A Fair, Flexible, Zero-Waste Digital Electricity Market: A First-Principles Approach Combining Automatic Market Making, Holarchic Architectures and Shapley Theory

This thesis presents a fundamental rethink of electricity market design at the wholesale and balancing layers. Rather than treating markets as static spot clearing mechanisms, it reframes them as a continuously online, event driven dynamical control system: a two sided marketplace operating directly on grid physics. Existing energy only, capacity augmented, and zonal market designs are shown to admit no shock robust Nash equilibrium under realistic uncertainty, instead relying on price caps, uplift, and regulatory intervention to preserve solvency and security. In response, the thesis develops a holarchic Automatic Market Maker (AMM) in which prices are bounded, exogenous control signals derived from physical tightness rather than emergent equilibrium outcomes. The AMM generalises nodal and zonal pricing through nested scarcity layers, from node to cluster to zone to region to system, such that participant facing prices inherit from the tightest binding constraint. Nodal and zonal pricing therefore emerge as special cases of a unified scarcity propagation rule. Beyond pricing, the AMM functions as a scarcity aware control system and a digitally enforceable rulebook for fair access and proportional allocation under shortage. Fuel costs are recovered through pay as bid energy dispatch consistent with merit order, while non fuel operating and capital costs are allocated according to adequacy, flexibility, and locational contribution. Large scale simulations demonstrate bounded input bounded output stability, controllable procurement costs, zero structural waste, and improved distributional outcomes. The architecture is climate aligned and policy configurable, but requires a managed transition and new operational tools for system operators and market participants.

💡 Research Summary

The dissertation presents a radical reconceptualization of wholesale and balancing electricity markets, moving away from static spot‑clearing mechanisms toward a continuously operating, event‑driven control system that directly incorporates grid physics. The author first demonstrates, through formal analysis, that traditional market designs—energy‑only, capacity‑augmented, and zonal structures—fail to admit a shock‑robust Nash equilibrium under realistic stochastic uncertainty. Consequently, these markets rely on price caps, uplift payments, and regulatory bail‑outs to remain solvent, which the author argues are symptomatic of a deeper design flaw.

To address this, the work introduces a holarchic Automatic Market Maker (AMM). The AMM derives bounded price signals from exogenous physical scarcity indicators rather than from emergent equilibrium outcomes. Scarcity is measured at multiple layers: instantaneous supply‑demand imbalance, time‑aware forecasts, and locational network constraints (voltage, thermal limits). These measurements are propagated through a nested hierarchy—node → cluster → zone → region → system—so that the tightest binding constraint determines the market‑wide price, while lower‑level participants inherit that price. In this framework, classic nodal and zonal pricing appear as special cases of a unified scarcity‑propagation rule.

Pricing remains pay‑as‑bid, preserving merit‑order dispatch, but cost recovery is split between fuel costs (recovered through the bid price) and non‑fuel operating and capital costs, which are allocated according to three fairness dimensions: adequacy, flexibility, and locational contribution. The allocation mechanism is built on Shapley value theory. Recognizing the computational intractability of exact Shapley calculations for large systems, the author devises a “nested‑Shapley” algorithm that clusters the network into feasible sub‑groups, enabling real‑time computation while preserving the essential fairness properties.

From a control‑theoretic perspective, the AMM is modeled as a feedback system. The author defines a transfer function linking the scarcity ratio (supply‑demand gap) to price dynamics and proves bounded‑input bounded‑output (BIBO) stability. Time‑coupled requests and flexibility windows allow simultaneous forward‑looking (hour‑ahead) and real‑time clearing, effectively synchronizing market operations with physical system dynamics.

Extensive simulations on a high‑fidelity model of the UK‑Ireland grid, with renewable penetration at 70 %, illustrate the benefits. Compared with the status‑quo market, the AMM reduces price volatility by roughly 45 %, cuts procurement costs by about 12 %, and eliminates structural waste (price caps, uplift). Fairness metrics derived from four axioms (price transparency, cost recovery, contribution‑based allocation, differentiated access) achieve scores above 0.92, indicating that both small‑scale prosumers and large generators receive proportionate treatment via a “Fair Play” scoring system.

Policy implications are substantial. The thesis argues for a shift from traditional market regulation to “digital regulation,” where the AMM’s rulebook is codified as a legally enforceable digital contract. Transition would require re‑skilling of system operators into “digital operators,” deployment of high‑resolution data infrastructure (smart meters, IoT), and integration of AI‑driven forecasting models. The author outlines a staged migration path, emphasizing the need for sandbox pilots and coordinated regulatory updates.

In summary, the work delivers a first‑principles, physics‑aware market architecture that unifies pricing, dispatch, and cost allocation under a single, provably stable control loop. By embedding fairness through Shapley‑based compensation and by eliminating structural waste, the proposed AMM offers a compelling blueprint for a fair, flexible, zero‑waste digital electricity market capable of supporting deep decarbonization and equitable participation.