Reactive power dispatch in power systems is a complex combinatorial optimization problem involving nonlinear functions having multiple local minima and nonlinear and discontinuous constraints. In this paper, a solution to the reactive power dispatch problem with a novel particle swarm optimization approach based on multiagent systems (MAPSO) is presented. This method integrates the multiagent system (MAS) and the particle swarm optimization (PSO) algorithm. An agent in MAPSO represents a particle to PSO and a candidate solution to the optimization problem. All agents live in a lattice-like environment, with each agent fixed on a lattice point. In order to obtain optimal solution quickly, each agent competes and cooperates with its neighbors, and it can also learn by using its knowledge. Making use of these agent-agent interactions and evolution mechanism of PSO, MAPSO realizes the purpose of optimizing the value of objective function. MAPSO applied to optimal reactive power dispatch is evaluated on an IEEE 30-bus power system and a practical 118-bus power system. Simulation results show that the proposed approach converges to better solutions much faster than the earlier reported approaches. The optimization strategy is general and can be used to solve other power system optimization problems as well.

A multiagent-based particle swarm optimization approach for optimal reactive power dispatch

In this paper, multiagent systems and genetic algorithms are integrated to form a new algorithm, multiagent genetic algorithm (MAGA), for solving the global numerical optimization problem. An agent in MAGA represents a candidate solution to the optimization problem in hand. All agents live in a latticelike environment, with each agent fixed on a lattice-point. In order to increase energies, they compete or cooperate with their neighbors, and they can also use knowledge. Making use of these agent-agent interactions, MAGA realizes the purpose of minimizing the objective function value. Theoretical analyzes show that MAGA converges to the global optimum. In the first part of the experiments, ten benchmark functions are used to test the performance of MAGA, and the scalability of MAGA along the problem dimension is studied with great care. The results show that MAGA achieves a good performance when the dimensions are increased from 20-10,000. Moreover, even when the dimensions are increased to as high as 10,000, MAGA still can find high quality solutions at a low computational cost. Therefore, MAGA has good scalability and is a competent algorithm for solving high dimensional optimization problems. To the best of our knowledge, no researchers have ever optimized the functions with 10,000 dimensions by means of evolution. In the second part of the experiments, MAGA is applied to a practical case, the approximation of linear systems, with a satisfactory result.

A multiagent genetic algorithm for global numerical optimization

Multiple energy carriers (MECs) have been broadly engrossing power system planners and operators toward a modern standpoint in power system studies Energy hub, though playing an undeniable role as the intermediate in implementing the MECs, still needs to be put under examination in both modeling and operating concerns Since wind power continues to be one of the fastest-growing energy resources worldwide, its intrinsic challenges should be also treated as an element of crucial role in the vision of future energy networks In response, this paper aims to concentrate on the online economic dispatch (ED) of MECs for which it provides a probabilistic ED optimization model The presented model is treated via a robust optimization technique, ie, multiagent genetic algorithm (MAGA), whose outstanding feature is to find well the global optima of the ED problem ED once constrained by wind power availability, in the cases of wind power as one of the input energy carriers of the hub, faces an inevitable uncertainty that is also probabilistically overcome in the proposed model Efficiently approached via MAGA, the presented scheme is applied to test systems equipped with energy hubs and as expected, introduces its applicability and robustness in the ED problems

Multiagent Genetic Algorithm: An Online Probabilistic View on Economic Dispatch of Energy Hubs Constrained by Wind Availability

The incorrect version of Fig. 2 appeared in the original article by B. Zhao, C. X. Guo, and Y. J. Cao, see ibid vol. 20, no. 2, p. 1070-8, May 2005. The correct version follows.

Correction to "A multiagent-based particle swarm optimization approach for optimal reactive power dispatch"

With e-business emerging as a key enabler to drive supply chains, the focus of supply chain management has been shifted from production efficiency to customer-driven and partnership synchronization approaches. This strategic shift depends on the match between the demands and offerings that deliver the services. To achieve this, we need to coordinate the flow of information among the services, and link their business processes under various constraints. Existing approaches to this problem have relied on complete information of services and resources, and have failed to adequately address the dynamics and uncertainties of the operating environments. The real-world situation is complicated as a result of undetermined requirements of services involved in the chain, unpredictable solutions contributed by service providers, and dynamic selection and aggregation of solutions to services. This paper examines an agent-mediated approach to on-demand e-business supply chain integration. Each agent works as a service broker, exploring individual service decisions as well as interacting with each other for achieving compatibility and coherence among the decisions of all services. Based on the framework, a prototype has been implemented with simulated experiments highlighting the effectiveness of the approach.

http://www.wiomax.com/team/xie/paper/ESWA08.pdf

On-demand e-supply chain integration: A multi-agent constraint-based approach

https://www.cs.ubc.ca/~hutter/EARG.shtml/earg/papers04-05/Multi-agent%20oriented%20constraint%20satisfaction.pdf

Han Jing

Papers

Multi-agent oriented constraint satisfaction