Solving MKP Applied to IoT in Smart Grid Using Meta-heuristics Algorithms: A Parallel Processing Perspective
This work addresses energy management in smart grids to mitigate electricity price increases and load shedding in South Africa, but it appears incremental as it builds on existing meta-heuristics and suggests future scalability options.
The paper tackles the optimization of energy management controllers in smart grids by addressing the Multiple Knapsack Problem (MKP) for Demand Side Management, proposing the iterative Discrete Flower Pollination Algorithm (iDFPA) and parallelization for scalability.
Increasing electricity prices in South Africa and the imminent threat of load shedding due to the overloaded power grid has led to a need for Demand Side Management (DSM) devices like smart grids. For smart grids to perform to their peak, their energy management controller (EMC) systems need to be optimized. Current solutions for DSM and optimization of the Multiple Knapsack Problem (MKP) have been investigated in this paper to discover the current state of common DSM models. Solutions from other NP-Hard problems in the form of the iterative Discrete Flower Pollination Algorithm (iDFPA) as well as possible future scalability options in the form of optimization through parallelization have also been suggested.