Flexible Optimal Operations of Energy Supply Networks with Renewable Energy Generation and Battery Storage

Bedingt durch Umweltbelange und steigende Kosten für fossile Brennstoffe werden immer mehr Wind- und Solaranlagen (distributed generation, DG) in Verteilernetzen (distribution networks, DNs) installiert. Es ist eine bekannte Tatsache, dass die Einspeisung durch erneuerbare Energieträger starken Schwankungen unterliegt. Ein möglicher Lösungsansatz zur Behandlung dieser Schwankungen ist die Nutzung von Energiespeichersystemen wie z. B. Batteriespeichersysteme (BSS). Der Einsatz solcher Systeme verwandelt traditionelle passive Verteilernetze (PDNs) in aktive Verteilernetze (ADNs). Folglich ist es wichtig, die Auswirkungen und Vorteile der Integration solcher Einheiten in konventionelle Verteilernetze zu untersuchen.In dieser Dissertation wird eine systematische Untersuchung (bestehend aus Modellierung, Simulation und Optimierung) des dynamischen Betriebs von Energieversorgungsnetzen mit eingebetteten erneuerbaren Energieträgern und Speichersystemen vorgenommen. Basierend auf komplexen Lastflussmodellen werden verschiedene Optimierungsprobleme mathematisch formuliert und gelöst.In dieser Arbeit werden neue mathematische Modelle und eine neue Problemformulierung für den kombinierten optimalen Lastfluss von Wirk- und Blindleistung (active-reactive optimal power flow, A-R-OPF) in PDNs (ohne DG-Anlagen und BSSs) und ADNs (mit DG-Anlagen und BSSs) vorgestellt. Typischerweise enthalten DNs zwei Spannungsebenen, nämlich Nieder- und Mittelspannung. Deshalb werden Untersuchungen in den beiden Spannungsebenen getrennt durchgeführt. Modellierungsverfahren für PDNs, ADNs und Energiepreise werden vorgestellt. Diese Verfahren dienen als Grundlage der vorliegenden Arbeit. Darauf aufbauend werden Simulationsstudien in PDNs zur Analyse der Betriebseigenschaften durchgeführt. Insbesondere wird die Auswirkung des Betriebs der Laststufenschalter der Haupttransformatoren hervorgehoben. Darüber hinaus wird ein Optimierungsverfahren zur Minimierung der gesamten Energieverluste in PDNs vorgestellt.In ADNs werden zwei Spannungsebenen mit jeweils zugehörigen realen Fallstudien getrennt betrachtet. Auf der Niederspannungsebene wird eine hohe Einspeisungsrate von PV-Anlagen (photovoltaic systems, PVSs) angenommen, um die Auswirkungen eines solchen Szenarios zu zeigen. Insbesondere wird die Fähigkeit der Inverter dieser PV-Anlagen zur Erzeugung von Blindleistung untersucht. Die Gesamteinnahmen aus den installierten PV-Anlagen werden maximiert, während gleichzeitig die Gesamtkosten der Energieverluste und die Nachfrage minimiert werden. Durch die Verwendung unterschiedlicher Preismodelle können viele interessante Ergebnisse generiert werden, z. B. besteht keine Notwendigkeit, BSSs in Niederspannungsnetzen für die Aufnahme überschüssiger PV-Energie zu installieren. Auf der Mittelspannungsebene wird ein DN mit einer hohen Einspeisungsrate von Windenergie und BSSs betrachtet. In diesem Fall werden die Gesamteinnahmen der Windparks und BSSs maximiert, während die Gesamtkosten der Energieverluste minimiert werden. Es zeigt sich, dass eine enorme Reduktion der Energieverluste und der Blindleistungsimporte erreicht werden kann. Um die Lebensdauer der BSSs zu verlängern wird nur ein fester Lade-/ Entlade-Zyklus pro Tag betrachtet. Diese Lösung liefert eine optimale Betriebsstrategie, welche die Zulässigkeit gewährleistet und den Profit signifikant erhöht. Aufgrund der Tatsache, dass die Profile der erneuerbaren Energien, der Nachfrage und der Preise von Tag zu Tag variieren, ist ein feststehender Betrieb der BSSs allerdings nicht optimal.Weiterhin wird ein flexibles Batterie-Management-System zur Behandlung solcher Schwankungen vorgestellt. Dies wird durch die Optimierung der Lade- und Entladezeiten der BSSs für jeden Tag erreicht. Daraus resultiert ein komplexes gemischt-ganzzahliges nichtlineares Optimierungsproblem (mixed-integer nonlinear program, MINLP). Für dessen Lösung wird ein iteratives zweistufiges Verfahren eingeführt. In der oberen Stufe werden die ganzzahligen Variablen (d. h. Lade- und Entladezeiten) optimiert und an die untere Stufe weitergegeben. In der unteren Stufe wird das A-R-OPF Problem mit einem NLP-Löser gelöst und der resultierende Wert der Zielfunktion wird an die obere Stufe für die nächste Iteration weitergegeben. Dieses Verfahren konvergiert, wenn eine Anzahl von Iterationen erreicht ist. Die Verwendung dieses flexiblen Ansatzes resultiert in bedeutend höheren Profiten.Due to environmental and fuel cost concerns more and more wind- and solar-based distributed generation (DG) units are embedded in distribution networks (DNs). It is, however, a well-known fact that renewable energy generators are highly fluctuating sources, and therefore, energy storage systems such as battery storage systems (BSSs) are considered as a solution to handle such fluctuations. In general, DG units and/or BSSs convert traditional passive DNs (PDNs) into active DNs (ADNs). Consequently, it is important to investigate the impact and benefits of integrating such entities in conventional DNs.This dissertation presents a systematic study consisting of modeling, simulation, and optimization of dynamic operations of energy supply networks with embedded renewable generation and storage. Based on complex power flow models, different optimization problems are mathematically formulated and solved. In this work, novel mathematical models and a new combined problem formulation for active-reactive optimal power flow (A-R-OPF) in PDNs (without DG units and BSSs) and ADNs (with DG units and BSSs) are studied. Typically, DNs consist of two different networks in terms of voltage levels, namely, low-voltage and medium-voltage DNs. For this reason, investigations are carried out separately on both networks. Modeling procedures for PDNs, ADNs, and energy prices are presented. These procedures serve as the basis for this work. Then, simulation studies in PDNs are made to analyze its operating characteristics. In particular, the operation of on-load-tap-changers of main transformers is highlighted. Moreover, an optimization framework is introduced to minimize the total energy losses in PDNs.In ADNs, two voltage levels with two real case studies are separately considered. On the low-voltage level, a high penetration level of photovoltaic (PV) systems (PVSs) is considered in the network in order to reveal the impact of such a scenario. In particular, the reactive power capability of the inverters of these PVSs is explored. The total revenue from the installed PVSs is maximized whilst the total cost of energy losses and demand is minimized. Using different price models many interesting results are found, e.g., no need to use BSSs in low-voltage DNs for accommodating expected spilled PV energy. On the medium-voltage level, a DN with a high penetration of wind energy and BSSs is considered. In this case, the total revenue from wind parks and BSSs is maximized and the total cost of energy losses is minimized. It is found that a huge reduction in energy losses and reactive energy imports can be achieved. To prolong the life of BSSs only one fixed charge/discharge cycle every day is considered. The solution provides an optimal operation strategy which ensures the feasibility and enhances the revenue significantly. However, due to the fact that the profiles of renewable energy generation, demand and prices vary from day to day a fixed operation of BSSs cannot be optimal.A flexible battery management system is proposed to adapt to such variations. This is accomplished by optimizing the lengths (hours) of charge and discharge periods of BSSs for each day, leading to a complex mixed-integer nonlinear program (MINLP). An iterative two-stage framework is proposed to address this problem. In the upper stage, the integer variables (i.e., hours of charge and discharge periods) are optimized and delivered to the lower stage. In the lower stage the A-R-OPF problem is solved by a NLP solver and the resulting objective function value is brought to the upper stage for the next iteration. This procedure will converge when number of iterations is reached. Using this flexible system a considerably higher revenue can be achieved

On variable reverse power flow-part II: an electricity market model considering wind station size and location

This is the second part of a companion paper on variable reverse power flow (VRPF) in active distribution networks (ADNs) with wind stations (WSs). Here, we propose an electricity market model considering agreements between the operator of a medium-voltage active distribution network (MV-ADN) and the operator of a high-voltage transmission network (HV-TN) under different scenarios. The proposed model takes, simultaneously, active and reactive energy prices into consideration. The results from applying this model on a real MV-ADN reveal many interesting facts. For instance, we demonstrate that the reactive power capability of WSs will be never utilized during days with zero wind power and varying limits on power factors (PFs). In contrast, more than 10% of the costs of active energy losses, 15% of the costs of reactive energy losses, and 100% of the costs of reactive energy imported from the HV-TN, respectively, can be reduced if WSs are operated as capacitor banks with no limits on PFs. It is also found that allocating WSs near possible exporting points at the HV-TN can significantly reduce wind power curtailments if the operator of the HV-TN accepts unlimited amount of reverse energy from the MV-ADN. Furthermore, the relationships between the size of WSs, VRPF and demand level are also uncovered based on active-reactive optimal power flow (A-R-OPF)

On variable reverse power flow-part I: active-reactive optimal power flow with reactive power of wind stations

It has recently been shown that using battery storage systems (BSSs) to provide reactive power provision in a medium-voltage (MV) active distribution network (ADN) with embedded wind stations (WSs) can lead to a huge amount of reverse power to an upstream transmission network (TN). However, unity power factors (PFs) of WSs were assumed in those studies to analyze the potential of BSSs. Therefore, in this paper (Part-I), we aim to further explore the pure reactive power potential of WSs (i.e., without BSSs) by investigating the issue of variable reverse power flow under different limits on PFs in an electricity market model. The main contributions of this work are summarized as follows: (1) Introducing the reactive power capability of WSs in the optimization model of the active-reactive optimal power flow (A-R-OPF) and highlighting the benefits/impacts under different limits on PFs. (2) Investigating the impacts of different agreements for variable reverse power flow on the operation of an ADN under different demand scenarios. (3) Derivation of the function of reactive energy losses in the grid with an equivalent-π circuit and comparing its value with active energy losses. (4) Balancing the energy curtailment of wind generation, active-reactive energy losses in the grid and active-reactive energy import-export by a meter-based method. In Part-II, the potential of the developed model is studied through analyzing an electricity market model and a 41-bus network with different locations of WSs

Real-Time Active-Reactive Optimal Power Flow with Flexible Operation of Battery Storage Systems

In this paper, a multi-phase multi-time-scale real-time dynamic active-reactive optimal power flow (RT-DAR-OPF) framework is developed to optimally deal with spontaneous changes in wind power in distribution networks (DNs) with battery storage systems (BSSs). The most challenging issue hereby is that a large-scale ‘dynamic’ (i.e., with differential/difference equations rather than only algebraic equations) mixed-integer nonlinear programming (MINLP) problem has to be solved in real time. Moreover, considering the active-reactive power capabilities of BSSs with flexible operation strategies, as well as minimizing the expended life costs of BSSs further increases the complexity of the problem. To solve this problem, in the first phase, we implement simultaneous optimization of a huge number of mixed-integer decision variables to compute optimal operations of BSSs on a day-to-day basis. In the second phase, based on the forecasted wind power values for short prediction horizons, wind power scenarios are generated to describe uncertain wind power with non-Gaussian distribution. Then, MINLP AR-OPF problems corresponding to the scenarios are solved and reconciled in advance of each prediction horizon. In the third phase, based on the measured actual values of wind power, one of the solutions is selected, modified, and realized to the network for very short intervals. The applicability of the proposed RT-DAR-OPF is demonstrated using a medium-voltage DN

Chance Constrained Optimal Power Flow Using the Inner-Outer Approximation Approach

In recent years, there has been a huge trend to penetrate renewable energy sources into energy networks. However, these sources introduce uncertain power generation depending on environmental conditions. Therefore, finding 'optimal' and 'feasible' operation strategies is still a big challenge for network operators and thus, an appropriate optimization approach is of utmost importance. In this paper, we formulate the optimal power flow (OPF) with uncertainties as a chance constrained optimization problem. Since uncertainties in the network are usually 'non-Gaussian' distributed random variables, the chance constraints cannot be directly converted to deterministic constraints. Therefore, in this paper we use the recently-developed approach of inner-outer approximation to approximately solve the chance constrained OPF. The effectiveness of the approach is shown using DC OPF incorporating uncertain non-Gaussian distributed wind power

A framework for real-time optimal power flow under wind energy penetration

Developing a suitable framework for real-time optimal power flow (RT-OPF) is of utmost importance for ensuring both optimality and feasibility in the operation of energy distribution networks (DNs) under intermittent wind energy penetration. The most challenging issue thereby is that a large-scale complex optimization problem has to be solved in real-time. Online simultaneous optimization of the wind power curtailments of wind stations and the discrete reference values of the slack bus voltage which leads to a mixed-integer nonlinear programming (MINLP) problem, in addition to considering variable reverse power flow, make the optimization problem even much more complicated. To address these difficulties, a two-phase solution approach to RT-OPF is proposed in this paper. In the prediction phase, a number of MINLP OPF problems corresponding to the most probable scenarios of the wind energy penetration in the prediction horizon, by taking its forecasted value and stochastic distribution into account, are solved in parallel. The solution provides a lookup table for optional control strategies for the current prediction horizon which is further divided into a certain number of short time intervals. In the realization phase, one of the control strategies is selected from the lookup table based on the actual wind power and realized to the grid in the current time interval, which will proceed from one interval to the next, till the end of the current prediction horizon. Then, the prediction phase for the next prediction horizon will be activated. A 41-bus medium-voltage DN is taken as a case study to demonstrate the proposed RT-OPF approach