Hasil untuk "Production of electric energy or power. Powerplants. Central stations"

Isabella Poli, Teresa Gatti, Yan Li et al.

This roadmap provides a comprehensive overview of the latest advancements in lead-free perovskite materials for photovoltaic and photoelectrochemical /photocatalytic applications. It highlights the urgent need for sustainable energy solutions, emphasizing the role of lead-free perovskites in addressing challenges related to toxicity, scalability, and efficiency. The roadmap is designed to guide the reader from application-driven perspectives to fundamental materials insights, characterization techniques, fabrication strategies and overreaching sustainability considerations. The document explores key material families, including tin-, bismuth-, antimony-, and copper-based perovskites, detailing their optoelectronic properties, fabrication techniques, and application potential. Special attention is given to advanced characterization methods, green processing strategies, the integration of artificial intelligence and machine learning for material design and optimization and lifecycle impact assessments to ensure environmental sustainability. By bringing together insights from global research communities, this roadmap serves as a strategic guide for advancing lead-free perovskite technology, fostering interdisciplinary collaboration, and accelerating the transition to next-generation solar energy solutions.

Sanghyun Lee, Loiy Al-Ghussain, Mohammad Alrbai et al.

C.S. Subash Kumar, R. Saravanan, S. Sankarakumar et al.

Наталя Панасівна Савченко, Оксана Миколаївна Довгалюк

Стаття присвячена питанню розробки удосконаленої структури для системи резервного живлення споживачів промислових підприємств, що є дуже актуальним для сьогодення електроенергетичної галузі України. В умовах нестабільного електропостачання, пов’язаного з руйнуванням енергетичної інфраструктури країни, системи резервного живлення все частіше починають використовуватись для забезпечення гарантованого та безперебійного живлення різних груп споживачів електричної енергії. Найбільш критичним це питання є для промислових підприємств, оскільки воно потребує дієвого вирішення через тяжкі і небезпечні наслідки від вимушених перерв електропостачання. В статті обґрунтовано, що запобіганню таких ситуацій сприяє впровадження систем резервного живлення до наявних систем електропостачання промислових підприємств, яке додатково сприяє вирішенню питань спрощення інтеграції відновлюваних джерел розосередженої генерації в електричну мережу підприємства, а також забезпеченню резервування та покращенню балансування потужності у досліджуваних системах електропостачання. Проведений аналіз наявних та найбільш поширених на сьогоднішній день схем реалізації систем резервного живлення у складі систем електропостачання промислових підприємств. Спільне застосування систем резервного живлення та джерел альтернативної енергетики при інтелектуалізації процесу керування режимами таких об’єктів є основою реалізації технології мікромереж у електропостачанні сучасних промислових підприємств. Модернізація систем електропостачання за рахунок побудови мікромережевої структури підвищує надійність енергозабезпечення як окремих, так і групових споживачів промислового підприємства. На основі проведеного аналізу особливостей виконання систем резервного живлення авторами запропоновано виконання локальної мікромережевої сітки низької напруги 0,4 кВ з використанням у якості джерел розосередженої генерації стаціонарних сонячних електростанцій та мобільних гібридних електростанцій з метою забезпечення додаткової мікрогенерації та балансування потужності у різних точках системи електропостачання промислового підприємства. Отримані результати дослідження режимів експлуатації розробленої гібридної системи резервного живлення з мікрогенерацією від мобільних електростанцій альтернативної енергетики показують доцільність та енергоефективність запропонованого рішення, що доводить перспективність подальшого розвитку та впровадження сучасних технологічних рішень при реалізації систем резервного живлення на промислових підприємствах.

Hui WANG, Yucheng DONG, Yuqi XIA et al.

To address the issues of low renewable energy accommodation rates, high carbon emissions, and poor operational economy in mining areas of Northwest China, this paper proposes an optimal scheduling model for coal mine integrated energy system (CMIES) that incorporates the mutual recognition of stepped carbon and green certificates, as well as gravity energy storage. Initially, the basic CMIES model was developed by considering the diverse utilization of mine resources, including coalbed methane and gravity energy storage of abandoned mines. Subsequently, to enhance the economic efficiency and energy utilization rate of the CMIES, coupling equipment such as carbon capture, power-to-gas, and combined cooling, heating, and power units was incorporated into the system. Additionally, a flexible load model for electricity, heat, and cooling was established to improve the system's operational flexibility. Furthermore, a mutual recognition mechanism for stepped carbon and green certificates was introduced to encourage the utilization of renewable energy equipment through market interactions. Finally, a mixed-integer programming model was formulated to minimize the total operating cost of the system and was solved using Cplex. The simulation results demonstrate that the proposed model significantly enhances the renewable energy accommodation rate in mining areas while reducing system carbon emissions, striking a balance with operational economics, thereby providing a theoretical foundation for the low-carbon and economically viable transition of CMIES.

Yipei Wang, Jun-Hyeong Kwon, Seong-Cheol Choi et al.

In this paper, a modular cell balancing circuit based on a bidirectional flyback converter (BFC) is designed, which is equipped with a symmetrical BFC for each cell. The primary side of all BFCs is in parallel with the battery pack, and the secondary side is connected to the individual cells. Such an input-parallel output-series structure allows for bidirectional and controllable energy transfer among the cells. The control of the charging/discharging for a specific cell can be realized by adjusting the PWM signal on the primary or secondary side of the corresponding BFC. Based on this, three cell balancing strategies are proposed: maximum voltage discharge (MXVD), minimum voltage charge (MNVC), and maximum and minimum voltage balancing (MX&MNB). For MX&MNB, which is essentially a combination of MXVD and MNVC, it controls the maximum voltage cell discharging and minimum voltage cell charging simultaneously, where the energy is transferred directly between the two cells with the largest voltage difference. A cell balancing prototype is built and tested to verify the feasibility and stability of the proposed strategy. All three proposed methods can implement cell balancing simply and effectively, while the MX&MNB provides a faster speed.

Siddhesh Pimpale

The introduction of Smart Electric Vehicles (SEVs) represents an increasingly disruption on automotive area, once integrates advanced computer and communication technologies to highly electrical cars, which come with high performances, environment friendly and user friendly characteristics . But the increasing complexity of SEVs prompted by greater dependence on interconnected systems, autonomous capabilities and electrification, presents new challenges in cybersecurity as well as functional safety. The safety and reliability of such vehicles is paramount, as unsafe or unreliable operation in either case represents an unacceptable risk in terms of the performance of the vehicle and safety of the passenger. This paper investigates the integrated development of cybersecurity and functional safety for SEVs, emphasizing the requirement for the parallel development of these domains as components that are not treated separately. In SEVs, cybersecurity is quite crucial in order to prevent the threats of hacking, data breaches and unauthorized access to vehicle systems. Functional safety ensures that important vehicle functions (braking, steering, battery control, etc.) keep working even if some part fails. This convergence of functional safety issues with cybersecurity issues is becoming more crucial, since a security incident can result in a failure of catastrophic consequences for a functional safety system and, conversely. This paper reports the current state of cybersecurity and functional safety standards for SEVs, highlighting challenges that include the weaknesses of communication networks, the potential security threats of over-the-air updates, and the demand for real-time responsive systems for failure.

Xuyuan Kang, Xiao Wang, Jingjing An et al.

Thermal energy storage (TES) is an effective method for load shifting and demand response in buildings. Optimal TES control and management are essential to improve the performance of the cooling system. Most existing TES systems operate on a fixed schedule, which cannot take full advantage of its load shifting capability, and requires extensive investigation and optimization. This study proposed a novel integrated load prediction and optimized control approach for ice-based TES in commercial buildings. A cooling load prediction model was developed and a mid-day modification mechanism was introduced into the prediction model to improve the accuracy. Based on the predictions, a rule-based control strategy was proposed according to the time-of-use tariff; the mid-day control adjustment mechanism was introduced in accordance with the mid-day prediction modifications. The proposed approach was applied in the ice-based TES system of a commercial complex in Beijing, and achieved a mean absolute error (MAE) of 389 kW and coefficient of variance of MAE of 12.5%. The integrated prediction-based control strategy achieved an energy cost saving rate of 9.9%. The proposed model was deployed in the realistic building automation system of the case building and significantly improved the efficiency and automation of the cooling system.

Changgang Wang, Wei Liu, Yu Cao et al.

In the context of the rising share of new energy generation, accurately generating new energy output scenarios is crucial for day-ahead power system scheduling. Deep learning-based scenario generation methods can address this need, but their black-box nature raises concerns about interpretability. To tackle this issue, this paper introduces a method for day-ahead new energy scenario generation based on an improved conditional generative diffusion model. This method is built on the theoretical framework of Markov chains and variational inference. It first transforms historical data into pure noise through a diffusion process, then uses conditional information to guide the denoising process, ultimately generating scenarios that satisfy the conditional distribution. Additionally, the noise table is improved to a cosine form, enhancing the quality of the generated scenarios. When applied to actual wind and solar output data, the results demonstrate that this method effectively generates new energy output scenarios with good adaptability.

Stefano Rinaldi, Dmitrii Vasenin, Alessandro Musatti et al.

Wenxuan Liu, Binghao He, Yusheng Xue et al.

Abstract The carbon market plays a critical role in promoting the transition toward renewable energy sources and reducing greenhouse gas emissions in the electricity generation and transmission. Extant research has overlooked the dynamic bilateral causality that exists between electricity and carbon markets. Moreover, these studies have frequently treated the macroeconomic effect as exogenous. To bridge this research gap, this paper presents a holistic modeling framework that comprehensively captures the intertwined nature of electricity and carbon markets and their concomitant interactions with the overarching economy. The suggested modeling framework is an integration of three principal modules, namely, a carbon market, an electricity market, and economic system. This synergistic blend provides an exhaustive understanding of the entire market operation cycle. It offers detailed clearance rules, and most importantly, it adopts a macroeconomic systematic modeling approach for evaluating the impact emanating from the interconnected electricity and carbon markets. To illustrate the practicality and effectiveness of the proposed approach, a case study anchored on empirical data sourced from the electricity and carbon markets in China is conducted. The empirical findings underscore the fact that incorporating a green certificate market into the modeling framework can precipitate a reduction in greenhouse gas emissions. Additionally, the results indicate that expanding the scale of the green certificate market from 1.9% in 2021 to 33% by 2023 will increase the generation of green electricity by 10%.

LÜ Jiawei, HUO Zhaoyi, LIU Feng et al.

ObjectivesThe advanced exergoeconomics analysis method based on exergetic analysis development can refine the economic costs of splitting system components and deeply explore the underlying reasons for the formation of economic costs.MethodsCombining advanced exergetic analysis, advanced exergoeconomics analysis method is used to split the costs of the components in the gas-steam combined cycle power generation system into endogenous, exogenous, avoidable and unavoidable costs, and calculate them.ResultsUnder the design conditions, the avoidable loss in the combustion chamber in the combined cycle power system is the largest, which is 28.41 MW, accounting for 26.55% of the combustion chamber loss. Based on the results of the analysis, different improvement measures are proposed for the turbine to reduce the endogenous and exogenous losses of the system. The largest share of the annualized cost of the system is the endogenous avoidable portion, and the bottom-cycle improvement is prioritized highest for the high-pressure cylinder, followed by the low-pressure cylinder. The exogenous share of annualized costs in the combined cycle power system is 80.59%, of which the exogenous avoidable portion is 40.04%.ConclusionsThe findings of the study can provide the system with a multifaceted energy efficiency evaluation perspective and an improvement direction to optimize the cost.

Fengliang XU, Keqian WANG, Wenhao WANG et al.

With the high penetration rate of distributed photovoltaic access and the promotion of re-electrification and electrical energy substitution, the volatility of the system source and load is intensified, and the traditional distribution network planning methods are difficult to adapt to the requirements of the new power system development. To address this problem, an optimal allocation model for hybrid energy storage in low-voltage distribution networks considering incentive-based demand response is firstly established. Then, based on the characteristics of energy storage devices and incentive-based demand-side response resources at different time scales, it is proposed to use the improved VMD algorithm to make a multi-scale decomposition and combined reconstruction of the net load curves, and the improved whale optimization algorithm is used to solve the optimal allocation model with the objective of the minimum sum of the total system cost and active power fluctuation value. Finally, the effectiveness of the proposed scheme is verified with practical examples.

Thore Wietzke, Jan Gall, Knut Graichen

This paper presents a new approach to predict the occupancy for building energy systems (BES). A Gaussian Process (GP) is used to model the occupancy and is represented as a state space model that is equivalent to the full GP if Kalman filtering and smoothing is used. The combination of GPs and mechanistic models is called Latent Force Model (LFM). An LFM-based model predictive control (MPC) concept for BES is presented that benefits from the extrapolation capability of mechanistic models and the learning ability of GPs to predict the occupancy within the building. Simulations with EnergyPlus and a comparison with real-world data from the Bosch Research Campus in Renningen show that a reduced energy demand and thermal discomfort can be obtained with the LFM-based MPC scheme by accounting for the predicted stochastic occupancy.

Zongbao Zhang, Jiao Hao, Wenmeng Zhao et al.

The rapid expansion of electric vehicles (EVs) has rendered the load forecasting of electric vehicle charging stations (EVCS) increasingly critical. The primary challenge in achieving precise load forecasting for EVCS lies in accounting for the nonlinear of charging behaviors, the spatial interactions among different stations, and the intricate temporal variations in usage patterns. To address these challenges, we propose a Multiscale Spatio-Temporal Enhanced Model (MSTEM) for effective load forecasting at EVCS. MSTEM incorporates a multiscale graph neural network to discern hierarchical nonlinear temporal dependencies across various time scales. Besides, it also integrates a recurrent learning component and a residual fusion mechanism, enhancing its capability to accurately capture spatial and temporal variations in charging patterns. The effectiveness of the proposed MSTEM has been validated through comparative analysis with six baseline models using three evaluation metrics. The case studies utilize real-world datasets for both fast and slow charging loads at EVCS in Perth, UK. The experimental results demonstrate the superiority of MSTEM in short-term continuous load forecasting for EVCS.

Pratik Harsh, Hongjian Sun, Debapriya Das et al.

The growing integration of distributed energy resources (DERs) into the power grid necessitates an effective coordination strategy to maximize their benefits. Acting as an aggregator of DERs, a virtual power plant (VPP) facilitates this coordination, thereby amplifying their impact on the transmission level of the power grid. Further, a demand response program enhances the scheduling approach by managing the energy demands in parallel with the uncertain energy outputs of the DERs. This work presents a stochastic incentive-based demand response model for the scheduling operation of VPP comprising solar-powered generating stations, battery swapping stations, electric vehicle charging stations, and consumers with controllable loads. The work also proposes a priority mechanism to consider the individual preferences of electric vehicle users and consumers with controllable loads. The scheduling approach for the VPP is framed as a multi-objective optimization problem, normalized using the utopia-tracking method. Subsequently, the normalized optimization problem is transformed into a stochastic formulation to address uncertainties in energy demand from charging stations and controllable loads. The proposed VPP scheduling approach is addressed on a 33-node distribution system simulated using MATLAB software, which is further validated using a real-time digital simulator.

Qi Wang, Chongqing Kang

LIANG Zhongrong, LAN Maowei, ZHENG Guo et al.

Aiming at the multi-objective optimization of boiler combustion system, on the basis of the established prediction model of boiler combustion system, the weighted-particle swarm algorithm and the multi-objective particle swarm optimization (MOPSO) algorithm were used to optimize the adjustable operating parameters of the boiler, which can realize the operating state of the boiler with high efficiency and low NOx emission. The analysis shows that the operating parameters obtained by the two optimization algorithms are similar, and the trend is consistent with the combustion characteristics analysis and combustion adjustment test results. It indicates that the intelligent algorithm is effective and feasible to optimize the combustion system of the power plant boiler. However, the weighted-particle swarm optimization algorithm has serious subjective dependence. It is difficult to select appropriate weights, and the optimization time is long and the results are few. However, the optimization time of the MOPSO algorithm is far less than the optimization time of the weighted-particle swarm optimization algorithm, the optimization results are more, and the optimization efficiency is higher. Therefore, the MOPSO algorithm is more beneficial to guide the actual operation of the boiler.

Bingning Wang, Seoung-Bum Son, Pavan Badami et al.

In our initial study on the high-voltage 5 V cobalt-free spinel LiNi_0.5Mn_1.5O₄ (LNMO) cathode, we discovered a severe delamination issue in the laminates when cycled at a high upper cut-off voltage (UCV) of 4.95 V, especially when a large cell format was used. This delamination problem prompted us to investigate further by studying the transition metal (TM) dissolution mechanism of cobalt-free LNMO cathodes, and as a comparison, some cobalt-containing lithium nickel manganese cobalt oxides (NMC) cathodes, as the leachates from the soaking experiment might be the culprit for the delamination. Unlike other previous reports, we are interested in the intrinsic stability of the cathode in the presence of a baseline Gen2 electrolyte consisting of 1.2 M of LiPF₆ in ethylene carbonate/ethyl methyl carbonate (EC/EMC), similar to a storage condition. The electrode laminates (transition metal oxides, transition metal oxides, TMOs, coated on an Al current collector with a loading level of around 2.5 mAh/cm²) or the TMO powders (pure commercial quality spinel LNMO, NMC, etc.) were stored in the baseline solution, and the transition metal dissolution was studied through nuclear magnetic resonance, such as ¹H NMR, ¹⁹F NMR, scanning electron microscope (SEM), X-ray photoelectron spectroscopy (XPS) and inductively coupled plasma mass spectrometry (ICP-MS). Significant electrolyte decomposition was observed and could be the cause that leads to the TM dissolution of LNMO. To address this TM dissolution, additives were introduced into the baseline electrolyte, effectively alleviating the issue of TM dissolution. The results suggest that the observed delamination is caused by electrolyte decompositions that lead to etching, and additives such as lithium difluorooxalato borate and p-toluenesulfonyl isocyanate can alleviate this issue by forming a firm cathode electrolyte interface. This study provides a new perspective on cell degradation induced by electrode/electrolyte interactions under storage conditions.

Hossien Shad, Majid Gandomkar, Javad Nikoukar

Abstract Despite improving various technical indices such as operation and voltage profile, the integration of synchronous distributed generations (SDGs) may lead to the loss of protection coordination in the distribution network (DN). To tackle the problem, this paper presents a multi‐objective siting and sizing of SDGs on DN considering operation, economic and protection coordination indices simultaneously. The proposed scheme is structured in the framework of a four‐objective optimization problem, which aims to minimize the energy losses, the worst voltage security index (VSI), planning cost of SDGs and the protection index (PI) (the deviation of coordination time interval). Therefore, it is constrained to power flow equations, VSI, and protection coordination of overcurrent relays (OCRs). Then, the Pareto optimization based on the method of the weighted functions summation obtains an integrated single‐objective problem for the proposed scheme. Next, a hybrid evolutionary algorithm formed by merging particle swarm optimization (PSO) and crow search algorithm (CSA) is incorporated to achieve the optimal solution with unique response approximate conditions. Eventually, the suggested scheme is applied on distribution portion of IEEE 30‐bus ring and IEEE 69‐bus radial DN and numerical results confirm the efficient performance of SDG planning in terms of technical indicators and protective devices coordination.