A combined resource allocation framework for PEVs charging stations, renewable energy resources and distributed energy storage systems

Optimal allocation of distributed generation and electric vehicle charging stations based on intelligent algorithm and bi‐level programming

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“…Based on these considerations, the work by Liu et al 10 builds a joint planning model of EVCSs and renewable energy with minimum voltage fluctuations, minimum load fluctuations and maximum capacity for EVCS energy storage connections, and the genetic particle swarm algorithm is used to determine the optimal construction scheme. Other works 11,12 consider the impacts of increasing the load demand, dynamic electricity pricing and DG power intermittence on the EVCS and DG coordination planning problem.…”

“…ADNs coordinate and control DG, the energy storage system, controllable loads and other power equipment in the distribution network by optimising the system management mode to promote the source absorption of green and clean energy. The planning models in [10][11][12] do not consider the adjustment ability of ADNs when considering the planning problem, and could not make full use of the positive effects of DG and EVCs on reducing network loss and improving system power flow distribution, which limited the development of DG and EVCS. The work by Zare et al 13 takes active management measures for DG connected to distribution network and establishes a distributed planning model of DG based on multiobjective two-layer distribution under the active management model.…”

Non-Wire Alternatives: An Additional Value Stream for Distributed Energy Resources

“…The parameter Γ i adjusts the robustness of the solution and is known as the protection level of the i th constraint (Γ i = |J i | produces the most robust solution). The interested reader is encouraged to consult [10] for a more detailed explanation of (11) and its robust counterpart (12).…”

“…A common technique to modulate the accuracy and solvability of a DER planning model is to modify the number of time-steps in the modeling horizon. Some change the number of representative days in a year or month (e.g., in [11], [12]) or modify the time step resolution (e.g., 4 hours per time step [22] or a year per time step [23], [24]). While the relatively coarse time step resolutions in [11], [12], [22], [23], [24] may be appropriate for their specific studies, there are two issues with the approach.…”