Energy management system
About: Energy management system is a(n) research topic. Over the lifetime, 4805 publication(s) have been published within this topic receiving 70082 citation(s). The topic is also known as: EMS.
18 Nov 2010-IEEE Transactions on Smart Grid
TL;DR: This paper presents an autonomous and distributed demand-side energy management system among users that takes advantage of a two-way digital communication infrastructure which is envisioned in the future smart grid.
Abstract: Most of the existing demand-side management programs focus primarily on the interactions between a utility company and its customers/users. In this paper, we present an autonomous and distributed demand-side energy management system among users that takes advantage of a two-way digital communication infrastructure which is envisioned in the future smart grid. We use game theory and formulate an energy consumption scheduling game, where the players are the users and their strategies are the daily schedules of their household appliances and loads. It is assumed that the utility company can adopt adequate pricing tariffs that differentiate the energy usage in time and level. We show that for a common scenario, with a single utility company serving multiple customers, the global optimal performance in terms of minimizing the energy costs is achieved at the Nash equilibrium of the formulated energy consumption scheduling game. The proposed distributed demand-side energy management strategy requires each user to simply apply its best response strategy to the current total load and tariffs in the power distribution system. The users can maintain privacy and do not need to reveal the details on their energy consumption schedules to other users. We also show that users will have the incentives to participate in the energy consumption scheduling game and subscribing to such services. Simulation results confirm that the proposed approach can reduce the peak-to-average ratio of the total energy demand, the total energy costs, as well as each user's individual daily electricity charges.
01 Feb 2014-Energy
Abstract: MES (multi-energy systems) whereby electricity, heat, cooling, fuels, transport, and so on optimally interact with each other at various levels (for instance, within a district, city or region) represent an important opportunity to increase technical, economic and environmental performance relative to “classical” energy systems whose sectors are treated “separately” or “independently”. This performance improvement can take place at both the operational and the planning stage. While such systems and in particular systems with distributed generation of multiple energy vectors (DMG (distributed multi-generation)) can be a key option to decarbonize the energy sector, the approaches needed to model and relevant tools to analyze them are often of great complexity. Likewise, it is not straightforward to identify performance metrics that are capable to properly capture costs and benefits that are relating to various types of MES according to different criteria. The aim of this invited paper is thus to provide the reader with a comprehensive and critical overview of the latest models and assessment techniques that are currently available to analyze MES and in particular DMG systems, including for instance concepts such as energy hubs, microgrids, and VPPs (virtual power plants), as well as various approaches and criteria for energy, environmental, and techno-economic assessment.
07 Apr 2011-Iet Renewable Power Generation
Abstract: This study presents a smart energy management system (SEMS) to optimise the operation of the microgrid. The SEMS consists of power forecasting module, energy storage system (ESS) management module and optimisation module. The characteristic of the photovoltaics (PV) output in different weather conditions has been studied and then a 1-day-ahead power forecasting module is presented. As energy storage needs to be optimised across multiple-time steps, considering the influence of energy price structures, their economics are particularly complex. Therefore the ESS module is applied to determine the optimal operation strategies. Accordingly, multiple-time set points of the storage device, and economic performance of ESS are also evaluated. Smart management of ESS, economic load dispatch and operation optimisation of distributed generation (DG) are simplified into a single-object optimisation problem in the SEMS. Finally, a matrix real-coded genetic algorithm (MRC-GA) optimisation module is described to achieve a practical method for load management, including three different operation policies and produces diagrams of the distributed generators and ESS.
06 Jun 2005-
Abstract: Methods and systems are provided for optimizing the control of energy supply and demand. An energy control unit includes one or more algorithms for scheduling the control of energy consumption devices on the basis of variables relating to forecast energy supply and demand. Devices for which energy consumption can be scheduled or deferred are activated during periods of cheapest energy usage. Battery storage and alternative energy sources (e.g., photovoltaic cells) are activated to sell energy to the power grid during periods that are determined to correspond to favorable cost conditions.
22 Jan 2013-IEEE Transactions on Smart Grid
TL;DR: The proposed EMS is implemented for a microgrid composed of photovoltaic panels, two wind turbines, a diesel generator and an energy storage system and the results show the economic sense of the proposal.
Abstract: A novel energy management system (EMS) based on a rolling horizon (RH) strategy for a renewable-based microgrid is proposed. For each decision step, a mixed integer optimization problem based on forecasting models is solved. The EMS provides online set points for each generation unit and signals for consumers based on a demand-side management (DSM) mechanism. The proposed EMS is implemented for a microgrid composed of photovoltaic panels, two wind turbines, a diesel generator and an energy storage system. A coherent forecast information scheme and an economic comparison framework between the RH and the standard unit commitment (UC) are proposed. Solar and wind energy forecasting are based on phenomenological models with updated data. A neural network for two-day-ahead electric consumption forecasting is also designed. The system is tested using real data sets from an existent microgrid in Chile (ESUSCON). The results based on different operation conditions show the economic sense of the proposal. A full practical implementation of the system for ESUSCON is envisioned.