Energy Conversion and Economics最新文献

Lithium-ion battery state-of-health (SOH) monitoring is essential for maintaining the safety and reliability of electric vehicles and efficiency of energy storage systems. When the SOH of lithium-ion batteries reaches the end-of-life threshold, replacement and maintenance are required to avoid fire and explosion hazards. This paper provides a comprehensive literature review of lithium-ion battery SOH estimation methods at the cell, module, and pack levels. Analysis and summary of the SOH definition based on the resistance, capacity, and energy indices are presented at each battery hierarchy level. A comparison of SOH indices in terms of modelling complexity, required measurement time, and accuracy is provided. To the best of knowledge, a comprehensive classification of SOH estimation methods at different battery hierarchy levels is presented for the first time in this review. In addition, SOH estimation methods are further classified based on the applied methodologies, including direct measurement, model-based methods, data-driven methods, and hybrid model-data methods. Advantages and disadvantages of SOH estimation methods are summarized and compared across different battery hierarchy levels. A detailed summary of typical SOH estimation methods is presented along with the battery topology, operating conditions, and performance. The challenges and research prospects of lithium-ion battery SOH estimation are discussed from the cell to pack levels.

Greenhouses need to supply CO₂ to crops while simultaneously emitting CO₂. To effectively harness the dual functionality of greenhouses as a carbon source and carbon consumer, this work incorporates carbon capture and emissions trading into a multi-energy greenhouse (MEG), which is equipped with various power and heat sources such as photovoltaic (PV) panels and a combined heat and power (CHP) unit and proposes that the captured CO₂ should be used to feed crops on-site. A low-carbon economic operation method is proposed for the coordinated environment-energy-carbon management of the MEG, and it considers various factors, including the power purchase/carbon supply costs, carbon emissions trading income, temperature/humidity/light intensity and CO₂ concentration requirements for crops, and operational constraints of various energy/environmental regulation equipment. The proposed method is validated using a tomato MEG. The results highlight the significant economic and environmental benefits of introducing carbon capture, emissions trading, and utilisation into MEGs.

Accurately and efficiently estimating the carbon market risk is paramount for ensuring financial stability, promoting environmental sustainability, and facilitating informed decision-making. Although classical risk estimation methods are extensively utilized, the implicit pre-assumptions regarding distribution are predominantly contained and challenging to balance accuracy and computational efficiency. A quantum computing-based carbon market risk estimation framework is proposed to address this problem with the quantum conditional generative adversarial network-quantum amplitude estimation (QCGAN-QAE) algorithm. Specifically, quantum conditional generative adversarial network (QCGAN) is employed to simulate the future distribution of the generated return rate, whereas quantum amplitude estimation (QAE) is employed to measure the distribution. Moreover, the quantum circuit of the QCGAN improved by reordering the data interaction layer and data simulation layer is coupled with the introduction of the quantum fully connected layer. The binary search method is incorporated into the QAE to bolster the computational efficiency. The simulation results based on the European Union Emissions Trading System reveals that the proposed framework markedly enhances the efficiency and precision of value-at-risk and conditional value-at-risk compared to original methods.

Renewable energy-based microgrids (MGs) strongly depend on the implementation of energy storage technologies to optimize their functionality. Traditionally, electrochemical batteries have been the predominant means of energy storage. However, technological advancements have led to the recognition of hydrogen as a promising solution to address the long-term energy requirements of microgrid systems. This study conducted a comprehensive literature review aimed at analysing and synthesizing the principal optimization and control methodologies employed in hydrogen-based microgrids within the context of building microgrid infrastructures. A comparative assessment was conducted to evaluate the merits and disadvantages of the different approaches. The optimization techniques for energy management are categorized based on their predictability, deployment feasibility, and computational complexity. In addition, the proposed ranking system facilitates an understanding of its suitability for diverse applications. This review encompasses deterministic, stochastic, and cutting-edge methodologies, such as machine learning-based approaches, and compares and discusses their respective merits. The key outcome of this research is the classification of various energy management strategy methodologies for hydrogen-based MG, along with a mechanism to identify which methodologies will be suitable under what conditions. Finally, a detailed examination of the advantages and disadvantages of various strategies for controlling and optimizing hybrid microgrid systems with an emphasis on hydrogen utilization is provided.

Packetized energy encapsulates energy into modulated, routable, and trackable energy packets, enhancing the flexibility of managing distributed energy resources and expediting prosumers’ participation in transactive energy markets. In the context of packetized energy trading (PET), energy prosumers are naturally deemed as self-interested agents seeking to obtain their own benefits. To align with prosumers’ demand, supply, quality of service (QoS), and system-level social welfare, it is necessary to explore the design of prosumers’ bidding strategies and the market clearing methods, considering prosumers’ utility and the best demand response to markets. This study addresses challenges arising from prosumers’ selfishness and asymmetric preferences by proposing a PET-oriented iterative double auction (IDA-PET) design, where prosumers are allowed to iteratively change the bids before the auctioneer clears the market. Moreover, IDA-PET accommodates system capacity constraints, energy balance, and economic constraints, providing cooperative strategies for both prosumers and the auctioneer. To validate the effectiveness of IDA-PET, a novel and dedicated co-simulation platform based on the hierarchical engine for large-scale infrastructure co-simulation platform is developed and case studies are conducted within a residential microgrid. The simulation results demonstrate that IDA-PET can efficiently enhance the revenue of the auction market while meeting prosumers’ QoS requirements.

To optimally harness the adjustable capabilities of reactive power sources for voltage control, a dynamic partitioning method that uses reactive power flow tracking for branch cutting through Binary Particle Swarm Optimisation (BPSO) is proposed for Active Distribution Networks (ADNs). Initially, the limitations of existing Voltage/Var Sensitivity (VVS) calculation methods are analysed, leading to the proposition of a novel VVS calculation method capable of capturing variations in source-load timing characteristics. Subsequently, the fuzzification of the VVS matrix between nodes is used to derive the membership degree matrix. Next, based on the membership relationship between reactive power source nodes, these nodes are pre-partitioned, and the number of leading nodes and zones alongside are preliminarily determined. Then, the range of the branch to be cut is established, guided by the reactive power flow direction of the branch. Employing the zonal comprehensive coupling degree as the objective function of the BPSO facilitates the identification of optimal branch cutting points, thereby determining the partitioning outcome. Finally, a reactive power reserve check is executed to rectify any non-compliant zones. In this study, numerical simulations are conducted using the enhanced IEEE 33-node power system to demonstrate the efficacy of the proposed method.

To promote the profit of generation companies and reduce carbon emissions from the generation sector, a two-layer decision-making model based on the cooperative game is proposed. Based on the analysis of a renewable-fossil energy generation alliance to participate in electricity-carbon-green certificate markets, a robust optimization model considering the price uncertainty in the electricity spot market is first established based on the information gap decision-making theory as the upper part of a two-layer optimization model. Subsequently, the clearing models of the electricity spot, carbon emission trading, and green certificate markets are established. This two-layer optimization model is transformed into a single-layer model based on the well-established KKT conditions. A profit allocation model for the members of the generation alliance is then presented based on the Shapley value and an improved nucleolus kernel method. Finally, the effectiveness of the proposed model is demonstrated by the IEEE 14-bus power system.

Peer-to-peer energy trading enhances distribution network resilience by reducing energy demand from central power plants and enabling distributed energy resources to support critical loads after extreme events. However, adequate reserves from main grids are still required to ensure real-time energy balance in distribution networks due to the uncertainty in renewable generation. This paper introduces a novel two-stage joint energy and reserve market for prosumers, wherein local flexible resources are fully utilized to manage renewable generation uncertainty. In contrast to cooperative optimization methods, the interactions between prosumers are modelled as a generalized Nash game, considering that prosumers are self-interested and should follow distribution network constraints. Then, linear decision rules are employed to ensure a feasible market equilibrium and develop a privacy-preserving algorithm to guide prosumers the market equilibrium with a proven convergence. Finally, the numerical study on a modified IEEE 33-power system demonstrates that the designed market effectively manages renewable generation uncertainty, and that the algorithm converges to the market equilibrium.

A power system can be regarded as a cyber-physical system with physical power networks and a cyber system based on increasing engagement with information communication technologies for smart grid functionalities for more efficient operations and control. Non-intrusive load monitoring (NILM), an emerging smart-grid technology, can be used to better understand the electricity usage profile and composition of smart meters using advanced data analysis algorithms. Although NILM enables various smart grid services, wider applications of NILM require addressing the challenges regarding cyber security and data privacy risks. Anomaly detection in appliance data is one of the most effective measures against potential cyber intrusions from a data perspective. This study proposes a framework of anomaly detection-based learning algorithms to identify the anomalous periods of electricity loading data, which may be a subject for potential cyber-attacks. Comparison studies with the hidden Markov model are performed to validate the proposed approaches. The simulation results show that these anomaly detection-based learning algorithms work well and can precisely determine anomalous loading periods. Moreover, these trained models perform well on the testing dataset without prior knowledge of the data, providing the possibility of the real-time assessment of power- loading states. The proposed framework can also be used to develop protective measures to ensure secure system operation and user data privacy.

Robust optimization is an essential tool for addressing the uncertainties in power systems. Most existing algorithms, such as Benders decomposition and column-and-constraint generation (C&CG), focus on robust optimization with decision-independent uncertainty (DIU). However, increasingly common decision-dependent uncertainties (DDUs) in power systems are frequently overlooked. When DDUs are considered, traditional algorithms for robust optimization with DIUs become inapplicable. This is because the previously selected worst-case scenarios may fall outside the uncertainty set when the first-stage decision changes, causing traditional algorithms to fail to converge. This study provides a general solution algorithm for robust optimization with DDU, which is called dual C&CG. Its convergence and optimality are proven theoretically. To demonstrate the effectiveness of the dual C&CG algorithm, we used the do-not-exceed limit (DNEL) problem as an example. The results show that the proposed algorithm can not only solve the simple DNEL model studied in the literature but also provide a more practical DNEL model considering the correlations among renewable generators.