Biechl, Helmuth
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Institute
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.
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.
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.