• Energy Research

On-line solution of constrained optimization problems is an essential requirement for a secure, reliable and economic smart grids operation. In this context, the stream of data acquired by the grid sensors should be promptly analyzed in order to identify proper control actions aimed at mitigating the effect of system perturbations, or adapting the grid state to new load and/or generation patterns. To solve this computationally intensive task, case-based reasoning could play a key role in extracting actionable knowledge from historical datasets. Due to time constraints, the search of similar patterns, although simple in principle, requires to face the large size of the historical smart grid data, which increase dynamically once new measured information is available. However, recent advances in Big Data offer efficient schemes for processing massive data. As an example, in this paper we present a solution aimed at searching k most similar cases based on the MapReduce paradigm. This paradigm allows to distribute data and jobs over a compute cluster of commodity hardware. Experimental results are intended to describe the solution behavior under different conditions, such as the dataset size, the number of available nodes/jobs, the block size.

The large-scale deployment of pervasive sensors and decentralized computing in modern smart grids is expected to exponentially increase the volume of data exchanged by power system applications. In this context, the research for scalable and flexible methodologies aimed at supporting rapid decisions in a data rich, but information limited environment represents a relevant issue to address. To this aim, this paper investigates the role of Knowledge Discovery from massive Datasets in smart grid computing, exploring its various application fields by considering the power system stakeholder available data and knowledge extraction needs. In particular, the aim of this paper is dual. In the first part, the authors summarize the most recent activities developed in this field by the Task Force on “Enabling Paradigms for High-Performance Computing in Wide Area Monitoring Protective and Control Systems” of the IEEE PSOPE Technologies and Innovation Subcommittee. Differently, in the second part, the authors propose the development of a data-driven forecasting methodology, which is modeled by considering the fundamental principles of Knowledge Discovery Process data workflow. Furthermore, the described methodology is applied to solve the load forecasting problem for a complex user case, in order to emphasize the potential role of knowledge discovery in supporting post processing analysis in data-rich environments, as feedback for the improvement of the forecasting performances.

An optimization approach to determine the value of optimum LC compensator for time-variant nonsinusoidal environments is proposed. The optimization technique minimizes the transmission loss and/or maximizes the power factor. All the source voltage harmonics follow the same pattern. The equivalent source and load impedances are presented by complex time variations functions. The performance of the LC compensator is discussed by means of numerical examples taken from previous publications.

Wind energy represents a promising alternative to replace traditional fossil-based energy sources. For this reason, increasing the efficiency in the conversion process from wind to electrical energy is crucial. Unfortunately, the presence of systematic errors (mostly related to the yaw and pitch angles) is one of the key factors causing underperformance, and for this reason, it requires adequate identification. The present work deals with diagnosing wind turbine static yaw error, occurring when the wind vane sensor is incorrectly aligned with the rotor shaft. A thorough investigation methodology is proposed by considering a unique experimental test-up shared by the Eolos Wind Research Station. A utility-scale wind turbine has been imposed to operate subjected to several static yaw errors and reference meteorological data collected nearby the wind turbine were available. By analyzing the relation between the meteorological data and the SCADA data collected by the wind turbine, a systematic alteration in the measurements of nacelle wind speed in the presence of the yaw error is explicitly shown. This phenomenon has been overlooked in the literature and leads to revisiting the methods mostly employed for the diagnosis of the error. Furthermore, a correlation between the presence of static error, increased blade pitch, and heightened levels of tower vibration is observed. In summary, this work provides a comprehensive characterization of the experimental evidence associated with the presence of a wind turbine static yaw error. This paves the way for more effective diagnostic techniques for wind turbine yaw errors, potentially revolutionizing data-driven maintenance strategies.

The present paper presents a Cyber Physical Power System (CPPS) framework based on the Service-Oriented Architecture for proactive transmission grids control, modeling and monitoring. CPPS paradigm aims at integrating and coordinating computation, networking, and physical processes according to a holistic vision of the transmission system. The key feature of the proposed framework is the ability of multiple entities to process, manage and share massive heterogeneous information. More specifically, the idea is to conceptualize a holistic architecture that enables the computing resources to deliver much more automation that the sum of its individually self-managed components, allowing the Transmission System Operator to improve the interoperability and the integration level of monolithic and hard to customize power system control and monitoring functions. Moreover, it allows TSO to develop content-based data extraction and aggregation from a host of pervasive sensors network and to exploit distributed embedded computing resources aimed at solving large-scale problems. To assess the benefits of the CPPS framework, the first experimental results obtained on a real test bed are presented and discussed.

A loadability policy established on the basis of the Overhead Transmission Lines (OHLs) static thermal rating is referred to the worst-case conditions (i.e. zero wind speed). Whilst it reduces the potential misoperations, at the same time it implies an underuse of the electrical infrastructures. The transition towards smart grids architectures calls for a reliable exploitation of these assets in order to improve the power system efficient operating. Such considerations are at the basis of the so called Dynamic Line Rating (DLR) paradigm which is incorporated in the smart grid vision. An open issue in this topic is the need to take under control the weather conditions and hence the OHL temperature along the entire OHL length. To overcome the pointed out issue, thanks also to the quick development of Wide Area Measurement System (WAMS) technology, an innovative approach can be pursued. OHL thermal dynamic can be derived from the measurement of the voltage and current synchrophasors at the OHL ends, so overcoming the need of spatially distributed sensors DLR system. It is worth noting anyway that in this case it deals with an average estimate of the OHL conductor temperature and not a punctual one as obtained by using distributed devices. With regard to the described framework, the present paper presents the first experimental findings obtained in an in-field DLR campaign carried out on the Italian power system. A recently proposed DLR estimation algorithm, never applied in reallife WAMS environment, is selected for the purpose. From its response, interesting considerations and valuable modifications to apply to the estimation algorithm mathematical framework are suggested in the work. The fundamental characteristics that the latter needs to have are well posed in evidence. The discussed experimental outcomes are referred to a real-life OHL included into the Italian WAMS monitoring program.

This paper presents a discussion of ''Solution of economic dispatch problems by seeker optimization algorithm'' by Binod Shaw et al. ''Expert Systems with Applications'', 39 January (2012) 508-519. The discussed paper presented economic dispatch problem by experimenting with four example systems considering 20, 38, 40 and 10-unit test systems considering convex and nonconvex cost functions with and without transmission loss. However in the reported results, the total cost calculations are different for the given generation schedule. In this discussion, clarification regarding cost calculations is presented.