Wind Integration Research Articles

Detailed analysis of Sustainable Infrastructure Design and Benefits for urban Cities

Addressing the issues of urbanization, climate change, and resource scarcity now centers on the junction of infrastructure development and sustainability. This review study looks at how new ideas and technologies are developing sustainable infrastructure solutions. It assesses research and development in important domains including smart cities, green infrastructure, renewable energy, circular economy, resilience, and social equality critically. The notion of green infrastructure is covered at the outset of the article, along with how it can be used to manage environmental issues including stormwater runoff, air quality, and urban heat islands. It examines the most recent developments in renewable energy infrastructure, evaluating the scalability, efficiency, and integration of solar, wind, hydropower, and geothermal systems into the current energy infrastructures. The analysis also looks at how smart cities and infrastructure have developed, with an emphasis on how IoT, AI, and data analytics are used to improve quality of life, mobility, and sustainability. It goes over case studies of prosperous smart city projects and how they've improved public services, strengthened urban infrastructure resilience, and decreased greenhouse gas emissions. The study concludes with a discussion of new developments and technologies, including digital twins, self-driving cars, decentralized energy systems, and green building materials, that will influence sustainable infrastructure in the future. It highlights the compensations and difficulties of numerous technologies and suggests directions for further study and advancement in the area.

Enhancing decentralized frequency regulation approach in mixed source of generation diversified with wind and PV integration deploying artificial gorilla troops algorithm

Nowadays, the penetration of renewable energy sources, mainly wind and solar photovoltaic systems, into the existing power systems, destabilizes the grid, especially in the aspect of frequency regulation. AGC is one of the most important tasks in this mixed-generation environment to maintain the balance between the generated and consumed electricity, thus keeping the system frequency at an acceptable level. Therefore, AGC needs to be optimized for the noisy and volatile output of RESs. AGTA is a recent optimization method developed based on the social foraging behavior of gorillas, being a sophisticated way of exploration and exploitation. Therefore, the method is implemented in AGC. By mimicking the social behaviour and foraging strategy, the group gives rise to a new technique for enhancing the efficiency and response of the AGC system in light of wind and PV energy generation variation. A two-area power system model has been formulated characterized by the existence of wind and PV generation in addition to the conventional sources of the power system. This model is meant to simulate diverse situations to give an idea as regards the capability of the new algorithm to enhance grid stability and adaptability. The simulated results show that the proposed AGTA significantly surpasses other conventional optimization methods for AGC and results in an effective frequency control strategy. it confirms the potential of the AGTA from a new perspective in providing a feasible option for decentralized frequency regulation in a multi-generation landscape.

A systematic approach for risk assessment and mitigation of wind integrated electricity markets

A risk-based double auction mechanism with dispatchable loads is one of the most cost-efficient trading mechanisms in the modern real-time electricity markets. Risks in the system due to various uncertainties, such as line failure, generator failure, load changes and other severe contingencies causes non-systematic execution of real-time electricity markets. This paper presents a simple and systematic procedure for reducing system risks in deregulated power markets considering wind power integrations. For maximizing the considerable system social welfare and minimizing the system’s economic risk, the optimal placement and operation of wind power producers (WPP) have been suggested. The critical buses have been identified by accessing the different assessment tools e.g., Value at Risk (VaR), Conditional Value at Risk (CVaR), and locational marginal pricing (LMP) based Marginal Pricing Index (MPI). The presented approach has been evaluated and validated on modified IEEE 30-bus system.

A Unit Commitment Model Considering the Flexibility Retrofit of Combined Heat and Power Units for Wind Integration

A Unit Commitment Model Considering the Flexibility Retrofit of Combined Heat and Power Units for Wind Integration

Effect of investment cost on technology preference in a flexible, low-carbon Finnish power system

This paper investigates the effects of varying investment costs in a cost-optimal portfolio of electricity-generating technologies to achieve a low-carbon power system to help mitigate climate emissions in Finland. Several different cost scenarios for nuclear, solar, wind, and battery technologies are constructed, considering both single- and multi-stage investment-strategy pathways, with sector coupling of the power system to the heating sector and hydrogen production. Potential new nuclear investments are assumed small modular reactors capable of flexible operation, and optional additional flexibility measures from heating and hydrogen sectors are considered. The scenarios are simulated with hourly resolution using GenX, an openly available capacity expansion optimization tool. Across a wide range of assumed investment costs, the results demonstrate a clear role for flexible nuclear power as a provider of both a solid base generation and flexibility to enhance integration of intermittent wind to the system. Despite optimistic cost assumptions for solar and batteries, their role is observed to remain marginal in the Finnish setting, likely due to large seasonal variation in the local solar availability.

Influence Analysis and Control Method of Ultra-Low Frequency Oscillation in a Hydro-Dominant Sending Power System with Wind Power Integration

To make clear the influence of renewable energy on ultra-low-frequency oscillation (ULFO) in a hydro-dominant system via an high voltage direct current (HVDC) transmission system, this paper studies the damping characteristics when wind power is integrated into the sending power system. The damping torque method is applied for the mechanism study and risk evaluation of ULFO. The study of the inner cause and outer performance proves that ULFO belongs to frequency oscillation rather than conventional power oscillation. According to the theoretical analysis, the suggested control mode is identified to reduce the risk of ULFO. Moreover, a robust controller is designed for wind units based on mixed H2/H∞ robust control theory, and the control method makes use of the fast response ability of the converter without compromising the primary frequency regulation ability. Finally, a simulation model of a hydro-dominant sending power system with wind integration is established in PSCAD Version 4.6 software. The simulations verify the proposed control can suppress ULFO effectively.

Hybrid renewable energy source optimization using black widow optimization techniques with uncertainty constraints

To encourage the growth and use of renewable energy such as PV Solar Energy, Wind Energy, India is speeding up the creation of a renewable portfolio standard. Scheduling practises for thermal power and other renewable energy producers in the electric power system will shift as a result of the adoption of the standard and accompanying marketable green credits. In this study, we develop a dynamic economic emission dispatch model for wind, solar, and hydro power under tradable green certificates, taking into account many objectives simultaneously. The drive to transition towards cleaner and more sustainable energy sources has underscored the significance of optimizing hybrid renewable energy systems. This study presents a novel approach that leverages the Black Widow Optimization (BWO) algorithm to address the challenges of optimizing such systems while accounting for uncertainty constraints. The core problem revolves around the effective integration and management of multiple renewable energy sources, including solar, wind, and hydro, within a framework that ensures robustness and adaptability. In the face of variable weather conditions and equipment reliability uncertainties, the BWO algorithm excels in balancing exploration and exploitation to yield optimal solutions. Through rigorous modelling and sensitivity analysis, this study provides quantitative insights into the system's performance, offering a clear understanding of the trade-offs between cost-effectiveness and reliability. Real-world case studies validate the practicality of the proposed approach, underlining its importance in achieving sustainable and resilient energy systems. This study's goal is to minimise the yearly cost of operating a hybrid wind-solar renewable energy system to meet a specified load. The objective was to minimise the yearly cost of the system by selecting the optimal level of system components to meet the required load in an efficient and cost-effective manner. Several computational strategies inspired by nature, such as Black Widow Optimization (BWO), were used to maximise fitness. The fitness function and the techniques used to create it where both coded in MATLAB.

Towards Sustainable Integration: Techno-Economic Analysis and Future Perspectives of Co-Located Wind and Hydrogen Energy Systems

Abstract This article presents a comprehensive study that focuses on the techno-economic analysis of co-located wind and hydrogen energy integration within an integrated energy system (IES). The research investigates four distinct cases, each exploring various configurations of wind farms, electrolyzers, batteries, hydrogen storage tanks, and fuel cells. To obtain optimal results, the study employs a sophisticated mathematical optimization model formulated as a mixed-integer linear program. This model helps determine the most suitable component sizes and hourly energy scheduling patterns. The research utilizes historical meteorological data and wholesale market prices from diverse regions as inputs, enhancing the study’s applicability and relevance across different geographical locations. Moreover, sensitivity analyses are conducted to assess the impact of hydrogen prices, regional wind profiles, and potential future fluctuations in component prices. These analyses provide valuable insights into the robustness and flexibility of the proposed IES configurations under varying market conditions and uncertainties. The findings reveal cost-effective system configurations, strategic component selections, and implications of future energy scenarios. Specifically comparing to configurations that only have wind and battery combinations, we find that incorporating an electrolyzer results in a 7% reduction in the total cost of the IES, and utilizing hydrogen as the storage medium for fuel cells leads to a 26% cost reduction. Additionally, the IES with hybrid hydrogen and battery energy storage achieves even higher and stable power output. This research facilitates decision-making, risk mitigation, and optimized investment strategies, fostering sustainable planning for a resilient and environmentally friendly energy future.

Long-term operation rules of a hydro–wind–photovoltaic hybrid system considering forecast information

The large-scale integration of wind and solar energy into cascade hydropower stations increases the complexity of hydraulic/electrical relationships and requires a modification of conventional scheduling mode for better energy system performance. This study focuses on the long-term operation rules for hydro–wind–photovoltaics (PV) hybrid energy systems (HESs). Based on the conventional operation chart, a cascade energy operation chart that couples wind, PV output, and runoff forecasts is proposed to obtain the total output of HES, which can promote the utilization of the joint regulation of cascade hydropower stations. A total output allocation method based on the cascade energy operation chart is established to coordinate the operation of each power station. Then an operation chart optimization model with objectives of maximizing power generation and output reliability is proposed to improve HES performance. The case study reveals that compared with the conventional reservoir operation chart, 1) the optimized cascade energy operation chart increased power generation of HES by 9.2 %; 2) the reservoir impounding timing was delayed, the drawdown timing was brought forward, and the drawdown depth was increased; 3) the reliability of HES power output was improved, and the output corresponding to the 95 % guaranteed rate requirement was increased by 18 %.

Five grand challenges of offshore wind financing in the United States

Offshore wind energy has the potential to play a critical role in fostering a renewable energy transformation in the United States. This owes to its massive technical potential, strategic location near densely populated coastlines, and—relative to onshore wind and solar—high capacity factors and consistent production. The Biden Administration's target to build 30 GW of offshore wind capacity by 2030 (from 0.04 GW today) requires the creation and swift development of a new industry that interlinks the wind and power industries with the maritime sector. Critical to its success is financing. While financial capital is abundant, deploying it for offshore wind faces major challenges. We identify and describe five grand challenges affecting offshore wind finance in the U.S. Failing to address these challenges may put deployment targets at risk. The challenges include (1) Early years financing: navigating the complexities, timing mismatches, and high costs of projects in the development phase; (2) Policy support for project financial solvency: addressing the uncertainty and systematic transfers of tax credits away from offshore wind, characteristic of the U.S. Investment Tax Credit; (3) Workforce development: building a skilled workforce for an emerging market; (4) Transmission and integration barriers: upgrading the power grid to reliably support large scale offshore wind integration; and (5) Floating wind development: financing the development and scale-up of floating offshore wind technologies. The second challenge has already been solved to a large extent by the Inflation Reduction Act.

Construction of Simulation Model of 'Lushijia No.1' under Wind and Wave Integration

Based on the new ' Lu Shijia No.1 ' platform, the project team has customized a set of force and motion response theory system for it. The existing model is extended, and the basic coordinate system of eight frames is proposed for the new platform, which simplifies the analysis process. By considering the design parameters of the platform, the linear potential flow theory, Stokes wave theory and blade element theory are used to analyze the force and motion response of the platform, and the numerical calculation standards of various forces are defined. At the same time, the H ∞ controller of the optimized fan is integrated to improve the overall control system of the fan. Based on the full coupling integration of wind turbine and platform, the project team uses AQWA hydrodynamic analysis software to simulate and analyze the platform in frequency domain. With the help of FAST-matlab, the time domain full coupling analysis of the platform is carried out. The simulation results show that the new ' Lu Shijia No.1 ' achieves the expected effect of ' reducing response and increasing power '. On the basis of simulation, the project team constructed a scaled down ' Lu Shijia No.1 ' model. Through the final launching test of the solid model, the feasibility of the theoretical design was proved.

Maximizing the cost effectiveness of electric power generation through the integration of distributed generators: wind, hydro and solar power

BackgroundThe transition towards renewable energy sources has become an imperative step to mitigate climate change, reduce carbon emissions and improve energy security and economic prosperity in a sustainable manner. Maximizing the cost effectiveness of electric power generation is crucial to making renewable energy sources viable and attractive options for clean energy production. The strategic allocation of wind, hydro and solar power systems is essential to achieving this goal. This paper attempts to demonstrate how the cost effectiveness of electrical power system could be maximized through the integration of wind, solar and hydropower systems and comparison at different penetration levels of 0, 25, 50, 75 and 100% on cost effectiveness of electric power generation. The different generator technologies were designed based on their electrical output attributions.ResultsThe cost of electric generation for the integration of each generator at the various buses were calculated at different penetration level for fair comparison. The results indicate that the minimum money loss for the integration of solar power was $743.90 at bus 4 and at 50% penetration level, the minimum money loss for the integration of wind power was $999.00 at bus 4 and at 25% penetration level while the minimum amount loss for the integration of hydropower was $546.50 at bus 4 and at 75% penetration level.ConclusionsThe magnitude to which the integration of the different generator affects the cost effectiveness of power production hinges on the type of generator, the penetration level and the location of the generator in the grid.

Large-scale offshore wind integration by wind-thermal-electrolysis-battery (WTEB) power system: A case study of Yangxi, China

Offshore wind power integration is a grand challenge due to the volatility, randomness and intermittency This work presents a wind-thermal-electrolysis-battery (WTEB) hybrid energy system designed to integrate large-scale offshore wind energy and reduce curtailment. The system composes of offshore wind farms (OWF), thermal power plants (TPP), electrolysis station, battery bank. The Qingzhou (geographic name) I-VII offshore wind farms (5000 MW) and the Yangxi (geographic name) coal-fired power plant units #5, #6 (2 × 1240 MW) are taken as a case study. The OWF is the indispensable generator and the renewable fraction increases with integration level. The TPP adjusts the load ratio to meet the changing net load. The electrolysis station consumes the excess electricity and the battery bank time-shifts the electricity to reduce the electrolyzer downtime. The recommended wind-thermal (W-T) capacity ratio is 1.452:1 in the studied case, namely 3600 MW OWF. The WTEB system breaks the trade-off relationship between renewable fraction (50.11%) and curtailment rate (1.48%) compared to traditional wind-thermal (WT) bundled system. The WPEB system improves the electricity shortage rate (4.74%), transmission line capacity factor (61.79%), load following precision (91.79%). The internal rate of return (IRR), levelized cost of electricity (LCOE), levelized cost of hydrogen (LCOH) of WTEB-W3600 are 34.70%, 0.5172 ¥/kWh, 29.76 ¥/kg. The WTEB hybrid energy system for a large-scale offshore wind power integration is first proposed and the techno-economic feasibility analysis proves the feasibility and forward-looking of the system. Collaborative capacity optimization of OWF, TPP, electrolysis station, battery can improve the system performance and economy. The wind-photovoltaic-thermal-electrolysis-battery (WPTEB) system co-locating the OWF and floating photovoltaic (PV) is an important development direction.

Optimized Configuration of Distributed Wind-Solar-Storage System Based on Wind-Solar Scene Generation

To achieve large-scale, high-proportion, high-quality sustainable development of new energy such as wind and solar, the integration of wind, solar, and storage is imperative. In this paper, the Latin hypercube sampling and rapid backward reduction methods are used to generate and reduce the wind-solar output data. Then, an optimization model based on distributed wind-solar-storage system is established, in which the storage capacity is the configurable variable and the system benefit is the optimization objective. Subsequently, the wind-solar output data is substituted into the model for solving, and the Cplex solver is used to solve the model. The results show that the optimal storage capacity of the model built in this paper is 5 MWh.

A Novel Data-Driven Self-Tuning SVC Additional Fractional-Order Sliding Mode Controller for Transient Voltage Stability With Wind Generations

This paper proposes a novel data-driven self-tuning additional sliding mode controller for power system transient voltage stability enhancement considering wind integration. We first develop a new additional fractional-order sliding mode controller (FOSMC) for the static var compensator (SVC). The tuning of FOSMC parameter settings is then reformulated as a Markov decision process (MDP) and solved by the deep reinforcement learning (DRL) algorithm. In addition, a data-driven estimation method is proposed for the identification of an equivalent transfer function that is further used to calculate reward during the training process, yielding the model-free training. After that, the well-trained agent allows us to tune the controller parameters considering system uncertainties and achieve robustness against various operating conditions. Comparative results with other state-of-art methods demonstrate that the proposed method can effectively suppress the chattering issue of the sliding mode controller and ensure transient voltage stability under different operating conditions.

Research on short-term joint optimization scheduling strategy for hydro-wind-solar hybrid systems considering uncertainty in renewable energy generation

Due to its randomness, intermittence, and volatility, the high-proportional integration of wind and solar power poses challenges to the safe and stable operation of power systems. Cascade hydropower stations have a high response speed, high adjustability, and stable output. This study proposed a hydro-wind-solar hybrid system and investigated its short-term optimal coordinated operation based on deep learning and a double-layer nesting algorithm. A stochastic complementary scheduling model was constructed to maximize the cascade energy storage. The particle swarm optimization algorithm-dynamic programming (PSO-DP) coupled with the inner and outer nesting optimization algorithm reduces the problem-solving complexity. The hybrid system was applied to a national comprehensive development base of renewable energy with integrated wind, solar, and hydropower in China. Studies have shown the following: The hydro-wind-solar hybrid system has a certain degree of scalability. The utilization of deep learning methods can fully consider the uncertainty of wind and solar. The internal and external nested optimization algorithm, which realizes the reasonable and efficient distribution of water and electricity, and improves the future power generation capacity of cascade hydropower stations, was used to solve the problem. This study provided a valuable reference for the large-scale utilization of other renewable energy sources worldwide.

Evaluation of Economic Impact of Renewable Energy Sources in Multi-period Optimal Operation of Power Systems

Abstract: In order to operate the electrical network economically and effectively, generation from various sources must be appropriately scheduled. Integrated systems are more common today because of the increase in the cost of fossil fuels and the technological advancements made in the field of renewable energy Resources. To be able to determine the best settings, the optimal power flow problem is posed with all relevant system characteristics, including generator outputs. The network might include conventional fossil fuel generators and renewable energy sources like wind power generators and solar photovoltaic. The classic problem of optimal power flow is a complex non-linear problem with non-linear constraints. In this research, it is suggested how thermal plants with wind and solar integration operate economically using General Algebraic Modeling System (GAMS) software's which has used Non-linear programming to resolve the Dynamic Economic Dispatch (DED) which is a part of optimal power flow problem. The impact of wind and solar integration on the cost-effective operation of thermal units while taking into considering IEEE-24 bus test system has been researched. In this paper, the reduction of a thermal plant's fuel consumption with the integration of wind and solar power is the primary focus of study.

Time-averaged wind power data hides variability critical to renewables integration

Most publicly available wind data are aggregated to a temporal resolution of 30 or 60 min. This is adequate for some purposes, such as large-scale wind integration studies. However, the consequent loss of high-frequency power fluctuations from the data can significantly impact analysis on local and regional scales. The importance of this missing variability to the accurate assessment of renewables integration is increasingly being recognised as wind power is considered for energy autarky and local energy systems. Here, we investigate the statistics of the lost variability using two high-temporal-resolution datasets from France and the US. In particular, we focus on the likelihood that minimum and maximum thresholds are exceeded and illustrate the importance of sub-half-hourly variability for assessing sector-coupling applications, such as wind farm – electrolyser systems. We find that using half-hourly averaged turbine data can underestimate the occurrence of zero power by a factor of two. This matters if a wind turbine is coupled to an electrolyser, either directly or with only short-duration storage, because of dynamic operating constraints. The lower output limit means that half-hourly wind output could overestimate the hydrogen production and energy storage capabilities of alkaline electrolysers by up to 70 %.

Topology of Wind Power DC Collection Converter Based on Double Bus Structure and Its Power Balancing Control Strategy

Wind power collection technology is the key to grid-connected wind power generation. Compared with AC wind integration, DC wind power collection is more advantageous. Using a triple active bridge (TAB) converter as a submodule, this paper proposes a wind power DC converter topology and its control strategy for medium-voltage dc (MVDC) grid connection. The converter adopts a double bus structure, with the MVDC bus connected to the grid and the low-voltage dc (LVDC) bus handling the mismatched power. It has independent maximum power point tracking (MPPT) control, power balancing, and autonomous voltage balancing on the output side. The converter system is simulated and verified on the MATLAB/SIMULINK software platform. The simulation results show the effectiveness of the control strategy and the ability of the converter to operate stably under multiple conditions.

Hydropower Advantages over Batteries in Energy Storage of Off-Grid Systems: A Case Study

Microgrids are decentralized power production systems, where the energy production and consumption are very close to each other. Microgrids generally exploit renewable energy sources, encountering a problem of storage, as the power production from solar and wind is intermittent. This research presents a new integrated methodology and discusses a comparison of batteries and pumped storage hydropower (PSH) as energy storage systems with the integration of wind and solar PV energy sources, which are the major upcoming technologies in the renewable energy sector. We implemented the simulator and optimizer model (HOMER), which develops energy availability usage to obtain optimized renewable energy integration in the microgrid, showing its economic added value. Two scenarios are run with this model—one considers batteries as an energy storage technology and the other considers PSH—in order to obtain the best economic and technical results for the analyzed microgrid. The economic analysis showed a lower net present cost (NPC) and levelized cost of energy (LCOE) for the microgrid with PSH. The results showed that the microgrid with the storage of PSH was economical, with an NPC of 45.8 M€ and an LCOE of 0.379 €/kWh, in comparison with the scenario with batteries, which had an NPC of 95.2 M€ and an LCOE of 0.786 €/kWh. The role of storage was understood by differentiating the data into different seasons, using a Python model. Furthermore, a sensitivity analysis was conducted by varying the capital cost multiplier of solar PV and wind turbines to obtain the best optimal economic solutions.