Application of Complex Network Theory in Power System Security Assessment

The power demand increases every year around the world with the growth of population and the expansion of cities. Meanwhile, the structure of a power system becomes increasing complex. Moreover, increasing renewable energy sources (RES) has linked to the power network at different voltage levels. These new features are expected to have a negative impact on the security of the power system. In recent years, complex network (CN) theory has been studied intensively in solving practical problems of large-scale complex systems. A new direction for power system security assessment has been provided with the developments in the CN field. In this thesis, we carry out investigations on models and approaches that aim to make the security assessment from an overview system level with CN theory. Initially, we study the impact of the renewable energy (RE) penetration level on the vulnerability in the future grid (FG). Data shows that the capacity of RE has been increasing over by 10% annually all over the world. To demonstrate the impact of unpredictable fluctuating characteristics of RES on the power system stability, a CN model given renewable energy integration for the vulnerability analysis is introduced. The numerical simulations are investigated based on the simplified 14-generator model of the South Eastern Australia power system. Based on the simulation results, the impact of different penetrations of RES and demand side management on the Australian FG is discussed. Secondly, the distributed optimization performance of the communication network topology in the photovoltaic (PV) and energy storage (ES) combined system is studied with CN theory. A Distributed Alternating Direction Method of Multipliers (D-ADMM) is proposed to accelerate the convergence speed in a large dimensional communication system. It is shown that the dynamic performance of this approach is highly-sensitive to the communication network topology. We study the variation of convergence speed under different communication network topology. Based on this research, guidance on how to design a relatively more optimal communication network is given as well. Then, we focus on a new model of vulnerability analysis. The existing CN models usually neglect the detailed electrical characteristics of a power grid. In order to address the issue, an innovative model which considers power flow (PF), one of the most important characteristics in a power system, is proposed for the analysis of power grid vulnerability. Moreover, based on the CN theory and the Max-Flow theorem, a new vulnerability index is presented to identify the vulnerable lines in a power system. The comparative simulations between the power flow model and existing models are investigated on the IEEE 118-bus system. Based on the PF model, we improve a power system cascading risk assessment model. In this research the risk is defined by the consequence and probabilities of the failures in the system, which is affected by both power factors and the network structure. Furthermore, a cascading event simulation module is designed to identify the cascading chain in the system during a failure. This innovation can form a better module for the cascading risk assessment of a power system. Finally, we argue that the current cyber-physical network model have their limitations and drawbacks. The existing “point-wise” failure model is not appropriate to present the interdependency of power grid and communication network. The interactions between those two interdependent networks are much more complicated than they were described in some the prior literatures. Therefore, we propose a new interdependency model which is based on earlier research in this thesis. The simulation results confirm the effectiveness of the new model in explaining the cascading mechanism in this kind of networks

The integration of distributed energy resources into electric power systems

Small-scale, residential, and distributed energy resources (DER), which are electric vehicles (EVs), heat pumps (HPs), and photovoltaic (PV) arrays, were studied to evaluate their impact on the UK future residential demand and their impact on UK distribution networks. Centralized and decentralized controllers were planned in order to defer reinforcement, while connecting DER units to distribution networks. The centralized controller allocates EV charging durations considering network constraints. The decentralized controller adjusts EV and HP loads based on consumer satisfaction, network constraints, and electricity prices. Normal probability distribution and median filter were used to predict aggregated power of EVs, HPs, and PV arrays on a half-hourly basis over a year. Because of an expected surplus of PV power generation, a considerable demand reduction followed by a sharp demand increase will occur with these residential DER units during summer days in 2035. A low voltage section of test network was used to study the impact of uncontrolled EV charging loads on a three-phase four-wire system. Different combinations of EVs, HPs, and PV arrays were used to investigate their uncertainties in a low voltage section of real network. Real-world trials were used to generate the individual power of residential customers and DER units. Results of unbalanced power flow indicated that network constraints exceeded their limits with a high number of these low carbon technologies. Using an extended section of the test network, the central controller maintains voltage magnitudes, voltage unbalance factors, and power flows within their limits, by re-allocating EV charging durations accordingly. The decentralized controller was designed to minimize electricity bills of EV and HP users. This controller adjusts EV and HP loads to maintain consumer satisfaction and network constraints within their specified boundaries. Consumer satisfaction was determined using mathematical models of EV battery state-of-charge levels and the indoor temperatures of HP houses. The decentralized controller was used to connect predicted numbers of EVs and HPs to a real distribution network, while overcoming the need for network reinforcement, third parties (aggregators), and extensive communication systems