Energy Optimization with Bio-Inspired Heuristics Techniques in Smart Grid


Due to the exponential growth in the human population, the demand of energy is increasing with increased use of technology and smart appliances in every eld of life. Energy consumers are increasing day by day due to their highly dependence on automatic operated appliances and power consuming devices. Because of this, reliable and high-quality electrical power system is extremely important to full the residential, commercial and industrial sectors energy demand. Meanwhile there is a rapid increase in the global natural resources pressure. Throughout the world, major blackouts occur due to the consumer energy demand and supply mismatch and system automation de ciencies. Therefore, a transition process from traditional electric power grid to smart grid, to integrate communication and information technologies, is the demand of the future. To ful ll the energy demands, new resources of energy generation are necessary to keep the balance between demands and generation. However, due to the realization of the fact that, with increased carbon emission and scarcity of fossil fuels, optimized and ecient utilization of the existing resources of energy is very important in the near future. Therefore, search and integration of new green renewable energy resources is obligatory in such circumstances. But, integration of green renewable energy resources needs a wide sort of design, planning and optimization. Di erent conventional optimization techniques, such as Linear Programming (LP), Non-Linear Programming (NLP), Integer Linear Programming (ILP), Mixed Integer Linear Programming (MILP), Dynamic Programming (DP) and Constrained Programming (CP) etc. have already been practiced in the near past. However, in the present situations, when integration of renewable energy resources is mandatory, and problems are non-linear and have numerous local optima, such conventional optimization techniques become obsolete. In the last decade, bio-inspired modern heuristic optimization techniques are getting popularity due to their stochastic nature of search mechanisms and avoidance of large convergence time for exact solution. In this research work, we have explored and analyzed di erent bio-inspired algorithms for energy optimization problem, such as; Ant Colony Optimization (ACO), Antlion Optimization (ALO), Bacterial Foraging Optimization (BFO) algorithm, Cuckoo Search Optimization Algorithm (CSOA), Firey Algorithm (FA), Genetic Algorithm (GA), Grasshopper Optimization Algorithm (GOA) and Moth-Flame Optimization (MFO) algorithm. We also proposed a hybrid version of Genetic and Moth Flame Optimization algorithms, named as, Time-constrained Genetic-Moth Flame Optimization (TG-MFO) algorithm. Three main objectives of the use of the aforementioned bio-inspired algorithms; (a) minimization of the consumed energy cost by shifting the appliances from high energy price hours (on-peak hours) to low energy demand or low price hours (o -peak hours), (b) minimization of the peak to average power ratio (PAR) for stability and further reduction of the energy cost, (c) Reduction in the consumer waiting time due to
shifting/scheduling of the appliances. Simulation results show that, the use of bio-inspired energy optimization algorithms gave comparative results in terms of the aforementioned objectives. Renewable energy sources (RESs) and battery storage units (BSUs) are also integrated for further reduction of total load and its cost. For analysis and validation of the proposed bio-inspired algorithms, we applied these algorithms on consumer’s di erent real life scenarios, such as; single home for one day, single home for thirty days, thirty di erent size homes for one day and thirty di erent size homes for thirty days in a residential sector, an oce in the commercial sector and a woolen mill in the industrial sector. We considered di erent size homes with di erent power rating appliances and di erent length of operational times (LOTs) to make our algorithms more practicable. We put con-straints on appliances starting times and their operation ending times to minimize the end user discomfort and frustration along with the reduction of electricity bill. In a single home, we categorized the appliances as; Fixed Load (i.e., non-shift able or non-interruptible load) and shiftable Load (i.e., shift-able or interruptible load) to minimize consumer electricity bill. We also divided the consumers into three types as; (a) non-active users (that are fully depended on utility and do not have their own energy generation or battery storage units, (b) semi-active users (that are partially dependent on utility due to their own renewable energy sources and (c) full-active users (those users who have their own energy generation from any renewable energy sources and battery storage units in their own premises). We proposed three di erent system models (residential, commercial and industrial) for considering all aforementioned parameters to make a reliable and sustainable demand side management (DSM) system in a Smart Grid (SG). Day ahead pricing (DAP) signals are applied for calculation of the consumed energy cost, to make the system more practicable. In most of our assumed system models, usually there is a trade-o between reduction of consumer’s electricity cost and waiting time. Therefore, we proposed hybrid algorithm for achieving our objectives in an ecient way.

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