Efficient Load Control Based Demand Side Management Schemes Towards a Smart Energy Grid System

• Published in: Sustainable cities and society 2020-08, Vol.59, p.102175, Article 102175
• Main Authors: Chakraborty, Nilotpal, Mondal, Arijit, Mondal, Samrat
• Format: Article
• Language: English
• Subjects: Demand side management; Direct load control; energy management; heuristic algorithm; scheduling; smart grid
• ISSN: 2210-6707; 2210-6715
• DOI: 10.1016/j.scs.2020.102175

•Direct load control based demand side management schemes are discussed.•Two generalized strip packing based heuristic algorithms are proposed for scheduling deferrable appliances.•Five iterative post-processing improvement schemes are proposed.•All the proposed mechanisms are evaluated on benchmark datasets as well as real-world energy consumption data. In this paper, we propose efficient load scheduling based demand side management schemes for the objective of peak load reduction. We propose two heuristic algorithms, named G-MinPeak and LevelMatch, which are based on the generalized two-dimensional strip packing problem, where each of the appliances has their specific timing requirements to be fulfilled. Furthermore, we have proposed some improvement schemes that try to modify the resulted schedule from the proposed heuristic algorithms to reduce the peak. All the proposed algorithms and improvement schemes are experimented using benchmark data sets for performance evaluation. Extensive simulation studies have been conducted using practical data to evaluate the performance of the algorithms in real life. The results obtained show that all the proposed methodologies are thoroughly effective in reducing peak load, resulting in smoother load profiles. Specifically, for the benchmark datasets, the deviation from the optimal values has been about 6% and 7% for LevelMatch and G-MinPeak algorithms respectively and by using the improvement schemes the deviations are further reduced up to 3% in many cases. For the practical datasets, the proposed improvement schemes reduce the peak by 5.21− 7.35 % on top of the peaks obtained by the two proposed heuristic algorithms without much computation overhead.