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

Yokoyama, Ryohei; Tokunaga, Akira; Wakui, Tetsuya

doi:10.1016/j.energy.2018.03.124

Cited by 11 publications

(7 citation statements)

References 32 publications

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“…One option is to incorporate uncertainty directly to the energy system modelling. In the context of energy system optimization under uncertainty, commonly used methods include stochastic optimization [10][11][12], robust optimization [13][14][15], fuzzy programming [16,17], and, less commonly, quadratic programming [18]. Stochastic methodology can be used to describe the short-term uncertainty of the temporal fluctuations in e.g.…”

Section: Introductionmentioning

confidence: 99%

Analyzing the effects of uncertainties on the modelling of low-carbon energy system pathways

Pilpola

Lund

2020

Energy

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Decarbonization is an important goal of the future energy transition, but its modelling is also subject to several uncertainties. Here we investigate the impacts of such uncertainties through analyzing the overall performance and operation of a modelled national energy system undergoing deep decarbonization. Finland was chosen as a case, as it intends to become carbon-neutral already by 2035. Uncertainties in costs, energy consumption, and renewable resource potential and how they affect the operation of a modelled energy system is analyzed using a Monte Carlo method linked to a national energy system model with hourly resolution. The importance of the different uncertainties for the overall system indicators such as annual cost, CO2 emissions, and reliability are assessed. The impacts on different modelled low-carbon pathways are compared. For the Finnish case study, the projected energy consumption seems to be the most important uncertainty factor for the future energy system scenarios (e.g. for the CO2 emissions), followed by the production of wind power and the potential of biomass. The results indicate that addressing input uncertainties will be highly relevant for energy system modelling when pursuing decarbonization. None of the modelled cost-optimal decarbonization pathways stands out as fully resilient in this respect. Highlights • The impacts of uncertainties on low-carbon energy system modelling are analyzed. • Monte Carlo analysis is used to examine uncertainties' effects on modelled systems.

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Section: Introductionmentioning

confidence: 99%

Analyzing the effects of uncertainties on the modelling of low-carbon energy system pathways

Pilpola

Lund

2020

Energy

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“…In order to de-risk the solutions one may also want to account for potential shortfalls [23,24] or regret [25] alongside the other design criteria, for instance using a multiobjective optimization approach. Such superstructure optimization models for risk-conscious decision-making under uncertainty are well developed and have been used for decision-making in various (bio)energy sectors, including CHP systems [20,26,27], distributed energy systems [28], and bioethanol supply-chains [29,30].…”

Section: Introductionmentioning

confidence: 99%

Risk-conscious optimization model to support bioenergy investments in the Brazilian sugarcane industry

et al. 2020

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The past decades have seen a diversification of the sugarcane industry with the emergence of new technology to produce bioenergy from by-product and waste process streams. Given Brazil's ambitious goal of reducing greenhouse gas emissions by over 40% below 2005 levels by 2030, it is of paramount importance to develop reliable decision-making systems in order to stimulate investment in these low-carbon technologies. This paper seeks to develop a more accurate optimization model to inform risk-conscious investment decisions for bioenergy generation capacity in sugarcane mills. The main objective is for the model to enable a better understanding of how Brazilian government policies, such as the electricity price in the regulated market, may impact these investments, by taking into account the uncertainty in sugar, ethanol and spot electricity markets and the interdependency between production and investment decisions in terms of saleable product mix. The proposed methodology combines portfolio optimization theory with superstructure process modeling and it relies on simple surrogates derived from a detailed sugarcane plant simulator to retain computational tractability and enable scenario analysis. The case study of an existing sugarcane plant is used to demonstrate the methodology and illustrate how the model can assist decision-makers. In all of the scenarios assessed, the model recommends investment in extra bioelectricity capacity via the anaerobic digestion of vinasse but advises against investment in second-generation ethanol production via the hydrolysis of surplus bagasse. Furthermore, the decision to upgrade the cogeneration system with a condensation turbine is highly sensitive to the electricity price practiced in the regulated market, capacity constraints on the sugar-ethanol mix, and the accepted level of risk. Another key insight drawn from the case study is that recent market conditions have favored a production focused on the sugar business, making it challenging for policy-makers to create attractive scenarios for biofuels. Long-term electricity contracting appears to be the main hedging strategy for de-risking other products and investments in the sugarcane business, provided it is priced adequately.

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“…Niu et al [13] planned a renewable cooling resource in a robust way considering demands and renewable energy uncertainties. Based on the minimax regret criterion, Yokoyama et al [14] carried out robust optimal design works for a gas turbine co-generation system in consideration of uncertain energy demands. Roberts et al [15] carried out a robust sizing for an energy system in a probabilistic scenario-based way, accounting for uncertain demands and natural resources.…”

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

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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.

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

Cited by 11 publications

References 32 publications

Analyzing the effects of uncertainties on the modelling of low-carbon energy system pathways

Analyzing the effects of uncertainties on the modelling of low-carbon energy system pathways

Risk-conscious optimization model to support bioenergy investments in the Brazilian sugarcane industry

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

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