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Home Energy Mangement

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Project log

Zain Ul Abideen
added 2 research items
In this paper, performance of energy management controller (EMC) based on meta-heuristic algorithms: Harmony Search Algorithm (HSA) and Firefly Algorithm (FA) are evaluated. Critical peak pricing (CPP) scheme is implemented to calculate the electricity cost. Appliances are categorized into three groups on the basis of power consumption. Electricity cost minimization and electricity load shifting from peak hours towards off peak hours are the main objectives of the paper. In simulation results, adopted approach reduces the Peak to Average Ratio (PAR) and total electricity cost. Furthermore, HSA shows better results than FA in terms of PAR and electricity cost.
In this paper performance of Home Energy Management System (HEMS) is evaluated using two meta-heuristic techniques: Harmony Search Algorithm (HSA) and BAT Algorithm. Appliances are classified into three categories according to their characteristics. Critical peak pricing is used for electricity price calculation as electricity pricing scheme. The main purpose is electricity cost reduction, electricity consumption, peak to average ratio reduction and maximizing User Comfort (UC) by reducing waiting time. Simulation results show the overall effectiveness of HSA.
Muhammad Azeem Sarwar
added 2 research items
In this paper, appliance scheduling scheme is proposed for residential area. Different types of heuristic and meta-heuristic optimization techniques are being used to solve the general problem of electricity demand. In this paper, a unique swarm based optimization technique Elephant Herding Optimization (EHO) is used to manage the electricity demand in order to manage the single home appliances in such a way that reduction of electricity cost is achieved and certain point of user comfort. For this purpose Real Time Pricing (RTP) scheme is used in this paper for electricity cost. To validate the effectiveness of proposed scheme simulations are performed. The results of EHO are compared with the results of Enhanced Differential Evolution (EDE). The simulations show that proposed scheme i.e. EHO provide best optimal results in achieving the minimum electricity cost and user comfort at certain point.
In smart grid several scheduling techniques have been proposed for load management in commercial, industrial and residential areas to minimize electricity cost, Peak to Average ratio (PAR) and provide user comfort maximization. Demand Side Management (DSM) is necessary for optimized results. Smart grid is a digital technology with two-way communication between the utility company and electricity consumers. Energy Management Controller (EMC) are used to maintain record of all appliances, operation time of appliances and cost which we have to pay for it. Smart grid motivates users to shift the load in Off Peak Hours (OPH) form Peak Hours (PH) through providing incentive in OPH. By this act consumers save money against load shifting from high price hours to low price hours. In this paper, Genetic Algorithm (GA) and Earthworm Optimization Algorithm (EWA) based schemes is proposed to minimize electricity cost and Peak to Average Ratio (PAR) while maximizing User Comfort (UC) via appliances scheduling.
Sakeena Javaid
added 2 research items
In this work, we propose a DSM scheme for electricity expenses and peak to average ratio (PAR) reduction using two well-known heuristic approaches: the cuckoo search algorithm (CSA) and strawberry algorithm (SA). In our proposed scheme, a smart home decides to buy or sell electricity from/to the commercial grid for minimizing electricity costs and PAR with earning maximization. It makes a decision on the basis of electricity prices, demand and generation from its own microgrid. The microgrid consists of a wind turbine and solar panel. Electricity generation from the solar panel and wind turbine is intermittent in nature. Therefore, an energy storage system (ESS) is also considered for stable and reliable power system operation. We test our proposed scheme on a set of different case studies. The simulation results affirm our proposed scheme in terms of electricity cost and PAR reduction with profit maximization.
In this paper, an integrated fog and cloud based environment for effective energy management is proposed in which fogs are connected to cloud in order to reduce the burden of cloud. It handles the data of clusters of buildings at consumers’ end. Six fogs are used on six different regions in the world which are based on six continents. Furthermore, each fog is connected to cluster of buildings and one fog is connected to one cluster. Each cluster comprises of multiple smart buildings and these buildings has at least 100 smart homes. Microgrids (MGs) are available near the buildings and accessible by the fogs. Energy is managed for these homes and fog helps the consumers to fulfill their load demands through nearby MGs and cloud servers’ communication. The requests are sent by the homes or buildings to the fog according to the energy demands and fog forwards these requests to nearby MGs to fulfill them. The MGs establish the connection and provide electricity to relevant homes in the building and requests are managed by the round robin algorithm. Proposed model is evaluated in terms of demand request time, demand response time and demand processing time and it performs efficiently during the peak demand periods.
Pamir Shams
added 2 research items
In smart grid (SG), demand side management (DSM) is a set or group of programs, allow consumers to play a vital role in transferring of their own load demand during peak time periods and minimizing their hourly based power consumption and total monetary cost of the electricity consumed and it also helps the electric utility in reducing higher power demand in the time of high energy demanded time slots. Where, this consequently results in reduction of the total electricity cost, maximization of power grid sustainability and reduction in carbon dioxide emissions which ultimately results in a pollution free environment. Nowadays, most of the DSM strategies available in existing literature concentrate on house hold appliances scheduling to decrease consumer delay time and peak to average ratio (PAR). However, they ignore the total electricity cost. In this paper, we employ load shifting strategy, to decrease total electricity payment. To gain above objective, we propose a hybrid of bat algorithm (BA) and crow search algorithm (CSA) i.e., bat-crow search algorithm (BCSA) and the results are compared with the existing BA and CSA. Simulations were conducted for a single home with 15 appliances, uses critical peak pricing (CPP) scheme for the computation of consumers electricity bill. The results show that load is successfully shifted to lower price time slots using our proposed BCSA technique, which ultimately leads to 31.191% reduction in total electricity payment.
Demand side management (DSM) in smart grid (SG) makes users able to take informed decisions according to the power usage pattern of the electricity users and assists the utility in minimizing peak power demand in the duration of high energy demand slots. Where, this ultimately leads to carbon emission reduction, total electricity cost minimization and maximization of grid efficiency and sustainability. Nowadays , many DSM strategies are available in existing literature concentrate on house hold appliances scheduling to decrease electricity cost. However, they ignore peak to average ratio (PAR) and consumers delay minimization. In this paper, a load shifting strategy of DSM is considered , to decrease PAR and waiting time. To gain aforementioned objectives , the flower pollination algorithm (FPA), grey wolf optimizer (GWO) and their hybrid i.e., flower grey wolf optimizer (FGWO) are used. Simulations were conducted for a single home consist of 15 appliances and critical peak pricing (CPP) tariff is used for computing users electricity payment. The results show and validate that load is successfully transferred to low price rate hours using our proposed FGWO technique, which ultimately leads to 50.425% reduction in PAR, 2.4148 hours waiting time and with 54.654% reasonable reduction in cost.
Ishtiaq Ali
added 2 research items
Today, electricity is the most worthwhile resource which makes human life very easy. To overcome the gap among demand and supply of electricity, new techniques and methods are being explored. However, electricity demand is increasing constantly, which causes serious crisis. To tackle this problem, demand side management integrated with traditional grids through intercommunication between utility and customers. In this research work, we comparatively look over the two meta-heuristic algorithms: strawberry algorithm (SBA) and enhanced differential evolution (EDE) algorithm in terms of cost minimization, peak to average ratio reduction and maximizing user comfort. For electricity bill calculation, critical peak pricing (CPP) scheme is used. Simulation results show that both optimization techniques work significantly to achieve the desired objectives. SBA performs better then EDE in term of cost minimization while EDE performs better then SBA in terms of user comfort (UC) maximization, PAR reduction and energy consumption minimization.
In this paper, we used two techniques: Enhanced Differential Evolution (EDE) and Crow Search Algorithm (CSA), in order to evaluate the performance of Home Energy Management System (HEMS). The total load is categorized into three groups based on their energy consumption pattern, and time of use of appliances. Critical Peak Pricing (CPP) scheme is used to calculate electricity bill. Our goals are electricity cost reduction, energy consumption minimization, Peak to Average Ratio (PAR) minimization, and user comfort maximization. However, there is trade-off between multiple objectives (goals). The simulation results show that, there is trade-off between PAR and total cost, and there is trade-off as well between PAR and waiting time. The simulation results also show that CSA performs better in terms of total cost and user comfort than EDE and unscheduled.
Muhammad Azeem Sarwar
added 2 research items
In this study, problem of scheduling of appliances in Home Energy Management System (HEMS) is analyzed and a solution is proposed. Although there are many heuristic algorithms for solving the scheduling problem however we considered a swarm based heuristic algorithm Elephant Herding Optimisation (EHO). EHO uses the herding behaviour of elephants to handle the problem. To validate our research work, we simulate the single home with 12 appliances and scheduling is performed using EHO. We divided the appliances into two categories Interruptible and non-interruptible. Time of Use (TOU) pricing signal is used. Simulation results show that EHO is efficient as compare to Enhanced Differential Evolution (EDE) and unscheduled case. EHO technique is efficient in scheduling the appliances and reducing the waiting time.
With the usage of demand side management (DSM) techniques, consumers such as residential, commercial and industrial are more flexible to use electricity according to their need. Many techniques are proposed to manage electricity cost, load, peak to average ratio (PAR) and user comfort of consumer appliances. In this paper we proposed a technique Earthworm Optimization Algorithm (EWA) that is developed for residential area in SG and compare with the Bacterial Foraging Algorithm (BFA). These algorithms are used for the scheduling the appliance load in real time pricing. Both algorithms are used to shifting the load from on-peak hours to off-peak hours in RTP and reduced the electric cost and PAR. We compare both algorithms in terms of electricity cost, PAR and used comfort. Our simulation results show that the EWA outperformed the BFA in terms of electricity cost however, BFA reduced the PAR as compared to EWA.
Private Profile
added 2 research items
Smart grid (SG) is an emerging technology which is considered as an ultimate solution to meet the increasing power demand challenges. Modern communication technologies have enabled the successful implementation of SG, which aims at provision of demand side management (DSM) mechanisms, such as demand response (DR). In this thesis, we propose teacher learning genetic optimization (TLGO) technique by combining genetic algorithm (GA) with teacher learning based optimization (TLBO) algorithm for residential load scheduling, assuming that electric prices are announced on a day-ahead basis. User discomfort is one of the key aspect which must be addressed along with cost minimization. The major focus of this work is to minimize consumer electricity bill at minimum user discomfort. Load scheduling is formulated as an optimization problem and an optimal schedule is achieved by solving the minimization problem. We also investigated the effect of power flexible appliances on consumers' bill. Furthermore, a relationship among power consumption, cost and user discomfort is also demonstrated by feasible region. Simulation results validate that our proposed technique performs better in terms of cost reduction and user discomfort minimization, and is able to obtain the desired trade-off between consumer electricity bill and user discomfort.
Home energy management controller (HEMC) is an important component for electricity consumer to actively participate in demand response program. It helps the consumer to manage electricity load in an effective way to reduce electricity bill. In this thesis, we design a HEMC based on two heuristic techniques: teaching learning based optimization (TLBO) and enhanced differential evolution (EDE). We also proposed a hybrid technique by combining the feature of TLBO and EDE to optimize the HEMC. The major objective of designing this controller is to minimize consumer electricity bill while preserving consumer satisfaction. For this purpose, we perform simulations for a single as well as multiple homes by utilizing day-ahead real time price (DA-RTP) and critical peak price (CPP) signals. Results show that our proposed hybrid technique achieves maximum user satisfaction at minimum electricity cost and peak to average ratio (PAR). A tradeoff analysis between user satisfaction and energy consumption cost is demonstrated in simulations.
Muhammad Azeem Sarwar
added 2 research items
In this study, problem of scheduling of appliances in Home Energy Management System (HEMS) is analyzed and a solution is proposed. Although there are many heuristic algorithms for solving the scheduling problem however we considered a swarm based heuristic algorithm Elephant Herding Optimisation (EHO). EHO uses the herding behaviour of elephants to handle the problem. To validate our research work, we simulate the single home with 12 appliances and scheduling is performed using EHO. We divided the appliances into two categories Interruptible and non-interruptible. Time of Use (TOU) pricing signal is used. Simulation results show that EHO is efficient as compare to Enhanced Differential Evolution (EDE) and unscheduled case. EHO technique is efficient in scheduling the appliances and reducing the waiting time.
Nowadays, Energy become the most valued necessity. Energy crisis becomes a critical issue of this era. Energy demand is increasing day by day, due to which peak load creation occurs. In order to handle the critical situation of the energy crisis, many techniques and methods are implemented. This can be done by replacing the traditional grid with smart grid and scheduling of appliances at Demand Side Management (DSM). Our main focus is on load management and minimization of cost which can be done by load shifting from on peak hours to off peak hours. We have achieved objectives by using two meta-heuristic optimization techniques; Harmony Search Algorithm (HSA) and EarthWorm optimization Algorithm (EWA). Simulation results show that the approaches we adopted reduce the cost, reduce the Peak Average Ratio (PAR) by load shifting from on peak to off peak hours between the min and max interval with a low difference.
Mashab Farooqi
added 2 research items
In this paper performance of Home Energy Management System (HEMS) is evaluated using two meta-heuristic techniques: Harmony Search Algorithm (HSA) and BAT Algorithm. Appliances are classified into three categories according to their characteristics. Critical peak pricing is used for electricity price calculation as electricity pricing scheme. The main purpose is electricity cost reduction, electricity consumption, peak to average ratio reduction and maximizing User Comfort (UC) by reducing waiting time. Simulation results show the overall effectiveness of HSA.
With the usage of demand side management (DSM) techniques, consumers such as residential, commercial and industrial are more flexible to use electricity according to their need. Many techniques are proposed to manage electricity cost, load, peak to average ratio (PAR) and user comfort of consumer appliances. In this paper we proposed a technique Earthworm Optimization Algorithm (EWA) that is developed for residential area in SG and compare with the Bacterial Foraging Algorithm (BFA). These algorithms are used for the scheduling the appliance load in real time pricing. Both algorithms are used to shifting the load from on-peak hours to off-peak hours in RTP and reduced the electric cost and PAR. We compare both algorithms in terms of electricity cost, PAR and used comfort. Our simulation results show that the EWA outperformed the BFA in terms of electricity cost however, BFA reduced the PAR as compared to EWA.