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Saverio Bolognani

Saverio Bolognani
Dr., Senior Researcher (Oberassistent)


Automatic Control Laboratory
Swiss Federal Institute of Technology
Physikstrasse 3, ETL I 34
CH-8092 Zurich

Phone: +41 44 632 5886
Email: bsaverio@ethz.ch

I recently joined IfA to work with Prof. Florian Dörfler on networked control and distributed optimization in power systems.

I earned my Ph.D. in the Automatic Control Group of the University of Padova, Italy, under the supervision of Prof. Sandro Zampieri.

Recent presentations

BIRS 2017, Banff, Canada

BIRS2017

CDC 2016, Las Vegas (NV), USA

CDC2016

ECC 2016, Aalborg, Denmark

ECC2016

PSCC 2016, Genova, Italy

PSCC2016

ACC 2016, Boston (MA), USA

ACC2016

Selected publications

For a complete list of my publication, see my ORCID page
ORCID logo
0000-0002-7935-1385

or download my bibliography file
BibTeX logo

Optimization on Manifolds for Power System Applications

In [HBH+16] we propose an unconventional approach to the problem of optimization in power systems. We consider a feedback control architecture in which the full non-linear AC power system is actuated based on real-time measurements. This setup can be casted in the framework of constrainted optimization on manifolds, adopting the grid model presented in [BD15].

An illustration of the effectiveness of this approach on a realistic power system test bed has been presented in [HZB+17].

[HBH+16] Hauswirth, A., Bolognani, S., Hug, G. & Dörfler, F.
Projected gradient descent on Riemannian manifolds with applications to online power system optimization.
In Proc. 54rd Annual Allerton Conference on Communication, Control, and Computing, 2016. [doi: 10.1109/ALLERTON.2016.7852234]
[BD15] Bolognani, S. & Dörfler, F.
Fast power system analysis via implicit linearization of the power flow manifold.
In Proc. 53rd Annual Allerton Conference on Communication, Control, and Computing, 2015. [doi: 10.1109/ALLERTON.2015.7447032]
[HZB+17] Hauswirth, A., Zanardi, A., Bolognani, S., Dorfler, F. & Hug, G.
Online Optimization in Closed Loop on the Power Flow Manifold.
In Proc. of the 12th IEEE PES PowerTech conference, 2017 (Best Paper Award).

Distributed Optimal Power Flow

We investigated the problem of designing distributed control algorithms for the solution of the optimal reactive power flow problem in a power distribution network, i.e. to exploit the reactive power compensation capabilities of compensators and microgenerators in order to minimize power losses and regulate the grid voltage.

One possible approach is based on a gossip (peer-to-peer) coordination among compensators [BZ11]. Interestingly, there is an interaction between the communication topology (who talks to who) and the electric grid topology. It is possible to show that neighbor-to-neighbor communication achieves the best performance [BZ13], possibly with a multi-hop approach to guarantee convergence for any initial condition [BCD+12].

Another approach consists in designing a distributed (networked) feedback law that drives the grid to a configuration where all voltages are between bounds, and power losses are minimized [BCC+15]. The feedback strategy is obtained from the dual decomposition of the original optimization problem, and requires no measurements from the loads.

In [CBC+16] we showed how there is a fundamental suboptimality gap when communication among agents is not allowed, and only purely local strategies can be employed.

[BZ11] Bolognani, S. & Zampieri, S.
A gossip-like distributed optimization algorithm for reactive power flow control.
In Proc. IFAC World Congress, 2011. [doi: 10.3182/20110828-6-IT-1002.03256]
[BZ13] Bolognani, S. & Zampieri, S.
A distributed control strategy for reactive power compensation in smart microgrids.
IEEE Transactions on Automatic Control, 58(11):2818-2833, 2013. [doi: 10.1109/TAC.2013.2270317]
[BCD+12] Bolognani, S., Carron, A., Di Vittorio, A., Romeres, D., Schenato, L. & Zampieri, S.
Distributed multi-hop reactive power compensation in smart micro-grids subject to saturation constraints.
In Proc. 51st Conference on Decision and Control, 2012. [doi: 10.1109/CDC.2012.6426317]
[BCC+15] Bolognani, S., Carli, R., Cavraro, G. & Zampieri, S.
Distributed reactive power feedback control for voltage regulation and loss minimization.
IEEE Transactions on Automatic Control, 60(4):966-981, 2015. [doi: 10.1109/TAC.2014.2363931]
[CBC+16] Cavraro, G., Bolognani, S., Carli, R. & Zampieri, S.
The value of communication in the voltage regulation problem.
In Proc. 55th IEEE Conference on Decision and Control, 2016. [doi: 10.1109/CDC.2016.7799158]

Power flow analysis

In [BD15], we show how the manifold of the power flow equation solutions can be locally approximated via a tangent plane, obtaining a sparse linear implicit model that can be efficiently used for fast decision and optimization problems in (possibly unbalanced) power systems. The resulting approximation is sparse, structure preserving, and computationally extremely efficient.

The source code for the approximation is available online [B15].

In [BZ16] we derived a linear approximation for the power flow analysis of a distribution grid, in order to enable the design of feedback control laws, identification algorithms, and state estimation procedures. We also provide explicit bounds on the quality of the approximation, and sufficient conditions for the existence of a solution, based on an implicit function theorem argument.

The source code for the approximation, together with a test feeder inspired by the IEEE123 distribution test feeder, is available online [B14].

In [ABD17] we show how the determinant of the power flow Jacobian, which is used to determine wheter a given power system state is voltage-stable, can be approximated extremely well via a voltage stability index which can be computed in a scalable and distributed fashion.

[BD15] Bolognani, S. & Dörfler, F.
Fast power system analysis via implicit linearization of the power flow manifold.
In Proc. 53rd Annual Allerton Conference on Communication, Control, and Computing, 2015. [doi: 10.1109/ALLERTON.2015.7447032]
[B15] Bolognani, S.
1ACPF - First-order AC power flow model.
GitHub repository, 2015.
[BZ16] Bolognani, S. & Zampieri, S.
On the existence and linear approximation of the power flow solution in power distribution networks.
IEEE Transactions on Power Systems, 31(1):163-172, 2016. [doi: 10.1109/TPWRS.2015.2395452]
[B14] Bolognani, S.
approx-pf - Approximate linear solution of power flow equations in power distribution networks.
GitHub repository, 2014.
[ABD17] Aolaritei, L., Bolognani, S. & Dörfler, F.
A distributed voltage stability margin for power distribution networks.
In Proc. IFAC World Congress, 2017.

Virtual inertia in power systems

In [PBD16] and [PBD17] we consider the problem of optimal allocation of virtual inertia (via storage, flywheels, spinning reserves, etc.) in a power grid, in order to mitigate the effect of disturbances (including fluctuating renewable power generation) on the system frequency. We develop a framework for the mathematical analysis of the perfomance of different allocation choices, and we derive an efficient numerical method to tackle large scale optimal allocation problems.

[PBD16] Poolla, B.K., Bolognani, S. & Dörfler, F.
Placing rotational inertia in power grids.
In Proc. American Control Conference, 2016. [doi: 10.1109/ACC.2016.7525263]
[PBD17] Poolla, B.K., Bolognani, S. & Dörfler, F.
Optimal Placement of Virtual Inertia in Power Grids.
IEEE Transactions on Automatic Control, 2017 (in press). [doi: 10.1109/TAC.2017.2703302]

Chance-constrained real-time decision

In [BAD17] we studied the problem of real-time chance-constrained decision making, with a particular focus on the operation of distribution network in presence of stochastic uncertainties. This decision problem has a specific structure: on one hand, we assume that some a-priori information about the unknown parameters is known, in the form of samples; on the other hand, we assume that it is possible to gather further information regarding the true value of these parameters via real-time measurements. We specialized the scenario-based approach towards this task, obtaining a two-phase algorithm composed of an offline processing of the samples, and an online part to be executed in real-time. This online part is extremely lightweight and is suited for implementation in embedded systems.

[BAD17] Bolognani, S., Arcari, E. & Dörfler, F.
A Fast Method for Real-Time Chance-Constrained Decision with Application to Power Systems.
IEEE Control Systems Letters, 1(1):152-157, 2017. [doi: 10.1109/LCSYS.2017.2711140]

Deferrable power loads

We considered the problem of deferrable loads (power loads that can be postponed up to a deadline) in a scenario where consumers face time varying energy prices. We showed how to compute the optimal consumption strategy of the individual users that observes the stochastic price signal [MBR+15].

We then consider a scenario where multiple flexible loads are subject to a coupling constraint on the power that they can request (e.g. car batteries in a charging station). Given their deadlines, we characterized the set of feasible power consumption decisions that guarantee the satisfaction of all the deadlines, while allowing full participation of each consumer to the energy market via a constrained auction [MBR+14].

[MBR+15] Materassi, D., Bolognani, S., Roozbehani, M. & Dahleh, M.
Optimal consumption policies for power-constrained flexible loads under dynamic pricing.
IEEE Transactions on Smart Grids, 6(4):1884-1892, 2015. [doi: 10.1109/TSG.2015.2393053]
[MBR+14] Materassi, D., Bolognani, S., Roozbehani, M. & Dahleh, M.A.
Deferrable loads in an energy market: coordination under congestion constraints.
In Proc. 22nd Mediterranean Conference on Control and Automation, 2014. [doi: 10.1109/MED.2014.6961443]

Identification of power distribution grid topology

We considered the problem of identifying the topology of the power distribution grid from nodal voltage measurements, in order to enable a plug-and-play deployment of control and automation in these grids. We showed how the topology can be inferred from the statistical properties of the voltage measurements, in particular from their conditional correlation [BBM+13].

[BBM+13] Bolognani, S., Bof, N., Michelotti, D., Muraro, R. & Schenato, L.
Identification of power distribution network topology via voltage correlation analysis.
In Proc. 52nd Conference on Decision and Control, 2013. [doi: 10.1109/CDC.2013.6760120]

State estimation in power distribution networks

PMU-based state estimation in power distribution grids is made difficult by the poor time synchronization between different sensors. We addressed this specific problem in [TCB14], where we proposed a distributed state estimation algorithm for this goal.

[TCB14] Todescato, M., Carli, R. & Bolognani, S.
State estimation in power distribution networks with poorly synchronized measurements.
In Proc. 53rd Conference on Decision and Control, 2014. [doi: 10.1109/CDC.2014.7039783]

Clock synchronization algorithms

In [BCL+16] we have shown how a very large family of clock synchronization algorithms (implemented on wireless or wired networks, and based on peer-to-peer or broadcast communications) can be cast into the same framework (that we called RandSync), for which sufficient conditions for convergence can be provided. We implemented RandSync on a wireless testbed [B14], showing how an accuracy of less than 0.1 ms can be achieved with just about 2 broadcasted messages from each node evey hour.

[BCL+16] Bolognani, S., Carli, R., Lovisari, E. & Zampieri, S.
A randomized linear algorithm for clock synchronization in multi-agent systems.
IEEE Transactions on Automatic Control, 61(7), 2016. [doi: 10.1109/TAC.2015.2479136]
[B14] Bolognani, S.
RandSync source code.
GitHub repository, 2014.

Distributed computing and network congestion control

In [KBD16], we consider the problem of optimal allocation of computing power for IaaS (Infrastructure as a Service) cloud computing applications. We formulate a bilevel optimization problem where bandwidth and computing capacity constraints for each involved node are taken into account, showing that the optimal allocation strategy is independent by the congestion control mechanism employed by the data network infrastructure.

[KBD16] Kottmann, F., Bolognani, S. & Dörfler, F.
A separation principle for optimal IaaS cloud computing distribution.
In Proc. EUSIPCO, 2016. [doi: 10.1109/EUSIPCO.2016.7760477]

Wireless sensor networks

We condidered a problem that arises when trying to infer agent-to-agent distances in a wireless sensor networks based on received power strength. We showed how it is possible to identify the parameters of the wireless channel and to compensate the measurement offsets via a distributed algorithm that require no central coordinator [BDS+10].

[BDS+10] Bolognani, S., Del Favero, S., Schenato, L. & Varagnolo, D.
Consensus-based distributed sensor calibration and least-square parameter estimation in WSNs.
International Journal of Robust and Nonlinear Control, 20(2):176-193, 2010. [doi: 10.1002/rnc.1452]

Control of quantum systems

We considered the problem of achieving attractivity and invariance of a quantum subspace by designing an appropriate feedback, discrete-time, control law. The control scheme consists in a quantum measurement, which is given, and a coherent control action, which has to be designed. An algorithm has been proposed to check feasibility of the control problem and to return a stabilizing unitary control.[BT10].

[BT10] Bolognani, S. & Ticozzi, F.
Engineering stable discrete-time quantum dynamics via a canonical QR decomposition.
IEEE Transactions on Automatic Control, 55(12):2721-2734, 2010. [doi: 10.1109/TAC.2010.2049291]

Model predictive control of electric drives

We designed and implemented a model predictive controller for PMSM electric drives, where we included both the electrical and the mechanical dynamics in a single aggregate model [BBP+09].

[BBP+09] Bolognani, S., Bolognani, S., Peretti, L. & Zigliotto, M.
Design and implementation of model predictive control for electrical motor drives.
IEEE Transactions on Industrial Electronics, 56(6):1925-1936, 2009. [doi: 10.1109/TIE.2008.2007547]

Teaching

For the material of the Game Theory and Control course, refer to the official course page.

Master Thesis and Semester projects

Open projects

Final reports and presentation are available upon request.

  • Elena Arcari, "Fast chance-constrained optimization using real-time measurements with applications to power systems," January 2017
  • Philipp Lütolf, "Optimal placement of virtual damping and inertia," January 2017
  • Liviu Aolaritei, "A decentralized voltage collapse distance for power distribution networks," October 2016
  • Alessandro Zanardi, "Constrained optimization over manifolds for power system application," September 2016
  • Jan Schulze, "Peer-to-peer clock synchronization in wireless sensor networks," February 2016
  • Panagiotis Kyriakis, "Formation of robust networks for secure exchange of cryptocurrencies," February 2016
  • Felix Kottmann, "Computational load and congestion control in cloud environments," November 2015
  • Dalibor Drzajic, "Energy theft detection using compressive sensing methods," August 2015

Misc

Wall academic calendar 2016-2017