Revisiting the Optimal PMU Placement Problem in Multi-Machine Power Networks

To provide real-time visibility of physics-based states, phasor measurement units (PMUs) are deployed throughout power networks. PMU data enable real-time grid monitoring and control -- and is essential in transitioning to smarter grids. Various considerations are taken into account when determining the geographic, optimal PMU placements (OPP). This paper focuses on the control-theoretic, observability aspect of OPP. A myriad of studies have investigated observability-based formulations to determine the OPP within a transmission network. However, they have mostly adopted a simplified representation of system dynamics, ignored basic algebraic equations that model power flows, disregarded including renewables such as solar and wind, and did not model their uncertainty. Consequently, this paper revisits the observability-based OPP problem by addressing the literature's limitations. A nonlinear differential algebraic representation (NDAE) of the power system is considered and implicitly discretized -- using various different discretization approaches -- while explicitly accounting for uncertainty. A moving horizon estimation approach is explored to reconstruct the joint differential and algebraic initial states of the system, as a gateway to the OPP problem which is then formulated as a computationally tractable integer program (IP). Comprehensive numerical simulations on standard power networks are conducted to validate various aspects of this approach and test its robustness to various dynamical conditions

A Novel Security Methodology for Smart Grids: A Case Study of Microcomputer-Based Encryption for PMU Devices

Coordination of a power system with the phasor measurement devices (PMUs) in real time on the load and generation sides is carried out within the context of smart grid studies. Power systems equipped with information systems in a smart grid pace with external security threats. Developing a smart grid which can resist against cyber threats is considered indispensable for the uninterrupted operation. In this study, a two-way secure communication methodology underpinned by a chaos-based encryption algorithm for PMU devices is proposed. (e proposed system uses the IEEE-14 busbar system on which the optimum PMU placement has been installed. (e proposed hyperchaotic system-based encryption method is applied as a new security methodology among PMU devices. (e success of results is evaluated by the completeness of data exchange, durations, the complexity of encryption-decryption processes, and strength of cryptography using a microcomputer-based implementation. (e results show that the proposed microcomputer-based encryption algorithms can be directly embedded as encryption hardware units into PMU and PDC devices which have very fast signal processing capabilities taking into considerations the acceptable delay time for power system protection and measuring applications and quality metering applications which is 2 ms and 10 ms, respectively. While proposed algorithms can be used in TCP or UDP over IP-based IEEE C37.118, IEC 61850, and IEC 61850-90-5 communication frameworks, they can also be embedded into electronic cards, smartcards, or smart tokens which are utilized for authentication among smart grid components.Türkiye Bilimsel ve Teknolojik Araştirma Kurum

Modern Machine Learning Tools for Monitoring and Control of Industrial Processes: A Survey

Over the last ten years, we have seen a significant increase in industrial data, tremendous improvement in computational power, and major theoretical advances in machine learning. This opens up an opportunity to use modern machine learning tools on large-scale nonlinear monitoring and control problems. This article provides a survey of recent results with applications in the process industry.Comment: IFAC World Congress 202

Extended particle-in-cell schemes for physics in ultrastrong laser fields: Review and developments.

We review common extensions of particle-in-cell (PIC) schemes which account for strong field phenomena in laser-plasma interactions. After describing the physical processes of interest and their numerical implementation, we provide solutions for several associated methodological and algorithmic problems. We propose a modified event generator that precisely models the entire spectrum of incoherent particle emission without any low-energy cutoff, and which imposes close to the weakest possible demands on the numerical time step. Based on this, we also develop an adaptive event generator that subdivides the time step for locally resolving QED events, allowing for efficient simulation of cascades. Further, we present a unified technical interface for including the processes of interest in different PIC implementations. Two PIC codes which support this interface, PICADOR and ELMIS, are also briefly reviewed

Classifying and modelling demand response in power systems

Demand response (DR) is expected to play a major role in integrating large shares of variable renewable energy (VRE) sources in power systems. For example, DR can increase or decrease consumption depending on the VRE availability, and use generating and network assets more efficiently. Detailed DR models are usually very complex, hence, unsuitable for large-scale energy models, where simplicity and linearity are key elements to keep a reasonable computational performance. In contrast, aggregated DR models are usually too simplistic and therefore conclusions derived from them may be misleading. This paper focuses on classifying and modelling DR in large-scale models. The first part of the paper classifies different DR services, and provides an overview of benefits and challenges. The second part presents mathematical formulations for different types of DR ranging from curtailment and ideal shifting, to shifting including saturation and immediate load recovery. Here, we suggest a collection of linear constraints that are appropriate for large-scale power systems and integrated energy system models, but sufficiently sophisticated to capture the key effects of DR in the energy system. We also propose a mixed-integer programming formulation for load shifting that guarantees immediate load recovery, and its linear relaxation better approximates the exact solution compared with previous models

Induced innovation, inventors and the energy transition

We study how individual inventors respond to incentives to work on 'clean' electricity technologies. Using natural gas price variation, we estimate output and entry elasticities of inventors and measure the medium-term impacts of a price increase mirroring the social cost of carbon. We find that the induced clean innovation response primarily comes from existing clean inventors. New inventors are less responsive on the margin than their average contribution to clean energy patenting would indicate. Our findings suggest a role for policy to increase the supply of clean inventors to help mitigate climate change

Symmetry in Chaotic Systems and Circuits

Symmetry can play an important role in the field of nonlinear systems and especially in the design of nonlinear circuits that produce chaos. Therefore, this Special Issue, titled “Symmetry in Chaotic Systems and Circuits”, presents the latest scientific advances in nonlinear chaotic systems and circuits that introduce various kinds of symmetries. Applications of chaotic systems and circuits with symmetries, or with a deliberate lack of symmetry, are also presented in this Special Issue. The volume contains 14 published papers from authors around the world. This reflects the high impact of this Special Issue

Chaos in music: historical developments and applications to music theory and composition

The Doctoral Dissertation submitted by Jonathan R. Salter, in partial fulfillment of the requirements for the degree Doctor of Musical Arts at the University of North Carolina at Greensboro comprises the following: 1. Doctoral Recital I, March 24, 2007: Chausson, Andante et Allegro; Tomasi, Concerto for Clarinet; Bartok, Contrasts; Fitkin, Gate. 2. Doctoral Recital II, December 2, 2007: Benjamin, Le Tombeau de Ravel ; Mandat, Folk Songs; Bolcom, Concerto for Clarinet; Kovacs, Sholem-alekhem, rov Fiedman! 3. Doctoral Recital III, May 3, 2009: Kalliwoda, Morceau du Salon; Shostakovich, Sonata, op. 94 (transcription by Kennan); Tailleferre, Arabesque; Schoen eld, Trio for Clarinet, Violin, and Piano. 4. Dissertation Document: Chaos in Music: Historical Developments and Applications to Music Theory and Composition. Chaos theory, the study of nonlinear dynamical systems, has proven useful in a wide-range of applications to scienti c study. Here, I analyze the application of these systems in the analysis and creation of music, and take a historical view of the musical developments of the 20th century and how they relate to similar developments in science. I analyze several 20th century works through the lens of chaos theory, and discuss how acoustical issues and our interpretation of music relate to the theory. The application of nonlinear functions to aspects of music including organization, acoustics and harmonics, and the role of chance procedures is also examined toward suggesting future possibilities in incorporating chaos theory in the act of composition. Original compositions are included, in both sheet music and recorded form

Forecasting the Short-term Value of Wind Power for Risk-aware Bidding Strategies in Single-imbalance Price Electricity Markets

The participation of wind energy in electricity markets and strategic bidding in the day-ahead market has been investigated with growing interest in recent years. However, markets adopting a single-price imbalance settlement where participants can increase their profits if they help to put the system back into balance have received very limited attention in the academic literature. In this thesis, new probabilistic models forecasting the short-term value of wind power are developed and their use in bidding in these types of markets is investigated. The proposed strategies are designed for participants who want to bid strategically in the day-ahead market to increase the value of the energy generated at a wind farm, where value is measured in terms of revenue and exposure to risk. Following an extensive analysis of the available market data, two alternative approaches are developed to generate day-ahead forecasts of the market quantities of relevance for the work. These forecasts are then combined with short-term predictions of wind power in a probabilistic framework. Bids are formulated to reflect the participant\u27s risk profile, conditioned upon the uncertainty in future wind power generation and electricity market conditions. The methodology is applied to a case study where the participation of a real wind farm in the new Irish electricity market is simulated over a test period. The benefits of the proposed models are clearly demonstrated as the strategies successfully improve the value of wind power for the participant by increasing their revenue while reducing the exposure to risk. Moreover, the market quantity forecasts developed in this work prove to be more valuable than a wind power forecast of higher accuracy for a risk-seeking participant