The Vulkan real estate site in Oslo is owned by Aspelin Ramm, and includes one of the largest parking garages used for EV charging in Europe. EV charging (both AC and DC) is managed for now predominately for costs reasons but also with relevance at further EV penetration level in this car parking location (mixed EV and ICE vehicles). This neighbourhood scale SEEV4-City operational pilot (OP) has 50 22 kW flexible AC chargers with two sockets each and two DC chargers of 50 kW with both ChaDeMo and CCS outlets. All EV chargers now have a smart control (SC) and Vehicle-to-Grid (V2G) functionality (though the latter may not be in place fully for DC chargers, as they may not be fully connected to the remote back-office system of the EV charging systems operator). A Lithium-ion Battery Energy Stationary Storage System (BESS) with a capacity of 50 kWh is pre-programmed to reduce the energy power peaks of the electric vehicle (EV) charging infrastructure and charges at other times from the central grid (which has a generation mix of 98% from hydro-electric power, and in the region covering Oslo also 1% from wind). The inverter used in the BESS is rated at 50 kW, and is also controlled to perform phase balancing of the 3-phase supply system.
Peer-to-peer (P2P) energy trading has been recognized as an important technology to increase the local self-consumption of photovoltaics in the local energy system. Different auction mechanisms and bidding strategies haven been investigated in previous studies. However, there has been no comparatively analysis on how different market structures influence the local energy system’s overall performance. This paper presents and compares two market structures, namely a centralized market and a decentralized market. Two pricing mechanisms in the centralized market and two bidding strategies in the decentralized market are developed. The results show that the centralized market leads to higher overall system self-consumption and profits. In the decentralized market, some electricity is directly sold to the grid due to unmatchable bids and asks. Bidding strategies based on the learning algorithm can achieve better performance compared to the random method.
Hybrid Energy Storage System (HESS) have the potential to offer better flexibility to a grid than any single energy storage solution. However, sizing a HESS is challenging, as the required capacity, power and ramp rates for a given application are difficult to derive. This paper proposes a method for splitting a given load profile into several storage technology independent sub-profiles, such that each of the sub-profiles leads to its own requirements. This method can be used to gain preliminary insight into HESS requirements before a choice is made for specific storage technologies. To test the method, a household case is investigated using the derived methodology, and storage requirements are found, which can then be used to derive concrete storage technologies for the HESS of the household. Adding a HESS to the household case reduces the maximum import power from the connected grid by approximately 7000 W and the maximum exported power to the connected grid by approximately 1000 W. It is concluded that the method is particularly suitable for data sets with a high granularity and many data points.
MULTIFILE
As electric loads in residential areas increase as a result of developments in the areas of electric vehicles, heat pumps and solar panels, among others, it is becoming increasingly likely that problems will develop in the electricity distribution grid. This research will analyse different solutions to such problems to determine Using a model developed as part of this project, we will simulate various cases to determine under which circumstances load balancing at a community-level is more (cost) effective than alternative solutions (e.g. grid reinforcement and/or household batteries).
