Michael Lau

In this paper, we present modeling to generate continuous alternatives (MGCA), an MGA post-processing methodology. MGCA can be utilized once an initial MGA exploration has been completed and consists of the following steps: 1. Dimensional reduction of the MGA data to capacity decisions at the generator cluster level and metric evaluations (defined as affine transformations of variables e.g. cost, emissions, land use). 2. Formulation of an exploratory problem which includes MGCA’s novel constraint implicitly encoding the convex hull of MGA solutions. 3. Utilization of the exploratory problem to explore the convex hull of MGA solutions via introduction of additional constraints and new objectives, ideally in an interactive setting. 4. Export and evaluation of discovered interpolated solutions (later referenced as interpolates) to an operational model to evaluate operational metric values consistent with economic dispatch. The key innovation of MGCA lies in a recognition that a convex hull can be fully described by the set of convex combinations of exterior solutions which can be encoded as a constraint in an optimization problem. MGCA introduces several new capabilities compared to previous work: 1. Implicitly define the shape of the convex hull of all investment decisions with full spatial and technical resolution as discovered by a previous MGA exploration. 2. Create new feasible solutions with estimates of their associated outcome metric values (note, Vågerö et al [25] simultaneously invented a similar methodology for estimating metric values). 3. Allow users to define new optimization problems within the identified near-optimal feasible space which solve in fractions of a second. The MGCA approach enhances the usability of MGA results by allowing utility planners and stakeholders to rapidly generate feasible capacity mixes which optimize over their constraints and objectives of interest with generator cluster-level detail and estimated operational characteristics.

As decarbonisation agendas mature, macro-energy systems modelling studies have increasingly focused on enhanced decision support methods that move beyond least-cost modelling to improve consideration of additional objectives and tradeoffs. One candidate is modelling to generate alternatives (MGA), which systematically explores new objectives without explicit stakeholder elicitation. This paper provides comparative testing of four existing MGA methodologies and proposes a new Combination vector selection approach. We examine each existing method’s runtime, parallelizability, new solution discovery efficiency, and spatial exploration in lower dimensional (N ⩽ 100) spaces, as well as spatial exploration for all methods in a three-zone, 8760 h capacity expansion model case. To measure convex hull volume expansion, this paper formalizes a computationally tractable high-dimensional volume estimation algorithm. We find random vector provides the broadest exploration of the near-optimal feasible region and variable Min/Max provides the most extreme results, while the two tie on computational speed. The new Combination method provides an advantageous mix of the two. Additional analysis is provided on MGA variable selection, in which we demonstrate MGA problems formulated over generation variables fail to retain cost-optimal dispatch and are thus not reflective of real operations of equivalent hypothetical capacity choices. As such, we recommend future studies utilize a parallelized combined vector approach over the set of capacity variables for best results in computational speed and spatial exploration while retaining optimal dispatch.  

Europe’s reliance on Russian natural gas, highlighted by the Russian-Ukraine conflict, poses a significant energy security risk. To address this, Europe must urgently reduce its dependency on Russian fossil fuels through demand reduction, alternative energy sources, and rapid clean energy infrastructure development. The European Commission’s REPowerEU plan targets a two-thirds reduction in Russian gas imports by 2022 end, but stops short of total elimination until 2027 and lacks a detailed policy analysis for immediate energy security. A new report offers detailed strategies for the EU and the UK to quickly sever Russian gas dependence. It suggests enhancing the REPowerEU plan with additional gas reductions for electricity and adjusting gas storage targets. This report, aligning with other studies, outlines immediate strategies for a Russian gas embargo, assessing policy implications and climate commitments. Its integrated electricity and gas models confirm the feasibility of eliminating Russian gas dependency while maintaining supply security.