Hybrid Renewable Energy Systems (HRES): Decision Guidance for Operation, Investment and Efficient Resilience Markets



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This dissertation focuses on Hybrid Renewable Energy Systems (HRES). HRES typically comprises an elaborated energy grid that relies on multiple sources – most prevalent of which are renewable sources such as solar, wind, and hydro, combined with more traditional sources such as fossil-fired power generators, and the placement of energy storage technology at key locations of the grid, to establish a reliable, cleaner and stable supply of power. This dissertation poses and addresses two interrelated problems of (1) establishing a modular framework for guiding optimized decisions on investment in and operations of HRES and (2) creating effective resilient mechanisms for minimizing economic losses across multiple entities during catastrophic power shortages. Regarding the first part, there is a lack of flexible solutions for integrating investment and operations optimization, across a complex composite power network, leveraging rigorous mathematical programming methods. As for the second part, most resilience solutions focus on areas such as prevention, corrective actions, and load restoration, and not enough attention is given to the need for mitigation solutions that preserve against economic loss as their capacities become degraded. To address these problems, a framework is first developed to support the introduction of renewable energy generation and carbon emissions constraints into an existing electric power network, and the key operational decisions regarding its configuration. It is followed by the implementation of a mixed-integer linear programming (MILP) model and tool to support short term operational decisions. Second, a formal modular and extensible analytic performance model (PM) for operational and investment decisions in the HRES is developed to represent the composite power network as well as a knowledge base of power network components, including metrics of interest and feasibility constraints as a function of investment and operation decision variables. Third, a Decision Guidance System (DGS) is developed, which leverages the knowledge base of reusable HRES models, to allow different decision-making mechanisms to include optimization, and trade-off analysis in support of investment and policy decisions. Fourth, a real-world microgrid case study, based on data from a municipal electric utility, is used to demonstrate the applicability of the HRES model and DGS. Lastly, a formal framework for a cooperative power rights market is proposed, designed to minimize the overall economic loss of participating business entities during a significant power supply shortage due to a catastrophic event. The formal market framework involves three stages: (1) a decision by each entity on bids and asks submitted to the market; (2) a market mechanism and resolution to determine bought and sold power allowances by the entities; and (3) given the market clearance, determination, by each entity, of its precise operational controls. Critically, the desired properties of the market system are defined, namely, (a) Pareto-optimality, (b) individual rationality, and (c) fairness. Algorithms for each of the three stages of the market system are presented, together with a formal proof that they satisfy the listed desirable properties. A simplified version of the combined algorithm and a numerical example is also presented for the special case of discrete, independent value-adding activities together with a simple application for a numerical example.



Cooperative Markets, Decision Guidance, Hybrid Renewable Energy System, Mixed Integer Linear Programming, Optimization, Resilience