Resumen de tesis. Implementation of Demand Response Programs in Intelligent Energy Management Systems Based on Distributed Control System

Abstract

[EN] Over the last decades, the hierarchical and centrally controlled approach of existing power distribution is moving toward a smart power grid paradigm. Nowadays, consumers are becoming part of the solution in the power system operation problem, where the role of aggregator and demand response are being lebalized in several cuntries. Therefore, technical features and economic, aspects of the consumer's participation in demand response programs, namely through an aggregator, require intensive modeling and validation.

The main contribution of this tesis is modeling of an aggregator that is responsible for demand response programs and respective events implementation and validation by simulating, emulating and performing actual control of devices. The proposed approach also considers both consumer participation in demand response events and the individual appliances used to obtain the required demand reduction.

In the scope of the main contribution, the DEEPDISEM platform, designed and developed in this thesis, provides support to the demand response implementation in the context of intelligent energy management. DEEPDISEM integrates realistic network models using real-time simulation, hardware-in-the-loop, several loads and distributed generation emulators, and real devices. The diversity of capabilities and features of DEEPDISEM make it a powerful tool to assay the demand response models by providing actual load management in the end-users. To run the realistic simulation, OP5600 is used as the real-time simulator machine to control laboratory emulators from the simulation environment and obtain realistic results. Also, DEEPDISEM utilizes several distributed programmable logic controllers and songle-board computers for descentrolized management, running linear programming, and intelligent approaches like decision trees and rule-based decisions.

Besides this, several key contributions are gathered together to accomplish and support the core contribution. These key contributions are classified in two main categories: a) power and energy system, and b) computer science. The key contributions related to the power and energy systems include demand response programs definition, resource aggregation, demand response gathering, distributed generation and demand response scheduling, renewables integration, local markets and communities, and irrigation management. The key contributions related to computer science consist of distributed control and intelligent applications. All these key contributions in both cotegories are validated through Supervisory Control And Data Acquisition systems, real-time simulation, laboratory emulation, and case studies.

The presented approach in this tesis is supported through various developed methods, aiming at practical features of demand response implementation and validation through a diversity of case studies, both simulated and comprising actual physical equipment. Various models, decision-making methods, and applications, from an insolated farm to a largue aggregator (i.e., 220 consumers and 86 producers) with several types of end-users, have been tested using the DEEPDISEM platform. The results of DEEPDISEM show significant energy savings and cost reductions for both the aggregator and the end-user. Also, the results demonstrate the actual impact of demand response implementation through actuation in the actual devices. Thus, the feasibility of field implmentation and widespread of innovate demand response models, which used to be mostly done by simulation models, disregarding the actual impact in the physical devices, was archived.