Towards Robust Scenarios of Spatio-Temporal Renewable Energy Planning: A GIS-RO Approach

Abstract

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.

Optimizing operational costs and PV production at utility scale: An optical fiber network analogy for solar park clustering

“…In this section, the emphasis is placed on comparing the results from the new SONET-based model (ring approach) against those of the former GREECE-OPSPV model (park approach). GREECE was originally implemented in Python using libraries depicted in [22] and [21], and led to the extraction of 133 potential sites along with their digitalized attributes (see section 4.1.1). Corresponding data items were then implemented into both former and updated (SONET-based) OPSPV module (see Figure 2).…”

“…This holistic approach does not link temporal scenarios with a territory's own geographical constraints and related costs. Accordingly, we recently proposed an integrated model framework, combining GIS and Robust Optimization (RO), called the GREECE-OPSPV system (Geographical REnewable Energy Candidate Extraction -Optimal Planning and Sizing of PV parks) [21,22], that fills the gap between pure GIS approaches and bottom-up models, by handling interdependent spatial and temporal constraints for site selection at regional scale. It tackles the optimization problem of identifying the best sites (location and size) that increase solar energy penetration into the power grid at minimal cost, while satisfying the region's specific constraints (terrain, resource dispersion, infrastructures, etc.…”

“…The GIS GREECE model is extensively depicted in [22] and [21]. This section comprises a summary of the underlying data and its processing, for proper understanding of the analysis.…”

Construction of a WebGIS Tool Based on a GIS Semiautomated Processing for the Localization of P2G Plants in Sicily (Italy)

“…Considering other applications, GIS-based processing models have been used for simulating the cost for energy wood supply chains in Finland, necessary due to the increasing use of bioenergy for traditional forest products and biofuels [27]. Furthermore, GIS processing and spatio-temporal data have been also used to define the localization, size, and capacity of solar plants, in order to maximize solar energy production and minimize costs [28]. Similar approaches have been followed in Saudi Arabia for the site selection of solar power plants, filtering in GIS the suitable lands to consider, taking into account environmental parameters, and the nearness to the main transport networks and the urban centers [14].…”

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

“…The optimization model is a robust optimization model [30], and has been published recently in a computer science conference [31]. This means that all forecast data are represented as intervals instead of a stochastic distribution [32].…”