Flexibility integration in power and energy management of smart local distribution operators
Creators: name: Angizeh, Farhad
Publisher: name: No Publisher Supplied
Year: 2023

description: This dissertation intends to construct an integrated decision-making framework that aids Local Distribution Operators (LDOs) in coordinating potential customer-sited resources into their power and energy procurement problems while satisfying various operational requirements. We argue that current active customers with emerging demand-side capabilities, e.g., rooftop solar photovoltaic (PV), can provide a range of flexibilities that an LDO can utilize in the planning and operation of its system if efficiently integrated. We also define flexibility as a measurable quantity that is defined by an LDO’s dynamically changing response to time-dependent internal and external events. The LDO’s response is formed from the available flexibility sources identified through a generic power flow-based operation model. We show that flexibility sources aid LDOs in enhancing controllability, which refers to adjusting the system’s operating state on multiple time scales under different operation conditions. Different LDOs ranging from single-node to multi-node systems are investigated. The operation problems of a single-node LDO are addressed in Chapters 2 and 3, where the LDO intends to coordinate behind-the-meter (BTM) flexibility sources to get benefits from energy and ancillary service markets in the former, while resilience enhancement is desired in the latter. We first propose a framework that helps single-node LDOs to efficiently size and optimally dispatch their BTM energy storage systems (ESS) to achieve potential energy and demand charge savings through utility bill management (UBM) and revenue streams from energy arbitrage and frequency regulation (FR) opportunities. Further, to investigate the impacts of facility behavior on the provided flexibilities, 24 facilities with different functionalities from different end-use sectors, including residential, commercial, and industrial, are simulated. Next, the same problem is addressed for resiliency purposes during grid outages. We propose a decision-making tool developed on a probabilistic energy procurement-based model to quantify the resilience enhancement for standalone and hybrid ESS (coupled with solar PV) configurations under the uncertainty of power outages. In Chapter 4, the potential flexibility sources are incorporated in a multi-node LDO framework which aids the LDO in coordinately integrating shared flexibility sources for resilience enhancement under upstream grid emergencies. We show that co-optimization of potential joint flexibility sources in a coordinated framework can increase the operational flexibility of the LDO during extreme events. An integrated optimization model, which is formulated as a mixed-integer linear programming (MILP) problem with approximated linear AC power flow equations, is developed to minimize the total operation cost of the LDO in both normal and emergency cases. To reveal the merits of the proposed model, multiple case studies are investigated through the modified IEEE 16-node test feeder, where we decompose the original system into 6- and 10-node systems. In Chapter 5, a real multi-node LDO is implemented where a broader set of potential flexibility sources are integrated to optimize the operation schedule of the LDO in an energy procurement-based framework. We propose a novel hierarchical optimization framework that couples the balancing problem of the LDO with building energy management (BEM) to form an integrated model capable of capturing potential joint-flexibilities of connected buildings and the LDO-operated assets. The proposed framework comprises a network-level power flow-based model to solve the energy procurement problem of the LDO and a building-level physics-aware simulation model to estimate a set of day-ahead aggregate load scenarios with potential flexibilities. A test case consisting of four verified EnergyPlus building models representing a real connected building community at Qatar University is implemented through various case studies to reveal the practicality of the proposed tiered two-level optimization framework. Finally, in Chapter 6, we further extend our investigation of flexible loads in the LDO context, where we define four generic flexible load classes modeled based on their inherent service controllability and/or interruptibility. A Load Rescheduling (LR) program that enables LDO to fully capture potential load flexibilities is introduced where the LDO directly energizes the eligible customers’ loads given the customers’ desired service requests that include service start and end times. Further, to make the proposed flexible load model more realistic, the service priority constraint is considered to model the operation correlations/ dependencies between the flexible loads. The simulation results on the modified IEEE 33-node test distribution system reveal that the LDO can potentially cut its total operation costs by 10% if it strategically incorporates all potential flexible loads.