https://hdl.handle.net/10419/246503 2026-10-23T10:56:53Z https://hdl.handle.net/10419/339528 Title: Marginal curtailment under network constraints: Evidence from the 2030 GB power system Authors: Chyong, Chi Kong; Newbery, David M. G. Abstract: As wind and solar expand, curtailment becomes unavoidable. It is usually reported as an average share of potential output, but investment decisions depend on marginal curtailment: the share of potential output from the next unit of capacity that is lost. Marginal curtailment can be several times higher than the average. Using a detailed model of Britain's 2030 power system that accounts for internal transmission constraints, we show that network congestion sharply increases these marginal losses, even after planned grid reinforcement, new interconnectors, and storage are taken into account. As a result, the actual cost of adding renewable capacity in constrained locations can be far higher than standard cost metrics suggest. In some areas, the cost of delivered electricity from additional renewables is up to 90% higher than implied by conventional levelised cost estimates. Current renewable auctions and support schemes largely ignore this effect, focusing on average costs rather than delivered value. Our results point to the need for more careful locational planning and for support mechanisms that reflect where renewable generation can actually be used, not just where it can be built. 2026-01-01T00:00:00Z https://hdl.handle.net/10419/339524 Title: Biomethane in Europe: Why scaling up is harder than it looks Authors: Olczak, Maria 2026-01-01T00:00:00Z https://hdl.handle.net/10419/339509 Title: Risk structure and financial hedging in nodal electricity markets Authors: Yang, Daniel Abstract: Since the deregulation of electricity markets in the 1990s, U.S. power grids have witnessed the creation of an increasingly diverse suite of financial instruments designed to mitigate risks caused by underlying generation and consumption patterns. However, the nature of risks is shifting rapidly as power grids absorb unprecedented changes in both demand and supply: on the demand side, the recent emergence of large AI data centers and increased electrification across heating, transport, and industrial applications have shifted demand patterns, increasing the risk of resource adequacy; on the supply side, the expansion of utility-scale renewables and storage and the retirement of coal-fired power plants have introduced variability and uncertainty, raising concerns around baseload reliability. Under these paradigms, new grid risks surface that challenge the adequacy of financial instruments designed for a power system dominated by predictable load growth and dispatchable thermal generation, thereby exposing market participants and system operators to operational challenges and unhedged risks. This paper analyzes changes to five main categories of grid risk for market participants - 1) locational congestion risk, 2) temporal price risk, 3) volume variability risk, 4) demand and supply-side tail risks, and 5) regulatory design risk - under the new demand and supply-side dynamics and assesses the effectiveness of a main financial hedging instrument, the Financial Transmission Right (FTR), in mitigating these risks for market participants. This paper specifically analyzes the impact of the increasing penetration of utility-scale energy storage assets on the payoff of FTRs as an example to illustrate potential risks to the grid that arise from inter-temporal dispatch decisions. Based on the theoretical discussion, this paper proposes the design of Multi-Period, Volume-Adjustable (MPVA) FTRs to address the inter-temporal changes in the modern grid. The discussion clarifies the boundary between risks that can be financialized and those that require physical or operational solutions as well as trade-off considerations in financial hedging instrument design, thus offering a potential roadmap for the next generation of electricity market design. 2026-01-01T00:00:00Z https://hdl.handle.net/10419/339526 Title: Reshaping the consumer-energy relationship through AI: Opportunities, risks and regulatory outlook Authors: Moreno, Valeria Karina; Poudineh, Rahmatallah Abstract: The promise of Artificial Intelligence (AI) in residential energy is consumer empowerment, yet without appropriate governance the emerging reality may be its opposite. As households become data producers, behavioural subjects, and operational assets within intelligent electricity networks, a troubling asymmetry is taking shape: while consumers gain new modes of participation and value creation, they simultaneously face deepening opacity, dependency, and systemic exposure. This asymmetry stems from a quiet migration of control. As optimisation and coordination become automated, decision-making shifts from individual households to platform operators, aggregators, and technology providers. A new layer of intermediation emerges-one where consumers engage markets not directly, but through algorithmic interfaces whose logic, data practices, and profit distribution remain largely invisible to them. More subtly, AI is beginning to govern household behaviour itself. Nudges and adaptive controls embedded in everyday devices steer users toward predefined goals-efficiency, load shifting-often without explicit awareness. These systems learn habits, anticipate preferences, and adjust operations accordingly, gradually transforming voluntary decisions into algorithmically mediated routines. The shift is foundational: from enabling informed choice to shaping choice itself. Yet this trajectory is not inevitable. The central argument of this paper is that AI's impact on the consumer-energy relationship depends less on technology than on the institutions governing it. Without proactive oversight, digitalisation risks entrenching inequality and eroding agency; with adaptive, transparent regulation, it can democratise participation and accelerate clean energy transitions. To navigate this, the paper proposes a framework linking consumer, system, and institutional governance to eight guiding principles: privacy, autonomy, fairness, coordination, stability, resilience, transparency, and accountability. Ultimately, governing AI in energy ultimately means governing intelligence itself-determining whether automation serves public purpose or silently reshapes it. 2026-01-01T00:00:00Z