Demand response (DR) refers to the active participation by retail customers in electricity markets, seeing and responding to prices as the prices change over time [1]. Occupants are excluded from traditional commercial building DR control loops. However, our experimental results suggest that motivated occupants can achieve significant load shaving with proper information. The analysis indicates more load shaving potentials with financial incentives. During the DR events, utility companies send DR requests to commercial building facility managers (FMs), who often send emails to occupants with generic instructions. Unless the buildings are equipped with advanced hardware, office building FMs have limited means to reduce significant peak load in the DR periods. We present a software-based, occupant-engaged fast DR system for commercial office buildings. Our collaborative DR (cDR) module is built upon our collaborative energy management and control (cEMC) platform. Through a web portal, occupants can submit a preferred temperature range and schedule. In this chapter, we present a novel occupant-engaged collaborative DR system from the perspective of cyber-physical-social systems (CPSSes). Traditional building automation systems (BASes) are designed based on thermal and mechanical requirements, without considering the impact of occupants' psychological motivations, such as peer pressure, social recognition, and gaming experiences. The cDR system is designed with cybernetics and social factor in mind. From the cybernetics perspective, we introduced a semantic building data model to enable micro-zoning scenarios. From the social behavior perspective, (1) we developed a zonal virtual energy meter algorithm to split the whole building energy consumption into individual occupants and (2) provide an embedded social network, in order to encourage an energy competition game. The effectiveness of the energy game is validated by real building experiments. In addition, we developed a game-theoretical optimal incentive design (OID) algorithm to allocate real financial incentives to individual occupants, and conducted a simulation-based study. During field experiments on a mid-size office building at Pittsburgh, in 2014 summer, we observed up to 1.6% load reduction for the traditional email-based DR method, and 11-15% load reduction using our cDR system without compromising on comfort. Up to 56.7% load was shed, with an acceptable loss of comfort.

Chapter Contents:

• Abstract
• 14.1 Introduction
• 14.1.1 Introduction to demand response
• 14.1.2 Commercial buildings as cyber-physical-social systems
• 14.1.2.1 Human-in-the-loop architecture
• 14.1.2.2 Cyber, physical and social factors
• 14.1.2.3 Psychological comfort models
• 14.1.2.4 Motivation of the proposed methodology
• 14.1.3 Occupant-engaged DR
• 14.2 Collaborative, occupant-engaged fast demand responses
• 14.2.1 The software architecture of cEMC
• 14.2.2 Design for engaging human-machine interface: Occupants' Dashboard and FM's HMI
• 14.2.3 Temperature and ventilation arbitrations using convex optimization
• 14.2.4 Collaboration for DR scenarios
• 14.2.5 Energy split algorithm for energy games
• 14.3 Experimental study
• 14.3.1 Deployment site
• 14.3.2 Baseline setup
• 14.3.3 Field experiment
• 14.4 Conclusions and future work
• Acronyms
• Acknowledgments
• References

Inspec keywords: energy consumption; game theory; building management systems

Other keywords: occupant-engaged fast demand response; collaborative demand response; building automation systems; game-theoretical optimal incentive design algorithm; micro-zoning scenarios; building energy consumption; commercial office buildings; energy competition game; financial incentives; cyber-physical-social systems; collaborative energy management and control platform

Subjects: Control of electric power systems; Game theory; Game theory; Probability theory, stochastic processes, and statistics; Automated buildings; Power utilisation; Buildings (energy utilisation)