An Innovative Coalitional Trading Model for a Biomass Power Plant Paired With Green Energy Resources

The role of biomass resources to diminish the dependency on fossil fuels is steadily increasing worldwide. More importantly, governments set goals to boost the share of renewable energy resources in the power sector to face up to global warming issues. In this paper, a coalitional game model for the trading of a Biomass Power Plant (BPP) paired with a concentrating solar power facility and a wind park is proposed. In the proposed coalitional trading architecture, the physical coupling between biomass and concentrating solar power facilities is embedded, while cost sources related to operation and maintenance of all units as well as harvesting and transportation of forestry residue are taken into account to represent a more pragmatic trading approach. The suggested coalitional trading model is formulated as a stochastic model with three sequential stages. Moreover, game theory concepts, i.e., $\tau$-value, nucleolus, and Shapley-value, are exploited and compared for profit allocation to the coalition members. A cost-benefit analysis is also conducted to investigate the effect of cooperative and non-cooperative trading models on the BPP’s investment feasibility. The results highlight the lucrativeness of the proposed coalitional trading model and remarkable reduction in the payback period of the BPP under a cooperative game framework.

[1]  Enzo Sauma,et al.  Review of Cooperative Game Theory applications in power system expansion planning , 2021, Renewable and Sustainable Energy Reviews.

[2]  Taher Niknam,et al.  Self-Scheduling Approach to Coordinating Wind Power Producers With Energy Storage and Demand Response , 2020, IEEE Transactions on Sustainable Energy.

[3]  Kaifeng Zhang,et al.  A robust offering strategy for wind producers considering uncertainties of demand response and wind power , 2020, Applied Energy.

[4]  L. Shapley.  A Value for n-person Games , 1988 .

[5]  Competitive low carbon economy in 2050 , 2013 .

[6]  Carl Dudley.  A NOTE ON REINVESTMENT ASSUMPTIONS IN CHOOSING BETWEEN NET PRESENT VALUE AND INTERNAL RATE OF RETURN , 1972 .

[7]  Chun Sing Lai,et al.  Day-Ahead and Intraday Dispatch of an Integrated Biomass-Concentrated Solar System: A Multi-Objective Risk-Controlling Approach , 2022, IEEE Transactions on Power Systems.

[8]  Xu Wang,et al.  Robust Bidding Strategy and Profit Allocation for Cooperative DSR Aggregators With Correlated Wind Power Generation , 2019, IEEE Transactions on Sustainable Energy.

[9]  Kittisak Jermsittiparsert,et al.  Risk-Constrained Stochastic Optimization of a Concentrating Solar Power Plant , 2020, IEEE Transactions on Sustainable Energy.

[10]  L. Lai.  Power system restructuring and deregulation : trading, perfomance and information technology , 2001 .

[11]  S. Tijs,et al.  Bounds for the core of a game and the t-value , 1981 .

[12]  Amjad Anvari-Moghaddam,et al.  Optimal Behavior of a Hybrid Power Producer in Day-Ahead and Intraday Markets: A Bi-Objective CVaR-Based Approach , 2021, IEEE Transactions on Sustainable Energy.

[13]  Mohamed Kareem AlAshery,et al.  Second-Order Stochastic Dominance Constraints for Risk Management of a Wind Power Producer's Optimal Bidding Strategy , 2020, IEEE Transactions on Sustainable Energy.

[14]  Ming Zhou,et al.  Profit-Sharing Mechanism for Aggregation of Wind Farms and Concentrating Solar Power , 2020, IEEE Transactions on Sustainable Energy.

[15]  Shuqiang Zhao,et al.  Look-ahead bidding strategy for concentrating solar power plants with wind farms , 2020 .

[16]  Long Bao Le,et al.  Sharing Profit From Joint Offering of a Group of Wind Power Producers in Day Ahead Markets , 2018, IEEE Transactions on Sustainable Energy.

[17]  E. Kohlberg.  On the Nucleolus of a Characteristic Function Game , 1971 .

[18]  Innocent Kamwa,et al.  Scenario-Wise Distributionally Robust Optimization for Collaborative Intermittent Resources and Electric Vehicle Aggregator Bidding Strategy , 2020, IEEE Transactions on Power Systems.

[19]  Javier Contreras,et al.  Risk-constrained self-scheduling of a hybrid power plant considering interval-based intraday demand response exchange market prices , 2021 .

[20]  Jean-François Toubeau,et al.  Deep Learning-Based Multivariate Probabilistic Forecasting for Short-Term Scheduling in Power Markets , 2019, IEEE Transactions on Power Systems.

[21]  Chongqing Kang,et al.  Optimal Offering Strategy for Concentrating Solar Power Plants in Joint Energy, Reserve and Regulation Markets , 2016, IEEE Transactions on Sustainable Energy.

[22]  Zacharie De Greve,et al.  An advanced day-ahead bidding strategy for wind power producers considering confidence level on the real-time reserve provision , 2020 .

[23]  Loi Lei Lai,et al.  A comprehensive review on large-scale photovoltaic system with applications of electrical energy storage , 2017 .

[24]  Fangxing Li,et al.  Coordinated Bidding Strategy of Wind Farms and Power-to-Gas Facilities Using a Cooperative Game Approach , 2020, IEEE Transactions on Sustainable Energy.

[25]  Pierluigi Siano,et al.  Risk-Involved Optimal Operating Strategy of a Hybrid Power Generation Company: A Mixed Interval-CVaR Model , 2021 .

[26]  J. P. Sánchez,et al.  Analysis of forest residual biomass potential for bioenergy production in Spain , 2016 .

[27]  Du Songhuai,et al.  A novel nucleolus-based loss allocation method in bilateral electricity markets , 2006, IEEE Transactions on Power Systems.

[28]  Miao Miao,et al.  Operation strategy of a hybrid solar and biomass power plant in the electricity markets , 2019, Electric Power Systems Research.

[29]  Henrik Madsen,et al.  A novel bidding method for combined heat and power units in district heating systems , 2018, Energy Systems.