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

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

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

Optimal integration of electric vehicles in smart grids with renewables and battery storage systems under uncertainty

There has been a huge trend to integrate Renewable Energies (REs) and Electric Vehicles (EVs) into energy networks (Figure 1). This is mostly due to the shrinking price of their application and the increasingly strict emission policy. However, the integration of REs and EVs brings new challenges to the network operation [1]. For instance, a considerable amount of REs cannot be accommodated in the network and thus has to be curtailed due to technical limitations [2-5]. For overcoming this problem, Battery Storage Systems (BSSs) can be used to store the surplus energy and consequently increase economic benefits [4].&nbsp;</p

Optimal E-Powertrain Solutions for Future Electric Vehicles

Owing to increasing emission specification, decreasing price of energy storage systems and power electronic devices, in addition to fast-developing technology, Electric Vehicles&nbsp; (EVs) will become a significant share of automotive market in the near future [1]. Therefore, there is a huge competition among car manufacturers to produce EVs. The final price and driving range are known as vital factors to win the competition.</p