• Energy Research

The current study presents a modified sine cosine optimization (MSCO) algorithm for solving the non-smooth environmental/economic power dispatch problem. In the proposed MSCO algorithm, random search agents’ population is initialized in the search domain for simultaneous optimization of both the combined economic and environmental objectives. Added to that, the proposed MSCO proposes an opposition strategy to preserve the diversity of solutions purposefully. Hence, the Pareto optimal solutions are customized according to the Pareto front concepts. These solutions are evolved using a modified version of the sine cosine algorithm (SCA), where the best agent is selected randomly from the stored Pareto solutions. Furthermore the parameter-based tuning mechanism is designed to improve the balance between the exploration and exploitation abilities. The correctness and effectiveness of the proposed MSCO are validated through experiments results and comparisons on EELD problem. Simulations were conducted on two test systems with non-smooth fuel cost and emission issues. The first system constitutes 6-unit benchmarking system, while the second one constitutes 10- units, and their results are compared with the results of other optimization techniques that were reported in the literature. The numerical comparisons reveal the robustness and effectiveness of the proposed MSCO algorithm.

The scheduling of the generating units is a very important issue in the power system operation, which is defined as unit commitment problem (UCP). The aim of the UCP scheduling to minimize the total generation costs with satisfying the power demand, spinning reserve, ramp rate and other operation constraints. In this paper, a proposed procedure binary Sine Cosine (BSC) optimization technique with optimal priority list (OPL) algorithm is introduced to procedure solves UCP. The proposed procedure is achieved through two stages; the first stage aims to shrunk search space by introducing optimal priority list (OPL), while the second stage adopts the BSC optimization technique to find the optimal solution of UCP, by accelerating the search process of BSC optimization technique. The different types of priority lists are introduced with binary particle swarm optimization (BPSO) technique then these results are comprised to find the optimal binary result, a lowest cost and OPL. The procedure proposed is applied to IEEE test system. The results of these algorithms are compared to show the capability and efficiently of the optimal algorithm. Based on the obtained results, it can be concluding that, the proposed procedure gives the best results and saves the computational efforts.

Many engineering optimization problems are typically multi-objective in their natures and multidisciplinary with a large number of decision variables. Furthermore, Pareto dominance loses its effectiveness in such situations. Thus, developing a robust optimization algorithm undoubtedly becomes a true challenge. This paper proposes a multi-objective orthogonal opposition-based crow search algorithm (M2O-CSA) for solving large-scale multi-objective optimization problems (LSMOPs). In the M2O-CSA, a multi-orthogonal opposition strategy is employed to mitigate the conflicts among the convergence and distribution of solutions. First, two individuals are randomly chosen to undergo the crossover stage and then orthogonal array is presented to obtain nine individuals. Then individuals are used in the opposition stage to improve the diversity of solutions. The effectiveness of the proposed M2O-CSA is investigated by implementing it on different dimensions of multi-objective optimization problems (MOPs). The Pareto front solutions of these MOPs have various characteristics such as convex, non-convex and discrete. It is also applied to solve multi-objective design applications with distinctive features such as four bar truss (FBT) design, welded beam (WB) deign, disk brake (DB) design, and speed reduced (SR) design, where they involve different characteristics. In this context, a new decision making tool based on multi-objective optimization on the basis of ratio analysis (MOORA) technique is employed to help the designer for extracting the operating point as the best compromise or satisfactory solution to execute the candidate engineering design. Simulation results affirm that the proposed M2O-CSA works efficiently and effectively.

Sustainability goals include the utilization of renewable energy resources to supply the energy needs in addition to wastewater treatment to satisfy the water demand. Moreover, hydrogen has become a promising energy carrier and green fuel to decarbonize the industrial and transportation sectors. In this context, this research investigates a wind-photovoltaic power plant to produce green hydrogen for hydrogen refueling station and to operate an electrocoagulation water treatment unit in Ostrava, Czech Republic's northeast region. The study conducts a techno-economic analysis through HOMER Pro® software for optimal sizing of the power station components and to investigate the economic indices of the plant. The power station employs photovoltaic panels and wind turbines to supply the required electricity for electrolyzers and electrocoagulation reactors. As an off-grid system, lead acid batteries are utilized to store the surplus electricity. Wind speed and solar irradiation are the key role site dependent parameters that determine the cost of hydrogen, electricity, and wastewater treatment. The simulated model considers the capital, operating, and replacement costs for system components. In the proposed system, 240 kg of hydrogen as well as 720 kWh electrical energy are daily required for the hydrogen refueling station and the electrocoagulation unit, respectively. Accordingly, the power station annually generates 6,997,990 kWh of electrical energy in addition to 85595 kg of green hydrogen. Based on the economic analysis, the project's NPC is determined to be €5.49 M and the levelized cost of Hydrogen (LCH) is 2.89 €/kg excluding compressor unit costs. This value proves the effectiveness of this power system, which encourages the utilization of green hydrogen for fuel-cell electric vehicles (FCVs). Furthermore, emerging electrocoagulation studies produce hydrogen through wastewater treatment, increasing hydrogen production and lowering LCH. Therefore, this study is able to provide practicable methodology support for optimal sizing of the power station components, which is beneficial for industrialization and economic development as well as transition toward sustainability and autonomous energy systems.

Identifying the parameters of photovoltaic (PV) modules is significant for their design and simulation. Because of the instabilities in the weather action and land surface of the earth, which cause errors in measuring, a novel fuzzy represen-tation-based PV module is formulated and developed. In this paper, a novel locomotion-based hybrid salp swarm algorithm (LHSSA) is presented to identify the parameters of PV modules accurately and reliably. In the LHSSA, better leader salps based on particle swarm optimization (PSO) are incorporated to the traditional salp swarm algorithm (SSA) in a serialized scheme with the aim of providing more valuable information for the leader salps of the SSA. By this integration, the proposed LHSSA can escape the local optima as well as guide the seeking process to attain the promising region. The proposed LHSSA is investigated on different PV models, i. e., single-diode (SD), double-diode (DD), and PV module in crisp and fuzzy aspects. By comparing with different algorithms, the comprehensive results affirm that the LHSSA can achieve a highly competitive performance, especially on quality and reliability.

AbstractMetaheuristic algorithms based on intelligent rules have been successfully developed and applied to solve many optimization areas over the past few decades. The sine–cosine algorithm (SCA) imitates the behaviour of transcendental functions while the sine and cosine functions are presented to explore and exploit the search space. SCA starts by random population and executes iterative evolution processes to update the standard evolutionary algorithm’s destination or the best location. SCA used linear transition rules to balance the exploration and exploitation searches while searching for the best or optimal solutions. Since Mirjalili proposed it in 2016, SCA has attracted many researchers’ attention to deal with several optimization problems in many fields due to its strengths in solving optimization tasks that include the simple concept, easiness of implementation, and rapid convergence. This paper aims to provide researchers with a relatively comprehensive and extensive overview of the Sine–Cosine optimization algorithm in the literature to inspire further research. It examines the available publications, including improvements, binary, chaotic, hybridizations, multi-objective variants, and different applications. Some optimization formulations regarding single-objective optimization problems, multi-objective optimization problems, binary-objective optimization problems, and more classifications regarding the optimization types are discussed. An extensive bibliography is also included.

Abstract This paper presents a novel conscious neighborhood strategy-based Laplacian barnacles mating algorithm, named NLBMA for solving solar cell diode models. It is an indispensable and prudent improvement to address insufficient diversification and intensification inclinations and the entrapment in the local optima of the original barnacles mating algorithm (BMA). In this regard, the proposed NLBMA introduces two new searching methodologies called Laplacian-based crossover search (LCS) and neighborhood-based wandering search (NWS) in order to boost the searching ability of barnacles by exploring different regions within the search space. In this sense, the LCS aims to improve the diversification of solutions while the NWS operates in enhancing the intensification ability by refining the solution quality and then the balancing between local and global searches can be enhanced consciously. The proposed NLBMA is used to optimize the parameters of single-diode (SD), and double-diode (DD) models of photovoltaic (PV) units. In all experiments, NLBMA is compared with the standard BMA and different state-of-the-art recent optimization algorithms. The comprehensive and statistical results indicate that NLBMA is more accurate and superior compared to other peers, where for all the studies PV models/modules, the NLBMA has proved to consistently achieve improved results for the root mean squared error (RMSE) of current than the others. For example, the results of R M S E found by NLBMA for R.T.C. France silicon solar cell are 7.73006e−4 A for SD model and 7.5242e−4 A for DD model, with percentage of improvement 83.23% and 90.94% on the original BMA variant, respectively. For the KC200GT module, the found R M S E values are 1.9827e−3 A and 2.0083e−3 A with saving 1.48% and 96.27% for the SD and DD modules over the original BMA variant, respectively. For the Photowatt PWP-201 module, the results are 3.3610e−2 A and 3.3043e−2 A with improvement 67.19% and 73.55% for the SD and DD models compared to the original BMA variant, respectively. Therefore, the NLBMA can efficiently prove its superiority and reliability to tackle the problem of parameters’ extraction of the PV models.

Economical power dispatch problem (EPDP) is urgent operation problem for system operators. The EPDP is formulated as multi-objective problem that simultaneously optimize two objectives namely generation fuel costs and emissions. EPDP is considered as a complex and non-convex problem. Because of its complexity in constraints and conflict nature, a multi-objective fruit-fly optimization algorithm (MO-FOA) is proposed to improve the economic issue with acceptable levels of emissions. This algorithm is initialized randomly with a swarm of fruit flies. During this initialization, the objective functions are simulated into single objective function. The application to the standard 30-bus IEEE system and ten unit generating system demonstrates the efficiency and robustness of the proposed MO-FOA to obtain well-scattered Pareto optimal solutions for the multi-objective EPDP. It is proven the promising characteristics of the proposed MO-FOA to solve highly complex problems in the single and multiobjective functions.