Optimization and Control of Storage in Smart Grids

The power system transformation brings new challenges and opportunities due to changes and uncertainties in electricity consumption and generation. High integration of intermittent solar and wind generation requires fast ramping resources to satisfy the growing demand, triggered by the electrification of the transportation sector. Energy storage is one possible solution to facilitate such a transformation. In this thesis Li-Ion battery, as energy storage, is used both at the level of individual consumers minimizing the cost of electricity and at the grid level for increasing reliability and stability of the power network. The cost of the batteries being still high, the importance is also given to the health of the battery taking into account its degradation in optimization and control formulations. Electricity consumers with local renewable generation such a rooftop solar generation can use a battery to minimize their cost of operation. We formulate storage optimization problem under time-varying electricity prices with different net-metering policies. The strong Lagrangian theory based storage operation solution is developed with threshold-based structure of the optimal charging policy. The proposed algorithm is computationally efficient with quadratic worst-case complexity with respect to the horizon length. Due to their high-cost batteries need to be used for more than one dedicated application for becoming financially viable. New billing mechanisms worldwide facilitate such a storage operation. In the co-optimization formulations, we consider storage performing energy arbitrage under net metering along with power factor correction, peak demand shaving, and energy backup for power outages. These formulations are evaluated on case studies using real data. To implement the above algorithms in real time, autoregressive forecast in context of receding/rolling horizon model predictive control is proposed. Further, prosumer storage applications are explored for low voltage consumers in Madeira and Uruguay. These control policies are tailored based on consumer contracts proposed by the utility. Large-scale storage applications facilitating dynamic regulation and phase balancing using storage are proposed for increasing power system reliability. A distributed stochastic control is implemented for a fleet of geographically distributed batteries for tracking fast timescale supply and demand imbalance, while also ensuring that the mean charge level of the fleet stays close to the desired level. Based on real-world data case studies for a substation in Madeira and network-based simulations, we show that phase unbalance can be an outcome of ad hoc distributed generation and electric vehicle charging placement in the context of phase imbalance. Storage control architectures are proposed for reducing the phase imbalance. Battery life is commonly quantified using cycle life and calendar life. In other words, battery degradation happens due to operational cycles and the age of the battery. To increase the operational life of the battery, age-based storage degradation should be matched with the operational cycle based degradation. We propose an effective mechanism to control the cycles of operation, thus reducing the stress on the battery. Such considerations of storage health are embedded in the control and the optimization design for the batteries. Several case studies using real data are conducted to evaluate the performance of the storage control and optimization algorithms. We observe that the ever-decreasing prices of batteries and the growing share of intermittent renewables will only increase the relevance of this work for future power networks.