|dc.description.abstract||Electric power systems are experiencing a movement toward increasing the share of renewable energy sources (RESs), while having to cope with the retirement of conventional generating units to facilitate an eco-friendly system. However, the uncertainty and variability associated with RESs and the demand, call for additional sources of flexibility. Residential, commercial, and industrial loads are a potential source of flexibility in power systems. In addition, recent deployments of energy storage systems (ESSs) can contribute significantly to power system flexibility. Therefore, the effective management of flexible sources can lead to an improved power system operation.
This thesis investigates options for capturing the flexibility of residential loads and ESSs in a power distribution system. A two-stage optimization framework is developed wherein multiple home energy management systems (HEMSs) simultaneously optimize their respective energy consumption patterns, and determines their flexibility provisions, which are communicated to the local distribution company (LDC). A flexibility evaluation approach is developed to estimate the residential energy hub (REH) flexibilities at each bus in the distribution system. Intra-hour flexibility indices are calculated to represent the REHs' willingness to alter their consumptions. Different clusters of residential customers are considered, classified by their ownership of photovoltaic (PV) panels and ESSs, and their preferred objectives. The LDC aggregates the controllable demand profiles and the flexibilities of each HEMS to optimize its operational performance and hence determines peak reduction signals that are sent to the HEMSs. Studies are carried out considering a 33-bus distribution system coordinating with 1,295 houses connected at different buses, with varying customer preferences and objectives, to demonstrate the applicability of the proposed scheme.
ESSs can improve the energy management in distribution systems, especially with the increasing penetration of HEMSs that schedule household appliances and render them as smart loads. A large number of uncoordinated HEMSs can result in significant changes to the aggregated load profile of the distribution system. Therefore, a new framework and mathematical model for integrating ESSs in the distribution grid is proposed to minimize the operation cost of the LDC and to alleviate the impact of uncoordinated HEMS operation on the distribution grid. A novel neural network (NN) based state-of-health (SoH) estimator for a lithium-ion (Li-ion) battery based ESS is proposed, which is incorporated within the LDC's planning problem. The results show that the proposed estimation model is an accurate estimation of the SoH of the ESS. Also, the LDC's ESS investment plan decisions are compared considering the proposed SoH of the ESS vis-_a-vis a linear degradation model, and when degradation of ESS is not considered in planning.
The third research problem addressed in the thesis investigates the ESS's role in providing the LDC with flexibility services. A novel flexibility service framework is developed based on the battery energy storage system (BESS)s' in providing different levels of charge rate (C-Rate). This work proposes a cooperative game theory based approach to determine the allocation of monetary benefits among flexible BESSs. The proposed model ensures a fair distribution of monetary gains among the coalition members and proposes a novel flexibility pricing scheme.||en