Variable Wind Power Research Articles

On the potential role of flexible Norwegian hydropower in managing challenging renewable energy variability events in Europe

Abstract Previous research has identified flexible Norwegian hydropower as one potential key resource for managing variations in wind and solar power in Northern Europe. There is, however, a need for further detailed examination of this potential role of Norwegian hydropower based on updated future scenarios and using the latest data and model tools available. We analyze potential power system impacts of expanding Norwegian hydropower flexibility and Norway-Europe transmission, considering renewable energy variability based on a simulation for the historical weather years 1991-2020. The simulations are performed using FanSi, a stochastic optimization model for analyzing large-scale power systems with significant shares of hydropower combined with high shares of wind/solar power. A year 2050 scenario for Europe from the integrated energy system model SCOPE SD is used as framework for our analysis with FanSi. Our results highlight how expanded hydropower and transmission can potentially reduce price spikes during periods of low wind/solar output, reduce wind/solar energy curtailment during periods of high wind/solar output; and reduce price differences between interconnected areas during periods of either low or high wind/solar output. We demonstrate that these effects are attributable to more dynamic operation and expanded operational ranges of hydropower and transmission in the simulations assuming expanded hydropower and transmission capacities. We acknowledge high fundamental uncertainty in modelling a future system for the year 2050.

Reconfigurable intelligent surfaces using NOMA with wind energy harvesting and power variation

Reconfigurable intelligent surfaces using NOMA with wind energy harvesting and power variation

A Raster-Based Multi-Objective Spatial Optimization Framework for Offshore Wind Farm Site-Prospecting

Siting an offshore wind project is considered a complex planning problem with multiple interrelated objectives and constraints. Hence, compactness and contiguity are indispensable properties in spatial modeling for Renewable Energy Sources (RES) planning processes. The proposed methodology demonstrates the development of a raster-based spatial optimization model for future Offshore Wind Farm (OWF) multi-objective site-prospecting in terms of the simulated Annual Energy Production (AEP), Wind Power Variability (WPV) and the Depth Profile (DP) towards an integer mathematical programming approach. Geographic Information Systems (GIS), statistical modeling, and spatial optimization techniques are fused as a unified framework that allows exploring rigorously and systematically multiple alternatives for OWF planning. The stochastic generation scheme uses a Generalized Hurst-Kolmogorov (GHK) process embedded in a Symmetric-Moving-Average (SMA) model, which is used for the simulation of a wind process, as extracted from the UERRA (MESCAN-SURFEX) reanalysis data. The generated AEP and WPV, along with the bathymetry raster surfaces, are then transferred into the multi-objective spatial optimization algorithm via the Gurobi optimizer. Using a weighted spatial optimization approach, considering and guaranteeing compactness and continuity of the optimal solutions, the final optimal areas (clusters) are extracted for the North and Central Aegean Sea. The optimal OWF clusters, show increased AEP and minimum WPV, particularly across offshore areas from the North-East Aegean (around Lemnos Island) to the Central Aegean Sea (Cyclades Islands). All areas have a Hurst parameter in the range of 0.55–0.63, indicating greater long-term positive autocorrelation in specific areas of the North Aegean Sea.

Application of an Optimal Fractional-Order Controller for a Standalone (Wind/Photovoltaic) Microgrid Utilizing Hybrid Storage (Battery/Ultracapacitor) System

Nowadays, standalone microgrids that make use of renewable energy sources have gained great interest. They provide a viable solution for rural electrification and decrease the burden on the utility grid. However, because standalone microgrids are nonlinear and time-varying, controlling and managing their energy can be difficult. A fractional-order proportional integral (FOPI) controller was proposed in this study to enhance a standalone microgrid’s energy management and performance. An ultra-capacitor (UC) and a battery, called a hybrid energy storage scheme, were employed as the microgrid’s energy storage system. The microgrid was primarily powered by solar and wind power. To achieve optimal performance, the FOPI’s parameters were ideally generated using the gorilla troop optimization (GTO) technique. The FOPI controller’s performance was contrasted with a conventional PI controller in terms of variations in load power, wind speed, and solar insolation. The microgrid was modeled and simulated using MATLAB/Simulink software R2023a 23.1. The results indicate that, in comparison to the traditional PI controller, the proposed FOPI controller significantly improved the microgrid’s transient performance. The load voltage and frequency were maintained constant against the least amount of disturbance despite variations in wind speed, photovoltaic intensity, and load power. In contrast, the storage battery precisely stores and releases energy to counteract variations in wind and photovoltaic power. The outcomes validate that in the presence of the UC, the microgrid performance is improved. However, the improvement is very close to that gained when using the proposed controller without UC. Hence, the proposed controller can reduce the cost, weight, and space of the system. Moreover, a Hardware-in-the-Loop (HIL) emulator was implemented using a C2000™ microcontroller LaunchPad™ TMS320F28379D kit (Texas Instruments, Dallas, TX, USA) to evaluate the proposed system and validate the simulation results.

Backstepping control of multilevel modified SVM inverter in variable speed DFIG-based dual-rotor wind power system

The backstepping control (BC) scheme has been successfully used in a variety of high-effectiveness industrial AC drives. This study presents the application of the BC technique based on multilevel modified space vector modulation (BC-MSVM) to get better the energy performance of a dual-rotor wind turbine system (DRWT). Two techniques are suggested to command the stator power of a doubly-fed induction generator (DFIG) driven by a DRWT. This work addresses the problems of the DRWT-based energy production system, such as stream fineness, power overshoot, and torque ripples. The use of a multilevel inverter in BC-MSVM led to an enhancement in the competence of the multilevel BC-MSVM technique and the system as a whole, and this is proven by the results performed using MATLAB software on a 1500 kW DFIG-DRWT. The use of a seven-level inverter led to a minimization in the rates of overshoot, ripples, steady-state error, and response time of active power by 26.66%, 53.84%, 2.09%, and 9.09%, respectively. Regarding the reactive power, the ratios were estimated at 18.12%, 34%, 25%, and 1.41%, respectively. These ratios prove that using a higher-level inverter significantly improves the voltage quality and characteristics of the DFIG-DRWT.

Enriching wind power utility through offshore wind-hydrogen-chemicals nexus: Feasible routes and their economic performance

The offshore wind-hydrogen strategy is crucial for achieving carbon neutrality, yet faces challenges like wind power variability, surplus hydrogen, and elevated costs. Recently, linking offshore wind, hydrogen, and chemicals is proposed to address these challenges. However, there is a lack of a comprehensive techno-economic analysis of this nexus. This study, as one of the first attempts, performs the techno-economic analysis of 8 feasible offshore wind-hydrogen-chemical nexus routes, in which factors of technology, economics, and financial policy are considered. The research indicates that Route 7, which incorporates offshore wind power utilizing proton exchange membrane technology for hydrogen production and blends it with natural gas on offshore platforms, demonstrates the most cost-effective outcome. The key factors influencing the cost reduction of these routes encompass the green hydrogen price, capital costs, internal rate of return, electricity price, value-added tax rate, and loan ratio. Among these, the green hydrogen price is the primary determinant influencing the cost of green products. If the price of green hydrogen decreases to 2.19 USD/kg, green synthetic ammonia can meet the current standard of 605.47 USD/t. Finally, the results can provide important reference to support stakeholders for seeking cost-effective routes of offshore wind-hydrogen-chemical nexus in China and other regions.

Integrated assessment of offshore wind and wave power resources in mainland Portugal

This study presents a comprehensive analysis of offshore wind and wave energy resources in mainland Portugal, aiming to facilitate sustainable energy exploitation. The study integrates the spatial distribution and temporal variability of those energy resources, as well as the interplay between wind and wave patterns and co-location feasibility. A 44-year dataset (1979–2022), derived from ERA5 reanalysis wind and SWAN wave model across the concession zones designated by the Portuguese government (labelled as P1 to P6) are utilised. The results indicate promising prospects for wind and wave power densities in these zones, emphasising the suitability of the offshore renewable energy extraction. Additionally, analysis revealed fluctuations in wind and wave power variability among the selected locations, with concession zone P3 (Leixões) emerging as a promising candidate due to its relatively lower variability and stable distribution patterns of both resources. The analysis on co-location feasibility demonstrated the potential for efficient energy extraction in location P5 proving to be the most favourable in wind-wave resource integration. Moreover, time lag analysis highlighted opportunities for optimising energy capture efficiency. Overall, this study provides valuable insights into the sustainable exploitation of wind and wave power resources, contributing to deliver informed decision-making for renewable energy investment.

Mesoscale weather systems and associated potential wind power variations in a midlatitude sea strait (Kattegat)

Abstract. Mesoscale weather systems cause spatiotemporal variability in offshore wind power, and insight into their fluctuations can support grid operations. In this study, a 10-year model integration with the kilometre-scale atmospheric model COnsortium for Small-scale MOdelling – CLimate Mode (COSMO-CLM) provided a wind and potential power fluctuation analysis in the Kattegat, a midlatitude sea strait with a width of 130 km and an irregular coastline. The model agrees well with scatterometer data away from coasts and small islands, with a spatiotemporal root-mean square difference of 1.35 m s−1. A comparison of 10 min wind speed at about 100 m with lidar data for a 2-year period reveals very good performance, with a slight model overestimation of 0.08 m s−1 and a high value for the Perkins skill score (0.97). From periodograms made using the Welch's method, it was found that the wind speed variability on a sub-hourly timescale is higher in winter compared to summer. In contrast, the wind power varies more in summer when winds often drop below the rated power threshold. During winter, variability is largest in the northeastern part of the Kattegat due to a spatial spin-up of convective systems over the sea during the predominant southwesterly winds. Summer convective systems are found to develop over land, driving spatial variability in offshore winds during this season. On average over the 10 summers, the mesoscale wind speeds are up to 20 % larger than the synoptic background at 17:00 UTC with a clear diurnal cycle. The winter-averaged mesoscale wind component is up to 10 % larger, with negligible daily variation. Products with a lower resolution like ERA5 substantially underestimate this ratio between the mesoscale and synoptic wind speed. Moreover, taking into account mesoscale spatial variability is important for correctly representing temporal variability in power production. The root-mean square difference between two power output time series, one ignoring and one accounting for mesoscale spatial variability, is 14 % of the total power generation.

Collaborative optimization of renewable energy power systems integrating electrolytic aluminum load regulation and thermal power deep peak shaving

Addressing renewable energy (RE) curtailment in power systems necessitates a comprehensive strategy leveraging peak regulation resources from both the power and load sides. On the power side, deep peak shaving of thermal power plants can mitigate surplus electricity during periods of high RE production. On the load side, energy-intensive industrial loads, characterized by large capacity and rapid adjustability, can respond effectively to energy deficits when RE is low. To enhance the peak regulation capacity for optimal RE accommodation, this paper proposes a collaborative optimization method combining electrolytic aluminum load (EAL) regulation with thermal power deep peak shaving (DPS). The study is initiated by developing a sophisticated peak regulation model for the electrolytic aluminum load (EAL), considering production characteristics, cost features, and safety constraints. Subsequently, a collaborative optimization model is formulated, integrating EAL regulation with thermal power deep peak shaving (DPS), aiming to minimize societal peak regulation costs (PRC). Applying this model to a practical regional grid in Yunnan Province reveals substantial reductions in RE curtailment rates, effective minimization of total social PRC, and enhanced operational economics of thermal power DPR under varying wind power scenarios. Notably, the proposed approach requires only minor adjustments to the EAL, ensuring production safety is maintained. This research provides valuable insights for the seamless integration of EAL into grid peak regulation services.

Characterizing Supply Reliability Through the Synergistic Integration of VRE towards Enhancing Electrification in Kenya

Decentralized electrical power systems, driven by variable renewable energy sources such as solar PV and wind, have the potential to provide accessible and sustainable energy, contributing to the realization of a zero-carbon transition. However, these sources are susceptible to extreme weather conditions, presenting a challenge to the reliability of the power system. With abundant resources and a significant rural population lacking access to electricity, Africa has emerged as a key area for research on variable renewable energy-based electricity generation. Despite this focus, there remains a substantial gap in understanding at regional-scale the potential and variability of solar and wind power across various time scales, as well asthe impact of available resource synergy. Thisstudy aims to bridge this knowledge gap by conducting comprehensive simulations of hybrid wind and solar energy systems, both on-grid and off-grid, across 20 geographically diverse locations in Kenya. Using high-resolution hourly time step data, we examine the effect of resource complementarity on system reliability at varying time scales: daily, monthly and annually. The study findings shows the available VRE resource exhibit moderate tendency for complementarity, and optimizing their deployment can reduce hourly variability by 20%, significantly enhancing supply reliability, especially in the northern and eastern regions.

An Initiative Towards Efficient and Sustainable V2G Technology Fed STATCOM for Grid connected Wind Energy System

The proliferation of renewable energy sources, particularly wind energy, has significantly contributed to the diversification of the global energy mix and the mitigation of greenhouse gas emissions. However, the intermittent nature of wind power generation poses challenges to the stability and reliability of the power grid. This paper explores the integration of Vehicle-to-Grid (V2G) technology with Static Synchronous Compensator (STATCOM) for grid-connected wind energy systems. Novelty. Vehicle-to-Grid (V2G) technology enables bidirectional energy flow between electric vehicles (EVs) and the power grid, allowing EV batteries to serve as energy storage devices. This capability can be leveraged to support grid stability and reliability by utilizing EV batteries to store surplus wind energy during periods of high generation and release it back to the grid during periods of low generation or high demand. Purpose. This effectively mitigates the intermittency and variability of wind power, smoothing out fluctuations and improving grid stability. Methods. By employing V2G technology and leveraging the dual-purpose battery solution can compensate the grid voltage fluctuations and improve power factors in grid-connected wind energy systems, enhancing overall system stability and reliability. Results. The Hystersis current controller (HCC) scheme is used for controlling STATCOM to enhance the dynamic performance. Practical value. The system is simulated in its entirety using the matrix laboratory (MATLAB) . The proposed sustainable model will pave the way for maintaining the true power supply under varying wind conditions.

Sizing Energy Storage Systems to Dispatch Wind Power Plants

Integrating wind power plants into the electricity grid poses challenges due to the intermittent nature of wind energy generation. Energy storage systems (ESSs) have shown promise in mitigating the intermittent variability associated with wind power. This paper presents a distributionally robust optimization (DRO) model for sizing energy storage systems to dispatch wind power plants. The variable wind power is formulated as a moment-based ambiguity set. Dispatchability is described by the expected value of the insufficient power of wind power relative to the dispatch command, which is a sum of nonlinear functions and is taken as the optimal index. A deterministic semi-definite positive model is derived to solve the problem effectively. Numerical studies are conducted to demonstrate the effectiveness and advantages of the proposed method.

Meeting the Energy Needs of Poultry Houses with Wind Turbine System under Tekirdağ Conditions and Its Environmental Effects

Türkiye has a favorable geographical location for renewable energy sources such as wind and solar. Accordingly, renewable energy sources can be used in the fast-growing bovine, ovine, and poultry farms that perform plant and animal production. Marmara Region is the leading production center of the Turkish poultry production industry. However, there are problems in maintaining environmental conditions in poultry houses. The fan-pad system plays an essential role in maintaining environmental conditions in the poultry houses. For this reason, the fan-pad system required for evaporative cooling in the poultry houses in Tekirdağ region was designed. The project was carried out in poultry houses with 6000 chickens and the dimension of the poultry house is 14 x 30 (420 m2). In the poultry house, a 16.8 m2 pad is needed for cooling, and a circulation pump with 0.2 kWh power is required for water circulation in the system. For mechanical ventilation, 11 units of aspirators with a diameter of 60 cm a power of 0.75 kWh, and a flow rate of 9500 m3h-1 will be sufficient for the system. For the ventilation and cooling system, an 8.45 kWh wind turbine system will supply the required energy. However, due to the variability of wind power, a turbine system larger than 8.45 kWh, such as 10 kWh, should be used. The cost of the cooling system planned with the wind turbine energy system reaches 35953 $. The use of wind turbine systems to generate electricity in agriculture will significantly reduce CO2 emissions to nature. In Tekirdağ conditions, 18250 kWh of electricity can be produced annually with a 10-kWh wind turbine system. With eco-friendly energy generation, it can play a major role in preventing climate change by avoiding the emission of 17155 kg of CO2 which is equivalent to the annual electricity production.

Multistage Stochastic optimization for mid-term integrated generation and maintenance scheduling of cascaded hydroelectric system with renewable energy uncertainty

The uncertainties resulting from the escalating penetration of renewable energy resources pose severe challenges to the efficient operation of modern power systems. Hydroelectricity is characterized by its flexibility, controllability, and reliability, and thus becomes one of the most ideal energy resources to hedge against such uncertainties. This paper studies the mid-term integrated generation and maintenance scheduling of a cascaded hydroelectric system (CHS) consisting of multiple cascaded reservoirs and hydroelectric units. To precisely describe the mid-term water regulation policies, the hydraulic coupling relationship and water-energy nexus of CHS are incorporated into the proposed optimization model. The uncertainties of natural water inflow and the power outputs of wind/solar energy generation are taken into consideration and captured via a stochastic process modeled by a scenario tree. A multistage stochastic optimization (MSO) approach is developed to coordinate the complementary operations of multiple energy resources, by optimizing the mid-term water resource management, generation scheduling, and maintenance scheduling of CHS. The proposed MSO model is formulated as a large-scale mixed-integer linear program that presents significant computational intractability. To address this issue, a tailored Benders decomposition algorithm is developed. Two real-world case studies are conducted to demonstrate the capability and characteristics of the proposed model and algorithm. The computational results show that the proposed MSO model can exploit the flexibility of hydroelectricity to efficiently respond to variable wind and solar power, and reserve water resources for the generation in peak months to reduce the consumption of fossil fuel. The proposed solution approach also exhibits promising computational efficiency when handling large-scale models.

Requirement on the Capacity of Energy Storage to Meet the 2 °C Goal

The inherent power fluctuations of wind, photovoltaic (PV) and bioenergy with carbon capture and storage (BECCS) create a temporal mismatch between energy supply and demand. This mismatch could lead to a potential resurgence of fossil fuels, offsetting the effects of decarbonization and affecting the realization of the Paris target by limiting global warming to below 2 °C in the 21st century. While application of energy storage is widely recommended to address this limitation, there is a research gap to quantify the impacts of energy storage limitation on global warming. Here, we analyzed the hourly variation of global wind and PV power during the period 1981–2020 and the monthly capacity of biomass production in 2019, and thus quantified the impact of decreasing the capacity of energy storage on global warming using a state-of-the-art Earth system model. We found that global warming by 2100 in the SSP1-2.6 scenario would increase by about 20% and exceed 2 °C without deploying energy storage facilities. Achieving the 2 °C target requires reducing power losses of wind and PV by at least 30% through energy storage. This requirement delivers to a cumulative storage capacity of 16.46 TWh using batteries during the period 2021–2100, leading to the international trade of cobalt and manganese across countries due to deficits of minerals at a country level. In the context of energy security, we highlight the importance of considering the limitations of energy storage and mineral shortage in the forthcoming policies of decarbonization.

Research on energy coordinated control strategy for offshore wind power based on distributed consistency algorithm

Offshore wind power is clean and has a large capacity, making it an important component of the development of new power systems. However, the randomness of offshore wind power affects the stability of the power grid. A coordinated control strategy of offshore wind power and multi-source energy based on a distributed consistency algorithm is proposed for offshore wind farms’ energy coordination management and optimization. By constructing a source network storage load model and analyzing the output and load fluctuations of offshore wind power, energy storage, and power grid, the constraint equation and balance equation of source network load storage power are derived. An adaptive distributed consistency methodology is employed based on a proportional utilization micro growth function to preserve the stability of offshore wind power and the power grid. It can enhance the power distribution of offshore wind power, energy storage, and distribution networks. The outcomes of the simulation show that the suggested approach may quickly equalize power, decrease frequency variations of offshore wind power, and enhance the commercial viability of offshore wind power.

Impact of supporting power source on a high-proportion renewable energy generation base

In recent years, China has organized three batches of high-proportion renewable energy generation bases, with the aim of resolving the geographical imbalance between renewable energy supply and consumption. Renewable electricity is transported from the resource-abundant western regions to the electricity-demanding eastern regions. Given the inherent variability and unpredictability of wind power and photovoltaic power generation, there is a pressing need for additional support from more reliable energy generation sources, including coal-fired power and concentrated solar power (CSP). This paper presents a power system optimization planning model that incorporates the internal energy transfer of CSP. It proposes strategies for establishing high-proportion renewable energy generation bases supported by both coal-fired power and CSP. Furthermore, it investigates the primary constraints affecting the energy generation base. The study also compares the peak shaving depth and output characteristics between coal-fired power and CSP. Moreover, this paper introduces the method of natural gas supplemental combustion to enhance the power supply stability of CSP, enabling the renewable energy generation base to supply electricity through conventional DC transmission.

Wind speed and wind power forecasting models

Sustainable energy resources have proved to be the best alternative in the wake of environmental degradation, depletion of ozone layer and ever-increasing demand for energy. Though wind energy is a promising resource, the non-linear nature and non-stationary characteristics of wind have remained a formidable challenge. Variability in wind power has posed numerous challenges in managing the power systems, especially in grid evacuation, penetration and integration. Forecasting wind is one of the powerful solutions to solve this problem. As the penetration of renewable energy sources is poised to increase in future, an accurate prediction can go a long way in helping the electricity grid to perform well. This article presents a review of existing research and recent trends in the forecasting of wind power and speed with a critical analysis of the contribution of every researcher. A review of forecasting technologies, data, time horizons, various forecasting approaches and error metrics has been presented in detail. The plethora of research issues that continue to challenge power system operators, wind farm owners and other stakeholders has been highlighted. The development of models for wind power or wind speed forecasting with excellent reliability and outstanding accuracy is the need of the hour.

Capacity Allocation in Distributed Wind Power Generation Hybrid Energy Storage Systems

Abstract The inherent variability and uncertainty of distributed wind power generation exert profound impact on the stability and equilibrium of power storage systems. In response to this challenge, we present a pioneering methodology for the allocation of capacities in the integration of wind power storage. Firstly, we introduce a meticulously designed uncertainty modeling technique aimed at optimizing wind power forecasting deviations, thus augmenting the controllability of distributed wind power variations. Subsequently, we establish a cutting-edge real-time dynamic optimization model for state of charge, which effectively mitigates the fluctuations associated with grid-connected wind power. Moreover, we employ a state-of-the-art distributed robust optimization algorithm to enhance the stability of the distributed wind power storage system. Through comprehensive simulation testing, our findings unequivocally demonstrate the efficacy of our approach in preserving a harmonious balance between wind power load and output demand, thereby assuring the unwavering operation of the entire system. Notably, our approach attains an exceptional capacity allocation efficiency of 91% in the rigorous wind power grid-smoothing test, outperforming comparable methodologies. Lastly, we proffer essential recommendations pertaining to attenuation optimization at the effective capacity level of the batteries, effectively safeguarding the long-term stability of the energy storage system.

Integrating High Levels of Wind in Island Systems: Lessons from Hawaii

such as wind and solar plants presents an operational challenge for grid operators. The economic incentives and technical challenges that accompany large amounts of variable generation in island power systems are often much greater. The Hawaiian Electric Company and its subsidiaries, the Maui Electric Company and the Hawaii Electric Light Company have considerable experience in planning and operating power systems with relatively high levels of wind power. The islands of Hawaii and Maui operate power systems with high levels of wind power (more than 10% by energy) and have experienced and addressed challenges associated with the variability and uncertainty of wind power. The island of Maui is anticipating further wind plant deployments in the near future. Recent analyses of possible near-term deployment of large amounts of wind power on the Oahu power system (500MW of wind power; approximately 1200MW peak and 520MW minimum annual load) has shown the potential for this system to accept almost 25% of its energy from wind and solar power. This paper will identify some of the wind integration challenges and highlight the benefits of a variety of strategies that are expected to improve system economics and operational reliability, including proposed modifications to the baseload thermal fleet (deeper turndown, higher ramp rates, and tuned droop characteristics), advanced wind turbine grid support features, new operating strategies, wind forecasting and refinements to the up and down reserve requirements. This paper will present the key findings in the context of useful insights and lessons learned that are relevant to other island power systems considering very high levels of wind power.