Uncertainties towards a fossil-free system with high integration of wind energy in long-term planning

Abstract

There is a large amount of parametric uncertainties that might affect long-term energy planning, due to the inherent variability connected to the future. Most of these uncertainties are stochastic, i.e. they cannot be reduced, but can be better characterized. In an attempt to address this issue, studies often explore different alternative scenarios or perform local sensitivity analyses. While acknowledging their importance, it is evident that their traditional scope must be reconsidered, as those methods cannot consider interactions among parameters and hence might omit parameters that are highly influential. This study aims to explore the whole uncertainty range in order to identify the most critical parameters towards fossil-free energy systems with high integration of wind-based electricity. Denmark is used as a case study of a country with large wind resources, which are increasingly exploited. It pursues three steps: 1) Selection of parameters and characterization of their uncertainties, 2) Global sensitivity analyses through Morris sampling, and 3) Uncertainty propagation and Monte Carlo runs using Latin Hypercube sampling. Offshore wind upscaling will depend on technological improvements related to capital costs or efficiencies as well as on the system integration constraints. Hence, increasing deployments of offshore wind would require policies that foster technological learning, while promoting the cost-efficient integration of an increase in participation in the power mix, such as grid transmission expansion. Therefore, methods that deal with the whole uncertainty space should, to a larger extent, be implemented when uncertainties are assessed in association with long-term planning of systems with high integration of fluctuating renewable energy.

Optimisation Techniques for Managing the Project Sustainability Objective: Application to a Shell and Tube Heat Exchanger

“…Monte Carlo is more sophisticated than the exhaustive search approach. However, its efficiency as an optimisation algorithm is still far from other existing optimisation techniques [24].…”

Exploring the Long-Term Development of the Ukrainian Energy System

“…The studies often apply energy system models representing the whole energy system of a country [14][15][16] or a wider geographical region [17][18][19] to analyse the role of different technologies (e.g., residential heat pumps [20], hydrogen [19], storage [21], district heating [22] or heating technologies [23]), policy measures (e.g., increasing efficiency [24], electrification [25,26] and nuclear reduction [27]) and other developments (e.g., growth in data centres [28]) in reducing GHG emissions or energy transition, often focusing on the period until 2050. Some articles address the uncertainties inherent to long-term energy planning studies such as the present one [29][30][31]. Some studies combine several models to obtain deeper sectoral insights and analyse the effects on the wider economy [32][33][34].…”

Broad Ranges of Investment Configurations for Renewable Power Systems, Robust to Cost Uncertainty and Near-Optimality

“…is uncertainty propagates through the model to strongly a ect the optimal and near-optimal system compositions, thus undermining any analysis of the trade-o s. Hence, it is crucial that apparent compromises are rigorously tested for robustness to technology cost uncertainty to raise con dence in conclusions about viable, cost-e ective power system designs. To thoroughly sweep the uncertainty space, we can fortunately avail of previous works on multi-dimensional global sensitivity analysis techniques in the context of least-cost optimisation [3,[16][17][18][19]. We expand their application to strengthening insights on the scope of near-optimal tradeo s, wherein the novelty of this contribution lies.…”