A fair electricity market strategy for energy management and reliability enhancement of islanded multi-microgrids

Abstract This paper proposes an electricity market strategy for the optimal operation of multi-microgrids (MMGs). A new techno-economical objective function is proposed that accounts for the profit of microgrid owners (MGOs), reduces energy not supplied (ENS), and enhances the reliability of microgrids (MGs). An MMG includes multiple MGs that can transfer their power to the upstream grid as well as other MGs in an optimized fashion. Each MG possesses various generation sources such as photovoltaic, wind turbine generators, combined heat and power units, diesel generators, and batteries. Weibull, beta, and normal distribution functions are used for probabilistic modeling of renewable energy sources and loads. Moreover, the security constraints of the MGs, and particular penalties for MGOs when their customers experience the power outage are considered. A new electricity market strategy and energy transaction method among MGs are proposed that improves the profit of the MGOs. Wild Goats Algorithm (WGA) is used as the optimization technique. Different test scenarios are simulated considering different MMG's operational modes. The proposed approach ensures that in an MMG environment all microgrids have the same percentage of profit increment compared to their maximum possible profit. Simulation results show that all MGOs can earn an equal percentage (around 72%) of their maximum possible profit by participating in the proposed electricity market. Moreover, it is shown that the proposed energy market improves customer satisfaction, enhances MG’s reliability, fairly allocates profit of MGOs, and minimizes the total cost.

[1]  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.

[2]  Nikos D. Hatziargyriou,et al.  State estimation in Multi‐Microgrids , 2011 .

[3]  C. Rindt,et al.  Techno-economic optimization of an energy system with sorption thermal energy storage in different energy markets , 2020 .

[4]  Osama A. Mohammed,et al.  A Multiagent-Based Game-Theoretic and Optimization Approach for Market Operation of Multimicrogrid Systems , 2019, IEEE Transactions on Industrial Informatics.

[5]  Christof Weinhardt,et al.  Designing microgrid energy markets , 2018 .

[6]  Peter Tzscheutschler,et al.  Integration of energy markets in microgrids: A double-sided auction with device-oriented bidding strategies , 2019, Applied Energy.

[7]  Chanan Singh,et al.  Optimal Deployment of Distributed Generation Using a Reliability Criterion , 2016 .

[8]  Ramesh Rayudu,et al.  Review of energy storage technologies for sustainable power networks , 2014 .

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

[10]  Josep M. Guerrero,et al.  A Novel Approach to Neighborhood Fair Energy Trading in a Distribution Network of Multiple Microgrid Clusters , 2018 .

[11]  Pierluigi Siano,et al.  A Two-Loop Hybrid Method for Optimal Placement and Scheduling of Switched Capacitors in Distribution Networks , 2020, IEEE Access.

[12]  Dan Wang,et al.  Integrated demand response in district electricity-heating network considering double auction retail energy market based on demand-side energy stations , 2019, Applied Energy.

[13]  Ebrahim Babaei,et al.  A Hybrid Optimization Technique Using Exchange Market and Genetic Algorithms , 2020, IEEE Access.

[14]  Tao Ding,et al.  Integrated demand response for a load serving entity in multi-energy market considering network constraints , 2019, Applied Energy.

[15]  Mousa Marzband,et al.  Optimal energy management for stand‐alone microgrids based on multi‐period imperialist competition algorithm considering uncertainties: experimental validation , 2016 .

[16]  Hongjie Jia,et al.  Energy storage capacity optimization for autonomy microgrid considering CHP and EV scheduling , 2018 .

[17]  Behnam Mohammadi-Ivatloo,et al.  Wild Goats Algorithm: An Evolutionary Algorithm to Solve the Real-World Optimization Problems , 2018, IEEE Transactions on Industrial Informatics.

[18]  Chengshan Wang,et al.  Energy management system for stand-alone diesel-wind-biomass microgrid with energy storage system , 2016 .

[19]  Behnam Mohammadi-Ivatloo,et al.  Optimal battery technology selection and incentive-based demand response program utilization for reliability improvement of an insular microgrid , 2019, Energy.

[20]  Ke Meng,et al.  Two-stage energy management for networked microgrids with high renewable penetration , 2018, Applied Energy.

[21]  Ali Bidram,et al.  Optimal integration of renewable energy sources, diesel generators, and demand response program from pollution, financial, and reliability viewpoints: A multi-objective approach , 2020, Journal of Cleaner Production.

[22]  M. T. Hagh,et al.  Optimal reliable and resilient construction of dynamic self‐adequate multi‐microgrids under large‐scale events , 2019, IET Renewable Power Generation.

[23]  Gordon Lightbody,et al.  An advanced retail electricity market for active distribution systems and home microgrid interoperability based on game theory , 2018 .

[24]  Mehdi Abapour,et al.  Dynamic and multi-objective reconfiguration of distribution network using a novel hybrid algorithm with parallel processing capability , 2020, Appl. Soft Comput..

[25]  Mousa Marzband,et al.  Non-cooperative game theory based energy management systems for energy district in the retail market considering DER uncertainties , 2016 .

[26]  Farhad Samadi Gazijahani,et al.  Stochastic multi-objective framework for optimal dynamic planning of interconnected microgrids , 2017 .

[27]  Meng Cheng,et al.  Peer-to-Peer energy trading in a Microgrid , 2018, Applied Energy.

[28]  Behnam Mohammadi-Ivatloo,et al.  Economic Dispatch of Renewable Energy and CHP-Based Multi-zone Microgrids Under Limitations of Electrical Network , 2020, Iranian Journal of Science and Technology, Transactions of Electrical Engineering.

[29]  Mehdi Savaghebi,et al.  Distributed Smart Decision-Making for a Multimicrogrid System Based on a Hierarchical Interactive Architecture , 2016, IEEE Transactions on Energy Conversion.

[30]  Mehdi Savaghebi,et al.  An Optimal Energy Management System for Islanded Microgrids Based on Multiperiod Artificial Bee Colony Combined With Markov Chain , 2017, IEEE Systems Journal.

[31]  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.

[32]  Kazem Zare,et al.  Optimal performance of microgrid in the presence of demand response exchange: A stochastic multi-objective model , 2019, Comput. Electr. Eng..

[33]  Kazem Zare,et al.  RETRACTED: Interval optimization based performance of photovoltaic/wind/FC/electrolyzer/electric vehicles in energy price determination for customers by electricity retailer , 2018, Solar Energy.

[34]  Edris Pouresmaeil,et al.  A Centralized Smart Decision-Making Hierarchical Interactive Architecture for Multiple Home Microgrids in Retail Electricity Market , 2018, Energies.