While solar PV generation is well-established on single-family houses, there is still a lack of installations on apartment buildings. To understand the effect of sharing distributed generation, we developed two energy sharing models, a welfare optimization, and a game theoretical (bi-level) model. We introduced two type of players, the owner of distributed generation (e.g. solar PV and energy storage) and the consumers. Furthermore, we included consumer preferences by multiple objectives such as emissions reduction and distributed generation in additions to cost in the model. We applied both models to a numerical example using data from the electricity market in Texas. The results showed that welfare is maximized in both models, but shared differently between the owner of the generation and the consumers. One exception is the bi-level model with uniform price auctions, which results in a reduction in system welfare to maximize owner revenues.

The economic value of photovoltaic (PV) systems depends on country-specific conditions. This study investigates the impact of grid fees, solar irradiance and local consumption on the profitability and penetration of PV systems and batteries in renewable energy communities. The linear optimization model calculates the optimal investments into PV and storages applied on a test community, which represents the European housing situation. The comparison of eight countries considers individual heat and cooling demands as well as sector coupling. Results show that renewable energy communities have the potential to reduce electricity costs due to community investments and load aggregation but do not necessarily lead to more distributed PV. Besides full-load hours, the energy component of electricity tariffs has the highest impact on PV distribution. Under current market conditions, battery energy storage systems are rarely profitable for increasing PV self-consumption but there is potential with power pricing. Renewable energy communities enable individuals to be a prosumer without the necessity of owning a PV system. This could lead to more (community) PV investments in the short term. Hence, it hinders investments in a saturated PV market.

Energy communities enable members to create jointly value by investing in and operating distributed energy resources (DERs), including generation and storage. In this paper, we develop value allocation and stabilization algorithms for energy communities based on cooperative game theory. The method considers realistic relationships between the consumers of energy within an energy community and potential outside investors, who can form coalitions, while accounting for investment and operation of DERs. We apply the model to a numerical example using data from the Austrian electricity market. The results show how the created value from DERs could be shared between the members of the energy community. Moreover, limited area for solar photovoltaic (PV) installations may destabilize the energy community by removing incentives to participate. Still, the introduction of capacity based payments or the expansion of the limited PV resources show an stabilization effect of the energy community.

As electric vehicle market penetration grows steadily and charging demand along with it, the analysis of daily usage gains in significance. We propose in this paper a simple yet powerful tool based on a Markov chain that can model the stochastic nature of day to day usage of a charging station if adequate datasets on travel patterns are available. The model is generic and therefore can be tailored to different locations with different features. Within this work, we conducted a case study with the aim to verify the algorithm. By an additional sensitivity analysis, impacts of the made assumptions are considered. With a final analysis of two charging tariff designs the model provides valuable stochastic information about electricity consumption and annual revenues at a location of interest.