Universal Smart Grid Agent for Distributed Power Generation Management

Abstract

Renewable energy sources provide an ever increasing amount of the global electric power generation. However, for many regions, even whole countries, the go-to primary renewable energy sources that are available in large quantities are wind and solar radiation, which are highly volatile since they depend on a factor that is not human-controllable: the weather. The traditional power grid featured centralized power generation and a hierarchical structure; but in addition to the volatility, renewable energy sources blur the distinction between generator and consumer: Through photovoltaic panels on rooftops, a consumer can alternate between becoming a generator and a consumer during any one day. Moreover, wind farms and larger photovoltaic power plants feed into the intermediate layer of the power grid, the distribution grid. Today, lower levels of the power grid feed back into the upper levels, voiding the traditional hierarchical structure of the power grid; power generation has also become distributed, just as the consumption of power. Due to technical restrictions that are inherent in the type of power plant, the traditional coal, oil, or nuclear power plants cannot accompany this volatility with arbitrarily changing their power generation.At the same time, the notion of the smart grid introduces a vast array of new data coming from sensors in the power grid, at wind farms, power plants, and consumers. The new wealth of information can help in managing the different actors in the power grid. This thesis proposes to view the outlined problem of power generation and distribution as a problem of information distribution and processing.To accommodate the new, decentralized architecture of the power grid, an equally decentralized approach to grid-wide information processing and distribution is sensible. Each power plant, substation, transformer, and large consumers, such as factories, become agents that exhibit proactive behavior i ii ABSTRACT and communicate to maintain the grid-wide power balance.Local forecasting is the basis for these entities. Every agent forecasts its future power balance or imbalance from historic data. The agents utilize individually trained Artificial Neural Networks to exhibit this forecast. The agent now seeks the help of other agents to solve this disequilibrium. The rules of this exchange are governed by a protocol designed in this thesis. The core principle of the rules that govern the information exchange is to arrive at a power equilibrium while being as scalable as possible without any agent having a priori knowledge of other agents. For this, the protocol remodels the power grid in the communication architecture to take advantage of the properties of the electric grid. Which agent contributes which part to the power equilibrium, however, remains a combinatorial problem. This thesis models the demand and supply of power in the Boolean domain. The power balance solver leverages Ternary Vector Lists and the XBOOLE system to master the emerging complexity. Thus, a distributed demand-suppl...

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“…This is for providing a basic understanding of the algorithm. For the technical details, please refer to [13] and [22].…”

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