Addressing the Scalability and Privacy Issues of Energy Communities
Presented By: Pierluigi Siano
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 Neighborhood 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 fulfill the privacy of prosumers.
Who Should Attend?
- Researchers, Academicians, and Professionals
About the Speaker
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 focused on demand response, energy management, the integration of distributed energy resources in smart grids, electricity markets, and 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, and Open Journal of the IEEE IES, IET Smart Grid, and IET Renewable Power Generation.
Tags & Topics for This Webinar:
- Aggregator; PV-battery systems; battery energy storage systems; up and down-regulation; ancillary services market; linear programming; quadratic programming; distributed optimization; alternating direction method of multipliers (ADMM)