Stochastic MILP Model for Merging EV Charging Stations with Active Distribution System Expansion Planning by considering Uncertainties

Zare, Peyman; Dejamkhooy, Abdolmajid; Majidabad, Sajjad Shoja; Davoudkhani, Iraj Faraji

doi:10.1080/15325008.2023.2286616

Cited by 7 publications

(2 citation statements)

References 42 publications

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“…This work adopts the MEVES modeling approach presented in 10 , 54 , which itself builds upon the foundational work in 85 , 86 , 88 , 89 . Figure 7 depicts the detailed model of a MEVES unit.…”

Section: Iumg: Investigation Analysis Mathematical Model and Modelingmentioning

confidence: 99%

“…By regulating the charging and discharging cycles of MEVES units in accordance with the central load dispatching center's LFC signal (C(s)), precise frequency management can be achieved 10,[85][86][87] . This work adopts the MEVES modeling approach presented in 10,54 , which itself builds upon the foundational work in 85,86,88,89 . Figure 7 depicts the detailed model of a MEVES unit.…”

Section: Mobile Ess (Meves)mentioning

confidence: 99%

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Robust load-frequency control of islanded urban microgrid using 1PD-3DOF-PID controller including mobile EV energy storage

Davoudkhani,

Zare,

Abdelaziz

et al. 2024

Sci Rep

Self Cite

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Electricity generation in Islanded Urban Microgrids (IUMG) now relies heavily on a diverse range of Renewable Energy Sources (RES). However, the dependable utilization of these sources hinges upon efficient Electrical Energy Storage Systems (EESs). As the intermittent nature of RES output and the low inertia of IUMGs often lead to significant frequency fluctuations, the role of EESs becomes pivotal. While these storage systems effectively mitigate frequency deviations, their high costs and elevated power density requirements necessitate alternative strategies to balance power supply and demand. In recent years, substantial attention has turned towards harnessing Electric Vehicle (EV) batteries as Mobile EV Energy Storage (MEVES) units to counteract frequency variations in IUMGs. Integrating MEVES into the IUMG infrastructure introduces complexity and demands a robust control mechanism for optimal operation. Therefore, this paper introduces a robust, high-order degree of freedom cascade controller known as the 1PD-3DOF-PID (1 + Proportional + Derivative—Three Degrees Of Freedom Proportional-Integral-Derivative) controller for Load Frequency Control (LFC) in IUMGs integrated with MEVES. The controller’s parameters are meticulously optimized using the Coati Optimization Algorithm (COA) which mimics coati behavior in nature, marking its debut in LFC of IUMG applications. Comparative evaluations against classical controllers and algorithms, such as 3DOF-PID, PID, Reptile Search Algorithm, and White Shark Optimizer, are conducted under diverse IUMG operating scenarios. The testbed comprises various renewable energy sources, including wind turbines, photovoltaics, Diesel Engine Generators (DEGs), Fuel Cells (FCs), and both Mobile and Fixed energy storage units. Managing power balance in this entirely renewable environment presents a formidable challenge, prompting an examination of the influence of MEVES, DEG, and FC as controllable units to mitigate active power imbalances. Metaheuristic algorithms in MATLAB-SIMULINK platforms are employed to identify the controller’s gains across all case studies, ensuring the maintenance of IUMG system frequency within predefined limits. Simulation results convincingly establish the superiority of the proposed controller over other counterparts. Furthermore, the controller’s robustness is rigorously tested under ± 25% variations in specific IUMG parameters, affirming its resilience. Statistical analyses reinforce the robust performance of the COA-based 1PD-3DOF-PID control method. This work highlights the potential of the COA Technique-optimized 1PD-3DOF-PID controller for IUMG control, marking its debut application in the LFC domain for IUMGs. This comprehensive study contributes valuable insights into enhancing the reliability and stability of Islanded Urban Microgrids while integrating Mobile EV Energy Storage, marking a significant advancement in the field of Load-Frequency Control.

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Section: Iumg: Investigation Analysis Mathematical Model and Modelingmentioning

confidence: 99%

Section: Mobile Ess (Meves)mentioning

confidence: 99%

Robust load-frequency control of islanded urban microgrid using 1PD-3DOF-PID controller including mobile EV energy storage

Davoudkhani,

Zare,

Abdelaziz

et al. 2024

Sci Rep

Self Cite

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Maiden application of mountaineering team-based optimization algorithm optimized 1PD-PI controller for load frequency control in islanded microgrid with renewable energy sources

Davoudkhani,

Zare,

Shenava

et al. 2024

Sci Rep

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Load Frequency Control (LFC) is essential for maintaining the stability of Islanded Microgrids (IMGs) that rely extensively on Renewable Energy Sources (RES). This paper introduces a groundbreaking 1PD-PI (one + Proportional + Derivative-Proportional + Integral) controller, marking its inaugural use in improving LFC performance within IMGs. The creation of this advanced controller stems from the amalgamation of 1PD and PI control strategies. Furthermore, the paper presents the Mountaineering Team Based Optimization (MTBO) algorithm, a novel meta-heuristic technique introduced for the first time to effectively tackle LFC challenges. This algorithm, inspired by principles of intellectual and environmental evolution and coordinated human behavior, is utilized to optimize the controller gains. The effectiveness of the proposed methodology is rigorously evaluated within a simulated IMG environment using MATLAB/SIMULINK. This simulated IMG incorporates diverse power generation sources, including Diesel Engine Generators (DEGs), Microturbines (MTs), Fuel Cells (FCs), Energy Storage Systems (ESSs), and RES units like Wind Turbine Generators (WTGs) and Photovoltaics (PVs). This paper employs the Integral Time Multiplied by the Squared Error (ITSE) and Integral of Time Multiplied By Absolute Error (ITAE) indicators as the primary performance metrics, conventionally used to mitigate frequency deviations. To achieve optimal controller parameter tuning, a weighted composite objective function is formulated. This function incorporates multiple components: modified objective functions related to both ITSE and ITAE, along with a term addressing overshoot and settling time. Each component is assigned an appropriate weighting factor to prioritize specific performance aspects. By employing distinct objective functions for different aspects of control performance, the derivation of optimized controller gains is facilitated. The efficacy and contribution of the proposed methodology are rigorously demonstrated within the context of RES-based IMGs, featuring a comparative analysis with well-known optimization algorithms, including Particle Swarm Optimization (PSO) and the Whale Optimization Algorithm (WOA). These algorithms are used to optimize the 1PD-PI controller, resulting in three control schemes: 1PD-PI/MTBO, 1PD-PI/WOA, and 1PD-PI/PSO. The effectiveness of these control schemes is evaluated under various loading conditions, incorporating parametric uncertainties and nonlinear factors of physical constraints. Three case studies, presented in eight scenarios (I-VIII), are utilized to comprehensively assess the efficiency, robustness, and sensitivity of the proposed approach. This analysis extends beyond the time domain, considering the stability evaluation of the proposed control scheme. Simulation results unequivocally establish the superior performance of the MTBO algorithm-optimized 1PD-PI controller compared to its counterparts. This superiority is evident in terms of minimized settling time, reduced peak undershoot and ...

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A Cost-Effective Corona-Virus Search Optimization Based Approach to Joint Expansion Planning of Distribution Networks with Heavy-Duty Electric Truck Dedicated Urban Charging Stations

Shayeghi,

Zare,

Davoudkhani

et al. 2024

2024 16th International Conference on Electronics, Computers and Artificial Intelligence (ECAI)

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Stochastic MILP Model for Merging EV Charging Stations with Active Distribution System Expansion Planning by considering Uncertainties

Cited by 7 publications

References 42 publications

Robust load-frequency control of islanded urban microgrid using 1PD-3DOF-PID controller including mobile EV energy storage

Robust load-frequency control of islanded urban microgrid using 1PD-3DOF-PID controller including mobile EV energy storage

Maiden application of mountaineering team-based optimization algorithm optimized 1PD-PI controller for load frequency control in islanded microgrid with renewable energy sources

A Cost-Effective Corona-Virus Search Optimization Based Approach to Joint Expansion Planning of Distribution Networks with Heavy-Duty Electric Truck Dedicated Urban Charging Stations

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