Consensus Algorithm-based Coalition Game Theory for Demand Management Scheme in Smart Microgrid

Abstract Power mismatching between the generated and consumed power is caused by the stochastic and unpredictable demand energy, resulting in increasing the electricity bill and the load energy waste. A consensus algorithm-based coalition game theory for optimal demand management scheme is proposed for multi-agent smart microgrids (SMGs). The consensus algorithm depends on the information and data transfer among the neighbors in the multi-agents SMGs. The consensus algorithm for the demand management system has been proposed to improve the coalition game theory. The allocation of the surplus energy on the deficient customers is based on Shapley value, which enables the unequal distribution of power according to the demand. The computational and storage units are shifted to the Fog layer to deal with the multi-agents SMGs' extensive data and information. The proposed method's main objectives are minimizing the energy cost, energy waste in the presence of packet losses, and power mismatching. A hypothetical SMG system has been simulated and modeled using the MATLAB environment program to prove the proposed method's effectiveness. Three scenarios are performed, including without a demand management system, coalition game theory only, and consensus algorithm-based coalition game theory. A comparison between the obtained results has been performed. Sensitivity analysis based on the increasing of iterations and the number of homes is performed to prove the effectiveness of the proposed method. Also, a comparison between the optimization outcomes obtained results is implemented using genetic algorithm (GA), particle swarm optimization (PSO), ant colony optimization (ACO), and artificial bee colony (ABC) optimization techniques. The results prove the applicability and feasibility of the proposed method for the demand management system in SMG. The proposed method achieves an improvement of 8.056%, 6.629%, and 98.888% for the incremental cost, the total electricity bill, and load energy waste concerning their values without applying the energy management system, respectively.

[1]  Maher Kayal,et al.  Cooperative energy management of a community of smart-buildings: A Blockchain approach , 2020 .

[2]  Mousa Marzband,et al.  A novel techno-economic multi-level optimization in home-microgrids with coalition formation capability , 2020, Sustainable Cities and Society.

[3]  Basil H. Jasim,et al.  A New Robust Energy Management and Control Strategy for a Hybrid Microgrid System Based on Green Energy , 2020, Sustainability.

[4]  Nadeem Javaid,et al.  An Innovative Heuristic Algorithm for IoT-Enabled Smart Homes for Developing Countries , 2018, IEEE Access.

[5]  Francesco Palmieri,et al.  Multi-layer cloud architectural model and ontology-based security service framework for IoT-based smart homes , 2018, Future Gener. Comput. Syst..

[6]  Nadeem Javaid,et al.  Coalition based game theoretic energy management system of a building as-service-over fog , 2019, Sustainable Cities and Society.

[7]  Ahmed M. A. Haidar,et al.  Sustainable energy planning for cost minimization of autonomous hybrid microgrid using combined multi-objective optimization algorithm , 2020 .

[8]  Mohammed Raiss El Fenni,et al.  Optimization of energy exchange in microgrid networks: a coalition formation approach , 2019 .

[9]  Nadeem Javaid,et al.  Enhanced Time-of-Use Electricity Price Rate Using Game Theory , 2019, Electronics.

[10]  Zheyuan Cheng,et al.  Resilient Collaborative Distributed Energy Management System Framework for Cyber-Physical DC Microgrids , 2020, IEEE Transactions on Smart Grid.

[11]  Alberto Leon-Garcia,et al.  A Fog-Based Internet of Energy Architecture for Transactive Energy Management Systems , 2018, IEEE Internet of Things Journal.

[12]  Mohammed Akherraz,et al.  Multi-objective optimization and the effect of the economic factors on the design of the microgrid hybrid system , 2020 .

[13]  David J. Hill,et al.  Multiagent System Based Microgrid Energy Management via Asynchronous Consensus ADMM , 2018, IEEE Transactions on Energy Conversion.

[14]  Andrea Gasparri,et al.  Multi-Agent Coordination of Thermostatically Controlled Loads by Smart Power Sockets for Electric Demand Side Management , 2019, IEEE Transactions on Control Systems Technology.

[15]  Mo-Yuen Chow,et al.  A Resilient Consensus-Based Distributed Energy Management Algorithm Against Data Integrity Attacks , 2019, IEEE Transactions on Smart Grid.

[16]  Byung-Seo Kim,et al.  Towards Real-Time Energy Management of Multi-Microgrid Using a Deep Convolution Neural Network and Cooperative Game Approach , 2020, IEEE Access.

[17]  Zhengtao Ding,et al.  Distributed Agent Consensus-Based Optimal Resource Management for Microgrids , 2018, IEEE Transactions on Sustainable Energy.

[18]  Mohammad S. Obaidat,et al.  Distributed Home Energy Management System With Storage in Smart Grid Using Game Theory , 2017, IEEE Systems Journal.

[19]  Mohammad Shahidehpour,et al.  Consensus‐based operational framework for self‐healing in multi‐microgrid systems , 2020 .

[20]  Mo-Yuen Chow,et al.  Robust Consensus-Based Distributed Energy Management for Microgrids With Packet Losses Tolerance , 2020, IEEE Transactions on Smart Grid.

[21]  Yinliang Xu,et al.  Distributed Optimal Resource Management Based on the Consensus Algorithm in a Microgrid , 2015, IEEE Transactions on Industrial Electronics.

[22]  Chandra Prakash Gupta,et al.  Coalition formation strategies for cooperative operation of multiple microgrids , 2019, IET Generation, Transmission & Distribution.

[23]  Razman Ayop,et al.  A rule-based energy management scheme for long-term optimal capacity planning of grid-independent microgrid optimized by multi-objective grasshopper optimization algorithm , 2020, Energy Conversion and Management.

[24]  Omar Chiotti,et al.  Cooperative energy management system for networked microgrids , 2020 .

[25]  Jasim Abdulateef,et al.  Multi-Objective optimisation of a micro-grid hybrid power system for household application , 2020 .

[26]  Afshin Izadian,et al.  DoS-Resilient Distributed Optimal Scheduling in a Fog Supporting IIoT-Based Smart Microgrid , 2020, IEEE Transactions on Industry Applications.

[27]  Tong Jiang,et al.  Adaptive Consensus Algorithm for Distributed Heat-Electricity Energy Management of an Islanded Microgrid , 2018 .

[28]  Anas Alseyat,et al.  A Computationally Efficient Consensus-Based Multiagent Distributed EMS for DC Microgrids , 2021, IEEE transactions on industrial electronics (1982. Print).

[29]  Jiming Chen,et al.  Consensus-Based Energy Management in Smart Grid With Transmission Losses and Directed Communication , 2017, IEEE Transactions on Smart Grid.

[30]  Frede Blaabjerg,et al.  A New Robust Control Strategy for Parallel Operated Inverters in Green Energy Applications , 2020, Energies.

[31]  Shahram Jadid,et al.  Optimal energy management for multi-microgrid considering demand response programs: A stochastic multi-objective framework , 2020, Energy.

[32]  Bishoy E. Sedhom,et al.  IoT-based optimal demand side management and control scheme for smart microgrid , 2021 .

[33]  Ali Zangeneh,et al.  Energy management in multi-microgrids considering point of common coupling constraint , 2020 .