Game Theoretic Model Predictive Control for Distributed Energy Demand-Side Management

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Stephens, Ted; Smith, David; Mahanti, Anirban


2015-12-31


Journal Article


IEEE Transactions on Smart Grids


5


3


1394 - 1402


Demand-side management (DSM) is a critical smart grid technique for improving and updating existing power infrastructure. Distributed energy generation and storage are widely investigated DSM technologies that are a scalable and integrable with contemporary systems. However, prior research has mainly focused on day-ahead optimization while neglecting forecasting errors and their often detrimental consequences. We propose a novel game theoretic model predictive control (MPC) approach that can adapt to real-time data. The MPC algorithm produced Pareto optimal strategies, and was shown to be more effective than a day-ahead scheme when mean forecasting errors greater than 10% are present. This robust and continuous MPC approach limits forecasting errors, and in doing so achieves greater electricity cost savings than the day-ahead optimization scheme.


Data61; NICTA; Smart grid, game theory, demand-side management, model predictive control


https://doi.org/10.1109/TSG.2014.2377292


English


nicta:8144


Stephens, Ted; Smith, David; Mahanti, Anirban. Game Theoretic Model Predictive Control for Distributed Energy Demand-Side Management. IEEE Transactions on Smart Grids. 2015-12-31; 5(3): 1394 - 1402. <a href="https://doi.org/10.1109/TSG.2014.2377292" target="_blank">https://doi.org/10.1109/TSG.2014.2377292</a>



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