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A new analytical approach based on dynamic programming method for the solution of cubic objective function with equality and inequality constraints is presented in this paper. A generalized recursive equation is derived to solve the problem. The proposed approach is non-iterative and directly gives the solution without any initial assumption. This method is implemented for economic power dispatch problem to minimize fuel cost and economic dispatch problem of chiller plant for saving energy. The superiority of this method is demonstrated by comparing the experimental results of the proposed method with lambda logic based algorithm and with gradient search method reported in the literature.

Although the tasks of managing carbon peaks and achieving carbon neutrality in China are arduous, they are also of great significance, which highlights China’s determination and courage in dealing with climate change. The power industry is not only a major source of carbon emissions but also an important area for carbon emission reduction. Thus, against the backdrop of carbon neutrality, understanding the development status of China’s power industry guided by the carbon neutrality background is important because it largely determines the completeness of China’s carbon reduction promises to the world. This study aims to review China’s achievements in carbon reduction in the electric industry, its causes and future policy highlights.

The methods used in this study include descriptive analyses based on official statistics, government documents and reports.

The research results show that, after years of development, the power industry has achieved positive results in low-carbon provisions and in the electrification of consumption, and carbon emission intensity has continued to decline. Policy initiatives play a key role in this process, including, but not limited to, technology innovations, low-carbon power replacement and supported policies for low-carbon transformation toward low-carbon economies.

This study provides a full picture of China’s power industry against the backdrop of low-carbon development, which could be used as a benchmark for other countries engaging in the same processes. Moreover, a careful review of China’s development status may offer profound implications for policymaking both for China and for other governments across the globe.

Purpose of this paper are Real power loss reduction, voltage stability enhancement and minimization of Voltage deviation.

In HLG approach as per Henry gas law sum of gas dissolved in the liquid is directly proportional to the partial pressure on above the liquid. Gas dissolving in the liquid which based on Henry gas law is main concept to formulate the proposed algorithm. Populations are divided into groups and all the groups possess the similar Henry constant value. Exploration and exploitation has been balanced effectively. Ranking and position of the worst agents is done in order to avoid the local optima. Then in this work Mobula alfredi optimization (MAO) algorithm is projected to solve optimal reactive power problem. Foraging actions of Mobula alfredi has been imitated to design the algorithm. String foraging, twister foraging and backward roll foraging are mathematically formulated to solve the problem. In the entire exploration space the Mobula alfredi has been forced to discover new regions by assigning capricious position. Through this approach, exploration competence of the algorithm has been improved. In all iterations, the position of the Mobula alfredi has been updated and replaced with the most excellent solution found so far. Exploration and exploitation capabilities have been maintained sequentially. Then in this work balanced condition algorithm (BCA) is projected to solve optimal reactive power problem. Proposed BCA approach based on the conception in physics- on the subject of the mass; incoming, exit and producing in the control volume. Preliminary population has been created based on the dimensions and number of particles and it initialized capriciously in the exploration space with minimum and maximum concentration. Production control parameter and Production probability utilized to control the exploration and exploitation.

Proposed Henry's Law based -soluble gas optimization (HLG) algorithm, Mobula alfredi optimization (MAO) algorithm and BCA are evaluated in IEEE 30 bus system with L-index (Voltage stability) and also tested in standard IEEE 14, 30, 57, 118, 300 bus test systems without L- index. Real power loss minimization, voltage deviation minimization, and voltage stability index enhancement has been attained.

For the first time Henry's Law based -soluble gas optimization (HLG) algorithm, Mobula alfredi optimization (MAO) algorithm and BCA is projected to solve the power loss reduction problem.

The main purpose behind this work is to explore the methods already proposed in various literatures to overcome the issues associated with VAr management in a competitive environment. Managing reactive power support service in competitive electricity market environment has become an important constituent of ancillary services. The characteristics of VAr generation/absorption do not allow its transmission over a long distance. The issues associated with the localized nature of reactive power must be considered during the valuation, planning, pricing and allocation of VAr producing/absorbing resources. In this review work, the key issues associated with the reactive support and the techniques used to tackle these issues in various utilities across the globe are been discussed in brief. In the literature, numerous renowned authors propose various methods to manage reactive power with various types of structural and operational scenarios. These methods are also discussed briefly in this paper. The experience with VAr management in some matured electricity market is also discussed in this paper.

Discussion of various issues associated with reactive power management and methods/techniques to overcome these, has been carried out in this paper. The methods were proposed in various literatures related to reactive power management by some of the renowned authors and adopted by various electric utilities.

The review work may be useful for utilities to develop a quick insight on reactive support services to control the voltage profile and also, it may be a useful asset for the researchers working in this area.

The paper is organized with different sections to elaborate the issues and associated methods. This paper is a single piece of work, which addresses reactive power planning, pricing for VAr support, market issues and valuation of VAr utilization.

Forecasting of stock indices is a challenging issue because stock data are dynamic, non-linear and uncertain in nature. Selection of an accurate forecasting model is very much essential to predict the next-day closing prices of the stock indices. The purpose of this paper is to develop an efficient and accurate forecasting model to predict the next-day closing prices of seven stock indices.

A novel strategy called quasi-oppositional symbiotic organisms search-based extreme learning machine (QSOS-ELM) is proposed to forecast the next-day closing prices effectively. Accuracy in the prediction of closing price depends on output weights which are dependent on input weights and biases. This paper mainly deals with the optimal design of input weights and biases of the ELM prediction model using QSOS and SOS optimization algorithms.

Simulation is carried out on seven stock indices, and performance analysis of QSOS-ELM and SOS-ELM prediction models is done by taking various statistical measures such as mean square error, mean absolute percentage error, accuracy and paired sample t-test. Comparative performance analysis reveals that the QSOS-ELM model outperforms the SOS-ELM model in predicting the next-day closing prices more accurately for all the seven stock indices under study.

The QSOS-ELM prediction model and SOS-ELM are developed for the first time to predict the next-day closing prices of various stock indices. The paired t-test is also carried out for the first time in literature to hypothetically prove that there is a zero mean difference between the predicted and actual closing prices.

The aim of this research paper is to examine the bio‐inspired optimization algorithms, namely, genetic algorithm (GA), particle swarm optimization (PSO) and bacterial foraging optimization (BFO) algorithm with adaptive chemotactic step for determining the steady‐state equivalent circuit parameters of the three‐phase induction motor using a set of manufacturer data.

The induction motor parameter determination issue is devised as a nonlinear constrained optimization problem. The nonlinear equations of various quantities (torque, current and power factor) are derived in terms of equivalent circuit parameters from a single and a double‐cage model, and then, equates to the corresponding manufacturer data. These equations are solved by the bio‐inspired algorithms. Using the squared error between the determined and the manufacturer data as the objective function, the parameter determination problem is transferred into an optimization process where the model parameters are determined that minimize the defined objective function. The objective function is iteratively minimized using GA, PSO and BFO techniques. In order to balance the exploration and exploitation searches of the BFO algorithm, an adaptive chemotactic step is utilized.

Comparisons of the results of GA, PSO, BFO and IEEE Std. 112‐F (using no‐load, locked‐rotor and stator resistance tests) methods for two sample motors are presented. Results show the superiority of the bio‐inspired optimization algorithms over the classical one. Besides, BFO‐based parameter determination method is observed to obtain better quality solutions quickly than GA and PSO methods.

The parameters obtained by the proposed approaches can be used in analyzing the stalling and/or reacceleration process of a loaded motor following a fault or during voltage sag condition as well as in system‐level studies.

The most significant contribution of the research is the potential to determine the equivalent circuit parameters of induction motor only from its manufacturer data without conducting any lab tests on the motor. The bio‐inspired optimization based parameter determination approaches are faster and less intrusive than the IEEE Std. 112‐F method.

This study aims to intend and implement the optimal power flow, where tuning the production cost is done with the inclusion of stochastic wind power and different kinds of flexible AC transmission systems (FACTS) devices. Here, the speed with fitness-based krill herd algorithm (SF-KHA) is adopted for deciding the FACTS devices’ optimal sizing and placement integrated with wind power. Here, the modified SF-KHA optimizes the sizing and location of FACTS devices for attaining the minimum average production cost and real power depletions of the system. Especially, the objective includes reserve cost for overestimation, cost of thermal generation of the wind power, direct cost of scheduled wind power and penalty cost for underestimation. The efficiency of the offered method over several popular optimization algorithms has been done, and the comparison over different algorithms establishes proposed KHA algorithm attains the accurate optimal efficiency for all other algorithms.

The proposed FACTS devices-based power system with the integration of wind generators is based on the accurate placement and sizing of FACTS devices for decreasing the actual power loss and total production cost of the power system.

Through the cost function evaluation of the offered SF-KHA, it was noted that the proposed SF-KHA-based power system had secured 13.04% superior to success history-based adaptive differential evolution, 9.09% enhanced than differential evolution, 11.5% better than artificial bee colony algorithm, 15.2% superior to particle swarm optimization and 9.09% improved than flower pollination algorithm.

The proposed power system with the accurate placement and sizing of FACTS devices and wind generator using the suggested SF-KHA was effective when compared with the conventional algorithm-based power systems.

A system operator (SO) of a transmission network consistently aims to minimise operating costs whilst still maintaining a certain degree of system adequacy. One of the ways to achieve this is by minimising the level of spinning reserve (SR) in the system. In order to do so, the level of SR must be analysed. This study looks at quantifying the risk of inadequacy when the SR is varied. A study was done for a period of 24 h with 30 m intervals to determine the risk level at each period. The number of generators despatched, system power margin and the system sell price was all taken into account. Risk was then computed by factoring the probability of generation inadequacy and the cost of purchasing the imbalance from the balancing market.