Developing optimal energy management of energy hub in the presence of stochastic renewable energy resources

Abstract Increasing the implementation of distributed generation and introducing multi-carrier energy systems highlight the need for energy hub systems. The energy hub is a new idea implemented in multi-carrier energy systems, sending, receiving, and storing different energy types. Therefore, the present paper proposes an improved energy hub consisting of different types of renewable energy-based DG units considering electricity and heating storage systems, which models the system’s operation and planning aspects. Furthermore, optimal planning and scheduling of multi-carrier energy hub system is modeled considering the stochastic behavior of wind and photovoltaic units. The operation section’s main challenge is determining the optimal interaction between different resources for supplying other loads in the system. The presented model is solved using a robust method based on a Quantum Particle Swarm Optimization (QPSO) approach to minimize the energy hub system’s total cost. The minimization of fuel consumption and pollutant emissions due to implementing the residential energy hub’s thermal storage system is evaluated. Simulation results show that the amount of consumed natural gas reduces by 48% after using CHP units produced heat to supply heating and cooling loads. After installing CHP and thermal storages in the energy hub system, the amount of CO2 has reduced by about 904 tons during a year. It can be concluded that the produced power of CHP is at the highest, which is equal to 61%, as it can generate electricity at all times during the day. Moreover, to evaluate the efficiency of the proposed methodology, the Genetic Algorithm (GA) and PSO algorithm are also implemented for optimization of the mentioned energy hub system. The performance of the mentioned algorithms is compared with each other, and the results depicted that the QPSO algorithm is the best and the convergence speed and global search ability of the QPSO algorithm are significantly better than PSO and GA algorithms The obtained numerical results verify the efficiency of the proposed method in the optimal scheduling and planning of the energy hub system in the presence of stochastic renewable energy systems.

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

[2]  Josep M. Guerrero,et al.  Optimal Operation of Energy Hubs Considering Uncertainties and Different Time Resolutions , 2020, IEEE Transactions on Industry Applications.

[3]  Chee Wei Tan,et al.  Optimal sizing of an autonomous photovoltaic/wind/battery/diesel generator microgrid using grasshopper optimization algorithm , 2019, Solar Energy.

[4]  Abdullah Abusorrah,et al.  Optimal Expansion Planning of Energy Hub With Multiple Energy Infrastructures , 2015, IEEE Transactions on Smart Grid.

[5]  Yung-Ruei Chang,et al.  Optimal sizing of renewable energy generations in a community microgrid using Markov model , 2017 .

[6]  Muhammad Khalid,et al.  An Improved Optimal Sizing Methodology for Future Autonomous Residential Smart Power Systems , 2018, IEEE Access.

[7]  Aboubakr Salem,et al.  High-Level Penetration of Renewable Energy Sources Into Grid Utility: Challenges and Solutions , 2020, IEEE Access.

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

[9]  X. Guan,et al.  Optimal planning of distributed hydrogen-based multi-energy systems , 2021 .

[10]  Mohammad Reza Mohammadi,et al.  Optimal planning of renewable energy resource for a residential house considering economic and reliability criteria , 2018 .

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

[12]  Fei Tao,et al.  Design of Optimal Attack-Angle for RLV Reentry Based on Quantum Particle Swarm Optimization , 2014 .

[13]  Morteza Nazari-Heris,et al.  Evaluating the impact of multi-carrier energy storage systems in optimal operation of integrated electricity, gas and district heating networks , 2020, Applied Thermal Engineering.

[14]  A. Jalilian,et al.  Optimal sizing and siting of renewable energy resources in distribution systems considering time varying electrical/heating/cooling loads using PSO algorithm , 2018 .

[15]  Wen-jing Niu,et al.  Multireservoir system operation optimization by hybrid quantum-behaved particle swarm optimization and heuristic constraint handling technique , 2020 .

[16]  N. Phuangpornpitak,et al.  Opportunities and Challenges of Integrating Renewable Energy in Smart Grid System , 2013 .

[17]  R. Meenakumari,et al.  An improved genetic algorithm-based optimal sizing of solar photovoltaic/wind turbine generator/diesel generator/battery connected hybrid energy systems for standalone applications , 2019, International Journal of Ambient Energy.

[18]  Kwang Y. Lee,et al.  Determining PV Penetration for Distribution Systems With Time-Varying Load Models , 2014, IEEE Transactions on Power Systems.

[19]  Sunliang Cao,et al.  Quantification of energy flexibility of residential net-zero-energy buildings involved with dynamic operations of hybrid energy storages and diversified energy conversion strategies , 2020 .

[20]  Ali Behbahaninia,et al.  Availability analysis of an Energy Hub with CCHP system for economical design in terms of Energy Hub operator , 2021 .

[21]  Mohammad Yusri Hassan,et al.  Optimal sizing of hybrid power systems using power pinch analysis , 2014 .

[22]  Andrzej M. Trzynadlowski,et al.  Wind speed and wind direction forecasting using echo state network with nonlinear functions , 2019, Renewable Energy.

[23]  Ke Peng,et al.  A cost-effective two-stage optimization model for microgrid planning and scheduling with compressed air energy storage and preventive maintenance , 2021 .

[24]  Wen-jing Niu,et al.  Simplex quantum-behaved particle swarm optimization algorithm with application to ecological operation of cascade hydropower reservoirs , 2019, Appl. Soft Comput..

[25]  Amir Abtahi,et al.  Optimization and energy management of a standalone hybrid microgrid in the presence of battery storage system , 2018 .

[26]  Mohammad Hassan Moradi,et al.  An energy management system (EMS) strategy for combined heat and power (CHP) systems based on a hybrid optimization method employing fuzzy programming , 2013 .

[27]  Yun Teng,et al.  Optimal Operation Strategy for Combined Heat and Power System Based on Solid Electric Thermal Storage Boiler and Thermal Inertia , 2019, IEEE Access.

[28]  Peng Wang,et al.  Optimum design of a multi-form energy hub by applying particle swarm optimization , 2020 .

[29]  Furong Li,et al.  Optimal design and operation of CHPs and energy hub with multi objectives for a local energy system , 2017 .

[30]  M. Bettayeb,et al.  Hybrid solar PV/PEM fuel Cell/Diesel Generator power system for cruise ship: A case study in Stockholm, Sweden , 2019, Case Studies in Thermal Engineering.

[31]  Ali Mohammad Ranjbar,et al.  A scenario-based optimization of Smart Energy Hub operation in a stochastic environment using conditional-value-at-risk , 2018 .

[32]  Hoseyn Sayyaadi,et al.  Comprehensive performance evaluation and demands’ sensitivity analysis of different optimum sizing strategies for a combined cooling, heating, and power system , 2021, Journal of Cleaner Production.

[33]  Pandian Vasant,et al.  Optimization of the hydropower energy generation using Meta-Heuristic approaches: A review , 2020 .

[34]  Bruno Sareni,et al.  Fast power flow scheduling and sensitivity analysis for sizing a microgrid with storage , 2017, Math. Comput. Simul..

[35]  Andrzej M. Trzynadlowski,et al.  Wind speed forecasting using an echo state network with nonlinear output functions , 2017, 2017 American Control Conference (ACC).

[36]  Yi Liu,et al.  Multi-objective optimization and selection of hybrid combined cooling, heating and power systems considering operational flexibility , 2020 .

[37]  Xing He,et al.  A strategy to optimize the multi-energy system in microgrid based on neurodynamic algorithm , 2019, Appl. Soft Comput..

[38]  Cost and Performance Evaluation of Hydrokinetic-diesel Hybrid Systems☆ , 2014 .

[39]  H. R. E. H. Bouchekara,et al.  Optimal sizing of PV/wind/diesel hybrid microgrid system using multi-objective self-adaptive differential evolution algorithm , 2018, Renewable Energy.

[40]  Alireza Zakariazadeh,et al.  Improvement of Demand Side Management and Social Welfare Index Using a Flexible Market-Based Approach , 2019, IEEE Transactions on Industry Applications.

[41]  João P.S. Catalão,et al.  Optimal sizing and siting of smart microgrid components under high renewables penetration considering demand response , 2019, IET Renewable Power Generation.

[42]  Abbas Ketabi,et al.  Techno‐economic comparative study of hybrid microgrids in eight climate zones of Iran , 2020, Energy Science & Engineering.

[43]  Masood Parvania,et al.  Intelligent Damage Classification and Estimation in Power Distribution Poles Using Unmanned Aerial Vehicles and Convolutional Neural Networks , 2020, IEEE Transactions on Smart Grid.

[44]  Azah Mohamed,et al.  Power Quality Impact of Renewable Energy based Generators and Electric Vehicles on Distribution Systems , 2013 .

[45]  Alireza Jalilian,et al.  Optimization of DG Units in Distribution Systems for Voltage Sag Minimization Considering Various Load Types , 2020 .

[46]  Bo Zhao,et al.  Optimal sizing, operating strategy and operational experience of a stand-alone microgrid on Dongfushan Island , 2014 .

[47]  Puppala Rajendhar,et al.  A Water Filling Energy distributive algorithm based HEMS in coordination with PEV , 2020 .

[48]  Hamidreza Zareipour,et al.  A Probabilistic Energy Management Scheme for Renewable-Based Residential Energy Hubs , 2017, IEEE Transactions on Smart Grid.

[49]  Barun Das,et al.  Techno-economic feasibility and size optimisation of an off-grid hybrid system for supplying electricity and thermal loads , 2021 .

[50]  Mehdi Abapour,et al.  Techno-economic and environmental assessment of the coordinated operation of regional grid-connected energy hubs considering high penetration of wind power , 2021 .

[51]  Mauricio Camargo,et al.  Multi-criteria optimization for the design and operation of distributed energy systems considering sustainability dimensions , 2021, Energy.

[52]  Alireza Jalilian,et al.  Optimal sizing and location of renewable energy based DG units in distribution systems considering load growth , 2018, International Journal of Electrical Power & Energy Systems.

[53]  Mostafa Sahraei-Ardakani,et al.  Tractable Stochastic Unit Commitment for Large Systems During Predictable Hazards , 2020, IEEE Access.

[54]  Hyery Kim,et al.  A two-stage stochastic p-robust optimal energy trading management in microgrid operation considering uncertainty with hybrid demand response , 2021 .

[55]  Mohammad Reza Rajati,et al.  Unit Sizing of a Stand-Alone Hybrid Power System Using Model-Free Optimization , 2007 .

[56]  Qiang Zhang,et al.  Comparison and selection of operation optimization mode of multi-energy and multi-level district heating system: Case study of a district heating system in Xiong’an , 2021 .

[57]  Alireza Zakariazadeh,et al.  Optimum energy resource scheduling in a microgrid using a distributed algorithm framework , 2018 .

[58]  Joao P. S. Catalao,et al.  Optimal operation of electrical and thermal resources in microgrids with energy hubs considering uncertainties , 2019, Energy.

[59]  Hamed Hashemi-Dezaki,et al.  Optimal Day-Ahead Self-Scheduling and Operation of Prosumer Microgrids Using Hybrid Machine Learning-Based Weather and Load Forecasting , 2020, IEEE Access.

[60]  Prachi Chauhan,et al.  Capacity optimization of grid connected solar/fuel cell energy system using hybrid ABC-PSO algorithm , 2020 .

[61]  Muhammad Mansoor,et al.  Optimal planning of thermal energy systems in a microgrid with seasonal storage and piecewise affine cost functions , 2021 .

[62]  Hamed Hashemi-Dezaki,et al.  Impacts of load modeling on generalized analytical reliability assessment of smart grid under various penetration levels of wind/solar/non-renewable distributed generations , 2019 .

[63]  Abdullah Abusorrah,et al.  Reliability-Based Optimal Planning of Electricity and Natural Gas Interconnections for Multiple Energy Hubs , 2017, IEEE Transactions on Smart Grid.

[64]  Arezoo Hasankhani,et al.  Stochastic energy management of smart microgrid with intermittent renewable energy resources in electricity market , 2021 .

[65]  Yi Chen,et al.  Multi-objective optimization of combined cooling, heating and power system integrated with solar and geothermal energies , 2019, Energy Conversion and Management.

[66]  Chuntian Cheng,et al.  Multi-objective quantum-behaved particle swarm optimization for economic environmental hydrothermal energy system scheduling , 2017 .

[67]  S. M. Hakimi,et al.  Intelligent energy management in off-grid smart buildings with energy interaction , 2020 .

[68]  Kittisak Jermsittiparsert,et al.  Optimal operation of CCHP and renewable generation-based energy hub considering environmental perspective: An epsilon constraint and fuzzy methods , 2019 .

[69]  An LUO,et al.  Overview of power quality analysis and control technology for the smart grid , 2016 .

[70]  Junyong Liu,et al.  Optimal planning and investment benefit analysis of shared energy storage for electricity retailers , 2021 .

[71]  Elnaz Azizi,et al.  Data-Driven load management of stand-alone residential buildings including renewable resources, energy storage system, and electric vehicle , 2020 .

[72]  M. Parastegari,et al.  An efficient scenario-based stochastic programming method for optimal scheduling of CHP-PEMFC, WT, PV and hydrogen storage units in micro grids , 2019, Renewable Energy.

[73]  Sayyad Nojavan,et al.  Robust optimization of renewable-based multi-energy micro-grid integrated with flexible energy conversion and storage devices , 2021 .

[74]  Mehdi Abapour,et al.  Optimal Operation of Integrated Electrical and Natural Gas Networks with a Focus on Distributed Energy Hub Systems , 2020 .

[75]  Faruk Ugranli,et al.  Probabilistic distribution planning: Including the interactions between chance constraints and renewable energy , 2020 .

[76]  Lin Lu,et al.  A novel optimal configuration model for a zero-carbon multi-energy system (ZC-MES) integrated with financial constraints , 2020 .

[77]  Lingfeng Wang,et al.  A Real-Time Rolling Horizon Chance Constrained Optimization Model for Energy Hub Scheduling , 2020 .