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This article emphasizes the design and analysis of an optimal intelligent controller for frequency regulation application. The load frequency control (LFC) mechanism is an eminent and essential mechanism to reinstate the system frequency and scheduled tie-line power to their nominal values. Employing an appropriate controller enhances the operation of the LFC mechanism. Hence, this article put forward an adoptive control policy-based fuzzy-fractional ordered PI controller parallel with fractional PIDN controller (i.e., Fuzzy (PIλf)+PIλDN) for the LFC mechanism. Besides that, a maiden attempt of using a new opposition-learning-based volleyball premier league (OVPL) algorithm is carried out to obtain optimal control parameters. The proposed optimal intelligent controller is explored for a nonlinear multi-area interconnected power system under deregulated environment. The proposed LFC schemes performance has been compared with several popular strategies for step and random perturb in load. Also, the robustness of the proposed scheme has been verified for a wide range of system parameter variations. On the other hand, the modified HVDC tie-line model and the impact of the inertia emulation technique (IET) using converter capacitors on transient behavior are illustrated in this article. Finally, the efficacy of the proposed LFC scheme has been verified over published literature on its platform. [ABSTRACT FROM AUTHOR]

This work presents a generic optimization framework using advanced mixed-integer programming techniques to integrate day-ahead and balancing markets with distributed energy resources such as storage, electric vehicles, demand response, and Transmission and Distribution System Operators' coordination schemes. The day-ahead model determines optimal initial energy scheduling, while the balancing model optimizes energy and reserve products for three market designs with varying Transmission and Distribution System Operators' coordination. The framework is applied to the interconnected Greek-Bulgarian-Romanian power system, considering 2030 installed capacities. The model outputs illustrate market coupling scenarios among Romania, Bulgaria, and Greece, highlighting clear price signals and distributed energy resources flexibility. Results reveal the significance of a diverse energy mix for energy security and show that Transmission and Distribution System Operators' coordination significantly influences ancillary service prices, with reductions of up to 80 % in certain scenarios. Net demand values determine electricity flow direction. Flexibility providers like storage can cover up to 100 % of the upward congestion management requirements and 11 % of upward balancing energy, helping smooth energy allocation from intermittent renewables. The impact of electric vehicle penetration on generation scheduling is minimal. The proposed model offers valuable insights for system operators, market participants, and policymakers, enabling them to provide accurate price signals and optimize resource allocation. The integrated day-ahead and balancing market models support efficient renewable energy integration, emissions reduction, enhanced grid stability, and investment in low-carbon technologies. This aligns with the United Nations Framework Convention on Climate Change goals, contributing to the development of sustainable and resilient energy systems. • A day-ahead market model provides optimal initial energy scheduling. • Congestion management capacity is added as a new market product. • Energy storage systems can play a decisive role in future power systems. • The impact of electric vehicles in horizon 2030 is not highly intensive. [ABSTRACT FROM AUTHOR]

In the interconnected power grid operating within a performance-based frequency regulation market, uncoordinated frequency control strategies and power fluctuations in interconnection lines can intensify conflicts of interest among grid operators, leading to frequent and severe frequency fluctuations. To address these challenges and enhance grid stability, the Squid-Inspired Cooperative Load Frequency Control (SC-LFC) method is proposed. This method mimics the distributed neural decision-making observed in squids, treating each unit within an area as an independent agent. In real-time applications, each unit independently collects local frequency and status information, thereby avoiding coordination failures due to inter-area communication delays or errors. To achieve efficient coordinated control across multiple objectives and regions in complex, random interconnected power grids, the Automatic Curriculum Multi-Agent Deep Meta Actor-Critic (ACMA-DMAC) algorithm is introduced. This approach employs a hybrid curriculum learning strategy, enabling gradual learning and adaptation, which enhances the robustness and efficiency of the SC-LFC strategy. Simulations based on a four-area load frequency control model of the China Southern Grid (CSG) validate the effectiveness and superior performance of the proposed method. • A SC-LFC method is proposed to balance the interests of operators. • An ACMA-DMAC is proposed to achieve multi-task collaborative learning. • The proposed method can effectively reduce the power fluctuation. [ABSTRACT FROM AUTHOR]

In Europe, the building sector accounts for approximately 35 % of the energy-related emissions. Hybrid systems coordinating heat pumps and gas boilers can avoid greenhouse gas (GHG) emissions from carbonized electricity production by providing demand-side flexibility without any service interruption. This work aimed to develop a control strategy for a fleet of hybrid heat pumps to reduce GHG emissions. The electricity and gas consumption of a fleet of 3000 hybrid heat pumps, heating 100,000 dwellings spread throughout France, was evaluated. A Modelica model of a district archetype was simulated in seven cities representative of the French climatic zones to obtain the national heating demand. The marginal emission factor of the electricity consumption was assessed using a French power system model coupled with marginal emission factors for interconnected power systems, which were assessed through linear regressions. Two types of control strategies (prioritizing the heat pump and fuel switch) are evaluated considering 4 different sizing for the heat pump (120 %, 50 %, 35 %, and 20 %). Between July 2018 and June 2019, a strategy prioritizing the heat pumps would have avoided between 8000 and 26,000 t CO2eq for the power system. A strategy switching between the heat pump and the boiler based on the marginal emission factor of the electricity consumption would have avoided around 38,000 t CO2eq , with a limited influence of the sizing of the heat pump. [Display omitted] • The impact of controlling a fleet of 3000 hybrid heat pumps on the power grid is evaluated. • Hybrid heat pumps can contribute to decarbonizing electricity consumption. • Between July 2018 and 2019, the new control strategy would have avoided 380 kg GHG eq per French dwelling. • The control strategy mainly decarbonized the electricity generation in the interconnected power systems. • The sizing of the heat pumps has a low impact on the avoided emissions. [ABSTRACT FROM AUTHOR]

China has implemented strong incentives to promote the market of plug-in hybrid electric vehicles (PHEVs). The increase of PHEV will lead to an increase in electricity consumption, and more power generation will also lead to more carbon dioxide emissions. Therefore, it is necessary to evaluate the environmental benefits of the subsidy policy. In this study, we calculated the CO 2 emission reduction and the increase of grid power generation and the CO 2 emission from the investment and construction of clean energy power generation facilities caused by the subsidy of PHEV from 2016 to 2020, and evaluated the effect of PHEV subsidy through the multi-period DID model. The results show that replacing ICEV with a PHEV will reduce CO 2 emissions by 12.38 kg per 100 km. Under the existing grid structure and power generation conditions, the promotion of PHEV will have a positive impact on CO 2 emissions, but the effect is short-lived. Without considering other costs, the investment in clean energy power generation facilities at this stage will achieve better CO 2 emission reduction effect than the promotion of PHEV. CO 2 Emission Reduction in Case of Subsidy Amount Transfer: [Display omitted] • The carbon reduction effect of China's increased PHEV due to subsidy policies was calculated. • Analyzed the development of power generation in China from 2000 to 2020. • The carbon reduction effect of China's PHEV subsidy policy was analyzed through a multi period DID model. • The analysis shows that the current focus of China's carbon reduction policy subsidies is on clean energy construction. Synopsis: this study calculates the CO 2 emission reduction effect of vehicles brought by China's PHEV subsidies and the CO 2 emission reduction effect brought by the construction of clean energy power generation facilities in the interconnected power grid under the current investment level. The multi-period DID model proves that the PHEV promotion subsidies have a positive effect on CO 2 emission reduction at this stage. On the basis of the research results, we have explored the "real key" to better promote China's road carbon emission reduction and achieve the "3060" goal by assuming that other costs are not considered. [ABSTRACT FROM AUTHOR]

Integrating wind power into the automatic generation control (AGC) of deregulated power system introduces significant operational challenges and complicates coordinated load–frequency control among independent operators. Although various AGC strategies exist, limited research focuses on advanced AGC frameworks capable of managing active power imbalances in deregulated environments. This paper addresses these challenges by proposing a cascade control scheme designed to reduce frequency oscillations and tie line active power deviations in a multi-machine, multi-area interconnected power system under deregulated conditions. Initially, the system is analyzed with areas interconnected by AC tie lines and later extended to a configuration with both AC and HVDC links to enhance power transfer in an open market scenario. Wind power integration is also considered, introducing frequency deviations but without participation in price-based market operations. The cascade controller’s performance is evaluated across various market scenarios, including contract violation case along with bilateral and unilateral, demonstrating its advantages over traditional PID control in minimizing frequency and active power deviations to improve stability of the system. Optimal gain parameters for both controllers are identified using the Rao algorithm with integral square error as cost function of the optimization problem, highlighting the cascade scheme’s effectiveness in maintaining stability in a deregulated, wind-integrated power system. [ABSTRACT FROM AUTHOR]

The analysis of signal transmission in Very Large Scale Integrated (VLSI) circuit, begins with a LC/RLC interconnect line representation which is lossless and low frequency interconnection. The transmission of signal of high frequency through interconnect networks can be modelled as a distributed RLCG transmission line interconnections. In this work delay and voltage models for RLCG type transmission lines are obtained by using single and two pole methodology using the concept of the first two moments of the transfer function of RLCG interconnects. In the proposed models, the closed form formulas are derived theoretically for the analytical delay, single pole delay and two-pole delay. Proposed single pole delay model and two-pole delay models’ estimations are within 2.8% and 2.1% of SPICE delay estimation. The proposed approaches for various voltage and delay models have advantages in accuracy, novelty and minimum time required for simulation over the other existing models. [ABSTRACT FROM AUTHOR]

Load-frequency-control (LFC) is employed for frequency stability and balanced power flow among control-areas. Inverters are becoming critical assets in modern power networks with increasing renewable energy. As these inverter-based resources prevail, the system inertia decreases, leading to potential frequency instability problems. Grid-forming (GFM) inverter development and applications are gaining significant attraction because of their ability to maintain quality power-grid operations. GFM inverter, acting as a voltage-source-converter, adjusts its output frequency by contributing a portion of the load change to reduce the frequency deviations. The virtual synchronous generator (VSG) control mechanism for GFM is implemented in this work. A two-area interconnected power system model emulating an enhanced IEEE 9-Bus system is developed and simulated in MATLAB-Simulink® for analysis. Secondary controllers are applied in each LFC and GFM-loop of the proposed LFC-GFM system with a magnetotactic-bacteria-optimization (MBO) algorithm for simultaneously tuning these parameters. The proposed control strategy's effectiveness is verified by comparing simulation results with the basic LFC system under varying inverter penetration levels. The simulated dynamic responses verify the efficacy of the controlled penetration of the GFM inverter in the proposed LFC-GFM system with enhanced damping characteristics, improving small-signal stability and reducing settling time by 43.46% and frequency deviation by 55.5%. [ABSTRACT FROM AUTHOR]

The reinforcement learning (RL)-based generation control strategies have been widely studied to address the limited adaptability of traditional automatic generation control (AGC) strategies to the load disturbance problem resulting from heterogeneous energy sources. To improve the control accuracy of the RL-based strategy in load frequency control (LFC), a double deep Q-network combined with an upper confidence bound (DDQN-UCB)-based strategy is designed to solve the problem of agent decision-making in a nonlinear environment. Firstly, the area control error (ACE) and control performance standard 1 (CPS1) of the LFC power system are considered in the design of the RL reward function. Secondly, the actual and estimated Q-values are calculated using the Q-network and the target Q-network combined with the reward value. Thirdly, the deviation loss of the two Q-values is calculated, and the network is updated based on the loss value using gradient descent. Finally, the UCB algorithm is introduced to equalize the frequency of being selected for each action during the random exploration of the actions, and the agent uses the greedy algorithm in combination with the UCB algorithm to select a power-compensated control action to send to the environment. In this paper, the IEEE multi-area LFC power system is used as an experimental validation model. A comparison of the proposed RL control algorithm with five other algorithms revealed that the pre-learning convergence accuracy was improved by 57.5%. Furthermore, the LFC effectiveness test demonstrated that the DDQN-UCB control strategy enhances LFC accuracy while simultaneously stabilizing the power exchange of the inter-area tie-line to within 1.8972 MW, thereby maintaining the stability of the power system. [ABSTRACT FROM AUTHOR]

Energy storage systems play a crucial role in modern power systems. Consequently, a mixed cloud energy storage (CES) system is proposed. The mixed CES system comprises consumers and prosumers. The consumers can only consume energy. The prosumers can either produce or consume energy at different time intervals. The proposed mixed CES system is designed for investigating the generation control challenges in mixed interconnected power systems. To optimize the active power balance and economic efficiency of the mixed CES system, a long-term deep reinforcement learning (LDRL) artificial intelligence approach is proposed as the real-time economic generation controller to control the mixed CES system. The LDRL consists of a reinforcement mechanism and two models: a long short-term memory model for economic dispatch and a deep neural networks model for smart generation control. The reinforcement framework updates policies for the prosumers. The efficacy of proposed method is validated across three mixed systems, i.e., the improved IEEE 300-bus, Polish 2383-bus, and mixed systems with varying structures. The numerical simulations verify that the LDRL method can efficiently and economically control the mixed CES systems with diverse structures. • The framework of cloud energy storage systems with varying structures is built. • The real-time economic generation controller is designed for the built system. • A long-term deep reinforcement learning method is proposed for this controller. • Long short-term memory and deep neural networks are used to reinforcement framework. • Higher control performance and optimal objectives are obtained simultaneously. [ABSTRACT FROM AUTHOR]

Interference amongst stability phenomena has been raising concerns due to the potential maladjustment of control parameters in large‐scale and interconnected power systems. In consequence of such interference, power system security is threatened. In order to preclude any inconsistency and ensure maximum security, a comprehensive study needs to be carried out in short‐term operational planning concentrating on the determination of an economic operating point with enhanced static and dynamic security. In this paper, an integrated approach with incorporation of voltage and rotor‐angle stabilities into the optimal operation of power system is proposed. This study is based on a many‐objective optimisation problem providing a cost‐effective security solution prior to real‐time operation whereby the operating point of power system is maximally secure and economic. The output of the proposed approach is a vector of adaptively adjusted control parameters consisting of terminal voltage and active power of generating units, reactive power of SVCs, and tap position of transformers. In addition, active power production and reactive power opportunity costs, static, and dynamic security criteria constitute the vector of objective functions with eight elements. The performance of the approach is assessed in several scenarios including various load patterns and different power limits of generators. [ABSTRACT FROM AUTHOR]

In recent decades the power system has become a complex network, to design a load frequency control (LFC) requires solving a complex equation. Optimisation techniques are essentially required to optimise the parameters of different controllers used for LFC issues in the power system. In a unified power system, the LFC is examined from all angles using different optimisation strategies to optimise the conventional PI, PID, cascaded, and fuzzy controllers as well as recently designed controllers. This manuscript specifically reviews the use of soft computing techniques in the frequency regulation of the power system with single/multiple areas that include conventional, renewable, and combinations of both, with FACTS devices and certain energy storage devices such as superconductor magnetic energy storage (SMES) and battery sources. Furthermore, deregulated power systems and microgrids are also considered for the study. To regulate LFC under various disturbances such as generation rate constraints (GRC) and dead band control, a few additional control approaches are utilised. Models of the power system are discussed and analysed. In addition, the merits and drawbacks of the studied techniques/structures that address design and implementation issues—as well as control issues that relate to the LFC problems—have been discussed. [ABSTRACT FROM AUTHOR]

This paper proposes a maiden intelligent controller design that consists of a fuzzy proportional-integral-derivative-double derivative (FPIDD) controller whose parameters are fine-tuned using the Gradient-Based Optimization algorithm (GBO). The proposed FPIDD2 regulator is employed as a secondary regulator for stabilizing the combined voltage and frequency loops in a two-area interconnected power system. The proposed FPIDD2 controller is tested in a two-area hybrid system, with each area comprising a mix of traditional (thermal, gas, and hydraulic power plants) and renewable generation units (wind and solar power). Additionally, the proposed controller takes into account system nonlinearities (such as generation rate limitations and governor deadband), system uncertainties, and load/renewables changes. The dynamic responses of the system demonstrate that FPIDD2 has superior ability to attenuate the deviations in voltage and frequency in both areas of the system. In the investigated hybrid system, the suggested FPIDD2 is compared to a GBO-tuned integral derivative tilted (ID-T) controller and FPID controller. As a fitness function for the GBO, the criteria of minimizing the integral time absolute error (ITAE) are applied. The results are presented in the form of MATLAB/SIMULINK time-domain simulations. The simulation outcomes prove that the presented controller has an outstanding performance compared with the other control strategies in the dynamic response of the system in terms of rising and settling times, maximum overshoot, undershoot values and ITAE. The ITAE value of the FPIDD2 regulator tuned by the GBO technique was enhanced by 90.9% with the GBO-based ID-T and 55.4% with GBO-based FPID controllers. [ABSTRACT FROM AUTHOR]

Nowadays, renewable energy (RE) sources are heavily integrated into the power system due to the deregulation of the energy market along with environmental and economic benefits. The intermittent nature of RE and the stochastic behavior of loads create frequency aberrations in interconnected hybrid power systems (HPS). This paper attempts to develop an optimization technique to tune the controller optimally to regulate frequency. A hybrid Sparrow Search Algorithm-Grey Wolf Optimizer (SSAGWO) is proposed to optimize the gain values of the proportional integral derivative controller. The proposed algorithm helps to improve the original algorithms' exploration and exploitation. The optimization technique is coded in MATLAB and applied for frequency regulation of a two-area HPS developed in Simulink. The efficacy of the proffered hybrid SSAGWO is first assessed on standard benchmark functions and then applied to the frequency control of the HPS model. The results obtained from the multi-area multi-source HPS demonstrate that the proposed hybrid SSAGWO optimized PID controller performs significantly by 53%, 60%, 20%, and 70% in terms of settling time, peak undershoot, control effort, and steady-state error values, respectively, than other state-of-the-art algorithms presented in the literature. The robustness of the proffered method is also evaluated under the random varying load, variation of HPS system parameters, and weather intermittency of RE resources in real-time conditions. Furthermore, the controller's efficacy was also demonstrated by performing a sensitivity analysis of the proposed system with variations of 75% and 125% in the inertia constant and system loading, respectively, from the nominal values. The results show that the proposed technique damped out the transient oscillations with minimum settling time. Moreover, the stability of the system is analyzed in the frequency domain using Bode analysis. [ABSTRACT FROM AUTHOR]

With the increase in penetration of wind generation on interconnected power systems, the importance of wind power ramp forecasting has continuously grown. Large power ramps caused by sudden weather changes raise more concerns due to their significant impact on the power system economics and stability. Correct wind power ramp forecasts can help the system operators and utility companies to tradeoff the risks when scheduling wind energy in the electricity market. In this paper, a day‐ahead wind power ramp forecasting algorithm is developed to provide probabilistic ramp forecasts for look‐ahead times up to 48 h using hourly wind speed forecasts from Environment Canada High Resolution Deterministic Prediction System (HRDPS). An image‐based similarity search strategy has been designed to build a direct link between the wind speed forecasts and the wind power ramp prediction, thus reducing the impact of the uncertainty from both the power production forecast model and the ramp identification process on the forecasting accuracy. A performance assessment and validation of the proposed ramp event forecasting method is conducted by using the forecast and operation data from six investigated wind farms across Canada. [ABSTRACT FROM AUTHOR]

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Electric Power Automation Equipment / Dianli Zidonghua Shebei is the property of Electric Power Automation Equipment Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Electric Power Automation Equipment / Dianli Zidonghua Shebei is the property of Electric Power Automation Equipment Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this article, a fractional-order proportional-integral-differential (FOPID) controller and its modified structure, called a MFOPID controller, are presented. To guarantee optimal system performance, the gains of the proposed FOPID and MFOPID controllers are well-tuned, employing the Jellyfish Search Optimizer (JSO), a novel and highly effective bioinspired metaheuristic approach. The proposed controllers are assessed in a hybrid system with two domains, where each domain contains a hybrid of conventional (gas, reheat, and hydro) and renewable generation sources (solar and wind). For a more realistic analysis, the presented system model includes practical limitations with nonlinear characteristics, such as governor dead zone/band (GDZ/GDB), boiler dynamics, generation rate limitation/constraint (GRL/GRC), system uncertainties, communication time delay (CTD), and load changes. The suggested methodology outperforms some newly developed heuristic techniques, including fitness-dependent optimizer (FDO), sine-cosine algorithm (SCA), and firefly algorithm (FA), for the interconnected power system (PS) of two regions with multiple generating units. Furthermore, the proposed MFOPID controller is compared with JSO-tuned PID/FOPID and PI controllers to ascertain its superiority. The results signify that the presented control method and its parametric optimization significantly outperforms the other control strategies with respect to minimum undershoot and peak overshoot, settling times, and ITSE in the system's dynamic response. The sensitivity analysis outcomes imply that the proposed JSO-MFOPID control method is very reliable and can effectively stabilize the load frequency and interconnection line in a multi-area network with interconnected PS. [ABSTRACT FROM AUTHOR]

Since modern power systems are susceptible to undesirable frequency oscillations caused by uncertainties in renewable energy sources (RESs) and loads, load frequency control (LFC) has a crucial role to get these systems' frequency stability back. However, existing LFC techniques may not be sufficient to confront the key challenge arising from the low-inertia issue, which is due to the integration of high-penetration RESs. Therefore, to address this issue, this study proposes an optimized intelligent fractional-order integral (iFOI) controller for the LFC of a two-area interconnected modern power system with the implementation of virtual inertia control (VIC). Here, the proposed iFOI controller is optimally designed using an efficient metaheuristic optimization technique, called the gray wolf optimization (GWO) algorithm, which provides minimum values for system frequency deviations and tie-line power deviation. Moreover, the effectiveness of the proposed optimal iFOI controller is confirmed by contrasting its performance with other control techniques utilized in the literature, such as the integral controller and FOI controller, which are also designed in this study, under load/RES fluctuations. Compared to these control techniques from the literature for several scenarios, the simulation results produced by the MATLAB software have demonstrated the efficacy and resilience of the proposed optimal iFOI controller based on the GWO. Additionally, the effectiveness of the proposed controller design in regulating the frequency of interconnected modern power systems with the application of VIC is confirmed. [ABSTRACT FROM AUTHOR]

Reducing the emissions of greenhouse gases has directed energy sectors toward using renewable energy sources (RESs) and decreasing the dependency on conventional energy sources. Recently, developing efficient load frequency control (LFC) schemes has become essential to face the reduced inertia due to RESs installations. This paper presents a modified tilt fractional order (FO) integral–tilt FO derivative with a fractional filter (TFOI-TFODFF or namely TI λ -TD μ FF) LFC method. Although the proposed controller uses the same elements of standard controllers, it adopts FO control capabilities and flexibilities, including the tilt, FO integral, FO derivative, and FO filter. Thence, a new control structure is obtained, merging the advantages of both controllers. Moreover, the proposed TFOI-TFODFF controller employs two control loops to be able to mitigate low-frequency as well as high-frequency disturbances in power grids. Additionally, a new modified marine predator algorithm (MMPA) is proposed for optimally tuning the parameters of the proposed TFOI-TFODFF LFC method. The performance of the MMPA is enhanced in terms of initialization and exploitation phases using the chaotic maps and weighting factor. A two-area interconnected power system case study is implemented with wind and photovoltaic RESs and electric vehicles (EVs) contribution. The proposed TFOI-TFODFF LFC is compared with the FOPID, TID, TI-DF, and FOTPID controllers, wherein the proposed TFOI-TFODFF has offered superior performance of the proposed controller. Moreover, the proposed modified MPA is compared with the original MPA and other competitive optimization algorithms, and statistical analyses are carried out through parametric and nonparametric tests. [ABSTRACT FROM AUTHOR]

This paper investigates the use of energy storage devices (ESDs) as back-up sources to escalate load frequency control (LFC) of power systems (PSs). The PS models implemented here are 2-area linear and nonlinear non-reheat thermal PSs besides 3-area nonlinear hydro-thermal PS. PID controller is employed as secondary controller in each control area and ESDs such as battery energy storage system, flywheel energy storage system and ultra-capacitor are employed to assist LFC task during crest load disturbances. PID controller parameters are optimized by salp swarm algorithm (SSA) using a new cost function. This function is innovative, improving system stability by increasing stability margin of the system. Contribution of the proposed approach are thoroughly justified by contrasting it against the renowned works in the state-of-the-art. The comparison analysis clearly unveils that SSA optimized PID controller with ESDs is able to significantly reduce settling time and unwanted oscillations of frequency and tie-line power deviations with a greater stability margin. Our proposal is also more economic than the existing solutions considering the trade-off between simplicity and effectiveness. [ABSTRACT FROM AUTHOR]

The increase in power demand, nonlinearity, complexity, varying structure, and other important causes has necessitated the implementation of artificial intelligent control methodologies for safe and acceptable operation of the electric power systems. Therefore, in this article, an improved two-degrees-of-freedom (2DOF-PID) control scheme is proposed for power/frequency control of a two-area interconnected electric power system. The parameters of the 2-DOF-PID control scheme are optimized using the Dragonfly Algorithm (DA) via a new integral-based weighted goal fitness function (IB-WGFF) (i.e., DF-2DOF-PID-IB-WGFF). The superiority of the suggested scheme is proved by comparing the results obtained using the proposed IB-WGFF with those obtained using the conventional controllers, and the 2DOF-PID controllers optimized using the DA and Genetic Algorithm (GA) via the frequently published performance criterion. To verify the stability, efficacy, and robustness of the proposed control scheme, a load disturbances and parameters perturbations with various percentages are implemented in the controlled system under the same controllers. Finally, verification results proved that the proposed 2DOF-PID optimized using DA via the IB-WGFF is more stable, efficient, and robust than the other controllers recently used in the literature. [ABSTRACT FROM AUTHOR]

Frequency, tie-line power, and the terminal voltages of synchronized generators must all be kept within prescribed limits to ensure the stability of an interconnected power grid through combined automatic generation control (AGC) and automatic voltage regulator (AVR) loops. Thermal power plants, electric vehicles, and renewable energy sources—including solar and wind, geothermal, and solar thermal power plants—form the two-area integrated power system in present research. A new cascade controller named the cascaded proportional integral derivative (PID) and fractional-order PID (CPID-FOPID) controller is proposed for the first time, whose performance is compared with the PID and FOPID controller. The results show that the proposed cascade controller outperforms PID and FOPID in delivering superior dynamic characteristics, including short settling times and low oscillation amplitudes. A new metaheuristic algorithm named the coot algorithm was applied to optimize the parameters of these controllers. The suggested controller outperforms FOPID in the combined AGC and AVR problem under uncertain conditions (random load disturbance, variable input of solar irradiation, and wind power). Robustness of the controller is tested with significant variation in the turbine time constant of the thermal and geothermal power plant. In this study, authors also investigated the best possible coordination between the superconducting magnetic energy storage (SMES) and gate-controlled series capacitor (GCSC) devices to control both voltage and frequency simultaneously. The effect of communication time to the power system is analyzed in this study. Additionally, the obtained results are satisfactorily validated using OPAL-RT real-time digital simulator. [ABSTRACT FROM AUTHOR]

The article presents the main analysis results of the frequency change in the interconnected power system (IPS) of Ukraine under the conditions of its isolated operation, as well as synchronous operation with the UPS/IPS and ENTSOE. The reasons for the change in the maximum and minimum values are considered, as well as the frequency histograms for the above modes of operation of the IPS of Ukraine are presented. [ABSTRACT FROM AUTHOR]

The article presents the discussion on electricity market in Great Britain being a complex intersection of engineering and economics. Topics include physical connections between demand and supply make up the electricity grid, and the most important part of the price that consumers pay is the wholesale electricity price; and wholesale electricity price is the price at which suppliers buy the electricity they use to supply to end consumers.

With the increasing scale of wind power installation and grid connection, the interconnection of power grids through tie lines can absorb wind power in a wider range, but the uncertainty of wind power output leads to an increase in the risk of system operation. Conditional value-at-risk (CVaR) was introduced in multi-regional unit commitment to quantify the risk, and a multi-regional unit commitment model considering conditional value at risk was proposed, so as to balance the operation economy and risk loss of multi-regional unit commitment with an interconnected power grid. Besides, the tie line operation mode was modeled in detail to coordinate the economy and flexibility of tie line operation. By introducing auxiliary variables, the tie line operation mode model was transformed into a mixed-integer linear constraint. Finally, the proposed multi-region unit commitment model was transformed into a mixed-integer linear programming form for the solution. With the improved two area system as a simulation example, the results demonstrate that this method can effectively control the tail risk of the system and improve the ability to resist the wind power output uncertainty while ensuring the operation economy of multi-area internet power grid. [ABSTRACT FROM AUTHOR]

• Reviewed on AGC challenges with various control approaches in power systems. • A detailed survey presented on AGC with renewable energy sources. • AGC problems with integration of energy storage devices & FACTS have addressed. • Research gaps and directions for future power systems is presented. This paper presents a comprehensive literature review and an up-to-date bibliography on automatic generation control (AGC)/load frequency control (LFC) of interconnected power systems integrated with conventional, renewable and sustainable energy sources. The objective of AGC is to quickly stabilize the deviations in frequency and tie-line power following load fluctuations. Various state-of-the-art AGC strategies have not been reviewed so far, hence, an appropriate review on recent AGC mechanism is indispensable. Salient points discussed and highlighted in this survey include different power system models with/without renewable sources, and integration of sophisticated concepts such as microgrids (MGs), smart grids (SGs), distributed generations (DGs), high voltage direct current (HVDC) links, and power system after liberalization. Furthermore, various examined control approaches like traditional controllers, intelligent methods, adaptive, robust, optimal, variable, soft computing, centralized and decentralized methods with their advantages and disadvantages have been pondered and compared. Further, AGC challenges with incorporation of flexible AC transmission systems (FACTS), energy storage devices and high penetration of renewable sources for modern and future power systems have been addressed. This review concludes the new control schemes namely, electrical vehicles (EVs), fractional-order (FO) based controllers, fuzzy aided controllers, and cascaded controllers with case studies. Research gaps and directions for future developments AGC of power systems are also discussed. [ABSTRACT FROM AUTHOR]

The performance and transient stabilizing using a unified power flow controller (UPFC) anticipated for a grid consisting of parallel connected two number of three-phase transmission lines is presented in this paper. The interruption that happened in absence of UPFC connected to three phase line increases the impedance of the transmission line and consequently reduces the flow of active power. The volume of active power flowing through the grid can be controlled by installing UPFC on the grid. The conditions for transient stability are suggested by the transient model and UPFC interconnected power grid. The complete dynamic control is modeled by the active and reactive power waves developed during transient events. A series of simulations are presented in this paper to show the effectiveness and correctness of the methodology applied for transient stability. [ABSTRACT FROM AUTHOR]

Modern power systems need the expansion of EHV and UHV power Transmission networks due to the exponential rise in power demand. The Ferranti effect needs the attention of researchers on the dynamic variation of loads in interconnected power systems. This article focuses on the development of closed-loop control circuitry for both Static Synchronous Compensator (STATCOM) and Static Var Compensator (SVC). The developed transmission system Simulink model has been simulated and Ferranti Effect is observed for both no-load and lightly loaded conditions. The closed-loop control circuits along with power circuits of STATCOM and SVC have been developed and simulated and the results show the significant suppression of the Ferranti effect by both of the shunt devices. [ABSTRACT FROM AUTHOR]

The effective and reliable operation of an interconnected power system (PS) is a major concern, because of the large amount of power contribution from the intermittent renewable energy sources to the power grid. Energy storage (ES) devices are employed to enhance the reliability of the PS operation. Electric vehicles (EVs) are a novel type of distributed ES device that may be utilized to compensate for power imbalances. This paper discusses the participation of EVs for combined frequency and voltage control under conventional PS along with a thermal, hydro, realistic dish‐Stirling solar thermal system (RDSTS). Appropriate physical constraints such as governor dead band and generation rate constraints are equipped with thermal and hydro plants, and communication delay between the generating unit and load dispatch center is considered. An aggregate EVs model, RDSTS, and a new cascade controller named fractional‐order proportional‐derivative with filter coefficient cascaded with tilt‐integral‐derivative (CFOPDN‐TID) controller are provided in all areas for various studies. An artificial flora algorithm is employed to determine the optimal value of the various controller parameters under several scenarios. The system dynamics of the CFOPDN‐TID controller outperform the proportional‐integral‐derivative (PID) and TID controllers under various conditions. The incorporation of EVs improves the system performance in terms of less settling time, and peak deviation, and reduce oscillations. The system performance degrades with increases in the communication time delay of the EVs. The integration of RDSTS into the system provides better dynamics than the dish‐Stirling solar thermal system. Numerous studies have been conducted to validate the robustness of the proposed CFOPDN‐TID controller. [ABSTRACT FROM AUTHOR]

Modern power systems are very complicated and have more obligation to uphold the supply of power be reliable, stable, and of decent quality at both the transmission and distribution phases. Preserving grid balance is a significant concern if there is an unforeseen generation deficit or grid disturbance, or intermittent renewable energy sources, such as bio‐diesel, electric vehicles (EVs), and solar power are incorporated into the energy mix. To compensate for this imbalance and enhance the power system's reliability and stability, the energy storage systems (ESS) can be regarded as an appropriate measure. Thus, ESS contributes to greenhouse gas (GHG) emission reductions by successfully integrating more renewable energy sources into the grid. This article presents multiple ESSs, such as pumped hydroelectric storage (PHS), accurate flywheel energy storage (AFES), battery energy storage (BES), capacitive energy storage (CE), and superconducting magnetic energy storage (SMEs) and their comparative performance analysis in unified voltage and frequency control of power system. The proposed interconnected power system includes thermal, solar‐thermal, bio‐diesel‐engine generator, and EVs. In both control systems, a maiden attempt was made to utilize Harris' Hawks optimization‐based fuzzy‐fractional‐order‐tilt‐integral‐derivative (FFOTID) controller. The repercussion of PHS proclaims a substantial amelioration in system performance than other ESSs. Likewise, the system exhibits better dynamics with the coalition of PHS and power system stabilizer (PSS) of AVR compared to their separate intervention. Lastly, sensitivity analysis of proposed controller is analyzed by alteration of load perturbation and nonlinearity of thermal plants, which ensures optimal controller parameters accomplished at nominal conditions are resilient enough. [ABSTRACT FROM AUTHOR]

This paper addresses a novel hunger games search (HGS) based on specular reflection-based learning (SRL) and dynamic quasi-opposition-based learning (DQOL), named HGS_RQ, for improving the optimization performance of the classical HGS while dealing with load frequency control task. By these learnings, a fitter solution can be generated whether by SRL or DQOL and therefore, the quality of the best solution can be refined. The effectiveness of the proposed HGS_RQ is demonstrated and validated on two-area interconnected power system with considering nonlinearity effect of governor dead band. Additional supplementary controller is proposed to reinforce frequency regulation through solid oxide fuel cell. The objective function is adapted to minimize the integral time absolute error in frequency deviations and tie line power. The efficacy and superiority are affirmed by the comparisons with some of prominent recent methods. It can be noted that the adequate response is proved, since the maximum frequency deviation is 0.088 Hz, the settling time is about 2 s, and the steady-state frequency change is zero in the two areas. On the other hand, there is a significant reduction in tie line power transient response with maximum deviation of 1.318% for the studied cases. Furthermore, the statistical measures and analysis of variance test are analyzed to exhibit the superior performance of the HGS_RQ in terms of accuracy and reliability. [ABSTRACT FROM AUTHOR]

The intermittency of renewable energy generation and the randomness of power load variations lead to voltage and frequency fluctuations among the interconnected power system. In this study, a control strategy based on sliding mode control theory and disturbance observer is proposed for the multi-area power system with parameter uncertainties to handle load frequency control. By choosing an appropriate integral-type sliding-mode surface and reaching law, a slidingmode load frequency controller is constructed. In order to reduce the influence of uncertainties, a disturbance observer is designed to estimate lumped disturbances. On this basis, the sliding mode controller is reconstructed to provide better control effect. And the improved honey badger algorithm is used to tune out the optimal parameters of the designed controller. Simulation studies are carried out on a two-region reference system and an unequal capacity three-area power system with nonlinearity connected wind turbine generators to illustrate the favorable performance of the power system under the proposed control strategy. The comparison results show that the proposed control method possesses excellent frequency control ability under the uncertainty of load disturbance, wind power fluctuations and parameter uncertainties. And it also achieves efficient frequency control under a certain range of communication delay. [ABSTRACT FROM AUTHOR]

This study aims to maintain the frequency stability of the power system penetrated by wind power. Hence, a battery energy storage system (BESS) is applied to smooth the wind power output in power systems and to enhance their load frequency control (LFC) capacity. A novel comprehensive control framework is proposed for power systems integrated with wind farms and BESS based on an adaptive fuzzy super-twisting sliding mode control (AF-SSMC) method. Firstly, the sliding functions and control laws of subsystems are designed according to different relative degrees. Then, the super-twisting algorithm is applied to suppress the chattering of the sliding mode control law. Furthermore, an adaptive fuzzy control method is used to adjust the control gains online for better control performance of the controllers. The Lyapunov stability theory is employed to prove the asymptotic stability of the subsystems. The model of an interconnected thermal power system with wind and BESS penetration is also constructed for simulation analyses. The results indicate that the AF-SSMC method effectively reduces the chattering, and the proposed framework stabilizes the frequency of the power system under system uncertainties and external disturbances. Moreover, the wind farm and BESS combined system accurately tracks a reference power to reduce wind power fluctuations. [ABSTRACT FROM AUTHOR]

This paper proposes a novel load frequency control (LFC) approach formulated on an optimal structure of the coefficient diagram method (CDM) in a two-area thermal power system. As part of a realistic analysis, nonlinearities related to governor dead band (GDB) and generation rate constraint (GRC) have been considered. In this article, a hybrid CDM method is combined with the optimization of its mathematical equations to achieve an innovative controller. Furthermore, a new metaheuristic optimization technique called the water cycle algorithm (WCA) is used to determine the optimal coefficients of the CDM controller. For the purpose of demonstrating the validity of the proposed scheme, a wide range of uncertainties in the dynamic parameters of a nonlinear power system were tested. In addition, a comparative study is presented between the results obtained from a classical integral, CDM alone, optimized fuzzy, optimized PID, and the suggested controller. In this new approach to improved control, algebraic support provides a robust and responsive controller that can provide fast and stable dynamic responses and effectively overcome the detrimental effects of nonlinearities and uncertainties in the parameters of the power system. [ABSTRACT FROM AUTHOR]

With the incorporation of renewable energy, load frequency control (LFC) becomes more challenging due to uncertain power generation and changeable load demands. The electric vehicle (EV) has been a popular transportation and can also provide flexible options to play a role in frequency regulation. In this paper, a novel adaptive composite controller is designed to solve the LFC problem for the interconnected power system with electric vehicles and wind turbine. EVs are used as regulation resources to effectively compensate the power mismatch. First, the sliding mode controller is developed to reduce the random influences caused by the wind turbine generation system. Second, an auxiliary controller with reinforcement learning is proposed to produce adaptive control signals, which will be attached to the primary proportion‐integration‐differentiation control signal in a real‐time manner. Finally, by considering random wind power, load disturbances and output constraints, the proposed scheme is verified on a two‐area power system under four different cases. Simulation results demonstrate that the proposed adaptive composite frequency control scheme has a competitive performance with regard to dynamic performance. [ABSTRACT FROM AUTHOR]

Oscillations are an intrinsic phenomenon in interconnected power systems, leading to steady-state stability, safety decline, transmission power limitation, and electric power systems' ineffective exploitation by developing power systems, particularly by connecting these systems to low-load lines. In addition, they affect the economic performance of the systems. In this study, PSS2B power system stabilizers and TCSC compensators are used to improve the stability margin of power systems. In order to coordinate TCSC compensators, the MOPSO multiobjective algorithm with integral of the time-weighted absolute error (ITAE) and figure of demerit (FD) objective functions was used. The MOPSO algorithm optimization results are compared with nondominated sorting genetic algorithm (NSGAII) and multiobjective differential evolution (MODE) algorithms. The optimization results indicated a better performance of the proposed MOPSO algorithm than NSGAII and MODE. The simulations were iterated in two scenarios by creating different loading conditions in generators. The results indicated that the proposed control system, where the coordination between PSS2B power system stabilizers and TCSC compensators using the MOPSO algorithm, is better than power systems in which PSS2B Stabilizers or TCSC compensators are utilized solely. All criteria, e.g., ITAE, FD, maximum deviation range, and the required time for power oscillation damping in hybrid control systems, have been obtained. This means more stability and accurate and proper performance. [ABSTRACT FROM AUTHOR]

Distributed multi‐area economic dispatch (MAED) can overcome the deficiencies of a large amount of information processing and regional privacy leakage faced by centralized scheduling. However, multiple iterations may increase communication and computing costs, as well as operational risk. Therefore, this paper proposes a double spatial scale search (DSSS) algorithm to accelerate the iterative process. First, each area performs the multi‐parametric quadratic programming (MPQP) with load and phase angles of boundary buses as parameters to pre‐process the MAED problem as a piecewise quadratic programming (PQP) problem. To reduce the complexity of MPQP, a parameter variables reduction method and a fitting‐based imprecise MPQP (i‐MPQP) algorithm are developed. Then, aided by the i‐MPQP results, we design a DSSS algorithm to optimize the PQP problem efficiently. Finally, two interconnected power systems of different sizes are used for numerical testing. The results show that the i‐MPQP can produce a 77.98% and 88.65% reduction in the number of precise MPQP (p‐MPQP) function segments. Compared with the existing algorithm, the iterations of DSSS algorithm are decreased by 75% and 76.35%, and it can converge with one iteration in most cases. Moreover, the performance of the proposed method is insensitive to the system settings and algorithm parameters. [ABSTRACT FROM AUTHOR]

Frequency, voltage, and power flow between different control zones in an interconnected power system are used to determine the standard quality of power. Therefore, the voltage and frequency control in an IPS is of vital importance to maintaining real and reactive power balance under varying load conditions. In this paper, a dandelion optimizer (DO)-based proportional-integral-proportional-derivative (PI-PD) controller is investigated for a realistic multi-area, multi-source, realistic IPS with nonlinearities. The output responses of the DO-based PI-PD were compared with the hybrid approach using artificial electric field-based fuzzy PID algorithm (HAEFA), Archimedes optimization algorithm (AOA)-based PI-PD, learning performance-based behavior optimization (LPBO)-based PI-PD and modified particle swarm optimization (MPSO)-based PI-PD control schemes in a two-area network with 10% step load perturbation (SLP). The proposed strategy was also investigated in a two- and three-area IPS in the presence of different nonlinearities and SLPs. The simulation results and the comprehensive comparison between the different control schemes clearly confirm that the proposed DO-based PI-PD is very effective for realistic, multi-area multi-source IPS with nonlinearities. [ABSTRACT FROM AUTHOR]

We propose an algorithm for solving a class of bi-objective multistage stochastic linear programs. We show that the cost-to-go functions are saddle functions, and we exploit this structure, developing a new variant of the stochastic dual dynamic programming algorithm. Our algorithm is implemented in the open-source stochastic programming solver SDDP.jl. We apply our algorithm to a hydro-thermal scheduling problem using data from the Brazilian Interconnected Power System. We also propose and implement a computationally tractable heuristic for bi-objective stochastic convex programs. [ABSTRACT FROM AUTHOR]

In this paper, Levy flight and Fitness Distance Balance (FDB)-based coyote optimization algorithm (LRFDBCOA) is proposed to improve the performance of automatic generation control (AGC) of three different PV-based interconnected power systems. First, the achievement of the proposed algorithm is evaluated on the two-area PV-reheat thermal power system with PI controller, which is widely used in the literature, and the results are compared with the results obtained using genetic algorithm (GA), firefly algorithm (FA), and modified whale optimization algorithm (M-WOA) introduced in previous studies. Then, the control performance of the proposed algorithm is demonstrated on the newly constructed two-area PV-non-reheat thermal power system with governor dead band (GDB) and two-area PV-multi-source power system with electrolyser and fuel cell. As the costs of solar energy systems decrease, the microgrids containing these resources increase. Therefore, such a study will make an important contribution to the literature. For the AGC of these two systems, proportional–integral–derivative (PID), tilt-integral-derivative (TID) and fractional order PID (FOPID) controllers are used. Thus, both the performance of the proposed algorithm on AGC and the compatibility of the two newly tested systems with PID, TID and FOPID controllers are revealed. As a result, the proposed LRFDBCOA algorithm outperforms algorithms such as GA, FA, M-WOA and arithmetic optimization algorithm (AOA) in interconnected two-area power systems controlled by PID, TID and FOPID, and it shows superior control performance in terms of settling time, maximum/minimum peak value of Δ f 1 , Δ f 2 and Δ P tie under varying load conditions and perturbed system parameters. [ABSTRACT FROM AUTHOR]

The interconnected power system is exposed to a wide range of disturbances that may induce electromechanical oscillations of small magnitude and often persist for long periods. Such oscillations may sustain and grow, causing system separation if no adequate damping is provided. Conventional Power System Stabilizers (PSSs) are often used to provide the necessary damping torque to suppress the oscillation through the excitation system. The design of PSS in the previous work is either nonlinear or entirely linear based on a linearized model around an equilibrium point. Nonlinear controllers provide very robust performance. However, their complexity limits their deployment. On the other hand, linear-based PSSs are simple, but their performance degrades as the operating conditions move away from the region of attraction. Unlike previously published work, the design of the PSS in this paper explicitly uses the nonlinear model of the power system and linear control theory. This paper presents a nonlinear PSS based on feedback linearization. The Riccati equation is used to construct the new coordinate's linear controller. The efficacy of the presented study is demonstrated through a comparison with nonlinear-based and linear-based PSSs. Performing an in-depth analysis of inertia's impact on the system's stability concludes the proposed study. [ABSTRACT FROM AUTHOR]