• Energy Research

Quantum and quantum-inspired algorithms have not yet been systematically classified in the context of potential Operations Research (OR) applications. Our systematic mapping is designed for quick consultation and shows which algorithms have been significantly explored in the context of OR, as well as which algorithms have been vaguely addressed in the same context. The study provides rapid access to OR professionals, both practitioners and researchers, who are interested in applying and/or further developing these algorithms in their respective contexts. We prepared a replicable protocol as a backbone of this systematic mapping study, specifying research questions, establishing effective search and selection methods, defining quality metrics for assessment, and guiding the analysis of the selected studies. A total of more than 2,000 studies were found, of which 149 were analyzed in detail. Readers can have an interactive hands-on experience with the collected data on an open-source repository with a website. An international standard was used as part of our classification, enabling professionals and researchers from across the world to readily identify which algorithms have been applied in any industry sector. Our effort also culminated in a rich set of takeaways that can help the reader identify potential paths for future work.

Accurate network topology information is critical for secure operation of smart power distribution systems. Line outages can change the operational topology of a distribution network. As a result, topology identification by detecting outages is an important task to avoid mismatch between the {topology that the operator believes is present and the actual topology}. Power distribution systems are operated as radial trees and are recently adopting the integration of sensors to monitor the network in real time. In this paper, an optimal sensor placement solution is proposed that enables outage detection through statistical tests based on sensor measurements. Using two types of sensors, node sensors and line sensors, we propose a novel formulation for the optimal sensor placement as a cost optimization problem with binary decision variables, i.e., {to place or not place a sensor at each bus/line}. The advantage of the proposed placement strategy for outage detection is that it incorporates various types of sensors, is independent of load forecast statistics and is cost effective. Numerical results illustrating the placement solution are presented.

Dispatchable energy storage is necessary to enable renewable-based power systems that have zero or very low carbon emissions. The inherent degradation behaviour of electrochemical energy storage (EES) is a major concern for both EES operational decisions and EES economic assessments. Here, we propose a decision framework that addresses the intertemporal trade-offs in terms of EES degradation by deriving, implementing and optimizing two metrics: the marginal benefit of usage and the average benefit of usage. These metrics are independent of the capital cost of the EES system, and, as such, separate the value of EES use from the initial cost, which provides a different perspective on storage valuation and operation. Our framework is proved to produce the optimal solution for EES life-cycle profit maximization. We show that the proposed framework offers effective ways to assess the economic values of EES, to make investment decisions for various applications and to inform related subsidy policies.

With the rapid integration of renewable energy resources, the power system develops into an expansive and complex system. One of the reason for this complexity is the enormous amount of information that is required to be transmitted from the local power system to the centralized controller. The accurate and timely communication of an enormous amount of data presents a considerable challenge to the existing communication system. Keeping this issue in mind, this paper presents a novel Singular Value Decomposition (SVD) based real-time load frequency regulation (LFC) of a power system. It is proposed that the measured data in local power system is decomposed using SVD and then is sent through the communication system, and only the most valuable information is transmitted to the control center. From the obtained results, it is shown that the proposed strategy is competent enough to significantly reduce the volume of the data being communicated through the communication system. Furthermore, it can accurately recover the original power system data.

This paper presents a fully distributed state estimation algorithm for wide-area monitoring in power systems. Through iterative information exchange with designated neighboring control areas, all the balancing authorities (control areas) can achieve an unbiased estimate of the entire power system's state. In comparison with existing hierarchical or distributed state estimation methods, the novelty of the proposed approach lies in that: 1) the assumption of local observability of all the control areas is no longer needed; 2) the communication topology can be different than the physical topology of the power interconnection; and 3) for DC state estimation, no coordinator is required for each local control area to achieve provable convergence of the entire power system's states to those of the centralized estimation. The performance of both DC and AC state estimation using the proposed algorithm is illustrated in the IEEE 14-bus and 118-bus systems.

Real time topology knowledge is essential for situational awareness of power distribution networks. Line outages change the topology of a distribution network. Hence, outage detection is an important task. Most of the existing outage detection algorithms are centralized, in which sensors communicate their data to a control center which performs outage detection using the received data. However, with the increasing size of the distribution network and with different areas of the network being monitored by different operators, communication is a bottleneck and scalability is a major concern. To address these issues, we propose a novel outage detection algorithm using a divide and conquer approach. First, we divide a distribution network into sub-networks, such that outage detection can be run in parallel in each sub-network independently ensuring scalability to large networks. Further, to reduce the latency, bandwidth and attenuation challenges associated with communications in a large network, we divide each sub-network into multiple control areas which communicate only with their neighbors. We employ a distributed iterative load estimation across the control areas of each sub-network and then use the load estimate for local outage detection in each control area. The performance of our algorithm is evaluated for multiple feeder models and compared against traditional centralized outage detection algorithms.

The papers in this special section focus on complex systems within current smart grid operations. The existing power grids, being recognized as one of the significant engineering accomplishments, work exceptionally well for the purposes they have been designed to achieve. Enabled by the advances in sensing, communication, computation, and actuation, smart power are rapidly growing in scale, inter-connectivity, and complexity. Major paradigm shifts in power grids include departing producer-controlled structures and transforming to more decentralized and consumer-interactive ones, being more distributed in electricity generation, enhancing the coupling between the physical and cyber layers, and operating in more variable and stochastic conditions. Driven by these emerging needs, power grids are anticipated to be complex and smart networked platforms in which large volume of high-dimensional and complex data is routinely generated, exchanged, and processed for various monitoring, control, and scheduling purposes. The papers in this section cover some of the recent research in the theory of complex systems with applications to power grid operations, which present novel research contributions in all aspects of complex and large-scale systems of relevance and significance in power grids.

Accurate topology information is critical for effective operation of power distribution networks. Line outages change the operational topology of a distribution network. Hence, outage detection is an important task. Power distribution networks are operated as radial trees and are recently adopting the integration of advanced sensors to monitor the network in real time. In this paper, a dynamic-programming-based minimum cost sensor placement solution is proposed for outage identifiability. We propose a novel formulation of the sensor placement as a cost optimization problem involving binary placement decisions, and then provide an algorithm based on dynamic programming to solve it in polynomial time. The advantage of the proposed placement strategy is that it incorporates various types of sensors, is independent of time varying load statistics, has a polynomial execution time and is cost effective. Numerical results illustrating the proposed sensor placement solution are presented for multiple feeder models including standard IEEE test feeders. arXiv admin note: text overlap with arXiv:1901.11104

Distributed generation resources have become significantly more prevalent in the electric power system over the past few years. This warrants reconsideration on how the coordination of generation resources is achieved. In this paper, we particularly focus on secondary frequency control and how to enhance it by exploiting peer-to-peer communication among the resources. We design a control framework based on a consensus-plus-global-innovation approach, which guarantees bringing the frequency back to its nominal value. The control signals of the distributed resources are updated in response to a global innovation corresponding to the ACE signal, and additional information exchanged via communication among neighboring resources. We show that such a distributed control scheme can be very well approximated by a PI controller and can stabilize the system. Moreover, since our control scheme takes advantage of both the ACE signal and peer-to-peer communication, simulation results demonstrate that our control scheme can stabilize the system significantly faster than the AGC framework. Also, an important feature of our scheme is that it performs $c��$-close to the centralized optimal economic dispatch, where $c$ is a positive constant depending only on the cost parameters and the communication topology and $��$ denotes the maximum rate of change of overall system. Submitted for publication