Challenges of the future Danish energy scenario with 50 % wind integration and how electric vehicles adaptive charging can help to mitigate.

Abstract

Denmark aims for a 50% wind integration in 2020. In the current scenario, where wind represents around 34% of domestic consumption, mothballing of conventional power plants and heavy dependency in interconnection lines is already the trend. One of the strategies from the Transmission System Operator is to integrate a big volume of flexible loads to adapt to wind production and mitigate some of its draw backs. EVs, even when a significant increase is expected in the following years, will not represent a volume of consumption that can really impact the load curve by 2020 and this type of response will rely in the short term in other flexible loads like Heat Pumps. Due to its configuration and advanced technology, EVs can participate to other services vital to the correct operation of the Electric Power System as it is provision of frequency reserves to maintain balance between consumption and generation. This work presents a solution using the adaptive charging capabilities of an EV to get the best respond in both the day-ahead market and the regulation market. The adaptive scheme will achieve: lower price for purchased electricity in the dayahead market, with higher levels of wind energy penetration, and the possibility to participate to the frequency regulation market and get revenues. All this features are gained without affecting the normal operation of the vehicle. Two different configurations for the battery of the EV are compared in this work: unidirectional and bidirectional. A fleet of 400 EVs have been modeled based on statistical survey data for EVs users driving profiles in weekdays and weekends. This fleet is managed by the figure of an aggregator who purchases electricity in the day-ahead market and bid on the frequency regulation market. The reference charging profile is a non-controlled consumption scheme of plug-and-charge. This reference model is compared first with a basic adaptive models based on weight coefficients varying according to the State of Charge of the battery and the level of wind penetration. Later on, the adaptive model is optimized, following the same indicators, seeking to maximize wind penetration while bidding to frequency regulation market the most number of times. The optimization algorithms used are Gradient Search, Genetic and Differential Evolution. The decision factor for the adaptive charging strategies is the forecast wind penetration signal with is the coefficient between the level of forecast wind production and the level of forecast consumption. The idea behind using this signal is that it will yield typically lower cost of electricity and high net wind penetration. Allowing high net wind penetration will reduce the presence of energy from other generation facilities and thus the CO2 content in the battery charge. Results show that the owner of an EV with bi-directional capabilities and Genetic Algorithm can reduce the final expenses on the EV by 20% in one year. If GSA is used instead, 36% more wind energy will be integrated in the vehicle. In addition, because currently upward regulation is provided by coal and gas fired units, 60% of the current emissions by providing this service could be cut with a GSA charging scheme.