Gibbs Sampling for Game-Theoretic Modeling of Private Network Upgrades with Distributed Generation

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Renewable energy is increasingly being curtailed,due to at times oversupply or network constraints. Curtailment can be partially avoided by smart grid management, such as ANM, but network reinforcement constitutes the long-term solution. Since network upgrades are expensive, recent interest has focused on incentivising private investors into participating in network investments. In this paper, we study settings where a private line investor installs a transmission line, but also provides access to other generators that pay a transmission fee. This model can be formulated as a Stackelberg game. Crucially the interdependent generation capacities built by renewable investors affect the resulting curtailment and profitability of projects. Optimal capacities rely jointly on stochastic variables such as the wind resource at the location. In this paper we how how big data and machine learning techniques, such as MCMC and Gibbs sampling, can be used generate observations from historic data and simulate multiple future scenarios, enabling optimal decision making regarding renewable energy investments. We present a game-theoretic formulation of the investment decision, and apply our methodology to a real network upgrade problem in the UK.
Original languageEnglish
Title of host publication2018 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe)
ISBN (Electronic)9781538645055
Publication statusPublished - 13 Dec 2018
Event8th IEEE PES Innovative Smart Grid Technologies Conference Europe - Sarajevo, Bosnia and Herzegovina
Duration: 21 Oct 201825 Oct 2018


Seminar8th IEEE PES Innovative Smart Grid Technologies Conference Europe
Abbreviated title2018 IEEE PES ISGT Europe
CountryBosnia and Herzegovina


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