Transmission Network Planning in Super Smart Grids: A Survey

For a utility company to reduce CO2 emissions along with managing load demands, it must strive for a 100 percent renewable electrical power generation. Europe takes initiatives to achieve this goal by developing a super smart grid (SSG) based on renewable energy resources (RERs) by 2050. The SSG is based on two exclusive alternatives: wide area and decentralized power generation using large number of RERs. Before developing such SSG, there is a need to address the critical issues associated with RERs, i.e., load flow balancing and transients stability. Considering a reliability issue involved with RERs and the random deviations between demand response and generation response patterns, load flow balancing and transient stability become challenging research issues in SSGs. These technical issues are also considered to be more challenging, if an unexpected outage in the form of an occurrence of three phase (L-L-L) faults (TPF) arises in SSGs, due to power quality disturbances. To address this problem, load flow balancing probabilistic modeling is performed in this research paper in order to formulate the complexity of randomness between generation and demand response patterns through transmission network planning (TNP) in the form of a super smart node (SSN) transmission network infrastructure. Moreover, a further optimization in SSN transmission network has been done with the addition of a cooperative control strategies in terms of an integrating vehicle to grid (V2G) technology in SSN transmission network in order to achieve further enhancement in load flow balancing and transient stability in SSGs. Furthermore, as SSGs power infrastructure is based on different clusters, therefore in order to accommodate various clusters for load flow balancing, a continuous spinning reserve (CSRs) probabilistic modeling has also been performed in this paper in terms of its integration in SSN transmission network. Considering above probabilistic analysis, future contingencies are easily predictable, before any kind of disruptive changes arises in a SSGs due an occurrence of a TPF. Moreover, from simulation results as performed in this paper, we can easily verified that our proposed probabilistic algorithm of load flow balancing and transients stability outperforms existing literature work and can also achieved near optimal performance, even for a broad range of variations in load and also in case of an arising of significant power quality disturbances in SSGs due to an occurrence of TPF.