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The transmission network of Kolkata (left); 2500 km 2 simulation area divided into 500 × 500 mesh (Google Earth) with city center (CBD), local distribution center (LDC-the railway freight unloading station), and substations marked (right).
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A major hurdle in increasing the economic feasibility of solar photovoltaic (SPV) plants is the ever-increasing share of location-dependent costs (land, transmission, labor, etc.) in total installation costs. Such costs are geospatial in nature, due to spatial socio-economics affecting them. Present geolocation methods, for locating SPV installatio...
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... and GIS, the MUFSP model empirically determines each of the costs at every location, within the limited suburban boundary. The modeling problem is defined to identify which of the prime spatial factors (x) can minimize the total location-dependent costs C loc (x). See Table ii for the list of variables used in the cost functions' construction. Fig. 1 represents the simulation boundary for the MUFSP model, for our case study of Kolkata. The simulation area is a squared 2500 km 2 area, south of the city of Kolkata. The northern and western suburban areas are heavily populated, while the eastern side is a marshy wetland, making these areas unsuitable for SPV plant development. In ...
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... 1 represents the simulation boundary for the MUFSP model, for our case study of Kolkata. The simulation area is a squared 2500 km 2 area, south of the city of Kolkata. The northern and western suburban areas are heavily populated, while the eastern side is a marshy wetland, making these areas unsuitable for SPV plant development. In addition, Fig. 1 also shows the transmission network 3 around Kolkata, from which three representative substations (SS1, SS2 and SS3) are selected for the MUFSP model construction. The outer boundary of the simulation area (50 km from the edge of metropolitan Kolkata) is terminated at half the distance from the nearest city (100 km away from ...
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... chain cost is the cost involved in transporting the material, by railway network, from the manufacturing plant to the local distribution center. The second part of the supply chain is where the material is transported by road freight from the local distribution center (LDC) to the simulation area point where the SPV plant might be installed. Fig. 1 shows the location of the LDC. Table 6 shows the transportation distance and the total freight load of the material (obtained from Oguz and Şentürk, 2019), along with the assumed freight rates for both road and rail. Eq. (10) below, represents the supply chain cost function at each point of the simulation ...
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... costs C loc (x), across the simulation area of 2500 km 2 , is shown in Figs. 3 and 4. Fig. 3 shows the details of the costs when only substations SS1 and SS2 are considered. Fig. 4 shows the details of the costs considering all the three substations of SS1 to SS3. The direction of observation of the 3D visualization is from northeast of Fig. 1. It is found that, there exist optimized locations, in the suburban region, where the spatial variation of the total location-based SPV installation costs is minimum, for each level of transmission voltage. Table 7 corresponds to Fig. 3, where SS2 influences the optima. Table 8 corresponds to Fig. 4, where SS3 influences the ...
Citations
... However, costs of SPV installation affected by social factors, such as land, labor and supply chain costs have only recently been explored in literature. A methodology to optimize location-dependent social costs of SPV plants by a multi-factor spatial parameterization (MUFSP) model is presented in (Basu et al., 2021). Such developments have increased the power parity of SPV more than any other renewable sources of energy. ...
... Such developments have increased the power parity of SPV more than any other renewable sources of energy. Despite these developments, variable renewable energies (VRE) such as SPV power generation are subject to intermittency depending on the weather and time of day (Kennedy, 2023;Joseph et al., 2022;Basu et al., 2021). Due to the intermittency, in countries like Japan and Germany, SPV is subject to curtailment when supply exceeds demand (Dumlao and Ishihara, 2020;Frysztacki and Brown, 2020). ...
... This study is the first to analyze the optimization of social factors associated with GES systems in any terrain. This paper adopts the MUFSP model proposed in (Basu et al., 2021) to GES deployment in the Fukuoka city region of Japan, which is a part of the Kyushu grid. The aim of this model is to geolocate the minimum cost-optimized point for construction of a GES system based on social factors. ...
p class="MsoNormal" style="margin-top: 10pt; text-align: justify;"> Gravity Energy Storage (GES) systems are recently being considered as a viable solution for storing intermittent renewable energy power, specifically in high curtailment zones. While a few studies have analyzed the material costs of GES systems, there is a paucity of literature on analyzing the socioeconomic costs of GES systems. This study analyzes the location-dependent costs of GES plants using a multi-factor spatial parameterization model for evaluating the existence of a point of minimum cost in a suburban mountainous geography. A case study of 500x500 points in a 50x50km2 area in the suburban area of Fukuoka city in Japan is performed. It is found that the cost of material transportation and transmission is more dominant in determining the position of an optimal cost location than factors of excavation and land costs. The position of the minima is also related to the principal urban area in that the line connecting the Center Business District (CBD) and suburban flat areas (line 1) is where the potential minima lie. The intersection point of an orthogonal to the line connecting the CBD with a substation nearest to the flat area with line 1, is the potential zone of minima location. The findings of this study are critical for urban energy planners and reveals how socioeconomic cost factors can aid in geolocation a suitable GES installation site. </p
... However, GES is hypothesized to be an improvement over PHES due to its non-limiting geographical applicability. Several costs like land, supply chain, transmission and labor are spatially variable even in a limited geographical distribution [10]. Moreover, construction and excavation are associated with the policies of local governing bodies, which determine costs according to geography and demography [30]. ...
... The authors of [32] discuss technological advances aimed at reducing working hours, focusing on the location dependency of labor costs for VRE technologies. This research extends the multi-factor spatial parameterization (MUFSP) model presented by [10] for geolocating the optimal location of a utility-scale, suburban SPV plant. The MUFSP created by the authors of [10] is a cost simulation model that analyzes socioeconomic and geographic factors depending on the location of the SPV plant, which shows ...
... This has brought the levelized cost of energy (LCOE) of SPV lower than fossil fuels in several countries like China [3,4], Japan [5,6] and India [3,7]. Socioeconomic analysis has also been carried out to reduce the LCOE of SPV further in terms of social acceptance [6], internal rate of returns (IRR) [8,9] and spatial cost variability [10]. However, in recent years, the rate of adoption of SPV power has increased manifold owing to the Paris Agreement's determined contributions and net zero targets [1,6], which has led to supply being significantly larger than demand, even during the non-peak production hours [11]. ...
The increased decentralization of renewable energy has increased curtailment rates in stagnating demand zones, increasing the levelized cost of energy (LCOE). The geographically dynamic nature of gravity energy storage (GES) is emerging in the field of mechanical energy storage, over pumped hydro. However, GES costs vary geospatially, specifically in decentralized suburban areas, due to the impact of urban socioeconomics. This study aims to find a mathematical approximation of a cost-optimized location for suburban Solar–GES hybrid systems in curtailment-prone areas. A multi-parameterization model mathematically programmed land, transmission, supply chain and excavation costs into geospatial matrix approximations for suburban areas of 2500 km2 in Fukuoka and Ibaraki in Japan. It was found that SPV-GES location-dependent costs were mainly affected by distance from the city’s economic center and flat plains in suburbs, and supply chain and transmission costs optimized the location-dependent cost for GES at a specific point. It was also found that flat terrains were more economical than mountainous terrains due to high GES supply chain costs. With GES found to be cost-competitive compared to other storage technologies in Japan, this study reveals that GES introduction benefits the LCOE of suburban, decentralized SPV when curtailment is >50% irrespective of terrain.
In recent decades, development in the field of solar energy has been a growing challenge in the context of the transition from conventional to renewable energy sources. However, there are cases where investments in photovoltaic farms do not reach the maximum potential of a territory for their emplacement. Therefore, the present study aims to analyse the site suitability for photovoltaic farms in Romania in relation to current investments in the field. The suitability map was obtained through a simple heuristic approach, based on the Multicriteria Evaluation of favourable/restrictive biophysical and anthropogenic data, integrated in the GIS-Weighted Overlay spatial analysis. The results were compared to the current distribution of photovoltaic farms, quantified by the visual interpretation of high-resolution images provided by Google Earth. Overall, the suitability map obtained shows a relatively high potential for the location and expansion of photovoltaic farms in the area, the very high and high suitability classes summing more than 7,000,000 ha (29.9% of the country's area). The highest potential has been obtained for the southern and south-eastern part of Romania, where the zonal calculated mean suitability scores highlighted the Bucharest-Ilfov (4.94), South-West Oltenia (4.64), South-Muntenia (4.63) and Southeast (4.42) development regions (NUTS II). However, analysing the frequency of existing photovoltaic farms, the study indicates a potential for solar energy not yet sufficiently exploited, but also possibly inappropriate between the degree of suitability and the photovoltaic farms implemented so far. Hence, the comparison of calculated mean suitability scores and the percentage of the total area allocated for existing photovoltaic farms indicates several mismatches between the investments made in the area and the modelled site suitability, mainly in the Ilfov, Tulcea, Constanța, Buzău, Satu-Mare, Brașov, Prahova and Galați counties. At the same time, the results reveal an alternate possibility of using/reusing degraded or contaminated land, through investments in the solar energy, several aspects being highlighted for the ceased mining areas, landfills near urban centres, or poorly productive agricultural lands. This study can provide important information to decision makers and potential investors, in order to streamline future projects by setting up photovoltaic farms in accordance with the modelled suitability classes.
