TRusT: A Two-stage Robustness Trade-off approach for the design of decentralized energy supply systems

Majewski, Dinah Elena; Lampe, Matthias; Voll, Philip; Bardow, André

doi:10.1016/j.energy.2016.10.065

Cited by 36 publications

(11 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Based on a life cycle assessment, Li et al [10] employed a fuzzy rough set to deal with the uncertainties existing in natural resources to evaluate the environmental impact for a distributed renewable energy system. In the robust approach, Majewski et al [11] addressed the uncertain data of energy demands and the prices in a CCHP system based on a proposed robust model. Luo et al [12] carried out robust design work for a CCHP system accounting for uncertain demands and photovoltaic output power.…”

Section: Introductionmentioning

confidence: 99%

Optimal Design of a Combined Cooling, Heating, and Power System and Its Ability to Adapt to Uncertainty

Zhang

Wang

et al. 2020

Energies

View full text Add to dashboard Cite

To realize the best performances of the distributed energy system (DES), many uncertainties including demands, solar radiation, natural gas, and electricity prices must be addressed properly in the planning process. This study aims to study the optimal sizing and performances of a hybrid combined cooling, heating, and power (CCHP) system under uncertainty in consideration of the operation parameters, including the lowest electric load ratio (LELR) and the electric cooling ratio (ECR). In addition, the ability of the system to adapt to uncertainty is analyzed. The above works are implemented separately under three operation strategies with multi-objectives in energy and cost saving, as well as CO2 reducing. Results show that the system with optimized operation parameters performs better in both the deterministic and uncertain conditions. When the ECRs in the summer and in mid-season as well as the LELR are set at 50.00%, 50.00%, and 20.00% respectively, the system operating in the strategy of following the electric load has the best ability to adapt to uncertainty. In addition, among all the uncertainties, the single uncertain natural gas price and the single uncertain heating demand have the smallest and largest effects on the optimal design respectively.

show abstract

Section: Introductionmentioning

confidence: 99%

Optimal Design of a Combined Cooling, Heating, and Power System and Its Ability to Adapt to Uncertainty

Zhang

Wang

et al. 2020

Energies

View full text Add to dashboard Cite

show abstract

“…In addition, most of these methods cannot produce a unique optimal solution but an interval one, which cannot support the decision-making for design. Majewski et al investigated the trade-off relationship in the objective function between the nominal and worst cases [23]. However, this method produces Pareto optimal solutions depending on the importance given to the nominal and worst cases, which is also unsuitable for design.…”

Section: Introductionmentioning

confidence: 99%

Robust optimal design of energy supply systems under uncertain energy demands based on a mixed-integer linear model

Yokoyama

Tokunaga

Wakui

2018

Energy

View full text Add to dashboard Cite

“…For a recent application with further references, see ref . Bardow and co-workers compare robust optimization with a two-stage optimization approach to quantify trade-offs in the costs to ensure robustness applied to energy supply systems. Stochastic programs with chance/probabilistic constraints: If the emphasis is on the reliability of a system, it can be claimed that the (system) constraints are not fulfilled for every value of the uncertain parameters but with a high predefined probability (see, e.g., ref for details and its application in process engineering). Recently, sigma-points have been used to calculate the probabilities needed in this approach …”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Efficient Approach for Calculating Pareto Boundaries under Uncertainties in Chemical Process Design

Bortz

Burger

Harbou

et al. 2017

Ind. Eng. Chem. Res.

View full text Add to dashboard Cite

Taking account of uncertain model parameters in simulation-based flowsheet optimization is crucial in order to quantify the reliability of the optimization results. Since chemical process design is a multicriteria optimization (MCO) task, methods to deal with uncertain Pareto boundaries are needed. The simplest of such methods consists of a sensitivity analysis of the Pareto boundary. In this work, it is shown how going beyond sensitivity analysis can yield favorable process designs not seen by sensitivity analysis alone. This is achieved by taking uncertainties into account by worst and best case Pareto boundaries or by considering the robustness of the Pareto boundary with respect to uncertain model parameters as additional objectives. In order to increase computational efficiency, for the first time, an adaptive scalarization approach is used to deal with uncertainties in MCO. The methods are illustrated by the calculation of a NQ curve of a distillation column.

show abstract

TRusT: A Two-stage Robustness Trade-off approach for the design of decentralized energy supply systems

Cited by 36 publications

References 37 publications

Optimal Design of a Combined Cooling, Heating, and Power System and Its Ability to Adapt to Uncertainty

Optimal Design of a Combined Cooling, Heating, and Power System and Its Ability to Adapt to Uncertainty

Robust optimal design of energy supply systems under uncertain energy demands based on a mixed-integer linear model

Efficient Approach for Calculating Pareto Boundaries under Uncertainties in Chemical Process Design

Contact Info

Product

Resources

About