Optimal Sizing and Scheduling of Community Batery Storage within a Local Market Nam Trong Dinh trongnam.dinh@adelaide.edu.au The University of Adelaide Adelaide, South Australia, Australia S. Ali Pourmousavi a.pourm@adelaide.edu.au The University of Adelaide Adelaide, South Australia, Australia Sahand Karimi-Arpanahi sahand.karimi- arpanahi@adelaide.edu.au The University of Adelaide Adelaide, South Australia, Australia Yogesh Pipada Sunil Kumar yogeshpipada.sunilkumar@adelaide.edu.au The University of Adelaide Adelaide, South Australia, Australia Mingyu Guo mingyu.guo@adelaide.edu.au The University of Adelaide Adelaide, South Australia, Australia Derek Abbott derek.abbott@adelaide.edu.au The University of Adelaide Adelaide, South Australia, Australia Jon A. R. Liisberg jon.liisberg@watts.dk Watts A/S Hovedgaden, Svinninge, Denmark ABSTRACT The ever-increasing uptake of distributed energy resources necessi- tates the introduction of local electricity markets at the residential level. Electric retailers, who are adversely afected by these changes, can make a proft by operating local trading platforms and ofering services through community-level battery storage. In this work, we propose a Stackelberg game-based approach for sizing the cen- tralized battery unit under the operation of a multi-interval local market. The optimization is formulated as a bilevel program, where the leader is the market aggregator responsible for determining the local prices and battery charging/discharging schedules. Also, the followers in the bilevel program are prosumers, who can vary electricity consumption with respect to their comfort and cost of electricity. Upon obtaining the optimal capacity of the community storage, we modify the algorithm to efciently operate the battery on a daily basis. The applicability of the proposed model is evaluated using real-world data of residential prosumers with rooftop photo- voltaic systems for two diferent pricing schemes, which represents the proft trade-of between the aggregator and prosumers. The results show the proftability of the proposed model for community storage installation, where a relatively short payback period can be achieved via either pricing scheme. CCS CONCEPTS · Hardware → Batteries;· Computing methodologies → Plan- ning and scheduling. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for proft or commercial advantage and that copies bear this notice and the full citation on the frst page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specifc permission and/or a fee. Request permissions from permissions@acm.org. e-Energy ’22, June 28–July 1, 2022, Virtual Event, USA © 2022 Association for Computing Machinery. ACM ISBN 978-1-4503-9397-3/22/06. . . $15.00 https://doi.org/10.1145/3538637.3538837 KEYWORDS Community storage, local electricity market, demand response, energy rebound efect, Stackelberg game, storage sizing ACM Reference Format: Nam Trong Dinh, S. Ali Pourmousavi, Sahand Karimi-Arpanahi, Yogesh Pipada Sunil Kumar, Mingyu Guo, Derek Abbott, and Jon A. R. Liisberg. 2022. Optimal Sizing and Scheduling of Community Battery Storage within a Local Market. In The Thirteenth ACM International Conference on Future Energy Systems (e-Energy ’22), June 28–July 1, 2022, Virtual Event, USA. ACM, New York, NY, USA, 13 pages. https://doi.org/10.1145/3538637.3538837 1 INTRODUCTION The electricity grid has been transitioning to a green and sustain- able system over the past couple of decades by adopting larger amount of distributed energy resources (DER) such as rooftop solar photovoltaic (PV) systems and behind-the-meter battery storage. Traditional customers can leverage their DER to lower the electric- ity consumption cost as well as to reduce their carbon footprint. Also, during the periods of excess solar generation, customers can export their surplus electricity to the utility grid through fxed feed- in tarif (FiT) schemes, which helps to decrease the overall energy cost. As a result, rooftop PV systems in Australia have seen a steady growth in the last decade and are expected to increase even more within the next 20 years [2]. However, the roll out of DER reduces the revenue of utility companies because of the self-consumption of PV owners in the residential sector. In other words, retailers’ sale, hence their rev- enue, have reduced compared to the past. At the same time, the distribution network service providers are dealing with the techni- cal and operational challenges to handle reverse power fow, which mostly occurs during mid-day. For instance, on October 31, 2021, the distribution network in South Australia (SA) observed a neg- ative net demand for nearly four hours with a dipping record of -69.4 MW due to the high export of rooftop solar PV systems [34]. To manage it, SA Power Networks has reduced the export limit from 5 kW to 1.5 kW in some congested areas resulting in higher 34