Modeling and Optimization of Renewable Energy Systems by Using Novel Approaches

This study proposes an artificial hummingbird algorithm (AHA) for energy management (EM) for optimal operation of a microgrid (MG), including conventional sources and renewable energy sources (RES), with an incentive-based demand response (DR). Due to the stochastic nature of solar and wind output power and the uncertainty of prices and load, a probabilistic EM with hybrid AHA and point estimation method (PEM) is proposed to model this uncertainty by utilizing the normal and Weibull distribution functions. The PEM method is considered a good tool for handling stochastic EM problems. It achieves good results using the same procedures used with the deterministic problems while maintaining low computational efforts. The proposed AHA technique is employed to solve a deterministic incentive DR program, with the goal of reducing the overall cost, which includes the cost of conventional generator fuel and the cost of power transaction with the main grid while taking into account the load demand. Two different case studies are tested. The simulation results of the proposed AHA is compared with the results of well-known metaheuristic algorithms to demonstrate its efficacy. According to AHA’s results, a total reduction of energy consumption by 104 KWh for the first case study and 2677 MWh for the second case study is achieved while achieving the lowest overall operating cost. The results demonstrate that the AHA is adequate for tackling the EM problem. Then, to examine the effect of uncertainty on the MG state, a probabilistic EM problem is solved using AHA-PEM.

2,550 views

25 citations

Original Research

05 May 2022

A Logistic Modelling Analysis for Wind Energy Potential Assessment and Forecasting its Diffusion in Pakistan

Shoaib Ahmed Khatri

, 4 more and

Laveet Kumar

There is increased focus to harness renewable energy resources in the 21st century to contain climate change and attain energy security. In this context, wind energy is attaining a significant marketplace to meet the ever-increasing electricity demand. This study, as such, undertakes wind energy assessment and forecasts wind power market penetration in Pakistan considering three different scenarios for the period 2020–2050. The modeling approach of this study is based on the Levenberg–Marquardt algorithm (LMA) optimization method, which is used to estimate the parameters of the logistic model to improve forecasting precision. It is revealed that around 55, 64, and 73% of wind potential could be technically exploited under each of the three scenarios, respectively. The Certified Emission Reductions (CERs) for each scenario are also estimated. The anticipated annual abatement of GHG emissions and CERs earnings at 30% capacity utilization factor is found to be 158 million CERs by the year 2050. These results suggest that wind energy offers great potential to attain energy security, environmental stability, and sustainable development in Pakistan. This study would assist energy professionals, government, and stakeholders to undertake wind energy market assessment and devise appropriate energy management plans.

6,743 views

6 citations

Original Research

01 April 2022

Optimal Sizing Grid-Connected Hybrid PV/Generator/Battery Systems Following the Prediction of CO2 Emission and Electricity Consumption by Machine Learning Methods (MLP and SVR): Aseer, Tabuk, and Eastern Region, Saudi Arabia

Khalid Almutairi

, 2 more and

Omar Marey

Researchers’ concentration has been on hybrid systems that can fulfill economic and environmental goals in recent years. In this study, first, the prediction of CO₂ emission and electricity consumption of Saudi Arabia by 2040 is made by employing multi-layer perceptron (MLP) and support vector regression (SVR) methods to see the rate of CO₂ emission and electricity consumption. In this regard, the most important parameters such as gross domestic product (GDP), population, oil consumption, natural gas consumption, and renewable consumption are considered. Estimating CO₂ emission by MLP and electricity consumption by SVR showed 815 Mt/year and 475 TWh/year, respectively, where R2 for MLP and SVR was 0.99. Prediction results showed a 31% and 39% increase in CO₂ emission and electricity consumption by 2040 compared to 2020. Second, the optimum combination of components for supplying demand load and desalination load in residential usages are found where 0% capacity shortage, 20–60$/t penalty for CO₂ emission, sell back to the grid, and both fixed and random grid outages are considered. Load demands were considered under two winter and non-winter times so that 4,266, 2,346, and 3,300 kWh/day for Aseer, Tabuk, and the Eastern Region were shown, respectively. Results show that 0.12, 0.11, and 0.12 (kW (PV))/(kWh/day(load)) and 0.1, 0.08, and 0.08 (kW(Bat))/(kWh/day(load)) are required under the assumption of this study for Aseer, Tabuk, and the Eastern Region, respectively. Also, COEs for the proposed systems are 0.0934, 0.0915, and 0.0910 $/kWh for Aseer, Tabuk, and the Eastern Region, respectively. Also, it was found that renewable fractions (RFs) between 46% and 48% for all of the case studies could have rational COE and NPCs and fulfill the increasing rate of CO₂ emission and electricity consumption. Finally, sensitivity analysis on grid CO₂ emission and its penalty, load and solar Global Horizontal Irradiance (GHI), PV, and battery prices showed 45%–55%, 42%–52%, and 43%–49% RFs for Aseer, Tabuk, and the Eastern Region, respectively.

4,188 views

10 citations

Review

25 April 2022

A Detailed Review Investigating the Mathematical Modeling of Solar Stills

Ahmadreza Ayoobi

and

Mahdi Ramezanizadeh

4,721 views

7 citations

Original Research

16 February 2022

A Comparison Study of Multi-Objective Bonobo Optimizers for Optimal Integration of Distributed Generation in Distribution Systems

Ahmad Eid

, 2 more and

Baseem Khan

In this paper, the three newly published Multi-Objective Bonobo Optimizer (MOBO) variants are assessed and evaluated using statistical analysis for solving the multi-objective optimization of Distributed Generation (DG) into distribution systems. The main objectives of the study are to minimize system loss and enhance voltage profile. While the first variant, MOBO1, depends on the sort and grid-index approach, the second variant, MOBO2, relies on the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) algorithm technique. The last variant, MOBO3, is inspired by the Multi-objective Evolutionary Algorithm based on Decomposition (MOEA/D). The three MOBO algorithms are compared to themselves and to other algorithms solving the same optimization problem. These algorithms include the MOJAYA, Multi-Objective Artificial Ecosystem-Based Algorithm (MOAEO), Multi-Objective Gravitational Search Algorithm (MOGSA), and Multi-Objective Particle Swarm Optimization (MOPSO). The 33-bus and 85-bus radial distribution systems are used test systems for solving the optimal allocation of single- and three-DG units operating at unity power factor. In order to find the best compromise solution, the Pareto Optimal front method is adopted with the help of a fuzzy-based function. The obtained results show the effectiveness of the MOBO variants compared with other algorithms in terms of different statistical parameters and multi-objective performance metrics such as diversity, hypervolume, spacing, and set coverage. While the MOBO algorithm reduces power loss and TVD by 39.59 and 68.31% for a single DG, they are reduced to 58.13 and 88.44% for three DG units allocated to the 33-bus distribution system, respectively. On the other hand, the MOBO algorithm reduces power loss and TVD by 37.28 and 66.84% for a single DG, respectively, they are decreased to 46.35 and 82.53% for three DG units assigned to the 85-bus distribution system. Among the three MOBO variants, it is found that the MOBO1 is superior for a single-DG allocation, while the MOBO3 is the best for the allocation of three-DG units.

2,541 views

12 citations

Original Research

15 February 2022

Performance Optimization of the Helical Heat Exchanger With Turbulator

Wang Xifeng

, 5 more and

Mostafa Abedini

In this paper, optimization on a two-tube helical heat exchanger with a fin is represented. The spiral pipes heat exchanger which is made of the cooper is adopted for investigation. The effects of three types of fins with the proposed geometric shapes on the overall heat transfer coefficient and pressure loss are investigated. The fins are located on the inner surface of the outer pipe. The obtained numerical results are compared with the experimental results, and a good agreement is observed between the results. The studies show that the total heat transfer coefficient has increased by 170% compared to an exchanger with no fin. Therefore, the best fin has been selected based on the benefit-cost-ratio (BCR) factor. Finally, using the new represented optimization algorithm, the height of the represented triangular fin is optimized to represent the best values for overall heat transfer coefficient and pressure loss of the helical heat exchanger. In addition, the results indicate that reducing the density and height of the triangular fin increases heat transfer and reduces pressure loss.

3,480 views

19 citations

Original Research

13 December 2021

Emplacement of the Photovoltaic Water Pumping System in Remote Areas by a Multi-Criteria Decision-Making Method: A Case Study

Fatemehzahra Heydari

, 2 more and

Siavash Haghighat

2,354 views

5 citations

Open for submission