Smart grid,Microgrid

My PhD Thesis final defence presentation.

In this paper, an efficient model based on factored conditional restricted boltzmann machine (FCRBM) is proposed for electric load forecasting of in smart grid (SG). This FCRBM has deep layers structure and uses rectified linear unit (RELU) function and multivariate autoregressive algorithm for training. The proposed model predicts day ahead and week ahead electric load for decision making of the SG. The proposed model is a hybrid model having four modules i.e., data processing and features selection module, FCRBM based forecaster module, GWDO (genetic wind driven optimization) algorithm-based optimizer module, and utilization module. The proposed model is examined using FE grid data of USA. The proposed model provides more accurate results with affordable execution time than other load forecasting models, i.e., mutual information, modified enhanced differential evolution algorithm, and artificial neural network (ANN) based model (MI-mEDE-ANN), accurate fast converging short term load forecasting model (AFC-STLF), Bi-level model, and features selection and ANN-based model (FS-ANN).

Electrical load forecasting is a challenging problem due to random and non-linear behavior of the consumers. With the emergence of the smart grid (SG) and advanced metering infrastructure (AMI), people are capable to record, monitor, and analyze such a complicated non-linear behavior. Electric load forecasting models are indispensable in the decision making, planning, and contract evaluation of the power system. In this regard, various load forecasting models are proposed in the literature, which exhibit trade-off between forecast accuracy and execution time (convergence rate). In this article, a fast and accurate short-term load forecasting model is proposed. The abstractive features from the historical data are extracted using modified mutual information (MMI) technique. The factored conditional restricted boltzmann machine (FCRBM) is empowered via learning to predict the electric load. Eventually, the proposed genetic wind driven optimization (GWDO) algorithm is used to optimize the performance. The remarkable advantages of the proposed framework are the improved forecast accuracy and convergence rate. The forecast accuracy is improved through the use of MMI technique and FCRBM model. On the other side, convergence rate is enhanced by GWDO algorithm. Simulation results illustrate that the proposed fast and accurate model outperforms existing models i.e., Bi-level, MI-artificial neural network (MI-ANN), and accurate fast converging short-term load forecast (AFC-STLF) in terms of forecast accuracy and convergence rate.

In this paper, an efficient model based on factored conditional restricted boltzmann machine (FCRBM) is proposed for electric load forecasting of in smart grid (SG). This FCRBM has deep layers structure and uses rectified linear unit (RELU) function and multivariate autoregressive algorithm for training. The proposed model predicts day ahead and week ahead electric load for decision making of the SG. The proposed model is a hybrid model having four modules i.e., data processing and features selection module, FCRBM based forecaster module, GWDO (genetic wind driven optimization) algorithm-based optimizer module, and utilization module. The proposed model is examined using FE grid data of USA. The proposed model provides more accurate results with affordable execution time than other load forecasting models, i.e., mutual information, modified enhanced differential evolution algorithm, and artificial neural network (ANN) based model (MI-mEDE-ANN), accurate fast converging short term load forecasting model (AFC-STLF), Bi-level model, and features selection and ANN-based model (FS-ANN).

The smart grid plays a vital role in decreasing electricity cost via Demand Side Management (DSM). Smart homes, being a part of the smart grid, contribute greatly for minimizing electricity consumption cost via scheduling home appliances. However, user waiting time increases due to the scheduling of home appliances. This scheduling problem is the motivation to find an optimal solution that could minimize the Peak to Average Ratio (PAR) and electricity cost with minimum user waiting time. There are many studies on Home Energy Management (HEM) for cost minimization and peak load reduction. However, none of the systems gave sufficient attention to tackle multiple parameters (i.e., electricity cost and peak load reduction) at the same time where user waiting time is considered to be minimum for residential consumers with multiple homes. Hence, in contribution 1, we propose an efficient HEM scheme using the well-known meta-heuristic Genetic Algorithm (GA), the recently developed Cuckoo Search Optimization Algorithm (CSOA) and the Crow Search Algorithm which can be used for electricity cost and peak load alleviation with minimum user waiting time. The integration of a smart electricity storage system is also taken into account for more efficient operation of the HEM System. Furthermore, we took the real-time electricity consumption pattern for every residence, i.e., every home has its own living pattern. The proposed scheme is instigated in a smart building which is comprised of thirty smart homes (apartments). Critical Peak Pricing (CPP) and Real-Time Pricing (RTP) signals are examined in terms of electricity cost assessment for both a single smart home and a smart building. In addition, feasible regions are presented for multiple and single smart homes, which show the relationship among the electricity cost, electricity consumption and user waiting time. Experimental results prove the effectiveness of our proposed scheme for multiple and single smart homes concerning electricity cost and PAR minimization. Moreover, there subsists a tradeoff between electricity cost and user waiting. With the emergence of automated environments, energy demand by consumers is increasing rapidly. More than 80% of total electricity is being consumed in the residential sector. This brings a challenging task of maintaining the balance between demand and generation of electric power. In order to meet such challenges, a traditional grid is renovated by integrating two-way communication between the consumer and generation unit. To reduce electricity cost and peak load demand, DSM is modeled as an optimization problem and the solution is obtained by applying metaheuristic techniques with different pricing schemes. In contribution 2, an optimization technique, the Hybrid Gray Wolf Differential Evolution (HGWDE) is proposed by merging the Enhanced Differential Evolution (EDE) and Gray Wolf Optimization (GWO) schemes using the same RTP and CPP tariffs. Load shifting is performed from on-peak hours to off-peak hours depending on the electricity cost defined by the utility. However, there is a trade-off between User Comfort (UC) and cost. To validate the performance of the proposed algorithm, simulations have been carried out in MATLAB. Results illustrate that using RTP, the PAR is reduced up to 53.02%, 29.02% and 26.55%, while the electricity bill is reduced up to 12.81%, 12.012% and 12.95%, respectively, for 15-min, 30-min and 60-min operational time intervals (OTI). On the other hand, the PAR and electricity bill are reduced up to 47.27%, 22.91%, 22% and 13.04%, 12%, 11.11% using the CPP tariff. Microgrid is a community-based power generation and distribution system that interconnects smart homes with renewable energy sources. Microgrid generates power for electricity consumers and operates in both islanded and grid-connected modes more efficiently and economically. In contribution 3, we propose optimization schemes for reducing electricity cost and minimizing PAR with maximum UC in a smart home. We consider a grid-connected microgrid for electricity generation which consists of wind turbine and photovoltaic (PV) panel. First, the problem was mathematically formulated through Multiple Knapsack (MKP) then it is solved by existing heuristic techniques: GWO, binary particle swarm optimization (BPSO), GA and Wind Driven Optimization (WDO). Furthermore, we also propose three hybrid schemes for electricity cost and PAR reduction: (1) hybrid of GA and WDO named as WDGA; (2) hybrid of WDO and GWO named as WDGWO; and (3) WBPSO, which is the hybrid of BPSO and WDO. In addition, a battery bank system has also integrated to make our proposed schemes more cost-efficient and reliable to ensure stable grid operations. Finally, simulations have been performed to verify our proposed schemes. Results show that our proposed schemes efficiently minimize the electricity cost and PAR. Moreover, our proposed techniques: WDGA, WDGWO and WBPSO outperform the existing heuristic techniques. The advancements in smart grid, both consumers and electricity providing companies can benefit from real-time interaction and pricing methods. In contribution 4, a smart power system is considered, where consumers share a common energy source. Each consumer is equipped with a Home Energy Management Controller (HEMC) as scheduler and a smart meter. The HEMC keeps updating the electricity proving utility with the load profile of the home. The smart meter is connected to power grid having an advanced metering infrastructure which is responsible for two way communication. Genetic teaching-learning based optimization, flower pollination teaching learning based optimization, flower pollination BAT and flower pollination genetic algorithm based energy consumption scheduling algorithms are proposed. These algorithms schedule the loads in order to shave the peak formation without compromising UC. The proposed algorithms achieve optimal energy consumption profile for the home appliances equipped with sensors to maximize the consumer benefits in a fair and efficient manner by exchanging control messages. Control messages contain energy consumption of consumer and RTP information. Simulation results show that proposed algorithms reduce the PAR by 34.56% and help the users to reduce their energy expenses by 42.41% without compromising the comfort. The daily discomfort is reduced by 28.18%.

With the emergence of the smart grid, both consumers and electricity providing companies can benefit from real-time interaction and pricing methods. In this work, a smart power system is considered, where consumers share a common energy source. Each consumer is equipped with a home energy management controller (HEMC) as scheduler and a smart meter. The HEMC keeps updating the utility with the load profile of the home. The smart meter is connected to a power grid having an advanced metering infrastructure which is responsible for two-way communication. Genetic teaching-learning based optimization, flower pollination teaching learning based optimization, flower pollination BAT and flower pollination genetic algorithm based energy consumption scheduling algorithms are proposed. These algorithms schedule the loads in order to shave the peak formation without compromising user comfort. The proposed algorithms achieve optimal energy consumption profile for the home appliances equipped with sensors to maximize the consumer benefits in a fair and efficient manner by exchanging control messages. Control messages contain energy consumption of consumer and real-time pricing information. Simulation results show that proposed algorithms reduce the peak-to-average ratio by 34.56% and help the users to reduce their energy expenses by 42.41% without compromising the comfort. The daily discomfort is reduced by 28.18%.

The integration of Smart Grid (SG) with cloud and fog computing has improved the energy management system. The conversion of traditional grid system to SG with cloud environment results in enormous amount of data at the data centers. Rapid increase in the automated environment has increased the demand of cloud computing. Cloud computing provides services at the low cost and with better efficiency. Although problems still exists in cloud computing such as Response Time (RT), Processing Time (PT) and resource management. More users are being attracted towards cloud computing which is resulting in more energy consumption. Fog computing is emerged as an extension of cloud computing and have added more services to the cloud computing like security, latency and load traffic minimization. In this paper a Cuckoo Optimization Algorithm (COA) based load balancing technique is proposed for better management of resources. The COA is used to assign suitable tasks to Virtual Machines (VMs). The algorithm detects under and over utilized VMs and switch off the under-utilized VMs. This process turn down many VMs which puts a big impact on energy consumption. The simulation is done in Cloud Sim environment, it shows that proposed technique has better response time at low cost than other existing load balancing algorithms like Round Robin (RR) and Throttled.

The integration of Smart Grid (SG) with cloud and fog computing has improved the energy management system. The conversion of traditional grid system to SG with cloud environment results in enormous amount of data at the data centers. Rapid increase in the automated environment has increased the demand of cloud computing. Cloud computing provides services at the low cost and with better efficiency. Although problems still exists in cloud computing such as Response Time (RT), Processing Time (PT) and resource management. More users are being attracted towards cloud computing which is resulting in more energy consumption. Fog computing is emerged as an extension of cloud computing and have added more services to the cloud computing like security , latency and load traffic minimization. In this paper a Cuckoo Optimization Algorithm (COA) based load balancing technique is proposed for better management of resources. The COA is used to assign suitable tasks to Virtual Machines (VMs). The algorithm detects under and over utilized VMs and switch off the under-utilized VMs. This process turn down many VMs which puts a big impact on energy consumption. The simulation is done in Cloud Sim environment, it shows that proposed technique has better response time at low cost than other existing load balancing algorithms like Round Robin (RR) and Throttled.

With the rapid pace in the evolution and development of technology, the demand of electrical energy is also increasing. Beside the production of energy from traditional and renewable energy sources, the energy management is also required to control the consumption of energy in commercial, industrial and residential houses. Improvement in technologies while reduction in cost has enabled consumers to interconnect the smart devices for reducing cost and energy consumption, this is called internet of things (IoTs). Such increase in the number of smart systems and energy management systems cause a huge amount of data which cannot be processed on traditional system. It requires high computing power and high storage which may be provided by cloud computing. Cloud computing provide resources to customers on demand with low investment and operational cost. The cloud resources are flexible, efficient, scalable and secure. In this paper we simulate the use of cloud computing in smart grid. The datacenters in cloud collect the building’s data, process it and send the results to the building. In this study, we calculate the total response time to each building, the number of requests coming from each building per our, the processing time of each datacenter and the cost of each datacenter (CRRP). The results are useful for energy service providers to select the optimal processing and data storage resources.

Short term load forecasting is indispensable for industrial, commercial, and residential smart grid (SG) applications. In this regard, a large variety of short term load forecasting models have been proposed in literature spaning from legacy time series models to contemporary data analytic models. Some of these models have either better performance in terms of accuracy while others perform well in convergence rate. In this paper, a fast and accurate short term load forecasting framework based on stacked factored conditional restricted boltzmann machine (FCRBM) and conditional restricted boltzmann machine (CRBM) is presented. The stacked FCRBM and CRBM are trained using rectified linear unit (RelU) and sigmoid functions, respectively. The proposed framework is applied to offline demand side load data of US utility. Load forecasts decide weather to increase or decrease the generation of an already running generator or to add extra units or exchange power with neighboring systems. Three performance metrics i.e., mean absolute percentage error (MAPE), normalized root mean square (NRMSE), and correlation coefficient are used to validate the proposed framework. The results show that stacked FCRBM and CRBM are accurate and robust as compared to artificial neural network (ANN) and convolutional neural network (CNN).

Micro-grid (MG) is an emerging component of a smart grid and it is increasing the efficiency and reliability of the power system with the passage of time. MGs often need power in order to fulfill its load requirements, which is transmitted form macro station (MS). Transmission of power from MS cause power line losses. To decrease these power line losses, a hierarchical based coordination (HBC) strategy is proposed for efficiently exchanging the power among MGs. HBC aims to decrease power line losses by making hierarchical coalitions. Results are evaluated and compared with conventional non-coordination model (NCM). This comparison shows the effectiveness of proposed HBC strategy. Results indicate that HBC has decreased the power line losses by 40.1% as compared to conventional NCM.

With the advent of Smart Grid (SG), it provides the consumers with the opportunity to schedule their power consumption load efficiently in such a way that it reduces their energy cost while also minimizing their Peak to Average Ratio (PAR) in the process. We in this paper target the appliances to schedule in such a way that it increases User Comfort (UC) and decreases electricity consumption load which benefits both consumer and utility. In this paper, we proposed hybrid of Bacterial Forging Algorithm (BFA) and Tabu Search (TS) Algorithm using different Operational time Interval (OTI) to schedule appliances while balancing User Comfort which is the main objective of the Demand Side Management (DSM). This paper tries to reduce both waiting time and electricity cost simultaneously in the new hybrid Bacterial Foraging Tabu Search (BFTS) technique. Real time pricing (RTP) scheme was used to get the total cost of electricity consumed. We compared the results of proposed hybrid scheme with Bacterial Forging (BFA) and Tabu Search (TS) Algorithm using different Operational time Interval (OTI). The result shows effectiveness of using hybrid Bacterial Foraging Tabu Search (BFTS) technique for Demand Side Management (DSM).

Micro-grid (MG) is an emerging component of a smart grid and it is increasing the efficiency and reliability of the power system with the passage of time. MGs often need power in order to fulfill its load requirements, which is transmitted form macro station (MS). Transmission of power from MS cause power line losses. To decrease these power line losses, a hierarchical based coordination (HBC) strategy is proposed for efficiently exchanging the power among MGs. HBC aims to decrease power line losses by making hierarchical coalitions. Results are evaluated and compared with conventional non-coordination model (NCM). This comparison shows the effectiveness of proposed HBC strategy. Results indicate that HBC has decreased the power line losses by 40.1% as compared to conventional NCM.

Short term load forecasting is indispensable for industrial, commercial, and residential smart grid (SG) applications. In this regard, a large variety of short term load forecasting models have been proposed in literature spaning from legacy time series models to contemporary data analytic models. Some of these models have either better performance in terms of accuracy while others perform well in convergence rate. In this paper, a fast and accurate short term load forecasting framework based on stacked factored conditional restricted boltzmann machine (FCRBM) and conditional restricted boltzmann machine (CRBM) is presented. The stacked FCRBM and CRBM are trained using rectified linear unit (RelU) and sigmoid functions, respectively. The proposed framework is applied to offline demand side load data of US utility. Load forecasts decide weather to increase or decrease the generation of an already running generator or to add extra units or exchange power with neighboring systems. Three performance metrics i.e., mean absolute percentage error (MAPE), normalized root mean square (NRMSE), and correlation coefficient are used to validate the proposed framework. The results show that stacked FCRBM and CRBM are accurate and robust as compared to artificial neural network (ANN) and convolutional neural network (CNN).

With the rapid pace in the evolution and development of technology, the demand of electrical energy is also increasing. Beside the production of energy from traditional and renewable energy sources, the energy management is also required to control the consumption of energy in commercial, industrial and residential houses. Improvement in technologies while reduction in cost has enabled consumers to interconnect the smart devices for reducing cost and energy consumption, this is called internet of things (IoTs). Such increase in the number of smart systems and energy management systems cause a huge amount of data which cannot be processed on traditional system. It requires high computing power and high storage which may be provided by cloud computing. Cloud computing provide resources to customers on demand with low investment and operational cost. The cloud resources are flexible, efficient , scalable and secure. In this paper we simulate the use of cloud computing in smart grid. The datacenters in cloud collect the building's data, process it and send the results to the building. In this study, we calculate the total response time to each building, the number of requests coming from each building per our, the processing time of each datacen-ter and the cost of each datacenter (CRRP). The results are useful for energy service providers to select the optimal processing and data storage resources.

Microgrid is a community-based power generation and distribution system that interconnects smart homes with renewable energy sources (RESs). Microgrid efficiently and economically generates power for electricity consumers and operates in both islanded and grid-connected modes. In this study, we proposed optimization schemes for reducing electricity cost and minimizing peak to average ratio(PAR) with maximum user comfort (UC) in a smart home. We considered a grid-connected microgrid for electricity generation which consists of wind turbine and photovoltaic (PV) panel. First, the problem was mathematically formulated through multiple knapsack problem (MKP) then solved by existing heuristic techniques: grey wolf optimization (GWO), binary particle swarm optimization (BPSO), genetic algorithm (GA) and wind-driven optimization (WDO). Furthermore, we also proposed three hybrid schemes for electric cost and PAR reduction: (1) hybrid of GA and WDO named WDGA; (2) hybrid ofWDO and GWO named WDGWO; and (3) WBPSO, which is the hybrid of BPSO and WDO. In addition, a battery bank system (BBS) was also integrated to make our proposed schemes more cost-efficient and reliable, and to ensure stable grid operation. Finally, simulations were performed to verify our proposed schemes. Results show that our proposed scheme efficiently minimizes the electricity cost and PAR. Moreover, our proposed techniques, WDGA, WDGWO and WBPSO, outperform the existing heuristic techniques.

In this paper, we propose a home energy management (HEM) scheme in the residential area for electricity cost and peak to average ratio (PAR) reduction. Furthermore, reduction in imported electricity from the external grid is also the objective of this study. Our proposed scheme schedules smart appliances as well as electrical vehicles (EVs) charging/discharging optimally according to the consumer preferences. Each consumer has its own grid-connected microgrid for electricity generation; which consists of wind turbine, solar panel, micro gas turbine (MGT) and energy storage system (ESS). Furthermore, the scheduling problem is mathematically formulated and solved by mixed integer linear programming (MILP). We also provide the comparison of the optimal solutions, while considering EVs with and without discharging capabilities. Findings from simulations affirm our proposed scheme in terms of above-mentioned objectives. Index Terms-Demand side management; home energy management ; mixed integer linear programming

Smart Grid (SG) plays vital role to utilize electric power with high optimization through Demand Side Management (DSM). Demand Response (DR) is a key program of DSM which assist SG for optimization. Smart Home (SH) is equipped with smart appliances and communicate bidirectional with SG using Smart Meter (SM). Usually, appliances considered as working for specific time-slot and scheduler schedule them according to tariff. If actual run and power consumption of appliances are observed closely, appliances may run in phases, major tasks, sub-tasks and run continuously. In the paper, these phases have been considered to schedule the appliances using three optimization algorithms. In one way, appliances were scheduled to reduce the cost considering continuous run for given time slot according to their power load given by company’s manual. In other way, actual running of appliances with major and sub-tasks were paternalized and observed the actual consumption of load by the appliances to evaluate true cost. Simulation showed, Binary Particle Swarm Optimization (BPSO) scheduled more optimizing scheduling compared to Fire Fly Algorithm (FA) and Bacterial Frogging Algorithm (BFA). A hybrid technique of FA and GA have also been proposed. Simulation results showed that the technique performed better than GA and FA.

With the emergence of automated environments, energy demand by consumers is increasing rapidly. More than 80% of total electricity is being consumed in the residential sector. This brings a challenging task of maintaining the balance between demand and generation of electric power. In order to meet such challenges, a traditional grid is renovated by integrating two-way communication between the consumer and generation unit. To reduce electricity cost and peak load demand, demand side management (DSM) is modeled as an optimization problem, and the solution is obtained by applying meta-heuristic techniques with different pricing schemes. In this paper, an optimization technique, the hybrid gray wolf differential evolution (HGWDE), is proposed by merging enhanced differential evolution (EDE) and gray wolf optimization (GWO) scheme using real-time pricing (RTP) and critical peak pricing (CPP). Load shifting is performed from on-peak hours to off-peak hours depending on the electricity cost defined by the utility. However, there is a trade-off between user comfort and cost. To validate the performance of the proposed algorithm, simulations have been carried out in MATLAB. Results illustrate that using RTP, the peak to average ratio (PAR) is reduced to 53.02%, 29.02% and 26.55%, while the electricity bill is reduced to 12.81%, 12.012% and 12.95%, respectively, for the 15-, 30- and 60-min operational time interval (OTI). On the other hand, the PAR and electricity bill are reduced to 47.27%, 22.91%, 22% and 13.04%, 12%, 11.11% using the CPP tariff.

Smart Grid (SG) plays vital role to utilize electric power with high optimization through Demand Side Management (DSM). Demand Response (DR) is a key program of DSM which assist SG for optimization. Smart Home (SH) is equipped with smart appliances and communicate bidirectional with SG using Smart Meter (SM). Usually, appliances considered as working for specific time-slot and scheduler schedule them according to tariff. If actual run and power consumption of appliances are observed closely, appliances may run in phases, major tasks, sub-tasks and run continuously. In the paper, these phases have been considered to schedule the appliances using three optimization algorithms. In one way, appliances were scheduled to reduce the cost considering continuous run for given time slot according to their power load given by companys manual. In other way, actual running of appliances with major and sub-tasks were paternalized and observed the actual consumption of load by the appliances to evaluate true cost. Simulation showed, Binary Particle Swarm Optimization (BPSO) scheduled more optimizing scheduling compared to Fire Fly Algorithm (FA) and Bacterial Frogging Algorithm (BFA). A hybrid technique of FA and GA have also been proposed. Simulation results showed that the technique performed better than GA and FA.

The smart grid plays a vital role in decreasing electricity cost through Demand Side Management (DSM). Smart homes, a part of the smart grid, contribute greatly to minimizing electricity consumption cost via scheduling home appliances. However, user waiting time increases due to the scheduling of home appliances. This scheduling problem is the motivation to find an optimal solution that could minimize the electricity cost and Peak to Average Ratio (PAR) with minimum user waiting time. There are many studies on Home Energy Management (HEM) for cost minimization and peak load reduction. However, none of the systems gave sufficient attention to tackle multiple parameters (i.e., electricity cost and peak load reduction) at the same time as user waiting time was minimum for residential consumers with multiple homes. Hence, in this work, we propose an efficient HEM scheme using the well-known meta-heuristic Genetic Algorithm (GA), the recently developed Cuckoo Search Optimization Algorithm (CSOA) and the Crow Search Algorithm (CSA), which can be used for electricity cost and peak load alleviation with minimum user waiting time. The integration of a smart Electricity Storage System (ESS) is also taken into account for more efficient operation of the Home Energy Management System (HEMS). Furthermore, we took the real-time electricity consumption pattern for every residence, i.e., every home has its own living pattern. The proposed scheme is implemented in a smart building; comprised of thirty smart homes (apartments), Real-Time Pricing (RTP) and Critical Peak Pricing (CPP) signals are examined in terms of electricity cost estimation for both a single smart home and a smart building. In addition, feasible regions are presented for single and multiple smart homes, which show the relationship among the electricity cost, electricity consumption and user waiting time. Experimental results demonstrate the effectiveness of our proposed scheme for single and multiple smart homes in terms of electricity cost and PAR minimization. Moreover, there exists a tradeoff between electricity cost and user waiting.

In the smart grid (SG) users in residential sector adopt various load scheduling methods to manage their consumption behavior with specific objectives. In this paper, we focus on the problem of load scheduling under utility and rooftop photovoltaic (PV) units. We adopt genetic algorithm (GA), binary particle swarm optimization (BPSO), wind driven optimization (WDO), and proposed genetic wind driven optimization (GWDO) algorithm to schedule the operation of interruptible appliances (IA) and non interruptible appliances (Non-IA) in order to reduce electricity cost and peak to average ratio (PAR). For energy pricing combined real time pricing (RTP) and inclined block rate (IBR) is used because in case of only RTP their is possibility of building peaks during off peak hours that may damage the entire power system. The proposed algorithm shift load from peak consumption hours to off peak hours and to hours with high generation from rooftop PV units. For practical consideration, we also take into consideration pricing scheme, rooftop PV units, and ESS in our system model, and analyze their impacts on electricity cost and PAR. Simulation results show that our proposed scheduling algorithm can affectively reflect and affect users consumption behavior and achieve the optimal electricity cost and PAR.

Renewable energy sources (RESs) are considered as future replacement of traditional energy generation sources with zero carbon emission and low price electricity producers. RESs are intermittent, uncertain and random in nature, they do not produce fixed amount of energy and heavily depend upon weather, season and area. In this paper, new trends in the integration of photovoltaic and wind turbine are presented. This paper discusses the integration of RESs at three level i.e. consumer level, micro grid level and main grid level. A comprehensive review of the intermittent and stochastic nature of RESs is also provided. Additionally, fault protection concerns and the feasibility of RESs are discussed. Moreover, the usage of storage system to deal with the fluctuating behavior of RESs is presented.

Traditional power grid and its demand-side management (DSM) techniques are centralized and mainly focus on industrial consumers. The ignorance of residential and commercial sectors in DSM activities degrades the overall performance of a conventional grid. Therefore, the concept of DSM and demand response (DR) via residential sector makes the smart grid (SG) superior over the traditional grid. In this context, this paper proposes an optimized home energy management system (OHEMS) that not only facilitates the integration of renewable energy source (RES) and energy storage system (ESS) but also incorporates the residential sector into DSM activities. The proposed OHEMS minimizes the electricity bill by scheduling the household appliances and ESS in response to the dynamic pricing of electricity market. First, the constrained optimization problem is mathematically formulated by using multiple knapsack problems, and then solved by using the heuristic algorithms; genetic algorithm (GA), binary particle swarm optimization (BPSO), wind driven optimization (WDO), bacterial foraging optimization (BFO) and hybrid GA-PSO (HGPO) algorithms. The performance of the proposed scheme and heuristic algorithms is evaluated via MATLAB simulations. Results illustrate that the integration of RES and ESS reduces the electricity bill and peak-to-average ratio (PAR) by 19.94% and 21.55% respectively. Moreover, the HGPO algorithm based home energy management system outperforms the other heuristic algorithms, and further reduces the bill by 25.12% and PAR by 24.88%.

The performance and comparative analysis of home energy management controller using three optimization techniques; genetic algorithm (GA), enhanced differential evolution (EDE) and optimal stopping rule (OSR) has been evaluated in this paper. In this regard, a generic system model consisting of home area network, advanced metering infrastructure, home energy management controller, and smart appliances has been proposed. Price threshold policy and priority of appliance have also been considered to depict monthly and yearly average electricity bill savings and appliance delay using day-ahead real-time pricing (DA-RTP). Simulation results validate that all our proposed schemes successfully shifts the appliance operations to off-peak times and results in reduced electricity bill with reasonable waiting time.

Smart grid (SG) is one of the most advanced technologies, which plays a key role in maintaining balance between demand and supply by implementing demand response (DR). In SG the main focus of the researchers is on home energy management (HEM) system, that is also called demand side management (DSM). DSM includes all responses, which adjust the consumerâ ˘ A ´ Zs electricity consumption pattern, and make it match with the supply. If the main grid cannot provide the users with sufficient energy, then the smart scheduler (SS) integrates renewable energy source (RES) with the HEM system. This alters the peak formation as well as minimizes the cost. Residential users basically effect the overall performance of traditional grid due to maximum requirement of their energy demand. HEM benefits the end users by monitoring, managing and controlling their energy consumption. Appliance scheduling is integral part of HEM system as it manages energy demand according to supply, by automatically controlling the appliances or shifting the load from peak to off peak hours. Recently different techniques based on artificial intelligence (AI) are being used to meet aforementioned objectives. In this paper, three different types of heuristic algorithms are evaluated on the basis of their performance against cost saving, user comfort and peak to average ratio (PAR) reduction. Two techniques are already existing heuristic techniques i.e. harmony search(HS) algorithm and enhanced differential evolution (EDE) algorithm. On the basis of aforementioned two algorithms a hybrid approach is developed i.e. harmony search differential evolution (HSDE). We have done our problem formulation through multiple knapsack problem (MKP), that the maximum consumption of electricity of consumer must be in the range which is bearable for utility and also for consumer in sense of electricity bill. Finally simulation of the proposed techniques will be conducted in MATLAB to validate the performance of proposed scheduling

Controlling power utilization in the residential area is one of the major challenges in the smart grid (SG). Demand response (DR) has played a vital role in energy management and improved it with the involvement of residential consumers who participate in such programs from utilities for scheduling their appliances to the off peak hours. In this paper, we have proposed a worldwide adaptive thermostat model for effectively managing the power in all countries of the world. The proposed approach has been evaluated with the help of fuzzy logic and its two inference systems (FIS): 1) Mamdani and 2) Takagi Sugeno. Utilizing the membership functions; outdoor temperature, user occupancy, utility price and initialized setpoints are evaluated for maintaining the buildings temperature. Furthermore, energy consumption in buildings is analyzed by tuning the indoor initialized setpoints while considering all the aforementioned parameters. Simulations are conducted in Matlab to validate the proposed system model and results show that energy consumption in cold countries is reduced upto 45% as compared to the existing programmable approach. Index Terms—Energy management, thermostat, smart grid, fuzzy logic, takagi sugeno fuzzy inference system, mamdani fuzzy inference system

Electricity is a controllable and convenient form of energy. In this paper we discus about the electricity control. In current years Demand Side Management (DSM) techniques are designed. For residential and commercial sectors. These techniques are very effective to control the load profile of customer in grid area network. In this paper we use two optimization techniques: Harmony Search Algorithm (HSA) and Firefly Algorithm (FA).In our work we categorize smart appliances in three different categories on the basis of their energy consumption. For energy pricing we use Time of Use (ToU)pricing signal.Simulation result verify our adopted approach significantly reduce the cost without compromise the user comfort.

With the emergence of smart grid (SG), the residents have the opportunity to integrate renewable energy sources (RESs) and take part in demand side management (DSM). In this regard, we design energy management control unit (EMCU) based on genetic algorithm (GA), binary particle swarm optimization (BPSO), and wind driven optimization (WDO) to schedule appliances in presence of objective function, constraints, control parameters, and comparatively evaluate the performance. For energy pricing, real time pricing (RTP) plus inclined block rate (IBR) is used. RESs integration to SG is a challenge due stochastic nature of RE. In this paper, two techniques are addressed to handle the stochastic nature of RE. First one is energy storage system (ESS) which smooths out variation in RE generation. Second one is the trading/cooperation of excess generation to neighboring consumers. The simulation results show that WDO perform more efficiently than unscheduled in terms of reduction in: electricity cost, the tradeoff between electricity cost and waiting time, and peak to average ratio (PAR). Moreover, incorporation of RESs into SG design increase the revenue and reduce carbon emission.

In this paper, an energy management controller (EMC) is designed using three optimization techniques: harmony search algorithm (HSA), firefly algorithm (FA) and enhanced differential evolution (EDE). The objectives of this work are to minimize electricity cost as well as peak to average ratio (PAR) while maintaining the user comfort (UC). Critical peak pricing (CPP) is used for the calculation of electricity bill. The trade-off between UC and electricity cost is exploited in such a way that a stability is achieved among UC and electricity price that is preferred by the consumer. Reduction in PAR is beneficial for both consumer and utility as it provides stability to the electric grid.

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.

Proliferation in smart grid gave rise to different Demand Side Management (DSM) techniques, designed for type of sectors i.e. domestic, trade and commercial sectors, very effective in smoothening load profile of the consumers in grid area network. To resolve energy crises in residential areas, smart homes are introduced; contains Smart Meters, allows bidirectional communication between utilities and customers. For this purpose, different heuristic techniques are approached to overcome state of the art energy crisis which provide best optimal solution. The purpose of our implementation is to reduce the total cost and Peak to Average Ratio value while keeping in mind that there is a trade-off of these with waiting time up to an acceptable limit. Our proposed scheme uses heuristic technique Harmony Search Algorithm with Fish Swarm Algorithm to achieve the defined goals. Real time prizing signal is used for bill calculation in Advanced Metering Infrastructure.

In smart grid, several optimization techniques are developed for residential load scheduling purpose. Preliminary all the conventional techniques aimed at minimizing the electricity consumption cost. This paper mainly focuses on minimization of electricity cost and maximiza-tion of user comfort along with the reduction of peak power consumption. We develop a multi-residential load scheduling algorithm based on two heuristic optimization techniques: genetic algorithm and binary particle swarm optimization. The day-ahead pricing mechanism is used for this scheduling problem. The simulation results validate that the proposed model has achieved substantial savings in electricity bills with maximum user comfort. Moreover, results also show the reduction in peak power consumption. We analyzed that user comfort has significant effect on electricity consumption cost.

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.

In the past few years, a number of optimization techniques have been designed for Home Energy Management System (HEMS). In this paper, we evaluated the performance of two heuristic algorithms, i.e., Harmony Search Algorithm (HSA) and Tabu Search (TS) for optimization in residential area. These algorithms are used for efficient scheduling of Smart Appliances (SA) in Smart Homes (SH). Evaluated results show that TS performed better than HSA in achieving our defined goals of cost reduction, improving User Comfort (UC) level and minimization of Peak to Average Ratio (PAR). However, there remains a trade-off between electricity cost and waiting time.

With the emergence of smart grid (SG), the residents have the opportunity to integrate renewable energy sources (RESs) and take part in demand side management (DSM). In this regard, we design energy management control unit (EMCU) based on genetic algorithm (GA), binary particle swarm optimization (BPSO), and wind driven optimization (WDO) to schedule appliances in presence of objective function, constraints, control parameters, and comparatively evaluate the performance. For energy pricing, real time pricing (RTP) plus inclined block rate (IBR) is used. RESs integration to SG is a challenge due stochastic nature of RE. In this paper, two techniques are addressed to handle the stochastic nature of RE. First one is energy storage system (ESS) which smooths out variation in RE generation. Second one is the trading/cooperation of excess generation to neighboring consumers. The simulation results show that WDO perform more efficiently than unscheduled in terms of reduction in: electricity cost, the tradeoff between electricity cost and waiting time, and peak to average ratio (PAR). Moreover, incorporation of RESs into SG design increase the revenue and reduce carbon emission.

Today’s buildings are responsible for about 40% of total energy consumption and 30–40% of carbon emissions, which are key concerns for the sustainable development of any society. The excessive usage of grid energy raises sustainability issues in the face of global changes, such as climate change, population, economic growths, etc. Traditionally, the power systems that deliver this commodity are fuel operated and lead towards high carbon emissions and global warming. To overcome these issues, the recent concept of the nearly zero energy building (nZEB) has attracted numerous researchers and industry for the construction and management of the new generation buildings. In this regard, this paper proposes various demand side management (DSM) programs using the genetic algorithm (GA), teaching learning-based optimization (TLBO), the enhanced differential evolution (EDE) algorithm and the proposed enhanced differential teaching learning algorithm (EDTLA) to manage energy and comfort, while taking the human preferences into consideration. Power consumption patterns of shiftable home appliances are modified in response to the real-time price signal in order to get monetary benefits. To further improve the cost and user discomfort objectives along with reduced carbon emission, renewable energy sources (RESs) are also integrated into the microgrid (MG). The proposed model is implemented in a smart residential complex of multiple homes under a real-time pricing environment. We figure out two feasible regions: one for electricity cost and the other for user discomfort. The proposed model aims to deal with the stochastic nature of RESs while introducing the battery storage system (BSS). The main objectives of this paper include: (1) integration of RESs; (2) minimization of the electricity bill (cost) and discomfort; and (3) minimizing the peak to average ratio (PAR) and carbon emission. Additionally, we also analyze the tradeoff between two conflicting objectives, like electricity cost and user discomfort. Simulation results validate both the implemented and proposed techniques.

Smart grid is envisioned to meet the 21st century energy requirements in a sophisticated manner with real time approach by integrating the latest digital communications and advanced control technologies to the existing power grid. It will dynamically connect all the stake holders of smart grid through enhanced energy efficiency awareness corridor. Smart Homes (SHs), Home Energy Management Systems (HEMS) and effect of home appli- ances scheduling in smart grid are now familiar research topics in electrical engineering. Peak load management and reduction of Peak to Average Ratio (PAR) and associated methods are under focus of researchers since decades. These topics have got new dimensions in smart grid environment. This dissertation aims at simulation study for effective Demand Side Management (DSM) in smart grid environment. This work is mainly focused on optimal load scheduling for energy cost minimization and peak load reduction. This work comprehensively reviews the smart grid applications, communication technologies, load management techniques, pricing schemes and related topics in order to provide an insight to the environment required for dynamic DSM. Various network attributes such as Internet Pro- tocol (IP) support, power usage, data rate etc. are considered to compare the communications technologies in smart grid context. Techniques suitable for Home Area Networks (HANs) such as ZigBee, Bluetooth, Wi-Fi, 6LoWPAN and Z-wave are discussed and compared in context of consumer concerns and network attributes. A similar approach in context of utilities’ concerns is adopted for wireless communications techniques for Neighborhood Area Networks (NANs), which include WiMAX and GSM based cellular standards. Issues and challenges regarding dynamic DSM in smart grid have been discussed briefly. DSM is supposed to have a vital role in future energy management systems and is one of the hot research areas. This study presents detailed review and analytical comparison of DSM tech- niques along with related technologies and implementation challenges in smart grid. It also covers consumers and utilities concerns in context of DSM to enhance the readers’ intuition about the topic. Two major types of DSM schemes, incentive based and dynamic pricing based, have been discussed and compared analytically. Dynamic pricing based HEMS are emphasized as important tools for peak load reduction and consumers’ energy cost minimization. Dynamic pricing based HEMS and their associated optimization techniques along with analytical comparison of the latest schemes have been described. Comparison of DSM techniques and study of latest HEMS scheme provided the base for new ideas of partial baseline load and reserved interrupting load to formulate two unique energy cost minimization problems. These models resulted the following two solutions in which scheduling has been carried out through many different algorithms to reduce peak load and consequently the PAR. This work includes novel appliance scheduling solution named; Comprehensive Home Energy Management Architecture (CHEMA), with multiple integrated scheduling options in smart grid environment. Multiple layers of enhanced architecture are modeled in Simulink with embed- ded MATLAB code. Single Knapsack is used for scheduling and four different cases for cost reduction are modeled. Fault identification and electricity theft control have also been added along with the carbon foot prints reduction for environmental concerns. Simulation results have shown the peak load reduction of 22.9% for unscheduled load with Persons Presence Controller (PPC), 23.15% for scheduled load with PPC and 25.56% for flexible load scheduling. Simi- larly total cost reduction of 23.11%, 24% and 25.7% has been observed, respectively. Smart grid interface layer and load forecasting layers are not implemented in current work and will be focused in future work. Another novel comparative approach has also been proposed in this research, which investi- gates the effect of multiple pricing schemes and optimization techniques for cost minimization and peak load reduction. The proposed model uses multiple pricing schemes including Time of Use (ToU), Real Time Pricing (RTP) day ahead case and Critical Peak Pricing (CPP). Pro- posed optimization problem has been solved with multiple optimization techniques including Knapsack, Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). Knapsack is used with two options of limited slots scheduling and whole day scheduling. Comparative results of the multiple pricing and optimization schemes have been discussed. Results show that the best combination achieved with GA and CPP with 39.9223% cost reduction. PSO showed the 43.73% cost reduction with all the pricing schemes. The proposed schemes have many applications for peak load reduction and energy cost mini- mization to benefit consumers and utilities. A user can schedule his load using one of the op- tions provided in CHEMA according to his preferences. Similarly, maintenance activities can be accommodated without disturbing the pre-defined schedule by using reserved interrupting slots. In large buildings, reserved slots can be used to schedule heavy loads without generating a peak.

The smart grid appears an advanced and upgraded form of the power grid. As an essential component of the smart grid, demand side management (DSM) enhances the energy efficiency of electricity infrastructure. In this thesis, we propose home energy management controller (HEMC) based on heuristic algorithms to reduce electricity expense, peak to average ratio (PAR), and maximize user comfort. We consider proposed HEMC for a single home and multiple homes. In particular, for multiple homes we classify modes of operation for the appliances according to their energy consumption with varying operation time slots. This strategy influences the consumers to reshape energy consumption profile in response to electricity cost. In order to achieve an optimal scheduling of energy consumption profile of the household appliances, we explore heuristic algorithms, such as wind-driven optimization (WDO), harmony search algorithm (HSA), and genetic algorithm (GA). We also propose a hybrid optimization algorithm genetic harmony search algorithm (GHSA) that can schedule energy consumption profile in an appropriate way. The existing and proposed optimization algorithms are investigated by considering single home and multiple homes with real-time electricity pricing (RTEP) and critical peak pricing (CPP) tariffs. Finally, simulation results are conducted which shows proposed algorithm GHSA performs efficiently to reduce electricity cost, PAR, and maximize user comfort.

In smart grid, several optimization techniques are developed for residential load scheduling purpose. Most of these conventional techniques of demand side management aim at minimizing the energy consumption cost. Maintaining a balance between two conflicting objectives: energy consumption cost and user comfort is still a challenging task to achieve. Therefore, in this paper, we focus on minimization of electricity cost and user discomfort while taking into account the peak energy consumption. In this regard, we implement and analyze the performance of a traditional technique; dynamic programming (DP) and two heuristic optimization techniques: genetic algorithm (GA) and binary particle swarm optimization (BPSO) for residential load. Based on these techniques, we propose a hybrid scheme; GAPSO for residential load scheduling, so as to optimize the desired objective function. In order to alleviate the complexity of the problem, the multi-dimensional knapsack is used to formulate the energy scheduling problem. The proposed model is evaluated based on two pricing schemes: day-ahead and critical peak pricing for single and multiple days. Furthermore, feasible regions are calculated and analyzed to develop a relationship between power consumption, electricity cost, and user discomfort. The simulation results are compared with DP, and validate that the proposed model along with the proposed hybrid scheme reflects substantial savings in electricity bills with minimum user discomfort. Moreover, results also show a phenomenal reduction in peak power consumption.

With the advent of smart grid and demand side management techniques, users have opportunity to reduce their electricity cost without compromising their comfort. In this thesis, we evaluate the performance of home energy management system based on user satisfaction using evolutionary computation. Our objective is to maximize the total user satisfaction within user defined budget. For the budget, three different scenarios are taken into account: 0.25/day, $0.50/day and $1.00/day. Problem formulation is performed using multiple knapsack problem. Feasible regions for three scenarios are also calculated. To obtain the desired satisfaction, three optimization techniques are used: genetic algorithm, enhanced differential evolution algorithm, and harmony search algorithm. The proposed techniques are evaluated and their simulation results are compared in terms of achieved satisfaction.

With the emergence of the smart grid (SG), the residents have the opportunity to integrate renewable energy sources (RESs) and take part in demand side management (DSM). In this thesis, we introduce generic home energy management control system (HEMCS) model having energy management control unit (EMCU) to efficiently schedule household load and integrate RESs. The EMCU based on genetic algorithm (GA), binary particle swarm optimization (BPSO), wind driven optimization (WDO), and our proposed genetic wind driven optimization (GWDO) algorithm to schedule appliances of single home and multiple homes. For energy pricing, combined real time pricing (RTP) and inclined block rate (IBR) is adopted, because in case of only RTP there is a possibility of building peaks during off peak hours that may damage the entire power system. Moreover, to control demand under the capacity of electricity grid station feasible region is defined and problem is formulated using multiple knapsack. Energy efficient integration of RESs in SG is a challenge due to time varying and intermittent nature of RE. In this thesis, two techniques are used to handle time varying and intermittent nature of RE. First one is energy storage system (ESS) that smooth out variations in RE generation. Second is trading of the surplus generation among neighboring consumers. The simulation results show that our proposed scheme can mitigate voltage rise problem in areas with high penetration of RESs and reduce electricity cost and peak to average ratio (PAR) of aggregated load.

Recently, Home Energy Management (HEM) controllers have been widely used for residential load management in a smart grid. Generally, residential load management aims {to reduce the electricity bills and also curtail the Peak-to-Average Ratio (PAR)}. In this paper, we design a HEM controller on the {basis} of four heuristic algorithms: Bacterial Foraging Optimization Algorithm (BFOA), Genetic Algorithm (GA), Binary Particle Swarm Optimization (BPSO), and Wind Driven Optimization (WDO). Moreover, we proposed {a} hybrid algorithm which is Genetic BPSO (GBPSO). All the selected algorithms are tested with the consideration of essential home appliances in Real Time Pricing (RTP) environment. Simulation results show that each algorithm in the HEM controller reduces the electricity cost and curtails the PAR. GA based HEM controller performs relatively better in term of PAR reduction; it curtails approximately $34\%$ PAR. Similarly, BPSO based HEM controller performs relatively better in term of cost reduction {, as} it reduces approximately $36\%$ cost. Moreover, GBPSO based HEM controller performs better than the other algorithms based HEM controllers in terms of both cost reduction and PAR curtailment.

In this paper, we propose a novel strategy for a Demand Side Management (DSM) in a Smart Grid (SG). In this strategy, three types of loads are considered, i.e., residential load, commercial load and industrial load. The larger number of appliances of different power rating for each type of load is considered in this work. The focus of this work is to minimize the Peak to Average Ratio (PAR) to increase the efficiency of SG, by increasing the utilization of spinning reserves. On the other hand, our aim is to minimize the electricity consumption cost. Tackling the large number of appliances in an SG is a challenging task, because it increases the complexity of the problem. However, in literature the focus is on small number of appliance. In this work, the load scheduling problem is mathematically formulated and solved by using genetic algorithm. The simulation results show that the propose algorithm reduces the cost, while reducing the peak load demand of the SG.

Demand Side Management (DSM) mechanism is used for the implementation of different strategies to encourage residential users to reduce electricity bill as well as energy demand. There is also a close relationship between the consumer and utility for equally benefiting to both in terms of grid stability and bill reduction. Extensive research is undertaken now a days in order to make practical implementation on the possible use of different DSM strategies to regulate the energy demand and carbon emission reduction in the World. The major objective of this work is to study the DSM-based approaches which could be helpful in achieving significant electricity demand reduction at the electricity distribution network which is directly connected to the commercial and residential sector especially. In this work, we use an optimization algorithm to obtain the optimal solution for residential electricity load management in a typical household setting. There are two major tasks of this algorithms; firstly, electricity bill minimization of residential user in time of use pricing models, secondly, peaks reduction of demand curve (peak shaving) which will eventually minimize the investment cost of utility including, peak power plants, and transmission lines. Three types of smart appliances are considered; without delay, delay of one hour, delay of five hours. To validate the effectiveness of the proposed algorithm, mathematical models of appliances based on their length of operation time is developed.

Efficient energy management requires smart approaches in demand side as well as demand response management assisted by smart, innovative, and computationally feasible schemes. Artificial intelligence algorithms are increasingly becoming helpful in generating multiple scenarios for a range of real world problems on the pattern of human Intelligence. This paper draws on employing algorithms such as Particle Swarm Optimization (PSO) and Genetic Algorithms (GA) to generate a hybrid algorithm inspired by the characteristics of these two. Finally simulations have been drawn graphically to compare the energy consumption patterns for appliance scheduling schemes as well as corresponding cost analysis for energy optimization.

This paper presents real time information based energy management algorithms to reduce electricity cost and peak to average ratio (PAR) while preserving user comfort in a smart home. We categorize household appliances into thermostatically controlled (tc), user aware (ua), elastic (el), inelastic (iel) and regular (r) appliances/loads. An optimization problem is formulated to reduce electricity cost by determining the optimal use of household appliances. The operational schedules of these appliances are optimized in response to the electricity price signals and customer preferences to maximize electricity cost saving and user comfort while minimizing curtailed energy. Mathematical optimization models of tc appliances, i.e., air-conditioner and refrigerator, are proposed which are solved by using intelligent programmable communication thermostat ( iPCT). We add extra intelligence to conventional programmable communication thermostat (CPCT) by using genetic algorithm (GA) to control tc appliances under comfort constraints. The optimization models for ua, el, and iel appliances are solved subject to electricity cost minimization and PAR reduction. Considering user comfort, el appliances are considered where users can adjust appliance waiting time to increase or decrease their comfort level. Furthermore, energy demand of r appliances is fulfilled via local supply where the major objective is to reduce the fuel cost of various generators by proper scheduling. Simulation results show that the proposed algorithms efficiently schedule the energy demand of all types of appliances by considering identified constraints (i.e., PAR, variable prices, temperature, capacity limit and waiting time).

In this paper, we introduce a generic architecture for demand side management (DSM) and use combined model of time of use tariff and inclined block rates. The problem formulation is carried via multiple knapsack and its solution is obtained via ant colony optimization (ACO). Simulation results show that the designed model for energy management achieves our objectives; it is proven as a cost-effective solution to increase sustainability of smart grid. The ACO based energy management controller performs more efficiently than energy management controller without ACO based scheduling in terms of electricity bill reduction, peak to average ratio minimization and user comfort level maximization.

In this paper we propose an ECG optimization model for a smart home based on DSM.The proposed model is an efficient SHEM strategy. The model is proposed keeping in view the minimization of energy consumption,energy consumption cost and energy generation cost.The model is based on efficient scheduling of appliances and an ECG optimization algorithm is proposed.We are using and optimizing energy from two energy sources namely lceg and lcd which are also known as macrogrid and microgrid respectively.The problem is solved as cost optimization problem using genetic algorithm and mathematically formulated using binary MNKP. The simulation results show that our ECG model efficiently reduces the cost of energy consumption, energy generation and energy consumption utilization.

Due to smart grid applications the consumers and producers are able to meet the demand of each others and thus take part in demand side management and demand response program. Hence smart grid leads to optimization of energy consumption and reduce high cost in today extensive demand of energy. In this research work we are reducing electricity consumption cost and load consumption using scheduling the appliances. The twenty appliances are used to schedule their energy consumption and load using heuristics techniques i.e. binary particle optimization, genetic algorithm and wind driven optimization, using the same data set for each technique and their results are compared with each other in order to find which technique do better optimization. Simulations are performed in matlab to show the cost and load reduction by the above three techniques and validate the experiment. The simulation results show that binary particle swarm optimization perform better than the other two techniques and wind driven optimization is better than genetic algorithm but not able to perform as binary particle swarm optimization, similarly genetic algorithm is least efficient as compared to both methods. Our research work is beneficial to meet the demand side management and help in reducing electricity cost and load for consumers.