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Connecting PowerMatcher to the electricity markets: an analysis of a Smart Grid application

Veen, N.A. van der (2015) Connecting PowerMatcher to the electricity markets: an analysis of a Smart Grid application. Master's Thesis / Essay, Artificial Intelligence.

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Abstract

In the Dutch electricity system, many agents make decisions every day, every minute, and some agents even every millisecond. These agents range from customers choosing to do the laundry to producers starting a power plant, to grid operators choosing to invest in new grid capacity. However, do we all make our decisions in the most optimal way? There is a large amount of activity in the field of Smart Energy Grids. A lot of people believe that decision making in the grid can be done more efficiently: Many decisions, especially on the demand side, are made without regarding the actions of other agents. For example, sometimes wind energy is curtailed during the night because there is barely any demand at that moment, while a few hours before, there had been a large demand peak while there was barely any wind. In the ideal Smart Grid, the actions of all agents are integrated in the most optimal way given the objectives of the stakeholders, including economic, environmental and safety objectives. How to arrange this? In this project we reviewed one Smart Grid solution: PowerMatcher-MC, which balances demand and supply of energy in a cluster of flexible devices and which trades the surplus on the Dutch energy markets. PowerMatcher-MC makes use of the PowerMatcher Smart Grid technology, a market-based multi-agent system for balancing supply and demand. First, we compared PowerMatcher-MC to several other solutions using qualitative descriptions and game theory. We tried to answer the following questions: Is the solution optimal? How beneficial is the solution to all the stakeholders? We used small dynamic games, where time is considered, to show the limits of different solutions. We show that non-cooperative approaches, such as dynamic pricing and PowerMatcher, are not always pareto optimal over time. Furthermore, three conceptual problems in non-cooperative solutions were discovered: similar bids give a sub-optimal solution, future unawareness and increased problem of valuation of flexibility. Secondly, we have tested PowerMatcher-MC using a simulation in which up to 2000 agents are interacting. The simulation results show the consequences of the three conceptual problems of non-cooperative approaches in one specific application. In an application as PowerMatcher-MC, in is very important to decrease the impact of the conceptual problems as much as possible. We give some suggestions for improving PowerMatcher-MC. The results suggest that non-cooperative approaches are probably not the best solution in every situation. For future work, we suggest to search for the limitations of the non-cooperative approaches more in depth. Furthermore, we suggest to compare non-cooperative solutions to global optimization and solutions using cooperative game theory in both a theoretic way (using theory of dynamic games) and an experimental way.

Item Type: Thesis (Master's Thesis / Essay)
Degree programme: Artificial Intelligence
Thesis type: Master's Thesis / Essay
Language: English
Date Deposited: 15 Feb 2018 08:07
Last Modified: 15 Feb 2018 08:07
URI: http://fse.studenttheses.ub.rug.nl/id/eprint/13255

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