This study examines the symmetric (linear) and asymmetric (nonlinear) impact of economic growth (EG), capital formation (CF), renewable and non-renewable energy (NRE) consumption on CO2 emissions and ecological footprint (EF) of seventeen OECD countries spanning data from 1970 to 2016. The autoregressive distributed lag (ARDL) model is used to examine the symmetric impact and the nonlinear autoregressive distributed lag (NARDL) model is employed to explore the asymmetric impact of the variables on the environment. The results indicate that economic growth and gross capital formation dampens environmental quality in the OECD region over the sampled period. Our estimation using the ARDL model shows that a 1% increase in renewable energy (RE) is projected to reduce CO2 emission by 0.2% and a 1% increase in NRE is estimated to increase CO2 emission by 1.08%. Similarly, a 1% rise in EG and NRE is expected to increase ecological footprint (EF) by 0.10% and 0.53%, respectively. Estimation using NARDL decomposed EG with positive (negative) shocks shows that a 1% increase (decrease) in EG is expected to reduce CO2 emissions by 0.4% (0.16%). Similarly, 1% positive (negative) shock in RE is expected to decrease CO2 emission by 0.5%. The findings indicate that conventional energy obtained from fossil fuels is observed to worsen the environment. Interestingly, renewable energy consumption enhances environmental quality for both fitted models with CO2 emission and ecological footprint. This is insightful for stakeholders and government administrators in the region. This demonstrates the importance of a paradigm shift towards renewable energy consumption in the OECD countries to improve economic growth and productive capital stock. This finding also aligns with the non-linear investigation of the pivotal role of renewable energy consumption for a cleaner environment. 

Symmetric and asymmetric impact of economic growth, capital formation, renewable and non-renewable energy consumption on environment in OECD countries

Grasshopper Optimization Algorithm (GOA) is a recent swarm intelligence algorithm inspired by the foraging and swarming behavior of grasshoppers in nature. The GOA algorithm has been successfully applied to solve various optimization problems in several domains and demonstrated its merits in the literature. This paper proposes a comprehensive review of GOA based on more than 120 scientific articles published by leading publishers: IEEE, Springer, Elsevier, IET, Hindawi, and others. It provides the GOA variants, including multi-objective and hybrid variants. It also discusses the main applications of GOA in various fields such as scheduling, economic dispatch, feature selection, load frequency control, distributed generation, wind energy system, and other engineering problems. Finally, the paper provides some possible future research directions in this area.

/pdf/grasshopper-optimization-algorithm-theory-variants-and-3nn15nvo6g.pdf

Grasshopper Optimization Algorithm: Theory, Variants, and Applications

Review on non-isolated DC-DC converters and their control techniques for renewable energy applications

Salp swarm algorithm (SSA) is a recent optimization technique inspired by behavior of the salp chains in deep oceans. However, the SSA is efficient, simple and easy to implement, it is susceptible to stagnation at local optima for some cases. The main contribution of this paper is proposing an improved salp swarm algorithm algorithm (ISSA) for enhancing the search capabilities of the original SSA to solve the optimal power flow (OPF) problem. In the proposed ISSA, both of exploration and the exploitation processes are enhanced. The exploration process is achieved by applying a random mutation to find new searching areas while an adaptive process is developed to enhance the exploitation process by focusing on the most promising search area. This strategy will balance the transformation between exploration and exploitation. The ISSA is employed to achieve OPF with non-smooth and non-convex generator fuel cost functions such as; minimizing quadratic fuel cost, piecewise quadratic cost, quadratic fuel cost considering the valve-point effect and prohibited zones. The main advantages of the ISSA are avoiding stagnation at local optima and can solve nonlinear and non-smooth optimization problems where its adaptive operators balance between the exploration and exploitation phases of this algorithm. However, the parameters of ISSA need to be carefully defined before application of algorithm. The proposed algorithm is validated using the standard IEEE 30-bus, IEEE 57-bus and IEEE 118-bus test systems. The performance of proposed algorithm is comprehensively compared with moth-flame optimization algorithm, improved harmony search algorithm, genetic algorithm and other reported optimization techniques. The results prove the effectiveness and superiority of the proposed algorithm compared with other optimization techniques.

An improved version of salp swarm algorithm for solving optimal power flow problem

The appropriate control and management of reactive power is of great relevance in the electrical reliability, stability, and security of power grids. This issue is considered in order to increase system efficiency and to maintain voltage under the acceptable value range. In this regard, novel technologies as FACTS, renewable energies, among others, are varying conventional grid behavior leading to unexpected limit capacity reaching due to large reactive power flow. Thus, optimal planning of this must be considered. This paper proposes a new application for a simple and easy implementation optimization algorithm, called Rao-3, to solve the constrained non-linear optimal reactive power dispatch problem. Moreover, the integration of solar and wind energy as the most applied technologies in electric power systems are exploited. Due to the continuous variation and the natural intermittence of wind speed and solar irradiance as well as load demand fluctuation, the uncertainties which have a global concern are investigated and considered in this paper. The proposed single-objective and multi-objective deterministic/stochastic optimal reactive power dispatch algorithms are validated using three standard test power systems, namely IEEE 30-bus, IEEE 57-bus, and IEEE 118-bus. The simulation results show that the proposed optimal reactive power dispatch algorithms are superior compared with two recent algorithms (Artificial electric field algorithm (AEFA) and artificial Jellyfish Search (JS) algorithm) and other optimization algorithms used for solving the same problem.

/pdf/optimal-reactive-power-dispatch-with-time-varying-demand-and-mhaiobxbp7.pdf

Optimal Reactive Power Dispatch With Time-Varying Demand and Renewable Energy Uncertainty Using Rao-3 Algorithm

The optimal reactive power dispatch (ORPD) problem is an important issue to assign the most efficient and secure operating point of the electrical system. The ORPD became a strenuous task, especially with the high penetration of renewable energy resources due to the intermittent and stochastic nature of wind speed and solar irradiance. In this paper, the ORPD is solved using a new natural inspired algorithm called the marine predators’ algorithm (MPA) considering the uncertainties of the load demand and the output powers of wind and solar generation systems. The scenario-based method is applied to handle the uncertainties of the system by generating deterministic scenarios from the probability density functions of the system parameters. The proposed algorithm is applied to solve the ORPD of the IEEE-30 bus system to minimize the power loss and the system voltage devotions. The result verifies that the proposed method is an efficient method for solving the ORPD compared with the state-of-the-art techniques.

https://www.mdpi.com/1996-1073/13/17/4316/pdf

Solving the Optimal Reactive Power Dispatch Using Marine Predators Algorithm Considering the Uncertainties in Load and Wind-Solar Generation Systems

Performance enhancement of grid-connected PV systems using adaptive reference PI controller

This paper proposes an adaptive grasshopper optimization algorithm (AGOA) for solving the optimal power flow (OPF) problem with the optimal incorporation of a center-node unified power flow controller (C-UPFC). The C-UPFC which is an advanced flexible AC transmission system (FACTS) device is inserted in series with a transmission line (TL) at its midpoint for providing the power flow control together with independent voltage control. The proposed AGOA is based on applying the Levy flight distribution and spiral path orientation of search agents to the traditional grasshopper optimization algorithm (GOA) to diminish the stagnation problem of the basic GOA at local optima and enhance its searching ability. Therefore, this AGOA technique is implemented for optimal sizing and siting of the C-UPFC on standard IEEE 30-bus and 57-bus systems as well as 26-bus system, and then compared with other well-known techniques to verify its effectiveness. To assess the installation of the C-UPFC in a power system, the optimal capacities and locations of the C-UPFC are determined for different objective functions, such as the fuel cost, fuel cost with a valve point loading effect (VPLE), piecewise cost and emission. Simulation results reveal that the proposed algorithm is more efficient and superior for OPF solution compared with the other algorithms reported in the literature. Furthermore, the optimal integration of the C-UPFC in the power system is considerably minimizing the power loss and improving the voltage profile.

/pdf/optimal-power-flow-solution-with-an-embedded-center-node-3vlhvuhnn5.pdf

Optimal Power Flow Solution With an Embedded Center-Node Unified Power Flow Controller Using an Adaptive Grasshopper Optimization Algorithm

The Egyptian irrigation system depends mainly on canals that take water from the River Nile; nevertheless, the arid climate that dominates most of the country influences the high rate of water losses, mainly through evaporation. Thus, the main objective of this study is to develop a practical approach that helps to accommodate solar photovoltaic (PV) panels over irrigation canals to reduce the water evaporation rate. Meanwhile, a solar PV panel can contribute effectively and economically to an on-grid system by generating a considerable amount of electricity. A hybrid system includes a solar PV panel and a diesel generator. Several factors such as the levelized cost of energy (LCOE), total net present cost, loss of power supply probability, and greenhouse gas emissions should be considered while developing a technoeconomically feasible grid-connected renewable integrated system. A mathematical formulation for the water loss was introduced and the evaporation loss was monthly estimated. Thus, this study also aims to enhance an innovative metaheuristic algorithm based on a cuckoo search optimizer to show the way forward for developing a technoeconomic study of an irrigation system integrated with an on-grid solar PV panel designed for a 20-year lifespan. The results are compared using the mature genetic algorithm and particle swarm optimization to delimit the optimal size and configuration of the on-grid system. The optimal technoeconomic feasibility is connected to the graphical information system to delimit the optimal length and direction of the solar PV accommodation covering the canals. Finally, based on the simulated results, the optimal sizing and configuration of the irrigation-system-integrated on-grid solar PV accommodation have less impact on the LCOE without violating any constraint and, at the same time, generating clean energy.

Energy Harvesting and Water Saving in Arid Regions via Solar PV Accommodation in Irrigation Canals

Large-scale wind turbines with a large blade radius rotates under fluctuating conditions depending on the blade position. The wind speed is maximum in the highest point when the blade in the upward position and minimum in the lowest point when the blade in the downward position. The spatial distribution of wind speed, which is known as the wind shear, leads to periodic fluctuations in the turbine rotor, which causes fluctuations in the generator output voltage and power. In addition, the turbine torque is affected by other factors such as tower shadow and turbine inertia. The space between the blade and tower, the tower diameter, and the blade diameter are very critical design factors that should be considered to reduce the output power fluctuations of a wind turbine generator. To model realistic characteristics while considering the critical factors of a wind turbine system, a wind turbine model is implemented using a squirrel-cage induction motor. Since the wind speed is the most important factor in modeling the aerodynamics of wind turbine, an accurate measurement or estimation is essential to have a valid model. This paper estimates the average wind speed, instead of measuring, from the generator power and rotating speed and models the turbine’s aerodynamics, including tower shadow and wind shear components, without having to measure the wind speed at any height. The proposed algorithm overcomes the errors of measuring wind speed in single or multiple locations by estimating the wind speed with estimation error less than 2%.

https://www.mdpi.com/1996-1073/12/10/1907/pdf

Ayman Alhejji

Papers

Solving the Optimal Reactive Power Dispatch Using Marine Predators Algorithm Considering the Uncertainties in Load and Wind-Solar Generation Systems

Performance enhancement of grid-connected PV systems using adaptive reference PI controller

Optimal Power Flow Solution With an Embedded Center-Node Unified Power Flow Controller Using an Adaptive Grasshopper Optimization Algorithm

Energy Harvesting and Water Saving in Arid Regions via Solar PV Accommodation in Irrigation Canals

Dynamic Modeling of Wind Turbines Based on Estimated Wind Speed under Turbulent Conditions