A novel hybrid algorithm for solving emerging electricity market pricing problem of microgrid

A novel order characteristic load shifting policy for load factor improvement, peak reduction, and economical operation of distribution systems

This paper performs optimal sizing of the distributed energy resources (DERs) of an islanded microgrid test system to minimize the generation cost. Two fossil fueled generators, two wind turbines and three fuel cells are the DERs involved to be sized for the minimization of generation cost. Utilization percentage shows the maximum usage of DERs in obtaining the minimal value of objective function. Grey Wolf Optimizer (GWO), its modified version (MGWO) and MGWO amalgamated with particle swarm optimization (PSO), sine cosine algorithm (SCA) and crow search algorithm (CSA) to perform the optimization. A comparative study is then made to prove the efficiency of the proposed hybrid MGWOCSA among the four other optimizers used and also among those available in literature.

Fuel cost of a thermal generator is its own load functions. In this research, Modified Improved Particle Swarm Optimization (MIPSO) is applied to calculate economic dispatch. Constriction Factor Approach (CFA) is used to modify IPSO algorithm because of the advantage to improve the ability of global searching and to avoid local minimum , so that the time needed to converge become faster. Simulation results achieved by using MIPSO method at the time of peak load of of 9602 MW, obtained generation cost is Rp 7,366,912,798,34 per hour, while generation cost of real system is Rp. 7,724,012,070.30 per hour. From the simulation result can be concluded that MIPSO can reduce the generation cost of 500 kV Jawa Bali transmission system of Rp 357,099,271.96 per hour or equal to 4,64%.

Impact of maximized utility benefit based on customer willingness for economic operation of a grid connected microgrid system

Multi-objective energy management using a smart charging technique of a microgrid with the charging impact of plug-in hybrid electric vehicles

Even with the advantages of the sine cosine algorithm (SCA) in solving multimodal problems, there are some shortcomings for this method. We observe that the random patterns utilized in SCA cause an increasing attraction toward local optima. This study developed a rationalized version of this technique to deal with several representative benchmark cases with different dimensions. The improved algorithm combines the chaotic local search mechanism and Lévy flight operator with the core trends of SCA. The new variant is named as CLSCA. The Lévy flight with long jumps is adopted to boost the exploratory tendencies of the algorithm, while the chaotic local search mechanism is used as a local search for the destination point, which helps to further enhance the exploitation capability of SCA. Therefore, a suitable equilibrium between the exploration and exploitation can be kept in the CLSCA by two embedded patterns. To investigate the effectiveness and strength of the developed method, the CLSCA was tested on many benchmark functions, including different types of tasks such as single modal, multi-modal, hybrid, and composition functions. We compare the CLSCA with well-known optimizers, like particle swarm optimization (PSO) algorithm, grey wolf optimizer (GWO), SCA with differential evolution (SCADE), opposition-based SCA (OBSCA), fuzzy self-tuning PSO (FST_PSO), chaotic salp swarm algorithm (CSSA), and Chaotic whale optimizer (CWOA). Numerical experimental results demonstrate that the exploratory and exploitative properties of the classical SCA are clearly improved. The experimental results also show that our improved CLSCA is a better technique for different kinds of optimization tasks.

Abstract: This study presents a comparative analysis of five metaheuristic algorithms for optimizing Proportional Integral Derivative (PID) controller parameters in a vehicle cruise control system. The selected algorithms include the Rain Optimization Algorithm (ROA), Student Psychology-Based Optimization (SPBO), Sine Cosine Algorithm (SCA), Gradient-Based Optimization (GBO), and Grey Wolf Optimization (GWO). The PID parameters are optimized using four common performance criteria: Integral Time Absolute Error (ITAE), Integral Absolute Error (IAE), Integral SquareError (ISE), and Integral Time Square Error (ITSE). Unlike conventional tuning methods, metaheuristic approaches provide more flexible and adaptive parameter selection, enhancing the control system's robustness. The study analyses the algorithms under several internal and external disturbances, including road incline, passenger mass, and measurement noise. The findings demonstrate that no singular algorithm is universally superior; instead, efficacy is contingent upon the particular system conditions and control objectives. The Sine Conine Algorithm (SCA) has superior overall performance under typical settings, while GWO attains the quickest response time for slope fluctuations. Conversely, ROA reduces overshoot, rendering it appropriate for applications that necessitate stability. Additionally, the proposed methods are compared with the classical Ziegler-Nichols tuning approach, demonstrating the advantages of metaheuristic algorithms in terms of reduced overshoot, faster settling time, and improved robustness. The findings indicate that the SCA with the ITAE criterion yields superior performance under varying disturbance conditions. However, the fastest system response under different slope conditions isachieved with the parameter values determined by the GWO employing the ITSE criterion. The system model is designed in a MATLAB environment and the detailed comparative results are provided in figures and tables.Cite this article as: F. Heybetli, Y. Danayiyen, A. Begüm Taşdemir and Ş. Şenyiğit, "Comparative analysis of metaheuristic algorithms in PID-based vehicle cruisecontrol systems," Electrica, 25, 0051, 2025. doi: 10.5152/electrica.2025.24051.

Microgrid (MG) systems can either operate in on-grid or off-grid mode. The on-grid mode of microgrid operation can further be divided into active and passive mode. In active mode the grid both buys and sells power to the microgrid whereas in passive mode the grid acts as a backup and only sells power to the microgrid. In this study, the grid power commitment is sized optimally to incur the lowest MG system generating cost. The generation cost is reduced for three different scenarios of a low voltage microgrid (LV MG) system that comprises of battery energy storage (BES), renewable energy sources (RES), and fossil fuel generators while running in grid-connected mode. The load demand model is taken into account and distributed generator (DG) scheduling ideas are percolated for reducing the second level Microgrid system’s generating costs. The optimization tool for the study was a newly established hybrid swarm intelligence algorithm that has previously been utilized to solve a variety of power system optimization challenges. Numerical results show that there was 25% increase in generation cost when passive participation of grid was considered and 58% increase when fixed electricity pricing was considered.

In present scenario economic dispatch for the microgrid (MG) is better suited to the requirements of a system in actual operation because it not only considers the lowest cost in a scheduling cycle but also coordinates between different distributed generations (DGs) over many periods. Wind energy and solar energy are subjected to random variations and intervals, so there is a great difficulty in solving the economic dispatch problem. In this paper, different distributed generations are employed in MG which includes photovoltaic arrays, wind turbine, fuel cell, diesel engines, gas-turbine and battery. In this work the MG is considered to be operating in islanding mode. The main objective of this work is to minimize the generation cost and emission cost of the MG while satisfying system hourly demand and system constraints. Economic dispatch for MG involving DGs is an optimization problem and in this paper Cuckoo Search Algorithm (CSA) is used to perform the optimization process. The results obtained from CSA is compared with Particle Swarm Optimization (PSO) algorithm and it is inferred that CSA shows better global convergence when compared to PSO and also gives good optimum solution by reducing system generation cost and emission cost.

Economic and environmental factors based multi-objective approach for optimizing energy management in a microgrid

This study will solve the classical vehicle routing problem, the goal is to generate k trips with the shortest distance for h customers with predetermined locations and needs.. The proposed solution to the classical vehicle routing problem is a hybrid sine cosine algorithm. The sine cosine algorithm is hybridized with the grey wolf optimizer, which is used in combination with the methods of tournament selection, opposition learning, and the mutation and crossover method to build the optimal routing plan for the means of transporting cement. To demonstrate the advantages of the developed hybrid sine cosine algorithm, this algorithm is evaluated and compared with modern algorithms such as sine cosine algorithm, dragonfly algorithm, grey wolf optimizer, ant lion optimizer, particle swarm optimization, modified hybrid particle swarm optimization, genetic algorithm, and the double-population genetic algorithm in case studies. The hybrid sine cos algorithm gives optimal results in these cases because it balances mining and exploration. Thus, the results of this study indicate that managers can use the developed hybrid sine cosine algorithm to create optimal vehicle routing plans to reduce transportation distances.

There are many challenges in incorporating the attenuation cost of energy storage into the optimization of microgrid operations due to the randomness of renewable energy supply, the high cost of controlled power generation, and the complexity associated with calculating the cost of battery attenuation. Therefore, this paper proposes a microgrid energy management scheme considering the attenuation cost of energy storage. This scheme analyzes the power generation mode and uncertainty factors of distributed generators in detail. The influence of charge and discharge depth on the cycle life and residual value of the energy storage system was analyzed, and the energy storage attenuation cost model was established. Finally, considering the cost of power generation, environmental treatment, and the deterioration cost of energy storage systems, the objective function of the comprehensive operation cost of microgrids is formulated. The improved sine cosine algorithm (SCA) is used to simulate the energy output optimization of various distributed generators in the microgrid. The results show that the algorithm can effectively reduce the comprehensive operation cost of microgrids and improve their energy utilization efficiency, which proves the practical significance and reference value of the method for microgrid energy management.

Performance of rime-ice algorithm for estimating the PEM fuel cell parameters

Modelling density of pure and binary mixtures of normal alkanes: Comparison of hybrid soft computing techniques, gene expression programming, and equations of state

The authors present a genetic approach for determining the priority order in the commitment of thermal units in power generation. The objective of the problem is to properly schedule the on/off states of all thermal units in a system to meet the load demand and the reverse requirement at each time interval, such that the overall system generation cost is a minimum, while satisfying various operational constraints. The authors examine the feasiblity of using genetic algorithms and report some simulation results in near-optimal commitment of thermal units in a power system.