Energy Conversion and Economics最新文献

The typhoon–rainstorm–flood disaster chain poses a significant flooding risk to urban distribution network (DN) equipment, often leading to power system outages. The increasing frequency and severity of this disaster chain in East Asia, driven by global warming, population growth, and land-use changes, highlight the need for improved disaster preparedness. Traditional studies focusing on individual meteorological disasters, such as typhoons or floods, may be insufficient for developing efective mitigation strategies. To address this gap, this study proposes a novel risk analysis method for enhancing the disaster defence strategy of DNs. First, a hybrid deep learning model is developed to forecast a 48-h rainstorm time series following a typhoon's landfall. Second, a one-dimensional pipe network and a two-dimensional surface-coupled urban flood model are constructed to predict flood depth based on the typhoon–rainstorm time series. Third, an influence factor set is established from environmental and societal perspectives, and spatial correlation analysis is applied to assess DN outage risk. To validate the proposed method, Typhoon Talim (2023), which made landfall in China, is used as a case study. The results demonstrate that the model effectively captures disaster-causing mechanisms and accurately identifies high-risk areas. This research provides a theoretical foundation for outage risk prevention in developing countries, particularly in mitigating the impacts of the typhoon–rainstorm–flood disaster chain.

In integrated energy systems (IESs), thermal energies with different characteristics and efficiencies are typically regarded as having the same thermal energy level, which leads to unreasonable assumptions regarding the thermal energy structure of the system. Moreover, the traditional optimal operation method does not consider the impact of expanding a single thermal energy flow into a multi-level thermal energy flow on the optimal operation results of the system. These problems pose challenges to the complexity of multi-level thermal energy flow mechanisms and optimal operation results of the IES. To tackle this challenge, first, this study establishes a multi-level thermal energy coupling (MTEC) model, which divides the thermal energy flow into three levels according to temperature, and re-models the production and conversion equipment based on thermal energy levels. Second, the energy hub matrix for MTEC-IDR joint operation is proposed, and the integrated demand response (IDR) is introduced to replace energy storage devices to solve the problem of rising costs caused by insufficient load flexibility. Finally, the system constraints and objective function are improved, and an optimal IES scheduling strategy under the MTEC-IDR mechanism is proposed. The effectiveness of the proposed strategy is proved from the perspectives of low-carbon implementation and economy.

The impact of climate change is leading to a phenomenal increase in the frequency and intensity of high-impact, low-probability (HILP) weather events, which cause widespread power outages. Consequently, there is a pressing need to develop resilient power distribution systems against such extreme events. Presently, the methods and metrics to assess grid resilience against HILP events are at an early stage of development and need further work to make them widely implementable in grid resilience investment planning. To address this issue, this study proposes a Monte Carlo-based framework to evaluate the resilience of distribution systems in the presence of distributed energy resources under two distinct phases: (1) during the event as the system succumbs to the extreme forces, and (2) in its aftermath as the restoration proceeds. This allows power system utilities to analyse the effectiveness of various resilience enhancement strategies for different phases of extreme weather events. The framework also establishes a mathematical relationship to determine the post-event restoration time based on the hierarchical sequence of component repairs, which depends on the inter-dependence of component failures and repair crew availability. The framework's effectiveness is demonstrated through case studies on the modified IEEE 69-bus system.

The increasing penetration of distributed energy resources (DERs) and renewable energy sources (RESs) requires more granular analysis for accurate carbon footprint tracing. Traditional tracing methodologies primarily utilized deterministic steady-state analyses, which inadequately addressed the significant uncertainties inherent in RESs. To address this gap, this study introduces two stochastic carbon footprint-tracing approaches that incorporate RES uncertainties into load-side carbon footprint assessments. The first method embeds a probabilistic analysis within the carbon emissions flow (CEF) framework, providing a comprehensive reference for the spatial distribution of carbon intensity across power system components. Recognizing that the CEF network complexity increases with higher DER penetration, the second method extends the initial approach to enhance computational efficiency while maintaining accuracy, thus ensuring scalability for large-scale power system topologies. The proposed models were validated and benchmarked using a synthetic 1004-bus test system in a case study, demonstrating their enhanced performance and advancements over conventional deterministic methods. The results underscore the effectiveness of the stochastic approaches in delivering more precise and reliable carbon footprint tracing, thereby contributing to the sustainable management of decarbonized power systems.

Multiple energy storage systems (ESSs) often face imbalances in charging–discharging operations, as well as the uncertainties of practical scenarios and influencing factors. To address these challenges, this study proposes a user-side cloud energy storage (CES) model with active participation of the operator. This CES model incorporates adjustable time-of-use (TOU) electricity pricing and state-of-charge (SOC) management. In the configuration process, the net load scenario generation reduction is performed first. Subsequently, demand response is implemented based on the updated TOU pricing. To address the imbalance of ESSs, an improved multiobjective particle swarm optimization is employed, followed by access verification of the multi-ESS aggregation. In the dispatch process, a two-stage interval optimization model is adopted. Specifically, day-ahead scheduling determines the SOC limit interval, and intra-day scheduling achieves rolling optimization to determine the exact charging–discharging duration. This ensures that the charging–discharging cycles are controllable, orderly, and efficient. Ultimately, a fair settlement method based on optimal pricing of various fees within the “cloud” is proposed, ensuring sustainable revenue growth for all types of users. A case study demonstrates that the proposed methods can achieve multifaceted value in energy management and enhance the socioeconomics of user-side ESS projects.

Accurate locational marginal price forecasting (LMPF) is crucial for the efficient allocation of resources. Nevertheless, the sudden changes in LMP make it inadequate for many existing long short-term memory (LSTM) network-based prediction models to achieve the required accuracy for practical applications. This study adopts a hierarchical method of three layers based on double quantum-inspired grey wolf optimisation (QGWO) to improve the LSTM model (HD-QGWO-LSTM) for a one-step LMPF. The top layer completes the data processing. The middle layer is a QGWO-optimised support vector machine (SVM) for classifing whether LMPs are price spikes. The bottom laver is a double QGWO-improved LSTM (QGWO-LSTM) model for a real LMPF, where one QGWO-LSTM is for the spike LMPF and the other is for the non-spike LMPF. To address the issue of excessively long training times during the design of the LSTM network structure and parameter selection, a QGWO algorithm is proposed and used to optimise four LSTM parameters. The simulation results on the New England electricity market show that the HD-QGWO-LSTM method achieves similar prediction accuracy to other four LSTM-based methods. The results also validate that the QGWO algorithm significantly reduces time consumption while ensuring optimisation effectiveness when optimising SVM and LSTM.

The accelerated growth of smart cities and the intensifying impact of climate change have introduced new demands for low-carbon commercial buildings. The majority of existing low-carbon scheduling methods for commercial buildings focus on operational carbon emissions embedded in consumed electricity from the electricity network without a lifecycle perspective, resulting in the underestimation of the carbon emissions of consumed electricity. This article proposes a Stackelberg game model for low-carbon scheduling of commercial building loads. In this model, the lifecycle unit carbon-emission factors are calculated and then transferred to commercial buildings employing the carbon-emission flow method. Subsequently, a low-carbon scheduling model considering the carbon transaction, demand response, and thermal comfort is established for commercial building loads. Finally, the Stackelberg game model is implemented to determine the interaction between commercial buildings and the electricity network. The case study indicates that approximately 23% of indirect carbon emissions from electricity used in commercial buildings originate from the extraction, construction, transportation, demolition, and recycling stage, while approximately 77% occur during the operation stage.

Frequent outbreaks of severe natural disasters underscore the importance of power system resilience. With high efficiency and rapid response, hydrogen energy can enhance power system resilience during such incidents. Traditional post-event resilience assessment methods, which are event-triggered, focus on a single indicator, leading to an ambiguous portrayal of the power capacity of coupled systems. To address this limitation, based on a two-stage electro–hydrogen coupled model, the concept of electro–hydrogen coupled region (EHCR) is proposed to illustrate the potential relationships between resilience indicators, exploring the accurate power capacity of the coupled system to critical loads during extreme events. The convex hull estimation is employed to determine the EHCR. A max–min diagnostic model is introduced as the convergence criterion for resilience margins. An external cutting-plane algorithm is developed to interactively obtain the EHCR by progressively eliminating non-capacity regions of the current space based on the diagnostic model. The efficacy of the proposed methods is validated through case studies based on an IEEE 30-bus and Belgium 20-node coupled system under ice disaster scenarios.

The widespread integration of photovoltaics (PVs) presents significant challenges to the operation and control of distribution systems, particularly in maintaining voltage stability at nodes with PV connections. To address these challenges, this paper proposes a voltage control algorithm based on distributed model-free adaptive control (MFAC). The control objective is to achieve real-time reactive-power-voltage coordination under constraints including PV power output limitations, voltage safety ranges, and the communication network topology. The proposed method estimates dynamic linearization parameters that represent the voltage control characteristics of the distribution systems by utilizing real-time data from distributed PVs and enabling communication between adjacent nodes. Rather than relying on a precise network model, the algorithm achieves robust voltage control by estimating these parameters from historical and real-time sampling data, employing a data-driven approach to iteratively update control strategies. Multi-scenario simulations of a 32-bus power system demonstrated the effectiveness and robustness of the algorithm across diverse operating conditions.

This study proposes an alternating current microgrid that integrates renewable energy sources to enhance energy sustainability. In this system, wind and solar power are initially converted to DC using DC–DC converters; subsequently, they are integrated into a common AC bus through parallel voltage source converters. The goal is to provide uninterrupted power to local loads while addressing power quality issues and efficiently managing power flow within the system. The main contribution of this study is the development of a unified power flow strategy that ensures reliable power delivery by considering peak and off-peak electricity pricing, as well as the battery state of charge for optimised grid and storage utilisation. Moreover, when the power electronics circuitry is integrated with renewable energy sources, the grid encounters power quality issues at the point of common coupling. Therefore, to mitigate power quality issues at the point of common coupling, particularly with power electronics integration, a frequency-locked loop based on an amplitude integrator, coupled with a harmonic decoupling network, is used to extract the fundamental components of the grid voltage and reduce harmonic distortion. The proposed topology and control strategies are validated through laboratory testing using a hardware prototype, with the test results demonstrating their effectiveness.