Energy Conversion and Economics最新文献

The economic viability of offshore wind-to-hydrogen systems depends heavily on the accuracy of wind-speed forecasting. We evaluated three probabilistic scenario generators (historical bootstrapping, parametric Weibull fitting, and calibrated long short-term memory (LSTM) sequence model) using a decision-coupled framework. From 61 years of ERA5, we created 1000 synthetic 23-year hourly scenarios per method, propagated them through a techno-economic model, and scored the continuous ranked probability score (CRPS) on economic distributions (levelized cost of hydrogen (LCOH), net present value (NPV), and interest rate of return (IRR)). We report the decision bandwidth and its elasticity to skill, and run a financing price sweep to meet probability targets. Relative to bootstrapping and Weibull, the LSTM lowers CRPS by 30% for LCOH and NPV (25% for IRR), narrowing risk bands at similar medians. The hydrogen price required to reach more than 90% probability is €7.76–7.78/kg for Pr(NPV > 0) and €9.16-9.18/kg for Pr(NPV > 0 and IRR > 10%). A cross-method spread of less than €0.02/kg indicates a threshold-saturated regime, in which better skills mainly contract risk. Robustness tests shift medians, but preserve method ranking and thresholds. This framework translates probabilistic forecasting skills into finance ability metrics for wind-to-hydrogen screening and policy design.

Non-intrusive load monitoring (NILM) aims to decompose total electricity usage into appliance-specific signals, facilitating detailed energy management without the need for further hardware installation. However, precisely isolating the consumption of specific appliances remains difficult because of overlapping signatures, transient fluctuations, and noise interference. In this study, we propose TransSense as a transformer-based NILM framework designed to overcome these challenges by using multiscale feature extraction and improved temporal modelling. TransSense integrates global and local attention processes to capture both long-term consumption trends and short-term switching events to balance comprehensive contextual comprehension with resilience to localized noise. The model incorporates rotary positional embeddings to capture relative temporal connections in energy consumption patterns. This approach dramatically enhances sequential sensitivity compared to conventional absolute positional encoding. We also implement a hierarchical feature extraction module that includes pooling operations and transformer blocks to reduce redundancy and enhance significant temporal features across various time scales. The results of an extensive experimental evaluation on the Reference Energy Disaggregation Data Set (REDD) and UK Domestic Appliance-Level Electricity (UK-Dale) datasets show that the TransSense model performed better than the conventional benchmark methods used for comparison. These results reflect its enhanced predictive accuracy, robust identification of different load states, and strong resilience to noise. Our findings underscore TransSense's potential as a scalable and generalizable solution for intelligent energy disaggregation on future smart grid infrastructure.

As the industry shifts towards smarter and greener power systems, enhancing the resilience of power systems against increasingly frequent extreme events has become an urgent priority that requires substantial investment. Therefore, effectively quantifying the value of resilience resources (i.e. the resources contributing to improved resilience) and providing transparent economic signals to incentivize their deployment poses a critical challenge. Inspired by locational marginal pricing for energy and capacity, this study proposes a methodology for locational marginal capacity pricing for power system resilience. We first evaluated power system resilience and generated pricing scenarios using a robust optimization approach. The resulting resilience prices reflect the marginal contribution of the maximum capacity of resilience resources to the overall resilience enhancement. During extreme events, resilient resources receiving revenues are obligated to ensure their availability, whereas customers paying fees gain pre-event resilience commitments and priority in load restoration. The case studies validate the effectiveness of the proposed resilience pricing, demonstrating its ability to capture node-specific demands for resilience resources and quantify their value in enhancing system-wide resilience. Moreover, the resilience price provides a transparent and effective economic signal to guide the optimal location and sizing of resilience resources, as well as inform line hardening and expansion strategies.

Low-frequency oscillations (LFOs) remain a major obstacle to maintaining stable dynamic performance in power systems with high levels of renewable energy integration. In particular, inter-area LFOs have emerged as a critical concern in large, interconnected grids. In recent years, wide-area damping control (WADC) has been widely studied in suppressing inter-area LFOs in power systems with renewable energy. This paper provides a comprehensive review of the key technologies underpinning WADC within the framework of wide-area measurement systems. First, it outlines the overall structure of WADC in renewable-integrated power systems. Next, it examines the central technical issues associated with WADC in detail. The paper then summarizes and compares various offline and online adaptive design methodologies for wide-area damping controllers. Finally, it discusses the major challenges facing WADC and highlights future development opportunities. Overall, this review aims to deliver a thorough and meaningful overview of current research on WADC for power systems with high renewable energy penetration.

To improve the adaptability of voltage regulation in active distribution networks (ADNs) with high photovoltaic (PV) penetration, this paper proposes a distributed Volt/Var control (VVC) strategy enabled by multi-agent deep reinforcement learning and implemented through heterogeneous PV inverters. First, a distributed VVC framework is established by partitioning the ADN into multiple sub-networks, each modelled as an agent, with the goal of minimizing voltage deviation. This control framework considers the heterogeneous operation modes of PV systems and utilizes both reactive power support and active power curtailment to maintain voltage within acceptable limits. Then, the VVC problem is formulated as a Markov game and solved using a multi-agent soft actor–critic algorithm. Simulation studies conducted on the IEEE 33-bus and 118-bus test systems validated the effectiveness of the proposed method, demonstrating its superior performance in reducing voltage fluctuations compared to benchmark approaches.

Under voltage sags, swells, stochastic load profiles and harmonic voltage distortion, conventional synchronisation mechanisms often fail to track the grid voltage immediately and accurately, leading to degraded control performance and potential power quality violations. Maintaining high power factor operation becomes critically important in polluted grid scenarios. To ensure stability under these challenging conditions, this paper focuses on maintaining balanced and accurate unit templates with a minimal phase delay and stable DC link voltage in the presence of unpredictable grid scenarios. The synchronising transfer switch provides smooth transitions between the grid-connected and islanded modes of operation, enhancing both power quality and system reliability. The optimisation of distributed energy resources to improve grid resilience, stability and overall efficiency of renewable energy integration is presented in this work. The key element of this integration is a multi-functional voltage source converter with a unique control strategy, enabling precise control over the charging and discharging processes of the battery. The system exhibits versatile capabilities, including harmonic mitigation, reactive power compensation, and seamless supply of active power to the grid. Performance assessments highlight the controller's ability to improve microgrid stability and smooth operational transition capabilities.

Many residential prosumers exhibit a high price tolerance for household electricity bills and a low response to price incentives. This is because household electricity bills are not inherently high, and the potential for saving electricity bills through participation in conventional shared energy storage (SES) is limited, which diminishes their motivation to actively engage in SES. Additionally, existing SES models often require prosumers to take additional actions, such as optimising rental capacity and bidding prices, which happen to be capabilities that typical household prosumers do not possess. To incentivise these high-price-tolerance residential prosumers to participate in SES, a novel SES aggregation framework is proposed, which does not require prosumers to take additional actions and allows them to maintain existing energy storage patterns. Compared to the conventional long-term operation of SES, the proposed framework introduces an additional short-term construction step during which the energy service provider (ESP) acquires control of the energy storage systems (ESS) and offers electricity deposit and withdrawal services (DWS) with dynamic coefficients, enabling prosumers to withdraw more electricity than they deposit without additional actions. Additionally, a matching mechanism is proposed to align prosumers’ electricity consumption behaviours with ESP optimisation strategies. Finally, the dynamic coefficients in the DWS and SES trading strategies are jointly optimised using a modified deep reinforcement learning algorithm. Combining neighbouring experience pool replay modifies the twin delay deep deterministic policy gradient (CNEPR-TD3), which introduces a multilabel neighbouring experience replay mechanism to improve learning efficiency and convergence stability. Simulation studies based on one-year real-world data validated the proposed approach. Ablation experiments showed that the inclusion of dynamic DWS and the matching mechanism increased the overall SES profit by 42.87%, confirming the effectiveness and economic value of the proposed framework.

Some sub-Saharan African countries face the paradox of overcapacity in the power sector, where investments in electricity generation far exceed both demand and grid capacity, despite millions still lacking access to electricity. This imbalance stems from inadequate planning and external pressures from donors and private investors, who often prioritize generation over expanding transmission infrastructure. As a result, some power plants remain underutilized, placing financial burdens on governments and raising electricity costs for consumers. This study assessed overcapacity in Rwanda's power system using two key indicators: the plant utilization factor and reserve margin. We propose a coordinated generation and transmission expansion planning model to better align generation capacity with demand and grid development. The problem was analysed using PLEXOS software. Overcapacity in Rwanda rose from 29% in 2017 to 35% in 2023, as generation additions outpaced demand. However, with electricity consumption projected to grow by 10% annually, overcapacity could decline to 21% by 2028, based on committed and ongoing generation projects. Model results show that delaying selected generation projects and strengthening the transmission network could reduce overcapacity by 79% within 5 years and lower operating costs by 11.7% compared to the current master plan. These findings underscore the importance of integrated planning to improve system utilization and efficiency, offering a practical framework for other African countries facing similar challenges.

Capacity markets (CMs) have been widely analysed and implemented in various regions to enhance the capacity adequacy and supply security in power systems with high renewable penetration. This study compares the performance of two market designs, an energy market combined with a CM and an energy-only market, using a capacity expansion model that incorporates long-term energy storage (ES). This study contributes significantly to the debate on CM by quantifying the improvement in the system reliability. Through simulations conducted on provincial power systems in China, we demonstrate that introducing a CM significantly enhances the system reliability by providing a stable revenue stream that incentivises capacity investments. Additionally, the effectiveness of implementing a CM is analysed through simulations under various market conditions, thereby presenting various advantages. Furthermore, the results of this study indicate that the reliable availability of conventional technologies is essential for future power systems with high renewable energy penetration. The supply security cannot be ensured with an excessive penetration level of renewable energy sources, despite long-term ES. These findings present critical insights into the design of hybrid electricity markets for transitioning power systems.

In this paper, an optimal power procurement decision-making model for the receiving-end power system considering uncertainties in two-level inter-provincial and intra-provincial markets is proposed. The model aims to achieve secure and economically efficient system dispatch under market coordination, ensuring rational resource utilization and intra-provincial supply-demand balance. First, a decision-making framework is established to mitigate renewable energy output fluctuations and enhance overall supply reliability under these two-level uncertainties. Second, leveraging scenario generation for inter-provincial market participant behavior uncertainties and introducing a fluctuation coefficient, an optimal procurement model based on information gap decision theory (IGDT) is developed to handle intra-provincial wind and solar power output uncertainty. Finally, the model is empirically validated using actual transaction data from a provincial receiving-end system in East China. Results demonstrate that increased inter-provincial bidding pressure and greater intra-provincial renewable output fluctuations both elevate system power supply costs. Crucially, the proposed model effectively quantifies the impact of uncertainty factors, enhancing the robustness of power procurement decisions.