Cluster identification through FACTS device installation in bulk power system

Optimal generation units start-up sequence during restoration of power system considering network reliability using bi-level optimization

There are a lot of uncertainties in planning and operation of electric power system, which is a complex, nonlinear, and non-stationary system. Advanced computational methods are required for planning and optimization, fast control, processing of field data, and coordination across the power system for it to achieve the goal to operate as an intelligent smart power grid and maintain its operation under steady state condition without significant deviations. State-of-the-art Smart Grid design needs innovation in a number of dimensions: distributed and dynamic network with two-way information and energy transmission, seamless integration of renewable energy sources, management of intermittent power supplies, real time demand response, and energy pricing strategy. One of the important aspects for the power system to operate in such a manner is accurate and consistent short term load forecasting (STLF). This paper presents a methodology for the STLF using the similar day concept combined with fuzzy logic approach and swarm intelligence technique. A Euclidean distance norm with weight factors considering the weather variables and day type is used for finding the similar days. Fuzzy logic is used to modify the load curves of the selected similar days of the forecast by generating the correction factors for them. The input parameters for the fuzzy system are the average load, average temperature and average humidity differences of the forecasted previous day and its similar days. These correction factors are applied to the similar days of the forecast day. The tuning of the fuzzy input parameters is done using the Particle Swarm Optimization (PSO) and Evolutionary Particle Swarm Optimization (EPSO) technique on the training data set of the considered data and tested. The results of load forecasting show that the application of swarm intelligence for load forecasting gives very good forecasting accuracy. Both the variants of Swarm Intelligence PSO and EPSO perform very well with EPSO an edge over the PSO with respect to forecast accuracies.

Distributed generation (DG) significantly improves power system reliability and voltage profile, and reduces power losses. These benefits can be maximized by optimizing the DG utilization, which greatly depends on adequate number, size, and placement of DG. This study proposes a high-convergence optimization technique for optimal DG placement in distribution networks, aiming to reduce power losses and improve voltage profile and voltage stability index. The technique is tested on a 7-bus system before being verified on the IEEE 33-bus system. After validation, it is tested on a 71-bus distribution network (DN). The estimated ideal size for a single DG is used as a limit for determining the optimal placement and size of multiple DG units. The results show that there is a loss reduction of 70.09%, 61.43%, and 71.15% for the 7-bus, IEEE 33-bus, and 71-bus systems, respectively, from their base case loss. The average absolute error of voltage of the aforementioned buses also shows similar enhancement, decreasing from 0.0144, 0.0561, and 0.0527, to 0.0058, 0.0209, and 0.0184, respectively. The voltage stability index of each DN has increased significantly in comparison to the base case. The comparison demonstrates that the proposed technique can provide better results for any size of distribution network.

Guest Editorial: Situational awareness of integrated energy systems

With the rise in electricity demand, various additional sources of generation, known as Distributed Generation (DG), have been introduced to boost the performance of power systems. A hybrid multi-objective Evolutionary Programming-Firefly Algorithm (MOEPFA) technique is presented in this study for solving multi-objective power system problems which are minimizing total active and reactive power losses and improving voltage profile while considering the cost of energy losses. This MOEPFA is developed by embedding Firefly Algorithm (FA) features into the conventional EP method. The analysis in this study considered DG with 4 different scenarios. Scenario 1 is the base case or without DG, scenario 2 is for DG with injected active power, scenario 3 is for DG injected with reactive power only and scenario 4 is for DG injected with both active and reactive power. The IEEE 69-bus test system is applied to validate the suggested technique.

Voltage stability analysis is the major concern in order to operate any power system safely. In this context considerable research work has been carried out to improve the voltage stability. FACTS devices can used for controlling power flow, transmission voltage, improving voltage profile, and reducing reactive losses. In this paper two different FACTS devices have been selected to place in suitable location to improve voltage stability in power system. The mathematical model of the power system equipped with a Static VAR Compensator (SVC) and Static synchronous compensator (STATCOM) separately is systematically derived and the parameters of the shunt FACTS are modeled into the power flow equations and used in the control strategy. The two models of FACTS are developed for the IEEE 30-bus system and implemented in Newton-Raphson load flow algorithm in order to control the voltage of the bus to which the shunt FACTS is connected. In this study a MATLAB written program is used for the solution of power flow problems and calculate voltages, active and reactive power at each bus of IEEE 30-bus system.

This paper presents an Evolutionary Particle Swarm Optimization (EPSO) for PSO model selection. It provides a new paradigm of meta-optimization that systematically estimates appropriate values of parameters in PSO model for efficiently solving various optimization problems. In order to further investigate the characteristics, i.e., exploitation and exploration in search, of the PSO model optimized by EPSO, we propose to use two fitness functions in EPSO, which are a temporally cumulative fitness of the best particle and a temporally cumulative fitness of the entire swarm for designing PSO models. Applications of the proposed method to some benchmark optimization problems well demonstrate its effectiveness. Our experimental results indicate that the former fitness function can generate a PSO model with higher fitness, and the latter can generate a PSO model with faster convergence.

A simulated rebounding algorithm applied to the multi-stage security-constrained transmission expansion planning in power systems

Optimal Reactive Power Dispatch (ORPD) is a critical optimization problem in power system operations, aiming to minimize Active Power Loss (APL) and Voltage Deviation (VD), and maintain voltage levels within acceptable limits. This study employs the Artificial Bee Colony (ABC) algorithm to solve the ORPD problem in the IEEE 30-bus system. The ABC algorithm, inspired by the foraging behavior of honeybee colonies, optimizes key control variables such as generator bus voltages, transformer tap settings, and reactive power compensations. The performance of the proposed method is evaluated by comparing it with other metaheuristic approaches, demonstrating its superior ability to reduce power losses and improve voltage profiles. Results indicate that the ABC algorithm significantly decreases APL and enhances voltage stability, outperforming conventional optimization methods. The study confirms the effectiveness and robustness of the ABC algorithm in addressing complex ORPD challenges, making it a viable tool for enhancing power system efficiency and stability.

The Phasor Measuring Units provide synchronized measurements for monitoring and control of Power System State for Power System protection. The high PMU cost and installation cost are main reasons for minimizing the number of PMUs and their optimal placement should ensure Power System full Observability. This paper presents the investigation of Optimal placement of Phasor Measuring Units (PMU) and the use of reduced number of PMUs for ensuring Power system full observability. The Optimal PMU Placement (OPP) problem has been tackled considering different cases like with and without conventional measurements and zero injection buses. The OPP is considered for different contingencies such as single PMU or line outage to ensure full system observability. The PMU Channels, which limit the number of measurements at the installed bus is also taken into account in OPP formulation. MAT LAB Simulations are carried out by Binary Integer Linear programming (BILP) method and tested on standard IEEE 14-bus, IEEE 30-bus, IEEE 57-bus and IEEE 118-bus systems. The results presented were compared with the recent literature and found the effectiveness of proposed method with regard to number of PMUs and execution time is appreciable.

This paper proposes a reactive power planning model, which incorporates flexible AC transmission system (FACTS) devices. The optimal location of multi-type FACTS devices and their control parameters are optimized by a novel bacterial swarming algorithm (BSA) to minimize the real power losses and also to improve voltage profile. The simulation studies have been undertaken on both IEEE 30-bus and 118-bus test systems. Simulation results show that the performance of the power system is improved with multi-type FACTS placed optimally in the reactive power planning model. The advantage of using BSA to determine the location of FACTS devices is demonstrated, in comparison with two other evolutionary algorithms (EAs), i.e., genetic algorithm (GA) and particle swarm optimizer (PSO).

Guest Editorial

The load stability in bulk power system may be seriously affected when a large amount of distributed generators (DG) stop simultaneously due to voltage sag. It is important to understand the mechanism of load instability, which cannot be analyzed by well-known P-V curve only. For analyzing load stability without detailed simulations, this paper presents torque-speed characteristic curves of induction motor considering power system condition and constant impedance loads. As the results of examining the curves in various conditions (model order reduction methods, load dropping out), this paper also presents some cautions for bulk power system modeling with DG. A bulk power system model whose lower voltage subsystems are reduced by an accurate model order reduction method is introduced in order to check out the problems by DG-stopping. As to the numerical simulation, a great number of DG-stopping leads to voltage instability in bulk power system, and the instability is greatly improved by DG-voltage regulation.

This study proposes a novel spatial‐temporal decomposition approach for systematically tracking dominant modes, mode shapes and coherent groups in bulk power systems using measurement data. First, components of the dominant oscillation modes, including frequencies and damping ratios, are identified from measurement data through a proposed recursive continuous wavelet transform. Second, cross‐wavelet transform is employed to estimate the mode shapes using the wavelet coefficient of the dominant modes. Furthermore, a reconstructed wavelet coefficient, which integrates the wavelet coefficients of all the estimated dominant modes, is used to identify the coherent groups of generators via the cross‐correlation coefficient. The proposed approach is evaluated on the simulation data from 16‐machine 68‐bus test system and China Southern Power Grid (CSG) as well as the field‐measurement data collected from phasor measurement units of CSG. It is demonstrated that the proposed approach performs with high accuracy, robustness and efficiency in tracking dominant modes, mode shapes and coherent groups in the bulk power systems.

Quantitative assessment of U.S. bulk power systems and market operations during the COVID-19 pandemic