Wind Energy Sources Research Articles

Reliability analysis of an active distribution network integrated with solar, wind and tidal energy sources

The global trends toward increasing clean and sustainable power generation have brought significant changes and developments in electrical distribution networks. The existing passive networks are being restructured to facilitate renewable distributed generations to ensure high degree of reliability of power supply to customers. A distribution system reliability assessment (DSRA) is crucial to analyze the risk and cost associated with power interruptions and take necessary preventive and corrective actions. This paper presents an approach to DSRA and carries out a comprehensive reliability assessment of an active distribution network (ADN) connected with solar, wind, and tidal energy sources. A multi-state reliability model is developed, combining the Markov and well-being frameworks to determine the reliability of a network energized by multiple renewable energy sources (RESs). The load point reliability and worth-oriented indices, namely SAIDI, SAIFI, CAIDI, AENS, EENS, ECOST and IEAR, are evaluated to analyze the impacts of the three RESs on the reliability of the ADN. The paper also proposes two new metrics to estimate the benefits of adding RESs from the reliability worth and cost perspectives. The analyses have been carried out on the Roy Billinton Test System. The results show that the maximum reliability worth and cost benefits can be extracted from a combined operation of multiple RESs. A combination of solar-wind-tidal energy sources, therefore, can be an effective solution for countries having long coastlines to fulfill not only the sustainable energy goals but also to improve the grid's reliability and reduce the monetary losses incurred due to customer interruptions.

RENEWABLE ENERGIES RESOURCES EVALUATION USING A GEOGRAPHIC INFORMATION SYSTEMS IN THE COLOMBIAN CARIBBEAN REGION

This manuscript presents an integral evaluation of the use of wind and solar resources in the Colombian Caribbean region. For the use of solar and wind energy sources, spatial restrictions according to topography, natural parks, water reserves, indigenous and sacred reserves were taken into account. In addition, studies of the energy potential of wind and solar radiation were carried out through geostatistical analysis of different points in meteorological stations located in the region. Finally, areas with wind and solar potential were determined using the Geographic Information System for Renewable Energies (SIGER). The results show that the department of Guajira has the highest values of solar brightness (229-240 Month day), while the departments of Valledupar, Cesar, and part of Magdalena decrease in the range of 229 to 207 Month day. In terms of solar radiation, the departments of Guajira and Atlántico showed global solar radiation values of up to 6,015 W/m2. The departments of Cesar, Magdalena, and part of Bolivar showed values close to 5,259 W/m2; while Sucre, and Cordoba showed low solar radiation values of 4,502 W/m2. It is concluded that the departments of Atlántico and Guajira are excellent candidates for solar and wind energy projects due to their characteristics. The results of this manuscript are a fundamental basis for future technical-economic feasibility studies of renewable energy in the Colombian Caribbean region. In addition, it allows the elaboration of energy policies that encourage the execution of this type of study at a real level.Keywords: SIGER, renewable energies, solar energy, wind energy, wind potentialJEL Classification: Q42DOI: https://doi.org/10.32479/ijeep.11612

Energy management using multi-criteria decision making and machine learning classification algorithms for intelligent system

Hybrid energy systems (HESs) are one of the most effective solutions for the power demand especially in remote areas. It is well-known that the HESs usually include renewables like solar and/or wind energy sources. Renewables are intermittent, fluctuating, and nonlinear. Therefore, an effective energy management plays an essential role in organizing the power flow in hybrid energy sources. In this work, a hybrid energy system (HES) composed of wind, gasoline and diesel generator is used as a case study to electrify a specific remote area. The sources of the HES are categorized in different arrangements to select the best combination from all available six energy sources combinations based on five criteria using the technique for order of preference by similarity to ideal solution (TOPSIS). This work is divided into two stages, in the first stage; a historical demand side dataset is used to model and calculate the five criteria. TOPSIS method results are combined with the five criteria and the demand side to form a dataset. In the second stage, machine learning algorithms, namely random forest (RF) and light gradient boosted machine (LightGBM) algorithms are used to predict the combination of the energy sources as a way of validating the proposed work. Evaluating the algorithms shows the superiority of RF algorithm with accuracy of 81.81% over LightGBM with accuracy of 68.6%. The behavior of both algorithms is explained using the confusion matrix. RF algorithm classifies the classes G1G2, and G2 correctly and misclassifies some values of the other classes. On the other hand, LightGBM algorithm classifies the class G2 correctly and misclassifies some values of the other classes.

Energy, exergy, economic and exergoenvironmental analyses of transcritical CO2 cycle powered by single flash geothermal power plant

Abstract The need for energy is increasing worldwide as the population has a continuous trend of increase. The restrictions on energy sources are becoming tougher as the authorities set these developed and developing countries. This leads to looking for other alternative energy sources to replace the conventional energy sources, leading to greenhouse emissions. Environmentally friendly energy sources (renewable energies), for example, geothermal, solar and wind, are viewed as clean and sustainable energy sources. Among these kinds of energy sources, geothermal energy is one of the best options because, like solar and wind energy sources, it does not depend on weather conditions. In this work, a single flash geothermal power plant is used to power a transcritical CO2 power plant is proposed. The energy and exergy analysis of the proposed combined power plant has been performed and the best possible operating mode of the power plant has been discussed. The effects of parameters such as separator pressure, CO2 condenser temperature and CO2 turbine inlet pressure and the pinch point on the energy efficiency, exergy efficiency and output power are determined and discussed. Our results indicate that the highest exergy destruction is in the CO2 vapor generator of 182.4 kW followed by the CO2 turbine of 106 kW, then the CO2 condenser of 82.81 kW and then the CO2 pump 58.76 kW. The lowest exergy destruction rates occur in the single flash geothermal power plant components where the separator has exactly zero exergy destruction rate. The results also show that the combined power plant produces more power and has better efficiencies (first law and second law) than the stand-alone geothermal power plant. Finally, Nelder–Mead simplex method is applied to determine the optimal parameters such as separator pressure, power output and pumps input power and second law efficiency. The results show that the power plant should be operated at a lower pinch temperature to reduce damage to the environment. As the condenser pressure increases, the environmental damage effectiveness coefficient decreases sharply until it reaches the minimum value of 1.2 to 1.7 MPa and then starts to increase. The trend of the impact of sports on environmental improvement is exactly the opposite of the trend of the effectiveness of environmental damage. Therefore, from an environmental point of view, it is recommended to operate the gas turbine at a high inlet pressure.

Analyzing the factors influencing the formation of the price of electricity in the deregulated markets of developing countries

The aim of this paper is to measure the impact of certain factors on the formation of the price of electricity, which is obtained from conventional and renewable energy sources, in the deregulated markets of developing countries, such as Montenegro. Another goal is to identify and analyze the criteria that can lead to a change in the electricity market structure. This study was especially inspired by the fact that no similar research has been conducted in this domain, according to the authors’ knowledge. A regression model is applied in the paper, which can be used to display the fluctuation of electricity prices based on the analyzed factors. In addition to price as a dependent variable, the model also examined eight factors that are independent variables. Apart from the basic model, another model has been formulated which, in addition to a price as dependent variable and the examined factors, also takes into consideration the production of electricity from renewable sources. The results of the research showed that an increase in the amount of electricity produced will lead to a slight increase in price, which is to be expected considering there is still one large producer servicing the market, which, despite the deregulation of the market, still has significant monopoly power. The generation of electricity by small hydropower plants will not significantly affect price change, and these entities will behave as if they were in a market of perfect competition. Further opening up the market to new competitors would lead to a fall in the price of electricity. Based on the predicted capacity of electricity generation from small hydropower plants, the electricity deficit could be reduced; in another words it could lead to the reduction of imports. Based on the results of the study, energy policy makers will be able to adopt informed strategies and make decisions related to developments in the energy market, most notably the creation of a common EU energy market. Also, the results of the conducted research expand the theoretical body of work dealing with the determination of the price of electricity. Future studies could address the issue of using natural potential for the generation of electricity from renewable sources, such as solar energy, wind energy and other sources.

Supervisory energy management of a hybrid battery/PV/tidal/wind sources integrated in DC-microgrid energy storage system

With increasing energy prices, nuclear energy concerns, climate changes, and electrical grid losses, the demand to rely on more renewable energy is growing. The majority of people currently prefer to live and work in smart environments, such as smart cities and smart institutions with an integrated smart microgrid. Energy management is a complex topic because a large amount of these smart microgrid systems rely on hybrid energy sources. As a result, a smart energy management controller needs to be created. The current research provides a new energy management control technique for a smart DC-microgrid based on a combined fuzzy logic controller (FLC) and high order sliding mode (HSMC) methods. The hybrid energy provider integrated into the DC-microgrid is made up of a battery bank, wind energy, photovoltaic (PV) energy, and tidal energy source. The new proposed intelligent control is intended to regulate source-side converters (SSCs) in order to capture the maximum energy from hybrid renewable energy sources (wind, tidal and PV) while also improving power quality in the DC-microgrid. To keep the microgrid as cost-effective as feasible, renewable energy sources are prioritized. The suggested controller offers a steady output power and sustained service. From the present simulation results under Matlab/Simulink and the comparative analysis, the proposed controller produces +1.02% wind power, +10% PV power, +100% tidal power, and +8.48% load power over intelligent fractional-order proportional–integral–derivative (PID) and more when compared to the super twisting fractional-order and PID controls. In addition, the suggested controller assures smooth output power and uninterrupted service.

Towards a Zero-Carbon Electricity System for India in 2050: IDEEA Model-Based Scenarios Integrating Wind and Solar Complementarity and Geospatial Endowments

This study evaluated a potential transition of India’s power sector to 100% wind and solar energy sources. Applying a macro-energy IDEEA (Indian Zero Carbon Energy Pathways) model to 32 regions and 114 locations of potential installation of wind energy and 60 locations of solar energy, we evaluated a 100% renewable power system in India as a concept. We considered 153 scenarios with varying sets of generating and balancing technologies to evaluate each intermittent energy source separately and their complementarity. Our analysis confirms the potential technical feasibility and long-term reliability of a 100% renewable system for India, even with solar and wind energy only. Such a dual energy source system can potentially deliver fivefold the annual demand of 2019. The robust, reliable supply can be achieved in the long term, as verified by 41 years of weather data. The required expansion of energy storage and the grid will depend on the wind and solar energy structure and the types of generating technologies. Solar energy mostly requires intraday balancing that can be achieved through storage or demand-side flexibility. Wind energy is more seasonal and spatially scattered, and benefits from the long-distance grid expansion for balancing. The complementarity of the two resources on a spatial scale reduces requirements for energy storage. The demand-side flexibility is the key in developing low-cost supply with minimum curtailments. This can be potentially achieved with the proposed two-level electricity market where electricity prices reflect variability of the supply. A modelled experiment with price signals demonstrates how balancing capacity depends on the price levels of guaranteed and flexible types of loads, and therefore, can be defined by the market.

Social Welfare Maximization of Competitive Congested Power Market Considering Wind Farm and Pumped Hydroelectric Storage System

The utilization of wind energy sources with energy storage systems has been increased in the power sector to satisfy the consumer’s energy demand with minimum price. This paper presents the impact of a wind farm (WF) and pumped hydroelectric storage (PHS) system in the competitive electricity market under a congested transmission system. The PHS system is used to compensate for the deviation of WF generation in the real-time electricity market. To investigate the impact of the proposed method, initially, the market-clearing power problem is solved without consideration of WF and PHS systems, and again it is solved with the WF and PHS systems. The optimal location of the WF and PHS systems is decided by the bus sensitivity factor (BSF) of these systems. The analysis is carried out by using generator sensitivity factor (GSF) with the help of the moth flame optimization (MFO) algorithm and thereby calculating market clearing price (MCP) and market clearing volume (MCV). The MFO algorithm is used here for the first time for solving the congested market-clearing power problem with the integration of WF and PHS systems under deregulated environment. The presented approach shows the improvement of social welfare after the placement of WF and PHS in the congested deregulated system. Modified IEEE 30 bus system is used to solve the market-clearing power problem and results obtained from the MFO algorithm are compared with the firefly algorithm (FA). Three different real-time wind speed data have been considered here to verify the proposed approach with uncertainty and the continuously changing nature of wind flow. It is discovered that social welfare is improved with the quantity addition of wind power, regardless of optimization techniques.

Design Of Horizontal Type Of Wind Turbine On A Highrise Building

In making a horizontal type wind turbine, of course, it is necessary to analyze it in depth, one of which is by predicting the production of wind energy produced by the wind turbine to estimate the wind power in the wind turbine which will later be applied. Wind energy sources that are commonly used are located in rural areas, fields and even there is such a large amount of energy that it is sometimes difficult to reach the power grid and other large areas including the roofs of high-rise buildings. There are many analytical models in wind energy estimation, one of which is often done by many researchers, namely by using the Weibull distribution method. From the measurement results that as many as 1516.37 kWh with a 1 kW wind turbine with a radius of 1 meter (capacity factor 30.09%). Modeling wind turbine blades with NACA 4412 using Qblade software to determine the torsional angle of the blade to be applied so that it is obtained that the torsion angle from the base and The tip of the blade has a tilt angle of 19.05? to 6.96° with a maximum Cp of 0.5 this is a pretty good value in designing wind turbine blades.

A New Particle Swarm Optimization Based Strategy for the Economic Emission Dispatch Problem Including Wind Energy Sources

Power dispatch has become an important issue due to the high integration of Wind Power (WP) in power grids. Within this context, this paper presents a new Particle Swarm Optimization (PSO) based strategy for solving the stochastic Economic Emission Dispatch Problem (EEDP). This problem was solved considering several constraints such as power balance, generation limits, and Valve Point Loading Effects (VPLEs). The power balance constraint is described by a chance constraint to consider the impact of WP intermittency on the EEDP solution. In this study, the chance constraint represents the tolerance that the power balance constraint cannot meet. The suggested framework was successfully evaluated on a ten-unit system. The problem was solved for various threshold tolerances to study further the impact of WP penetration.

Thermodynamic Interactions as a Descriptor of Cross-Over in Nonaqueous Redox Flow Battery Membranes.

Grid-scale energy storage is increasingly needed as wind, solar, and other intermittent renewable energy sources become more prevalent. Redox flow batteries (RFBs) are well suited to this application because of the advantages in scalability and modularity over competing technologies. Commercial aqueous flow batteries often have low energy density, but nonaqueous RFBs can offer higher energy density. Nonaqueous RFBs have not been studied as extensively as aqueous RFBs, and the use of organic solvents and organic active materials in nonaqueous RFBs presents unique membrane separator challenges compared to aqueous systems. Specifically, organic active material cross-over, which degrades battery performance, may be affected by membrane/active material thermodynamic interactions in a fundamentally different way than ionic active material cross-over in aqueous RFB membranes. Hansen solubility parameters (HSPs) were used to quantify these interactions and explain differences in organic active material permeability properties. Probe molecules with a more unfavorable HSP-determined enthalpy of mixing with the membrane polymer exhibited lower permeability or cross-over properties. The HSP approach, which accounts for the uncharged polymer backbone and the charged side chain, revealed that interactions between the uncharged organic probe molecule and the hydrophobic polymer backbone were more important for determining permeability or cross-over properties than interactions between the probe molecule and the hydrophilic side chain. This result is significant for nonaqueous RFBs because it suggests a decoupling of ionic conduction expected to predominantly occur in charged polymer regions and cross-over of organic molecules via hydrophobic or uncharged polymer regions. Such decoupling is not expected in aqueous systems where active materials are often polar or ionic and both cross-over and conduction occur predominantly in charged polymer regions. For nonaqueous RFBs, or other membrane applications where selective organic molecule transport is important, HSP analysis can guide the co-design of the polymer separator materials and soluble organic molecules.

Hybrid generation of renewables increases the energy system's robustness in a changing climate

Hybrid generation of renewables (e.g., hydro, solar, and wind energy sources) is an increasingly important technology to improve energy use efficiency and reliability. However, renewables are usually influenced by climate factors. In a changing climate, it is essential to know whether the integration of renewables enhances or weakens the robustness of the energy system, which is defined as “the insensitivity of the system's operational performances to climate change”. To answer this question, we first modeled the long-term operation of several hybrid renewable energy systems (HRESs) (i.e., hydro–solar–wind, hydro–wind, and hydro–solar systems) and a hydropower energy system using historical hydro-meteorological data and stochastic dynamic programming. Then, we constructed uncertain climatic conditions using a multi-variable stochastic simulation technique, which considered spatial-temporal correlations between inflow, solar, and wind power output. Finally, we compared the robustness of the operating rules under the simulated climatic conditions. A hypothetical case study based on China's Longyangxia hydro–photovoltaic (PV) power plant showed that: (1) the integration of PV and/or wind power significantly improved the system's robustness compared to only hydropower sources under uncertain climatic conditions; (2) the hydro–solar–wind HRES was the most robust system, meeting the energy demand more than 90% of the climatic conditions; (3) the hydro–solar–wind HRES was most sensitive to inflow change, followed by changes to wind and PV power. These findings hightlight the importance to implement hybrid generation of hydro, solar, and wind energy sources in a changing climate.

Well-Being Analysis Applied to the Study of Composite Systems Flexibility Considering Wind Energy Sources

This paper presents a methodology to evaluate, via well-being analysis, the flexibility of composite electrical systems, considering wind energy sources. By adding wind farms to different system bars, it is possible to assess which configuration provides the best adequacy and safety level for the system. The evaluation is based on probabilistic indices of Loss of Load Expectation (LOLE) and Expected Marginal State (EMS). Such indices are obtained by well-being analysis, combining deterministic criteria with probabilistic methods. Non-Sequential Monte Carlo Simulation is used to consider the probabilistic and intermittent characteristics of wind sources. The methodology is applied to the IEEE-RTS test system.

Design and Development of a Solar-Powered Smart Heater

Renewable energy sources are those that replenish naturally without depletion. Example of such sources are bioenergy, hydropower, geothermal, wind and solar energy sources. The alarming rate of global energy demand and consumption necessitates an immediate solution for energy conservation and maximum efficiency of new technological gadgets now being built. As a gadget, a solar water heater transforms dc electricity into heat energy, which is then transferred to the water in which it is immersed. The size of the heater is determined by its design, capacity, and intended purpose; it might be for household or industrial use. As an alternative to conventional electric water heaters, this effort focuses on the design and development of a solar-powered smart water heater. In tests using a variety of settings, the solar-powered smart heater that was constructed performed admirably. The components used include a PIC16F876A microcontroller, a 12 V/300 W monocrystalline PV, a 12 V/ 40 A charge controller, and 12 V / 150 W dc submersible heating element among others The temperature control capacity of the fabrication makes it useful in water management system applications such as in aquaculture fingerling hatchery, hot water dispenser and shower among others. The system showed excellent performance in water temperature control using a 150 watts dc heater. Keywords:Solar, heater, photo-voltaic, temperature-controlled, direct current, alternating current .

Potential of Utilization of Renewable Energy Technologies in Gulf Countries

This critical review report highlights the enormous potentiality and availability of renewable energy sources in the Gulf region. The earth suffers from extreme air pollution, climate changes, and extreme problems due to the enormous usage of underground carbon resources applications materialized in industrial, transport, and domestic sectors. The countries under Gulf Cooperation Council, i.e., Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the United Arab Emirates, mainly explore those underground carbon resources for crude oil extraction and natural gas production. As a nonrenewable resource, these are bound to be exhausted in the near future. Hence, this review discusses the importance and feasibility of renewable sources in the Gulf region to persuade the scientific community to launch and explore renewable sources to obtain the maximum benefit in electric power generation. In most parts of the Gulf region, solar and wind energy sources are abundantly available. However, attempts to harness those resources are very limited. Furthermore, in this review report, innovative areas of advanced research (such as bioenergy, biomass) were proposed for the Gulf region to extract those resources at a higher magnitude to generate surplus power generation. Overall, this report clearly depicts the current scenario, current power demand, currently installed capacities, and the future strategies of power production from renewable power sources (viz., solar, wind, tidal, biomass, and bioenergy) in each and every part of the Gulf region.

The determinants of renewable energy sources for the fueling of green and sustainable economy

This study seeks to evaluate potential solutions to Pakistan's energy shortages based on a renewable green hydrogen source provided by geothermal, wind, biomass, and solar energy. To this end, the application of multi-Criteria Decision Analysis (MCDA) and the Fuzzy-analytical hierarchical process was tested on four primary criteria: social acceptance, economic, commercialization, and environmental. The Data Envelopment Analysis (DEA) is used to analyze the development of hydrogen energy using existing renewable sources under the set parameters. Based on results from the fuzzy-led DEA study, the efficiency of wind energy sources is best adapted to produce hydrogen energy for all four criteria in Pakistan. The DEA-led analysis also deems wind energy to be Pakistan's effective source of hydrogen energy. In other words, to produce hydrogen energy, the findings revealed the best optimal rank 1.00 for wind energy, second highest score 0.97 for biomass, third rank for solar energy with a score of 0.75, and geothermal ranked at last position with a score of 0.662. The findings emphasize that the development of wind energy projects will help to fulfill local energy requirements and minimize fossil energy usage. This study can assist policymakers design fact-based initiatives in their particular regions of hydrogen energy.

Data-driven battery operation for energy arbitrage using rainbow deep reinforcement learning

As the world seeks to become more sustainable, intelligent solutions are needed to increase the penetration of renewable energy. In this paper, the model-free deep reinforcement learning algorithm Rainbow Deep Q-Networks is used to control a battery in a microgrid to perform energy arbitrage and more efficiently utilise solar and wind energy sources. The grid operates with its own demand and renewable generation, as well as dynamic energy pricing from a real wholesale energy market. Four scenarios are tested including using demand and price forecasting produced with local weather data. The algorithm and its subcomponents are evaluated against an actor-critic method and a linear programming model with Rainbow able to outperform all other methods. This research shows the importance of using the distributional approach for reinforcement learning for complex environments, as well as how it can visualise and contextualise the agents’ behaviour for real-world applications.

Primary Energy Use and Environmental Effects of Electric Vehicles

The global market of electric vehicles has become one of the prime growth industries of the 21st century fueled by marketing efforts, which frequently assert that electric vehicles are “very efficient” and “produce no pollution.” This article uses thermodynamic analysis to determine the primary energy needs for the propulsion of electric vehicles and applies the energy/exergy trade-offs between hydrocarbons and electricity propulsion of road vehicles. The well-to-wheels efficiency of electric vehicles is comparable to that of vehicles with internal combustion engines. Heat transfer to or from the cabin of the vehicle is calculated to determine the additional energy for heating and air-conditioning needs, which must be supplied by the battery, and the reduction of the range of the vehicle. The article also determines the advantages of using fleets of electric vehicles to offset the problems of the “duck curve” that are caused by the higher utilization of wind and solar energy sources. The effects of the substitution of internal combustion road vehicles with electric vehicles on carbon dioxide emission avoidance are also examined for several national electricity grids. It is determined that grids, which use a high fraction of coal as their primary energy source, will actually increase the carbon dioxide emissions; while grids that use a high fraction of renewables and nuclear energy will significantly decrease their carbon dioxide emissions. Globally, the carbon dioxide emissions will decrease by approximately 16% with the introduction of electric vehicles.

Optimized Integration of Hybrid Renewable Sources with Long‐Life Battery Energy Storage in Microgrids for Peak Power Shaving and Demand Side Management under Different Tariff Scenario

Herein, a novel cost‐effective demand side management and peak power shaving are demonstrated by optimized scheduling of renewable energy source integrated grid‐connected hybrid microgrid and vanadium redox flow battery (VRFB) storage. To promote the waste to energy for the rural and urban communities, the biogas energy source is integrated along with the combined solar photovoltaic (PV) and wind energy sources. As a long life and scalable battery storage solution, VRFB storage is adopted for peak shaving and microgrid performance reliability. Power generation from the renewable sources, VRFB charge–discharge, and grid power usage are scheduled considering two practical electricity tariff profiles, thus making the overall microgrid system operation cost‐effective and efficient. The optimized cost of energy management is determined considering the operation and maintenance cost of energy source and battery storage, grid tariff profile, and power import and export. The performance of the overall control scheme is experimentally validated by a grid‐connected hybrid microgrid consisting of 10 kWp solar PV, 1 kW wind turbine, 15 kVA biogas engine generator, and 1 kW 6 h VRFB storage. The proposed energy management scheme is scalable and a generalized one that claims to be suitable for large‐scale renewable energy integrated power systems as well.

Hybrid cross entropy—cuckoo search algorithm for solving optimal power flow with renewable generators and controllable loads

AbstractThe demand of energy is increasing due to the growing population of the world and improvements of technology. One of the best significant solution techniques to fulfill this energy demand is utilization of renewable energy sources (RESs). Modern power systems, which integrate RESs, such as wind, small hydro or solar energy sources need to carry out the uncertainty by the accessibility of demanded or injected power. Therefore, it is necessary to consider uncertainty costs in optimal power flow (OPF) problems. This paper proposed a novel hybrid meta‐heuristic algorithm entitled cross entropy—cuckoo search algorithm (CE‐CSA). The application of levy flights in the cuckoo search algorithm (CSA) improves the local exploitation capability while the CE method is used in the initial stage for global exploration due to its fast convergence. The effectiveness of the proposed hybrid algorithm has been demonstrated in solving the OPF problem, considering RESs and controllable loads for different stochastic scenarios in a benchmark system to minimize the total operation cost. To verify its effectiveness, its performance is compared with the most advanced and recently proposed hybrid meta‐heuristic techniques. Simulation results show that the proposed algorithm can solve the OPF problems with RESs and controllable loads efficiently and can give better solutions compared to different techniques. The conventional statistical method called analysis of variance (ANOVA) test, Tukey honestly significant difference test, and Wilcoxon sign rank test are performed for comparative analysis of different techniques. The results of this test show the validation of CE‐CSA compared to different optimization techniques.