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Interpretable machine learning for building energy management: A state-of-the-art review 用于建筑能源管理的可解释机器学习:最新综述
Q1 ENERGY & FUELS Pub Date : 2023-02-01 DOI: 10.1016/j.adapen.2023.100123
Zhe Chen , Fu Xiao , Fangzhou Guo , Jinyue Yan

Machine learning has been widely adopted for improving building energy efficiency and flexibility in the past decade owing to the ever-increasing availability of massive building operational data. However, it is challenging for end-users to understand and trust machine learning models because of their black-box nature. To this end, the interpretability of machine learning models has attracted increasing attention in recent studies because it helps users understand the decisions made by these models. This article reviews previous studies that adopted interpretable machine learning techniques for building energy management to analyze how model interpretability is improved. First, the studies are categorized according to the application stages of interpretable machine learning techniques: ante-hoc and post-hoc approaches. Then, the studies are analyzed in detail according to specific techniques with critical comparisons. Through the review, we find that the broad application of interpretable machine learning in building energy management faces the following significant challenges: (1) different terminologies are used to describe model interpretability which could cause confusion, (2) performance of interpretable ML in different tasks is difficult to compare, and (3) current prevalent techniques such as SHAP and LIME can only provide limited interpretability. Finally, we discuss the future R&D needs for improving the interpretability of black-box models that could be significant to accelerate the application of machine learning for building energy management.

在过去十年中,由于大量建筑运营数据的可用性不断增加,机器学习已被广泛用于提高建筑能源效率和灵活性。然而,由于机器学习模型的黑箱性质,最终用户理解和信任机器学习模型是具有挑战性的。为此,机器学习模型的可解释性在最近的研究中引起了越来越多的关注,因为它可以帮助用户理解这些模型做出的决策。本文回顾了以前采用可解释机器学习技术进行建筑能源管理的研究,以分析如何提高模型的可解释性。首先,这些研究根据可解释机器学习技术的应用阶段进行了分类:事前和事后方法。然后,根据具体技术对研究进行了详细分析,并进行了批判性比较。通过回顾,我们发现可解释机器学习在建筑能源管理中的广泛应用面临以下重大挑战:(1)使用不同的术语来描述模型的可解释性,这可能会导致混乱;(2)可解释机器学习在不同任务中的性能难以比较;(3)目前流行的技术,如SHAP和LIME,只能提供有限的可解释性。最后,我们讨论了提高黑箱模型可解释性的未来研发需求,这对于加速机器学习在建筑能源管理中的应用可能具有重要意义。
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引用次数: 39
Operation of a circular economy, energy, environmental system at a wastewater treatment plant 废水处理厂循环经济、能源和环境系统的运行
Q1 ENERGY & FUELS Pub Date : 2022-12-01 DOI: 10.1016/j.adapen.2022.100109
Davis Rusmanis , Yan Yang , Richen Lin , David M. Wall , Jerry D. Murphy

Decarbonising economies and improving environment can be enhanced through circular economy, energy, and environmental systems integrating electricity, water, and gas utilities. Hydrogen production can facilitate intermittent renewable electricity through reduced curtailment of electricity in periods of over production. Positioning an electrolyser at a wastewater treatment plant with existing sludge digesters offers significant advantages over stand-alone facilities. This paper proposes co-locating electrolysis and biological methanation technologies at a wastewater treatment plant. Electrolysis can produce oxygen for use in pure or enhanced oxygen aeration, offering a 40% reduction in emissions and power demand at the treatment facility. The hydrogen may be used in a novel biological methanation system, upgrading carbon dioxide (CO2) in biogas from sludge digestion, yielding a 54% increase in biomethane production. A 10 MW electrolyser operating at 80% capacity would be capable of supplying the oxygen demand for a 426,400 population equivalent wastewater treatment plant, producing 8,500 tonnes dry solids per annum (tDS/a) of sludge. Digesting the sludge could generate 1,409,000 m3CH4/a and 776,000 m3CO2/a. Upgrading the CO2 to methane would consume 22.2% of the electrolyser generated hydrogen and capture 1.534 ktCO2e/a. Hydrogen and methane are viable advanced transport fuels that can be utilised in decarbonising heavy transport. In the proposed circular economy, energy, and environment system, sufficient fuel would be generated annually for 94 compressed biomethane gas (CBG) heavy goods vehicles (HGV) and 296 compressed hydrogen gas fuel cell (CHG) HGVs. Replacement of the equivalent number of diesel HGVs would offset approximately 16.1 ktCO2e/a.

通过整合电力、水和燃气公用事业的循环经济、能源和环境系统,可以促进经济脱碳和环境改善。氢气生产可以通过减少在生产过剩期间的电力削减来促进间歇性可再生电力。与独立设施相比,将电解槽与现有的污泥消化池一起放置在污水处理厂具有显著的优势。提出了在某污水处理厂电解与生物甲烷化共存的工艺方案。电解可以产生用于纯氧或强化氧曝气的氧气,使处理设施的排放和电力需求减少40%。氢气可以用于一种新型的生物甲烷化系统,升级污泥消化产生的沼气中的二氧化碳(CO2),使生物甲烷产量增加54%。一个10兆瓦的电解槽以80%的产能运行,将能够为一个426,400人口的污水处理厂提供氧气需求,每年产生8,500吨干固体污泥(tDS/ A)。污泥的消化可产生1,409,000 m3CH4/a和776,000 m3CO2/a。将二氧化碳升级为甲烷将消耗22.2%的电解槽产生的氢气,并捕获1.534千吨二氧化碳当量/年。氢和甲烷是可行的先进运输燃料,可用于重型运输脱碳。在提出的循环经济、能源和环境系统中,每年将为94辆压缩生物甲烷(CBG)重型货车(HGV)和296辆压缩氢气燃料电池(CHG)重型货车(HGV)产生足够的燃料。更换同等数量的柴油hgv将抵消大约16.1千吨二氧化碳当量/年。
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引用次数: 4
A semantic ontology for representing and quantifying energy flexibility of buildings 一种用于表示和量化建筑物能量灵活性的语义本体
Q1 ENERGY & FUELS Pub Date : 2022-12-01 DOI: 10.1016/j.adapen.2022.100113
Han Li, Tianzhen Hong

Energy flexibility of buildings can be an essential resource for a sustainable and reliable power grid with the growing variable renewable energy shares and the trend to electrify and decarbonize buildings. Traditional demand-side management technologies, advanced building controls, and emerging distributed energy resources (including electric vehicle, energy storage, and on-site power generation) enable the transition of the building stock to grid-interactive efficient buildings (GEBs) that operate efficiently to meet service needs and are responsive to grid pricing or carbon signals to achieve energy and carbon neutrality. Although energy flexibility has received growing attention from industry and the research community, there remains a lack of common ground for energy flexibility terminologies, characterization, and quantification methods. This paper presents a semantic ontology—EFOnt (Energy Flexibility Ontology)—that extends existing terminologies, ontologies, and schemas for building energy flexibility applications. EFOnt aims to serve as a standardized tool for knowledge co-development and streamlining energy flexibility related applications. We demonstrate potential use cases of EFOnt via two examples: (1) energy flexibility analytics with measured data from a residential smart thermostat dataset and a commercial building, and (2) modeling and simulation to evaluate energy flexibility of buildings. The compatibility of EFOnt with existing ontologies and the outlook of EFOnt's role in the building energy data tool ecosystem are discussed.

随着可再生能源份额的不断增长以及建筑电气化和脱碳的趋势,建筑的能源灵活性可以成为可持续和可靠电网的重要资源。传统的需求侧管理技术、先进的建筑控制和新兴的分布式能源(包括电动汽车、能源存储和现场发电)使建筑存量能够向电网互动高效建筑(geb)过渡,这些建筑能够有效地满足服务需求,并对电网定价或碳信号做出反应,以实现能源和碳中和。尽管能源灵活性已经受到工业界和研究界越来越多的关注,但能源灵活性的术语、表征和量化方法仍然缺乏共同点。本文提出了一个语义本体——efont(能源灵活性本体),它扩展了建筑能源灵活性应用的现有术语、本体和模式。EFOnt旨在作为知识共同开发和简化能源灵活性相关应用的标准化工具。我们通过两个例子展示了EFOnt的潜在用例:(1)使用来自住宅智能恒温器数据集和商业建筑的测量数据进行能源灵活性分析,以及(2)建模和仿真以评估建筑物的能源灵活性。讨论了EFOnt与现有本体的兼容性以及EFOnt在建筑能源数据工具生态系统中的作用。
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引用次数: 15
Using crowdsourced data to estimate the carbon footprints of global cities 利用众包数据估算全球城市的碳足迹
Q1 ENERGY & FUELS Pub Date : 2022-12-01 DOI: 10.1016/j.adapen.2022.100111
Xinlu Sun , Zhifu Mi , Andrew Sudmant , D'Maris Coffman , Pu Yang , Richard Wood

Cities are at the forefront of the battle against climate change. However, intercity comparisons and responsibility allocations among cities are hindered because cost- and time-effective methods to calculate the carbon footprints of global cities have yet to be developed. Here, we establish a hybrid method integrating top-down input–output analysis and bottom-up crowdsourced data to estimate the carbon footprints of global cities. Using city purchasing power as the main predictor of the carbon footprint, we estimate the carbon footprints of 465 global cities in 2020. Those cities comprise 10% of the global population but account for 18% of the global carbon emissions showing a significant concentration of carbon emissions. The Gini coefficients are applied to show that global carbon inequality is less than income inequality. In addition, the increased carbon emissions that come from high consumption lifestyles offset the carbon reduction by efficiency gains that could result from compact city design and large city scale. Large climate benefits could be obtained by achieving a low-carbon transition in a small number of global cities, emphasizing the need for leadership from globally important urban centres.

城市处于对抗气候变化的最前线。然而,城市间的比较和责任分配受到阻碍,因为计算全球城市碳足迹的成本和时间效益的方法尚未开发出来。本文建立了自上而下的投入产出分析与自下而上的众包数据相结合的全球城市碳足迹估算方法。利用城市购买力作为碳足迹的主要预测指标,我们估计了2020年全球465个城市的碳足迹。这些城市占全球人口的10%,但占全球碳排放量的18%,显示出碳排放的显著集中。运用基尼系数表明全球碳不平等小于收入不平等。此外,高消费生活方式带来的碳排放增加抵消了紧凑城市设计和大型城市规模可能带来的效率提高所带来的碳减排。通过在少数全球城市实现低碳转型,可以获得巨大的气候效益,强调需要全球重要城市中心发挥领导作用。
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引用次数: 12
Moving toward the low-carbon hydrogen economy: Experiences and key learnings from national case studies 向低碳氢经济迈进:国家案例研究的经验和关键学习
Q1 ENERGY & FUELS Pub Date : 2022-12-01 DOI: 10.1016/j.adapen.2022.100108
Gunhild A. Reigstad , Simon Roussanaly , Julian Straus , Rahul Anantharaman , Robert de Kler , Maxine Akhurst , Nixon Sunny , Ward Goldthorpe , Lionel Avignon , Jonathan Pearce , Stefan Flamme , Gianfranco Guidati , Evangelos Panos , Christian Bauer

The urgency to achieve net-zero carbon dioxide (CO2) emissions by 2050, as first presented by the IPCC special report on 1.5 °C Global Warming, has spurred renewed interest in hydrogen, to complement electrification, for widespread decarbonization of the economy. We present reflections on estimates of future hydrogen demand, optimization of infrastructure for hydrogen production, transport and storage, development of viable business cases, and environmental impact evaluations using life cycle assessments. We highlight challenges and opportunities that are common across studies of the business cases for hydrogen in Germany, the UK, the Netherlands, Switzerland and Norway. The use of hydrogen in the industrial sector is an important driver and could incentivise large-scale hydrogen value chains. In the long-term hydrogen becomes important also for the transport sector. Hydrogen production from natural gas with capture and permanent storage of the produced CO2 (CCS) enables large-scale hydrogen production in the intermediate future and is complementary to hydrogen from renewable power. Furthermore, timely establishment of hydrogen and CO2 infrastructures serves as an anchor to support the deployment of carbon dioxide removal technologies, such as direct air carbon capture and storage (DACCS) and biohydrogen production with CCS. Significant public support is needed to ensure coordinated planning, governance, and the establishment of supportive regulatory frameworks which foster the growth of hydrogen markets.

IPCC关于1.5°C全球变暖的特别报告首次提出了到2050年实现二氧化碳净零排放的紧迫性,这激发了人们对氢的新兴趣,以补充电气化,实现经济的广泛脱碳。我们提出了对未来氢需求的估计,氢生产、运输和储存基础设施的优化,可行商业案例的开发以及使用生命周期评估进行环境影响评估的思考。我们强调了在德国、英国、荷兰、瑞士和挪威的氢商业案例研究中共同面临的挑战和机遇。氢在工业部门的使用是一个重要的驱动因素,可以激励大规模的氢价值链。从长远来看,氢对运输部门也很重要。通过捕获和永久储存产生的二氧化碳(CCS)从天然气中制氢,可以在不久的将来大规模制氢,并与可再生能源制氢形成互补。此外,及时建立氢气和二氧化碳基础设施可以作为支持二氧化碳去除技术部署的基础,例如直接空气碳捕获和储存(DACCS)和利用CCS生产生物氢。需要大量的公众支持,以确保协调规划、治理和建立支持性监管框架,促进氢市场的增长。
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引用次数: 11
Why we must move beyond LCOE for renewable energy design 为什么我们必须超越LCOE进行可再生能源设计
Q1 ENERGY & FUELS Pub Date : 2022-12-01 DOI: 10.1016/j.adapen.2022.100112
Eric Loth , Chris Qin , Juliet G. Simpson , Katherine Dykes

The inherent intermittency of wind and solar energy challenges the relevance of Levelized Cost of Energy (LCOE) for their future design since LCOE neglects the time-varying price of electricity. The Cost of Valued Energy (COVE) is an improved valuation metric that takes into account time-dependent electricity prices. In particular, it integrates short-term (e.g., hourly) wind and solar energy “generation devaluation”, whereby high wind and/or solar energy generation can lead to low, and even negative, energy prices for grids with high renewable penetration. These aspects are demonstrated and quantified with examples of two large grids with high renewable shares using three approaches to model hourly price: (1) residual demand, (2) wind and solar generation, and (3) statistical price-generation correlation. All three approaches indicate significant generation devaluation. The residual demand approach provides the most accurate price information while statistical correlations show that generation devaluation is most pronounced for the Variable Renewable Energy (VRE) that dominates market share (e.g., solar for California and wind for Germany). In some cases, the cost of valued energy relative to levelized cost can be 43% higher for solar (CAISO) and 129% higher for wind (ERCOT). This indicates that COVE is a much more relevant metric than LCOE in such markets. This is because COVE is based on the annualized system costs relative to the annualized spot market revenue, and thus considers economic effects of costs vs. revenue as well as those of supply vs. demand. As such, COVE (instead of LCOE) is recommended to design and value next-generation renewable energy systems, including storage integration tradeoffs. However, more work is needed to develop generation devaluation models for projected grids and markets and to better classify grid characteristics as we head to a carbon-neutral energy future.

风能和太阳能固有的间歇性挑战了能源平准化成本(LCOE)与未来设计的相关性,因为LCOE忽略了电力的时变价格。有价值能源成本(COVE)是一种改进的评估指标,它考虑了与时间相关的电价。特别是,它将短期(例如,每小时)风能和太阳能“发电贬值”结合起来,因此,风能和/或太阳能的高发电量可以导致可再生能源渗透率高的电网的能源价格较低,甚至为负。这些方面通过两个具有高可再生能源份额的大型电网的例子进行了演示和量化,使用三种方法来模拟小时价格:(1)剩余需求,(2)风能和太阳能发电,以及(3)统计价格-发电相关性。所有这三种方法都表明了显著的代际贬值。剩余需求方法提供了最准确的价格信息,而统计相关性显示,占市场份额的可变可再生能源(VRE)的发电贬值最为明显(例如,加州的太阳能和德国的风能)。在某些情况下,太阳能(CAISO)和风能(ERCOT)的价值能源成本相对于平准化成本可能高出43%和129%。这表明在这样的市场中,COVE是一个比LCOE更相关的指标。这是因为COVE是基于年化系统成本相对于年化现货市场收入,因此考虑了成本与收入的经济影响,以及供应与需求的经济影响。因此,COVE(而不是LCOE)被推荐用于设计和评估下一代可再生能源系统,包括存储集成权衡。然而,在我们迈向碳中和能源未来的过程中,需要做更多的工作来为预计的电网和市场开发发电量贬值模型,并更好地对电网特征进行分类。
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引用次数: 16
Defining and applying an electricity demand flexibility benchmarking metrics framework for grid-interactive efficient commercial buildings 为电网互动高效商业建筑定义和应用电力需求灵活性基准指标框架
Q1 ENERGY & FUELS Pub Date : 2022-12-01 DOI: 10.1016/j.adapen.2022.100107
Jingjing Liu , Rongxin Yin , Lili Yu , Mary Ann Piette , Marco Pritoni , Armando Casillas , Jiarong Xie , Tianzhen Hong , Monica Neukomm , Peter Schwartz

Building demand flexibility (DF) research has recently gained attention. To unlock building DF as a predictable grid resource, we must establish a quantitative understanding of the resource size, performance variability, and predictability based on large empirical datasets. Researchers have proposed various sets of theoretical metrics to measure this performance. Some metrics have been applied to simulation results, but most fall short of exploring the complexities in real building applications. There are practical metrics used in individual demand response field studies but they alone cannot fulfil the job of DF benchmarking across a diverse group of buildings. The electrical grid's geographically diverse and changing nature presents challenges to comparing building DF performance measured under different conditions (i.e., benchmarking DF). To address this challenge, a novel DF benchmarking framework focused on load shedding and shifting is presented; the foundation is a set of simple, proven single-event metrics with attributes describing event conditions. These enable benchmarking and visualization in different dimensions for identifying trends that represent how these attributes influence DF. To test its feasibility and scalability, the DF framework was applied to two case studies of 11 office buildings and 121 big-box retail buildings with demand response participation data. These examples provided a pathway for using both building level benchmarking and aggregation to extract insights into building DF about magnitude, consistency, and influential factors. Potential applications of the framework and real-world values have been identified for grid and building stakeholders.

建筑需求灵活性(DF)的研究近年来引起了人们的关注。为了将DF构建为可预测的网格资源,我们必须建立对资源大小、性能可变性和基于大型经验数据集的可预测性的定量理解。研究人员提出了各种理论指标来衡量这种表现。一些指标已经应用于模拟结果,但大多数都没有探索实际建筑应用中的复杂性。个别需求响应现场研究中使用了一些实用指标,但仅凭这些指标无法完成对不同建筑群体进行DF基准测试的工作。电网的地理多样性和不断变化的性质给比较在不同条件下测量的建筑DF性能(即基准DF)带来了挑战。为了解决这一挑战,提出了一种新颖的DF基准测试框架,重点关注减载和转移;基础是一组简单的、经过验证的单事件度量,其中包含描述事件条件的属性。这些特性支持在不同维度上进行基准测试和可视化,以确定表示这些属性如何影响DF的趋势。为了验证其可行性和可扩展性,将DF框架应用于11个办公楼和121个大型零售建筑的需求响应参与数据的两个案例研究。这些示例提供了使用构建级别基准测试和聚合的途径,以提取构建DF的大小、一致性和影响因素的见解。已经为网格和建筑利益相关者确定了框架的潜在应用和实际价值。
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引用次数: 9
Batteries, fuel cells, or engines? A probabilistic economic and environmental assessment of electricity and electrofuels for heavy goods vehicles 电池、燃料电池还是发动机?重型货车用电力和电燃料的概率经济和环境评估
Q1 ENERGY & FUELS Pub Date : 2022-12-01 DOI: 10.1016/j.adapen.2022.100110
Nathan Gray , Richard O'Shea , David Wall , Beatrice Smyth , Piet N.L. Lens , Jerry D. Murphy

Uncertainty surrounding the total cost of ownership, system costs, and life cycle environmental impacts means that stakeholders may lack the required information to evaluate the risks of transitioning to low-carbon fuels and powertrains. This paper assesses the life cycle costs and well-to-wheel environmental impacts of using electricity and electrofuels in Heavy Good Vehicles (HGVs) whilst considering input parameter uncertainty. The complex relationship between electricity cost, electrolyser capacity factor, CO2 capture cost and electricity emissions intensity is assessed within a Monte Carlo based framework to identify scenarios where use of electricity or electrofuels in heavy goods vehicles makes economic and environmental sense. For vehicles with a range of less than 450 km, battery electric vehicles achieve the lowest total cost of ownership for an electricity cost less than 100 €/MWh. For vehicles that require a range of up to 900 km, hydrogen fuel cell vehicles represent the lowest long-term cost of abatement. Power-to-methane and power-to-liquid scenarios become economically competitive when low-cost electricity is available at high-capacity factors and CO2 capture costs for fuel synthesis are below 100 €/tCO2; these fuels may be more applicable to decarbonise shipping and aviation. Battery electric HGVs reduce greenhouse gas emissions by 50% compared to the diesel baseline with electricity emissions of 350 gCO2e/kWh. Electricity emissions less than 35 gCO2e/kWh are required for the power-to-methane and power-to-liquid scenarios to meet EU emissions savings criteria. High vehicle capital costs and a lack of widespread refuelling infrastructure may hinder initial uptake of low-carbon fuels and powertrains for HGVs.

总拥有成本、系统成本和生命周期环境影响的不确定性意味着利益相关者可能缺乏必要的信息来评估向低碳燃料和动力系统过渡的风险。本文在考虑输入参数不确定性的情况下,评估了重型货车(hgv)使用电力和电燃料的生命周期成本和井对车轮的环境影响。电力成本、电解槽容量系数、二氧化碳捕获成本和电力排放强度之间的复杂关系在蒙特卡洛框架内进行评估,以确定在重型货车中使用电力或电燃料具有经济和环境意义的情况。对于续航里程低于450公里的汽车,纯电动汽车的总拥有成本最低,电力成本低于100欧元/兆瓦时。对于需要续航里程达到900公里的汽车来说,氢燃料电池汽车代表了最低的长期减排成本。当高容量的低成本电力和燃料合成的二氧化碳捕获成本低于100欧元/吨二氧化碳时,电力制甲烷和电力制液体方案在经济上具有竞争力;这些燃料可能更适用于脱碳航运和航空。与柴油车相比,电池电动hgv减少了50%的温室气体排放,其电力排放量为350克二氧化碳当量/千瓦时。要达到欧盟的减排标准,电力制甲烷和电力制液体方案的电力排放量必须低于35克二氧化碳当量/千瓦时。高昂的车辆资本成本和缺乏广泛的加油基础设施可能会阻碍低碳燃料和动力系统在hgv中的初步应用。
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引用次数: 8
Exploring the trilemma of cost-efficiency, landscape impact and regional equality in onshore wind expansion planning 探索陆上风电扩张规划中成本效益、景观影响和区域平等的三难困境
Q1 ENERGY & FUELS Pub Date : 2022-09-01 DOI: 10.1016/j.adapen.2022.100102
Jann Michael Weinand , Russell McKenna , Heidi Heinrichs , Michael Roth , Detlef Stolten , Wolf Fichtner

Onshore wind development has historically focused on cost-efficiency, which may lead to uneven turbine distributions and public resistance due to landscape impacts. Using a multi-criteria planning approach, we show how onshore wind capacity targets can be achieved by 2050 in a cost-efficient, visually unobtrusive and evenly distributed way. For the case study of Germany, we build on the existing turbine stock and use open data on technically feasible turbine locations and data on scenicness of landscapes to plan the optimal expansion. The analysis shows that while the trade-off between optimizing either cost-efficiency or landscape impact of the turbines is rather weak with about 15% higher costs or scenicness, an even distribution has a large impact on these criteria. However, a more evenly distributed expansion is necessary for the achievement of the targeted south quota, a policy target that calls for more wind turbine additions in southern Germany. Our analysis assists stakeholders in resolving the onshore wind expansion trilemma.

陆上风电开发历来关注的是成本效益,这可能导致涡轮机分布不均匀,以及由于景观影响而引起的公众阻力。使用多标准规划方法,我们展示了如何以经济高效、视觉上不引人注目和均匀分布的方式实现2050年的陆上风电装机容量目标。对于德国的案例研究,我们以现有的涡轮机存量为基础,并使用技术上可行的涡轮机位置和景观景观数据的开放数据来规划最佳扩展。分析表明,虽然优化涡轮机的成本效率或景观影响之间的权衡相当弱,成本或景观性提高约15%,但均匀分布对这些标准有很大影响。然而,更均匀分布的扩张对于实现南方目标配额是必要的,这一政策目标要求在德国南部增加更多的风力涡轮机。我们的分析有助于利益相关者解决陆上风电扩张的三难困境。
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引用次数: 7
Cities are not isolates: To reduce their impacts a change in urban-rural interdependencies and the direction of modernity are required 城市不是孤立的:为了减少城市的影响,需要改变城乡之间的相互依赖关系和现代化的方向
Q1 ENERGY & FUELS Pub Date : 2022-09-01 DOI: 10.1016/j.adapen.2022.100104
Stephanie Pincetl

It has become assumed that most humans will live in cities going forward and that they can be made to mitigate their environmental impacts. These assumptions come out of a period that has enjoyed ample energy from fossil fuels, and invisible to most, enormous resource flows from non-urban areas. For cities to reduce their GHGs, that means they must be reduced in resourcing areas, challenging our current deep dependence on fossil energy. This perspective suggests there is a need for new research that investigates how to reduce GHGs in resourcing areas through intensive agroecology, how to build climate appropriate, low embedded GHG emissions buildings, low energy technologies, to move to a future where we begin to live within the limits of the planet.

人们已经假设,未来大多数人将生活在城市里,城市可以减轻对环境的影响。这些假设产生于这样一个时期:化石燃料提供了充足的能源,而大多数人却看不到来自非城市地区的巨大资源流动。城市要想减少温室气体排放,就必须在资源丰富的地区减少温室气体排放,挑战我们目前对化石能源的严重依赖。这一观点表明,有必要进行新的研究,调查如何通过集约化生态农业来减少资源地区的温室气体,如何建造适合气候的、低嵌入式温室气体排放的建筑,低能源技术,以走向我们开始在地球极限内生活的未来。
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引用次数: 3
期刊
Advances in Applied Energy
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