Scheduling of Energy Storage Systems with Geographically Distributed Renewables

Renewable energy sources (RES) are very likely to continue the upward capacity trend witnessed in the past years. The reasons for adoption are varied and respond to both market pressures, influence of government intervention and a raised awareness of the consequences on the environment of the current generation fleet. The change from dispatch able generation to an environment in which Independent System Operators (ISO's), Regional Transmission Operators (RTO's) and consumers, among others, accommodate the demand to the available generation, requires fundamental changes in the way the system is managed. Also, to better harness the energy from renewable sources, both new methods and technologies need to be adopted, counteracting for the sometimes unpredictable behavior of these sources. This study proposes a method with multi-period optimization to help prescribe the optimal placement and usage of RES and Energy Storage Systems (ESS) with full information of the system. Four cases are analyzed in their dispatches, as well as the benefits to the participants in the wholesale market, for a reduced 30-bus network. While the data requirements are high, and the use of a reduced system limits the applications herein proposed, the policy implications from the results obtained provide useful insights into an ongoing debate regarding on how to direct investment in the electrical system.

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

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

[3]  C. Lindsay Anderson,et al.  Reducing the Variability of Wind Power Generation for Participation in Day Ahead  Electricity Markets , 2008, Proceedings of the 41st Annual Hawaii International Conference on System Sciences (HICSS 2008).

[4]  N. Kobayashi,et al.  THE SODIUM-SULFUR BATTERY FOR UTILITY-SCALE APPLICATIONS , 2008 .

[5]  Ray Zimmerman,et al.  A Symbiotic Role for Plug-in Hybrid Electric Vehicles in an Electric Delivery System , 2009 .

[6]  Robert J. Thomas,et al.  MATPOWER's extensible optimal power flow architecture , 2009, 2009 IEEE Power & Energy Society General Meeting.

[7]  M. O'Malley,et al.  Wind power myths debunked , 2009, IEEE Power and Energy Magazine.

[8]  Alberto J. Lamadrid,et al.  Geographical averaging and ancillary services for stochastic power generation , 2010, 45th International Universities Power Engineering Conference UPEC2010.

[9]  Alberto J. Lamadrid,et al.  Dynamic optimization for the management of stochastic generation and storage , 2010, 2010 IEEE/PES Transmission and Distribution Conference and Exposition: Latin America (T&D-LA).

[10]  Anjali Sheffrin,et al.  Adapting California's energy markets to growth in renewable resources , 2010, IEEE PES General Meeting.