Generic Demand Model Considering the Impact of Prosumers for Future Grid Scenario Analysis
Hesamoddin Marzooghi, Shariq Riaz, Gregor Verbic, Archie C. Chapman,, and David J. Hill

TL;DR
This paper introduces a generic demand model for prosumers with PV-battery systems, enabling future grid scenario analysis by capturing their aggregated impact on demand and system stability.
Contribution
It proposes a market-structure-agnostic bi-level demand model that accounts for prosumer behavior, suitable for analyzing future power systems with high renewable penetration.
Findings
High prosumer penetration improves system loadability.
The model effectively captures demand changes due to prosumers.
It is applicable for future grid stability studies.
Abstract
The increasing uptake of residential PV-battery systems is bound to significantly change demand patterns of future power systems and, consequently, their dynamic performance. In this paper, we propose a generic demand model that captures the aggregated effect of a large population of price-responsive users equipped with small-scale PV-battery systems, called prosumers, for market simulation in future grid scenario analysis. The model is formulated as a bi-level program in which the upper-level unit commitment problem minimizes the total generation cost, and the lower-level problem maximizes prosumers' aggregate self-consumption. Unlike in the existing bi-level optimization frameworks that focus on the interaction between the wholesale market and an aggregator, the coupling is through the prosumers' demand, not through the electricity price. That renders the proposed model market…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsSmart Grid Energy Management · Electric Vehicles and Infrastructure · Microgrid Control and Optimization
