Advances in Applied Energy最新文献

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.

The success of the energy transition relies on effectively utilizing flexibility in the power system. Dynamic tariffs are a highly discussed and promising innovation for incentivizing the use of residential flexibility. However, their full potential can only be realized if households achieve significant benefits. This paper specifically addresses this topic. We examine the leverage of household flexibility and the financial benefits of using dynamic tariffs, considering household heterogeneity, the costs of home energy management systems and smart meters, the impact of higher electricity prices and price spreads and the differences between types of prosumers. To comprehensively address this topic, we use the EVaTar-building model, a simulation framework that includes embedded optimization designed to simulate household electricity consumption patterns under the influence of a home energy management system or in response to dynamic tariffs. The study's main finding is that households can achieve significant cost savings and increase flexibility utilization by using a home energy management system and dynamic electricity tariffs, provided that electricity prices and price spreads reach higher levels. When comparing price levels in a low and high electricity price scenario, with an increase of the average electricity price by 15.2 €ct/kWh (67 % higher than the average for the year 2019) and an increase of the price spread by 8.9 €ct/kWh (494 % higher), the percentage of households achieving cost savings increases from 3.9 % to 62.5 %. Households with both an electric vehicle and a heat pump observed the highest cost benefits. Sufficiently high price incentives or sufficiently low costs for home energy management systems and metering point operation are required to enable households to mitigate rising electricity costs and ensure residential flexibility for the energy system through electric vehicles and heat pumps.

Air-conditioning systems have great potential to provide energy flexibility services to the power grids of high-renewable penetration, due to their high power consumption and inherent energy flexibilities. Direct load control by switching off some operating chillers is the simplest and effective means for air-conditioning systems in buildings to respond to urgent power reduction requests of power grids. However, the implementation of this approach in today's buildings, which widely adopt demand-based feedback controls, would result in serious problems including disordered cooling distribution and likely extra energy consumption. This study, therefore, proposes a reconfigurable control strategy to address these problems. This strategy consists of supply-based feedback control, incorporated with the conventional demand-based feedback control, a control loop reconfiguration scheme and a setpoint reset scheme, facilitating effective control under limited cooling supply and smooth transition between supply-based and demand-based feedback control modes. The proposed control strategy is deployed in a commonly-used digital controller to conduct hardware-in-the-loop control tests on an air-conditioning system involving six AHUs. Test results show that the reconfigurable control achieves commendable control performance. Proper chilled water distribution enables even thermal comfort control among the building zones during demand response and rebound periods. Temperature deviation of the building zones is controlled below 0.2 K most of the time. 11.6 % and 27 % of power demand reductions are achieved during demand response and rebound periods respectively, using the proposed reconfigurable control compared with that using conventional controls.

As the enthusiasm for electric vehicles passes the range anxiety and other tests, large-scale transportation electrification becomes a prominent topic in research and policy discussions. In consequence, the public attention has shifted upstream and holistically towards the integration of large-scale transportation electrification to power systems. This paper proposes a method to identify bottlenecks in power transmission systems to accommodate large-scale and stochastic electric vehicles charging demands. First, a distributionally robust chance-constrained direct current optimal power flow model is developed to quantify the impacts of stochastic electric vehicles charging demands. Subsequently, an agent-based model with the trip-chain method is applied to obtain the spatiotemporal distributions of electric vehicles charging demands for both light-duty electric vehicles and medium and heavy-duty electric vehicles. The first two moments of those distributions are extracted to build an ambiguity set of electric vehicles charging demands. Finally, a 121-bus synthetic transmission network for New York State power systems is used to validate the future transportation electrification in New York State from 2025 to 2035. Results show that the large-scale transportation electrification in New York State will account for approximately 13.33 % to 16.79 % of the total load demand by 2035. The transmission capacity is the major bottleneck in supporting New York State to achieve its transportation electrification. To resolve the bottlenecks, we explore some possible solutions, such as transmission capacity expansion and distributed energy resources investment. Wind power shows an advantage over solar energy in terms of total operational costs due to better peak alignment between wind power and electric vehicles charging demand.

The variability of solar photovoltaic (PV) power output, driven by rapidly changing cloud dynamics, hinders the transition to reliable renewable energy systems. Information on future sky conditions, especially cloud coverage, holds the promise for improving PV output forecasting. Leveraging recent advances in generative artificial intelligence (AI), we introduce SkyGPT, a physics-constrained stochastic video prediction model, which predicts plausible future images of the sky using historical sky images. We show that SkyGPT can accurately capture cloud dynamics, producing highly realistic and diverse future sky images. We further demonstrate its efficacy in 15-minute-ahead probabilistic PV output forecasting using real-world power generation data from a 30-kW rooftop PV system. By coupling SkyGPT with a U-Net-based PV power prediction model, we observe superior prediction reliability and sharpness compared with several benchmark methods. The propose approach achieves a continuous ranked probability score (CRPS) of 2.81 kW, outperforming a classic convolutional neural network (CNN) baseline by 13% and the smart persistence model by 23%. The findings of this research could aid efficient and resilient management of solar electricity generation, particularly as we transition to renewable-heavy grids. The study also provides valuable insights into stochastic cloud modeling for a broad research community, encompassing fields such as solar energy meteorology and atmospheric sciences.

Leveraging demand-side flexibility resources (e.g., buildings) is a crucial and cost-effective strategy for addressing the operational and infrastructure-related challenges in power grids pursuing deep decarbonization with high renewable energy penetration. However, the demand flexibility opportunities and financial benefits for residential space heating, which are sizeable demand-side flexibility resources, through emerging building energy management solutions (i.e., smart control and phased change material (PCM) thermal storage) across the US are not fully understood. In this paper, we systematically assess the demand flexibility and cost-saving/revenue potentials in residential space heating through detailed building-level simulations for five consecutive years at a 5-min temporal resolution in 20 metro areas across the high-heating-demand regions of the US. The results show a high degree of synergy between PCM thermal storage and smart control, which enables substantial demand flexibility potential, reaching 98.5% of peak load shifting, and electricity cost-saving/revenue potential, reaching 338.3% of electricity cost reductions, for residential space heating in the US. By achieving such performance, adopting smart control and PCM thermal storage is financially viable in 50% of the tested metro areas. The results reveal that the demand flexibility and cost-saving/revenue potentials of residential space heating in the US are further enhanced by higher volatilities in electricity prices. Active PCM thermal storage has lower energy efficiency but much higher energy flexibility than passive PCM thermal storage. Based on the findings, recommendations for integrating PCM thermal storage and smart control systems within residential space heating are provided.

A virtual power plant (VPP) refers to an active aggregator of heterogeneous distributed energy resources (DERs), which creates a promising pathway to expand renewable energy and demand-side electrification for deep decarbonization. The VPP market is projected to have a significant growth potential, with the global investment surging from $6.47 billion in 2022 to $16.90 billion by 2030. Up to now, VPPs still face technical challenges in dealing with the inherent uncertainty of DERs, and data emerge as a promising and essential resource to handle this issue. This paper makes the first effort to review the development of VPP technologies from a data-centric perspective, and then analyze the major role of data within every decision phase of VPPs. We examine the VPP energy management through a data lifecycle lens, and extensively survey the progress in data creation, data communication, data-driven decision support, data sharing and privacy, as well as technical solutions stemming from reinforcement learning, peer-to-peer sharing, blockchain, and market participation. In addition, we offer a unique overview of open data and recent real-world projects around the world to showcase the latest VPP practices. We finally discuss the major challenges and future opportunities in detail, with a focus on topics such as public benchmarks, internet of things, 5G, explainable artificial intelligence, and federated learning. We highlight the need for technical advances in data management and support systems for the growing scale of future VPP systems, and suggest VPPs delivering more ancillary grid services in the future.

Building energy flexibility plays a critical role in demand-side management for reducing utility costs for building owners and sustainable, reliable, and smart grids. Realizing building energy flexibility in tropical regions requires solar photovoltaics and energy storage systems. However, quantifying the energy flexibility of buildings utilizing such technologies in tropical regions has yet to be explored, and a robust control sequence is needed for this scenario. Hence, this work presents a case study to evaluate the building energy flexibility controls and operations of a net-zero energy office building in Singapore. The case study utilizes a data-driven energy flexibility quantification workflow and employs a novel data-driven model predictive control (MPC) framework based on the physically consistent neural network (PCNN) model to optimize the building energy flexibility. To the best of our knowledge, this is the first instance that PCNN is applied to a mathematical MPC setting, and the stability of the system is formally proved. Three scenarios are evaluated and compared: the default regulated flat tariff, a real-time pricing mechanism, and an on-site battery energy storage system (BESS). Our findings indicate that incorporating real-time pricing into the MPC framework could be more beneficial to leverage building energy flexibility for control decisions than the flat-rate approach. Moreover, adding BESS to the on-site PV generation improved the building self-sufficiency and the PV self-consumption by 17% and 20%, respectively. This integration also addresses model mismatch issues within the MPC framework, thus ensuring a more reliable local energy supply. Future research can leverage the proposed PCNN-MPC framework for different data-driven energy flexibility quantification types.