Impacts of Stochastic Wind Power and Storage Participation on Economic Dispatch in Distribution Systems

Evaluating the impact related to stochastic wind generation and generic storage on economic dispatch in distribution system operation is an important issue in power systems. This paper presents the analysis of the impacts of high wind power and storage participation on a distribution system over a period of 24 h using grid reconfiguration for electrical distribution system (EDS) radial operation. In order to meet this objective, a stochastic mixed integer linear programming (SMILP) is proposed, where the balance between load and generation has to be satisfied minimizing the expected cost during the operation period. The model also considers distributed generation (DG) represented by wind scenarios and conventional generation, bus loads represented through a typical demand profile, and generic storage. A case study provides results for a weakly meshed distribution network with 70 buses, describing in a comprehensive manner the effects of stochastic wind scenarios and storage location on distribution network parameters, voltage, substation behavior as well as power losses, and the expected cost of the system.

[1]  A. Borghetti A Mixed-Integer Linear Programming Approach for the Computation of the Minimum-Losses Radial Configuration of Electrical Distribution Networks , 2012, IEEE Transactions on Power Systems.

[2]  M. J. Rider,et al.  A mixed-integer LP model for the reconfiguration of radial electric distribution systems considering distributed generation , 2013 .

[3]  Seyed Mehdi Hosseini,et al.  A new improved adaptive imperialist competitive algorithm to solve the reconfiguration problem of distribution systems for loss reduction and voltage profile improvement , 2014 .

[4]  Lei Tang,et al.  A survey on distribution system feeder reconfiguration: Objectives and solutions , 2014, 2014 IEEE Innovative Smart Grid Technologies - Asia (ISGT ASIA).

[5]  Ehab F. El-Saadany,et al.  Incorporating load variation and variable wind generation in service restoration plans for distribution systems , 2013 .

[6]  Lei Yang,et al.  Stochastic optimization based economic dispatch and interruptible load management with distributional forecast of wind farm generation , 2014, 53rd IEEE Conference on Decision and Control.

[7]  Ehab F. El-Saadany,et al.  Distribution system reconfiguration for energy loss reduction considering the variability of load and local renewable generation , 2013 .

[8]  E. Lopez,et al.  Minimal loss reconfiguration using genetic algorithms with restricted population and addressed operators: real application , 2006, IEEE Transactions on Power Systems.

[9]  B. Vahidi,et al.  Reconfiguration and Capacitor Placement Simultaneously for Energy Loss Reduction Based on an Improved Reconfiguration Method , 2012, IEEE Transactions on Power Systems.

[10]  R. Jabr,et al.  Minimum Loss Network Reconfiguration Using Mixed-Integer Convex Programming , 2012, IEEE Transactions on Power Systems.

[11]  A. C. Rueda-Medina,et al.  A mixed-integer linear programming approach for optimal type, size and allocation of distributed generation in radial distribution systems , 2013 .

[12]  A. Arabali,et al.  Cost analysis of a power system using probabilistic optimal power flow with energy storage integration and wind generation , 2013 .

[13]  Colleen Lueken,et al.  Distribution grid reconfiguration reduces power losses and helps integrate renewables , 2012 .

[14]  Allan C. Nerves,et al.  Multi-objective optimization of distribution network reconfiguration with capacitor and distributed generator placement , 2014, TENCON 2014 - 2014 IEEE Region 10 Conference.

[15]  Joao P. S. Catalao,et al.  A fast method for the unit scheduling problem with significant renewable power generation , 2015 .

[16]  M. Rider,et al.  Imposing Radiality Constraints in Distribution System Optimization Problems , 2012 .

[17]  Alexander V. Gorpinich,et al.  Combination of capacitor placement and reconfiguration for loss reduction in distribution systems using selective PSO , 2013 .

[18]  F. Bouffard,et al.  Stochastic security for operations planning with significant wind power generation , 2008, 2008 IEEE Power and Energy Society General Meeting - Conversion and Delivery of Electrical Energy in the 21st Century.

[19]  A. K. Srivastava,et al.  Impact of battery energy storage on power system with high wind penetration , 2012, PES T&D 2012.

[20]  Amirsaman Arabali,et al.  A stochastic framework for power system operation with wind generation and energy storage integration , 2014, ISGT 2014.

[21]  M. Kowsalya,et al.  Optimal Distributed Generation and capacitor placement in power distribution networks for power loss minimization , 2014, 2014 International Conference on Advances in Electrical Engineering (ICAEE).

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

[23]  Ehab F. El-Saadany,et al.  A generalized power flow analysis for distribution systems with high penetration of distributed gene , 2011 .

[24]  Carmen L. T. Borges,et al.  A Flexible Mixed-Integer Linear Programming Approach to the AC Optimal Power Flow in Distribution Systems , 2014, IEEE Transactions on Power Systems.

[25]  G. Joos,et al.  A Stochastic Optimization Approach to Rating of Energy Storage Systems in Wind-Diesel Isolated Grids , 2009, IEEE Transactions on Power Systems.

[26]  J. G. Slootweg,et al.  Integrating smart grid solutions into distribution network planning , 2013, 2013 IEEE Grenoble Conference.

[27]  M. O'Malley,et al.  Stochastic Optimization Model to Study the Operational Impacts of High Wind Penetrations in Ireland , 2011, IEEE Transactions on Power Systems.