Energy Conversion and Economics最新文献

State estimation of multi-area interconnected power systems is crucial for reflecting system operations and guiding actuator responses. However, sensor faults, communication link failures, and external random disturbances can inevitably lead to power system failures. Therefore, designing a highly effective state estimation method capable of timely and accurate detection of sensor faults in the power grid to mitigate losses is of significant practical importance. This paper addresses the fault estimation problem in multi-area power systems with parameter uncertainties and proposes a fault-tolerant state estimator that accounts for communication link failures between network layers in each area. These communication link failures are modelled as Bernoulli-distributed variables. State estimation is achieved using information from adjacent power system areas. Sufficient conditions for the error system's $�H_{\infty }$ performance are provided using Lyapunov stability theory and linear matrix inequality methods. Finally, simulations on a three-area interconnected power system validate the proposed method's effectiveness in mitigating the effects of communication link failures and random disturbances. The method accurately and rapidly estimates sensor faults and the system state, ensuring stable operation and enhancing grid reliability.

This paper proposes tokenising generators' revenue streams as a novel financial instrument for electricity market risk management. The core idea is that tokenised revenue streams (RevToks) holders can directly claim a portion of a generator's energy market revenues. The novelty lies in exploring a tranched structure, creating a tiered hierarchy of RevTok claims. This approach addresses the financial challenges of non-dispatchable renewable energy generation and volatile electricity spot markets, which expose generation firms to volumetric and price risks, potentially deterring project financiers. The manuscript introduces tranching, a scheme for fractionalising energy market revenues based on RevTok seniority. Project financiers might hold senior RevToks, ensuring first claim on generator revenues, while bulk offtakers could purchase junior RevToks as a hedge against high wholesale prices. Case study market simulations indicate that these tranched revenue sharing arrangements can, in principle, help generators, financiers, and offtakers to better manage their risk exposure. The proposed system would allow RevTok holders to directly claim shares of specific generators' revenue streams, offering a new risk management tool for various electricity market participants.

This study introduces a comprehensive framework aimed at advancing research and policy development in the realm of decarbonization within electric power systems. The framework focuses on three key aspects—carbon accounting, carbon-aware decision making, and carbon-electricity market design—and proposes solutions to existing problems. In contrast to traditional pool-based emission models, this framework proposes a novel flow-based emission model that incorporates the underlying physical power grid and power flows. Thus, the framework allows accurate carbon accounting at both the temporal and spatial scales, thereby facilitating informed decision-making to achieve grid decarbonization goals. The framework is built on a flow-based carbon accounting methodology and utilizes the carbon-aware optimal power flow technique as a theoretical foundation for decarbonization decision-making. Additionally, this study explores the potential design of carbon-electricity markets and pricing mechanisms to incentivize decentralized decarbonization actions. Critical issues of data availability, infrastructure development, fairness and equity considerations are also discussed.

The increasing impact of climate change raises concerns regarding the vulnerability of overhead transmission lines to ice disasters. To address this issue, this study reviews icing growth modelling in two categories: physical-driven models (PDMs) and data-driven models (DDMs), covering current advances and future directions. First, PDMs are summarised, focusing on the thermodynamic and fluid mechanics mechanisms. Existing PDMs are compared based on principles, analysing their advantages, disadvantages, and challenges faced. Second, the summarisation of DDMs involves four aspects: data preparation, algorithm selection, model training, and model evaluation. In data preparation, techniques such as preprocessing methods are reviewed to handle multisource data. In algorithm selection, various modelling algorithms are compared and analysed, from basic to deep learning approaches. In model training, processes are summarised to enhance practical applicability, including data partitioning, hyperparameter adjustment, generalisation capability, and model interpretability. In model evaluation, the predictive capabilities are analysed, covering both regression and classification tasks. Subsequently, based on the analyses, a comparison of PDMs and DDMs across various aspects is presented. Finally, future directions in icing growth modelling are outlined. The aim is to enhance icing assessment by understanding the underlying mechanism in attempt to reduce vulnerability and ensure reliability against adverse weather conditions.

The power fluctuation of distributed photovoltaic (PV) systems significantly impacts the balance of the power system, leading to risks like PV curtailment and load shedding. This paper proposes a multi-level rolling warning method for distributed PV power fluctuation (DPPF) based on interval analysis, aiming to establish a framework for proactively mitigating the potential adverse effects of fluctuations in distributed PV systems. Firstly, the power control mechanism to deal with DPPF is clarified, and warning levels are defined to determine the range of fluctuations that can be controlled by different power control measures. Secondly, based on the probability density of DPPF, the probabilities of each warning level are obtained by integrating the probability densities within each warning range. Finally, the differences in the forecasting accuracy of PV power fluctuations at different time scales are analysed, and the rolling warning of DPPF is achieved by periodically updating PV power output to adjust the warning results. Simulation results demonstrate that the proposed method identifies the thresholds for each warning range and provides warnings for different system operating conditions and PV power fluctuation events, confirming its effectiveness and applicability.

This article proposes a novel online reinforcement learning-based linear quadratic regulator for the three-level neutral-point clamped DC/AC voltage source inverter. The proposed controller employs online updated fixed-weight recurrent neural network (NN) and policy iteration to dynamically adjust the optimal control gains based on real-time measurements without any knowledge of the system model or offline pre-training. Moreover, it produces a constant switching frequency with low current harmonics. Compared to the existing control methods, it provides superior control performance, guaranteed control stability, and simplified NN design. Experimental results are presented to verify the effectiveness of the proposed control method.

Human activities have been driving massive greenhouse gas emissions, causing global warming, and triggering increasingly frequent extreme weather events that severely threaten the environment. Power generation is the leading contributor to anthropogenic emissions, making precise, real-time measurement and monitoring of power plant carbon emissions crucial in reducing climate change. This study uses a new sophisticated pipeline that combines tropospheric monitoring instrument satellite data, power plant attributes, and advanced artificial intelligence algorithms to build a predictive carbon emission model. The approach utilizes multimodal data processing, encoding, and model optimisation. Experimental results confirm that this pipeline can automatically extract and utilize vast amounts of relevant data, thereby enabling the artificial intelligence model to accurately predict power plant carbon emissions and providing a vital tool for reducing global warming.

Load forecasting with distributed energy resources (DERs) behind-the-meter is more challenging owing to transformed data patterns. Traditional forecasting method which is only based on unmasked-load could not suit the present limited masked-load. To bridge the divergence between unmasked-load and masked-load, this article proposes a masked-load forecasting (MLF) method based on transfer learning technique and Bayesian optimization, which is Maximum Mean Discrepancy-Neural Network with Bayesian optimization (MMD-NN^b). At first, common feature vectors between unmasked-load and masked-load are extracted and an outcome predictor could be established based on feature vectors from historical unmasked-load. The feature vectors from masked-load could therefore accommodate to the outcome predictor, and the masked-load could be forecast. Owing to the excessive hyperparameters involved in training, Bayesian optimization is adopted for hyperparameters fine-tuning. MMD-NN^b was tested and compared with four related models. The improvements from MMD-NN^b were observed in all comparison scenarios. Also, MMD-NN^b was proved to have high resilience to the different DERs and not requiring additional DERs-data.

The integration of seasonal loads, such as cereal baking and aquatic-product processing loads, often leads to significant voltage deviations and severe peak loads of the distribution system during specific periods, resulting in increased network losses. Traditional approaches for reducing network losses are becoming less effective and cost-efficient due to the spatiotemporally uneven distribution characteristics of seasonal loads. To address this issue, this study proposes an optimisation model that collaboratively integrates mobile energy storage, switching capacitors, and tie lines to minimise annual network losses in special planning scenarios affected by seasonal loads. The deployment strategies of multiple reinforcement methods are thoroughly analysed, greatly enhancing the explainability and feasibility of the collaborative deployment model. Then, the proposed model is reformulated to a mixed-integer linear programming model using the inscribed regular dodecagon approximation approach, thereby making it trackable for state-of-the-art solvers. To illustrate the effectiveness of the model, case studies are conducted on a unique 55-bus distribution system located in East China, which contains feeders with substantial seasonal variation aquaculture loads and with general loads. The effectiveness of multiple reinforcement methods is thoroughly analysed through detailed numerical results. Furthermore, a sensitivity analysis of the investment budget is conducted.

Liu, W., He, B., Xue, Y., Huang, J., Zhao, J., Wen, F.: A comprehensive modelling framework for coupled electricity and carbon markets. Energy Convers. Econ. 5, 1–14 (2024). https://doi.org/10.1049/enc2.12108

In funding information, the text “National Natural Science Foundation of China, Grant/Award Numbers: 72171206, 71931003, 72061147004, 72192805; NARI Science and Technology Project” was incorrect. This should have read: ‘The Science and Technology Project of NARI Technology Co., Ltd. on “Interaction and Coordination Technology of Information-Physical-Social Elements” (GF-GFWD-210338).’

We apologize for this error.