Advances in Applied Energy最新文献

Renewable energy (RE) generation systems are rapidly being deployed on the grid. In parallel, electrified devices are quickly being added to the grid, introducing additional electric loads and increased load flexibility. While increased deployment of RE generation contributes to decarbonization of the grid, it is inherently variable and unpredictable, introducing uncertainty and potential instability in the grid. One way to mitigate this problem is to deploy utility-scale storage. However, in many cases the deployment of utility-scale battery storage systems remain unfeasible due to their cost. Instead, utilizing the increased amounts of data and flexibility from electrified devices on the grid, advanced control can be applied to shift the demand to match RE generation, significantly reducing the capacity of required utility-scale battery storage. This work introduces the novel forecast-aided predictive control (FAPC) algorithm to optimize this load shifting in the presence of forecasts. Extending upon an existing coordinated control framework, the FAPC algorithm introduces a new electric vehicle charging control algorithm that has the capability to incorporate forecasted information in its control loop. This enables FAPC to better track a realistic RE generation signal in a fully correlated simulation environment. Results show that FAPC effectively shifts demand to track a RE generation signal under different weather and operating conditions. It is found that FAPC significantly reduces the required capacity of the battery storage system compared to a baseline control case.

Electrification and distributed energy resources (DERs) are vital for reducing the building sector's carbon footprint. However, conventional reactive control is insufficient in addressing many current building-operation-related challenges, impeding building decarbonization. To reduce building carbon emissions, it is essential to consider dynamic grid electricity mix and incorporate the coordination between DERs and building energy systems in building control. This study develops a novel model predictive control (MPC)-based integrated energy management framework for buildings with multiple DERs considering dynamic grid electricity mix and pricing. A linear, integrated high-fidelity model encompassing adaptive thermal comfort, building thermodynamics, humidity, space conditioning, water heating, renewable energy, electric energy storage, and electric vehicle, is developed. An MPC controller is developed based on this model. To demonstrate the applicability, the developed framework is applied to a single-family home with an energy management system through whole-year simulations considering three climate zones: warm, mixed, and cold. In the simulations, the framework reduces the whole-building electricity costs and carbon emissions by 11.9% - 38.3% and 7.2% - 25.1%, respectively, compared to conventional control. Furthermore, the framework can reduce percent discomfort time from 25.7% - 47.4% to nearly 0%, compared to conventional control. The framework also can shift 86.4% - 100% of peak loads to off-peak periods, while conventional control cannot achieve such performance. The case study results also suggest that pursuing cost savings is possible in tandem with carbon emission reduction to achieve co-benefits (e.g., simultaneous 37.7% and 21.9% reductions in electricity costs and carbon emissions, respectively) with the proposed framework.

The energy transition is increasingly being discussed and implemented to cope with the growing environmental crisis. However, great challenges remain for effectively harvesting and utilizing solar energy in cities related to time and location-dependant supply and demand, which needs more accurate forecasting- and an in-depth understanding of the electricity production and dynamic balancing of the flexible energy supplies concerning the electricity market. To tackle this problem, this article discusses the development of solar cities over the past few decades and proposes a refined and enriched concept of a sustainable solar city with six integrated modules, namely, land surface solar irradiation, three-dimensional (3D) urban surfaces, spatiotemporal solar distribution on 3D urban surfaces, solar photovoltaic (PV) planning, solar PV penetration into different urban systems, and the consequent socio-economic and environmental impacts. In this context, Geographical Information Science (GIScience) demonstrates its potent capability in building the conceptualized solar city with a dynamic balance between power supply and demand over time and space, which includes the production of multi-sourced spatiotemporal big data, the development of spatiotemporal data modelling, analysing and optimization, and geo-visualization. To facilitate the development of such a solar city, this article from the GIScience perspective discusses the achievements and challenges of (i) the development of spatiotemporal big data used for solar farming, (ii) the estimation of solar PV potential on 3D urban surfaces, (iii) the penetration of distributed PV systems in digital cities that contains the effects of urban morphology on solar accessibility, optimization of PV systems for dynamic balancing between supply and demand, and PV penetration represented by the solar charging of urban mobility, and (iv) the interaction between PV systems and urban thermal environment. We suggest that GIScience is the foundation, while further development of GIS models is required to achieve the proposed sustainable solar city.

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.