Sambuddha Chakrabarti

Bio: Sambuddha Chakrabarti is an academic researcher from University of Texas at Austin. The author has contributed to research in topics: Distributed algorithm & Message passing. The author has an hindex of 4, co-authored 7 publications receiving 813 citations. Previous affiliations of Sambuddha Chakrabarti include Royal Institute of Technology.

A Survey of Distributed Optimization and Control Algorithms for Electric Power Systems

Abstract: Historically, centrally computed algorithms have been the primary means of power system optimization and control. With increasing penetrations of distributed energy resources requiring optimization and control of power systems with many controllable devices, distributed algorithms have been the subject of significant research interest. This paper surveys the literature of distributed algorithms with applications to optimization and control of power systems. In particular, this paper reviews distributed algorithms for offline solution of optimal power flow (OPF) problems as well as online algorithms for real-time solution of OPF, optimal frequency control, optimal voltage control, and optimal wide-area control problems.

Toward Distributed/Decentralized DC Optimal Power Flow Implementation in Future Electric Power Systems

Abstract: This paper reviews distributed/decentralized algorithms to solve the optimal power flow (OPF) problem in electric power systems. Six decomposition coordination algorithms are studied, including analytical target cascading, optimality condition decomposition, alternating direction method of multipliers, auxiliary problem principle, consensus+innovations, and proximal message passing. The basic concept, the general formulation, the application for dc-OPF, and the solution methodology for each algorithm are presented. We apply these six decomposition coordination algorithms on a test system, and discuss their key features and simulation results.

Security Constrained Optimal Power Flow via proximal message passing

Abstract: In this paper, we propose a distributed algorithm to solve the Security Constrained Optimal Power Flow (SC-OPF) Problem. We consider a network of devices, each with its own dynamic constraints and objective, subject to reliability constraints across multiple scenarios. Each scenario corresponds to the failure or degradation of a set of devices and has an associated probability of occurrence. The network objective is to minimize the cost of operation of all devices, over a given time horizon, across all scenarios subject to the constraints of transmission limit, upper and lower generating limits, generation-load balance etc. This is a large optimization problem, with variables for consumption and generation for each device, in each scenario. In this paper, we extend the proximal message passing framework to handle reliability constraints across scenarios. The resulting algorithm is extremely scalable with respect to both network size and the number of scenarios.

Financial Transmission Rights: A New Proposal

Abstract: In this paper, we will consider the problems of Revenue Adequacy (RA) and Hedging to Risk (H2R), faced by the Independent System Operators (ISOs) and holders of Financial Transmission Rights (FTRs) (or, Congestion Revenue Rights, or CRRs as they are variously known), respectively. It is well known, that the main driver for these two problems is the difference in the topology of the network that is used while solving the FTR auction and allocation process, to that used in the Day-Ahead (DA) or Real Time (RT) market dispatch calculations. As we will see in this paper, that the problems of RA and H2 $R$ form a set of conflicting requirements, especially when situations corresponding to changing network topologies are considered. We will, therefore, in the present work, propose a newer type of FTR such that both the above-mentioned problems are averted. We will also present the revised auction mechanism of this new FTR, in order to incentivize both the ISOs and the potential holders to sell and purchase them, respectively.

A Survey of Distributed Optimization and Control Algorithms for Electric Power Systems

Abstract: Historically, centrally computed algorithms have been the primary means of power system optimization and control. With increasing penetrations of distributed energy resources requiring optimization and control of power systems with many controllable devices, distributed algorithms have been the subject of significant research interest. This paper surveys the literature of distributed algorithms with applications to optimization and control of power systems. In particular, this paper reviews distributed algorithms for offline solution of optimal power flow (OPF) problems as well as online algorithms for real-time solution of OPF, optimal frequency control, optimal voltage control, and optimal wide-area control problems.

Foundations and Challenges of Low-Inertia Systems (Invited Paper)

Abstract: The electric power system is currently undergoing a period of unprecedented changes. Environmental and sustainability concerns lead to replacement of a significant share of conventional fossil fuel-based power plants with renewable energy resources. This transition involves the major challenge of substituting synchronous machines and their well-known dynamics and controllers with power electronics-interfaced generation whose regulation and interaction with the rest of the system is yet to be fully understood. In this article, we review the challenges of such low-inertia power systems, and survey the solutions that have been put forward thus far. We strive to concisely summarize the laid-out scientific foundations as well as the practical experiences of industrial and academic demonstration projects. We touch upon the topics of power system stability, modeling, and control, and we particularly focus on the role of frequency, inertia, as well as control of power converters and from the demand-side.

Peer-to-peer and community-based markets: A comprehensive review

Abstract: The advent of more proactive consumers, the so-called “prosumers”, with production and storage capabilities, is empowering the consumers and bringing new opportunities and challenges to the operation of power systems in a market environment. Recently, a novel proposal for the design and operation of electricity markets has emerged: these so-called peer-to-peer (P2P) electricity markets conceptually allow the prosumers to directly share their electrical energy and investment. Such P2P markets rely on a consumer-centric and bottom-up perspective by giving the opportunity to consumers to freely choose the way they buy their electric energy. A community can also be formed by prosumers who want to collaborate, or in terms of operational energy management. This paper contributes with an overview of these new P2P markets that starts with the motivation, challenges, market designs moving to the potential future developments in this field, providing recommendations while considering a test-case.

Grand challenges in the science of wind energy

Abstract: Harvested by advanced technical systems honed over decades of research and development, wind energy has become a mainstream energy resource. However, continued innovation is needed to realize the potential of wind to serve the global demand for clean energy. Here, we outline three interdependent, cross-disciplinary grand challenges underpinning this research endeavor. The first is the need for a deeper understanding of the physics of atmospheric flow in the critical zone of plant operation. The second involves science and engineering of the largest dynamic, rotating machines in the world. The third encompasses optimization and control of fleets of wind plants working synergistically within the electricity grid. Addressing these challenges could enable wind power to provide as much as half of our global electricity needs and perhaps beyond.