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)

Solar-based energy is an intermittent power resource whose potential pattern varies in space and time. Planning the penetration of such resource into a regional power network is a strategic problem that requires both to locate and bound candidate parcels subject to multiple geographical restrictions and to determine the subset of these and their size so that the solar energy production is maximized and the associated costs minimized. The problem is also permeated with uncertainty present in the estimated forecast energy demand, resource potential and technical costs. This paper presents a novel combination of Geographic Information Systems (GIS) and Robust Optimization (RO) to develop strategic planning scenarios of a collection of parcels that accounts for their spatio-temporal characteristics, and specifically their hourly radiation patterns that are location dependent, to best fit the network temporal demand and minimize technical costs. The problem is formulated as a GIS spatial placement problem and a RO fractional knapsack problem to plan the effective power penetration and geographical suitability of new PV facilities. The combination GIS-RO generates an excellent decision support system that allows for the computation of optimized parcel scenarios (locations, sizes and power). The qualitative and quantitative effectiveness of the approach is demonstrated on real data on the French Guiana region. Results show that the proposed approach provides reliable fine grained planning that also accounts for the risk adversity of the decision maker towards forecast demand and solar potentials.

Modeling analogy with optical network design for optimal PV site clustering • Enhanced risk management of operational costs and energy stability • Comparative study with an existing GIS-optimization model on real data • Novel implementation strategy for PV site selection at utility scale