Zone-Distributed Optimization System for Energy Management in Smart Grids

Abstract

Energy management system (EMS) is an important component of smart grid operation. A proper EMS is the key to the integration of smart grid (SG) features, which include two-way communication, smart metering as well advanced control algorithms, in order to operate different components of SG efficiently and constructively. EMSs are both applied on the entity’s and the system’s level. On the entity level, individual entities don’t coordinate with the system operator to optimize the objectives globally which would lead to inefficient solutions. System level EMS is implemented using centralized and decentralized approaches. Some of crucial drawbacks of the centralized EMS approach are that it’s incapable of considering customer preferences while optimizing the operation of the networks, in addition to the lack of flexibility and scalability. Nevertheless, in the decentralized EMS approach, the customers and the system operator share information and interact constructively in optimizing their objectives. However, this approach still requires central coordination and long back-forth process to converge, as there is no shared background of mathematical foundation. In addition to that, most of the proposed decentralized techniques, do not consider network constraints, especially for unbalanced systems. This thesis proposes a Zone-Distributed Optimization System (ZDOS) using distributed semi-definite programming for energy management of a SG. ZDOS divides distribution systems into numerous micro grid-like regions, or zones, to facilitate smart grid operation. The proposed ZDOS divides the grid into a number of Zone-Specific Optimization Subsystem (ZSOS), each responsible for controlling and managing the activities inside a zone. The ZDOS clusters the distribution system based on the customer-class (residential, commercial and industrial). Each class is controlled by ZSOS in order to optimize a certain objective function, as well as a set of constraints which are consistent with customer’s nature, preferences, requirements and the applied Demand Response (DR) strategy. Furthermore, ZSOS’s of connected zones exchange local information at the point of connectivity indicating the desired power exchange and voltage level until the iterative process of every ZSOS is satisfied. Simulations and analysis are conducted on a modified 123-IEEE test system, which include diesel generators, renewable energy resources, and energy storage systems. The system is tested under different scenarios and demand response strategies. The analysis has shown that the results obtained by ZDOS are valid as the supply and demand are always balanced. Furthermore, the performance of ZDOS for minimizing operational costs has significantly improved when applying the DR, compared to ZDOS results without DR. The effectiveness of a multi-objective ZDOS when taking into consideration the preferences and requirements of different customers has been proven.