Extreme events in time series aggregation: A case study for optimal residential energy supply systems

Teichgraeber, Holger; Lindenmeyer, Constantin P.; Baumgärtner, Nils; Kotzur, Leander; Stolten, Detlef; Robinius, Martin; Bardow, André; Brandt, Adam R.

doi:10.1016/j.apenergy.2020.115223

Cited by 31 publications

(9 citation statements)

References 35 publications

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“…Thus, the benefits of the presence of the storage options could be further explored by ensuring these exchanges are done in an optimal way. More realistic relationships can be used to describe the performance of the renewable energy technologies (e.g., variation of efficiency of generation units at part-loads [46,47], defining alternating power function and state of charge for storage systems [28,48]), or using clustering techniques to select typical operating periods [49,50].…”

Section: Discussionmentioning

confidence: 99%

A Model Predictive Control-Based Decision-Making Strategy for Residential Microgrids

Mechleri

Dorneanu

Arellano‐García

2022

Eng

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This work presents the development of a decision-making strategy for fulfilling the power and heat demands of small residential neighborhoods. The decision on the optimal operation of a microgrid is based on the model predictive control (MPC) rolling horizon. In the design of the residential microgrid, the new approach different technologies, such as photovoltaic (PV) arrays, micro-combined heat and power (micro-CHP) units, conventional boilers and heat and electricity storage tanks are considered. Moreover, electricity transfer between the microgrid components and the national grid are possible. The MPC problem is formulated as a mixed integer linear programming (MILP) model. The proposed novel approach is applied to two case studies: one without electricity storage, and one integrated microgrid with electricity storage. The results show the benefits of considering the integrated microgrid, as well as the advantage of including electricity storage.

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Section: Discussionmentioning

confidence: 99%

A Model Predictive Control-Based Decision-Making Strategy for Residential Microgrids

Mechleri

Dorneanu

Arellano‐García

2022

Eng

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“…For instance, Renaldi and Friedrich [31] apply multiple time grids to reduce computation time when modelling a solar district heating system. In Teichgraeber et al [32] optimization of residential energy supply uses TSR, but introduces an iterative process, that performs a feasibility check with a full time-series to successively improve the reduced time-series. Lastly, instead of directly applying off-the-shelf solvers, specialized solution algorithms, often based on decomposition, can greatly reduce computation time [33,34].…”

Section: Discussionmentioning

confidence: 99%

Adequacy of time-series reduction for renewable energy systems

Göke,

Kendziorski

2021

Preprint

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To manage computational complexity, models of macro-energy systems commonly deploy reduced sets of time-series data. This paper evaluates the adequacy of time-series reduction when modelling energy systems with fully renewable generation and a consequent dependency on storage. Analysis includes various methods to derive reduced time-series and to implement them into models, either as time-slices, also referred to as representative days, or continuous timesteps. All methods are tested with regard to unmet demand and accuracy of estimated system costs using a simple capacity expansion model of the power sector within a renewable energy system. Some methods achieve little unmet demand, but instead their results regarding storage are biased and favour seasonal at the expense of short-term storage. We conclude that renewable energy systems limit the adequacy of time-series reduction and future research should focus on alternative methods to reduce computational complexity.

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“…Recently, other methods have proposed tackling the problem of underestimating the variance of time series with respect to clustering in general. Some add iteratively feasible time steps if the operation of an energy system optimized for an aggregated time series is not feasible for operation with the original time series [120] while others use synthesized variations [66,118,121] in time series and simulate the operation of energy systems designed for one scenario with all of the others [118], or re-run the optimization, including the most expensive time steps from the first optimization run [121,122].…”

Section: Extreme Periodsmentioning

confidence: 99%