Microgrids (MGs) and active distribution networks (ADNs) are important platforms for distributed energy resource (DER) consumption. The increasing penetration of DERs has motivated the development ADNs coupled with MG...Microgrids (MGs) and active distribution networks (ADNs) are important platforms for distributed energy resource (DER) consumption. The increasing penetration of DERs has motivated the development ADNs coupled with MGs. This paper proposes a distributedco-optimization method for peer-to-peer (P2P) energy trading and network operation for an ADN integrated with multiple microgrids(MMGs). A framework that optimizes P2P energy trading among MMGs and ADN operations was first established. Subsequently, anenergy management model that aims to minimize the operation and energy trading costs was constructed for each MG. Accordingly, theMMGs’ cooperative game model was established based on Nash bargaining theory to incentivize each stakeholder to participate in P2Penergy trading, and a distributed solution method based on the alternating direction method of multipliers was developed. Moreover, analgorithm that adjusts the amount of energy trading between the ADN and MG is proposed to ensure safe operation of the distributionnetwork. With the communication between the MG and ADN, the MMGs’ P2P trading and ADN operations are optimized in a coordinated manner. Finally, numerical simulations were conducted to verify the accuracy and effectiveness of the proposed method.展开更多
The energy landscape for the Low-Voltage(LV)networks is undergoing rapid changes.These changes are driven by the increased penetration of distributed Low Carbon Technologies,both on the generation side(i.e.adoption of...The energy landscape for the Low-Voltage(LV)networks is undergoing rapid changes.These changes are driven by the increased penetration of distributed Low Carbon Technologies,both on the generation side(i.e.adoption of micro-renewables)and demand side(i.e.electric vehicle charging).The previously passive‘fit-and-forget’approach to LV network management is becoming increasing inefficient to ensure its effective operation.A more agile approach to operation and planning is needed,that includes pro-active prediction and mitigation of risks to local sub-networks(such as risk of voltage deviations out of legal limits).The mass rollout of smart meters(SMs)and advances in metering infrastructure holds the promise for smarter network management.However,many of the proposed methods require full observability,yet the expectation of being able to collect complete,error free data from every smart meter is unrealistic in operational reality.Furthermore,the smart meter(SM)roll-out has encountered significant issues,with the current voluntary nature of installation in the UK and in many other countries resulting in low-likelihood of full SM coverage for all LV networks.Even with a comprehensive SM roll-out privacy restrictions,constrain data availability from meters.To address these issues,this paper proposes the use of a Deep Learning Neural Network architecture to predict the voltage distribution with partial SM coverage on actual network operator LV circuits.The results show that SM measurements from key locations are sufficient for effective prediction of the voltage distribution,even without the use of the high granularity personal power demand data from individual customers.展开更多
“Multidimensional international world”refers to understanding the world through multiple dimensions beyond traditional economic or political measures,fostering cross-cultural collaboration,and creating systems that ...“Multidimensional international world”refers to understanding the world through multiple dimensions beyond traditional economic or political measures,fostering cross-cultural collaboration,and creating systems that balance global integration with local needs.This also includes management of global business operations across diverse cultures in a multipolar international landscape.The paper briefs the developed and already tested in numerous applications high-level Spatial Grasp Model and Technology(SGT),which can help investigate and manage complex systems with a holistic spatial approach effectively covering various physical and virtual dimensions,their interrelations,and integration as a whole.Different areas will be investigated with examples of practical solutions in them and their combinations in a high-level Spatial Grasp Language(SGL),the key element of SGT.This allows for the creation and distributed management of very large spatial networks with different orientation which can be self-spreading,self-analyzing,self-modifying,and self-recovering in complex terrestrial and celestial environments,and also organize dynamic multi-networking solutions supporting global evolution and integrity.展开更多
基金supported by the State Key Laboratory of Technology and Equipment for Defense against Power System Operational Risks Program(grant number SGNR0000KJJS2302139).
文摘Microgrids (MGs) and active distribution networks (ADNs) are important platforms for distributed energy resource (DER) consumption. The increasing penetration of DERs has motivated the development ADNs coupled with MGs. This paper proposes a distributedco-optimization method for peer-to-peer (P2P) energy trading and network operation for an ADN integrated with multiple microgrids(MMGs). A framework that optimizes P2P energy trading among MMGs and ADN operations was first established. Subsequently, anenergy management model that aims to minimize the operation and energy trading costs was constructed for each MG. Accordingly, theMMGs’ cooperative game model was established based on Nash bargaining theory to incentivize each stakeholder to participate in P2Penergy trading, and a distributed solution method based on the alternating direction method of multipliers was developed. Moreover, analgorithm that adjusts the amount of energy trading between the ADN and MG is proposed to ensure safe operation of the distributionnetwork. With the communication between the MG and ADN, the MMGs’ P2P trading and ADN operations are optimized in a coordinated manner. Finally, numerical simulations were conducted to verify the accuracy and effectiveness of the proposed method.
基金This work was performed as part of the Network Constraints Early Warning System(NCEWS)projectThe authors acknowledge the support of Innovate UK(project no.B16N12241)and the UK OFGEM(Network Innovation Allowance NIA_SPEN0016 and NIA_SPEN034)+1 种基金Robu and Flynn also acknowledge the support of UKRI projects Centre for Energy Systems Integration(CESI)[EP/P001173/1]and Community Energy Demand Reduction in India(ReFlex)[EP/R008655/1]Finally,the authors are grateful for the recognition of our work by UK’s Institute of Engineering and Technology’s(IET),through the award of the IET and E&T 2019 Innovation of the Year Award[43].
文摘The energy landscape for the Low-Voltage(LV)networks is undergoing rapid changes.These changes are driven by the increased penetration of distributed Low Carbon Technologies,both on the generation side(i.e.adoption of micro-renewables)and demand side(i.e.electric vehicle charging).The previously passive‘fit-and-forget’approach to LV network management is becoming increasing inefficient to ensure its effective operation.A more agile approach to operation and planning is needed,that includes pro-active prediction and mitigation of risks to local sub-networks(such as risk of voltage deviations out of legal limits).The mass rollout of smart meters(SMs)and advances in metering infrastructure holds the promise for smarter network management.However,many of the proposed methods require full observability,yet the expectation of being able to collect complete,error free data from every smart meter is unrealistic in operational reality.Furthermore,the smart meter(SM)roll-out has encountered significant issues,with the current voluntary nature of installation in the UK and in many other countries resulting in low-likelihood of full SM coverage for all LV networks.Even with a comprehensive SM roll-out privacy restrictions,constrain data availability from meters.To address these issues,this paper proposes the use of a Deep Learning Neural Network architecture to predict the voltage distribution with partial SM coverage on actual network operator LV circuits.The results show that SM measurements from key locations are sufficient for effective prediction of the voltage distribution,even without the use of the high granularity personal power demand data from individual customers.
文摘“Multidimensional international world”refers to understanding the world through multiple dimensions beyond traditional economic or political measures,fostering cross-cultural collaboration,and creating systems that balance global integration with local needs.This also includes management of global business operations across diverse cultures in a multipolar international landscape.The paper briefs the developed and already tested in numerous applications high-level Spatial Grasp Model and Technology(SGT),which can help investigate and manage complex systems with a holistic spatial approach effectively covering various physical and virtual dimensions,their interrelations,and integration as a whole.Different areas will be investigated with examples of practical solutions in them and their combinations in a high-level Spatial Grasp Language(SGL),the key element of SGT.This allows for the creation and distributed management of very large spatial networks with different orientation which can be self-spreading,self-analyzing,self-modifying,and self-recovering in complex terrestrial and celestial environments,and also organize dynamic multi-networking solutions supporting global evolution and integrity.
