It is generally recognized that a higher penetration of renewable power on the electric grid, along with the attendant environmental benefits,... Show moreIt is generally recognized that a higher penetration of renewable power on the electric grid, along with the attendant environmental benefits, is limited by its inherent high variability and intermittency. An approach to alleviating this issue is to install grid scale energy storage as buffer. However, the economic viability of such an endeavor is dependent on the optimal sizing and placement (OSP) of storage units, which in turn requires the specification of an appropriate storage management policy. While stochastic programming with recourse is recognized as the standard approach to stage-wise optimal decision-making under uncertainty, Economic Model Predictive Control (EMPC) is put forward as a deterministic simplification of the former and demonstrated to be a viable economic dispatch strategy for networks with a high proportion of renewable energy and storage. Then, a numerical, EMPC-based gradient search strategy is proposed to address the OSP problem. Since both the operating policy and OSP questions are invariably massive optimization problems in real systems, strong emphasis is laid on computational tractability. Therefore, the analytical nature of a surrogate stochastic control policy, Economic Linear Optimal Control (ELOC), is exploited to develop innovative modifications to both algorithms. The end products are (1), an Approximate Infinite Horizon EMPC (AIH-EMPC) strategy, a relatively low computational cost variant of EMPC and (2), a hybrid EMPC-ELOC OSP strategy that essentially sidesteps the inherent combinatorial complexity of the unit location problem. Show less
