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With rapid increase in the use of technology, the world is now moving towards smart cities which requires the communication and collaboration of Internet-of-Things (IoT) devices. The smart city enhances the use of technology to share information and data among devices. These devices are producing a huge volume of data that needs to be tackled very carefully. Different works have already been done to provide a communication network for the IoT devices though, nothing is founded more effective in terms of resource utilization. Hybrid network architecture is the combination of a centralized and distributed network. The centralized network is used for the communication of IoT devices with edge nodes and distributed network for communicating miner nodes with edge nodes. In this way, the network utilizes a lot of resources. In this paper, we are proposing a single network which is the combination of both edge nodes and miner nodes. Blockchain is also implemented in this network to provide secure communication between the devices. The evaluation of the proposed model is done using different performance parameters such as time and cost against the number of devices. Limited number of devices are used to perform this evaluation. Furthermore, the results are obtained by utilizing Proof-of-Work (PoW) consensus mechanism.
Recently a massive increase in the demand of energy has been reported in residential, industrial and commercial sectors. Traditional Grid (TG) with the aging infrastructure is unable to address the increasing demand problem. Smart Grid (SG) enhanced the TG by adopting information and communication based technological solutions to address the increasing electricity demand. Smart Home Energy Management System (SHEMS) plays an important role in the efficacy of SG. In this paper, an Improved Algorithm for Peak to average ratio Reduction (IAPR) in SHEMS is developed. To validate the effectiveness of the IAPR, comparison is made with the renowned meta-heuristic optimization approaches namely Strawberry Algorithm (SA) and Salp Swarm Algorithm (SSA) using two different pricing scheme. It is illustrated by simulations results that the IAPR reduced the PAR to a greater degree as compare to SA and SSA.
The day by day increase in population is producing a gap between the demand and supply of electricity. Installation of new electricity generation system is not a good solution to tackle the high demand of electricity. To get the most out of the existing system, several demand response schemes have been presented by researchers. These schemes try to schedule the appliances in such a way that electricity consumption cost and peak-to-average ratio are minimized along with maximum user comfort. However, there exists a trade-off between user comfort and electricity consumption cost. In this paper, a novel scheme is developed for the home energy management system to schedule the home appliances in such a way that comforts the consumers economically. To evaluate the effectiveness of our proposed scheme, comparison is performed with two well known meta-heuristic techniques namely Flower Pollination Algorithm (FPA) and Jaya Optimization Algorithm (JOA). Experimental results shows that the proposed scheme outperforms FPA and JOA in appliances waiting time reduction. Furthermore, the proposed scheme reduced the electricity consumption cost and peak to average ratio by 58% and 56% respectively as compared to unscheduled scenario.
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- Sakeena Javaid
- Nadeem Javaid
Signature of Student: Summary of the Research This synopsis describes that residential energy management systems which are leveraged by the penetration of the renewable energy sources (RESs) such as: photovoltaic (PV) systems and wind turbines. These are the major sources used in residential energy and the concurrent penetration of these resources will considerably change the residential energy management systems' (EMS) functionality. This research focuses on smart homes and buildings which are equipped with automation technologies to enhance occupants' comfort level and provides savings on the electricity bills. The occupants' activities can be controlled or monitored by the EMS in order to schedule the appliances daily energy consumption patterns. Another significant aspect of this research is integration of the smart homes into smart grid architecture. It can support the traditional power grid by integrating smart grid's energy management programs (i.e., demand side management programs). In addition, energy management in the residential sector is challenged by many factors. The major challenge is that energy consumption is continuously growing despite of the enforcement of the various energy efficiency policies. Some important factors of the energy growth are: increasing the usage and number of appliances, improper schedules and control of the appliances and more comfort demand. For improving these challenges, EMSs are used to manage and reduce residential energy usage and cost by incorporating different energy management programs, such as: energy conservation and energy efficiency. EMSs utilize RESs for residential energy generation to fulfill users' requirements and to support reliability and robustness of energy supply systems. For effectively managing the residential users' energy, home energy management systems (HEMS) and buildings energy management systems (BEMS) are the focus of this research and they are comprised of the following applications which are based on controlling the daily usage of appliances and seasonally usage of appliances: 1) monitoring and scheduling the flexible loads for accommodating daily activities, user preferences and requirements of residential consumers, 2) reduction of peak load demand through optimal control of flexible residential loads, storages and generation systems, 3) cost effectiveness with market oriented strategy of implementing EMSs by effectively managing energy use in response of fluctuating energy prices. Furthermore, we also consider the prevention measures for rebound effects by informing consumers to utilize more energy after the implementation of RESs. In addition, some major barriers to the large scale (multiple homes and residential buildings) implementations are also considered as: peak formation, cost maximization and user comfort sacrifices by analyzing complexity of the proposed solutions. For solving the aforementioned issues, this research considers the meta-heuristic algorithms (genetic wind driven algorithm (GWD)) for the efficient HEMS in the residential units (single and multiple homes) at first stage. Since meta-heuristic algorithms are well suited for solving the stochastic nature of problems as randomness in energy consumption patterns and users' schedules. Using these algorithms; we are simulating two scenarios for the above mentioned applications: 1) energy cost and user comfort without RESs for single, multiple homes and buildings, 2) energy cost and user comfort with RESs for single, multiple homes and buildings. We will also consider the HEMS and BEMS using fuzzy logic in our subsequent work for the aforementioned scenarios while considering the seasonally used appliances (i.e., by providing heating and air conditioning system control). Furthermore, the comparative analysis of these techniques will also be conducted.
In the last decade, high energy demand is observed due to increase in population. Every home has now large number of smart appliances for the daily routine operations. Due to high demand of energy, numerous challenges in the existing power systems are raised, i.e., robustness, stability and sustainability. The objective of our new schemes Random Cell Elimination Scheme (RCES) and Memory Updation Heuristic Scheme (MUHS) is to automate the Energy Management System (EMS), so that the consumer gets facilitated. Price forecasting is performed to reduce the burden on utility and also ease the consumers. This work is focused on the residential sector EMS, especially for the smart homes. To fulfill the high load demand of electricity consumers, we have defined a fitness criterion. The researchers are working on new approaches to enhance and improve the power demand. The increasing demand of electricity creates peaks on utility. Therefore an improved Home Energy Management System (HEMS) is necessary for the automation of smart home to reduce the cost and peaks on utility. We have utilized three pricing systems, Time of Use (ToU) and Real Time Pricing (RTP) and Critical Peak Pricing (CPP) in our experiments. Furthermore, The vital part of the smart grid is electricity price forecasting because it makes grid cost effective. Although, existing systems for price forecasting may be challenging to manage with enormous price data in the grid. As repetition from the feature cannot be avoided and an integrated system is needed for regulating the plans in price. To handle this problem, a new price forecasting system is developed. This proposed model particularly integrated with three systems. Initially, features are selected from the random data by combining the Mutual Information (MI) and Random Forest (RF). The Grey Correlation Analysis (GCA) is used to remove the redundancy from the selected features. Secondly, the Recursive Feature Elimination (RFE) scheme is used to reduce the dimensions. Finally, classification is done based on Enhanced-Convolutional Neural Network (E-CNN) classifier to forecast the price. The experimental results shows that the proposed schemes performed better than mentioned benchmark schemes in reducing PAR and user discomfort and but also increase the price forecasting accuracy.
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Smart grid technologies ensures reliability, availability and efficiency of energy which contribute in economic and environmental benefits. On other hand, communities have smart homes with private energy backups however, unification of these backups can beneficial for the community. A community consists of certain number of smart homes (SH) which have their own battery based energy storage system. In this paper, 12 smart communities are connected with 12 fog computing environment for power economy sharing within the community. Each community has 10 smart homes with battery bases energy storage system. These communities are evaluated for load and cost profiles with three scenarios; SHs without storage system, SHs with storage system for individual SH requirements and SHs with unified energy storage system (unified-ESS). Unified-ESS is formed with the help of home and fog based agents. Simulations show that, unfied-ESS is efficient to have reduced cost for SHs within the community.
Home energy management systems (HEMSs) based on demand response (DR) synergized with renewable energy sources (RESs) and energy storage systems (ESSs) optimal dispatch (DRSREOD) are used to implement demand-side management in homes. Such HEMSs benefit the consumer and the utility by reducing energy bills, reducing peak demands, achieving overall energy savings and enabling the sale of surplus energy. Further, a drastically rising demand of electricity has forced a number of utilities in developing countries to impose large-scale load sheddings (LSDs). A HEMS based on DRSREOD integrated with an LSD-compensating dispatchable generator (LDG) (DRSREODLDG) ensures an uninterrupted supply of power for the consumers subjected to LSD. The LDG operation to compensate the interrupted supply of power during the LSD hours; however, accompanies the release of GHGs emissions as well that need to be minimized to conserve the environment. A 3-step simulation based posteriori method is proposed to develop a scheme for eco-efficient operation of DRSREODLDG-based HEMS. The method provides the tradeoffs between the net cost of energy (CEnet) to be paid by the consumer, the time-based discomfort (T BD) due to shifting of home appliances (HAs) to participate in the HEMS operation and minimal emissions (T EM iss) from the local LDG. The search has been driven through multi-objective genetic algorithm and Pareto based optimization. The surface fit is developed using polynomial models for regression based on the least sum of squared errors and selected solutions are classified for critical tradeoff analysis to enable the consumer by choosing the best option and consulting a diverse set of eco-efficient tradeoffs between CEnet, T BD and T EM iss.
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Presently, power systems have the capacities to accommodate different framework for incorporating economic dispatch, transmission, storage, and electricity consumption. This can provide an efficient energy management for controlling, coordinating, planning and operations. This paper focuses on coordinating the behaviors of a typical energy management of microgrid which is an issue on energy interconnections. A setup of microgrid, electricity users, storage and utility company is designed. Initially, optimal solution is formulated as a three stage Stackelberg game in which each player is allow to maximize its payoffs, while ensuring load stability and reliability. The method of backward induction is applied to examine the non cooperative game problem. We further proposed an efficient Jaya-based conditional restricted Boltzmann machine for microgrid power output forecasting which enable the microgrid make strategic decision. Simulation results validate the fact that accurate prediction of renewable energy can influence the choice of microgrid strategies.
The emergence of Demand Response(DR) program optimizes the energy consumption pattern of customers and improves the efficacy of energy supply. The pricing infra structure of DR program is time-based rate where prices changed according to user usage. The variation in price rate is due to the consume electricity and extra generation cost. The main objective of DR is to encourage the consumer to shifts the peak load and get incentives in terms of cost reduction. Users who shift the load and who did not have to pay the same rate. In this work, game theory based pricing strategy is evaluated where each user have different price rates according to the consume energy during Shoulder-peak and On-peak hours. Moreover, to avoid peek formation during the Off-peak hours Salp swarm algorithm is used to schedule the home appliances. The experimental results prove the effectiveness of proposed pricing scheme as well proposed scheduling scheme.
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- Haseeb Hassan Asif
- Nadeem Javaid
In this paper, we have discussed a problem of demand side management i.e. forecasting proposed a new solution to short term price forecasting using data analytics. Accurate Price Forecasting plays very important role in Demand Side Management. There are many techniques that can be tuned to forecast Price at very good accuracy. However, if compared at individual level, there is still room for optimization. And for that purpose, we have proposed a naïve approach towards optimization. In our paper we have compared the working of many different benchmark regressors on two different datasets. Our model introduces the ability for one learning algorithm to share its knowledge with another and tweak the forecast results accordingly to give an optimum forecast. Our resulting accuracy improves 1% in the overall average accuracy.
Demand Response Management (DRM) is considered one of the crucial aspects of the smart grid as it helps to lessen the production cost of electricity and utility bills. DRM becomes a fascinating research area when numerous utility companies are involved and their announced prices reflect consumer’s behavior. This paper discusses a Stackelberg game plan between consumers and utility companies for efficient energy management. For this purpose, analytical consequences (unique solution) for the Stackelberg equilibrium are derived. Besides this, this paper presents a distributed algorithm which converges for consumers and utilities. Moreover, different power consumption activities on the basis of time series are becoming a basic need for load prediction in smart grid. Load forecasting is taken as the significant concerns in the power systems and energy management with growing technology. The better precision of load forecasting minimizes the operational costs and enhances the scheduling of the power system. The literature has discussed different techniques for demand load forecasting like neural networks, fuzzy methods, Naïve Bayes, and regression based techniques. This paper presents a novel knowledge based system for short-term load forecasting. The algorithms of Affinity Propagation and Binary Firefly Algorithm are integrated in knowledge based system. Besides, the proposed system has minimum operational time as compared to other techniques used in the paper. Moreover, the precision of the proposed model is improved by a different priority index to select similar days. The similarity in climate and date proximity are considered all together in this index. Furthermore, the whole system is distributed in sub-systems (regions) to measure the consequences of temperature. Additionally, the predicted load of the entire system is evaluated by the combination of all predicted outcomes from all regions. The paper employs the proposed knowledge based system on real time data. The proposed scheme is compared with Deep Belief Network and Fuzzy Local Linear Model Tree in terms of accuracy and operational cost. In addition, the presented system outperforms other techniques used in the paper and also decreases the Mean Absolute Percentage Error (MAPE) on a yearly basis. Furthermore, the novel knowledge based system gives more efficient outcomes for demand load forecasting.