A multi-agent trading platform for electricity contract market

Yuan, Jiahai; Shun-kun, Yu; Hu, Zhaoguang

doi:10.1109/ipec.2005.207058

Cited by 13 publications

(9 citation statements)

References 13 publications

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“…Agents are endowed with historical information about the market including past strategies of other players, and have strategic behaviour to face the market. (Jia-hai et al, 2005) presents the architecture and negotiation strategy of an agent-based negotiation platform for power generating and power consuming companies in contract electricity market. An intelligent agent implements the negotiation process by selecting a strategy using learning algorithms.…”

Section: Related Workmentioning

confidence: 99%

An Agent-based Approach for Smart Energy Grids

Amato

Martino

Scialdone

et al. 2014

Proceedings of the 6th International Conference on Agents and Artificial Intelligence

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The increasing demand for energy and the availability of several solutions of renewable energy sources has stimulated the formulation of plans aiming at expanding and upgrading existing power grids in several countries. According to NIST, smart grid will be one of the greatest achievements of the 21st century. By linking information technologies with the electric power grid to provide electricity with a brain, the smart grid promises many benefits, including increased energy efficiency, reduced carbon emissions, and improved power reliability. In this paper we present an agent based architecture for supporting collection and processing of information about local energy production and storage resources of neighborhoods of individual houses and to schedule the energy flows using negotiation protocols.

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Section: Related Workmentioning

confidence: 99%

An Agent-based Approach for Smart Energy Grids

Amato

Martino

Scialdone

et al. 2014

Proceedings of the 6th International Conference on Agents and Artificial Intelligence

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“…In this very scenario, an agent represents a peer inside an electricity distribution net in which producers and consumers are the main components of the market. [5,6]). Most of the previously published articles are dealing with the negotiation step (e.g.…”

Section: Related Workmentioning

confidence: 99%

Context‐awareness in the deregulated electric energy market: an agent‐based approach

Capodieci

Alsina

Cabri

2013

Concurrency and Computation

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Multiagent systems are commonly used for simulation of new paradigms of energy distribution. Especially when considering Smart Grids, the autonomicity deployed by goal-driven agents implies the need for being aware of multiple aspects connected to the energy distribution context. With ‘context’, we refer to the outside world variables such as weather, stock market trends, location of the users, government actions, and so on; therefore, an architecture highly context-aware is needed. We propose a model in which every important factor concerning the electric energy distribution is presented by modeling context-aware agents able to identify the impact of these factors. Moreover, some tests have been performed regarding the web service integration in which agents contracting energy will automatically retrieve data to be used in adaptive and collaborative aspects; an explicative example is represented by the retrieval of weather forecasting that provides input on ongoing demand and data for the predicted availability (in case of photovoltaic or wind powered environments)

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“…The point of this conception is reactivity, autonomy, proactivity and social-ability, and multi-agent system is a system consisted of more than one interactive agents populated in a common environment [7]. Literature [8][9] discusses the application of MAS in electricity market research.…”

Section: B Masmentioning

confidence: 99%

“…Assuming that the goal of the company is to increase the load factor to above 85% with bill loss less than 2,000 Yuan per day. According to the shape of load curve the utility agent sets 24-7 hour as valley time, [8][9][10][11] and 18-21 hour as peak time while 12-17, 22-23 as shoulder time. Utility agent firstly give out a TOU price policy randomly and customer agents response by changing electricity consumption behavior.…”

Section: Infrastructure Ofthe Simulation Laboratorymentioning

confidence: 99%

Simulation of Large Customer Price Response Under Time-of-Use Electricity Pricing Based on Multi-Agent System

Yuan

Jing

2006

2006 International Conference on Power System Technology

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The mechanism of customer TOU response is the key for success of Time-of-use pricing. However different customers have different price response behavior and no simple analytic model is possible for it. Therefore multi-agents simulation is introduced. In simulation research TOU price response is taken as a decentralized decision process of all related customers, while Power Supply Corporation can dynamically adjust its policy according to customer response. The paper discusses in details with the design principle and framework of the simulation system. In the system customer agent is implemented as responsive agent with its response rules built on fuzzy logic, utility agent is implemented as learning agent with Classifier Learning Algorithm as its learning rules, and environment management module manages the interaction among them. Then the simulation flow is given and finally a case study based on a real power supply corporation is given and simulation results reported.

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A multi-agent trading platform for electricity contract market

Cited by 13 publications

References 13 publications

An Agent-based Approach for Smart Energy Grids

An Agent-based Approach for Smart Energy Grids

Context‐awareness in the deregulated electric energy market: an agent‐based approach

Simulation of Large Customer Price Response Under Time-of-Use Electricity Pricing Based on Multi-Agent System

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