Demand Side Storage to Increase Hydroelectric Generation Efficiency

In this paper, a new generation control strategy is proposed based on combined use of different resources including large hydroelectric power plants and directly controlled industrial responsive loads. Water pumping in water storage systems is modeled as a virtual power plant (VPP) in transmission system. The balance model is in the economic dispatch (ED) time frame and loads are scheduled to be dispatched optimally along with the swing hydroelectric plant, which realizes load-following and frequency regulation. The model is implemented on the IEEE 24 bus reliability test system with hydroelectric generation units. Real-field data of responsive loads are used and the results show the value of flexibility of responsive loads in increasing generation efficiency of hydroelectric power plants operated in swing mode.

[1]  Ali Keyhani,et al.  Automatic Generation Control Structure for Smart Power Grids , 2012, IEEE Transactions on Smart Grid.

[2]  Secundino Soares,et al.  Minimum loss predispatch model for hydroelectric power systems , 1997 .

[3]  E.L. da Silva,et al.  Solving the hydro unit commitment problem via dual decomposition and sequential quadratic programming , 2006, IEEE Transactions on Power Systems.

[4]  Duncan S. Callaway Tapping the energy storage potential in electric loads to deliver load following and regulation, with application to wind energy , 2009 .

[5]  G. A. Nash,et al.  A practical hydro dynamic unit commitment and loading model , 2001, PICA 2001. Innovative Computing for Power - Electric Energy Meets the Market. 22nd IEEE Power Engineering Society. International Conference on Power Industry Computer Applications (Cat. No.01CH37195).

[6]  Alec Brooks,et al.  Demand Dispatch , 2010, IEEE Power and Energy Magazine.

[7]  Khosrow Moslehi,et al.  A Reliability Perspective of the Smart Grid , 2010, IEEE Transactions on Smart Grid.

[8]  Allen J. Wood,et al.  Power Generation, Operation, and Control , 1984 .

[9]  Farrokh Rahimi,et al.  Demand Response as a Market Resource Under the Smart Grid Paradigm , 2010, IEEE Transactions on Smart Grid.

[10]  Le-Ren Chang-Chien,et al.  Incorporating Demand Response With Spinning Reserve to Realize an Adaptive Frequency Restoration Plan for System Contingencies , 2012, IEEE Transactions on Smart Grid.

[11]  Philip G. Hill,et al.  Power generation , 1927, Journal of the A.I.E.E..

[12]  Farrokh Albuyeh,et al.  Grid of the future , 2009, IEEE Power and Energy Magazine.

[13]  Hoay Beng Gooi,et al.  Effective economic dispatch model and algorithm , 2007 .

[14]  Mohamed A. El-Sharkawi,et al.  Optimal Combined Bidding of Vehicle-to-Grid Ancillary Services , 2012, IEEE Transactions on Smart Grid.

[15]  J. Oyarzabal,et al.  A Direct Load Control Model for Virtual Power Plant Management , 2009, IEEE Transactions on Power Systems.

[16]  Probability Subcommittee,et al.  IEEE Reliability Test System , 1979, IEEE Transactions on Power Apparatus and Systems.

[17]  Wai-yu. Ng Generalized Generation Distribution Factors for Power System Security Evaluations , 1981, IEEE Transactions on Power Apparatus and Systems.

[18]  Nick Jenkins,et al.  Investigation of Domestic Load Control to Provide Primary Frequency Response Using Smart Meters , 2012, IEEE Transactions on Smart Grid.

[19]  Danny Pudjianto,et al.  Virtual power plant and system integration of distributed energy resources , 2007 .

[20]  B.J. Kirby Load Response Fundamentally Matches Power System Reliability Requirements , 2007, 2007 IEEE Power Engineering Society General Meeting.

[21]  S. Ali Pourmousavi,et al.  Real-Time Central Demand Response for Primary Frequency Regulation in Microgrids , 2012, IEEE Transactions on Smart Grid.