GitHub - MartinRepo/SmartGrid: Implementation of some smart grid sheduling algorithms, with a visualization page.

Scheduling for Smart Grid

Authored by: @Martingale

Click here to visit demo site. Just try it. :D

Content

Core strategies for following algorithms

Feasible Graph

Fixed Parameter

Pseudocode as following

Input: a set of job J 
Output: an optimal configuration of J 
{bi} i=1...τ/2+1 ← the boundaries {0, 2, 4, 6, ..., τ} 
{Di} i=1...τ/2 ← the windows where Di is bounded by bi (inclusively) and bi+1 (exclusively) 
{Ci} i=1...τ/2 ← the collection of job sets where Ci is the set of jobs in Di 
Tleft ← a configuration that labels all jobs j ∈ J to be not yet executed for i from 1 to τ/2 do 
Tright ← ListConfigurations(Di, Ci) 
Tleft ← ConcatenateTables(Tleft, Tright) 
Tleft ← FilterTable(Tleft) 
return any configuration in Tleft

Greedy

Job is assigned to the machine with the smallest number of jobs currently assigned.

Genetic

Regard a solution as an individual. Then through constant crossover mutations (crossover: crossing two schedules to generate a new schedule.

Mutation: randomly changing the position or time of a task within a feasible interval.).

It uses the fitness function (calculating peak cost) to decide which scheduling scheme is kept and which is filtered out.

Dataset Preview

There are 11 jobs, 6 windows, 7 boundaries.Their width(duration) and height(power cost) is adjusted

Simulation

Performance Comparison

Load function: Sum of jobs' height in per unit time t. 𝐿(𝑡) = ∑𝐽:𝑡 𝐻(𝑗)

Total cost

Total cost function: Total cost of the entire scheduling solution S. 𝐶𝑜𝑠𝑡(𝑆) = ∑ 𝐿(𝑡)^𝛼