Adaptive inertia emulation control for high-speed flywheel energy storage systems

Low-inertia power systems suffer from a high rate of change of frequency (ROCOF) during a sudden imbalance in supply and demand. Inertia emulation techniques using storage systems, such as flywheel energy storage systems (FESSs), can elp to reduce the ROCOF by rapidly providing the needed power to balance the grid. In this work, a new adaptive ontroller for inertia emulation using high-speed FESS is proposed. The controller inertia and damping coefficients vary using a combination of bang–bang control approaches and self-adaptive ones, to simultaneously improve both the ROCOF and the frequency nadir. The performance of the proposed adaptive controller has been initially validated and compared with several existing adaptive controllers by means of offline simulations, and then validated with experimental results. The proposed controller has been implemented on a real 60 kW high-speed FESS, and its performance has been evaluated by means of power hardware-in-the-loop (PHIL) testing of the FESS in realistic grid conditions. Both Simulations and PHIL testing results confirm that the proposed inertia emulation control for the FESS outperforms several previously reported controllers, in terms of reducing the maximum ROCOF and improving the frequency nadir during large disturbances

Advanced control techniques for modern inertia based inverters

”In this research three artificial intelligent (AI)-based techniques are proposed to regulate the voltage and frequency of a grid-connected inverter. The increase in the penetration of renewable energy sources (RESs) into the power grid has led to the increase in the penetration of fast-responding inertia-less power converters. The increase in the penetration of these power electronics converters changes the nature of the conventional grid, in which the existing kinetic inertia in the rotating parts of the enormous generators plays a vital role. The concept of virtual inertia control scheme is proposed to make the behavior of grid connected inverters more similar to the synchronous generators, by mimicking the mechanical behavior of a synchronous generator. Conventional control techniques lack to perform optimally in nonlinear, uncertain, inaccurate power grids. Besides, the decoupled control assumption in conventional VSGs makes them nonoptimal in resistive grids. The neural network predictive controller, the heuristic dynamic programming, and the dual heuristic dynamic programming techniques are presented in this research to overcome the draw backs of conventional VSGs. The nonlinear characteristics of neural networks, and the online training enable the proposed methods to perform as robust and optimal controllers. The simulation and the experimental laboratory prototype results are provided to demonstrate the effectiveness of the proposed techniques”--Abstract, page iv

Providing Virtual Inertia Through Power Electronics

VSC-HVDC (voltage source converter based HVDC) system with its inherent merits for renewable energy integration has captured increasing research attentions. However, compared with AC systems dominated by synchronous generators (SGs), VSC-HVDC systems with general vector control cannot provide inertia for the grid due to lack of kinetic energy. This tends to degrade the safety and stability of the grid with the increasing penetration of renewable energy sources. To cope with this issue, virtual synchronous generator (VSG) has been proposed. In this thesis, firstly, a comprehensive introduction of various typologies of VSG schemes is made to illustrate their deficiencies and merits. The simulation results established in Simulink/Plecs show that VSG can not only participate into the regulation of frequency and voltage in case of power disturbances but guarantee the inertia provision for the grid. Although the integration of VSG control enhances the inertia and damping response of inverts, researches show that plenty of issues relative with VSG should be ameliorated. The fluctuation performances of SGs are introduced into the output active power and current of inverters when incorporates VSG control. This threatens the stability and safety of VSG operation, for power electronic based inverters are more vulnerable during the oscillations of current and frequency. Hence, to solve these issues, various enhanced VSG strategies have been constructed to improve its robustness and output performance. In this thesis, the structures and properties of enhanced VSG schemes are fully discussed. The results show that the dynamic properties of VSG during transient periods are enhanced in comparison of that of normal VSG. Modular multilevel converters (MMC) and alternate arm converters (AAC), as the representatives for enhanced topologies of VSC-HVDC system, have more complicated inner structures in comparison with 2/3 level converters. In this thesis, VSG control is applied into MMC/AAC models to strengthen their power and frequency regulation ability. In addition, a four-terminal multi terminal direct current (MTDC) system is incorporated with VSG control to provide primary frequency and voltage response for the grid. The results show that the integration of VSG improves the stability operation and inertia response of MMC/AAC/MTDC systems

Virtual Synchronous Generator Control Using Twin Delayed Deep Deterministic Policy Gradient Method

This paper presents a data-driven approach that adaptively tunes the parameters of a virtual synchronous generator to achieve optimal frequency response against disturbances. In the proposed approach, the control variables, namely, the virtual moment of inertia and damping factor, are transformed into actions of a reinforcement learning agent. Different from the state-of-the-art methods, the proposed study introduces the settling time parameter as one of the observations in addition to the frequency and rate of change of frequency (RoCoF). In the reward function, preset indices are considered to simultaneously ensure bounded frequency deviation, low RoCoF, fast response, and quick settling time. To maximize the reward, this study employs the Twin-Delayed Deep Deterministic Policy Gradient (TD3) algorithm. TD3 has an exceptional capacity for learning optimal policies and is free of overestimation bias, which may lead to suboptimal policies. Finally, numerical validation in MATLAB/Simulink and real-time simulation using RTDS confirm the superiority of the proposed method over other adaptive tuning methods

Parameter adaptive model predictive control strategy of NPC three-level virtual synchronous generator

The Virtual Synchronous Generator (VSG) emulates the characteristics of a synchronous generator to provide inertia and damping for renewable energy systems. In the case of using the NPC three-level converter structure, traditional control methods require complex dual-loop control and internal PI parameter tuning. Furthermore, although fixed-parameter VSG control can provide inertia and damping when a significant power load is switched in an islanded microgrid, it cannot guarantee frequency regulation performance. To address these issues, this paper proposes an NPC three-level VSG parameter adaptive finite control set model predictive control strategy. This method eliminates the need for dual-loop control and PI parameter tuning. By incorporating angular velocity deviation and its rate of change into adaptive adjustment, a Tracking-Differentiator (TD) is designed to calculate the rate of change of angular velocity. This approach avoids frequent fluctuation of adaptive parameters during load power switching and improves the frequency stability of the microgrid. The effectiveness of the proposed strategy is validated through simulation and experimental verification

Virtual Synchronous Machine Control with Adaptive Inertia Applicable to an MMC Terminal

Renewable energy sources (RES) penetration levels are increasing in the power grid. However, it does not have inertia as a traditional synchronous generator, causing a reduction in the inertia and damping in the power grid, impacting the stability during power changes in the grid, causing large frequency deviations. The virtual synchronous machine (VSM) concept has become an attractive solution to emulate the synchronous machine characteristics and supply the inertia and damping property in the system. It consists of emulating the synchronous machine’s static and dynamic properties by power electronic converters and energy storage systems. Nevertheless, the implementation and design of the VSM is a challenge since it must be flexible in the presence of load fluctuations, preventing the oscillations and frequency overshoot from increasing during system disturbances. Hence, the VSM with adaptive inertia has become a potential solution because it provides the inertia and damping factor to the grid according to the load variations and different RES penetration levels in the system. Therefore, the inertia estimation is necessary to use the special techniques that guarantee the balance between the power and frequency response..

Review on Multi-Objective Control Strategies for Distributed Generation on Inverter-Based Microgrids

[EN] Microgrids have emerged as a solution to address new challenges in power systems with the integration of distributed energy resources (DER). Inverter-based microgrids (IBMG) need to implement proper control systems to avoid stability and reliability issues. Thus, several researchers have introduced multi-objective control strategies for distributed generation on IBMG. This paper presents a review of the different approaches that have been proposed by several authors of multi-objective control. This work describes the main features of the inverter as a key component of microgrids. Details related to accomplishing efficient generation from a control systems' view have been observed. This study addresses the potential of multi-objective control to overcome conflicting objectives with balanced results. Finally, this paper shows future trends in control objectives and discussion of the different multi-objective approaches.Gonzales-Zurita, Ó.; Clairand, J.; Peñalvo-López, E.; Escrivá-Escrivá, G. (2020). Review on Multi-Objective Control Strategies for Distributed Generation on Inverter-Based Microgrids. Energies. 13(13):1-29. https://doi.org/10.3390/en13133483S1291313Ross, M., Abbey, C., Bouffard, F., & Joos, G. (2015). Multiobjective Optimization Dispatch for Microgrids With a High Penetration of Renewable Generation. IEEE Transactions on Sustainable Energy, 6(4), 1306-1314. doi:10.1109/tste.2015.2428676Murty, V. V. S. N., & Kumar, A. (2020). Multi-objective energy management in microgrids with hybrid energy sources and battery energy storage systems. Protection and Control of Modern Power Systems, 5(1). doi:10.1186/s41601-019-0147-zKatircioğlu, S., Abasiz, T., Sezer, S., & Katırcıoglu, S. (2019). Volatility of the alternative energy input prices and spillover effects: a VAR [MA]-MGARCH in BEKK approach for the Turkish economy. Environmental Science and Pollution Research, 26(11), 10738-10745. doi:10.1007/s11356-019-04531-5Olivares, D. E., Mehrizi-Sani, A., Etemadi, A. H., Canizares, C. A., Iravani, R., Kazerani, M., … Hatziargyriou, N. D. (2014). Trends in Microgrid Control. IEEE Transactions on Smart Grid, 5(4), 1905-1919. doi:10.1109/tsg.2013.2295514Akinyele, D., Belikov, J., & Levron, Y. (2018). Challenges of Microgrids in Remote Communities: A STEEP Model Application. Energies, 11(2), 432. doi:10.3390/en11020432Benamar, A., Travaillé, P., Clairand, J.-M., & Escrivá-Escrivá, G. (2020). Non-Linear Control of a DC Microgrid for Electric Vehicle Charging Stations. International Journal on Advanced Science, Engineering and Information Technology, 10(2), 593. doi:10.18517/ijaseit.10.2.10815Lakshmi, M., & Hemamalini, S. (2018). Nonisolated High Gain DC–DC Converter for DC Microgrids. IEEE Transactions on Industrial Electronics, 65(2), 1205-1212. doi:10.1109/tie.2017.2733463Yin, C., Wu, H., Locment, F., & Sechilariu, M. (2017). Energy management of DC microgrid based on photovoltaic combined with diesel generator and supercapacitor. Energy Conversion and Management, 132, 14-27. doi:10.1016/j.enconman.2016.11.018Chen, D., Xu, Y., & Huang, A. Q. (2017). Integration of DC Microgrids as Virtual Synchronous Machines Into the AC Grid. IEEE Transactions on Industrial Electronics, 64(9), 7455-7466. doi:10.1109/tie.2017.2674621Abhinav, S., Schizas, I. D., Ferrese, F., & Davoudi, A. (2017). Optimization-Based AC Microgrid Synchronization. IEEE Transactions on Industrial Informatics, 13(5), 2339-2349. doi:10.1109/tii.2017.2702623Liu, Z., Su, M., Sun, Y., Li, L., Han, H., Zhang, X., & Zheng, M. (2019). Optimal criterion and global/sub-optimal control schemes of decentralized economical dispatch for AC microgrid. International Journal of Electrical Power & Energy Systems, 104, 38-42. doi:10.1016/j.ijepes.2018.06.045Khatibzadeh, A., Besmi, M., Mahabadi, A., & Reza Haghifam, M. (2017). Multi-Agent-Based Controller for Voltage Enhancement in AC/DC Hybrid Microgrid Using Energy Storages. Energies, 10(2), 169. doi:10.3390/en10020169Asghar, F., Talha, M., & Kim, S. (2017). Robust Frequency and Voltage Stability Control Strategy for Standalone AC/DC Hybrid Microgrid. Energies, 10(6), 760. doi:10.3390/en10060760Lotfi, H., & Khodaei, A. (2017). Hybrid AC/DC microgrid planning. Energy, 118, 37-46. doi:10.1016/j.energy.2016.12.015Kerdphol, T., Rahman, F., & Mitani, Y. (2018). Virtual Inertia Control Application to Enhance Frequency Stability of Interconnected Power Systems with High Renewable Energy Penetration. Energies, 11(4), 981. doi:10.3390/en11040981Rodrigues, Y. R., Zambroni de Souza, A. C., & Ribeiro, P. F. (2018). An inclusive methodology for Plug-in electrical vehicle operation with G2V and V2G in smart microgrid environments. International Journal of Electrical Power & Energy Systems, 102, 312-323. doi:10.1016/j.ijepes.2018.04.037Ghosh, S., & Chattopadhyay, S. (2020). Three-Loop-Based Universal Control Architecture for Decentralized Operation of Multiple Inverters in an Autonomous Grid-Interactive Microgrid. IEEE Transactions on Industry Applications, 56(2), 1966-1979. doi:10.1109/tia.2020.2964746Mohapatra, S. R., & Agarwal, V. (2020). Model Predictive Control for Flexible Reduction of Active Power Oscillation in Grid-Tied Multilevel Inverters Under Unbalanced and Distorted Microgrid Conditions. IEEE Transactions on Industry Applications, 56(2), 1107-1115. doi:10.1109/tia.2019.2957480Ziouani, I., Boukhetala, D., Darcherif, A.-M., Amghar, B., & El Abbassi, I. (2018). Hierarchical control for flexible microgrid based on three-phase voltage source inverters operated in parallel. International Journal of Electrical Power & Energy Systems, 95, 188-201. doi:10.1016/j.ijepes.2017.08.027Golshannavaz, S., & Mortezapour, V. (2018). A generalized droop control approach for islanded DC microgrids hosting parallel-connected DERs. Sustainable Cities and Society, 36, 237-245. doi:10.1016/j.scs.2017.09.038Safa, A., Madjid Berkouk, E. L., Messlem, Y., & Gouichiche, A. (2018). A robust control algorithm for a multifunctional grid tied inverter to enhance the power quality of a microgrid under unbalanced conditions. International Journal of Electrical Power & Energy Systems, 100, 253-264. doi:10.1016/j.ijepes.2018.02.042Andishgar, M. H., Gholipour, E., & Hooshmand, R. (2017). An overview of control approaches of inverter-based microgrids in islanding mode of operation. Renewable and Sustainable Energy Reviews, 80, 1043-1060. doi:10.1016/j.rser.2017.05.267Li, Z., Zang, C., Zeng, P., Yu, H., Li, S., & Bian, J. (2017). Control of a Grid-Forming Inverter Based on Sliding-Mode and Mixed ${H_2}/{H_\infty }$ Control. IEEE Transactions on Industrial Electronics, 64(5), 3862-3872. doi:10.1109/tie.2016.2636798Hossain, M. A., Pota, H. R., Squartini, S., & Abdou, A. F. (2019). Modified PSO algorithm for real-time energy management in grid-connected microgrids. Renewable Energy, 136, 746-757. doi:10.1016/j.renene.2019.01.005Shokoohi, S., Golshannavaz, S., Khezri, R., & Bevrani, H. (2018). Intelligent secondary control in smart microgrids: an on-line approach for islanded operations. Optimization and Engineering, 19(4), 917-936. doi:10.1007/s11081-018-9382-9Safari, A., Babaei, F., & Farrokhifar, M. (2019). A load frequency control using a PSO-based ANN for micro-grids in the presence of electric vehicles. International Journal of Ambient Energy, 42(6), 688-700. doi:10.1080/01430750.2018.1563811Miveh, M. R., Rahmat, M. F., Ghadimi, A. A., & Mustafa, M. W. (2016). Control techniques for three-phase four-leg voltage source inverters in autonomous microgrids: A review. Renewable and Sustainable Energy Reviews, 54, 1592-1610. doi:10.1016/j.rser.2015.10.079Rokrok, E., Shafie-khah, M., & Catalão, J. P. S. (2018). Review of primary voltage and frequency control methods for inverter-based islanded microgrids with distributed generation. Renewable and Sustainable Energy Reviews, 82, 3225-3235. doi:10.1016/j.rser.2017.10.022Bouzid, A. M., Guerrero, J. M., Cheriti, A., Bouhamida, M., Sicard, P., & Benghanem, M. (2015). A survey on control of electric power distributed generation systems for microgrid applications. Renewable and Sustainable Energy Reviews, 44, 751-766. doi:10.1016/j.rser.2015.01.016Vásquez, V., Ortega, L. M., Romero, D., Ortega, R., Carranza, O., & Rodríguez, J. J. (2017). Comparison of methods for controllers design of single phase inverter operating in island mode in a microgrid: Review. Renewable and Sustainable Energy Reviews, 76, 256-267. doi:10.1016/j.rser.2017.03.060Shen, X., Wang, H., Li, J., Su, Q., & Gao, L. (2019). Distributed Secondary Voltage Control of Islanded Microgrids Based on RBF-Neural-Network Sliding-Mode Technique. IEEE Access, 7, 65616-65623. doi:10.1109/access.2019.2915509Arbab-Zavar, B., Palacios-Garcia, E., Vasquez, J., & Guerrero, J. (2019). Smart Inverters for Microgrid Applications: A Review. Energies, 12(5), 840. doi:10.3390/en12050840Bullich-Massagué, E., Díaz-González, F., Aragüés-Peñalba, M., Girbau-Llistuella, F., Olivella-Rosell, P., & Sumper, A. (2018). Microgrid clustering architectures. Applied Energy, 212, 340-361. doi:10.1016/j.apenergy.2017.12.048Kerdphol, T., Rahman, F., Mitani, Y., Hongesombut, K., & Küfeoğlu, S. (2017). Virtual Inertia Control-Based Model Predictive Control for Microgrid Frequency Stabilization Considering High Renewable Energy Integration. Sustainability, 9(5), 773. doi:10.3390/su9050773Hajiakbari Fini, M., & Hamedani Golshan, M. E. (2018). Determining optimal virtual inertia and frequency control parameters to preserve the frequency stability in islanded microgrids with high penetration of renewables. Electric Power Systems Research, 154, 13-22. doi:10.1016/j.epsr.2017.08.007Jung, J., & Villaran, M. (2017). Optimal planning and design of hybrid renewable energy systems for microgrids. Renewable and Sustainable Energy Reviews, 75, 180-191. doi:10.1016/j.rser.2016.10.061Baharizadeh, M., Karshenas, H. R., & Guerrero, J. M. (2018). An improved power control strategy for hybrid AC-DC microgrids. International Journal of Electrical Power & Energy Systems, 95, 364-373. doi:10.1016/j.ijepes.2017.08.036Serban, I., & Ion, C. P. (2017). Microgrid control based on a grid-forming inverter operating as virtual synchronous generator with enhanced dynamic response capability. International Journal of Electrical Power & Energy Systems, 89, 94-105. doi:10.1016/j.ijepes.2017.01.009Tavakoli, M., Shokridehaki, F., Marzband, M., Godina, R., & Pouresmaeil, E. (2018). A two stage hierarchical control approach for the optimal energy management in commercial building microgrids based on local wind power and PEVs. Sustainable Cities and Society, 41, 332-340. doi:10.1016/j.scs.2018.05.035Cagnano, A., De Tuglie, E., & Cicognani, L. (2017). Prince — Electrical Energy Systems Lab. Electric Power Systems Research, 148, 10-17. doi:10.1016/j.epsr.2017.03.011Zhang, H., Meng, W., Qi, J., Wang, X., & Zheng, W. X. (2019). Distributed Load Sharing Under False Data Injection Attack in an Inverter-Based Microgrid. IEEE Transactions on Industrial Electronics, 66(2), 1543-1551. doi:10.1109/tie.2018.2793241Yang, L., Hu, Z., Xie, S., Kong, S., & Lin, W. (2019). Adjustable virtual inertia control of supercapacitors in PV-based AC microgrid cluster. Electric Power Systems Research, 173, 71-85. doi:10.1016/j.epsr.2019.04.011Rahman, F. S., Kerdphol, T., Watanabe, M., & Mitani, Y. (2019). Optimization of virtual inertia considering system frequency protection scheme. Electric Power Systems Research, 170, 294-302. doi:10.1016/j.epsr.2019.01.025Farrokhabadi, M., Canizares, C. A., Simpson-Porco, J. W., Nasr, E., Fan, L., Mendoza-Araya, P. A., … Reilly, J. (2020). Microgrid Stability Definitions, Analysis, and Examples. IEEE Transactions on Power Systems, 35(1), 13-29. doi:10.1109/tpwrs.2019.2925703Yoldaş, Y., Önen, A., Muyeen, S. M., Vasilakos, A. V., & Alan, İ. (2017). Enhancing smart grid with microgrids: Challenges and opportunities. Renewable and Sustainable Energy Reviews, 72, 205-214. doi:10.1016/j.rser.2017.01.064Rajesh, K. S., Dash, S. S., Rajagopal, R., & Sridhar, R. (2017). A review on control of ac microgrid. Renewable and Sustainable Energy Reviews, 71, 814-819. doi:10.1016/j.rser.2016.12.106Marzal, S., Salas, R., González-Medina, R., Garcerá, G., & Figueres, E. (2018). Current challenges and future trends in the field of communication architectures for microgrids. Renewable and Sustainable Energy Reviews, 82, 3610-3622. doi:10.1016/j.rser.2017.10.101Singh, A., & Suhag, S. (2018). Trends in Islanded Microgrid Frequency Regulation – A Review. Smart Science, 7(2), 91-115. doi:10.1080/23080477.2018.1540380Hou, X., Sun, Y., Lu, J., Zhang, X., Koh, L. H., Su, M., & Guerrero, J. M. (2018). Distributed Hierarchical Control of AC Microgrid Operating in Grid-Connected, Islanded and Their Transition Modes. IEEE Access, 6, 77388-77401. doi:10.1109/access.2018.2882678SHI, R., ZHANG, X., HU, C., XU, H., GU, J., & CAO, W. (2017). Self-tuning virtual synchronous generator control for improving frequency stability in autonomous photovoltaic-diesel microgrids. Journal of Modern Power Systems and Clean Energy, 6(3), 482-494. doi:10.1007/s40565-017-0347-3Toub, M., Bijaieh, M. M., Weaver, W. W., III, R. D. R., Maaroufi, M., & Aniba, G. (2019). Droop Control in DQ Coordinates for Fixed Frequency Inverter-Based AC Microgrids. Electronics, 8(10), 1168. doi:10.3390/electronics8101168Shuai, Z., Fang, J., Ning, F., & Shen, Z. J. (2018). Hierarchical structure and bus voltage control of DC microgrid. Renewable and Sustainable Energy Reviews, 82, 3670-3682. doi:10.1016/j.rser.2017.10.096Agundis-Tinajero, G., Segundo-Ramírez, J., Visairo-Cruz, N., Savaghebi, M., Guerrero, J. M., & Barocio, E. (2019). Power flow modeling of islanded AC microgrids with hierarchical control. International Journal of Electrical Power & Energy Systems, 105, 28-36. doi:10.1016/j.ijepes.2018.08.002Ali, A., Li, W., Hussain, R., He, X., Williams, B., & Memon, A. (2017). Overview of Current Microgrid Policies, Incentives and Barriers in the European Union, United States and China. Sustainability, 9(7), 1146. doi:10.3390/su9071146Cui, Y., Geng, Z., Zhu, Q., & Han, Y. (2017). Review: Multi-objective optimization methods and application in energy saving. Energy, 125, 681-704. doi:10.1016/j.energy.2017.02.174Yazdi, F., & Hosseinian, S. H. (2019). A novel «Smart Branch» for power quality improvement in microgrids. International Journal of Electrical Power & Energy Systems, 110, 161-170. doi:10.1016/j.ijepes.2019.02.026Bassey, O., Butler-Purry, K. L., & Chen, B. (2020). Dynamic Modeling of Sequential Service Restoration in Islanded Single Master Microgrids. IEEE Transactions on Power Systems, 35(1), 202-214. doi:10.1109/tpwrs.2019.2929268Chang, E.-C. (2018). Study and Application of Intelligent Sliding Mode Control for Voltage Source Inverters. Energies, 11(10), 2544. doi:10.3390/en11102544Das, D., Gurrala, G., & Shenoy, U. J. (2018). Linear Quadratic Regulator-Based Bumpless Transfer in Microgrids. IEEE Transactions on Smart Grid, 9(1), 416-425. doi:10.1109/tsg.2016.2580159Nguyen, H. K., Khodaei, A., & Han, Z. (2018). Incentive Mechanism Design for Integrated Microgrids in Peak Ramp Minimization Problem. IEEE Transactions on Smart Grid, 9(6), 5774-5785. doi:10.1109/tsg.2017.2696903Xiao, Z., Guerrero, J. M., Shuang, J., Sera, D., Schaltz, E., & Vásquez, J. C. (2018). Flat tie-line power scheduling control of grid-connected hybrid microgrids. Applied Energy, 210, 786-799. doi:10.1016/j.apenergy.2017.07.066Baghaee, H. R., Mirsalim, M., Gharehpetian, G. B., & Talebi, H. A. (2018). A Decentralized Robust Mixed $H_{{2}}/ H_{{{\infty }}}$ Voltage Control Scheme to Improve Small/Large-Signal Stability and FRT Capability of Islanded Multi-DER Microgrid Considering Load Disturbances. IEEE Systems Journal, 12(3), 2610-2621. doi:10.1109/jsyst.2017.2716351Panda, S. K., & Ghosh, A. (2020). A Computational Analysis of Interfacing Converters with Advanced Control Methodologies for Microgrid Application. Technology and Economics of Smart Grids and Sustainable Energy, 5(1). doi:10.1007/s40866-020-0077-xZhang, L., Chen, K., Lyu, L., & Cai, G. (2019). Research on the Operation Control Strategy of a Low-Voltage Direct Current Microgrid Based on a Disturbance Observer and Neural Network Adaptive Control Algorithm. Energies, 12(6), 1162. doi:10.3390/en12061162Zhu, K., Sun, P., Zhou, L., Du, X., & Luo, Q. (2020). Frequency-Division Virtual Impedance Shaping Control Method for Grid-Connected Inverters in a Weak and Distorted Grid. IEEE Transactions on Power Electronics, 35(8), 8116-8129. doi:10.1109/tpel.2019.2963345Samavati, E., & Mohammadi, H. R. (2019). Simultaneous voltage and current harmonics compensation in islanded/grid-connected microgrids using virtual impedance concept. Sustainable Energy, Grids and Networks, 20, 100258. doi:10.1016/j.segan.2019.100258Shi, K., Ye, H., Song, W., & Zhou, G. (2018). Virtual Inertia Control Strategy in Microgrid Based on Virtual Synchronous Generator Technology. IEEE Access, 6, 27949-27957. doi:10.1109/access.2018.2839737Fathi, A., Shafiee, Q., & Bevrani, H. (2018). Robust Frequency Control of Microgrids Using an Extended Virtual Synchronous Generator. IEEE Transactions on Power Systems, 33(6), 6289-6297. doi:10.1109/tpwrs.2018.2850880Amoateng, D. O., Al Hosani, M., Elmoursi, M. S., Turitsyn, K., & Kirtley, J. L. (2018). Adaptive Voltage and Frequency Control of Islanded Multi-Microgrids. IEEE Transactions on Power Systems, 33(4), 4454-4465. doi:10.1109/tpwrs.2017.2780986Sopinka, A., & Pitt, L. (2013). British Columbia Electricity Supply Gap Strategy: A Redefinition of Self-Sufficiency. The Electricity Journal, 26(3), 81-88. doi:10.1016/j.tej.2013.03.003Baghaee, H. R., Mirsalim, M., Gharehpetian, G. B., & Talebi, H. A. (2018). Decentralized Sliding Mode Control of WG/PV/FC Microgrids Under Unbalanced and Nonlinear Load Conditions for On- and Off-Grid Modes. IEEE Systems Journal, 12(4), 3108-3119. doi:10.1109/jsyst.2017.2761792Gholami, S., Saha, S., & Aldeen, M. (2018). Robust multiobjective control method for power sharing among distributed energy resources in islanded microgrids with unbalanced and nonlinear loads. International Journal of Electrical Power & Energy Systems, 94, 321-338. doi:10.1016/j.ijepes.2017.07.012Mousazadeh Mousavi, S. Y., Jalilian, A., Savaghebi, M., & Guerrero, J. M. (2018). Autonomous Control of Current- and Voltage-Controlled DG Interface Inverters for Reactive Power Sharing and Harmonics Compensation in Islanded Microgrids. IEEE Transactions on Power Electronics, 33(11), 9375-9386. doi:10.1109/tpel.2018.2792780Fani, B., Zandi, F., & Karami-Horestani, A. (2018). An enhanced decentralized reactive power sharing strategy for inverter-based microgrid. International Journal of Electrical Power & Energy Systems, 98, 531-542. doi:10.1016/j.ijepes.2017.12.023Khayat, Y., Naderi, M., Shafiee, Q., Batmani, Y., Fathi, M., Guerrero, J. M., & Bevrani, H. (2019). Decentralized Optimal Frequency Control in Autonomous Microgrids. IEEE Transactions on Power Systems, 34(3), 2345-2353. doi:10.1109/tpwrs.2018.2889671Arcos-Aviles, D., Pascual, J., Marroyo, L., Sanchis, P., & Guinjoan, F. (2018). Fuzzy Logic-Based Energy Management System Design for Residential Grid-Connected Microgrids. IEEE Transactions on Smart Grid, 9(2), 530-543. doi:10.1109/tsg.2016.2555245Alyazidi, N. M., Mahmoud, M. S., & Abouheaf, M. I. (2018). Adaptive critics based cooperative control scheme for islanded Microgrids. Neurocomputing, 272, 532-541. doi:10.1016/j.neucom.2017.07.027Buduma, P., & Panda, G. (2018). Robust nested loop control scheme for LCL‐filtered inverter‐based DG unit in grid‐connected and islanded modes. IET Renewable Power Generation, 12(11), 1269-1285. doi:10.1049/iet-rpg.2017.0803Batiyah, S., Sharma, R., Abdelwahed, S., & Zohrabi, N. (2020). An MPC-based power management of standalone DC microgrid with energy storage. International Journal of Electrical Power & Energy Systems, 120, 105949. doi:10.1016/j.ijepes.2020.105949Baghaee, H. R., Mirsalim, M., Gharehpetan, G. B., & Talebi, H. A. (2018). Nonlinear Load Sharing and Voltage Compensation of Microgrids Based on Harmonic Power-Flow Calculations Using Radial Basis Function Neural Networks. IEEE Systems Journal, 12(3), 2749-2759. doi:10.1109/jsyst.2016.2645165Benhalima, S., Miloud, R., & Chandra, A. (2018). Real-Time Implementation of Robust Control Strategies Based on Sliding Mode Control for Standalone Microgrids Supplying Non-Linear Loads. Energies, 11(10), 2590. doi:10.3390/en11102590California Carbon Market Watch: A Comprehensive Analysis of the Golden State’s Cap-and-Trade Program, Year One—2012–2013. 2014https://www.issuelab.org/resource/california-carbon-market-watch-a-comprehensive-analysis-of-the-golden-state-s-cap-and-trade-program-year-one-2012-2013.htmlExploring the Best Possible Trade-Off between Competing Objectives: Identifying the Pareto Fronthttps://pythonhealthcare.org/2018/09/27/93-exploring-the-best-possible-trade-off-between-competing-objectives-identifying-the-pTeekaraman, Y., Kuppusamy, R., & Nikolovski, S. (2019). Solution for Voltage and Frequency Regulation in Standalone Microgrid using Hybrid Multiobjective Symbiotic Organism Search Algorithm. Energies, 12(14), 2812. doi:10.3390/en12142812Zeng, Z., Li, H., Tang, S., Yang, H., & Zhao, R. (2016). Multi‐objective control of multi‐functional grid‐connected inverter for renewable energy integration and power quality service. IET Power Electronics, 9(4), 761-770. doi:10.1049/iet-pel.2015.0317Wu, Y., Guerrero, J. M., Vasquez, J. C., & Wu, Y. (2019). Bumpless Optimal Control over Multi-Objective Microgrids with Mode-Dependent Controllers. Energies, 12(19), 3619. doi:10.3390/en12193619Sedighizadeh, M., Esmaili, M., & Eisapour-Moarref, A. (2017). Voltage and frequency r