Smart Grids are increasing their generation capacity by means of adding Renewable Energy Resources (RES) to their supplies, mainly thanks to their negligible carbon footprints and high economic benefits. Unfortunately, fluctuations in solar radiation, wind speed or direction among others, can produce unbalances between supply and demand in the grid. To overcome the RES uncertainty and therefore the unbalances various Demand Response (DR) programs have been developed. Moreover, flexible load management carried out by aggregators has been proposed to provide ancillary services in DR plans. In this thesis a methodology to develop a complete aggregation system in a smart grid scheme is shown, proposing flexible load models and direct load control structures able to operate in DR plans and to provide ancillary services to the System Operator (SO). The methodology is developed considering a framework for loads aggregation and it is assessed on two flexible loads: Water Booster Pressure Systems (WBPSs) and Thermoelectric Refrigeration Units (TERs). For both loads a dynamical model is presented, simulated and a cluster of a large population is studied to characterize its flexibility capacities. Then, a power controller is synthetized using Virtual Reference Feedback Tuning (VRFT) algorithm, which is a data-driven method to regulate the electrical power consumption of the cluster and the practical implementation of the aggregation system is presented. Finally, a Central Allocator (ACTR) strategy is proposed with a hierarchical structure to synchronize the low-level aggregation systems. In order to provide ancillary services to the SO and to estimate in real time the flexibility capacity serving as a feedback signal to the SO, improving the operation of the overall scheme. As result, the complete aggregation system has been validated by simulations fulfilling the ancillary services’ response time and the power capacity variation requested by the SO.
Le Smart Grids stanno aumentando la loro capacità di generazione energetica aggiungendo Fonti di Energia Rinnovabile (RES) alle loro forniture, grazie alla ridotta emissione di CO2 e agli elevati vantaggi economici. Sfortunatamente, le fluttuazioni della radiazione solare, della velocità o della direzione del vento, tra le altre, possono produrre squilibri tra domanda e offerta nella rete. Per superare l'incertezza dagli RES e gli squilibri, sono stati proposti vari programmi di Demand Response (DR). Inoltre, è stata proposta una gestione flessibile del carico svolta dagli aggregatori per fornire servizi ausiliari nei piani DR. In questa tesi viene mostrata una metodologia di sviluppo di un sistema di aggregazione completo nel contesto delle smart grids, proponendo modelli di carico flessibili e strutture di controllo diretto del carico in grado di operare nei piani di DR e fornire servizi ausiliari all’Operatore di Sistema (SO). Questa metodologia è valutata su due carichi flessibili: Water Booster Pressure Systems (WBPSs) e unità di refrigerazione termoelettrica (TERs). Per entrambi i carichi viene presentato e simulato un modello dinamico e studiato un cluster di un'ampia popolazione per caratterizzarne le capacità di flessibilità. Quindi, un controllore di potenza viene sintetizzato utilizzando l'algoritmo VRFT (Virtual Reference Feedback Tuning), che è un metodo data-driven, per regolare il consumo di energia elettrica del cluster e viene presentata l'implementazione pratica del sistema di aggregazione. Infine, viene proposta una strategia di Central Allocator (ACTR) con una struttura gerarchica per sincronizzare i sistemi di aggregazione di basso livello. Questo per fornire servizi ausiliari al SO e stimare in tempo reale la loro capacità di flessibilità in modo da generare un feedback al SO, migliorando il funzionamento del sistema complessivo. Come risultato, il sistema di aggregazione completo è stato convalidato da simulazioni che soddisfano il tempo di risposta dei servizi ausiliari e la variazione della capacità di potenza richiesta dal SO.
Methodology to develop a complete aggregation system in a smart grid framework
Quintero Plata, Javier Mario
2019/2020
Abstract
Smart Grids are increasing their generation capacity by means of adding Renewable Energy Resources (RES) to their supplies, mainly thanks to their negligible carbon footprints and high economic benefits. Unfortunately, fluctuations in solar radiation, wind speed or direction among others, can produce unbalances between supply and demand in the grid. To overcome the RES uncertainty and therefore the unbalances various Demand Response (DR) programs have been developed. Moreover, flexible load management carried out by aggregators has been proposed to provide ancillary services in DR plans. In this thesis a methodology to develop a complete aggregation system in a smart grid scheme is shown, proposing flexible load models and direct load control structures able to operate in DR plans and to provide ancillary services to the System Operator (SO). The methodology is developed considering a framework for loads aggregation and it is assessed on two flexible loads: Water Booster Pressure Systems (WBPSs) and Thermoelectric Refrigeration Units (TERs). For both loads a dynamical model is presented, simulated and a cluster of a large population is studied to characterize its flexibility capacities. Then, a power controller is synthetized using Virtual Reference Feedback Tuning (VRFT) algorithm, which is a data-driven method to regulate the electrical power consumption of the cluster and the practical implementation of the aggregation system is presented. Finally, a Central Allocator (ACTR) strategy is proposed with a hierarchical structure to synchronize the low-level aggregation systems. In order to provide ancillary services to the SO and to estimate in real time the flexibility capacity serving as a feedback signal to the SO, improving the operation of the overall scheme. As result, the complete aggregation system has been validated by simulations fulfilling the ancillary services’ response time and the power capacity variation requested by the SO.| File | Dimensione | Formato | |
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https://hdl.handle.net/10589/166940