A novel software package for the robust design of off-grid power systems

Brivio, Claudio; Matteucci, Matteo; Mandelli, Stefano; Merlo, Marco

doi:10.1016/j.jclepro.2017.08.069

Cited by 28 publications

(20 citation statements)

References 23 publications

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“…The literature regarding the optimal sizing of power systems under uncertainties is very rich, especially for hybrid systems with multiple energy sources, isolated or linked to the national grid [10][11][12][13][14]. Typical methodologies iteratively simulate the operation of the system, testing several combinations of the size of the components [8,[15][16][17][18] in order to find the design that minimizes total system costs, also including reliability aspects and other socio-economic indicators [16,19].…”

Section: Uncertainties In Sizing Methodologiesmentioning

confidence: 99%

“…Conventional commercial software usually optimizes only a single deterministic scenario of load and renewable generation [17], while the scientific literature is recently proposing several approaches to make decisions under uncertainty, which means taking into account many possible profiles of load and renewable generation. Authors in [20] applied for instance a chanceconstrained method to size and locate distributed energy sources, assuming that load flows can exceed the maximum capability of feeders according to a probability density function assessed by simulating multiple scenarios of load, distributed generation, and energy price.…”

Section: Uncertainties In Sizing Methodologiesmentioning

confidence: 99%

“…Authors in [11] proposed a stochastic approach based on NSGA-II method to size a hybrid system able to meet the thermal demand under uncertainties in solar and wind production; the outcome is the Pareto front between the economic value of the energy-not-served and the investment costs. The study [17] details a robust approach by using multiple scenarios to size rural systems in developing countries, but robustness seems to lead to rather conservative results. Conversely, papers [8,12,15,23] propose to approximate the stochastic objective function of the problem into a weighted sum of the deterministic objective functions of single scenarios.…”

Section: Uncertainties In Sizing Methodologiesmentioning

confidence: 99%

See 2 more Smart Citations

Stochastic sizing of isolated rural mini-grids, including effects of fuel procurement and operational strategies

Fioriti

Giglioli

Poli

et al. 2018

Electric Power Systems Research

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The governments of developing countries struggle to guarantee the universal access to electricity on their territory and 1.2 billion people are still without any service, especially in remote areas. Hybrid mini-grids can be an effective solution since they exploit local renewable resources integrated with energy storage devices, reduce the use of fuel generators, and defer the construction of long and expensive grids until the growth of demand makes it profitable. Off-grid mini-grids are typically operated with simple load-following dispatching strategies, but predictive approaches can provide better performances, although at the expense of additional computational requirements. This paper investigates the benefits of using rolling-horizon dispatching strategies during the mini-grid design stage, also comparing how the optimal size of components is affected by several technical and economical parameters. Moreover, we propose the use of a stochastic sizing procedure that captures the uncertainties related to the load, to the renewable generation, and to the time required for the fuel procurement and delivery. A case study with real load data collected from an existing mini-grid placed in Habaswein, Kenya, is presented and discussed. The optimal sizing of some

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Section: Uncertainties In Sizing Methodologiesmentioning

confidence: 99%

Section: Uncertainties In Sizing Methodologiesmentioning

confidence: 99%

Section: Uncertainties In Sizing Methodologiesmentioning

confidence: 99%

See 1 more Smart Citation

Stochastic sizing of isolated rural mini-grids, including effects of fuel procurement and operational strategies

Fioriti

Giglioli

Poli

et al. 2018

Electric Power Systems Research

View full text Add to dashboard Cite

show abstract

“…This section focuses on methods and approaches for the long-term forecasting of energy demand, which is a pivotal aspect for implementing a reliable and appropriate planning of the energy supply options, as discussed in the Introduction [9][10][11][12][13][14].…”

Section: Approaches To Forecast the Long-term Evolution Of The Energymentioning

confidence: 99%

Long-term energy planning and demand forecast in remote areas of developing countries: Classification of case studies and insights from a modelling perspective

Riva

Tognollo

Gardumi

et al. 2018

Energy Strategy Reviews

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More than half a billion people are expected to still lack reliable and affordable electric energy in 2040 and around 1.8 billion may remain reliant on traditional solid biomass for cooking. Long-term energy planning could help to achieve the energy access targets in developing countries, especially in remote rural areas. Different studies exist on long-term rural energy planning, but the different foci, terminology and methodologies make it difficult to track their similarities, weaknesses and strengths. With this work, we aim at providing a critical analysis of peer-reviewed studies on long-term rural energy planning, to help researchers in the field move across the diverse know-how developed in the last decades. The work resulted in the analysis of 126 studies and categorisation of 84 of them, under a number of rules clearly defined in the first part of the paper. The studies are then classified in two consecutive steps, first according to their type and afterwards according to the methodology they employ to forecast the energy demand, which is one the most critical aspects when dealing with long-term rural energy planning. The work also provides specific insights, useful to researchers interested in rural modelling. Few studies assume a dynamic demand over the years and most of them do not consider any evolution of the future energy load, or forecast its growth through arbitrary trends and scenarios. This however undermines the relevance of the results for the purpose of long-term planning and highlights the necessity of further developing the forecasting methodologies. We conclude that bottom-up approaches and system-dynamics seem appropriate approaches to forecast the evolution of the demand for energy in the long-term; we analyse their potential capability to tackle the context-specific complexities of rural areas and the nexus causalities among energy and socioeconomic dynamics.

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“…On the other hand, the optimization of generation designs is a widely-studied problem from the perspective of an individual off-grid system [11,12]. Some of the methods are based on classical optimization techniques such as Mixed-Integer Programming (MIP) [13], whereas others apply heuristic algorithms [14], metaheuristic techniques [15][16][17][18], or artificial intelligence methods [19]. Most methods minimize the cost of the system, although some methods include other criteria such as minimizing carbon emissions [20].…”

Section: Introductionmentioning

confidence: 99%

Optimizing Off-Grid Generation in Large-Scale Electrification-Planning Problems: A Direct-Search Approach

2019

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Off-grid systems play a prominent role in rural electrification planning. The problem of optimizing the generation design of a single off-grid system has received a significant amount of attention in the literature, and several software tools and algorithms have addressed it. However, methods and tools designed for individual mini-grids are not directly applicable to regional planning, where it is necessary to estimate the generation cost of potentially thousands of mini-grids. Conversely, most regional planning tools estimate the generation cost of mini-grids with rules of thumb or analytical expressions. These estimations are useful, but they lack the accuracy necessary to develop a rural electrification plan. This paper presents a method to estimate the generation cost of any potential off-grid system in a large-scale rural electrification planning problem, which is currently implemented in the Reference Electrification Model (REM). The method uses a master-slave decomposition that exploits the structure of the problem and combines continuous and discrete variables. The algorithm is illustrated with a case study that shows that a direct application of a discrete model may lead to suboptimal results in large-scale planning.

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A novel software package for the robust design of off-grid power systems

Cited by 28 publications

References 23 publications

Stochastic sizing of isolated rural mini-grids, including effects of fuel procurement and operational strategies

Stochastic sizing of isolated rural mini-grids, including effects of fuel procurement and operational strategies

Long-term energy planning and demand forecast in remote areas of developing countries: Classification of case studies and insights from a modelling perspective

Optimizing Off-Grid Generation in Large-Scale Electrification-Planning Problems: A Direct-Search Approach

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