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This paper presents an efficient mixed-integer linear programming approach to unit commitment (UC) in isolated power systems while maintaining frequency stability under loss of generation contingencies. The dynamic frequency constraints considered are formulated using the center of inertia (COI) concept to determine the dominant parameters affecting system frequency response, specifically addressing the rate of change of frequency and the frequency nadir. Another significant contribution of this work is to propose a simple and computationally efficient method to obtain the lower bounds for the frequency constraints through the random sampling of generation unit combinations. Additionally, this paper introduces a load frequency sensitivity index-based method for determining the procured volume of dynamic regulation reserve from the ancillary service market. By incorporating these methods into the UC framework, the system can better adapt to system frequency dynamics. The proposed approach is validated using the benchmark system of Taiwan Power Company. Simulation results demonstrate the efficacy of the proposed methods in enhancing frequency stability during both low and high load demand days. Comparative analyses also highlight the benefits of Taipower’s internally provided dynamic regulation reserves. These reserves are enforced through frequency constraints in the proposed UC model, resulting in increased online units and enhanced frequency nadir compared to the conventional UC method. External procurement of dynamic regulation reserves is more cost-effective and recommended during low load demand days.

This paper presents an Equilibrium Optimizer (EO)-based method that simultaneously determines locations and sizes of multiple photovoltaic (PV) units for maximizing a distribution system’s PV hosting capacity (PVHC). The proposed method avoids various search-space restrictions seen in recent literature. The constraints in this paper are separated into two types based on the necessity of power flow analysis. The proposed method is implemented through co-simulation between MATLAB and OpenDSS. PV output and load demand variation is considered through a 24-hour profile representing a season. Smart inverter is considered through the PV inverter volt-var control (VVC) scheme. The proposed method is tested on the IEEE 123-bus benchmark system. Four test cases are considered to determine the impact of reverse power flow (RPF) limit and the impact of VVC toward the optimal PV planning. The results show that EO produces superior results compared to Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), and Coyote Optimization Algorithm (COA).

The optimal planning for distributed generations (DGs) associated with photovoltaics (PVs) in the utility-owned distribution system is crucial for increasing high penetration of renewables while against practical system operation constraints. Such PV-DG planning is categorized as a complicated mixed-integer nonlinear programming (MINLP) problem and is extremely difficult to solve by using conventional methods. In recent years, several bio-inspired metaheuristic algorithms have been proposed to tackle various complicated real-parameter optimization problems. This paper proposes a two-stage approach including a new bio-inspired algorithm, Coyote Optimization Algorithm (COA), to solve the large-scale MINLP PV-DG sizing problem considering different load levels. The objective function terms under consideration include the total system power loss and voltage regulator tap changes at different load levels while against limits of rms bus voltages, tap changes, and PV-DG constraints at each candidate bus. The proposed method is tested using the IEEE 123-bus unbalanced benchmark system and an actual utility distribution network. Results obtained are then compared with those obtained by a classic MINLP solver-based and four other bio-inspired methods. Moreover, results also show that the proposed method leads to lower loss, a minimum number of regulator tap changes, and higher PV penetration capacity among the compared methods and is suitable for solving the large-scale PV-DG planning problem in distribution systems.

Wind energy is among the fastest-growing electric energy resources worldwide. As the electric power generated by wind turbines (WTs) varies, the WT-connected bus voltage fluctuates. This paper presents a study on implementing a swarm-based proportional and integral (PI) controller for GTO-STATCOM voltage regulator to mitigate the voltage fluctuation caused by the output variations of an offshore wind farm. The proposed swarm-based algorithm for the PI controller is Harris Hawks Optimization (HHO). Simulation results obtained by the HHO algorithm are compared with three other swarm-based algorithms and show that STATCOM with HHO-based PI controller can effectively regulate the WT-connected bus voltage under different wind power output conditions. It shows that the STATCOM compensation performance of the proposed algorithm is superior to that of the compared solutions in maintaining the stable WT-connected bus voltage.

Penetration of grid connected renewable energy systems and smart loads based on power electronics technology has been increasing in low and medium voltage distribution. Power Electronics is a key technology for distribution networks which can transfer electrical power from renewable energy sources to grids or generate regulated frequency and/or voltage for different loads such as variable speed drives and battery chargers. New demands for a) cost and size reduction, b) performance and quality improvement and c) flexibility on power management have promoted power electronics applications extensively in industrial, commercial and residential sectors such as transportation, utility and home appliances in recent years.

With widespread non-linear loads and the increasing penetration of distributed generations in the power system, harmonic pollution has become a great concern. The causes of harmonic pollution not only include the integer harmonics, but also interharmonics, which exacerbate the complexity of harmonic analysis. In addition, the output variability of highly non-linear loads and renewables such as electric arc furnaces and photovoltaic solar or wind generation may lead to weakly time-varying harmonics and interharmonics in both frequency and magnitude. These features present challenges for accurate assessment of associated power-quality (PQ) disturbances. To tackle such increasing time-varying PQ problems, a hybrid detection method using synchrosqueezing wavelet transform (SSWT) is proposed. The proposed method first obtains the proper parameter values for the mother wavelet according to numerical computations. The wavelet transform-based synchrosqueezing and a clustering method are applied to determine each frequency component of the waveform under assessment. The time-domain waveform and the associated magnitude of each frequency component is then reconstructed by the inverse SSWT operation. The novelty of the proposed method is that it can decompose the measured waveform containing both harmonics and interharmonics into intrinsic mode functions without the need for fundamental frequency detection. Compared to other time–frequency analysis methods, SSWT has better anti-noise and higher resolution of time–frequency curves; even the measured signal has close frequency components. Simulation results and actual measurement validations show that the proposed method is effective and relatively accurate in time-varying harmonic and interharmonic detection and is suitable for applications in power networks and microgrids that have high penetration of renewables or non-linear loads causing time-varying voltage or current waveforms.

It is known that artificial neural network is a powerful scheme for function learning and modeling nonlinear loads. However, a direct application of artificial neural network for modeling time-varying loads may lead to inaccuracies. This paper presents an accurate neural-network-based method for modeling the highly nonlinear voltage-current characteristic of an ac electric arc furnace (EAF). The neural-network-based model can be effectively used to assess waveform distortions, voltage fluctuations, and performances of reactive power compensation devices associated with the EAF in a power system. Simulation results obtained by using the proposed model are compared with the actual measured data and two other traditional neural network models. It is shown that the proposed method yields favorable performance and can be applied for modeling similar types of nonlinear loads for power engineering studies. [ABSTRACT FROM AUTHOR]

Abstract-This paper presents a new method for planning single-tuned passive harmonic filters to control harmonic voltage distortion throughout a power system. In the problem, the probabilistic characteristics of the harmonic source currents and network harmonic impedances in the filter planning are taken into account. The objective is to minimize the total filter installation cost, while the harmonic voltage limits and filter component constraints are satisfied with predetermined confidence levels. To obtain the optimal size of each filter component of the planning problem, the proposed procedure is first to find the candidate filter buses based on the sensitivity analysis. Next, the formulated probability-constrained problem is transformed into a deterministic nonlinear programming problem and is solved by a genetic-algorithm-based optimizer. The proposed solution procedure is tested with an actual distribution network and is verified by the conventional deterministic approach and by the Monte Carlo simulation. Numerical experiences show that the proposed method yields favorable results compared with the other two approaches. [ABSTRACT FROM AUTHOR]

This paper presents a new approach based on the linear approximation method and chance-constrained programming model for the optimal planning of single-tuned harmonic passive filters in an industrial power system. In the problem, the effects of the probabilistic characteristics of the nonlinear load currents and harmonic impedances of the system and linear loads are considered in the filter planning. The objective is to minimize the total filter installation cost, while the harmonic currents limits and filter component constraints are satisfied with predetermined confidence levels. The formulated probability-constrained problem is then transformed into a deterministic nonlinear programming problem and is solved by a sequential quadratic programming (SQP) solver. The proposed solution procedure is tested with an actual industrial power system and is verified by the conventional deterministic approach and by the Monte Carlo simulation. Numerical experiences show that the proposed method yields favorable results for the filter planning. [ABSTRACT FROM AUTHOR]

The black-box arc models are commonly used for describing the interaction between a switching arc and the surrounding circuit during a circuit breaker interruption process. This paper proposes an extended Mayr's equation based arc model for the SF6 circuit breaker to characterize the transients associated with switching off a shunt reactor. The proposed model includes Mayr's arc model with estimated parameters input, a time-controlled switch, and a dielectric recovery voltage control unit. From the transient simulation aspects, the paper not only provides the guidelines for estimating parameters required for Mayr's equation to simulate the arc dynamics, but also describes a method for modeling post-arc behavior that cannot be simulated by conventional black-box models. Several study cases are performed to show the usefulness of the proposed model. Results obtained are compared with the measured and calculated data. It shows that the proposed SF6 circuit breaker arc model is relatively accurate for characterizing the shunt reactor switching transients. [ABSTRACT FROM AUTHOR]

An efficient steady-state compensation method and a conceptual design for sizing the three-phase three-wire shunt active power filter (APF) under nonsinusoidal source voltages are presented in this paper. Conventionally, the compensation currents injected by the APF are determined to eliminate harmonic currents of the nonlinear load as well as to compensate reactive power and maintain the sinusoidal source currents. However, the subject related to the optimal control of the APF to meet the IEEE-519 harmonic current limits and other constraints, such as minimum load power factor requirement after compensation with an optimal kilovolt-ampere rating, are rarely investigated. In this paper, an optimization-based solution algorithm is proposed to determine the three-phase three-wire APF current injections to meet different constraints with an optimal filter size, where the voltage compensation-based control strategy is formulated as a nonlinear programming problem and is solved to find the optimal compensator gains and APF current injections. Results obtained by simulations with Matlab/Simulink show that the proposed approach is very flexible and effective for compensating harmonic currents generated by the nonlinear load with an optimal size of the APF. [ABSTRACT FROM AUTHOR]