Distributed Control over Energy Management Using Multi-agent Coordination Techniques for Nanogrids

碩士 === 國立清華大學 === 資訊系統與應用研究所 === 104 === We design and implement a distributed control energy management system (EMS) based on the multi-agent platform for nanogrids in which multi-agents can coordinate by negotiation to maintain power stability with minimal cost. Specifically, we model the decision...

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Bibliographic Details
Main Authors: Wu, Li Wei, 吳立為
Other Authors: Soo, Von Wun
Format: Others
Language:en_US
Published: 2016
Online Access:http://ndltd.ncl.edu.tw/handle/98170452880960853791
Description
Summary:碩士 === 國立清華大學 === 資訊系統與應用研究所 === 104 === We design and implement a distributed control energy management system (EMS) based on the multi-agent platform for nanogrids in which multi-agents can coordinate by negotiation to maintain power stability with minimal cost. Specifically, we model the decision making and negotiation of multi-agents by allowing them to know constraints and objectives, to receive messages of power signals and can collaborate together to find a compromised bus voltage via communication and negotiation with other agents in nanogrids. Since the demand power from loads and supply power from solar cell are both intermittent, it is hard to balance supply and demand power. We use market-oriented model to implement the negotiation protocol between every devices in EMS. The market-oriented model is to balance the current of demand side and supply side. The EMS in a nanogrid must maintain stable bus voltages so that power supply and power demand are balanced and the maximal power can be delivered from power supply to the consumption devices. In terms of currents, it is that the total supply currents must be equal to the total consumption currents, e.g. Ig + Is + Ib + Ip = Il1 + Il2 + … Iln. We also investigate a prediction system for EMS to predict the behavior of intermittent supply and demand of power under a specific window time period. Furthermore, we build a protocol for EMS to make transaction with an electric company in which the system would buy or sell power from and to the electronic company respectively. Finally, we measure the performance of gain/cost over the 10 days simulated data that are modeled the intermittent behaviors of power generation and consumption using Poisson and Gaussian probability models.