Multi-Objective Evolutionary Framework for Prediction in PV-Integrated Microgrids with Electric Vehicles in Modern Power Systems

Cybersecurity is crucial for modern power systems due to their high digitization. The open information and communication technology, that is (ICT) being used for the operation of such systems, is highly vulnerable to cyber threats. The adopted smart grid concept around the globe enables the utilization of demand-side for providing ancillary services based on well-known demand response (DR) programs. These programs aggregate smart appliances in homes and electric vehicles (EVs) for providing vital services such as frequency regulation and voltage support. Since the aggregation is based on cyber layer, any cyber threat could affect the ancillary services that are being delivered from the aggregators, which might lead to stability and security issues resulting in brownout or massive blackouts. This chapter discusses the cybersecurity in DR program and shows its importance for modern and future smart power systems due to their stability and security margins. Furthermore, the cyberattack case study is implemented in a power system with demand side program responsible for providing primary frequency support ancillary service, where the results confirm the high vulnerability of modern power systems to cyber threats on DR-active power reserve providers. Moreover, technical suggestions are provided for enhancing the cybersecurity in DR programs in power systems with high power share from renewable energy sources.

In modern power systems, there is a greater penetration of renewable sources, resulting in the expansion of microgrid. By implementing RES instead of conventional synchronous machines, the system’s overall inertia is significantly reduced. Modern and future power systems must reduce system inertia and keep the frequency at its nominal value since frequency oscillation impacts on the functionality, stability, and resilience of the system. Therefore, creating a stable, scalable, and reliable virtual inertia control system is crucial to effectively minimize the deviations during significant contingencies. Consequently, taking into account the probable issues, the foremost objective of this research work is to improve the dynamic security of an island microgrid through the implementation of a frequency control concept based on virtual inertia control. In the research study, the proposed microgrid system comprises of photovoltaics, wind-generating units, thermal power units, storage units, electric vehicles (EVs), and loads. A cascaded PIDFN controller is optimally designed using a novel metaheuristic modified differential evolution (MDE) algorithm that regulates the frequency based on virtual inertia control. In the MATLAB®/SIMULINK environment, various operating situations such as load variations, RES, and EVs disconnection are investigated, and the performance of the VIC-based MDE controller was compared to that of other controllers based on evolutionary optimization algorithms such as DE and TLBO. The results validate that the recommended virtual inertia control strategy enhances the reliability of the system.

Due to the enormous increase in the interconnection of electric vehicles (EVs) into utility power grids, more and more challenges are arising in modern power systems. To handle these challenges, many attempts have been proposed to achieve the satisfactory performance of EV charging/discharging, the improvement of utility power grids operation, and an adaptive coordination strategy for the EV–grid connection. Energy storage systems (ESSs) may play a vital role in this process by injecting/absorbing their energy into/from the grid, including EVs. This paper proposes an adaptive coordination method based on fuzzy logic control (FLC) for EVs and superconducting magnetic energy storage (SMES) to enhance the charge and discharge processes of EVs during off and on peak loading periods. FLC is utilized to organize the charging/discharging process for both EVs and SMES. The state of charge (SoC) of SMES and EVs is considered as the input for EVs’ FLC besides the electricity price (EP) signal. To verify the effectiveness of the proposed coordination method, uncertain numbers of EVs, the rate of EP, and daily residential load profile are included in this study. The results demonstrate that the proposed coordination method can improve the performance of EV charging/discharging. Moreover, the cooperative operation with SMES and the reliability improvement of the utility power are preserved using the proposed coordination strategy.

Nanogrids are becoming an essential part of modern home power systems, offering sustainable solutions for residential areas. These medium-to-low voltage, small-scale grids, operating at medium-to-low voltage, enable the integration of distributed energy resources such as wind turbines, solar photovoltaics, and battery energy storage systems. However, ensuring power quality, stability, and effective energy management remains a challenge due to the variability of renewable energy sources and evolving customer demands, including the increasing charging load of electric vehicles. This paper reviews the current research on nanogrid architecture, functionality in low-voltage distribution systems, energy management, and control systems. It also explores power-sharing strategies among nanogrids within a microgrid framework, focusing on their potential for supplying off-grid areas. Additionally, the application of blockchain technology in providing secure and decentralized energy trading transactions is explored. Potential challenges in future developments of nanogrids are also discussed.

This paper deals with the maiden application of opposition-based interactive search algorithm (OISA) for a multi-area, multi-source power system having thermal, hydro and gas generating units. For realistic approach, physical constraints like boiler dynamics (BD), governor dead band (GDB) and generation rate constraint (GRC) have been considered in the thermal system, while the effects of GRC and GDB are included in hydro and gas systems. Since modern power systems are focussing more towards renewable energy resources, for the purpose of analysis distributed generation (DG) and electric vehicle (EV) units are also incorporated in the system. A novel cascade combination of two-degree-of-freedom proportional–integral–derivative with a filter (PIDN) and fractional order integral–derivative (FOID) is used as the proposed controller for the system (2DOF-PIDN-FOID). The parameters of the proposed controller are optimised using the OISA algorithm. For analysis of the proposed system in deregulated environment, a step load perturbance (SLP) of 0.01 per unit is done in area-1. The effect of integration of distributed generation (DG) and electric vehicle (EV) on the system is analysed in both combined manner and separately. In both the cases, the system dynamics show marked improvement in comparison with previous cases. Analysis of system dynamics for various cases reveals the superiority of the proposed control scheme. Further, the superiority of the proposed algorithm is proven among other well-known evolutionary techniques like genetic algorithm (GA), particle swarm optimisation (PSO), interactive search algorithm (ISA) and whale optimisation algorithm (WOA) by comparison under same test conditions. Moreover, the robustness of the system is validated against system parameter variations. Finally, the superiority of the proposed control scheme is shown by comparison with published results in the literature under same test conditions.

Identification of combined power quality disturbances in the modern power systems by considering the development of different types of loads and distribution generations has become increasingly important. The novelty of this paper comes from the accurate and fast identification of the combined power quality disturbances in the presence of different distributed generations and loads such as photovoltaic cell, wind turbine with doubly fed induction generators, diesel engines, electric arc furnace, DC machine, 6-pulse and 12-pulse rectifier loads. In this paper, the features are extracted using variational mode decomposition, just from voltage waveforms. To reduce the redundant data, dimension of features vector, and time, the Relief-F method and correlation feature selection method are applied on the extracted features and these two methods are compared together. In this paper, the K-nearest neighbors classifier is used to classify the multiple power quality disturbances. To verify the effectiveness of the proposed method, different scenarios such as misfiring, variation of sun radiation and wind speed, entrance and exit of loads, capacitors and distributed generators, different fault at the grid in half-load to full-load were simulated. This method can be used as an added algorithm for smart metering in modern and smart power systems.

Renewable energy sources (RES) and low-carbon technology users play a vital role in modern power systems. However, RES generation is easily affected by the environment. Meanwhile, the load, such as electric vehicles (EVs) and prosumers, accounts for most low-carbon technology users. Their power is usually superimposed on peak loads without dispatching, which also exacerbates the instability of the power system. Current optimal dispatching mechanisms mainly rely on centralized organizations, while their dispatching process is not open and transparent. In this article, we propose a blockchain-based trustworthy dispatching approach for the distribution network in high renewable energy penetrated power systems. We first develop an optimal dispatching model considering EVs’ charging behavior and the prosumers’ economic benefits. With the model, prosumers can be dispatched to balance power and consume renewable energy, reducing the impact of disorderly charging on the grid and the abandonment of RES generation. An orderly charging iteration optimization (OCIO) algorithm is proposed to implement orderly EV charging while considering the charging cost and the period. We also propose a modified particle swarm optimization (mPSO) algorithm to publish dispatching tasks based on real-time power balance. Furthermore, blockchain is applied as an open and transparent ledger to record each entity’s power generation and consumption information, ensuring that the dispatching process is trustworthy. Finally, the effectiveness of the dispatching approach is verified in the modified IEEE 33-bus test system and Ethereum-based smart contracts.

Nowadays, the growing integration of new industrial technologies into modern power systems (MPSs) such as distributed energy resources (DERs) such as various types of renewable energy sources (RESs), electric vehicles (EVs), and energy storage systems (ESSs), caused various adverse impacts that should be considered broadly. These technologies cause various deteriorations on the performance indices (PIs), e. g. voltage deviation and reverse power flows. Therefore, the hosting capacity (HC) concept is declared to ensure that integration is managed efficiently to accommodate them without any PI violations. Various research fields investigated HC technologies in terms of calculation methods, tools, enhancement techniques, etc. in several articles and books. However, the book entitled “Hosting Capacity for Smart Power Grids, Ahmed F. Zobaa, Shady H.E. Abdel Aleem et al. Springer, 1 2020”, is the most recent prominent comprehensive reference for HC strategies in MPSs. Hence, this article proposes a book review and discussion of its most important contributions which helps the reader in realizing the recent developments in HC technologies based on this book’s contents.

Modern power systems are experiencing a rapid movement from fossil-based generations to renewable energy resources (RERs) due to concerns about the environment and the dependence on fossil fuel sources. However, the replacement of conventional generation with intermittent and low inertia RERs in the power grid can negatively affect system regulation capability. Also, the increasing integration of electric vehicles (EVs) presents concerns over power system stability and reliability. To maintain the frequency of the system within stable limits, load frequency control (LFC) regulates the power output of the generating units in response to the changes in load demand. Therefore, appropriate control strategies and optimization algorithms are required for effective LFC while RER/EV penetration increases. In this paper, an advanced LFC model of a multi-area interconnected system with EV and RER integration is developed which considers the change in system inertia that arises from generator outages or the replacement of conventional units with RERs. A non-sequential Monte Carlo simulation (MCS) is implemented to calculate the system inertia and regulation capacity of each area considering the generator failure rates and load changes. The contribution of inertial response and primary frequency regulation from EVs to the system is assessed using a simulation-based approach. A particle swarm optimization (PSO) algorithm is used to tune the parameters of the PID controllers. The efficacy of the developed algorithm is demonstrated on a two-area model of the IEEE RTS-96 system with varying RER penetration levels.

At present, electric vehicles (EVs), small-scale wind power, and solar power have been increasingly integrated into modern power system via the combined cooling heating and power based microgrid (CCHP-MG). However, inside the microgrid the uncertainties of EVs charging, wind power, and solar power significantly impact the economy of CCHP-MG operation. Therefore to improve the economy deteriorated by the uncertainties, this paper presents a two-stage adjustable robust optimization to achieve the minimal operational cost for CCHP-MG. Before the realizations of the uncertainties, the day-ahead stage as the first stage decides an operational strategy that can withstand the worst-case uncertainties. As long as the uncertainties are observed, the real-time stage as the second stage adjusts the operational units to compensate the errors caused by the day-ahead operational strategy. Due to the difficulties of the model solution, this paper further adopts the duality theory, Big-M method, and column-and-constraint generation (C & CG) decomposition to convert the model into two tractable mixed integer linear programming (MILP) problems. Further, C & CG iteration algorithm is also employed to solve the MILPs, which can ultimately provide an optimal economic day-ahead dispatch strategy capable of handling uncertainties. The experimental results demonstrate the effectiveness of the presented approach.

Electric vehicles (EVs), as flexible distributed energy storage units, play an important role in maintaining the balance between supply and demand in modern power systems. However, the stochasticity of their arrival times and the uncertainty of the demand for charging will affect the accuracy of any scheduling. Therefore, an Adaptive Robust Optimization model considering the uncertainty of EV charging demands is proposed. Firstly, an adaptive data-driven uncertainty set is constructed to address the issue of unknown probability distribution associated with the demand for charging, effectively capturing the multimodal characteristics of the uncertain data. Secondly, to eliminate extreme scenarios during real-time scheduling, a scenario reduction strategy based on the Jensen-Shannon divergence is proposed. The strategy effectively reduces the model’s conservatism. Thirdly, to prevent grid overload resulting from the large-scale integration of EVs, a hybrid demand response strategy is proposed, which guides EV users to charge in an orderly manner. A new column-and-constraint generation algorithm with a parallel alternating optimization procedure (C&CG-PAOP) is employed to obtain the optimal solution. Experimental results demonstrate the effectiveness of our method in reducing the operating costs of charging stations.

The growing prominence of electric vehicle (EV) aggregators in the modern power system is drawing more attention towards modeling their behavior in the short-term electricity markets. The demand-side flexibility offered by the EVs can be leveraged to reduce their charging costs. In this paper, the participation of an EV aggregator in the intraday and balancing market is modeled as a multistage stochastic programming problem. The computational complexity introduced by the peculiarities of the intraday market is solved by a progressive hedging algorithm (PHA), a scenario-based decomposition technique. A randomized scenario sampling approach is implemented to accelerate the PHA which is further improved with a parallel randomized PHA. Finally, an asynchronous version of the parallel randomized PHA is leveraged to speed up the multistage model of EV aggregator trading. We compare the computation time of the modified versions of the PHA algorithm with the conventional PHA for the proposed EV aggregator model. Furthermore, we also show the value of EV aggregator trading in the intraday and balancing markets by comparing its cost to baseline models.

(English) Dynamic behavior of modern power system is increasingly influenced by the dynamics of power converters. At the generation level, a high volume of wind and solar energy with their large-scale power converters are replacing conventional synchronous generators. At the transmission level, many High Voltage Direct Current (HVDC) projects based on Modular Multilevel Converter (MMC) technology are commissioned to transfer offshore wind to the load centers, interconnect two non-synchronous AC grids, or two different nodes of the same AC grid (as an embedded link) to enhance the security of supply. The consumption level also faces such high integration of power converters since the modern flexible loads, such as electric vehicles, industrial motor drives, and recent hydrogen electrolyzers, are connected to the power system via power converters. Such transformation in generation, transmission, and consumption levels leads to the power dynamics that have never been experienced before in the conventional power systems. The high penetration of power converters may cause several challenges: reduction in the system total inertia and short-circuit current, excitation of undesirable interactions among control loops, reduction in stability margin, and loss of devices in the context of fault-ride through (FRT). Such challenges can be addressed by appropriate adjustments in the control systems of power converters. At the transmission level, this thesis focuses on improving the dynamic behavior of HVDC systems. A methodology is suggested to study the small-signal stability and evaluate potential interactions that may happen among control loops of two HVDC converter stations. Next, a multivariable controller based on H-inf design practice is developed to optimize the dynamic behavior of the system. As a follow-up study, mu-synthesis control approach is adopted to make HVDC control resilient against AC grid impedance variation. In the context of FRT performance, the fault operation of the HVDC system is improved by appropriate modification of the MMC control loops. Finally, a brief study on the DC voltage droop control design is conducted on a multi-terminal HVDC system. At the generation level, this thesis investigates the dynamic behavior of grid-forming control during fault events in a low-strength power system. It investigates the complication related to the existing grid codes and suggests two FRT controls for AC voltage support during fault recovery. As the last step, this thesis evaluates the feasibility of modern flexible loads to provide grid-forming services to the low-strength power grid. A hydrogen electrolyzer is used as an example and the operational constraints related to the hydrogen production are linked to the grid-forming functionalities. (Català) El comportament dinàmic del sistema de potència modern està cada vegada més influenciat per la dinàmica dels convertidors de potència. A nivell de generació, un gran volum d'energia eòlica i solar amb els seus convertidors de potència a gran escala està substituint els generadors síncrons convencionals. A nivell de transmissió, molts projectes de corrent continu d'alta tensió (HVDC) basats en la tecnologia Modular Multilevel Converter (MMC) s'encarreguen de transferir l'eòlica marina als centres de càrrega o interconnectar dues xarxes de CA per millorar la seguretat del subministrament. El nivell de consum també s'enfronta a una integració tan alta dels convertidors de potència, ja que les càrregues flexibles modernes, com ara vehicles elèctrics, accionaments de motors industrials i electrolitzadors d'hidrogen recents, es connecten al sistema elèctric mitjançant convertidors de potència. Aquesta transformació en els nivells de generació, transmissió i consum condueix a una dinàmica de potència que mai s'havia experimentat abans en els sistemes de potència convencionals. L'elevada penetració dels convertidors de potència pot provocar diversos reptes: reducció de la inèrcia total del sistema i de la potència de curtcircuit, excitació d'interaccions indesitjables entre llaços de control, reducció del marge d'estabilitat i pèrdua de dispositius en el context de fallada (FRT). Aquests reptes es poden abordar mitjançant ajustos adequats en els sistemes de control dels convertidors de potència. A nivell de transmissió, aquesta tesi se centra en la millora del comportament dinàmic dels sistemes HVDC. Es proposa una metodologia per estudiar l'estabilitat del senyal petit i avaluar les interaccions potencials que poden passar entre els bucles de control de dues estacions convertidores HVDC. A continuació, es desenvolupa un controlador multivariable basat en la pràctica de disseny H-inf per optimitzar el comportament dinàmic del sistema. Com a estudi de seguiment, s'adopta un enfocament de control de sintetització-mu per fer que el control HVDC sigui resistent a les variacions en la impedància de la xarxa de CA. En el context del rendiment FRT, el funcionament de falla del sistema HVDC es millora mitjançant les modificacions adequades als bucles de control MMC. Finalment, es realitza un breu estudi sobre el disseny de control de caiguda de tensió de CC en un sistema HVDC multiterminal. A nivell de generació, aquesta tesi investiga el comportament dinàmic del control de formació de xarxa durant esdeveniments de falla en un sistema d'energia de baixa inèrcia. Investiga la complicació relacionada amb els codis de xarxa existents i suggereix dos controls FRT per al suport de la tensió de CA durant la recuperació de fallades. Com a darrer pas, aquesta tesi avalua la viabilitat de càrregues flexibles modernes per proporcionar serveis de formació de xarxa a la xarxa elèctrica de baixa resistència. Com a exemple s'utilitza un electrolitzador d'hidrogen i les limitacions operatives relacionades amb la producció d'hidrogen estan vinculades a les funcionalitats de formació de la xarxa. (Español) El comportamiento dinámico de los sistemas eléctricos modernos está cada vez más influido por la dinámica de los convertidores de potencia. A nivel de generación, un gran volumen de energía eólica y solar con sus convertidores de potencia a gran escala están sustituyendo a los generadores síncronos convencionales. A nivel de transmisión, se están encargando muchos proyectos de corriente continua de alta tensión (HVDC) basados en la tecnología de convertidores multinivel modulares (MMC) para transferir la energía eólica marina a los centros de carga, interconectar dos redes de CA no síncronas o dos nodos diferentes de la misma red de CA (como enlace integrado) para mejorar la seguridad del suministro. El nivel de consumo también se enfrenta a una integración tan elevada de los convertidores de potencia, ya que las cargas flexibles modernas, como los vehículos eléctricos, los accionamientos de motores industriales y los recientes electrolizadores de hidrógeno, se conectan al sistema eléctrico a través de convertidores de potencia. Esta transformación en los niveles de generación, transmisión y consumo da lugar a una dinámica de potencia nunca antes experimentada en los sistemas eléctricos convencionales. La alta penetración de los convertidores de potencia puede causar varios retos: reducción de la inercia total del sistema y de la corriente de cortocircuito, excitación de interacciones indeseables entre los bucles de control, reducción del margen de estabilidad y pérdida de dispositivos en el contexto de la conmutación por fallo (FRT). Estos retos pueden abordarse mediante ajustes adecuados en los sistemas de control de los convertidores de potencia. A nivel de transmisión, esta tesis se centra en mejorar el comportamiento dinámico de los sistemas HVDC. Se propone una metodología para estudiar la estabilidad de pequeña señal y evaluar las posibles interacciones que pueden producirse entre los lazos de control de dos estaciones convertidoras HVDC. A continuación, se desarrolla un controlador multivariable basado en la práctica de diseño H-inf para optimizar el comportamiento dinámico del sistema. Como estudio complementario, se adopta un enfoque de control mu-síntesis para que el control HVDC sea resistente a la variación de la impedancia de la red de CA. En el contexto del rendimiento FRT, se mejora el funcionamiento en fallo del sistema HVDC mediante la modificación adecuada de los bucles de control MMC. Por último, se realiza un breve estudio sobre el diseño del control de droop de tensión de CC en un sistema HVDC multiterminal. A nivel de generación, esta tesis investiga el comportamiento dinámico del control de formación de red durante eventos de fallo en un sistema eléctrico de baja tensión. Investiga la complicación relacionada con los códigos de red existentes y sugiere dos controles FRT para el soporte de tensión de CA durante la recuperación de fallos. Como último paso, esta tesis evalúa la viabilidad de las cargas flexibles modernas para proporcionar servicios de formación de red a la red eléctrica de baja potencia. Se utiliza como ejemplo un electrolizador de hidrógeno y se relacionan las restricciones operativas relacionadas con la producción de hidrógeno con las funcionalidades de formación de red.

The restoration of modern power systems after large-scale outages poses significant challenges due to the increasing integration of renewable energy sources (RES) and electric vehicles (EVs), both of which introduce new dimensions of uncertainty and flexibility. This paper presents a Hierarchical Modern Power System Restoration (HMPSR) model that employs a two-level architecture to enhance restoration efficiency and system resilience. At the upper level, Graph Neural Networks (GNNs) are used to predict fault locations and optimize network topology by analyzing the spatial and topological features of the grid. At the lower level, Distributionally Robust Optimization (DRO) is applied to manage uncertainty in generation and demand through scenario-based dispatch planning. The model specifically considers solar and wind power as the primary RES, and incorporates both grid-connected and mobile EVs as flexible energy resources to support the restoration process. Simulation results on an enhanced IEEE 33-bus test system demonstrate that the HMPSR model reduces restoration time by 18.6% and total cost by 15.4%, while maintaining a Grid Stability Index above 85% under high variability conditions. These results confirm the effectiveness of a tightly integrated, hierarchical strategy for power system restoration, providing a robust and adaptive framework for real-world deployment.

Modern power systems which integrate renewable energy sources (RESs), such as wind or solar energy sources and plug-in electric vehicles (PEVs), need to carry out the uncertainty due to the access of demanded or injected power. Therefore, it is necessary to consider uncertainty costs in optimal power flow problems. To develop the uncertainty cost functions, log-normal probability distribution function (PDF) for solar irradiance, normal PDF for unloading and loading behavior of PEVs, and Rayleigh PDF for wind speed are considered. In this work, a dynamic optimal power flow (DOPF) model is formulated by considering the uncertainties of RESs and PEVs. A newly hybrid metaheuristic algorithm named cross entropy (CE) covariance matrix adaption evolutionary strategy (CMAES) is proposed to evaluate the DOPF problem in a modified IEEE 118-bus system. The application of adaptive step length in the CMAES method improves the local exploitation capability, while the CE method is used in the initial stage for global exploration due to its fast convergence. Simulation results indicate that the proposed technique can solve the DOPF problems with RESs and PEVs effectively and can provide high-quality solutions compared to different techniques. The conventional statistical method called ANOVA test is performed for comparative analysis of different techniques. The results of this test show the validation of CE-CMAES compared to different optimization techniques. Therefore, the proposed method could be easily and efficiently applied by power system operators in real-time optimal power flow operations.