Microgrids: Resilience, Reliability, and Market Structure

This dissertation addresses several challenges corresponding to the operation and planning of microgrids in distribution networks including the formation, energy supply resilience, the contribution of microgrids in the short-term and mid-term operation of bulk power networks and the computational challenges in operation of interdependent infrastructure systems. An approach to transforming the active distribution network with distributed energy resources into multiple autonomous microgrids is presented in the first section. The distribution network consists of several generation resources and demand entities that could be clustered into autonomous microgrids. Eigen decomposition in the graph spectra of the distribution network is leveraged to determine the boundaries for microgrids and a mixed-integer programming problem that minimizes the expansion cost within microgrids. Once microgrids are formed, in the next section, a framework is proposed to identify the vulnerable components, and ensure the resilient operation of coordinated electricity and natural gas infrastructures considering deliberate disruptions. The microgrid demands, which consist of electricity and heat demands, are served by the interdependent electricity and natural gas supplies. The proposed approach addressed the vulnerability of multiple energy carrier microgrids to deliberate disruptions, in order to apply preventive reinforcements to improve the resilience of energy supply. The proposed methodology is formulated as a bi-level optimization problem to address the optimal and secure operation of multiple energy carrier microgrids. To investigate the contribution of microgrids in the bulk power network, in the next section, a hierarchical structure for the electricity market is proposed to facilitate the coordination of energy markets in distribution and transmission networks. The proposed market structure facilitates the integration of microgrids in the electricity markets to provide energy and ancillary services. In the proposed hierarchical structure, microgrids participate in the energy market at the distribution networks settled by the distribution network operator, and load aggregators interact with microgrids and generation companies to import/export energy to/from the distribution network electricity markets from/to the wholesale electricity market. Furthermore, the impact of microgrids in the mid-term operation of bulk power systems is investigated. A new framework for risk-averse mid-term generation maintenance scheduling in the power systems is presented. Microgrid aggregators facilitate the participation of microgrids in the wholesale market. The effect of microgrids as controllable demand entities on the generation maintenance scheduling practices in the power system is investigated. The uncertainties in the marginal cost of generation in microgrids, the generation capacity installed within the microgrids, and the system electricity demand are captured using respective nominal values and uncertainty intervals. Moreover, the contingencies in transmission network are addressed by introducing additional variables. A two-stage robust optimization problem is formulated to determine a trade-off between the performance and conservativeness of the procured solution in the long-term operation horizon. The last section of this dissertation addresses the coordinated operation of interdependent electricity and natural gas infrastructure systems to improve the security and reliability measures in both infrastructure systems and mitigate the risk of demand curtailment. The electricity and natural gas network operation problems are non-convex mixed-integer nonlinear programming problems that are hard to solve in polynomial time. The non-convex feasible region is formed by the Weymouth constraint and the introduced binary commitment decision variables in the natural gas and electricity network operation problems respectively. A sparse semidefinite programming (SDP) relaxation is utilized to procure the optimal solution for the coordinated operation of electricity and natural gas networks. The presented algorithm leverages the sparseness of the natural gas network to construct several small matrices of lifting variables that are used to form a tight and traceable SDP relaxation. A set of valid constraints that tighten the relaxation ensures the exactness of the solution procured from the relaxed problem