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Mit dem Wetter sparen
(2010)
Beim vorliegenden EnBau-Forschungsvorhaben sollte im Rahmen des ENOB-Förderprogramm ein Langzeitmonitoring des Neubauvorhabens Solar Info Center Freiburg (SIC) mit folgenden Untersuchungsschwerpunkten durchgeführt werden:
• Natürliche Klimatisierung mit Nachtlüftung und Einzelanbindung der Büroflächen
• Erdsondenkühlung für Seminarraum und Foyer
• Zonenweise Abschaltung und Optimierung des Heizbetriebs
• Optimierung Lüftungsbetrieb
• Sonnenschutzanlagen
• Analyse Stromverbrauch / Gesamtenergiebilanz
• Bedarfsanalyse der Nutzer
• Erstellung einer „Betriebsanleitung“ für das Gebäude
• Kurzzeitmessungen
• Gebäudeautomation
Die gesamte Projektlaufzeit wurde auf drei Jahre angesetzt die Datenerfassung für das Monitoring sollte dabei mindestens 2 Jahre betragen.
Three real-lab trigeneration microgrids are investigated in non-residential environments (educational, office/administrational, companies/production) with a special focus on domain-specific load characteristics. For accurate load forecasting on such a local level, à priori information on scheduled events have been combined with statistical insight from historical load data (capturing information on not explicitly-known consumer behavior). The load forecasts are then used as data input for (predictive) energy management systems that are implemented in the trigeneration microgrids. In real-world applications, these energy management systems must especially be able to carry out a number of safety and maintenance operations on components such as the battery (e.g. gassing) or CHP unit (e.g. regular test runs). Therefore, energy management systems should combine heuristics with advanced predictive optimization methods. Reducing the effort in IT infrastructure the main and safety relevant management process steps are done on site using a Smart & Local Energy Controller (SLEC) assisted by locally measured signals or operator given information as default and external inputs for any advanced optimization. Heuristic aspects for local fine adjustment of energy flows are presented.
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.
This paper presents the use of model predictive control (MPC) based approach for peak shaving application of a battery in a Photovoltaic (PV) battery system connected to a rural low voltage gird. The goals of the MPC are to shave the peaks in the PV feed-in and the grid power consumption and at the same time maximize the use of the battery. The benefit to the prosumer is from the maximum use of the self-produced electricity. The benefit to the grid is from the reduced peaks in the PV feed-in and the grid power consumption. This would allow an increase in the PV hosting and the load hosting capacity of the grid.
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.