Tie-line Power Flow Deviations Research Articles

AGC Including ELD and Emission Coordination Using Sine Cosine Algorithm

The aim of conventional AGC is to regulate the demand load of the area with taking into account the sharing power with the others area and also the frequency deviation and tie-line power deviation within minimum error steady-state. The proposed coordination's goal is to regulate the demand load between the interconnected area when taking into account the optimal dispatch and emission effect. The two-area that be consist of three units in each one (hydro, thermal, and gas) is used of proposed system. The optimization algorithms were used to find the best operating point of the system by tuning the integral gain are located in ACE that named primary control and Generation allocation logic named secondary controller. The Particle Swarm Optimization (PSO) and Sine Cosine Algorithm (SCA) are used to tune gains of the integral (I) controller to show the superiority in identifying robust controller. The simulation results prove that the SCA with proposed coordination is very effectiveness as compared with PSO algorithm in enhance the dynamic performance and reduce overshoot, maximum frequency deviation, and net tie line power flow deviation error for a given load change and disturbed the demand load between two-area as economic.

Problem of Thermal Generating Unit Solve Through Different Controller

In a thermal generating station, electricity is generated by different generating unit which are operated parallelly to fulfill the demand arises from the different consumers. In a huge and complex structure of power system, load is continuously varied so the problem of thermal generating unit (T.G.U) is to mismatch the frequency and tie line power deviations due to continuously changing of load. To solve the problem of T.G.U, different controller like; PI, PID and fuzzy controllers applied, which gives the satisfied result in normal operation but in abnormal condition, some controller takes large time to settle down the load changes problem, which is dangerous for the system. In this paper model of T.G.U has been developed and simulated in MATLAB Software. Response of the developed model has been tabulated for better comparison. The tabulated result shows that the fuzzy controller gives the good results over the other controller due to settle down the frequency and tie line power flow deviations in less time and maintain the system constancy.

GA Technique to Solve the AGC Problem of Thermal Generating Unit

In a large-scale power system, Automatic Generation Control (AGC) is responsible to area load changes and abnormal imprecise system operating parameters essentially means very fast minimization of area frequency deviations and mutual tie line power flow deviations of the areas for satisfactory and stable operation of the system. Some technique gives the good results in normal operation but in abnormal condition, it take large time to settle down the load disturbance, which is harmful for the system. New approach (Genetic Algorithm (GA) Technique) provides better control performance over frequency deviations and tie line power flow deviations due to a normal and abnormal operating condition of sudden load changes. In this paper six area model of thermal generating units has been developed and simulated in MATLAB Simulink software. Response of the developed model has been obtained by GA technique and compared with the other technique like; fuzzy, PID. Tabulated result shows that the GA technique give the better performance over the other technique due to settle down the frequency and tie line power flow deviations in less time and maintain the system constancy.

Assessment and Control of Microgrid Impacts on Distribution Networks by Using Experimental Tests

The main aim of this article is to assess and control impacts that a grid-tied microgrid may have on a distribution network. For this purpose, the tie-line power flow has been considered in a performance index to be minimized. In particular, three statistical indices have been compared on the microgrid operated under a classical day-ahead dispatching policy and an online optimal control strategy. The online optimization process starts from the same day-ahead dispatching policy and tries to compensate the tie-line power flow deviations from the scheduled value. For this purpose, a real-time economic dispatching algorithm based on a proportional integral derivative controller has been developed and implemented on the PrInCE Lab microgrid, located at the Polytechnic University of Bari, Bari, Italy.

Optimal AGC Scheme Design Using Hybrid Particle Swarm Optimisation and Gravitational Search Algorithm

In this paper, a novel hybrid particle swarm optimisation and gravitational search algorithm (HPSO-GSA) is proposed to design an optimal automatic generation control (AGC) scheme in interconnected power system. The proposed algorithm combines the advantages of both particle swarm optimisation (PSO) and gravitational search algorithm (GSA). This new meta-heuristic HPSO-GSA is applied to achieve the optimal proportional-integral-derivative (PID) controller parameters. Hence, the optimal PID controller is used to reduce the system fluctuations with the best dynamic performances. The AGC issue is formulated as an optimal load frequency control problem, where the frequency fluctuations and the tie-line power flow deviations are to be minimised in the same time. In order to test the performance of the proposed HPSO-GSA strategy, the integral time multiplied by absolute error (ITAE) is used as an objective function. To evaluate the efficiency of the proposed approach over disturbances, the standard two-area power system is used for the simulation. The obtained simulation results are compared to those yielded using classical and heuristic optimisation techniques surfaced in the recent state-of-the-art literature. The comparative study demonstrates the potential of the proposed strategy and shows its robustness to solve the optimal AGC problem.

Grid of the future and the need for a decentralised control architecture: the web-of-cells concept

This study describes a decentralised control scheme for reserves activations in the future (2030+) power system. The proposed web-of-cells (WoC) concept has been developed within the European ELECTRA Integrated Research Programme on smart grids. The concept proposes a decentralised control scheme, focusing on local inter-cell tie-line power flow deviations. The WoC concept, the different roles, and the associated control scheme are described and discussed in detail here, including an outlook of validating its robustness and responsiveness.

A novel optimized hybrid fuzzy logic intelligent PID controller for an interconnected multi-area power system with physical constraints and boiler dynamics

In the fast developing world nowadays, load frequency control (LFC) is considered to be a most significant role for providing the power supply with good quality in the power system. To deliver a reliable power, LFC system requires highly competent and intelligent control technique. Hence, in this article, a novel hybrid fuzzy logic intelligent proportional-integral-derivative (FLiPID) controller has been proposed for LFC of interconnected multi-area power systems. A four-area interconnected thermal power system incorporated with physical constraints and boiler dynamics is considered and the adjustable parameters of the FLiPID controller are optimized using particle swarm optimization (PSO) scheme employing an integral square error (ISE) criterion. The proposed method has been established to enhance the power system performances as well as to reduce the oscillations of uncertainties due to variations in the system parameters and load perturbations. The supremacy of the suggested method is demonstrated by comparing the simulation results with some recently reported heuristic methods such as fuzzy logic proportional-integral (FLPI) and intelligent proportional-integral-derivative (PID) controllers for the same electrical power system. the investigations showed that the FLiPID controller provides a better dynamic performance and outperform compared to the other approaches in terms of the settling time, and minimum undershoots of the frequency as well as tie-line power flow deviations following a perturbation, in addition to perform appropriate settlement of integral absolute error (IAE). Finally, the sensitivity analysis of the plant is inspected by varying the system parameters and operating load conditions from their nominal values. It is observed that the suggested controller based optimization algorithm is robust and perform satisfactorily with the variations in operating load condition, system parameters and load pattern.

Using differential search algorithm for solving optimal frequency regulation problem in interconnected power system

In order to improve the frequency stability and control in an interconnected power system including various power generating units, the automatic generation control (AGC) needs to be more robust and efficient. On the other hand, the difficulties in AGC are not only to design a robust controller but it also to optimise its parameters effectively for a robust optimal solution. In this paper, an application of a new efficient optimisation method called the differential search algorithm (DSA) was proposed to solve the AGC problem. The proposed approach was applied to reach the proportional-integral-derivative (PID) controller parameters. This latter was used to maintain the frequency at nominal value using the integral time multiplied by absolute error (ITAE) as fitness function. To show the robustness of the proposed strategy, a four area interconnected electrical network was investigated for the simulation. The obtained DSA results were compared to other approaches available in literature. The simulation results of this research show that DSA strategy is able to solve AGC problem and achieve less frequency and tie-line power flow deviations than those determined by other methods used in this paper.

Amalgamated Firefly Algorithm with Migration Effect of Biogeography Based Optimization for Proportional Integral and Derivative Controller Parameters Optimization in Two Area Interconnected Power System

In this study, Firefly Algorithm and Amalgamated Firefly Algorithm with Migration Effect of Biogeography Based Optimization techniques are proposed to optimize the proportional, integral and derivative gains of PID controller in two equal areas Interconnected Power System for quenching the deviations of frequency and make the tie line power flow deviation to zero. The considered performance index for minimization is Integral of Time weighted Absolute value of Error. Two different operating conditions are taken. First operating condition is taken as, the occurrence of 5% step load perturbation in area 1 and 10% step load perturbation in area 2. Second operating condition is taken as, the occurrence of 15% step load perturbation in area 1 and 10% step load perturbation in area 2. Importance of this interconnected power system is to provide reliable and efficient power to consumers. For this reason, the responses are analyzed and discussed. Finally, it is concluded with the identification of better optimization technique which provides solution for supply of reliable and efficient power to consumers in an interconnected power system. From the response and analysis, amalgamated firefly algorithm with migration effect of biogeography based optimization gives better performance compared from firefly algorithm. Migration effect of biogeography based optimization technique improves the local search of firefly algorithm in the amalgamated performance.

Reservoir based learning network for control of two-area power system with variable renewable generation

The penetration of renewable energy sources into the electric power system is rapidly increasing. Integrating variable renewable energy sources into the transmission grid introduces challenges in real time power system operation. This causes power and frequency fluctuations and raises stability concerns. In this paper, a 200MW photovoltaic (PV) plant is integrated into a two-area four-machine power system. In order to maintain the system frequency, a dynamic tie-line power flow control is implemented using predicted PV power as an input to the automatic generation controller in Area 1, which transfers power to Area 2 with PV generation. The prediction performances of two learning reservoir based networks, an echo state network (ESN) and an extreme learning machine (ELM), are investigated for day and night time operations. The experimental study is performed using actual weather data from Clemson, SC and a real time simulation of a utility-scale PV plant integrated power system. Phasor measurement units (PMUs) are used to provide input signals to automatic generation controllers in the two area power system. Typical tie-line power flow control results based on ESN and ELM models are presented to show the impact of predicting PV power in improved automatic generation control with variable generation. ESN and ELM models provide minimal tie-line power flow deviations from reference power flows during day and night time operations, respectively.

Coordination of Economic Load Dispatch and Load Frequency Control for Interconnected Power System

This paper deals with the coordination of economic load dispatch and load frequency control concepts of the interconnected power system. The total change in the particular control area is shared by each unit according to their participation factor obtained from the calculation of the economic load dispatch. In this work, two control areas are considered, the first control area contains the combination of hydro, thermal and gas generating unit and in the second control area, it contains the combination of the thermal and hydro generating units. Integral controller is used for secondary controller for load frequency control mechanism. A digital simulation is used in conjunction with the genetic algorithm (GA) technique to determine the optimum parameters of the individual gain of integral controller. Three different types of performance indices are considered to measure the appropriateness of the proposed controller. The optimum values of the gains improve the dynamic performance of the controller and reduce the overshoot and maximum frequency deviation and net tie-line power flow deviation error for a particular load change. To show the effectiveness of the proposed controller, simulation result is shown in result and discussion section.

Solving LFC problem in an interconnected power system using superconducting magnetic energy storage

This paper proposes the combination of a load frequency control (LFC) with superconducting magnetic energy storage (SMES) to solve the LFC problem in interconnected power systems. By using this combination, the speed damping of frequency and tie-line power flow deviations is considerably increased. A new control strategy of SMES is proposed in this paper. The problem of determining optimal parameters of PID and SMES control loop is considered as an optimization problem and a pattern search algorithm (PS) optimization is employed to solve it. The simulation results show that if an SMES unit is installed in an interconnected power system, in addition to eliminating oscillations and deviations, the settling time in the frequency and tie-line power flow responses is considerably reduced.

Evolutionary computation based three-area automatic generation control

In this paper, various novel heuristic stochastic search techniques have been proposed for optimization of proportional–integral-derivative gains used in Sugeno fuzzy logic based automatic generation control of multi-area thermal generating plants. The techniques are genetic algorithm, various types of particle swarm optimization and bacteria foraging optimization. Numerical results show that all optimization techniques are more or less equally very effective in yielding optimal transient responses of area frequency and tie-line power flow deviations, but still MCASO and BFO yield much more global true optimal results. Particle swarm optimizations take the least time to achieve the same optimal gains. These gains are for nominal system parameters. For varying off-nominal on-line system parameters, fast acting Sugeno fuzzy logic manipulates the nominal gains adaptively to determine transient responses.

ＦＦＣ‐ＴＢＣ系統連系での蓄電池による連系線潮流変動抑制に関する基礎検討

In recent years, a lot of distributed generations such as photovoltaic and wind power generations are going to be installed in power systems. However, the fluctuation of these generator outputs affects the system frequency. Therefore, introduction of battery system to the power system has been considered in order to level the fluctuation of the total power output of the distributed generations. This research aims at suppressing the system frequency fluctuation and the tie line power flow deviation due to short-period fluctuation of wind power output within a certain range by cooperative control of LFC generators and battery system. It is assumed that a TBC based small control area with a large penetration of wind power plants is interconnected into a FFC based large control area. In this paper, the fluctuations of the system frequency and the tie line power flow are analyzed and LFC signal dispatching methods for effective control of the LFC generators and the battery system are proposed. Then, the required battery capacity for suppressing the tie line power deviation within a given level is evaluated.

Optimizations of PID gains by particle swarm optimizations in fuzzy based automatic generation control

In this paper, various novel heuristic stochastic search techniques have been proposed for optimization of proportional–integral–derivative gains used in Sugeno fuzzy logic based automatic generation control of multi-area thermal generating plants. The techniques are classical particle swarm optimization, hybrid particle swarm optimizations and hybrid genetic algorithm simulated annealing. Numerical results show that all optimization techniques are more or less equally very effective in yielding optimal transient responses of area frequency and tie-line power flow deviations. The gains obtained by particle swarm optimization are more optimal than those obtained by GA/hybrid GA-simulated annealing. Particle swarm optimizations take the least time to achieve the same optimal gains. These gains are for nominal system parameters. For varying off-nominal on-line system parameters, fast acting Sugeno fuzzy logic manipulates the nominal gains adaptively to determine transient responses.

Study of Identification for the Inertia Constant in a Small Power System for LFC

This paper has tried the identification for the inertia constant in a small power system, for the betterment of the LFC control characteristic. The power demand estimation method was used for the method of identifying an inertia constant power system. Until now, the method that used as an output set-point control by the power demand estimation is improved to the identification method. The power demand estimation method is using the frequency variation, the tie line power flow deviation, the output deviation of power plant, the inertia coefficient and damping coefficient of power system. In this paper, the actual electric power demand, the output of power plant, etc. was observed and the inertia coefficient is calculated using this estimation method. Then, the inertia constant and the damping constant are not observed.In this paper, the inertia coefficient of the system of 2000kW small-scale system was calculated.

Strategies for Minimizing Operational Impacts of Large Wind Turbine Arrays on Automatic Generation Control Systems

A primary application of wind electric generation is expected to be large clusters or arrays of MW-size wind turbines connected to the utility transmission network. Array output power variations due to the uncontrolled nature of the wind resource can cause undesirable dynamic impacts on the utility system, such as excessive frequency and/or tie-line power flow deviations. As a consequence, spinning reserve, unloadable generation, and load following requirements and their associated economic penalties tend to increase as wind electric generation is added to the system. However, if array power variations are limited and/or anticipated, the associated operation and economic penalties can be significantly reduced. In this paper, a number of operational concepts are considered which have the potential to enhance the effectiveness of wind turbine arrays in the utility system.