Effect of price responsive demand on the operation of microgrids

In this paper, a demand elasticity model is developed and tested for the dispatch of microgrids. The price obtained from dispatching the network in a base-case scenario is used as input to a demand elasticity model; this demand model is then used to determine the price-responsive demand for the next iteration, assuming that the load schedule is defined a day ahead. Using this scheme, trends for demand, hourly prices, and total operation costs for a microgrid can be obtained, to study the impact of demand response on unit commitment. This way, for a microgrid, the effect on the scheduling of diesel generators and energy storage systems can be analyzed with respect to price-elastic loads. The results for a benchmark microgrid show that the proposed 24-hour model eventually converges to a steady state, with prices and costs at their lowest values for different scenarios. Moreover, it is confirmed that elastic demand in a microgrid reduces electricity price variability and mitigates the need for storage in the presence of high penetration of renewable energy.

[1]  E. Bompard,et al.  The Demand Elasticity Impacts on the Strategic Bidding Behavior of the Electricity Producers , 2007, IEEE Transactions on Power Systems.

[2]  Mohammad Kazem Sheikh-El-Eslami,et al.  Investigation of Economic and Environmental-Driven Demand Response Measures Incorporating UC , 2012, IEEE Transactions on Smart Grid.

[3]  B. Hobbs,et al.  Optimal Generation Mix With Short-Term Demand Response and Wind Penetration , 2012, IEEE Transactions on Power Systems.

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

[5]  Enrique Castillo,et al.  Building and Solving Mathematical Programming Models in Engineering and Science , 2001 .

[6]  L. Olmos,et al.  Demand Response in an Isolated System With High Wind Integration , 2012, IEEE Transactions on Power Systems.

[7]  Javier Contreras,et al.  Optimization of control strategies for stand-alone renewable energy systems with hydrogen storage , 2007 .

[8]  Wenxian Yang,et al.  A Statistical Demand-Price Model With Its Application in Optimal Real-Time Price , 2012, IEEE Transactions on Smart Grid.

[9]  K. Strunz,et al.  Design of benchmark of medium voltage distribution network for investigation of DG integration , 2006, 2006 IEEE Power Engineering Society General Meeting.

[10]  Chuin-Shan Chen,et al.  Time-of-use pricing for load management programs in Taiwan Power Company , 1994 .

[11]  K. C. Divya,et al.  Battery Energy Storage Technology for power systems-An overview , 2009 .

[12]  A. Jofré,et al.  A distribution company energy acquisition market model with integration of distributed generation and load curtailment options , 2005, IEEE Transactions on Power Systems.

[13]  Fred Schweppe,et al.  Homeostatic Utility Control , 1980, IEEE Transactions on Power Apparatus and Systems.

[14]  H. Madsen,et al.  Controlling Electricity Consumption by Forecasting its Response to Varying Prices , 2013, IEEE Transactions on Power Systems.

[15]  A. K. David Load forecasting under spot pricing , 1988 .

[16]  M. Carrion,et al.  A computationally efficient mixed-integer linear formulation for the thermal unit commitment problem , 2006, IEEE Transactions on Power Systems.

[17]  M. Lijesen The real-time price elasticity of electricity , 2007 .

[18]  Kankar Bhattacharya,et al.  Optimal Operation of Residential Energy Hubs in Smart Grids , 2012, IEEE Transactions on Smart Grid.