In this paper, we propose a home energy management system which employs load shifting strategy of demand side management to optimize the energy consumption patterns of a smart home. It aims to manage the load demand in an efficient way to minimize electricity cost and peak to average ratio while maintaining user comfort through coordination among home appliances.
In order to meet the load demand of electricity consumers, we schedule the load in day-ahead and real-time basis. We propose a fitness criterion for proposed hybrid technique which helps in balancing the load during On-peak and Off-peak hours. Moreover, for real-time rescheduling, we present the concept of coordination among home appliances. This helps the scheduler to optimally decide the ON/OFF status of appliances in order to reduce the waiting time of appliance.
For this purpose, we formulate our real-time rescheduling problem as knapsack problem and solve it through dynamic programming. This study also evaluates the behavior of the proposed technique for three pricing schemes including: time of use, real-time pricing and critical peak pricing. Simulation results illustrate the significance of the proposed optimization technique with 95% confidence interval.
The optimization of SG components using DSM and DR is a challenging task. Several solutions have been proposed and implemented while aiming at Single Objective Optimization (SOO) or Multi Objective Optimization (MOO) with trade-off between different targeted objectives. Some are quoted and also their systematic review is given in Table I. Due to the importance of DSM and its emergent need, it has been an active topic from a couple of decades. Articles target the user comfort, cost minimization on the consumer side and PAR reduction which directly or in directly benefits the utility using mathematical, statistical and some bio-inspired artificial intelligence optimization techniques.
Since its existence, the DSM came up with numerous challenges like: load shifting, communication, fairness, security and privacy. DSM strategies are developed to overcome the irregular usage of electric load on the consumer end. This irregular usage is a challenging task to handle, because it increases the load on the utility and generation units. The extra generation of electricity is required because of high demand in some specific hours(On-peak hours) resulting in high electricity cost. In this regard, efficient algorithm for HEMS is required which helps users to shift the load from On-peak to Off-peak hours in a balanced way.
PROPOSED SYSTEM MODEL
The graphical representation of the linear Equation 4a is shown in Fig. 4 for On-peak and Off-peak hours. This graphical representation illustrates that the selected feasible solution from the defined feasible region not only reduces the cost, but also maintains the load curve which further helps to avoid the peak formation (i.e., O3). On the other hand, for real-time rescheduling, objective O4 is considered.
SIMULATION RESULTS AND DISCUSSION
Regardless of adopted electricity price, Fig. 12 shows that a swarm based approach has low price and PAR as compared to evolutionary one’s, where their hybrid outperformed both techniques. Fig. 12 evidently revealed 5%, 8% and 8% less cost than the without coordination scheduled for GA, BFA and HBG, respectively, using RTP.
CONCLUSION AND FUTURE WORK
In this work, a HEMS is proposed, in which the load shifting strategy of DSM is implemented to achieve the desired objectives. This study considers a single home which consists of multiple home appliances. Each appliance in the smart home is scheduled using the proposed optimization technique, HBG. The proposed technique helps to find the most optimum schedule of each home appliance considering system constraints. The scheduling is done based on day-ahead and real-time basis. The real-time rescheduling procedure helps to handle interrupts generated by the energy consumer to turn on some other home appliance.
Therefore, the most feasible solution is to allow dynamic scheduling through the coordination of appliances. For this purpose, we have incorporated a coordination among home appliances using dynamic programming techniques. Whereas, the performance of the HEMS is evaluated using different pricing schemes. From the simulation results we conclude that the coordination in real-time rescheduling does not effect the electricity cost and PAR significantly. Moreover, we compare the results of the proposed approach with the GA and the BFA to check its efficiency.
Results show that the PAR and electricity costs are reduced upto 50% and 40%, respectively. Further, we analyze the behavior of RTP, TOU and CPP to check the performance of HEMS. However, these price signals do not affect the performance of the scheduler as we analyze the behavior of these pricing schemes. This shows that our proposed approach efficiently schedules home appliances using all the pricing signals. However, TOU is economical as compared to the rest of other techniques.
Source: University of Idaho
Authors: Adia Khalid | Nadeem Javaid | Mohsen Guizani | Musaed Alhussein | Khursheed Aurangzeb | Manzoor Ilahi