1 Generic Demand Model Considering the Impact of Prosumers for Future Grid Scenario Analysis Shariq Riaz, Student Member, IEEE, Hesamoddin Marzooghi, Member, IEEE, Gregor Verbiˇ c, Senior Member, IEEE, Archie C. Chapman, Member, IEEE, and David J. Hill, Life Fellow, IEEE Abstract—The increasing uptake of residential PV-battery sys- tems 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 structure agnostic, making it suitable for future grid studies where the market structure is potentially unknown. As a case study, we perform steady-state voltage stability analysis of a simplified model of the Australian National Electricity Market with significant penetration of renewable generation. The simulation results show that a high prosumer penetration changes the demand profile in ways that significantly improve the system loadability, which confirms the suitability of the proposed model for future grid studies. Index Terms—Demand response, aggregators, prosumers, PV- battery systems, generic demand model, future grids, scenario analysis, bi-level optimization. NOMENCLATURE B Set of buses. G Set of generators g. M Set of demand aggregators m. L Set of lines: L⊆B×B. R Set of regions r. H Set of time slots h. x / x Minimum/maximum value of x. r +/− g Ramp-up/down rate of supplier g. τ u/d g Minimum up/down time of supplier g. B i,j Susceptance of line between buses i and j . θ i,h Voltage angle at bus i in h. p i,j l,h Power flow on line i-j : p i,j l,h = B i,j (θ i,h - θ j,h ). Shariq Riaz, Gregor Verbiˇ c, Archie C. Chapman, and David J. Hill are with the School of Electrical and Information Engineering, The University of Syd- ney, Sydney, New South Wales, Australia. e-mails: shariq.riaz, gregor.verbic, archie.chapman, david.hill@sydney.edu.au. Hesamoddin Marzooghi is with School of Engineering and Technology, Central Queensland University (CQ University), Brisbane, Australia. e-mail: h.marzooghi@cq.edu.au. Shariq Riaz is also with the Department of Electrical Engineering, Univer- sity of Engineering and Technology Lahore, Lahore, Pakistan. David J. Hill is also with the Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong. Δp i,j l,h Power loss on line i-j . p g,h Generated power of supplier g in h. p m b,h Battery storage power of aggregator m in h. e m b,h Battery storage state of charge of aggregator m in h. p inf,m d,h Inflexible demand of aggregator m in h. p flx,m d,h Flexible demand of aggregator m in h. p u,m d,h Underlying prosumer demand of aggregator m in h. p m pv,h PV generation of aggregator m in h. p res r,h Required reserve in region r in the system in h. s g,h Binary decision variable on/off status of supplier g. u g,h Binary start-up decision variable of supplier g. d g,h Binary shut-down decision variable of supplier g. c fix/var g Fix/variable cost of supplier g. c su/sd g Start-up/shut-down cost of supplier g. η b Battery round-trip efficiency. λ Dual variable associated with an equality constraint. μ Dual variable associated with an inequality constraint. b Binary variable introduced to maintain complementary slackness. M Large positive number used in the MILP formulation of the lower-level KKT conditions. I. I NTRODUCTION P OWER systems are undergoing a major transformation driven by the increasing uptake of variable renewable energy sources (RES). At the demand side, the emergence of cost-effective “behind-the-meter” distributed energy resources, including on-site generation, energy storage, electric vehicles, and flexible loads, and the advancement of sensor, computer, communication and energy management technologies are changing the way electricity consumers source and consume electric power. Indeed, recent studies suggest that rooftop PV- battery systems will reach retail price parity from 2020 in the USA grids and the Australian National Electricity Market (NEM) [1]. A recent forecast by Morgan Stanley has suggested that the uptake can be even faster, by boldly predicting that up to 2 million Australian households could install battery storage by 2020 [2]. This has been confirmed by the Energy Networks Australia and the Australian Commonwealth Scientific and Industrial Research Organisation (CSIRO) who have estimated the projected uptake of solar PV and battery storage in 2050 to be 80 GW and 100 GWh [3], which will represent between 30%–50% of total demand, a scenario called “Rise of the Prosumer” [4]. Here, the prosumer they refer to is a small- scale (residential, commercial and small industrial) electricity consumer with on-site generation. A similar trend has been arXiv:1605.05833v5 [math.OC] 13 Sep 2017