By 2030 the sales of electric vehicles (EV) are set to increase 6 to 30 folds compared to the levels of 2019 thereby leading to an increase of discarded EV batteries. This creates a challenge to sustainably handle the waste by repurpose and recycling the EV batteries. In this paper, a method to analyze the usage of EV batteries throughout their life cycle in light of their contributions to a circular economy is proposed. The study is divided into two stages which are modelled with mixed-integer linear optimization. Stage 1 considers the optimal charging strategy for an EV and stage 2 represents the second-life of the EV battery as stationary energy storage in a residential building. Six scenarios were created for both stages; stage 1 includes smart charging and/or Vehicle to Grid (V2G) and stage 2 adds demand side management and/or PV self-consumption maximization to stage 1. A sensitivity analysis is performed to assess the impact of electricity prices. The results are analysed to assess the operational lifetime and economic savings for an EV owner. The results show that using second-life batteries in a residentail building can extend the lifetime of an EV battery by 3-5 years while allowing savings above 23%.

Increasing environmental restrictions, create stringent regulations for the use and management of rivers and water bodies, thereby jeopardizing the operational flexibility of hydropower plants. The national plan for new environmental regulations in Sweden is expected to be implemented in 2025. Hence, it is imperative for the hydropower producers to consider alternatives which can overcome the environmental restrictions for providing flexibility. In this paper, a Mixed Integer Linear Programming (MILP) based algorithm for the short-term regulation of hydropower plants with energy storage is proposed. There are two stages of the algorithm: Future electricity market prices model using machine learning techniques, and the combined hydropower plant and battery system (CHBS)'s operation model using MILP. The designed algorithm is used to analyze the techno-economic feasibility of the operation of three hydropower plants Skattungbyn, Unnan and Hansjo, located in lower Orealven river in Sweden. Three different electricity market scenarios were developed based on Swedish nuclear energy targets for 2040. The forecasted market prices act as inputs for the designed optimization algorithm. The algorithm optimizes the operation of hydropower plants with an objective to maximize revenue through harnessing flexibility enabled by storage. It is observed from the results that with the current battery costs (approximate to 0.36 million euro/MWh), the CHBS for short-term regulation is not profitable with NPV ranging from -1 to -4 million euro and the cost of battery needs to be less than 50 000 euro/MWh to make it economically feasible.