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Energy and AI 2024年第2期16卷 331-345页

作者： Rahman Heidarykiany Cristinel Ababei MESS Laboratory EECE DepartmentMarquette UniversityMilwaukeeWIUSA

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%.

关键词： Day ahead demand side management(DSM) Appliance energy usage prediction Residential energy usage scheduling flexibility Market incentives Non-cooperative game theory(GT) Dynamic price(DP) Energy cost minimization Electricity cost minimization Peak-to-average ratio(PAR)minimization Machine learning(ML) Long short-term memory(LSTM) Smart Home Energy Management(SHEM) load shifting Internet of Things(ioT)applications Smart grid Heating Ventilation and air conditioning(HVAC)

Future evolution of automated demand response system in smart grid for low-carbon economy

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Journal of Modern Power Systems and Clean Energy 2015年第1期3卷 72-81页

作者： Huaguang YAN Bin LI Songsong CHEN Ming ZHONG Dezhi LI Limin JIANG Guixiong HE China Electric Power Research Institute Beijing 100192China School of Electric and Electronic Engineering North China Electric Power UniversityBeijing 102206China

Smart grid construction is an important carrier and an effective way to promote the development of lowcarbon *** response(DR)is commonly regarded as an important core technology in smart grid field,and it reflects the flexible and interactive features of the core business in smart *** is the developing direction of automated demand response(ADR)technology,and its main features are the standardization of information exchange,together with the intelligence of decision-making and the automation of *** technology can improve the efficiency of the whole power system and enhance the ability to accept new energy *** paper analyzes the role of demand response in improving efficiency and low-carbon energy saving power *** automated demand response system architecture is investigated,and the ADR roadmap of commercial/industrial and residential customer is *** key technologies for ADR system are analyzed,including demand response strategy,information exchanging model,measurement and verification techniques,and multi-agent scheduling *** ensure the interoperability between the grid side and the user side,the ADR business in smart grid user interface standards is concluded to support further demand side management project.

关键词： Demand response Information model System architecture load shifting

Model-Based Quantification of load Shift Potentials and Optimized Charging of Electric Vehicles

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Smart Grid and Renewable Energy 2013年第5期4卷 398-408页

作者： Tobias Hahn Martin Schonfelder Patrick Jochem Vincent Heuveline Wolf Fichtner Institute of Process Engineering in Life Sciences Karlsruhe Institute of Technology(KIT)KarlsruheGermany Chair of Energy Economics Karlsruhe Institute of Technology(KIT)KarlsruheGermany University of Heidelberg Interdisciplinary Center for Scientific ComputingHeidelbergGermany

Managing the charging process of a large number of electric vehicles to decrease the pressure on the local electricity grid is of high interest to the utilities. Using efficient mathematical optimization techniques, the charging behavior of electric vehicles shall be optimally controlled taking into account network, vehicle, and customer requirements. We developed an efficient algorithm for calculating load shift potentials defined as the range of all charging curves meeting the customer’s requirements and respecting all individual charging and discharging constraints over time. In addition, we formulated a mixed integer linear program (MIP) applying semi-continuous variables to find cost-optimal load curves for every vehicle participating in a load shift. This problem can be solved by e.g. branch-and-bound algorithms. Results of two scenarios of Germany in 2015 and 2030 based on mobility studies show that the load shifting potential of EV is significant and contribute to a necessary relaxation of the future grid. The maximum charging and discharging power and the average battery capacity are crucial to the overall load shift potential.

关键词： Electric Mobility load shifting Charging Management Optimization

Artificial Neural Network-Aided Energy Management Scheme for Unlocking Demand Response

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Artificial Neural Network-Aided Energy Management Scheme for...

第32届中国控制与决策会议

作者： Xiangping Chen Jinbiao Li Anqian Yang Qilong Zhang IEEE Electrical Engineering School Guizhou University

European Horizon 2020 raises three important targets-calling for 20% reduction green-house emission,20% increase of renewable resources in energy sectors,20% energy efficiency *** to demand response（DR） scheme is commonly regarded as one of the key technologies to realize these targets across the world where 70% of the total DR potential is estimated from residential and tertiary *** advantage of model predictive control（MPC） lies on its potential in shifting controlled demands towards strong generation or low-tariff periods which in turn encourage the change of energy consumption behavior of end-users under economic *** research develops a smart energy management system based on MPC scheme by using dynamic program to formulate the optimization problems in the residential *** paper presents a smart energy management system for unlocking demand response in the residential *** approach comprises the predictive estimation of one-hour energy demand and PV generation（supply） for rescheduling the 24-h ahead demand profiles by shifting potential flexible ***-time electrical demand is met by combining power supplies from PV,grid and batteries while minimizing consumer s cost of *** results show that the peak-to-average ratio is reduced by 22.9% with the cost saving of 34.6% for the selected day.

关键词： demand response model predictive scheme photovoltaic peak-to-average ratio load shifting

来源： cnki会议评论

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峰谷分时电价:一种现行有效的DSM措施

电力需求侧管理 2002年第Z1期4卷 30-34页

作者：王治华唐为民李扬李军红张长沪 DSM Research Institute of Southeast University Nanjing Power Supply Bureau

TOU tariff is one of the measures for implementing the DSM. This paper introduces the appearance of DSMand its measures and main contents. TOU tariff's principle、structure and strategy of price-setting are also expou... 详细信息

关键词： TOU load shifting