Year: 2020

Yan passed her GBO

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Yan Jiang, a Ph.D. student in ECE from our lab, passed her Graduate Board Oral Examination! Congratulations!

1 paper accepted to TSUSC

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Our paper on online EV scheduling for adaptive charging networks [1] has been accepted to IEEE Transactions on Sustainable Computing!

[1] [doi] A. Bahram, M. H. Hajiesmaili, Z. Lee, N. Crespi, and E. Mallada, “Online EV Scheduling Algorithms for Adaptive Charging Networks with Global Peak Constraints,” IEEE Transactions on Sustainable Computing, 2020.
[Bibtex] [Abstract] [Download PDF]

This paper tackles online scheduling of electric vehicles (EVs) in an adaptive charging network (ACN) with local and global peak constraints. Given the aggregate charging demand of the EVs and the peak constraints of the ACN, it might be infeasible to fully charge all the EVs according to their charging demand. Two alternatives in such resource-limited scenarios are to maximize the social welfare by partially charging the EVs (fractional model) or selecting a subset of EVs and fully charge them (integral model). The critical challenge is the need for online solution design since in practical scenarios the scheduler has no information of future arrivals of EVs in a time- coupled underlying problem. For the fractional model, we devise both offline and online algorithms. We prove that the offline algorithm is optimal. Using competitive ratio as the performance measure, we prove the online algorithm achieves a competitive ratio of 2. The integral model, however, is more challenging since the underlying problem is strongly NP-hard due to 0/1 selection criteria of EVs. Hence, efficient solution design is challenging even in offline setting. We devise a low-complexity primal-dual scheduling algorithm that achieves a bounded approximation ratio. Built upon the offline approximate algorithm, we propose an online algorithm and analyze its competitive ratio in special cases.

@article{bhlcm2019tsusc,
  abstract = {This paper tackles online scheduling of electric vehicles (EVs) in an adaptive charging network (ACN) with local and global peak constraints. Given the aggregate charging demand of the EVs and the peak constraints of the ACN, it might be infeasible to fully charge all the EVs according to their charging demand. Two alternatives in such resource-limited scenarios are to maximize the social welfare by partially charging the EVs (fractional model) or selecting a subset of EVs and fully charge them (integral model). The critical challenge is the need for online solution design since in practical scenarios the scheduler has no information of future arrivals of EVs in a time- coupled underlying problem. For the fractional model, we devise both offline and online algorithms. We prove that the offline algorithm is optimal. Using competitive ratio as the performance measure, we prove the online algorithm achieves a competitive ratio of 2. The integral model, however, is more challenging since the underlying problem is strongly NP-hard due to 0/1 selection criteria of EVs. Hence, efficient solution design is challenging even in offline setting. We devise a low-complexity primal-dual scheduling algorithm that achieves a bounded approximation ratio. Built upon the offline approximate algorithm, we propose an online algorithm and analyze its competitive ratio in special cases.},
  author = {Bahram, Alina and Hajiesmaili, Mohammad H. and Lee, Zachary and Crespi, Noel and Mallada, Enrique},
  bdsk-url-3 = {https://doi.org/10.1109/TSUSC.2020.2979854},
  doi = {10.1109/TSUSC.2020.2979854},
  grants = {CAREER-1752362, CPS-1544771, ENERGISE-DE-EE0008006, AMPS-1736448, TRIPODS-1934979,EPCN-1711188,},
  journal = {IEEE Transactions on Sustainable Computing},
  month = {1},
  title = {Online EV Scheduling Algorithms for Adaptive Charging Networks with Global Peak Constraints},
  url = {https://mallada.ece.jhu.edu/pubs/2020-TSUSC-BHLCM.pdf},
  year = {2020}
}

2 papers accepted to ECC 20

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Our papers on minimum-time charging of energy storage via approximate conic relaxation [1] and on generation cost reduction [2] have been accepted to European Control Conference 2020!

[1] [doi] J. Guthrie and E. Mallada, “Minimum-Time Charging of Energy Storage in Microgrids via Approximate Conic Relaxation,” in 19th IEEE European Control Conference (ECC), 2020, pp. 1713-1718.
[Bibtex] [Abstract] [Download PDF]

We study the problem of maximizing energy transfer to a load in a DC microgrid while respecting constraints on bus voltages and currents, and accounting for the impact of neighboring constant power loads. Both the objective and dynamics give rise to indefinite quadratic terms, resulting in a non-convex optimization problem. Through change of variables and relaxations we develop a closely related second-order cone program. The problem retains the same feasible set as the original problem but utilizes a linear approximation of the non-convex objective. We demonstrate how this can be used to design approximately optimal charging profiles for periodic pulsed loads in real time.

@inproceedings{gm2020ecc,
  abstract = {We study the problem of maximizing energy transfer to a load in a DC microgrid while respecting constraints on bus voltages and currents, and accounting for the impact of neighboring constant power loads.  Both the objective and dynamics give rise to indefinite quadratic terms, resulting in a non-convex optimization problem. Through change of variables and relaxations we develop a closely related second-order cone program. The problem retains the same feasible set as the original problem but utilizes a linear approximation of the non-convex objective. We demonstrate how this can be used to design approximately optimal charging profiles for periodic pulsed loads in real time.},
  author = {Guthrie, James and Mallada, Enrique},
  bdsk-url-3 = {https://doi.org/10.23919/ECC51009.2020.9143992},
  booktitle = {19th IEEE European Control Conference (ECC)},
  doi = {10.23919/ECC51009.2020.9143992},
  grants = {CAREER-1752362, CPS-1544771, ENERGISE-DE-EE0008006, AMPS-1736448, TRIPODS-1934979, EPCN-1711188, ARO-W911NF-17-1-0092},
  keywords = {Power Networks},
  month = {5},
  pages = {1713-1718},
  title = {Minimum-Time Charging of Energy Storage in Microgrids via Approximate Conic Relaxation},
  url = {https://mallada.ece.jhu.edu/pubs/2020-ECC-GM-b.pdf},
  year = {2020}
}
[2] [doi] Y. Shen, M. Bichuch, and E. Mallada, “On the Value of Energy Storage in Generation Cost Reduction,” in 19th IEEE European Control Conference (ECC), 2020, pp. 1526-1532.
[Bibtex] [Abstract] [Download PDF]

This work seeks to quantify the benefits of using energy storage toward the reduction of the energy generation cost of a power system. A two-fold optimization framework is provided where the first optimization problem seeks to find the optimal storage schedule that minimizes operational costs. Since the operational cost depends on the storage capacity, a second optimization problem is then formulated with the aim of finding the optimal storage capacity to be deployed. Although, in general, these problems are difficult to solve, we provide a lower bound on the cost savings for a parametrized family of demand profiles. The optimization framework is numerically illustrated using real-world demand data from ISO New England. Numerical results show that energy storage can reduce energy generation costs by at least 2.5 percent.

@inproceedings{sbm2020ecc,
  abstract = {This work seeks to quantify the benefits of using energy storage toward the reduction of the energy generation cost of a power system.  A two-fold optimization framework is provided where the first optimization problem seeks to find the optimal storage schedule that minimizes operational costs. Since the operational cost depends on the storage capacity, a second optimization problem is then formulated with the aim of finding the optimal storage capacity to be deployed. Although, in general, these problems are difficult to solve, we provide a lower bound on the cost savings for a  parametrized family of demand profiles.
The optimization framework is numerically illustrated using real-world demand data from ISO New England. Numerical results show that energy storage can reduce energy generation costs by at least 2.5 percent.},
  author = {Shen, Yue and Bichuch, Maxim and Mallada, Enrique},
  bdsk-url-3 = {https://doi.org/10.23919/ECC51009.2020.9143772},
  booktitle = {19th IEEE European Control Conference (ECC)},
  doi = {10.23919/ECC51009.2020.9143772},
  grants = {CAREER-1752362, CPS-1544771, ENERGISE-DE-EE0008006, AMPS-1736448, TRIPODS-1934979, EPCN-1711188, ARO-W911NF-17-1-0092},
  keywords = {Power Networks},
  month = {5},
  pages = {1526-1532},
  title = {On the Value of Energy Storage in Generation Cost Reduction},
  url = {https://mallada.ece.jhu.edu/pubs/2020-ECC-SBM.pdf},
  year = {2020}
}

1 paper accepted to ACC 20

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Our paper on implicit trajectory planning for feedback linearizable systems [1] has been accepted to American Control Conference 2020!

[1] [doi] T. Zheng, J. W. Simpson-Porco, and E. Mallada, “Implicit Trajectory Planning for Feedback Linearizable Systems: A Time-varying Optimization Approach,” in American Control Conference (ACC), 2020, pp. 4677-4682.
[Bibtex] [Abstract] [Download PDF]

We develop an optimization-based framework for joint real-time trajectory planning and feedback control of feedback-linearizable systems. To achieve this goal, we define a target trajectory as the optimal solution of a time-varying optimization problem. In general, however, such trajectory may not be feasible due to , e.g., nonholonomic constraints. To solve this problem, we design a control law that generates feasible trajectories that asymptotically converge to the target trajectory. More precisely, for systems that are (dynamic) full-state linearizable, the proposed control law implicitly transforms the nonlinear system into an optimization algorithm of sufficiently high order. We prove global exponential convergence to the target trajectory for both the optimization algorithm and the original system. We illustrate the effectiveness of our proposed method on multi-target or multi-agent tracking problems with constraints.

@inproceedings{zsm2020acc,
  abstract = { We develop an optimization-based framework for joint real-time trajectory planning and feedback control of feedback-linearizable systems. To achieve this goal, we define a target trajectory as the optimal solution of a time-varying optimization problem. In general, however, such trajectory may not be feasible due to , e.g., nonholonomic constraints. To solve this problem, we design a control law that generates feasible trajectories that asymptotically converge to the target trajectory. More precisely, for systems that are (dynamic) full-state linearizable, the proposed control law implicitly transforms the nonlinear system into an optimization algorithm of sufficiently high order. We prove global exponential convergence to the target trajectory for both the optimization algorithm and the original system. We illustrate the effectiveness of our proposed method on multi-target or multi-agent tracking problems with constraints.},
  author = {Zheng, Tianqi and Simpson-Porco, John W. and Mallada, Enrique},
  bdsk-url-3 = {https://doi.org/10.23919/ACC45564.2020.9147997},
  booktitle = {American Control Conference (ACC)},
  doi = {10.23919/ACC45564.2020.9147997},
  grants = {CPS-1544771, CAREER-1752362, ARO-W911NF-17-1-0092},
  month = {7},
  pages = {4677-4682},
  title = {Implicit Trajectory Planning for Feedback Linearizable Systems: A Time-varying Optimization Approach},
  url = {https://mallada.ece.jhu.edu/pubs/2020-ACC-ZSM.pdf},
  year = {2020}
}