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Heuristic Algorithm based Home Energy Management System in Smart Grid
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Recently, Home Energy Management (HEM) controllers have been widely used for residential load management in a smart grid. Generally, residential load management aims at reducing the electricity bills and curtailing the Peak-to-Average Ratio (PAR). In this thesis, we design a HEM controller on the bases 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 the 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 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.
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... Authors in [3] have proposed three optimization algorithms EDE,BPSO and GA to achieve cost minimization, load shifting and reduction in carbon emission. Day ahead pricing scheme is used for load shifting by mapping to a multiple knapsack problems.Main concern is not only scheduling of appliances but also considering priority of an appliance that is given by consumer.Grid sustainability is ensured by balancing load at generation and consumptions units. ...
... HEMs economic operation problem using mixed integer linear programming was presented in [16]. An interesting concept of HEM was presented in [5] as a human-centric smart home energy management system (SHE), while [17] proposes Smart Home Energy Management System (SHEMS) that reduces energy usage based on three states of residents' activity: active, away, or sleep. ...
Currently, smart energy meters are being implemented in many countries. In some countries, consumers also
have an in-home display (IHD), a device that facilitates energy management by presenting interesting information about
energy consumption. In this article, the authors presented their own design of an advanced in-home display. Two-way
communication with such a display is necessary for its proper operation so that it could be an interactive device. The authors
wanted households to have only one device for managing energy in a comprehensive way, and not many, each of which
would have reduced functionality.
... Authors in [3] have proposed three optimization algorithms EDE,BPSO and GA to achieve cost minimization, load shifting and reduction in carbon emission. Day ahead pricing scheme is used for load shifting by mapping to a multiple knapsack problems.Main concern is not only scheduling of appliances but also considering priority of an appliance that is given by consumer.Grid sustainability is ensured by balancing load at generation and consumptions units. ...
With the emergence of automated environment,
energy demand by consumer is increasing day by day. More than
80% of total electricity is being consumed in residential sector.
In this paper, a heuristic optimization technique is proposed for
the efficient utilization of energy sources to balance load between
demand and supply sides. An optimization technique is proposed
which is a hybrid of Enhanced differential evolution (EDE)
algorithm and Gray wolf optimization (GWO). The proposed
scheme is named as hybrid gray wolf differential evolution
(HGWDE) . It is applied for home energy management (HEM)
with the objective function of cost minimization and reducing
peak to average ratio (PAR). Load shifting is performed from
on peak hours to off peak hours on basis of user preference
and real time pricing (RTP) tariff defined by utility. However,
there is a trade off between user comfort and above mentioned
parameters. To validate the performance of proposed algorithm,
simulations have been carried out in MATLAB. Results illustrate
that PAR and electricity bill have been reduced to 53.02%, and
12.81% respectively.
In this work, we propose a Realistic Scheduling Mechanism (RSM) to reduce user frustration and enhance appliance utility by classifying appliances with respective constraints and their time of use effectively. Algorithms are proposed regarding functioning of home appliances. A 24 hour time slot is divided into four logical sub-time slots, each composed of 360 min or 6 h. In these sub-time slots, only desired appliances (with respect to appliance classification) are scheduled to raise appliance utility, restricting power consumption by a dynamically modelled power usage limiter that does not only take the electricity consumer into account but also the electricity supplier. Once appliance, time and power usage limiter modelling is done, we use a nature-inspired heuristic algorithm, Binary Particle Swarm Optimization (BPSO), optimally to form schedules with given constraints representing each sub-time slot. These schedules tend to achieve an equilibrium amongst appliance utility and cost effectiveness. For validation of the proposed RSM, we provide a comparative analysis amongst unscheduled electrical load usage, scheduled directly by BPSO and RSM, reflecting user comfort, which is based upon cost effectiveness and appliance utility.
In this paper, we propose a solution to the problem of scheduling of a smart home appliance operation in a given time range. In addition to power-consuming appliances, we adopt a photovoltaic (PV) panel as a power-producing appliance that acts as a micro-grid. An appliance operation is modeled in terms of uninterruptible sequence phases, given in a load demand profile with a goal of minimizing electricity cost fulfilling duration, energy requirement, and user preference constraints. An optimization algorithm, which can provide a schedule for smart home appliance usage, is proposed based on the mixed-integer programming technique. Simulation results demonstrate the utility of our proposed solution for appliance scheduling. We further show that adding a PV system in the home results in the reduction of electricity bills and the export of energy to the national grid in times when solar energy production is more than the demand of the home.
In this paper, we propose mathematical optimization models of household energy units to optimally control the major residential energy loads while preserving the user preferences. User comfort is modeled in a simple way which considers appliance class, user preferences and weather conditions. The Wind Driven Optimization (WDO) algorithm with the objective function of comfort maximization along with minimum electricity cost is defined and implemented. On the other hand, for maximum electricity bill and peak reduction, Min-max Regret based Knapsack Problem (K-WDO) algorithm is used. To validate the effectiveness of the proposed algorithms, extensive simulations are conducted for several scenarios. The simulations show that the proposed algorithms provide with the best optimal results with fast convergence rate, as compared to the existing techniques
The drive towards a modern, efficient and information-driven grid - the 'Smart Grid' - necessitates the incorporation of computationally intelligent infrastructure. For instance, residential load management strategies may require the scheduling of appliances in order to achieve certain objectives such as load factor maximization/peak-to-average (PAR) ratio minimization or minimization of energy cost. This paper presents an approach to one of such load scheduling problem, which involves the use of the metaheuristic optimization technique that is Genetic Algorithms (GA). We consider a scenario in which dynamic pricing is adopted and the objective is to minimize the overall cost of electricity payment while satisfying a set of constraints. MATLAB was used as the simulation platform and results confirm that Genetic Algorithm can optimize energy consumption over a set of constraints we have defined, thus minimizing overall electricity cost for the Nigerian consumer in a smart pricing environment.
In smart grid, residential consumers adopt different load scheduling methods to manage their power consumptions with specific objectives. The conventional load scheduling methods aim to maximize the consumption payoff or minimize the consumption cost. In this paper, we introduce a novel concept of cost efficiency-based residential load scheduling framework to improve the economical efficiency of the residential electricity consumption. The cost efficiency is defined as the ratio of consumer's total consumption benefit to its total electricity payment during a certain period. We develop a cost-efficient load scheduling algorithm for the demand-side's day-ahead bidding process and real-time pricing mechanism by using a fractional programing approach. Results show that the proposed scheduling algorithm can effectively reflect and affect user's consumption behavior and achieve the optimal cost-efficient energy consumption profile. For practical consideration, we also take into account the service fee and distributed energy resources (DERs) in our framework, and analyze their impacts on the cost efficiency. Simulation results confirm that the proposed algorithm significantly improves consumer's cost efficiency. It is shown that a higher service fee will decrease the cost efficiency, while the integration of DERs can effectively improve the cost efficiency.
With the arrival of smart grid era and the advent of advanced communication and information infrastructures, bidirectional communication, advanced metering infrastructure, energy storage systems and home area networks would revolutionize the patterns of electricity usage and energy conservation at the consumption premises. Coupled with the emergence of vehicle-to-grid technologies and massive distributed renewable energy, there is a profound transition for the energy management pattern from the conventional centralized infrastructure towards the autonomous responsive demand and cyber-physical energy systems with renewable and stored energy sources. Under the sustainable smart grid paradigm, the smart house with its home energy management system (HEMS) plays an important role to improve the efficiency, economics, reliability, and energy conservation for distribution systems. In this paper, a brief overview on the architecture and functional modules of smart HEMS is presented. Then, the advanced HEMS infrastructures and home appliances in smart houses are thoroughly analyzed and reviewed. Furthermore, the utilization of various building renewable energy resources in HEMS, including solar, wind, biomass and geothermal energies, is surveyed. Lastly, various home appliance scheduling strategies to reduce the residential electricity cost and improve the energy efficiency from power generation utilities are also investigated.
In this paper we propose novel and more realistic analytical models for the determination of the peak demand under four power demand control scenarios. Each scenario considers a finite number of appliances installed in a residential area, with diverse power demands and different arrival rates of power requests. We develop recursive formulas for the efficient calculation of the peak demand under each scenario, which take into account the finite population of the appliances. Moreover, we associate each scenario with a proper real-time pricing process in order to derive the social welfare. The proposed analysis is validated through simulations. Moreover, the performance evaluation of the proposed formulas reveals that the absence of the assumption of finite number of appliances could lead to serious peak-demand over-estimations.
In this paper, we study an electricity load scheduling problem in a residence. Compared with previous works in which only limited sets of appliances are considered, we classify various appliances into five sets considering their different energy consumption and operation characteristics, and provide mathematical models for them. With these appliance models, we propose an electricity load scheduling algorithm that controls the operation time and energy consumption level of each appliance adapting to time-of-use pricing in order to maximize the overall net utility of the residence while satisfying its budget limit. The optimization problem is formulated as a mixed integer nonlinear programming (MINLP) problem, which is in general, difficult to solve. In order to solve the problem, we use the generalized Benders decomposition approach with which we can solve the MINLP problem easily with low computational complexity. By solving the problem, we provide an algorithm to obtain the optimal electricity load scheduling of various appliances with different energy consumption and operation characteristics in a unified way.
This paper presents a comprehensive and general optimization-based home energy management controller, incorporating several classes of domestic appliances including deferrable, curtailable, thermal, and critical ones. The operations of the appliances are controlled in response to dynamic price signals to reduce the consumer's electricity bill whilst minimizing the daily volume of curtailed energy, and therefore considering the user's comfort level. To avoid shifting a large portion of consumer demand toward the least price intervals, which could create network issues due to loss of diversity, higher prices are applied when the consumer's demand goes beyond a prescribed power threshold. The arising mixed integer nonlinear optimization problem is solved in an iterative manner rolling throughout the day to follow the changes in the anticipated price signals and the variations in the controller inputs while information is updated. The results from different realistic case studies show the effectiveness of the proposed controller in minimizing the household's daily electricity bill while {preserving} comfort level, as well as preventing creation of new least-price peaks.