Addressing the Scalability and Privacy Issues of Energy Communities

06 Apr 2021
Pierluigi Siano
Video Length / Slide Count:
Time 00:45:48
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 PVbattery systems in the market for the ancillary service (ASM) is proposed in this presentation. To consider both reserve capacity and reserve energy, dayahead 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.