Virtual power plants planning in the distribution network constrained to system resiliency under extreme weather events

The present study finds the proper location and size of virtual power plants (VPPs) in distribution networks, while taking into account network resilience to severe weather conditions. This is formulated as an optimization problem with two objective functions to minimize both the planning cost of the VPPs in a year and expected energy not-supplied of the network during natural events like flood and earthquake. The problem is subject to some boundaries, including AC power flow relationships, limits on operating and resilience situation, and planning terms of the VPP. There are also uncertain quantities present in the problem, such as those related to the amount of load, renewable power generation, price of energy, and accessibility of equipment and VPP devices. The uncertainties are modeled using the stochastic programming. The two objectives are merged to be a single objective by adopting Pareto optimization based on epsilon-constraint method. Krill herd optimization (KHO) and grey wolf optimization (GWO) are also combined to introduce a new hybrid algorithm so that a dependable optimal solution that shows low dispersion. Finally, a 33-bus distribution network is used to test the validity of the proposed method. According to the result, the optimal size and site of VPPs are found and the network achieves more suitable economic, operating, and resilience indices.(c) 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Automated summary

The present study optimizes the location and size of virtual power plants (VPPs) in distribution networks with consideration for network resilience. Uncertainties are modeled using stochastic programming, and Pareto optimization and hybrid algorithms are employed to find a dependable optimal solution. Experimental results confirm the effectiveness of the proposed method.