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With the advancements of the battery energy storage systems (BESSs), reduction of their manufacturing costs and government subsidies, the BESS uptake is likely to increase rapidly in power distribution networks. This paper investigates the effects of residential BESSs on low-voltage (LV) networks using the actual household load profiles equipped with BESS and solar-photovoltaic (PV) systems. The electricity consumption data collected via smart meters (2200 households with PV/BESS, 1950 households with PV only and 1000 households without a PV or a BESS) at different solar-PV penetration levels and network types are used to simulate real network operating scenarios. A real LV distribution network in Australia is analysed in DIgSILENT PowerFactory under different scenarios, such as, customers with and without a solar-PV/BESS, with a solar-PV but without a BESS, and without a solar-PV, by using both the power flow and quasi-dynamic simulation studies under balanced and the unbalanced network loading conditions. According to the study, customers experience large voltage excursions from solar-PV power exports, which could be resolved by the household BESS, provided that the BESS charging is coordinated with the solar-PV production. Moreover, quasi-dynamic simulation shows that the BESS could reduce the violation of the over-voltage limit during the solar peak hours (midday) by lowering the worst-case feeder voltage by 3%. Finally, extreme-event (high solar PV generation scenario) simulation shows that the implementation of the BESS controller to facilitate charging BESS during afternoon solar-PV export may reduce the negative grid impact and will assist to avoid network upgrades. [ABSTRACT FROM AUTHOR]

This article develops a practical framework for the multiobjective optimal planning of a grid-connected renewable-battery system considering a long-period operation. The capacities of wind turbine, solar photovoltaic (PV), and battery storage are optimized by minimizing three objective functions: cost of electricity (COE), grid dependence (GD), and total curtailed energy (TCE). A new rule-based energy management is developed for the long-period operation, where: 1) the capacity degradations of PV and battery are applied; 2) purchase and sell electricity prices are updated for each year using interest and escalation rates; and 3) the salvation value of the components is considered to achieve a realistic economic analysis of the planning problem. The developed multiobjective optimal planning model is examined using the long-period (ten years) real data of wind speed, solar insolation, ambient temperature, and load consumption for a grid-connected household in Australia. It is found that a household with the minimum GD (0.008%) results in a COE of 116 ¢/kWh with a TCE of 100 MWh in ten years. The proposed optimal planning framework based on the long-period operation is compared with the short-period operation. [ABSTRACT FROM AUTHOR]

An increasing number of residential customers have installed hybrid rooftop solar battery systems (HRSBSs). Currently, most HRSBSs are installed behind-the-meter (BTM), where only customers’ net load is measured by smart meters. This invisibility poses significant challenges to the system operation. Disaggregating BTM PV generation and battery charging/discharging profile of customers can enhance the grid-edge observability. This article proposes an energy disaggregation method to jointly separate the PV generation and battery charging/discharging power from the net load. First, a Home Smart Battery Management model is built to simulate the battery charging/discharging profile. Second, an optimal disaggregation model is established based on a contextually supervised source separation method. Furthermore, the feature vectors of PV, load, and battery are extracted as the input of the disaggregation model. Case studies on two datasets from Australia and China show that the proposed method has a promising disaggregation performance. [ABSTRACT FROM AUTHOR]

A precise estimation of the state of charge (SoC) of the lithium-ion battery is crucial for reducing range-anxiety and improving the performance of the electric vehicle (EV) battery management system. An accurate estimation of the SoC, however, has remained elusive due to the complex and nonlinear behavior of the battery. In this article, a new mixed estimation model (MEM) for the battery parameters and the SoC estimation is proposed, where the route is specified before the travel. The new MEM uses a combination of a battery power-based method (BPBM), a combined model, and a partial adaptive forgetting factor recursive least-square (PAFF-RLS) SoC calibration algorithm to make use of the best characteristics of each model to determine a better and more accurate SoC estimation. The partial adaptive forgetting factors solves the issue of the different rate changes in the battery parameters and reduces the complexity of the algorithm compared to the fully adaptive recursive models. The BPBM allows various traveling factors to be included in the model, such as the environmental conditions, the effect of auxiliary loads, and the traffic congestion. To verify the validity of the PAFF-RLS algorithm, two laboratory tests using real-time driving cycles have been conducted on a 2012 Nissan Leaf 31.1 Ah Manganese-oxide Li-ion battery cell. The effectiveness of the MEM model has been demonstrated by driving the Nissan Leaf along two selected routes in Australia. The results demonstrate the great accuracy of the proposed method for the SoC estimation, when compared with those from the previous models. [ABSTRACT FROM AUTHOR]

This article determines the optimal capacity of solar photovoltaic (PV) and battery energy storage (BES) for grid-connected households to minimize the net present cost of electricity. The real-time rule-based home energy management systems using actual annual data of solar insolation, ambient temperature, household electricity consumption, and electricity rates are used in the optimization process. The above-mentioned technique is applied to two system configurations—household with a solar PV and a BES. The uncertainty analysis is implemented using ten years of real data to confirm the optimal results. An accurate cash flow analysis is also presented to illustrate the customer payment in each year during the project lifetime. The sensitivity analysis is conducted by varying the cost and capacity of system components, grid constraint, average daily electricity demand, and retail price of electricity. A typical grid-connected household in South Australia is considered as the case study. A practical guideline is presented for the residential consumers in South Australia to select the optimal PV/BES based on their daily average electricity demand and the available rooftop space for PV installation. Finally, the proposed optimization method is applied to households of other Australian States and a comparison of results is presented. [ABSTRACT FROM AUTHOR]