HVAC energy cost minimization in smart grids: A cloud-based demand side management approach with game theory optimization and deep learning  

作　　者：Rahman Heidarykiany Cristinel Ababei 

机构地区：[1]MESS Laboratory,EECE Department,Marquette University,Milwaukee,WI,USA

出　　处：《Energy and AI》2024年第2期331-345,共15页能源与人工智能（英文）

基　　金：supported by the National Science Foundation(NSF)grant ECCF 1936494.

摘　　要：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) 

分 类 号：TM76[电气工程—电力系统及自动化]