Merge pull request #124 from StanfordASL/update_bib

Fixed issues with escaping dollar sign.

_bibliography/ASL_Bib.bib

@@ -2129,18 +2129,18 @@ @inproceedings{RossiPavone2013
   timestamp = {2017-02-20}
 }
 
-@inproceedings{RossiIglesiasEtAl2018,
+@InProceedings{RossiIglesiasEtAl2018,
   author    = {Rossi, F. and Iglesias, R. and Alizadeh, M. and Pavone, M.},
   title     = {On the Interaction Between {Autonomous Mobility-on-Demand} Systems and the Power Network: Models and Coordination Algorithms},
   booktitle = proc_RSS,
   year      = {2018},
-  note      = {{Extended version available at }\url{https://arxiv.org/abs/1709.04906}},
-  abstract  = {We study the interaction between a fleet of electric, self-driving vehicles servicing on-demand transportation requests (referred to as Autonomous Mobility-on-Demand, or AMoD, system) and the electric power network. We propose a joint linear model that captures the coupling between the two systems stemming from the vehicles' charging requirements. The model subsumes existing network flow models for AMoD systems and linear models for the power network, and it captures time-varying customer demand and power generation costs, road congestion, and power transmission and distribution constraints. We then leverage the linear model to jointly optimize the operation of both systems. We propose an algorithmic procedure to losslessly reduce the problem size by bundling customer requests, allowing it to be efficiently solved by state-of-the-art linear programming solvers. Finally, we study the implementation of a hypothetical electric-powered AMoD system in Dallas-Fort Worth, and its impact on the Texas power network. We show that coordination between the AMoD system and the power network can reduce the overall energy expenditure compared to the case where no cars are present (despite the increased demand for electricity) and yield savings of \$78M per year compared to an uncoordinated scenario. Collectively, the results of this paper provide a first-of-a-kind characterization of the interaction between electric-powered AMoD systems and the electric power network, and shed additional light on the economic and societal value of AMoD.},
   address   = {Pittsburgh, Pennsylvania},
   month     = jun,
-  url       = {/wp-content/papercite-data/pdf/Rossi.Iglesias.Alizadeh.Pavone.RSS18.pdf},
+  note      = {{Extended version available at }\url{https://arxiv.org/abs/1709.04906}},
+  abstract  = {We study the interaction between a fleet of electric, self-driving vehicles servicing on-demand transportation requests (referred to as Autonomous Mobility-on-Demand, or AMoD, system) and the electric power network. We propose a joint linear model that captures the coupling between the two systems stemming from the vehicles' charging requirements. The model subsumes existing network flow models for AMoD systems and linear models for the power network, and it captures time-varying customer demand and power generation costs, road congestion, and power transmission and distribution constraints. We then leverage the linear model to jointly optimize the operation of both systems. We propose an algorithmic procedure to losslessly reduce the problem size by bundling customer requests, allowing it to be efficiently solved by state-of-the-art linear programming solvers. Finally, we study the implementation of a hypothetical electric-powered AMoD system in Dallas-Fort Worth, and its impact on the Texas power network. We show that coordination between the AMoD system and the power network can reduce the overall energy expenditure compared to the case where no cars are present (despite the increased demand for electricity) and yield savings of \$ 78M per year compared to an uncoordinated scenario. Collectively, the results of this paper provide a first-of-a-kind characterization of the interaction between electric-powered AMoD systems and the electric power network, and shed additional light on the economic and societal value of AMoD.},
   owner     = {frossi2},
-  timestamp = {2018-06-30}
+  timestamp = {2018-06-30},
+  url       = {/wp-content/papercite-data/pdf/Rossi.Iglesias.Alizadeh.Pavone.RSS18.pdf},
 }
 
 @Article{RossiIglesiasEtAl2018b,
@@ -2173,17 +2173,17 @@ @inproceedings{RossiBandyopadhyayEtAl2018
   timestamp = {2018-02-01}
 }
 
-@phdthesis{Rossi2018,
+@PhdThesis{Rossi2018,
   author    = {Rossi, F.},
   title     = {On the Interaction between {Autonomous Mobility-on-Demand} Systems and the Built Environment: Models and Large Scale Coordination Algorithms},
   school    = ios_univ_Stanford_AA,
   year      = {2018},
-  abstract  = {Autonomous Mobility-on-Demand systems (that is, fleets of self-driving cars offering on-demand transportation) hold promise to reshape urban transportation by offering high quality of service at lower cost compared to private vehicles. However, the impact of such systems on the infrastructure of our cities (and in particular on traffic congestion and the electric power network) is an active area of research. In particular, Autonomous Mobility-on-Demand (AMoD) systems could greatly increase traffic congestion due to additional "rebalancing" trips required to re-align the distribution of available vehicles with customer demand; furthermore, charging of large fleets of electric vehicles can induce significantly stress in the electric power network, leading to high electricity prices and potential network instability. In this thesis, we build analytical tools and algorithms to model and control the interaction between AMoD systems and our cities. We open our work by exploring the interaction between AMoD systems and urban congestion. Leveraging the theory of network flows, we devise models for AMoD systems that capture endogenous traffic congestion and are amenable to efficient optimization. These models allow us to show the key theoretical result that, under mild assumptions that are substantially verified for U.S. cities, AMoD systems do not increase congestion compared to privately-owned vehicles for a given level of customer demand if empty-traveling vehicles are properly routed. We leverage this insight to design a real-time congestion-aware routing algorithm for empty vehicles; microscopic agent-based simulations with New York City taxi data show that the algorithm significantly reduces congestion compared to a state-of-the-art congestion-agnostic rebalancing algorithm, resulting in 22\% lower wait times for AMoD customers. We then devise a randomized congestion-aware routing algorithm for customer-carrying vehicles and prove rigorous analytical bounds on its performance. Preliminary results based on New York City taxi data show that the algorithm could yield a further reduction in congestion and, as a result, 5\% lower service times for AMoD customers. We then turn our attention to the interaction between AMoD fleets with electric vehicles and the power network. We extend the network flow model developed in the first part of the thesis to capture the vehicles' state-of-charge and their interaction with the power network (including charging and the ability to inject power in the network in exchange for a payment, denoted as "vehicle-to-grid"). We devise an algorithmic procedure to losslessly reduce the size of the resulting model, making it amenable to efficient optimization, and test our models and optimization algorithms on a hypothetical deployment of an AMoD system in Dallas-Fort Worth, TX with the goal of maximizing social welfare. Simulation results show that coordination between the AMoD system and the power network can reduce electricity prices by over \$180M/year, with savings of \$120M/year for local power network customers and \$35M/year for the AMoD operator. In order to realize such benefits, the transportation operator must cooperate with the power network: we prove that a pricing scheme can be used to enforce the socially optimal solution as a general equilibrium, aligning the interests of a self-interested transportation operator and self-interested power generators with the goal of maximizing social welfare. We then design privacy-preserving algorithms to compute such coordination-promoting prices in a distributed fashion. Finally, we propose a receding-horizon implementation that trades off optimality for speed and demonstrate that it can be deployed in real-time with microscopic simulations in Dallas-Fort Worth. Collectively, these results lay the foundations for congestion-aware and power-aware control of AMoD systems; in particular, the models and algorithms in this thesis provide tools that will enable transportation network operators and urban planners to foster the positive externalities of AMoD and avoid the negative ones, thus fully realizing the benefits of AMoD systems in our cities.},
   address   = {Stanford, California},
   month     = mar,
-  url       = {/wp-content/papercite-data/pdf/Rossi.PhD18.pdf},
+  abstract  = {Autonomous Mobility-on-Demand systems (that is, fleets of self-driving cars offering on-demand transportation) hold promise to reshape urban transportation by offering high quality of service at lower cost compared to private vehicles. However, the impact of such systems on the infrastructure of our cities (and in particular on traffic congestion and the electric power network) is an active area of research. In particular, Autonomous Mobility-on-Demand (AMoD) systems could greatly increase traffic congestion due to additional "rebalancing" trips required to re-align the distribution of available vehicles with customer demand; furthermore, charging of large fleets of electric vehicles can induce significantly stress in the electric power network, leading to high electricity prices and potential network instability. In this thesis, we build analytical tools and algorithms to model and control the interaction between AMoD systems and our cities. We open our work by exploring the interaction between AMoD systems and urban congestion. Leveraging the theory of network flows, we devise models for AMoD systems that capture endogenous traffic congestion and are amenable to efficient optimization. These models allow us to show the key theoretical result that, under mild assumptions that are substantially verified for U.S. cities, AMoD systems do not increase congestion compared to privately-owned vehicles for a given level of customer demand if empty-traveling vehicles are properly routed. We leverage this insight to design a real-time congestion-aware routing algorithm for empty vehicles; microscopic agent-based simulations with New York City taxi data show that the algorithm significantly reduces congestion compared to a state-of-the-art congestion-agnostic rebalancing algorithm, resulting in 22\% lower wait times for AMoD customers. We then devise a randomized congestion-aware routing algorithm for customer-carrying vehicles and prove rigorous analytical bounds on its performance. Preliminary results based on New York City taxi data show that the algorithm could yield a further reduction in congestion and, as a result, 5\% lower service times for AMoD customers. We then turn our attention to the interaction between AMoD fleets with electric vehicles and the power network. We extend the network flow model developed in the first part of the thesis to capture the vehicles' state-of-charge and their interaction with the power network (including charging and the ability to inject power in the network in exchange for a payment, denoted as "vehicle-to-grid"). We devise an algorithmic procedure to losslessly reduce the size of the resulting model, making it amenable to efficient optimization, and test our models and optimization algorithms on a hypothetical deployment of an AMoD system in Dallas-Fort Worth, TX with the goal of maximizing social welfare. Simulation results show that coordination between the AMoD system and the power network can reduce electricity prices by over \$ 180M/year, with savings of \$ 120M/year for local power network customers and \$ 35M/year for the AMoD operator. In order to realize such benefits, the transportation operator must cooperate with the power network: we prove that a pricing scheme can be used to enforce the socially optimal solution as a general equilibrium, aligning the interests of a self-interested transportation operator and self-interested power generators with the goal of maximizing social welfare. We then design privacy-preserving algorithms to compute such coordination-promoting prices in a distributed fashion. Finally, we propose a receding-horizon implementation that trades off optimality for speed and demonstrate that it can be deployed in real-time with microscopic simulations in Dallas-Fort Worth. Collectively, these results lay the foundations for congestion-aware and power-aware control of AMoD systems; in particular, the models and algorithms in this thesis provide tools that will enable transportation network operators and urban planners to foster the positive externalities of AMoD and avoid the negative ones, thus fully realizing the benefits of AMoD systems in our cities.},
   owner     = {frossi2},
-  timestamp = {2018-03-19}
+  timestamp = {2018-03-19},
+  url       = {/wp-content/papercite-data/pdf/Rossi.PhD18.pdf},
 }
 
 @inproceedings{RoelofsLandryEtAl,
@@ -2250,7 +2250,7 @@ @InProceedings{RibeiroLukeEtAl2023
   volume    = {43},
   address   = {Doha, Qatar},
   month     = dec,
-  abstract  = {Although vehicle electrification and utilization of on-site solar PV generation can begin reducing the greenhouse gas emissions associated with bus fleet operations, a method to intelligently coordinate bus-route assignments, bus charging, and energy storage is needed to fully decarbonize fleet operations while simultaneously minimizing electricity costs. This paper proposes a 24/7 Carbon-Free Electrified Fleet digital twin framework for modeling, controlling, and analyzing an electric bus fleet, co-located solar PV arrays, and a battery energy storage system. The framework consists of forecasting modules for marginal grid emissions factors, solar generation, and bus energy consumption that are input to the optimization module, which determines bus and battery operations at minimal electricity and emissions costs. We present a digital platform based on this framework, and for a case study of Stanford University's Marguerite Shuttle, the platform reduced peak charging demand by 99\%, electric utility bill by \$2778, and associated carbon emissions by 100\% for one week of simulated operations for 38 buses. When accounting for operational uncertainty, the platform still reduced the utility bill by \$784 and emissions by 63\%.},
+  abstract  = {Although vehicle electrification and utilization of on-site solar PV generation can begin reducing the greenhouse gas emissions associated with bus fleet operations, a method to intelligently coordinate bus-route assignments, bus charging, and energy storage is needed to fully decarbonize fleet operations while simultaneously minimizing electricity costs. This paper proposes a 24/7 Carbon-Free Electrified Fleet digital twin framework for modeling, controlling, and analyzing an electric bus fleet, co-located solar PV arrays, and a battery energy storage system. The framework consists of forecasting modules for marginal grid emissions factors, solar generation, and bus energy consumption that are input to the optimization module, which determines bus and battery operations at minimal electricity and emissions costs. We present a digital platform based on this framework, and for a case study of Stanford University's Marguerite Shuttle, the platform reduced peak charging demand by 99\%, electric utility bill by \$ 2778, and associated carbon emissions by 100\% for one week of simulated operations for 38 buses. When accounting for operational uncertainty, the platform still reduced the utility bill by \$ 784 and emissions by 63\%.},
   doi       = {10.46855/energy-proceedings-11033},
   owner     = {jthluke},
   timestamp = {2024-10-28},
@@ -2981,7 +2981,7 @@ @Article{LukeRibeiroEtAl2024
   year      = {2025},
   volume    = {377},
   number    = {124506},
-  abstract  = {Electrifying a commercial fleet while concurrently adopting distributed energy resources can significantly reduce the cost and carbon footprint of its operation. However, coordinating fleet operations with distributed resources requires an intelligent system to determine joint dispatch. In this paper, we propose a 24/7 Carbon-Free Electrified Fleet digital twin framework for the coordination of an electric bus fleet, co-located photovoltaic solar arrays, and a battery energy storage system. The framework optimizes electric bus and battery storage operations to minimize costs and emissions with the consideration of on-site solar generation, hourly marginal grid emissions factors, and predictions of bus energy consumption through a surrogate model. We evaluate the framework in a case study of Stanford University's Marguerite Shuttle electric bus fleet for both a campus depot, whereby non-controllable loads are coupled behind-the-meter, and a stand-alone depot. In a techno-economic analysis, we find that joint optimization of a campus depot's battery storage and bus operations saves at least \$1.79M USD in electricity costs over a 10-year horizon while also reducing 98\% of carbon emissions associated with the depot. For a stand-alone depot, sensitivity analyses show that 100\% elimination of depot emissions is achievable without any trade-off with bill savings, whereas for depots with reduced on-site solar capacity, using an emissions-aware optimization model can reduce the depot's carbon footprint by an additional 17\% at a carbon abatement cost of \$66 USD/tCO compared to a model that only minimizes electricity costs. Furthermore, optimized bus and battery operations have even greater impact in reducing electricity costs under new net billing tariff policies ("net energy metering (NEM) 3.0") compared to previous NEM 2.0 policies. As adoption of electric buses continues to grow, fleet operators may leverage our flexible framework to ensure smart, low-cost, and low-emissions fleet operations.},
+  abstract  = {Electrifying a commercial fleet while concurrently adopting distributed energy resources can significantly reduce the cost and carbon footprint of its operation. However, coordinating fleet operations with distributed resources requires an intelligent system to determine joint dispatch. In this paper, we propose a 24/7 Carbon-Free Electrified Fleet digital twin framework for the coordination of an electric bus fleet, co-located photovoltaic solar arrays, and a battery energy storage system. The framework optimizes electric bus and battery storage operations to minimize costs and emissions with the consideration of on-site solar generation, hourly marginal grid emissions factors, and predictions of bus energy consumption through a surrogate model. We evaluate the framework in a case study of Stanford University's Marguerite Shuttle electric bus fleet for both a campus depot, whereby non-controllable loads are coupled behind-the-meter, and a stand-alone depot. In a techno-economic analysis, we find that joint optimization of a campus depot's battery storage and bus operations saves at least \$ 1.79M USD in electricity costs over a 10-year horizon while also reducing 98\% of carbon emissions associated with the depot. For a stand-alone depot, sensitivity analyses show that 100\% elimination of depot emissions is achievable without any trade-off with bill savings, whereas for depots with reduced on-site solar capacity, using an emissions-aware optimization model can reduce the depot's carbon footprint by an additional 17\% at a carbon abatement cost of \$ 66 USD/tCO compared to a model that only minimizes electricity costs. Furthermore, optimized bus and battery operations have even greater impact in reducing electricity costs under new net billing tariff policies ("net energy metering (NEM) 3.0") compared to previous NEM 2.0 policies. As adoption of electric buses continues to grow, fleet operators may leverage our flexible framework to ensure smart, low-cost, and low-emissions fleet operations.},
   doi       = {10.1016/j.apenergy.2024.124506},
   owner     = {jthluke},
   timestamp = {2024-10-28},