Stochastic Energy Management and Cyber-Physical Security of Battery Energy Storage Systems in Smart Distribution Systems

• Author / Creator

  Peng Zhuang

• Battery energy storage systems (BESSs) are vital for improving the sustainability, efficiency, and resiliency of smart distribution systems (SDSs). With the proper energy management, BESSs can provide a wide range of applications for both demand-side and grid-scale services in SDSs. However, there are various elements in SDSs with randomnesses, such as renewable energy sources (RES) power output, load demand, electricity price, and the mobility of residential electric vehicles (EVs) and electric buses (EBs), which can significantly affect the performance of energy management of BESSs. Moreover, due to the highly coupled cyber and physical systems in SDSs, the cyber-physical attacks that leverage common cyber attacks to stealthily cause cascaded physical failures seriously threaten the effective and reliable energy management of BESSs. In this thesis, the stochastic energy management and cyber-physical security of BESSs in SDSs are investigated from four main aspects to improve BESS integration in SDSs.

  To improve the performance of BESSs in demand-side electricity usage cost reductions under randomness, the stochastic energy management of demand-side BESSs is investigated with its application in greenhouses. A stochastic multi-timescale energy management scheme for greenhouses with RES and energy storage systems is proposed. The optimal energy management problem is formulated as a multi-timescale Markov decision process (MMDP) to address the randomness. In particular, a fast-timescale (FTS) process is used to model the rapidly changing electrical process with fine granularity, while a slow-timescale (STS) process is used to model the gradually varying thermal process to reduce computational complexity. The exact solution of the optimal energy management problem is derived to minimize the greenhouse operating cost. Then, an approximation solution is developed to reduce the computational complexity further.

  In SDSs, the highly penetrated demand-side BESSs that are driven by the objective of electricity usage cost reductions can unintentionally induce residential electricity usage to negatively impact power system operations. In the second work, the stochastic energy management of residential BESSs at high penetration levels in SDSs is studied. A hierarchical and decentralized stochastic energy management scheme is proposed. This energy management problem is formulated in a two-layer hierarchical architecture and is solved in a decentralized manner. In the lower layer, individual BESS's energy management problem is formulated as a Markov decision process (MDP) to minimize the electricity usage cost. In the upper layer, the solutions of individual BESSs are used to minimize line losses and regulate voltage levels, which is formulated as a decentralized partially observable MDP (POMDP). A heuristic search and pruning method is proposed to reduce the computational complexity for practical applications.

  The EBs with bus-to-grid (B2G) capabilities can function as mobile BESSs to provide energy storage capabilities. However, the mobility of EBs introduces great challenges to efficient energy management. In the third work, the stochastic energy management of electric bus charging stations (EBCSs) for EBs with B2G capabilities is investigated. The RES with integrated BESSs are included for the sustainable charging with reduced costs. By treating EBCSs as energy prosumers, the day-ahead dynamic prices are used to mitigate charging impacts. This problem is formulated as a distributionally robust MDP (DRMDP) to address the inaccuracies of probability density function estimations. An event-based ambiguity set with combined statistical distance and moment information is developed to achieve minimax-regret criterion for robust solutions that are less conservative. To reduce the computational complexity, a heuristic regret function is proposed for tractable solutions, based on which the day-ahead dynamic prices are determined.

  For the cyber-physical security analysis, the understanding of attack structure is essential as it provides helpful guidance for the development of effective countermeasures. Thus, in the fourth work, the mechanisms of cyber-physical attacks against BESSs in SDSs are analyzed. More specifically, a numerical model of false data injection attacks (FDIAs) against distribution system states estimation (DSSE) of SDSs is developed, which is used to construct stealthy cyber attacks targeting system information integrity of SDSs. In the developed FDIAs, the three-phase feeder model is leveraged to consider the unbalanced loads and unsymmetrical line parameters of practical SDSs. The virtual self admittance is added to address the missing phase(s) in practical multiphase SDSs. Further, based on the developed FDIAs, the mechanism of FDIAs against the state of charge (SoC) estimation of BESSs in SDSs is studied.

• Subjects / Keywords

  • Security
  • Energy

• Graduation date

  Fall 2020

• Type of Item

  Thesis

• Degree

  Doctor of Philosophy

• DOI

  https://doi.org/10.7939/r3-pmee-ch65

• License

  Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of these terms. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.