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The paper illustrates the status quo of a research project for the development of a control system enabling CHP units for a demand-oriented electricity production by an intelligent management of the heat storage tank. Thereby the focus of the project is twofold. One is the compensation of the fluctuating power production by the renewable energies solar and wind. Secondly, a reduction of the load on the power grid is intended by better matching local electricity demand and production.
In detail, the general control strategy is outlined, the method utilized for forecasting heat and electricity demand is illustrated as well as a correlation method for the temperature distribution in the heat storage tank based on a Sigmoid function is proposed. Moreover, the simulation model for verification and optimization of the control system and the two field test sites for implementing and testing the system are introduced.
The coupling of the heat and power sector is required as supply and demand in the German electricity mix drift further and further apart with a high percentage of renewable energy. Heat pumps in combination with thermal energy storage systems can be a useful way to couple the heat and power sectors. This paper presents a hardware-in the-loop test bench for experimental investigation of optimized control strategies for heat pumps. 24-hour experiments are carried out to test whether the heat pump is able to serve optimized schedules generated by a MATLAB algorithm. The results show that the heat pump is capable of following the generated schedules, and the maximum deviation of the operational time between schedule and experiment is only 3%. Additionally, the system can serve the demand for space heating and DHW at any time.
Micro grids often consist of energy generators, storages and consumers with controllers which are not prepared for their integration into communication networks for energy systems. In this paper it will be presented, how standards from the field of energy automation can be applied in such controllers. The data for communication interfaces can be structured according to the IEC 61850- or the VHPREADY standard. It is investigated which requirements must be supported to implement such data models within the controllers. For the transmission of the data we propose the OPC UA protocol, which supports extensive security measures and which is today available for nearly all modern types of controllers and computers.
The majority of people in sub-Saharan Africa (SSA) rely on so-called “paratransit” for their mobility needs. The term refers to a large informal transport sector that runs independent of government, of which 83% comprises minibus taxis (MBT). MBT technology is often old and contribute significantly to climate change with their high carbon dioxide (CO2) emissions. Issues related to sustainability and climate change are becoming more important world-wide and hardly any attention is given to MBTs. Converting the MBTs from internal combustion engines (ICEs) to electric motors could be a possible solution. The existing power grid in SSA is largely based on fossil power plants and is unstable. This can be seen by frequent local power blackouts. To avoid further strain on the existing power grid, it would therefore make sense to charge the electric minibus taxis (eMBTs) through a grid consisting of renewable energies. A mobility map is created via simulations with collected data points of the MBTs. By using this mobility map, the energy demand of the eMBTs is calculated. Furthermore, a region-specific photovoltaic (PV) and wind simulation can be realised based on existing weather data, and a tool to size the supply system to charge the eMBTs is developed after all data has been collected. With the help of this work, it can be determined to what extent renewable energies such as PV and wind power can be used to support the transition from ICEs to electric engines in the MBT sector.
The objective of the project presented here is to develop an intelligent control algorithm for an energy system consisting of a biogas CHP (combined heat and power), various storage technologies, such as thermal energy storages (TES) and gas storages, and other renewable energy sources, such as photovoltaics. A corresponding algorithm based on the Monte-Carlo method has already been developed at Reutlingen University for CHP units running on natural gas and for heat pumps. The project presented here concentrates on the further development of this algorithm for application to biogas CP units. In this context, an adequate implementation of the gas storage is of primary importance, as it mainly determines the flexibility of the plant. In the course of the validation of the new optimization algorithm, simulations were carried out based on data from the Lower Lindenhof, an agricultural experimental station of the University of Hohenheim. Both an optimization with regard to onsite electricity utilization and an optimization driven by residual load were investigated. Preliminary results show that the optimization algorithm can improve the operation of the biogas CHP unit depending on the selected target function.
This paper examines the deployment of Power to-X technologies in the US energy system through 2040. For this analysis, Power-to-X technologies have been added to an input database representing the US energy system as a single region, which is used in conjunction with an energy system optimization model called Tools for energy model optimization and analysis (Temoa). Detailed data for each individual technology, including water electrolysis, hydrogen compression and storage, chemical processing to synthetic natural gas (SNG) and methanol was collected and entered to the input database. Under a deep decarbonization scenario, Power-to-X is deployed beginning in 2035 under the assumption of no new nuclear power plants installed and a restriction on biodiesel production based on limited area for growing crops. The major portion of the hydrogen generated by electrolysis from excess PV- and wind-generated electricity goes into the production of methanol. This result suggests that Power-to-X is used to generate transport fuels in order to reduce CO2 emissions especially in this sector.
This article presents a two-level optimisation approach for the management of controllable and distributed converters with storage systems across different energy sectors. It aims at the reduction of electrical peak load and at the economical optimisation of the electrical energy exchange with the grid, based on a dynamic external incentive, e.g. through dynamic energy price tariffs. By means of a secure, standardised and lean communication with two different internal price signals, an optimal flexibility provision shall be achieved. The two-level optimisation approach consists of a centralised and several distributed decentralised entities. At the centralised level, the distributed flexibilities are invoked for optimal scheduling on the basis of an internal price algorithm for stimulating the decentralised entities. Based on that internal incentive and on the expected demands for electricity, heating and cooling, the decentralised optimisation algorithms provide optimal generation schedules for the energy converters. The suggested interaction between the central and decentral entities is successfully tested and the principle potential for peak shaving and the adaption to dynamic energy-related market prices could be demonstrated and compared to different energy management strategies such as the standard heat-led operation. Further, variations of the system parameters such as load shifting potential, installed capacity and system diversification are evaluated against cost saving potential for the energy supply and overall system performance.
The integration of renewable energy sources in single family homes is challenging. Advance knowledge of the demand of electrical energy, heat, and domestic hot water (DHW) is useful to schedule projectable devices like heat pumps. In this work, we consider demand time series for heat and DHW from 2018 for a single family home in Germany. We compare different forecasting methods to predict such demands for the next day. While the 1-day-back forecast method led to the prediction of heat demand, the N-day-average performed best for DHW demand when Unbiased Exponentially Moving Average (UEMA) is used with a memory of 2.5 days. This is surprising as these forecasting methods are very simple and do not leverage additional information sources such as weather forecasts.
The main challenge when driving heat pumps by PV-electricity is balancing differing electrical and thermal demands. In this article, a heuristic method for optimal operation of a heat pump driven by a maximum share of PV-electricity is presented. For this purpose, the (DHW) are activated in order shift the operation of the heat pump to times of PV-generation. The system under consideration refers to thermal and electrical demands of a single family house. It consists of a heat pump, a thermal energy storage for DHW and of grid connected heating and generation of domestic hot water, the heat pump runs with two different supply temperatures and thereby achieving a maximum overall COP. Within the algorithm for optimization a set of heuristic rules is developed in a way that the operational characteristics of the heat pump in terms of minimum running and stopping times are met as well as the limiting constraints of upper and lower limits of room temperature and energy content of electricity generated, a varying number of heat pump schedules fulfilling the bundary conditions are created. Finally, the schedule offering the maximum on-site utilization of PV-electricity with a minimum number of starts of the heat pump, which serves as secondary condition, is selected. Yearly simulations of this combination have been carried out. Initial results of this method indicate a significant rise in on-site consumption of the PV-electricity and heating demand fulfilment by renewable electricity with no need for a massive TES for the heating system in terms of a big water tank.
The increasing share of renewable energy with volatile production results in higher variability of prices for electrical energy. Optimized operating schedules, e.g., for industrial units, can yield a considerable reduction of energy costs by shifting processes with high power consumption to times with low energy prices. We present a distributed control architecture for virtual power plants (VPPs) where VPP participants benefit from flexible adaptation of schedules to price forecasts while maintaining control of their operating schedule. An aggregator trades at the energy market on behalf of the participants and benefits from more detailed and reliable load profiles within the VPP.