Grid planning algorithm under uncertainties for an optimal integration of electric vehicles
Wegener, Tammo (2021)
Diplomityö
2021
School of Energy Systems, Energiatekniikka
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi-fe2021110954423
https://urn.fi/URN:NBN:fi-fe2021110954423
Tiivistelmä
With the integration of electric vehicles charging infrastructure into the medium and low voltage grid, new challenges for the distribution grid operators arise. The power demand due to electric vehicle charging occurs with high simultaneity and power compared to the conventionally connected dwelling loads. In order to effectively integrate electric vehicles into the existing grids, methods are required to prevent voltage band violations and the over-utilization of lines, which could threaten smooth grid operation.
A classic approach to overcome grid bottlenecks, based on the NOVA principle, is the reinforcement of grids. This work investigated the cost effective reinforcement of low voltage networks that have been overloaded due to the integration of electric vehicle charging equipment, using a grid reinforcement algorithm. For this investigation, the cigre benchmark network was utilized based on which, basic assumptions were made regarding the number and charging power of electric vehicles. The number of dwellings within the benchmark grid was derived using the simultaneity factor for different degrees of household electrification, while the quantity of electric vehicles was estimated using current vehicle statistics and future set goals by the government. Furthermore, the costs for different grid reinforcement options in urban areas was established. Novel methods using conventional grid reinforcement, variable transformers, lithium-ion storage and combined heat and power were considered. Using the load torque approach and sensitivity matrices, the most vulnerable grid nodes were ascertained. In addition, uncertainties due to cable temperature and temperature coefficient were depicted and evaluated using a Monte-Carlo simulation.
In this thesis, it has been found that the grid reinforcement costs for the integration of electric vehicle charging equipment across the grid can be accurately estimated by placing all charging equipment at a single grid node. To determine the maximum and minimum grid expansions costs, it is sufficient to analyse the most and least sensitive grid nodes. This leads to a reduction of required computational power, since only two grid nodes need to be taken into consideration. Additionally, the cost influences of cable uncertainties are less than 4 %, and variable transformer are not a viable option to circumvent conventional grid reinforcement when considering the integration of larger amounts of electric vehicle charging equipment. Furthermore, grid reinforcement using lithium-ion storage is 10,6 times more expensive in comparison to conventional grid reinforcement, even when taken predicted lithium-ion battery prices for 2025. As for combined heat and power reinforcement costs, the applicability without making use of the extant heat is compared to the conventional grid reinforcement costs is 5,7 times greater is comparison to the cost for conventional grid reinforcement.
A classic approach to overcome grid bottlenecks, based on the NOVA principle, is the reinforcement of grids. This work investigated the cost effective reinforcement of low voltage networks that have been overloaded due to the integration of electric vehicle charging equipment, using a grid reinforcement algorithm. For this investigation, the cigre benchmark network was utilized based on which, basic assumptions were made regarding the number and charging power of electric vehicles. The number of dwellings within the benchmark grid was derived using the simultaneity factor for different degrees of household electrification, while the quantity of electric vehicles was estimated using current vehicle statistics and future set goals by the government. Furthermore, the costs for different grid reinforcement options in urban areas was established. Novel methods using conventional grid reinforcement, variable transformers, lithium-ion storage and combined heat and power were considered. Using the load torque approach and sensitivity matrices, the most vulnerable grid nodes were ascertained. In addition, uncertainties due to cable temperature and temperature coefficient were depicted and evaluated using a Monte-Carlo simulation.
In this thesis, it has been found that the grid reinforcement costs for the integration of electric vehicle charging equipment across the grid can be accurately estimated by placing all charging equipment at a single grid node. To determine the maximum and minimum grid expansions costs, it is sufficient to analyse the most and least sensitive grid nodes. This leads to a reduction of required computational power, since only two grid nodes need to be taken into consideration. Additionally, the cost influences of cable uncertainties are less than 4 %, and variable transformer are not a viable option to circumvent conventional grid reinforcement when considering the integration of larger amounts of electric vehicle charging equipment. Furthermore, grid reinforcement using lithium-ion storage is 10,6 times more expensive in comparison to conventional grid reinforcement, even when taken predicted lithium-ion battery prices for 2025. As for combined heat and power reinforcement costs, the applicability without making use of the extant heat is compared to the conventional grid reinforcement costs is 5,7 times greater is comparison to the cost for conventional grid reinforcement.