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Smart Grids

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Private Profile
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The increasing load demand in residential area and irregular electricity load profile encouraged us to propose an efficient Home Energy Management System (HEMS) for optimal scheduling of home appliances. We propose a multi-objective optimization based solution that shifts the electricity load from On-peak to Off-peak hours according to the defined objective load curve for electricity. It aims to manage the trade-off between conflicting objectives: electricity bill, waiting time of appliances and electricity load shifting according to the defined electricity load pattern. The defined electricity load pattern helps in balancing the load during On-peak and Off-peak hours. Moreover, for real time rescheduling, concept of coordination among home appliances is presented. This helps the scheduler to optimally decide the ON/OFF status of appliances to reduce the waiting time of the appliance. Whereas, electricity consumers have stochastic nature, for which, nature-inspired optimization techniques provide optimal solution. For optimal scheduling, we proposed two optimization techniques: binary multi-objective bird swarm optimization and a hybrid of bird swarm and cuckoo search algorithms to obtain the Pareto front. Moreover, dynamic programming is used to enable coordination among the appliances so that real-time scheduling can be performed by the scheduler on user's demand. To validate the performance of the proposed nature-based optimization techniques, we compare the results of proposed schemes with existing techniques such as multi-objective binary particle swarm optimization and multi-objective cuckoo search algorithms. Simulation results validate the performance of proposed techniques in terms of electricity cost reduction, peak to average ratio and waiting time minimization. Also, test functions for convex, non-convex and discontinuous Pareto front are implemented to prove the efficacy of proposed techniques.
Private Profile
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Energy consumption minimization and user comfort enhancement in Home Energy Management System (HEMS) are the major challenges in a smart grid. In HEMS, appliances of Heating, Ventilation, and Air Conditioning (HVAC) have a large impact on the energy consumption. For user comfort, one needs to take into account different environmental factors among which humidity plays an important role in determining the suitable temperature for optimal user comfort. In order to minimize energy consumption without compromising user comfort, fuzzy logic techniques are widely used without considering humidity. In this paper, we tune the Fuzzy Inference System (FIS) by including humidity as well as we propose a method for the automatic rule generation for FIS. Automatic rule generation is devised using combinatorics. The proposed system is evaluated by the membership functions of the input parameters and the results are compared with Mamdani FIS and Sugeno FIS. Indoor temperature, outdoor temperature, occupancy, price, initialized set points of thermostat, and humidity are the input parameters of the system. Performance metrics used for the evaluation are energy consumption, Peak-to-Average Ratio (PAR), cost, and efficiency gain. Simulation of one month energy consumption with proposed technique is performed in MATLAB®. Simulation results validate the proposed technique and show that despite all the energy savings, the proposed technique manages to be in the user comfort zone while achieving electricity cost reduction up to 24%. Moreover, optimization using FIS provides the reduced energy consumption up to 28%. The proposed technique seems to have a potential for improved demand-side energy management in a smart grid.
Energy consumption in residential sector is the 25% of all the sectors. Maintaining user comfort and energy optimization are the major tasks of Home Energy Management System. Appliances of Heating, Ventilation and Air Conditioning (HVAC) and lighting devices constitute up to 64% and 4% of energy consumption respectively in residential buildings. Different techniques like Demand Response (DR) and pricing tariffs like Time Of Use (TOU) has been used to make user participate in the energy consumption reduction. In the literature review, many techniques have been discussed which use Fuzzy Logic System integrated with other techniques for energy consumption minimization. However, user comfort is often sacrificed in these techniques. In this thesis, we have proposed a Fuzzy Inference System (FIS) that uses humidity as an input parameter in order to maintain the thermostat set-point according to user comfort. Additionally, we have used indoor room temperature variation as a feedback to proposed FIS in order to get the better energy consumption simulation. When defining FIS, number of rules in the rule base plays an important role in the correct working. With the increase in number of rules, task of defining them in FIS becomes time consuming and chances of manual errors increase. In this research, we have also proposed the automatic rule base generation using combinatorial method. Proposed techniques are evaluated using Mamdani FIS and Sugeno FIS. The input parameters of proposed FIS are indoor temperature, outdoor temperature, occupancy, price rate, initialized set points and humidity whereas energy consumption is the output of the system. Performance metrics used for the evaluation of the MATLAB simulation results are energy consumption, Peak-to-Average Ratio (PAR), cost, efficiency gain and user comfort. In addition, a model has been proposed which will quantify the user comfort with respect to different energy consumption levels. Simulation result validates that proposed technique reduces energy consumption by 28%.
Ashfaq Ahmad
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In this paper, we investigate a joint real-time load scheduling and energy storage management at a grid-connected solar powered electric vehicle. Without any a-priori knowledge, we consider a finite time approach with arbitrary dynamics of system inputs. Our aim is to minimize an average aggregated system cost through joint optimization of electric vehicle's energy procurement price, load scheduling delays, photovoltaic sufficiency in terms of locally generated renewable energy mix, and battery degradation. Through subsequent modification and reformulation of the joint optimization problem, we utilize the concept of one-slot look-ahead queue stability to solve the problem by employing the Lyapunov optimization technique. We show that the joint optimization problem is separable into sub-problems which are sequentially solved with asymptotic optimality and a bounded performance guarantee. Simulations are carried in different scenarios and under varying weather conditions. Results show that our proposed algorithm can achieve a daily electric vehicle's photovoltaic sufficiency up to 50.50%, a monthly bill reduction up to 72.61%, and a yearly reduced CO $_2$ emission level up to 6.06 kg, while meeting electric vehicle user's energy and delay requirements.
Muhammad IDREES Khan
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Acute oral toxicity and antioxidant studies of an amine-based diselenide
 
Abdul Mateen
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Smart Grid (SG) plays a noteworthy role in minimizing the Electricity Cost (EC) through Demand Side Management (DSM). Smart homes are the part of SG, pays a lot in minimizing EC via scheduling the appliances. Home Energy Management (HEM) have been extensively used for energy management in smart homes. In this paper, for the effective utilization of energy in a smart home, we propose a solution that consists of bio-inspired techniques: Genetic Algorithm (GA), Flower Pollination Algorithm (FPA) and hybrid of these two, Genetic Flower Pollination Algorithm (GFPA). All of these techniques applied to the appliances that are essential in a home. Our proposed solution leads to find an optimal scheduling pattern that reduces EC, Peak to Average Ratio (PAR) and maximize User Comfort (UC). In our work, we have considered one home. We divide appliances into three categories, non-interruptible, interruptible and fixed appliances. Simulation results show that our proposed schemes performed better in terms of EC, UC and PAR. We have done this work for three different Operational Time Intervals (OTIs) 15, 30 and 60 minutes for each appliance.
Private Profile
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Demand side management (DSM) in smart grid (SG) makes users able to take informed decisions according to their power usage pattern and assists the electric utility in minimizing higher power demand in the duration of higher energy demand intervals. Where, this ultimately leads to carbon emission reduction, electricity monetary cost minimization and maximization of power grid efficiency and sustainability. Nowadays, a large number of the DSM strategies 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 thesis, we consider a load shifting strategy of DSM, to decrease PAR, delay time and total electricity cost. To gain aforementioned objectives, the crow search algorithm (CSA) and enhanced differential evolution (EDE) are employed. In addition, flower pollination algorithm (FPA), grey wolf optimizer (GWO) and their hybrid i.e., flower-grey wolf optimizer (FGWO) are also used. Moreover, bat algorithm (BA), CSA and their hybrid algorithm i.e., bat-crow search algorithm (BCSA) are also used. For simulation of EDE and CSA, a home with 13 appliances are considered. Furthermore, for the simulation of FPA, GWO, FGWO, BA, CSA, and BCSA, a single home consists of 15 appliances are taken into account. For computing monetary cost, Critical peak pricing (CPP) tariff is employed.
Private Profile
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For the better management of energy, a coordination based energy management system as-a-service on fog is presented. An efficient system model is introduced to handle a social networking problem in order to maintain the balance between the produced and the required energy. This social network problem is formulated as a game theory based coalition method. With the increase in number of electricity consumers, the computational complexity of energy management system is becoming a threat for system efficiency in real-time environment. To deal with this dilemma, the service providers shift their computational and storage units on cloud and fog. The fog is an intermittent layer between the cloud and the end user which helps to make the system faster as compared to the cloud. In this scenario, a building with multiple apartments is considered. Where, each apartment is taken as a player and the surplus power as pay-off. The surplus energy will be distributed among the energy deficient apartments using Shapley value that unevenly distributes the power according to the demand. The experimental results show that 13 kW extra power is saved and distributed among energy deficient apartments during different times of a day.
Abdul Mateen
added 4 research items
Since the development of Smart Grid (SG), Home Energy Management (HEM) systems are emerged widely into it and consumers have an opportunity to schedule their smart appliances efficiently in smart homes. In this research, meta-heuristic techniques Harmony Search Algorithm (HSA), Pigeon Inspired Optimization (PIO) and our proposed Harmony Pigeon Inspired Optimization (HPIO) are adopted to efficiently schedule smart appliances in smart home. The aim of using the above proposed techniques is to reduce Electricity Cost (EC) and Peak-to-Average Ratio (PAR). HEM is proposed to further evaluate the performance of evaluated techniques. In this work, single home and multiple homes which consist of 10 ,30 and 50 homes are considered equipped with multiple smart appliances. These appliances are divided into three sets, which are thermostatically and non-thermostatically controllable, and non-controllable appliances under Time-ofUse (ToU) pricing scheme. Simulations are carried out on these parameters and results shows that proposed technique HPIO performed better than HSA and PIO in terms of minimizing waiting time and PAR. We have considered User Comfort (UC) in terms of waiting time.
Nowadays, different schemes and ways are proposed to meet the user's load requirement of energy towards the Demand Side (DS) in order to encapsulate the energy resources. However, this Load Demand (LD) increases day by day. This increase in LD is causing serious energy crises to the utility and DS. As the usage of energy increases with the increase in user's demand respectively, the peak is increased in these hours which affect the customer's in term of high-cost prices. This issue is tackled using some schemes and their proper integration. Two-way communication is done by the utility through Smart Grid (SG) between utility and customers. Customers that show some good behavior and helps the utility to control this LD, can perform a key role here. In this paper, our main focus is to control the Customer Side Management (CSM) by reducing the peak generation from on-peak hours. In our scenario, we focus on saving the cost expenditure of users by giving them comfort and shifting the load of appliances from high LD hours to low LD hours. In this study, we adopt the optimization algorithms, like Bacterial Foraging Optimization Algorithm (BFOA), Flower Pollination Algorithm (FPA) and proposed our Hybrid Bacterial Flower Pollination Algorithm (HBFPA) to optimize the solution of our problem using the famous electricity scheme named as Critical Peak Pricing(CPP) with three different Operational Time intervals (OTIs). Simulations and results show that our scheme reduces the cost and peak to the average ratio by proper shifting the appliances from highly load demanding hours to the low demanding hours with the negligibly small difference between the maximum and minimum 90% of confidence interval.
Abdul Mateen
added a research item
Daily operations and planning in a smart grid require a day-ahead load forecasting of its customers. The accuracy of day-ahead load-forecasting models has a significant impact on many decisions such as scheduling of fuel purchases, system security assessment, economic scheduling of generating capacity, and planning for energy transactions. However, day-ahead load forecasting is a challenging task due to its dependence on external factors such as meteorological and exogenous variables. Furthermore, the existing day-ahead load-forecasting models enhance forecast accuracy by paying the cost of increased execution time. Aiming at improving the forecast accuracy while not paying the increased executions time cost, a hybrid artificial neural network-based day-ahead load-forecasting model for smart grids is proposed in this paper. The proposed forecasting model comprises three modules: (i) a pre-processing module; (ii) a forecast module; and (iii) an optimization module. In the first module, correlated lagged load data along with influential meteorological and exogenous variables are fed as inputs to a feature selection technique which removes irrelevant and/or redundant samples from the inputs. In the second module, a sigmoid function (activation) and a multivariate auto regressive algorithm (training) in the artificial neural network are used. The third module uses a heuristics-based optimization technique to minimize the forecast error. In the third module, our modified version of an enhanced differential evolution algorithm is used. The proposed method is validated via simulations where it is tested on the datasets of DAYTOWN (Ohio, USA) and EKPC (Kentucky, USA). In comparison to two existing day-ahead load-forecasting models, results show improved performance of the proposed model in terms of accuracy, execution time, and scalability.