Transactive Control of Electric Railway Networks by David Humberto D'Achiardi Pascualy B.S. in Mechanical Engineering and Economics Massachusetts Institute of Technology, 2016 Submitted to the Department of Mechanical Engineering in partial fulfillment of the requirements for the degree of Master of Science in Mechanical Engineering at the MASSACHUSETTS INSTITUTE OF TECHNOLOGY February 2019 @ Massachusetts Institute of Technology 2019. All rights reserved. Signature redacted A uthor ......................... Department of Mechanical Engineering January 18, 2019 C ertified by .......... Signature redacted ............. Anuradha M. Annaswamy Director, Active-adaptive Control Laboratory Senior Research Scientist, Department of Mechanical Engineering, MIT Thesis Supervisor Signature redacted Accepted by ....................... MASSACHUSrS ISMUTE OF ICHNOWOGY Nkcolas Hadjiconstantinou Chairman, Committee on Graduate Students FEB 252019 LIBRARIES ARCHIVES 2 Transactive Control of Electric Railway Networks by David Humberto D'Achiardi Pascualy Submitted to the Department of Mechanical Engineering on January 18, 2019, in partial fulfillment of the requirements for the degree of Master of Science in Mechanical Engineering Abstract This thesis proposes a transactive control mechanism that minimizes the operational costs of individual trains and integrates rail-side distributed energy resources (DERs) within an electric railway system. The operation of each individual train is posed as an energy cost minimization problem that is constrained by both the dynamics of the train and the specific schedule it must meet. The solution of this problem yields an optimal power profile for the train to follow. For each of the railway's segments, the proposed transactive controller determines the minimum average cost of electricity and the dispatch of the DERs based on the energy usage of the trains across space and time. By iterating between the transactive controller and the cost minimiza- tion scheme of the individual trains, the proposed methodology yields optimal power profiles of all the trains in the system and the dispatch of the generation assets. This methodology is tested through numerical simulation of the Amtrak Northeast Corridor service between University Park Station in the outskirts of Boston, Mas- sachusetts and New Haven Station in Connecticut. Simulations of the southbound service over the course of a year demonstrate that the minimum cost operation re- duces retail energy supply and delivery charges by 10% when compared to minimum work operation. We test the addition of solar generation across the system as an example of DER integration. The price signal of the transactive controller converges to within 1% after just 3 iterations for a single PV array and one train and within 4 iterations for two PV arrays and two trains. Thesis Supervisor: Anuradha M. Annaswamy Title: Director, Active-adaptive Control Laboratory Senior Research Scientist, Department of Mechanical Engineering, MIT 3 4 Acknowledgments I would like to first thank my advisor, Anuradha Annaswamy, who exudes a contagious passion for knowledge and a persistence in research that I deeply admire. Beyond the countless lessons on all aspects of my graduate studies I have had the privilege to learn from her human touch, which is on par with her contributions to a truly vast range of research areas. I also thank the other members of the Active Adaptive Control Laboratory and the 3-441 office, for their feedback and suggestions, and especially Yohan John and Stefanos Baros who were always willing to talk through problems and new ideas. I am also grateful to my undergraduate advisors, Professors Amos Winter and Bengt Holmstr6m, as well as my mentor Professor Doug Hart, who provided words of wisdom in key transitions and encouraged me to pursue graduate studies. There are countless others that inspired and enabled my journey through MIT. I extend a very special note of appreciation to my undergraduate scholarship donor, Carlos Rodriguez-Pastor as well as Doug and Sara Bailey, the donors of my fellowship during the first year of graduate school. The work of this thesis was made possible by award 1644877 of the Cyber-Physical Systems program within the National Science Foundation as well as the collaborators of that project. I also thank my partner, Maria Roldin, for encouraging me to confront obstacles that held me back, and with her constant love helped me through some of the most difficult years I have lived. Lastly, I would like to thank my parents who provided the foundation of my education, prompted my desire to help others and developed my inquisitiveness about the world around me. I will forever be in debt to your unconditional love and support. 5 6 ,. , ----:. g.-.-. ~ s:--:..-0 <-. %;- - --- .lr3'' - 'p-9 'il'T U-. m-'-''Y ."' I-.I. - .'o.f -rRo l-, -I-, -----.. II-. m.- -:ll ..i: -.-I-r- -m1-. -1 v-- .. s- -- - --- - .. p.l-l-:-- P-r-R - '.- I.-sp e"--M I'-M -'mm..-"----. --- '"- - , .. , y e Contents 1 Introduction 21 2 Background 25 2.1 Electrified Railway Systems .... ...... ...... ....... 25 2.1.1 Traction System Architecture ... ............ ... 26 2.1.2 Regenerative Braking ........ ........ ....... 28 2.2 Train Dynamics ...... ...... ....... ...... ..... 29 2.2.1 Free Body Diagram . ........ ........ ....... 29 2.2.2 Davis Equation .. ............ ........... 31 2.2.3 Traction Curve .... ............ .......... 32 2.3 Trajectory Optimization .. ............ ........... 33 2.4 Transactive Control ....... ...... ...... ...... ... 34 3 Minimum Cost Operation 35 3.1 Problem Formulation . ...... ...... ...... ...... .. 35 3.2 Baseline Power Profile - Work Minimization ...... ........ 38 3.3 Selection of Amtrak NEC ...... ............ ...... 38 3.4 Field Data Collection ........................... 41 3.5 Wholesale Energy Market Pricing .................... 43 3.6 Retail Tariff Cost Minimization - Yearly Operational Cost Reductions 45 4 Transactive Controller 49 4.1 Iterative Market Clearing ......................... 49 7 4.2 Pricing Signal and Revenue Allocation ... ...... ....... 52 4.3 Integration of Rail-Side Distributed Energy Resources (DERs) . ... 53 4.4 Transactive Control for Multi-Train Coordination with DERs .. ... 56 5 Contributions and Future Research Directions 59 5.1 Dynamic Market Mechanisms ... .... .... .... .... ... 61 5.2 Demand Charge Management .... ..... ..... ..... ... 61 5.3 Regulation and Reserve Market Participation .... ...... ... 61 5.4 Mass Transit Systems .. ..... ...... ..... .... ..... 62 A Matlab Code 67 A. 1 Elevation from Position Dataset - University Park Station MA to Prov- idence Station in Rhode Island .. ...... ...... ...... .. 67 A.2 Elevation from Position Dataset - Providence Station in Rhode Island to New Haven Station in Connecticut . ....... ...... .... 71 B Field Data Collection Guide 75 C Datasets 81 8 List of Figures 2-1 Traction system frequency and voltage of the three traction systems in the northeast corridor of the United States. The trains employed by the Amtrak Acela Express and the Northeast Regional, which are the two Amtrak services that travel between Boston, MA and Washington, DC, must interface with all three three traction systems. This graphic was developed using Google Earth Pro [30]. .............. 27 2-2 Free body diagram of electric train 1. The resulting traction force FT, friction and drag force FDF and the gravitational force component in the direction of motion of the train m1gsina are identified. Newton's second law of motion is written for the i direction, along the direction of m otion of the train. ...................... .... 30 2-3 Traction curve of a Siemens ACS-64 locomotive used by Amtrak in the Northeast Regional service along the NEC corridor [13]. ........ 32 3-1 Map of the four pricing regions identified along Amtrak's Northeast Corridor service between University Park Station in Massachusetts and New Haven Station in Connecticut. This graphic was developed using Google Earth Pro [30]. ........... ............ ... 40 9 3-2 Position, elevation and speed data collection methodology of the South- bound Amtrak Acela Express 2171 between South Station in Boston, MA and Pennsylvania Station in New York City, NY on Sunday, July 1, 2018. The train ride was recorded using the GPS and accelerometer of an iPhone 6 in 1 second intervals using a third party application, M yTracks for iOS [47]. .... ...... ...... ..... ..... 42 3-3 Map of the ISO-NE pricing nodes and four substations at Sharon, MA; New Warwick, RI; London, CT; and Branford, CT along the track be- tween University Park Station in Massachusetts and New Haven Sta- tion in Connecticut. This map was developed using SNL Energy [46]. 43 3-4 Position [km], velocity [m/s], power [MW], and LMP [$/Mwh] are plotted against time for the three operating modes (minimum work, minimum cost, and observed or field mode) for a southbound trip on Amtrak Acela between University Park Station in Massachusetts and New Haven Station in Connecticut with a stop in Providence Station in Rhode Island. ....... .......... .......... .. 44 3-5 Estimated delivery charge tariffs faced by Amtrak at the four pric- ing sections (corresponding to ACC1 to ACC 4) between University Park Station in Massachusetts and New Haven Station in Connecti- cut. Delivery charges across all regions exhibit fixed [$/month], energy [$/MWh] and demand [$/MW] charges; however, given that the en- ergy cost minimization proposed in section 3.1. is only based on energy costs, these