Multi-area Power System Research Articles

Enhanced sliding mode controller design via meta‐heuristic algorithm for robust and stable load frequency control in multi‐area power systems

AbstractThis article introduces a novel approach named HBA‐dHoSMO, which combines a continuous decentralized higher‐order sliding mode controller‐based observer (dHoSMO) with the honey badger algorithm (HBA), specifically designed for load frequency control in multi‐area power systems (MAPSs). Traditional sliding mode controllers (SMCs) employed in load frequency control of MAPSs often face challenges related to chattering and oscillations, leading to decreased robustness and stability. Additionally, tuning the parameters for these SMC designs to achieve optimal performance in MAPSs can be challenging. The HBA‐dHoSMO is proposed to address the issues of chattering and oscillations, while the optimal parameters for SMC design are obtained using HBA. The stability analysis of the entire system is conducted using linear matrix inequality and the Lyapunov stability theory, affirming the reliability and feasibility of the approach. A comprehensive set of case studies is performed under various configurations and conditions. Additionally, particle swarm optimization and tuna swarm optimization, in conjunction with SMC‐based and proportional–integral–derivative controllers, are examined for performance comparison. Simulation results demonstrate the superior performance of the proposed controller across all case studies. This is evidenced by the lowest integral time absolute error values recorded as 0.0133, 6.45 × 10−4, and 0.0167 for single‐, two‐, and three‐area power systems, respectively.

Research on large-scale clean energy optimal scheduling method based on multi-source data-driven

With the large-scale growth and grid connection of intermittent renewable energy such as wind and solar, the problem of increasing renewable energy curtailment rate and system backup flexibility has become increasingly prominent. In order to solve the problem of high proportion of renewable energy scientific consumption and flexible and stable operation of energy system. We propose a flexible and economical dispatch method based on data-driven multi-regional power system. For the problem of economic dispatch of multi-area power system, a mathematical calculation model is established to satisfy the constraints of unit output, system power balance, unit ramp rate, and valve point effect, and to consider the requirement of minimizing the cost of multi-area power load comprehensively. Based on data-driven, this paper adopts an improved fruit fly optimization algorithm to quickly find the global optimal solution. The calculations are performed by IEEE6 simulation test system, and the results verify the feasibility of the proposed algorithm. The improved fruit fly optimization algorithm is compared and analyzed with other algorithms considering the quality of the obtained solutions. The results show the effectiveness and superiority of the proposed algorithm in solving multi-area economic dispatching problems in real power systems.

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.

ADP-Based Decentralized Load Frequency Control Schemes to Multiarea Asynchronous Markov Jumping Power Systems With Experience Replay.

This article investigates the problem of robust decentralized load frequency control (LFC) in multiarea interconnection power systems, in the presence of the external disturbances and stochastic abrupt variations, such as component failures and different load demands. To capture the different operating conditions of the load in the multiarea power systems, a Markov superposition technique is skillfully employed to model the system's component matrices. To solve the Nash equilibrium solution of the zero-sum differential game problem, an improved online adaptive dynamic programming (ADP) algorithm based on the experience replay technique (ERT) is developed, which addresses the nonlinear coupling difficulties encountered in solving the game algebraic Riccati equations (Game AREs). The proposed algorithm weakens the condition that traditional policy iteration online solutions of zero-sum games require an initial stabilizing control policy pair, and considers the fact that only the transition probability matrix is known, while the prior knowledge of the dynamics is completely unknown. The algorithm is used to obtain both a minimum LFC and maximum disturbance policy for a given disturbance rejection level. Additionally, a safer LFC controller of the power systems with the Markov jumping parameters is designed. The stability and convergence of the proposed algorithm are demonstrated. Finally, the effectiveness and good performance of the proposed design method are validated using a two-area four-mode simulation example.

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.

Improved Memory Event-Triggered Load Frequency Control in Multi-Area Power System With Renewable Energy

Improved Memory Event-Triggered Load Frequency Control in Multi-Area Power System With Renewable Energy

Adaptive Model Predictive Control Scheme for Application of SMES for Load Frequency Control

The use of superconducting magnetic energy storage (SMES) has been widely reported in the literature to mitigate various load frequency control (LFC) issues in a multi-area power system. Most of these are thyristor based SMES, employing proportional type controllers. In this paper, a supervisory adaptive model predictive control scheme (AMPC) is proposed for a voltage source converter based relatively small rating SMES for LFC application. The reference power command for the SMES is derived from the AMPC in such a manner that it effectively handles the operational constraints of the SMES system. A representative first-order system emulating the inner loop dynamics of the SMES chopper system, is tuned offline via genetic algorithm (GA) and is used to derive dynamic constraints for the cost function in AMPC. The effectiveness of the scheme is demonstrated through simulations.

Application of Reinforcement Learning-Based Adaptive PID Controller for Automatic Generation Control of Multi-Area Power System

Application of Reinforcement Learning-Based Adaptive PID Controller for Automatic Generation Control of Multi-Area Power System

Load Frequency Control and Optimization of Load Allocation for Multi-Area Power System with Electric Vehicle Charging Load

The integration of electric vehicles (EVs) into power systems has introduced new challenges for load frequency control due to the additional charging load they impose. This research article investigates the design and analysis of automatic load frequency control in a two-area power system, considering the presence of EV charging load. The study employs Artificial Neural Network (ANN) based PI control to manage both traditional load demand and the dynamic charging requirements of EVs. Maintaining a stable power system frequency and balancing generation with the EV charging load have become crucial tasks. Automatic Generation Control (AGC) or Load Frequency Control (LFC) systems need to adapt and account for the variability and uncertainty associated with EV charging patterns. The integration of ANN-based PI control provides an intelligent and adaptive approach to address these challenges. Using MATLAB, a power system model is simulated to evaluate the effectiveness of the proposed control scheme. This investigation conducts a comparative analysis of the system's frequency responses under various scenarios, including different EV charging load profiles. It highlights the benefits and challenges of utilizing ANN-based PI control to manage the combined load of traditional demand and EV charging. Moreover, the load distribution among the distribution stations varied from 0.08% to 12.50% when compared between particle swarm optimization and genetic algorithm, respectively. By considering the dynamic interaction between power system operation and EV charging, this research aims to enhance the reliability, efficiency and sustainability of power systems in the context of evolving transportation trends and the increasing electrification of vehicles.

Robust Load Frequency Control for Uncertainties Multi-area Power Systems with Couplings of Systems Dynamics and Reconfigurable Communication Networks

Robust Load Frequency Control for Uncertainties Multi-area Power Systems with Couplings of Systems Dynamics and Reconfigurable Communication Networks

Cloud-Based Frequency Control for Multiarea Power Systems: Privacy Preserving via Homomorphic Encryption

Cloud-Based Frequency Control for Multiarea Power Systems: Privacy Preserving via Homomorphic Encryption

T-S Fuzzy based Load Frequency Control of Multi-Area Power System with Hybrid Delays: Performance Analysis and Improvement

T-S Fuzzy based Load Frequency Control of Multi-Area Power System with Hybrid Delays: Performance Analysis and Improvement

Switching-like dynamic event-triggered load frequency control for multi-area power systems with electric vehicles under DoS attacks

The load frequency control (LFC) for multi-area power systems with electric vehicles (EVs) is investigated under Denial of Service (DoS) attacks. First, a new dynamic event-triggered mechanism is proposed, where the event parameters can be switched according to whether DoS attacks occur or not. Then, a time-delay model with parameter uncertainty is developed by considering the state of charge (SOC) of EVs batteries and DoS attacks. Particularly, asymptotic stability criterion with prescribed H∞ performance is derived, and the relationship between the packet losses induced by DoS attacks and the event parameters is revealed. Further, a co-design scheme of the controller gain and the event-triggered weighting matrix is provided. Finally, simulation results show that the proposed method is advantageous to resist DoS attacks and save communication resources.

DMPC-based load frequency control of multi-area power systems with heterogeneous energy storage system considering SoC consensus

The energy storage system (ESS) has been widely used for the load frequency control (LFC) of power systems. The heterogeneous ESS (HESS) consisting of various types of energy storage units (ESUs) with different regulation characteristics creates many difficulties in designing a high-performance LFC strategy for a multi-area power system. To obtain the satisfied dynamic response of the LFC scheme against load disturbances and keep the state of charge (SoC) balance of the HESS, this paper proposes a distributed model predictive control (DMPC) method to design the LFC strategy. Firstly, the dynamic model of the power system with HESS is established. Then, the distributed predictive model for LFC design is formulated by considering the LFC objective, the SoC balance requirement, and the operational constraints of the generators and the HESS. Subsequently, the optimal LFC strategy is obtained by solving the distributed predictive model based on the linear quadratic programming (LQP) method. Finally, we make case studies to validate the effectiveness of the proposed method. Compared with the PI-based LFC, the proposed DMPC-based LFC performs better in stabilizing the system frequency, narrowing the area control error (ACE), and keeping the SoC of all the ESUs consistent.

Application of an optimal tilt controller in a partial loading schedule of multi-area power system considering HVDC link and virtual inertia

Maintaining the reliability of the modern power system is becoming increasingly challenging due to the continuous growth in its size and complexity. One of the most significant challenges PS faces is frequency regulation, which is essential for maintaining a stable and reliable operation of PS. This article investigates the automatic generation control (AGC) of a multi-area PS considering partial loading scheduled in a hydro and thermal generating unit. A bird swarm algorithm (BSA) based two-degree of freedom tilt integral derivative (2DOF-TID) controller has been suggested. The superiority of the optimal 2DOF-TID controller has been selected by comparing the system dynamics with other conventional controllers. Also, the ascendancy of suggested BSA has been shown over the grasshopper algorithm and particle swarm optimization techniques. The investigation on partial loading schedules for the hydro-thermal system showed that the system's dynamic response deteriorates when loading on hydro units exceeds their nominal loading. Also, system dynamics are enhanced when thermal units are loaded beyond their capacity. The investigations are also conducted to analyse the effect of high voltage direct current (HVDC) link and virtual inertia emulation with an energy storage system. The studies with alternating current (AC)/HVDC and virtual inertia revealed that dynamic responses are better with the latter when compared with AC and HVDC links alone.

A Distributed Robust System-Wide State Estimation Method for Power Systems Based on Maximum Correntropy

The distribution of measurement noise applied in practical power system state estimation (PSSE) can deviate from the assumed Gaussian model and the performance of an estimator becomes bad if the Gaussian model is still used. This paper proposes a new distributed robust PSSE method for multi-area power systems. The non-Gaussian model is utilized to fit the measurement noise distribution to reach high model accuracy. The proposed distributed method is derived based on the maximum correntropy criterion to further reduce the impact of non-Gaussian measurement noise and outliers. The influence function combined with the finite-time average consensus algorithm is used to implement the proposed distributed robust method in a fully distributed manner. Simulations conducted on the IEEE 30-bus, 118-bus and 300-bus systems and the Polish 2383-bus system demonstrate the robustness and effectiveness of the proposed distributed method. Each local area can get the system-wide robust state estimation solution by only using local information and small amounts of data from neighboring areas. Our proposed distributed robust method has at least twelve percent improvement in reducing the mean squared error under Gaussian-Uniform noise. It is verified the communication network applied for the proposed method is fairly flexible while the existing distributed approaches use a fixed communication network when the power systems are partitioned. The simulation results also verify the robustness of the proposed method to bad data and communication failure.

Frequency regulation scheme in power system using an observer-based waiting event-triggered control with random features

Addressed in this paper is the observer-based waiting event-triggered control issue in multi-area load frequency control (LFC) power system subject to random communication delay and data packet dropout. In order to maintain the grid frequency stability of the multi-area LFC power system in an open communication network, an observer-based waiting event-triggered scheme (WETS) is proposed to deal with frequency fluctuation and extend the life of control equipments. Compared with the existing event-triggered mechanism, the proposed WETS has the merits of continuous event-triggered scheme and the periodic sampling event-triggered scheme in the both of theory and application. Then, by well considering the negative factors of data packet dropout and random time delay in data transmission, a stochastic delay-dependent multi-area LFC model is established, which involves the information of observer-based WETS, random communication delay and packet dropout in a unified structure. Further, an improved Lyapunov–Krasovskii functional (LKF) is constructed by dividing the interval of transmitted delay. Based on the constructed Lyapunov functional, Park’s theorem and Jensen’s integral inequality, some less conservative stabilization criteria are obtained. Finally, the validity of the proposed control method is verified via numerical examples.

Hierarchical bi-level load frequency control for multi-area interconnected power systems

To overcome the shortcomings of proportional–integral (PI) control, a linear quadratic regulator (LQR) is proposed in this study, to determine the set points of the PI controllers in a two-level hierarchical manner. The application of the proposed scheme is studied on load frequency control (LFC) of a power system with multiple control areas (CAs). At the lower control level, a discrete PI controllers are used to regulate the frequency and tie-line powers of each of the CAs. To achieve an improved closed loop performance, the local PI controllers are optimally tuned using moth flame optimization (MFO) algorithm by minimizing the Integral Square Error (ISE) of the state errors with respect to local information and interactions with neighboring CAs. While at the upper control layer, an optimized finite-horizon LQR is applied as a guide to establish the optimal set-points of the lower level PI controllers. Since only few of the state variables can be accessed, Kalman filter (KF) is applied as an observer for the estimation of the remaining states. The efficacy of the proposed scheme is verified by implementing it on a three-CA system perturbed with a step and random net disturbances formed by combining the fluctuations in the output power of DFIG-based wind turbine generator and random load demand. From the simulation results, it is established that the proposed LQR-PI supervisory scheme has outperformed the conventional PI control scheme in optimality and stability.

Adaptive event-triggered PID load frequency control for multi-area interconnected wind power systems under aperiodic DoS attacks

In this article, an adaptive event-triggered proportional–integral–derivative (PID) load frequency control (LFC) strategy is designed for multi-area interconnected wind power systems under aperiodic denial-of-service (DoS) attacks. First, to relieve the frequency fluctuation caused by wind power integration, a dynamic model including the doubly-fed induction generator is established for multi-area interconnected wind power systems. Second, to avoid superfluous occupation of network communication bandwidth, an adaptive event-triggered mechanism with memory property is proposed under aperiodic DoS attacks. Then, with the aid of intermittent control method, a memory-based PID LFC strategy is achieved to stabilize the frequency of wind power systems. Third, by constructing a switched Lyapunov-Krasovskii functional, two exponential stability criteria meeting the H∞ norm bound are derived. Finally, simulation examples are provided to verify the effectiveness of the presented control strategy.

Interconnected power control on unequal, deregulated multi-area power system using three-degree-of-freedom-based FOPID-PR controller

Interconnected power control on unequal, deregulated multi-area power system using three-degree-of-freedom-based FOPID-PR controller