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Abstract This study proposes a holistic data‐driven method for the optimal sizing and operation of a building‐level islanded microgrid with renewable energy resources in an urban setting. Firstly, various metres are integrated on an energy monitoring platform where field data are collected. A randomised learning‐based forecasting model is designed for supply/demand prediction in the microgrid. Based on the forecasting results, data‐driven uncertainty modelling is used to characterise the uncertainties associated with renewable energy supply and loads. An optimal sizing approach is then proposed to determine the optimal sizes for energy storage systems (ESSs) and distributed generators with the overall aim of minimising the investment and maintenance costs. Based on the optimal sizing and uncertainty scenarios, a two‐stage coordinated energy management method is proposed to minimise the operating cost under uncertainties. To validate the proposed method, it is compared with a benchmark method. Simulation results show that the proposed method can reduce the system cost while preserving the ESS lifetime. The developed methods are packaged onto a real‐time platform for implementation.

Network frequency control function is incorporated into a grid-connected wind farm-dual battery energy storage system (BESS) scheme. The design of the scheme takes advantage of the rapid response characteristics of the BESS and the in-built short-term overloading capability of the associated power conversion devices. A control strategy to regulate the BESS output power is then proposed. It is shown that the frequency control action offered by the BESS complements amicably with that of conventional generators in enhancing the frequency regulation attribute of the grid system.

Integer linear programming (ILP) has been widely applied to solve the optimal phasor measurement unit (PMU) placement (OPP) problem for its computational efficiency. Using ILP, a placement with minimum number of Phasor Measurement Units (PMUs) and maximum measurement redundancy can be obtained while ensuring system observability. Author response: please delete this above sentence. However, the existing ILP-based OPP methods does not guarantee full coverage of solutions to the optimization problem, which may sequentially results in suboptimal supervision of the system. In this paper, a hybrid ILP-based method is proposed to cover all solutions to the OPP problem without any omission. Comparing with the existing exhaustive searching methods, the proposed method is more computationally efficient, which makes finding all solutions in a large system a more feasible problem.

The number of Stationary Battery Systems (SBS) connected to various power distribution networks across the world has increased drastically. The increase in the integration of renewable energy sources is one of the major contributors to the increase in the number of SBS. SBS are also used in other applications such as peak load management, load-shifting, voltage regulation and power quality improvement. Accurately modeling the charging/discharging characteristics of such SBS at various instances (charging/discharging profile) is vital for many applications. Capacity loss due to the aging of the batteries is an important factor to be considered for estimating the charging/discharging profile of SBS more accurately. Empirical modeling is a common approach used in the literature for estimating capacity loss, which is further used for estimating the charging/discharging profiles of SBS. However, in the case of SBS used for renewable integration and other grid related applications, machine-learning (ML) based models provide extreme flexibility and require minimal resources for implementation. The models can even leverage existing smart meter data to estimate the charging/discharging profile of SBS. In this paper, an analysis on the performance of different ML approaches that can be applied for lithium iron phosphate battery systems and vanadium redox flow battery systems used as SBS is presented for the scenarios where the aging of individual cells is non-uniform.