Flexibility Aggregation of Temporally Coupled Resources in Real Time Balancing Markets Using Machine Learning

In modern power systems with high penetration of renewable energy sources, the flexibility provided by distributed energy resources is becoming invaluable. Demand aggregators offer balancing energy in the real-time balancing market on behalf of flexible resources. A challenging task is the design of the offering strategy of an aggregator. In particular, it is difficult to capture the flexibility cost of a portfolio of flexibility assets within a price-quantity offer, since the costs and constraints of flexibility resources exhibit inter-temporal dependencies. In this article, we propose a generic method for constructing aggregated balancing energy offers that best represent the portfolio's actual flexibility costs, while accounting for uncertainty in future timeslots. For the case study presented, we use offline simulations to train and compare different machine learning (ML) algorithms that receive the information about the state of the flexible resources and calculate the aggregator's offer. Once trained, the ML algorithms can make fast decisions about the portfolio's balancing energy offer in the real-time balancing market. Our simulations show that the proposed method performs reliably towards capturing the flexibility of the Aggregator's portfolio and minimizing the aggregator's imbalances.</p

Διαχείριση πόρων με επίγνωση πλαισίου για ενοποιημένα κινητά και σταθερά δικτυακά συστήματα

“Context”, as a research notion, has been invented and roughly exploited in many fields of computer science since 1960s and refers to the general idea that computers can sense, react and adapt their functionalities based on the information they acquire from their environment. In mobile and wireless networking research field, context awareness concepts have been widely adopted as means to provide more intelligent functionalities in terms of context information acquisition, exchange and evaluation, while business logic breakthroughs are being proposed, too. Context-aware resource management is a new research field dealing with ways that traditional resource management algorithms in mobile and wireless networking systems can have more intelligent decision making mechanisms by fully exploiting all context information being available in their geographical environment. Additionally, taking into account the fact that the silos between mobile and fixed networking systems are gradually breaking down, decision making mechanisms have to be realized from an overall system perspective (i.e. for converged mobile and fixed network infrastructures), that is jointly take into consideration mobile and fixed networking systems resources availability for efficient resource management procedures. Finally, during the last years, due to the continuous convergence of computing and networking systems, context awareness concepts appear to be a major research “glue-point” of such kind of heterogeneous environments’ integration. Implications of Cloud Computing (CC) paradigm, which are applicable in mobile and wireless networking area are increasingly gaining ground and Mobile Cloud Computing/Networking (MCC/MCN) is an emerging research area introducing itself as the integration of CC into the existing and upcoming 4G HetNet and beyond setups.In this PhD thesis, novel context-aware resource management schemes and algorithms are proposed for: a) 4G heterogeneous wireless network (HetNet) environments, b) mobile and fixed networking systems’ convergence, and c) hybrid/mobile cloud infrastructures, while their performance is evaluated in comparison with related existing state-of-the-art solutions. In a nutshell, this thesis’ main contribution is that it introduces several architectural and algorithmic innovations for context aware resource management in convergent mobile and fixed networking systems towards realizing novel building blocks of the next generation mobile networking continuum.Η έννοια του “γενικότερου/ευρύτερου πλαισίου” (context) στην έρευνα εισήχθη καιαξιοποιήθηκε για πρώτη φορά στο χώρο των επιστημών της μηχανικής Η/Υ τη δεκαετία του1960 και αναφέρεται στη γενική ιδέα ότι οι ηλεκτρονικές συσκευές μπορούν να“αισθάνονται”, να αντιδρούν και να προσαρμόζουν τη λειτουργία τους βάσει τωνπληροφοριών που συλλέγουν από το περιβάλλον τους. Στο πεδίο των κινητών καιασύρματων επικοινωνιών, οι έννοιες που είναι σχετικές με την επίγνωση του πλαισίου(context awareness) έχουν υιοθετηθεί ευρέως για την παροχή εξυπνότερων λειτουργικοτήτωνπου έχουν σχέση με τη συλλογή, την ανταλλαγή και την αξιολόγηση των πληροφοριών, ενώέχουν προταθεί και καινοτόμα επιχειρηματικά μοντέλα και λύσεις που βρίσκουν εφαρμογήστην πραγματική αγορά. Η “διαχείριση πόρων με επίγνωση πλαισίου” είναι ένα νέοερευνητικό πεδίο που ασχολείται με τη βελτίωση των παραδοσιακών αλγορίθμων διαχείρισηςπόρων στα κινητά και ασύρματα δικτυακά συστήματα, έτσι ώστε να μπορούν να παίρνουνεξυπνότερες και αποδοτικότερες αποφάσεις λόγω της πλήρους αξιοποίησης τωνπληροφοριών που βρίσκεται στο περιβάλλον τους (context information). Επιπρόθετα,λαμβάνοντας υπόψιν το γεγονός ότι στις μέρες μας παρατηρείται μία συνεχής ανάγκη γιασύγκλιση των κινητών και σταθερών δικτυακών συστημάτων, οι μηχανισμοί λήψηςαποφάσεων θα πρέπει να λαμβάνουν υπόψιν τους τη νέα αυτή πραγματικότητα για τηναποδοτικότερη διαχείριση δικτυακών πόρων. Τέλος, λόγω του ότι τα τελευταία χρόνιαπαρατηρείται επίσης μία σύγκλιση μεταξύ των δικτυακών και των υπολογιστικών(computing) συστημάτων, επίγνωση του πλαισίου σημαίνει ότι οι νέοι αλγόριθμοιδιαχείρισης πόρων θα πρέπει να λαμβάνουν ταυτόχρονα υπόψιν τους διαθέσιμους δικτυακούςκαι υπολογιστικούς πόρους για τη μεταφορά οποιασδήποτε υπηρεσίας στις κινητέςηλεκτρονικές συσκευές οποιουδήποτε χρήστη. Έτσι, η εισαγωγή εννοιών του υπολογιστικούνέφους (cloud computing) στα υπάρχοντα ετερογενή ασύρματα δικτυακά περιβάλλοντα 4ηςγενιάς αποτελεί μία ιδιαίτερη πρόκληση για την παγκόσμια ερευνητική κοινότητα.Στην παρούσα διδακτορική διατριβή, προτείνονται καινοτόμες αρχιτεκτονικές και καινοτόμοιαλγόριθμοι για: α) ετερογενή ασύρματα δικτυακά περιβάλλοντα 4ης γενιάς, β) ενοποιημένακινητά και σταθερά δικτυκά συστήματα, και γ) υβριδικές και κινητές υποδομέςυπολογιστικού νέφους (hybrid/mobile cloud infrastructures). Οι προτεινόμενεςαρχιτεκτονικές και προτεινόμενοι αλγόριθμοι αξιολογούνται και συγκρίνονται μευπάρχουσες λύσεις από τη διεθνή βιβλιογραφία

Electricity market equilibria analysis on the value of demand-side flexibility portfolios’ mix and the strategic demand aggregators’ market power

Electricity market equilibrium analysis is becoming increasingly important within the today's liberalized electricity market and regulatory context for both market participants and policy makers. The former ones want to make informed business decisions to optimize their market position, while the latter need to promote efficient equilibria that satisfy both supply and demand-side requirements and optimize social welfare. In this paper, we focus on modeling the strategic demand aggregators’ (DA) behavior via an Equilibrium Problem with Equilibrium Constraints (EPEC) formulation. We model typical demand flexibility portfolios that comprise of several types of distributed flexibility assets and renewable energy resources. We then quantify the value of demand flexibility portfolios’ mix and respective value of market power that a strategic DA may exercise with respect to each DA's cost decrease and social welfare as a function of time for a day-ahead market use case. Simulation results show that: (i) there is an optimal equilibrium point where overall DAs’ costs decrease, while social welfare's deteriorates negligibly, (ii) there may be a trade-off between the rate of a certain DA's cost decrease and the rate of market power (or else % share of total demand flexibility) that this DA possesses, so the strategic DA should take this into account in order to find its optimal CAPEX/OPEX balance for its portfolio, (iii) competition among strategic DAs at the demand-side can mitigate grid congestion problems and serve as a counter-balance to the strategic behavior of the supply-side market participants.</p

Designing a Distribution Level Flexibility Market using Mechanism Design and Optimal Power Flow

Modern Smart Grids with high RES penetration at the distribution level, face great challenges related to congestion avoidance and voltage control. The development of a flexibility market that guarantees the constraint satisfaction of the distribution network and truthful (as opposed to strategic) player participation is necessary. This paper proposes an efficient flexibility market architecture that facilitates flexibility service provision in a distribution network. It leverages an optimal and incentive compatible mechanism in order to achieve efficiency (energy services with lower cost) and truthful participation

Network and Market-Aware Bidding to Maximize Local RES Usage and Minimize Cost in Energy Islands with Weak Grid Connections

The increasing renewable energy sources RES penetration in today&rsquo;s energy islands and rural energy communities with weak grid connections is expected to incur severe distribution network stability problems (i.e., congestion, voltage issues). Tackling these problems is even more challenging since RES spillage minimization and energy cost minimization for the local energy community are set as major pre-requisites. In this paper, we consider a Microgrid Operator (MGO) that: (i) gradually decides the optimal mix of its RES and flexibility assets&rsquo; (FlexAsset) sizing, siting and operation, (ii) respects the physical distribution network constraints in high RES penetration contexts, and (iii) is able to bid strategically in the existing day-ahead energy market. We model this problem as a Stackelberg game, expressed as a Mathematical Problem with Equilibrium Constraints (MPEC), which is finally transformed into a tractable Mixed Integer Linear Program (MILP). The performance evaluation results show that the MGO can lower its costs when bidding strategically, while the coordinated planning and scheduling of its FlexAssets result in higher RES utilization, as well as distribution network aware and cost-effective RES and FlexAsset operation

Sizing of electric vehicle charging stations with smart charging capabilities and quality of service requirements

The increasing penetration of electric vehicles (EVs) inherently couples the transportation system with the electricity system through charging stations (CSs). Today's regulatory context highly incentivizes CS infrastructure investments that are expected to have a significant impact on reducing air pollution, cutting emissions and promoting environmentally sustainable cities. The Sizing problem of a CS typically concerns the minimization of the investment cost for charging facilities, subject to the CS being able to fulfill a certain level of charging requests. Several studies have shown the potential of Smart Charging technologies, towards controlling the charging profiles of EVs, so as to achieve a lower operational cost or a lower peak to average power consumption ratio for the CS, by shifting the charging of some EVs. By making more efficient use of charging facilities, Smart Charging can also help reducing the amount of chargers required in order to achieve a certain Quality of Service (QoS) for the CS's clients. In this paper we solve the CS's sizing problem (i.e. decisions on number and types of installed chargers) through an optimization framework that minimizes the investment cost of CS operators, subject to achieving a certain QoS for their clients (EV owners). In particular, we extend the existing CS sizing models by taking into account also the smart charging capabilities during operation. We present a novel formulation for the QoS level of the CS using chance-constraints and propose some relaxations that constitute the problem solvable. Finally, we present a methodology that enhances the scalability of the optimal sizing algorithm. The proposed methodology is able to offer valuable services to CS operators in competitive environments

Demand Response as a Service: Clearing Multiple Distribution-Level Markets

The uncertain and non-dispatchable nature of renewable energy sources renders Demand Response (DR) a critical component of modern electricity distribution systems. Demand Response (DR) service provision takes place via aggregators and special distribution-level markets (e.g., flexibility markets), where small, distributed DR resources, such as building energy management systems, electric vehicle charging stations, micro-generation and storage, connected to the low-voltage distribution grid, offer DR services. In such systems, energy balancing (and thus, also DR decisions) have to be made close to real-time. Thus, market clearing algorithms for DR service provision must fulfill several requirements related to the efficiency of their operation. More specifically, a DR market clearing algorithm needs to be optimal in terms of cost-efficiency, scalable in terms of number of assets and locations, and able to satisfy real-time constraints. In order to cope with these challenges, this paper presents a distributed DR market clearing algorithm based on Lagrangian decomposition, combined with an optimal cloud resource allocation algorithm for assigning the required computation power. A heuristic algorithm is also presented, able to achieve a near-optimal solution, within negligible computational time. Simulations, performed on a testbed, demonstrate the computational burden introduced by various DR models, as well as the heuristic algorithm&#x0027;s near-optimal performance. The resource allocation algorithm is able to service multiple DR requests (e.g. in multiple distribution networks), and minimize the cost of computational resources while respecting the execution time constraints of each request. This enables third parties to offer cost-efficient and competitive DR operation as a service