Renewable Energy Sources Output Research Articles

Enhancing resilience of distribution systems: Integrating mobile energy storage systems and information gap decision theory for uncertainty management

Power Distribution Systems (PDSs) have seen considerable disruption owing to events and the intrinsic uncertainty associated with renewable energy sources (RES). The fundamental purpose of this project is to identify methods to enhance the resilience of Mobile Energy Storage Systems (MESSs) against unexpected cyber and natural disasters. Information Gap Decision Theory (IGDT) is used to effectively handle the uncertainties linked with RES's outputs. It also applies the Seasonal Autoregressive Integrated Moving Average (SARIMA) model to predict important aspects that affect the output of RES. The shift to microgrid mode prioritizes the delivery of critical loads through MESSs, which are crucial amid adverse situations. A risk-averse strategy mitigates risks associated with wind turbine and photovoltaic (PV) outputs, therefore enhancing the resilience of the PDS. An optimization model utilizing Mixed-Integer Linear Programming (MILP) was formulated. The approach's efficacy in reducing uncertainties in RES has been proved by simulations conducted on the IEEE 69 bus and Dakota transport system. Assuming the most severe situation, the Uncertainty Radius (UR) for wind turbines is 0.7385, but for PVs, it is 0.8753, thereby guaranteeing the robustness of the system against power failures and disturbances.

Improving High-Voltage Cycling Stability and High-Rate Capability of Sodium-Ion Layered Cathode Oxides through Trace Amounts of Low-Valence Metals.

With the increasing demand for clean energy sources, the need for large-scale energy storage systems to ensure the stable output of renewable energy sources, such as wind and solar, has also increased. Sodium-ion batteries have emerged as a potential solution for these storage systems owing to their high energy density, abundance in the Earth's crust, and low cost. However, the larger atomic radius of sodium ions results in higher energy barriers for ion migration in cathode materials, which can affect the cycle life and rate performance of the battery. Therefore, developing a suitable structure that facilitates rapid sodiation and desodiation and maintains good cycling stability remains a significant challenge. This study aimed to reduce the content of trivalent manganese ions and minimize the impact of the Jahn-Teller effect to enhance the capacity retention of manganese-based layered oxides. Additionally, a series of P2-type Na0.78Li0.1ZnxNi0.15-xMn0.75O2 compounds were successfully synthesized through doping with divalent zinc ions. Structural analyses of the doped material indicated that Zn doping did not alter the crystal structure but increased the interlayer distance of the transition metals. Electrochemical performance tests revealed that appropriate Zn2+ doping promoted sodium-ion diffusion and improved the reversible capacity of the battery. This study provides a promising approach for developing sodium-ion batteries with rapid charging and discharging capabilities.

Collaborative Planning of Distribution Network, Data Centres and Renewable Energy in the Power Distribution IoT via Interval Optimization

With the development of the power distribution Internet of Things (IoT), the escalating power demand of data centers (DCs) poses a formidable challenge to the operation of distribution networks (DNs). To address this, the present study considers the operational flexibility of DCs and its impact on DNs and constructs a collaborative planning framework of DCs, renewable energy sources (RESs), and DNs. This framework employs the interval optimization method to mitigate uncertainties associated with RES output, wholesale market prices, carbon emission factors, power demand, and workloads, and the collaborative planning model is transformed into an interval optimization problem (IOP). On this basis, a novel hybrid solution method is developed to solve the IOP, where an interval order relation and interval possibility method are employed to transform the IOP into a deterministic optimization problem, and an improved integrated particle swarm optimization algorithm and gravitational search algorithm (IIPSOA-GSA) is presented to solve it. Finally, the proposed planning framework and solution algorithm are directly integrated into an actual integrated system with a distribution network and DC to verify the effectiveness of the proposed method.

Load frequency control in power systems with high renewable energy penetration: A strategy employing P[formula omitted](1+PDF) controller, hybrid energy storage, and IPFC-FACTS

The high penetration of Renewable Energy Sources (RESs) in the modern power system poses a challenge to power system stability. This stability is affected by the stochastic, fluctuating output of RESs, which is influenced by weather conditions, and a lack of inertia resulting from reduced rotating mass. To address this issue, a new controller, referred to as Proportional-Fractional Integrator Plus Proportional-Derivative with Filter, PIλ(1+PDF), is designed for Load Frequency Control (LFC) with the support of a Hybrid Energy Storage System (HESS) for power systems with high-RES penetration. The HESS comprises a Superconducting Magnetic Energy Storage System (SMES) and a Vanadium Redox Flow Battery (VRFB) coupled with an Interline Power Flow Controller Flexible AC Transmission Systems (IPFC-FACTs) controller. The HESS, working in conjunction with the proposed LFC, injects virtual inertia and maintains power flow to expedite the frequency stability process. These systems are also integrated with Alternating Current (AC) and High Voltage Direct Current (HVDC) transmission lines to collectively enhance both the system's stability and the capacity of its transmission lines. To optimize the PIλ(1+PDF) controller parameters, Zebra Optimization Algorithm (ZOA) is employed utilizing an Integral Time Absolute Error (ITAE) objective function. The proposed controller is tested on a four-area power system integrated with a wind turbine, photovoltaic (PV) panels, a biodiesel generator, and a hydrogen aqua electrolyzer fuel cell, representing a high penetration of RESs in modern power systems. The results are compared with those obtained using Proportional-Integral-Derivative (PID) and Fractional Order Proportional Integral Derivative (FOPID) controllers. Sensitivity analysis and robustness tests are also performed to verify the stability of the power network by changing system parameters and under randomly chosen loading conditions. The proposed PIλ(1+PDF) controller tuned with ZOA outperforms PID and FOPID controllers by minimizing settling time for frequency changes by 62 %, eliminating overshoot, and reducing undershoots for frequency and tie-line power changes by 73 % and 55 %, respectively. Simulation results demonstrate that the proposed controller outperforms PID and FOPID controllers by effectively damping frequency and tie-line deviations, resulting in reduced frequency overshoots, undershoots, and shorter settling times.

Enhancing Grid Stability and Sustainability: Energy-Storage-Based Hybrid Systems for Seamless Renewable Integration

In the face of escalating global energy demand, the shift towards renewable energy sources has emerged as a sustainable solution. However, the integration of renewable energy into the electrical grid introduces challenges such as intermittent and instability. The concept of energy-storage-based hybrid systems, which combines renewable energy systems with energy storage, presents a promising approach to overcome these hurdles. These hybrid systems enhance grid stability by ensuring a consistent energy supply, compensating for the variable output of renewable energy sources, and providing ancillary services to the grid. Furthermore, they pave the way for a more resilient and reliable energy infrastructure, fostering the seamless integration of a substantial share of renewable energies. This paper offers a comprehensive exploration of energy-storage-based hybrid systems, discussing their structure, functioning, and the pivotal role they play in bolstering grid stability and promoting the unobstructed integration of renewable energy sources.

Equalizing multi-temporal scale adequacy for low carbon power systems by co-planning short-term and seasonal energy storage

Power supply faces seasonal security risks due to the large seasonal volatility of renewable energy sources (RES) generation. Power systems with high shares of RES generation are more difficult to keep seasonal and daily/hourly supply adequacy at the same level. Therefore, this paper first addresses multi-temporal adequacy detail accounting for differences in RES output and timing of production, and demand. Then a planning approach is proposed for sizing short-term and seasonal energy storage accompanying with RES to achieve multi-temporal adequacy equilibrium. Unlike existing methods based on linking typical days or 8760-h simulations, the seasonal electricity adequacy constraints are captured by orderly clustering yearly net power curves, then the energy balance constraints are decoupled into long-term (i.e., seasonal) and short-term (i.e., hourly) energy balances. At the short-term scale, the robust optimization is used to address the uncertainty and randomness of wind and solar generation, in the same time realizing the coordination of short-term and seasonal energy storage. A modified column and constraint generation algorithm is used to solve the min-max-min model with binary variables. Finally, case studies are carried out on the Garver-6 system and the Northwest China Power Grid. The results demonstrate that the proposed planning method ensures multi-temporal scale adequacy equilibrium for high-RES power systems, and improving the planned system's economic performance.

Mapping Europe renewable energy landscape: Insights into solar, wind, hydro, and green hydrogen production

The study offers an in-depth examination of the capabilities and output of renewable energy sources, specifically focusing on solar, wind, hydroelectric, and green hydrogen technologies, within 27 countries of the European Union (EU) and the United Kingdom (UK). The outcomes reveal significant strides in renewable energy production, with countries such as Austria, Belgium, and Germany leading in their respective sectors. Germany emerges as a frontrunner, with the highest total renewable energy production. The study also highlights the pivotal role of green hydrogen as an alternative fuel source, with Germany and France showcasing considerable production capacities. The relationship between electricity consumption and renewable energy potential indicates a trend towards countries with high energy demands leading the shift to renewable energy. Regional analysis uncovers areas such as Northern Sweden, Germany, and France as potential hubs for green electricity production due to their abundant renewable resources. The analysis identifies areas that have either an excess or shortage of renewable energy resources, highlighting the critical need for equilibrium between supply and demand. It emphasizes the advancements made by the EU and UK in shifting towards renewable energy sources and stresses the ongoing need for dedication to achieving sustainability objectives in energy.

AC POWER REGULATION TECHNIQUES FOR RENEWABLE ENERGY SOURCES

This article explores different AC power regulation techniques that can be employed to optimize the output of renewable energy sources, such as solar and wind power systems. The article provides an overview of the challenges associated with regulating AC power output from renewable sources and examines various techniques that can be used to improve the performance of power regulation systems. These techniques include voltage control, phase control, reactive power compensation, and power factor correction. The article also discusses the benefits and limitations of each technique, as well as their potential applications in renewable energy systems. Overall, this article provides valuable insights for engineers and researchers working to optimize power auto consumption in renewable energy systems.

Examining the Relationship of Renewable Energy Use, Urbanization, and Industrialization in Shaping Carbon Emissions in South Asia

This research investigates the dynamic impact of various factors including economic growth, the expansion of renewable energy sources, urbanization, industrialization, tourism, agriculture, and forestry on carbon dioxide (CO2) emissions in Pakistan, India, and Bangladesh from 1980-2022. The research centers on the three most populous nations of the three countries. To quantify the correlation between emissions and the myriad of distinct categories of drivers, national time series data annually are utilized. To ascertain the immediate and enduring consequences of emissions fluctuations, DOLS methods are used for data analysis. The results indicate that increasing levels of energy consumption, GDP per capita, tourism arrivals, agricultural activities, and industrialization are all significant long-term contributors to rising CO2 emissions in each of the three countries. However, there is some evidence that implementing sustainable agricultural practices, expanding forest cover, and increasing the output of renewable energy sources could potentially serve as mitigating factors. Based on the study's results, to reconcile the pursuit of development objectives with emission reduction, it is recommended that economic growth strategies prioritize low-carbon sectors, accelerate the adoption of renewable energy sources, promote sustainable agricultural and tourism practices, and foster regional collaboration. Integrated planning for inclusive, environmentally friendly, and sustainable growth trajectories can be enhanced by incorporating comprehensive understandings of the interplay among interconnected factors that impact emission patterns. Customized development trajectories ought to be established for Pakistan, India, and Bangladesh.

Hierarchical consistency tracking (HCT) model: A rapid and efficient approach for dynamic thermal analysis of alkaline water electrolysis

The intermittent power output of renewable energy sources necessitates alkaline water electrolysis (AWE) systems to operate under fluctuating conditions, emphasizing the need for a comprehensive understanding of their dynamic thermal behavior. However, current methods for dynamic thermal detection in AWE systems suffer from limitations such as limited temperature signals, insufficient temperature distribution analysis, and a lack of dynamic thermal analysis methods. To address these challenges, a hierarchical consistency tracking (HCT) model is proposed, which incorporates three hierarchical procedures: segmental characteristic temperatures (CT) extraction, surface consistency extraction, and dynamic consistency tracking. By analyzing infrared images captured from the electrolyzer, the HCT model extracts CT, quantifies temperature distribution patterns, and enables the assessment of thermal consistency during dynamic processes. This model offers a rapid and efficient approach with several advantages, including a more comprehensive understanding of temperature distribution, quantification of dynamic thermal consistency, and optimization of operating parameters and internal structures to improve temperature uniformity. Moreover, the HCT model provides a cost-effective and efficient solution for thermal characterization by eliminating the need for additional temperature sensors during dynamic operation.

Robust Bilevel Optimal Dispatch of Park Integrated Energy System Considering Renewable Energy Uncertainty

This paper focuses on optimizing the park integrated energy system (PIES) operation, and a robust bilevel optimal dispatch is proposed. Firstly, the robust uncertainty set is constructed based on the K-means++ algorithm to solve the uncertainty of renewable energy sources output in PIES. Then, the bi-level dispatch model is proposed, with the operator as the leader and consumers as the follower. The upper model establishes an electricity-heat-gas integrated energy network, and the lower model considers the demand response of consumers. Optimizing the pricing strategies of energy sources to determine the output of each energy conversion equipment and the demand response plan. Moreover, analyzing the decision-making process of the robust bi-level model and the solution method is given. Finally, case studies show that the proposed dispatch model can increase operator profits and reduce consumers’ energy costs. The in-sample and out-of-sample simulations demonstrate that the proposed ellipsoid uncertainty set possesses high compactness, good robustness, and low conservatism.

Development of new reliability metrics for microgrids: Integrating renewable energy sources and battery energy storage system

Microgrids (MGs) are gaining popularity due to their ability to provide reliable and resilient power supply, especially when integrated with renewable energy sources (RESs) and battery energy storage systems (BESS). Reliability is a critical factor for MG owners and policy makers. However, existing reliability indices such as loss of load expectation (LOLE) and expected energy not supplied (EENS) may not offer a comprehensive understanding of MG reliability and resiliency. To address this gap, this paper proposes three new indices for MGs to provide supplementary information on the performance of RESs: the Microgrid Resiliency Index (MRI), the Microgrid Renewable Energy Availability Index (MREAI), and the Microgrid Renewable Energy Energy Index (MREEI). The MRI assesses the MG’s ability to recover from interruptions, providing insights into its resiliency. The MREAI and MREEI offer additional information beyond LOLE and EENS, specifically highlighting the contribution of RESs to the availability and energy losses in the MG. These indices enable a more comprehensive assessment of MG reliability. The formulation for calculation of the indices are provided and applied to a MG with integrated solar photovoltaic (PV), wind turbine (WT), and BESS components. To evaluate the effectiveness of the proposed indices in providing supplementary information on resiliency and the contribution of RESs, various scenarios are examined. These scenarios include the impact of using BESS, changes in RES availability, and variations in RES output. The results demonstrate that the proposed indices effectively capture the contribution of RES to MG reliability and offer valuable insights for decision-making related to RES installation. Also, by integrating BESS, the contribution of RESs to outage hours and lost energy is effectively decreased from 89.41% and 87.41% to 32.69% and 28.94% respectively. Additionally, results highlight the substantial enhancement in the resiliency of the MG, which witnessed an impressive 68.43% improvement.

A chance constrained dynamic network reconfiguration based on Minty algorithm in distribution networks

Abstract With high renewable energy sources (RESs) penetration in distribution networks, handling the uncertainties of RESs outputs and multi-time coupling problems in the dynamic network reconfiguration (DNR) is a big challenge. Besides, the existing mathematical and artificial intelligence algorithms for network reconfiguration face the problem of falling into local optima and poor convergence. To address the above challenge and problem, this paper first establishes a chance-constrained programming model to handle the uncertainties. Then the Minty algorithm is applied for efficiency and accurate static network reconfiguration (SNR) in each time interval. Finally, a branch exchange-based method is proposed to eliminate violations for the operation times of switches. Numerical simulations on the IEEE 33 system and an actual 151-bus distribution network show the superiority of the proposed algorithm over existing methods.

Creep-Fatigue Life Property of P91 Welded Piping Subjected to Bending and Torsional Moments at High Temperature

Abstract In recent years, the role of thermal power plants has shifted from providing a baseload to providing supplemental supply to compensate for fluctuations in the output of renewable energy sources. Thus, the operation of these plants involves frequent startup and shutdown cycles, which lead to extensive damage caused by creep and fatigue interactions. In addition, the piping utilized in thermal plants is subjected to a combined stress state composed of bending and torsional moments. In this study, a high-temperature fatigue testing machine capable of generating such a bending-torsional loading was developed. Creep-fatigue tests were conducted on P91 steel piping with weldment. The results clarified that the creep-fatigue life was reduced by the superposition of the torsional and bending moments and that it was further reduced by a holding load. It was shown that the creep-fatigue life of piping with weldment can be estimated accurately using the equivalent bending moment, which is composed of the torsional and bending moments. It was also confirmed that crack occurred in the heat-affected zone (HAZ) of the welded part, which has been often observed in actual thermal power equipment. From the finite element analysis, it was identified that cracking was initiated in the HAZ due to the accumulation of creep strain and increase in the hydrostatic pressure component during a holding load.

A fast and robust DOBC based frequency and voltage regulation scheme for future power systems with high renewable penetration

AbstractThis paper proposes a disturbance‐observer‐based control (DOBC) scheme for frequency and voltage regulation in modern power systems with high renewable energy sources (RES) penetration. The proposed approach acts as a feed‐forward control that improves the dynamic performance of the conventional proportional‐integral‐derivative (PID) controller. The proposed voltage and frequency control has been validated through hardware‐in‐loop (HIL) implementation on OPAL‐RT, and testing on laboratory‐scale experimental test setup. The robustness of the proposed control scheme has been validated through simulations under worst‐case and stochastic uncertainties to mitigate real‐time variability in RES output and load. Real‐time simulation results depict superior performance of the proposed control strategy in comparison to several well‐established techniques under practical operating conditions, in the presence of communication delay and white noise. To validate the proposed control on laboratory‐scale experimental setup, the digital twin of the physical plant transfer function has been designed. Results reveal that the proposed DOBC control scheme drastically improves the system performance without rendering much computational burden under practical operation scenarios.

A Reliability Study of Photovoltaics Energy Systems in DC Microgrid

In this paper, a lifetime of Photovoltaics PV is studied based on the evaluation of the reliability of the DC microgrid. The analysis has a comprehensive consideration of the elements of the inverter and converter composed of a renewable energy system and its process during bad weather as a supplementary measure for the intermittent output of renewable energy sources connected to the DC microgrid. A battery and a boost converter for the battery were used to keep the voltage constant. The output of the battery varies according to the output fluctuation of the renewable energy source. At this time, the life analysis result of the switching devices that are used in the boost converter is introduced. To prevent system downtime and increase maintenance costs due to converter failures connected to the battery, a converter life analysis is required. Considering that the main failure mechanism of switching devices used in converters is thermal stress, and power loss due to thermal stress. Based on the above, the lifetime characteristics of semiconductor devices are analyzed. The main contribution of this work is conducting the lifetime analysis of solar energy system that is used in the DC microgrid with different weather conditions

A flexibility-based multi-objective model for contingency-constrained transmission expansion planning incorporating large-scale hydrogen/compressed-air energy storage systems and wind/solar farms

The remarkable arrival of uncertainties given by the fluctuations in the output of renewable energy sources (RESs) calls for a more flexible power grid. In confronting this issue, large-scale energy storage systems play a vital role in achieving higher flexibility. In this regard, the authors of this study present here a new multi-objective model for contingency-constrained transmission expansion planning that incorporates large-scale hydrogen/compressed-air energy storage systems and wind/solar farms to simultaneously boost both supply-demand-related flexibility (SDFX) and grid-related flexibility (GDFX). The proposed model is formulated as a non-convex mixed-integer nonlinear master-slave optimization problem. The planning objectives, modeled through the master problem, are intended to minimize the investment and operation costs, while maximizing the SDFX enhancement metric minus the GDFX degradation metric. The slave problem, however, aims to maximize the community welfare function (CIWF) and microgrid welfare function (MIWF) under both normal and N-1 operating conditions. To solve the resulting non-convex mixed-integer nonlinear master-slave optimization problem, a potent symphony orchestra search algorithm (SOSA)—empowered with a multi-computational-step, multi-dimensional, multiple-homogeneous structure—is employed to allow better diversification and exploration. Following the determination of optimal solutions, a conservative-based fuzzy satisfying approach is applied to characterize the best trade-off solution that suits cost-effectiveness and flexibility requirements. The newly developed model was implemented on the IEEE 24-bus and IEEE 118-bus test grids, and the simulation results proved its effectiveness and practicality.

TFODn‐FOPI multi‐stage controller design to maintain an islanded microgrid load‐frequency balance considering responsive loads support

AbstractModern electrical power system design will increase renewable energy sources (RES) dominance. Rotating masses, the main source of inertia in power systems, have been greatly decreased in renewable energy producing systems. Thus, load–frequency equilibrium is an important indicator of these systems' performance and safety. This work introduces a fractional‐order (FO) operator‐based cascade control structure for islanded microgrid (μG) load‐frequency control (LFC). The structure utilizes a tilt–FO derivative with filter (TFODn) in the first level to reduce noise. The second level implements the proportional integral (PI) controller's FO form (FOPI), making it a tilt–FO derivative with a filter cascaded to the FOPI controller (TOFDn‐FOPI). The optimal controller parameters for quick dynamic responses with low frequency fluctuation are determined using a unique cost function and prairie dog optimization (PDO) algorithm. The LFC control loops have frequency deviation‐based responsive loads (RL). Control signal delays, RES output changes, parameter uncertainties, and cyber vandalism are examined. Laboratory‐scale tests also assessed the controller's practicality. The proposed controller outperforms standard and FO type proportional integral derivative (PID) and PD‐PI controllers. The TFODn‐FOPI controller is suitable for complicated closed‐loop systems like islanded μGs due to its faster error clearing, reduced RL capacity, and superior time and frequency domain indicators.

Investigating the Power of LSTM-Based Models in Solar Energy Forecasting

Solar is a significant renewable energy source. Solar energy can provide for the world’s energy needs while minimizing global warming from traditional sources. Forecasting the output of renewable energy has a considerable impact on decisions about the operation and management of power systems. It is crucial to accurately forecast the output of renewable energy sources in order to assure grid dependability and sustainability and to reduce the risk and expense of energy markets and systems. Recent advancements in long short-term memory (LSTM) have attracted researchers to the model, and its promising potential is reflected in the method’s richness and the growing number of papers about it. To facilitate further research and development in this area, this paper investigates LSTM models for forecasting solar energy by using time-series data. The paper is divided into two parts: (1) independent LSTM models and (2) hybrid models that incorporate LSTM as another type of technique. The Root mean square error (RMSE) and other error metrics are used as the representative evaluation metrics for comparing the accuracy of the selected methods. According to empirical studies, the two types of models (independent LSTM and hybrid) have distinct advantages and disadvantages depending on the scenario. For instance, LSTM outperforms the other standalone models, but hybrid models generally outperform standalone models despite their longer data training time requirement. The most notable discovery is the better suitability of LSTM as a predictive model to forecast the amount of solar radiation and photovoltaic power compared with other conventional machine learning methods.

Estimation Method for Transmission Line Overloads Due to Renewable Energy Source (RES) Output Forecast Errors in Daily Supply and Demand Planning

AbstractWe propose a method to estimate transmission‐line overloads due to forecast errors of power output of renewable energy sources. The proposed method involves particle swarm optimization and optimal power flow calculations. The effectiveness of the method is confirmed via numerical simulations using a modified IEEE RTS 24‐bus system model. The results indicate that the proposed method can determine the forecast errors of the renewable power output that causes thermal overload, and further predict the occurrences of transmission‐line overload under generator activation in supply and demand planning. Furthermore, we explain the occurrence mechanism of transmission‐line overload due to forecast errors, which may contribute to improve the daily supply and demand planning. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.