Open AccessStochastic scheduling of compressed air energy storage in DC SCUC framework for high wind penetration
Author(s) -
Gupta Pranda Prasanta,
Jain Prerna,
Chand Sharma Kailash,
Bhakar Rohit
Publication year - 2019
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/iet-gtd.2019.0330
Subject(s) - compressed air energy storage , wind power , power system simulation , electric power system , mathematical optimization , energy storage , scheduling (production processes) , integer programming , benders' decomposition , computer science , reliability engineering , engineering , power (physics) , electrical engineering , mathematics , physics , quantum mechanics
High intermittent wind generation necessitates integration of bulk energy storage systems (ESSs) for maintaining security and reliability in power system operation. Considering this, stochastic security constrained unit commitment (SCUC) including compressed air energy storage (CAES) as bulk ESS for high wind penetration and with wind uncertainty modelling is addressed. Network constraints for pre‐ and post‐line contingency are modelled using DC power flow. Injection sensitivity factors (ISFs) are conventionally used in power flow equations which, however, make N − 1 network security constrained formulation huge and computationally demanding for the proposed stochastic model. Therefore, this study proposes a line outage distribution factor (LODF) to reduce the number of coefficients of post contingency DC power flow equations. This is a compact formulation with the lower computational requirement. Wind uncertainty is modelled as probable scenarios. The proposed SCUC is a complex mixed integer linear programming problem and solved using Benders decomposition technique for IEEE 30‐bus and 118‐bus system. Simulation results to analyse the impact of CAES, wind uncertainty and line contingency with ISF and LODF on overall operation costs, CAES scheduling, wind curtailment, locational marginal price and computational time. Results show that the proposed model is computationally efficient for system operation under high wind penetration.