Robust optimization of distributed generation investment in buildings

Rezvan, A. Taghipour; Gharneh, Naser Shams; Gharehpetian, Gevork B.

doi:10.1016/j.energy.2012.10.011

Cited by 52 publications

(18 citation statements)

References 18 publications

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“…Rezvan el al. [101] performed robust optimization of EnergyPlus energy system model of a 400-bed hospital to face the uncertain energy demand. By adjusting the penalty and degree of solution robustness parameters in the objective function, they reached robust optimal solutions with lower objective costs and more stable system performances.…”

Section: 6mentioning

confidence: 99%

A review on simulation-based optimization methods applied to building performance analysis

2014

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Recent progress in computer science and stringent requirements of the design of "greener" buildings put forwards the research and applications of simulation-based optimization methods in the building sector. This paper provides an overview on this subject, aiming at clarifying recent advances and outlining potential challenges and obstacles in building design optimization. Key discussions are focused on handling discontinuous multi-modal building optimization problems, the performance and selection of optimization algorithms, multi-objective optimization, the application of surrogate models, optimization under uncertainty and the propagation of optimization techniques into real-world design challenges. This paper also gives bibliographic information on the issues of simulation programs, optimization tools, efficiency of optimization methods, and trends in optimization studies. The review indicates that future researches should be oriented towards improving the efficiency of search techniques and approximation methods (surrogate models) for largescale building optimization problems; and reducing time and effort for such activities. Further effort is also required to quantify the robustness in optimal solutions so as to improve building performance stability.

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Section: 6mentioning

confidence: 99%

A review on simulation-based optimization methods applied to building performance analysis

2014

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“…In [30], an inexact two-stage stochastic robust programming was proposed for residential micro-grid management-based on random demand. In [31,32], a robust optimization method was introduced to determine the optimum capacity of distributed generation technologies for buildings under uncertain energy demands. The work in [33] used a robust optimization model for managing combined heat and power systems via linear decision rules.…”

Section: Introductionmentioning

confidence: 99%

A robust optimization method for energy management of CCHP microgrid

Luo

et al. 2017

J. Mod. Power Syst. Clean Energy

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Energy management is facing new challenges due to the increasing supply and demand uncertainties, which is caused by the integration of variable generation resources, inaccurate load forecasts and non-linear efficiency curves. To meet these challenges, a robust optimization method incorporating piecewise linear thermal and electrical efficiency curve is proposed to accommodate the uncertainties of cooling, thermal and electrical load, as well as photovoltaic (PV) output power. Case study results demonstrate that the robust optimization model performs better than the deterministic optimization model in terms of the expected operation cost. The fluctuation of net electrical load has greater effect on the dispatching results of the combined cooling, heating and power (CCHP) microgrid than the fluctuation of the cooling and thermal load. The day-ahead schedule is greatly affected by the uncertainty budget of the load demand. The economy of the optimal decision could be achieved by adjusting different uncertainty budget levels according to control the conservatism of the model.

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“…Due to conservativeness under worst-case constraints, this technique has not gained much popularity [18]. These conservative property problems have been resolved by [33], by proposing the concept of adjustable robust optimization.…”

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confidence: 99%

“…The accuracy of solution is sensitive to the technique used for scenario generation in stochastic optimization but RO only needs information about the upper and lower bounds [18].…”

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confidence: 99%

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Robust Optimization-Based Scheduling of Multi-Microgrids Considering Uncertainties

2016

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Scheduling of multi-microgrids (MMGs) is one of the important tasks in MMG operation and it faces new challenges as the integration of demand response (DR) programs and renewable generation (wind and solar) sources increases. In order to address these challenges, robust optimization (RO)-based scheduling has been proposed in this paper considering uncertainties in both renewable energy sources and forecasted electric loads. Initially, a cost minimization deterministic model has been formulated for the MMG system. Then, it has been transformed to a min-max robust counterpart and finally, a traceable robust counterpart has been formulated using linear duality theory and Karush-Kuhn-Tucker (KKT) optimality conditions. The developed model provides immunity against the worst-case realization within the provided uncertainty bounds. Budget of uncertainty has been used to develop a trade-off between the conservatism of solution and probability of unfeasible solution. The effect of uncertainty gaps on internal and external trading, operation cost, unit commitment of dispatchable generators, and state of charge (SOC) of battery energy storage systems (BESSs) have also been analyzed in both grid-connected and islanded modes. Simulations results have proved the robustness of proposed strategy.

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Robust optimization of distributed generation investment in buildings

Cited by 52 publications

References 18 publications

A review on simulation-based optimization methods applied to building performance analysis

A review on simulation-based optimization methods applied to building performance analysis

A robust optimization method for energy management of CCHP microgrid

Robust Optimization-Based Scheduling of Multi-Microgrids Considering Uncertainties

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