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Institute
Microgrids can be operated in on-grid mode, but also in off-grid mode (island operation). In off-grid mode, grid forming units have to ensure the grid's voltage and frequency stability. For more than one grid forming unit, the active and reactive power sharing has to be handled. This paper presents a method for voltage and reactive power control for systems without a superordinated control system or a communication link between the grid forming units. A failsafe concept is included, that means that a stable operation is given also in case that one grid forming unit is disconnected.
Microgrids with a high penetration of distributed generation (DG) in combination with energy storage systems (ESS), but also in combination with fuel-driven generation units (gensets) can be operated in on-grid mode, but also in off-grid mode (island operation). For grid restoration in island mode, a black start strategy is needed. This scientific work deals with a black start concept for island grids with a high amount of non-controllable DG units and non-controllable loads which is investigated by mathematical modeling and simulation for different scenarios. The assumed underlying control behavior of the DG units is described in the German application guide VDE-AR-N 4105. The corresponding mathematical modeling is presented and a verification by specific measurements is presented.
Due to the increasing share of volatile renewable energy sources, like photovoltaics (PV) and wind energy in nearly Zero Energy Buildings (nZEB), there is an increasing need for demand-side management (DSM) or demand response (DR) programs to balance the production and consumption in the grid. The flexibility that can be obtained for smart grids from such DR methods is not limited to appliances like water heaters or dishwashers but can also be achieved with space heating and air-conditioning. In such an interdisciplinary investigation, often one part is simplified, in this case, typically either the thermal models or the implemented DR strategy are very detailed. In this work, a detailed thermal model of a control center is obtained and calibrated in IDA ICE building-modelling software with measurements from a test site in Germany. Afterward, several price-based load matching algorithms are applied to the model to see the possible flexibility exploitation with the thermal capacity of this small building. Not all investigated algorithms show good performance but some of them show promising results. Thus, this model can be used for DR methods and should be extended to work with more DSM strategies and provide ancillary services.
Determination methods for controller parameters of back-to-back converters in electric power grids
(2016)
The paper presents a new optimization method for PI controllers of back-to-back voltage source converters using a vector control scheme to enable the control of active and reactive power transmission between two independent grids, for example, an emulator as a load or a source between the medium voltage distribution grid and a low voltage island grid. The control principle based on three phase systems in dq-components enables an independent control of active and reactive power with a simple structure using PI controllers. The presented optimization method using pole placement (PP) technique for tuning of the controllers leads to a higher degree of freedom and therefore to better results compared to the modulus optimum (MO) optimization method discussed in [1], [2]. A cascaded control model consisting of inner current and outer power/voltage control loops is being used for the optimization of the system's transient response. The mathematical modeling of the control system as well as the evaluation of the controller parameters are described in detail. A comparison of the presented optimization method for controllers with existing methods is shown by simulation results using the software PSCAD.
This paper investigates the use of common thermal storage systems for demand side management in off-grid situations for nearly zero energy buildings. Typical parameters and characteristics were analyzed to develop mathematical models for freezers, water heaters and space heating/cooling. The models used in this work are based on simplified equations derived from differential equations. Simplified models of a battery storage and a PV-system have been added. Models for the thermal storages, PV-system and battery storage were merged to one system model. All models and simulations were designed and conducted with Matlab. Various pre-defined price based set point calculation algorithms were modified to work with the off-grid system based on the system’s voltage and available PV-power. Voltage and battery’s state of charge based algorithms are developed in this work. In a system with a freezer, water heater and space heating/cooling that is powered by a PV-system only, a possible battery storage capacity reduction of up to 50% with PV-power based and up to 36% with SOC based algorithms compared to the same system with fixed set point thermostatic control could be achieved. Additionally, the capacity could even be reduced by up to 18% by solely reacting to voltage drops.
Mathematical modeling and dynamic behavior of a Lithium-Ion battery system for microgrid application
(2016)
This paper deals with the analysis and simulation of a stationary battery system for microgrid application, where the system structure including battery cells, inverters, filters, transformers, control system and a simplified grid model is described and modeled mathematically. For the simulation of the whole system the software PSCADTM is used. In the first part several equivalent circuit models for Lithium-Ion cells will be compared in order to model the dynamic behavior of the battery system. Particularly the evaluation of the effect of the model's complexity on the dynamics of the entire system will be investigated. In the second part, the dependency of state of charge (SOC), temperature and aging effects of the Lithium-Ion cells on electrical system quantities will be shown. It is also investigated the fact that a high frequency battery model has to be taken into account to describe the cells' dynamics if an inverter with Pulse Width Modulation is used.
The research work presents an approach to set-up simplified mathematical models of microgrid components based on detailed models. The verification is done by a comparison with measurement results of a real system. Using simplified models allows an accurate analysis and optimization of the dynamic behavior of existing as well as planned microgrids. The paper shows simulation and measurement results for different combinations of microgrid components in island mode operation.
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.
The paper presents the dynamic modeling and stability analysis of Low Voltage (LV) microgrids in island operation using simplified electrical models for Distributed Generations (DGs). These simplified models are used to simulate electrical (excluding switching) as well as control dynamics for each DG to setup and facilitate system level simulations. The paper focuses on the operation of components in grid forming mode using a droop based primary control. This approach is applied on a real microgrid which is set up within the IREN2 research project framework. The demonstrator incorporates a Li-Ion based Battery Energy Storage System (BESS), a plant oil driven generator as well as a BESS emulator. First, a brief overview of the detailed model for each DG including its simplification is discussed. Next, the microgrid is set up using simplified models for transient simulations and the comparison with real measurements is shown for different microgrid topologies. Later, overall microgrid stability i.e., various instability aspects in LV island grids are discussed. In this regard, an analytical method based on Eigenvalue analysis for identification of stability limits for relevant electrical and control parameters and under various loading conditions is presented. Finally, the complete microgrid model is simulated for potential instable conditions and a comparison with the analytical solution is shown.
The paper investigates the transient stability issues
in islanded microgrids with both grid forming as well as grid
following Distributed Generation (DG) units participating in
the microgrid. The focus is to identify high frequency stability
challenges due to short time transients that generally arise
from fast load changes. It is shown that the primary control
in DGs, type of load and grid impedances requires significant
considerations for transient grid stability. An extended microgrid
simulation model with two Battery Storage Systems (BSSs)
namely BSS1 and BSS2, a Back-to-back Station (B2B) as well as
a resistive load bank is modeled in this regard [1]. The models
of these DGs are based on real system components integrated
in a real microgrid demonstrator and are simplified to simulate
electrical (excluding switching) as well as control dynamics for
each DG to setup and facilitate system level simulations [2]. The
B2B and BSS1 are operated in grid forming mode (VSI inverter)
and the primary control is based on the classical droop control to
regulate output voltage and frequency. The BSS2 is operated in
grid following mode (CSI inverter) and emulates a prosumer with
a primary control that regulates BSS output active and reactive
power. The microgrid has no secondary microgrid controller and
the microgrid stability under islanded operation is exclusively
considered in this paper.
In the future more and more conventional power plants, which provide ancillary services such as provision of reactive power for voltage control and primary control power (active power) for frequency control to the transmission system and thus secure the energy supply, are going to be replaced by renewable energy sources. Due to this fact new concepts for providing these services by renewable power sources will be necessary in the future to maintain stability of the network operation. This refers to the delivery of active and reactive power by distributed generation (DG) and distributed storage (DS). Beyond that DG and DS can be found nowadays in households or also called nanogrids. This research work presents a concept of a nanogrid that can provide ancillary services to the distribution grid in the low voltage level and transfer reactive power as well as primary control power to higher voltage levels by upscaling, which means the connection of many nanogrids. The implementation of the concept is done in a real system and also in a simulation environment that uses simplified mathematical models.
The paper presents the effect of network impedances on the transient stability of Low Voltage (LV) microgrids intended for islanded operation. A simulation model is developed using simplified models of Distributed Generations (DGs). These simplified models are used to simulate electrical (excluding switching) as well as control dynamics for each DG to setup and facilitate system level simulations [1]–[3]. The paper focuses on the operation of DGs in grid forming mode using a droop based primary control. This approach is applied on a real microgrid which is set up within the pebbles research project framework. These DGs are connected through cable impedances to a resistive load bank at the point of common coupling (PCC). The effect of varying individual impedances between DGs and PCC under loading conditions on the microgrid stability is investigated. The location of load between DGs and its impact is also discussed. Finally, a control modification utilizing concept of virtual impedances (VIs) in Voltage Source Inverters (VSI) is proposed to improve the transient stability of the discussed microgrid.
In this paper, the small signal stability of a Battery Energy Storage System (BESS) used in a low voltage islanded microgrid is investigated for an ohmic load case using eigenvalue sensitivity analysis. Two approaches namely Quasi Steady State (QSS) and Dynamic Phasor Modeling (DPM) are presented and compared for a reference BESS in a real microgrid. The QSS approach is considered as a traditional method to model system dynamics assuming that they are slow enough to apply steady state rules. The DPM approach on the other hand considers the electrical dynamics in the control feedback loop and the coupling between the parallel inverters. The evaluation of the mathematical models for both approaches as well as simulation and measurement results are presented. The classical QSS stability analysis applied to the BESS does not show the dependency of stability margins on droop parameters, smoothing time constant or load parameters. This problem can be overcome by the presented DPM method. The sensitivity of the BESS and load parameters on stability limits is studied in detail.
This research work presents an operation mechanism for supplying a scheduled value of reactive power at the medium voltage (MV) side of the distribution transformer by a group of interlinked nanogrids in the low voltage level (LV), which are part of a topological power plant (TPP). The operation mechanism takes into account the self-consumption of the nanogrid and network constraints, such as the permissible voltage band for each node and the loading of the transformer as well as cables. Furthermore, the minimization of active power losses within the TPP is taken into consideration while the scheduled reactive power at the MV side should be accomplished.
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.
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
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.
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.
The widespread implementation of smart meters (SM) and the deployment of the advanced metering infrastructure (AMI) provide large amounts of fine-grained data on prosumers. Machine learning (ML) algorithms are used in different techniques, e.g. non-intrusive load monitoring (NILM), to extract useful information from collected data. However, the use of ML algorithms to gain insight on prosumer behavior and characteristics raises not only numerous technical but also legal concerns. This paper maps electricity prosumer concerns towards the AMI and its ML based analytical tools in terms of data protection, privacy and cybersecurity and conducts a legal analysis of the identified prosumer concerns within the context of the EU regulatory frameworks. By mapping the concerns referred to in the technical literature, the main aim of the paper is to provide a legal perspective on those concerns. The output of this paper is a visual tool in form of a table, meant to guide prosumers, utility, technology and energy service providers. It shows the areas that need increased attention when dealing with specific prosumer concerns as identified in the technical literature.
Balancing the energy production and consumption is a huge challenge for future smart grids. In this context, many demand-side management programs are being developed to achieve flexibility from different loads like space heating. As space heating models for flexibility simulations are an interdisciplinary field of work, complex civil engineering thermal models need to be combined with complex electrical engineering control simulations in different software frameworks. Traditionally used methods have shortcomings in one of those two domains as the publications that provide complex control strategies for demand response are lacking complex thermal models and vice versa. Co-simulations overcome this problem but are computationally expensive and have compatibility limitations. Thus, the aim of this work is to develop a methodology for designing space heating/cooling models, intended for positive energy district- or smart city simulations, which provide high accuracy at low computational expense. This could be achieved by synthesizing neural network object models from IDA-ICE civil engineering models in Matlab. These machine learning models showed improvements of more than 30% in different error metrics and a simulation time reduction of more than 80% compared to other methods, making them suitable for use in microgrid simulations, including flexibility analyses.