Why Energy Exchange without Social Dynamics will fail
Introduction and Approach — Part 1
This blog is first in the series to understand integration of social dynamics in energy exchange platforms. The blog lays out the issues and approach on integrating social dynamism with not just policies, but also in the software of the energy exchange platforms.
The reason for this is that India is very complex in terms of its people, with different social, cultural and economic backgrounds of households within and outside the communities. What will work in one village, might not work in the next. These backgrounds vary so much temporally and spatially, that one single solution is impossible for all. For example, even with the presence of energy exchange platform, there is a high chance that people would not be ready to exchange energy due to differences in castes, economic status, or simply, affordability issues. This affects the sustenance and success of the energy exchange projects. One of such project in making is Energy Bazaar, where we bring decentralized energy exchanges to rural Indian households, with grid optimizations and control.
Thus, provided an energy accessibility solution which Energy Bazaar brings, there needs to be an additional layer of “social dynamism” which makes a solution custom made for the respective community. This means incorporating the social norms of the community within the offered energy exchange platforms. These social norms are not restricted to the preference for a community (e.g. caste, religion divide), but the implications of social norms reflect on other characteristics of projects as well (e.g. preference of paying mechanisms).
Now let’s understand how this is actually done. All the existing solutions look at either supply side issues, meaning the perspective from the energy provider, or they focus only on the demand side issues, meaning the consumer perspective. None of them focus their solution on a community level. This approach tries to understand how communities can adapt a solution as a whole. This takes in the approach of developing the policies with the dynamic community systems and around formal-informal regulations (or institutions) present in the community.
In my masters thesis in policy analysis at TU Delft, I worked on this problem and came up with a framework, which tries to model how regulations or rules can be adapted around the changing community. The framework works on two principles:
- Understand that how technologies get adapted in a community with the principle of “innovation diffusion”
- Understand that how changing desires, social-economic characteristics of communities and the technologies affects the actions of the people. This in-turn affect interactions of people in the community and finally impact the energy exchange projects.
Such an ideology of community level actions was first brought by Nobel Laureate and economist Elinor Ostrom (Ostrom, 2015). In fact, she worked with rural communities of India to showcase the need of such community management influences.
The former influence can be exemplified as follows: a consumer takes the decision to buy solar panels only after hearing the benefits of the solar panels from a neighbor. Next to the positive feedback from peers, they also need to consider their own constraints (income, etc.) and then the consumers decide to buy the solar panel or not. These effects help understand a household as a function of it’s choices, influences, constraints and demands. All these functions define a “utility” function for the consumer. This makes every household as an “Agent” in the network which has its own utility function defined. Agent-based models help here to model and simulate such networks and understand that how such communities would interact together. Thus, developing agent-based models from such systems help to test out various policies and social structures of a community. These agent based models and policies would be discussed in further blogs.
The latter influence (how interactions of households in such networks effect energy exchange) can be studied through understanding the exact patterns of interactions within the community, and the different actions of the households around energy usage that help them to take decisions. These influences are found to affect the “utility” at three levels:
- Individual
- Community
- Energy Providing Firms
The household, when considering the individual utility, tries to tweak the regulation around their own actions or energy usage patterns. For example, if the battery starts performing worse with time due to continuous usage of the appliances, the households use the appliances in intervals and not continuously. These rules can be simply labeled as “individual rules.” Note that the utility I discuss here is not purely gained or reduced by money, but also dependent on relationships and other social relations (higher trust in the community is higher utility of the household). The utility also can be “exchanged” with not just money but even goods, e.g. payment-in-kind is very common in many parts of rural India.
Households, especially rural, live in close communities and thus also think about the good of community, leading to better usage rules of resources. For example, in case of an energy exchange platform, if the demand is too high than the available supply, there is need to prioritize the most important demands, such as hospitals, schools etc. Thus every household, or the node on this energy exchange network, needs to have some form of “weight”. Note that these weights already exist in the communities, like with castes, making anonymity of nodes in the network important as well (such policies will be discussed in next blog in this series). These rules are defined based on the gap between the resource and community utility and are labeled as “usage rules”. The following image clearly shows how usage rules differ according to the resource (energy) in the system compared to demand.
Finally, it is also important for the energy providing firms to bring regulations and be more flexible in terms of the dynamism of the community. For example, when the households are not able to pay, they need to have options to borrow money, or extend the duration to pay, and so on. This flexibility in regulations should not come at the expense of running the project (e.g. losses on the operations) and thus such flexible regulations need to be recorded on the exchange platform. These rules are labeled as “top-down rules”.
All in all, these policies and regulations reflect that looking at all the households equally and as “rational agents” would not help incorporating the exact demands of the communities, especially in rural India when the societal norms are strong, and the economic distribution is diverse. This idea is also proposed in famous literature for markets designs (Kirman, 1991).
There is need of considering every household differently. This can be done with modeling every household as an “agent” in a network. Artificial intelligence based computational models and negotiation platforms can help test different policy scenarios for such heterogeneous networks. Here AI helps in defining automated negotiations between households. Most important thing to consider here is that as much there is need to consider the collective action of the people (taken together to save resources, etc.), it is also important to look at the action of individual actor. This is so because collections of individual usage actions lead to immense influence on the energy projects (even if the actions are independent of each other).
All of these agents would exchange energy to maximize their own “utility”. AI based learning can also help predict the energy markets in terms of availability of energy and prices, based on the exchange histories. This agent-based modelling and AI prediction mechanisms which help consider the social dynamics of every agent and will be incorporated in the final products of Energy Bazaar. How this is done (with glimpses of codes and results being worked in ETH Zurich Computational Social Science lab) will be discussed in the following blogs in this series.
Summarizing, the community and individual level policies around energy usage needs to be well understood at a very individual household level to utilize the maximum potential of energy exchange platforms. The game-theoretical approaches used in Energy Bazaar platform integrates very well with these social dynamism based models and thus Energy Bazaar can provide a complete sustained solution for energy exchange in rural India.
So now, the final question remains that how all of this can look integrated at the block-chain level: this can take various forms. One of the cases how we envision is that Energy Bazaar would have not just monetary value coins, but also “social value” coins. What this means is basically every participant has a wallet with different values of the tokens they can trade and it is not limited to the energy they are trading, but also the social cost they incur (losing relations from community reduces their social value coins and doing operations and maintenance for the community micro-grid increases these coins). These coins can compensate each other in different ratios e.g. the social value can have same or lower or higher weight than the energy exchange coins’ value. One of the other ways is also to allow people to not just sell, buy energy but additionally “rent” or “loan” energy allowing more flexibility to households at an energy exchange platform. This helps in realising the energy access and democratisation pillars at Energy Bazaar. The work of Power for All in India is already commendable in this field of energy access and we look forward to contribute in similar direction.
Interested to know how we pull off all this? Follow our Medium blog, follow us on Linkedin and join our slack community if you similar interesting ideas to share!
The complete research on this topic can be read at the TU Delft repository. Readers are asked to look at the executive summary for a complete idea of the work in given limited amount of time. I would like to thank Yvo Hunink , Dirk van den Biggelaar and Rob de Jeu for their input on the blog. I would like to also thank Rural Spark, CEEW, Piconergy , Barefoot College, SolShare and TU Delft for providing resources for the research.
References:
Ostrom, Elinor. Governing the commons. Cambridge university press, 2015.
Kirman, Alan, and Annick Vignes. “Price dispersion: theoretical considerations and empirical evidence from the Marseilles fish market.” Issues in contemporary economics. Palgrave Macmillan UK, 1991. 160–185.
