Siting renewable power generation assets with combinatorial optimisation

Abstract

This paper studies the problem of siting renewable power generation assets using large amounts of climatological data while accounting for their spatiotemporal complementarity. The problem is cast as a combinatorial optimisation problem selecting a pre-specified number of sites so as to minimise the number of simultaneous low electricity production events that they experience relative to a pre-specified reference production level. It is shown that the resulting model is closely related to submodular optimisation and can be interpreted as generalising the well-known maximum coverage problem. Both deterministic and randomised algorithms are discussed, including greedy, local search and relaxation-based heuristics as well as combinations of these algorithms. The usefulness of the model and methods is illustrated by a realistic case study inspired by the problem of siting onshore wind power plants in Europe, resulting in instances featuring over ten thousand candidate locations and ten years of hourly-sampled meteorological data. The proposed solution methods are benchmarked against a state-of-the-art mixed-integer programming solver and several algorithms are found to consistently produce better solutions at a fraction of the computational cost. The physical nature of solutions provided by the model is also investigated, and all deployment patterns are found to be unable to supply a constant share of the electricity demand at all times. Finally, a cross-validation analysis shows that, except for an edge case, the model can successfully and reliably identify deployment patterns that perform well on previously unseen climatological data from historical data spanning a small number of weather years.

Assessing the impact of offshore wind siting strategies on the design of the European power system

“…Spatiotemporal complementarity. An approximate solution method relying on a mixed-integer relaxation (MIR) of problem (2) followed by a local search algorithm inspired by the simulated annealing algorithm [35] is used to tackle (2a)-(2f) [27]. The mixed-integer relaxation is formed by relaxing the integrality constraint (2f) of the time window variables.…”

“…A framework siting RES assets such that the occurrence of simultaneous, system-wide lowgeneration events is minimized has been recently proposed by Berger et al [18]. The problem has since been cast as an integer program (IP) for which several solution methods have been proposed [27]. Although they offer a valuable overview of different siting criteria proposed in the literature, a common drawback of all these studies is that they fall short in evaluating the implications of the corresponding outcomes on the design and economics of power systems.…”

“…This siting scheme selects a collection of disjoint subsets of locations so as to maximize their spatiotemporal complementarity. Recall that, in this paper, locations are considered complementary if they rarely experience simultaneous low electricity production events (compared with a pre-specified reference production level) [18,27]. The framework that makes it possible to cast this problem as an IP is discussed next.…”

Exploring the Complementarity of Offshore Wind Sites to Reduce the Seasonal Variability of Generation

“…The application of reanalysis products has evolved for studies in the field of energy, and different studies have employed ERA5 as a primary source of information for wind farm simulations [13,18,19,47,48]. We selected a 30-year period of ERA5 data (1990-2020) from specific locations to characterize Brazilian winds for wind farm simulation.…”

“…Olauson et al (2018) employed ERA5 and MERRA-2 reanalysis while studying the aggregated wind power of five different countries and the generation of individual wind turbines in Sweden [17]. Berger et al (2022) proposed an optimization method for wind farm siting in Europe based on reanalysis data [18]. Radu et al (2022) assessed the impact of offshore wind farms on the design of the European Power System in order to maximize the aggregate power output and its spatiotemporal complementarity [19].…”