Showing 4 results for Multi-Agent System
F. Daneshfar, H. Bevrani, F. Mansoori,
Volume 7, Issue 2 (6-2011)
Abstract
Bayesian Networks (BN) provides a robust probabilistic method of reasoning under uncertainty. They have been successfully applied in a variety of real-world tasks but they have received little attention in the area of load-frequency control (LFC). In practice, LFC systems use proportional-integral controllers. However since these controllers are designed using a linear model, the nonlinearities of the system are not accounted for and they are incapable to gain good dynamical performance for a wide range of operating conditions in a multi-area power system.
A strategy for solving this problem due to the distributed nature of a multi-area power system, is presented by using a BN multi-agent system.
This method admits considerable flexibility in defining the control objective. Also BN provides a flexible means of representing and reasoning with probabilistic information. Efficient probabilistic inference algorithms in BN permit answering various probabilistic queries about the system. Moreover using multi-agent structure in the proposed model, realized parallel computation and leading to a high degree of scalability.
To demonstrate the capability of the proposed control structure, we construct a BN on the basis of optimized data using genetic algorithm (GA) for LFC of a three-area power system with two scenarios.
Sh. Yousefi, M. Parsa Moghaddam, V. Johari Majd,
Volume 7, Issue 3 (9-2011)
Abstract
In this paper, an agent-based structure of the electricity retail market is presented based on which day-ahead (DA) energy procurement for customers is modeled. Here, we focus on operation of only one Retail Energy Provider (REP) agent who purchases energy from DA pool-based wholesale market and offers DA real time tariffs to a group of its customers. As a model of customer response to the offered real time prices, an hourly acceptance function is proposed in order to represent the hourly changes in the customer’s effective demand according to the prices. Here, Q-learning (QL) approach is applied in day-ahead real time pricing for the customers enabling the REP agent to discover which price yields the most benefit through a trial-and-error search. Numerical studies are presented based on New England day-ahead market data which include comparing the results of RTP based on QL approach with that of genetic-based pricing.
M. Mozaffari Legha, E. Farjah,
Volume 16, Issue 2 (6-2020)
Abstract
This paper aims to establish an Arduino and IoT-based Hierarchical Multi-Agent System (HMAS) for management of loads’ side with incentive approach in a micro-grid. In this study, the performance of the proposed algorithm in a micro-grid has been verified. The micro-grid contains a battery energy storage system (BESS) and different types of loads known as residential consumer (RC), commercial consumer (CC), and industrial consumer (IC). The user interface on a smartphone directly communicates with the load management system via an integrated Ethernet Shield server which uses Wi-Fi communication protocol. Also, the communication between the Ethernet Shield and the Arduino microcontroller is based on Wi-Fi communication. A simulation model is developed in Java Agent Development Environment (JADE) for dynamic and effective energy administration, which takes an informed decision and chooses the most feasible action to stabilize, sustain, and enhance the micro-grid. Further, the environment variable is sensed through the Arduino microcontroller and sensors, and then given to the MAS agents in the IoT environment. The test results indicated that the system was able to effectively control and regulate the energy in the micro-grid.
P. Paliwal,
Volume 18, Issue 1 (3-2022)
Abstract
The determination of a suitable technology combination for an isolated micro-grid (IMG) based on hybrid renewable energy resources (HRES) is a challenging task. The intermittent behavior of RES leads to an adverse impact on system reliability and thus complicates the planning process. This paper proposes a two-fold approach to provide a suitably designed HRES-IMG. Firstly, a reliability-constrained formulation based on load index of reliability (LIR) is developed with an objective to achieve a minimum levelized cost of energy (LCOE). Multi-state modeling of HRES-IMG is carried out based on hardware availability of generating units and uncertainties due to meteorological conditions. Modeling of battery storage units is realized using a multi-state probabilistic battery storage model. Secondly, an efficient optimization technique using a decentralized multi-agent-based approach is applied for obtaining high-quality solutions. The butterfly-PSO is embodied in a multi-agent (MA) framework. The enhanced version, MA-BFPSO is used to determine optimum sizing and technology combinations. Three different technology combinations have been investigated. The combination complying with LIR criterion and least LCOE is chosen as the optimal technology mix. The optimization is carried out using classic PSO, BF-PSO, and, MA-BFPSO and obtained results are compared. Further, in order to add a dimension in system planning, the effect of uncertainty in load demand has also been analyzed. The study is conducted for an HRES-IMG situated in Jaisalmer, India. The technology combination comprising of solar, wind, and battery storage yields the least LCOE of 0.2051 $/kWh with a very low value of LIR (0.08%). A reduction in generator size by 53.8% and LCOE by 16.5% is obtained with MABFPSO in comparison with classic PSO. The results evidently demonstrate that MA-BFPSO offers better solutions as compared to PSO and BF-PSO.
