Affiliations: [a] Galgotias College of Engineering and Technology, Greater Noida, India
| [b] Industry Integration Cell, Shri Vishwakarma Skill University, Palwal, Haryana, India
| [c] Campus Director, Chandigarh Group of Colleges, Landran, Panjab, India
Correspondence:
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Corresponding author. Ravindra Kumar Yadav, Assistant Professor, Galgotias College of Engineering and Technology, Greater Noida, India. E-mail: yash.rkyadav@gmail.com.
Abstract: The increasing demand for electrical energy is a result of advancing technologies and changing lifestyles worldwide. Meeting this escalating energy need poses a substantial challenge, especially the difficulty in constructing new conventional power plants due to limited fossil fuel resources. To address this, demand-side management (DSM) in smart grid (SG), integrated with solar photovoltaic energy (SPE) have emerged as a crucial tool for effectively managing electricity demand, ensuring flexibility and reliability. DSM achieves optimal electricity utilization by rescheduling the operation schedules of consumer appliances and carefully adjusting their demand profiles. Integrating DSM into a smart grid framework is highly advantageous for the power industry’s pursuit of sustainable energy goals. While various heuristic-based optimization techniques have been employed for DSM, the focus on SPE has been limited to small-scale residential loads. This study utilizes the Ant Colony Optimization (ACO) algorithm to tackle a day ahead DSM minimization problem, considering SPE in areas with large number of appliances. The DSM minimization problem falls into the category of discrete combinatorial problems, making it well-suited for ACO optimization. The self-healing, self-protection, and self-organizing attributes of ACO make it particularly effective for DSM solutions. Residential, commercial, and industrial loads, with and without SPE integration, are considered to demonstrate the efficacy of the proposed ACO algorithm. Simulation results are compared with other studies in the literature, including Evolutionary Algorithm (EA), Moth Flame Optimization (MFO), and Bacterial Foraging Optimization (BFO), in terms of reducing consumer’s cost of energy (CCE) and utility peak load (UPL). The findings indicate that the proposed ACO algorithm outperforms the other algorithms considered in the current context.
Keywords: Demand side management, ant colony optimization, solar photovoltaic energy, utility peak load, consumer’s cost of energy
