Abstract
Global warming and climate change are two key probing issues in the present context. The electricity sector and transportation sector are two principle entities propelling both these issues. Emissions from these two sectors can be offset by switching to greener ways of transportation through the electric vehicle (EV) and renewable energy technologies (RET). Thus, effective scheduling of both resources holds the key to sustainable practice. This paper presents a scheduling scenario-based approach in the smart grid. Problem formulation with dual objective function including both emissions and cost is developed for conventional unit commitment with EV and RET deployment. In this work, the scheduling and commitment problem is solved using the fireworks algorithm which mimics explosion of fireworks in the sky to define search space and the distance between associated sparks to evaluate global minimum. Further, binary coded fireworks algorithm is developed for the proposed scheduling problem in the smart grid. Thereafter, possible scenarios in conventional as well as smart grid are put forward. Following that, the proposed methodology is simulated using a test system with thermal generators.
Highlights
With the overwhelming importance that sustainability has gained in recent time, it is vital for the significant entities of global emissions like transportation and power generation to abridge their emissions keeping in mind the end goal to cut down them to 80% by 2050 [1], thereby accomplishing the goals set in endorsing Kyoto Protocol launched by United Nations Framework Convention on Climate Change (UNFCCC) [2]
Afterwards, algorithms like ant colony optimization (ACO) deduced from the natural behavior of ants in discovering the shortest path looking for food, are exploited in successful handling of commitment and scheduling of thermal units to minimize the generation cost [22]
The simulations for solving Unit commitment problem (UCP) using BGWA are carried out in MATLAB R2012b environment operating on Mac OS X version 10.9.1 and 2.7 GHz processor
Summary
With the overwhelming importance that sustainability has gained in recent time, it is vital for the significant entities of global emissions like transportation and power generation to abridge their emissions keeping in mind the end goal to cut down them to 80% by 2050 [1], thereby accomplishing the goals set in endorsing Kyoto Protocol launched by United Nations Framework Convention on Climate Change (UNFCCC) [2]. Intelligent handling of the PEVs in conjunction with the utility resources like thermal units and RETs can minimize the cost and burden of the network [10]. Thereupon, inspired by swarm intelligence like social behavior and coordination principles, particle swarm optimization (PSO) is proposed and developed to improve the quality of UCP solution [18]. Afterwards, algorithms like ant colony optimization (ACO) deduced from the natural behavior of ants in discovering the shortest path looking for food, are exploited in successful handling of commitment and scheduling of thermal units to minimize the generation cost [22]. The UCP is an optimization problem in which for the given load and available generation units, the total operational cost is minimized by intelligent commitment and optimal power allocation under specified network and unit constraints. The operational cost includes fuel and startup costs, incorporates system constraints like load balance, reserve and units constraints like up/down ramp rates, times, generation limits etc
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
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 call
