Multi-year stochastic planning of off-grid microgrids subject to significant load growth uncertainty: overcoming single-year methodologies

Fioriti, Davide; Poli, Davide; Martínez, Pablo Dueñas; Pérez-Arriaga, Ignacio J.

doi:10.1016/j.epsr.2021.107053

Cited by 28 publications

(15 citation statements)

References 36 publications

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“…The second approach is Pareto-based optimization, which uses the Pareto-dominance concept. The Pareto-front is the set of all solutions for which the corresponding objective vectors cannot be improved in any dimension without degradation in another [20]. When considering three objectives, the Pareto-front becomes a three-dimensional surface.…”

Section: Methods For Sizing Autonomous Microgridsmentioning

confidence: 99%

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Multi-Objective Optimization of Autonomous Microgrids with Reliability Consideration

Riou¹,

Dupriez-Robin

Grondin³

et al. 2021

Energies

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Microgrids operating on renewable energy resources have potential for powering rural areas located far from existing grid infrastructures. These small power systems typically host a hybrid energy system of diverse architecture and size. An effective integration of renewable energies resources requires careful design. Sizing methodologies often lack the consideration for reliability and this aspect is limited to power adequacy. There exists an inherent trade-off between renewable integration, cost, and reliability. To bridge this gap, a sizing methodology has been developed to perform multi-objective optimization, considering the three design objectives mentioned above. This method is based on the non-dominated sorting genetic algorithm (NSGA-II) that returns the set of optimal solutions under all objectives. This method aims to identify the trade-offs between renewable integration, reliability, and cost allowing to choose the adequate architecture and sizing accordingly. As a case study, we consider an autonomous microgrid, currently being installed in a rural area in Mali. The results show that increasing system reliability can be done at the least cost if carried out in the initial design stage.

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Section: Methods For Sizing Autonomous Microgridsmentioning

confidence: 99%

“…Refs. [19][20][21][22][23] have considered long-term sizing with multi-step investments using optimization techniques. Some reviews also focus on available tools for the design and planning of hybrid renewable energy systems [24][25][26].…”

Section: Literature Review On Autonomous Microgrid Designmentioning

confidence: 99%

Multi-Objective Optimization of Autonomous Microgrids with Reliability Consideration

Riou¹,

Dupriez-Robin

Grondin³

et al. 2021

Energies

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“…However, the planning process of energy systems is subject to considerable uncertainty, making the selection and sizing of appropriate energy technologies challenging. Small oversights can lead to unreliable electric supply, unnecessary high costs [2], underestimated environmental impacts through unexpected fossil fuel consumption, and, ultimately, failing energy systems [3]. Most pressing issues are time varying factors, such as demand changes, price volatilities and component degradation [3].…”

Section: Introductionmentioning

confidence: 99%

“…Small oversights can lead to unreliable electric supply, unnecessary high costs [2], underestimated environmental impacts through unexpected fossil fuel consumption, and, ultimately, failing energy systems [3]. Most pressing issues are time varying factors, such as demand changes, price volatilities and component degradation [3]. The former are particularly prevalent in developing countries within areas with no prior access to electricity.…”

Section: Introductionmentioning

confidence: 99%

“…At the same time, fossil fuel prices are expected to rise in the future due to decreasing availability, increasing awareness of their environmental impact, and overall rising energy consumption [6]. Components' degradation over time and use leads to efficiency losses and unavoidable reinvestment, affecting the performance and cost of the power system and creating project risks [3]. Consideration of these circumstances is particularly relevant in developing countries, where second-hand products are often used and proper maintenance is not always guaranteed.…”

Section: Introductionmentioning

confidence: 99%

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Sustainable Energy System Planning in Developing Countries: A Decision Support System Considering Variations Over Time

Eckhoff¹,

Hart²,

Breitner³

2022

Proceedings of the Annual Hawaii International Conference on System Sciences

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Planning energy systems is subject to changes in components' health and installation costs, fossil fuel prices, and load demand. Especially in developing countries, electrical loads are reported to increase drastically after electrification. Improper sizing of the energy system's components can lead to reduced environmental sustainability, decreased reliability, and long-term project failures. As no tools for energy system planning exist that aim at developing countries and sufficiently account for temporal variations, we modify the software NESSI4D in a design science cycle to provide the comprehensive decision support system NESSI4D + . We conduct an applicability check with a representative rural village in mountainous Nepal that validates NESSI4D + 's relevance and shows the importance of considering temporal variations for economically, ecologically, and socially long-term sustainable energy projects.

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Risk-constrained multi-period investment model for Distributed Energy Resources considering technology costs and regulatory uncertainties

Heleno

Valenzuela

2022

Applied Energy

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Multi-year stochastic planning of off-grid microgrids subject to significant load growth uncertainty: overcoming single-year methodologies

Cited by 28 publications

References 36 publications

Multi-Objective Optimization of Autonomous Microgrids with Reliability Consideration

Multi-Objective Optimization of Autonomous Microgrids with Reliability Consideration

Sustainable Energy System Planning in Developing Countries: A Decision Support System Considering Variations Over Time

Risk-constrained multi-period investment model for Distributed Energy Resources considering technology costs and regulatory uncertainties

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