Tri-objective optimal scheduling of smart energy hub system with schedulable loads

Abstract Many researches have investigated the optimal energy scheduling to meet the demand regarding the sustainable development issues and the economic and environmental indices. The energy hub system (EHS) is introduced as an appropriate framework in which the demands are supplied by multi carrier energy (MCE) such as electricity, natural gas, and thermal energy. In this paper, the optimal scheduling problem of an EHS is modeled as a tri-objective optimization problem in which the operation cost, the emission pollution, and the deviation of the electrical load profile from its desired value is minimized. In the proposed model, the third objective function as a Demand-side Management (DSM) strategy is considered for the optimal shifting of the electrical deferrable loads (EDLs) based on the day-ahead electrical energy prices. Moreover, the reserve scheduling is proposed by the interruptible loads (ILs) of the thermal and the electrical demand. The proposed model is solved using the augmented e-constraint method in the General Algebraic Modeling System (GAMS) optimization software environment. The generated solutions by the augmented e-constraint method have diverse non-dominated solutions in which the best solution is selected by the fuzzy approach. In order to validate the proposed approach, three Case studies are investigated and their results are compared with each other. The operation cost and the emission pollution of the EHS in the first case is 402130.12 $ and 36162.26 kg. With the participation of EDLs in the second case, the operation cost and the emission reduces 0.4% and 3.02%, respectively. Finally, with the simultaneous participation of EDLs and ILs in the third case, the operation cost and the emission pollution reduces 0.38% and 2.53% in comparison with the second case.

[1]  Jared L. Cohon,et al.  Multiobjective programming and planning , 2004 .

[2]  Mostafa Sedighizadeh,et al.  Stochastic multi-objective energy management in residential microgrids with combined cooling, heating, and power units considering battery energy storage systems and plug-in hybrid electric vehicles , 2018, Journal of Cleaner Production.

[3]  Luis Romeral,et al.  Multiobjective Optimization of Multi-Carrier Energy System Using a Combination of ANFIS and Genetic Algorithms , 2018, IEEE Transactions on Smart Grid.

[4]  Na Li,et al.  Optimal Residential Demand Response in Distribution Networks , 2014, IEEE Journal on Selected Areas in Communications.

[5]  Pascal Henry Biwole,et al.  Multi-Objective Optimization Methodology for Net Zero Energy Buildings , 2018 .

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

[7]  Kelum A. A. Gamage,et al.  Demand side management in smart grid: A review and proposals for future direction , 2014 .

[8]  N. Amjady,et al.  Stochastic Multiobjective Market Clearing of Joint Energy and Reserves Auctions Ensuring Power System Security , 2009, IEEE Transactions on Power Systems.

[9]  Javier Contreras,et al.  Medium-term energy hub management subject to electricity price and wind uncertainty , 2016 .

[10]  Noradin Ghadimi,et al.  Robust optimization based optimal chiller loading under cooling demand uncertainty , 2019, Applied Thermal Engineering.

[11]  Wei Gao,et al.  Different states of multi-block based forecast engine for price and load prediction , 2019, International Journal of Electrical Power & Energy Systems.

[12]  G. Chiandussi,et al.  Comparison of multi-objective optimization methodologies for engineering applications , 2012, Comput. Math. Appl..

[13]  Behnam Mohammadi-Ivatloo,et al.  Robust scheduling of thermal, cooling and electrical hub energy system under market price uncertainty , 2019, Applied Thermal Engineering.

[14]  Behnam Mohammadi-Ivatloo,et al.  Stochastic optimization of energy hub operation with consideration of thermal energy market and demand response , 2017 .

[15]  Kazem Zare,et al.  Integration of Smart Energy Hubs in Distribution Networks Under Uncertainties and Demand Response Concept , 2019, IEEE Transactions on Power Systems.

[16]  Taher Niknam,et al.  Scenario-based dynamic economic emission dispatch considering load and wind power uncertainties , 2013 .

[17]  Shahram Jadid,et al.  Optimal electrical and thermal energy management of a residential energy hub, integrating demand response and energy storage system , 2015 .

[18]  Wanzhong Zhao,et al.  Parameter optimization design of vehicle E-HHPS system based on an improved MOPSO algorithm , 2018, Adv. Eng. Softw..

[19]  Shahab Bahrami,et al.  From Demand Response in Smart Grid Toward Integrated Demand Response in Smart Energy Hub , 2016, IEEE Transactions on Smart Grid.

[20]  Alfredo Vaccaro,et al.  A goal programming methodology for multiobjective optimization of distributed energy hubs operation , 2014 .

[21]  Ayda Darvishan,et al.  Fuzzy-based heat and power hub models for cost-emission operation of an industrial consumer using compromise programming , 2018, Applied Thermal Engineering.

[22]  Bo Sun,et al.  Initiative optimization operation strategy and multi-objective energy management method for combined cooling heating and power , 2016, IEEE/CAA Journal of Automatica Sinica.

[23]  Thillainathan Logenthiran,et al.  Demand Side Management in Smart Grid Using Heuristic Optimization , 2012, IEEE Transactions on Smart Grid.

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

[25]  Halit Üster,et al.  Optimization for Design and Operation of Natural Gas Transmission Networks , 2014 .

[26]  Alireza Soroudi,et al.  Binary PSO-based dynamic multi-objective model for distributed generation planning under uncertainty , 2012 .

[27]  Kazem Zare,et al.  Optimal scheduling of heating and power hubs under economic and environment issues in the presence of peak load management , 2018 .

[28]  Nima Amjady,et al.  Multi-objective electricity market clearing considering dynamic security by lexicographic optimization and augmented epsilon constraint method , 2011, Appl. Soft Comput..

[29]  Chunyan Wang,et al.  Braking sense consistency strategy of electro-hydraulic composite braking system , 2018, Mechanical Systems and Signal Processing.

[30]  Ziyad M. Salameh,et al.  Optimum photovoltaic array size for a hybrid wind/PV system , 1994 .

[31]  Zhiqiang Zhai,et al.  Particle swarm optimization for redundant building cooling heating and power system , 2010 .

[32]  Saman Soheyli,et al.  Modeling a novel CCHP system including solar and wind renewable energy resources and sizing by a CC-MOPSO algorithm , 2016 .

[33]  Elaheh Mashhour,et al.  A comprehensive model for self-scheduling an energy hub to supply cooling, heating and electrical demands of a building , 2016 .

[34]  Javier Contreras,et al.  A Stochastic Bilevel Model for the Energy Hub Manager Problem , 2017, IEEE Transactions on Smart Grid.

[35]  Amany El-Zonkoly.  Optimal scheduling of observable controlled islands in presence of energy hubs , 2017 .

[36]  George Mavrotas,et al.  Effective implementation of the epsilon-constraint method in Multi-Objective Mathematical Programming problems , 2009, Appl. Math. Comput..

[37]  Albert Moser,et al.  Uncertainty modeling in optimal operation of energy hub in presence of wind, storage and demand response , 2014 .

[38]  Li Yao,et al.  Stochastic Day-Ahead Scheduling of Integrated Energy Distribution Network With Identifying Redundant Gas Network Constraints , 2019, IEEE Transactions on Smart Grid.

[39]  Wanzhong Zhao,et al.  Parametric optimization of novel electric–hydraulic hybrid steering system based on a shuffled particle swarm optimization algorithm , 2018, Journal of Cleaner Production.

[40]  Bohong Wang,et al.  An MILP model for optimal design of multi-period natural gas transmission network , 2018 .

[41]  G. Andersson,et al.  Optimal Power Flow of Multiple Energy Carriers , 2007, IEEE Transactions on Power Systems.

[42]  Shanlin Yang,et al.  A systematic review of supply and demand side optimal load scheduling in a smart grid environment , 2018, Journal of Cleaner Production.

[43]  G. Andersson,et al.  Energy hubs for the future , 2007, IEEE Power and Energy Magazine.

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

[45]  Karzan Wakil,et al.  RETRACTED: Risk-assessment of photovoltaic-wind-battery-grid based large industrial consumer using information gap decision theory , 2018, Solar Energy.

[46]  Noradin Ghadimi,et al.  Optimal offering and bidding strategies of renewable energy based large consumer using a novel hybrid robust-stochastic approach , 2019, Journal of Cleaner Production.

[47]  Ali Reza Seifi,et al.  Multi-objective operation management of a multi-carrier energy system , 2015 .