IET Renewable Power Generation最新文献

DC microgrids (DCMGs) are gaining popularity due to the rise of DC devices, increased use of solar power, and the absence of frequency and reactive power concerns. Key challenges in DCMGs include voltage fluctuations due to unpredictable changes in renewable energy resources (RERs), power flow management, and power distribution among distributed generations (DGs). To ensure the stability and reliability of DCMGs, it is crucial to maintain proper DC bus voltage levels and effectively manage power flow between different components. This paper presents a new scheduling framework for bipolar DCMGs (BPDCMGs) that simultaneously considers voltage variations and imbalances. A novel objective function focused on voltage variations is developed based on advanced load flow equations for BPDCMGs. Also, distributionally robust optimization (DRO) is utilized for RERs and load consumption uncertainties based on the Kullback–Leibler divergence metric. Following the reformulation of the multiobjective DRO problem, an optimal compromise solution is found using min–max fuzzy criteria. The proposed model has been tested on the IEEE 33 bus system, simulating an islanded BPDCMG. Detailed analysis demonstrates the model's effectiveness in managing voltage fluctuations and imbalances, with numerical results indicating over a 90% reduction in voltage fluctuations and over 40% decrease in unbalancing.

In this study, thermodynamic and environmental assessments of waste heat driven cogeneration systems are carried out. The cogeneration systems include basic and modified configurations of Organic Rankine cycle (ORC), Reverse osmosis (RO) unit and Proton exchange membrane (PEM) electrolyser. The ORCs are aimed at transforming waste heat into power for the operation of a reverse osmosis (RO) unit and a proton exchange membrane (PEM) electrolyser, for generation of fresh water and hydrogen, respectively. The systems were simulated in engineering equation solver (EES). Among the studied configurations and working fluids, the findings demonstrate that the ORC configuration that combines both an internal heat exchanger and a mixing chamber (HMORC) and employing Isopentane as working fluid showed optimal performance, and showcasing energy and exergy efficiencies, and sustainability index values of 19.31%, 24.63%, and 2.033, respectively. Furthermore, this setup achieves maximum flow rates of 5.211 m³/h for fresh water and 2.737 kg/h for hydrogen. Moreover, The parametric study indicates that performance of the cogeneration systems improves with rise in evaporator pressure and drop in condenser pressure. The results highlight the promise of optimised system configuration for effective waste heat recovery and sustainable resource use.

Historically heavily reliant on coal, Indonesia is at a crucial juncture to accelerate its transition to renewable energy in order to meet its net-zero goals. However, most transition pathways generally do not focus on the fast transition from fossil fuels to achieve carbon neutrality. This study compares the techno-economic feasibility of three distinctive energy transition pathways. The net-zero pathway, with an accelerated transition from coal to renewable energy, leads to a 42% reduction in annualised energy system cost and achieves carbon neutrality by 2050, compared to a pathway dependent on continued coal investments. This is enabled by the cost competitiveness of solar photovoltaics-based electrification of the energy system, supported by batteries, transmission grid expansion across the main islands, and various power-to-X routes for flexibility. Furthermore, Indonesia should carefully consider the environmental impact of large-scale palm oil cultivation and biofuel expansion. We find that the land efficiency of solar photovoltaics-based e-fuel production is much higher than cultivating oil palm for biofuel synthesis. Thus, a fast transition towards a net-zero energy system will bring various direct and indirect benefits to Indonesia.

Accurate prediction of short-term wind power ramps is essential for effective smart grid management. This study introduces the swinging door algorithm for ramp detection, which outperforms traditional methods by precisely identifying ramp events. Additionally, a long short-term memory (LSTM) network is evaluated against established models such as support vector machines, artificial neural networks, convex multi-task feature learning, and random forest for wind power ramp forecasting. The LSTM model demonstrates superior performance, achieving the lowest weighted mean absolute percentage error of 8.36% and normalized root mean squared error of 0.60, alongside the highest R-squared (R²) value of 0.73, indicating strong predictive accuracy and correlation with observed data. Furthermore, the combined swinging door algorithm-LSTM framework improved ramp event detection by 15% compared to traditional methods, showcasing its robustness in capturing both mild and extreme ramp events. This research underlines LSTM's effectiveness in wind power forecasting, marking a notable advancement in prediction methodologies. By illustrating the strengths of LSTM and swinging door algorithm, the study contributes to the refinement of prediction models for smart grid applications, highlighting their potential to transform wind power ramp prediction and detection.

In scenarios of partial shading, the effectiveness of power transmission within a photovoltaic system experiences a notable decline, potentially leading to hotspots within the photovoltaic array. While incorporating bypass diodes can mitigate this challenge, it may lead to numerous power peaks along the power–voltage (P–V) characteristics, thus complicating the task of maximum power tracking. Addressing this issue, using metaheuristic algorithms for maximum power point tracking (MPPT) offers promising outcomes by circumventing convergence towards local power peaks and easing the computational strain on the microcontroller. This study presents a fresh approach to MPPT technique utilizing the proportional–integral–derivative-based search algorithm to effectively identify the MPP under varying partial shading conditions. Compared to existing methods, the proposed algorithm demonstrates superior performance in power tracking efficiency, tracking time, stability with fewer fluctuations, and achieving higher maximum power output. Evaluation against state-of-the-art algorithms like particle swarm optimization and JAYA confirms the effectiveness of the proposed MPPT technique. MATLAB/Simulink software-based analysis and its validation using real-time analysis from the typhoon based hardware-in-the-loop (HIL-402) emulator support its efficacy.

Adding photovoltaic (PV) systems in distribution networks, while desirable for reducing the carbon footprint, can lead to voltage violations under high solar-low load conditions. The inability of traditional volt-VAr control in eliminating all the violations is also well-known. This article presents a novel coordinated inverter control methodology that leverages system-wide situational awareness to significantly improve hosting capacity (HC). The methodology employs a real-time voltage-reactive power (VQ) sensitivity matrix in an iterative linear optimizer to calculate the minimum reactive power intervention from PV inverters needed for mitigating over-voltage without resorting to active power curtailing or requiring step voltage regulator setting changes. The algorithm is validated using the EPRI J1 feeder under an extensive set of realistic use cases and is shown to provide 3x improvement in HC under all scenarios.

In recent years, due to its cost effectiveness and environmental advantages, demand for renewable energy resources has grown and their contributions to grid power has therefore increased while requiring effective frequency and voltage regulation. DC link voltage instability is a potential problem in solar energy microgrids, especially during an intermittency, where the system reliability degrades and DC link capacitor is under higher stress. In this article, a novel reserve energy management scheme based on battery and super capacitor storage is presented to stabilize the DC link voltage and reduce capacitor stress, while enhancing the system reliability. The scheme is tested in four different scenarios: Inverter connected DC-microgrid with irradiance intermittencies, standalone DC-microgrid without inverter and irradiance intermittencies, standalone DC-microgrid without inverter and load intermittencies, and standalone DC-microgrid with inverter under irradiance intermittencies. Simulation results indicate that the proposed control strategy stabilizes DC link voltage over all scenarios, even subject to large instances of irradiance or load changes. During low solar irradiance, the battery and super-capacitor promote voltage stability by compensating power deficits from the utility grid in the inverter connected grid case. In stand alone mode, the battery provides power during intermittencies and the supercapacitor provides fast transient voltage compensation. The strategy is notable in reducing stress on DClink capacitors and mitigating inverter voltage fluctuations, ultimately enhancing inverter longevity. The results show that the proposed control scheme can improve voltage stability, mitigate the transient effects and guarantee the reliable operation of solar microgrids in variable conditions.

The increasing use of renewable energy and decreasing inertia from large generators necessitate studying transient energy storage systems (TESSs) for better frequency stability. This paper examines UK National Grid data from 2014 to 2022 to propose initial design requirements for a TESS power-train. It assesses second-by-second historical frequency data across various time frames to explore the impact on TESS sizing and strategies using the enhanced frequency response service 1 approach. The results establish an approach for determining the suitable battery energy capacity of TESSs offering frequency control services, contributing to the reduction of power demand and energy losses in the distribution grid. Moreover, the study presents a suitable deadband strategy for the enhanced frequency response service. The findings provide insights into the design and operational needs of TESSs in supporting grid frequency response, utilizing a statistical methodology driven by frequency data from the UK transmission network managed by National Grid Electricity Transmission. The primary aims of the paper are (a) to develop a suitable power and energy management philosophy for TESSs and thus inform future control objectives, and (b) assess the system energy specification requirements.

A high proportion of renewable energy is connected to the current power grid through power electronics interface devices, which makes the power system to have the low-inertia dynamic characteristics. In order to improve the inertial response and voltage supporting capability, this article proposes a third-order model of the synchronous generator based active support control strategy for the power electronics transformer (PET) based on a single multi-winding medium-frequency transformer (MWMFT) and AC choppers (ACCs) for renewable generation. First, the structure of the multi-winding medium-frequency power electronics transformer (MWMFPET) is introduced. Second, the average model of the MWMFPET is especially established through two modulation ratios. Third, the active support control strategy for this PET has been elaborated in detail. Finally, the simulation results verify the feasibility and effectiveness of the proposed control strategy for the MWMFPET.

As wind turbine grow in size, the uneven distribution of wind speeds within the rotor-wept area results in unbalanced loads, which have become a significant factor affecting wind turbine performance. Load feedback-based individual pitch control (IPC) for wind turbine addresses this issue. However, traditional proportional-integral (PI) controllers may not have optimal proportional and integral coefficients in engineering applications, and fixed-coefficient PI controllers are not ideal for complex operating conditions. Therefore, this paper proposes an offline optimization-online self-correction control method based on particle swarm optimization (PSO) and fuzzy logic. The PSO algorithm is employed offline to minimize unbalanced rotor loads by optimizing the PI coefficients, which are then subjected to online self-correction based on fuzzy logic. Joint simulations using OpenFAST and MATLAB demonstrate that the proposed offline optimization-online self-correction control method enhances the load reduction effectiveness of IPC in wind turbine.