@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} }
 @article{rahbari-asr_chow_chen_deng_2016, title={Distributed Real-Time Pricing Control for Large-Scale Unidirectional V2G With Multiple Energy Suppliers}, volume={12}, ISSN={["1941-0050"]}, DOI={10.1109/tii.2016.2569584}, abstractNote={With the increasing trend in adoption of plug-in hybrid and plug-in electric vehicles, they will play a prominent role in the future electric energy market by acting as responsive loads to increase the grid stability and facilitate the integration of renewables. However, due to the large number of controllable devices in the future grid, central vehicle to grid (V2G) management would be challenging and vulnerable to single points of failure. This paper introduces a novel distributed approach for optimal management of unidirectional V2G considering multiple energy suppliers. Each charging station as well as each energy supplier is equipped with a local price regulator to control the price paid to the energy suppliers and the price paid by the vehicles through coordination with their neighbors. In response to the updated prices, the vehicles adjust their charging rates and energy suppliers adjust their production to maximize their benefit. The main advantages of the proposed approach are that it manages unidirectional V2G in a fully distributed way considering multiple energy suppliers and vehicles, and it converges to the global optimum despite the greedy behavior of the individuals.}, number={5}, journal={IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS}, author={Rahbari-Asr, Navid and Chow, Mo-Yuen and Chen, Jiming and Deng, Ruilong}, year={2016}, month={Oct}, pages={1953–1962} }
 @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} }
 @article{rahbari-asr_chow_2014, title={Cooperative Distributed Demand Management for Community Charging of PHEV/PEVs Based on KKT Conditions and Consensus Networks}, volume={10}, ISSN={["1941-0050"]}, DOI={10.1109/tii.2014.2304412}, abstractNote={Efficient and reliable demand side management techniques for community charging of plug-in hybrid electrical vehicles (PHEVs) and plug-in electrical vehicles (PEVs) are needed, as large numbers of these vehicles are being introduced to the power grid. To avoid overloads and maximize customer preferences in terms of time and cost of charging, a constrained nonlinear optimization problem can be formulated. In this paper, we have developed a novel cooperative distributed algorithm for charging control of PHEVs/PEVs that solves the constrained nonlinear optimization problem using Karush-Kuhn-Tucker (KKT) conditions and consensus networks in a distributed fashion. In our design, the global optimal power allocation under all local and global constraints is reached through peer-to-peer coordination of charging stations. Therefore, the need for a central control unit is eliminated. In this way, single-node congestion is avoided when the size of the problem is increased and the system gains robustness against single-link/node failures. Furthermore, via Monte Carlo simulations, we have demonstrated that the proposed distributed method is scalable with the number of charging points and returns solutions, which are comparable to centralized optimization algorithms with a maximum of 2% sub-optimality. Thus, the main advantages of our approach are eliminating the need for a central energy management/coordination unit, gaining robustness against single-link/node failures, and being scalable in terms of single-node computations.}, number={3}, journal={IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS}, author={Rahbari-Asr, Navid and Chow, Mo-Yuen}, year={2014}, month={Aug}, pages={1907–1916} }
 @article{rahbari-asr_ojha_zhang_chow_2014, title={Incremental Welfare Consensus Algorithm for Cooperative Distributed Generation/Demand Response in Smart Grid}, volume={5}, ISSN={["1949-3061"]}, DOI={10.1109/tsg.2014.2346511}, abstractNote={In this paper, we introduce the incremental welfare consensus algorithm for solving the energy management problem in a smart grid environment populated with distributed generators and responsive demands. The proposed algorithm is distributed and cooperative such that it eliminates the need for a central energy-management unit, central price coordinator, or leader. The optimum energy solution is found through local peer-to-peer communications among smart devices. Each distributed generation unit is connected to a local price regulator, as is each consumer unit. In response to the price of energy proposed by the local price regulators, the power regulator on each generation/consumer unit determines the level of generation/consumption power needed to optimize the benefit of the device. The consensus-based coordination among price regulators drives the behavior of the overall system toward the global optimum, despite the greedy behavior of each unit. The primary advantages of the proposed approach are: 1) convergence to the global optimum without requiring a central controller/coordinator or leader, despite the greedy behavior at the individual level and limited communications; and 2) scalability in terms of per-node computation and communications burden.}, number={6}, journal={IEEE TRANSACTIONS ON SMART GRID}, author={Rahbari-Asr, Navid and Ojha, Unnati and Zhang, Ziang and Chow, Mo-Yuen}, year={2014}, month={Nov}, pages={2836–2845} }
 @inproceedings{rahbari-asr_chow_yang_chen_2013, title={Network cooperative distributed pricing control system for large-scale optimal charging of PHEVs/PEVs}, DOI={10.1109/iecon.2013.6700146}, abstractNote={Efficient demand management policies at the grid side are required for large scale charging of Plug-in Hybrid Electric Vehicles and Plug-in Electric vehicles (PHEVs/PEVs). The SoC level and Charging Cost should be optimized while the aggregate load is kept under a safety limit to avoid overloads. Conventionally, optimal managing of the charging rates requires gathering and processing data in a center. However, as the scale of the problem increases to consider thousands of charging stations distributed over a vast geographical area, the central approach suffers from vulnerability to single node/link failures as well as scalability. This paper introduces a novel decentralized network cooperative approach for controlling the PHEV/PEV charging rates. In this approach, each charging station acts as a local retailer of energy, selling the power to the plugged in vehicle while coordinating the price with its neighbors. In response to the offered price, the Smart-Charger of the vehicle adjusts the charging current to maximize the utility of the PHEV/PEV user. By iteratively repeating this process, the convergence to the global optimum is attained without the requirement for any central unit. Robustness to single link/node failures is another advantage of our method.}, booktitle={39th annual conference of the ieee industrial electronics society (iecon 2013)}, author={Rahbari-Asr, N. and Chow, M. Y. and Yang, Z. Y. and Chen, J. M.}, year={2013}, pages={6148–6153} }