Distribution Network Planning and Operation With Autonomous Agents

With the restructured power system, different system operators and private investors are responsible for operating and maintaining the electricity networks. Moreover, with incentives for a clean environment and reducing the reliance on fossil fuel generation, future distribution networks adopt a considerable penetration of renewable energy sources. However, the uncertainty of renewable energy sources poses operational challenges in distribution networks. This thesis addresses the planning and operation of the distribution network with autonomous agents under uncertainty. First, a decentralized energy management system for unbalanced networked microgrids is developed. The energy management schemes in microgrids enhance the utilization of renewable energy resources and improve the reliability and resilience measures in distribution networks. While microgrids operate autonomously, the coordination among microgrid and distribution network operators contributes to the improvement in the economics and reliability of serving the demand. Therefore, a decentralized energy management framework for the networked microgrids is proposed. Furthermore, the unbalanced operation of the distribution network and microgrids, as well as the uncertainty in the operating modes of the microgrids, renewable energy resources, and demand, are addressed. The second research work presents a stochastic expansion planning framework to determine the installation time, location, and capacity of battery energy storage systems in the distribution network with considerable penetration of photovoltaic generation and data centers. The presented framework aims to minimize the capital cost of the battery energy storage and the operation cost of the distribution network while ensuring the security of energy supply for the data centers that serve end-users in the data network as well as the reliability requirements of the distribution network. The third research work proposes a coordinated expansion planning of natural gas-fired distributed generation in the power distribution and natural gas networks considering demand response. The problem is formulated as a distributionally robust optimization problem in which the uncertainties in the photovoltaic power generation, electricity load, demand bids, and natural gas demand are considered. The Wasserstein distance metric is employed to quantify the distance between the probability distribution functions. The last research work proposes a decentralized operation of the distribution network and hydrogen refueling stations equipped with hydrogen storage, electrolyzers, and fuel cells to serve hydrogen and electric vehicles. The uncertainties in the electricity demands, PV generation, hydrogen supply, and hydrogen demands are captured, and the problem is formulated as a Wasserstein distance-based distributionally robust optimization problem. The proposed framework coordinates the dispatch of the distributed generation in the distribution network with the hydrogen storage, electrolyzer, and fuel cell dispatch considering the worst-case probability distribution of the uncertain parameters. The proposed frameworks limit the information shared among these autonomous operators using Benders decomposition