Fakultät 07 / Institut für Elektrische Energietechnik
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Maximising Distribution Grid Utilisation by Optimising E-Car Charging Using Smart Meter Gateway Data
(2023)
The transition towards climate neutrality will result in an increase in electrical vehicles, as well as other electric loads, leading to higher loads on electrical distribution grids. This paper presents an optimisation algorithm that enables the integration of more loads into distribution grid infrastructure using information from smart meters and/or smart meter gateways. To achieve this, a mathematical programming formulation was developed and implemented. The algorithm determines the optimal charging schedule for all electric vehicles connected to the distribution grid, taking into account various criteria to avoid violating physical grid limitations and ensuring non-discriminatory charging of all electric vehicles on the grid while also optimising grid operation. Additionally, the expandability of the infrastructure and fail-safe operation are considered through the decentralisation of all components. Various scenarios are modelled and evaluated in a simulation environment. The results demonstrate that the developed optimisation algorithm allows for higher transformer loads compared to a P(U) control approach, without causing grid overload as observed in scenarios without optimisation or P(U) control.
To realize a reliable and cost-effective application of high-temperature superconductive (HTS) equipment at high-voltage (HV) levels, the influence of thermally induced gas bubbles on the dielectric strength of different solid insulating materials in liquid nitrogen (LN2) was investigated. A heatable copper tape electrode arrangement was developed simulating HTS tapes with insulation in between. AC breakdown measurements were performed without and with forced boiling on insulating papers, polypropylene laminated paper (PPLP) and polyimide (PI) films. Under nucleate boiling the influence of bubbles on the dielectric strength of all materials was not significant. However under film boiling the dielectric strength of the insulating papers decreased to a level comparable to their dielectric strength in air, demonstrating the insufficient impregnation of porous materials under film boiling. For PI there was no degradation at all. PPLP retained about 70% of its basic dielectric strength in LN2.
Ghana suffers from frequent power outages, which can be compensated by off-grid energysolutions. Photovoltaic-hybrid systems become more and more important for rural electrificationdue to their potential to offer a clean and cost-effective energy supply. However, uncertainties relatedto the prediction of electrical loads and solar irradiance result in inefficient system control and canlead to an unstable electricity supply, which is vital for the high reliability required for applicationswithin the health sector. Model predictive control (MPC) algorithms present a viable option to tacklethose uncertainties compared to rule-based methods, but strongly rely on the quality of the forecasts.This study tests and evaluates (a) a seasonal autoregressive integrated moving average (SARIMA)algorithm, (b) an incremental linear regression (ILR) algorithm, (c) a long short-term memory (LSTM)model, and (d) a customized statistical approach for electrical load forecasting on real load data of aGhanaian health facility, considering initially limited knowledge of load and pattern changes throughthe implementation of incremental learning. The correlation of the electrical load with exogenousvariables was determined to map out possible enhancements within the algorithms. Results showthat all algorithms show high accuracies with a median normalized root mean square error (nRMSE)<0.1 and differing robustness towards load-shifting events, gradients, and noise. While the SARIMAalgorithm and the linear regression model show extreme error outliers of nRMSE >1, methods viathe LSTM model and the customized statistical approaches perform better with a median nRMSE of0.061 and stable error distribution with a maximum nRMSE of <0.255. The conclusion of this study isa favoring towards the LSTM model and the statistical approach, with regard to MPC applicationswithin photovoltaic-hybrid system solutions in the Ghanaian health sector.
This paper presents a life cycle assessment (LCA) of photovoltaic (PV) solar modules whichhave been integrated into electric vehicle applications, also called vehicle integrated photovoltaics(VIPV). The LCA was executed by means of GaBi LCA software with Ecoinvent v2.2 as a backgrounddatabase, with a focus on the global warming potential (GWP). A light utility electric vehicle (LUV)named StreetScooter Work L, with a PV array of 930 Wp, was analyzed for the location of Cologne,Germany. An operation time of 8 years and an average shadowing factor of 30% were assumed.The functional unit of this LCA is 1 kWh of generated PV electricity on-board, for which an emissionfactor of 0.357 kg CO2-eq/kWh was calculated, whereas the average grid emissions would be 0.435 kgCO2-eq/kWh. Hence, charging by PV power hence causes lower emissions than charging an EV bythe grid. The study further shows how changes in the shadowing factor, operation time, and otheraspects affect vehicle’s emissions. The ecological benefit of charging by PV modules as compared togrid charging is negated when the shadowing factor exceeds 40% and hence exceeds emissions of0.435 kg CO2-eq/kWh. However, if the operation time of a vehicle with integrated PV is prolonged to12 years, emissions of the functional unit go down to 0.221 kg CO2-eq/kWh. It is relevant to point outthat the outcomes of the LCA study strongly depend on the location of use of the vehicle, the annualirradiation, and the carbon footprint of the grid on that location.