Planning the decarbonisation of energy systems: The importance of applying time series clustering to long-term models

Novo, Riccardo; Marocco, Paolo; Giorgi, Giuseppe; Lanzini, Andrea; Santarelli, Massimo; Mattiazzo, Giuliana

doi:10.1016/j.ecmx.2022.100274

Cited by 15 publications

(16 citation statements)

References 43 publications

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“…The addition of storage to the OSeMOSYS model followed the Traditional (TRAD) methodology [18] and uses a different time representation to account for years (y) and seasons (s), but also daytypes (ld), dailytimebrackets (lh), and timeslices (l) to obtain a modelling timeline. For this, Seasons, DayTypes, and DailyTimeBrackets parameters were then added to the Text files.…”

Section: 6electricity Storage Performance Datamentioning

confidence: 99%

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Techno-economic data adopted for long-term energy system modelling in Morocco

Caillard,

Yeganyan,

Cannone

et al. 2024

Preprint

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Morocco is facing the critical challenge of achieving the ambitious targets set by the Nationally Determined Contributions (NDCs) and the Green Hydrogen Roadmap whilst not having a clear published informed plan for development of the required new technologies. This gap highlights the necessity for long-term modelling to make these targets achievable in time. To bridge this gap, a comprehensive dataset is presented, leveraging open-source tools, specifically OSeMOSYS, to explore the dynamics of exports and storage in the electricity sector. The dataset presented encompasses a wide range of data from 2015 to 2050, covering aspects such as electricity generation and consumption, imports and exports of electricity and estimated energy potentials. Furthermore, it delves into power technologies costs and performance parameter, renewable energy reserves, compiling information from diverse sources including the Moroccan Ministry of Energy (MEM), the International Renewable Energy Agency (IRENA), and direct communications with the National Office of Electricity and Drinking Water (ONEE). This extensive dataset aims to enhance the availability of critical energy data for policymakers, stakeholders, and researchers.

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Section: 6electricity Storage Performance Datamentioning

confidence: 99%

“…As storage technologies, batteries and GH2 storage technologies were added to the model. Li-ion batteries technologies were chosen as they present good round-trip efficiency and are the most used globally [18]. Converter and Inverter technologies were added to link the batteries to the power plants and to the grid.…”

Section: 6electricity Storage Performance Datamentioning

confidence: 99%

Techno-economic data adopted for long-term energy system modelling in Morocco

Caillard,

Yeganyan,

Cannone

et al. 2024

Preprint

View full text Add to dashboard Cite

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“…Constraints linking periods, such as storage, complicate time series aggregation since they require chronology of representative periods to be preserved. A number of solutions have been proposed; they include merging only periods that are adjacent chronologically (Pineda & Morales, 2018;Tso et al, 2020;De Guibert et al, 2020), aggregating periods from different parts of the year separately (Welsch et al, 2012;Samsatli & Samsatli, 2015;Timmerman et al, 2017), and linking storage levels between representative periods (Gabrielli et al, 2018;Tejada-Arango et al, 2018;Kotzur et al, 2018b;van der Heijde et al, 2019;Novo et al, 2022).…”

Section: Inter-period Links and Storagementioning

confidence: 99%

Reducing climate risk in energy system planning: a posteriori time series aggregation for models with storage

Hilbers¹,

Brayshaw²

2022

Preprint

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The growth in variable renewables such as solar and wind is increasing the impact of climate uncertainty in energy system planning. Addressing this ideally requires high-resolution time series spanning at least a few decades. However, solving capacity expansion planning models across such datasets often requires too much computing time or memory.To reduce computational cost, users often employ time series aggregation to compress demand and weather time series into a smaller number of time steps. Methods are usually a priori, employing information about the input time series only. Recent studies highlight the limitations of this approach, since reducing statistical error metrics on input time series does not in general lead to more accurate model outputs. Furthermore, many aggregation schemes are unsuitable for models with storage since they distort chronology.In this paper, we introduce a posteriori time series aggregation schemes for models with storage. Our methods adapt to the underlying energy system model; aggregation may differ in systems with different technologies or topologies even with the same time series inputs. Furthermore, they preserve chronology and hence allow modelling of storage technologies.We investigate a number of approaches. We find that a posteriori methods can perform better than a priori ones, primarily through a systematic identification and preservation of relevant extreme events. We hope that these tools render long demand and weather time series more manageable in capacity expansion planning studies. We make our models, data, and code publicly available.

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“…Tan et al [22] decomposed the aggregated heating load profiles into daily average components and hourly fluctuation components, and planned the long-term and short-term storage devices to respond to daily and hourly components, respectively. Novo et al [23] observed the SoC variation of long-term storage was unique for each typical period throughout the year. Therefore, the deviation of SoC between the start and the end of one typical period was the same, and it's feasible to model the initial SoC between two typical periods via the deviation.…”

Section: Introductionmentioning

confidence: 99%

“…Novo et al. [23] observed the SoC variation of long‐term storage was unique for each typical period throughout the year. Therefore, the deviation of SoC between the start and the end of one typical period was the same, and it's feasible to model the initial SoC between two typical periods via the deviation.…”

Section: Introductionmentioning

confidence: 99%

An efficient local multi‐energy systems planning method with long‐term storage

Jian

Zhang

Wen

et al. 2023

IET Renewable Power Gen

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Long‐term storage will play a crucial role in future local multi‐energy systems (MES) with high penetration renewable energy integration for demand balancing. Local MES planning with long‐term energy storage is essentially a very large‐scale program because numerous decision variables, including binary variables, should be used to model long‐term energy dependencies for accurate operational cost estimation. How to largely reduce decision variables as well as guarantee the planning model accuracy becomes one main concern. To this end, this paper proposes a novel efficient aggregation and modeling method for local MES planning. The aggregation method first decomposes input time series data (renewable energy output and energy demand) into hourly and daily components, based on which more accurate aggregation results with a few typical scenarios can be derived. By incorporating similar decomposition into the operation model of energy devices, the planning model can describe the long‐term energy cycle and the hourly operation characteristic at the same time and yield accurate optimization results with limited complexity. Experimental results show that the proposed method can considerably decrease the complexity of the problem while maintaining agreement with the results based on the optimization of the full‐time series.

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Planning the decarbonisation of energy systems: The importance of applying time series clustering to long-term models

Cited by 15 publications

References 43 publications

Techno-economic data adopted for long-term energy system modelling in Morocco

Techno-economic data adopted for long-term energy system modelling in Morocco

Reducing climate risk in energy system planning: a posteriori time series aggregation for models with storage

An efficient local multi‐energy systems planning method with long‐term storage

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