Plug Load Energy Control In Commercial Building

Building energy management (BEM) systems are a critical component to manag-ing energy demand. The future vision of BEM is to promote the integration of renew-able energy and demand response by exploiting smart grid technologies. However, itwill introduce the uncertainty and randomness signifcantly both in energy supply anddemand, which poses great challenge in scheduling the electricity procurement andload control efciently.To remedy the critical issues, we present an agent-based BEM system framework,using the independence and autonomy of each agent to solve given problems and usingthe collaboration of diferent agents to solve the large-scale complicated problems inordertoensuretherapidityandaccuracyandreducetheinformationinteraction. Basedon this frame work, two types of plug load control are studies.More specifcally, for pure plug load, we propose a low-complexity and efectivedistributed algorithm based on dual decomposition for electricity procurement, whichcan handle with sufcient speed and computational efciency to make them practicalfor control and reduce information interaction. The simulation results show that theagent-based multi-timescale BEM system can efectively address the randomness anduncertainties of renewable energy resources and plug load, and has the potential toreduce the electricity consumption in buildingS.For plug-in electric Vehicle (PEV) which can be act as load when charging or asdistributed energy storage when discharging, we focus on energy unbalance in BEMsystem at real time and consider a scenario using PEV to resolve this problem. Therewhereaplug-inelectricvehicle(PEV)chargingstationoraparkinglotisintegratedintoa commercial building, and we investigate how to align incentives to encourage PEVowners to participate in building energy balance for BEM system. For this purpose, inthis paper, we introduce a two stage Stackelberg game between the PEV agent and the PEV owners. We use backward induction to analyze the formulated game and derivethe optimal pricing scheme. We assess the performance of our proposed scheme. Thesimulation results show that this algorithm can efectively address the unbalance of theBEM system and is a win-win strategy because it can increase the proft for both BEMsystem and PEV.