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In recent times, the energy consumed by buildings facilities became considerable. Efficient local energy management is vital to deal with building power demand penalties. This operation becomes complex when a hybrid energy system is included in the power system. This study proposes new energy management between photovoltaic (PV) system, Battery Energy Storage System (BESS) and the power network in a building by controlling the PV/BESS inverter. The strategy is based on explicit model predictive control (MPC) to find an optimal power flow in the building for one-day ahead. The control algorithm is based on a simple power flow equation and weather forecast. Then, a cost function is formulated and optimised using genetic algorithms-based solver. The objective is reducing the imported energy from the grid preventing the saturation and emptiness of BESS. Including other targets to the control policy as energy price dynamic and BESS degradation, MPC can optimise dramatically the efficacy of the global building power system. The strategy is implemented and tested successfully using MATLAB/SimPowerSystems software, compared to classical hysteresis management, MPC has given 10% in energy cost economy and 25% improvement in BESS lifetime.
Ziel der vorliegenden Arbeit ist das netzdienliche Betreiben einer Wärmepumpe. Um diese Netzdienlichkeit zu erreichen, wird ein modellprädiktiver Regler entwickelt und implementiert, dessen Ziel es ist die Stromkosten einer Wärmepumpe zu senken. Dazu werden die Variablen Stromkosten und ein simulierter Heizbetrieb betrachtet.
Die Entwicklung eines modellprädiktiven Reglers setzt zunächst eine Modellierung der Komponenten des Heizsystems voraus. Ebenfalls muss eine Kostenfunktion formuliert werden, die es zu minimieren gilt. In einem Optimierungsproblem werden die Modelle als Randbedingungen und die Kostenfunktion als Zielfunktion der Optimierung formuliert. Dazu müssen gewisse Vereinfachungen getroffen werden, um das Optimierungsproblem zuverlässig und ohne enormen Rechenaufwand in einer Regelungsschleife lösen zu können.
Nun wird das Optimierungsproblem mit externen Modulen verknüpft, die eine Kommunikation mit der realen Wärmepumpen, Strompreisprognosen und Wettervorhersagen ermöglichen. Der dabei entwickelte Algorithmus wird auf einem Raspberry Pi Einplatinencomputer gespeichert und dort in einem regelmäßigen Zeitintervall von 15 Minuten ausgeführt, um den Betrieb der Wärmepumpe zu regeln.
Schließlich wird der modellprädiktive Regler in Betrieb genommen. Anschließend kann der modellprädiktive Betrieb mit dem konventionellen Betrieb verglichen werden. Aus dem Vergleich wird deutlich, dass eine modellprädiktive Regelung tatsächlich die Netzdienlichkeit einer Wärmepumpe verbessern kann. Andererseits werden auch die Entwicklungspotentiale identifiziert.
Dissertation D. Dongol
In rural low voltage grid networks, the use of battery in the households with a grid connected Photovoltaic (PV) system is a popular solution to shave the peak PV feed-in to the grid. For a single electricity price scenario, the existing forecast based control approaches together with a decision based control layer uses weather and load forecast data for the on–off schedule of the battery operation. These approaches do bring cost benefit from the battery usage. In this paper, the focus is to develop a Model Predictive Control (MPC) to maximize the use of the battery and shave the peaks in the PV feed-in and the load demand. The solution of the MPC allows to keep the PV feed-in and the grid consumption profile as low and as smooth as possible. The paper presents the mathematical formulation of the optimal control problem along with the cost benefit analysis . The MPC implementation scheme in the laboratory and experiment results have also been presented. The results show that the MPC is able to track the deviation in the weather forecast and operate the battery by solving the optimal control problem to handle this deviation.
The increase in households with grid connected Photovoltaic (PV) battery system poses challenge for the grid due to high PV feed-in as a result of mismatch in energy production and load demand. The purpose of this paper is to show how a Model Predictive Control (MPC) strategy could be applied to an existing grid connected household with PV battery system such that the use of battery is maximized and at the same time peaks in PV energy and load demand are reduced. The benefits of this strategy are to allow increase in PV hosting capacity and load hosting capacity of the grid without the need for external signals from the grid operator. The paper includes the optimal control problem formulation to achieve the peak shaving goals along with the experiment set up and preliminary experiment results. The goals of the experiment were to verify the hardware and software interface to implement the MPC and as well to verify the ability of the MPC to deal with the weather forecast deviation. A prediction correction has also been introduced for a short time horizon of one hour within this MPC strategy to estimate the PV output power behavior.