Electricity grid modelling
Gjalt Huppes
Dear Modellers,
I am not a modeler but interested in systematic modelling. My background is in environmental sciences, co-originating models for Life Cycle Analysis (LCA), Material Flow Accounting (MFA/SFA), and Environmentally Extended Input-Output Analysis (EEIOA). My interest has shifted to policy analysis in this Industrial Ecology domain. My background is in Economics and Political Science, with a PhD in Macro-Environmental Policy, Principles and Design, in environmental sciences.
The electricity market & grid is collapsing currently and needs a deep institutions redesign. Such revisions are due regularly. The last one took place near globally around the start of the twenty first century.
I made a sketch for that institutional redesign, see the draft report/paper as attached.
One element that needs attention beyond my capabilities is the design of the grid-to-come. It will be a meshed grid, of some sort. The organizational structure is that of a linked system of small nodes. They are externally connected to large-scale and small-scale primary and secondary producers, and to final and intermediate users, also small and large. Producers and users are at all relevant voltage levels; a best guess now is four or five levels. Each node is to link to at least three suppliers, in-grid or to the grid, and similarly to three purchasers, in-grid or external users. Each node covers two voltage levels at maximum, to avoid the building up of monopolistic power. These conditions, and a few more, lead to a both resilient and competitive (non-monopolistic) Market & Grid system. In the paper there is a sketch of a single voltage grid structure, see also below.
My core question is how such a system may be designed systematically, at lowest cost (and linked materials use). The system to have in mind is transcontinental, as described in the adjoining paper: (Yang, Deshmukh, & Suh, 2023). They reckon with spatial aspects, which I would like to be covered more abstractly, in terms of distances between producers and users, some sort of measure on that.
How such a system may be designed seems to be a topology question, or at least is touching on that domain.
I look for advice and preferably even support.
Kind regards,
Gjalt
Yang, H., Deshmukh, R., & Suh, S. (2023). Global transcontinental power pools for low-carbon electricity. Nature communications, 14(1), 8350. doi:10.1038/s41467-023-43723-z
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Ahmad Mahmoudi
Hi Gjalt,
Thank you for sharing your draft report and the grid sketch. Your framing to treat “market rules + physical infrastructure” as a coupled institutional design problem, aligns closely with the direction of my PhD work.
In my case, I've taken a deliberately practical step; I built a reproducible decision support pipeline for an industrial process heat transition decision (boiler pathway choice) that couples facility-level hourly demand with regional grid constraints at the Grid Exit Point (GXP) level and then stress-tests that decision under deep uncertainty using paired futures. The intent is not to claim a universal “best” grid design in one step, but to demonstrate how transparent, auditable artefacts can move between models and decision scales so infrastructure and institutional choices remain decision-relevant rather than purely conceptual.
From that perspective, your topology question becomes most tractable when anchored to a clearly specified decision model: which objective(s) are being optimised (e.g., total system cost, materials intensity, resilience metrics, or market power constraints), at what spatial/voltage resolution, and over what operational timescales. Put simply: systematic design needs an explicit objective function and constraint set before scaling to a highly general transcontinental formulation.
Methodologically, I see a useful decomposition:
- Design space (candidate topologies + institutional levers): node granularity, voltage-layer boundaries, HVDC overlay structure, minimum connectivity/resilience rules (e.g., k-link), and ownership/market-access rules intended to prevent monopolistic control.
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Evaluation layer (performance under uncertainty): dispatch or investment simulation and robustness testing across plausible futures (demand growth, renewables intermittency, technology costs, reliability standards, behavioural flexibility).
This is where open-source power-system tooling can be valuable. a network optimisation engine can evaluate candidate networks and produce decision-grade outputs (e.g., congestion hours, curtailment, expansion choices, nodal prices/constraints), while a decision framework (like mine) can orchestrate experiments, enforce paired-futures comparability, and track provenance via thin-waist artefacts. Likewise, macro-scale models (e.g., national energy system models) can provide scenario-consistent bounds and trajectories, while higher-resolution engines quantify operational feasibility and congestion risk.
I think it could be started with what a minimal “first decision model” could look like for your redesign question, starting from a single-voltage abstraction with distance-based costs, explicit connectivity constraints, and a small set of institutional levers, and then scaling to multi-voltage layers and HVDC overlays.
Kind regards,
Ahmad
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Gjalt Huppes
Robbie Morrison
Hello Gjalt
Market design is certainly central. However I would be cautious about suggesting that larger, finer‑grained primary market structures will naturally dampen out rent seeking opportunities.
Empirical and modeled investigations both suggest that nodal pricing, for instance, can easily give rise to deficient competition.
In nodal pricing too, a node cannot cover two voltage levels. Perhaps you are thinking more of networked markets?
Agent-based energy system models have been developed for these kinds of questions. Open-source examples include AMIRIS and EMLab-Generation.
Other market designs have been proposed such as progressive pricing, whereby consumers benefit from the cheaper technologies being dispatched along the merit order.
The work I quote below is quite dated, but there is doubtless newer research.
HTH, best, Robbie
References
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Browne, Oliver, Stephen Poletti, and David Young (1 April 2012). "Simulating market power in the New Zealand electricity market". New Zealand Economic Papers. 46 (1): 35–50. ISSN 0077-9954. doi:10.1080/00779954.2011.649566.
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Elliott, Matthew (2015). "Inefficiencies in networked markets". American Economic Journal: Microeconomics. 7 (4): 43–82. ISSN 1945-7669. doi:10.2139/ssrn.2445658.
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Harvey, Scott M and William W Hogan (10 January 2000). Nodal and zonal congestion management and the exercise of market power. Cambridge, Massachusetts, USA: John F Kennedy School of Government, Harvard University.
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O'Neill, Richard P, Paul M Sotkiewicz, Benjamin F Hobbs, Michael H Rothkopf, and William R Stewart (1 July 2005). "Efficient market-clearing prices in markets with nonconvexities". European Journal of Operational Research. 164 (1): 269–285. ISSN 0377-2217. doi:10.1016/j.ejor.2003.12.011.
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