In this paper, a comprehensive harmonic domain reference frame (HDRF) model of a voltage source converter (VSC) grid interactive photovoltaic (PV) system is presented. The model is useful for assessing the harmonic coupling between the PV system and the network. Different components of the PV system such as inverter, LCL filter and interconnecting transformer have been incorporated in the model. Using this model, harmonic currents from PV system connected to both distorted and undistorted networks have been quantified. Also, the model has been deployed in investigating resonance occurrence in a medium-voltage distribution network (MVDN) where the results provide interesting technical insight and understanding.
This paper presents a two-stage framework for optimal Electric Vehicle (EV) charging/discharging strategy for DC Microgrid (MG) with Distributed Generators (DGs). A multi-objective optimisation task aimed at minimising system losses and EV battery degradation with Vehicle-to-Grid (V2G) peak shaving service has been realised. This coordinated EV integration into the DCMG was formulated as a directed weighted single source shortest path problem that was solved using a modified Dijkstra's algorithm. The weights of the edges were obtained using primal-dual interior point method. The proposed framework has been experimentally verified using simulations with a test DCMG system with practical IEEE European low voltage test feeder load profiles. Results show realisation of peak demand shaving leveraging on EV discharge with minimal on-board battery degradation as well as reduced system losses. It is also shown that the proposed two-stage framework reduces the battery state of charge (SOC) sample space requirements in the analysis, thus, reducing the computational burden.
The growing integration of rooftop photovoltaics (PVs) and energy storage units (ESUs) in customer households has resulted in changes in the customer load profiles. This is likely to influence the accuracy of state estimation (SE) carried out based on previously assumed load profiles. In this paper, a statistical model for modern low voltage (LV) customers was developed using Gaussian mixture model (GMM). The resulting model was subsequently applied to SE using weighted least squares (WLS) algorithm. LV network with high penetration of customer-owned PV and ESUs have been simulated. Different scenarios which include load profiles: with PVs integrated but without ESUs, ESUs alone, and with hybrid systems (combination of PVs and ESUs) have been considered. The results are presented and discussed.
The ambition to decarbonize the source of energy for heat and transport sector through electricity from renewable energy has led to significant challenge in the way power distribution networks (DNs) are planned, designed and operated. Traditionally, DN was put in place to support the demand passively. Now with renewable generation, storage and demand side management through automation, provision of network support services have transformed the character of the DNs. Active management of the DN requires fast power flow analysis, state estimation, reactive power support etc. This paper proposes a method of power flow analysis which incorporates the challenges of distributed generator (DG) characteristics, demand side management and voltage support. The proposed approach reformulated the Jacobian matrix of the well-known modified augmented nodal analysis (MANA) method; thus, improving the robustness and solvability of the formulation. Reactive powers of the DGs, node voltages and currents of 'non-constitutive' elements were the chosen state variables. The performance of this method is compared with the MANA. Results are discussed and the effectiveness of the proposed approach is demonstrated with two example case studies.
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