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Recent years have seen a rapid uptake in distributed energy resources (DER). Such technologies pose a number of challenges to network operators, which ultimately can limit the amount of rooftop solar photovoltaic (PV) systems that can be connected to a network. The objective of this industry-based research was to determine the potential network effects of forecast levels of customer-owned rooftop solar PV on Energy Queensland's distribution network and formulate functions that can be used to determine such effects without the requirement for detailed network modeling and analysis. In this research, many of Energy Queensland's distribution feeders were modeled using DIgSILENT PowerFactory and analyzed with forecast levels of solar PV and customer load. Python scripts were used to automate this process, and quasi-dynamic simulation (QDSL) models were used to represent the dynamic volt–watt and volt–var response of inverters, as mandated by the Australian Standard AS/NZS 4777. In analyzing the results, linear relationships were revealed between the number of PV systems on a feeder and various network characteristics. Regression was used to form trend equations that represent the linear relationships for each scenario analyzed. The trend equations provide a way of approximating network characteristics for other feeders under various levels of customer-owned rooftop solar PV without the need for detailed modeling. [ABSTRACT FROM AUTHOR]

• The impacts of COVID lockdown on six aged care communities' energy and peak demand are studied. • The lockdown period was compared with the same period of similar temperature in previous years. • The impacts of COVID lockdown varied enormously in different climate regions. • Effectiveness and limitations of existing energy KPIs are discussed. In late March to April 2020 residential aged care facilities (RAC) in Australia were under COVID-19 lockdown. This paper explores whether the resultant restrictions on entry and exit and elimination of site group activities within RACs impacted on the total electricity use, peak demand and electrical load profiles. Six RACs in four different climate zones are analysed, comparing historical electricity load and demand profiles with the lockdown period. The facilities in warm regions showed largest reductions in energy use and peak demands. There was a peak demand timing shift in temperate regions and hot regions' changes were negligible for energy use or peak demands. This study revealed the limitations of using aggregate data as the key performance indicator (KPI) – energy use per bed. Also, KPIs in relation to cooling degree days (or total cooling and heating degree days) have been examined and are not recommended for temperate regions or temperate seasons. The potential CO 2 emission reduction from onsite renewable generation is quantified. Further research is to investigate energy use changes at circuit levels under lockdowns, and develop more nuanced KPIs that include the rate of energy use and the timing of that use. [ABSTRACT FROM AUTHOR]

• A framework for aggregating large-scale prosumers in electricity markets is proposed. • The proposed bi-level framework is linearized by duality theory and big-M method. • Cases studies based on 120,000 prosumers from Australia are conducted. • Increasing prosumers enlarges the gap between strategic and non-strategic bidding. Increased penetration of distributed energy resources is unleashing the flexibility of large-scale prosumers in deregulated markets. To explore prosumers' potential market revenues, some existing studies have focused on the strategic bidding of prosumers aggregation. A majority of those studies assume the price-taker role of the aggregator while a few studies assume the price-maker role of the aggregator. However, it remains an open question as to how the increasing number of prosumers influences the profit of a strategic aggregator. Therefore, we conduct a numerical analysis in this paper to quantify the profits of aggregating large-scale prosumers. A stochastic bi-level optimization model is proposed to depict the strategic behavior of prosumers aggregation bidding in joint energy and regulation markets. This bi-level model is transformed into a mixed-integer linear programming model by employing the Karush-Kuhn-Tucker conditions based on strong duality theory. Case studies based on 120,000 prosumers from Australia demonstrate that the strategic bidding behavior of an aggregator can lead to a 7.5% decrease in operation costs, and increasing the number of prosumers will lead to a larger gap between non-strategic and strategic behavior. [ABSTRACT FROM AUTHOR]