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

Abstract

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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“…To ensure that any chromosome after crossover operation satisfies the constraints given by (12) and (13), a specialized crossover operator is designed, as illustrated in Figure 3. Figure 3 depicts that the customized crossover operator consists three crossover operations, which is detailed as follows:…”

“…Mutation operator might cause the chromosome to violate the constraints given by (12) and (13). To avoid this situation, a specialized mutation operator is designed in this paper, as illustrated in Figure 4.…”

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