Smart Energy最新文献_第6页

Integrating bottom-up GIS and machine learning models for spatial-temporal analysis of electric mobility impact on power system 基于自底向上GIS和机器学习模型的电动交通对电力系统影响的时空分析

IF 5.4 Q2 ENERGY & FUELS

Smart Energy

Pub Date : 2025-06-20 DOI: 10.1016/j.segy.2025.100185

Corrado Maria Caminiti, Davide Fratelli, Matteo Spiller, Aleksandar Dimovski, Marco Merlo

The ongoing electrification in the light-duty transportation sector represents a pivotal shift that deeply influences electricity distribution networks’ operations, introducing a peculiar demand profile characterised by spatial and temporal variability. To address these challenges posed by the increasing adoption of Electric Vehicles (EVs), this work integrates a Machine Learning (ML) model for the vehicle selection procedure in a holistic Spatial-Temporal Model (STM) that accurately simulates the most typical stochastic behaviour within the transportation and electricity networks. The methodology assesses traffic behaviour, evaluates the grid impact of charging processes, and extends the analysis to flexibility services, particularly the provision of primary frequency regulation. The methodology is applied to the Lombardy region in Italy, adopting the 2030 e-mobility scenario defined by policymakers as a reference. This framework selects EVs diverting from linear probabilistic extraction models based on penetration rates by exploiting behavioural patterns and the socio-economic characterisation of EV drivers. Relying purely on open-source data, the work demonstrates the frequency regulation potential of EVs fostered by smart charging algorithms, which increase the power band available for grid services. The results of the procedure provide actionable insights for grid operators and urban planners, bridging the gap between transportation and electrical infrastructure.

轻型运输部门正在进行的电气化代表了一个关键的转变，它深刻地影响着配电网络的运营，引入了一个以空间和时间变化为特征的特殊需求剖面。为了解决电动汽车（ev）日益普及带来的这些挑战，本研究将用于车辆选择过程的机器学习（ML）模型集成到一个整体时空模型（STM）中，该模型精确模拟了交通和电力网络中最典型的随机行为。该方法评估交通行为，评估收费过程对电网的影响，并将分析扩展到灵活性服务，特别是提供主要频率调节。该方法应用于意大利伦巴第地区，采用政策制定者定义的2030年电动交通情景作为参考。该框架通过利用电动汽车驾驶员的行为模式和社会经济特征，从基于渗透率的线性概率提取模型中选择电动汽车。纯粹依靠开源数据，这项工作证明了智能充电算法促进电动汽车的频率调节潜力，这增加了电网服务的可用功率频带。该程序的结果为电网运营商和城市规划者提供了可行的见解，弥合了交通和电力基础设施之间的差距。

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引用次数: 0

Assessing the role of storage and thermoelectric plants in the energy transition: a short- and medium-term scenario analysis with Italy as a case study 评估储能和热电厂在能源转型中的作用：以意大利为例的短期和中期情景分析

IF 5.4 Q2 ENERGY & FUELS

Smart Energy

Pub Date : 2025-06-13 DOI: 10.1016/j.segy.2025.100186

Andrea Franzoso, Michel Noussan, Paolo Marocco, Marco Badami, Gabriele Fambri, Marta Gandiglio

Global warming is pushing many countries worldwide to adopt decarbonization strategies aimed at reducing the dependence on fossil fuels. The successful development of these strategies critically depends on the ability to model and evaluate alternative options, thereby enabling policymakers to identify and implement the most effective solutions. In this context, the present study introduces a detailed operational analysis of the Italian energy system under the 2030 and 2040 horizons, based on authoritative scenarios developed by national transmission system operators. The primary goal is to complement these scenarios by highlighting short- and medium-term operational challenges, particularly concerning the role of thermoelectric power plants and electricity storage systems. To this aim, a set of key performance indicators is introduced to systematically assess scenario impacts. The analysis captures the effects of rising electricity demand, driven by the diffusion of electric vehicles and heat pumps, on system operation, highlighting a projected 25% increase in peak demand along with an 8.3% increase in peak thermoelectric generation. Despite a marked decline in the capacity factor of thermoelectric power plants (from 0.54 to 0.18), these units remain essential to meet demand during extended periods of low renewable generation, with peak capacity requirements remaining close to 40 GW. The results also underscore the role of electricity storage in providing short-term flexibility. However, the benefits of additional storage become marginal beyond 230 GWh of capacity.

全球变暖正在推动世界上许多国家采取旨在减少对化石燃料依赖的脱碳战略。这些战略的成功制定在很大程度上取决于对备选方案进行建模和评估的能力，从而使决策者能够确定和实施最有效的解决方案。在此背景下，本研究基于国家输电系统运营商制定的权威方案，对2030年和2040年的意大利能源系统进行了详细的运行分析。主要目标是通过强调短期和中期运营挑战，特别是关于热电厂和电力储存系统的作用，来补充这些情景。为此目的，引入了一套关键绩效指标来系统地评估情景影响。该分析抓住了由电动汽车和热泵的普及所驱动的电力需求上升对系统运行的影响，强调了预计峰值需求将增长25%，峰值热电发电量将增长8.3%。尽管热电厂的容量系数明显下降（从0.54降至0.18），但这些机组仍然是满足低可再生能源发电长期需求的必要条件，峰值容量需求仍接近40吉瓦。研究结果还强调了电力储存在提供短期灵活性方面的作用。然而，超过230吉瓦时的容量，额外存储的好处就变得微不足道了。

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引用次数: 0

Generating building-level heat demand time series by combining occupancy simulations and thermal modeling 结合使用模拟和热建模，生成建筑层热需求时间序列

IF 5.4 Q2 ENERGY & FUELS

Smart Energy

Pub Date : 2025-05-01 DOI: 10.1016/j.segy.2025.100181

Simon Malacek , José Portela , Yannick Werner , Sonja Wogrin

Despite various efforts, decarbonizing the heating sector remains a significant challenge. To tackle it by smart planning, the availability of highly resolved heating demand data is key. Several existing models provide heating demand only for specific applications. Typically, they either offer time series for a larger area or annual demand data on a building level, but not both simultaneously. Additionally, the diversity in heating demand across different buildings is often not considered. To address these limitations, this paper presents a novel method for generating temporally resolved heat demand time series at the building level using publicly available data. The approach integrates a thermal building model with stochastic occupancy simulations that account for variability in user behavior. As a result, the tool serves as a cost-effective resource for cross-sectoral energy system planning and policy development, particularly with a focus on the heating sector. The obtained data can be used to assess the impact of renovation and retrofitting strategies, or to analyze district heating expansion. To illustrate the potential applications of this approach, we conducted a case study in Puertollano (Spain), where we prepared a dataset of heating demand with hourly resolution for each of 9,298 residential buildings. This data was then used to compare two different pathways for the thermal renovation of these buildings. By relying on publicly available data, this method can be adapted and applied to various European regions, offering broad usability in energy system optimization and analysis of decarbonization strategies.

尽管做出了各种努力，但供暖部门的脱碳仍然是一项重大挑战。要通过智能规划解决这一问题，获得高分辨率的供暖需求数据是关键。现有的几种型号仅为特定应用提供加热需求。通常，它们要么提供更大区域的时间序列，要么提供建筑层面的年度需求数据，但不能同时提供这两种数据。此外，不同建筑之间采暖需求的多样性往往没有被考虑。为了解决这些限制，本文提出了一种利用公开数据在建筑水平上生成时间解决的热需求时间序列的新方法。该方法将热建筑模型与考虑用户行为可变性的随机占用模拟相结合。因此，该工具是跨部门能源系统规划和政策制定的成本效益资源，特别是以供热部门为重点。获得的数据可用于评估改造和改造策略的影响，或分析区域供热扩张。为了说明这种方法的潜在应用，我们在Puertollano（西班牙）进行了一个案例研究，在那里我们准备了9,298座住宅建筑的每小时供暖需求数据集。这些数据随后被用于比较这些建筑热改造的两种不同途径。该方法依赖于公开可用的数据，可适用于欧洲各地区，在能源系统优化和脱碳战略分析方面具有广泛的可用性。

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引用次数: 0

Reliability and electrical safety of grid-connected household PV systems: Data-driven risk analysis and insights 并网家庭光伏系统的可靠性和电气安全：数据驱动的风险分析和见解

IF 5.4 Q2 ENERGY & FUELS

Smart Energy

Pub Date : 2025-05-01 DOI: 10.1016/j.segy.2025.100182

Rade Ciric , Eivind Lundemoen Håkedal , Oddvin Tesaker Pedersen , Knut Ola Dørum

Home photovoltaic generators (PVGs) offer many benefits, including reduced energy costs and environmental sustainability. Ensuring electrical safety in PVGs is crucial to prevent hazards such as electric shock, fires, and system malfunctions. As PVG components age, the likelihood of electrical issues increases. This research assesses the reliability of key components and evaluates the risk of electric shock in household PVGs using fault tree analysis. Due to limited data on failure rate of small-scale PVGs, component reliability was analysed based on survey feedback from 85 Norwegian PVG owners. To gain deeper insights into home PVG vulnerabilities, a Simulink model was applied to simulate various faults, including failures in PV modules, inter-string connections, MOSFETs, and both the DC and AC sides of the inverter. The findings indicate that inverters are the most failure-prone components in household PVGs while the residual current devices (RCDs), as critical protection units, also lose reliability over time. These findings underscore the critical importance of implementing a comprehensive suite of protective measures in PVG systems to ensure both safety and reliability, as well as importance of proactive condition monitoring, particularly for the inverter, battery charger, RCD, and insulation resistance.

家用光伏发电机（PVGs）有很多好处，包括降低能源成本和环境可持续性。确保PVGs的电气安全对于防止触电、火灾和系统故障等危险至关重要。随着PVG组件的老化，电气问题的可能性增加。本研究采用故障树分析法对家用PVGs关键部件的可靠性进行评估，并对其触电风险进行评估。由于小型PVG的故障率数据有限，基于85位挪威PVG车主的调查反馈，对组件可靠性进行了分析。为了更深入地了解家用PVG漏洞，应用Simulink模型模拟了各种故障，包括光伏模块、串间连接、mosfet以及逆变器直流和交流侧的故障。研究结果表明，逆变器是家用pvg中最容易发生故障的部件，而作为关键保护单元的剩余电流装置（rcd）也会随着时间的推移而失去可靠性。这些发现强调了在PVG系统中实施一套全面的保护措施以确保安全性和可靠性的重要性，以及主动状态监测的重要性，特别是对于逆变器、电池充电器、RCD和绝缘电阻。

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引用次数: 0

Flexibility in short-term electricity markets for renewable integration and uncertainty mitigation: A comprehensive review 可再生能源整合和不确定性缓解的短期电力市场灵活性：全面审查

IF 5.4 Q2 ENERGY & FUELS

Smart Energy

Pub Date : 2025-05-01 DOI: 10.1016/j.segy.2025.100183

Lander Mentens , Herbert Peremans , Johan Springael , Philippe Nimmegeers

Globally, the share of renewable energy sources in the electricity mix is increasing. However, higher levels of renewable energy sources and especially those with an intermittent nature introduce uncertainty in delivering reliable and secure electricity. Hence, flexibility becomes more important to counteract imbalances between generation and consumption. This review paper contributes to the nuanced ways in which flexibility can be strategically employed to navigate and mitigate uncertainties amid evolving market dynamics and increasing share of renewable sources in the energy mix. It investigates the impact of renewable energy sources on short-term electricity markets, with a specific focus on day-ahead, intraday, and balancing markets in the Central Western Europe region. It explores the design characteristics and parameters of these markets, emphasizing how these markets deal with the uncertainty arising from the limited predictability of renewable resources. In this context, flexibility becomes a crucial element in reducing this uncertainty. The primary objective is to fully understand and analyze the intricate interplay between the sequential short-term markets and the imperative for flexibility. First, the current short-term markets are discussed. Next, the paper examines how flexibility in its three dimensions (i.e., time, space, and demand-response) can strategically function to not only address but also proactively alleviate uncertainties within these markets. Advancements in market coupling, forecasting accuracy, and increased liquidity have significantly enhanced the efficiency of these markets, particularly in accommodating the growing presence of renewable energy sources.

在全球范围内，可再生能源在电力结构中的份额正在增加。然而，更高水平的可再生能源，特别是间歇性的可再生能源，在提供可靠和安全的电力方面带来了不确定性。因此，灵活性对于抵消发电和消费之间的不平衡变得更加重要。在不断变化的市场动态和可再生能源在能源结构中的份额不断增加的情况下，灵活性可以战略性地用于导航和减轻不确定性。它调查了可再生能源对短期电力市场的影响，特别关注中欧西欧地区的前一天，日内和平衡市场。它探讨了这些市场的设计特点和参数，强调这些市场如何处理由可再生资源的有限可预测性引起的不确定性。在这种情况下，灵活性成为减少这种不确定性的关键因素。主要目标是充分理解和分析连续短期市场和灵活性之间复杂的相互作用。首先，讨论当前的短期市场。接下来，本文考察了其三个维度（即时间、空间和需求响应）的灵活性如何在战略上发挥作用，不仅可以解决这些市场中的不确定性，还可以主动缓解这些不确定性。市场耦合、预测准确性和流动性增加方面的进步大大提高了这些市场的效率，特别是在适应日益增长的可再生能源方面。

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引用次数: 0

Smart flexibility in energy communities: Scenario-based analysis of distribution grid implications and economic impacts 能源社区的智能灵活性：基于场景的配电网影响和经济影响分析

IF 5.4 Q2 ENERGY & FUELS

Smart Energy

Pub Date : 2025-05-01 DOI: 10.1016/j.segy.2025.100184

Magnus Askeland, Sigurd Bjarghov, Rubi Rana, Andrei Morch, Henning Taxt

The transition of the power system towards increased renewable energy generation necessitates enhanced flexibility at all system levels, especially within distribution grids. This study investigates the integration of Energy Communities (ECs) as a potential strategy to manage consumer-level flexibility within the context of Norwegian distribution grids. An equilibrium model is developed to analyse both implicit and explicit flexibility activation mechanisms to investigate potential strategies for distribution grid operators (DSOs) and assess the interactions between different stakeholders. The scenarios are evaluated based on their impact on peak load reduction, cost efficiency, and grid usage. Results from a Norwegian case study show that EC flexibility activation can reduce total system costs by 1.8% while lowering peak grid capacity needs by 13.1%. These reductions contribute to mitigating distribution grid congestion and deferring costly infrastructure reinforcements. Although derived from a specific geographical context, the findings offer valuable insights applicable to other regions with similar grid conditions and regulatory frameworks. The study concludes that combining active DSO strategies with local coordination in ECs enhances the cost-efficiency of flexibility activation, though careful consideration of pricing structures is necessary to realise the potential while preventing unintended consequences. Our scenario-based framework illustrates the potential of smart flexibility activation mechanisms to optimise grid operations, reduce peak loads, and enhance cost-efficiency. Key challenges and prerequisites to overcome them are also highlighted. By integrating advanced flexibility mechanisms and leveraging local market coordination, this study underscores the role of energy communities in accelerating the transition to decentralised smart energy systems.

电力系统向增加可再生能源发电的过渡需要在所有系统级别，特别是在配电网内提高灵活性。本研究调查了能源社区（ec）的整合作为在挪威配电网背景下管理消费者级灵活性的潜在策略。建立了一个均衡模型来分析隐式和显式柔性激活机制，以研究配电网运营商（dso）的潜在策略，并评估不同利益相关者之间的相互作用。这些场景是根据它们对峰值负载减少、成本效率和电网使用的影响来评估的。挪威的一个案例研究结果表明，EC灵活性激活可以将系统总成本降低1.8%，同时将电网峰值容量需求降低13.1%。这些减少有助于缓解配电网拥堵和推迟昂贵的基础设施加固。尽管研究结果来自特定的地理环境，但它为具有类似电网条件和监管框架的其他地区提供了有价值的见解。该研究的结论是，将主动DSO策略与ec中的本地协调相结合，可以提高灵活性激活的成本效益，尽管需要仔细考虑定价结构，以实现潜力，同时防止意外后果。我们基于场景的框架说明了智能灵活性激活机制在优化电网运行、降低峰值负荷和提高成本效益方面的潜力。报告还强调了主要挑战和克服这些挑战的先决条件。通过整合先进的灵活机制和利用当地市场协调，本研究强调了能源社区在加速向分散式智能能源系统过渡中的作用。

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引用次数: 0

Urban Smart Energy Systems from a Climate Change Perspective: Technical, Economic and Environmental Optimization Analysis 气候变化视角下的城市智能能源系统：技术、经济和环境优化分析

IF 5.4 Q2 ENERGY & FUELS

Smart Energy

Pub Date : 2025-04-11 DOI: 10.1016/j.segy.2025.100180

Federico Battini , Andrea Menapace , Giulia Stradiotti , Ariele Zanfei , Francesco F. Nicolosi , Daniele Dalla Torre , Massimiliano Renzi , Giovanni Pernigotto , Francesco Ravazzolo , Maurizio Righetti , Andrea Gasparella , Jakob Zinck Thellufsen , Henrik Lund

In response to the growing need for sustainable urban development, energy systems modelling must provide long-term carbon-neutral solutions at the city scale while balancing competing criteria. This work introduces a multi-objective optimization approach addressing technical, economic, and environmental criteria for urban smart energy systems designed to achieve 100% renewable energy integration. The analysis incorporates climate change impacts on both energy demand and production. Two optimization strategies are evaluated using Bozen-Bolzano, Italy, as a case study. Specifically, the energy systems were modelled using EnergyPLAN, integrated with Python for automation. Grid search and Non-dominated Sorting Genetic Algorithm-II (NSGA-II) were adopted as optimization methods to compare the advantages and limitations of two different approaches. The results show that both methods produce similar solutions on the Pareto front, with the grid search slightly outperforming due to the consideration of extreme input ranges. However, NSGA-II generated a significantly larger number of Pareto solutions, demonstrating its effectiveness in exploring the solution space more comprehensively. This study underscores the importance of incorporating climate change into multi-objective optimization for robust decision-making in the design of smart urban energy systems for sustainable development.

为了应对城市可持续发展日益增长的需求，能源系统建模必须在平衡竞争标准的同时，在城市规模上提供长期的碳中和解决方案。这项工作介绍了一种多目标优化方法，解决了城市智能能源系统的技术、经济和环境标准，旨在实现100%的可再生能源整合。该分析纳入了气候变化对能源需求和生产的影响。以意大利Bozen-Bolzano为例，对两种优化策略进行了评价。具体来说，能源系统使用EnergyPLAN进行建模，并与Python集成以实现自动化。采用网格搜索和非支配排序遗传算法- ii （NSGA-II）作为优化方法，比较两种方法的优缺点。结果表明，两种方法在Pareto前沿产生相似的解，由于考虑了极端输入范围，网格搜索的性能略好。然而，NSGA-II生成的Pareto解的数量明显更多，表明其在更全面地探索解空间方面的有效性。该研究强调了将气候变化纳入多目标优化的重要性，以便在可持续发展的智能城市能源系统设计中进行稳健决策。

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引用次数: 0

Exploring the advantages of a multi-year-adaptive approach on cost-optimal long-term mini-grid design under different demand evolution scenarios 探讨不同需求演变情景下，多年自适应方法在成本最优长期微电网设计中的优势

IF 5.4 Q2 ENERGY & FUELS

Smart Energy

Pub Date : 2025-02-28 DOI: 10.1016/j.segy.2025.100178

Milky Ali Gelchu , Jimmy Ehnberg , Dereje Shiferaw , Erik O. Ahlgren

Mini-grids are essential for rural electrification in sub-Saharan Africa, but due to uncertainty about future demand evolution in non-electrified communities, cost-optimal long-term sizing and design is particularly difficult. Standard, non-adaptive design approaches single-year and multi-year, are highly susceptible to demand evolution uncertainties. Despite potentially great advantages there is a lack of studies investigating adaptive design approaches. Thus, this study, using particle swarm optimization, explores the advantages of a multi-year-adaptive approach on cost-optimal long-term solar PV mini-grid component sizing under three demand evolution scenarios, considering the impacts of load flexibility, varying discount rates, and potential future mini-grid component cost reductions. The results show that the multi-year-adaptive approach helps to manage demand evolution challenges. It leads to significant cost-savings, up to three-quarters, in higher demand evolution scenarios, compared to multi-year and single-year approaches. These cost-savings increase with load flexibility (up to 4 % with 10 % flexibility), higher discount rates (up to 9.4 % with rates from 7 % to 20 %), and component cost reductions (up to 3.6 % per 1 % reduction). The study demonstrates how an adaptive approach can be utilized to optimize mini-grid component sizing and enhance cost efficiency.

迷你电网对于撒哈拉以南非洲地区的农村电气化至关重要，但由于非电气化社区未来需求演变的不确定性，成本最优的长期规模和设计尤其困难。标准的、非自适应的设计方法是单年和多年的，非常容易受到需求演变不确定性的影响。尽管具有潜在的巨大优势，但缺乏对适应性设计方法的研究。因此，本研究采用粒子群优化方法，在三种需求演变情景下，考虑负载灵活性、不同贴现率和未来潜在的微网组件成本降低的影响，探讨了多年自适应方法在成本最优的长期太阳能光伏微网组件规模上的优势。结果表明，多年自适应方法有助于管理需求演变的挑战。与多年和单年方法相比，在更高的需求演变场景中，它可以节省高达四分之三的成本。这些成本节约增加了负载灵活性（10%的灵活性可达4%），更高的折扣率（从7%到20%的折扣率可达9.4%），以及组件成本降低（每降低1%可达3.6%）。该研究展示了如何利用自适应方法来优化微型电网组件大小并提高成本效率。

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引用次数: 0

Optimizing district heating operations: Network modeling and its implications on system efficiency and operation 优化区域供热操作：网络建模及其对系统效率和运行的影响

IF 5.4 Q2 ENERGY & FUELS

Smart Energy

Pub Date : 2025-02-13 DOI: 10.1016/j.segy.2025.100175

Pascal Friedrich , Thanh Huynh , Stefan Niessen

Efficient utilization of local heat sources in urban areas necessitates integrating various suppliers into District Heating Systems (DHSs), considering the diverse ownership and physical characteristics of these sources. This study addresses the challenges in operational planning and pricing through local heat markets, emphasizing the importance of accurately representing the District Heating Network (DHN) physics for reliable market matching. We explore different DHN modeling approaches for day-ahead operational planning, balancing between numerical efficiency, economic viability, and operational feasibility. Our models, ranging from mixed-integer linear to non-linear, aim to maximize social welfare under steady-state conditions and are tested on small scenarios to highlight potential synergies between Heatpumps (HPs) and Combined Heat and Power Units (CHPs). Assuming regulations enable cost-competitive operations between HPs and CHP units, we anchor our energy price assumptions in 2030 forecasts for Germany. This approach allows us to highlight the techno-economic advantages of leveraging non-linear model flexibility during the transition to sustainable heat supply. The model’s operational schedules are further validated through detailed physical simulations in Modelica, revealing the impact of transient effects on actual performance, particularly the risks associated with thermo-hydraulic oscillations. The study concludes by discussing the required model complexity for effective DHS scheduling.

考虑到这些热源的不同所有权和物理特性，城市地区当地热源的有效利用需要将各种供应商纳入区域供热系统。本研究通过当地供热市场解决了运营规划和定价方面的挑战，强调了准确代表区域供热网络（DHN）物理特性对于可靠的市场匹配的重要性。我们探索了不同的DHN建模方法，用于日前运营规划，在数值效率、经济可行性和运营可行性之间取得平衡。我们的模型，从混合整数线性到非线性，旨在在稳态条件下最大化社会福利，并在小场景下进行测试，以突出热泵（hp）和热电联产机组（CHPs）之间的潜在协同作用。假设法规允许热电联产和热电联产之间的成本竞争，我们将德国2030年的能源价格预测作为基础。这种方法使我们能够突出在向可持续供热过渡期间利用非线性模型灵活性的技术经济优势。通过Modelica的详细物理模拟，进一步验证了该模型的运行计划，揭示了瞬态效应对实际性能的影响，特别是与热液振荡相关的风险。最后讨论了有效国土安全部调度所需的模型复杂度。

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引用次数: 0

Optimizing storage capacity in 100 % renewable electricity supply: A GIS-based approach for Italy 在100%可再生电力供应中优化存储容量：意大利基于gis的方法

IF 5.4 Q2 ENERGY & FUELS

Smart Energy

Pub Date : 2025-02-03 DOI: 10.1016/j.segy.2025.100177

Vittoria Battaglia , Aseed Ur Rehman , Laura Vanoli

The sustainability of energy systems relies on the integration of renewable local sources. This study aimed to optimize Italy's electricity supply by leveraging a hybrid PV-wind energy system, employing advanced optimization techniques. The primary goal was pinpointing the minimum storage capacity necessary for Italy's power grid in a scenario completely reliant on PV and wind energy. To achieve this, the potential of both PV and wind energy was evaluated through a GIS-based analysis, while dynamic simulation was used to estimate power generation across regions. The Mixed-integer linear programming algorithm underwent a three-step process: computing the hourly residual load for diverse PV and wind capacity combinations, determining the hourly storage requirements and ultimately identifying the mix with the least storage capacity. Applying Mixed-integer linear programming to Italy's complete PV and wind energy potential revealed a necessity for 33 TWh of storage capacity. To decrease the required storage capacity, two new scenarios were proposed: the island scenario, in which the total annual electricity production from solar and wind energy is equal to the annual electricity demand, and the peak hour scenario, where generation from PV and wind is matched to the consumption in peak hour electric demand. The economic analysis of the proposed scenarios shows that although hydrogen can be used to store enormous amounts of energy, the inefficiencies in the conversion processes make it less cost-effective compared to other technologies. Pumped-hydro storage is the most cost-effective option for energy storage. The results show that the most economically viable scenario is the island scenario with an optimal mix of 16.9 % PV and 83.1 % wind, requiring a storage capacity of 7.04 TWh and a 3.34 trillion euro investment for pump-hydro storage.

能源系统的可持续性依赖于当地可再生能源的整合。本研究旨在通过采用先进的优化技术，利用混合光伏-风能系统来优化意大利的电力供应。主要目标是在完全依赖光伏和风能的情况下，确定意大利电网所需的最小存储容量。为了实现这一目标，通过基于gis的分析评估了光伏和风能的潜力，同时使用动态模拟来估计区域间的发电量。混合整数线性规划算法经历了三个步骤：计算不同光伏和风能容量组合的每小时剩余负荷，确定每小时存储需求，最终确定存储容量最小的组合。将混合整数线性规划应用于意大利完整的光伏和风能潜力，发现需要33太瓦时的存储容量。为了减少所需的存储容量，提出了两种新的情景：孤岛情景，其中太阳能和风能的年总发电量等于年电力需求；高峰时段情景，其中光伏和风能的发电量与高峰时段电力需求的消费量相匹配。对提议方案的经济分析表明，尽管氢可以用来储存大量的能量，但与其他技术相比，转换过程中的低效率使其成本效益较低。抽水蓄能是能源储存最具成本效益的选择。结果表明，经济上最可行的方案是岛屿方案，其最佳组合为16.9%的光伏和83.1%的风能，需要7.04太瓦时的存储容量和3.34万亿欧元的抽水蓄能投资。

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引用次数: 0