We introduce a novel, decision-focused, end-to-end methodology that directly generates storage charge and discharge profiles at sub-hourly resolutions, leveraging past pricing data and storage activity. This approach does not rely on future price predictions or intermediary price forecasting models. Our method incorporates the prior knowledge of the storage model and infers the hidden reward that incentivizes the energy storage decisions. This is achieved through a dual-layer framework, combining a predictive layer with an optimization layer. We have also developed a hybrid loss function for effective model training. The numerical experiments on synthetic and real-world energy storage data show that our approach achieves the best performance against existing benchmark methods, which shows the effectiveness of our method.

Electricity storage is used for intertemporal price arbitrage and for regulation, i.e., for balancing unforeseen fluctuations in electricity supply and demand. We present an optimization model that computes bids for both arbitrage and regulation, and ensures that storage operators can honor their market commitments at all times for all fluctuation signals in an uncertainty set that is inspired by applicable market regulations. We encode this requirement with an infinite number of functional constraints. We show that the robust constraints with functional uncertainties are equivalent to a finite number of deterministic constraints, which leads to an exact bilinear reformulation that can be solved with off-the-shelf software.

The electric vehicle (EV) battery supply chain will face challenges in sourcing scarce, expensive minerals required for manufacturing and in disposing of hazardous retired batteries. Integrating recycling technology into the supply chain has the potential to alleviate these issues; however, players in the battery market must design investment plans for recycling facilities. In this work, we propose a two-stage stochastic optimization model for computing minimum cost recycling capacity decisions, in which retired batteries are recycled and recovered materials are used to manufacture new batteries. The model is a separable concave minimization subject to linear constraints, a class for which we design a new finitely convergent global optimization algorithm based on piecewise linear approximation that solves up to 10x faster than comparable algorithms. We propose an equivalent reformulation of the model that reduces the total number of variables by introducing integrality constraints. The reformulation can also be solved by our global algorithm with drastically reduced solve times. We detail a cut grouping strategy for Benders' decomposition in the second stage which improves convergence relative to single-cut and multi-cut implementations. To produce a set of second-stage scenarios, we design an approach for generating time-series projections for new battery demand, retired battery supply, and material costs. Analysis of the optimal solutions shows that effective investment in recycling can reduce battery manufacturing costs by 22\% and reduce environmental impacts by up to 7\%.

In recent years, several metropolitan areas worldwide have announced ambitious electrification targets for their public bus systems. The problem of electrifying these large-scale public transit systems poses significant logistic challenges. It involves long-term investment decisions, including planning the acquisition of different types of battery electric buses (BEBs), the retirement of conventional buses, and the location of charging infrastructure. At the operational level, the activities of an evolving mixed fleet of conventional and electric buses must be precisely scheduled to respect the charging dynamics of BEBs and ensure a sufficient level of service throughout the transition horizon. In this work, we present a multi-period integer programming formulation of the bus fleet transition problem that includes the joint planning of yearly investment decisions and hourly operations. We show that the feasible solutions to the operational problem can be uniquely mapped to a set of cycles in an underlying graph. Taking advantage of this structure, we propose an equivalent cycle-based formulation of the problem and develop a range of policy restriction and column generation heuristics that rely on both formulations. Extensive experiments on large-scale instances based on real data from North American cities show that our approach can generate solutions with an average optimality gap of less than 2\% in a few hours. In comparison, general-purpose MILP solvers generally cannot identify a feasible solution with the same computational budget.