Area Power System Research Articles

Scenario Generation Based on Ant Colony Optimization for Modelling Stochastic Variables in Power Systems

Uncertainty is an important subject in optimization problems due to the unpredictable nature of real variables in the power system area, which can condition the solution’s accuracy. The effective modelling of stochastic variables can contribute to the reduction in losses in the system under evaluation and facilitate the implementation of an effective response in advance. To model uncertainty variables, the most extended technique is the scenario generation (SG) method. This method evaluates possible combinations of complete curves. Classical scenario generation methods are founded on probability distributions or robust stochastic optimization. This paper proposes a novel approach for constructing scenarios using the Ant Colony Optimization (ACO) algorithm, referred to as ACO-SG. This methodology does not require a previous statistical study of uncertainty data to generate new scenarios. A historical dataset and the desired number of scenarios are the inputs inserted into the algorithm. In the case study, the algorithm used historical data from the Savona Campus Smart Polygeneration Microgrid of the University of Genoa. The approach was applied to generate scenarios of photovoltaic generation and building consumption. The low values of the Euclidean distance were used in order to check the validity of the scenarios. Moreover, the error deviation of the scenarios generated with the goal of daily power were 1.77% and 0.144% for the cases of PV generation and building consumption, respectively. The different results for both cases are explained by the characteristics of the specific cases. Despite these different results, both were significantly low, which indicates the capability of the algorithm to generate any kind of feature within curves and its adaptability to any case of SG.

Chaos quasi-opposition crayfish based modified new controller designed for hybrid deregulated power environment considering cyber-attack

Frequency security is critical to the stability and dependability of the electrical systems. The integration of renewable energy resources presents new challenges for power networks and frequency security. In order to provide a more reliable control mechanism for improved frequency regulation in multi area deregulated environments with penetrations of renewable energy sources like Solar photovoltaic and wind system has been included considering along with its intermitance nature. Additionally, to get more realistic approach and its analysis, nonlinearities such as generation rate constant and governor dead band has been taken into consideration for the anticipated Load Frequency Control (LFC). A novel modified cascaded Dual-Loop Linear Active Disturbance Rejection based Tilted controller has been proposed for the suggested LFC paradigm. A modified version of the Chaotic Quasi-Opposition Crayfish Optimisation algorithm (CQOCOA) has been offered as a novel optimization approach for optimal parameters of the proposed controller. ITSE has been taken as objective function for optimization purpose. Moreover, a comprehensive detailed examination for the proposed LFC system has been investigated and successfully implemented. The performance has been examined under a variety of operating scenarios, including multiple-step, random load disturbances and Distributed Generation (DG) analysis along with its technical core advancements. This study has been investigated and validated over large multi area power system i.e., IEEE-118 bus system. This work also put forward the deep analysis and implementation of learning-based attack detection and mitigation method for the grid's frequency regulation with cyber-physical model. The effectiveness of the proposed controller has been compared and verified over previous published literature work of international repute. The compressive results analysis provide strong evidence in favour of effectiveness and efficacy of the proposed control methodology. Furthermore, this work also suggests its potential to implement in hybrid multi area power system for enhancing the performance and stability in deregulated power structure.

Automatic Generation Control in Renewables-Integrated Multi-Area Power Systems: A Comparative Control Analysis

Electrical load dynamics result in system instability if not met with adequate power generation. Therefore, monitoring and control plans are necessary to avoid potential consequences. Tie-line-bias control has facilitated power exchange between interconnected areas to cope with load dynamics. However, this approach presents a challenge, as load variation in either area leads to frequency deviations and power irregularities in each of the interconnected areas, which is undesirable. The load frequency control loop method is used to address this issue, which utilizes area control errors. This study focuses on the control of inter-area oscillations in a six-area power system under the effect of renewable energy sources. It evaluates the area control errors in response to changes in load and the penetration of renewable energy into the system. To mitigate these errors efficiently, an adaptive-PID controller is proposed, and its results are compared with PI and PID controllers optimized with heuristic and meta-heuristic algorithms. The findings demonstrate the superiority of the proposed controller over traditional controllers in mitigating tie-line power errors and frequency deviations in each area of the interconnected power system, thus helping to mitigate inter-area oscillations and restore system stability.

Analysis of Load Frequency Control of Two Area Power System using Different Control Techniques

To maintain stability is a mandatory requirement of any kind of power network. Interconnected power systems have significant advantages in maintaining stability and frequency at their nominal values. The control of the interconnected power systems is performed by Automatic Generation Control (AGC), which has two parts: the Automatic Voltage Regulator (AVR) and Load Frequency Control (LFC). When a disturbance occurs in the system, Load Frequency Control (LFC) acts to re-establish the system into its steady state with zero error as well as the desired transient response characteristics. This paper exhibits the design and implementation of a PID and Fuzzy Logic Controller for two area power systems, taking the steady state error and percentage of overshoot, undershoot, and pre-shoot as comparison parameters. MATLAB/Simulink software was used to bring out the implementation and obtain results. System dynamic performance is observed for PID and Fuzzy Logic Controllers in Simulink. The comparison study indicated that the proposed Fuzzy Logic controller has better performance than the PID controller. IUBAT Review—A Multidisciplinary Academic Journal, 7(1): 62-79

A Graph Reinforcement Learning-Based Handover Strategy for Low Earth Orbit Satellites under Power Grid Scenarios

Amidst the escalating need for stable power supplies and high-quality communication services in remote regions globally, due to challenges associated with deploying a conventional power communication infrastructure and its susceptibility to natural disasters, LEO satellite networks present a promising solution for broad geographical coverage and the provision of stable and high-speed communication services in remote regions. Given the necessity for frequent handovers to maintain service continuity, due to the high mobility of LEO satellites, a primary technical challenge confronting LEO satellite networks lies in efficiently managing the handover process between satellites, to guarantee the continuity and quality of communication services, particularly for power services. Thus, there is a critical need to explore satellite handover optimization algorithms. This paper presents a handover optimization scheme that integrates deep reinforcement learning (DRL) and graph neural networks (GNN) to dynamically optimize the satellite handover process and adapt to the time-varying satellite network environment. DRL models can effectively detect changes in the topology of satellite handover graphs across different time periods by leveraging the powerful representational capabilities of GNNs to make optimal handover decisions. Simulation experiments confirm that the handover strategy based on the fusion of message-passing neural network and deep Q-network algorithm (MPNN-DQN) outperforms traditional handover mechanisms and DRL-based strategies in reducing handover frequency, lowering communication latency, and achieving network load balancing. Integrating DRL and GNN into the satellite handover mechanism enhances the communication continuity and reliability of power systems in remote areas, while also offering a new direction for the design and optimization of future power system communication networks. This research contributes to the advancement of sophisticated satellite communication architectures that facilitate high-speed and reliable internet access in remote regions worldwide.

A hybrid SCSO-QNN approach based load frequency control of three area power system with renewable sources using FOPID controller

ABSTRACT This research proposes a hybrid SCSO-QNN method for designing a FOPID controller in a three-area power system to regulate load frequency. The proposed hybrid approach combines quantum neural networks with sand cat swarm optimization, and it is commonly named as the SCSO-QNN method. The proposed method’s primary goal is to overcome load disturbances and attain the intended output of the interconnected system. The SCSO generates controller parameters tuned by the FOPID controller, providing a significant improvement in performance, while the QNN is employed to forecast the optimal control signal of the converter. Reducing steady-state errors and improving system stability are areas where the FOPID performs better than a traditional integer-order PID controller. Moreover, SCSO-QNN optimization technique exhibits excellent convergence properties, resulting in improved control performance. The proposed method ensures system frequency control by controlling frequency deviation and power fluctuations in the connect line during load disturbances. The proposed method is executed on the MATLAB platform and contrasted with current techniques. The proposed technique outperforms all current techniques, including ABC-FOPID, Genetic Algorithm, and Artificial Bee Colony PID. The proposed method Area control error is 1.8%, which is less than other existing methods.

Structure at infinity and defect of transfer matrices with time-varying coefficients, with application to exact model-matching

We study the structure at infinity of transfer matrices with time-varying coefficients. Such transfer matrices have their entries in a skew field F of rational fractions, i.e. of quotients of skew polynomials. Any skew rational fraction is the quotient of two proper ones, the latter forming a ring Fpr (a subring of F) on which a “valuation at infinity” is defined. A transfer matrix G has both a “generalized degree” and a valuation at infinity, the sum of which is the opposite of the “defect” of G. The latter was first defined by Forney in the time-invariant case to be the difference between the total number of poles and the total number of zeros of G (poles and zeros at infinity included and multiplicities accounted for). In our framework, which covers both continuous- and discrete-time systems, the classic relation between the defect and Forney's left- and right-minimal indices is extended to the time-varying case. The exact model-matching problem is also completely solved. These results are illustrated through an example belonging to the area of power systems.

Generation Control of Multi-Area Multi-Source Power System using Fire-Fly Algorithm

This article presents automatic generation control (AGC) of an interconnected three area power system having different production sources such as hydropower, thermal and gas power plants. The control areas are provided with single reheat turbine and generation rate constraints of 3%/min. An attempt has been made to apply a Proportional derivative–Proportional integral derivative (PD–PID) cascade controller in AGC. Controller gains are optimized simultaneously using more robust and powerful evolutionary computational technique Firefly Algorithm (FFA). Performance of classical controllers such as Proportional Integral (PI) and Proportional Integral Derivative (PID) controller are investigated and compared with PD–PID cascade controller. Investigations reveal that PI, and PID provide more or less same response where as PD–PID cascade controller provides much better response than the later. The system dynamic performances are studied with 1% and 2% step load perturbation in Area1. Sensitivity analysis reveals that the FFA optimized PD–PID Cascade controller parameters obtained at nominal condition of loading, size and position of disturbance and system parameter (Inertia constant, H) are robust and need not be reset with wide changes in system loading, size, position of disturbance and system parameters.

Fuzzy analytic hierarchy process based generation management for interconnected power system

Decision makers consistently face the challenge of simultaneously assessing numerous attributes, determining their respective importance, and selecting an appropriate method for calculating their weights. This article addresses the problem of automatic generation control (AGC) in a two area power system (2-APS) by proposing fuzzy analytic hierarchy process (FAHP), an multi-attribute decision-making (MADM) technique, to determine weights for sub-objective functions. The integral-time-absolute-errors (ITAE) of tie-line power fluctuation, frequency deviations and area control errors, are defined as the sub-objectives. Each of these is given a weight by the FAHP method, which then combines them into an single final objective function. This objective function is then used to design a PID controller. To improve the optimization of the objective function, the Jaya optimization algorithm (JOA) is used in conjunction with other optimization techniques such as sine cosine algorithm (SCA), Luus–Jaakola algorithm (LJA), Nelder–Mead simplex algorithm (NMSA), symbiotic organism search algorithm (SOSA) and elephant herding optimization algorithm (EHOA). Six distinct experimental cases are conducted to evaluate the controller’s performance under various load conditions, with data plotted to show responses corresponding to fluctuations in frequency and tie-line exchange. Furthermore, statistical analysis is performed to gain a better understanding of the effectiveness of the JOA-based PID controller. For non-parametric evaluation, Friedman rank test is also used to validate the performance of the proposed JOA-based controller.

Design and modelling of an AI governed type-2 Fuzzy tilt control strategy for AGC of a multi-source power grid in constraint to optimal dispatch

The robust design and application of a novel type-2 fuzzy tilt controller (Type2-FTC) has been well demonstrated in this manuscript. This controller is anticipated to build automatic generation control (AGC) of a multi-area hybrid power system in concern to the optimal power generation scheduling. The combined action of AGC and optimal scheduling of a power system is referred as economic AGC or commonly ELD-AGC. At the initial stage of the analysis, the concept of only AGC has been discussed through various robust approaches. Then the extensive study on ELD-AGC of the proposed power system has been progressed under various controller actions. Further, the proposed Type2-FTC controller is designed ideally with employing an innovative quassi-opposition path finder algorithm (QO-PFA) in various operating conditions. The effectiveness and the viability of the proposed Type2-FTC controller has been verified over type-I fuzzy PID and conventional PID controllers for AGC as well as ELD-AGC of the multi area power system. In controller comparison study, it has been revealed that proposed Type2-FTC is a potential candidate to improve frequency stability of the system. Further, the analysis over technique study justifies supremacy of the suggested QO-FPA algorithm over standard PFA and PSO algorithms for optimal designing all implemented controllers.

Distributed Generation (DG) system using COPRAS method

Distributed Generation (DG) system. Distributed Generation (DG) power systems, Expansion of power systems in remote areas, and Very recently as a sustainable way of electrification popular. Depletion of conventional fossil fuels, Fuel price volatility, and emissions Awareness environment about reduction, Due to this its demand is increasing. DG systems have additional power quality challenges. There are many types of DG power sources that produce electrical power with different voltages at various frequencies. RES-Based Distributed Generation (DG) Systems are conventional energy systems In finding new modern solutions for planning have contributed Widespread use of DG is old and Defines new views, Current delivery system The only way to deal with the project Calling for more complex defenses. COPRAS (Complex Proportionality Assessment) is very Multiple criteria used in decision-making methods is one. This technique is different Decision making by researchers Used to solve problems. Investment cost, operating cost, Primary energy consumption, CO2 emissions. Energy storage system, fuel system, Wind Solar Hybrid System, Conventional System, PV energy storage system. Involvement of Distributed Generation (DG) system PV energy storage system ranked 1st, and the Conventional system also got 5th position.

Optimum Coordination of Multi‐Band Power System Stabilizers and STATCOM Using the Modified Particle Swarm Algorithm

In this paper, to improve the small signal stability of the power system, the optimal coordination of multi‐band power system stabilizers (MB‐PSSs) and static synchronous compensator (STATCOM) has been done. The study is conducted on a standard four machines and two areas power system. To create coordination, a modified version of the particle swarm algorithm as well as the integral time absolute error (ITAE) criterion are proposed. To evaluate the proposed stability method in different operating conditions and in the form of two scenarios, a three‐phase fault with a duration of 200 ms has been applied in the communication line between the two areas. After the simulation, the values of overshoot, undershoot, settling time, and ITAE criteria for the proposed stability method have been calculated and their values have been compared with the results of power system stabilizer (PSS) and MB‐PSS stabilization methods. The results indicate that the proposed combined method of MB‐PSS and STATCOM performed better than the other two methods in improving the dynamic stability of the power system and has the lowest value of the criteria. The values of ITAE for the PSS, MB‐PSS stabilization methods and the proposed MB‐PSS and STATCOM combined method in the first scenario are equal to 0.002354, 0.0.001496, and 0.001242 p.u. and in the second scenario it is equal to 0.0.007418, 0.004015, and 0.003435 p.u., respectively.

Integrative analysis of diverse hybrid power systems for sustainable energy in underdeveloped regions: A case study in Indonesia

<abstract> <p>There is a goal for practical renewable electrification and renewable energy investments in underdeveloped regions. Indonesia's experience underscores the complexities and challenges in implementing such projects effectively. A study on the effects of various socio-economic factors on Carbon dioxide (CO<sub>2</sub>) emissions in Indonesia highlights the significant impact of forest area, urbanization, and industrialization on carbon emissions. A hybrid system consists of PV, a Biogas Generator, and a Wind Turbine that are successfully deployed. However, no economic analysis has been conducted to obtain the best configuration of the hybrid system. We propose to delve into the effective integration combination of hybrid power systems. In this study, we thoroughly analyzed hybrid power systems in underdeveloped areas using the HOMER software. We examined five different hybrid system configurations: Solely biogas, complete generator integration, a biogas and hydrogen combo, biogas coupled with a PV system, and biogas combined with a wind turbine. Our findings indicated varying levels of economic viability, operational performance, and environmental impact across the configurations, providing crucial insights for policymakers and stakeholders in underdeveloped regions like Indonesia. The results showed the Wind-Hydrogen and Hydrogen Only schemes as the most cost-effective, with a Total Net Present Cost (NPC) of, 969.27 and Levelized Cost of Energy (LCOE) at zhongwenzy.218. Moreover, while CO<sub>2</sub> emissions were similar across all schemes, around 27,744 kg/year, the All-Generator scheme had slightly higher emissions at 27,667 kg/year but led in electricity production with 29,101 kWh/year. These results underscore the importance of balancing cost, energy output, and environmental impact in hybrid power system schemes for underdeveloped regions.</p> </abstract>

Event-triggered data-driven load frequency control for multi-area power system with wind penetration under deregulation environment

In order to solve the problem of power exchange between areas of multi-area power systems under the power market, this paper designs a power trading contract based on the generation participation matrix, so as to simulate the specific process of power change in each area. With the integration of wind power into the large-scale power system, it is difficult to model the multi-area power system. For this situation, this paper designs a data-driven model-free adaptive load frequency control algorithm based on collecting input and output data, which gets rid of the dependence of the power system on the model. Along with the frequent transmission of input and output data in each area, the communication load of the power system also increases. Aiming at saving communication resources, this paper designs an event-triggered mechanism to reduce the communication bandwidth. The stability of the control algorithm is demonstrated theoretically. Finally, a three-area power system with wind penetration is used as an example to simulate and verify the effectiveness of the proposed algorithm in this paper.

A Reinforcement Learning Based Coordinated but Differentiated Load Frequency Control Method With Heterogeneous Frequency Regulation Resources

As the diversification of frequency regulation resources, it is increasingly important to find a load frequency control (LFC) scheme that can effectively coordinate different resources. This paper proposes a coordinated but differentiated LFC control method that utilizes heterogeneous frequency regulation resources with their characteristics fully considered. The optimal LFC models for the single area and multi-area power systems are formulated as partially observable Markov decision process model (POMDP) and partially observable Markov game (POMG) considering the accessibility of system information. A deep reinforcement learning method called recurrent proximal policy optimization is leveraged to solve the POMDP and POMG models. Simulations based on historical data demonstrate that the proposed control scheme can better coordinate different resources and achieve superior control performance.

Experimental Validation of a Diesel Genset Frequency Dynamics Model for Use in Remote Area Power Systems

Experimental Validation of a Diesel Genset Frequency Dynamics Model for Use in Remote Area Power Systems

Inertia Estimation of a Power System Area Based on Iterative Equation Error System Identification

Inertia Estimation of a Power System Area Based on Iterative Equation Error System Identification

Application of Pattern Search Algorithm based Automatic Generation Control of an Interconnected Power System

This paper demonstrates the advantage of the selection of the Pattern Search algorithm (PS) in the design of a Proportional-Integral controller used in Automatic Generation Control for a two area non-reheat interconnected thermal system. The design problem involves the implementation of PS algorithm to find out the value of parameters of the controller of the proposed power system. Here we have implemented three objective functions by using ITAE, damping ratio of dominant eigenvalues and settling time with weight coefficients for the design problem to get an optimized parameter of the Proportional Integral controller. The robustness of the designed PI controller using the Pattern search algorithm has been demonstrated after comparing other PI controllers using various optimization techniques like Genetic Algorithm, Bacteria Foraging Optimization Algorithm for the system under study. Further, best objective function uses the PS optimization algorithm in PI/PID controller parameters, tunning is obtained in presence of physical constraints like governor dead band non linearity of the proposed two area power system. The modified model to test the sensitivity analysis of PI/PID controller by using different loading condition to show the PID controller performance of frequency and tie line power deviation improved better as compared to the PI controller of the same power system. Further, it is extended to multi-area multi-source power for effectiveness and robustness of the system under different disturbances to check the transient stability.

Out-of-Step Detection based on Phasor Measurement Unit

The electrical power systems operate as a huge, interconnected network that extends across a large area. In the power system, there is an equilibrium between generated power and a load. Any disturbance in the system, such as a fault or a change in load, will lead to imbalance and electromechanical oscillations. As a result, the power flow between two areas varies. This is known as a power swing." Large system disturbances could lead to large rotor angle deviations between groups of generators, resulting in a loss of synchronism between generators or between interconnected systems. This is known as an out-of-step condition. To avoid equipment damage and power outages, the interconnected area must be isolated as soon as possible before the electrical system loses synchronization. In this paper, PMU data is used to measure the current, voltage, and phase angle of the three phases at both ends of two interconnected area power systems. The measured data is then used to distinguish between a power swing or fault condition and predict the future phase angle difference during the disturbances to evaluate the system stability condition. If a swing is detected, then it will be ascertained whether the swing is stable or not. The performance of the proposed method has been tested on a simulated system using MATLAB / Simulink software.

An advanced secondary voltage control strategy for future power systems

The network voltage control together with the regulation of reactive power, has always been recognized as vital feature of power systems. It is aimed at containing the bus voltage within acceptable limits for guaranteeing system security, first and then assessing power quality. The voltage control is a key field in which several Transmission System Operators are currently investing resources to be well prepared in managing power systems with a high share of renewable energy sources, overcoming the drawbacks of the existing architectures. As is well known the voltage control is realized through a hierarchical structure, which is based on classical primary control, secondary control, and tertiary optimization. The secondary control is concerned with the regional level, implying proper coordination at both the geographical and temporal level. Due to its central role in power system operation, the secondary voltage control cannot be excluded from the effects of the dramatic changes that power systems are experiencing worldwide, even though regulators work by implementing well consolidated methodologies and strategies which show excellent performances. The motivation for this study in fact relies on the observation of some oscillatory phenomena in some areas of the Italian power system characterized by a high number of regulating resources highly coupled each other on the basis of the grid topology: despite these oscillatory phenomena are not so frequent, with the increasing of regulating resources enslaved by secondary regulation (such as renewable-based power plants) could potentially be worsened in the near future. Thus, in this paper, an optimal control strategy is proposed by tailoring a proper linear quadratic integral control which allows guaranteeing the desired profile for pilot buses voltage, ensuring a time response of aperiodic type without any oscillations. The aim of the proposed approach is to preserve the strengths of the existing secondary voltage regulation while enhancing the overall stability so enabling the progressive large penetration of renewable energy sources-based power plants and minimize the occurrence of oscillatory phenomena especially in highly electrically coupled areas. The robustness of the proposed regulation is verified first with respect to the WSCC 9-bus test system and successively on an actual regional area of Italian transmission network.