IEES - Institut für Elektrische Energiesysteme
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The goal for solving the expansion planning (EP) problem in electrical systems involves the search for the optimal allocation of output power among available generators to serve the system load in a given time horizon. Currently, environmental aspects and the continuing search for alternative energy sources, push for the integration of wind power generators in the EP problem. In order to fulfill this new requirement, this paper developes a long-term energetic analysis for expansion planning under high wind power penetration scenarios in Colombia and its effect in neighboring countries. The simulations were developed using SDDP ™ software package, a tool based in stochastic dual dynamic programming technique for hydrothermal-wind least-cost dispatch.
The control of mechanical power in wind turbogenerators involves the participation of many subsystems. One of them is related to pitch mechanism, which usually employs conventional PI strategies. This paper presents an alternative for estimating the parameters of this PI controller, using Fuzzy Logic (FL). Matlab-Simulink™ software was used in order to verify the performance of the FL-based parameter estimator in some general cases.
This work constitutes the second part of a study that aims to analyze the technical and economic implications of the penetration of wind power in the Colombian Interconnected System (SIN). In the previous part of the work, steady state studies were carried out (loading at transmission corridors, short circuit and the most important contingencies were applied). A summary with the main problems encountered and their respective solutions were proposed. In this second part, the same group of wind farms and time horizon were employed, while dynamic models were fit to ensure the proper operation of controls against the contingences. Using the software DigSilent™, studies of stability (with and without controls), power quality (flicker analysis), and voltage ride through capability (VRT) were addressed. Finally, technical recommendations and further work are presented.
This work is the first part of a study that aims to analyze the technical and economic implications of the penetration of wind power in the Colombian Interconnected system (SIN). Using the software DigSilent™, studies of steady state, contingencies and short-circuit were conducted in order to identify problems of stress, overload, voltage profiles, transmission bottlenecks and the most neuralgic elements for the operation of the proposed wind parks. Additional electrical studies, such as stability, power quality, voltage ride through capability, and the development of some indicators associated with the penetration of wind power in Colombia, will be addressed on a second part of this work.
The paper presents an approach for modelling a Battery Energy Storage System (BESS). This approach consists of four stages. In the first stage a detailed model is developed taking into consideration all the electrical details of the original system. In stage two the detailed model will be validated using real measurements. In the third stage the complexity of the detailed model is reduced resulting in a simplified model which is able to represent the relevant electrical dynamics of the original system and to decrease the simulation time significantly. In the last stage the simplified model is validated by a comparison with simulation results of the detailed model.
Impact of Load Matching Algorithms on the Battery Capacity with different Household Occupancies
(2019)
Due to an increasing use of renewable energy sources in the power grid, it is of high importance to balance supply and demand for grid utilities and microgrid operators. If there are mismatches in the balancing, microgrids with islanded operation capabilities would be preferrable. In islanded mode, nearly zero energy buildings commonly use a stand-alone photovoltaics power supply with a battery storage. A battery storage is expensive and the capacity in case of off-grid operation depends on the electricity consumption of the dwelling's occupants. Using thermostatically controlled appliances like a freezer, water heater and space heating as additional storage systems can reduce the capacity of the battery storage system or increase the operation time in islanded mode for a fixed battery size. This paper analyzes the battery capacity dependency both on the control algorithms for the thermal storages and on the occupancy of the dwelling. Possible battery reductions for different selected occupancies are presented in this work by comparing the simulation results of different load matching algorithms to each other and between the different occupancies. The analysis of those results enables recommendations on the most suitable algorithm for most occupancy scenarios of an existing dwelling with respect to a minimized battery capacity. This can be particularly useful, for example, for dwelling and apartment owners who are renting out dwellings.
Increasing shares of renewable energy sources in combination with rising popularity of demand response applications and flexibility programs forces higher awareness for production and consumption balancing. Accurate models for forecasting are not just necessary for PV- or wind power sources in smart cities, but also the prediction of loads respectively consumption, which can be based on time series analysis or machine learning methods. Three of those methods, namely a linear regression (LM), a long short-term memory network (LSTM) and a neural network model (NN), have been selected to see their performance on predicting the load of a large smart city on the example of the Estonian electricity consumption data. Hourly data of the year 2019 was used as training data to predict the first 20 days of 2020. For this kind of prediction, the LM showed the lowest root mean square error (RMSE) and had the lowest computational time. The neural network was slightly less accurate. The LSTM showed the worst performance in terms of accuracy and computational time. Thus, LSTM is not the preferred method for this kind of prediction and the recommendation for forecasting such loads would be a LM because the RMSE and computational effort needed are lower than for a NN
Due to an increasing share of renewable energy sources the balancing of energy production and consumption is getting a lot of interest considering future smart grids. In this context, many investigations on demand-response programs are being conducted to achieve flexibility from different energy storages and loads. As space heating is an important schedulable load for flexibility simulations, there are different modelling approaches due to its interdisciplinary nature. Models can be built from the civil engineering or electrical engineering point of view, depending on the computational expense and accuracy level. Scheduling optimizations need a lot of simulations, preferably with computationally light models. Thus, this work will use a computationally light neural network load prediction model for space heating which is based on a detailed civil engineering model. Simulations with different scheduling times were conducted to see the long- and short-term effects of the demand response action. Results show, that applying the same demand response action at different times results in different behaviors of the system resp. energy consumption, which requires further studies for developing optimized scheduling methods.
To reduce greenhouse gas emissions, volatile energy production from renewable sources is highly encouraged by international agreements. This leads to balancing challenges of demand and supply which can be addressed with smart grids or even smart city concepts. Demand side management control strategies for flexibility harvesting often include energy storage systems, like flywheel- (FESS) and battery (BESS) storages. To investigate different control strategies for a hybrid energy storage system with a flywheel and battery storage in an islanded microgrid, an existing flywheel is modernized with state-of-the-art components to support real time power hardware in the loop simulations. Testing a load levelling control strategy with this test bench showed that the cyclic lifetime of the battery storage system could be increased with peak shaving due to a reduced amount of charging and discharging operations. An excessive energy buffering control method could increase the islanded operation time by using nearly 10% of the otherwise lost energy. However, these results with the testbench showed limited use for research with the current setup due to low capacity and high self-discharge rate of the existing FESS. But due to the MATLAB-based programming interface, it is perfectly suitable as an educational setup for the demonstration of possible implementations of the European Green Deal.
Microgrids play an important role in the energy transition. The change from few centralized power plants to thousands of small renewable and volatile energy resources requires, but also enables new grid control strategies.
The full potential of microgrids is deployed by the extension of island grid operation.
This leads to an increased reliability and resilience of the distribution of electrical energy.
Further, the island capability of microgrids can support a greater grid area in case of failures in higher voltage levels by a temporary disconnection from the main grid and later by resynchronization in the frame of a cellular approach.
For the modeling and control of microgrids in island operation, this thesis delivers mathematical models for typical assets verified by specifc measurements at an existing microgrid (Microgrid Campus Wildpoldsried - MCW) with island capability.
Further, the interaction of inverter-based assets, such as battery storage systems in grid forming, but also in grid support mode, and photovoltaic systems as well as rotating generation units by using droop control, is investigated by simulation; the results are confirmed by measurements at the MCW.
Based on these verified simulation models, further control strategies for the microgrid are developed and analyzed by simulation. This includes a centralized approach for a microgrid controller, including active and reactive power management, strategies for black start and resynchronization as well as energy scheduling for the battery storage systems.
Further, also a failsafe control strategy for active and reactive power sharing between distributed generation, battery storage systems and rotating generation units is investigated for systems without communication system (e.g., temporarily caused by a failure or permanent to reduce installation and maintenance costs). All investigations were performed under consideration of the practical application in real grids. This includes existing rules like VDE AR-N4105 for the distributed generation and the DIN EN50160 for voltage quality on the one hand, but also communication dead times caused by Ethernet-based communication links, protocol converters and control platforms.
Therefore, this thesis provides insightful methods for the planning, dimensioning and analysis of islanded microgrids.
