Integration Of Renewable Energy Sources Research Articles

Examining the drivers of the imbalance price: Insights from the balancing mechanism in the United Kingdom

The increasing integration of renewable energy sources in the UK electricity sector has posed challenges to the stability of the system, leading to a sharp rise in the costs of balancing services. This study analyses half-hourly data from January 2017 to December 2023 to examine the factors determining the imbalance price in the UK's balancing mechanism, with particular focus on the sharp price increases in 2021-2022. Employing a Generalised Additive Model (GAM) to account for non-linear relationships, the analysis finds that the imbalance price is positively affected by the net imbalance volume (demand-side factor) and negatively impacted by the de-rated margin (supply-side factor). The wholesale electricity price, however, is identified as the dominant factor driving both the mean and volatility of the imbalance price during 2021-2022. These findings suggest that the balancing mechanism is functioning effectively and that a reduction in the wholesale price would lead to a lower imbalance price and thus lower costs of balancing services.

Leveraging machine learning for sustainable integration of renewable energy generation

Long-term economic benefits and sustainability are provided by the integration of renewable energy sources (RESs) into electrical networks. However, because of their intermittent nature and reliance on environmental factors, RESs pose issues in production and consumption balance. Because renewable energy sources like wind and solar are unpredictable, forecasting their output is essential for planning purposes and maintaining grid stability. This thesis focuses on developing effective instruments and algorithms to improve renewable energy generation estimates and handle abnormalities in consumption. These tools and algorithms include maximum power point tracking and machine learning models like random forest (RF), adaptive boosting (AdaBoost), and extreme gradient boosting (XGBoost). The methods' effectiveness is confirmed by accuracies higher than 80%, which provides speedier and more user-friendly solutions in comparison to the traditional ways. In the end, our effort seeks to offer practical instruments for anticipatory modelling and mitigating intermittentness in renewable energy sources, enabling their assimilation into current power structures to adequately supply energy requirements in a sustainable manner.

Online Prediction and Correction of Static Voltage Stability Index Based on Extreme Gradient Boosting Algorithm

With the increasing integration of renewable energy sources into the power grid and the continuous expansion of grid infrastructure, real-time preventive control becomes crucial. This article proposes a real-time prediction and correction method based on the extreme gradient boosting (XGBoost) algorithm. The XGBoost algorithm is utilized to evaluate the real-time stability of grid static voltage, with the voltage stability L-index as the prediction target. A correction model is established with the objective of minimizing correction costs while considering the operational constraints of the grid. When the L-index exceeds the warning value, the XGBoost algorithm can obtain the importance of each feature of the system and calculate the sensitivity approximation of highly important characteristics. The model corrects these characteristics to maintain the system’s operation within a reasonably secure range. The methodology is demonstrated using the IEEE-14 and IEEE-118 systems. The results show that the XGBoost algorithm has higher prediction accuracy and computational efficiency in assessing the static voltage stability of the power grid. It is also shown that the proposed approach has the potential to greatly improve the operational dependability of the power grid.

Innovative Strategies for Thermal Energy Optimization and Renewable Energy Integration in Net-Zero-Energy Buildings: A Comprehensive Review

The thermal performance and energy efficiency of buildings are critical factors in achieving sustainable energy systems as energy needs for heating and cooling are expected to represent more than 50% of global final energy consumption. This study analyzes conventional renewable energy systems for heating and cooling in buildings, focusing on strategies for developing net-zero-energy buildings. This review covers the integration of renewable energy, the use of intelligent energy management systems, and the optimization of thermal processes. It also compares various systems based on their advantages and limitations and analyzes emerging trends in the thermal management of buildings in different climate zones. The synthesis of recent literature highlights practical recommendations for achieving high thermal performance in buildings, including the importance of selecting appropriate energy systems based on local climatic conditions, optimizing system efficiency, and taking advantage of new materials and advanced technologies. This review aims to contribute to promoting sustainable construction practices with the integration of renewable energy sources and improving the energy efficiency of buildings.

Smart Integration of Renewable Energy Sources Employing Setpoint Frequency Control—An Analysis on the Grid Cost of Balancing

The increasing installation of Renewable Energy Sources (RES) presents significant challenges to the stability and reliability of power systems. This paper introduces an advanced control method to mitigate the adverse effects of intermittent generation from RES on the power system frequency stability. The proposed approach emphasizes the critical role of Battery Energy Storage Systems (BESS) and RES in enhancing the resilience of modern power networks. The Generation Export Management Schemes (GEMS) are employed to curtail the excessive export of RES, thereby contributing to improved frequency stability. This research involves a comprehensive analysis of the dynamic behavior of the network under various operational scenarios, particularly focusing on power exchanges between RES, BESS, and synchronous generation units. Furthermore, this paper focuses on the economic implications of integrating RES into the grid, with a detailed cost of balancing (COB) modelling and analysis conducted to assess the financial viability of the proposed frequency management solutions. The analysis encompasses both short-term and long-term perspectives, providing insights into the development of economically sustainable smart power networks that effectively integrate renewable energy and storage technologies while maintaining system stability.

Quantifying Uncertainty Costs in Renewable Energy Systems Considering Probability Function Behavior and CVaR at Low-Probability Generation Extremes using Deterministic Equations

The increase and integration of renewable energy sources in electrical power systems implies an increase in uncertainty variables, both in costs and production, of economic dispatch (ED) and currently have a significant influence on wholesale electricity markets (MEM). Uncertainty costs refer to the quantification of additional expenses or economic losses associated with the variability inherent in the generation of renewable energy, such as wind, solar, or hydroelectric. Therefore, this article presents deterministic equations related to cost overestimation and underestimation, as well as CVaR, to model and evaluate the stochasticity of risks associated with the integration of renewable sources, allowing system operators and planners to make informed decisions. To mitigate or use said risks in energy systems with high penetration of elements, mainly smart networks. In this study, a mathematical analysis is carried out using the histogram spectrum formed by the power generated by the probability density function (PDF) for solar generation, although it is possible to consider other types of functions to determine energy generation. The objective of the proposed model is to provide another tool to the system operator for energy management and planning, which relieves a little of the weight of the computational load and at the same time presents more precision in the results by being able to work with a database. Historical data if these values are available. Commonly, for this type of analysis, values are estimated using probabilistic calculations by density functions when integrating these functions, or in other recent cases by estimating them by analytical methods of the same functions. A validation of the model is presented by comparing the result with the Monte Carlo simulation, developing the total cost of uncertainty only from "low probability generation extremes". Furthermore, the results are presented through analytical uncertainty cost functions (AUCF). This analysis includes the calculation of uncertainty costs for low and high-probability energy generation, determined by the Conditional Value at Risk (CVaR), using deterministic equations.

Analysis of multidimensional impacts of electric vehicles penetration in distribution networks.

Moving towards a cleaner, greener, and more sustainable future, expanding electric vehicles (EVs) adoption is inevitable. However, uncontrolled charging of EVs, especially with their increased penetration among the utility grid, imposes several negative technical impacts, including grid instability and deteriorated power quality in addition to overloading conditions. Hence, smart and coordinated charging is crucial in EV electrification, where Vehicle-to-Grid (V2G) technology is gaining much interest. Owing to its inherited capability of bi-directional power flow, V2G is capable of enhancing grid stability and resilience, load balancing, and congestion alleviation, as well as supporting renewable energy sources (RESs) integration. However, as with most emerging technologies, there are still technical research gaps that need to be addressed. In addition to these technical impacts, other multidisciplinary factors must be investigated to promote EVs adoption and V2G implementation. This paper provides a detailed demonstration of the technical problems associated with EVs penetration in distribution networks along with quantifiable insights into these limitations and the corresponding mitigation schemes. In addition, it discusses V2G benefits for power systems and consumers, as well as explores their technical barriers and research directions to adequately regulate their services and encourage EV's owners to its embracement. Moreover, other factors, including regulatory, social, economic and environmental ones that affect EV market penetration are being studied and related challenges are analyzed to draw recommendations that aid market growth.

Surrogate Modeling for Solving OPF: A Review

The optimal power flow (OPF) problem, characterized by its inherent complexity and strict constraints, has traditionally been approached using analytical techniques. OPF enhances power system sustainability by minimizing operational costs, reducing emissions, and facilitating the integration of renewable energy sources through optimized resource allocation and environmentally aligned constraints. However, the evolving nature of power grids, including the integration of distributed generation (DG), increasing uncertainties, changes in topology, and load variability, demands more frequent OPF solutions from grid operators. While conventional methods remain effective, their efficiency and accuracy degrade as computational demands increase. To address these limitations, there is growing interest in the use of data-driven surrogate models. This paper presents a critical review of such models, discussing their limitations and the solutions proposed in the literature. It introduces both Analytical Surrogate Models (ASMs) and learned surrogate models (LSMs) for OPF, providing a thorough analysis of how they can be applied to solve both DC and AC OPF problems. The review also evaluates the development of LSMs for OPF, from initial implementations addressing specific aspects of the problem to more advanced approaches capable of handling topology changes and contingencies. End-to-end and hybrid LSMs are compared based on their computational efficiency, generalization capabilities, and accuracy, and detailed insights are provided. This study includes an empirical comparison of two ASMs and LSMs applied to the IEEE standard six-bus system, demonstrating the key distinctions between these models for small-scale grids and discussing the scalability of LSMs for more complex systems. This comprehensive review aims to serve as a critical resource for OPF researchers and academics, facilitating progress in energy efficiency and providing guidance on the future direction of OPF solution methodologies.

Power Quality Improvement for Hybrid Microgrid Integrated with Renewable Energy Sources

Given the present energy crisis and depletion of fossil fuel reserves, there is a pressing need to enhance the integration of Renewable Energy Sources (RES) into the power system. This research focuses on designing and managing a Hybrid Microgrid (HMG) in fluctuating RES. The Direct Current (DC) sub-grid comprises a Wind Turbine (WT), a solar Photovoltaic (PV) array equipped with a Perturb-and-Observation (P&O) Maximum Power Point Tracking (MPPT) method, a boost conversion, and a Battery Energy Storage System (B-ESS) connected to DC demands. The Alternative Current (AC) sub-grid comprises a Permanent Magnet Synchronous Generator (PMSG) WT and a Fuel Cell (FC) integrated with an inverter circuit synced with the grid to fulfill its load requirements. A reversible Interlinking Conversion (IC) connects the AC and DC sub-grid, enabling efficient power interchange across the two grids. The IC's control technique allows it to function as an active energy filter and exchange operator. The IC's active energy filtering function ensures that the power supply of the microgrid complies with the criteria set by IEEE 519. It does this by offering reactive power assistance and decreasing the harmonic levels to below 5%. The suggested method enables the HMG to function in grid-connected and islanded states. During grid-connected operation, power is transferred across the DC and AC sub-grids, ensuring that all load requirements are fulfilled. In the islanded method, a diesel generator provides power to the AC sub-grid to fulfill the needs of essential loads, while the B-ESS sustains the DC microgrid. The suggested model is constructed and simulated with MATLAB, and its outcomes are examined.

Mitigating Risk and Emissions in Power Systems: A Two-Stage Robust Dispatch Model with Carbon Trading

The large-scale integration of renewable energy sources is crucial for reducing carbon emissions. Integrating carbon trading mechanisms into electricity markets can further maximize this potential. However, the inherent uncertainty in renewable power generation poses significant challenges to effective decarbonization, renewable energy accommodation, and the security and cost efficiency of power system operations. In response to these challenges, this paper proposes a two-stage robust power dispatch model that incorporates carbon trading. This model is designed to minimize system operating costs, risk costs, and carbon trading costs while fully accounting for uncertainties in renewable energy output and the effects of carbon trading mechanisms. This model is solved using the column-and-constraint generation algorithm. Validation of an improved IEEE 39-bus system demonstrates its effectiveness, ensuring that dispatch decisions are both robust and cost-efficient. Compared to traditional dispatch models, the proposed model significantly reduces system risk costs, enhances the utilization of renewable energy, and, through the introduction of a ladder carbon trading mechanism, achieves substantial reductions in carbon emissions during system operation.

The Role of Employee Competencies in the Sustainable Development and Energy Efficiency of Agile Organizations

The aim of this study was to examine how the development of employee competencies affects the efficiency of green energy utilization in organizations and how these activities support the sustainable development of enterprises. This research focused on identifying the specific agile competencies that have the greatest impact on energy efficiency and on integrating these competencies with sustainable development goals. Quantitative and qualitative analyses were conducted based on data collected through online surveys among employees of companies using green energy. The research results indicate a significant relationship between the level of agility in employee competencies and the energy efficiency in enterprises. Most companies achieve better results through the application of agile practices. This study’s findings confirm that investments in the development of agile employee competencies and the integration of renewable energy sources can significantly contribute to improving energy efficiency and increasing the competitiveness and innovation of organizations. Moreover, the need for further research on methods for assessing and implementing agile practices in various operational settings, with a focus on sustainability aspects, was emphasized.

Design of a High-Voltage Arbitrary Waveform Generator Using a Modular Cascaded H-Bridge Topology

As the integration of renewable energy sources into the grid increases, the insulation systems of grid components such as transformers and switchgear encounter significant challenges due to the transients and harmonics generated by power-electronic-based converters. A test generator capable of replicating these component stresses is essential to accurately evaluate these insulation systems under real-grid conditions. This paper proposes a modular cascaded H-bridge-based high-voltage arbitrary waveform generator, prototyped with three stages to generate customized waveforms (triangular, sawtooth, pulse, and complex) up to 8 kV. The H-bridge modules are designed using Si MOSFETs with a maximum blocking voltage of 4.5 kV. The input to the HV H-bridge module is provided by a 10 kV medium-frequency transformer, whose design is described with a focus on the insulation system and winding configuration. This transformer is driven by a zero-voltage switching driver. This arbitrary waveform generator excels in several aspects, including a straightforward design procedure, compact size, high voltage capability, ease of integration, and cost.

Analysis of the Principle of Gas Turbine and State-of-art Applications

Abstract. As a matter of fact, gas turbines have become a cornerstone of modern energy systems, playing a pivotal role in power generation, the oil and gas industry, and the integration of renewable energy sources. This study provides an in-depth analysis of the principles underlying gas turbine operation, alongside a summary of state-of-the-art facilities that represent the pinnacle of current technological advancements. These turbines, known for their high efficiency and flexibility, are crucial for meeting the growing global demand for reliable and cleaner energy. However, significant challenges remain, particularly in terms of material limitations, emissions reduction, and the need for greater operational flexibility to accommodate renewable energy sources. The future of gas turbines may involve a shift towards alternative fuels like hydrogen, which could drastically reduce their environmental impact. These results highlight the ongoing evolution of gas turbines and underscore the importance of continuous innovation to address these challenges, ensuring that gas turbines can meet the energy needs of the future while minimizing their ecological footprint. The findings of this research are essential for advancing the understanding of gas turbine technology and guiding future developments in this field.

Optimizing power output in hybrid photovoltaic/wind systems: a nonlinear back-stepping approach for enhanced efficiency and stability

Abstract This paper investigates the challenge of controlling hybrid renewable energy systems (HRES), specifically those combining wind energy and photovoltaic sources, under varying environmental conditions such as fluctuating wind speeds and partial shading. The primary objective is to develop a robust backstepping control strategy that enhances the system’s stability and energy efficiency while ensuring seamless grid integration through the use of dual-fed induction generators. The study uses advanced modeling techniques, including maximum power point tracking for wind turbines and particle swarm optimization for photovoltaic systems, to optimize energy capture. A detailed simulation framework was designed to validate the effectiveness of the control strategy under different climatic scenarios. Quantitative results show that the wind turbine achieved over 95% power recovery, the DC link voltage remained stable within 0.5% of the reference, and photovoltaic energy extraction was optimized with 98% accuracy, even under partial shading. These findings indicate that the proposed control strategy significantly enhances the performance, reliability, and adaptability of the HRES. This work offers a promising contribution to the integration of renewable energy sources into the electrical grid, supporting a more sustainable energy future.

Optimal operating strategy of hybrid heat pump − boiler systems with photovoltaics and battery storage

The growing need to reduce energy consumption and greenhouse gas emissions is driving the search for more efficient heating solutions in buildings. Hybrid heating systems, which combine air-to-water heat pumps (AWHP) with traditional gas boilers, are a common solution after refurbishment investments. However, managing these systems effectively, particularly when integrated with photovoltaic (PV) panels and battery energy storage systems (BESS), remains a complex task. For instance, heat pumps perform poorly in very cold conditions, making boilers a more efficient option; however, it might be advantageous to use it to increase electricity self-consumption. Optimal management depends on multiple factors, including future forecast data. In this paper, a daily optimization program is developed by means of a brute-force approach using forecast data. The core innovation of this paper is the use of an artificial neural network (ANN) that, trained on predictive optimization results, can determine the optimal solution in real-time without the need for future forecasts. The ANN achieved a 99.16% accuracy in new scenarios, successfully optimizing costs, CO2 emissions, and primary energy use. Results indicate up to 19% cost savings in colder cities, a 12% reduction in CO2 emissions, and a 3% decrease in primary energy consumption. This approach holds significant potential for enhancing the integration of renewable energy sources, contributing to long-term sustainability goals.

Optimization of emergency frequency control strategy for power systems considering both source and load uncertainties

With the increasing integration of renewable energy sources and the presence of numerous controllable loads such as electric vehicles and energy storage in the modern power system, higher nonlinearities and uncertainty both sources and loads are introduced. These factors pose challenges in achieving fast and accurate emergency frequency control. Therefore, this paper addresses the issue of dual source-load uncertainties in power system and presents an optimization strategy based on the Soft Actor Critic (SAC) algorithm that involves the participation of controllable loads in emergency frequency control. Firstly, the spatio-temporal uncertainties of wind farm power output on power supply side and power demand on the load side are described using Weibull and normal probability distributions, respectively. Secondly, an improved Markov Decision Process (MDP) model for emergency frequency control is established, which considers the characteristics of the dual source-load uncertainties. Finally, an optimization of the SAC algorithm is conducted based on Deep Reinforcement Learning (DRL), aiming to achieve rapid system frequency recovery and minimize the cost of removing controllable loads. The presented approach in the paper enhances the emergency frequency control strategy for uncertain power systems and effectively addresses the issue of source-load uncertainty compounded by fault power shortages.

Multi-Objective Optimal Power Flow Analysis Incorporating Renewable Energy Sources and FACTS Devices Using Non-Dominated Sorting Kepler Optimization Algorithm

In the rapidly evolving landscape of electrical power systems, optimal power flow (OPF) has become a key factor for efficient energy management, especially with the expanding integration of renewable energy sources (RESs) and Flexible AC Transmission System (FACTS) devices. These elements introduce significant challenges in managing OPF in power grids. Their inherent variability and complexity demand advanced optimization methods to determine the optimal settings that maintain efficient and stable power system operation. This paper introduces a multi-objective version of the Kepler optimization algorithm (KOA) based on the non-dominated sorting (NS) principle referred to as NSKOA to deal with the optimal power flow (OPF) optimization in the IEEE 57-bus power system. The methodology incorporates RES integration alongside multiple types of FACTS devices. The model offers flexibility in determining the size and optimal location of the static var compensator (SVC) and thyristor-controlled series capacitor (TCSC), considering the associated investment costs. Further enhancements were observed when combining the integration of FACTS devices and RESs to the network, achieving a reduction of 6.49% of power production cost and 1.31% from the total cost when considering their investment cost. Moreover, there is a reduction of 9.05% in real power losses (RPLs) and 69.5% in voltage deviations (TVD), while enhancing the voltage stability index (VSI) by approximately 26.80%. In addition to network performance improvement, emissions are reduced by 22.76%. Through extensive simulations and comparative analyses, the findings illustrate that the proposed approach effectively enhances system performance across a variety of operational conditions. The results underscore the significance of employing advanced techniques in modern power systems enhance overall grid resilience and stability.

Optimizing peak shaving operation in hydro-dominated hybrid power systems with limited distributional information on renewable energy uncertainty

The increasing integration of renewable energy sources (RES) in power systems poses challenges for peak shaving operations due to RES uncertainty. However, it is difficult to obtain complete distributional information for uncertainty modeling. This study focuses on optimizing peak shaving in hydro-dominated hybrid power systems under such uncertainty. We utilize limited distributional information of RES forecast errors, specifically the first two moments, to build a moment ambiguity set. Employing distributionally robust chance-constrained programming (DRCCP), we develop a peak shaving model that quantifies the flexibility reserve of hydropower by risk level and the forecast errors. To enhance computational tractability, we apply the Chebyshev inequality to reformulate the moment-based DRCCP model into a mixed-integer linear programming model. Numerical simulations conducted on a provincial power grid in China validate the model's effectiveness. Key findings indicate that: (1) The model effectively leverages hydropower to provide ramping flexibility for peak shaving and quantifies the flexibility reserve needed for RES forecast errors. (2) This uncertainty modeling approach is more practical than probability distribution function-based methods, ensuring reliable peak shaving scheduling and reducing conservatism. (3) Decision-makers can adjust risk level to modify hydropower flexibility reserve, balancing robustness and conservatism of peak shaving scheduling.

Performance Evaluation of Solar PV Integrated with Custom Power Device under Various Load Conditions

The non-linear properties and rapid switching of power electronic equipment are the primary causes of power quality issues in power systems, particularly in the power distribution systems. The widespread use of delicate equipment, which continuously pollutes the environment, is making power quality problems worse. The increasing integration of renewable energy sources into the generation mix and the decarburization of the economy have created new challenges for smart grid technology, requiring creative solutions such as energy storage systems and smart transformers. This article describes a solar photovoltaic integrated unified power quality conditioner (UPQC) that uses a deep learning method based on neural networks and a novel compensating technique. Here, two Deep Neural Network algorithms are used, one in the solar PV system to obtain the maximum power under various irradiance situations, and the other to manage the UPQC under various load conditions. When compared to the conventional UPQC based on PQ Theory, DNN-UPQC produces superior results in terms of reducing total harmonic distortion. This iterative strategy, which is focused on soft computing, provides faster convergence to the target condition while maintaining the updating weight within a predetermined limit. PV-based UPQC has been connected individually to reduce voltage sag, swell, and unbalance in variable load conditions. The system's dynamic and steady state performance are assessed by modelling it with a MATLAB-Simulink in different load condition.

Big Data and Machine Learning-Based Iot Models for Sustainable Energy Prediction

Integrating Big Data and Internet of Things (IoT) platforms is the focus of this research, which aims to improve energy management. The problem statement is centered on the potential for development through advanced technologies and the inefficiencies in traditional energy management methods. The objectives are to analyze energy consumption patterns, develop an innovative Home Energy Management System (HEMS) architecture, and offer energy-saving solutions. Synthetic energy consumption data is generated, normalized, and divided into training and testing sets from a methodological perspective. K-nearest neighbors, Decision Trees, Support Vector Regression, and Random Forest are the machine learning models trained and evaluated. The Random Forest model outperforms other models in terms of the accuracy of its predictions of energy consumption. The integration of renewable energy sources with cutting-edge technology to revolutionize energy management practices is the essence of novelty. In conclusion, this investigation underscores the importance of utilizing advanced technologies to promote sustainable energy management, providing practitioners and policymakers with practical insights.