A GIS-AHP approach for determining the potential of solar energy to meet the thermal demand in southeastern Spain productive enclaves

Romero-Ramos, José A.; Gil, Juan D.; Cardemil, José M.; Escobar, Rodrigo; Arias, Ignacio; García, Manuel Pérez

doi:10.1016/j.rser.2023.113205

Cited by 28 publications

(5 citation statements)

References 59 publications

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“…It breaks down a complex problem into constituent factors and forms a hierarchy by the dominant relationship, and then uses pairwise comparisons to derive a scale of relative importance for decision alternatives. Broadly speaking, it can be carried out in the following four steps: (1) establishing hierarchical structure, (2) structuring all judgment matrices in each level, (3) ranking order and testing the uniformity, (4) ranking overall order and testing the uniformity 27,[37][38][39] .…”

Section: Data Source and Study Methodsmentioning

confidence: 99%

GIS-based risk assessment of flood disaster in the Lijiang River Basin

Tang

Liju

et al. 2023

Sci Rep

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This study is designed to provide a scientific reference for the establishment of rainstorm and flood disaster prevention system in Guilin region and improve the risk assessment of rainstorm and flood disasters. To realize the goal, a flood risk evaluation model is established by weight analysis methods including the entropy weight method and the analytic hierarchy process from 3 aspects, i.e., risk of disaster causing factors, sensitivity of disaster-pregnant environment and vulnerability of disaster bearing body. For the model, the daily precipitation 1980–2020 of 6 representative national meteorological stations in the Lijiang River Basin was used as reference data of disaster causing factors; six indicators, i.e., NDVI, river network density, geological hazard, slope, slope aspect and terrain undulation were selected as the sensitivity of disaster-pregnant environment; NPP, potential of farmland production, and population density were taken as the criteria for determining the vulnerability of disaster bearing capacity. Meanwhile, ArcGIS was used for analysis and calculation to complete the risk assessment of flood disaster in Lijiang River Basin, Guangxi. The results indicate that: (1) the hazard level of flood disaster causing factors in Lijiang River Basin shows a decreasing distribution pattern from north to south, and high-risk areas cover 3108.47 km2, accounting for 21.29%; (2) the stability grade of disaster-pregnant environment shows a decreasing trend from the surrounding mountains to the plains, and the low-stability and lower-stability areas are mostly found in the low-lying areas around Lijiang River, with an area of 4218.63 km2, accounting for 28.69%; (3) the vulnerability of the disaster bearing body is generally at a low level, and the areas with high level cover 246.96 km2, accounting for only 1.69%; (4) under the combined effect of the above factors, the northern part of Guilin City in the Lijiang River Basin has a high risk of flood disaster.

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Section: Data Source and Study Methodsmentioning

confidence: 99%

GIS-based risk assessment of flood disaster in the Lijiang River Basin

Tang

Liju

et al. 2023

Sci Rep

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“…The moving base storage amount in period t, N t means the storage amount which should be charged for effective demand-to-supply management in period t. On the other hand, it shows the appropriate amount of electricity in the storage battery to conduct effective demand-to-supply management. N t is calculated by the input amount determination parameter over period t, β t and cumulative probability distribution of the remaining storage amount L t , F(L t ) as shown in Equation (15).…”

Section: Parameters Explanation and Calculation Methods Equation Of C...mentioning

confidence: 99%

“…However, they did not conduct demand forecasting based on real power consumption data. Romero-Ramos et al ( 2023) [15] evaluated the potential of solar power generation to satisfy the heat demands in an industrial area in southeastern Spain. Their results indicated that 5% of the total area of southeastern Spain is an effective territory to develop an industrial area supplied by solar energy.…”

Section: Introductionmentioning

confidence: 99%

Seasonal Analysis and Capacity Planning of Solar Energy Demand-to-Supply Management: Case Study of a Logistics Distribution Center

Takada,

Ijuin,

Matsui

et al. 2023

Energies

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In recent years, global warming and environmental problems have become more serious due to greenhouse gas (GHG) emissions. Harvesting solar energy for production and logistic activities in supply chains, including factories and distribution centers, has been promoted as an effective means to reduce GHG emissions. However, it is difficult to balance the supply and demand of solar energy, owing to its intermittent nature, i.e., the output depends on the daylight and season. Moreover, the use of large-capacity solar power generation systems and batteries incurs higher installation costs. In order to maintain low costs, demand-to-supply management of solar energy, based on appropriate seasonal analysis of power generation and consumption and the capacity planning for power generation and the storage battery, is necessary. In this study, the on-demand cumulative control method is applied to actual power consumption data and solar power generation data estimated at a distribution center. Moreover, the monthly, seasonal, and temporal characteristics of power generation and consumption at the distribution center are analyzed. Additionally, the total amount of power purchased is investigated for solar energy demand-to-supply management.

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“…( 3) = class score on subcriteria for parameter i i = class interval Ba = upper limit of class Bb = lower limit of class Using GIS, spatial-based multi-criteria decision-making modeling will be carried out for bioenergy agroindustry site selection. The GIS analyses and evaluates spatial and nonspatial data sub-criteria to select bioenergy agroindustry locations classified by weight [22]. Determining the location of bioenergy agroindustry needs to consider multi-criteria.…”

Section: Table 2 Index Random Consistencymentioning

confidence: 99%

Decision Support System for Bioenergy Supply Chain Optimization: A Case Study at Lebak District, Banten Indonesia

Krisnaningsih,

Arkeman,

Marimin

et al. 2024

IJSDP

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This study aims to design a decision support system (DSS) development model in sustainable bioenergy supply chains. Our approach involves 1) identifying the bioenergy supply chain model through determining the spatial potential model of agroindustry area using geographic information system (GIS) and analytical hierarchy process (AHP); 2) determining the optimization model for aggregate production process planning using fuzzy goal programming for bioenergy production to design the biomass inventory level determination model using adaptive neuro-fuzzy inference system (ANFIS) approach; 3) designing the concept of DSS model in bioenergy supply chain. The results showed the identification of the supply chain model from the spatial model of potential agroindustry locations with three regional categories: 19.34% potential, 16.93% not potential, and 63.70% developing. Aggregate planning is appropriate based on three objective functions to be achieved in production planning for determining inventory levels using ANFIS using three input variables and comparing performance with RMSE, MAPE, and R2 inventory levels so that the model can predict inventory levels adaptively. The concept of the DSS Model on the bioenergy supply chain from agricultural centers to users by adding Internet of Things (IoT) technology can increase the effectiveness and efficiency of the bioenergy supply chain. The managerial implications of this research can provide relevant insights for the design and improvement of renewable energy management programs. Utilization of local biomass resources becomes more optimal.

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A GIS-AHP approach for determining the potential of solar energy to meet the thermal demand in southeastern Spain productive enclaves

Cited by 28 publications

References 59 publications

GIS-based risk assessment of flood disaster in the Lijiang River Basin

GIS-based risk assessment of flood disaster in the Lijiang River Basin

Seasonal Analysis and Capacity Planning of Solar Energy Demand-to-Supply Management: Case Study of a Logistics Distribution Center

Decision Support System for Bioenergy Supply Chain Optimization: A Case Study at Lebak District, Banten Indonesia

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