@article{rahbari-asr_chow_zhang_2016, title={Consensus-based distributed scheduling for cooperative operation of distributed energy resources and storage devices in smart grids}, volume={10}, ISSN={1751-8687 1751-8695}, url={http://dx.doi.org/10.1049/iet-gtd.2015.0159}, DOI={10.1049/iet-gtd.2015.0159}, abstractNote={Optimal dispatch of storage devices is crucial for the economic operation of smart grids with distributed energy resources. Through appropriate scheduling, storage devices can store the energy when the renewable production is high or electricity price is low, and support the demand when electricity is expensive. Conventionally, this scheduling requires a control centre to gather information from the entire system and find the optimal schedule in the required horizon for the controllable devices. This study proposes a fully distributed scheduling methodology based on discrete-time optimal control, primal-dual gradient descent, and consensus networks. In the proposed approach, the requirement for the control centre is eliminated and the optimal schedule for all the devices is found solely through iterative coordination of each device with its neighbours. The application of the algorithm is demonstrated in a 5-bus system and its convergence to the global optimum is validated through Monte Carlo simulations. Further, it is shown that the algorithm is robust against communication link failures provided that the communications topology remains connected or reconnects after being disconnected.}, number={5}, journal={IET Generation, Transmission & Distribution}, publisher={Institution of Engineering and Technology (IET)}, author={Rahbari-Asr, Navid and Chow, Mo-Yuen and Zhang, Yuan}, year={2016}, month={Apr}, pages={1268–1277} }
 @inproceedings{rahbari-asr_zhang_chow_2016, title={Cooperative distributed energy scheduling for storage devices and renewables with resiliency against intermittencies}, DOI={10.1109/isie.2016.7744959}, abstractNote={Cost-effective operation of microgrids relies on optimal scheduling of energy resources and storage devices. Scheduling considering storage devices is inherently a multi-step optimization problem and its complexity grows with the increasing of the device number, and the schedule time resolution. Conventional centralized approaches raise concerns regarding privacy of the system as well as its vulnerability to single point of failure. Fully distributed approaches require iterative communications among distributed components where both the number of iterations and the communications packet size grow as the number of time steps increases. The situation is aggravated due to the intermittency of the renewable resources, since scheduling needs to be repeated once there is considerable change in forecasted profiles. To resolve the issue, this paper proposes a two layer fully distributed resilient scheduling methodology. In the first layer (scheduling layer), the distributed components communicate with each other to find the long term set points for charging/discharging of storage devices. At the second layer (regulatory layer), the distributed devices run a high resolution short term optimization considering the real-time data and the calculated set points from the scheduling layer. The numerical results demonstrate that using the double layer structure, the system shows resiliency against intermittencies and the objective values track the optimal values.}, booktitle={Proceedings of the ieee international symposium on industrial}, author={Rahbari-Asr, N. and Zhang, Y. and Chow, M. Y.}, year={2016}, pages={612–617} }
 @article{zhang_rahbari-asr_duan_chow_2016, title={Day-Ahead Smart Grid Cooperative Distributed Energy Scheduling With Renewable and Storage Integration}, volume={7}, ISSN={["1949-3029"]}, DOI={10.1109/tste.2016.2581167}, abstractNote={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.}, number={4}, journal={IEEE TRANSACTIONS ON SUSTAINABLE ENERGY}, author={Zhang, Yuan and Rahbari-Asr, Navid and Duan, Jie and Chow, Mo-Yuen}, year={2016}, month={Oct}, pages={1739–1748} }
 @inproceedings{jia_li_du_zhang_gopalakrishnan_xun_zhang_2016, title={Influence based analysis of community consistency in dynamic networks}, booktitle={Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining ASONAM 2016}, author={Jia, X. W. and Li, X. Y. and Du, N. and Zhang, Y. and Gopalakrishnan, V. and Xun, G. X. and Zhang, A. D.}, year={2016}, pages={1–8} }
 @inproceedings{zhang_chow_2016, title={Microgrid cooperative distributed energy scheduling (CoDES) considering battery degradation cost}, DOI={10.1109/isie.2016.7744978}, abstractNote={Obtaining an optimal charging and discharging schedule of battery energy storage devices in a microgrid is essential to the economic and reliable operation of the system. The depth of discharge (DoD) is a key variable that affects the cycle life of a battery. In this paper, an energy scheduling problem is formulated for a microgrid considering battery degradation cost under different DoD scenarios. The formulated scheduling problem is solved by using the cooperative distributed energy scheduling (CoDES) algorithm in a distributed way. The operation of the CoDES algorithm is demonstrated and the economic benefit of using battery energy storage devices in a microgrid is analyzed under different DoD scenarios.}, booktitle={Proceedings of the ieee international symposium on industrial}, author={Zhang, Y. and Chow, M. Y.}, year={2016}, pages={720–725} }
 @inproceedings{zeng_zhang_chow_2015, title={A resilient distributed energy management algorithm for economic dispatch in the presence of misbehaving generation units}, booktitle={2015 Resilience Week (RSW)}, author={Zeng, W. T. and Zhang, Y. and Chow, M. Y.}, year={2015}, pages={12–16} }
 @article{zhang_rahbari-asr_chow_2016, title={A robust distributed system incremental cost estimation algorithm for smart grid economic dispatch with communications information losses}, volume={59}, ISSN={["1084-8045"]}, DOI={10.1016/j.jnca.2015.05.014}, abstractNote={With an increasing number of controllable distributed energy resources deployed and integrated into the power system, how to economically manage these distributed resources will become a challenge for the future smart grid. To solve the issue, consensus based distributed economic dispatch algorithms have been introduced in the literature as computationally scalable approaches. However, in real-world applications with imperfect communications networks, the performance of consensus-based economic dispatch algorithms degrades when information losses occur. In this paper, a robust distributed system incremental cost estimation (RICE) algorithm is introduced to solve the Economic Dispatch Problem (EDP) in a smart grid environment in a distributed way considering communications information losses. Unlike the existing consensus-based algorithms to solve EDP, RICE algorithm has two updating layers running in parallel in each distributed controller: one layer uses the gossip updating rule to estimate the system׳s average power mismatch, while the other layer uses the consensus updating rule to update the system Incremental Cost (IC) estimation. In this approach, the vulnerability of consensus-based algorithms to communications information losses is eliminated. The convergence and optimality of the algorithm are guaranteed as long as the undirected communications topology among local controllers is connected. Several case studies are presented to illustrate the performance of the proposed algorithm, and show the robustness under different information loss scenarios with different communications topologies.}, journal={JOURNAL OF NETWORK AND COMPUTER APPLICATIONS}, author={Zhang, Yuan and Rahbari-Asr, Navid and Chow, Mo-Yuen}, year={2016}, month={Jan}, pages={315–324} }
 @inproceedings{rahbari-asr_zhang_chow_2015, title={Cooperative distributed scheduling for storage devices in microgrids using dynamic KKT multipliers and consensus networks}, DOI={10.1109/pesgm.2015.7286376}, abstractNote={Scheduling of storage devices in microgrids with multiple renewable energy resources is crucial for their optimal and reliable operation. With proper scheduling, the storage devices can capture the energy when the renewable generation is high and utility energy price is low, and release it when the demand is high or utility energy price is expensive. This scheduling is a multi-step optimization problem where different time-steps are dependent on each other. Conventionally, this problem is solved centrally. The central controller should have access to the real-time states of the system as well as the predicted load and renewable generation information. It should also have the capability to send dispatch commands to each storage device. However, as the number of devices increases, the centralized approach would not be scalable and will be vulnerable to single point of failure. Combining the idea of dynamic KKT multipliers with consensus networks, this paper introduces a novel algorithm that can optimally schedule the storage devices in a microgrid solely through peer-to-peer coordination of devices with their neighbors without using a central controller.}, booktitle={2015 ieee power & energy society general meeting}, author={Rahbari-Asr, N. and Zhang, Y. and Chow, M. Y.}, year={2015} }
 @inproceedings{zhang_chow_2015, title={Distributed optimal generation dispatch considering transmission losses}, DOI={10.1109/naps.2015.7335143}, abstractNote={Economically dispatching the generation is essential to the efficient operations of a power system. As an approximation to the nonconvex AC optimal power flow (ACOPF) problem, the convex DC optimal power flow (DCOPF) problem is used in many studies. In this paper, the DCOPF with transmission line losses (DCOPFL) is formulated to better approximate the ACOPF problem. A Cooperative Distributed Optimal Dispatch (CDOD) algorithm is proposed to solve the DCOPFL problem in a distributed manner. The convergence and correctness of the CDOD algorithm are verified through two representative case studies. The DCOPFL is also verified to have the smallest approximation error comparing with DCOPF and economic dispatch considering transmission losses (EDL) by taking ACOPF solution as reference.}, booktitle={2015 north american power symposium (naps)}, author={Zhang, Y. and Chow, M. Y.}, year={2015} }
 @inproceedings{zhang_asr_chow_2015, title={Online convergence factor tuning for robust cooperative distributed economic dispatch}, DOI={10.1109/pesgm.2015.7285652}, abstractNote={Solving economic dispatch problem (EDP) in a distributed way has attracted lots of attention in recent years due to its scalability and robustness to single points of failure. Robust distributed system Incremental Cost Estimation (RICE) algorithm has been proposed to solve the classic EDP in a distributed way considering communications information losses. However, assuring the stability of the algorithm without knowing the global information of the system is a challenging issue. This paper provides a distributed online approach to tune a certain parameter of the algorithm called “convergence factor” using only local information to assure the algorithm is stable. To do this, a local energy function is defined for each agent. As the algorithm proceeds, each agent uses a decaying mechanism to tune its convergence factor to ensure that its local energy function is within a certain bound. The summation of local energy functions represents an energy function for the entire network. Therefore, if each agent uses the tuning mechanism, the energy of the system would be forced to be constrained and the system will become stable. The effectiveness of the proposed approach is verified through several case studies.}, booktitle={2015 ieee power & energy society general meeting}, author={Zhang, Y. and Asr, N. R. and Chow, M. Y.}, year={2015} }
 @article{zeng_zhang_chow_2017, title={Resilient Distributed Energy Management Subject to Unexpected Misbehaving Generation Units}, volume={13}, ISSN={["1941-0050"]}, DOI={10.1109/tii.2015.2496228}, abstractNote={Distributed energy management algorithms are being developed for the smart grid to efficiently and economically allocate electric power among connected distributed generation units and loads. The use of such algorithms provides flexibility, robustness, and scalability, while it also increases the vulnerability of smart grid to unexpected faults and adversaries. The potential consequences of compromising the power system can be devastating to public safety and economy. Thus, it is important to maintain the acceptable performance of distributed energy management algorithms in a smart grid environment under malicious cyber-attacks. In this paper, a neighborhood-watch-based distributed energy management algorithm is proposed to guarantee the accurate control computation in solving the economic dispatch problem in the presence of compromised generation units. The proposed method achieves the system resilience by performing a reliable distributed control without a central coordinator and allowing all the well-behaving generation units to reach the optimal operating point asymptotically. The effectiveness of the proposed method is demonstrated through case studies under several different adversary scenarios.}, number={1}, journal={IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS}, author={Zeng, Wente and Zhang, Yuan and Chow, Mo-Yuen}, year={2017}, month={Feb}, pages={208–216} }