Distributed reactive power feedback control for voltage regulation and loss minimization

We consider the problem of exploiting the microgenerators dispersed in the power distribution network in order to provide distributed reactive power compensation for power losses minimization and voltage regulation. In the proposed strategy, microgenerators are smart agents that can measure their phasorial voltage, share these data with the other agents on a cyber layer, and adjust the amount of reactive power injected into the grid, according to a feedback control law that descends from duality-based methods applied to the optimal reactive power flow problem. Convergence to the configuration of minimum losses and feasible voltages is proved analytically for both a synchronous and an asynchronous version of the algorithm, where agents update their state independently one from the other. Simulations are provided in order to illustrate the performance and the robustness of the algorithm, and the innovative feedback nature of such strategy is discussed

A Distributed Feedback Control Approach to the Optimal Reactive Power Flow Problem

We consider the problem of exploiting the microgenerators connected to the low voltage or medium voltage grid in order to pro-vide distributed reactive power compensation in the power distribution network, solving the optimal reactive power flow problem for the mini-mization of power distribution losses subject to voltage constraints. The proposed strategy requires that all the intelligent agents, located at the generator buses, measure their voltage and share these data with the other agents via a communication infrastructure. The agents then adjust the amount of reactive power injected into the grid according to a policy which is a specialization of duality-based methods for constrained con-vex optimization. Convergence of the algorithm to the configuration of minimum losses and feasible voltages is proved analytically. Simulations are provided in order to demonstrate the algorithm behavior, and the innovative feedback nature of such strategy is discussed

Real-time optimization of energy networks with battery storage systems under uncertain wind power penetration

Es gibt einen starken Trend, erneuerbare Energien in Verteilungsnetze (VNs) der Elektroenergieversorgung einzuspeisen. Jedoch muss wegen technischer Beschränkungen dieser Anteil um eine beträchtliche Menge gekürzt werden. Batteriespeichersysteme (BSSs) können optimal genutzt werden, um die Energie zu speichern, den gekürzten Anteil der zu senken somit den ökonomischen Vorteil zu erhöhen. Allerdings werden durch die BSSs dynamische Terme in das Problem des optimalen Lastflusses (engl.: optimal power flow (OPF)) eingeführt. Weiterhin tritt die Windenergie intermittierend auf, weshalb der Netzbetreiber die Betriebsstrategie schnell entsprechend aktualisieren muss. Diese Aufgabe sollte durch eine Online-Optimierung durchgeführt werden, die auf die Bestimmung einer enormen Anzahl von gemischt-ganzzahligen Entscheidungsvariablen abzielt und auf ein dynamisches Echtzeit Wirk-/Blindleistungs-OPF-Problem (engl.: real-time dynamic active-reactive optimal power flow problem (RT-DAR-OPF problem)) führt. Deshalb ist die Entwicklung eines geeigneten Rahmens für das RT-DAR-OPF-Problem von größter Bedeutung für die Gewährleistung von sowohl Optimalität als auch Umsetzbarkeit in der Betriebsführung von VNs mit BSSs unter intermittierender Windenergieeinspeisung. Das herausforderndste Merkmal dabei ist, dass ein hochdimensionales, dynamisches, gemischt-ganzzahliges nichtlineares Optimierungsproblem (engl.: mixed-integer nonlinear programming problem (MINLP)) in Echtzeit gelöst werden muss. Zusätzlich wird die Problemkomplexität sowohl durch die Betrachtung der Wirk- als auch der Blindleistung des BSSs mit flexiblen Betriebsstrategien genauso wie durch die Minimierung der aufgewendeten Lebensdauerkosten der BSSs erhöht. Um dieses Problem zu lösen, wird ein Mehrphasen- und Mehrfachzeitskalen-RT-DAR-OPF-Rahmen in dieser Dissertation entwickelt, der sich mit der optimalen Behandlung spontaner Änderungen bei der Windenergie in VNs und BSSs beschäftigt. In der ersten Phase werden eine enorme Anzahl an gemischt-ganzzahligen Entscheidungsvariablen simultan optimiert und damit wird die Betriebsstrategie für den kommenden Tag berechnet. Die Variablen der BSSs, die in der ersten Phase berechnet wurden, werden in den anderen Phasen als feste Eingangsparameter verwendet. Zu vermerken ist, dass in der nächsten Phase andere Entscheidungsvariablen erneut berechnet werden. In der zweiten Phase werden basierend auf den vorhergesagten Windenergiewerten für kurze Vorhersagehorizonte die wahrscheinlichsten Windenergieszenarios generiert, um die Unsicherheiten bei der Windenergie mit einer Nicht-Gaußschen Verteilung zu beschreiben. Dann werden die MINLP-AR-OPF-Probleme entsprechend der Szenarios parallel im Vorfeld des Vorhersagehorizonts gelöst und in einer Lookup-Tabelle gespeichert. Ein neuer Abgleichsalgorithmus wird vorgeschlagen, um sowohl die Optimalität als auch die Umsetzbarkeit der Lösungen in der Lookup-Tabelle zu garantieren. In der dritten Phase wird basierend auf den Messungen der aktuellen Werte der Windenergie eine der Lösungen ausgewählt, modifiziert und schließlich am Netz für kurze Zeitintervalle realisiert. Die Demonstration der Anwendbarkeit des vorgeschlagenen RT-DAR-OPF-Ansatzes erfolgt unter Verwendung eines Mittelspannung-VN.There has been a huge trend to penetrate renewable energies into distribution networks (DNs). However, a considerable amount of this generation may need to be curtailed due to technical constraints in the network. Battery storage systems (BSSs) can be optimally used to store the energy, decrease the curtailment and consequently increase economic benefits. However, BSSs introduce dynamic terms to the problem of optimal power flow (OPF). In addition, considering both active and reactive power of the BSSs with flexible operation strategies, as well as maximizing the lifetime of the batteries further increase the complexity of the problem. Furthermore, wind power is intermittent, and therefore the network operator has to fast update the operation strategies correspondingly. This task should be carried out by an online optimization aiming at determining huge number of mixed-integer decision variables leading to a real-time dynamic active-reactive OPF (RT-DAR-OPF) problem. Therefore, developing a suitable framework for RT-DAR-OPF is of utmost importance for ensuring both optimality and feasibility in the operation of DNs with BSSs under intermittent wind energy penetration. The most challenging issue hereby is that a large-scale dynamic mixed-integer nonlinear programming (MINLP) problem has to be solved in real-time. To solve this problem, a multi-phase multi-time-scale RT-DAR-OPF framework is developed in this dissertation to optimally deal with the spontaneous changes of wind power in DNs with BSSs. In the first phase, a huge number of mixed-integer decision variables are simultaneously optimized to compute operation strategies of BSSs on a day-to-day basis. The variables of BSSs computed in the first phase will be used as fixed input parameters for the second phase. Note that in the next phase, other decision variables will be recomputed. In the second phase, based on the forecasted wind power values for short prediction horizons, the most probable wind power scenarios are generated to describe uncertain wind power with a non-Gaussian distribution. Then MINLP active-reactive OPF problems corresponding to the scenarios are solved in parallel in advance of each prediction horizon resulting in a lookup table. A new reconciliation algorithm is proposed to ensure both the feasibility and optimality of the solutions in the lookup table. In the third phase, based on the measured actual values of wind power, one of the solutions is selected, modified and finally realized to the network for very short intervals. The applicability of the proposed RT-DAR-OPF framework is demonstrated using a medium-voltage DN

Modeling, Control and Identification of a Smart Grid

We are in front of an epochal change in the power distribution and generation scenario. The increasing request of energy, the energy dependency of several countries from few foreign nations endowed with oilfield or gas field, and, on the other hand, the climate change and environmental issues are the main explanation of the recent development and spread of renewable distributed energy generation technologies. Examples of them are photovoltaic panels, wind turbines or geothermal, biomass, or hydroelectric. They are called small-size generators, or micro-generator, since the amount of power they can produce is significantly lower than the one produced by the huge, classical power plants. These distributed energy resources (DERs) are located close to where electricity is used, in the distribution network. Furthermore, they are connected to the electrical grid via electronic interfaces, the inverters, that could allow us to control the power injected into the grid. This thesis is focused on the study of some crucial aspects of this new energetic scenario: 1. Modeling: we recall the classical models and a recent linearized one of the power systems, that will be very useful for the design and the analysis of our algorithms. 2. Optimal Reactive Power Flow (OPRF) problem: in this part we recall classical and recent algorithms that deal with the reactive power regulation. In particular, we focus on the ones that solve the OPRF problem, i.e. the problem of the amount of reactive power to be injected by each micro-generators, in order to achieve “optimal” performance. We choose, as an optimality achievement, the minimization of the line losses. Finally we derive and propose our OPRF algorithm, providing formal proves of its convergence to the optimal solution. 3. Optimal Power Flow (OPF) problem: the OPF problem’s aim is to find an operating point of the power system that optimize a cost function (tipically the generation cost) satisfying the power demand and some operative constraints. After recalling the most popular algorithms that solve the OPF problem, we propose two of them. In this framework there are mainly two possible scenarios. The first is related to the “utility point of view”, where the total cost accounts for the production cost of the energy (that comes from big generation plants such as nuclear or hydro-electrical plants) and for the remuneration to be paid to the owners of DERs. In this framework, the utility imposes a behavior procedure to be followed by the producers to compute the amount of energy they have to inject into the grid to minimize the total cost. The first algorithm deal with this scenario. The second one is related to the “producer point of view”. Since the owners of the DERs are paid proportionally to the energy that they inject, they would like to maximize the power they inject, while keeping satisfied some operative constraints. The result is a game among the agents. A first treatment on this scenario is given by the second algorithm. 4. Switches monitoring for topology identification: in this part, after a literature review, we propose a algorithm for the identification of switches actions. They modify the topology of the electrical grid, whose knowledge is fundamental for monitoring, control and estimation. This algorithm works analyzing how the phasorial voltage profile vary and recognize a kind of signature left by the switches status change