This paper proposes a novel hybrid arithmetic–trigonometric optimization algorithm (ATOA) using different trigonometric functions for complex and continuously evolving real-time problems. The proposed algorithm adopts different trigonometric functions, namely sin, cos, and tan, with the conventional sine cosine algorithm (SCA) and arithmetic optimization algorithm (AOA) to improve the convergence rate and optimal search area in the exploration and exploitation phases. The proposed algorithm is simulated with 33 distinct optimization test problems consisting of multiple dimensions to showcase the effectiveness of ATOA. Furthermore, the different variants of the ATOA optimization technique are used to obtain the controller parameters for the real-time pressure process plant to investigate its performance. The obtained results have shown a remarkable performance improvement compared with the existing algorithms.

/pdf/an-arithmetic-trigonometric-optimization-algorithm-with-1r0asevp.pdf

An Arithmetic-Trigonometric Optimization Algorithm with Application for Control of Real-Time Pressure Process Plant

This paper presents a new hybrid metaheuristic algorithm, the hybrid Harris Hawks Optimizer-Arithmetic Optimization Algorithm (hHHO-AOA), as we have named it. It is proposed for sizing optimization and design of autonomous microgrids. The proposed hybrid algorithm has been developed based on operating the Harris Hawks Optimizer (HHO) and the Arithmetic Optimization Algorithm (AOA) in a uniquely cooperative manner. The developed algorithm is expected to increase the solution accuracy by increasing the solution diversity during an optimization process. The performance is verified with the evaluation metrics and the well-known statistical tests. According to the Friedman ranking test, the new algorithm performs 77.9&#x0025; better than HHO and 78.6&#x0025; better than AOA. Similarly, the performance checked with the Wilcoxon signed-rank test has revealed a significant superiority in solution accuracy compared to HHO and AOA alone. Later, the hybrid algorithm is tested on a microgrid that consists of a photovoltaic (PV) system, a wind turbine (WT) system, a battery energy storage system (BESS), diesel generators (DGs), and a commercial type load. For the optimal capacity planning of these components, a problem in which the loss of power supply probability (LPSP) and the cost of energy (COE) are defined as the objective function is formulated. The optimization done by the proposed algorithm has produced the lowest LPSP and the COE along with the highest rate of renewable fraction (RF). In conclusion, it is demonstrated that the new hHHO-AOA has proved itself in designing reliable, economical, and eco-friendly autonomous microgrids in the best optimal way. 

/pdf/the-hybrid-harris-hawks-optimizer-arithmetic-optimization-2tmd7tkw.pdf

The Hybrid Harris Hawks Optimizer-Arithmetic Optimization Algorithm: A New Hybrid Algorithm for Sizing Optimization and Design of Microgrids

This paper presents a new hybrid metaheuristic algorithm, the hybrid Harris Hawks Optimizer-Arithmetic Optimization Algorithm (hHHO-AOA), as we have named it. It is proposed for sizing optimization and design of autonomous microgrids. The proposed hybrid algorithm has been developed based on operating the Harris Hawks Optimizer (HHO) and the Arithmetic Optimization Algorithm (AOA) in a uniquely cooperative manner. The developed algorithm is expected to increase the solution accuracy by increasing the solution diversity during an optimization process. The performance is verified with the evaluation metrics and the well-known statistical tests. According to the Friedman ranking test, the new algorithm performs 77.9% better than HHO and 78.6% better than AOA. Similarly, the performance checked with the Wilcoxon signed-rank test has revealed a significant superiority in solution accuracy compared to HHO and AOA alone. Later, the hybrid algorithm is tested on a microgrid that consists of a photovoltaic (PV) system, a wind turbine (WT) system, a battery energy storage system (BESS), diesel generators (DGs), and a commercial type load. For the optimal capacity planning of these components, a problem in which the loss of power supply probability (LPSP) and the cost of energy (COE) are defined as the objective function is formulated. The optimization done by the proposed algorithm has produced the lowest LPSP and the COE along with the highest rate of renewable fraction (RF). In conclusion, it is demonstrated that the new hHHO-AOA has proved itself in designing reliable, economical, and eco-friendly autonomous microgrids in the best optimal way.

https://ieeexplore.ieee.org/ielx7/6287639/9668973/09712318.pdf

Short-term hydrothermal scheduling (STHTS) is a highly non-linear, multi-model, non-convex, and multi-dimensional optimization problem that has been worked upon for about 5 decades. Many research articles have been published in solving different test cases of STHTS problem, while establishing the superiority of one type of optimization algorithm over the type, in finding the near global best solution of these complex problems. This paper presents the implementation of an improved version of a variant of the Particle Swarm Optimization algorithm (PSO), known as Accelerated Particle Swarm Optimization (APSO) on three benchmark test cases of STHTS problems. The adaptive and variable nature of the local and global search coefficients for the proposed APSO significantly improve its performance in obtaining the optimal solution for the STHTS test cases. Two of these cases are non-cascaded cases of STHTS problem (NCSTHTS) and one case is cascaded case of STHTS problem (CSTHTS). The results are compared with the results of the previous implementations of the other algorithms as presented in the literature. Due to the stochastic nature of the meta-heuristic algorithms, the parametric and non-parametric statistical tests have been implemented to establish the superiority of results of one type of algorithm over the results of the other type of algorithms.

/pdf/implementation-of-apso-and-improved-apso-on-non-cascaded-and-hi9m0v8wb5.pdf

Implementation of APSO and Improved APSO on Non-Cascaded and Cascaded Short Term Hydrothermal Scheduling

The World Energy Council, in its 2019 World Energy Scenarios Report, advised policymakers to identify innovative opportunities for the integration of renewable energy resources into existing electrical power systems to achieve a fast and affordable solution. However, large-scale industries with cogeneration units are facing problems in handling the higher penetration levels of intermittent renewable energies. This paper addresses large-size photovoltaic power integration problems and their optimal operation. This work considers the case of a chemical industry having both cogeneration power and solar photovoltaics. Here, a modified firefly algorithm and a hybrid power resource optimization solver are proposed. The results of the proposed method are compared with other benchmark techniques, to confirm its advantages. The proposed techniques can be used in industries having cogeneration power plants with photovoltaics for better optimization and to meet the guidelines specified in IEEE 1547. The voltage ramp index is proposed to determine the voltage ramp up and down with intermittent solar irradiance. Additionally, a machine learning technique is used to predict the cogeneration plant efficiency at different loads and the solar irradiance under varying weather conditions. Finally, this paper proposes the effectiveness of the modified heuristic technique and certain guidelines, including solvers for industrial use.

Optimal Operation of a Photovoltaic Integrated Captive Cogeneration Plant with a Utility Grid Using Optimization and Machine Learning Prediction Methods

Short term hydrothermal scheduling (STHTS) is a non-linear, multi-modal and very complex constrained optimization problem which has been solved using several conventional and modern metaheuristic optimization algorithms A number of research articles have been published addressing STHTS using different techniques This article presents a comprehensive review of research published for solving the STHTS problem in the last four decades

/pdf/conventional-and-metaheuristic-optimization-algorithms-for-4prjdepquw.pdf

Conventional and Metaheuristic Optimization Algorithms for Solving Short Term Hydrothermal Scheduling Problem: A Review

In this paper, the concept of Finite Control Set Model Predictive Control (FCS-MPC) for a 17-level, single-phase switch ladder multilevel inverter (SP-SLMLI) connected to the grid is proposed. The current control of the inverter is a primal objective with the least computational effort. The inverter has 17-level output and hence the 17 control inputs. The inverter’s discrete model is employed to determine the future state of the control variable. In each sampling interval, there are 17 states to be checked, and the state that best minimizes the certain objective function defined in terms of the control variable is selected to be applied in the next sampling instant incorporating the controller delay due to calculations. Multilevel inverters with low switching loss and hence less power loss and suitable for medium and high power applications come with a substantial computational burden on the controller side. In order to stave off the calculation burden, conformal mapping has been introduced in the controller structure, and search space is reduced, thus requiring less time to search the optimal state per sampling interval. The idea can be extended to other Multilevel inverters with a higher number of switching levels.

Muhammad Anas Baig

Papers

Conventional and Metaheuristic Optimization Algorithms for Solving Short Term Hydrothermal Scheduling Problem: A Review

Implementation of APSO and Improved APSO on Non-Cascaded and Cascaded Short Term Hydrothermal Scheduling

Improved Finite Control Set Model Predictive Control of Switch Ladder Multilevel Inverter