Energy Conversion and Economics最新文献

The battery thermal management system effectively limits the temperature of each lithium-ion battery (LIB) to below 45°C and minimises the temperature difference between different LIBs to extend their service life. Given the volume constraints, the finite element method (FEM) was used to perform the structural optimisation calculation of battery thermal management systems (BTMS). However, owing to their high calculation costs, optimisation methods based on surrogate models are preferred. The k-means clustering strategy of the stochastic reduced-order model (SROM) method, as implemented within the domain of uncertainty analysis, was shown in this study to enhance the initial observation point sampling strategy of the Kriging optimisation method. The use of an active sampling strategy has been demonstrated to enhance the representativeness of observation points with respect to the overall grid points, which in turn accelerates the convergence rate of the Kriging optimisation method. In the multiphysics simulation example of an LIB liquid cooling system modelled in COMSOL software, the relative error of the improved Kriging method is reduced from 0.24% to 0.11% compared with the traditional Kriging method, and the calculation efficiency is increased by 86.7%. This provided a quantitative verification of the effectiveness of the proposed method.

High-penetration renewable energy systems (HPRES) are characterized by the extensive deployment of distributed energy resources (DERs), such as the grid-side independent storage, consumer-side distributed storage, and the combination of consumer-side distributed storage with distributed photovoltaics and wind turbines. Additionally, numerous DERs interacting with the grid significantly vary the operating characteristics of the grid. These changes introduce significant complexity in the analysis of carbon emissions, thereby necessitating advanced methodologies to accurately capture and manage the impact of these DERs on the overall carbon footprint of the power system. This study presents a novel methodology for accurately quantifying the distribution of carbon emissions in power systems comprising DERs. To the underlying concept of this approach is the quantification of the carbon emission characteristics, which is achieved by analysing the carbon emission intensity specific to various DERs. We further analyse the impact of these entities on the flow of electricity carbon emissions. To comprehensively address these dynamics, we develop a bidirectional electricity carbon emission flow model corresponding to the unique attributes of the emerging HPRES. To demonstrate the viability and effectiveness of the proposed approach, we perform a simulation based on the modified IEEE 39-bus system, along with a comparison with the original carbon-emission flow model. The findings of this study contribute significantly to research on the demand response, power grid planning, and low-carbon operations.

Electric vehicle (EV) charging station scheduling can maximize profits by optimizing charging prices. Many existing scheduling methods emphasize aggregator profits and still have limited consideration of inter-station coordination and the dynamic service radius. The prediction accuracy of schedulable capacity indirectly affects the profits of aggregators. In addition, the prediction accuracy of schedulable capacity is affected by the uncertainty of station selection, which has also been neglected. To address these issues, a pricing-based coordinated scheduling framework for multiple charging stations is proposed. The propose framework incorporates a dynamic service radius and schedulable capacity prediction models. The framework includes an analysis of EV station selection behaviour under joint decision-making and the development of a dynamic service radius model for charging stations. Additionally, a schedulable capacity prediction model is constructed by integrating physical modelling with a data-driven approach based on long short-term memory networks. Compared with the peak-valley pricing-based schedule method and Stackelberg-based pricing method, the aggregator profit is enhanced by the application of the proposed framework.

Accurately estimating the State of Health (SOH) of lithium-ion batteries is essential for ensuring their reliable operation. The constant-current charging voltage curves of batteries at different aging levels show significant deviations. Traditional methods based on one-dimensional time-series data face limitations in capturing and characterizing these complex patterns. To address this issue, this paper leverages the one-dimensional (1D) time series data of the lithium battery constant-current charging voltage segment, selected using incremental capacity analysis. This data is then transformed into a two-dimensional representation using the Gramian angular summation field algorithm. Utilizing the exceptional image-recognition capabilities of ResNet, this approach achieves high-accuracy SOH estimation. Validation using publicly available datasets from the University of Oxford and the University of Maryland demonstrates a significant improvement in battery SOH estimation accuracy compared to traditional techniques, which directly input voltage segments into the network.

The credible capacity formation is a critical task in the design of a virtual power plant (VPP) and serves as the foundation for maintaining stability between the VPP and the power grid. In this study, an optimal configuration method for distributed generations (DGs) for units within a VPP is proposed, based on the concept of credible capacity. The expected energy not served (EENS) is used as the system reliability index to evaluate the credible capacity of the VPP. To optimize the benefit function of cooperative operation between the VPP and the power grid, cooperative game theory is applied to configure the capacities of the VPP's DG resources—namely, wind, solar, and storage units. Multiple scenarios of EENS and credible capacity were analysed to validate the effectiveness of the proposed approach. The results demonstrate that the method can successfully achieve credible capacity for a VPP by optimally configuring the capacities of individual DG units.

Constant power loads (CPLs) introduce negative impedance in direct current microgrids (DCMGs), which is a major challenge. This negative impedance can significantly reduce the overall damping of the system, making it less stable and harder to control. To address this issue, output virtual resistance (VR) shaping is commonly employed to enhance system damping and improve power-sharing amongst distributed generators (DGs). The technique proposed in this work involves an adaptive variation of the DG virtual output resistance ($�R_V$) linearly with the output current. This shows improved power sharing between sources. The work compares the small signal stability criteria and the minor loop gain methods for linear, non-linear, and inverse droop controllers to determine the controller parameters with constant power loads. The control scheme is extensively tested through simulations for four different droop control schemes. The work also validates the DCMG performance when the DERs work with different droop controllers (heterogenous of controllers) to assess constant power load penetration, performance in meshed configurations, and DG plug-and-play operations. Additionally, improved power sharing performance was validated through a controller hardware in the loop (CHIL) based implementation.

As the integration of renewable energy sources, such as wind power and photovoltaics, continues, the issue of system uncertainty has become more pronounced. This paper proposes a stochastic power system dispatch method based on affinely adjustable robust optimization (AARO) with a generalized linear polyhedron (GLP) uncertainty set that can accurately quantify the flexibility of the power system supply and demand as well as enhance the optimality of dispatch strategies. First, a GLP uncertainty set was established to characterize both the temporal stochasticity and spatial correlation of multiple renewable energy outputs. A correlation envelope was employed to reflect renewable energy outputs from historical data, and a polyhedral set was proposed to accurately describe the uncertainty for model formulation, which can effectively reduce model conservatism by minimizing empty regions. Furthermore, the range of net load variations was analysed to build a demand flexibility quantification model for the power system. Next, based on the expected operational value, a robust optimization dispatch model that considers the flexible supply and demand balance is developed within the affine strategy framework. Finally, simulations of a modified 6-bus system and modified IEEE 57-bus system validate the effectiveness of the proposed GLP-AARO method for power system flexibility quantification and dispatch strategy optimization.

Ancillary services are crucial for supporting the reliable operation of power systems and constitute an integral part of the power market. The increasing integration of volatile renewable energy sources has introduced new challenges into China's traditional ancillary service markets, such as escalating ancillary service costs. Historically, the ancillary service cost-sharing approach in China has been a redistribution of revenue among generators, resulting in increasing cost-sharing pressure on the supply side. Therefore, based on the basic market logic of ‘who causes the demand, who pays,’ sharing the ancillary service costs with power consumers becomes urgent. This paper presents an overview of the latest research and practical experiences in China and other countries, and proposes an ancillary service cost allocation mechanism considering the participation of consumers. First, the ancillary service cost allocation mechanisms in China and other countries are summarized, including common rules and individual characteristics. Subsequently, a framework for the rights and responsibilities associated with ancillary services is systematically outlined from a market design perspective. Moreover, an ancillary service cost allocation mechanism was introduced based on the principle of ‘common but differentiated responsibilities (CBDR).’ Finally, the construction path of the ancillary service cost allocation mechanism under the new round of power industry reforms was proposed. The findings summarized in this study can promote the reasonable allocation of ancillary service costs and improve the flexibility of power systems and the consumption of renewable energy.

The collaborative energy scheduling of source-load-energy storage has great potential to meet the active control requirements of power-distribution networks. In this study, a federated deep reinforcement learning framework was developed to facilitate collaborative energy scheduling and maximize the total economic benefit in a distribution network. Then, considering the application of Markov decision processes for energy scheduling, a spatial temporal graph convolutional network transformer based power generation packaging model for renewable energy sources was presented, and a collaborative energy scheduling strategy based on a federated duelling deep Q-network was designed. The simulation results indicate that the developed collaborative scheduling strategy can maximize the economic benefits of a power distribution network while ensuring data privacy.

The increasing uncertainty caused by volatile renewable generation and random electricity demand has always been a critical challenge in power system operations. Robust optimization (RO) is a powerful tool for effectively addressing this uncertainty. As the interplay between uncertain factors and decision-making becomes more prevalent, RO with decision-dependent uncertainty (DDU) has attracted increasing attention. DDU significantly changes how the uncertainty set in RO is modelled and how the problems are solved. This study provides a comprehensive overview of the recent developments in RO with DDU for power system problems. We begin by introducing various models of DDU, classified according to their underlying causes. Next, we summarize the state-of-the-art solution algorithms for RO with DDU, such as variants of the column-and-constraint generation (C&CG) algorithm, variants of Benders decomposition, and multiparametric programming. Furthermore, we explore the application of RO with DDU in power systems. Based on our findings, we propose several research directions that may be valuable for future studies.