Optimal planning and secure grid operation are new challenges facing modern power systems. Conventional generation assets are becoming a thing of the past. Similarly, conventional grids for power transmission and power distribution are also transforming their paradigms. Now, due to the rising power of the digital age and an era of sustainable development, renewable sources have taken the place of fossil fuels, and smart digital grids are also being used [1,2,3].
Renewable power plants, such as wind power generation (WEG), solar power generation (PVG), and small hydro power plants (SHP), together with the new conception of smart grids, are now being chosen more often because, firstly, they are sustainable and environmentally friendly and can thus curb the issue of global warming, and secondly, they are more workable and reliable in commercial use [4,5]. With the increasing penetration of grids with high levels of stochastic resources, the development of innovative computing tools is necessary in order to obtain risk-based power generation estimations by evaluating the stochasticity of primary sources (wind, solar, and river flow).
Different algorithmic approaches must be applied to meet these modern power system needs and to obtain new solutions for the planning and operation of power systems. Through this approach, new research in this area is looking to expand the application of advanced computing algorithms for the solution of power system problems.
As far as impact to industry practices, commercial relevance, and economic impact are concerned with modern power systems, this Special Issue assuredly stands out thanks to its efficient and remarkable capabilities. The new, impactful, and tractable formulations/algorithms contained in this issue are applicable to all industries where smart grids and intermittent renewable energy resources are being used. This will be possible through this peer-reviewed new issue consisting of an innovative and modern uncertainty handling method, looking for scalability and commercial relevance through the application of the mentioned mathematical expansion. In this way, the proposed algorithms are beyond existing operation scheduling practices.
With sufficient development and adjustments in the algorithms presented and tests in pilot projects, the proposed techniques can achieve massive utilization as part of existing or new power system simulation tools, thus realizing the scheduling/operation of electrical networks and power grids worldwide.
Funding
This research received no external funding.
Conflicts of Interest
The author declares no conflict of interest.
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