• Energy Policies

    Big-Data Mechanisms and Energy Policy Design

    In this project, we study the role of non-cash incentives, to mitigate peak electricity demand. In particular, we investigate the impact of these incentives on motivating consumers to participate in Ontario’s peaksaverPLUS program — wherein they set their thermostats a few degrees higher during summer afternoons. Non-cash incentives are based on cognitive biases, and find their foundations in Behavioral Economics and Psychology. We use a game theoretic framework for mathematically modelling the behavior of consumers and the utility company, under our proposed incentives. For obtaining quantitative insights into the consumers’  behavioral preferences, we designed and conducted a wide-spread psychometric survey.  Ultimately, our work in this project entails the use of analytical and Big Data-based approaches to study non-cash incentives in policy design; so that appropriate policy decisions can be made based on evidence.

    Forecasting Residential Solar PV and Battery Adoption in Ontario: An Agent-Based Approach

    Distributed Energy Resources (DERs), especially rooftop solar PhotoVoltaic (PV) installations, will achieve higher diffusion levels as DER costs become more competitive. This diffusion of rooftop PV systems is already giving utility operators cause for concern: an impending `death spiral’ of the electric grid. As more customers generate their own energy and disconnect from the grid due to comparably high grid prices, fewer and fewer customers are left to pay for the cost of maintaining the grid. This would result in even higher electricity prices, and the vicious cycle continues. However, the death spiral seems to be a worst-case scenario as there is a possibility for customers to sell electricity back to the grid, therefore, staying connected.

    We conducted a scenario analysis to evaluate the technical feasibility and financial profitability for typical Ontario households to disconnect from the grid. We found that given the current Feed-in Tariff (FiT) for solar installations in Ontario and the projections of battery costs, it would not be profitable for a typical household in Ontario to disconnect from the grid anytime soon. Considering the unlikelihood of grid defection in Ontario, we decided to focus on the adoption of PV-battery systems. We conducted a survey in Ontario, asking respondents to choose which PV-battery systems they will purchase under different cost and benefit conditions. In addition, the survey respondents were asked to provide sentiments associated with purchasing PV-battery systems based on Affect Control Theory (ACT). Using an agent-based model, we study the impact of different policies on the adoption of PV-battery systems in Ontario, and the secondary impacts on the energy system in Ontario such as changes in hourly electrical load profiles, electric grid stability, and potential for massive grid defection.

    Agent-based models to study EV adoption

    Electric vehicles (EVs) are still a maturing technology. Barriers to their adoption include price and range anxiety. EV batteries are significant in determining both EV prices and costs. In this work, we focus on the impact of a high-capacity battery and EV rebates on an EV ecosystem. Using survey data from Los Angeles, California, we simulate different cases of battery costs and prices by means of an agent-based EV ecosystem model. We find that even in Los Angeles, a geographically spread out city, the price of EVs is a more significant barrier to adoption than EV range. In fact, even a quintupling of battery size at no additional costs improves EV adoption by only 5 %. Therefore, policy makers should focus more on affordability than range in promoting EV adoption.

    Critiquing Time-of-Use Pricing

    Since 2006, with the progressive deployment of Advanced Metering Infrastructure, jurisdictions in the Canadian province of Ontario have been increasingly using Time-Of-Use (TOU) pricing with the objective of reducing the mean peak-to-average load ratio and thus excess generation capacity. We analyse the hourly aggregate load data to study whether the choice of TOU parameters (i.e., number of seasons, season start and end times, and choice of peak and off-peak times) adequately reflects the aggregate load, and to study whether TOU pricing has actually resulted in a decrease in the mean peak-to-average ratio. We find that since the introduction of TOU pricing, not only has the mean peak-to-average load ratio actually increased but also that the currently implemented TOU parameters are far from optimal. Based on our findings, we make concrete recommendations to improve the TOU pricing scheme in Ontario.