Please note this is a comparison between Version 2 by Jason Zhu and Version 3 by Jason Zhu.

The transition towards net-zero emissions is inevitable for humanity’s future. Of all the sectors, electrical energy systems emit the most emissions. This urgently requires the witnessed accelerating technological landscape to transition towards an emission-free smart grid. It involves massive integration of intermittent wind and solar-powered resources into future power grids. Additionally, new paradigms such as large-scale integration of distributed resources into the grid, proliferation of Internet of Things (IoT) technologies, and electrification of different sectors are envisioned as essential enablers for a net-zero future. However, these changes will lead to unprecedented size, complexity and data of the planning and operation problems of future grids. It is thus important to discuss and consider High Performance Computing (HPC), parallel computing, and cloud computing prospects in any future electrical energy studies.

- parallel computing
- optimization
- power system studies

To date, the global mean temperature continues to rise, and emissions continue to grow, creating great risk to humanity. Efforts and pathways are drawn by many jurisdictions to limit warming to 2 °C and reach net zero CO2 emissions ^{[1]}. This is largely due to the outdated electrical energy system operation and infrastructure, which causes the largest share of emissions of all sectors. Electrical energy systems are, however, witnessing a transition, consequently causing a growth in the scale and complexity of their planning and operation problems. The changing grid topology, decarbonization, electricity market decentralization, and grid modernization mean innovations and new elements are continuously introduced to the inventory of factors considered in grid operation and planning. Moreover, with the accelerating technological landscape and policy changes, the number of potential future paths to Net-Zero increases, and finding the optimal transition plan becomes an inconceivable task.

The use of parallel techniques becomes inescapable, and defaulting HPC competence by the electrical energy and power system community is inevitable in the face of the presumed future and its upcoming challenges. With some algorithmic modifications, parallel computing unlocks the potential to solve huge power system problems that are conventionally intractable. This helps in the reduction of cost and CO2

emissions indirectly through detailed models that help people find less conservative operational solutions—which reduce thermal generation commitment and dispatch—and plan the transition to a net-zero grid with optimal placement of the continually growing inventory of Renewable Energy (RE) resources and smart component investmen. Moreover, parallel processing on multiple units is inherently more efficient and reduces energy use. Using multi-threading causes the energy consumption of multi-processors to increase drastically ^{[2]}. Thus, in the case of resource abundance, it is more efficient to distribute work on separate hardware. Resource sharing is more effective than resource distribution, which reduces the demand for hardware investment and larger servers. All of these factors make it increasingly important for electrical engineering scientists to familiarize themselves with efficient resource allocation and parallel computation strategies.

North America ^{[3]}, the EU ^{[4]}, and many other countries ^{[5]} set a target to completely retire coal plants earlier than 2035 and decarbonize the power system by 2050. In addition, the development of Carbon Capture and Storage Facilities is growing ^{[6]}. Renewable energy penetration targets are set, with evidence of fast-growing proliferation across the globe, including both transmission-connected Variable Renewable Energy (VRE) ^{[7]} and behind the meter distributed resources ^{[8]}. The demand profile is changing with increased electrification of various industrial sectors ^{[9]} and the transportation sector ^{[10]} building electrification, energy efficiency ^{[11][12]}, and the venture into a Sharing Economy ^{[13]}.

The emerging IoT, facilitated by low latency, low-cost next-generation 5G communication networks, helps roll out advanced control technologies and Advanced Metering Infrastructure ^{[14][15]}. This gives more options for contingency remedial operational actions to increase the grid reliability, and cost-effectiveness, such as Transmission Switching ^{[16]}, Demand Response ^{[17]}, adding more micro-grids, and other Transmission–Distribution coordination mechanisms ^{[18]}. Additionally, they allow lower investment in transmission lines and look for other future planning solutions, such as flow management devices and FACTs ^{[19]}, Distributed Variable Renewable Energy ^{[20]}, and Bulk Energy Storage ^{[21]}.

The asynchronous parallelization of OPF first appeared on preventative ^{[107]}, and corrective SCOPF ^{[108]} targeting online applications ^{[109]} motivated by the heterogeneity of solution time of different scenarios. Both SIMD and MIMD machines were used, emphasizing portability as “Getsub and Fifo” routines were carried out. On the same token, MPI protocols were used to distribute and solve SCOPF, decomposing the problem with GRMES and solving it with the non-linear IPM method varying the number of processors ^{[110]}. Real-time application potential was later demonstrated by using Benders decomposition instead for distributed SCOPF ^{[111]}. Benders decomposition is one of the most commonly used techniques to create parallel structures in power system optimization problems, and it shows up in different variations in the present literature.

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