Develop the hybrid DR with HES approach to predict the price and reduce congestion during Load Variation

Authors

  • M. Kamalakkannun, N. D. Sridhar

Keywords:

Demand response; Off-Grid Operation; Locational Marginal Price; Full-Scale Tests; Wind Power Uncertainty; GPFC; Hydrogen Energy Storage; Emergency Active Power Shortage

Abstract

An energy shift was supported by the creation of a digital distributed less CO2 power system, the major components of which are renewable-based producing plants. In 2022, 15 Photovoltaic Power Plants (PVPs) with a total installed capacity of 364 MW are commissioned in India, accounting for 21.08% of the country's total installed PVP capacity. With global carbon emissions gaining significance, reducing carbon emissions in power systems is a critical problem. Carbon expense was first added to an optimization model's goal variables or restrictions in early research. The carbon cost, on the other hand, is unable to reflect the time-varying CO2 emissions brought on by load energy usage. To solve the issue; this study presents a Demand Response (DR) approach that uses Locational Marginal Prices (LMP) on electrical power and carbon emissions to restructure the load demand framework, guiding the demand of an electricity and a carbon perspective. An approach could decrease power system carbon emissions while taking into account the financing of energy purchasing and the conventional DR to Peak Load Shifting (PLS). In the meantime, to account for Monte Carlo sampling, scenario reduction wind power uncertainty, and historical information fitting, approaches are applied. The Hydrogen Energy Storage (HES) system is created to benefit of the CO2 markets and energy at the same time using proper charging methods. The simulation findings utilizing the PJM 5-bus network demonstrate that the proposed Locational Marginal Electricity (LME)-CO2 cost could reduce the power system's carbon emissions while assuring economic performance. The load demand reflects the pattern of changing CO2 emission levels and the variation in power cost when the CO2 emission component is included in pricing.

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Published

13.07.2024

How to Cite

M. Kamalakkannun. (2024). Develop the hybrid DR with HES approach to predict the price and reduce congestion during Load Variation. International Journal of Intelligent Systems and Applications in Engineering, 12(4), 1443–1450. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/6418

Issue

Vol. 12 No. 4 (2024)

Section

Research Article

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