the activation of demand-side flexibility potential.Hopefully,this paper can also provide novel insights into the development and further integration of the sharing economy in LEMs.展开更多
In this paper, we present a novel cloud-based demand side management (DSM) optimization approach for the cost reduction of energy usage in heating, ventilation and air conditioning (HVAC) systems in residential homes ...In this paper, we present a novel cloud-based demand side management (DSM) optimization approach for the cost reduction of energy usage in heating, ventilation and air conditioning (HVAC) systems in residential homes at the district level. The proposed approach achieves optimization through scheduling of HVAC energy usage within permissible bounds set by house users. House smart home energy management (SHEM) devices are connected to the utility/aggregator via a dedicated communication network that is used to enable DSM. Each house SHEM can predict its own HVAC energy usage for the next 24 h using minimalistic deep learning (DL) prediction models. These predictions are communicated to the aggregator, which will then do day ahead optimizations using the proposed game theory (GT) algorithm. The GT model captures the interaction between aggregator and customers and identifies a solution to the GT problem that translates into HVAC energy peak shifting and peak reduction achieved by rescheduling HVAC energy usage. The found solution is communicated by the aggregator to houses SHEM devices in the form of offers via DSM signals. If customers’ SHEM devices accept the offer, then energy cost reduction will be achieved. To validate the proposed algorithm, we conduct extensive simulations with a custom simulation tool based on GridLab-D tool, which is integrated with DL prediction models and optimization libraries. Results show that HVAC energy cost can be reduced by up to 36% while indirectly also reducing the peak-to-average (PAR) and the aggregated net load by up to 9.97%.展开更多
Because of the rapid growth of new energy and the accompanying considerable uncertainty in the power market,the demand for flexibility in a power sys-tem has risen sharply.In the meantime,the market structure of auxil...Because of the rapid growth of new energy and the accompanying considerable uncertainty in the power market,the demand for flexibility in a power sys-tem has risen sharply.In the meantime,the market structure of auxiliary services has changed,resulting in market participants(MPs)benefiting less than expected from providing flexible services.To encourage MPs to provide flexibility,this study proposes a dynamic design framework for an auxiliary service compensation mech-anism.To evaluate the proposed framework,a case study is conducted,examining a peak-shaving service in Liao-ning province in northeast China.First,the operational status and limitations of the typical product,the peak-shaving service,in China’s flexibility auxiliary ser-vices market are analyzed.Then,taking into consideration the time value of the flexible products provided by the MPs,a dynamic mechanism for hierarchical compensation of flexibility auxiliary service costs is proposed,and a mathematical model aimed at optimizing the MPs’comprehensive income is constructed.The results show that,compared with the existing traditional mechanism,the proposed method can effectively guarantee fair remu-neration for the flexibility provider,while easing the tense supply-demand relationship in the flexibility market.展开更多
Nowadays,transmission system operators(TSOs)encounter the challenges of securely operating power grids with high penetration of renewables.In this context,more flexibility is needed than ever to maintain system reliab...Nowadays,transmission system operators(TSOs)encounter the challenges of securely operating power grids with high penetration of renewables.In this context,more flexibility is needed than ever to maintain system reliability.With rapid development of coordinated transmission and distribution technology recently,active distribution networks(ADNs)which have abundant flexible resources,have the potential to provide flexibility to the TSO.Hence,an integrated transmission-distribution flexibility market is proposed in this paper.In the proposed framework,the energy market is first cleared to obtain baseline generation schedules.Then the baseline generation schedules are treated as inputs to the flexibility market.Furthermore,the integrated flexibility market is cleared to facilitate flexibility trading between transmission and distribution networks.To preserve the data privacy of different market participants,an alternating direction method of multipliers(ADMM)based method is utilized to clear the markets in a distributed manner.In the energy market and flexibility market-clearing model,transmission network market clearing models are linear programming(LP)models,and distribution network market clearing models are second order cone programming(SOCP)models.Through the proposed method,only limited information is exchanged between TSO and DSOs.Case studies are conducted on a revised IEEE 30-bus transmission network with two 33-node ADNs.Numerical results demonstrate the proposed flexibility market framework can enhance competitiveness of generation companies in the transmission network and distributed generators(DGs)in the ADNs.Moreover,flexibility purchasing cost is reduced by 17.7%compared to traditional flexibility supplied by generators according to the case study results,and ADNs can gain additional flexibility profit by providing flexibility.展开更多
文摘With an increase in the electrification of end-use sectors,various resources on the demand side provide great flexibility potential for system operation,which also leads to problems such as the strong randomness of power consumption behavior,the low utilization rate of flexible resources,and difficulties in cost recovery.With the core idea of“access over ownership”,the concept of the sharing economy has gained substantial popularity in the local energy market in recent years.Thus,we provide an overview of the potential market design for the sharing economy in local energy markets(LEMs)and conduct a detailed review of research related to local energy sharing,enabling technologies,and potential practices.This paper can provide a useful reference and insights for the activation of demand-side flexibility potential.Hopefully,this paper can also provide novel insights into the development and further integration of the sharing economy in LEMs.
基金supported by the National Science Foundation(NSF)grant ECCF 1936494.
文摘In this paper, we present a novel cloud-based demand side management (DSM) optimization approach for the cost reduction of energy usage in heating, ventilation and air conditioning (HVAC) systems in residential homes at the district level. The proposed approach achieves optimization through scheduling of HVAC energy usage within permissible bounds set by house users. House smart home energy management (SHEM) devices are connected to the utility/aggregator via a dedicated communication network that is used to enable DSM. Each house SHEM can predict its own HVAC energy usage for the next 24 h using minimalistic deep learning (DL) prediction models. These predictions are communicated to the aggregator, which will then do day ahead optimizations using the proposed game theory (GT) algorithm. The GT model captures the interaction between aggregator and customers and identifies a solution to the GT problem that translates into HVAC energy peak shifting and peak reduction achieved by rescheduling HVAC energy usage. The found solution is communicated by the aggregator to houses SHEM devices in the form of offers via DSM signals. If customers’ SHEM devices accept the offer, then energy cost reduction will be achieved. To validate the proposed algorithm, we conduct extensive simulations with a custom simulation tool based on GridLab-D tool, which is integrated with DL prediction models and optimization libraries. Results show that HVAC energy cost can be reduced by up to 36% while indirectly also reducing the peak-to-average (PAR) and the aggregated net load by up to 9.97%.
基金supported by the National Key Research and Development Program“Renewable Energy and Thermal Power Coupling Integration and Flexible Operation Control Technology”(No.2019YFB1505400).
文摘Because of the rapid growth of new energy and the accompanying considerable uncertainty in the power market,the demand for flexibility in a power sys-tem has risen sharply.In the meantime,the market structure of auxiliary services has changed,resulting in market participants(MPs)benefiting less than expected from providing flexible services.To encourage MPs to provide flexibility,this study proposes a dynamic design framework for an auxiliary service compensation mech-anism.To evaluate the proposed framework,a case study is conducted,examining a peak-shaving service in Liao-ning province in northeast China.First,the operational status and limitations of the typical product,the peak-shaving service,in China’s flexibility auxiliary ser-vices market are analyzed.Then,taking into consideration the time value of the flexible products provided by the MPs,a dynamic mechanism for hierarchical compensation of flexibility auxiliary service costs is proposed,and a mathematical model aimed at optimizing the MPs’comprehensive income is constructed.The results show that,compared with the existing traditional mechanism,the proposed method can effectively guarantee fair remu-neration for the flexibility provider,while easing the tense supply-demand relationship in the flexibility market.
基金supported in part by the joint project of NSFC of China and EPSRC of UK (No.52061635103 and EP/T021969/1)the National Natural Science Foundation of China (No.52007026)。
文摘Nowadays,transmission system operators(TSOs)encounter the challenges of securely operating power grids with high penetration of renewables.In this context,more flexibility is needed than ever to maintain system reliability.With rapid development of coordinated transmission and distribution technology recently,active distribution networks(ADNs)which have abundant flexible resources,have the potential to provide flexibility to the TSO.Hence,an integrated transmission-distribution flexibility market is proposed in this paper.In the proposed framework,the energy market is first cleared to obtain baseline generation schedules.Then the baseline generation schedules are treated as inputs to the flexibility market.Furthermore,the integrated flexibility market is cleared to facilitate flexibility trading between transmission and distribution networks.To preserve the data privacy of different market participants,an alternating direction method of multipliers(ADMM)based method is utilized to clear the markets in a distributed manner.In the energy market and flexibility market-clearing model,transmission network market clearing models are linear programming(LP)models,and distribution network market clearing models are second order cone programming(SOCP)models.Through the proposed method,only limited information is exchanged between TSO and DSOs.Case studies are conducted on a revised IEEE 30-bus transmission network with two 33-node ADNs.Numerical results demonstrate the proposed flexibility market framework can enhance competitiveness of generation companies in the transmission network and distributed generators(DGs)in the ADNs.Moreover,flexibility purchasing cost is reduced by 17.7%compared to traditional flexibility supplied by generators according to the case study results,and ADNs can gain additional flexibility profit by providing flexibility.
