No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
Appliances Scheduling using Hybrid Scheme of Genetic Algorithm and Elephant Herd Optimization for Residential Demand Response
Abstract
The invention of Smart Grid (SG) have revolutionized the traditional electricity consumption pattern as well as
distribution. The technology of communication and information
have been involved in almost every domain so for Smart Grids.
Production of electricity is not cheap, hence with the help of
information and technology, Smart Meters (SM) play vital role
to control, manage and perform optimization to utilize the electric
power efficiently on consumer side and called Demand Side Management (DSM). In the proposed research paper, a Home Energy
Management System (HEMS) have been proposed to optimize the
home appliances to reduce the maximum cost. Elephant Herd
Optimization (EHO) algorithm have been implemented along
with our own hybrid of EHO algorithm. This EHO is hybrid
of Genetic Algorithm (GA) and called Genetic Elephant Herd
Optimization (GEHO). Results of simulation shows that GEHO
scheduled the appliance more efficiently to reduce maximum cost
when comparing with regular EHO and unscheduled schemes.
Peak to Average Ration (PAR) also have been observed. GEHO
and unscheduled have equal PAR due to scheduling of maximum
appliances on either sides but EHO have small PAR. For further
understanding of cost optimization different Operation Time
Interval (OTI) have been applied. Trends of load and cost with
all of three schemes have been discussed in detail.
ResearchGate has not been able to resolve any citations for this publication.
The smart grid plays a vital role in decreasing electricity cost through Demand Side
Management (DSM). Smart homes, a part of the smart grid, contribute greatly to minimizing electricity
consumption cost via scheduling home appliances. However, user waiting time increases due to
the scheduling of home appliances. This scheduling problem is the motivation to find an optimal
solution that could minimize the electricity cost and Peak to Average Ratio (PAR) with minimum user
waiting time. There are many studies on Home Energy Management (HEM) for cost minimization
and peak load reduction. However, none of the systems gave sufficient attention to tackle multiple
parameters (i.e., electricity cost and peak load reduction) at the same time as user waiting time
was minimum for residential consumers with multiple homes. Hence, in this work, we propose an
efficient HEM scheme using the well-known meta-heuristic Genetic Algorithm (GA), the recently
developed Cuckoo Search Optimization Algorithm (CSOA) and the Crow Search Algorithm (CSA),
which can be used for electricity cost and peak load alleviation with minimum user waiting time.
The integration of a smart Electricity Storage System (ESS) is also taken into account for more
efficient operation of the Home Energy Management System (HEMS). Furthermore, we took the
real-time electricity consumption pattern for every residence, i.e., every home has its own living
pattern. The proposed scheme is implemented in a smart building; comprised of thirty smart homes
(apartments), Real-Time Pricing (RTP) and Critical Peak Pricing (CPP) signals are examined in
terms of electricity cost estimation for both a single smart home and a smart building. In addition,
feasible regions are presented for single and multiple smart homes, which show the relationship
among the electricity cost, electricity consumption and user waiting time. Experimental results
demonstrate the effectiveness of our proposed scheme for single and multiple smart homes in terms
of electricity cost and PAR minimization. Moreover, there exists a tradeoff between electricity cost
and user waiting.
To avoid the complexity and time consumption of traditional statistical and mathematical programming, intelligent techniques have gained great attention in different financial research areas, especially in banking decisions’ optimization. However, choosing optimum bank lending decisions that maximize the bank profit in a credit crunch environment is still a big challenge. For that, this paper proposes an intelligent model based on the Genetic Algorithm (GA) to organize bank lending decisions in a highly competitive environment with a credit crunch constraint (GAMCC). GAMCC provides a framework to optimize bank objectives when constructing the loan portfolio, by maximizing the bank profit and minimizing the probability of bank default in a search for a dynamic lending decision. Compared to the state-of-the art methods, GAMCC is considered a better intelligent tool that enables banks to reduce the loan screening time by a range of 12%–50%. Moreover, it greatly increases the bank profit by a range of 3.9%–8.1%.
Energy saving is a hot topic due to the proliferation of climate changes and energy challenges globally. However, people's perception about using smart technology for energy saving is still in the concept stage. This means that people talk about environmental awareness readily, yet in reality, they accept to pay the given energy bill. Due to the availability of electricity and its integral role, modulating consumers' attitudes towards energy savings can be a challenge. Notably, the gap in today's smart technology design in smart homes is the understanding of consumers' behaviour and the integration of this understanding into the smart technology. As part of the Paris Climate change agreement (2015), it is paramount for Singapore to introduce smart technologies targeted to reduce energy consumption. This paper focused on the perception of Singapore households on smart technology and its usage to save energy. Areas of current research include: (1) energy consumption in Singapore households, (2) public programs and policies in energy savings, (3) use of technology in energy savings, and (4) household perception of energy savings in smart homes. Furthermore, three case studies are reviewed in relation to smart homes and smart technology, while discussing the maturity of existing solutions.
In the actual era of smart homes and smart grids, advanced technological systems that allow the automation of domestic tasks are developing rapidly. There are numerous technologies and applications that can be installed in smart homes today. They enable communication between home appliances and users, and enhance home appliances’ automation, monitoring and remote control capabilities. This review article, by introducing the concept of the smart home and the advent of the smart grid, investigates technologies for smart homes. The technical descriptions of the systems are presented and point out advantages and disadvantages of each technology and product today available on the market. Barriers, challenges, benefits and future trends regarding the technologies and the role of users have also been discussed.
Smart homes hold the potential for increasing energy efficiency, decreasing costs of energy use, decreasing the carbon footprint by including renewable resources, and transforming the role of the occupant. At the crux of the smart home is an efficient electric energy management system that is enabled by emerging technologies in the electricity grid and consumer electronics. This paper presents a discussion of the state of the art in electricity management in smart homes, the various enabling technologies that will accelerate this concept, and topics around consumer behavior with respect to energy usage.
In this paper, a communication-based load scheduling protocol is proposed for in-home appliances connected over a home area network. Specifically, a joint access and scheduling approach for appliances is developed to enable in-home appliances to coordinate power usage so that the total energy demand for the home is kept below a target value. The proposed protocol considers both “schedulable” appliances which have delay flexibility, and “critical” appliances which consume power as they desire. An optimization problem is formulated for the energy management controller to decide the target values for each time slot, by incorporating the variation of electricity prices and distributed wind power uncertainty. We model the evolution of the protocol as a two-dimensional Markov chain, and derive the steady-state distribution, by which the average delay of an appliance is then obtained. Simulation results verify the analysis and show cost saving to customers using the proposed scheme.
Smart and energy-efficient buildings have recently become a trend for future building industry. The major challenge in the control system design for such a building is to minimize the power consumption without compromising the customers comfort. For this purpose, a hierarchical multiagent control system with an intelligent optimizer is proposed in this study. Four types of agents, which are switch agent, central coordinator-agent, local controller-agent, and load agent, cooperate with each other to achieve the overall control goals. Particle swarm optimization (PSO) is utilized to optimize the overall system and enhance the intelligence of the integrated building and microgrid system. A Graphical User Interface (GUI) based platform is designed for customers to input their preferences and monitor the results. Two sets of case studies are carried out and corresponding simulation results are presented in this paper.
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.
This is the second part of a two-part paper that has arisen from the work of the IEEE Power Engineering Society's Multi-Agent Systems (MAS) Working Group. Part I of this paper examined the potential value of MAS technology to the power industry, described fundamental concepts and approaches within the field of multi-agent systems that are appropriate to power engineering applications, and presented a comprehensive review of the power engineering applications for which MAS are being investigated. It also defined the technical issues which must be addressed in order to accelerate and facilitate the uptake of the technology within the power and energy sector. Part II of this paper explores the decisions inherent in engineering multi-agent systems for applications in the power and energy sector and offers guidance and recommendations on how MAS can be designed and implemented. Given the significant and growing interest in this field, it is imperative that the power engineering community considers the standards, tools, supporting technologies, and design methodologies available to those wishing to implement a MAS solution for a power engineering problem. This paper describes the various options available and makes recommendations on best practice. It also describes the problem of interoperability between different multi-agent systems and proposes how this may be tackled.
Coordination of different distributed generation (DG) units is essential to meet the increasing demand for electricity. Many control strategies, such as droop control, master-slave control, and average current-sharing control, have been extensively implemented worldwide to operate parallel-connected inverters for load sharing in DG network. Among these methods, the droop control technique has been widely accepted in the scientific community because of the absence of critical communication links among parallel-connected inverters to coordinate the DG units within a microgrid. Thus, this study highlights the state-of-the-art review of droop control techniques applied currently to coordinate the DG units within a microgrid.
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.
Smart grid literature shows a potential way to implement smart grid using Multi-Agent System (MAS) which is a distributed computational intelligence technique that comprises of multiple interactive intelligent agents in an environment. This paper presents an application of MAS technique for improving the efficiency and optimizing the energy usage of smart homes. The smart appliances in a smart home are modeled as agents and optimization algorithms are used in decision making of the agents. These agents work together to reduce energy consumption while striking a balance between consumers comfort, energy cost and peak energy saving in the distribution grid. Such a way, a Home Energy Management System (HEMS) was designed and developed using MAS. This research enables smart homes to communicate, interact and negotiate with energy sources and devices in smart homes that achieve maximum overall energy efficiency and minimum electricity bill. Simulation studies carried out on the developed MAS have shown that it has the potential to provide the optimum solutions. How MAS communication influences on decision making of the agents and getting the optimum solution is also demonstrated in this paper.
In this paper, an algorithm for energy management system (EMS) based on multi-layer ant colony optimization (EMS-MACO) is presented to find energy scheduling in Microgrid (MG). The aim of study is to figure out the optimum operation of micro-sources for decreasing the electricity production cost by hourly day-ahead and real time scheduling. The proposed algorithm is based on ant colony optimization (ACO) method and is able to analyze the technical and economic time dependent constraints. This algorithm attempts to meet the required load demand with minimum energy cost in a local energy market (LEM) structure. Performance of MACO is compared with modified conventional EMS (MCEMS) and particle swarm optimization (PSO) based EMS. Analysis of obtained results demonstrates that the system performance is improved also the energy cost is reduced about 20% and 5% by applying MACO in comparison with MCEMS and PSO, respectively. Furthermore, the plug and play capability in real time applications is investigated by using different scenarios and the system adequate performance is validated experimentally too.
In this paper, a robust approach is developed to tackle the uncertainty of PV power output for load scheduling of smart homes integrated with household PV system. Specifically, a robust formulation is proposed and further transformed to an equivalent quadratic programming problem. Day-ahead load schedules with different robustness can be generated by solving the proposed robust formulation with different predefined parameters. The validity and advantage of the proposed approach has been verified by simulation results. Also, the effects of feed-in tariff and PV output have been evaluated.
Distributed power and energy resources are now being used to meet the combined electric power, heating, and cooling demands of many buildings. The addition of on-site renewables and their accompanying intermittency and non-coincidence requires even greater dynamic performance from the distributed power and energy system. Load following generators, energy storage devices, and predictive energy management are increasingly important to achieve the simultaneous goals of increased efficiency, reduced emissions, and sustainable economics. This paper presents two optimization strategies for the dispatch of a multi-chiller cooling plant with cold-water thermal storage. The optimizations aim to reduce both costs and emissions while considering real operational constraints of a plant. The UC Irvine campus micro-grid operation between January 2009 and December 2013 serves as a case study for how improved utilization of energy storage can buffer demand transients, reduce costs and improve plant efficiency. A predictive control strategy which forecasts campus demands from weather predictions, optimizes the plant dispatch, and applies feedback control to modify the plant dispatch in real-time is compared to best-practices manual operation. The dispatch optimization and predictive control algorithms are shown to reduce annual utility bill costs by 12.0%, net energy costs by 3.61%, and improve energy efficiency by 1.56%.
Optimization of energy use is a vital concept in providing solutions to many of the energy challenges in our world today. Large chemical, mechanical, pneumatic, hydraulic, and electrical systems require energy efficiency as one of the important aspects of operating systems. At the micro-scale, the built environment experiences progressive changes in energy consumption. There is steady increase in the annual energy consumption of households as a result of population growth, increase of economic activity and increased use of domestic appliances. The drive to reduce this growing trend has stimulated many mechanisms such as the use of energy labels on domestic appliances. Home appliance manufacturers research into developing energy-efficient products with the end-goal of making consumer devices more energy efficient. One important step towards energy efficiency and savings in the built environment is the introduction of Smart Home Energy Management Systems. (SHEMS). This paper analyses how the implementation of such solutions could contribute to energy savings and enhance efficiency for residential customers. The paper presents a general overview of energy efficiency measures focusing on residential loads. It further highlights, via modelling and simulation the potentials of Smart Home Energy Management Solutions for future domestic applications.
Modeling price elasticity of electricity demand using AIDS
- W Yang
W. Yang, et al., "Modeling price elasticity of electricity demand using
AIDS," in Innovative Smart Grid Technologies Conference (ISGT), 2014
IEEE PES, 2014, pp. 1-5
Home Appliances Coordination Scheme for Energy Management (HACS4EM) Using Wireless Sensor Networks in Smart Grids
- A Mahmood
A. Mahmood, et al., "Home Appliances Coordination Scheme for
Energy Management (HACS4EM) Using Wireless Sensor Networks in
Smart Grids," Procedia Computer Science, vol. 32, pp. 469-476, 2014.
Environmental Impacts and Benefits of Smart Home Automation: Life Cycle Assessment of Home Energy Management System
- J.-N Louis
J.-N. Louis, et al., "Environmental Impacts and Benefits of Smart Home
Automation: Life Cycle Assessment of Home Energy Management
System," IFAC-PapersOnline, vol. 48, pp. 880-885, 2015.
Implementation of demand side management of a smart home using multi-agent system
- Weixian Li
Li, Weixian, et al. "Implementation of demand side management of
a smart home using multi-agent system." Evolutionary Computation
(CEC), 2016 IEEE Congress on. IEEE, 2016.
- A Uc Irvine Case
- Study
A UC Irvine case study." International Journal of Electrical
Power & Energy Systems 65 (2015): 179-190.