Advances in Applied Energy最新文献

Transportation electrification plays a crucial role in mitigating greenhouse gas (GHG) emissions and enabling the decarbonization of power systems. However, current research on electric vehicles (EVs) only provides a fragmented examination of their impact on power system planning and operation, lacking a comprehensive overview across both transmission and distribution levels. This limits the effectiveness and efficiency of power system solutions for greater EV adoption. Conducting a systematic review of the effects of EVs on power transmission and distribution systems (e.g., grid integration, planning, operation, etc.), this paper aims to bridge the fragmented literature on the topic together by focusing on the interplay between transportation electrification and power systems. The study sheds light on the interplay between transportation electrification and power systems, delving into the importance of classifying EVs and charging infrastructure based on powertrain design, duty cycle, and typical features, as well as methods of capturing charging patterns and determining spatial-temporal charging profiles. Furthermore, we provide an in-depth discussion on the benefits of smart charging and the provision of grid-to-vehicle (G2V) and vehicle-to-grid (V2G) services for maintaining power system reliability. With the holistic systems approach, this paper can identify the main objectives and potential barriers of power transmission and distribution systems in accommodating transportation electrification at scale. Concurrently, it paves the way for a comprehensive understanding of technological innovation, transportation-power system decarbonization, policy pathways, environmental advantages, scenario designs, and avenues for future research.

In man-made energy systems like the electricity system, new concepts have the potential to influence and shape the development of the system. Sometimes the influence leads to a positive development and in other cases the new concept may lead into disadvantageous pathways. In this paper we argue that when a new concept is introduced, it may give rise to an unhinged paradox. An unhinged paradox implies that introducing a new concept, such as a new governance or management model, might lead to unintended consequences where some parts or the whole system become more unstable, or less resilient or unhinged. The transition of energy systems includes many “wicked” problems, i.e., aspects that are difficult to foresee the outcome of. The need for a rapid transition with an urgent need to implement new concepts together with a lack of or delayed feedback loops may give rise to wicked problems and unhinged systems. This unhinged paradox is likely to be found even beyond the scope of energy systems and will be further discussed in this paper in relation to the deregulation of the energy market, improved energy efficiency and energy flexibility.

To meet the European Union's 2050 climate neutrality target, future electricity generation is expected to largely rely on variable renewable energy (VRE). VRE supply, being dependant on weather, is susceptible to changing climate conditions. Based on spatiotemporally explicit climate data under a Paris-proof climate scenario and a comprehensive energy conversion model, this study assesses the projected changes of European VRE supply from the perspective of average production, production variability, spatiotemporal complementarity, and risk of concurrent renewable energy droughts.

For the period 2045–2055, we find a minor reduction in average wind and solar production for most of Europe compared to the period 1990–2010. At the country level, the impact of climate change on average VRE production is rather limited in magnitude (within $\pm 3\%$ for wind and $\pm 2\%$ for solar). The projected mid-term changes in other aspects of VRE supply are also relatively small. This suggests climate-related impacts on European VRE supply are less of a concern if the Paris-proof emission reduction pathway is strictly followed.

Based on spectral analysis, we identify strong seasonal wind-solar complementarities (with an anticorrelation between -0.6 and -0.9) at the cross-regional level. This reduces the demand for seasonal storage but requires coordinated cross-border efforts to develop a pan-European transmission infrastructure.

The risk of concurrent renewable energy droughts between a country and the rest of Europe remains non-negligible, even under the copperplate assumption. Central Western European countries and Poland are most vulnerable to such risk, suggesting the need for the planning of adequate flexibility resources.

This paper analyzes the operation of an energy hub on a community level with an integrated P2X facility and with access to energy markets. In our case, P2X allows converting power to hydrogen, heat, methane, or back to power. We consider the energy hub as a large prosumer who can be both a producer and consumer in the markets with the novelty that P2X technology is available. We investigate how such a P2X energy hub trades optimally in the electricity market and satisfies local energy demand under the assumption of a long-term strong climate scenario in year 2050. For numerical analysis, a case study of a mountain village in Switzerland is used. One of the main contributions of this paper is to quantify key conditions for profitable operations of such a P2X energy hub. In particular, the analysis includes impacts of influencing factors on profits and operational patterns in terms of different degrees of self-sufficiency and different availability of local renewable resources. Moreover, the access to real-time wholesale market electricity price signals and a future retail hydrogen market is assessed. The key factors for the successful operation of a P2X energy hub are identified to be sufficient local renewable resources and access to a retail market of hydrogen. The results also show that the P2X operation leads to an increased deployment of local renewables, especially in the case of low initial deployment; on the other hand, seasonal storage plays a subordinated role. Additionally, P2X lowers for the community the wholesale electricity market trading volumes.

Urban Building Energy Modeling (UBEM) provides a framework for decarbonization decision-making on an urban scale. However, existing UBEM systems routinely neglect microclimate effects on building energy consumption, potentially leading to major sources of error. In this work, we attempt to address these sources of error by proposing the large scale collection of remote sensing and climate modeling data to improve the capabilities of existing systems. We explore situations when remote sensing might be most valuable, particularly when high quality weather station data might not be available. We show that lack of access to weather station data is unlikely to be driving existing errors in energy models, as most buildings are likely to be close enough to collect high quality data. We also highlight the significance of Landsat8’s thermal instrumentation to capture pertinent temperatures for the buildings through feature importance visualizations. Our analysis then characterizes the seasonal benefits of microclimate data for energy prediction. Landsat8 is found to provide a potential benefit of an 8% reduction in electricity prediction error in the spring and summertime of New York City. In contrast, NOAA RTMA may provide a benefit of a 2.5% reduction in natural gas prediction error in the winter and spring. Finally, we explore the potential of remote sensing to enhance the quality of energy predictions at a neighborhood level. We show that benefits for individual buildings translates to the regional level, as we can achieve improved predictions for groups of buildings.

As one of the most important and advanced technology for carbon-mitigation in the building sector, building performance simulation (BPS) has played an increasingly important role with the powerful support of building energy modelling (BEM) technology for energy-efficient designs, operations, and retrofitting of buildings. Owing to its deep integration of multi-disciplinary approaches, the researchers, as well as tool developers and practitioners, are facing opportunities and challenges during the application of BEM at multiple scales and stages, e.g., building/system/community levels and planning/design/operation stages. By reviewing recent studies, this paper aims to provide a clear picture of how BEM performs in solving different research questions on varied scales of building phase and spatial resolution, with a focus on the objectives and frameworks, modelling methods and tools, applicability and transferability. To guide future applications of BEM for performance-driven building energy management, we classified the current research trends and future research opportunities into five topics that span through different stages and levels: (1) Simulation for performance-driven design for new building and retrofit design, (2) Model-based operational performance optimization, (3) Integrated simulation using data measurements for digital twin, (4) Building simulation supporting urban energy planning, and (5) Modelling of building-to-grid interaction for demand response. Additionally, future research recommendations are discussed, covering potential applications of BEM through integration with occupancy and behaviour modelling, integration with machine learning, quantification of model uncertainties, and linking to building monitoring systems.

The progression of electrification in the building and transportation sectors brings new opportunities for energy decarbonization. With higher dependence on the grid power supply, the variation of the grid carbon emission intensity can be utilized to reduce the carbon emissions from the two sectors. Existing coordinated control methods for buildings with distributed energy resources (DERs) either consider electricity price or renewable energy generation as the input signal, or adopt optimization in the decision-making, which is difficult to implement in the real-world environment. This paper aims to propose and validate an easy-to-deploy rule-based carbon responsive control framework that facilitates coordination between all-electric buildings and electric vehicles (EVs). The signals of the grid carbon emission intensity and the local photovoltaics (PV) generation are used for shifting the controllable loads. Extensive simulations were conducted using a model of an all-electric mixed-use community in a cold climate to validate the control performance with metrics such as emissions, energy consumption, peak demand, and EV end-of-day state-of-charge (SOC). Our study identifies that 4.5% to 27.1% of annual emission reduction can be achieved with limited impact on energy costs, peak demand, and thermal comfort. Additionally, up to 32.7% of EV emission reduction can be obtained if the EV owners reduce the target SOC by less than 21.2%.

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.