Fluctuating electricity prices offer potential economic savings for the consumption of electricity by flexible assets such as Electric Vehicles (EVs). This study proposes an operational bidding framework that minimizes the charging costs of an EV fleet by submitting an optimized bid to the day-ahead electricity market. The framework consists of a bidding module that determines the most cost-effective bid by considering an electricity price and an EV charging demand forecast module. In this study we develop and evaluate several regression and machine learning models that forecast the electricity price and EV charging demand. Furthermore, we examine the composition of a most optimal operational bidding framework by comparing the outcome of the bidding module when fed with each of the forecast models. This is determined by considering the day-ahead electricity price and imbalance costs due to forecast errors. The study demonstrates that the best performing self-contained forecast models with the objective of electricity price and EV charging demand forecasting, do not deliver the best overall results when included in the bidding framework. Additionally, the results show that the best performing framework obtains a 26% cost savings compared to a reference case where EVs are charged inflexibly. This corresponds to an achieved savings potential of 92%. Consequently, along with the developed bidding framework, these results provide a fundamental basis for effective electricity trading on the day-ahead market.

Power ow simulations form an essential tool for electricity network analysis but conventional models are designed to work on a separated transmission or distribution network only. The continuing growth of electricity consumption, demand side participation, and renewable resources makes the electricity net- works co-dependent. Integrated models incorporate the coupling of the net- works and interaction that they have on each other, representing the power ow within this changing environment accurately. Several numerical methods are available to solve the power ow problem on integrated networks. They can be categorized as a untied or as a splitting method and networks can be modelled as a homogeneous or hybrid network. In this paper, we review and assess these methods on the network models by running simulations on small test networks and comparing the outcome on their numerical performance, ie on convergence rate and CPU-time. The re- view shows that the convergence rate is comparable for most of the methods, but that hybrid networks have a slight advantage in computational time. Realistic network models, running on millions of buses and with large distribution networks, should give a better insight into the speed of the computations.

An integrated network consists of a transmission network and at least one distribution network which are connected to each other via a substation. One way to do power flow simulations on these integrated networks is the Master-Slave splitting method. This method splits the integrated network and iterates between the separate transmission (the master) and distribution (the slave) network. In this paper, we extend the method to hybrid networks: a network consisting of a balanced transmission and an unbalanced distribution network. An extra handling is necessary to get the Master-slave splitting to work on hybrid networks. We explain two approaches to use the Master-Slave splitting on a hybrid network and compare these approaches on accuracy, computational time, and convergence, by doing test-simulations. The Master-Slave splitting is interesting when distribution and transmission systems have different characteristics, are in geographically distinct locations, or when system operators are not able or allowed to share data of their network with each other. The extension to hybrid networks makes this method generally applicable and an interesting choice to do power flow simulations on integrated networks.