Abstract

The electrical power system is evolving in a way that requires new measures for ensuring its secure and reliable operation. Demand-side aggregation represents one of the more interesting ways to provide ancillary services by the coordinated management of a multitude of different distributed resources. In this framework, aggregators play the main role in ensuring the effectiveness of the coordinated action of the distributed resources, usually becoming mediators in the relation between distribution system operators and smart prosumers. The research project DEMAND recently introduced a new concept in demand-side aggregation by proposing a scheme without a central aggregator where prosumers can share and combine their flexibility with a collaboration–competition mechanism in a platform called Virtual Aggregation Environment (VAE). This paper, after a brief introduction to the DEMAND project, presents the algorithm for the day-ahead estimation of prosumers’ flexibility and the cooperative–competitive algorithm for the bottom-up aggregation. The first algorithm evaluates various couples of power variation and desired remuneration to be sent to the VAE for further elaborations and, for showing its potentiality, is applied to two different case studies: a passive user with only controllable loads and prosumers with controllable loads, photovoltaics and a storage system. The aggregation algorithm is instead discussed in detail, and its performance is evaluated for different population sizes.

Highlights

  • The power system is experiencing a period of major innovations, most of which are caused by the widespread of both grid-scale and behind-the-meter unpredictable renewable energy sources (RES) and battery energy storage systems (BESS)

  • Alongside utility-scale BESS and other centralized solutions, demand response (DR) and the aggregation of distributed resources is a very current research topic due to the need for power systems for solving the several issues caused by the unpredictability of RES and the increasing power peaks on lines and transformers

  • The absence of a third-party aggregator allows simplifying the coordination issues and reducing the overall cost of the aggregation platform, allowing distribution system operator (DSO), TransmissionSystem Operator (TSO) and market operators to directly contact the end-users participating in the DEMAND project for exploiting their flexibility

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Summary

Introduction

The power system is experiencing a period of major innovations, most of which are caused by the widespread of both grid-scale and behind-the-meter unpredictable renewable energy sources (RES) and battery energy storage systems (BESS). In [20], a P2P aggregation platform was proposed for purchasing energy in the liberalized energy market, where prosumers are evaluated and clustered according to their reliability and to the width of the range of regulation allowed In this context, the Italian research project DEMAND proposed an innovative way for managing the aggregation of prosumers connected to the distribution network without the need of a third-party aggregator and allowing the participation of all kinds of prosumers (industrial, commercial and residential prosumers) [21,22]. While a detailed description of the EMS operation has already been presented in previous papers by the same research group [23,24], where the potentiality of DEMAND and its main features are described in detail, the present work has the aim to describe in greater detail the operative steps defined for the day-ahead management phase and the formulation of the flexibility economic offer from the prosumer. The rest of the paper is structured as follows: Section 2 contains the description of the algorithm for the day-ahead management phase; Section 3 reports the cooperative–competitive algorithm for the bottom-up aggregation in the VAE; Section 4 contains the discussion of two case studies, showing the application of the day-ahead management phase algorithm; Section 5 reports an assessment of the performance of the aggregation algorithm; Section 6 contains the conclusion of the work

Day-Ahead Management Phase Algorithm
Bottom-Up
Case Studies
Dissatisfaction
Performance of the Bottom-Up Aggregation Process
15. Deployment
Conclusions
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