The increased popularity of small-scale DER has replaced the well-established concept of conventional generating plants around the world. In the present energy scenario, a significant share of energy production now comes from the grid integrated DERs installed at various consumer premises. These DERs are being renewable-based generates only intermittent power, which in turn makes the scheduling of electrical dispatch a tough task. The Virtual Power Plant (VPP) is a potential solution to this challenge, which coordinates and aggregates the DERs generation into a single controllable profile. In this paper, a modified PSO-based multi-objective optimization is proposed for the VPP scheduling in distribution network applications such as energy cost minimization, peak shaving, and reliability improvement. For feasibility analysis of the VPP, a case study of state power utility is taken, which includes a 90 bus industrial feeder with grid integrated PVs as DER. The optimized results are computed in both grid-connected and autonomous mode reveal that the operating cost, peak demand, and EENS are declined by 31.70%, 23.59%, and 62.30% respectively. The overall results obtained are compared by the results obtained from other well-established optimization techniques and it is found that the proposed technique is comparatively more cost-effective than others.
In this paper, a demand side management (DSM) technique for minimizing the peak load of the electric grid is proposed. The grid optimization problem is formulated and analyzed to find the optimized strategies. A smart power system with multi-residential end-users connected to a single energy source is considered. The end-users attempt to minimize peak-to-average ratio (PAR) of the electric grid by using energy scheduler (ES) as well as their energy production and storage strategies. Some end-users posses renewable energy sources (RESs), distribution generators (DGs), storage devices or both. Users charge their storage devices at low-demand periods and discharge them at high-demand periods. Likewise, end-users produces electricity during the peak hours to minimize their energy cost.
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