The global energy landscape is going through major shifts triggered by consumer preferences, regulations, and technological development. My dissertation develops optimization models to de-rive insights into strategic decisions in energy operations management. In the first essay, I examine how blockchain-enabled peer-to-peer energy trading shifts electricity consumers’ investment in renewable energy. Using the equilibrium model, I show that electricity consumers are always better off by participating in the virtual network, with their resulting cost savings averaging 9.7%. I also prove that blockchain is able to fully coordinate heterogeneous participants in the network to minimize the total cost in the system. The second essay addresses how a Transmission System Operator (TransCo) can optimally invest in a long-distance transmission line to allow renewable energy development by a Power Generation Company (GenCo) in a geographically remote region. Using a continuous-time, infinite-horizon, Stackelberg game between TransCo and GenCo, I show that transmission and generation act as complements with regard to the value functions for both companies. I derive the value-maximizing transmission fee charged by TransCo to GenCo for each unit of energy exported via transmission lines. I characterize a Pareto-improving cost-sharing contract through which both companies can improve the value of their investment. The third essay focuses on how to better manage a decentralized supply chain of an oil-field service company. To minimize the transportation and inventory holding costs of different members in a cross-docking supply chain, I formulate multi-period, mixed-integer programming models. I use structural properties of optimal solutions to show that different collaborations in the supply chain can generate significant cost savings for individual supply chain members, whereas the quantified cost savings exhibit significant variations depending on product weight and holding cost. I also develop a Stackelberg pricing game ...