Intelligent Decision Support System for Energy Management in Demand Response Programs and Residential and Industrial Sectors of the Smart Grid

This PhD thesis addresses the complexity of the energy efficiency control problem in residential and industrial customers of Smart electrical Grid, and examines the main factors that affect energy demand, and proposes an intelligent decision support system for applications of demand response. A multi criteria decision making algorithm is combined with a combinatorial optimization technique to assist energy managers to decide whether to participate in demand response programs or obtain energy from distributed energy resources

A multi-agent intelligent decision making support system for home energy management in smart grid: A fuzzy TOPSIS approach

In the context of intelligent home energy management in smart grid, the occupants' consumption behavior has a direct effect on the demand and supply of the electrical energy market. Correspondingly, the policies of the utility providers affect consumption behavior so techniques and tools are required to analyse the occupants' preferences, habits and lifestyles in order to support and facilitate their decision-making regarding the curtailing of their energy consumption and costs. The uncertainty about householders' preferences increases the uncertainty of appliance prioritization and makes it difficult to determine the consistency of preferences in terms of energy consumption. In this complex system, the preferences and judgments of householders are represented by linguistic and vague patterns. This paper proposes a much better representation of this linguistics that can be developed and refined by using the evaluation methods of fuzzy set theory. The proposed approach will apply the fuzzy Technique for Order Preference by Similarity to Ideal Solution (fuzzy TOPSIS) for achieving preferences. Based on our detailed literature review of the multi-agent system approach in this field, it is expected that the proposal model will offer a robust tool for communication and decision-making between occupant agents and dynamic environmental variables. It is shown that the proposed fuzzy TOPSIS approach will enable and assist householders to maximize their participation in demand response programs

A Knapsack Problem Approach For Achieving Efficient Energy Consumption in Smart Grid for Endusers’ Life Style

In order to achieve an efficient energy consumption level in the residential sector of a smart grid, the end-users are equipped with various smart home energy controller technologies. The devices are provided to inform the consumers about their consumption pattern by showing or sending different kinds of consumptional information to them. This kind of information is provided to assist them in making decisions about altering their consumption behaviour or to urge them to modify their life style during peak hours. We propose that the energy home controllers should offer preferred and optimal scenarios to support end-users when making a decision about their consumption. Effective scenarios should emerge from consumer's life style and preferences. In this paper, we will apply AHP methodology to quantify the consumer's preferences for using appliances during peak periods when the price has increased, and use the Knapsack problem approach to achieve the optimal solution for managing the appliances. With this approach, not only will the cost of electricity not escalate during peak hours, but also user preferences, satisfaction and minimum change to current life style will be considered

Subjective Quality Assessments in Virtual Environments

Being able to make objective Quality of Service (QoS) judgments or assessments is a challenging and crucial activity. The process of making these assessments is compounded when the environment in which the assessments have to be made are virtual; in the sense the interacting parties might not have necessarily metwith each other physically. In a broad sense Quality of Service assessments could be broadly categorized into two areas, namely objective assessments and subjective assessments. In this paper, we propose a suite of metrics to carry out subjective quality assessment in a virtual environment

A fuzzy TOPSIS approach for home energy management in smart grid with considering householders' preferences

It is expected that demand response programs will be designed to decrease electricity consumption or shift it from on-peak to off-peak periods depending on consumers' preferences and lifestyles. This paper demonstrates a fuzzy TOPSIS decision-making approach to quantify and evaluate consumers' preferences at the micro-level when using electrical devices according to a real-time price scheme of demand response in order to best manage the use of appliances. This enables and supports householders to maximize their participation in demand response programs

Versatile Energy Scheduler Compatible with Autonomous Demand Response for Home Energy Management in Smart Grid: A System of Systems Approach

The future home energy management system (HEMS) in smart grid will need to include contractual grid regulations imposed by the utility while also taking into consideration the domestic users’ comfort, preferences, budget, and security. The emerging autonomous demand response (ADR) programs have initiated steps in utilizing sophisticated software algorithms for the scheduling and optimization of HEMS. This paper proposes a system of systems approach as a versatile energy scheduling system that takes into account the components, characteristics and methodologies required for achieving an efficient level of energy consumption in the residential sector of the smart grid

Intelligent Decision Support System for Including Consumers’Preferences in Residential Energy Consumption Management in Smart Grid

Smart Grid is a novel initiative the aim of which is to deliver energy to the users and also to achieve consumption efficiency by means of two-way communication. The Smart Grid architecture is a combination of various hardware devices, management and reporting software tools that are combined within an ICT infrastructure. This infrastructure is needed to make the smart grid sustainable, creative and intelligent. One of the main goals of Smart Grid is to achieve Demand Response(DR) by increasing the end users' participation in decision making and increasing the awareness that will lead them to manage their energy consumption in an efficient way. Approaches proposed in the literature achieve demand response at the different levels of the Smart Grid, but no approach focuses on the users' point of view at the home level on a continuous basis and in an intelligent way to achieve demand response. In this paper, we develop such an approach by which demand response can be achieved on a continuous basis at the home level. To achieve this, the dynamic notion of price will be utilized to develop an intelligent decision-making model that will assist the users in achieving demand response