Incentive Design for Direct Load Control Programs

We study the problem of optimal incentive design for voluntary participation of electricity customers in a Direct Load Scheduling (DLS) program, a new form of Direct Load Control (DLC) based on a three way communication protocol between customers, embedded controls in flexible appliances, and the central entity in charge of the program. Participation decisions are made in real-time on an event-based basis, with every customer that needs to use a flexible appliance considering whether to join the program given current incentives. Customers have different interpretations of the level of risk associated with committing to pass over the control over the consumption schedule of their devices to an operator, and these risk levels are only privately known. The operator maximizes his expected profit of operating the DLS program by posting the right participation incentives for different appliance types, in a publicly available and dynamically updated table. Customers are then faced with the dynamic decision making problem of whether to take the incentives and participate or not. We define an optimization framework to determine the profit-maximizing incentives for the operator. In doing so, we also investigate the utility that the operator expects to gain from recruiting different types of devices. These utilities also provide an upper-bound on the benefits that can be attained from any type of demand response program.Comment: 51st Annual Allerton Conference on Communication, Control, and Computing, 201

Achieving Reliable Coordination of Residential Plug-in Electric Vehicle Charging: A Pilot Study

Wide-scale electrification of the transportation sector will require careful planning and coordination with the power grid. Left unmanaged, uncoordinated charging of electric vehicles (EVs) at increased levels of penetration will amplify existing peak loads, potentially outstripping the grid's capacity to reliably meet demand. In this paper, we report findings from the OptimizEV Project - a real-world pilot study in Upstate New York exploring a novel approach to coordinated residential EV charging. The proposed coordination mechanism seeks to harness the latent flexibility in EV charging by offering EV owners monetary incentives to delay the time required to charge their EVs. Each time an EV owner initiates a charging session, they specify how long they intend to leave their vehicle plugged in by selecting from a menu of deadlines that offers lower electricity prices the longer they're willing to delay the time required to charge their EV. Given a collection of active charging requests, a smart charging system dynamically optimizes the power being drawn by each EV in real time to minimize strain on the grid, while ensuring that each customer's car is fully charged by its deadline. Under the proposed incentive mechanism, we find that customers are frequently willing to engage in optimized charging sessions, allowing the system to delay the completion of their charging requests by more than eight hours on average. Using the flexibility provided by customers, the smart charging system was shown to be highly effective in shifting the majority of EV charging loads off-peak to fill the night-time valley of the aggregate load curve. Customer opt-in rates remained stable over the span of the study, providing empirical evidence in support of the proposed coordination mechanism as a potentially viable "non-wires alternative" to meet the increased demand for electricity driven growing EV adoption.Comment: 19 pages, 12 figure

Preemptive Scheduling of EV Charging for Providing Demand Response Services

We develop a new algorithm for scheduling the charging process of a large number of electric vehicles (EVs) over a finite horizon. We assume that EVs arrive at the charging stations with different charge levels and different flexibility windows. The arrival process is assumed to have a known distribution and that the charging process of EVs can be preemptive. We pose the scheduling problem as a dynamic program with constraints. We show that the resulting formulation leads to a monotone dynamic program with Lipschitz continuous value functions that are robust against perturbation of system parameters. We propose a simulation based fitted value iteration algorithm to determine the value function approximately, and derive the sample complexity for computing the approximately optimal solution.Comment: 21 pages, submitted to SEGA

Examining How Federal Infrastructure Policy Could Help Mitigate and Adapt to Climate Change: Hearing Before the H. Comm. on Transp. & Infrastructure, 116th Cong., Feb. 26, 2019 (Statement of Vicki Arroyo)

As the Fourth National Climate Assessment, released in November, describes, the United States is already experiencing serious impacts of climate change—and the risks to communities all across the country are growing rapidly. These findings, along with those in the 2018 Intergovernmental Panel on Climate Change (IPCC)report, are clear and should be a call to immediate action. Even if we manage to limit planetary warming to just 2 degrees C, the world will still face increased chances of economic and social upheaval from more severe flooding, droughts, heatwaves, and other climate impacts as well as devastating environmental consequences, the IPCC report warns. The scientific consensus as described in the IPCC Special Report is that countries around the world must rapidly decarbonize their economies, cutting greenhouse gas emissions in half by 2030 and to near zero by 2050. Yet the current trends are going in the wrong direction. Despite our increasing understanding of the narrowing window to act, U.S. GHG emissions increased by 3.4% in 2018, according to a January report from the Rhodium Group. Clearly more action is needed. The encouraging news is that many states and cities have committed to taking action. They are taking steps to reduce emissions through legislation, executive orders, and pledges made in collaborations such as the US Climate Alliance –now covering roughly half the US population and GDP. In my testimony, I will be focusing on the transportation sector, which is the largest contributor of GHG emissions in the United States, and is already facing significant impacts from climate change. Federal standards have been important in increasing efficiency and reducing emissions, yet transportation-sector emissions are increasing as more vehicle miles are driven, more freight is transported in trucks, and airline travel continues to grow. Transportation is becoming an increasingly large share of U.S. economy-wide emissions as the power sector decarbonizes as a result of market shifts and policy. There is an urgent need, therefore, to transition to a low-carbon transportation system. Such a transition would not only reduce emissions and fight climate change, it also would bring additional important benefits, including protecting public health by reducing conventional air pollution, providing more mobility options, and driving innovation and economic growth through policy action and through public and private investment