Energy system modelling – interactions and synergies in a highly renewable Pan-European power system

It is very likely that the European power supply system will be transformed in the next decades to a low carbon system based almost entirely on Renewable Energy Sources (RES). However, due to the natural fluctuations of the most powerful RES (wind and solar energy), it is also very likely that a significant amount of balancing and controllable backup power capacities will be required to ensure a stable grid operation. This implies high additional investments and operating costs. Therefore this work provides an overview of potential options and possibly more cost-effective alternatives to the installation of costly storage capacities, namely grid expansion, demand side management, an optimised mix between different RES as well as the use of overcapacities. Furthermore, the paper provides an approximation of the maximum RES penetration of the German electricity system in the absence of significant storage capacities. Our calculations show that from a numerical perspective on average approximately half of the load can be met by RES if flexible conventional power stations would provide the remaining electricity demand. However, in a 100% RES scenario a significant amount of storage capacities as well as limited overcapacities are required to ensure a reliable electricity supply

Ultrathin Nano-Absorbers in Photovoltaics: Prospects and Innovative Applications

Approaching the first terawatt of installations, photovoltaics (PV) are about to become the major source of electric power until the mid-century. The technology has proven to be long lasting and very versatile and today PV modules can be found in numerous applications. This is a great success of the entire community, but taking future growth for granted might be dangerous. Scientists have recently started to call for accelerated innovation and cost reduction. Here, we show how ultrathin absorber layers, only a few nanometers in thickness, together with strong light confinement can be used to address new applications for photovoltaics. We review the basics of this new type of solar cell and point out the requirements to the absorber layer material by optical simulation. Furthermore, we discuss innovative applications, which make use of the unique optical properties of the nano absorber solar cell architecture, such as spectrally selective PV and switchable photovoltaic windows

Fuel Cell Electrical Vehicles as Mobile Coupled Heat and Power Backup-Plant in Neighbourhoods

Fuel cell electric vehicles (FCEVs) can be used during idle times to convert hydrogen into electricity in a decentralised manner, thus ensuring a completely renewable energy supply. In addition to the electric power, waste heat is generated in the fuel cell stack that can also be used. This paper investigates how the energy demand of a compiled German neighbourhood can be met by FCEVs and identifies potential technical problems. For this purpose, energy scenarios are modelled in the Open Energy System Modelling Framework (oemof). An optimisation simulation finds the most energetically favourable solution for the 10-day period under consideration. Up to 49% of the heat demand for heating and hot water can be covered directly by the waste heat of the FCEVs. As the number of battery electric vehicles (BEVs) to be charged increases, so does this share. 5 of the 252 residents must permanently provide an FCEV to supply the neighbourhood. The amount of hydrogen required was identified as a problem. If the vehicles cannot be supplied with hydrogen in a stationary way, 15 times more vehicles are needed than required in terms of performance due to the energy demand

Assessment of the regionalised demand response potential in Germany using an open source tool and dataset

With the expansion of renewable energies in Germany, imminent grid congestion events occur more often. One approach for avoiding curtailment of renewable energies is to cover excess feed-in by demand response. As curtailment is often a local phenomenon, in this work we determine the regional demand response potential for the 401 German administrative districts. The load regionalisation is based on weighting factors derived from population and employment statistics, locations of industrial facilities, etc. Using periodic and temperature-dependent load profiles and technology specific parameters, load shifting potentials were determined with a temporal resolution of 15 minutes. Our analysis yields that power-to-heat technologies provide the highest potentials, followed by residential appliances, commercial and industrial loads. For the considered 2030 scenario, power-to-gas and e-mobility also contribute a significant potential. The cumulated load increase potential of all technologies ranges from $5 - 470~MW$ per administrative district. The median value is $25~MW$, which would suffice to avoid the curtailment of 8 classical wind turbines. Further, we calculated load shifting cost-potential curves for each district. Industrial processes and power-to-heat in district heating have the lowest load shifting investment cost, due to the largest installed capacities per facility. We distinguished between different size classes of the installed capacity of heat pumps, yielding $23\%$ lower average investment cost for heat pump flexibilisation in the city of Berlin compared to a rural district. The variable costs of most considered load shifting technologies remain under the average compensation costs for curtailment of renewable energies of 110~\text{\euro{}}/MWh. As all results and the developed code are published under open source licenses, they can be integrated into energy system models

Economic Assessment of Demand Response Using Coupled National and Regional Optimisation Models

In this work, we investigate the economic viability of demand response (DR) as a balancing option for variable renewable energies, such as wind and solar. Our assessment is based on a highly resolved national energy system model for Germany coupled with a regional DR optimisation model. First, this allows us to determine the spatially resolved flexibility demand, e.g., for avoiding transmission grid congestion. Second, a high number of DR technologies from the residential, commercial and industrial sector, as well as sector coupling, can be considered to cover the regional flexibility demand. Our analysis is based on a scenario for 2035 with a 66% share of renewable energy sources in the power generation. The results show that the largest DR capacity is being installed in the west of Germany, an area with a high density of population and industry. All DR units have an aggregated capacity below 100 MW per transmission grid node. For the economic assessment, we further differentiate between two cases. In the first case with full DR cost consideration, the optimisation selects only large-scale technologies with low specific investment costs. The second case assumes that the required communication components are already installed. Here, we consider only variable costs and disregard the investment costs. As a result, several small-scale DR technologies are used, such as e-mobility. We publish the developed methodology as an open-source model, which allows reuse for other scientific purposes

Voltage-Based Load Recognition in Low Voltage Distribution Grids with Deep Learning

Due to the increasing penetration of renewable energies in lower voltage level, there is a need to develop new control strategies to stabilize the grid voltage. For this, an approach using deep learning to recognize electric loads in voltage profiles is presented. This is based on the idea to classify loads in the local grid environment of an inverter’s grid connection point to provide information for adaptive control strategies. The proposed concept uses power profiles to systematically generate training data. During hyper-parameter optimizations, multi-layer perceptron (MLP) and convolutional neural networks (CNN) are trained, validated, and evaluated to determine the best task configurations. The approach is demonstrated on the example recognition of two electric vehicles. Finally, the influence of the distance in a test grid from the transformer and the active load to the measurement point, respectively, onto the recognition accuracy is investigated. A larger distance between the inverter and the transformer improved the recognition, while a larger distance between the inverter and active loads decreased the accuracy. The developed concept shows promising results in the simulation environment for adaptive voltage control

Particle Swarm Optimization for Energy Disaggregation in Industrial and Commercial Buildings

This paper provides a formalization of the energy disaggregation problem for particle swarm optimization and shows the successful application of particle swarm optimization for disaggregation in a multi-tenant commercial building. The developed mathmatical description of the disaggregation problem using a state changes matrix belongs to the group of non-event based methods for energy disaggregation. This work includes the development of an objective function in the power domain and the description of position and velocity of each particle in a high dimensional state space. For the particle swarm optimization, four adaptions have been applied to improve the results of disaggregation, increase the robustness of the optimizer regarding local optima and reduce the computational time. The adaptions are varying movement constants, shaking of particles, framing and an early stopping criterion. In this work we use two unlabelled power datasets with a granularity of 1 s. Therefore, the results are validated in the power domain in which good results regarding multiple error measures like root mean squared error or the percentage energy error can be shown.Comment: 10 pages, 13 figures, 3 table

A Non-Intrusive Load Monitoring Approach for Very Short Term Power Predictions in Commercial Buildings

This paper presents a new algorithm to extract device profiles fully unsupervised from three phases reactive and active aggregate power measurements. The extracted device profiles are applied for the disaggregation of the aggregate power measurements using particle swarm optimization. Finally, this paper provides a new approach for short term power predictions using the disaggregation data. For this purpose, a state changes forecast for every device is carried out by an artificial neural network and converted into a power prediction afterwards by reconstructing the power regarding the state changes and the device profiles. The forecast horizon is 15 minutes. To demonstrate the developed approaches, three phase reactive and active aggregate power measurements of a multi-tenant commercial building are used. The granularity of data is 1 s. In this work, 52 device profiles are extracted from the aggregate power data. The disaggregation shows a very accurate reconstruction of the measured power with a percentage energy error of approximately 1 %. The developed indirect power prediction method applied to the measured power data outperforms two persistence forecasts and an artificial neural network, which is designed for 24h-day-ahead power predictions working in the power domain.Comment: 15 pages, 14 figures, 4 table