About

Yacov Shamash is a Professor at the Department of Electrical and Computer Engineering at Stony Brook University. His research focuses on Control Theory & Systems, Energy Systems, and Industry/University Partnerships. He is involved in advancing control systems and energy-related research, contributing to the development of innovative solutions in these fields. As a faculty member, he collaborates on projects that bridge academic research and industry applications, fostering partnerships that enhance technological progress and practical impact in electrical engineering.

Research topics

• Computer Science
• Engineering
• Mathematics
• Control engineering
• Electrical engineering
• Algorithm
• Computer network
• Distributed computing

Selected publications

• Neuromorphic traveling-wave protection for IoT-enabled autonomous microgrids

  Cybernetics and Intelligence · 2026-03-01

  articleOpen accessSenior author

  This paper explores the implementation of Traveling Wave Protection (TWP) in microgrids through the integration of Internet of Things (IoT) technologies and a Spiking Recurrent Neural Network (SRNN). Microgrids present unique fault de-tection challenges, as conventional protection techniques can be hindered by reduced fault currents, bidirectional power flow, and communication latency. By leveraging high-frequency traveling wave signals, TWP offers rapid and precise fault localization. In parallel, IoT-enabled sensing provides real-time data acquisition and decentralized decision-making. The proposed SRNN further enhances fault classification and location accuracy by combining spiking neuron dynamics with recurrent memory. Hardware-in-the-loop experiments on both simplified and complex microgrids demonstrate the method’s effectiveness in minimizing misclas-sification while maintaining low latency and reduced power consumption. This work extends our previous IoT-based TWP research by adopting a neuromorphic framework suitable for microgrid edge deployments, paving the way for more adaptive and robust protection solutions in modern distribution networks.

  PublisherOA PDFDOI

• A Distributed Model Predictive Coordination Strategy for Multi-Agent Systems

  Research Square · 2026-02-25

  preprintOpen access1st authorCorresponding
  PublisherOA PDFDOI

• Reforming Quantum Microgrid Formation

  IEEE Transactions on Power Systems · 2025-01-09 · 1 citations

  articleSenior author

  This letter introduces a novel compact and lossless quantum microgrid formation (qMGF) approach to achieve efficient operational optimization of the power system and improvement of resilience. This is achieved through lossless reformulation to ensure that the results are equivalent to those produced by the classical MGF by exploiting graph-theory-empowered quadratic unconstrained binary optimization (QUBO) that avoids the need for redundant encoding of continuous variables. Additionally, the qMGF approach utilizes a compact formulation that requires significantly fewer qubits compared to other quantum methods thereby enabling a high-accuracy and low-complexity deployment of qMGF on near-term quantum computers. Case studies on real quantum processing units (QPUs) empirically demonstrated that qMGF can achieve the same high accuracy as classic results with a significantly reduced number of qubits.

  PublisherDOI

• Quantum contingency analysis for power system steady-state security identification

  Scientific Reports · 2025-04-30 · 3 citations

  articleOpen access

  Unprecedented extreme climate events cause devastating infrastructure outages within power systems. Comprehensive outage identification is essential for the identification of critical components to ensure the uninterrupted power supply in a secure manner to withstand extreme weather events. Accurate outage identification, however, requires simulations of a large number of outage scenarios necessitating highly scalable computations thus challenging classical computing paradigms. Quantum computing provides a promising resolution by exploiting exponential scalability achieved through superposition and entanglement of voltage states. This paper devises a quantum contingency analysis (QCA) method to identify outage scenarios on Noisy Intermediate-Scale Quantum (NISQ) devices. Advanced quantum circuits incorporating Pauli-twirling, dynamic decoupling, and matrix-free measurement are designed to mitigate hardware-induced errors. A preconditioned hybrid method is devised to alleviate the computation burden of parameter optimization of quantum gates. Case studies identify line and generation outages via QCA in typical power systems. Our research underscores that quantum computing exhibits exponential scalability in identifying power grid outages and critical components.

  PublisherOA PDFDOI

• Distributed secondary control of energy storage units in a droop-controlled DC microgrid

  Results in Engineering · 2025-09-11 · 1 citations

  articleOpen accessSenior author

  In the control and management of an energy storage system consisting of multiple energy storage units, bus voltage regulation, load power sharing, and energy level balancing are important objectives. To achieve these objectives, we propose a distributed secondary control scheme for each energy storage unit in a droop-controlled multi-bus DC microgrid. This control scheme is composed of two auxiliary control inputs. By constructing a distributed voltage observer based on the dynamic average consensus algorithm of a first-order multi-agent system, we design the first auxiliary control input such that global voltage regulation is achieved. Moreover, based on the leaderless consensus algorithm of a second-order multi-agent system, we design the second auxiliary control input such that proportional power sharing and state-of-energy balancing are simultaneously achieved. Through simulation studies in MATLAB/Simulink, we validate the effectiveness of the proposed control scheme and highlight the appealing feature of state-of-energy balancing over state-of-charge balancing for a battery energy storage system. • A distributed secondary control scheme based on V-P droop control is designed. • Global voltage regulation, load power sharing and SoE balancing are all achieved. • MATLAB/Simulink simulations validate the effectiveness of the proposed control. • SoC and SoE metric differences due to the battery terminal voltage are highlighted.

  PublisherDOI

• New Conditions and Controllers for State-of-Charge Balancing in Battery Energy Storage Systems

  IEEE Transactions on Automatic Control · 2025-09-01

  articleSenior author

  We investigate the state-of-charge (SoC) balancing control problem for a battery energy storage system, which consists of multiple battery units. These battery units are allowed to have heterogeneous battery parameters and are connected in parallel to deliver a desired total power. Existing power allocating controllers have been developed to achieve SoC balancing without taking balancing speed into consideration. Motivated by this observation, we aim to design new power allocating controllers such that accelerated SoC balancing is achieved. To facilitate our control design, we first introduce a new concept, the powered SoC, and establish new sufficient conditions that guarantee SoC balancing among battery units in the discharging and the charging modes. Based on these new sufficient conditions, we design a power allocating controller for each battery unit. It is shown that the proposed power allocating controllers achieve accelerated SoC balancing while delivering the desired total power. One key merit of our control method lies in achieving faster SoC balancing by tuning the power in the powered SoC. Moreover, we provide in-depth discussions on the SoC evolution trends among the battery units, the limiting case, and the parameter choice. Simulation results are given to validate our analytical results.

  PublisherDOI

• Efficient Online Uncertainty Evaluation for Microgrid Systems

  2025-10-05

  articleSenior author

  In this study, we present a method for online estimation of the mean performance output in microgrid systems subject to high-dimensional and dynamic uncertainties. We integrate an efficient Multivariate Probabilistic Collocation Method (MPCM) based sampling strategy with a Copula-based conditional probability distribution. This integrated method enables online evaluation of system outputs with high estimation accuracy and efficiency. The online evaluation algorithm is developed, and its theoretical analysis is provided. Real Time Digital Simulator (RTDS) experiments validate the method, demonstrating its feasibility for practical applications.

  PublisherDOI

• Ultra-resilient dynamic microgrid formation with renewable integration

  IET conference proceedings. · 2025-02-01 · 1 citations

  article

  Increasingly frequent natural disasters and cyber/physical attacks have posed an urgent need for resilient microgrids in communities. Dynamic microgrid formation (DMGF) is a novel strategy for boosting grid resilience against extreme events. However, traditional DMGF approaches do not consider the impact of renewable energy uncertainty and microgrid switching on system frequency and voltage, failing to provide a safety-guaranteed operation strategy. To address the challenge, this paper develops a new optimization formulation for ultra-resilient DMGF, where the renewable uncertainty and microgrid switching are systematically integrated by including a scenario dimension in the state variables and incorporating safety-assured transitional state constraints. A case study on a modified IEEE 37 node test feeder demonstrates that the developed method can significantly improve the resilience and safety of distribution systems with high renewable penetration under various uncertainties and disturbances.

  PublisherDOI

• A multi-objective hierarchical control framework for battery units in DC microgrids using improved adaptive droop control

  Automatica · 2025-12-22

  articleOpen accessSenior author

  We investigate the primary/secondary control problem for battery units with adaptive droop control in DC microgrids. A majority of existing control methods for state-of-charge (SoC) balancing among battery units require that the terminal voltage of each battery unit remains constant. To remove such a requirement, at the primary control level, we propose an improved adaptive droop control method by introducing an auxiliary battery state in terms of the battery parameters, the terminal voltage, and a power function of the SoC. The designed droop coefficient is inversely proportional to the auxiliary battery state. It is shown that the proposed adaptive droop control achieves proportional power sharing and SoC balancing, albeit at a cost of compromised voltage regulation. A salient feature of the proposed adaptive droop control method is that the battery’s terminal voltage is allowed to be time-varying. Moreover, to compensate for the voltage deviation caused by the improved adaptive droop control, we propose a consensus-based distributed control scheme at the secondary control level. It is shown that the resulting hierarchical control framework, consisting of the primary and secondary control levels, guarantees that the control objectives of voltage regulation, proportional power sharing, and SoC balancing are all achieved. Simulation studies based on an accurate electrical battery model validate the effectiveness of our multi-objective hierarchical control framework.

  PublisherDOI

• Distributed Quantum Computing for Fast Restoration of Power Distribution Systems With Grid-Forming IBRs

  IEEE Transactions on Industry Applications · 2025-10-07 · 1 citations

  articleSenior author

  The combinatorial nature of restoration decisions, especially under increased grid-forming inverter-based resources (IBRs), renders classical optimization intractable for large systems. To potentially address this, this paper develops a coordination-free distributed quantum computing (DQC) framework for rapid and reliable distribution system restoration. A two-stage approach is developed, where microgrids (MGs) first restore loads locally using distributed quantum solvers, followed by a network restoration. To enable execution on noisy intermediate-scale quantum hardware, a compact quantumcompatible formulation is constructed via a tailored variablereduction strategy and a constraint-scaling penalization method that yields minimal quadratic unconstrained binary optimization models with high fidelity. Case studies on modified IEEE 37- and 123-node feeders demonstrate that the proposed method accelerates restoration by up to 30% while maintaining high restoration optimality, and is compatible with both annealingand circuit-based quantum platforms.

  PublisherDOI

Frequent coauthors

• Zongli Lin

  University of Virginia

  83 shared

• Ben M. Chen

  Chinese University of Hong Kong

  44 shared

• Ali Saberi

  Washington State University

  26 shared

• Peddapullaiah Sannuti

  Rutgers, The State University of New Jersey

  18 shared

• Yang‐Yang Qian

  University of Virginia

  12 shared

• Yan Wan

  Donghua University

  12 shared

• Peng Zhang

  8 shared

• Siva S. Banda

  United States Air Force Research Laboratory

  8 shared

Labs

• Electrical and Computer Engineering, Stony Brook UniversityPI

Education

• Ph.D., Electrical Engineering

  University of California, Berkeley

  1986

• M.S., Electrical Engineering

  University of California, Berkeley

  1982

• B.S., Electrical Engineering

  Technion - Israel Institute of Technology

  1979