GIS based site suitability assessment for wind and solar farms in Songkhla, Thailand

Ali, Shahid; Taweekun, Juntakan; Techato, Kuaanan; Waewsak, Jompob; Gyawali, Saroj

doi:10.1016/j.renene.2018.09.035

Cited by 164 publications

(75 citation statements)

References 41 publications

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“…They may also represent zones too far from specific objects (e.g. electrical grid) [23]. Geographic elements for which distance must be computed (in yellow) can be of any kind but are generally related to connection and accessibility costs such as road and grid networks.…”

Section: Geographical Data Layersmentioning

confidence: 99%

“…Buffers surrounding geographic objects depend both on their type and on the kind of power station, e.g. wind [23], biomass [42] or solar PV [24]. Distance thresholds to given layers (e.g.…”

Section: Control Parametersmentioning

confidence: 99%

“…Distance thresholds to given layers (e.g. road network, electrical grid) stand for the limit beyond which establishing a facility gets economically irrelevant and so corresponding plots must be excluded [23]. Depending on the values defined for buffers and thresholds, corresponding restricted areas will be more or less significant.…”

Section: Control Parametersmentioning

confidence: 99%

“…For example grassland is never restricted, and agricultural lands may not be restricted (permissive scenario), or be restricted and take different buffer values accordingly (restrictive and intermediate scenarios). In the case of the maximum distance to road and electrical networks, a common value is 10 km [23]. We have preferred to double it in order to give priority to the optimization module for assessing the cost of the furthest zones (Table 3).…”

Section: Geographical Constraints and Land Managementmentioning

confidence: 99%

“…A refinement is then performed using multi-criteria analysis, in which relevance of the available zones is ranked according to predefined weighted decision criteria [22]. Criterion selection and ranking is mostly based on literature consensus and opinion from local expertise [23]. As these studies are not focused on energy demand management, the methodology is based on static data, i.e.…”

Section: Introduction and Related Workmentioning

confidence: 99%

See 4 more Smart Citations

An integrated GIS and robust optimization framework for solar PV plant planning scenarios at utility scale

et al. 2020

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Today, the overall goal of energy transition planning is to seek an optimal strategy for increasing the share of renewable sources in existing power networks, such that the growing power demand is satisfied at manageable short/long term investment. In this paper we address the problem of PV penetration in electricity networks, by considering both 1) the spatial issue of site selection and size, and 2) the temporal aspect of hourly load and demand satisfaction, in addition with the investment and maintenance costs to guarantee a viable and reliable solution. We propose to address this spatio-temporal optimization problem through an integrated GIS and robust optimization model, that allows handling of the ubiquitous dependencies between resource and demand time variability and the selection of optimal sites of renewable power generation. Our approach contributes to the integration of the multi-dimensional and combinatorial aspects of this problem, gathering geographical layers (regional or national scale) and temporal packing (hourly time stamp) constraints, and cost functions. This model computes the optimal geographical location and size of PV facilities allowing energy planning targets to be met at minimal cost in a reliable manner. In this paper, we illustrate our approach by studying the penetration of large-scale solar PV in the French Guiana's power system. Among the results, we show for instance that: 1) our approach performs geographical aggregation with real contextual data, i.e. balances the intermittency of RE sources by spreading out the corresponding installations (location + size) across the territory; 2) the total installed PV capacity can be doubled by removing the 35 % penetration limit on intermittent power without exceeding hourly demand; 3) the safest investment scenario is below 30 MW of new PV facilities (≈ 45 Me and 2 plants), though it is theoretically possible to install up to 45 MW (>120 Me and 11 plants). Nomenclature B iBoolean variable equal to 1 if site PS i is selected, 0 otherwise Ccap i Capital cost for implementation of a new PV power plant (e) Ccon i Connection cost for each new PV plant, transmission lines and substation (e) Clan Transmission line unit cost (e/m) Cop i Annual fixed operational cost per new PV plant (e) Csta Substation power unit cost (e/kW) Csta i Capital cost for new substation (e) Dem h Estimated hourly power demand (kWh) Dg i Minimum distance from the grid to the centroid of a candidate (m) Eint h Current hourly production from intermittent sources (kWh) * Corresponding author Email addresses: benjamin.pillot@ird.fr (Benjamin Pillot), nadeem.alkurdi@ird.fr (Nadeem Al-Kurdi), carmen.gervet@umontpellier.fr (Carmen Gervet), laurent.linguet@univ-guyane.fr (Laurent Linguet)

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Section: Geographical Data Layersmentioning

confidence: 99%

“…Buffers surrounding geographic objects depend both on their type and on the kind of power station, e.g. wind [23], biomass [42] or solar PV [24]. Distance thresholds to given layers (e.g.…”

Section: Control Parametersmentioning

confidence: 99%

Section: Control Parametersmentioning

confidence: 99%

Section: Geographical Constraints and Land Managementmentioning

confidence: 99%

Section: Introduction and Related Workmentioning

confidence: 99%

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An integrated GIS and robust optimization framework for solar PV plant planning scenarios at utility scale

et al. 2020

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Hybrid neurofuzzy wind power forecast and wind turbine location for embedded generation

et al. 2020

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Wind energy uptake in South Africa is significantly increasing both at the micro-and macro-level and the possibility of embedded generation cannot be undermined considering the state of electricity supply in the country. This study identifies a wind hotspot site in the Eastern Cape province, performs an in silico deployment of three utility-scale wind turbines of 60 m hub height each from different manufacturers, develops machine learning models to forecast very short-term power production of the three wind turbine generators (WTG) and investigates the feasibility of embedded generation for a potential livestock industry in the area. Windographer software was used to characterize and simulate the net output power from these turbines using the wind speed of the potential site. Two hybrid models of adaptive neurofuzzy inference system (ANFIS) comprising genetic algorithm and particle swarm optimization (PSO) each for a turbine were developed to forecast very short-term power output. The feasibility of embedded generation for typical medium-scale agricultural industry was investigated using a weighted Weber facility location model. The analytical hierarchical process (AHP) was used for weight determination. From our findings, the WTG-1 was selected based on its error performance metrics (root mean square error of 0.180, mean absolute SD of 0.091 and coefficient of determination of 0.914 and CT = 702.3 seconds) in the optimal model (PSO-ANFIS). Criteria were ranked based on their order of significance to the agricultural industry as proximity to water supply, labour availability, power supply and road network. Also, as a proof of concept, the optimal location of the industrial facility relative to other criteria was X = 19.24 m, Y = 47.11 m. This study reveals the significance of resource forecasting and feasibility of embedded generation, thus improving the quality of preliminary resource assessment and facility location among site developers.

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Unlocking renewable energy potential: A case study of solar and wind site selection in the Kasserine region, central‐western Tunisia

Rekik,

El Alimi

2023

Energy Science & Engineering

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Developing wind and solar photovoltaics on a large scale requires substantial financial investments, making it crucial to identify the most suitable locations beforehand. To address this issue, a spatial analysis is carried out to determine the most potential sites for hosting large‐scale solar photovoltaic and wind systems in the region of Kasserine, central‐western Tunisia. To this end, an integrated model based on Step‐wise Assessment Ratio Analysis (SWARA), Decision‐Making Trial and Evaluation Laboratory (DEMATEL), and Geographic Information System is proposed. An extensive literature survey is conducted to establish suitability criteria and constraints. The SWARA‐DEMATEL is used to assign weights and capture the interdependencies among the considered criteria, while the raster calculator tool from the ArcGis 10.8 software is utilized to extract the final suitability maps. The obtained results indicate that the region of Kasserine exhibits great solar and wind potential, with areas of 635 and 467 km2 extremely fit for installing solar and wind systems, respectively. Furthermore, 349 km2 are identified as potential locations for hosting solar‐wind hybrid systems. Considering these outcomes, policymakers can take the initiative to accelerate the deployment of these facilities, which would assist the country in achieving its plans by 2030.

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GIS based site suitability assessment for wind and solar farms in Songkhla, Thailand

Cited by 164 publications

References 41 publications

An integrated GIS and robust optimization framework for solar PV plant planning scenarios at utility scale

An integrated GIS and robust optimization framework for solar PV plant planning scenarios at utility scale

Hybrid neurofuzzy wind power forecast and wind turbine location for embedded generation

Unlocking renewable energy potential: A case study of solar and wind site selection in the Kasserine region, central‐western Tunisia

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