A smart load management system based on the grasshopper optimization algorithm using the under-frequency load shedding approach

Load management represents one of the main constraints when considering smart grids. In addition, load management is a significant challenge for power system security operations. The smart techniques in load management endow the system with the ability to restore its normal or stable operation after being subjected to any disturbances. When the load exceeds the generation, the system stability is affected, which leads to cascade outages and shutdown of the major parts of the power system, causing the frequency decay effect. Fast load shedding (LS) is the best way to avoid cascading outages and power system blackouts. This paper proposes an innovative, accurate, reliable and fast under-frequency load shedding (UFLS) technique based on the grasshopper optimization algorithm (GOA). LS is considered in this research as a constrained optimization problem. The objective function is to minimize the amount of load shed while maximizing the lowest swing frequency at all stages. To validate the proposed GOA-based UFLS, a comparison with adaptive, particle swarm optimization (PSO) and genetic algorithm (GA) UFLS is carried out for different disturbances. Two different case study systems are considered to test the accuracy and reliability of the proposed algorithm: the IEEE 9-bus and 39-bus systems. The proposed GOA and PSO are coded using the MATLAB environment. Different operating cases involving outage of multiple generators and increasing load are implemented to validate the proposed GOA. The DigSilent power factory software is used as a platform for simulating the power system under study when subjected to different disturbance levels. The results verify the accuracy and reliability of GOA in minimizing the amount of load shed and maximizing the lowest swing frequency while satisfying the constraints. Moreover, GOA achieves a faster solution than PSO and GA.

[1]  Ertuğrul Çam,et al.  Application of fuzzy logic for load frequency control of hydroelectrical power plants , 2007 .

[2]  Meng Tao,et al.  A control strategy for improved efficiency in direct-coupled photovoltaic systems through load management , 2018 .

[3]  Mohamed Menaa,et al.  Optimal load frequency control based on artificial bee colony optimization applied to single, two and multi-area interconnected power systems , 2015, 2015 3rd International Conference on Control, Engineering & Information Technology (CEIT).

[4]  Andrew J. Bernoff,et al.  A model for rolling swarms of locusts , 2007, q-bio/0703016.

[5]  K. Uma Rao,et al.  Partial load shedding using ant colony algorithm in smart grid environment , 2015, TENCON 2015 - 2015 IEEE Region 10 Conference.

[6]  Dongmin Yu,et al.  Dynamic multi agent-based management and load frequency control of PV/Fuel cell/ wind turbine/ CHP in autonomous microgrid system , 2019, Energy.

[7]  Basab Chakraborty,et al.  Demand side management in developing nations: A mitigating tool for energy imbalance and peak load management , 2016 .

[8]  Emmanuel Kakaras,et al.  Smart energy management algorithm for load smoothing and peak shaving based on load forecasting of an island’s power system , 2019, Applied Energy.

[9]  Liao Qingfen,et al.  Dynamic optimization of adaptive under-frequency load shedding based on WAMS , 2016, 2016 IEEE Information Technology, Networking, Electronic and Automation Control Conference.

[10]  Mohd Wazir Mustafa,et al.  Optimal Voltage and Frequency Control of an Islanded Microgrid using Grasshopper Optimization Algorithm , 2018, Energies.

[11]  Turaj Amraee,et al.  Semi-Adaptive Setting of Under Frequency Load Shedding Relays Considering Credible Generation Outage Scenarios , 2019, IEEE Transactions on Power Delivery.

[12]  Mehran Sanjabi Asasi,et al.  A Grasshopper Optimization Algorithm to solve optimal distribution system reconfiguration and distributed generation placement problem , 2017, 2017 IEEE 4th International Conference on Knowledge-Based Engineering and Innovation (KBEI).

[13]  Guglielmo Frigo,et al.  PMU-Based ROCOF Measurements: Uncertainty Limits and Metrological Significance in Power System Applications , 2019, IEEE Transactions on Instrumentation and Measurement.

[14]  Arun G. Phadke,et al.  Power System Relaying , 1992 .

[15]  Mohamed Talaat,et al.  A hybrid model of an artificial neural network with thermodynamic model for system diagnosis of electrical power plant gas turbine , 2018, Eng. Appl. Artif. Intell..

[16]  Shengwei Mei,et al.  Optimal load shedding strategy based on particle swarm optimization , 2009 .

[17]  Ahmed Eltom,et al.  Hardware Implementation of an Automatic Adaptive Centralized Underfrequency Load Shedding Scheme , 2014, IEEE Transactions on Power Delivery.

[18]  Javad Salehi,et al.  Risk-based energy management of renewable-based microgrid using information gap decision theory in the presence of peak load management , 2018 .

[19]  Ying-Yi Hong,et al.  Genetic-Based Underfrequency Load Shedding in a Stand-Alone Power System Considering Fuzzy Loads , 2012, IEEE Transactions on Power Delivery.

[20]  Jun-yong Liu,et al.  An improved particle swarm optimization algorithm , 2008, 2008 IEEE International Conference on Granular Computing.

[21]  Eleonora Riva Sanseverino,et al.  A Generalized Framework for Optimal Sizing of Distributed Energy Resources in Micro-Grids Using an Indicator-Based Swarm Approach , 2014, IEEE Transactions on Industrial Informatics.

[22]  Amin Khodabakhshian,et al.  A new undervoltage load shedding method to reduce active power curtailment , 2017 .

[23]  Andrew Lewis,et al.  Grasshopper Optimisation Algorithm: Theory and application , 2017, Adv. Eng. Softw..

[24]  Manoj Kumar.  Artificial Neural Network Approach for Under Frequency Load Shedding , 2012 .

[25]  Mayur Barman,et al.  A regional hybrid GOA-SVM model based on similar day approach for short-term load forecasting in Assam, India , 2018 .

[26]  Dongmin Yu,et al.  Peak load management based on hybrid power generation and demand response , 2018, Energy.

[27]  M. Burrows,et al.  Mechanosensory-induced behavioural gregarization in the desert locust Schistocerca gregaria , 2003, Journal of Experimental Biology.

[28]  Mohamed A. Farahat,et al.  Renewable power integration: Experimental and simulation study to investigate the ability of integrating wave, solar and wind energies , 2019, Energy.

[29]  Ze-kun Zhou,et al.  The improvement of particle swarm optimization , 2016, 2016 3rd International Conference on Systems and Informatics (ICSAI).

[30]  A.A.M. Zin,et al.  Static and dynamic under-frequency load shedding: a comparison , 2004, 2004 International Conference on Power System Technology, 2004. PowerCon 2004..

[31]  Cheng-Ting Hsu,et al.  Artificial Neural Network Based Adaptive Load Shedding for an Industrial Cogeneration Facility , 2008, 2008 IEEE Industry Applications Society Annual Meeting.

[32]  A. Raghami,et al.  Representing an intelligent load shedding algorithm with utilization of frequency deviation integration , 2012 .

[33]  Lukas Sigrist.  A UFLS Scheme for Small Isolated Power Systems Using Rate-of-Change of Frequency , 2015, IEEE Transactions on Power Systems.

[34]  Guglielmo Frigo,et al.  Under Frequency Load Shedding based on PMU Estimates of Frequency and ROCOF , 2018, 2018 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe).

[35]  Canbing Li,et al.  Enabling strategies of electric vehicles for under frequency load shedding , 2018, Applied Energy.

[36]  Ying-Yi Hong,et al.  Multiobjective Underfrequency Load Shedding in an Autonomous System Using Hierarchical Genetic Algorithms , 2010, IEEE Transactions on Power Delivery.

[37]  Swakkhar Shatabda,et al.  An ant colony optimization algorithm for load shedding minimization in smart grids , 2016, 2016 5th International Conference on Informatics, Electronics and Vision (ICIEV).

[38]  Rafael Mihalic,et al.  WAMS-Based Underfrequency Load Shedding With Short-Term Frequency Prediction , 2016, IEEE Transactions on Power Delivery.

[39]  Wenyuan Li,et al.  A New Under-Frequency Load Shedding Scheme Considering Load Frequency Characteristics , 2006, 2006 International Conference on Power System Technology.

[40]  Emily M. Craparo,et al.  Integrated supply- and demand-side energy management for expeditionary environmental control , 2019, Applied Energy.

[41]  Chao-Rong Chen,et al.  Optimal load shedding planning with genetic algorithm , 2011, 2011 IEEE Industry Applications Society Annual Meeting.

[42]  Salah Kamel,et al.  Optimal Allocation of Compensators , 2018 .

[43]  Mark Gillott,et al.  Optimum community energy storage for renewable energy and demand load management , 2017 .

[44]  Alireza Soroudi,et al.  Probabilistic Under Frequency Load Shedding Considering RoCoF Relays of Distributed Generators , 2018, IEEE Transactions on Power Systems.

[45]  Gazmend Kabashi,et al.  Re-Design of Load Shedding Schemes of the Kosovo Power System , 2011 .

[46]  E.J. Thalassinakis,et al.  A Monte-Carlo simulation method for setting the underfrequency load shedding relays and selecting the spinning reserve policy in autonomous power systems , 2004, IEEE Transactions on Power Systems.

[47]  Seung Ho Hong,et al.  A real-time decision model for industrial load management in a smart grid , 2016 .