Participatory Energy Management in Building Networks

PARTICIPATORY ENERGY MANAGEMENT IN BUILDING NETWORKS Mina Rahimian, Daniel Cardoso-Llach and Lisa Domenica Iulo Department of Architecture, Penn State Stuckeman School, PA, USA; email: {mxr446, dzc10, ldi1} @psu.edu ABSTRACT Research on delivering high quality energy-related information on users’ activities and consumption rates signify the effectiveness of such information for inspiring and motivating users to change their behavior towards more energy saving ones. However the issue of making these behavior changes durable and integrated to one’s lifestyle is still remaining a topic for further investigation. This paper attempts to encourage new ways of thinking about users’ engagement in the energy management system of their community-based microgrid by combining computational means of feedback delivery with an incentive program which requires users’ self-organized collaboration and participation in the shared-energy community endeavor. KEYWORDS: Management Participatory, Collective Intelligence, Microgrids, Energy INTRODUCTION Energy production is typically a regional enterprise, with the majority of energy produced far from the main areas of demand. This causes tremendous problems in terms of lack of resiliency and flexibility in handling the ever changing demands at the users’ end and the continuous changes of the dynamic environment (Kang, Park, Oh, & Park, 2014 - Farhangi, 2010 - Amin & Wollenberg, 2005 - Villareal, Erickson, & Zafar, 2014). Microgrids, on the other hand - as localized energy infrastructures support resiliency in the electrical grid by exercising greater control over the production by generating energy close to its point of consumption. Microgrid integrate various techniques of automation, optimization, pervasive control and computation on both the supply and demand side (Rahimian, 2015 - Sherman, 2007 Paglia, 2011). At the demand side, microgrids empower the users to interact with the energy management system to adjust their energy use and reduce their energy costs (Farhangi, 2010). Energy metering devices and feedback technologies act as a medium for communication between the grid and the user by making the grid visible to them and facilitating energy management. The mere display of users’ energy consumption feedback information, gives users the ability to control their consumption pattern based on the energy pricing rates throughout the day (Ipakchi & Albuyeh, 2009 - Kang, Park, Oh, & Park, 2014 - Rahimian, Iulo, & Cardoso Llach, In Press). Energy feedback technologies are based on the hypothesis that most people lack awareness and understanding about how their everyday behavior affects the environment and therefore limits the consumer’s capacity on deciding to take conservation actions (Lutzenhiser, 1993 - Froehlich, et al., 2010). In microgrids, it is expected that displaying energy feedback information at the demand side will increase users’ knowledge on consumption and consequently leads to managing energy usage by adopting long lasting conservation behaviors. Several studies suggest that delivering high quality energy-related information on users’ activities and consumption rates has the potential to motivate users change behavior towards more energy saving ones (Yu, Fung, Haghighat, Yoshino, & Morofsky, 2011). However, feedback delivery alone may not suffice to change people’s consumption behavior in long term (Hargreaves, Nye, & Burgess, 2010). Thus the issue of how to make energy-related behavior change durable and integrated to one’s lifestyle remains a topic for further investigation. BACKGROUND Studies of human behavior in the context of energy feedback technologies have shown that although displaying energy consumption information is necessary and valuable for increasing awareness and helping consumers control their consumption, broader psychological, social and cultural patterns of household energy use must be accounted for in order to encourage long-lasting changes in behavior (Aune, 2007 Hargreaves, et al., 2010). This is because metered provisions acquire meaning after going through each household’s interpretive and discursive lens, point of view and cultural practices. After energy feedback information is individually processed it holds persuasive ability and the potential to solve the gap of “energy illiteracy”, but it does not necessarily inspire users to adopt long-lasting behavior change. Over the past fifty years, environmental discourses in the field of human-computer interaction (HCI), and studies on motivations for environmentally positive behavior in the field of environmental psychology have been popular subjects for research. (Goodman, 2009). An extensive literature review on 139 resources by Froehlich et al on the study and design of environmental HCI1, as the intersection of environmental psychology and HCI, have explored two distinct approaches addressing the design and evaluation of feedback technologies which in consequence have resulted in a profound gap between these two disciplines. These researchers find this divergence to be the main reason that feedback technologies’ lack success in promoting longlasting behavior change (Froehlich, Findlater, & Landay, 2010). This is while environmental psychologists have largely focused on the effects of energy feedback information per se and HCI researchers have concentrated on the iterations of feedback design and the production of the artifact itself with an emphasis on understandability, usability and aesthetics (Froehlich, Findlater, & Landay, 2010). A PARTICIPATORY MODEL FOR ENERGY MANAGEMENT A growing body of literature suggests that combining feedback delivery on consumption patterns with other strategies such as goal setting, incentive programs, economic penalties, etc., is a more effective way of nudging users towards more responsible energy consumption habits. (Costanzo, Archer, Aronson, & Pettigrew, As Froehlich et al assert, environmental HCI is the study and design of eco-feedback technologies which provides feedback on individual or group behaviors with the goal of reducing environmental impact (Froehlich, Findlater, & Landay, 2010) 1 1986 - Fischer, 2008 - Froehlich, Findlater, & Landay, 2010 - Hargreaves, Nye, & Burgess, 2010). These studies have emphasized the complexities of human behavior and highlighted a body of environmental psychology literature offering techniques and inspiration on behavior change strategies to guide and/or complement persuasive energy feedback technologies (Lutzenhiser, 1993 - Hargreaves, Nye, & Burgess, 2010). Building upon the reviewed literature, this paper recommends a shift in focus more on the community’s energy use rather than individual energy consumers as the key unit of analysis (Rahimian, 2015). Toward this end, a conceptual prototype of an energy exchange system —a collaborative energy sharing network for small-scale community microgrids— with a diversity of intense energy users, structured on a collaborative incentive program with interactive and comprehensive energy feedback information is proposed as a possible solution. The focus is not on directly educating users about their energy consumption, but rather on fostering cooperative and energy-saving dynamics by coupling energy feedback technologies and an incentive program which requires users’ self-organized collaboration and participation in sharing energy within their community’s microgrid. The interface system described below is proposed as a way to increase the possibility of a community microgrid to be energy responsive through its users. In this system the visualization of energy use through feedback devices, an aesthetically appealing method for inducing behavior change, is combined with game-like built in incentives to motivate long term behavior modification. The system seeks to foster collaboration and participation among users, advancing a new view on energy consumption as a community endeavor. The “game” can provide targeted incentives for users of a microgrid to alter their consumption patterns and shape the use of shared-energy resources, resulting in new patterns of energy responsive collaboration and participation in the microgrid, linking resiliency to a community’s collective intelligence. SYSTEM OVERVIEW The section below discusses how the proposed conceptual prototype energy exchange system operates and introduces an interface structured upon an incentive program that motivates users’ participation. Conceptual Prototype: The proposed system is a small-scale community microgrid constituted of a moderate number of interconnected houses with a diverse set of energy users, introduced as a method to address the assumptions and principles laid out in the previous section. The variegation of users in this system play an important role in driving the system since it intensifies the possibility of exchanges to take place. Energy Supply: For this proposal community energy is supplied by a common source of renewable energy such as solar, wind power or fuel cells. As a small-scale microgrid the intent is to reach a level of independency on fossil fuels, in which the community’s need for energy is mostly provided by renewables. This is because dependence on clean energies is a challenging task since the amount of renewable energy harvested normally doesn’t match the amount of energy consumed in homes (Zhu, et al., 2013). While sharing energy will keep the supply and demand rate balanced in a microgrid community, it also serves as an efficient strategy addressing this challenge. Operation: In this conceptual prototype the operation of the energy exchange mechanism results from a pro-environmental strategy combining energy feedback technologies with an incentive structure promoting user’s participation and collaboration for saving energy in the community. As a computational strategy, the energy exchange system has two layers: One is the layer of computation and algorithms which technically drives the system, handles communication among different households’ energy profiles and is responsible for the energy transactions. The other layer, which is mainly discussed in this paper, is a simplified translation of the computation layer into an interactive user-friendly interface. Fig ure 1- The tra nsla tio n o f the a lg o rithms into a use r-frie ndly inte rfa c e THE INCENTIVE-SRUCTURED INTERFACE The interface used in this strategy is a communicative web-service device serving as a medium between the user, the community and the grid, accessed from anywhere in the house and outside and presented in many manifestations: through tablets, phones, website profiles and home dashboards. The interface graphically displays three different, but related set of data on the household’s personal energy information for instance the debit and credit-energy accounts in the “YOURS” tab (these energy accounts will be discussed later), the community’s general energy information such as the community-energy account in the “OUR” tab, and recommended energy conservation suggestions and tips customized based on the household’s overall consumption pattern in the “TIPS” tab. The information displayed in these tabs help users perceive their personal and group benefits of making more efficient energy consumption decisions and understand the payoffs of taking conservation actions. “YOURS”: as shown in the image this tab represents household’s personal energy information and includes several graphics: 1the energy accounts (energy-debit and energycredit) and the user’s current energy status in terms of consumption 2- the current overall energy efficiency status of the house 3- and the plans display how efficiently energy is being consumed in different locations of the house. Fig ure 2- The “ YO URS” ta b o f the inte rfa c e “OURS”: With the features outlined below, this tab attempts to abstractly incentivize communal collaboration and participation among the users for saving and sharing energy in the community. Additionally it gives general information on how the community is doing energy-wise.  Comparison: Self and else comparison has showed to be an effective reason for taking energy saving actions particularly when it is combined with feedback about performance (Froehlich, Findlater, & Landay, 2010). Accordingly, the interface is featured with a visual evaluation of each household’s current and past energy behavior in addition to graphically displaying the user’s stance in the overall community’s energy consumption.  Rewards and Penalties: Rewards and penalties are consequence motivation techniques coming after a behavior. Research into the effects of rewards have found that people respond to rewards even if they are nominal in nature (e.g., an acknowledgement of positive behavior). One of the main drivers that makes offering rewards a strong motive for users is that they become thrived to set goals for themselves to reach those rewards. When the goal of winning a reward is set, a sense of commitment, a promise to behave in a specific way to attain that goal will thrive in the user and consequently the commitment increases the possibility of pursuing specific behaviors (Froehlich, Findlater, & Landay, 2010). “TIPS”: For outlining any motivational strategy it’s important to highlight and lay out the personal benefits derived from taking action (Ouyang & Hokao, 2009). The designed interface clearly states the personal and financial benefits of specific conservation actions in specific situations by means of graphical visualization under this tab. Fig ure 3- The “ O URS” ta b o f the inte rfa c e Fig ure 4- The “ TIPS” ta b o f the inte rfa c e HOW IT WORKS Below is a brief description on how the system works: The power of the community is supplied by a common source of renewable energy leading to the resource being shared among community users. Energy-tokens (eT), each at an established value based on price of electricity and average energy use per capita, are the suggested currency use in the exchange system. Tokens can be exchanged in three ways according to user conditions:  Debit-energy tokens (Debit-tokens): represents the first and main share of energy that each household receives each month. The amount of debit-tokens assigned to each household is based upon the number of family members.  Credit-energy tokens (Credit-tokens): represents a trading mechanism for obtaining additional energy-tokens, in which case the household gets charged by energytokens.  Community-energy tokens (Community-tokens): a system for sharing energy-tokens were individual households may either sell extra energy-tokens at the end of the month or purchase energy-tokens if all debit and credit-tokens are used. Each month every household receives two constant share of debit-energy tokens and credit-energy tokens which can be accessed and viewed through the “YOURS” tab of the interface. The household’s monthly energy usage is tracked using debit-energy tokens first. In several typical schenarios household energy can be managed through the use and exchange of debit and credit tokens. If a household’s consumption goes beyond the limits of the second share of energy (credit-energy token), in a month, leaving the user in need of extra energy-tokens, the shared community-token account can be borrowed against, providing household’s access to additional energy tokens. By using energy-tokens from this shared account, the user owes the community both energy-tokens and actual money due to the end of the following month. In order to prevent users from continuously depending on the credit and community energy accounts for purchasing extra energy, the energy price follows an ascending pattern. In a community home to a diverse number of households with different energy consumption patterns, at the end of the month there are always users which have been efficient2 and users which have been more or less inefficient3 in terms of energy consumption. This means every month there are always some users selling their extra energy-tokens to the community, some buying the energy-tokens, some staying in the limits of their debit-energy share and receiving monetary rewards and some crossing the lines of efficiency and paying back money to the system. By this conceptual prototype and the introduced framework it is expected that energy and money transactions constantly occur in the scale of the community and become the main driver of the energy exchange system. In this scenario the system will be host to users’ participation in this community endeavor. CONCLUSION AND NEXT STEPS The proposed conceptual model of an energy exchange system operates based upon a set of principles and fundamentals, illustrating energy consumption and energy efficiency as a more social and collective process rather than individual. Researchers argue that it’s not effective nor laudable to aim at reducing users’ life quality, health and safety while promoting conservation behavior. This while users automatically 2 Their consumption have not exceeded their share of debit-tokens 3 Their consumption reached to the point of using from their credit-tokens or from the communitytokens adjust their behavior to a good balance between conserving energy and their own acceptable quality of life (Aune, 2007 - Ouyang & Hokao, 2009).The proposed sharing system is not aiming at challenge the mentality of “the home as haven”, as Margarethe Aune expresses, but rather attempts to address it. Rather than focusing on behavior change specifically the consumption patterns of different users and the dynamism of their behavioral attributes becomes the basis of the energy exchange mechanism. Therefore, the energy exchange mechanism operates based on the user’s tacit knowledge. That is, users perceive the payoffs proposed by sharing, and they themselves chose and decide to borrow or lend energy to their similarly situated others in the community based on the energy information provided by feedback technologies. Ultimately, the HCI and feedback technologies in this system are integrated in this system for information and communication facilitation. Therefore the focus is on the users as the smartest component of the system rather than any socalled smart technological device. ACKNOWLEDGEMENTS This research was supported by the Alma Heinz and August Louis Pohland Scholarship in the Stuckeman School of architecture and landscape architecture. Professor Ute Poerschke’s support during this research is gratefully acknowledged. Some information covered in this paper, including a more thorough literature review, will be published in the forthcoming proceedings for the “Architecture and Resilience on a Human Scale” conference at the University of Sheffield School of Architecture, 10-12 September 2015. REFERENCES Amin, M., & Wollenberg, B. F. (2005). Toward a Smart Grid. IEEE Power and Energy, 34 41. Aune, M. (2007). Energy Comes Home. Energy Policy, 5457 - 5465. Costanzo, M., Archer, D., Aronson, E., & Pettigrew, T. (1986). Energy Conservation Behavior, The Difficult Path From Information to Action. American Psychologist , 521 - 528. Farhangi, H. (2010). The Path of the Smart Grid. IEEE Power & Energy, 18 - 28. Fischer, C. (2008). Feedback on household electricity consumption: a tool for saving energy? Energy Efficiency, 79 - 104. Froehlich, J., Findlater, L., & Landay, J. (2010). The Design of Eco-Feedback Technology. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, (pp. 1999-2008). Atlanta. Goodman, E. (2009). Three Environmental Discourses in Human-Computer Interaction. In CHI'09 Extended Abstracts on Human Factors in Computing Systems, (pp. 25352544). Hargreaves, T., Nye, M., & Burgess, J. (2010). Making energy visible: A qualitative field study of how householders interact with feedback from smart energy monitors. Energy Policy, 6111-6119. Ipakchi, A., & Albuyeh, F. (2009). Grid of The Future, Are we ready to transion to a smart grid? IEEE Power & Energy, 52 - 62. Kang, S. J., Park, J., Oh, K.-Y., & Park, H. (2014). Scheduling-based real time energy flow control strategy for buildingenergy management system. Energy and Building, 239 248. Lutzenhiser, L. (1993). Social and Behavioral Aspects of Energy Use. Annual Review of Energy and the Environment, 247-289. Ouyang, J., & Hokao, K. (2009). Energy-saving potential by improving occupants’ behavior in urban residential sector in Hangzhou City, China. Energy and Buildings, 711 720. Paglia, T. K. (2011). Energy Improvement Districts and Local Energy Production (Master of Regional Planning Dissertation, Cornell University). Rahimian, M. (2015). A Participatory Approach for Constructing Energy Resiliency. University Park, PA, USA: Unpublished Master Dissertation. Pennsylvania State University. Rahimian, M., Iulo, L. D., & Cardoso Llach, D. (In Press). The Case for a Collaborative Energy Sharing Network. Submitted for publication at the proceedings for the "Architecture and Resilience on a Human Scale" conference at University of Sheffield, School of Architecture, 10-12 September 2015. Sherman, G. R. (2007). Sharing Local Energy Infrastructure - Organizational Models for Implementing Microgrids and District Energy Systems in Urban Commercial Districts (Master of City Planning, Massachusetts Institute of Technology). Villareal, C., Erickson, D., & Zafar, M. (2014). Microgrids: A Regulatory Perspective. CPUC Policy & Planning Division. Yu, Z., Fung, B. C., Haghighat, F., Yoshino, H., & Morofsky, E. (2011). A systematic procedure to study the influence of occupant behavior on building energy consumption. Energy and Buildings, 1409 - 1417. Zhu, T., Huang, Z., Su, J., Irwin, D., Mishra, A., Menasche, D., & Shenoy, P. (2013). Sharing Renewable Energy in Smart Microgrids. In Proceedings of the ACM/IEEE 4th International Conference on Cyber-Physical Systems, (pp. 219-228).