Ali Arefi

The paper analyses the Electromagnetic Transient (EMT) stability analysis of a modified Medium Voltage (MV). This Microgrid (MG) comprises a Diesel Generator (DG) and a Photovoltaic (PV) source under balanced load conditions. The system model was operated in a stable grid-connected mode with only DG's, but the authors proposed a new system with DG and PV in an islanded operation scenario. Islanded MG must operate in a stable mode where controllers can take prompt action in the fault event and make the system more reliable. In this regard, the droop control and PI control manage DG's active power and PV system's power operation, respectively. The Western Electricity Coordination Council (WECC) plant control is utilised as the benchmark model with PV only for a large-scale PV plant model. The control parameters are optimised for both machines to improve the system's dynamic response. The effectiveness and robustness of the MV islanded system are examined under different operating conditions through extensive simulation studies in the DigSILENT PowerFactory software.

Photovoltaic (PV) generation estimation plays a crucial role in the operation and planning of distribution networks (DNs). Existing literature primarily relies on model-based and data-driven methods for estimating PV generation in low voltage (LV) feeders. However, model-based techniques require accurate information regarding PV panel orientation, dust, shading, and cloud effects, which are often limited in DNs. On the other hand, supervised and semi-supervised data-driven approaches necessitate a ground-truth dataset for feeder or customer level estimation. Consequently, unsupervised data-driven techniques have gained traction in estimating behind-the-meter (BtM) PV generation in recent years. This paper focuses on feeder-level estimation, which is more practical and useful for utilities, as it eliminates the need for netload measurements at the customer level. We propose a new data-driven method using a signal-processing approach to estimate aggregated PV at the feeder-level. The PV scaling factor is estimated through first-order differencing and a moving average (MA) filter. The filter is designed to reduce the impact of quantization noise after employing the differentiator, based on the signal-to-noise ratio (SNR) on sunny days. By applying these techniques, the proposed approach offers an accurate estimation of BtM PV generation in LV feeders, enabling efficient operation and planning. To demonstrate the effectiveness of our method, we present comprehensive comparisons using a real dataset provided by an Australian distribution utility.

Electronic-based loads such as computers, smart phones, etc. are fast expanding all around the world. On the other hand, the aging of distribution systems to deliver power to these users results in a low quality of power. To this aim, in this study, renewable energy sources (RESs) connected to networks by smart inverters, are allocated to support networks' weakness related to harmonic distortion, resulting in delivering high-quality power to customers. In other words, solar and wind energy resources are optimally allocated in reconfigurable distribution networks with the aim of minimizing total harmonic distortion (THD). Due to the nonlinearity of the problem, the proposed methodology is then optimized by using the differential evolution algorithm (DEA). The simulation results from a typical 33-bus IEEE test network demonstrate that network reconfiguration in the presence of renewable energies with the interface of smart inverters has an active role in THD compensation, loss minimization, etc.

This paper proposes a model for energy sharing of interconnected microgrids (MGs), mainly where some MGs are owned by an entity, such as the government, which is the case study in Western Australia (WA). In the proposed model, MGs are able to trade energy among themselves when some of them have surplus generation, and others have lack of generations to meet their demand; however, they are obliged to pay for the use of distribution network, called network charge, and the share of network loss due to this energy transaction. In doing so, the network loss is taken into account and calculated through a power flow. The possibility of energy trading with the main grid is also considered through the wholesale electricity market. Considering the uncertainty of Photovoltaic (PV) generation and load involved, the decision making to inject or import energy to/from the main grid as well as to trade between MGs is obtained through a bi-level linear optimization. In the upper level, the distribution network operator intends to manage the energy exchange between MGs and energy trading with upstream grid, while in the lower level, each MG attempt to minimize its operational cost relating to PV and energy storage system (ESS). Finally, the proposed method is applied to a real project in Western Australia.