Demand Based Cost Optimization of Electric Bills for Household Users

Abstract- Internet of Things (IoT) is increasingly becoming the vehicle to automate, optimize and enhance the performance of systems in the energy, environment, and health sectors. In this paper, we use Wi-Fi wrapped sensors to provide online and in realtime the current energy consumptions at a device level, in a manner to allow for automatic control of peak energy consumption at a household, factory level, and eventually at a region level, where a region can be defined as an area supported by a distinct energy source. This allows to decrease the bill by avoiding heavily and controllable loads during high tariff slice and/or peak period per household and to optimize the energy production and distribution in a given region. The proposed model relies on adaptive learning techniques to help adjust the current load, while taking into consideration the actual and real need of the consumer. The experiments used in this study makes use of current and voltage sensors, Arduino platform, and simulation system. The main performance indexes used are the control of a peak consumption level, and the minimum time needed to adjust the distribution of load in the system. The system was able to keep the maximum load at a maximum of 10 kW in less than 10 seconds of response time. The level and response time are controllable parameters

Achieving an optimal trade-off between revenue and energy peak within a smart grid environment

We consider an energy provider whose goal is to simultaneously set revenue-maximizing prices and meet a peak load constraint. In our bilevel setting, the provider acts as a leader (upper level) that takes into account a smart grid (lower level) that minimizes the sum of users' disutilities. The latter bases its decisions on the hourly prices set by the leader, as well as the schedule preferences set by the users for each task. Considering both the monopolistic and competitive situations, we illustrate numerically the validity of the approach, which achieves an 'optimal' trade-off between three objectives: revenue, user cost, and peak demand

Mathematical optimization techniques for demand management in smart grids

The electricity supply industry has been facing significant challenges in terms of meeting the projected demand for energy, environmental issues, security, reliability and integration of renewable energy. Currently, most of the power grids are based on many decades old vertical hierarchical infrastructures where the electric power flows in one direction from the power generators to the consumer side and the grid monitoring information is handled only at the operation side. It is generally believed that a fundamental evolution in electric power generation and supply system is required to make the grids more reliable, secure and efficient. This is generally recognised as the development of smart grids. Demand management is the key to the operational efficiency and reliability of smart grids. Facilitated by the two-way information flow and various optimization mechanisms, operators benefit from real time dynamic load monitoring and control while consumers benefit from optimised use of energy. In this thesis, various mathematical optimization techniques and game theoretic frameworks have been proposed for demand management in order to achieve efficient home energy consumption scheduling and optimal electric vehicle (EV) charging. A consumption scheduling technique is proposed to minimise the peak consumption load. The proposed technique is able to schedule the optimal operation time for appliances according to the power consumption patterns of the individual appliances. A game theoretic consumption optimization framework is proposed to manage the scheduling of appliances of multiple residential consumers in a decentralised manner, with the aim of achieving minimum cost of energy for consumers. The optimization incorporates integration of locally generated and stored renewable energy in order to minimise dependency on conventional energy. In addition to the appliance scheduling, a mean field game theoretic optimization framework is proposed for electric vehicles to manage their charging. In particular, the optimization considers a charging station where a large number of EVs are charged simultaneously during a flexible period of time. The proposed technique provides the EVs an optimal charging strategy in order to minimise the cost of charging. The performances of all these new proposed techniques have been demonstrated using Matlab based simulation studies

Integrating Consumer Flexibility in Smart Grid and Mobility Systems - An Online Optimization and Online Mechanism Design Approach

Consumer flexibility may provide an important lever to align supply and demand in service systems. However, harnessing dispersed flexibility endowments in the presence of self-interested agents requires appropriate incentive structures. This thesis quantifies the potential value of consumers\u27 flexibility in smart grid and mobility systems. In order to include incentives, online optimization approaches are augmented with methods from online mechanism design

Review of trends and targets of complex systems for power system optimization

Optimization systems (OSs) allow operators of electrical power systems (PS) to optimally operate PSs and to also create optimal PS development plans. The inclusion of OSs in the PS is a big trend nowadays, and the demand for PS optimization tools and PS-OSs experts is growing. The aim of this review is to define the current dynamics and trends in PS optimization research and to present several papers that clearly and comprehensively describe PS OSs with characteristics corresponding to the identified current main trends in this research area. The current dynamics and trends of the research area were defined on the basis of the results of an analysis of the database of 255 PS-OS-presenting papers published from December 2015 to July 2019. Eleven main characteristics of the current PS OSs were identified. The results of the statistical analyses give four characteristics of PS OSs which are currently the most frequently presented in research papers: OSs for minimizing the price of electricity/OSs reducing PS operation costs, OSs for optimizing the operation of renewable energy sources, OSs for regulating the power consumption during the optimization process, and OSs for regulating the energy storage systems operation during the optimization process. Finally, individual identified characteristics of the current PS OSs are briefly described. In the analysis, all PS OSs presented in the observed time period were analyzed regardless of the part of the PS for which the operation was optimized by the PS OS, the voltage level of the optimized PS part, or the optimization goal of the PS OS.Web of Science135art. no. 107

Demand Side Management in the Smart Grid

Résumée L'objectif du présent projet est de développer des solutions pour améliorer l'efficacité énergétique dans les réseaux électriques. L'approche adoptée dans cette recherche est basée sur un concept nouveau dans le Smart Grids (réseaux électriques intelligents), l’optimisation du Demand/Response, qui permet la mise en œuvre de la gestion autonome de la demande de énergie pour une grande variété de consommateurs, des les maisons à les bâtiments, usines, centres commerciaux, les campus, les bases militaires, et même les micro-réseaux. La première partie de cette thèse présente le thème de la Smart Grid et évalue l'état de l'art par rapport aux portées du projet. Ensuite, nous introduisons une architecture pour la gestion autonome de la charge du côté de la demande. Cette architecture est composée par trois couches principales, dont deux, l’ordonnancement en ligne et l'ordonnancement au moindre coût, sont pleinement pris en compte, tandis que la troisième couche, la Demande/Response, est laissé comme une extension future. Une telle architecture tire profit de la séparation des des échelles de temps de la consommation d'énergie, et elle est évolutif et flexible. La deuxième partie de ce projet est axé sur la mise en œuvre de l'architecture proposée dans Matlab/Simulink, après une preuve de concept est donnée par des simulations et résultats expérimentaux. Mots-clés: programmation optimale de la charge, la charge de pointe de rasage, autonome Demand-Side Management (DSM), bâtiments intelligents, la demande / réponse, l'efficacité énergétique.----------ABSTRACT The objective of the present project is to develop solutions to improve energy eciency in electric grids. The basic approach adopted in this research is based on a new concept in the Smart Grid, namely Demand/Response Optimization, which enables the implementation of the autonomous demand side energy management for a big variety of consumers, ranging from homes to buildings, factories, commercial centers, campuses, military bases, and even micro-grids. The rst part of this thesis presents the topic of the Smart Grid and assesses the state of the art with respect to the scopes of the project. Afterward, we introduce an architecture for autonomous demand side load management composed of three main layers, of which two, online scheduling and minimum-cost scheduling, are fully addressed, while the third layer, Demand/Response, is left as future extension. Such architecture takes advantage of time-scale separation of energy consumption. It is scalable and exible. The second part of this project is focused on the implementation of the proposed architecture in Matlab/Simulink and a proof of concept is given through simulations and experimental results. Keywords: Optimal load scheduling, Peak-load shaving, Autonomous Demand-Side Man- agement (DSM), Smart Buildings, Demand/Response, Energy eciency