Wind Power Sources Research Articles

An Improved Non-dominated Sorting Genetic Algorithm for the Optimal Economic Emission Dispatch Problem with Wind Power Sources

An Improved Non-dominated Sorting Genetic Algorithm for the Optimal Economic Emission Dispatch Problem with Wind Power Sources

Optimal solution of multiobjective stable environmental economic power dispatch problem considering probabilistic wind and solar PV generation

The Environmental Economic Power Dispatch (EEPD) problem, a widely studied bi-objective nonlinear optimization challenge in power systems, traditionally focuses on the economic dispatch of thermal generators without considering network security constraints. However, environmental sustainability necessitates reducing emissions and increasing the penetration of renewable energy sources (RES) into the electrical grid. The integration of high levels of RES, such as wind and solar PV, introduces stability issues due to their uncertain and intermittent nature. This paper addresses these concerns by formulating and solving the Stable Environmental Economic Power Dispatch (SEEPD) problem, which includes fixed zonal reserve capacity from conventional thermal generators and uncertain reserves from RES. Uncertainties in RES and load demand are modeled using random variable generation techniques, applying Gaussian, Weibull, and log-normal probability density functions (PDFs) for load demand, wind velocity, and solar irradiance, respectively. The stochastic SEEPD problem extends to multiple periods by replicating the single-period problem for each interval in the planning horizon, linking periods through intertemporal ramping costs, physical ramp rate, and fixed zonal reserve constraints on dispatch variables. Multi-Objective Evolutionary Algorithms (MOEAs) have gained importance for solving complex nonlinear problems involving multi-objective functions. This paper applies the latest MOEAs to tackle the proposed SEEPD problem, incorporating stochastic wind and solar PV power sources. Network security constraints, such as transmission line capacities and bus voltage limits, are considered along with constraints on generator capabilities and intertemporal spinning reserves, ramp-up and ramp-down constraints for thermal generators. A bidirectional coevolutionary-based multi-objective evolutionary algorithm is employed, integrating an advanced constraint-handling technique to ensure compliance with system constraints. The simulation results show that the proposed formulation achieves a better trade-off between various conflicting objective functions compared to other state-of-the-art MOEAs.

Critical lines dynamic identification for grid-connected wind power system under N-k contingency based on Oscillating equations

Abstract As the penetration rate of wind power and other renewable energy sources increases, power systems are more prone to experiencing multiple component failures, known as N-k contingency Identifying and then closely monitoring and protecting critical components during N-k contingency can effectively prevent widespread cascading failures. The primary task in identifying critical components is to model the power system; A refined model better captures the time-varying characteristics of electrical components. Therefore, a dynamic model of line power flow decay and oscillation based on swing equations is established; it introduces time-varying saturated cut-set and transfer margin indices to identify critical lines. Simulations on the IEEE-118 nodes system, along with MATCASC cascading failure simulations, demonstrate that targeting identified critical lines as deliberate attack points in the grid results in a normalized remaining load demand decrease to 50% of the original.

Multi‐objective multiperiod stable environmental economic power dispatch considering probabilistic wind and solar PV generation

AbstractThe economic‐environmental power dispatch (EEPD) problem, a widely studied bi‐objective non‐linear optimization challenge in power systems, traditionally focuses on the economic dispatch of thermal generators without considering network security constraints. However, environmental sustainability necessitates reducing emissions and increasing the penetration of renewable energy sources (RES) into the electrical grid. The integration of high levels of RES, such as wind and solar PV, introduces stability issues due to their uncertain and intermittent nature. This article addresses these concerns by formulating and solving the economic environmental and stable power dispatch (EESPD) problem, which includes fixed zonal reserve capacity from conventional thermal generators and uncertain reserves from RES. Uncertainties in RES and load demand are modelled using random variable generation techniques, applying Gaussian, Weibull, and log‐normal probability density functions (PDFs) for load demand, wind velocity, and solar irradiance, respectively. The stochastic EESPD problem extends to multiple periods by replicating the single‐period problem for each interval in the planning horizon, linking periods through intertemporal ramping costs, physical ramp rate, and fixed zonal reserve constraints on dispatch variables. Multi‐objective evolutionary algorithms (MOEAs) have gained prominence for solving complex non‐linear problems involving multi‐objective functions. This article applies the latest MOEAs to tackle the proposed EESPD problem, incorporating stochastic wind and solar PV power sources. Network security constraints, such as transmission line capacities and bus voltage limits, are considered along with constraints on generator capabilities and intertemporal spinning reserves, ramp‐up and ramp‐down constraints for thermal generators. A bidirectional coevolutionary‐based multi‐objective evolutionary algorithm is employed, integrating an advanced constraint‐handling technique to ensure compliance with system constraints. The simulation results show that the proposed formulation achieves a better trade‐off between various conflicting objective functions compared to other state‐of‐the‐art MOEAs.

OPTIMAL CONTROL OF A HYBRID PV-WIND POWER SYSTEM FOR ISLANDED HYBRID MICROGRID USING MOGA-FUZZY LOGIC CONTROLLER

The paper presents a comprehensive approach to managing the complexities associated with the integration of photovoltaic (PV) and wind energy systems in islanded microgrid environments. These microgrids, often isolated from the main power grid, face unique challenges due to the variability and intermittency of renewable energy sources, which can lead to instability and inefficiencies in power supply. To address these challenges, the study introduces a novel control strategy that combines a Multi-Objective Genetic Algorithm (MOGA) with a Fuzzy Logic Controller. The MOGA is utilized to explore a broad solution space and identify optimal control parameters that balance multiple objectives, including minimizing power fluctuations, maximizing the utilization of renewable energy, and ensuring a stable and reliable power supply. This optimization is crucial in islanded microgrids, where the lack of a larger grid connection necessitates highly efficient and responsive energy management systems to maintain stability. The Fuzzy Logic Controller, on the other hand, provides a flexible and adaptive control mechanism that responds to the dynamic and often unpredictable nature of renewable energy generation. By interpreting input variables in a way that mimics human decision-making, the Fuzzy Logic Controller can effectively handle the inherent uncertainties and non-linearities in the power system, adjusting the operation of the PV and wind power sources, as well as any supplementary energy storage systems, to optimize performance. This adaptive capability is particularly beneficial in scenarios where rapid changes in weather conditions can significantly impact energy generation and consumption patterns. The integration of MOGA with Fuzzy Logic not only enhances the decisionmaking process but also allows for the simultaneous consideration of multiple objectives, which is a critical advancement over traditional single-objective optimization techniques. This dual approach ensures that the hybrid PV-wind power system operates at its highest efficiency, balancing the need for sustainability with the practical requirements of reliability and economic viability. The results from extensive simulations, which model various operational scenarios and disturbances, demonstrate that the proposed MOGA-Fuzzy Logic Controller significantly improves the stability and efficiency of the islanded microgrid. The system is shown to effectively manage power flows, reduce dependency on fossil fuel-based backup generators, and increase the overall penetration of renewable energy. The proposed MOGA-Fuzzy Logic Controller stands out as a promising solution for the optimal control of hybrid PV-wind power systems, offering a viable pathway for achieving sustainable energy goals in islanded and other decentralized grid settings. This work not only advances academic knowledge but also has practical implications for the design and operation of future energy systems, making it a significant contribution to the field of renewable energy and power system engineering.

Robust LFC design using adaptive neuro‐fuzzy inference‐aided optimal fractional‐order PIDA control for perturbed power systems with solar and wind power sources

AbstractMaintaining stability in modern power systems is challenging due to complex structures, rising power demand, and load disturbances. The integration of renewable energy sources further threatens stability by causing imbalances between generation and demand. Conventional load frequency stabilization methods fall short in such scenarios. This paper proposes an optimal fractional‐order proportional‐integral‐derivative‐acceleration (FOPIDA) controller, enhanced by a robust adaptive neuro‐fuzzy inference system (ANFIS), to improve load frequency control and reliability in power systems with wind and solar generators. First, the dynamical model of a multi‐area interconnected power system, including a thermal power plant, wind turbine, and solar photovoltaic generators, is developed. A decentralized ANFIS‐FOPIDA controller is then designed for load frequency control objectives. The gains of this controller are optimized using the whale optimization algorithm (WOA), focusing on frequency deviation and tie‐line power exchange. Simulations on a New England IEEE 10‐generator 39‐bus power system demonstrate the approach's effectiveness under various disturbances, including random load‐generation disturbances and nonlinear generation behaviors. Comparisons with other strategies, such as fractional order (FO) beetle swarm optimization algorithm (FOBSOA)‐FOPIDA, WOA‐PIDA, and WOA‐ANFIS‐PIDA, and recent control approaches highlight the superior performance of the WOA‐ANFIS‐FOPIDA method in enhancing power system stability.

Optimal power dispatch in hybrid power system for medium- and large-scale practical power systems using self-adaptive bonobo optimizer algorithm

Incorporating renewable energy sources (RESs) introduces a notable amount of uncertainty in the optimal planning and operation of electrical power grids. Under these circumstances, this paper proposes the application of a recently introduced metaheuristic optimization technique to solve the stochastic optimal power flow (OPF) problem involving wind and solar power sources. The self-adaptive bonobo optimizer (SaBO) is used to minimize three distinct objective functions: (i) Total generation cost (TGC) minimization, including both thermal and wind/solar generation costs, (ii) Power loss minimization, (iii) Combined generation cost and emissions effect minimization. The costs associated with the stochastic generation of wind and solar power included direct costs, reserves and penalty costs from the overestimation and underestimation of available wind and solar power, respectively. The performance of the proposed algorithm is evaluated on two power systems: the modified IEEE 30-bus and the Algerian DZA 114-bus test systems. To demonstrate the efficacy of the SaBO, the obtained results have been compared with those obtained from the Kepler optimization algorithm (KOA) and other recently published optimizers under the same case studies and constraints. The comparative results clearly show the superiority of the SaBO algorithm over all other well-known optimization algorithms provided in the literature for solving the OPF problem. This is evidenced by minimizing total generation costs of 781.2363 $/h for the modified IEEE 30-bus and 16,706.1630 $/h for the Algerian DZA-114-bus system. Furthermore, the integration of RES led to a notable 2.33% and 11.67% reduction in total generation cost for the IEEE 30-bus and Algerian DZA 114-bus systems, respectively, compared to their initial configurations without RESs. The promising findings highlight the powerful of the optimizer to solve non-linear and complex optimization problems in power systems.

Techno-economic analysis and comparison of hydrogen supply using offshore wind power sources on the coast of the Korean peninsula

In response to the rapidly changing industry caused by climate change, the Korean government has set a goal of supplying 3 million tons of green hydrogen by 2050. This study aims to determine the leveled cost of hydrogen (LCOH) resulting from floating offshore wind power generation. Initially, we created a numerical model to represent the power curve for large-capacity offshore wind power generation. Additionally, we accounted for the variability in electrolytic cell efficiency by modeling predictions based on real PEMEC parameters using the Aspen Custom Model. The power-hydrogen quantity was then predicted using Aspen Plus. A comparison is also made regarding the transportation of gaseous hydrogen and liquid hydrogen. Gaseous hydrogen is produced through pipelines, while liquid hydrogen is produced using a modified Claude cycle and transported via trucks. Finally, a Techno-economic analysis was conducted, considering the net present value (NPV) derived from capital expenditure (CAPEX) and operating expenditure (OPEX) throughout the entire process. The analysis of LCOH indicated that the most economically viable option for the region with the lowest LCOH per kg is the supply of liquid hydrogen, priced at $11.04/kgH2. Our sensitivity analysis also reveals that while liquid hydrogen is more cost-effective at up to 250 MW, gaseous hydrogen becomes cheaper as its scale exceeds 500 MW because of its greater sensitivity to production volume.

Research on the Changing Trends in Electricity Prices in Gansu Province Considering High Future Penetration of Sustainable Energy

Carbon peaking and carbon neutrality goals have put forward new requirements for the development of sustainable energy. As an important part of the energy system, the power system plays an important role in achieving sustainable energy development. Future power systems will be sustainable, with a high proportion of renewable energy such as photovoltaic and wind power. With advancements in technology and the sustainable development of power systems, the construction costs of wind power, photovoltaics, and other renewable energy sources will continue to decline. At the same time, in order to cope with the uncertainty of renewable energy, more flexible resources need to be built. Under a high proportion of sustainable energy penetration, the utilization rate of flexible resources is low, which will lead to an increase in adjustment costs. Economic issues have an important impact on the development of new power systems, and power system construction will in turn affect social and economic development. Therefore, it is necessary to analyze changing trends in electricity prices in the process of power system sustainability from a quantitative perspective, so as to provide guidance for future planning. In view of the above problems, this paper takes Gansu Province as an example, predicts future power installation trends, by analyzing the power system form evolution, and uses production operation simulation technology to determine the power generation and consumption situation. On this basis, the costs of various power supplies and power grids are calculated, and trends in electricity prices in Gansu Province are forecast.

Capacity Demand Analysis of Rural Biogas Power Generation System with Independent Operation Considering Source-Load Uncertainty

With the greatly increased penetration rate of wind power, photovoltaic, and other new energy sources in the power system, the proportion of controllable units gradually decreased, resulting in increased system uncertainty. The biogas power generation system can effectively alleviate the pressure caused by source-load uncertainty in such high-permeability systems of new energy sources such as wind power and photovoltaic. Hence, from the perspective of the power system, this paper introduces a capacity demand analysis method for a rural biogas power generation system capable of independent operation amidst source-load uncertainty. To enhance the depiction of pure load demand uncertainty, a scene set generation method is proposed, leveraging quantile regression analysis and Gaussian mixture model clustering. Each scene’s data and probability of occurrence elucidate the uncertainty of pure load demand. An integrated optimal operation model for new energy and biogas-generating units, free from energy storage capacity constraints, is established based on the generated scenario set. Addressing considerations such as biogas utilization rate and system operation cost, a biogas storage correction model, utilizing the gas storage deviation degree index and the cost growth rate index, is developed to determine biogas demand and capacity. The example results demonstrate the significant reduction in gas storage construction costs and charging and discharging imbalances achieved by the proposed model while ensuring systemic operational cost effectiveness.

Synergizing Wind and Solar Power: An Advanced Control System for Grid Stability

In response to the escalating global energy crisis, the motivation for this research has been derived from the need for sustainable and efficient energy solutions. A gap in existing renewable energy systems, particularly in terms of stability and efficiency under variable environmental conditions, has been recognized, leading to the introduction of a novel hybrid system that combines photovoltaic (PV) and wind energy. The innovation of this study lies in the methodological approach that has been adopted, integrating dynamic modeling with a sophisticated control mechanism. This mechanism, a blend of model predictive control (MPC) and particle swarm optimization (PSO), has been specifically designed to address the fluctuations inherent in PV and wind power sources. The methodology involves a detailed stability analysis using Lyapunov’s theorem, a critical step distinguishing this system from conventional renewable energy solutions. The integration of MPC and PSO, pivotal in enhancing the system’s adaptability and optimizing the maximum power point tracking (MPPT) process, improves control efficiency across key components like the doubly fed induction generator (DFIG), rectifier-sourced converter (RSC), and grid-side converter (GSC). Through rigorous MATLAB simulations, the system’s robust response to changing solar irradiance and wind velocities has been demonstrated. The key findings confirm the system’s ability to maintain stable power generation, underscoring its practicality and efficiency in renewable energy integration. Not only has this study filled a crucial gap in renewable energy control systems, but it has also set a precedent for future research in sustainable energy technologies.

Dynamic Modelling of Wind Farms: a Comparative Study Between Two Modelling Approaches.

World’s energy needs have encouraged wind power sources. An increasing number of wind farms are being connected to the power grid and accurate wind farm models need to be developed to study the dynamic behaviour of them. This paper reports an investigation to compare two different modelling approaches of a real wind farm. This study is applied to the dynamic model of a real wind farm located at El Perdón, in Navarre, (Northern Spain). It consists of 30 Squirrel Cage Induction Machine (SCIM) wind generators of 660 kW and a nominal voltage of 690V. The wind farm feeds three local loads at 66 kV and is connected through two transformers to the network of 220KV.

Unraveling energy hub dynamics and political economy toward sustainable urban energy: A multifaceted examination of policy decision making and implementation

In this study, we introduce a visionary approach to sustainable urban energy systems by proposing a multi-carrier power grid or energy hub (EH) that seamlessly integrates electric and gas sources. Our analysis spans the course of four distinct seasons, where we meticulously scrutinize the dynamic demand profiles encompassing power, heat, and cooling power. Our findings reveal significant season-dependent variations in the performance of the EH system, highlighting the propensity for heightened energy consumption during warmer seasons, primarily driven by cooling needs, while a contrasting trend emerges during colder seasons with an emphasis on heating requirements. Furthermore, we investigate the pivotal role of user engagement in demand response programs, elucidating how their active participation results in a notable reduction in overall power consumption, particularly during the warmer months, thereby reshaping energy utilization patterns. To address the intricate optimization challenges inherent in this multifaceted urban energy ecosystem, we introduce the innovative balanced seagull optimization algorithm, which offers an effective solution. Leveraging simulations of the EH system, which harnesses the potential of solar and wind power sources, our study demonstrates compelling outcomes, including amplified EH profits, a decrease in energy system costs, and a reduction in social welfare expenditures. This multifaceted examination underscores the complex interplay between policy decisions, the imperative for sustainable urban energy solutions, and the ever-evolving political-economic landscape. As we navigate toward a more sustainable and resilient urban energy future, this research provides invaluable insights that empower policymakers, urban planners, and stakeholders to make informed decisions for the benefit of society and the environment.

Low‐carbon economic optimal operation strategy of rural multi‐microgrids based on asymmetric Nash bargaining

AbstractThe multi‐microgrids operation can greatly improve the proportion of renewable energy integration and power reliability of rural microgrids through energy trading between adjacent microgrids. Therefore, this paper proposes an optimization method for the low‐carbon economic operation of rural microgrids which contain wind power, photovoltaic, biogas, and other common rural renewable energy sources. Using the asymmetric Nash bargaining model which can form the non‐linear energy mapping function to quantify the bargaining power of each microgrid to establish the multi‐microgrids energy trading model, and the non‐convex trading optimization problem is equivalently transformed into two convex optimization subproblems, subproblem 1 is microgrids alliance costs containing the emission costs, and subproblem 2 is cooperative benefits distribution. Then, the accelerated Alternating Direction Method of Multipliers (ADMM) method is used to solve subproblem 1 and subproblem 2, respectively, and the privacy of transaction information is also protected. The results show that: compared with the single‐microgrid operation, the combined operation of rural multi‐microgrids with biogas can greatly improve the renewable energy consumption rate, reduce carbon emission, and improve the profit income of the alliance and each microgrid. Meanwhile, the accelerated ADMM algorithm used can greatly improve the solution speed.

Comparison of onshore/offshore wind power hydrogen production through water electrolysis by life cycle assessment

A comprehensive life cycle assessment (LCA) was conducted to evaluate three prominent water electrolysis technologies: alkaline electrolysis technology (AEL), polymer electrolyte membrane electrolysis technology (PEM), and solid oxide electrolysis technology (SOEC). These technologies were analyzed in conjunction with both onshore and offshore wind power sources. The assessment encompassed four environmental factors: global warming potential, acidification potential, ozone depletion potential, and eutrophication potential. By quantitatively comparing all six methods while considering their environmental impacts, it has been concluded that the combination of onshore wind power and polymer electrolyte membrane electrolysis technology for hydrogen production demonstrates the lowest global warming potential with a value of 0.0936 kg CO2-eq. This method offers significant advantages in terms of its environmental impact on the entire ecosystem.

Mapping the potential for pumped storage using existing lower reservoirs

The increasing utilization of wind and solar power sources to lower CO2 emissions in the electric sector is causing a growing disparity between electricity supply and demand. Consequently, there is a heightened interest in affordable energy storage solutions to address this issue. Pumped Hydropower Storage (PHS) emerges as a promising option, capable of providing both short and long-term energy storage at a reasonable cost, while also offering the advantage of freshwater storage. To identify potential PHS locations in Brazil existing hydroelectric reservoirs as the lower reservoirs, we employed an innovative methodology that combines (i) plant-siting model that leverages high-resolution topographical and hydrological data to identify the most promising sites for further studies. (ii) An economic methodology was applied to configure PSH projects identified by the plant-siting model in terms of their installed capacity and discharge time, and to select the most attractive projects. (iii) A comprehensive analysis of the socio-environmental impacts of the projects was carried out, which enables the elimination of projects with severe impacts. Results created a ranking of 5600 mutually exclusive projects by net present value (NPV). The highest NPV is 2145 USD which refers to a PHS plant in the Doce Basin and Salto Grande dam as the lower reservoir. The upper reservoir stores 0.36 km3 of water and a 75 m high dam, the PHS has a 2 km tunnel, a 1 GW power capacity and discharge rate of 220 h. The paper shows a vast potential for weekly, monthly, and seasonal PHS with existing lower reservoirs in Brazil.

Model Predictive Secondary Frequency Control for Islanded Microgrid under Wind and Solar Stochastics

As microgrids are the main carriers of renewable energy sources (RESs), research on them has been receiving more attention. When considering the increase in the penetration of renewable energy sources/distributed generators (DGs) in microgrids, their low inertia and high stochastic power disturbance pose more challenges for frequency control. To address these challenges, this paper proposes a model predictive control (MPC) secondary control that incorporates an unknown input observer and where RESs/DGs use a deloading virtual synchronous generator (VSG) control to improve the system’s inertia. An unknown input observer is employed to estimate the system states and random power disturbance from the RESs/DGs and load to improve the effect of the predictive control. The distributed restorative power of each DG is obtained by solving the quadratic programming (QP) optimal problem with variable constraints. The RESs/DGs are given priority to participate in secondary frequency control due to the proper weighting factors being set. An islanded microgrid model consisting of multiple photovoltaic and wind power sources was built. The simulation results demonstrate that the proposed method improves the system frequency, restoration speed, and reduces frequency deviations compared with the traditional secondary control method.

Large-scale wind power grid integration challenges and their solution: a detailed review.

Despite global warming, renewable energy has gained much interest worldwide due to its ability to generate large-scale energy without emitting greenhouse gases. The availability and low cost of wind energy and its high efficiency and technological advancements make it one of the most promising renewable energy sources. Hence, capturing large amounts of wind energy is essential today. The large-scale integration of wind power sources must be evaluated and mitigated to develop a sustainable future power system. Wind energy research and the government are working together to overcome the potential barriers associated with its penetration into the power grid. This paper reviews the social, environmental, and cost-economic impacts of installing large-scale wind energy plants. A valuable review of wind energy technology and its challenges is also presented in this paper, including the effects of wind farms on nearby communities, generation uncertainty, power quality issues, angular and voltage stability, reactive power support, and fault ride-through capability. Besides, socioeconomic, environmental, and electricity market challenges due to the grid integration of wind power are also investigated. Finally, potential technical challenges to integrating large-scale wind energy into the power grid are reviewed regarding current research and their available mitigation techniques.

A leader supply-demand-based optimization for large scale optimal power flow problem considering renewable energy generations

The supply-demand-based optimization (SDO) is among the recent stochastic approaches that have proven its capability in solving challenging engineering tasks. Owing to the non-linearity and complexity of the real-world IEEE optimal power flow (OPF) in modern power system issues and like the existing algorithms, the SDO optimizer necessitates some enhancement to satisfy the required OPF characteristics integrating hybrid wind and solar powers. Thus, a SDO variant namely leader supply-demand-based optimization (LSDO) is proposed in this research. The LSDO is suggested to improve the exploration based on the simultaneous crossover and mutation mechanisms and thereby reduce the probability of trapping in local optima. The LSDO effectiveness has been first tested on 23 benchmark functions and has been assessed through a comparison with well-regarded state-of-the-art competitors. Afterward, Three well-known constrained IEEE 30, 57, and 118-bus test systems incorporating both wind and solar power sources were investigated in order to authenticate the performance of the LSDO considering a constraint handling technique called superiority of feasible solutions (SF). The statistical outcomes reveal that the LSDO offers promising competitive results not only for its first version but also for the other competitors.

A combined wind-PV power prediction method based on clustering prescreening

Due to the volatility caused by the high uncertainty of renewable energy, the large-scale integration of renewable energy into the electric power grid brings huge challenges to the scheduling and operation of the electric power system. Effective power prediction of power source can not only relieve the pressure of power system peaking and frequency regulation, but also improve the accuracy of decision-making. So as to make the most of the information between wind and photovoltaic power source, this paper puts forward a wind-PV joint power forecasting model. First, the raw power data is pre-screened by clustering to select the closer connection between wind and PV power; next, the screened data are passed through the upper CNN structure respectively after pre-processed. Then, the outputs from CNN modules are fed together into the prediction layer, which include LSTM layers and fully connected layers. Finally, the proposed wind-PV joint prediction model is verified by simulation experiments.