Conference Paper

Heuristic Algorithm Based Energy Management System in Smart Grid

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Abstract

Smart grid is one of the most advanced technologies which plays a key role in maintaining balance between demand and supply by implementing demand response (DR). Residential users basically effect the overall performance of traditional grid due to maximum requirement of their energy demand. Home energy management (HEM) benefit the end user by monitoring, managing and controlling their energy consumption. Appliance scheduling is integral part of HEM as it manages energy demand according to supply by automatically controlling the appliances or by shifting the load from peak to off peak hours. Recently different techniques based on artificial intelligence (AI) are used to meet these objectives. In this research work, we evaluate the performance of HEM which is designed on the basis of heuristic algorithms; wind driven optimization (WDO), ganetic algorithm (GA) and binary particle swarm optimisation (BPSO). Finally, simulations are conducted in MATLAB to validate the performance of scheduling techniques in terms of cost, reduced peak to average ratio (PAR) and equally distributed energy consumption pattern. The simulation results prove that WDO algorithm based HEM proves to perform efficiently than BPSO and GA.

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... At the HEMS level, GAs are used to find the optimal switching time of each appliances. In this case, a population's individual x i is constituted by a set of binary values (x i ¼ fx i1 ;x i2 ;…;x iJ g) stating if the corresponding appliance is on or off at the considered time j [136,137], as explained below. For retailers' or aggregators' price scheme optimisation, GA usually consider individuals that consist in a set of prices p i ¼ fp i1 ;p i2 ;…;p iJ g, with p ij the price for the j th period of the day [130,134,[138][139][140]. ...
... PSO is an iterative process, where a population (swarm) of particles is randomly determined during the first step of the iteration by choosing the x 0 ijp values randomly for each particle p (or x 0 ijp can be initialised using the result of the optimisation of a simplified problem using Mixed Integer Linear Programming [161]). In parallel with the choice of the swarm's particles initial position (x 0 ijp ), the aggregator determines the utility function he wants to minimise, which is often given by the cost: c ¼ P time j P assets i p j ⋅P i ⋅x ij in the case where x ij is a binary variable, but it could also be a multi-objective function that also integrates the Peak to Average Ratio [136,[157][158][159]. ...
... In Herath et al. [165] the CLONALG-based [168] Artificial Immune System (AIS) algorithm, derived from the processes found in biological immune systems [169], is used to determine the aggregators' pricing scheme. Developed on the annealing concept, 7 the simulated annealing method is employed for DR in Spinola et al. [86], and the Wind Driven Optimisation (WDO) algorithm [136], which is based on atmospheric motion, is used to determine an optimal scheduling of appliances at the household level. ...
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... . O sistema de energia moderno incorporadoà rede inteligente gerencia a demanda de eletricidade selecionando as prioridades do usuário por meio da comunicação nos dois sentidos. Em Smart Grid, o uso extensivo de comunicação e automação tornam inteligentes tanto a rede de distribuição de energia quanto a Demand Side Management (DSM), que consiste nos usuários da energia elétrica realizar ações afim de equilibrar a demanda, principalmente em momentos de pico (Rehman et al., 2016). ...
... Segundo (Rehman et al., 2016), no cenário de Smart Home, as técnicas de otimização convencionais, por exemplo, programação linear (Yong e Choi, 2014), programação não linear (Yong e Choi, 2014), programação convexa (Richard e Pistikopoulos, 2016), são muito lentas em relaçãoà taxa de convergência e tornam-se muito demoradas quando o número de aparelhos faz parte da resposta da demanda, do inglês Demand Response (DR), tornando-as inviáveis para o uso diário. Contudo, técnicas modernas baseadas em heurística, como PSO (Particle Swarm Optimization), ACO (Ant Colony optimization) (Raka et al., 2016) e GA (Genetic Algorithm) (et al, 2013) superaram essas desvantagens. ...
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... Scheduling and control of loads constitute the second characteristic. Here, we note that significantly more algorithms are developed at the consumer level [63][64][65] than at the aggregator level [66,67], especially with the aim of reducing energy costs and energy consumption [24]. Another characteristic of data usage comprises the design of pricing or incentive schemes, which both affect the success of the DR scheme [68][69][70][71][72]. ...
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... The implementation results using PSO confirm a performance enhancement with a better energy efficiency along with the flexible adjustment of mixing energy rate. The use of SI for energy management is also investigated in [201]. Here, a binary PSO algorithm is developed to implement the home energy management scheduling by following the energy consumption patterns of customers in a fashion the total power cost is minimized while satisfying user comfort. ...
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... The implementation results using PSO confirm a performance enhancement with a better energy efficiency along with the flexible adjustment of mixing energy rate. The use of SI for energy management is also investigated in [201]. Here, a binary PSO algorithm is developed to implement the home energy management scheduling by following the energy consumption patterns of customers in a fashion the total power cost is minimized while satisfying user comfort. ...
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... For example, AI systems and the IoT combined with 'smart' capabilities, such as domestic energy management to automatically control appliances (e.g. Rehman et al., 2016) and big data analysis, could enable energy systems to transition from supplying energy to matching demand, reinforcing the growing demand for electrical energy (see Cluster 3). According to some authors, these technologies could lead to progress in the development of smart grids and more efficient networks, supporting the uptake of renewables and enhancing efficiency, resulting in a continuous joint optimisation of demand and supply in all walks of life (e.g. ...
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... A PSO is a swarm intelligence algorithm that is frequently implemented for the optimal scheduling of the HEMS [25]. Rehman et al. developed a binary PSO (BPSO) algorithm in Rehman et al. [26]. Their proposed algorithm automatically controlled the appliance scheduling by shifting the consumers' load from a high-pricing time to a low-pricing time. ...
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... To solve these issues they worked on a performance of following metrics: reduced cost, PAR and energy consumption by using TOU. Different heuristic algorithms WDO, BPSO and GA are used by [10], to evaluate the results for this purpose. It is proved that GA algorithm outperforms by achieving objectives as compared to other heuristic algorithms. ...
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Demand response (DR) for residential and small commercial buildings is estimated to account for as much as 65% of the total energy savings potential of DR, and previous work shows that a fully automated Energy Management System (EMS) is a necessary prerequisite to DR in these areas. In this paper, we propose a novel EMS formulation for DR problems in these sectors. Specifically, we formulate a fully automated EMS's rescheduling problem as a reinforcement learning (RL) problem (referred to as the device based RL problem), and show that this RL problem decomposes over devices under reasonable assumptions. Compared with existing formulations, our new formulation (1) does not require explicitly modeling the user's dissatisfaction on job rescheduling, (2) enables the EMS to self-initiate jobs, (3) allows the user to initiate more flexible requests and (4) has a computational complexity linear in the number of devices. We also propose several new performance metrics for RL algorithms applied to the device based RL problem, and demonstrate the simulation results of applying Q-learning, one of the most popular and classical RL algorithms, to a representative example.
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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.
Article
This paper introduces optimized operation of household appliances in a Demand-Side Management (DSM) based simulation tool. DSM can be defined as the implementation of policies and measures to control, regulate, and reduce energy consumption. The principal purpose of the simulation tool is to illustrate customer-driven DSM operation, and evaluate an estimate for home electricity consumption while minimizing the customer's cost. An optimization algorithm i.e. Binary Particle Swarm Optimization (BPSO) is used for optimizing the DSM operation of the tool. The tool also simulates the operation of household appliances as a Hybrid Renewable Energy System (HRES). The resource management technique is implemented using an optimization algorithm, i.e. Particle Swarm Optimization (PSO), which determines the distribution of energy obtained from various sources depending on the load. The validity of the tool is illustrated through an example case study for various household situations.
Conference Paper
Particle Swarm Optimization (PSO) is a relatively recent heuristic search method whose mechanics are inspired by the swarming or collaborative behavior of biological populations. PSO is similar to the Genetic Algorithm (GA) in the sense that these two evolutionary heuristics are population-based search methods. In other words, PSO and the GA move from a set of points (population) to another set of points in a single iteration with likely improvement using a combination of deterministic and probabilistic rules. The GA and its many versions have been popular in academia and the industry mainly because of its intuitiveness, ease of implementation, and the ability to effectively solve highly nonlinear, mixed integer optimization problems that are typical of complex engineering systems. The drawback of the GA is its expensive computational cost. This paper attempts to examine the claim that PSO has the same effectiveness (finding the true global optimal solution) as the GA but with significantly better computational efficiency (less function evaluations) by implementing statistical analysis and formal hypothesis testing. The performance comparison of the GA and PSO is implemented using a set of benchmark test problems as well as two space systems design optimization problems, namely, telescope array configuration and spacecraft reliability-based design.
Conference Paper
In this paper, we consider a smart power infrastructure, where several subscribers share a common energy source. Each subscriber is equipped with an energy consumption controller (ECC) unit as part of its smart meter. Each smart meter is connected to not only the power grid but also a communication infrastructure such as a local area network. This allows two-way communication among smart meters. Considering the importance of energy pricing as an essential tool to develop efficient demand side management strategies, we propose a novel real-time pricing algorithm for the future smart grid. We focus on the interactions between the smart meters and the energy provider through the exchange of control messages which contain subscribers' energy consumption and the real-time price information. First, we analytically model the subscribers' preferences and their energy consumption patterns in form of carefully selected utility functions based on concepts from microeconomics. Second, we propose a distributed algorithm which automatically manages the interactions among the ECC units at the smart meters and the energy provider. The algorithm finds the optimal energy consumption levels for each subscriber to maximize the aggregate utility of all subscribers in the system in a fair and efficient fashion. Finally, we show that the energy provider can encourage some desirable consumption patterns among the subscribers by means of the proposed real-time pricing interactions. Simulation results confirm that the proposed distributed algorithm can potentially benefit both subscribers and the energy provider.
Conference Paper
This paper introduces a novel nature-inspired global optimization technique, which we call Wind Driven Optimization. WDO is a population based iterative global optimization method, where the velocity and the position of wind controlled air parcels are updated based on the physical equations that govern atmospheric motion. The effectiveness of WDO as a tool in optimization problems is first shown with its performance on benchmark functions and then with its application to optimizing the design for a thin double-sided AMC surface at 10 GHz.
Article
In recent years, load management (LM) programs are introduced as an impressive option in all energy policy decisions. Under deregulation, the scope of LM programs has considerably been expanded to include demand response programs (DRPs). Basically, DRPs are divided into two main categories namely, incentive-based programs (IBPs) and time-based rate (TBR) programs. In this paper, an economic model of responsive loads is derived based upon price elasticity of demand and customers' benefit function. In order to investigate the economic-driven and environmental-driven measures of demand response programs, a new linearized formulation of cost-emission based unit commitment problem associated with DRPs (UCDR) is presented. Here, UCDR is modeled as a mixed-integer programming (MIP) problem. The proposed model is applied to determine loads provided by DRPs and schedule commitment status of generating units. Moreover, the optimum value of incentive as a crucial issue for implementing DRPs is derived. Several analyses are conducted to investigate the impact of some important factors such as elasticity on the UCDR problem. The strategy success index (SSI) is employed to prioritize DRPs from the independent system operator (ISO) perspective. The conventional 10-unit test system is used to demonstrate effectiveness of the proposed methodology.
Article
Demand side management (DSM) is one of the important functions in a smart grid that allows customers to make informed decisions regarding their energy consumption, and helps the energy providers reduce the peak load demand and reshape the load profile. This results in increased sustainability of the smart grid, as well as reduced overall operational cost and carbon emission levels. Most of the existing demand side management strategies used in traditional energy management systems employ system specific techniques and algorithms. In addition, the existing strategies handle only a limited number of controllable loads of limited types. This paper presents a demand side management strategy based on load shifting technique for demand side management of future smart grids with a large number of devices of several types. The day-ahead load shifting technique proposed in this paper is mathematically formulated as a minimization problem. A heuristic-based Evolutionary Algorithm (EA) that easily adapts heuristics in the problem was developed for solving this minimization problem. Simulations were carried out on a smart grid which contains a variety of loads in three service areas, one with residential customers, another with commercial customers, and the third one with industrial customers. The simulation results show that the proposed demand side management strategy achieves substantial savings, while reducing the peak load demand of the smart grid.
Conference Paper
In this paper we present particle swarm optimization with Gaussian mutation combining the idea of the particle swarm with concepts from evolutionary algorithms. This method combines the traditional velocity and position update rules with the ideas of Gaussian mutation. This model is tested and compared with the standard PSO and standard GA. The comparative experiments have been conducted on unimodal functions and multimodal functions. PSO with Gaussian mutation is able to obtain a result superior to GA. We also apply the PSO with Gaussian mutation to a gene network. Consequently, it has succeeded in acquiring better results than those by GA and PSO alone.
Article
Differential evolution (DE) has emerged as one of the fast, robust, and efficient global search heuristics of current interest. This paper describes an application of DE to the automatic clustering of large unlabeled data sets. In contrast to most of the existing clustering techniques, the proposed algorithm requires no prior knowledge of the data to be classified. Rather, it determines the optimal number of partitions of the data "on the run." Superiority of the new method is demonstrated by comparing it with two recently developed partitional clustering techniques and one popular hierarchical clustering algorithm. The partitional clustering algorithms are based on two powerful well-known optimization algorithms, namely the genetic algorithm and the particle swarm optimization. An interesting real-world application of the proposed method to automatic segmentation of images is also reported.
Genetic-algorithm-based optimization approach for energy management
  • Arabali Amirsaman
Genetic algorithm methodology applied to intelligent house control
  • Filipe Fernandes
Fernandes, Filipe, et al. "Genetic algorithm methodology applied to intelligent house control." Computational Intelligence Applications In Smart Grid (CIASG), 2011 IEEE Symposium on. IEEE, 2011.
Particle swarm optimization: basic concepts, variants and applications in power systems
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Wind Driven Optimization (WDO): A novel nature-inspired optimization algorithm and its application to electromagnetics
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Demand response simulation implementing heuristic optimization for home energy management
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