Large-Scale Optimization for Planning of Reliable Power Systems and Design of Sustainable Biomass Supply Chains

Abstract

Power systems have become more vulnerable to cascading failures and blackouts with the growth of transmission and generation networks. Critical loads and power blackouts can threaten with massive economic damage to the society The situation worsens due to the added complexity of their intermittent behavior of the growing penetration of renewable energy sources. Thus, the importance to achieve a reliable power system, i.e., a system able to meet the end-users' electricity needs when considering unexpected contingencies or any other factor that may reduce the electricity availability. In the first part of this dissertation, stochastic and robust optimization approaches are applied for large-scale modeling of reliable expansion and operations of power systems under consecutive contingencies. In the two-stage stochastic optimization model, the expansion planning and operational variables under normal conditions are the "here and now" decisions. The operational variables under contingency are the "wait and see" decisions with a respective contingency probability assigned. First, a study for reliable power grid expansion considering N-1-1 contingencies is presented. The N-1-1 contingency consists of the consecutive loss of two components in a power system with intervening time for system adjustments between failures. Second, a two-stage stochastic power grid expansion considering multiple N-1-1 contingencies is studied. A reliable power system expansion planning may be achieved by placing new transmission lines and generation units while checking the grid's survivability under different contingency scenarios with defined probabilities. Finally, a study of N-1-1 contingency-constrained unit commitment with renewable integration and corrective actions is presented. Meeting the end-use customers' demands is crucial for energy companies, even when unexpected and consecutive failures are present. This task has increased its complexity considerably due to the high integration of renewable energies and their intermittent behaviors. Therefore, it is important to achieve reliable power based on a criterion closer to real-life power system operations and capable of addressing consecutive failures. In the second part of this dissertation, as for the biomass supply chain, nowadays, the consideration of life cycle analysis in the supply chains is called for by the emergence of bioeconomy strategies worldwide. In the U.S., the Sustainability Bioeconomy for Arid Regions (SBAR) is a multi-level research project for a sustainable bioeconomy through the cultivation of guar and guayule in the Southwestern regions. This study is part of the SBAR project. Here, a stochastic approach is applied to large-scale mixed-integer linear optimization models for the design and operations of guar and guayule supply chains. In the two-stage stochastic optimization model, the construction of the processing facilities are the "here and now" decisions. The harvesting, transportation, processing, and distribution operations are the "wait and see" decisions given an adoption rate probability for guar and guayule. First, a study for integrating environmental and social impacts into the optimal design of guayule and guar supply chains is presented. Guayule and guar are two desert-dwelling crops that can provide raw materials year-round for bioproducts such as rubber, resin, guar gum, and guar meal. Second, the former study is expanded to the American Southwest including the states of Arizona, New Mexico, and Texas. Finally, the optimal production planning and machinery scheduling for semi-arid farms is studied. Scarce water resources have made production planning a key management decision in the agriculture sector, especially in arid and semi-arid regions. To address this issue, farmers can gradually adopt low-water-use crops, such as guar and guayule, which have great potential for the agricultural economy of the Southwestern U.S. The farmers' profits can be further increased by reducing machinery transportation costs through optimized scheduling.