A Utility Roadmap for Expanding Customer Adoption of Electric Vehicles

Portland General Electric (PGE) is one of only a few electric utilities in the United States actively conducting evaluations of their pilots in support of transportation electrification (TE). This article offers insights into PGE’s efforts to provide EV-related outreach and education to its customers. The article also examines interest in and use of PGE’s public charging infrastructure, particularly among transportation network company (TNC) drivers. The authors conducted an analysis of utilization data from PGE’s public charging stations to examine usage and the effectiveness of a peak pricing surcharge during peak electricity demand periods. The research pulls from additional data sources including (1) online customer surveys, (2) ride-and-drive intercept surveys, (3) and an online focus group. Findings illuminate the utility’s experience after three years of implementation and provide concrete guidance for other utilities seeking to expand customer adoption of EVs, while also exploring how pricing mechanisms can be effective at managing increased system load associated with increased EV charging. Findings also highlight the barriers environmental justice communities face with EVs and provide insights into how utilities can address misconceptions and increase awareness of the benefits of EVs for these groups.

The Cost of Photovoltaic Forecasting Errors in Microgrid Control with Peak Pricing

Model predictive control (MPC) is widely used for microgrids or unit commitment due to its ability to respect the forecasts of loads and generation of renewable energies. However, while there are lots of approaches to accounting for uncertainties in these forecasts, their impact is rarely analyzed systematically. Here, we use a simplified linear state space model of a commercial building including a photovoltaic (PV) plant and real-world data from a 30 day period in 2020. PV predictions are derived from weather forecasts and industry peak pricing is assumed. The effect of prediction accuracy on the resulting cost is evaluated by multiple simulations with different prediction errors and initial conditions. Analysis shows a mainly linear correlation, while the exact shape depends on the treatment of predictions at the current time step. Furthermore, despite a time horizon of 24h, only the prediction accuracy of the first 75min was relevant for the presented setting.

Optimal Operation of Critical Peak Pricing for an Energy Retailer Considering Balancing Costs

Recently, there have been frequent fluctuations in the wholesale prices of electricity following the increased penetration of renewable energy sources. Therefore, retailers face price risks caused by differences between wholesale prices and retail rates. As a hedging against price risk, retailers can utilize critical peak pricing (CPP) in a price-based program. This study proposes a novel multi-stage stochastic programming (MSSP) model for a retailer with self-generation photovoltaic facility to optimize both its bidding strategy and the CPP operation, in the face of several uncertainties. Using MSSP, decisions can be determined sequentially with realization of the uncertainties over time. Furthermore, to ensure a global optimum, a mixed integer non-linear programming is transformed into mixed integer linear programming through three linearization steps. In a numerical simulation, the effectiveness of the proposed MSSP model is compared with that of a mean-value deterministic model based on a rolling horizon method. We also investigate the optimal strategy of a retailer by changing various input parameters and perform a sensitivity analysis to assess the impacts of different uncertain parameters on the retailer’s profit. Finally, the effect of the energy storage system on the proposed optimization problem is investigated.