Multiple time grids in operational optimisation of energy systems with short- and long-term thermal energy storage

Renaldi, Renaldi; Friedrich, Daniel

doi:10.1016/j.energy.2017.05.120

Cited by 43 publications

(20 citation statements)

References 42 publications

(55 reference statements)

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“…Renaldi and Friedrich [32] introduce multiple time grids for the operational optimization of an energy system which also relies on seasonal storage. This approach is popular 3 for controlling process plants or electrical grids and makes use of the different time constants of different elements of the system considered.…”

Section: Typical Periods and Storage Modelingmentioning

confidence: 99%

“…Taking the state of the art into account, we combine the approach of describing the operation by a sequence of clustered typical periods byGabrielli et al [24] with the idea of describing part of the system dynamics on a second time layer, similar to Renaldi and Friedrich [32]: The first layer, named the intra-period time layer, models the operation within a typical period. The second layer, the inter-period time layer, considers state changes between these periods.…”

Section: Idea and Structure Of The Papermentioning

confidence: 99%

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Time series aggregation for energy system design: Modeling seasonal storage

et al. 2018

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The optimization-based design of renewable energy systems is a computationally demanding task because of the high temporal fluctuation of supply and demand time series. In order to reduce these time series, the aggregation of typical operation periods has become common. The problem with this method is that these aggregated typical periods are modeled independently and cannot exchange energy. Therefore, seasonal storage cannot be adequately taken into account, although this will be necessary for energy systems with a high share of renewable generation.To address this issue, this paper proposes a novel mathematical description for storage inventories based on the superposition of inter-period and intra-period states. Interperiod states connect the typical periods and are able to account their sequence. The approach has been adopted for different energy system configurations. The results show that a significant reduction in the computational load can be achieved also for long term storage-based energy system models in comparison to optimization models based on the full annual time series.

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Section: Typical Periods and Storage Modelingmentioning

confidence: 99%

Section: Idea and Structure Of The Papermentioning

confidence: 99%

Time series aggregation for energy system design: Modeling seasonal storage

et al. 2018

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“…An overview of equipment modelling, control, and input data are given in the following paragraphs, while more detailed descriptions can be found in Ref. [33].…”

Section: Trnsys Model Of Drake Landing Solar Communitymentioning

confidence: 99%

Techno-economic analysis of a solar district heating system with seasonal thermal storage in the UK

2019

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Heat demand in buildings is responsible for around 40% of all energy use in middle to high latitude countries. The combination of district heating systems with solar thermal energy and seasonal thermal energy storage has successfully reduced the carbon intensity of heating in different countries, such as Denmark, Germany and Canada. The potentials of such systems to decarbonise the heat demand in the UK has also been highlighted in different reports. Nevertheless, bottom-up quantitative studies to support or dismissive these potentials are very limited. The quantification can be provided by simulating a solar district heating system using UK-specific inputs, such as heat demand and weather profiles. In this study, a validated simulation model is used to study the performance of solar district heating systems with seasonal thermal storage deployed in the UK. The case studies are based on the Drake Landing Solar Community in Okotoks, Canada, which has a relatively high solar fraction. The results show that the system is technically feasible to be implemented in the UK but that it has lower technical performance. A systematic analysis of the influence of the main components on the system performance shows that not only the solar supply and heat demand need to be balanced but also that the long-term storage needs to be appropriately sized. The relatively lower solar fraction could be offset by installing more long-term storage and implementing the system to supply new-built houses with better energy performance rather than the current building stock of older homes. Financially, the system still needs to be supported by encouraging policies to make it competitive with incumbent technologies. The results and the validated model open the possibility to design bespoke solar district heating systems for the UK and other countries in middle to high latitudes.

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“…The proposed method for time-series aggregation maintains chronology within each period; however, no chronology between periods is considered, and thus seasonal storage cannot be represented. To account for seasonal storage, a second time grid could be introduced as proposed by Renaldi and Friedrich (2017). Quite recently, Gabrielli et al (2017) and Kotzur et al (2017) considered seasonal storage in a synthesis problem by introduction of a second time grid to describe the sequence of typical periods, these authors further improved the approach of Renaldi and Friedrich (2017) by assigning all continuous variables to the full time horizon, while only the binary variables are considered for the sequence of aggregated typical periods.…”

Section: Aggregation Of Periods To Typical Periodsmentioning

confidence: 99%

“…To account for seasonal storage, a second time grid could be introduced as proposed by Renaldi and Friedrich (2017). Quite recently, Gabrielli et al (2017) and Kotzur et al (2017) considered seasonal storage in a synthesis problem by introduction of a second time grid to describe the sequence of typical periods, these authors further improved the approach of Renaldi and Friedrich (2017) by assigning all continuous variables to the full time horizon, while only the binary variables are considered for the sequence of aggregated typical periods. In contrast to our previous work on non-chronological time series (Bahl et al, 2017a), typical periods allow aggregation in following two dimensions:…”

Section: Aggregation Of Periods To Typical Periodsmentioning

confidence: 99%

Typical Periods for Two-Stage Synthesis by Time-Series Aggregation with Bounded Error in Objective Function

et al. 2018

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Two-stage synthesis problems simultaneously consider here-and-now decisions (e.g., optimal investment) and wait-and-see decisions (e.g., optimal operation). The optimal synthesis of energy systems reveals such a two-stage character. The synthesis of energy systems involves multiple large time series such as energy demands and energy prices. Since problem size increases with the size of the time series, synthesis of energy systems leads to complex optimization problems. To reduce the problem size without loosing solution quality, we propose a method for time-series aggregation to identify typical periods. Typical periods retain the chronology of time steps, which enables modeling of energy systems, e.g., with storage units or start-up cost. The aim of the proposed method is to obtain few typical periods with few time steps per period, while accurately representing the objective function of the full time series, e.g., cost. Thus, we determine the error of time-series aggregation as the cost difference between operating the optimal design for the aggregated time series and for the full time series. Thereby, we rigorously bound the maximum performance loss of the optimal energy system design. In an initial step, the proposed method identifies the best length of typical periods by autocorrelation analysis. Subsequently, an adaptive procedure determines aggregated typical periods employing the clustering algorithm k-medoids, which groups similar periods into clusters and selects one representative period per cluster. Moreover, the number of time steps per period is aggregated by a novel clustering algorithm maintaining chronology of the time steps in the periods. The method is iteratively repeated until the error falls below a threshold value. A case study based on a real-world synthesis problem of an energy system shows that time-series aggregation from 8,760 time steps to 2 typical periods with each 2 time steps results in an error smaller than the optimality gap of the synthesis problem (2%). This corresponds to a reduction of the number time steps and thus a reduction of the size of the synthesis problem by a factor of 1,000 with excellent accuracy in cost estimation. Thus, the proposed method enables an efficient and accurate synthesis of energy systems.

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Multiple time grids in operational optimisation of energy systems with short- and long-term thermal energy storage

Cited by 43 publications

References 42 publications

Time series aggregation for energy system design: Modeling seasonal storage

Time series aggregation for energy system design: Modeling seasonal storage

Techno-economic analysis of a solar district heating system with seasonal thermal storage in the UK

Typical Periods for Two-Stage Synthesis by Time-Series Aggregation with Bounded Error in Objective Function

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