Advances in Applied Energy最新文献

This work evaluates the effectiveness of chemical-based solutions for storing large amounts of renewable electricity. Four “Power-to-X-to-Power” pathways are examined, comprising hydrogen, methane, methanol, and ammonia as energy carriers. The pathways are assessed using a model scenario, where they are produced with electricity from an onshore wind farm, stored in suitable facilities, and then reconverted to electricity to meet the energy demand of a chemical site. An energy management and storage capacity estimation tool is used to calculate the annual load coverage resulting from each pathway. All four pathways offer a significant increase in load coverage compared to a scenario without storage solution ($56.19\%$). The hydrogen-based pathway has the highest load coverage ($71.88\%$) and round-trip efficiency ($36.93\%),$ followed by the ammonia-based ($69.62\%,31.37\%$), methanol-based ($67.85\%,27.00\%),$ and methane-based ($67.64\%,26.47\%$, respectively) pathways. The substantially larger storage capacity required for gaseous energy carriers to ensure a steady supply to the consumer could be a decisive factor. The hydrogen pathway requires a storage volume up to 10.93 times larger than ammonia and 16.87 times larger than methanol. Notably, ammonia and methanol, whose load coverages are only 2.26 and 4.03 percentage points lower than that of hydrogen, offer the possibility of implementing site-specific storage solutions, avoiding potential bottlenecks due to limited pipeline and cavern capacities.

Big data has been advocated as a dominant driving force to unleash the great waves of the next-generation industrial revolution. While the ever-increasing proliferation of heterogeneous data contributes to a more sustainable energy system, considerable challenges remain for breaking down the barrier of data sharing across monopolistic sectors and fully exploiting data asset value in a trustworthy environment. Here, we focus on a global aspiration and interest regarding the challenges, techniques, and prospects of data sharing in energy systems. In this paper, a conceptual framework for data sharing is designed, in which we introduce the commodity attribute of data assets and explain the bottlenecks of data trading. Two critical issues, i.e., right confirmation and privacy protection, are then systematically reviewed, which provide a fundamental guarantee for credible data openness. A detailed data market is conceived by elaborating on market bids, data asset valuation and pricing strategy, and game-based clearing. Finally, we conduct a discussion about some low-hanging fruit of data sharing in energy systems.

Hybrid power plants, namely those consisting of variable renewable energy (VRE) generators and energy storage in the same location, are growing in popularity and interact differently with the electrical grid than either component would individually. We investigate plant-grid dynamics in highly congested regions to determine whether stand-alone VRE, stand-alone storage, and hybrid VRE-plus-storage plants will reduce or increase the need for nearby transmission. The focus on congested regions offers empirical insight into future grid conditions, as VRE penetration continues to grow. Near congested load centers, we find that hybrid, stand-alone VRE and stand-alone storage plants each reduce transmission value, defined in terms of production costs. On the other hand, in congested areas with high VRE penetration, stand-alone storage and VRE generators have opposing effects, decreasing and increasing the need for transmission, respectively. Importantly, whether or not a hybrid plant’s optimal operation increases or decreases local transmission value depends on the plant’s technological specifications (i.e., lowering degradation costs of battery cycling reduces transmission value) and regulatory environment (i.e., allowing a hybrid to utilize grid charging reduces transmission value). Therefore, technological advances in energy storage and policy decisions will influence which variation of these results are realized. We also assess the financial implications of transmission expansion on hybrid and stand-alone plants. In VRE-rich areas, we find that wind plants stand to gain significantly more from transmission expansion than do solar plants, with a typical energy market revenue increase equal to that from hybridizing with four hours worth of storage. Results are based on real-time nodal price data and location-specific solar and wind generation profiles for 2018–2021 at 23 existing wind and solar plant locations in the United States that experience congestion patterns representative of regions with either high VRE penetration or high demand.

Blockchain technologies are considered one of the most disruptive innovations of the last decade, enabling secure decentralized trust-building. However, in recent years, with the rapid increase in the energy consumption of blockchain-based computations for cryptocurrency mining, there have been growing concerns about their sustainable operation in electric grids. This paper investigates the tri-factor impact of such large loads on carbon footprint, grid reliability, and electricity market price in the Texas grid. We release open-source high-resolution data to enable high-resolution modeling of influencing factors such as location and flexibility. We reveal that the per-megawatt-hour carbon footprint of cryptocurrency mining loads across locations can vary by as much as 50% of the crude system average estimate. We show that the flexibility of mining loads can significantly mitigate power shortages and market disruptions that can result from the deployment of mining loads. These findings suggest policymakers to facilitate the participation of large mining facilities in wholesale markets and require them to provide mandatory demand response.

Electrical energy is essential in today's society. Accurate electrical load forecasting is beneficial for better scheduling of electricity generation and saving electrical energy. In this paper, we propose an adaptive deep-learning load forecasting framework by integrating Transformer and domain knowledge (Adaptive-TgDLF). Adaptive-TgDLF introduces the deep-learning model Transformer and adaptive learning methods (including transfer learning for different locations and online learning for different time periods), which captures the long-term dependency of the load series, and is more appropriate for realistic scenarios with scarce samples and variable data distributions. Under the theory-guided framework, the electrical load is divided into dimensionless trends and local fluctuations. The dimensionless trends are considered as the inherent pattern of the load, and the local fluctuations are considered to be determined by the external driving forces. Adaptive learning can cope with the change of load in location and time, and can make full use of load data at different locations and times to train a more efficient model. Cross-validation experiments on different districts show that Adaptive-TgDLF is approximately 16% more accurate than the previous TgDLF model and saves more than half of the training time. Adaptive-TgDLF with 50% weather noise has the same accuracy as the previous TgDLF model without noise, which proves its robustness. We also preliminarily mine the interpretability of Transformer in Adaptive-TgDLF, which may provide future potential for better theory guidance. Furthermore, experiments demonstrate that transfer learning can accelerate convergence of the model in half the number of training epochs and achieve better performance, and online learning enables the model to achieve better results on the changing load.

Due to their thermal inertia, buildings equipped with electric heating and cooling systems can help to stabilize the electricity grid by shifting their load in time, and can thus facilitate energy flexible urban energy systems with the right control system in place. Because of minimum capacity requirements, they can often only participate in demand response schemes, such as secondary frequency reserves through aggregation. Such an aggregation could also take the form of entire district heating and cooling systems with connected buildings that are supplied by large-scale heat pumps and chillers. However, there is a lack of studies investigating the control of such configurations, both in simulation and in application. We present a two-level control scheme based on robust Model Predictive Control with affine policies to offer frequency reserves with a district system, where we exploit the thermal inertia of buffer storage tanks and a subset of the connected buildings. We leverage data-driven model generation methods to overcome the bottleneck of physics-based building modeling. In a numerical case study based on one-year historical data of a real system, we compare the approach to a situation where only the buffer storage is used for flexibility and demonstrate that the reserves offered increase substantially if the inertia of a subset of the connected buildings is also exploited. Furthermore, we validate the control approach in a first-of-its-kind experiment on the actual system, where we show that reserves can be offered by the district system without compromising the comfort in the connected buildings.

Microgrid provides a promising solution to consume more distributed renewable energies. To coordinate the increasingly developed distributed renewable generators in a high flexibility and high efficiency way, distributed event-triggered mechanisms have been widely investigated in the literature to reduce the communication requirement and hence improve the control performance of microgrids. However, most of the event-triggered mechanisms mandate continuous calculation of complicated triggering conditions, which may in turn impose the computation burden of the controller and increase additional energy cost. To this end, this paper presents a distributed self-triggered control strategy for the frequency restoration in islanded microgrids with the aid of a linear clock. With this self-triggered solution, each distributed generator’s controller decides its own control and communication actions based on monitoring the linear clock, which excludes continuous calculation of any triggering conditions. Thus, the communication and computation costs can be reduced simultaneously. Moreover, Zeno behavior can be naturally excluded by the above design. The results of theoretical analysis and simulations show that the proposed distributed self-triggered control scheme can effectively coordinate distributed renewable generators with very low communication and computation requirements. Therefore, this research can improve the coordination efficiency of microgrids greatly, which is very useful for guiding the efficient operation of large-scale distributed renewable generators.

The clean energy transition plays an essential role in achieving climate mitigation targets. As for the transportation sector, battery and fuel cell electric vehicles (EVs) have emerged as a key solution to reduce greenhouse gasses from transportation emissions. However, the rapid uptake of EVs has triggered potential supply risks of critical metals (e.g., lithium, nickel, cobalt, platinum group metals (PGMs), etc.) used in the production of lithium-ion batteries and fuel cells. Material flow analysis (MFA) has been widely applied to assess the demand for critical metals used in transportation electrification on various spatiotemporal scales. This paper presents a quantitative review and analysis of 78 MFA research articles on the critical metal requirement of transportation electrification. We analyzed the characteristics of the selected studies regarding their geographical and temporal scopes, transportation sectors, EV categories, battery technologies, materials, and modeling approaches. Based on the global forecasts in those studies, we compared the annual and cumulative global requirements of the four metals that received the most attention: lithium, nickel, cobalt, and PGMs. Although major uncertainties exist, most studies indicate that the annual demand for these four metals will continue to increase and far exceed their production capacities in 2021. Global reserves of these metals may meet their cumulative demand in the short-term (2020–2030) and medium-term (2020–2050) but are insufficient for the long-term (2020–2100) needs. Then, we summarized the proposed policy implications in these studies. Finally, we discuss the main findings from the four aspects: environmental and social implications of deploying electric vehicles, whether or not to electrify heavy-duty vehicles, opportunities and challenges in recycling, and future research direction.

As greenhouses are being widely adopted worldwide, it is important to improve the energy efficiency of the control systems while accurately regulating their indoor climate to realize sustainable agricultural practices for food production. In this work, we propose an artificial intelligence (AI)-based control framework that combines deep reinforcement learning techniques to generate insights into greenhouse operation combined with robust optimization to produce energy-efficient controls by hedging against associated uncertainties. The proposed control strategy is capable of learning from historical greenhouse climate trajectories while adapting to current climatic conditions and disturbances like time-varying crop growth and outdoor weather. We evaluate the performance of the proposed AI-based control strategy against state-of-the-art model-based and model-free approaches like certainty-equivalent model predictive control, robust model predictive control (RMPC), and deep deterministic policy gradient. Based on the computational results obtained for the tomato crop's greenhouse climate control case study, the proposed control technique demonstrates a significant reduction in energy consumption of 57% over traditional control techniques. The AI-based control framework also produces robust controls that are not overly conservative, with an improvement in deviation from setpoints of over 26.8% as compared to the baseline control approach RMPC.