Enhanced Slack Time based Price Driven Demand Response for Future Effectual Smart Communities
Bhagya Nathali Silva1, Murad Khan2, Kijun Han3
1Bhagya Nathali Silva, School of Computer Science and Engineering, Kyungpook National University, Daegu, Korea.
2Murad Khan, School of Computer Science and Engineering, Kyungpook National University, Daegu, Korea.
3Kijun Han*, School of Computer Science and Engineering, Kyungpook National University, Daegu, Korea.
Manuscript received on November 19, 2019. | Revised Manuscript received on December 08, 2019. | Manuscript published on December 30, 2019. | PP: 87-95 | Volume-9 Issue-2, December, 2019. | Retrieval Number: B3528129219/2019©BEIESP | DOI: 10.35940/ijeat.B3528.129219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: TEvolution of smart grid concept aims to address the imbalance between electricity demand and supply. Owing to consideration on sustainable energy, user comfort, and cost efficiency, residential Demand Response (DR) has gained a remarkable popularity over the past few years. To further enhance these benefits, herein we propose a residential appliance scheduling algorithm inspired by Least Slack Time (LST) algorithm. The conventional LST algorithm is amended with consumption thresholds and waiting factor constraints to derive proposed Minimum Slack Time (MST) algorithm, which increase cost and comfort efficiency during DR. Proposed algorithm was experimented in a simulated residential community consists of 50 houses. Further experiments were conducted by aggregating renewable energy sources using aggregated MST (AMST) algorithm. All instances were compared with an existing scheduling mechanism to assure superiority of proposed MST and AMST algorithms, in terms of grid electricity consumption, cost, Peak-to-Average Ratio (PAR), and waiting time.
Keywords: Cost efficient scheduling, Minimum slack time, Peak load reduction, Residential demand response, User convenience