Modelling utility-aggregator-customer interactions in interruptible load programmes using non-cooperative game theory
journal contributionposted on 2021-08-16, 06:15 authored by Soheil MohseniSoheil Mohseni, Alan BrentAlan Brent, S Kelly, WN Browne, Daniel BurmesterDaniel Burmester
Aggregator-activated demand response (DR) is widely recognised as a viable means for increasing the flexibility of renewable and sustainable energy systems (RSESs) necessary to accommodate a high penetration of variable renewables. To this end, by acting as DR aggregators and offering energy trading capabilities to smaller customers, energy retailers unlock additional sources of demand-side flexibility to ensure the cost-optimal operation of RSESs. Accordingly, a growing body of literature has highlighted the ways in which non-cooperative game theory could be used to reduce the gaps between modelled and real-world results for aggregator-mediated DR schemes. This paper aims to contribute to the trends of giving a realistic grounding to research on distributed DR-integrated energy scheduling by using insights from non-cooperative game theory to determine: (1) the optimal trade-off between importing electricity and utilising DR capacity in grid-tied RSESs, (2) the impact of the price elasticity of DR supply of different customer classes – especially, new sources of electricity demand, such as e-mobility – on the system-level dispatch of DR resources, and (3) the financial implications of harnessing the flexibility potential of a large number of end-consumers across different sectors. Accordingly, the principal goal of the paper is to develop an operational planning optimisation model that can be directly applied to real-world aggregator-mediated, market-based demand-side flexibility provisioning domains. To this end, this paper presents the first DR elasticity-aware, non-cooperative game-theoretic DR scheduling model that: (1) yields the best compromise solution between imported power and dispatched DR resources from the utility's perspective, (2) characterises the utility-aggregator-customer interactions during the market-based DR trade process with several customer categories involved, and (3) disaggregates the total sectoral load on the system to individual end-consumers, which has potential implications for pre-feasibility and business case assessments. The application of the model to a conceptual micro-grid for the town of Ohakune, in New Zealand, demonstrates its effectiveness in reducing the daily system operational cost (over the critical peak hours) by ~66% and ~47% on a representative summer and winter day, respectively. Importantly, the paper provides statistically significant evidence supporting that activating the flexibility potential of small- to medium-scale end-consumers through specifically defined third-party aggregators in a market-based approach – that is aware of strategic interactions among instrumentally rational economic agents involved in the dispatch and delivery of DR resources – plays a significant role in the cost-optimal transition to 100%-renewable electricity generation systems within the smart grid paradigm.