Energy consumption scheduler for energy management under RES and Utility in smart grid

Our project is based on scheduling different loads in a smart homes connected with smart grid.  We have proposed different techniques such as Genetic Algorithm(GA), Wind Driven Optimization(WDO), Particle Swarm Optimization(PSO), Bacteria Foraging Algorithm(BFA), Hybrid of GA and PSO

Project Title

Energy consumption scheduler for energy management under RES and Utility in smart grid

Project Area of Specialization

Electrical/Electronic Engineering

Project Summary

Our project is based on scheduling different loads in a smart homes connected with smart grid.  We have proposed different techniques such as Genetic Algorithm(GA), Wind Driven Optimization(WDO), Particle Swarm Optimization(PSO), Bacteria Foraging Algorithm(BFA), Hybrid of GA and PSO is (HGPSO), Ant colony Optimization(ACO) to schedule the appliances on hourly basis in a day to achieve best outputs for the consumer as well as for the supplier(utility). There are different types of renewable energy sources but here we have taken Photovoltaic system, Wind turbines, Fuel Cells, along with Batteries and Electric Vehicles(EVs). Batteries and EVs are used as the back-up sources when the other renewable sources are unavailable and will be used in on peak hours. That will help the consumers in cost minimization and load will be reduced on grid.

Project Objectives

Normally Smart Grid is vast field and has many objectives but here we have focused on four objectives in our FYP. Which are truly considered for both the supplier and consumer benefits.

1) Minimization of electricity cost per hour.

2) Reduction of Peak to Average Ratio (PAR)

3) Maximizing the user comfort

4) Reduction of Carbon Emission to the environment

Project Implementation Method

Our project is software based and it is all done in MATLAB(2018a). All implementation is completed by coding. First the generated powers are collected from each renewable source and given to batteries and EVs for charging. Appliances were run on unscheduled base by the consumer. From that we have high user comfort(desired) but in the meantime it increased the cost and PAR too. Then the appliances were scheduled by the different optimization techniques. Best results were given by HGPSO in minimizing cost and PAR.

User comfort was derived from scheduling the load, that will also help in cost and load minimization on grid. While carbon emission is related to the cost of the energy consumed by load per day. It was found in pounds. All the above results were shown by graphs.

Benefits of the Project

The benefits of this project are clear from the objectives, as achieved it will help the whole society. By cost minimization it will not let the consumer to pay in less amount but will use electricity in a proper way and will consume as much the consumer wants. This idea motivates the consumers to manage their way of consumption. Similarly, from reducing the peak to average ratio, load will be drastically decreased on the grid in on peak hours which will not allow the grid to use extra power plants.

Consumers always want to use energy any time they want. But it is not possible because if all consumers in a society need electricity at the same time. It will damage the grid machinery by increasing load and cost also increases for that specific duration. So by achieving maximum user comfort, the loads should be scheduled in a proper way to check all the critical points. In user comfort we will not provide the desired amount of electricity only to the consumer but will also be careful about the other aspects too.

In the recent decade global warming is big issue for everyone and it is almost going out of the control for especially developing and under developed countries. Carbon is one of the gas that is produced from generation and consumption of energy. From electricity aspect it can be controlled by scheduling loads to be run for minimum time. In the end we can conclude that for all four objectives scheduling of the loads is needed.

Technical Details of Final Deliverable

A smart grid is an electrical grid which includes a variety of operation and energy measures including smart meters, smart appliances, renewable energy resources, and energy efficient resources.  Here in this project we have used Photovoltaic system, Wind turbine, Fuel cell as renewable energy sources. For back-up we have used Batteries and Electric Vehicles(EV). Loads are Refrigerator, Lights, Washing machine, Cloth dryer, Water Heater, Electric vehicles. For scheduling these loads we have Genetic Algorithm(GA), Particle Swarm Optimization(PSO), Bacteria Foraging Optimization(BFO), Wind Driven Optimization(WDO), Ant Colony Optimization(ACO) and Hybrid of GA and PSO is (HGPO).

The produced power from the renewable sources is added together and given to the battery and electric vehicles to be charged, which can be used later in ON-Peak hours when cost is high and load is increased on grid.  First we run our loads on unscheduled base and found out our objectives. Then numbers wise we applied one by one technique to found out our objectives.   There is clear minimization in all objectives from the unscheduled objectives values.

We will add a hybrid of ACO and WDO and will check their output and we are sure that their result will be the best of all.

Final Deliverable of the Project

Software System

Core Industry

Energy

Other Industries

Security

Core Technology

Clean Tech

Other Technologies

Shared Economy, Others

Sustainable Development Goals

Affordable and Clean Energy, Decent Work and Economic Growth, Responsible Consumption and Production, Partnerships to achieve the Goal

Required Resources

If you need this project, please contact me on contact@adikhanofficial.com