Junjie Qin

Junjie Qin, Han-I Su, Ram Rajagopal

Risk Limiting Dispatch (RLD) was proposed recently as a mechanism that utilizes information and market recourse to reduce reserve capacity requirements, emissions and achieve other system operator objectives. It induces a set of simple dispatch rules that can be easily embedded into the existing dispatch systems to provide computationally efficient and reliable decisions. Storage is emerging as an alternative to mitigate the uncertainty in the grid. This paper extends the RLD framework to incorporate fast-ramping storage. It developed a closed form threshold rule for the optimal stochastic dispatch incorporating a sequence of markets and real-time information. An efficient algorithm to evaluate the thresholds is developed based on analysis of the optimal storage operation. Simple approximations that rely on continuous-time approximations of the solution for the discrete time control problem are also studied. The benefits of storage with respect to prediction quality and storage capacity are examined, and the overall effect on dispatch is quantified. Numerical experiments illustrate the proposed procedures.

Dipanjan Ghose, Junjie Qin, S Sivaranjani

Dynamic wireless charging (DWC) is an emerging technology that has the potential to reduce charging downtime and on-board battery size, particularly in heavy-duty electric vehicles (EVs). However, its spatiotemporal, dynamic, high-power demands pose challenges for power system operations. Since DWC demand depends on traffic characteristics such as speed, density, and dwell time, effective infrastructure planning must account for the coupling between traffic behavior and EV energy consumption. In this paper, we propose a novel traffic-aware microgrid planning framework for DWC. First, we use the macroscopic cell transmission model to estimate spatio-temporal EV charging demand along DWC corridors and integrate this demand into an AC optimal power flow formulation to design a supporting microgrid. Our framework explicitly links traffic patterns with energy demand and demonstrates that traffic-aware microgrid planning yields significantly lower system costs than worst-case traffic-based approaches. We demonstrate the performance of our model on a segment of I-210W in California under a wide range of traffic conditions.

Minghao Mou, Junjie Qin

Transportation electrification introduces strong coupling between the power and transportation systems. In this paper, we generalize the classical notion of Braess' paradox to coupled power and transportation systems, and examine how the cross-system coupling induces new types of Braess' paradoxes. To this end, we model the power and transportation networks as graphs, coupled with charging points connecting to nodes in both graphs. The power system operation is characterized by the economic dispatch optimization, while the transportation system user equilibrium models travelers' route and charging choices. By analyzing simple coupled systems, we demonstrate that capacity expansion in either transportation or power system can deteriorate the performance of both systems, and uncover the fundamental mechanisms for such new Braess' paradoxes to occur. We also provide necessary and sufficient conditions of the occurrences of Braess' paradoxes for general coupled systems, leading to managerial insights for infrastructure planners. For general networks, through characterizing the generalized user equilibrium of the coupled systems, we develop novel charging pricing policies to mitigate them.

José I. Caiza, Junjie Qin, Philip E. Paré

In this paper, we propose an SIR spread model in a population network coupled with an infrastructure network that has a pathogen spreading in it. We develop a threshold condition to characterize the monotonicity and peak time of a weighted average of the infection states in terms of the global (network-wide) effective reproduction number. We further define the distributed reproduction numbers (DRNs) of each node in the multilayer network which are used to provide local threshold conditions for the dynamical behavior of each entity. Furthermore, we leverage the DRNs to predict the global behavior based on the node-level assumptions. We use both analytical and simulation results to illustrate that the DRNs allow a more accurate analysis of the networked spreading process than the global effective reproduction number.

Siddharth Patel, Mohammad Rasouli, Junjie Qin et al.

The presence of behind-the-meter rooftop photovoltaics and storage in the residential sector is poised to increase significantly. Here we quantify in detail the value of these technologies to consumers and service providers. We characterize the heterogeneity in household electricity cost savings under time-varying prices due to consumption behavior differences. Different pricing policies significantly alter how households fare with respect to one another. Furthermore, household savings in absolute terms are not strongly correlated with savings normalized by PV and storage system size. We characterize the financial value of improved forecasting capabilities for a household, finding that it is a relatively small fraction of a household's cost savings. Coordination services that combine the resources available at all households can reduce costs by an additional 10% to 15% of the original total cost. Surprisingly, coordination service providers will not encourage adoption beyond 35-55% within a group. We present a simple model that explains the value of coordination and its relationship to the pricing of distribution services.