Navid Rahbari-Asr

Incremental Welfare Consensus Algorithm for Cooperative Distributed Generation/Demand Response in Smart Grid

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.

Cooperative Distributed Demand Management for Community Charging of PHEV/PEVs Based on KKT Conditions and Consensus Networks

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.

A robust distributed system incremental cost estimation algorithm for smart grid economic dispatch with communications information losses

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 systems 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. Display Omitted The classic Economic Dispatch Problem is solved in a distributed manner.The proposed RICE algorithm is robust to communication information losses.The proposed RICE algorithm is highly scalable.Rigorous analysis is provided to prove the convergency and optimality.

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.

Network cooperative distributed pricing control system for large-scale optimal charging of PHEVs/PEVs

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.

Distributed Real-Time Pricing Control for Large-Scale Unidirectional V2G With Multiple Energy Suppliers

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.

Cooperative distributed scheduling for storage devices in microgrids using dynamic KKT multipliers and consensus networks

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.

Asynchronous distributed cooperative energy management through gossip-based incremental cost consensus algorithm

The energy management problem in smart grid is a complex optimization problem of a Cyber-Physical System. Distributed cooperative energy management algorithms have great potential to solve this class of problems. In addition to the synchronous distributed algorithms, asynchronous distributed algorithms are more flexible, robust to packet loss and do not require global clock synchronization. In this paper, we have extended the synchronous Incremental Cost Consensus (ICC) algorithm to a gossip-based asynchronous version. The new algorithm is able to converge to the optimal solution in a distributed fashion with pairwise information exchange between neighbors without the need for any global synchronizing clock. The characteristics of the asynchronous ICC algorithm can be controlled by tuning the weighting of the updating matrix. Several case studies with different system configurations have been used to discuss the characteristics of the proposed algorithm.

Online and Offline Stability Analysis Methods for the Power Electronic-Based Components in Design and Operational Stages

Power electronic-based components (PECs) are at the heart of the enabling technologies for the smart-grids. They improve the controllability of the power system and provide excellent features such as load regulation, high power factor, and transient performance. However, they can behave as negative impedance due to their capability to operate as constant power loads, and jeopardize the stability of the power systems. Therefore, stability analysis of the power electronic-based distribution systems is crucial for development of the future smart-grids. This paper provides two methods to analyze stability: real-time (online) and offline. In real-time approach, the systems small-signal stability is investigated based on d - q impedance measurement and unit circle criterion and by calculating source and load impedances simultaneously and in a range of frequencies. In offline approach, the system dynamics are identified from the bode plots and then based on the eigenvalue analysis, the stability of the system under different loading conditions is analyzed. Furthermore, small-signal stability of a solid state transformer (SST) as an advanced PEC with power factor correction is investigated via the proposed methods. In addition, hardware experiment is developed though power hardware-in-the-loop experiment to assess stability of an SST in load variation and validate the real-time capability of the proposed technique.

Cooperative distributed energy scheduling for storage devices and renewables with resiliency against intermittencies

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.