Jie Duan

Day-Ahead Smart Grid Cooperative Distributed Energy Scheduling With Renewable and Storage Integration

Day-ahead scheduling of generation units and storage devices is essential for the economic and efficient operation of a power system. Conventionally, a control center calculates the dispatch schedule by gathering information from all of the devices. However, this centralized control structure makes the system vulnerable to single point of failure and communication failures, and raises privacy concerns. In this paper, a fully distributed algorithm is proposed to find the optimal dispatch schedule for a smart grid with renewable and energy storage integration. The algorithm considers modified dc power flow constraints, branch energy losses, and energy storage charging and discharging efficiencies. In this algorithm, each bus of the system is modeled as an agent. By solely exchanging information with its neighbors, the optimal dispatch schedule of the conventional generators and energy storage can be achieved in an iterative manner. The effectiveness of the algorithm is demonstrated through several representative case studies.

Economic impact of data integrity attacks on distributed DC optimal power flow algorithm

A variety of distributed energy management algorithms are being developed for DC optimal power flow (DCOPF) application owing to their flexibility and scalability in the presence of high distributed Energy Resources (DERs) penetration. However, these algorithms are vulnerable to malicious cyber attacks due to the absence of control centers. In this paper, we study and analyze the economic impact of the data integrity attack to distributed DC-OPF algorithms. In particular, we demonstrate how a malicious generator could gain more economic profit by compromising the distributed controller of its bus, modifying the information sent to neighboring buses and manipulating the power dispatch commands. To our best knowledge, this is the first paper to show the economic impact of malicious attacks in distributed DC-OPF. By revealing such potential financial risks, this paper conveys the message that besides the efforts of designing novel distributed energy management algorithms to address the DERs integration challenges, it is equally important to protect the distributed energy management algorithms from possible malicious attacks to avoid potential economic loss. The economic impact of the data integrity attack is illustrated in the Future Renewable Electric Energy Delivery and Management (FREEDM) system.

An attack-resilient distributed DC optimal power flow algorithm via neighborhood monitoring

Distributed DC optimal power flow (DC-OPF) is vulnerable to malicious cyber attacks due to the absence of a control center. In our previous work, we demonstrated a data integrity attack can manipulate the power dispatch result of distributed DC-OPF by compromising the distributed controller on a bus and modifying the information being sent to the neighboring buses. This vulnerability, in turn, could be exploited by attackers for financial arbitrage in a distributed electricity market. Thus, there is a growing need for attack-resilient control techniques that can fit into the distributed power system framework to ensure the global optimality of the power dispatch result in the presence of unexpected adversaries. In this paper, we proposed a resilient distributed DC-OPF algorithm against data integrity attacks by using a neighborhood monitoring scheme. On one hand, the resilient distributed DC-OPF algorithm is an efficient approach to deal with significant increasing amount of distributed energy resources (DERs) thanks to its flexibility and scalability. On the other hand, its neighborhood monitoring scheme enables its built-in defense to identify the misbehaving distributed controllers relying on each buss local information and recover the optimal power dispatch from the malicious impact of data integrity attacks.

Resilient Distributed DC Optimal Power Flow Against Data Integrity Attack

This paper investigates and addresses the vulnerability of the distributed DC optimal power flow (DC-OPF) algorithm to data integrity attacks. In particular, we first show that a compromised distributed controller on a single bus could manipulate the power dispatch result by sharing false information to neighboring buses. Two malicious scenarios of launching the data integrity attack are considered, namely economic-driven and infeasibility-driven attacks, respectively. These vulnerabilities demonstrate a growing need for anomaly detection and mitigation mechanisms that fit into the distributed power system framework to counteract highly skilled malicious cyber attackers. We then introduce a resilient distributed DC-OPF algorithm with an embedded attack-resilient control mechanism. It performs two major functions in a fully distributed way: 1) verifying the correctness of the shared information from neighboring buses while protecting each other’s privacy and 2) identifying the compromised distributed controllers and recovering the optimal power dispatch result from the impact of data integrity attacks. The effectiveness of the proposed attack-resilient mechanism is illustrated through case studies in the IEEE 14-bus system.

Reliability assessment and comparison between centralized and distributed energy management system in islanding microgrid

As the number of DER in the microgrid increases, the electrical interfaces and communication interactions are more sophisticated and frequent than ever, which poses a great challenge for microgrid Energy Management System (EMS). Efforts has been made to address this challenge. The solutions generally fall into two categories: centralized and distributed solution. Comparing with centralized EMS, distributed EMS is commonly considered as a more promising and reliable approach. Nonetheless, the current microgrid reliability research are focused on the physical layer of microgrid, such as overhead lines, transformers, circuit breakers and the DER device itself. The reliability analysis that considers controller failure and EMS framework are not reported in current publications. In this paper, a general reliability assessment methodology based on Monte Carlo simulation is presented to assess the reliability of both centralized EMS and distributed EMS in islanding microgrid. Different performance metrics such as system-average-interruption-frequency-index (SAIFI) and expected-energy-not-supplied (EENS) are used to quantify and evaluate the reliability. Simulation results indicates that using the same controller, the microgrid with distributed EMS is able to achieve better reliability indices, and the distributed EMS can achieve same level of reliability using less reliable controllers.

Resilient cooperative distributed energy scheduling against data integrity attacks

Distributed energy management algorithms eliminate the control center from the conventional energy management systems and calculate the optimal schedule for all devices through iterative coordination among neighbors. Most of the existing distributed approaches are developed under the assumption that all devices are secure and willing to achieve an optimal system performance together in a “collaborative” environment. However, unexpected faults and adversaries may emerge in the network and disrupt the convergence of those distributed approaches. In this paper, we extend the cooperative distributed energy scheduling (CoDES) algorithm to improve its resilience against data integrity attacks. Two types of data integrity attacks are considered in this paper - faulty attacks and random attacks. A distributed attack detection algorithm is developed to verify the state of neighboring devices without infringing their private information. A reputation-based mitigation algorithm is introduced to identify the compromised device and act accordingly to maintain the optimal energy scheduling result. The effectiveness of the proposed resilient distributed energy scheduling algorithm is evaluated in the Future Renewable Electric Energy Delivery and Management (FREEDM) microgrid system.