Joint optimization model of generation side and user side based on energy-saving policy

Abstract China is actively promoting energy saving policies to achieve the commitments promised in Copenhagen. Meanwhile, the relevant energy-saving programs are being implemented in the upstream and downstream of the electric power industry, including energy saving dispatching in the power generation side, and Time-of-Use (TOU) price mechanisms in the user side, etc. In order to optimize the energy efficiency of the power industry chain, it is necessary to implement joint optimization of the generation side and user side. In this paper, considering the constraints such as the user demand for electricity and benefits of the grid company, the joint optimization model of generation side and user side is built, with objective function of minimizing the coal consumption. In the user side, the TOU price is implemented and the fluctuation level of the load curve is reduced by adjusting the tariff of the peak periods and valley periods. In the power generation side, the electricity demand load optimized by TOU in the user side will be balanced in accordance with the principle of energy saving generation dispatching. Optimization results indicate that the response of demand load to TOU price will contribute to achieving energy saving benefits in the generation side.

[1]  Li Li,et al.  Energy conservation and emission reduction policies for the electric power industry in China , 2011 .

[2]  R. Jabr Rank-constrained semidefinite program for unit commitment , 2013 .

[3]  S.S. Choi,et al.  An Energy-Saving Series Compensation Strategy Subject to Injected Voltage and Input-Power Limits , 2008, IEEE Transactions on Power Delivery.

[4]  Narayana Prasad Padhy,et al.  Thermal unit commitment using binary/real coded artificial bee colony algorithm , 2012 .

[5]  Jiang Hai-yang Analysis model on the impact of user TOU electricity price on generation coal-saving , 2009 .

[6]  I. E. Lane,et al.  Industrial power demand response analysis for one-part real-time pricing , 1998 .

[7]  Jiang Hai-yang,et al.  Optimization Model for Designing Peak-valley Time-of-use Power Price of Generation Side and Sale Side at the Direction of Energy Conservation Dispatch , 2009 .

[8]  Babak Mozafari,et al.  Designing time-of-use program based on stochastic security constrained unit commitment considering reliability index , 2012 .

[9]  Provas Kumar Roy,et al.  Solution of unit commitment problem using gravitational search algorithm , 2013 .

[10]  Massimo Filippini,et al.  Short- and long-run time-of-use price elasticities in Swiss residential electricity demand , 2011 .

[11]  Zhongfu Tan,et al.  Demand response in China , 2010 .

[12]  Zwe-Lee Gaing,et al.  Unit Commitment with Security Assessment Using Chaotic PSO Algorithm , 2011, J. Circuits Syst. Comput..

[13]  Hai-ying He,et al.  Electricity demand price elasticity in China based on computable general equilibrium model analysis , 2011 .

[14]  Judith Gurney BP Statistical Review of World Energy , 1985 .

[15]  Wu Yuan-Kang,et al.  Resolution of the unit commitment problems by using the hybrid Taguchi-ant colony system algorithm , 2013 .

[16]  Juan Alvarez Lopez,et al.  A MIQCP formulation to solve the unit commitment problem for large-scale power systems , 2011 .

[17]  Wang Cheng-wen Classification Linkage Optimization Model of Time of Use Power Price Between Generating Side and Retail Side , 2007 .

[18]  Li Yang,et al.  Evolution of China’s power dispatch principle and the new energy saving power dispatch policy , 2010 .

[19]  B. Vahidi,et al.  A Solution to the Unit Commitment Problem Using Imperialistic Competition Algorithm , 2012, IEEE Transactions on Power Systems.