Renewable Energy Focus最新文献

In light of the growing integration of renewable energy sources (RES) into power networks, this study presents a new hybrid islanding detection method (IDM) designed to improve the islanding detection efficiency of hybrid MGs. The proposed hybrid IDM combines the strengths of two existing islanding detection approaches: The Rate of Change of Phase Angle Difference (ROCPAD) and the Intermittent-Bilateral Reactive Power Variation (IB-RPV). By combining these two techniques, the hybrid strategy uses their complementary qualities, minimizing the drawbacks of each method and improving detection accuracy overall. With analyzed performance, the suggested hybrid IDM’s compatibility with IEEE 1547 standards shows it complies with industry standards. Comparative performance analysis of the proposed IDM with existing IDM shows improved islanding detection accuracy, fast detection of islanding events, and no nuisance tripping. The method is noteworthy for its zero non-detection zone (NDZ) and negligible impact on power quality. The proposed hybrid IDM suits Hybrid Microgrids (HMG) with inverter-based Distributed Generation (DG).

In this study an eleven-level multilevel inverter (ELMLI) with reduced switching requirements and lower voltage stress is introduced. The architecture comprises of eight semiconductor devices and only one capacitor, yielding a voltage boosting output of 1.67. Gate pulses for the inverter circuit are provided through a straight forward nearest level control (NLC) method. To highlight the superiority of this topology, a assessment is conducted with recent designs concerning the numbers of semiconductor devices, diodes, sources, and capacitors employed. The Total Harmonic Distortion (THD) is measured at 7.61%, which nearly meets acceptable limits. Subsequently, the suggested circuit is simulated operationally and functionally using software tools such as PLECS and MATLAB (SIMULINK). Experimental validation is also carried out under various loading conditions using the setup.

The increasing demand for sustainable energy solutions and the escalating energy demand have facilitated the emergence of renewable energy sources (RES), such as photovoltaic (PV) sources. Combining RES with battery energy storage system (BESS) technology reduces peak hour demand and allows for economical charging and discharging for time-based energy pricing. Integrating PV and BESS in an unbalanced system with multiple RES sources is a challenging task. It requires a robust algorithm to minimise power loss in the distribution system and decrease the voltage unbalance factor (VUF). This paper presents a new multi-objective Pelican optimisation algorithm (MOPOA) for the optimal allocation of PV and BESS. The MOPOA helps find the best placement of PV and BESS in an IEEE-33 bus unbalanced radial distribution system (URDS). The proposed algorithm combines multiple benefits from a lower net present cost (NPC) and a higher voltage profile enhancement index (VPEI). The case studies and simulation results show that the proposed method places PV and BESS in IEEE 33-bus URDS optimally, satisfying all of the system’s requirements. The results indicate an improvement in the minimum VUF factor of 4.4% in a winter day and 4.3% in a summer day; a reduction in active power loss of 16% in a winter day and 7.1% in a summer day; and a reduction in reactive power loss of 7.5% in a winter day and 7.2% in a summer day. Thus, the recommended strategy effortlessly accelerates to a suboptimal solution.

The increasing use of inverter-based generation (IBG) in power grids raises concern about system strength. This is partly due to the inherent interactions among multiple IBGs in close proximity to one another. This paper proposes an approach to identifying the existential boundary of interaction in a network using the relative electrical distance (RED) concept. The mathematical formulation of the RED concept to address the interaction problem among the IBGs involved utilising the power system network’s admittance matrix to capture its structural characteristics. An interaction matrix derived from the RED values of all IBG pairs was then developed to identify the interacting IBG groups. The proposed approach was demonstrated using the IEEE 39-bus system and a practical 72-bus Nigerian power grid. Results showed that RED values effectively group interacting IBGs, with values closer to 0 signifying higher interaction levels, values closer to 1 indicating lower interaction, and a value of 1 denoting no interaction. Time-domain simulations confirmed the accuracy of the approach, demonstrating that the effect of control interaction propagates proportionally to neighbouring IBGs based on RED values. However, fault currents can influence the impact of control interactions. This approach, which requires less computational effort, provides a quick identification tool for potential areas of concern based on the degree of interaction, enhancing the reliability of power grids with high IBG penetration.

The advent of advanced technologies in power and energy systems is fortifying the grid’s resilience and enhancing the availability of power supply through a network of electrical and communication apparatus. The notable technologies include cyber-physical power systems (CPPS) and transactive energy systems (TES). The CPPS, a derivative of cyber-physical system (CPS), is for operational enhancement, and to boost performance. TES is an energy solution that uses economic and control techniques that enhance the dynamic balance between the supplied energy and energy demand across the electrical infrastructure. Integration of intelligence systems and information and communication technologies has brought new objections and threats to CPPS and TES, where adversaries capitalize on the vulnerabilities in cyber systems to manipulate the system deceitfully. Furthermore, the susceptibility of CPPS to information attacks inherently has the potential to cause cascading failures. Researchers have extensively focused their searchlight on applications of advanced technologies within CPPS. However, leaving out the impact of cascaded failures on the CPPS’ efficiency. This work critically assesses intelligent-based techniques used for cyber threat detection and mitigation. It offers insights on how to guide against some of the approaches adopted by cyber-attackers, identifies corresponding gaps, and presents future research directions. Also presented is the conceptualization of applying CPS models for the cyber-security enhancement of TES solutions. The articles selected for this review were evaluated based on recency and the application of intelligent approaches for intrusion and cyberattack detection in CPPS. It was uncovered from the review that topological models are often used to describe cyberattack processes in CPPS. Also, researchers based their investigation on False-Data Injection Attacks and IEEE-118 Bus systems for validation. It was discovered that the deep Reinforcement Learning-based Graph Convolutional Network is a promising solution for intrusion and cyberattack detection in TES owing to its security, detection accuracy, reliability, and scalability.

The global increase in renewable energy share and grid-resilience risks posed by climate change make distributed energy resources (DERs) a key priority for sustainable energy. While previous studies have explored the required changes for achieving a renewable energy source (RES)-based system, they have paid little attention to different transition strategies based on grid paradigms and their adaptability to local contexts. This study fills this research gap by showing transition pathways toward DER- and RES-based systems through a literature review of DERs, focusing on complementarity elements in electricity systems. We found that the transition pathway must be associated with changes in the following three complementarity elements: (1) the expansion and empowerment of prosumers; (2) the design and arrangement of the energy market and its mechanism in favor of the DER-based system; and (3) the adjustment of tasks and functions of existing stakeholders. These findings make a novel contribution to arguments about incumbents’ sustainability transitions, particularly incumbents’ adoption of new business models and adaptation to new institutions.

Amidst the growing integration of renewable energy sources (RES), the efficient administration of load-frequency control (LFC) in microgrids (MG) continues to be a significant challenge. In response to the complexities of modern MGs operations, this study introduces a novel two-stage fuzzy controller aimed at enhancing the system’s dynamic responses. The proposed controller consists of two levels, each of which contains a separate and autonomous fuzzy inference system (FIS). The proposed controller includes proportional (P) and derivative (D) control operators in the first level, and in the second level, the combination of proportional and integral (I) operators is used. The suggested fuzzy P-fuzzy D multiplied by 1+(fuzzy P - fuzzy I) which is named FPFD-(1+FPFI) controller parameters are tuned by solving an optimization problem to reduce energy wastage and prevent undesirable dynamic responses. The parameters of the controller and the membership functions (MF) at each level are both optimized. The optimization process utilizes an enhanced particle swarm optimization (PSO) algorithm. The decisive superiority of the FPFD-(1+FPFI) controller has been confirmed by evaluating its performance in an all-renewable MG compared to conventional controllers. Reduction of frequency deviation in the face of disturbances in the demand side or production of renewables, better performance in the presence of uncertainty in the parameters of the system model, better dynamic responses against nonlinear factors such as time delays, and also, robustness against cyberattacks are prominent features of the proposed FPFD-(1+FPFI) controller. In addition, the results of the studies show that the controller, with its fast and accurate performance, reduces the dependence on the energy storage systems to maintain the stability of the system by more than 40%. The efficiency of the proposed controller is also verified through laboratory scale evaluation.

Increasing electricity demand and concerns about climate change and fossil fuel consumption have highlighted the importance of renewable energy resources and storage systems. This paper proposes a method for exploring untapped pumped hydro storage potentials to accommodate intermittent renewable energy generation profiles. Hourly measured data from 2022 in Benzie County, Michigan, United States, were gathered for system sizing and a thorough, realistic analysis. By employing the multi-objective grey wolf optimization algorithm, we formulated optimal sizing and energy-management strategies for three different scenarios. Unlike similar studies, the 3^rd with triple objective functions (OFs) scenario aims to maximize both reliability and ecological OFs while minimizing the cost OF. It has shown promising results with multiple solutions, considering economic, environmental, and reliability factors. A case study conducted in Crystal Lake, Michigan, revealed that although Crystal Lake would function only as a micro-hydro power facility, it is a promising and huge storage unit with a substantial storage capacity of around 14.9734GWh. The system investigated is significant in the USA due to its rapid deployment capabilities, minimal construction requirements, and ease of integration with the distribution grid. The fuzzy logic method was employed to identify the best non-dominant solution among the other solutions. These outcomes include a notably low levelized cost of energy at 0.046147$/kWh, a robust index of reliability of 99.705%, and a significant reduction in CO₂ emissions amounting to 7.9142×10³ tons/year, when considering the triple OFs. The paper’s methodology provides valuable insights for regions aiming to utilize renewable energy from untapped storage sources.

Offshore wind farms are becoming more and more important to sustainable energy strategies, yet their maintenance presents unique logistical challenges. The focus of this research is the Technician Assignment Problem (TAP) which involves a complicated and fluctuating scheduling problem. The key objective is to determine the most effective assignment of technicians to maintenance tasks to maximize operational efficiency and ensure reliable service. This study introduces a mathematical optimization model that processes numerous variables—technician availability, skill sets, and temporal constraints—to minimize unmet maintenance needs and ensure equitable workload distribution. The model is based on assumptions derived from actual operational conditions and human resource practices, guaranteeing that its results are not only theoretically valid but also practically feasible. TAP adheres to labor regulations, employs human resource capabilities, and aims for a smart assignment of workforce. It complies with restrictions that prevent excessive work, require breaks, and ensure that technicians are assigned tasks that match their skills, thus promoting the well-being of the workforce and the efficiency of operations. The computational investigation of the model shows that it has a remarkable ability to improve scheduling decisions which effectively reduce unassigned positions and uniformly distribute work hours. In essence, this research contributes a methodologically robust framework to the field of workforce scheduling, with the potential to inform the maintenance strategies of offshore wind farms and similar complex service systems.

Solar energy plays a critical part in lowering CO₂ emissions and other greenhouse gases when integrated into the grid. Higher solar energy penetration is hindered by its intermittency leading to reliability issues. To forecast solar energy production, this study suggests a three-step forecasting method that selects weather variables with a moderate to strong positive correlation to solar radiation using Pearson correlation coefficient analysis. Low-level data fusion is used to combine weather inputs from a reliable local weather station and an on-site weather station, significantly improving the forecasting model’s accuracy regardless of the machine learning method used. Weather data was obtained from the Kisanhub Weather Station located in Cranfield University, UK and the meteorological station in Bedford, UK. In addition, PV power supply data was obtained from four solar plants. Using the Regression Learner app in MATLAB, the proposed architecture is tested on a utility scale solar plant (1 MW), showing a 6% and 13% prediction accuracy improvement when compared to solely using data from the on-site and local weather station respectively. It is further validated using data from three residential rooftop solar systems (8 kW, 10.5 kW and 15 kW), achieving root-mean square values of 0.0984, 0.0885, and 0.1425 respectively. The data was pre-processed using both rescaling and list-wise deletion methods. Training and testing data from the 1 MW solar plant was divided into 75% and 25% respectively, while 100% of the residential rooftop solar plants was used for validation.