Advances in Applied Energy最新文献

Resource adequacy, or ensuring that electricity supply reliably meets demand, is more challenging for wind- and solar-based electricity systems than fossil-fuel-based ones. Here, we investigate how the number of years of past weather data used in designing least-cost systems relying on wind, solar, and energy storage affects resource adequacy. We find that nearly 40 years of weather data are required to plan highly reliable systems (e.g., zero lost load over a decade). In comparison, this same adequacy could be attained with 15 years of weather data when additionally allowing traditional dispatchable generation to supply 5 % of electricity demand. We further observe that the marginal cost of improving resource adequacy increased as more years, and thus more weather variability, were considered for planning. Our results suggest that ensuring the reliability of wind- and solar-based systems will require using considerably more weather data in system planning than is the current practice. However, when considering the potential costs associated with unmet electricity demand, fewer planning years may suffice to balance costs against operational reliability.

Soaring solar cell temperature hindered photovoltaic (PV) efficiency, but a novel radiative cooling (RC) cover developed in this study offered a cost-effective solution. Using a randomly particle-doping structure, the radiative cooling cover achieved a high “sky window” emissivity of 95.3% while maintaining a high solar transmittance of 94.8%. The RC-PV system reached a peak power output of 147.6 W/m${}^2$. A field study to explore its potential in various provinces in China revealed significant efficiency improvements, with yearly electricity outputs surpassing those of ordinary PV systems by a relative improvement of 2.78%–3.72%. The largest increases were observed under clear skies and in dry, cool climates, highlighting the potential of RC-PV systems under real weather and environmental conditions. This work provided the theoretical foundation for designing scalable radiative cooling films for PV systems, unlocking the full potential of solar energy.

Linear programs are frequently employed to optimize national energy system models, which are used to find a minimum-cost energy system. For the operation, they assume perfect forecasting of the weather and demands over the whole optimization horizon and can therefore perfectly fit the energy systems’ design and operation. Therefore, they will yield lower costs than any real energy system that only has partial forecasting available. We compare linear programming with a priority list, a heuristic operation strategy which uses no forecasting at all, in a model of a climate-neutral German energy system. We find a 28% more expensive energy system under the priority list. Optimizing the same energy system model with both strategies envelopes the cost and design of any energy system that has partial forecasting. We demonstrate this by incorporating some rudimentary forecasting into a modified priority list, which actually reduces the gap to 22%. This is thus an approach to find an upper bound for how much a linear program possibly underestimates the costs of a real energy system in Germany in regard to imperfect forecasting. We also find that the two approaches differ mainly in the dimensioning and operation of energy storage. The priority list yields 63% less batteries, 73% less thermal storage and 54% more hydrogen storage. The use of renewables and other components in the system is very similar.

The integrated community energy system (ICES) has emerged as a promising solution for enhancing the efficiency of the distribution system by effectively coordinating multiple energy sources. However, the concept and modeling of ICES still remain unclear, and operational optimization of ICES is hindered by the physical constraints of heterogeneous integrated energy networks. This paper, therefore, provides an overview of the state-of-the-art concepts for techno–economic modeling of ICES by establishing a Multi-Network Constrained ICES (MNC-ICES) model. The proposed model underscores the diverse energy devices at community and consumer levels and multiple networks for power, gas, and heat in a privacy-protection manner, providing a basis for practical network-constrained community operation tools. The corresponding operational optimization in the proposed model is formulated into a constrained Markov decision process (C-MDP) and solved by a Safe Reinforcement Learning (RL) approach. A novel Safe RL algorithm, Primal-Dual Twin Delayed Deep Deterministic Policy Gradient (PD-TD3), is developed to solve the C-MDP. By optimizing operations and maintaining network safety simultaneously, the proposed PD-TD3 method provides a solid backup for the ICESO and has great potential in real-world implementation. The non-convex modeling of MNC-ICES and the optimization performance of PD-TD3 is demonstrated in various scenarios. Compared with benchmark approaches, the proposed algorithm merits training speed, higher operational profits, and lower violations of multi-network constraints. Potential beneficiaries of this work include ICES operators and residents who could be benefited from improved ICES operation efficiency, as well as reinforcement learning researchers and practitioners who could be inspired for safe RL applications in real-world industry.

This paper proposed a user-friendly and adaptive home intelligent agent with self-learning capability for optimal scheduling of smart home energy systems. The intelligent agent autonomously identifies model parameters based on system operation data, eliminating the need for manual input and making it more user-friendly and practical to implement. It can also self-learn the latest energy consumption information from an updated dataset and adaptively adjust model parameters to accommodate changing conditions. Utilizing these determined models as input, the intelligent agent performs day-ahead optimal scheduling using the proposed many-objective integer nonlinear optimization model and automatically controls system operation. Experimental studies were conducted on a laboratory-based smart home energy system to verify the effectiveness of the developed intelligent agent in different scenarios. The results consistently demonstrate Mean Absolute Percentage Errors below -12.7 % across all three scenarios, indicating the accuracy of the intelligent agent. Furthermore, the optimal scheduling significantly enhances system performances. After optimization, daily operational costs, peak-valley differences, and CO₂ emissions were reduced by 34.1 % to 81.6 %, 29.2 % to 36.7 %, and 19.6 % to 43.2 %, respectively. Moreover, the PV generation self-consumption rate and self-sufficiency rate improved by 29.6 % to 38.0 % and 40.5 % to 49.4 %, respectively. The proposed intelligent agent provides invaluable guidance for optimal dispatch of smart home energy systems in real-world settings.

This work examines the daily bidding problem of a grid-connected microgrid with locally deployed resources for electricity generation, storage and its own electricity demand. Trading electricity in energy markets may offer economic incentives but exposes the microgrid to financial risk caused by market commitments. Hence, a multi-objective, two-stage stochastic mixed integer linear programming (MILP) model is formulated, extending prior work of a risk-neutral microgrid bidding approach. The multi-objective model minimises both expected total cost of day-ahead microgrid operations and financial risk from bidding measured by conditional value-at-risk (CVaR). Bidding curves derived as first stage decisions are always feasible under present market rules – including a limitation on the number of break points per submitted curve – while being near optimal for the microgrid’s day-ahead recourse schedule. The proposed optimisation model is embedded in a variant of the $\varepsilon$-constrained method to generate bidding curve candidates with different trade-offs between the two conflicting objectives. Moreover, scenario reduction is used to compromise accuracy of the uncertainty set for better computational performance. Particularly, the marginal relative probability distance between initial and reduced scenario set is suggested to make a decision on the extent of scenario reduction. The proposed solution procedure is tested in a computational study to demonstrate its applicability to generate optimal microgrid bidding curve candidates with different emphasis between total cost and CVaR in reasonable computational time.

Ammonia, a reliable low-carbon alternative fuel with energy storage capabilities, has garnered increasing attention for its application of co-firing in coal-fired power plants as a strategy to mitigate direct carbon emissions. However, various types of ammonia production technologies result in diverse economic feasibility and emission intensities. Simultaneously, each stage, spanning from upstream processes such as raw material extraction to downstream applications, contributes to carbon emissions, which cannot be ignored. It is crucial to select the appropriate assessment method to determine the transformation pathways for co-firing systems. To this end, this review presents a comprehensive life cycle assessment of ammonia co-firing systems from a whole industrial chain perspective, encompassing the entire gamut of processes from fuel production and transportation to co-firing. Studies of the industrial chain perspective and of life cycle assessment methodology that are uniquely tailored for co-firing systems are presented. A nuanced exploration of distinct technologies across the spectrum of system processes ensues, including the advantages, limitations, and trends in advancement, based on carbon emissions and economic criteria. Considering the diverse fuel production, especially ammonia, typologies and intricate processes have undergone comprehensive review. The combustion characteristics, emissions, and economic factors associated with the co-firing process are systematically summarized, drawing upon aspects such as dynamics, experiments, simulations, and demonstration projects. This study illuminates the progression and technology selection of co-firing systems across multiple stages of the whole industry chain, thereby furnishing insights relevant to the low-carbon transformation of ammonia co-firing with coal in power plants.

As the demand for artificial intelligence (AI) models and applications continues to grow, data centers that handle AI workloads are experiencing a rise in energy consumption and associated carbon footprint. This work proposes a variational quantum computing-based robust optimization (VQC-RO) framework for control and energy management in large-scale data centers to address the computational challenges and overcome limitations of conventional model-based and model-free strategies. The VQC-RO framework integrates variational quantum circuits (VQCs) with classical optimization to enable efficient and uncertainty-aware control of energy systems in AI data centers. Quantum algorithms executed on noisy intermediate-scale quantum (NISQ) devices are used for value function estimation trained with Q-learning, leading to the formulation of a robust optimization problem with uncertain coefficients. The quantum computing-based robust control strategy is designed to address uncertainties associated with weather conditions and renewable energy generation while optimizing energy consumption in AI data centers. This work also outlines the computational experiments conducted at various AI data center locations in the United States to analyze the reduction in power consumption and carbon emission levels associated with the proposed quantum computing-based robust control framework. This work contributes a novel approach to energy-efficient and sustainable data center operation, promising to reduce carbon emissions and energy consumption in large-scale data centers handling AI workloads by 9.8 % and 12.5 %, respectively.

Wind power exhibits low controllability and is situated in dispersed geographical locations, presenting complex coupling and aggregation characteristics in both temporal and spatial dimensions. When large-scale wind power is integrated into the power grid, it will bring a significant technical challenge: the highly variable nature of wind power poses a threat to the safe and stable control of the power, frequency, and voltage in the power system. Simultaneously, the model predictive control (MPC) technology provides more opportunities for investigating control issues related to large-scale wind power integration in power systems. This paper provides a timely and systematic overview of the applications of MPC in the field of wind power for the first time, innovatively embedding MPC technology into multi-level (e.g., wind turbines, wind farms, wind power cluster, and power grids) and multi-objective (e.g., power, frequency, and voltage) control. Firstly, the basic concept and classification criteria of MPC are developed, and the available modeling methods in wind power are carefully compared. Secondly, the application scenarios of MPC in multi-level and multi-objective wind power control are summarized. Finally, how to use a variety of optimization algorithms to solve these models is discussed. Based on the broad review above, we summarize several key scientific issues related to predictive control and discuss the challenges and future development directions in detail. This paper details the role of MPC technology in multi-level and multi-objective control within the wind power sector, aiming to help engineers and scientists understand its substantial potential in wind power integration in power systems.

Hydrate-based CO₂ sequestration (HBCS) emerges as a promising solution to sequestrate CO₂ as solid hydrates for the benefit of reducing CO₂ concentration in the atmosphere. The natural conditions of high-pressure and low-temperature in marine seabed provide an ideal reservoir for CO₂ hydrate, enabling long-term sequestration. A significant challenge in the application of HBCS is the identification of an environmental-friendly promoter to enhance or tune CO₂ hydrate kinetics, which is intrinsically sluggish. In addition, the promoter identified should be effective in all CO₂ sequestration conditions, covering CO₂ injection as gas or liquid. In this study, we introduced sodium lignosulfonate (SL), a by-product from the papermaking industry, as an eco-friendly kinetic promoter for CO₂ hydrate formation. The impact of SL (0–3.0 wt.%) on the kinetics of CO₂ hydrate formation from gaseous and liquid CO₂ was systematically investigated. CO₂ hydrate morphology images were acquired for both gaseous and liquid CO₂ in the presence of SL for the explanation of the observed promotion effect. The promotion effect of SL on CO₂ hydrate formation is optimal at 1.0 wt.% with induction time reduced to 5.3 min and 21.1 min for gaseous and liquid CO₂, respectively. Moreover, CO₂ storage capacity increases by around two times at 1.0 wt.% SL, reaching 85.1 v/v and 57.1 v/v for gaseous and liquid CO₂, respectively. The applicability of SL as an effective kinetic promoter for both gaseous and liquid CO₂ was first demonstrated. A mechanism explaining how SL promotes CO₂ hydrate formation was formulated with additional nucleation sites by SL micelles and the extended contact surface offered by generated gas bubbles or liquid droplets with SL. The study demonstrates that SL as an effective promoter for CO₂ hydrate kinetics is possible for adoption in large-scale HBCS projects both nearshore and offshore.