A trust-region based sequential linear programming approach for AC optimal power flow problems

© 2018 Elsevier B.V. This study proposes a new trust-region based sequential linear programming algorithm to solve the AC optimal power flow (OPF) problem. The OPF problem is solved by linearizing the cost function, power balance and engineering constraints of the system, followed by a trust-region to control the validity of the linear model. To alleviate the problems associated with the infeasibilities of a linear approximation, a feasibility restoration phase is introduced. This phase uses the original nonlinear constraints to quickly locate a feasible point when the linear approximation is infeasible. The algorithm follows convergence criteria to satisfy the first order optimality conditions for the original OPF problem. Studies on standard IEEE systems and large-scale Polish systems show an acceptable quality of convergence to a set of best-known solutions and a substantial improvement in computational time, with linear scaling proportional to the network size.National Research Foundation, Singapore

Experiments with hybrid Bernstein global optimization algorithm for the OPF problem in power systems

This paper presents an algorithm based on the Bernstein form of polynomials for solving the optimal power flow (OPF) problem in electrical power networks. The proposed algorithm combines local and global optimization methods and is therefore referred to as a `hybrid' Bernstein algorithm in the context of this work. The proposed algorithm is a branch-and-bound (B&B) procedure wherein a local search method is used to obtain a good upper bound on the global minimum at each branching node. Subsequently, the Bernstein form of polynomials is used to obtain a lower bound on the global minimum. The performance of the proposed algorithm is compared with the previously reported Bernstein algorithm to demonstrate its effi cacy in terms of the chosen performance metrics. Furthermore, the proposed algorithm is tested by solving the OPF problem for several benchmark IEEE power system examples and its performance is compared with generic global optimization solvers such as BARON and COUENNE. The test results demonstrate that the algorithm HBBB delivers satisfactory performance in terms of solution optimality.This research is supported by the National Research Foundation, Prime Ministers Office, Singapore, under its CREATE programme

A hierarchical EMS for aggregated BESSs in energy and performance-based regulation markets

The battery energy storage systems (BESSs) have been increasingly installed in the power system, especially with the growing penetration rate of the renewable energy sources. However, it is difficult for BESSs to be profitable due to high capital costs. In order to boost the economic value of BESSs, this paper proposes a hierarchical energy management system (HiEMS) to aggregate multiple BESSs, and to achieve multi-market business operations. The proposed HiEMS optimizes the multi-market bids considering a realistic BESS performance model, and coordinates the BESSs and manages their state of charge (SOC) values, according to their price penalties based on dynamically generated annualized cost. By taking part in the energy market and regulation market at the same time, the cost-performance index (CPI) of the BESS aggregation is greatly improved. The impact of photovoltaic generation (PV) on system performance and CPI is also studied.This work is supported in part by DNV GL Energy (formerly KEMA) Technology Centre, Nanyang Technological University, and the Energy Innovation Research Programme (EIRP, Award No. NRF2014EWT-EIRP002-005), administrated by the Energy Market Authority (EMA). The EIRP is a competitive grant call initiative driven by the Energy Innovation Programme Office, and funded by the National Research Foundation (NRF) Singapore

Wind-thermal power system dispatch using MLSAD model and GSOICLW algorithm

The decision support model of mean-lower semi-absolute deviation (MLSAD) and the optimization algorithm of group search optimizer with intraspecific competition and lévy walk (GSOICLW) are presented to solve wind-thermal power system dispatch. MLSAD model takes the profit and downside risk into account simultaneously brought by uncertain wind power. Using a risk tolerance parameter, the model can be converted to a single-optimization problem, which is solved by an improved optimization algorithm, GSOICLW. Afterwards, both the model and the algorithm are tested on a modified IEEE 30-bus power system. Simulation results demonstrate that the MLSAD model can well solve wind-thermal power system dispatch. The study also verifies GSOICLW obtains better convergent dispatching solutions, in comparison with other evolutionary algorithms, such as group search optimizer and particle swarm optimizer.NRF (Natl Research Foundation, S’pore)EDB (Economic Devt. Board, S’pore)Accepted versio

Large-scale unit commitment under uncertainty: an updated literature survey

The Unit Commitment problem in energy management aims at finding the optimal production schedule of a set of generation units, while meeting various system-wide constraints. It has always been a large-scale, non-convex, difficult problem, especially in view of the fact that, due to operational requirements, it has to be solved in an unreasonably small time for its size. Recently, growing renewable energy shares have strongly increased the level of uncertainty in the system, making the (ideal) Unit Commitment model a large-scale, non-convex and uncertain (stochastic, robust, chance-constrained) program. We provide a survey of the literature on methods for the Uncertain Unit Commitment problem, in all its variants. We start with a review of the main contributions on solution methods for the deterministic versions of the problem, focussing on those based on mathematical programming techniques that are more relevant for the uncertain versions of the problem. We then present and categorize the approaches to the latter, while providing entry points to the relevant literature on optimization under uncertainty. This is an updated version of the paper "Large-scale Unit Commitment under uncertainty: a literature survey" that appeared in 4OR 13(2), 115--171 (2015); this version has over 170 more citations, most of which appeared in the last three years, proving how fast the literature on uncertain Unit Commitment evolves, and therefore the interest in this subject

A Hierarchical EMS for Aggregated BESSs in Energy and Performance-based Regulation Markets

The battery energy storage systems (BESSs) have been increasingly installed in the power system, especially with the growing penetration rate of the renewable energy sources. However, it is difficult for BESSs to be profitable due to high capital costs. In order to boost the economic value of BESSs, this paper proposes a hierarchical energy management system (HiEMS) to aggregate multiple BESSs, and to achieve multi-market business operations. The proposed HiEMS optimizes the multi-market bids considering a realistic BESS performance model, and coordinates the BESSs and manages their state of charge (SOC) values, according to their price penalties based on dynamically generated annualized cost. By taking part in the energy market and regulation market at the same time, the cost-performance index (CPI) of the BESS aggregation is greatly improved. The impact of photovoltaic generation (PV) on system performance and CPI is also studied

Wavelet Transform-Spectral Kurtosis Based Hybrid Technique for Disturbance Detection in a Microgrid

This paper proposes a combined Wavelet Transform-Spectral Kurtosis based approach for detecting islanding and power quality (PQ) issues in microgrids. Islanding and PQ disturbances are generated in a microgrid comprising renewable energy sources such as wind and solar photovoltaic apart from diesel generators, fuel cells and flywheel/battery energy storage systems (FESS/BESS). Different microgrid configurations are considered to test the detection capabilities of the proposed approach. The negative sequence component of the voltage signal is measured at the point of common coupling (PCC) and processed through Short Time Fourier Transform (STFT), Wavelet Transform (WT) and Wavelet Transform-Spectral Kurtosis (SK) under no-noise and 20-dB noise conditions. Furthermore, performance indices such as energy and kurtosis are calculated in all case studies to detect disturbances based on a suitably selected threshold. The results of the case studies demonstrate the superior performance and robustness of SK when compared with STFT and WT for detecting islanding and PQ disturbances in MGs

Firefly algorithm scaled fractional order fuzzy PID based PSS for transient stability improvement

This paper presents an improvement in stability in a single machine connected to infinite bus power system by designing an optimal fractional order fuzzy PID based power system stabilizer (FOFPID-PSS). The low frequency oscillations resulting from load switching are damped out by the designed PSS under different operating conditions. In this paper, a bio-inspired algorithm called firefly algorithm (FA) has been employed for tuning the parameters of the proposed FOFPID-PSS controller. The robustness of the proposed controller is tested for enhancing the transient stability under different operating conditions like step and random variations in load demand. In addition to the graphical results, a comparative analysis of the proposed FOFPID-PSS controller with that of conventional PID-PSS and fuzzy PID-PSS (FPID-PSS) is also presented in terms of the performance indices (PIs) like maximum overshoot, settling time and integral squared error (ISE). The results suggest that the proposed FOFPID-PSS outperforms the FPID-PSS and PID-PSS controllers

A Hybrid Firefly-Swarm Optimized Fractional Order Interval Type-2 Fuzzy PID-PSS for Transient Stability Improvement

This article focuses on the implementation of a hybrid firefly algorithm-particle swarm optimization (FAPSO) scheme for optimizing the parameters of an interval type-2 fractional order fuzzy proportional integral derivative (IT2FOFPID)-based power system stabilizer (PSS) to minimize the low-frequency oscillations in a power system. Here, the IT2FOFPID-based PSS is designed by considering speed deviation and acceleration as input signals. In this article, a single machine infinite bus system and the New England 10 machine 39-bus power system are used for testing and comparing the approaches. Stability studies are also performed using OPAL-RT's OP5600, a real-time digital simulator. The comparative studies demonstrate that the hybrid FAPSO optimized IT2FOFPID-PSS provides better damping and stability performance when compared with the PSSs based on the FA/PSO/ hybrid genetic algorithm and bacterial foraging optimization and hybrid differential evolution and pattern search optimized IT2FOFPID approaches under various operating scenarios