Advances in Applied Energy最新文献

Partial shading condition (PSC) causes underperformance, unreliability, and fire risks in photovoltaic (PV) systems. Accurate estimation of PV behaviors is crucial to fundamental understanding and further mitigation. However, current modeling methods lack full consideration of the physical behaviors, system complexities, and shading pattern diversities, ending in coarse and simple analysis. Herein, an innovative modeling approach with high-performance algorithms is proposed to address these challenges simultaneously. Based on rigorous analysis, physics models considering the reverse-biased behaviors, the system complexities, and shading pattern diversities, are developed at the cell, module, and array levels, respectively. Then, a strict and progressive validation via measurement data is conducted to justify the effectiveness of the developed method. The method is valid for mainstream PV technologies in the market and can predict cell behaviors and module electrical characteristics perfectly. Notably, the proposed method is more computationally efficient than Simulink when simulating the same PV array. Lastly, to demonstrate its exclusive advantages, two case studies are conducted. The localized power dissipation can be quantified. The observed energy loss justifies the necessity of reverse biased behaviors and high-resolution simulation. This method can be coded in any development environment, providing an efficient and comprehensive tool to analyze PV systems.

This paper presents a first step in the field of probabilistic forecasting of co-located wind and photovoltaic (PV) parks. The effect of aggregation is analyzed with respect to forecast accuracy and value at a co-located park in Sweden using roughly three years of data. We use a fixed modelling framework where we post-process numerical weather predictions to calibrated probabilistic production forecasts, which is a prerequisite when placing optimal bids in the day-ahead market. The results show that aggregation improves forecast accuracy in terms of continuous ranked probability score, interval score and quantile score when compared to wind or PV power forecasts alone. The optimal aggregation ratio is found to be 50%–60% wind power and the remainder PV power. This is explained by the aggregated time series being smoother, which improves the calibration and produces sharper predictive distributions, especially during periods of high variability in both resources, i.e., most prominently in the summer, spring and fall. Furthermore, the daily variability of wind and PV power generation was found to be anti-correlated which proved to be beneficial when forecasting the aggregated time series. Finally, we show that probabilistic forecasts of co-located production improve trading in the day-ahead market, where the more accurate and sharper forecasts reduce balancing costs. In conclusion, the study indicates that co-locating wind and PV power parks can improve probabilistic forecasts which, furthermore, carry over to electricity market trading. The results from the study should be generally applicable to other co-located parks in similar climates.

With the advent of sustainable and clean energy transitions, lithium-ion batteries have become one of the most important energy storage sources for many applications. Battery management is of utmost importance for the safe, efficient, and long-lasting operation of lithium-ion batteries. However, the frequently changing load and operating conditions, the different cell chemistries and formats, and the complicated degradation patterns pose challenges for traditional battery management. The data-driven solutions that have emerged in recent years offer great opportunities to uncover the underlying data mapping within a battery system. In particular, transfer learning improves the performance of data-driven strategies by transferring existing knowledge from different but related domains, and if properly applied, would be a promising approach for smarter battery management. To this end, this paper presents a systematic review for the applications of transfer learning in the field of battery management for the first time, with particular focuses on battery state estimation and ageing prognostics. Specifically, the general issues faced by conventional battery management are identified and the applications of transfer learning to these issues are summarized. Then, the specific challenges of each topic are identified and the potential solutions based on transfer learning are explained, followed by a discussion of the state of the art in terms of principles, algorithm frameworks, advantages and disadvantages. Finally, future trends of data-driven battery management with transfer learning are discussed in terms of key challenges and promising opportunities.

Since the energy sector is the dominant contributor to global greenhouse gas emissions, the decarbonization of energy systems is crucial for climate change mitigation. Two major challenges of energy systems decarbonization are renewable transition planning and sustainable systems operations. To address the challenges, incorporating emerging information and communication technologies can facilitate both the design and operations of future smart energy systems with high penetrations of renewable energy and decentralized structures. The present work provides a comprehensive overview of the applicability of emerging information and communication technologies in renewable transition and smart energy systems, including artificial intelligence, quantum computing, blockchain, next-generation communication technologies, and the metaverse. Relevant research directions are introduced through reviewing existing literature. This review concludes with a discussion of the industrial use cases and demonstrations of smart energy technologies.

Floating offshore wind power, an emerging technology in the offshore wind industry, has attracted increasing attention for its potential to cooperate with other renewable energies to decarbonize energy systems. The environmental effects of the floating offshore wind farm in deep-sea areas should be considered, and methods to enhance the low-carbon effect should be devised. There have been a few studies assessing the environmental effects of the floating offshore wind farm, but the scales of these studies were relatively small. This study evaluated the environmental impacts of a floating wind farm with 100 wind turbines of 6.7 MW using life cycle assessment (LCA) method, based on the Chinese core life cycle database. Results showed that the carbon footprint of the wind farm was 25.76 g CO₂-eq/kWh, which was relatively low in terms of global warming potential. Additionally, the floating offshore wind farm contributed most to eutrophication potential. A ± 20% variation in steel resulted in a ±3% to ±15% variation in the indicator score of each environmental category, indicating that the environmental performance of the wind farm was mainly influenced by this parameter. Moreover, scenario analysis showed that electric arc furnace routes can reduce the cumulative greenhouse gas emissions from upstream process of the floating offshore wind farm by 1.75 Mt CO₂-eq by 2030. Emission reduction of the steel industry will further reduce the carbon footprint of the floating offshore wind farm. In the future, more baseline data need to be collected to improve the reliability of LCA. The effects of the floating offshore wind farm on marine ecology and atmospheric physical characteristics remain to be investigated in depth.

As pressures to decarbonize the electricity grid increase, the grid edge is witnessing a rapid adoption of distributed and renewable generation. As a result, traditional methods for reactive power management and compensation may become ineffective. Current state-of-art for reactive power compensation, which rely primarily on capacity payments, exclude distributed generation (DG). We propose an alternative: a reactive power market at the distribution level designed to meet the needs of decentralized and decarbonized grids. The proposed market uses variable payments to compensate DGs equipped with smart inverters, at an increased spatial and temporal granularity, through a distribution-level Locational Marginal Price (d-LMP). We validate our proposed market with a case study of the US New England grid on a modified IEEE-123 bus, while varying DG penetration from 5% to 160%. Results show that our market can accommodate such a large penetration, with stable reactive power revenue streams. The market can leverage the considerable flexibility afforded by inverter-based resources to meet over 40% of reactive power load when operating in a power factor range of 0.6 to 1.0. DGs participating in the market can earn up to 11% of their total revenue from reactive power payments. Finally, the corresponding daily d-LMPs determined from the proposed market were observed to exhibit limited volatility.

This paper proposes a novel data-driven robust model predictive control (MPC) framework for a multi-zone building considering thermal comfort and uncertain weather forecast errors. The control objective is to maintain each zone's temperature and relative humidity within the specified ranges by minimizing the energy usage of the underlying heating system. A state-space model is developed to use a hybrid physics-based and data-driven method for the multi-zone building's temperature and relative humidity. The temperature and humidity RMSEs between the state-space model and the EnergyPlus-based model are less than 0.25 °C and 5.9%, respectively. The uncertainty space is based on historical weather forecast error data, which are clustered by using a k-means clustering algorithm. Machine learning approaches, including principal component analysis and kernel density estimation, are used to construct each basic uncertainty set and reduce the conservatism of resulting robust control action under disturbances. A robust MPC framework is built upon the proposed state-space model and data-driven disjunctive uncertainty set. An affine disturbance feedback rule is employed to obtain a tractable approximation of the robust MPC problem. Besides, the feasibility and stability of the proposed MPC are discussed in detail. A case study of controlling temperature and relative humidity of a multi-zone building in Ithaca, New York, USA, is presented. The results demonstrate that the proposed framework can reduce up to 8.8% of total energy consumption compared to conventional robust MPC approaches. Moreover, the proposed framework can essentially satisfy the thermal constraints that certainty equivalent MPC and robust MPC largely violate.

Resilient operation of multi-energy microgrid is a critical concept for decarbonization in modern power system. Its goal is to mitigate the low probability and high damaging impacts of electricity interruptions. Electrical vehicles, as a key flexibility provider, can react to unserved demand and autonomously schedule their operation in order to provide resilience. This paper presents a distributed control strategy for a population of electrical vehicles to enhance resilience of an urban energy system experiencing extreme contingency. Specifically, an iterative algorithm is developed to coordinate the charging/discharging schedules of heterogeneous electrical vehicles aiming at reducing the essential load shedding while considering the local constraints and multi-energy microgrid interconnection capacities. Additionally, the gap between electrical vehicle energy and the required energy level at the departure time is also minimised. The effectiveness of the introduced distributed coordinated approach on energy arbitrage and congestion management is tested and demonstrated by a series of case studies.

In light of current energy policies responding to rapid climate change, much attention has been directed to developing feasible approaches for transitioning energy production from fossil-based resources to renewable energy. Although existing studies analyze regional dispatch of renewable energy sources and capacity planning, they do not fully explore the impacts of the energy storage system technology's technical and economic characteristics on renewable energy integration and energy transition, and the importance of energy storage systems to the energy transition is currently ignored. To fill this gap, we propose an integrated optimal power flow and multi-criteria decision-making model to minimize system cost under operational constraints and evaluate the operational performance of renewable energy technologies with multidimensional criteria. The proposed method can identify the most critical features of energy storage system technologies to enhance renewable energy integration and achieve New York State's climate goals from 2025 to 2040. We discover that lead-acid battery requires an additional 38.66 GW capacity of renewable energy sources than lithium-ion battery to achieve the zero carbon dioxide emissions condition. Based on the cross-sensitivity analysis in the multidimensional evaluation, the vanadium redox flow battery performs the best, and the nickel-cadmium battery performs the worst when reaching the zero carbon dioxide emissions target in 2040. The results of the proposed model can also be conveniently generalized to select ESS technology based on the criteria preferences from RE integration and energy transition studies and serve as a reference for ESS configurations in future energy and power system planning.