Bottom-Up Demand Response by Following Local Energy Generation Voluntarily

We present an open-source low-budget hardware and software prototype of a smart plug, and the principles behind its capability to align power demand with a reference signal, e.g. from local renewable energy generation. We envision its use on a platform that combines social-media with energy networks, where users provide bottom-up demand response voluntarily. This article has two main objectives: 1. to illustrate the concept of voluntary demand response, and 2. to give researchers a tool to test behavioral flexibility assumptions by extending the open source prototype plug with their own concepts, e.g., implementing more complex autonomous decision-making. In contrast to social media based energysaving contests, which engage the user socially but do not interact with the power flow directly1, the smart plug hardware contains sensors and a relay. Thus, physical demand response innovations (Palensky and Dietrich 2011) can be tested with this hardware voluntarily. As a bottom-up demand response innovation, we suggest to transfer the intuitive concept of ’following’ from social networks to smart grids: a power generator publishes its energy generation measurements and consumers can choose a target to follow with their flexible loads, e.g. an electric vehicle can follow the owner’s or neighbor’s solar panel. The flexible loads then aim to balance some of the fluctuation in generation, thus increasing the tolerable penetration limits of fluctuating energy sources. The proposed proof-of-concept interface of the Open Energy Exchange (OEEX) shows details of nearby generators and their operators, from which users select the preferred target. In contrast to Renewable Energy Certificates, which provide market segmentation and price discrimination but are prone to ’greenwashing’ (Gillespie 2008), the ’following’ concept aligns the real power flows in the network, while building a personal relation with the energy consumed. Comparable approaches either lack the real-time demand mapping or may be used in a single household only2. Copyright c © 2015, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. See welectricity.com and social.opower.com. See vandebron.nl and sunnyplaces.com. The choices in designing this prototype were thus guided by the following realities: 1. Legislation adapts slowly, often delaying the introduction of innovative concepts. Since ’following’ with the prototype is voluntary, it is feasible under current legislation. 2. Power demand measurements may not be available yet. The prototype therefore includes a local measurement of consumption; other measurements (e.g. householdor distribution grid load) could be added to the system if available to improve alignment of demand with supply. 3. Research assumptions may diverge from practical wisdom if the researcher is not exposed to the realities, e.g. a washing machine running at night is an impractical, but common academic example. Both hardware plans and software of the smart plug are open source3, lowering the threshold for researchers to become part of the system they are studying, which fosters realistic assumptions. Software The smart plug software runs on a Spark Core, and the proof-of-concept system architecture is build around the private cloud solution provided by the Spark Cloud4. Figure 1 depicts the interaction of different components schematically: each flexible device is connected via a smart plug that modulates its activation in response to a target signal. Available on michaelkaisers.com. For details see www.spark.io.