Game-theoretic modeling of curtailment rules and network investments with distributed generation

Renewable energy sources (RES) have achieved high penetration rates in many areas, leading to curtailment, especially when existing network infrastructure is insufficient and energy generated cannot be exported. In this context, Distribution Network Operators (DNOs) face a significant knowledge gap about how to implement curtailment rules that achieve desired operational objectives, but at the same time minimise disruption and economic losses for RES generators. We study the properties of several curtailment rules widely used in UK, and their effect on the viability of RES investments. We propose a new rule which guarantees fair allocation of curtailment amongst all generators with minimal disruption. Another key knowledge gap faced by DNOs is how to incentivise private network upgrades, especially in settings where several generators can use the same line against the payment of a transmission fee. We provide a solution by using tools from algorithmic game theory. Specifically, this setting can be modelled as a Stackelberg game between the private line investor and local RES generators, required to pay a transmission fee to access the line. We provide a method for computing the equilibrium of this game, using a model that captures the stochastic nature of RES generation and demand. Finally, we use the KintyreHunterston grid reinforcement project and a large dataset (17 years) of wind speed measurements and demand to validate our model. We show that charging a transmission fee as a proportion of the feed-in tariff price between 15% and 75% would allow both investors to implement their projects and achieve desirable distribution of the profit. Overall, our results show how using gametheoretic tools can help network operators setting the optimal curtailment rule and determining transmission charges for private network infrastructure. In the future, we plan to extend the model to multi-location settings and dispatch decisions that include flexibility on the demand side, such as demand response or energy storage, which can be used to partially defer curtailment. Figure 1: Kintyre-Hunterston mapFigure 2: Profits at equilibrium depending on transmission feeReferences ETP Annual Conference 2017Edinburgh, 10 October 2017 [1] M. Andoni, V. Robu, W.-G. Früh and D. Flynn, "Game-theoretic modeling of curtailement rules andnetwork investments with distributed generation," Applied Energy, vol. 201, pp. 174-187, 2017.