Fuzzy-based heat and power hub models for cost-emission operation of an industrial consumer using compromise programming

Abstract In addition to economic issue, an emission issue should also be considered in the operation of an industrial consumer in order to reduce greenhouse gases like NO2, SO2 and CO2 to the atmosphere. Also, multi-carrier energy hub system can be used to supply heat and power demand by an industrial consumer. Therefore, this paper proposes a conflict bi-objective model for cost-emission based operation of industrial consumer in the presence of peak load management. Compromise programming is proposed to solve the proposed bi-objective model in order to obtain the Pareto solutions. Furthermore, fuzzy decision making approach is provided to select the trade-off solution from the Pareto solutions. Finally, peak load management is employed to flat the load profile in order to reduce the operation cost and emission. The proposed model is formulated as a mixed-integer linear program which is solved by using CPLEX solver in the GAMS optimization software. Two case studies have been used and obtained results are compared to validate the performance of proposed model.

[1]  Shengwei Wang,et al.  Performance of distributed energy systems in buildings in cooling dominated regions and the impacts of energy policies , 2017 .

[2]  Mohammad Ghiasi,et al.  Extracting Appropriate Nodal Marginal Prices for All Types of Committed Reserve , 2019 .

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

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

[5]  Behnam Mohammadi-Ivatloo,et al.  Risk-constrained scheduling of solar Stirling engine based industrial continuous heat treatment furnace , 2018 .

[6]  Sayyad Nojavan,et al.  A cost-emission model for fuel cell/PV/battery hybrid energy system in the presence of demand response program: ε-constraint method and fuzzy satisfying approach , 2017 .

[7]  R. I. Karimov,et al.  The efficient frontier for spot and forward purchases: an application to electricity , 2004, J. Oper. Res. Soc..

[8]  Hanne Sæle,et al.  Demand Response From Household Customers: Experiences From a Pilot Study in Norway , 2011, IEEE Transactions on Smart Grid.

[9]  Behnam Mohammadi-Ivatloo,et al.  Application of fuel cell and electrolyzer as hydrogen energy storage system in energy management of electricity energy retailer in the presence of the renewable energy sources and plug-in electric vehicles , 2017 .

[10]  Abbas Rabiee,et al.  Probabilistic Multi Objective Optimal Reactive Power Dispatch Considering Load Uncertainties Using Monte Carlo Simulations , 2015 .

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

[12]  Sayyad Nojavan,et al.  A hybrid approach based on IGDT–MPSO method for optimal bidding strategy of price-taker generation station in day-ahead electricity market , 2015 .

[13]  Sayyad Nojavan,et al.  Optimal stochastic short-term thermal and electrical operation of fuel cell/photovoltaic/battery/grid hybrid energy system in the presence of demand response program , 2017 .

[14]  Noradin Ghadimi,et al.  Fuzzy stochastic long-term model with consideration of uncertainties for deployment of distributed energy resources using interactive honey bee mating optimization , 2014 .

[15]  K. A. Subramanian,et al.  Control of greenhouse gas emissions (CO2, CH4 and N2O) of a biodiesel (B100) fueled automotive diesel engine using increased compression ratio , 2017 .

[16]  Kazem Zare,et al.  Large Consumer Electricity Acquisition Considering Time-of-Use Rates Demand Response Programs , 2014 .

[17]  Kazem Zare,et al.  Risk-based optimal performance of a PV/fuel cell/battery/grid hybrid energy system using information gap decision theory in the presence of demand response program , 2017 .

[18]  Hossein Shayeghi,et al.  Application of a new hybrid forecast engine with feature selection algorithm in a power system , 2019 .

[19]  Sayyad Nojavan,et al.  An efficient cost-reliability optimization model for optimal siting and sizing of energy storage system in a microgrid in the presence of responsible load management , 2017 .

[20]  Chi-Keung Woo,et al.  Efficient frontiers for electricity procurement by an LDC with multiple purchase options , 2006 .

[21]  Chi-Keung Woo,et al.  Managing electricity procurement cost and risk by a local distribution company , 2004 .

[22]  Noradin Ghadimi,et al.  A new feature selection and hybrid forecast engine for day-ahead price forecasting of electricity markets , 2017, J. Intell. Fuzzy Syst..

[23]  L. Yaan,et al.  Purchase Allocation and Demand Bidding in Electric Power Markets , 2002, IEEE Power Engineering Review.

[24]  Moataz Elsied,et al.  Energy management and optimization in microgrid system based on green energy , 2015 .

[25]  Nima Amjady,et al.  Solar energy forecasting based on hybrid neural network and improved metaheuristic algorithm , 2018, Comput. Intell..

[26]  Mohsen Mohammadi,et al.  Small-Scale Building Load Forecast based on Hybrid Forecast Engine , 2017, Neural Processing Letters.

[27]  Behnam Mohammadi-Ivatloo,et al.  Selling price determination by electricity retailer in the smart grid under demand side management in the presence of the electrolyser and fuel cell as hydrogen storage system , 2017 .

[28]  Sayyad Nojavan,et al.  A cost-emission framework for hub energy system under demand response program , 2017 .

[29]  Moataz Elsied,et al.  Optimal economic and environment operation of micro-grid power systems , 2016 .

[30]  Tian Zhao,et al.  Heat recovery and storage installation in large-scale battery systems for effective integration of renewable energy sources into power systems , 2017 .

[31]  Noradin Ghadimi,et al.  Short-term management of hydro-power systems based on uncertainty model in electricity markets , 2015 .

[32]  Mohammed H. Albadi,et al.  A summary of demand response in electricity markets , 2008 .

[33]  Adel Akbarimajd,et al.  A new prediction model based on multi-block forecast engine in smart grid , 2018, J. Ambient Intell. Humaniz. Comput..

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

[35]  Farkhondeh Jabari,et al.  Optimal short-term scheduling of a novel tri-generation system in the presence of demand response programs and battery storage system , 2016 .

[36]  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 .

[37]  Hongbo Ren,et al.  Multi-objective Optimization of Integrated Renewable Energy System Considering Economics and CO2 Emissions☆ , 2016 .

[38]  Kazem Zare,et al.  A multi-objective model for optimal operation of a battery/PV/fuel cell/grid hybrid energy system using weighted sum technique and fuzzy satisfying approach considering responsible load management , 2017 .

[39]  Noradin Ghadimi,et al.  The price prediction for the energy market based on a new method , 2018 .

[40]  B. Daryanian,et al.  Optimal Demand-Side Response to Electricity Spot Prices for Storage-Type Customers , 1989, IEEE Power Engineering Review.

[41]  Noradin Ghadimi,et al.  A new prediction model of battery and wind-solar output in hybrid power system , 2019, J. Ambient Intell. Humaniz. Comput..