A method for aggregating external operating conditions in multi-generation system optimization models

Lythcke-Jørgensen, Christoffer Ernst; Münster, Marie; Ensinas, Adriano V.; Haglind, Fredrik

doi:10.1016/j.apenergy.2015.12.050

Cited by 23 publications

(29 citation statements)

References 38 publications

(75 reference statements)

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“…This approach results in a significant deviation of the optimal scale of the long term storage if it is compared to the optimal result based on the full time series. As this problem would be expected [7,28], new methods are required to solve the issue.…”

Section: Typical Periods and Storage Modelingmentioning

confidence: 99%

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%

Time series aggregation for energy system design: Modeling seasonal storage

et al. 2018

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“…o Selection, location, and dimensioning of processes o Systematic heat and mass integration using pinch analysis o Flexible operation optimization with respect to both short-term market fluctuations and long-term energy system development through the application of the Characteristic Operating Pattern (CHOP) method [30] o Input data to the design methodology includes process and equipment models, energy system data, local resource data, and life cycle inventory data. The input data is structured prior to the optimization, which is conducted by a hybrid genetic algorithm/mixed integer-linear programming model.…”

Section: Methodsmentioning

confidence: 99%

Optimization of a flexible multi-generation system based on wood chip gasification and methanol production

et al. 2017

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“…Aggregation always involves averaging and thus neglects extreme conditions of the full time series. To circumvent this shortcoming, additional peak demands are commonly considered in aggregation methods (Mavrotas et al, 2008;Domínguez-Muñoz et al, 2011;Ortiga et al, 2011;Voll et al, 2013;Fazlollahi et al, 2014;Bungener et al, 2015;Lythcke-Jøgensen et al, 2016). In contrast to typical periods, these peak demands are single time steps with a duration ∆t t of zero and without any chronological order to other time steps.…”

Section: Solution Of Optimization Problemsmentioning

confidence: 99%

“…Al-Wakeel et al (2017) estimate future energy demands based on historical data and assess the accuracy of the estimation by error measures in the domain of the time series. A tailored twostage clustering algorithm is presented by Lythcke-Jøgensen et al (2016). The accuracy of typical periods is evaluated before optimization in the domain of the time series, but further analysis of the solution after the optimization is suggested, which relies on the experience of the designer.…”

Section: Introductionmentioning

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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A method for aggregating external operating conditions in multi-generation system optimization models

Cited by 23 publications

References 38 publications

Time series aggregation for energy system design: Modeling seasonal storage

Time series aggregation for energy system design: Modeling seasonal storage

Optimization of a flexible multi-generation system based on wood chip gasification and methanol production

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

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