Print Email Facebook Twitter Data-driven Dynamic Programming: a Peak Shaving Application Title Data-driven Dynamic Programming: a Peak Shaving Application Author Silva Cruz Guerreiro Mestre, Francisca (TU Delft Mechanical, Maritime and Materials Engineering; TU Delft Delft Center for Systems and Control) Contributor Mohajerin Esfahani, P. (mentor) Sharifi Kolarijani, M.A. (graduation committee) Mazo, M. (graduation committee) Tindemans, S.H. (graduation committee) Degree granting institution Delft University of Technology Programme Mechanical Engineering | Systems and Control Date 2020-11-10 Abstract The rising number of electricity consumers poses a challenge to power generators and grid operators in maintaining a balanced grid. Peak shaving is a technique that consists of shifting electricity consumption from hours of high demand to times of low demand, and has been gaining popularity in recent years. It allows to reduce peak loads which must be met at all times, which in turn reduces the need to resort to less efficient and more expensive power plants to see that all peaks are met. This paper proposes two methods to operate a battery energy storage system (BESS) for peak shaving of daily load profiles, based on the dynamic programming (DP) algorithm. The first approach treats historical profiles as deterministic paths and solves the DP algorithm for each profile. Then, an operating plan is obtained in real-time by combining the policies calculated for each path. The second approach treats data as stochastic paths, from which it derives a probabilistic model for the target path. Given this model, the DP algorithm obtains an operating plan for the entire day ahead. We also propose three modifications that can be applied to the two methods which aim at the improving the reliability of the methods. The performance of the proposed methods is examined through numerical experiments on both artificial and real data. Subject Dynamic programmingPeak shavingBattery energy storage system (BESS) To reference this document use: http://resolver.tudelft.nl/uuid:9fd26b50-ef9a-463d-9403-20456c3f89d1 Part of collection Student theses Document type master thesis Rights © 2020 Francisca Silva Cruz Guerreiro Mestre Files PDF Thesis_FranciscaMestre_20 ... 9_DCSC.pdf 7.69 MB Close viewer /islandora/object/uuid:9fd26b50-ef9a-463d-9403-20456c3f89d1/datastream/OBJ/view