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Towards Heuristic Algorithms: GA, WDO, BPSO, and BFOA for Home Energy Management in Smart Grid
Abstract
In this paper, we analyse the scheduling of residential appliances to: 1) reduce cost, and 2) reduce Peak to Average Ratio (PAR) by smoothing load profile. We consider 10 different residential appliances which are categorized into three different groups: shiftable interruptible, shiftable uninterruptible and regular appliances to flexibly control the load. To schedule appliances, Home Energy Management (HEM) systems are designed by using four different heuristic algorithms: Bacterial Forging Optimization Algorithm (BFOA), Genetic Algorithm (GA), Binary Particle Swarm Optimization (BPSO) and Wind Driven Optimization (WDO).
... • An objective function is mathematically formulated, which is subject to practical energy consumption con-straints to perform DSM via scheduling in order to ensure energy and cost savings, alleviate PAR, reduce CO 2 emissions, and improve user comfort. • In [20]- [22] the electricity cost and peak demand load ratio are formulated as an optimization problem, whereas, in this paper in addition to electricity cost and PAR, CO 2 emissions and user-comfort are also formulated and investigated by solving the DSM optimization problem via scheduling demand-side load of residential, commercial, and industrial service areas under pricebased DR programs. • A novel HBFPSO algorithm-based EMC is proposed to solve the DSM problem by optimally scheduling load of three service areas like residential, commercial, and industrial. ...
... The cost of operating these commercial appliances over a time period T can be calculated using (20), ...
The development of advanced metering infrastructure (AMI) in smart grid (SG) had enabled consumers to participate in demand-side management (DSM) using the price-based demand response (DR) programs offered by the distribution companies (DISCO). This way, not only the consumers minimize their electricity bills and discomfort, but also the DISCOs can handle peak power demand and reduce the carbon (CO
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) emissions in a controlled manner. Building an optimization framework that will minimize cost, peak demand, waiting time, and CO
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emission is not only a challenging task but also a concern of DSM. Most analyses are based on cost and peak-to-average ratio (PAR) minimization, but the effectiveness of the DSM framework is equally determined by user comfort and CO
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emission. Considering only one objective (cost) or two objectives (cost and PAR) is not sufficient. Thus, for DSM framework to achieve these four relatively independent objectives at the same time, minimized cost, PAR, CO
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emission, and user discomfort, an energy management controller (EMC) based on our proposed algorithm hybrid bacterial foraging and particle swarm optimization (HBFPSO) is employed that return optimal power usage schedule for consumers. A novel DSM framework consists of four units: (i) DISCO, (ii) multi-layer perceptron (MLP) based forecast engine, (iii) AMI, and (iv) demand-side energy management modules is successfully developed in this work. To validate the proposed model, extensive simulations are conducted and results are compared with the benchmark models like genetic algorithm (GA), bacterial foraging optimization algorithm (BFOA), binary particle swarm optimization (BPSO), and a hybrid combination of genetic and binary particle swarm optimization (GBPSO) in terms of electricity cost, PAR, user comfort, and CO
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emissions. The simulation results demonstrate effectiveness of our proposed model to outperform all the benchmark models in optimizing the consumer and DISCO objectives. The proposed scheme has reduced electricity cost, user discomfort, PAR, and CO
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emission for the residential sector by 15.14%, 4.6%, 61.6%, and 52.86% in scenario 1, 62.60%, 4.56%, 60.77%, and 27.77% in scenario 2, and 26.03%, 4.54%, 63.78%, and 23.02% in scenario 3, as compared to without an EMC. Similarly, for commercial sector the proposed HBFPSO algorithm reduces electricity cost, user discomfort, PAR, and CO
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emission by 11.31%, 5.5%, 60.9%, and 38.18% in scenario 1, 64.9%, 5.56%, 44.08%, and 58.8% in scenario 2, 15.31%, 5.26%, 78.22%, and 15.58% in scenario 3. Likewise, the proposed algorithm also has superior performance for the industrial sector for all the three scenarios.
... This nature-inspired algorithm is one of the fastest optimization algorithms with respect to convergence speed for solving problems. When all the people of a population are close to the desired value, then the population diversity disappears and it is located in the local optimum and the best solution is obtained (Bayraktar et al., 2010;Naseem et al., 2016). Fig. 9 shows the flowchart of the WDO algorithm. ...
Determining the properties of hydrocarbons, especially density, is one of the most important measures in the oil and gas industry. In this study, robust artificial intelligence techniques have been investigated to predict the density of pure and binary mixtures of normal alkanes (between C1 and C44). Since there are various conditions in oil and gas reservoirs, it has been tried to study the properties of different hydrocarbons in a wide range of temperature (up to 522 K) and pressure (up to 275 MPa). An extensive databank, including 2143 and 985 data points for pure and binary mixtures of normal alkanes, respectively, was extracted from the literature. The crow search algorithm (CSA), firefly algorithm (FFA), grey wolf optimization (GWO), and wind-driven optimization (WDO) algorithms were utilized to improve the learning process of least square support vector machine (LSSVM) and radial basis function neural network (RBFNN) models, which were developed to predict the density of hydrocarbons. Also, gene expression programming (GEP) correlations were presented using complex mathematical calculations to estimate the density of hydrocarbons. The results obtained from the models were also compared with seven equations of state (EOSs). The obtained results showed that the predictions of the proposed techniques are in a great match with the experimental data. By performing a comparison on the models’ outcomes, LSSVM-GWO and RBFNN-CSA were found to be the most accurate models for pure and binary mixtures of normal alkanes with overall average absolute percent relative error (AAPRE) values of 0.0622% and 0.0098%, respectively. It is noteworthy that the GEP correlations with the AAPRE values of 0.1955% and 0.3525% for pure and binary mixtures of normal alkanes, respectively, have a high accuracy compared to the equations of state and are suitable practical correlations for estimating the density of hydrocarbons.
... In [20,21,22] the electricity cost and peak demand load ratio are formulated as an optimization problem, whereas, in this paper in addition to electricity cost and PAR, CO 2 emissions and user-comfort are also formulated and investigated by solving the DSM optimization problem via scheduling demand-side load of residential, commercial, and industrial service areas under price-based DR programs. ...
... This study aims to mitigate peak load demand and cost of electricity simultaneously. A novel WDO algorithm is developed for solving household appliances scheduling in References [44,45]. The EMCs employed based on the WDO algorithm and its variants are for the purpose to minimize the cost of electricity and UC in terms of waiting time. ...
There will be a dearth of electrical energy in the prospective world due to exponential increase in electrical energy demand of rapidly growing world population. With the development of internet-of-things (IoT), more smart devices will be integrated into residential buildings in smart cities that actively participate in electricity market via demand response (DR) programs to efficiently manage energy in order to meet this increasing energy demand. Thus, with this incitement, an energy management strategy using price-based DR program is developed for IoT-enabled residential buildings. We propose a wind-driven bacterial foraging algorithm (WBFA), which is a hybrid of wind-driven optimization (WDO) and bacterial foraging optimization (BFO) algorithms. Subsequently, we devised a strategy based on our proposed WBFA to systematically manage the power usage of IoT-enabled residential building smart appliances by scheduling to alleviate peak-to-average ratio (PAR), minimize cost of electricity, and maximize user comfort (UC). This increases effective energy utilization, which in turn increases the sustainability of IoT-enabled residential buildings in smart cities. The WBFA-based strategy automatically responds to price-based DR programs to combat the major problem of the DR programs, which is the limitation of consumer’s knowledge to respond upon receiving DR signals. To endorse productiveness and effectiveness of the proposed WBFA-based strategy, substantial simulations are carried out. Furthermore, the proposed WBFA-based strategy is compared with benchmark strategies including binary particle swarm optimization (BPSO) algorithm, genetic algorithm (GA), genetic wind driven optimization (GWDO) algorithm, and genetic binary particle swarm optimization (GBPSO) algorithm in terms of energy consumption, cost of electricity, PAR, and UC. Simulation results show that the proposed WBFA-based strategy outperforms the benchmark strategies in terms of performance metrics.
... Simulation results show that the all the designed models proved their efficacy in increasing the reliability of Smart Grid. A home energy management system by using different heuristic algorithms is proposed in [20] for scheduling of home appliances to reduce electricity consumption cost and peak to average ratio. ...
Smart Gird is a technology that has brought many advantages with its evolution. Smart Grid is indispensable as it will lead us towards environmentally sustainable economic growth. Home energy management in Smart Grid is a hot research topic now a days. It aims at reducing the energy cost of users, gaining energy self-reliance and decreasing Greenhouse gas emissions. Renewable energy technologies nowadays are best suitable for off grid services without having to build extensive and complicated infrastructure. With the advent of Smart Grid (SG), the occupants have the opportunity to integrate with renewable energy sources (RESs) and to actively take part in demand side Management (DSM). This review paper is comprehensive study of various optimization techniques and their implementation with respect to electricity cost diminution, load balancing, power consumption and user's comfort maximization etc. for Home Energy Management in Smart Grid. This paper summarizes recent trends of energy usage from hybrid renewable energy integrated sources. It discusses several methodologies and techniques for hybrid renewable energy system optimization.
... Power optimization and electricity cost minimization strategy is proposed using GA for residential area in [35]. In [36], a HEMS is designed by using BFOA, GA, BPSO, and WDO to reduce monetary cost of the energy consumed by consumers and PAR. However, they ignored electricity customer satisfaction. ...
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.
... For optimizing the results, regrouping particle swarm optimization (RegPSO) is utilized by the authors. To reduce the PAR and energy cost, Naseem et al. [36] scheduled the residential loads by four different heuristic optimization techniques i.e. GA, Binary Particle Swarm Optimization (BPSO), wind driven optimization (WDO) and Bacterial Forging Optimization Algorithm (BFOA). ...
Development of smart grid technology provides an opportunity to various consumers in context for scheduling their energy utilization pattern by themselves. The main aim of this whole exercise is to minimize energy utilization and reduce the peak to average ratio (PAR) of power. The two way flow of information between electric utilities and consumers in smart grid opened new areas of applications. The main component is this management system is energy management controller (EMC), which collects demand response (DR) i.e. real time energy price from various appliances through the home gateway (HG). An optimum energy scheduling pattern is achieved by EMC through the utilization of DR information. This optimum energy schedule is provided to various appliances via HG. The rooftop photovoltaic system used as local generation micro grid in the home and can be integrated to the national grid. Under such energy management scheme, whenever solar generation is more than the home appliances energy demand, extra power is supplied back to the grid. Consequently, different appliances in consumer premises run in the most efficient way in terms of money. Therefore this work provides the comprehensive review of different smart home appliances optimization techniques, which are based on mathematical and heuristic one.
... For optimizing the results, regrouping particle swarm optimization (RegPSO) is utilized by the authors. To reduce the PAR and energy cost, Naseem et al. [36] scheduled the residential loads by four different heuristic optimization techniques i.e. GA, Binary Particle Swarm Optimization (BPSO), wind driven optimization (WDO) and Bacterial Forging Optimization Algorithm (BFOA). ...
Development of smart grid technology provides an opportunity to various consumers in context for scheduling
their energy utilization pattern by themselves. The main aim of this whole exercise is to minimize energy utilization
and reduce the peak to average ratio (PAR) of power. The two way flow of information between electric utilities
and consumers in smart grid opened new areas of applications. The main component is this management system is
energy management controller (EMC), which collects demand response (DR) i.e. real time energy price from various
appliances through the home gateway (HG). An optimum energy scheduling pattern is achieved by EMC through the
utilization of DR information. This optimum energy schedule is provided to various appliances via HG. The rooftop
photovoltaic system used as local generation micro grid in the home and can be integrated to the national grid. Under
such energy management scheme, whenever solar generation is more than the home appliances energy demand, extra
power is supplied back to the grid. Consequently, different appliances in consumer premises run in the most efficient
way in terms of money. Therefore this work provides the comprehensive review of different smart home appliances
optimization techniques, which are based on mathematical and heuristic one.
... We propose a Genetic BPSO (GBPSO) algorithm to solve load management problem. This hybrid technique incorporates the functionalities of GA and BPSO to create new individuals (Naseem et al., 2016). In GBPSO, we modify the method of updating position by using genetic operators (crossover and mutation) to further improve the performance of BPSO. ...
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.
Present advancements in the power systems paved way for introducing the smart grid (SG). A smart grid is beneficial to consumers which enables the bi-directional flow of information between the utility and customer. Demand-side management (DSM) techniques are crucial as load-side management techniques to attain the better stability of the grid. Home energy management systems (HEMS) play a indispensable part in the DSM. Countless traditional optimization techniques are utilized to implement HEMS, but the limitations of traditional Math heuristic methods gave rise to a concept-based optimization techniques called the Meta heuristic methods. Recent advancements introduced smart optimization techniques powered by Artificial Intelligence (AI). This article elucidates the applications of AI-based optimization techniques and their advantages over other methods. Various Machine learning (ML) and Deep Learning (DL) algorithms and their utilization for HEMS are discussed in brief.
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.
The smart grid is widely considered to be the informationization of the power grid. As an essential characteristic of the smart grid, demand response can reschedule the users’ energy consumption to reduce the operating expense from expensive generators, and further to defer the capacity addition in the long run. This survey comprehensively explores four major aspects: 1) programs; 2) issues; 3) approaches; and 4) future extensions of demand response. Specifically, we first introduce the means/tariffs that the power utility takes to incentivize users to reschedule their energy usage patterns. Then we survey the existing mathematical models and problems in the previous and current literatures, followed by the state-of-the-art approaches and solutions to address these issues. Finally, based on the above overview, we also outline the potential challenges and future research directions in the context of demand response.
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.
Demand-side management, together with the integration of distributed energy storage have an essential role in the process of improving the efficiency and reliability of the power grid. In this paper, we consider a smart power system in which users are equipped with energy storage devices. Users will request their energy demands from an energy provider who determines their energy payments based on the load profiles of users. By scheduling the energy consumption and storage of users regulated by a central controller, the energy provider tries to minimize the square Euclidean distance between the instantaneous energy demand and the average demand of the power system. The users intend to reduce their energy payment by jointly scheduling their appliances and controlling the charging and discharging process for their energy storage devices. We apply game theory to formulate the energy consumption and storage game for the distributed design, in which the players are the users and their strategies are the energy consumption schedules for appliances and storage devices. Based on the game theory setup and proximal decomposition, we also propose two distributed demand side management algorithms executed by users in which each user tries to minimize its energy payment, while still preserving the privacy of users as well as minimizing the amount of required signaling with the central controller. In simulation results, we show that the proposed algorithms provide optimality for both energy provider and users.
In recent years the use of several new resources in power systems, such as distributed generation, demand response and more recently electric vehicles, has significantly increased. Power systems aim at lowering operational costs, requiring an adequate energy resources management. In this context, load consumption management plays an important role, being necessary to use optimization strategies to adjust the consumption to the supply profile. These optimization strategies can be integrated in demand response programs. The control of the energy consumption of an intelligent house has the objective of optimizing the load consumption. This paper presents a genetic algorithm approach to manage the consumption of a residential house making use of a SCADA system developed by the authors. Consumption management is done reducing or curtailing loads to keep the power consumption in, or below, a specified energy consumption limit. This limit is determined according to the consumer strategy and taking into account the renewable based micro generation, energy price, supplier solicitations, and consumers' preferences. The proposed approach is compared with a mixed integer non-linear approach.
Bacterial foraging optimization algorithm (BFOA) has been widely accepted as a global optimization algorithm of current interest
for distributed optimization and control. BFOA is inspired by the social foraging behavior of Escherichia coli. BFOA has already drawn the attention of researchers because of its efficiency in solving real-world optimization problems
arising in several application domains. The underlying biology behind the foraging strategy of E.coli is emulated in an extraordinary manner and used as a simple optimization algorithm. This chapter starts with a lucid outline
of the classical BFOA. It then analyses the dynamics of the simulated chemotaxis step in BFOA with the help of a simple mathematical
model. Taking a cue from the analysis, it presents a new adaptive variant of BFOA, where the chemotactic step size is adjusted
on the run according to the current fitness of a virtual bacterium. Nest, an analysis of the dynamics of reproduction operator
in BFOA is also discussed. The chapter discusses the hybridization of BFOA with other optimization techniques and also provides
an account of most of the significant applications of BFOA until date.
Many areas in power systems require solving one or more nonlinear optimization problems. While analytical methods might suffer from slow convergence and the curse of dimensionality, heuristics-based swarm intelligence can be an efficient alternative. Particle swarm optimization (PSO), part of the swarm intelligence family, is known to effectively solve large-scale nonlinear optimization problems. This paper presents a detailed overview of the basic concepts of PSO and its variants. Also, it provides a comprehensive survey on the power system applications that have benefited from the powerful nature of PSO as an optimization technique. For each application, technical details that are required for applying PSO, such as its type, particle formulation (solution representation), and the most efficient fitness functions are also discussed.
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.
The Bacterial Foraging Optimization Algorithm is a swarm intelligence optimization algorithm. This paper first analyzes the chemotaxis, as well as elimination and dispersal operation, based on the basic Bacterial Foraging Optimization Algorithm. The elimination and dispersal operation makes a bacterium which has found or nearly found an optimal position escape away from that position, which greatly affects the convergence speed of the algorithm. In order to avoid this escape, the sphere of action of the elimination and dispersal operation can be altered in accordance with the generations of evolution. Secondly, we put forward an algorithm of an adaptive adjustment of step length we called improved bacterial foraging optimization (IBFO) after making a detailed analysis of the impacts of the step length on the efficiency and accuracy of the algorithm, based on chemotaxis operation. The classic test functions show that the convergence speed and accuracy of the IBFO algorithm is much better than the original algorithm.
The elastic behavior of the demand consumption jointly used with other available resources such as distributed generation (DG) can play a crucial role for the success of smart grids. The intensive use of Distributed Energy Resources (DER) and the technical and contractual constraints result in large-scale non linear optimization problems that require computational intelligence methods to be solved. This paper proposes a Particle Swarm Optimization (PSO) based methodology to support the minimization of the operation costs of a virtual power player that manages the resources in a distribution network and the network itself. Resources include the DER available in the considered time period and the energy that can be bought from external energy suppliers. Network constraints are considered. The proposed approach uses Gaussian mutation of the strategic parameters and contextual self-parameterization of the maximum and minimum particle velocities. The case study considers a real 937 bus distribution network, with 20310 consumers and 548 distributed generators. The obtained solutions are compared with a deterministic approach and with PSO without mutation and Evolutionary PSO, both using self-parameterization.
Task scheduling is a critical issue in achieving high performance in grid computing. Bacterial foraging optimization is an algorithm that has its thrust on the group foraging behaviour of Escherichia coli (E-Coli) present in the human intestine and has been used to solve optimization problems and is applied to task scheduling. BFO has been hybridized with different algorithms in order to examine its local and global search properties separately in a multi processing environment. This paper presents a broad overview on the formalization of works carried out on task scheduling by BFO and its hybridized variants.
This paper presents mathematical optimization models of residential energy hubs which can be readily incorporated into automated decision making technologies in smart grids, and can be solved efficiently in a real-time frame to optimally control all major residential energy loads, storage and production components while properly considering the customer preferences and comfort level. Novel mathematical models for major household demand, i.e., fridge, freezer, dishwasher, washer and dryer, stove, water heater, hot tub, and pool pumps are formulated. Also, mathematical models of other components of a residential energy system including lighting, heating, and air-conditioning are developed, and generic models for solar PV panels and energy storage/generation devices are proposed. The developed mathematical models result in Mixed Integer Linear Programming (MILP) optimization problems with the objective functions of minimizing energy consumption, total cost of electricity and gas, emissions, peak load, and/or any combination of these objectives, while considering end-user preferences. Several realistic case studies are carried out to examine the performance of the mathematical model, and experimental tests are carried out to find practical procedures to determine the parameters of the model. The application of the proposed model to a real household in Ontario, Canada is presented for various objective functions. The simulation results show that savings of up to 20% on energy costs and 50% on peak demand can be achieved, while maintaining the household owner's desired comfort levels.
Various forms of demand side management (DSM) programs are being deployed by utility companies for load flattening amongst the residential power users. These programs are tailored to offer monetary incentives to electricity customers so that they voluntarily consume electricity in an efficient way. Thus, DSM presents households with numerous opportunities to lower their electricity bills. However, systems that combine the various DSM strategies with a view to maximizing energy management benefits have not received sufficient attention. This study therefore proposes an intelligent energy management framework that can be used to implement both energy storage and appliance scheduling schemes. By adopting appliance scheduling, customers can realize cost savings by appropriately scheduling their power consumption during the low peak hours. More savings could further be achieved through smart electricity storage. Power storage allows electricity consumers to purchase power during off-peak hours when electricity prices are low and satisfy their demands when prices are high by discharging the batteries. For optimal cost savings, the customers must constantly monitor the price fluctuations in order to determine when to switch between the utility grid and the electricity storage devices. However, with a high penetration of consumer owned storage devices, the charging of the batteries must be properly coordinated and appropriately scheduled to avoid creating new peaks. This paper therefore proposes an autonomous smart charging framework that ensures both the stability of the power grid and customer savings.
Energy storage is traditionally well established in the form of large scale pumped-hydro systems, but nowadays is finding increased attraction in medium and smaller scale systems. Such expansion is entirely complementary to the forecasted wider integration of intermittent renewable resources in future electrical distribution systems (Smart Grids). This paper is intended to offer a useful tool for analyzing potential advantages of distributed energy storages in Smart Grids with reference to both different possible conceivable regulatory schemes and services to be provided. The Smart Grid Operator is assumed to have the ownership and operation of the energy storage systems, and a new cost-based optimization strategy for their optimal placement, sizing and control is proposed. The need to quantify benefits of both the Smart Grid where the energy storage devices are included and the external interconnected grid is explored. Numerical applications to a Medium Voltage test Smart Grid show the advantages of using storage systems related to different options in terms of incentives and services to be provided.
Interruptible loads represent highly valuable demand side resources within the electricity industry. However, maximizing their potential value in terms of system security and scheduling is a considerable challenge because of their widely varying and potentially complex operational characteristics. This paper investigates the use of binary particle swarm optimization (BPSO) to schedule a significant number of varied interruptible loads over 16 h. The scheduling objective is to achieve a system requirement of total hourly curtailments while satisfying the operational constraints of the available interruptible loads, minimizing the total payment to them and minimizing the frequency of interruptions imposed upon them. This multiobjective optimization problem was simplified by using a single aggregate objective function. The BPSO algorithm proved capable of achieving near-optimal solutions in manageable computational time-frames for this relatively complex, nonlinear and noncontinuous problem. The effectiveness of the approach was further improved by dividing the swarm into several subswarms. The proposed scheduling technique demonstrated useful performance for a relatively challenging scheduling task, and would seem to offer some potential advantages in scheduling significant numbers of widely varied and technically complex interruptible loads.
Sambarta Dasgupta, and Ajith Abraham “Bacterial foraging optimization algorithm: theoretical foundations, analysis, and applications
- Swagatam Das
- Arijit Biswas
LingfengWang “Smart charging and appliance scheduling approaches to demand side management” Electrical Power Systems
- O Chrostopher
- Adika
An Efficient Power Scheduling Scheme for Residential Load Management in Smart Homes
- Muhammad Rasheed
- Nadeem Babar
- Ashfaq Javaid
- Zahoor Ali Ahmad
- Umar Khan
- Qasim
Optimal integration of distributed energy storage devices in smart grids
- Guido Carpinelli
- Shahab Khormali
- Fabio Mottola
- Daniela Proto
Smart charging and appliance scheduling approaches to demand side management
- O Chrostopher
- Lingfeng Adika
- Wang
Chrostopher O. Adika, Lingfeng Wang "Smart charging and appliance scheduling approaches
to demand side management" Electrical Power Systems 2014, 57, 232-240.