Co-optimizing the Smart Grid and Electric Public Transit Bus System

TL;DR

This paper presents a co-optimization model for integrating electric public transit buses with urban power systems, aiming to reduce costs and enhance sustainability through deterministic and stochastic approaches.

Contribution

It introduces a novel co-optimization framework for electric transit and power systems, incorporating stochastic renewable energy management and different recourse strategies.

Findings

Coordinated strategies reduce operational costs.

Different recourse actions impact system reliability.

Pricing strategies influence co-optimization outcomes.

Abstract

As climate change provides impetus for investing in smart cities, with electrified public transit systems, we consider electric public transportation buses in an urban area, which play a role in the power system operations in addition to their typical function of serving public transit demand. Our model considers a social planner, such that the transit authority and the operator of the electricity system co-optimize the power system to minimize the total operational cost of the grid, while satisfying additional transportation constraints on buses. We provide deterministic and stochastic formulations to co-optimize the system. Each stochastic formulation provides a different set of recourse actions to manage the variable renewable energy uncertainty: ramping up/down the conventional generators, or charging/discharging of the transit fleet. We demonstrate the capabilities of the model and the benefit obtained via a coordinated strategy. We compare the efficacies of these recourse actions to provide additional managerial insights. We analyze the effect of different pricing strategies on the co-optimization. We also conduct congestion analysis in the power network, comparing our cooperative approach to a non-cooperative strategy when we assume electrified fleet sizes grow with greater battery capacities. Given the recent momentum towards building smarter cities and electrifying transit systems, our results provide policy directions towards a sustainable future. We test our models using modified MATPOWER case files and verify our results with different sized power networks. This study is motivated by a project with a large transit authority in California.