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User Comfort Oriented Residential Power Scheduling for Smart Homes
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Smart grid (SG) is an emerging technology which is considered as an ultimate solution to meet the increasing power demand challenges. Modern communication technologies have enabled the successful implementation of SG, which aims at provision of demand side management (DSM) mechanisms, such as demand response (DR). In this thesis, we propose teacher learning genetic optimization (TLGO) technique by combining genetic algorithm (GA) with teacher learning based optimization (TLBO) algorithm for residential load scheduling, assuming that electric prices are announced on a day-ahead basis. User discomfort is one of the key aspect which must be addressed along with cost minimization. The major focus of this work is to minimize consumer electricity bill at minimum user discomfort. Load scheduling is formulated as an optimization problem and an optimal schedule is achieved by solving the minimization problem. We also investigated the effect of power flexible appliances on consumers' bill. Furthermore, a relationship among power consumption, cost and user discomfort is also demonstrated by feasible region. Simulation results validate that our proposed technique performs better in terms of cost reduction and user discomfort minimization, and is able to obtain the desired trade-off between consumer electricity bill and user discomfort.
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... The smart grids (SGs) have emerged during the lasts years allowing better integration of DR into electrical systems. Both SG and DR concepts benefited from the development of advanced metering infrastructure, smart meter, intelligent control system and advanced communication technologies [1]. ...
... Heuristic algorithms have shown their effectiveness to solve the optimization problems, like GA [25], [26], BPSO [27], [28] and ACO [29]. A lot of work done in community detection through particle swarm optimization algorithm. ...
Appliance scheduling on the user’s defined preferences plays a pivotal role in smart home energy management systems. In this context, energy management controllers are largely used to satisfy the users’ demand preferences within their financial budget constraints. This work proposes a robust technique based on demand-side energy management for efficiently monitoring and controlling the domestic loads. A nature-inspired crescive consumer satisfaction algorithm (NCSA) is proposed for the optimal scheduling pattern based on time and device preferences. The proposed algorithm maximizes user satisfaction at the preset user budget by producing optimum appliance scheduling patterns. It considers household appliances input data such as time of use, power ratings, and the absolute maximum satisfaction for optimal scheduling. The proposed algorithm is evaluated on three budget schemes, and the simulation results reveal that the proposed algorithm achieves a better satisfaction index at a lower cost per unit satisfaction. Results also show that the proposed algorithm has a good convergence rate and is generalizable to any random budget scenario.
Voltage instability in a power system produces low-frequency oscillations (LFOs), causing adverse effects in power distribution. Intelligent control schemes can overcome the limitations of fixed-parameter structures in power system stabilizers (PSS). Flexible alternating current transmission system (FACTS) control along with some supplementary control have remarkable potential in damping the oscillations. This paper proposes an adaptive neurofuzzy based recurrent wavelet control (ANRWC) scheme to enhance the power system stability. The proposed scheme utilizes recurrent Gaussian as antecedent part’s membership function and recurrent wavelet function in consequent parts. Our scheme uses gradient descent, adadelta, adaptive moment estimation (ADAM) and proximal gradient descent algorithms for optimization in which parameters of the scheme are updated using a back-propagation algorithm. A multi-machine power system is used for testing the controller. We evaluate the proposed control scheme in comparison to conventional lead-lag control and an adaptive neurofuzzy takagi sugeno kang (ANFTSK) control scheme. For comparison, we calculate the performance indices (PIs) for different controllers. Both quantitative and qualitative evaluations assert the effectiveness of the proposed control as compared to other schemes.
High consumption and ever-increasing demand for electricity at commercial, residential, and industrial levels have attracted the research community to look for new technologies for the future grid. A smart grid is an advanced technology-enabled electrical grid system with the incorporation of information and communication technology. The smart grid also enables two-way power flow, and enhanced metering infrastructure capable of self-healing, resilient to attacks, and can forecast future uncertainties. This paper surveys various smart grid frameworks, social, economic, and environmental impacts, energy trading, and integration of renewable energy sources over the years 2015 to 2021. Energy storage systems, plugin electric vehicles, and a grid to vehicle energy trading are explored which can potentially minimize the need for extra generators. This study shows that the integration of renewable energy sources, plug-in electric vehicles, and energy storage systems provide long-term economic and environmental benefits and have identified challenges, opportunities, and future directions. This study is expected to serve as a potential source of guidance for the researchers to explore various aspects of the smart grid over the last 06 years which shows them the future directions. This survey will also serve as a guiding source for transmission and distribution systems operators while showing them the right path to transform their traditional grids into smart grids.
In recent years, due to the unnecessary wastage of electrical energy in residential buildings, the requirement of energy optimization and user comfort has gained vital importance. In the literature, various techniques have been proposed addressing the energy optimization problem. The goal of each technique was to maintain a balance between user comfort and energy requirements such that the user can achieve the desired comfort level with the minimum amount of energy consumption. Researchers have addressed the issue with the help of different optimization algorithms and variations in the parameters to reduce energy consumption. To the best of our knowledge, this problem is not solved yet due to its challenging nature. The gap in the literature is due to the advancements in the technology and drawbacks of the optimization algorithms and the introduction of different new optimization algorithms. Further, many newly proposed optimization algorithms which have produced better accuracy on the benchmark instances but have not been applied yet for the optimization of energy consumption in smart homes. In this paper, we have carried out a detailed literature review of the techniques used for the optimization of energy consumption and scheduling in smart homes. The detailed discussion has been carried out on different factors contributing towards thermal comfort, visual comfort, and air quality comfort. We have also reviewed the fog and edge computing techniques used in smart homes.
Energy management controller (EMC) is widely adopted for residential load management in smart grid (SG). Its main focus is on minimizing cost with minimal consumer interaction. EMC effectiveness is improved in the context of demand response (DR) program. In the era of demand side management (DSM) EMC plays a significant role in residential energy management by curtailing the peak to average ratio (PAR) and electricity bill. In this research work, we are reducing the electricity cost and PAR by maximizing the user comfort. In our research work, we consider a smart home with three smart appliances and schedule these appliances energy consumption and user comfort by using heuristic techniques: binary particle swarm optimization (BPSO) and firefly algorithm (FA). Simulations are conducted by utilizing the day-ahead real time pricing (DA-RTP). Simulation results depict that firefly algorithm (FA) has better performance as compared to BPSO in term of minimizing cost and PAR, while also maximizing user satisfaction level. A trade-off analysis between user satisfaction and energy consumption cost is demonstrated in simulations.
Demand Side Management (DSM) will play a significant role in the future smart grid by managing loads in a smart way. DSM programs, realized via Home Energy Management (HEM) systems for smart cities, provide many benefits; consumers enjoy electricity price savings and utility operates at reduced peak demand. In this paper, Evolutionary Algorithms (EAs) (Binary Particle Swarm Optimization (BPSO), Genetic Algorithm (GA) and Cuckoo search) based DSM model for scheduling the appliances of residential users is presented. The model is simulated in Time of Use (ToU) pricing environment for three cases: (i) traditional homes, (ii) smart homes, and (iii) smart homes with Renewable Energy Sources (RES). Simulation results show that the proposed model optimally schedules the appliances resulting in electricity bill and peaks reductions.
Electricity usage at electricity rush hour (peak hour) may vary from each and every service area such as industrial area, commercial area and residential area. Equalizing the power consumption in industry may lead to the utilization of power in other service areas in an efficient way. Although industries have comparably lesser number of power consuming device types than other service areas the power consumption is quite high. To meet the demands rising in the industry, shiftable loads (devices) can be redistributed equally to all the working time slots based on the average power utilization. It can be done in a flexible manner by shaping the loads using Demand Side Management (DSM) technique in Smart Grid. The main objective is to minimize the power utilization during the electricity rush hour by effectively distributing the power available during off-peak hour. Evolutionary algorithm can be well adapted to problems where optimization is the core criteria. Any maximization or minimization problem can be solved efficiently using evolutionary algorithm. Hence, to obtain the optimized fitness function of load redistribution in industry Genetic Algorithm in Demand Side Management (GA-DSM) is chosen and it has benefited with an overall reduction of 21.91% which is very remarkable. In addition to this the evaluation of the fitness function using GA-DSM is carried out in other two industrial dataset models (steel plant and wind power plant) which is unavailable so far in the literature.
Smart Home Energy Management (SHEM) systems can introduce adjustments in the working period and operations of the home appliances to allow for energy cost savings, which can however affect the Quality of Experience (QoE) perceived by the user. This paper analyses this issue and proposes a QoE-aware SHEM system, which relies on the knowledge of the annoyance suffered by the users when the operations of appliances are changed with respect to the ideal user's preferences. Accordingly, a number of profiles which describe different usages are created in the design phase. At the deployment stage, users behavior and annoyance are registered to assign one of these profiles per appliance. The assigned profile is then exploited by the QoE-aware Cost Saving Appliance Scheduling and the QoEaware Renewable Source Power Allocation algorithms. The former is aimed at scheduling controlled loads based on users profile preferences and electricity prices making use of a greedy approach. The latter re-allocates appliances' operations whenever a surplus of energy has been made available by renewable energy sources. Experimental results demonstrate that the annoyance perceived by the users is severely diminished with respect to a QoE-unaware strategy, at the expenses of only a limited reduction in energy saving.
Keyword: Load management PV system Battery state of charge Load following capability Mixed integer linear programing a b s t r a c t One of the challenges of utilizing solar PV in residential buildings is the management of load in accordance to the intermittent nature of solar resource. In this paper, Mixed Integer Linear Programming (MILP) is proposed to effectively plan and allocate load for consumption in such a way that the user defined priority of loads are conserved. In this way, the loads are made to follow the battery state of charge in accordance to the intermittent nature of solar resource without adversely affecting the quality of life of the user. In doing this, energy audit of a typical 4-bedroom residential building belonging to a medium-income family is carried out and classified into controllable and non-controllable loads. The energy audit is also used to size the PV system and the battery bank taking into consideration the financial status of the user (i.e. adequately sized and under-sized PV system based on financial status of the user). A load manager based on MILP with solar resource load following capability is thereafter developed and implemented in a computational program to manage the load based on user's priority. Six scenarios are created to gain insights into the effectiveness of the proposed load manager. The results reveal that the proposed technique is effective in allocating loads based on the available solar resource in such a way that the user priority is maintained.
Demand side management (DSM) will play a crucial role in the development of future smart grids to optimize the energy balance of the system. Various techniques of DSM such as load control strategies aim at modifying the load profile effectively to reduce energy expenses as well as the peak energy demands on the grid. In this work, we utilize the controlled load operation techniques with incorporation of renewable generators assisted by storage devices to modify the load profile. Thus, minimizing the expenditure of energy consumption on the consumer's end. Genetic algorithm (GA) is used to address the non-linear nature of the controlled appliances. While the appliance operations are controlled to minimize the energy expenses, we also incorporate the objective of maximizing user's satisfaction with the problem. While the approach effectively finds a balance between the two objectives of minimum cost and maximum satisfaction, it also helps in effectively reducing the Peak-to-average ratio (PAR) of the load profile on the grid side thus being beneficial to both the consumer and the grid.
h i g h l i g h t s DSM scheme that could maximize satisfaction within user's budget is presented. Load-satisfaction algorithm using the scheme is developed based on GA. We quantified user satisfaction based on certain rules. We develop cost per unit satisfaction index, k($) that relates budget to satisfaction. The proposed algorithm is effective in offering the maximum satisfaction. a b s t r a c t This paper presents a demand side load management technique that is capable of controlling loads within the residential building in such a way that the user satisfaction is maximized at minimum cost. Load-satisfaction algorithm was developed based on three postulations that allow satisfaction to be quantified. The input data required by the algorithm includes the power ratings of the electrical devices, its time of use, kW h electrical consumption as well as the satisfaction of the user on each electrical appliance at every hour of the day. From the data, the algorithm is able to generate an energy usage pattern, which would give the user maximum satisfaction at a predetermined user-budget. A cost per unit satisfaction index (k) which relates the expenditure of a user to the satisfaction achievable is also derived. To test the applicability of the proposed load-satisfaction management technique, three budget scenarios of $0.25/day, $0.5/day and $1.00/day are performed. The result of each of the scenarios using the proposed techniques is compared to cases in which the electrical appliances are randomly used. The results obtained revealed that the proposed algorithm offered the maximum satisfaction and minimum cost per unit satisfaction for all the scenarios.
Dynamic tariffs are expected to become a relevant pricing scheme in the context of smart grids. In this framework, active management of residential loads can play an important role to optimize the usage of end-use energy resources while minimizing energy cost. This paper presents an evolutionary algorithm to optimize the integrated usage of multiple residential energy resources (local generation, shiftable loads, thermostatically controlled loads and storage systems) considering a large set of management strategies. Customized solution encoding and operators are developed for different groups of loads. The multi-objective model considers as objective functions the minimization of the energy cost and the minimization of end-user's dissatisfaction associated with management strategies. Results have shown that significant savings can be achieved mainly through demand response actions implemented over thermostatically controlled loads. Savings are also dependent on the end-user's preferences and degree of willingness to accept automated control.
In this work, the optimal power scheduling for a grid-connected hydrokinetic-battery hybrid system is proposed to sufficiently explore hydrokinetic energy and to benefit customers at demand side. The developed model for the hybrid system’s optimal power flow management aims to minimize electricity cost subject to the power balance, hydrokinetic and battery storage outputs as well as other operational constraints. With respect to demand side management, an optimal control method is developed to schedule the power flow of hybrid system over 24-h. Simulations are performed using MATLAB (R2016a), and the results demonstrate that operating the proposed hybrid system under the developed optimal energy management model can reduce the operation cost and allow consumers to generate substantial income by selling power to the grid.
In this paper, we study a multi-residential electricity load scheduling problem with multi-class appliances in smart grid. Compared with the previous works in which only limited types of appliances are considered or only single residence grids are considered, we model the grid system more practically with jointly considering multi-residence and multi-class appliance. We formulate an optimization problem to maximize the sum of the overall satisfaction levels of residences which is defined as the sum of utilities of the residential customers minus the total cost for energy consumption. Then, we provide an electricity load scheduling algorithm by using a PL-Generalized Benders Algorithm which operates in a distributed manner while protecting the private information of the residences. By applying the algorithm, we can obtain the near-optimal load scheduling for each residence, which is shown to be very close to the optimal scheduling, and also obtain the lower and upper bounds on the optimal sum of the overall satisfaction levels of all residences, which are shown to be very tight.
The home power management system is proposed with the objective to reduce the electricity cost and also to avoid the problem of high peak demand. Recently more methods have been discussed in the area of Home energy management, but prioritizing the operation of power units from customer point of view has its own benefits depending on the comfort level. The Proposed home consists of a smart electrical appliance, photovoltaic system with battery, smart communication network and a robust controller. This controller schedules the power units in response to electricity price at the Time of Use (ToU). The available power units comprising of solar power, battery power, grid supply and the utilization of home appliance are categorized and monitored regularly. The Primary power units, preferably solar are chosen automatically as per the priority of the customer. When the primary power unit (solar) is not able to supply power, due to its intermittent nature of generation, the controller shifts to next power units accordingly. The simulation results show that the proposed system based on Home Energy Management (HEM) algorithm reduces the electricity cost, peak demand problem and enhances the efficiency of energy use. The Time of Use (ToU) is considered for reducing the peak demand. The smart controller is operated based on HEM algorithm and selects the power units accordingly. Also, there is a necessity of Energy conversion from DC (solar) to AC (grid/appliance), there is feasibility of power quality disturbance. This quality of power is improved by using Selective Harmonic elimination (SHE) method. The proposed system is developed and simulated in MATLAB/SimPowerSystem (SPS).