Distributed power flow loss minimization control for future grid

In this paper, a novel decentralized algorithm is proposed to minimize power flow loss in a large-scale future grid connecting with many real-time-distributed generation systems by which power flows bi-directionally. The DC-power loss at each link is defined as the product of resistance and the square of current that can be considered as a quadratic flow cost. We employ the notion of tie-sets that reduces the complexity of the power flow loss problem by dividing a power network into a set of loops that forms a linear vector space on which the power loss problem can be formulated as a convex optimization problem. As finding a solution in each tie-set enables global optimization, we realize parallel computing within a system of independent tie-sets by integrating autonomous agents. Simulation results demonstrate the minimization of the power loss on every link by iteratively optimized power flows and show the superiority against the traditional centralized optimization scheme

Fully distributed AC power flow (ACPF) algorithm for distribution systems

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/163883/1/stg2bf00044.pd

An Analytical Approach for Loss Minimization and Voltage Profile Improvement in Distribution Systems with Renewable Energy Sources

Loss minimization and voltage profile improvement are of prime importance in distribution system operation. This paper presents an analytical approach for coordinating distributed energy resources to reduce the active power loss in distribution networks. The proposed approach is based on the network admittance matrix and has an explicit solution if all loads in the network are constant current loads. Furthermore, it is shown that when loads are modeled with other characteristics such as constant power loads, a numerically efficient solution of loss minimization can also be obtained. The resulting solution reduces network losses while ensuring a nominal voltage at the point of common coupling. Case studies on a 19-node distribution system are presented to validate the proposed approach

Distributed power flow loss minimization control for future grid

In this paper, a novel decentralized algorithm is proposed to minimize power flow loss in a large-scale future grid connecting with many real-time-distributed generation systems by which power flows bi-directionally. The DC-power loss at each link is defined as the product of resistance and the square of current that can be considered as a quadratic flow cost. We employ the notion of tie-sets that reduces the complexity of the power flow loss problem by dividing a power network into a set of loops that forms a linear vector space on which the power loss problem can be formulated as a convex optimization problem. As finding a solution in each tie-set enables global optimization, we realize parallel computing within a system of independent tie-sets by integrating autonomous agents. Simulation results demonstrate the minimization of the power loss on every link by iteratively optimized power flows and show the superiority against the traditional centralized optimization scheme

Distributed power flow loss minimization control for future grid

In this paper, a novel decentralized algorithm is proposed to minimize power flow loss in a large-scale future grid connecting with many real-time-distributed generation systems by which power flows bi-directionally. The DC-power loss at each link is defined as the product of resistance and the square of current that can be considered as a quadratic flow cost. We employ the notion of tie-sets that reduces the complexity of the power flow loss problem by dividing a power network into a set of loops that forms a linear vector space on which the power loss problem can be formulated as a convex optimization problem. As finding a solution in each tie-set enables global optimization, we realize parallel computing within a system of independent tie-sets by integrating autonomous agents. Simulation results demonstrate the minimization of the power loss on every link by iteratively optimized power flows and show the superiority against the traditional centralized optimization scheme

Distributed Control Approaches for Power Systems

The energy industry is undergoing through a reconstruction from a monopolistic electricity market to a more open and transactive one. The next ­generation grid is a level playing field in terms of electricity transactions, where all customers have an equal opportunity. The emerging concepts of electricity prosumers are expected to have a significant impact on the retail electricity market. As a result, there is an urgent need to control the interactions among numerous consumers and pro­sumers. The existing control approaches can be divided into three categories, namely, centralized control, decentralized control, and distributed control. The majority of existing literature focuses on the centralized control. In most cases, the dedicated communication links are required to ex­change data between the central controller and the local agents. The centralized control approaches are suitable for relatively small­-scale systems without reconstructing the existing communication and control networks. However, as the number of consumers and prosumers are increasing to hun­dreds of thousands, there are some technical barriers on the centralized control-­based economic operations such as heavy computation burden and single point of failure. The decentralized control is an intermediate solution to address the above mentioned challenges. The overall objective is to maximize the benefits of local agents and there is no guarantee that the decisions made by each local agents can contribute to the global optimal decision of the entire system. The distributed control has the potential to solve the economic operation problems of multiple consumers and prosumers. Lo­cal agents can share information through two-­way communication links in order to find the global optimal decision. Application of distributed control methods in power system increase system’s scalability, alleviate monopoly and monopsony, improve the privacy and distribute computational load among various entities.Ph.D.College of Engineering & Computer ScienceUniversity of Michigan-Dearbornhttps://deepblue.lib.umich.edu/bitstream/2027.42/151932/1/Hajir Pourbabak Final Dissertation.pdfDescription of Hajir Pourbabak Final Dissertation.pdf : Dissertatio