Abstract
In a smart grid, several optimization techniques have been developed to schedule load in the residential area. Most of these techniques aim at minimizing the energy consumption cost and the comfort of electricity consumer. Conversely, maintaining a balance between two conflicting objectives: energy consumption cost and user comfort is still a challenging task. Therefore, in this paper, we aim to minimize the electricity cost and user discomfort while taking into account the peak energy consumption. In this regard, we implement and analyse the performance of a traditional dynamic programming (DP) technique and two heuristic optimization techniques: genetic algorithm (GA) and binary particle swarm optimization (BPSO) for residential load management. Based on these techniques, we propose a hybrid scheme named GAPSO for residential load scheduling, so as to optimize the desired objective function. In order to alleviate the complexity of the problem, the multi dimensional knapsack is used to ensure that the load of electricity consumer will not escalate during peak hours. The proposed model is evaluated based on two pricing schemes: day-ahead and critical peak pricing for single and multiple days. Furthermore, feasible regions are calculated and analysed to develop a relationship between power consumption, electricity cost, and user discomfort. The simulation results are compared with GA, BPSO and DP, and validate that the proposed hybrid scheme reflects substantial savings in electricity bills with minimum user discomfort. Moreover, results also show a phenomenal reduction in peak power consumption.
Highlights
It has been observed that the residential area is a major cause of energy consumption and greenhouse gas (GHG) emissions
We have noticed that there is no significant difference between cost reduction by GAPSO and dynamic programming (DP); we still prefer GAPSO over DP due to its computational efficiency which is clear from Table 7
We have modelled a residential energy management system proposing a hybrid technique for residential load scheduling
Summary
It has been observed that the residential area is a major cause of energy consumption and greenhouse gas (GHG) emissions. The results validate that the proposed models have efficiently and optimally reduced the electricity consumption cost of the consumers. In [40,41], authors proposed a model for the scheduling of large number of devices with an objective of cost minimization and reduction of peak power consumption. The proposed models perform well in terms of cost minimization and peak demand reduction, consumers’ comfort is not addressed, which is a key component for end users’ to participate in DR programs. There is a need to develop such an optimization method which can improve search efficiency and precision and adequate to handle multiple constraints Based on these heuristic techniques, a hybrid technique is proposed with the objectives of cost and discomfort minimization.
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