Appraising the Optimal Power Flow and Generation Capacity in Existing Power Grid Topology with Increase in Energy Demand

• Main Authors: Hamam, Y. (Author), Nnachi, G.U (Author), Richards, C.G (Author)
• Format: Article
• Language: English
• Published: MDPI 2022
• Subjects: Acoustic generators; Alternating current; alternating current model; Alternating current model; Constraint relaxation; cost of constraint relaxation; Cost of constraint relaxation; Current modeling; Deep learning; deep reinforcement learning; direct current model; Direct current model; Direct-current; Electric load flow; Electric power transmission; Electric power transmission networks; energy demand; Energy demands; Energy management; Generation capacity; linear programming; Linear programming; Linear-programming; maximum generation capacity; Maximum generation capacity; Maximum power; maximum power flow; Maximum power flow; Optimal generation; optimal generation capacity; Optimal generation capacity; optimal power flow; Optimal power flows; Population statistics; Power flows; Reinforcement learning; Topology
• Online Access: View Fulltext in Publisher

 Several socioeconomic factors such as industrialization, population growth, evolution of modern technologies, urbanization and other social activities do heavily influence the increase in energy demand. A thorough understanding of the effects of energy demand to power grid is highly essential for effective planning and operation of a power system network in terms of the available generation and transmission line capacities. This paper presents an optimal power flow (OPF) with the aim to determine the exact nodes through which the network capacities can be increased. The problem is formulated as a Direct Current (DC) OPF model, which is a linearized version of an Alternating Current (AC) OPF model. The DC-OPF model was solved as a single period OPF problem. The model was tested in several case studies using the topology of the IEEE test systems, and the computation speeds of the different cases were compared. The results suggested dual variables of the problem’s constraints as an extra tool for the network designer to see where to increase the network capacities. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.