Number Of Renewable Energy Sources Research Articles

Regional marketing mechanisms for industrial energy flexibility enabled by service-oriented IT platforms

Abstract An increasing number of renewable energy sources driven by ambitious sustainability efforts is both changing and challenging electricity systems worldwide. Successfully dealing with a variable and highly decentralized electricity supply associated with the rising share of renewable energy sources will be crucial in the near future. This paper investigates the potential of information technology (IT) for regional marketing of energy flexibility from the machine level in factories to the electricity-grid level. In this context, energy flexibility describes the capability to react quickly and cost-efficiently to alternating electricity availability. Based on a literature review of regional marketing mechanisms, existing concepts of IT platforms, and current real-world model regions, this paper aims to apply a holistic research approach to better understand the challenges regarding the energy transition on a regional level and corresponding requirements for IT platforms used for flexibility marketing. The approach allows to identify relevant key challenges and flexibility marketing use cases, attributing core importance to underlying production processes. In particular, target processes for respective use cases are derived based on a description of current regional marketing of demand flexibility. Successful implementation of target processes is required to provide regional energy flexibility via IT platforms. A comparison between current processes and target processes finally allows defining the need for change and development, e.g., of new intelligent interfaces, already during the conceptual test of new IT platforms. The introduced research approach was applied to one exemplary use case within the energy-flexible model region Augsburg of the Kopernikus project SynErgie, Germany. The paper especially illustrates that service-oriented IT platforms simplify the communication process between the relevant players in energy flexibility marketing.

Optimal Network Reconfiguration and Power Curtailment of Renewable Energy Sources to Eliminate Overloads of Power Lines

The increasing number of renewable energy sources in power systems contributes to overloads of power lines in emergency situations. Lines made with relatively small cross-section cables, which in the past were designed for an operating temperature of 40 °C, are particularly exposed to overloads. Currently, they constitute the so-called “bottlenecks” in network capacity. This is manifested in the fact that when carrying out expert opinions aimed at examining the impact of a source on the network, computational analyses show overloads of its elements. This article proposes a methodology for eliminating these overloads. It involves the use of two methods at the same time, namely optimal network reconfiguration combined with minimisation of the total power curtailment in RE sources. The search for the optimal network configuration will also allow for minimising power curtailment in renewable energy sources, and thus reduce the costs of this type of operation. With such a tool, network operators will be able to achieve the effect of relieving the line load with the lowest possible cost of redistribution. Based on the IEEE 118 bus test network, calculations were performed that confirmed the effectiveness of the proposed approach. The operation of the proposed methodology is presented with the example of two selected network failure states. The novelty of the proposed solution lies in the simultaneous use of two methods of eliminating line overloads. This streamlines the entire process and improves its effectiveness.

Power Flow Regulation Effect and Parameter Design Method of Phase-Shifting Transformer

In the context of carbon peaking and carbon neutrality goals, the integration of a large number of renewable energy sources may trigger significant tidal changes, leading to transmission congestion, wind and light abandonment, power oscillation, voltage and frequency fluctuations, etc. A phase-shifting transformer is a cost-effective and reliable power flow control equipment, but its key parameters lack systematic design methods. Based on the equivalent model of phase-shifting transformers, detailed design principles and methods have been proposed, and the configuration method of winding turns has been improved to facilitate calculation and control strategy formulation. The regulation effect of the phase-shifting transformer designed through simulation of power flow regulation in a typical 500 kV network is simulated. By adjusting the phase-shifting transformer, the power flow of heavy load lines can be transferred to light load lines, optimizing the power flow distribution and improving the transmission capacity of the cross-section.

A Multi-Timescale Coordinated Optimization Framework for Economic Dispatch of Micro-Energy Grid Considering Prediction Error

The integration of a large number of renewable energy sources (RES) into the micro-energy grid (MEG) brings certain challenges to its optimal dispatching, especially the impact of RES and load prediction error on optimal dispatching. Therefore, this paper proposes a multi-timescale coordinated optimization framework for economic dispatch of MEG considering prediction error, including three stages: day-ahead economic dispatch, intra-day rolling optimization and real-time adjustment. In the day-ahead and intra-day stages, the device's output is solved with the objective of optimal system economy. The MEG is divided into three subsystems in the real-time adjustment stage using distributed model predictive control (DMPC). The subsystems cooperate to correct the intra-day device output, which reduces the complexity of solving system optimization problems. Meanwhile, considering that the prediction horizon in DMPC usually remains constant during the optimization, which affects the control performance of DMPC. This paper analyzes the impact of the prediction horizon and control horizon on the control performance of DMPC. Then a DMPC method with adaptive adjustment of the prediction horizon according to the prediction errors of RES and loads power is proposed, which can balance the stability and prediction accuracy of the system. Finally, the case studies verify the feasibility of the proposed strategy.

Three‐Phase Unbalance Reduction and Energy Optimization for Active Distribution Network Using Flexible Multi‐State Switch

Active distribution network is integrated with a large number of renewable energy sources and flexible loads such as electric vehicles(EVs). The unstable output of distributed generation and the random access to different phases cause three phase unbalance in the distribution network, which increase the energy loss and carbon emissions. Three phase unbalance reduction requires high real‐time dispatch and optimization of the power flow. This paper attempts to reduce the energy loss and carbon emission by using flexible multi‐state switch (FMSS) with optimal energy dispatch strategy. A multi‐objective optimization model is established based on second‐order cone relaxation to reduce the three‐phase unbalance and energy loss. EVs regulation strategy is proposed by analyzing their charging flexibility. A fast solution method is brought up based on second‐order cone model transformation. The effectiveness and rapidity of the method is verified by multiple cases. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

Assessment of Harmonic Contribution of a Photovoltaic Installation Based on Field Measurements

Quantifying harmonic contribution of customer installations to the voltage distortion in the network is still one of the main challenges of utilities with respect to power quality, particularly when integrating large number of renewable energy sources. This paper discusses different aspect of evaluating the low-order harmonic emission of a 100 kW photovoltaic (PV) installation consisting of 16 single-phase inverters. Based on field measurements of one week the harmonic currents of the installation are analyzed with respect to different operating conditions. The measured harmonic currents are compared with emission values provided by the manufacturer as well as emission limits specified in standards. Finally, different methods for the determination of the contribution of the PV installation to the voltage distortion in the network are compared.

A Blockchain-Based Intelligent Data Management Platform for Power Grid Applications

With the access of a large number of renewable energy sources in the power system, the increasingly complex power system operations bring considerable safety concerns for data management. While measurement data analytics in the power grid play an important role in different control and alarming applications, smart and reliable data management is required. Blockchain technology is a decentralized database based on cryptography and distributed storage, which is immutable and traceable. This paper analyzes the matching degree between the characteristics of blockchain technology and the demand for measurement data management, showing the advantages of blockchain for power grid data management. In this paper, a data intelligent management platform based on blockchain technology is designed. A standardized interface design of data service is elaborated, and the application scenarios of intelligent data are listed, providing new possibilities for developing advanced applications based on the measurement data.

YOLO-SS-Large: A Lightweight and High-Performance Model for Defect Detection in Substations.

With the development of deep fusion intelligent control technology and the application of low-carbon energy, the number of renewable energy sources connected to the distribution grid has been increasing year by year, gradually replacing traditional distribution grids with active distribution grids. In addition, as an important component of the distribution grid, substations have a complex internal environment and numerous devices. The problems of untimely defect detection and slow response during intelligent inspections are particularly prominent, posing risks and challenges to the safe and stable operation of active distribution grids. To address these issues, this paper proposes a high-performance and lightweight substation defect detection model called YOLO-Substation-large (YOLO-SS-large) based on YOLOv5m. The model improves lightweight performance based upon the FasterNet network structure and obtains the F-YOLOv5m model. Furthermore, in order to enhance the detection performance of the model for small object defects in substations, the normalized Wasserstein distance (NWD) and complete intersection over union (CIoU) loss functions are weighted and fused to design a novel loss function called NWD-CIoU. Lastly, based on the improved model mentioned above, the dynamic head module is introduced to unify the scale-aware, spatial-aware, and task-aware attention of the object detection heads of the model. Compared to the YOLOv5m model, the YOLO-SS-Large model achieves an average precision improvement of 0.3%, FPS enhancement of 43.5%, and parameter reduction of 41.0%. This improved model demonstrates significantly enhanced comprehensive performance, better meeting the requirements of the speed and precision for substation defect detection, and plays an important role in promoting the informatization and intelligent construction of active distribution grids.

Analyzing enviromental management accounting to increase university awareness towards sustainability

Currently, universities are concerned about environmental issues and their impacts. As a university with a conservation perspective, Universitas Negeri Semarang plays a significant role in solving environmental problems. This study explores environmental management accounting (EMA) practices and the motivations for their use in universities. The focus was on waste management, energy, and climate change. Qualitative content analysis was used in this research with the main source of the UI Green Metrics document and in-depth interviews with the head of the Conservation Technical Implementation Unit and its divisions. The results show that the two categories support environmental management accounting. Both energy and climate change contributed 21% to the EMA, and waste management accounted for 18%. Energy and climate change include energy-efficient appliance usage, smart building implementation, number of renewable energy sources on campus, number of renewable energy sources on campus, and total carbon footprint divided by the total campus population (metric tons per person). Furthermore, the waste in the college will be recycled through several programs, such as paper and plastic reduction, organic and inorganic waste management, sewage disposal, and toxic waste treatment.

A Novel Multi Level Dynamic Decomposition Based Coordinated Control of Electric Vehicles in Multimicrogrids

This paper presents a novel tetra-level dynamic decomposition-based control approach for coordinated operation of electric vehicles in multimicrogrids, which is comprehensive, generic, modular, and secure in nature, to maximize the utilization of renewable energy sources, while meeting the load demands with the resources available. There are a number of microgrids that are connected to the grid. Each microgrid consists of a number of renewable energy sources, energy storage systems, non-renewable energy sources, electric vehicles, and loads. Each distributed energy source or load is controlled by a microsource controller. All microsource controllers with a similar nature are controlled by a unit controller, and all the unit controllers in a microgrid are controlled by a microgrid controller. There is a single multimicrogrid controller at the top. The proposed control scheme was verified through simulation-based case studies.

Optimal sizing of battery energy storage in solar microgrid considering peak load shaving

The power system faces a lot of challenges due to incorporation of a large number of renewable energy sources. Here, Battery Energy Storage System (BESS) can be used effectively to deal with the grid’s rising peak demands without the necessity of upgrading the grid capacity. An energy management strategy is considered based on peak load shaving combined with transformer power limit and BESS State of Charge (SOC). Cooperation Search Algorithm (CSA) is utilised to determine the number of batteries in BESS and Photovoltaic (PV) panels with the goal of maximising economic benefits. The optimisation model of solar microgrid energy storage is established and solved for three scenarios of the grid only (Base Case), BESS without PV (Case-A) and BESS with PV (Case-B). From the result, it is verified that in Case-B, the customer will pay 31% and 34% less compared to the Base Case and Case-A, respectively, towards energy costs in a day.

Advanced Cascaded H-Bridge & Modular Multilevel Converter Applied to a Wind Energy Generation

As the number of renewable energy sources increases worldwide, wind energy is one of themwho stand out from the crowd. With the development of technology, wind turbine projects have had a significant impact increased their capacity, reaching a higher capacity mainly for offshore installations. One of the most important challenges are power Converter, specifically semiconductor components that have limited voltage and current characteristics. This gave birth to the concept of multilevel Converter , which increased the voltage level and thus carried a higher power level. Apart from the application In multilevel converter , it is possible to increase the voltage and power level by using an generator. This context was investigated in this work the use of a multi-level converter (three-level cascaded H-bridge in series), which controlsan open coil of Generator Analysis of the proposed system was developed dynamicallySimulation of 1.71 MW WECS using PLECS software including basic system modeling components: generator, power converters, system control, filter and grid connection. The score show that the goal of achieving 5-level behavior of the output voltage is achieved by using OEW connection. In addition, a low harmonic concentration is achieved in the machine flow as such current supplied to the grid. In addition, it is possible to control the distribution of power between converter, indicating that lower power converter can be connected in a higher direction Wind power

Elimination of Line Overloads in a Power System Saturated with Renewable Energy Sources

The increasing number of renewable energy sources (RESs) connected to power grids contributes to the emergence of not only balancing problems but also technical ones, such as the overloading of power lines. If renewable sources with a high generation level are planned to be connected in the area under consideration, then a large number of significant overloads should be expected, especially during contingency analysis. As a rule, high-voltage networks have a mesh topology, which is why the concept of using advanced mathematical algorithms was developed, with the help of which the resulting threats can be eliminated. This article presents a proposal for a new method of eliminating line overloads and determining the currently available nodal generation levels. Its innovation is a new method of eliminating problems related to the capacity of power grids. The high efficiency of the method results from the appropriately defined response of properly selected RES sources to the state of network congestion. The problem under consideration is illustrated with the example of a modified IEEE 118-bus test network. In order to eliminate line overloads, the article proposes a two-stage approach. In the first step, the sources that are most responsible for the occurring overloads are determined. In the second step, a metaheuristic algorithm is used to solve a nonlinear optimisation problem with constraints. This task involves reducing the power generated in the sources selected in the previous step in such a way that the resulting line overloads are eliminated, and, at the same time, the total value of the curtailed generation is minimal.

Control strategy of vibration zone avoidance based on a hydropower-battery hybrid unit

As a growing number of renewable energy sources (RESs) are integrated into the power system, how to reduce the wear and tear on units while enhancing the regulating capability becomes one of the most pressing concerns for hydropower plants (HPPs). A hydropower flexibility enhancement innovation technology is studied in this paper, which can provide a real-time control ability to avoid the vibration zones for hydropower units (HUs) by the ‘power sources coordination’ between the HU and the unit-side battery. Firstly, the hydropower-battery hybrid unit (HBHU) model and the zone division of HU and battery are introduced. Secondly, the general principles of hydropower-battery coordinated control are proposed. Based on the state of charge (SOC) management and fast response ability of the battery, a control strategy of vibration zone avoidance for HU is suggested, of which feasibility is verified by the simulation in four typical scenarios. The results show that the coordination of the battery not only helps the HU avoid the vibration zones effectively but also improves the regulation performance, as well as reduces the wear and tear of the unit.

Performance of Deep Learning Techniques for Forecasting PV Power Generation: A Case Study on a 1.5 MWp Floating PV Power Plant

Recently, deep learning techniques have become popular and are widely employed in several research areas, such as optimization, pattern recognition, object identification, and forecasting, due to the advanced development of computer programming technologies. A significant number of renewable energy sources (RESs) as environmentally friendly sources, especially solar photovoltaic (PV) sources, have been integrated into modern power systems. However, the PV source is highly fluctuating and difficult to predict accurately for short-term PV output power generation, leading to ineffective system planning and affecting energy security. Compared to conventional predictive approaches, such as linear regression, predictive-based deep learning methods are promising in predicting short-term PV power generation with high accuracy. This paper investigates the performance of several well-known deep learning techniques to forecast short-term PV power generation in the real-site floating PV power plant of 1.5 MWp capacity at Suranaree University of Technology Hospital, Thailand. The considered deep learning techniques include single models (RNN, CNN, LSTM, GRU, BiLSTM, and BiGRU) and hybrid models (CNN-LSTM, CNN-BiLSTM, CNN-GRU, and CNN-BiGRU). Five-minute resolution data from the real floating PV power plant is used to train and test the deep learning models. Accuracy indices of MAE, MAPE, and RMSE are applied to quantify errors between actual and forecasted values obtained from the different deep learning techniques. The obtained results show that, with the same training dataset, the performance of the deep learning models differs when testing under different weather conditions and time horizons. The CNN-BiGRU model offers the best performance for one-day PV forecasting, while the BiLSTM model is the most preferable for one-week PV forecasting.

Critical Analysis of the GreenMetric World University Ranking System: The Issue of Comparability

The Universitas Indonesia GreenMetric World Ranking is the most widely adopted system nowadays to rank worldwide universities’ sustainability. The number of participating universities has consistently increased throughout the last decade. An in-depth analysis of this ranking system is made to assess how sustainability in universities is measured through specific indicators. Specifically, based on expert knowledge, common logic and the scientific literature, these indicators are assessed with respect to whether they can be used to fairly quantify and rank worldwide universities’ sustainability development. Some indicators proposed by the ranking system, such as the number of renewable energy sources on campus and the number of various types of programs for sustainable development, were found to be unable to measure any sustainability development effectively and fairly. Many others, such as the opted sewage disposal modality, the percentage of university budget for sustainability efforts and the ratio of sustainability research funding to total research funding, were found to need adjustment to account for context-specific factors such as availability of renewable energy sources, weather, landscape, original construction and the cultural habits of the enrolled people. Taking into account these considerations, a fairer evaluation and comparison of universities’ sustainability could be achieved which provides universities with information on how to effectively improve their sustainability.

Adaptive robust economic dispatch and real-time control of distribution system considering controllable inverter air-conditioner clusters

With a tremendous number of renewable energy sources (RES) integrated into the distribution system, the inherent uncertainty of RES power generation brings about significant challenges in distribution and power balance within the distribution system. This article proposes an adaptive robust economic dispatch (ARED) model and a real-time control strategy for distribution systems as countermeasures, which make full use of the adjustable capabilities of controllable inverter air-conditioner (IAC) clusters. Firstly, the concept of the adjustable capacity curve (ACC) is developed to accurately quantify the adjustable capacity of an IAC cluster. Afterward, a two-stage adaptive robust optimization is formulated for ARED, which comprehensively takes the adjustable capacity of the IAC cluster and the uncertainty of RES into consideration. Meanwhile, the solution methodology of ARED is also designed based on the column and constraint generation (C&CG) algorithm, where the master problem is quadratic programming with quadratic constraints (QCQP), and the max-min sub-problem is reformulated to a mixed integer linear programming (MILP) form by taking advantage of linear duality theory and big-M method. Finally, a novel real-time decentralized control strategy for IAC clusters is also proposed for purpose of hedging against stochastic RES power fluctuation after every round of ARED decisions. The results of the case study validate the effectiveness of ARED model and real-time control strategy under different uncertainty scenarios of RES power generation.

Integrated Duty Cycle Control for Multiple Renewable Energy Sources in a Grid Connected System

Due to their abundance, affordability, and clean energy production, Renewable Energy Sources (RESs) have emerged as crucial sources of electricity production. As a result, considerable effort has been made to integrate renewable energy sources into the grid to help it meet energy demands. One of the challenges is the Maximum Power Extraction (MPE), where the maximum power needs to be extracted from each RES. The cost of implementation rises linearly with the number of RESs if the MPE arrangements are being made for each RES individually. Additionally, the MPE through multiple RES systems gets more challenging, especially when several PhotoVoltaic (PV) strings are connected and each PV string receive non-uniform irradiance. This paper proposes an integrated MPE control system for a multi-RES grid-connected system. The proposed solution uses a single microcontroller to maximize power from all sources to overcome the linearly growing cost problem. Moreover, we propose an improved Multi-Dimensional Cuckoo (MDC) algorithm for MPE to tackle the non-uniform irradiation problem with multi-string PV, in contrast to prior works with grid-connected multiple sources. The proposed technique is first put up against the individual MPE control system, and then it is put up against the Jaya algorithm that is documented in the literature for a typical one-dimensional MPE.

An Evaluation of Pumped Hydroelectric Storage Systems

In smart grids, storage systems are needed to increase the number of renewable energy sources connected to distribution systems and to ensure the continuity of energy. By supporting the system elements, energy storage can provide many services such as energy time shifting, interruption control, ensuring continuity in transmission and power quality improvements. In smart grid applications, difficulties are encountered in energy storage due to various problems such as charge/discharge, safety, size and cost. For this reason, energy storage systems should be developed for energy capacity, control and protection that can increase storage performance. Since there are interruptions and waves in renewable energy sources such as wind and solar, batteries are used for storage. In order to expand the use of clean energy and to ensure energy continuity, mechanical storage methods in large powerful systems have been emphasized. Storage studies have been carried out to increase efficiency, provide flexibility in electrical systems, reduce costs and improve storage time, and reduce power fluctuations. In this study, pumped hydroelectric storage plants, which is one of the storage methods, were examined. Advantages and disadvantages of pumped storage hydroelectric system are mentioned and hybrid pumped hydro storage is explained. Its economic contribution is also briefly mentioned. This storage method of PHES has also been found to be hybrid systems that can be connected to variable speed turbines, groundwater, seawater and renewable energies to increase efficiency, reduce costs and save space. Among these systems, it has been observed that the system in which solar and wind are used as a hybrid is also advantageous in providing high profitability in the energy market. Thanks to its integration with the sun and wind, carbon emissions are reduced.

RISK ANALYSIS METHODOLOGIES FOR THE FINANCIAL EVALUATION OF WIND ENERGY POWER GENERATION PROJECTS

Pursuant to European legislative and energetics trends, Croatia must significantly increase the number of renewable energy sources in the electrical energy system. Wind power plants are among the most attractive on the market. Since the European experiences of the development, construction and utilization of power plants predominantly employ the project financing model, it is necessary to establish a framework for the analysis of the financial profitability of investments in the construction of a wind power plant. Moreover, in order to increase the security of such investments, a rapid and efficient system is required for the analysis of the investment risks of the project in order to reduce them on the planning or acceptable economic levels. With these goals, a model for the financial evaluation of wind energy power generation projects is presented, together with the elements of the risk analysis employed. The data used are from a project for a wind power plant with a power rating of 18 MW, which is planned for construction on the territory of Zadar County in the environs of Benkovac. In cases where there are no reference documents for Croatia, data and experiences from European projects are used.