Integration of demand response and renewable resources for power generation management

A single-period optimal dispatching problem is considered for a network of energy utilities connected via multiple transmission lines, where we seek to find the lowest operationalcost dispatching of various energy sources to satisfy demand. Our model includes traditional thermal resources and renewable energy resources available generation capabilities within the grid. A key novel addition is the consideration of demand reduction as a virtual generation source that can be dispatched quickly to hedge against the risk of unforeseen shortfall in supply. Demand reduction is dispatched in response to incentive signals sent to consumers. The control options of our optimization model consist of the dispatching order and dispatching amount of the thermal generators together with the rebate signals sent to end-users at each node of the network under a simple demand response policy. Numerical experiments based on our analysis of representative data are presented to illustrate the effectiveness of demand response as a hedging option.

[1]  Leon F. McGinnis,et al.  An Optimization Model for Production Costing in Electric Utilities , 1983 .

[2]  James A. Momoh,et al.  Optimal power flow : solution techniques, requirements, and challenges , 1996 .

[3]  Philippe Artzner,et al.  Coherent Measures of Risk , 1999 .

[4]  R. Rockafellar,et al.  Optimization of conditional value-at risk , 2000 .

[5]  M. Bollen,et al.  Real time optimal interruptible tariff mechanism incorporating utility-customer interactions , 2000 .

[6]  James D. McCalley,et al.  Risk based optimal power flow , 2001, 2001 IEEE Porto Power Tech Proceedings (Cat. No.01EX502).

[7]  Philippe Artzner,et al.  Risk Management: Coherent Measures of Risk , 2002 .

[8]  M. Milligan,et al.  Assessing Wind Integration Costs with Dispatch Models: A Case Study of PacifiCorp; Preprint , 2003 .

[9]  R. Belmans,et al.  Usefulness of DC power flow for active power flow analysis , 2005, IEEE Power Engineering Society General Meeting, 2005.

[10]  David Morrow,et al.  Distributed generation as a balancing resource for wind generation , 2007 .

[11]  Y. Xue,et al.  Dispatchable Distributed Generation Network - A New Concept to Advance DG Technologies , 2007, 2007 IEEE Power Engineering Society General Meeting.

[12]  J. Morales,et al.  Point Estimate Schemes to Solve the Probabilistic Power Flow , 2007, IEEE Transactions on Power Systems.

[13]  Zuyi Li,et al.  A Multiperiod Energy Acquisition Model for a Distribution Company With Distributed Generation and Interruptible Load , 2007, IEEE Transactions on Power Systems.

[14]  Deqiang Gan,et al.  Optimal Automatic Generation Control (AGC) Dispatching and Its Control Performance Analysis for the Distribution Systems with DGs , 2007, 2007 IEEE Power Engineering Society General Meeting.

[15]  C. V. Zeljkovic,et al.  A method for cost minimization applicable to load centers containing distributed generation , 2009, 2009 IEEE Bucharest PowerTech.

[16]  Hsan Hadj Abdallah,et al.  Economic Dispatch for Power System included Wind and Solar Thermal energy , 2009 .

[17]  Mohammad Kazem Sheikh-El-Eslami,et al.  Hedging risks with interruptible load programs for a load serving entity , 2009, Decis. Support Syst..

[18]  Bikash C. Pal,et al.  Intermittent wind generation in optimal power flow dispatching , 2009 .

[19]  Xing Wang,et al.  Generation dispatch in a smart grid environment , 2010, 2010 Innovative Smart Grid Technologies (ISGT).

[20]  Dmitriy Katz,et al.  Incentive Design for Lowest Cost Aggregate Energy Demand Reduction , 2010, 2010 First IEEE International Conference on Smart Grid Communications.