Stochastic Unit Commitment of a Distribution Network with Non-ideal Energy Storage

The need for secure and flexible operation of distribution power systems with renewable generation and the decline in battery prices have made energy storage projects a viable option. The storage characterization currently utilized for power system models relies on two significant assumptions: the storage efficiency and power limits are constant. This approach can lead to an overestimation of the available battery power and energy, thus, threatening the system reliability. We introduce a stochastic operating model for distribution systems with non-ideal energy storage, that allows the purchasing of energy and reserves from the electricity market through the interconnection with the transmission system. The model's objective is the centralized minimization of the operational costs derived from the energy and reserves purchase at the electricity market, as well as those incurred while operating the system's fuel-based and renewable generation, and energy storage system. The proposed energy storage model is computationally validated and compared on a modified IEEE 33-bus electric distribution system.

[1]  Davor Škrlec,et al.  Review of energy storage allocation in power distribution networks: applications, methods and future research , 2016 .

[2]  David Pozo,et al.  Non-Ideal Linear Operation Model for Li-Ion Batteries , 2019, IEEE Transactions on Power Systems.

[3]  S. Low,et al.  Zero Duality Gap in Optimal Power Flow Problem , 2012, IEEE Transactions on Power Systems.

[4]  R. Jabr Radial distribution load flow using conic programming , 2006, IEEE Transactions on Power Systems.

[5]  Enzo Sauma,et al.  Unit commitment with ideal and generic energy storage units , 2016, 2016 IEEE Power and Energy Society General Meeting (PESGM).

[6]  Marko Aunedi,et al.  Whole-Systems Assessment of the Value of Energy Storage in Low-Carbon Electricity Systems , 2014, IEEE Transactions on Smart Grid.

[7]  Carleton Coffrin,et al.  The QC Relaxation: A Theoretical and Computational Study on Optimal Power Flow , 2017, IEEE Transactions on Power Systems.

[8]  Santanu S. Dey,et al.  Strong SOCP Relaxations for the Optimal Power Flow Problem , 2015, Oper. Res..

[9]  T. Funaki,et al.  Economic and Efficient Voltage Management Using Customer-Owned Energy Storage Systems in a Distribution Network With High Penetration of Photovoltaic Systems , 2013, IEEE Transactions on Power Systems.

[10]  Enzo Sauma,et al.  Energy storage and transmission expansion planning: substitutes or complements? , 2017 .

[11]  H. Zareipour,et al.  A Bilevel Model for Participation of a Storage System in Energy and Reserve Markets , 2018, IEEE Transactions on Sustainable Energy.

[12]  Santanu S. Dey,et al.  Inexactness of SDP Relaxation and Valid Inequalities for Optimal Power Flow , 2014, IEEE Transactions on Power Systems.

[13]  A. G. Expósito,et al.  Reliable load flow technique for radial distribution networks , 1999 .

[14]  M. Fotuhi‐Firuzabad,et al.  A Stochastic Framework for Short-Term Operation of a Distribution Company , 2013, IEEE Transactions on Power Systems.

[15]  Ilhami Colak,et al.  Multi-period Prediction of Solar Radiation Using ARMA and ARIMA Models , 2015, 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA).