1. Demand response assisted energy and reserve procurement in renewable integrated dynamic energy market.
- Author
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Sharma, Akanksha and Sharma, Sumedha
- Subjects
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OPTIMIZATION algorithms , *ENERGY industries , *RESERVES (Accounting) , *ELECTRICAL load , *RELIABILITY in engineering - Abstract
As wind power integration increases, the need for heightened reserve procurement becomes imperative for maintaining system reliability. In the dynamic and deregulated multi-objective environment, this study investigates the impact of varying degrees of uncertainty in wind conditions on the acquisition of energy and spinning reserve ancillary services. The investigation focuses on obtaining reserve capacity through thermal generators and demand response (DR) offers, while addressing wind generation variability. To achieve this, the study employs the multi-objective particle swarm and electric field hybrid optimization algorithm to co-optimize total cost and emissions in the simultaneous market clearing of energy and SRAS procurement. The model's assessment involves a comprehensive evaluation over a one-day period, encompassing 24 hourly intervals on both IEEE 30-bus and IEEE 118-bus test systems. The performance evaluation of the developed algorithm includes a comparative analysis with other heuristic methods, assessing objectives and statistical performance metrics like convergence and spread, complemented by corresponding box plots. The findings highlight how the presented model successfully tackles the challenges posed by fluctuating wind conditions and dynamic DR offers, providing valuable insights for optimizing energy procurement and reserve capacity in wind-integrated power systems. • Investigations on a day-ahead dynamic multi-objective energy and SRAS procurement. • Analysis of model for different wind uncertainty levels. • Demand response participation in providing reserves accounting for wind uncertainty. • Explicated Pareto-based hybrid algorithm and compared its performance with others. • Measured the algorithm performance by statistical measures. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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