Abstract
As the world population and its dependency on energy is growing exponentially day by day, the existing energy generating resources are not enough to fulfill their needs. In the conventional grid system, most of the generated energy is wasted because of improper demand side management (DSM). This leads to a difficulty in keeping the equilibrium between the user need and electric power production. To overcome these difficulties, smart grid (SG) is introduced, which is composed of the integration of two-way communication between the user and utility. To utilize the existing energy resources in a better way, SG is the best option since a large portion of the generated energy is consumed by the educational institutes. Such institutes also need un-interrupted power supply at the lowest cost. Therefore, in this paper, we have taken a university campus load. We have not only applied two bio-inspired heuristic algorithms for energy scheduling—namely, the Firefly Algorithm (FA) and the Lion Algorithm (LA)—but also proposed a hybrid version, FLA, for more optimal results. Our main objectives are a reduction in both, that is, the cost of energy and the waiting time of consumers or end users. For this purpose, in our proposed model, we have divided all appliances into two categories—shiftable appliances and non-shiftable appliances. Shiftable appliances are feasible to be used in any of the time slots and can be planned according to the day-ahead pricing signal (DAP), provided by the utility, while non-shiftable appliances can be used for a specified duration and cannot be planned with the respective DAP signal. So, we have scheduled shiftable appliances only. We have also used renewable energy sources (RES) for achieving maximum end user benefits. The simulation results show that our proposed hybrid algorithm, FLA, has reduced the cost excellently. We have also taken into consideration the consumers’ waiting times, due to scheduling of appliances.
Highlights
We considered that the campus is receiving a day ahead pricing (DAP) signal from the utility provider, according to which we have to schedule our appliances to achieve our desired objectives
In this paper, we have studied the nature of both these algorithms and introduced the combination of firefly and lion algorithms, namely, the Firefly Lion algorithm (FLA)
Different energy pricing schemes are used in the literature to give energy costs either on a daily basis or an hourly basis such as time of use (TOU), inclined block rate (IBR), critical peak pricing (CPP) and day ahead pricing (DAP)
Summary
In TEPG, the energy flow is unidirectional while in the Smart Grid, the energy flows bi-directionally [3] This enables consumers to take energy from the utility in times of need and supply it back to the utility provider when extra energy is generated at the consumer end. SM transmits the pricing signal from utility to EMC, at the same time SM gathers the information about energy consumption of customers and sends these data to the utility provider. Most researchers have focused only on the minimization of energy cost (electricity bill of the consumer), by scheduling appliances in the residential, commercial or industrial sectors. The conclusions and future work are presented in the last section of the paper
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
