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Systemtechnik
(2010)
Mit dem Wetter sparen
(2010)
Kühlen mit Wärme
(2009)
Solartechnik
(2007)
Die Hochschule Offenburg begleitet seit Juli 2006 in Zusammenarbeit mit dem Fraunhofer ISE in Freiburg, die solar unterstützte Klimatisierung der Festo AG & Co. KG in Esslingen im Rahmen des Forschungsvorhabens Solarthermie2000plus. Dabei wurde die bereits bestehende Adsorptionskälteanlage, die bisher mit Gaskesseln und Kompressorenabwärme betrieben wurde, durch eine Solaranlage als dritte Wärmequelle ergänzt.
The PHOTOPUR project aims to develop a photocatalytic process as a type of AOPs (Advanced Oxidation Processes) for the elimination of plant protection products (PPP) of the cleaning water used to wash sprayers. At INES a PV based energy supply for the photocatalytic cleaning system was developed within the framework of two bachelor theses and assembled as a demonstration unit. Then the system was step by step extended with further process automation features and pushed to a remote operating device. The final system is now available as a mobile unit mounted on a lab table. The latest step was the photocatalytic reactor module which completed the first PHOTOPUR prototype. The system is actually undergoing an intensive testing phase with performance checks at the consortium partners. First results give an overview about the successful operation.
An energy oriented design concept was developed within the research project PHOTOPUR which has the development of a PV powered water cleaning system as main focus. During a wine season Plant Protection Products (PPP) are several times sprayed on plants to protect them of undesired insects and herbs or avoid hazardous fungus
types. A work package of the project partner INES in Offenburg led to a design introducing energy profiling already in the early beginning of a product design. The concept is based on three pillars respecting first the
requirements of the core process making up filtering and cleaning and secondary aspects which run, support, maintain and monitor the system to secure availability and product reliability.
The presented paper shows that the results of the design tools guided the developers to assemble a functional model of the water decontamination unit which was manually tested with its concatenated steps of the water cleaning process.
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.
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.
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.
In
this paper, a new method is demonstrated for onlin
e remote simulation of photovo
ltaic systems. The required
communication technology for the data exchange is introduced a
nd the methods of PV generato
r parameter extraction for the
simulation models are analysed. The method
shown for parameter extraction from the ma
nufacturer data is especially useful
for the commissioning procedure, where the measured installed pow
er is transferred to standard test conditions using the
simulation model and can then be easily compared with the de
sign power. At a simulation accuracy of 2% using the software
environment INSEL
®
any problems with the PV gene
rator can reliably be detected.
Online simulation of a grid connected PV generator is then
carried out during the operation of the photovoltaic plant. The
visualisation includes both the monitored and
the simulated online data sets, so that a very efficient fault detection scheme i
s
available. The method is implemented and
validated on several grid connected photovolta
ic power plants in Germany. It is
excellently suited to provide automatic and real time fault
detection and significantly impr
ove the commissioning procedure
for photovoltaic plants of all sizes.
Prädiktive Betriebsverfahren
(2010)
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.
Große Solaranlagen
(2011)