Optimal offering and bidding strategies of renewable energy based large consumer using a novel hybrid robust-stochastic approach

Abstract In the restructured power market, the participation of large consumer in the market is a challenging issue due to their considerable load demand. To provide the required load of the large consumers with the lowest possible cost, appropriate strategies should be taken by the operator of the large consumer. In this paper, in order to get optimal offering and bidding strategies for a large industrial consumer, a new mathematical model is proposed. The uncertainties of load demand, power market prices, solar radiation, temperature and wind speed are taken into account in the proposed model by using hybrid robust-stochastic approach. The load uncertainty is modeled using robust optimization approach while the other uncertainties are modeled using the stochastic approach. A linear model with integer variables is developed to derive offering and bidding curves, which are robust against the uncertainty associated with the load demand of the large consumer. Obtained results show that the higher amount of uncertain parameter is considered, the higher procurement price has resulted for the large consumer. Although, the higher paid price, the higher robustness against load uncertainty has resulted. In addition, total power procurement cost of the large consumer without considering load uncertainty, obtained as $40,060 while this amount is increased to $50,560 in order to be robust against load uncertainty.

[1]  Kazem Zare,et al.  Heating and power hub models for robust performance of smart building using information gap decision theory , 2018 .

[2]  Behnam Mohammadi-Ivatloo,et al.  Risk-based bidding of large electric utilities using Information Gap Decision Theory considering demand response , 2014 .

[3]  Sayyad Nojavan,et al.  Stochastic energy procurement of large electricity consumer considering photovoltaic, wind-turbine, micro-turbines, energy storage system in the presence of demand response program , 2015 .

[4]  Jamshid Aghaei,et al.  Mixed integer programming of multiobjective hydro-thermal self scheduling , 2012, Appl. Soft Comput..

[5]  Qi Zhang,et al.  Long-Term Electricity Procurement for Large Industrial Consumers under Uncertainty , 2018 .

[6]  Kazem Zare,et al.  Electricity procurement for large consumers based on Information Gap Decision Theory , 2010 .

[7]  G. Zakeri,et al.  Integrating Consumption and Reserve Strategies for Large Consumers in Electricity Markets , 2016 .

[8]  Antonio J. Conejo,et al.  Weekly self-scheduling, forward contracting, and pool involvement for an electricity producer. An adaptive robust optimization approach , 2015, Eur. J. Oper. Res..

[9]  B. Goss,et al.  Forecast Errors and Efficiency in the US Electricity Futures Market , 2001 .

[10]  Naser Nourani Esfetanaj,et al.  Heating hub and power hub models for optimal performance of an industrial consumer , 2017 .

[11]  Istvan Vokony,et al.  Evaluation of business possibilities of energy storage at commercial and industrial consumers – A case study , 2018, Applied Energy.

[12]  Behnam Mohammadi-Ivatloo,et al.  Optimal bidding strategy of electricity retailers using robust optimisation approach considering time-of-use rate demand response programs under market price uncertainties , 2015 .

[13]  K. Zare,et al.  Risk-Based Electricity Procurement for Large Consumers , 2011, IEEE Transactions on Power Systems.

[14]  Jamshid Aghaei,et al.  Mixed integer programming of generalized hydro-thermal self-scheduling of generating units , 2013 .

[15]  Ahmed Abdulaal,et al.  Two-stage discrete-continuous multi-objective load optimization: An industrial consumer utility approach to demand response , 2017 .

[16]  A. Conejo,et al.  Risk-constrained electricity procurement for a large consumer , 2006 .

[17]  Antonio J. Conejo,et al.  Power generation scheduling considering stochastic emission limits , 2018 .

[18]  A. Conejo,et al.  Strategic Bidding for a Large Consumer , 2015, IEEE Transactions on Power Systems.

[19]  Lucia Morales,et al.  European power markets–A journey towards efficiency , 2018 .

[20]  Mahdi Zarif,et al.  Self-scheduling approach for large consumers in competitive electricity markets based on a probabilistic fuzzy system , 2012 .

[21]  Debin Fang,et al.  A double auction model for competitive generators and large consumers considering power transmission cost , 2012 .

[22]  Sayyad Nojavan,et al.  Energy storage system and demand response program effects on stochastic energy procurement of large consumers considering renewable generation , 2016 .

[23]  M. H. Javidi,et al.  Self-Scheduling of Large Consumers With Second-Order Stochastic Dominance Constraints , 2013, IEEE Transactions on Power Systems.

[24]  M. Karami,et al.  Scenario-based security-constrained hydrothermal coordination with volatile wind power generation , 2013 .

[25]  Melvyn Sim,et al.  Robust discrete optimization and network flows , 2003, Math. Program..

[26]  Mehdi Ehsan,et al.  IGDT Based Robust Decision Making Tool for DNOs in Load Procurement Under Severe Uncertainty , 2013, IEEE Transactions on Smart Grid.

[27]  Long Bao Le,et al.  Optimal Bidding Strategy for Microgrids Considering Renewable Energy and Building Thermal Dynamics , 2014, IEEE Transactions on Smart Grid.

[28]  Ahmad Zahedi,et al.  Multi-objective based economic operation and environmental performance of PV-based large industrial consumer , 2017 .

[29]  Antonio J. Conejo,et al.  Multi-market energy procurement for a large consumer using a risk-aversion procedure , 2010 .

[30]  Antonio J. Conejo,et al.  Energy procurement for large consumers in electricity markets , 2005 .

[31]  Kazem Zare,et al.  Robust optimal offering strategy of large consumer using IGDT considering demand response programs , 2016 .