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Martina Vandebroek

Publications and source records attributed to Martina Vandebroek.

2 recordsLinked to original sources

An Integrated Optimization Framework for Smart Charging of Electric Bus Fleets under Dynamic Electricity Prices with On-Site Solar Generation, Energy Storage, and V2G operations

The rapid electrification of city bus fleets presents public transportation operators (PTOs) with the complex challenge of managing charging operations to minimize energy costs. Most existing studies on electric bus (EB) charging management rely on a discrete-time-based discretization approach, which is operationally unrealistic and limits their scalability for realistic applications. This study proposes a discrete-event optimization (DEO) approach for daily EB fleet charging management that considers peak power charges, photovoltaic (PV) generation with an energy storage system (ESS), vehicle-to-grid (V2G) operations, and battery degradation costs. We apply the DEO approach to a real-world case in Brussels involving 28 articulated EBs and 232 trips. A set of parametric instances is used to assess computational scalability. The results demonstrate that the DEO formulation can solve instances of realistic size within practical computation times with tight optimality gaps. A thorough cost analysis was conducted to evaluate the added value of V2G benefits and on-site PV generation. Key findings indicate that incorporating demand charges into the optimization reduces daily costs by 5% and decreases the share of peak power costs by 9%, underscoring the importance of load management. Integrating PV and ESS results in a total net cost reduction of up to 56%, with ESS primarily used for energy arbitrage rather than direct bus charging. V2G participation is highly sensitive to battery degradation costs and policy incentives. Combining all extensions results in a 58% reduction in total operational expenses compared to the baseline, demonstrating the significant value of smart (dis)charging tools for PTOs.

math.OC

Fitting mixed logit random regret minimization models using maximum simulated likelihood

This article describes the mixrandregret command, which extends the randregret command introduced in Gutiérrez-Vargas et al. (2021, The Stata Journal 21: 626-658) incorporating random coefficients for Random Regret Minimization models. The newly developed command mixrandregret allows the inclusion of random coefficients in the regret function of the classical RRM model introduced in Chorus (2010, European Journal of Transport and Infrastructure Research 10: 181-196). The command allows the user to specify a combination of fixed and random coefficients. In addition, the user can specify normal and log-normal distributions for the random coefficients using the commands' options. The models are fitted using simulated maximum likelihood using numerical integration to approximate the choice probabilities.

econ.EM