An Extreme-Scale Distributed Parallel Optimization for Addressing the Scalability and Privacy Issues of Energy Communities
Modern power systems are evolving from a centralized paradigm, according to which electrical energy was mainly generated by large power plants at the transmission level, to a new model where Distributed Generation (DG), often based on Renewable Energy Sources (RES) represents a relevant portion of the produced electrical energy. In this new model, the provision of ancillary services to the Transmission System Operator (TSO) should take into account the possible flexibility furnished by new distributed resources, such as dispersed and small generators, also based on RES, and frequently endowed with small batteries. In particular, distributed Battery Energy Storage Systems (BESSs), also of small scale, that were mainly used to decrease the uncertainty due to RES and to increase the energy self-consumption for the end-user, can be also managed to provide energy flexibility to the TSO. A novel scalable and privacy-preserving distributed parallel optimization that allows the participation of large-scale aggregation of prosumers with residential PV-battery systems in the market for the ancillary service (ASM) is proposed in this presentation. To consider both reserve capacity and reserve energy, day-ahead and real-time stages in the ASM are considered. A method, based on hybrid Variable Neighbourhood Search (VNS) and distributed parallel optimization is designed for the day ahead and real-time optimization. Different distributed optimization methods are compared and designed and a new distributed optimization method based on Linear Programming (LP) is designed that overcomes previous methods based on integer and Quadratic programming (QP). The proposed LP-based optimization can be easily coded up and implemented on microcontrollers and connected to a designed Internet of Things (IoT) based architecture. As confirmed by simulation results, carried out considering different realistic case studies, both day-ahead and real-time proposed optimization methods, by allocating the computational effort among local resources, are highly scalable and fulfil the privacy of prosumers.
Pierluigi Siano (M’09–SM’14) received the M.Sc. degree in electronic engineering and the Ph.D. degree in information and electrical engineering from the University of Salerno, Salerno, Italy, in 2001 and 2006, respectively. He is a Professor and Scientific Director of the Smart Grids and Smart Cities Laboratory with the Department of Management & Innovation Systems, University of Salerno. His research activities are centered on demand response, on energy management, on the integration of distributed energy resources in smart grids, on electricity markets and on planning and management of power systems. In these research fields he has co-authored more than 550 articles including more than 300 international journal papers that received in Scopus more than 10100 citations with an H-index equal to 49. In 2019 and 2020 he received the award as Highly cited Researcher by ISI Web of Science Group. He has been the Chair of the IES TC on Smart Grids. He is Editor for the Power & Energy Society Section of IEEE Access, IEEE TRANSACTIONS ON POWER SYSTEMS, IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, IEEE Systems, Open Journal of the IEEE IES, IET Smart Grid and IET Renewable Power Generation.
High Reporting Rate Measurements for Smart[er] Grids
Modern control algorithms in the emerging power systems process information delivered mainly by distributed, synchronized measurement systems, and available in data streams with different reporting rates. Multiple measurement approaches are used: on one side, the existing time-aggregation of measurements are offered by currently deployed IEDs (SCADA framework), including smart meters and other emerging units; on the other side, the high-resolution waveform-based monitoring devices like phasor measurement units (PMUs) use high reporting rates (50 frames per second or higher) and can include fault-recorder functionality. There are several applications where synchronized data received with high reporting rate has to be used together with aggregated data from measurement equipment having a lower reporting rate (complying with power quality data aggregation standards) and the accompanying question is how adequate are the energy transfer models in such cases. For example, state estimators need both types of measurements: the so-called “classical” one, adapted for a de facto steady-state paradigm of relevant quantities and the “modern” one, i.e. with fewer embedded assumptions on the variability of same quantities. Another example is given by emerging active distribution grids operation, which assumes higher variability of the energy transfer and consequently a new model approximation for its characteristic quantities (voltages, currents) is needed. Such a model is required not only in order to be able to correctly design future measurement systems but also for better assessing the quality of existing “classical” measurements, still in use for power quality improvement, voltage control, frequency control, network parameters’ estimation etc. The main constraint so far is put by the existing standards where several aggregation algorithms are recommended, with specific focus on the information compression. The further processing of rms values (already the output of a filtering algorithm) results in significant signal distortion. Presently there is a gap between (i) the level of approximation used for modeling the current and voltage waveforms which is implicitly assumed by most of the measurement devices deployed in power systems and (ii) the capabilities and functionalities exhibited by the high fidelity, high accuracy and high number of potential reporting rates of the newly deployed synchronized measurement units.
The talk will address:
The measurement paradigm in power systems. – System inertia, real time and steady-state. – Instrument transformers; limited knowledge on the infrastructure. – PQ, SCADA and PMUs. – Power system state estimation; WAMCS. – IEDs, PMUs, microPMUs. – Time-stamped versus synchronized measurements.
Measurement channel quality and models for energy transfer. – Voltage and frequency variability; rate of change of frequency. – The steady-state signal and rapid voltage changes (RVC); rms-values reported with 100 frames/s. – Measurement data aggregation; filtering properties. – Time-aggregation algorithms in the PQ framework. – Statistical approaches.
Applications and challenges. – Communication channel requirements; delay assessment in WAMCS. – Smart metering with high reporting rate (1s).
The presentation provides an overview of these techniques, with examples from worldwide measurement solutions for smart grids deployment.
Mihaela M. Albu (M’96, SM’07) is from Craiova, Romania. She graduated from “Politehnica” University of Bucharest (UPB) in 1987 and holds the Ph.D. degree (1998) from the same university. Since 2002 she is a Professor of Electrical Engineering at UPB. Teaching activity presently counts: Advanced Topics in Instrumentation and Measurement (Master); Smart Distribution Grids (Master); Signal Processing (Bachelor) at the Dept. of El. Engineering of UPB; and “Elektrische Meßtechnik”; “Sensoren” at the German Department of UPB.
Her research interests include wide area measurement systems as well as synchronized measurements and evaluation of the associated uncertainty considered in the state estimation algorithms; smart energy grids including optimal use of renewables and real time control; smart metering technologies; DC grids as an innovative solution for future intelligent grids, for which a demonstration platform was realized in a fully equipped laboratory fed from a DC bus at 230 V rated voltage and a proposal of power quality assessment in DC grids; power quality and signal processing for power quality assessment, nonlinear phenomena in power systems; distributed and computer-controlled measurement systems, IEEE and IEC standards in power, power system protection, virtual and Internet-based laboratories. She is the founder of an interdisciplinary group MicroDERLab at UPB, and coordinates several research teams working on projects funded by national and international grants. Dr. Albu was spending a leave at Arizona State University as a Fulbright Fellow 2002 – 2003 and in 2010. Her work includes coordination of a monograph (on measurements in power systems ), 7 Book chapters; 21 Journal Publications; more than 70 papers published in International Conference Proceedings; 40 presentations, invited papers and other non-refereed publications; 13 Laboratory notes, and more than 50 Technical Reports (recently on smart grids topics).
Since 2009 she is Vice-Chair of the Intellicis -Working Group 2: Reliable management and control of electric power systems, 2009-2013. The professional service is highlighted by active membership in IEEE – IMS, CIGRE, VDE, and IRE (Romanian Power Engineers Society). As an IEEE member, she volunteers for the Instrumentation and Measurement Society since 2009, while she became part of the AdCom. She is also involved in the IEEE local activities –as a vice-chair of the PES-Romania Chapter and contributes to the dialogue between the Romanian electrical engineering community and the IEEE.