Economically Effective and Intelligently Responsive Home Energy Management System

Energy management in home is one of the major issue now-a-days. There are different types of load like shiftable, non-shiftable, seasonal loads and auxiliary loads. In this research article, an energy management system is proposed for home which helps to schedule different loads on the basis of their types and price. It will help to minimize the cost of electricity by shifting load from peak time to off peak time. Emission will be minimized by charging penalty by adopting multi-objective optimization. Each source of energy has its own price of penalty with respect to time. Penalty is charged to minimize the use of sources like commercial supply and diesel generators which emits hazardous gases. In proposed model, user will get electricity from commercial supply, diesel generators and solar panels to provide continuous supply of electricity to fulfil the energy demand. The shiftable loads will be shifted from peak time to off peak time and higher price source to lower price source to minimize the overall price. In this research, we have proposed an EEIR (Economically Effective and Intelligently Responsive) HEMS (Home Energy Management System) by solving multi-objective optimization problem from BILP (Binary Integer Linear Programming) using branch and bound algorithm.

have little information about the peak timing and when load runs at that time, the electricity cost increases. We has proposed an energy management system to minimize the cost of energy and greenhouse gas emission as well.
Solution to this problem is proposed as a system model for load management in building which introduces demand side management in smart grid. One of the proposed model [1] has three main modules; load balancing, admission control and demand response management. It enables interconnection between different components and integration of different energy sources with-in the system. It also helps in system maintenance and up-gradation.
Hence it is helpful for handling of autonomous energy management and consumption. An architecture was designed for load management in smart buildings and demand side management in smart grid [2]. Countering the problems of [2], an ICHEMS (Intelligent Cloud Home Energy Management System) was proposed in [3], which allows system loads to shift on priority basis at low price. Chen et.
al. [4] showed that the appliances are connected through home area network, a communication based protocol was designed for load scheduling. A join access and scheduling technique was used to coordinate the appliances so that the demand values of power should be kept below the set value. The supplier gives the benefits to the user to use energy at off-peak time instead of peak time [5]. By using different demand side management techniques like peak clipping and valley filling, the loads are shifted from peak time to off-peak time to reduce the cost. The consumer can interact with the supplier to facilitate the load management at the supplier side [6]. The user can also schedule their load based on the price of energy by development of smart grid. The model by Zhao et. al. [7] has two parts, energy management system based on home area network and efficient scheduling. Wang et. al. [8] uses a vital methodology to integrate the vehicles for energy storage at off peak time to save energy and to sell this energy at peak time. Smart homes has the potential to improve the efficiency of the system. It also decreases the emission gases by using renewable energy sources. Another work made by Zipperer et. al. [9] gives the brief discussion for smart HEMS. Chang et. al. [10] proposed an architecture based on coordinated HEMS. The developed architecture helps the user to coordinate with their neighbor and schedule their appliances according to their supply. The user can manage his load depending upon the tariff of electricity by using demand side management in smart grid.
Each user finds an optimal time to start the appliances and operation time as well. Penalty is charged to overcome abrupt changes in the scheduling [11]. Han et. al. [12] proposed the HEMS in which power generation and power consumption both are considered. ZigBee network was used to monitor the power consumption of the appliances and lights. While PLC (Programmable Logic Controller) was used to monitor the total power generation from the solar panels. The home server collects the data of power generation and power consumption and analyzes the data and then schedules the appliances to minimize the energy cost. In Nguyen et. al. [13] an architecture was designed for energy scheduling with solar assisted heating, ventilation and air-conditioning system for household appliances. The benefit of the system was to generate the energy from solar at peak time and utilize the energy at off peak time. In this manner the price of energy was reduced.
Optimal energy management is challenging issue associated with the smart micro-grids. The author has developed the multi-objective mixed integer nonlinear programming model for optimal energy used in smart homes [14]. Vivekananthan et. al. [15] has proposed another model for HEMS by scheduling, real time monitoring and real time control. By scheduling, user can meet their needs by using available resources. The home energy management helps to reduce the cost of electricity [16]. Althaher et. al. [17]  for Home energy management [18]. Pan e. al. [19] uses HEMS for thermostatically controlled appliances.
Decentralization techniques are also in use to monitor and control the load for domestic users, in which the primary balancing technique has a mechanism of receiving feedback from the users and updating the load schedule [20]. Some researchers have built outlet based sensors for measuring the actual loading of various devices [21]. Using these sensors, the HEMS schedules can become much more accurate. A group used Dijkstra algorithm for HEMS for simpler solutions [22]. Another attempt to understanding the need of HEMS at its best, compared load management without heating devices for regions where normally winter does not remain for long times [23]. Zhao et. al. [24] formulated HEMS of mixed supply types, in which AC and

METHODOLOGY
The Fig. 2

PROBLEM FORMULATION
In this research we minimize the cost by shifting the loads from peak to off-peak time and reducing penalties. The problem formulation is given Equation (1): In Equation (1) In Equation (2) The Equations (3-4) represent constraint C 1 and C 2 are used for continuous time to finish the work. For continuous finish time, if C 1 and C 2 are equal to t, then it will finish its work in given time otherwise it will not be able to finish the work in given time.
In Equation (5) the constraints C 3 is for peak clipping so that the maximum power consumption at a time should be minimized. In peak clipping the load is shifted from peak time to off peak time. This technique helps to reduce the power consumption at peak time.
In Equation (6) C 4 is for valley filling. In valley filling the user is appreciated to build or use the load where use of load is minimum.
In Equation (7) C 5 is for decision variable. Value of C 5 will be either zero or one. If C 5 is one, then the appliance 'a' at any time 't' will be switched ON. If C 5 is zero, then the appliances 'a' at any time 't' will be switched OFF. The main objective of the proposed model is to minimize the cost of the energy used and the emission of carbon based hazardous gases. In this problem weighted sum is used to minimize the cost and emission. The W 1 is used to minimize the cost of energy used and W 2 is used to minimize the carbon emission gases.

SIMULATION RESULTS AND DISCUSSION
The simulation is performed on MATLAB version R2013a running on Intel dual core i3 system having 4GB of RAM  Fig. 3 the U Tou is the utility energy source, the D Tou diesel energy source and S Tou is the energy generated by the solar cells. All these energy resources have different energy price at different time. The price of tariff and penalty is given in PKR (Pakistani Rupee). Penalty is also charged for using the tariff. Table 2 shows the price of electricity generated from solar cells. The price of electricity is given is different at different time slots. Table 3 shows the price of electricity provided by electricity Supplier Company. The price of electricity is given different at different time slots. Table 4 describes the price of electricity generated from Diesel generators. The price of electricity is given is different at different time slots. Fig. 4 shows the tariff of penalty for using the different energy tariff like solar tariff, diesel tariff and utility tariff.
Each tariff has its own price of penalty that is charged to use the tariff. We have used weighted sum W  =0.9 then it means the weightage for price minimization is 0.9 and for emission is 0.1. The factor which has more value will be minimized more than other factor. If we set value of W=0.8 then the emission will be further minimized more but price of electricity bill will not minimum. Emission is also most important factor, because emission gases are harmful to the environment. It can be analyzed when value of W 1 =0.2 the value of W 2 will be equal to 0.8, so price will be high but emission will be minimized.
The penalty is charged due to emission gases emitted during generation of electricity.   is main factor to minimize and cost has less weitage. It can be seen that cost goes high from PKR 2400/-to PKR 3400/because in this case emission was main factor and penalty is charged due to which cost goes high but emission is minimized. By analyzing the both results shown in Figs. 6-7 the maximum cost is minimized in Fig. 6 because the emission factor is neglected. In this way cost and emission are minimized by using the proposed model by solving BILP using BBA. In Fig. 7 Table 5 shows the comparison of different energy management systems with our proposed energy management system. The cost saved by different energy management system is 1.8-8.1%. The cost saved by our proposed energy management system is 10.25%. EE&IR HEMS is also applicable for small as well as large industries.

CONCLUSIONS
Economically effective and intelligently responsive HEMS is proposed to minimize the cost and emission. By using