14,968 research outputs found
A Review on the Application of Natural Computing in Environmental Informatics
Natural computing offers new opportunities to understand, model and analyze
the complexity of the physical and human-created environment. This paper
examines the application of natural computing in environmental informatics, by
investigating related work in this research field. Various nature-inspired
techniques are presented, which have been employed to solve different relevant
problems. Advantages and disadvantages of these techniques are discussed,
together with analysis of how natural computing is generally used in
environmental research.Comment: Proc. of EnviroInfo 201
Improving the resilience of post-disaster water distribution systems using a dynamic optimization framework
The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Improving the resilience of water distribution systems (WDSs) to handle natural disasters (e.g., earthquakes) is a critical step towards sustainable urban water management. This requires the water utility to be able to respond quickly to such disaster events and in an organized manner, to prioritize the use of available resources to restore service rapidly whilst minimizing the negative impacts. Many methods have been developed to evaluate the WDS resilience, but few efforts are made so far to improve resilience of a post-disaster WDS through identifying optimal sequencing of recovery actions. To address this gap, a new dynamic optimization framework is proposed here where the resilience of a post-disaster WDS is evaluated using six different metrics. A tailored Genetic Algorithm is developed to solve the complex optimization problem driven by these metrics. The proposed framework is demonstrated using a real-world WDS with 6,064 pipes. Results obtained show that the proposed framework successfully identifies near-optimal sequencing of recovery actions for this complex WDS. The gained insights, conditional on the specific attributes of the case study, include: (i) the near-optimal sequencing of recovery strategy heavily depends on the damage properties of the WDS, (ii) replacements of damaged elements tend to be scheduled at the intermediate-late stages of the recovery process due to their long operation time, and (iii) interventions to damaged pipe elements near critical facilities (e.g., hospitals) should not be necessarily the first priority to recover due to complex hydraulic interactions within the WDS
On green routing and scheduling problem
The vehicle routing and scheduling problem has been studied with much
interest within the last four decades. In this paper, some of the existing
literature dealing with routing and scheduling problems with environmental
issues is reviewed, and a description is provided of the problems that have
been investigated and how they are treated using combinatorial optimization
tools
Design of evacuation plans for densely urbanised city centres
The high population density and tightly packed nature of some city centres make emergency planning for these urban spaces especially important, given the potential for human loss in case of disaster. Historic and recent events have made emergency service planners particularly conscious of the need for preparing evacuation plans in advance. This paper discusses a methodological approach for assisting decision-makers in designing urban evacuation plans. The approach aims at quickly and safely moving the population away from the danger zone into shelters. The plans include determining the number and location of rescue facilities, as well as the paths that people should take from their building to their assigned shelter in case of an occurrence requiring evacuation. The approach is thus of the locationβallocationβrouting type, through the existing streets network, and takes into account the trade-offs among different aspects of evacuation actions that inevitably come up during the planning stage. All the steps of the procedure are discussed and systematised, along with computational and practical implementation issues, in the context of a case study β the design of evacuation plans for the historical centre of an old European city
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Όλ¬Έ (λ°μ¬)-- μμΈλνκ΅ λνμ : νκ²½λνμ νλκ³Όμ μ‘°κ²½νμ 곡, 2019. 2. μ΄λκ·Ό.곡κ°κ³ν κ³Όμ μμ λ€μν μ΄ν΄κ΄κ³μμ κ²°λΆλ λͺ©νμ μ μ½ μ건μ λ§μ‘±μν€λ κ²μ 볡μ‘ν λΉμ νμ λ¬Έμ λ‘μ ν΄κ²°νκΈ° μ΄λ €μ΄ κ²μΌλ‘ μλ €μ Έ μλ€. κ·Έλ¬λ μ΅κ·Ό μ΄λ¬ν λ¬Έμ μ μ μ μκ³ λ¦¬μ¦ (genetic algorithms), λ΄κΈμ§ κΈ°λ² (simulated annealing), κ°λ―Έ κ΅°μ§ μ΅μ ν (ant colony optimization) λ±μ λ€λͺ©μ μ΅μ ν μκ³ λ¦¬μ¦μ΄ μμ©λκ³ μμΌλ©°, κ΄λ ¨ μ°κ΅¬ μμ κΈμ¦νκ³ μλ€. μ΄ μ€ μ μ μκ³ λ¦¬μ¦μ κ³΅κ° μ΅μ ν λΆλ¬Έμ κ°μ₯ λΉλ λκ² μ μ©λ μ΅μ ν μκ³ λ¦¬μ¦μΌλ‘ explorationκ³Ό exploitationμ κ· νμΌλ‘ ν©λ¦¬μ μΈ μκ° λ΄μ μΆ©λΆν μ’μ κ³νμμ μ μν μ μλ€. κ·Έλ¬λ κ³΅κ° μ΅μ ν μ°κ΅¬κ° 보μ¬μ€ μ’μ μ±κ³Όμλ λΆκ΅¬νκ³ λλΆλΆμ μ°κ΅¬κ° νΉμ μ©λ νΉμ μμ€μ λ°°μΉμ μ§μ€λμ΄ μμΌλ©°, κΈ°νλ³ν μ μ, μ¬ν΄ κ΄λ¦¬, κ·Έλ¦°μΈνλΌ κ³νκ³Ό κ°μ μ΅κ·Όμ νκ²½ μ΄μλ₯Ό λ€λ£¬ μ¬λ‘λ λ§€μ° λ―Έν‘νλ€. λ°λΌμ λ³Έ μ°κ΅¬μμλ μ μ μκ³ λ¦¬μ¦κ³Ό λΉμ§λ°° μ λ ¬ μ μ μκ³ λ¦¬μ¦ (non-dominated sorting genetic algorithm II)μ κΈ°μ΄νμ¬ κΈ°νλ³ν μ μ, μ¬ν΄ κ΄λ¦¬, λμμ λ
Ήμ§ κ³ν λ±κ³Ό κ°μ νκ²½ μ΄μλ₯Ό 곡κ°κ³νμ λ°μν μ μλ μΌλ ¨μ κ³΅κ° μ΅μ ν λͺ¨λΈμ μ μνμλ€. κ°λ³ νκ²½ μ΄μμ λ°λΌ κ³΅κ° ν΄μλ, λͺ©μ , μ μ½μκ±΄μ΄ λ€λ₯΄κ² ꡬμ±νμμΌλ©°, 곡κ°μ λ²μκ° μ’μμ§κ³ 곡κ°ν΄μλλ λμμ§λ μμλλ‘ λμ΄νμλ€.
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Όλ¬Έμ 첫λ²μ§Έ μ₯μμλ νμ ꡬμ λ κ·λͺ¨ (province scale, ν΄μλ 1γ’)μμ λ―Έλμ κΈ°νλ³νμ μ μνκΈ° μν ν μ§μ΄μ© μλ리μ€λ₯Ό λͺ¨μν μ μλ κ³΅κ° μ΅μ ν λͺ¨λΈμ μ μνμλ€. κΈ°νλ³νκ° λ¨Ό λ―Έλκ° μλ, νμ¬ μ΄λ―Έ μ§νλκ³ μμΌλ©° κ΄λ ¨ν λ€μμ νΌν΄κ° κ΄μ°°λκ³ μκΈ° λλ¬Έμ 곡κ°μ κ΄μ μμ κΈ°νλ³νμ λν μ μμ νμμ±μ΄ μ§μ λμ΄ μλ€. κ·Έλ¬λ ꡬ체μ μΌλ‘ κΈ°νμ λν ν볡 νλ ₯μ±μ ν₯μμν€κΈ° μνμ¬ ν μ§μ΄μ©μ 곡κ°μ ꡬμ±μ μ΄λ»κ² λ³νμμΌμΌ ν μ§μ λν λ°©λ²λ‘ μ μλ λ―Έν‘νλ€. μ§μκ³νμμ κΈ°νλ³ν μν₯μ κ³ λ €ν ν μ§μ΄μ© λ°°λΆμ λ§€μ° μ μ©ν, κΈ°λ³Έμ μΈ μ€μ₯κΈ° μ μ μ λ΅μ ν΄λΉνλ€. λ³Έ μ°κ΅¬μμλ λ€λͺ©μ μ μ μκ³ λ¦¬μ¦ (MOGA, multi-objective genetic algorithm)μ κΈ°μ΄νμ¬ 9,982γ’μ 350λ§μ μΈκ΅¬κ° κ±°μ£Όνλ νκ΅μ μΆ©μ²λ¨λ λ° λμ κ΄μμ μΌλλ₯Ό λμμΌλ‘ κΈ°νλ³ν μ μμ μν ν μ§μ΄μ© μλ리μ€λ₯Ό μ μνμλ€. μ§μμ μΈ κΈ°νλ³ν μν₯κ³Ό κ²½μ μ μ¬κ±΄μ κ³ λ €νμ¬ μ¬ν΄ νΌν΄ λ° μ νλμ μ΅μν, λ²Ό μμ°λ, μ’
νλΆλ 보μ , κ²½μ μ κ°μΉμ μ΅λν λ± λ€μ― κ°μ§μ λͺ©μ μ μ ννμλ€. κ° λͺ©μ λ³ κ°μ€μΉλ₯Ό λ³νμν€λ©° μ¬μ― κ°μ§ κ°μ€μΉ μ‘°ν©μ λν 17κ°μ νλ ν μ΅μ ν μ§μ΄μ© μλ리μ€λ₯Ό μμ±νμλ€. λλΆλΆμ μλ리μ€λ μ λμ μ°¨μ΄λ μμΌλ νμ¬μ ν μ§μ΄μ©μ λΉν΄ κΈ°νλ³ν μ μ λΆλΆμμ λ μ’μ νΌν¬λ¨Όμ€λ₯Ό 보μμΌλ―λ‘, κΈ°νλ³νμ λν ν볡νλ ₯μ±μ΄ κ°μ ν μ μμ κ²μΌλ‘ νλ¨νμλ€. λν κ³΅κ° μ΅μ ν λͺ¨λΈμ μ μ°ν ꡬ쑰λ₯Ό κ³ λ €νμμ λ, μ§μμ μ€λ¬΄μ μμ κ°μ€μΉμ κ°μ λͺ¨λΈμ νλΌλ―Έν°, κΈ°νλ³ν μν₯ νκ°μ κ°μ μ
λ ₯μλ£λ₯Ό λ³κ²½ν¨μΌλ‘μ¨ ν¨μ¨μ μΌλ‘ μλ‘μ΄ μλ리μ€λ₯Ό μμ± λ° μ ννλ κ²μ΄ κ°λ₯ν κ²μΌλ‘ μμνμλ€.
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Όλ¬Έμ λ λ²μ§Έ μ₯μμλ νμ ꡬμ κ΅° κ·λͺ¨ (local scale, ν΄μλ 100m)μμ κΈ°νλ³νμ λ°λ₯Έ μ¬ν΄ νΌν΄λ₯Ό κ΄λ¦¬νκΈ° μν ν μ§μ΄μ© μλ리μ€λ₯Ό λͺ¨μν μ μλ κ³΅κ° μ΅μ ν λͺ¨λΈμ μ μνμλ€. μ°μ
μ§νμμ νμ°λ‘ μΈν μ°μ¬νλ μΈλͺ
κ³Ό μ¬μ°μ μ¬κ°ν νΌν΄λ₯Ό μ΄λν μ μλ κ²μΌλ‘ μλ €μ Έ μλ€. λμ±μ΄ κΈ°νλ³νμ λ°λ₯Έ κ°μ°μ λ³λμ± μ¦κ°λ‘ μ΄λ¬ν μ°μ¬ν λΉλ λ° κ°λ μμ μ¦λλ κ²μΌλ‘ μμλλ€. μΌλ°μ μΌλ‘ μ°μ¬ν 리μ€ν¬κ° λμ μ§μμ νΌν΄ κ°λ°μ§μμ λ°°μΉνλ κ²μ΄ νΌν΄λ₯Ό μ κ° νΉμ ννΌν μ μλ κ°μ₯ ν¨κ³Όμ μΈ μ λ΅μΌλ‘ μλ €μ Έ μμΌλ, μ€μ 곡κ°μμμ κ³νμ λ§€μ° λ³΅μ‘ν λΉμ νμ λ¬Έμ λ‘μ μ΄κ²μ μ€ννλ λ° μ΄λ €μμ΄ μλ€. λ°λΌμ λ³Έ μ°κ΅¬μμλ λΉμ§λ°° μ λ ¬ μ μ μκ³ λ¦¬μ¦ IIμ κΈ°μ΄νμ¬ μ°μ¬ν 리μ€ν¬ λ° μ νλ, ννΈνμ μ΅μν λ±μ λ€μν λͺ©μ μ λ§μ‘±μν€λ μ’
ν©μ μΈ ν μ§μ΄μ© λ°°λΆ κ³νμ μ μνμλ€. λμμ§λ 2018λ
λκ³μ¬λ¦Όν½ κ°μ΅μ§μΈ νκ΅μ νμ°½κ΅°μΌλ‘μ 2006λ
μ μ°μ¬νλ‘ μΈν λκ·λͺ¨μ νΌν΄λ₯Ό κ²½ννμμΌλ, μ¬λ¦Όν½ νΉμ λ±μ κ°λ°μλ ₯μΌλ‘ μΈν λκ°λ°μ΄ μ°λ €λλ μ§μμ΄λ€. μ΅μ’
μ μΌλ‘ νλ²μ λͺ¨μλ₯Ό ν΅ν΄ νμ¬μ ν μ§μ΄μ© λ³΄λ€ μ μ΄λ νκ°μ§ μ΄μμ λͺ©μ μμ μ’μ νΌν¬λ¨Όμ€λ₯Ό 보μ΄λ 100κ°μ νλ ν μ΅μ κ³νμμ μμ±νμλ€. λν 5κ°μ λνμ μΈ κ³νμμ μ μ νμ¬ μ°μ¬ν리μ€ν¬ μ΅μνμ μ νλ μ΅μν κ°μ λ°μνλ μμ ν¨κ³Όλ₯Ό μ€λͺ
νμλ€. λ³Έ μ°κ΅¬κ²°κ³Όλ κΈ°νλ³νμ κ΄λ ¨λ κ³΅κ° μ μ μ λ΅μ μ립, λ³΄λ€ ν₯μλ κ°λ°κ³νμ μν μμ¬κ²°μ μ ν¨κ³Όμ μΌλ‘ μ§μν μ μμ κ²μΌλ‘ μμνμλ€.
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Όλ¬Έμ μΈ λ²μ§Έ μ₯μμλ λΈλ‘ κ·λͺ¨(neighborhood scale, 2m)μμ λμ λ΄ λ
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Ήμ§ 곡κ°μ λμλ―Όμ μΆμ μ§μ κ²°μ μ μΈ μν₯μ λ―ΈμΉκΈ° λλ¬Έμ λ€μν λμ μ¬μ λ° κ°λ°κ³νμλ λ
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ν©μ , μ λμ μΌλ‘ κ³ λ €λ μ¬λ‘λ λ§€μ° λ―Έν‘νλ€. λ°λΌμ λ³Έ μ°κ΅¬μμλ λΉμ§λ°° μ λ ¬ μ μ μκ³ λ¦¬μ¦ IIμ κΈ°μ΄νμ¬ λ
Ήμ§μ μνμ μ°κ²°μ± μ¦μ§, μ΄μ¬ ν¨κ³Ό μνμ κ°μ λ€μν ν¨κ³Όμ μ€μΉμ λ°λ₯΄λ λΉμ©μ μ’
ν©μ μΌλ‘ κ³ λ €νμ¬ μ μ ν λ
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Ήμ§κ³νμμ μ μνμλ€. λΈλ‘ κ·λͺ¨μ κ°μμ λμμ§μ λ³Έ μ΅μ ν λͺ¨λΈμ μ μ©ν¨μΌλ‘μ¨ 30κ°μ νλ ν μ΅μ λ
Ήμ§κ³νμμ μμ±νμμΌλ©°, κ° λͺ©μ κ° νΌν¬λ¨Όμ€λ₯Ό λΉκ΅νμ¬ λ
Ήμ§μ μ΄μ¬ μν ν¨κ³Όμ μνμ μ°κ²°μ± μ¦μ§ ν¨κ³Ό κ°μ μμΉ κ΄κ³ (synergistic relationship), μ΄λ¬ν κΈμ μ ν¨κ³Όμ λΉμ© μ κ° κ°μ μμ ν¨κ³Ό (trade-off relationship)λ₯Ό λΆμνμλ€. λν λ€μν κ³νμ μ€ λνμ μΈ νΉμ±μ μ§λλ κ³νμ, λ€μμ κ³νμμμ 곡ν΅μ μΌλ‘ λ
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νμλ€. λ³Έ μ°κ΅¬μμ μ μλ λͺ¨λΈμ κ³νμμ μμ μμλΆν° μ λμ νκ°, κ³νμ μ νμ μ΄λ₯΄λ μΌλ ¨μ κΈμ μ μΈ νΌλλ°± κ³Όμ μ μμμ΄ λ°λ³΅ν¨μΌλ‘μ¨ κΈ°μ‘΄μ λ
Ήμ§κ³ν κ³Όμ μ κ°μ νλ λ° κΈ°μ¬ν μ μμ λΏλ§ μλλΌ λͺ¨λΈμ κ²°κ³Ό μμ λ€μκ° νλ ₯μ λμμΈ (co-design)μ μν μ΄μμΌλ‘μ νμ©λ μ μμ κ²μΌλ‘ μμνμλ€.The meeting of heterogeneous goals while staying within the constraints of spatial planning is a nonlinear problem that cannot be solved by linear methodologies. Instead, this problem can be solved using multi-objective optimization algorithms such as genetic algorithms (GA), simulated annealing (SA), ant colony optimization (ACO), etc., and research related to this field has been increasing rapidly. GA, in particular, are the most frequently applied spatial optimization algorithms and are known to search for a good solution within a reasonable time period by maintaining a balance between exploration and exploitation. However, despite its good performance and applicability, it has not adequately addressed recent urgent issues such as climate change adaptation, disaster management, and green infrastructure planning. It is criticized for concentrating on only the allocation of specific land use such as urban and protected areas, or on the site selection of a specific facility.
Therefore, in this study, a series of spatial optimizations are proposed to address recent urgent issues such as climate change, disaster management, and urban greening by supplementing quantitative assessment methodologies to the spatial planning process based on GA and Non-dominated Sorting Genetic Algorithm II (NSGA II). This optimization model needs to be understood as a tool for providing a draft plan that quantitatively meets the essential requirements so that the stakeholders can collaborate smoothly in the planning process. Three types of spatial planning optimization models are classified according to urgent issues. Spatial resolution, planning objectives, and constraints were also configured differently according to relevant issues. Each spatial planning optimization model was arranged in the order of increasing spatial resolution.
In the first chapter, the optimization model was proposed to simulate land use scenarios to adapt to climate change on a provincial scale. As climate change is an ongoing phenomenon, many recent studies have focused on adaptation to climate change from a spatial perspective. However, little is known about how changing the spatial composition of land use could improve resilience to climate change. Consideration of climate change impacts when spatially allocating land use could be a useful and fundamental long-term adaptation strategy, particularly for regional planning. Here climate adaptation scenarios were identified on the basis of existing extents of three land use classes using Multi-objective Genetic Algorithms (MOGA) for a 9,982 km2 region with 3.5 million inhabitants in South Korea. Five objectives were selected for adaptation based on predicted climate change impacts and regional economic conditions: minimization of disaster damageand existing land use conversionmaximization of rice yieldprotection of high-species-richness areasand economic value. The 17 Pareto land use scenarios were generated by six weighted combinations of the adaptation objectives. Most scenarios, although varying in magnitude, showed better performance than the current spatial land use composition for all adaptation objectives, suggesting that some alteration of current land use patterns could increase overall climate resilience. Given the flexible structure of the optimization model, it is expected that regional stakeholders would efficiently generate other scenarios by adjusting the model parameters (weighting combinations) or replacing the input data (impact maps) and selecting a scenario depending on their preference or a number of problem-related factors.
In the second chapter, the optimization model was proposed to simulate land use scenarios for managing disaster damage due to climate change on local scale. Extreme landslides triggered by rainfall in hilly regions frequently lead to serious damage, including casualties and property loss. The frequency of landslides may increase under climate change, because of the increased variability of precipitation. Developing urban areas outside landslide risk zones is the most effective method of reducing or preventing damageplanning in real life is, however, a complex and nonlinear problem. For such multi-objective problems, GA may be the most appropriate optimization tool. Therefore, comprehensive land use allocation plans were suggested using the NSGA II to overcome multi-objective problems, including the minimization of landslide risk, minimization of change, and maximization of compactness. The study area is Pyeongchang-gun, the host city of the 2018 Winter Olympics in Korea, where high development pressure has resulted in an urban sprawl into the hazard zone that experienced a large-scale landslide in 2006. We obtained 100 Pareto plans that are better than the actual land use data for at least one objective, with five plans that explain the trade-offs between meeting the first and the second objectives mentioned above. The results can be used by decision makers for better urban planning and for climate change-related spatial adaptation.
In the third chapter, the optimization model was proposed to simulate urban greening plans on a neighborhood scale. Green space is fundamental to the good quality of life of residents, and therefore urban planning or improvement projects often include strategies directly or indirectly related to greening. Although green spaces generate positive effects such as cooling and reduction of rainwater runoff, and are an ecological corridor, few studies have examined the comprehensive multiple effects of greening in the urban planning context. To fill this gap in this fields literature, this study seeks to identify a planning model that determines the location and type of green cover based on its multiple effects (e.g., cooling and enhancement of ecological connectivity) and the implementation cost using NSGA II. The 30 Pareto-optimal plans were obtained by applying our model to a hypothetical landscape on a neighborhood scale. The results showed a synergistic relationship between cooling and enhancement of connectivity, as well as a trade-off relationship between greenery effects and implementation cost. It also defined critical lots for urban greening that are commonly selected in various plans. This model is expected to contribute to the improvement of existing planning processes by repeating the positive feedback loop: from plan modification to quantitative evaluation and selection of better plans. These optimal plans can also be considered as options for co-design by related stakeholders.1. INTRODUCTION
2. CHAPTER 1: Modelling Spatial Climate Change Land use Adaptation with Multi-Objective Genetic Algorithms to Improve Resilience for Rice Yield and Species Richness and to Mitigate Disaster Risk
2.1. Introduction
2.2. Study area
2.3. Methods
2.4. Results
2.5. Discussion
2.6. References
2.7. Supplemental material
3. CHAPTER 2: Multi-Objective Land-Use Allocation Considering Landslide Risk under Climate Change: Case Study in Pyeongchang-gun, Korea
3.1. Introduction
3.2. Material and Methods
3.3. Results
3.4. Discussion
3.5. Conclusion
3.6. References
4. CHAPTER 3: Multi-Objective Planning Model for Urban Greening based on Optimization Algorithms
3.1. Introduction
3.2. Methods
3.3. Results
3.4. Discussion
3.5. Conclusion
3.6. References
3.7. Appendix
5. CONCLUSION
REFERENCESDocto
Reducing the Vulnerability of Electric Power Infrastructure Against Natural Disasters by Promoting Distributed Generation
Natural disasters cause significant damage to the electrical power infrastructure every year. Therefore, there is a crucial need to reduce the vulnerability of the electric power grid against natural disasters. Distributed generation (DG) represents small-scale decentralized power generation that can help reduce the vulnerability of the grid, among many other benefits. Examples of DG include small-scale photo-voltaic (PV) systems. Accordingly, the goal of this paper is to investigate the benefits of DG in reducing the vulnerability of the electric power infrastructure by mitigating against the impact of natural disasters on transmission lines. This was achieved by developing a complex system-of-systems (SoS) framework using agent-based modeling (ABM) and optimal power flow (OPF). N-1 contingency analysis and optimization were performed under two approaches: The first approach determined the minimum DG needed at any single location on the electric grid to avoid blackouts. The second approach used a genetic algorithm (GA) to identify the minimum total allocation of DG distributed over the electric grid to mitigate against the failure of any transmission line. Accordingly, the model integrates ABM, OPF, and GA to optimize the allocation of DG and reduce the vulnerability of electric networks. The model was tested on a modified IEEE 6-bus system as a proof of concept. The outcomes of this research are intended to support the understanding of the benefits of DG in reducing the vulnerability of the electric power grid. The presented framework can guide future research concerning policies and incentives that can strategically influence consumer decision to install DG and reduce the vulnerability of the electric power infrastructure
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