The Electric Power Grid: Today and Tomorrow

AbstractIn the coming decades, electricity's share of total global energy is expected to continue to grow, I and more intelligent processes will be introduced into the electric power delivery (transmission and distribution) networks. It is envisioned that the electric power grid will move from an electromechanically controlled system to an electronically controlled network in the next two decades. A key challenge is how to redesign, retrofit, and upgrade the existing electromechanically controlled system into a smart self-healing grid that is driven by a well-designed market approach. Revolutionary developments in both information technology and materials science and engineering promise significant improvements in the security, reliability, efficiency, and cost effectiveness of electric power delivery systems. Focus areas in materials and devices include sensors, smart materials and structures, microfabrication, nanotechnology, advanced materials, and smart devices.

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Challenges in reliability, security, efficiency, and resilience of energy infrastructure: Toward smart self-healing electric power grid

2008 IEEE Power and Energy Society General Meeting - Conversion and Delivery of Electrical Energy in the 21st Century ◽

10.1109/pes.2008.4596791 ◽

2008 ◽

Cited By ~ 58

Author(s):

Massoud Amin

Keyword(s):

Electric Power ◽

Power Grid ◽

Self Healing ◽

Energy Infrastructure ◽

Electric Power Grid

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Optimal Allocation of Large-Capacity Distributed Generation with the Volt/Var Control Capability Using Particle Swarm Optimization

Energies ◽

10.3390/en14113112 ◽

2021 ◽

Vol 14 (11) ◽

pp. 3112

Author(s):

Donghyeon Lee ◽

Seungwan Son ◽

Insu Kim

Keyword(s):

Particle Swarm Optimization ◽

Electric Power ◽

Distributed Generation ◽

Optimal Allocation ◽

Power Grid ◽

Particle Swarm ◽

Renewable Energy Resources ◽

Swarm Optimization ◽

Large Capacity ◽

Electric Power Grid

Widespread interest in environmental issues is growing. Many studies have examined the effect of distributed generation (DG) from renewable energy resources on the electric power grid. For example, various studies efficiently connect growing DG to the current electric power grid. Accordingly, the objective of this study is to present an algorithm that determines DG location and capacity. For this purpose, this study combines particle swarm optimization (PSO) and the Volt/Var control (VVC) of DG while regulating the voltage magnitude within the allowable variation (e.g., ±5%). For practical optimization, the PSO algorithm is enhanced by applying load profile data (e.g., 24-h data). The objective function (OF) in the proposed PSO method considers voltage variations, line losses, and economic aspects of deploying large-capacity DG (e.g., installation costs) to transmission networks. The case studies validate the proposed method (i.e., optimal allocation of DG with the capability of VVC with PSO) by applying the proposed OF to the PSO that finds the optimal DG capacity and location in various scenarios (e.g., the IEEE 14- and 30-bus test feeders). This study then uses VVC to compare the voltage profile, loss, and installation cost improved by DG to a grid without DG.

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