77
views
0
recommends
+1 Recommend
0 collections
0
shares
• Record: found
• Abstract: found
• Article: found
Is Open Access

Initialization-free Distributed Algorithms for Optimal Resource Allocation with Feasibility Constraints and its Application to Economic Dispatch of Power Systems

Preprint

Bookmark
There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

Abstract

In this paper, a class of distributed algorithms are proposed for resource allocation optimization problem. The allocation decisions are made to minimize the sum of all the agents' local objective functions while satisfying both the global network resource constraint and the local allocation feasibility constraints. Here, the data corresponding to each agent in the considered separable optimization problem, such as the network resources, the local allocation feasibility constraint, and the local objective function, is only accessible to individual agent and cannot be shared with others, rendering a novel distributed optimization problem. In this regard, we propose a category of projection-based continuous-time algorithms to solve this distributed optimization problem in an initialization-free and scalable manner. Thus, no re-initialization is required even if operation environment or network configuration is changed, making it possible to achieve a "plug-and-play" optimal operation of networked heterogeneous agents. The algorithm convergence is established for strictly convex objective functions, and the exponential convergence is proved for strongly convex functions. The proposed algorithm is applied to the distributed economic dispatch problem in a power grid, illustrating that it can adaptively achieve global optimum in a scalable way, even if the generation cost, system load, or network configuration is varying. The application primarily demonstrates the promising implications of the proposed algorithm.

Most cited references29

• Record: found
• Abstract: found
• Article: found
Is Open Access

Constrained Consensus

(2008)
We present distributed algorithms that can be used by multiple agents to align their estimates with a particular value over a network with time-varying connectivity. Our framework is general in that this value can represent a consensus value among multiple agents or an optimal solution of an optimization problem, where the global objective function is a combination of local agent objective functions. Our main focus is on constrained problems where the estimate of each agent is restricted to lie in a different constraint set. To highlight the effects of constraints, we first consider a constrained consensus problem and present a distributed projected consensus algorithm'' in which agents combine their local averaging operation with projection on their individual constraint sets. This algorithm can be viewed as a version of an alternating projection method with weights that are varying over time and across agents. We establish convergence and convergence rate results for the projected consensus algorithm. We next study a constrained optimization problem for optimizing the sum of local objective functions of the agents subject to the intersection of their local constraint sets. We present a distributed projected subgradient algorithm'' which involves each agent performing a local averaging operation, taking a subgradient step to minimize its own objective function, and projecting on its constraint set. We show that, with an appropriately selected stepsize rule, the agent estimates generated by this algorithm converge to the same optimal solution for the cases when the weights are constant and equal, and when the weights are time-varying but all agents have the same constraint set.
Bookmark
• Record: found

Efficiency Loss in a Network Resource Allocation Game

(2004)
Bookmark
• Record: found

Equivalent differentiable optimization problems and descent methods for asymmetric variational inequality problems

(1992)
Bookmark

Author and article information

Journal
1510.08579

Numerical methods