Power Generation Profile Research Articles

Increasing revenues of offshore wind farms with co-located energy storage systems

Abstract Co-located onshore and offshore energy storage systems exhibit the potential of diversifying and increasing revenue streams of offshore wind farms. However, these systems should be designed and sized considering the power generation profile, the route to market of these assets, i.e. power trading strategy, and dynamics of the various segments of the power market. In this study, the feasibility of deploying subsea hydro-pneumatic storage systems was investigated. It was seen that if a 50MW capacity system with a storage duration of 4 hours is deployed in an offshore wind farm of 1000MW capacity, the wind farm can potentially participate in the day-ahead and ancillary services markets more confidently from a risk perspective. The relatively higher volatility in these markets presents an opportunity to improve economics of these offshore wind projects as long as costs can be kept under control.

Historical reconstruction dataset of hourly expected wind generation based on dynamically downscaled atmospheric reanalysis for assessing spatio-temporal impact of on-shore wind in Japan

ABSTRACT Wind power is crucial for achieving carbon neutrality, but its output can vary due to local wind conditions. The spatio-temporal behavior of wind power generation connected to the power grid can have a significant impact on system operations. To assess this impact, the use of long-term reanalysis results of wind data based on a numerical weather prediction (NWP) model is considered valid. However, in Japan, the behavior of on-shore wind power generation is influenced by diverse topographical and meteorological features (TMFs) of the installation site, making it challenging to assess possible operational impacts based solely on power curve-based estimates using a popular conversion equation. In this study, a nonparametric machine learning-based post-processing model that learns the statistical relationship between the TMFs at the target location and the actual wind farm (WF) output was developed to represent the expected per-unit output at each location. Focusing on historical reconstruction results and using this post-processing model to reproduce the real-world WF output behavior created a set of expected wind power generation profiles. The dataset includes hourly long term (1958–2012) wind power generation profiles expected under the WF installation assumptions at various on-shore locations in Japan with a 5 km spatial resolution and is expected to contribute to an accurate understanding of the impact of spatio-temporal wind power behavior. The dataset is publicly accessible at https://doi.org/10.5281/zenodo.11496867 (Fujimotoet al., 2024).

The impact of future UK offshore wind farm distribution and climate change on generation performance and variability

The UK plans to significantly increase offshore wind generation capacity as part of the effort to achieve net zero targets. Current installation is densely located in a few areas, particularly off the east coast of England, and although current siting proposals include new offshore regions, significant volumes of wind generation capacity are yet to be located to meet 2050 installation targets. This paper uses a recent dataset of multi-decadal offshore wind power capacity factor timeseries to assess how UK offshore wind generation is likely to be affected by both the spatial distribution of future wind farms, and by the impacts of near-future (2020–2050) climate change. We determine that a wider geographic spread of offshore capacity results in a much-improved and less-variable UK-aggregated power generation profile, with substantial reductions in periods of low generation and extreme wind power ramping events, without negatively impacting mean or peak generation outputs. The impact of near-term climate change appears to be minor, slightly reducing overall generation and possibly resulting in an underestimation of future installation requirements, but this climate signal is outweighed by the effects of spatial distribution, and even more so by inherent hourly to inter-annual wind speed variability. This study implies that the intermittency of wind generation can be partly mitigated through increasing the spatial diversity of the existing wind farm distribution. Alongside a more in depth investigation of future climate change, and a holistic assessment of relevant geospatial factors such as Levelised Cost of Energy, infrastructure, and environmental constraints, this study could be used for optimisation of future offshore wind siting.

Geospatial optimisation of wind and solar energy generation: A framework for optimised resource allocation

Wind and solar power generation profiles exhibit a high degree of power output variability and uncertainty compared to conventional fossil fuel generation plants. However, it is generally accepted that this variability and uncertainty can be mitigated by geographic siting and dispersion of the renewable energy plants, particularly with the view to leverage aggregation effects. In this context, this paper proposes a framework for the geospatial optimisation of wind and solar PV generation for scenarios with a high penetration of wind and solar renewable energy. The proposed optimisation framework represents a probabilistic risk-based approach that seeks to minimise the number of events where high residual load values require ancillary service interventions to maintain power balance. In this approach, the wind and solar resources are categorised in terms of the statistical properties of the associated spatiotemporal wind and solar power profiles for a given set of daily and seasonal Time of Use periods. It is thereby recognised that the resource characteristics and grid impact of wind and solar generation profiles can be interpreted with reference to the daily and seasonal cycles exhibited by the demand profiles, wherein some Time of Use periods are more important than others. The proposed framework is implemented for a number of baseline case studies and optimisation case studies, using wind and solar resource data sets covering South Africa. Overall, it is concluded that this approach can leverage geospatial site optimisation to substantially reduce the risks associated with high residual load events. Furthermore, the proposed framework is highly flexible in that the formulation of the minimum and maximum allocation constraints allows application for real-world scenarios whereby stringent capacity allocation constraints can be applied.

Stability analysis and energy harvesting in lumped parameter systems with internally coupled resonators

This article explores internally coupled resonators in metamaterial systems, focusing on mechanical and electromechanical coupling. The article provides a thorough examination of stability within the context of internally coupled resonators. It establishes stability criteria, emphasizing the importance of strictly stable systems in practical applications. Furthermore, it analyzes stability through simulations, revealing how various parameters impact system behavior and highlighting the challenges and benefits of achieving stability in metamaterial systems. Additionally, the article explores the impact of damping coefficients and resonator characteristics, on displacement and power generation profiles. Nonlinear behavior in internally coupled resonators is examined, revealing the presence of bifurcation in simulation and offering insights into multi-stability and system behavior.

Harnessing solar power with aesthetic innovation: An in‐depth study on spherical and hemispherical photovoltaic configurations

AbstractIn pursuing advancing solar energy systems, this research uniquely occupies a position at the intersection of photovoltaic (PV) efficiency, innovative design and aesthetic integration into urban landscapes. This study explores the potential of thin solar cells applied to spherical and hemispherical surfaces and the influence of temperature variations throughout the day. By conducting a comprehensive analysis of voltage–current diagrams, the study deciphers the intricate interconnections of spherical geometries and their response to external thermal impacts, elucidating their subsequent effects on energy conversion efficiency. Moreover, it delved into the dynamics of connecting multiple spherical modules, unveiling their potential to form artistic and utilitarian constructs such as solar trees and architectural embellishments. The findings indicate that while spherical configurations provide superior aesthetics and a power generation profile comparable to solar tracking systems, hemispherical configurations offer a 32% increase in efficiency compared with the spherical configuration and a notable reduction in land footprint. This research underscores the importance of striking a balance between aesthetic appeal, efficiency and land utilization, providing valuable insights for the future of PV technology integration into urban and agricultural landscapes.

Estimation of the hourly solar radiation

Hybrid facility investments from renewable energy sources have increased in recent years. In general, solar power is the secondary energy source in hybrid systems. The reason why solar energy is most commonly used in hybrid power systems is that it is cheaper than other types of renewable energy. Solar-Hydroelectric (SHE) is one of the foremost compatible hybrid energy pairs, as solar and hydroelectric power generation profiles complement one another. Hybrid systems consisting of solar and hydropower have complementary characteristics due to the shared use of infrastructure systems and different periodicities in power generation. Energy management is important for SHE-integrated facilities. Since only a limited amount of energy can be injected into the grid from transformer capacities, energy management in hybrid systems is of great importance. To manage energy in hybrid energy systems, the amount of energy that can be produced each hour must be determined. In hybrid energy plants, there is usually already another renewable energy plant in place, and solar energy is added on top and optimized. Since there are no pyrometers in the existing plants, the daily radiation data from the National Aeronautics and Space Administration (NASA) is used, but the daily energy production amount may be insufficient for accurate energy management. To realize this, it’s necessary to reveal the energy generation on an hourly basis. During this study, the quantity of radiation on an hourly basis was determined to calculate solar power generation. Empirical and econometric models utilized in radiation amount determination were performed, and the most appropriate method was clarified by comparing them with one another. Hourly-based radiation is achieved with an empirical method by using National Aeronautics and Space Administration (NASA) daily radiation.

Improved Anomaly Detection and Localization Using Whitening-Enhanced Autoencoders

Anomaly detection is of considerable significance in engineering applications, such as the monitoring and control of large-scale energy systems. This paper investigates the ability to accurately detect and localize the source of anomalies, using an autoencoder neural network-based detector. Correlations between residuals are identified as a source of misclassifications, and whitening transformations that decorrelate input features and/or residuals are analyzed as a potential solution. For two use cases, regarding spatially distributed wind power generation and temporal profiles of electricity consumption, the performance of various data processing combinations was quantified. Whitening of the input data was found to be most beneficial for accurate detection, with a slight benefit for the combined whitening of inputs and residuals. For localization of anomalies, whitening of residuals was preferred, and the best performance was obtained using standardization of the input data and whitening of the residuals using the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ZCA</i> or <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ZCA-cor</i> whitening matrix with a small additional offset.

Estimation of the hourly solar radiation

Hybrid facility investments from renewable energy sources have increased in recent years. In general, solar power is the secondary energy source in hybrid systems. The reason why solar energy is most commonly used in hybrid power systems is that it is cheaper than other types of renewable energy. Solar-Hydroelectric (SHE) is one of the foremost compatible hybrid energy pairs, as solar and hydroelectric power generation profiles complement one another. Hybrid systems consisting of solar and hydropower have complementary characteristics due to the shared use of infrastructure systems and different periodicities in power generation. Energy management is important for SHE-integrated facilities. Since only a limited amount of energy can be injected into the grid from transformer capacities, energy management in hybrid systems is of great importance. To manage energy in hybrid energy systems, the amount of energy that can be produced each hour must be determined. In hybrid energy plants, there is usually already another renewable energy plant in place, and solar energy is added on top and optimized. Since there are no pyrometers in the existing plants, the daily radiation data from the National Aeronautics and Space Administration (NASA) is used, but the daily energy production amount may be insufficient for accurate energy management. To realize this, it’s necessary to reveal the energy generation on an hourly basis. During this study, the quantity of radiation on an hourly basis was determined to calculate solar power generation. Empirical and econometric models utilized in radiation amount determination were performed, and the most appropriate method was clarified by comparing them with one another. Hourly-based radiation is achieved with an empirical method by using National Aeronautics and Space Administration (NASA) daily radiation.

Forecasting sustainable power generation profiles to achieve net zero emissions using multi-objective techno-ecological framework: A study in the context of India

Climate change is becoming the most pronounced issue worldwide, with its origins dating back to the time of industrialization. It has now become imperative to take remedial steps to mitigate its harmful effects throughout the globe. In today's era, climate change is expected to worsen due to air emissions from power generation systems as they primarily depend on coal and fossil fuels. Moreover, as the global population increases, the demand for electricity will rise, further exacerbating the problem. Given this situation, populous nations like India and China are seen as key players in achieving net-zero emissions worldwide by transitioning to sustainable power generation. This study presents a multi-objective optimization method to meet the future electricity demand while ensuring net-zero emissions by mitigating pollution through a techno-ecological synergy (TES). In turn, this study incorporates the ecosystem as a process system, unlike the conventional decision-making methods that ignore the ecosystem's ability to mitigate pollution. As a case study, Delhi, the capital of India, which is known as one of the world's most polluted cities, is selected to showcase the proposed methodology. The study proposes various scenarios for sustainable power generation for Delhi to achieve net-zero emissions. The findings reveal that solar energy could be a viable alternative to conventional fossil fuels to meet Delhi's energy demand by 2050. This is achievable by implementing an energy transition plan that involves the aggressive phasing out of the coal power plant, which aligns with the urgent requirement to reduce emissions.

Mechanism that determines the economics of 100% renewable power systems

Many studies have evaluated the economic feasibility of 100% renewable power systems using the optimization approach, but the mechanisms determining the results remain unclear, making this issue still debatable. This study presents a mathematical formulation of the mechanism that only the demand and power generation profiles determine the optimal capacities of generation and storage and their trade-off relationship. Furthermore, this study demonstrates the comprehensive quantification of the corresponding relationships among the factor cost of technologies, their optimal capacities, and total system cost. Based on these findings, the study also shows that hybrid systems comprising multiple renewable energy sources and different types of storage, including long-duration energy storage, are critical to reducing the total system cost by using actual profile data for multiple years and regions in Japan. This suggests that large-scale deployment of current-level power-to-gas technologies, such as water electrolysis, can contribute to the economics of 100% renewable power systems.

A novel prediction and control method for solar energy dispatch based on the battery energy storage system using an experimental dataset

The high power generation growth by photovoltaic systems needs to forecast the power generation profile during a day. It is also required to evolve the high-efficient and optimal on-grid/off-grid photovoltaic power generation units. Furthermore, some advantages can be achieved by integrating photovoltaic systems with storage devices such as battery energy storage systems. Thus, optimizing the hybrid systems comprising photovoltaic and battery energy storage systems is needed to evaluate the best capacity. In the present work, a novel control and sizing scheme is proposed for the battery energy storage system in a photovoltaic power generation plant in one-hour ahead and one-day ahead during the dispatching phase. Then, the proposed prediction strategy is recommended for solar irradiation and power utilization. The control approach comprises a predictive control method concerning a Radial Basis Function network optimized by Levenberg-Marquardt back-propagation learning algorithm. Using the RBF network for simulation leads to a WAPE% =1.68 %.

SWHORD simulator: A platform to evaluate energy transition targets in future energy systems with increasing renewable generation, electric vehicles, storage technologies, and hydrogen systems

This paper presents the simulation platform SWHORD, specially designed for the analysis of future energy systems under energy transition targets. The model is implemented in GAMS as a cost minimization mixed integer programming problem of a hydro-thermal power system, which includes high penetration of non-dispatchable renewable generation, storage technologies, electric vehicles, and hydrogen systems. Simulations are performed on an hourly basis for one year of operation, enabling the evaluation of both short-term dynamics and the seasonal behaviour of the system and including the hourly power generation profile by technology, fuel and emission costs, CO2 emissions and storage levels, as well as the renewable curtailment needed to balance the system. The model was validated by backtesting with historical data of the Portuguese power system and, from a comprehensive statistical analysis of the dispatchable generation, it is concluded that the simulation results present a good fit with the real data. An illustrative use case is presented to evaluate the consistency of the Portuguese targets for 2030. Simulation results put in evidence the advantages of the SWHORD simulator to study the complex interactions among the new drivers of future energy systems, such as electric vehicles, storage technologies, and hydrogen systems.

Efficient Power Coupling in Directly Connected Photovoltaic‐Battery Module

Due to the intermittent nature of solar irradiance and the temporal mismatch between solar power generation and consumption profiles, the combination of photovoltaic (PV) devices with energy storage is essential. The maximum power point tracking (MPPT) devices are commonly used for the connection of PV to electrochemical storage and load, ensuring power matching and providing flexibility in system design. Herein, the usability of direct PV‐battery coupling as an alternative to MPPT under realistically varied battery state of charge (SoC), irradiance, temperature of the PV module, and applied load is investigated. A stable power coupling factor above 90% is demonstrated between a silicon heterojunction solar module and Li‐ion battery in the whole range of measured SoC (12.5–75%) and a wide range of load power. The dependence of power coupling on temperature and irradiance is calculated and compared to the power generation profile of a PV plant installed in southern Germany. In the region of highest power generation, the direct connection provides coupling efficiencies above 95%, reaching 100%, for the usable range of battery SoC. These results show that direct PV‐to‐battery coupling is feasible for a variety of practical applications and scales.

Method for spatiotemporal wind power generation profile under hurricanes: U.S.-Caribbean super grid proposition

Wind turbines hit by hurricanes produce high variability of power generation profile. This research proposes a method to estimate this variability and solutions to smooth it. The method calculates the hurricane wind-speed profile in each turbine by Holland's parametric equation in spatiotemporal steps of the hurricane trajectory. The calculated wind speed profile is converted into a power generation profile using wind turbine characteristic curves. Since the simulations show high variability of power profile, this research investigates two strategies to smooth such variability. The first one is systemic. A U.S.-Caribbean super grid interconnecting the Caribbean islands with the U.S. power grid by submarine high voltage direct current (HVDC) cables is proposed. This super grid shows technical feasibility since the proposed route of cables meets some installation criteria, such as a maximum water depth of 3 km and cable length not exceeding 879 km. The U.S.-Caribbean super grid reduces power profile variability by draining the wind power peak caused by hurricanes passing through the Caribbeans and transmitting it to the US power grid by the Florida-Bahamas HVDC interconnector. Another strategy, with special turbines, was tested to evaluate a potential reduction in power variability. However, the special turbines with a high cutout speed showed a limited effect on reducing the variability compared to the U.S.-Caribbean super grid. The proposed power profile estimation method is an essential tool for planning, preliminary design, and reinforcements of high voltage grids in hurricane-prone regions with wind power capacity.

Enhancing wind power forecast accuracy using the weather research and forecasting numerical model-based features and artificial neuronal networks

Forecasting with accuracy the quantity of energy produced by wind power plants is crucial to enabling its optimal integration into power systems and electricity markets. Despite the remarkable improvements in the wind forecasting systems in recent years, large errors can still be observed, especially for longer time horizons. This work focuses on identifying new numerical weather prediction (NWP)-based features aiming to improve the overall quality of wind power forecasts.The methodology also incorporates a sequential forward feature selection algorithm. This algorithm was designed to select iteratively the meteorological features which minimize the wind forecast errors.The methodology was applied separately to seven wind parks in Portugal with different climate characteristics. The proposed approach allowed a reduction between 13% and 37% in the root mean square errors of wind power forecasts, compared with a baseline scenario. While the meteorological features identified for each wind park showed similarities within regions with analogous wind power generation profiles, each wind park required specific meteorological parameters as input data to obtain the best performance. Thus, the results show to be crucial to select the most relevant features of a specific site to maximize the accuracy of a wind power forecast.

Cross domain fusion in power electronics dominated distribution grids

In the near future, a drastic change in the structure of the electric grid is expected due to the increasing penetration of power electronics interfaced renewable energy sources (e.g. solar and wind), highly variable loads (e.g. electric vehicles and air conditioning) and unexpected energy demanding events (e.g. pandemics or natural disasters). Energy balancing management, voltage and frequency stability, reduced system inertia, grid resilience to fault conditions, and power quality of the supply are a few of the main challenges in the future power electronics dominated grids. Power electronics can solve these by integrating information and communication technology in new intelligent, highly reliable, and efficient devices like smart transformers. Smart transformers can increase the power flow flexibility by enabling the correct meshed-hybrid grid operations, as long as load mission and power generation profiles are known. Those profile are generally driven by heterogeneous, highly sparse and often incomplete data that belong to different domains. This article highlights the necessity of new approaches and models to identify patterns and events of interest that can serve as a common base. The resulting patterns can then be cross-fused in a common language and form the basis of further data analytics in future distribution grids.

Optimal deployment of distributed rooftop photovoltaic systems and batteries for achieving net-zero energy of electric bus transportation in high-density cities

Using rooftop solar photovoltaics (PV) and batteries together to power electric buses is considered a novel and feasible approach to reducing carbon emissions and tackling street-level air pollution in high-density cities like Hong Kong. However, associated optimal deployment is highly challenging due to the involved high-dimensional system planning, non-linear component sizing, and complex solar potential accurate characterization. To address this challenge, this study proposed a strategy to minimize the payback period of the deployed rooftop PV and batteries for achieving net-zero energy of electric bus transportation by taking associated grid impacts into consideration. In this strategy, a genetic algorithm-integer linear programming (GA-ILP) approach was developed to solve the high-dimensional rooftop PV planning problem and the non-linear PV and battery sizing problem. Our 3D-GIS (geographic information system) and DL (deep learning) integrated approach was used to attain accurate spatiotemporal rooftop solar power generation profiles. To verify the proposed strategy, a case study covering 5 bus terminuses with 28 bus routes and a total of 1,224 building rooftops surrounding the terminuses was conducted in a real region of Hong Kong. The effectiveness of the developed strategy was verified by comparing the optimized deployment with 150 million Monte-Carlo-generated solutions. The optimized deployment achieved the shortest payback period of 3.98 years by effectively tackling design issues such as battery oversizing, PV misallocation, and battery misallocation. To meet the increasing requirement on peak export power reduction (representing the grid impact constraint in the study), the proposed strategy can find a cost-effective balance between shifting PVs from higher-density solar potential terminuses to lower ones and increasing battery capacity. The proposed strategy can be adopted in practice to facilitate the development of cost-effective and grid-friendly community-solar-powered electric bus networks in high-density cities.

Temperature Droop-Based Dynamic Reactive Power Sharing Technique to Improve the Lifetime of Power Electronic Converter

Thermal stress because of mean junction temperature (<inline-formula><tex-math notation="LaTeX">$T_{{{jm}}}$</tex-math></inline-formula>) and junction temperature swing (<inline-formula><tex-math notation="LaTeX">$\Delta T_{{j}}$</tex-math></inline-formula>) reduces the lifetime of the power electronic (PE) converters. It is beneficial to distribute the stress evenly among the converters operating in parallel in a system like microgrid to improve their lifetime. The existing techniques usually only distribute stress due to <inline-formula><tex-math notation="LaTeX">$T_{{{jm}}}$</tex-math></inline-formula>. A few techniques are based on the accumulated damage in PE converters do not necessarily mitigate the effect of <inline-formula><tex-math notation="LaTeX">$\Delta T_{{j}}$</tex-math></inline-formula>. Moreover, most of the the existing techniques are not applicable for nondispatchable energy source like PV. The intermittent nature of active power generation profile of the PV inverter further reduces the lifetime of the PE converters operating in an islanded microgrid. To address this issue, a temperature droop-based dynamic reactive power sharing (TDDRPS) technique is proposed for an islanded ac microgrid to distribute the thermal stress due to both <inline-formula><tex-math notation="LaTeX">$T_{{{jm}}}$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$\Delta T_{{j}}$</tex-math></inline-formula>. The stability of the proposed TDDRPS technique is mathematically analyzed. Finally, the proposed technique is validated through simulation and experimentation on a scaled down laboratory prototype of microgrid.

ELECTRIC LOAD AND POWER FORECASTING USING ENSEMBLE GAUSSIAN PROCESS REGRESSION

We propose a new forecasting method for predicting load demand and generation scheduling. Accurate week-long forecasting of load demand and optimal power generation is critical for efficient operation of power grid systems. In this work, we use a synthetic data set describing a power grid with 700 buses and 134 generators over a 365-days period with data synthetically generated at an hourly rate. The proposed approach for week-long forecasting is based on the Gaussian process regression (GPR) method, with prior covariance matrices of the quantities of interest (QoI) computed from ensembles formed by up to twenty preceding weeks of QoI observations. Then, we use these covariances within the GPR framework to forecast the QoIs for the following week. We demonstrate that the the proposed ensemble GPR (EGPR) method is capable of accurately forecasting weekly total load demand and power generation profiles. The EGPR method is shown to outperform traditional forecasting methods including the standard GPR and autoregressive integrated moving average (ARIMA) methods.