Showing papers on "Energy management system published in 2016"
TL;DR: In this paper, a brief overview on the architecture and functional modules of smart HEMS is presented, and various home appliance scheduling strategies to reduce the residential electricity cost and improve the energy efficiency from power generation utilities are also investigated.
Abstract: 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.
565 citations
TL;DR: In this paper, the authors summarize the control objectives and development methodologies in the recently proposed microgrid supervisory controllers (MGSC) and energy management systems (EMS) and provide a detailed methodology review with emphasis on representative applications and research works.
Abstract: Microgrids (MGs), featured by distributed energy resources, consumption and storage, are designed to significantly enhance the self-sustainability of future electric distribution grids. In order to adapt to this new and revolutionary paradigm, it is necessary to control MGs in intelligent and coordinated fashion. To this aim, a new generation of advanced Microgrid Supervisory Controllers (MGSC) and Energy Management Systems (EMS) has emerged. The aim of this paper is to summarize the control objectives and development methodologies in the recently proposed MGSC/EMS. At first, a classification of control objectives is made according to the definition of hierarchical control layers in MGs. Then, focusing on MGSC/EMS related studies, a detailed methodology review is given with emphasis on representative applications and research works. Finally, the conclusions are summarized and the proposals of future research directions in this area are given.
293 citations
TL;DR: In this article, a systematic review of existing academic journal publications on energy management in industry is presented, where five essential key elements of an energy management based on overarching themes are identified within the body of literature (strategy/planning, implementation/operation, controlling, organization and culture).
274 citations
TL;DR: In this article, the authors consider an energy management system that controls a cluster of price-responsive demands and manages a wind-power plant and an energy storage facility, and propose a two-stage procedure based on robust optimization.
Abstract: We consider an energy management system that controls a cluster of price-responsive demands. Besides these demands, it also manages a wind-power plant and an energy storage facility. Demands, wind-power plant, and energy storage facility are interconnected within a small size electric energy system equipped with smart grid technology and constitute a virtual power plant that can strategically buy and sell energy in both the day-ahead and the real-time markets. To this end, we propose a two-stage procedure based on robust optimization. In the first stage, the bidding strategy in the day-ahead market is decided. In the second stage, and once the actual scheduling in the day-ahead market is known, we decide the bidding strategy in the real-time market for each hour of the day. We consider that the virtual power plant behaves as a price taker in these markets. Robust optimization is used to deal with uncertainties in wind-power production and market prices, which are represented through confidence bounds. Results of a realistic case study are provided to show the applicability of the proposed approach.
250 citations
TL;DR: In this article, 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).
223 citations
TL;DR: This paper presents how a learning system should be designed to learn the energy consumption model of HVACs, how to integrate the learning mechanism with optimization techniques to generate optimal demand response policies, and how a data structure should bedesigned to store and capture current home appliance behaviors properly.
Abstract: This paper focuses on developing an interdisciplinary mechanism that combines machine learning, optimization, and data structure design to build a demand response and home energy management system that can meet the needs of real-life conditions. The loads of major home appliances are divided into three categories: 1) containing fixed loads; 2) regulate-able loads; and 3) deferrable loads, based on which a decoupled demand response mechanism is proposed for optimal energy management of the three categories of loads. A learning-based demand response strategy is developed for regulateable loads with a special focus on home heating, ventilation, and air conditioning (HVACs). This paper presents how a learning system should be designed to learn the energy consumption model of HVACs, how to integrate the learning mechanism with optimization techniques to generate optimal demand response policies, and how a data structure should be designed to store and capture current home appliance behaviors properly. This paper investigates how the integrative and learning-based home energy management system behaves in a demand response framework. Case studies are conducted through an integrative simulation approach that combines a home energy simulator and MATLAB together for demand response evaluation.
204 citations
TL;DR: In this paper, a review of existing optimization objectives, constraints, solution approaches and tools used in microgrid energy management is presented, which can provide a foundation to embark on an in depth study in the area of energy management for smart microgrid network.
Abstract: Microgrid equipped with heterogenous energy resources and a bank of energy storage devices presents the idea of small scale distributed energy management (DEM). DEM facilitates the minimization of the transmission and operation costs, peak load and environmental pollution. Microgrid also enables active customer participation by giving them the access to the real time information and control. The capability of fast restoration against physical/cyber attack, integration of renewable energy resources and information and communication technologies (ICT) make microgrid as an ideal candidate for distributed power systems. The energy management system of microgrid can perform real time energy forecasting of renewable resources, energy storage elements and controllable loads in making proper short term scheduling to minimize total operating costs. Cost benefit analysis of microgrid reveals that cooperation among different microgrids can play an important role in the reduction of import energy cost from the utility grid. Cooperation among microgrids in smart microgrid network (SMN) brings the energy sharing and management issues. In this paper we present a review of existing optimization objectives, constraints, solution approaches and tools used in microgrid energy management. This review paper can provide a foundation to embark on an in depth study in the area of energy management for smart microgrid network.
194 citations
TL;DR: In this article, a new multi-agent based distributed energy management system architecture is proposed in which the distributed generation system is composed of several distributed energy resources and a group of loads, and non-cooperative game theory is used for the multiagent coordination in the system.
168 citations
TL;DR: The results indicated that, in comparison with a nonrobust approach, the proposed formulation adequately integrated the uncertainty into the EMS, increasing the robustness of the microgrid by using the diesel generator as spinning reserve, and the operating costs were also slightly increased due to the additional reserves.
Abstract: Microgrids have emerged as an alternative to alleviate increasing energy demands. However, because microgrids are primarily based on nonconventional energy sources (NCES), there is high uncertainty involved in their operation. The aim of this paper is to formulate a robust energy management system (EMS) for a microgrid that uses model predictive control theory as the mathematical framework. The robust EMS (REMS) is formulated using a fuzzy prediction interval model as the prediction model. This model allows us to represent both nonlinear dynamic behavior and uncertainty in the available energy from NCES. In particular, the uncertainty in wind-based energy sources can be represented. In this way, upper and lower boundaries for the trajectories of the available energy are obtained. These boundaries are used to derive a robust formulation of the EMS. The microgrid installed in Huatacondo was used as a test bench. The results indicated that, in comparison with a nonrobust approach, the proposed formulation adequately integrated the uncertainty into the EMS, increasing the robustness of the microgrid by using the diesel generator as spinning reserve. However, the operating costs were also slightly increased due to the additional reserves. This achievement indicates that the proposed REMS is an appropriate alternative for improving the robustness, against the wind power variations, in the operation of microgrids.
143 citations
TL;DR: In this paper, a real-time interactive energy management system (EMS) framework for the utility and multiple electrically coupled MGs is proposed, where a hierarchical bi-level control scheme with primary and secondary level controllers is applied in this regard.
Abstract: In this paper, a comprehensive real-time interactive energy management system (EMS) framework for the utility and multiple electrically coupled MGs is proposed. A hierarchical bi-level control scheme (BLCS) with primary and secondary level controllers is applied in this regard. The proposed hierarchical architecture consists of sub-components of load demand prediction, renewable generation resource integration, electrical power-load balancing, and responsive load demand. In the primary level, EMSs are operating separately for each microgrid (MG) by considering the problem constraints, power set-points of generation resources, and possible shortage or surplus of power generation in the MGs. In the proposed framework, minimum information exchange is required among MGs and the distribution system operator. It is a highly desirable feature in future distributed EMS. Various parameters such as load demand and renewable power generation are treated as uncertainties in the proposed structure. In order to handle the uncertainties, Taguchi $^\prime$ s orthogonal array testing approach is utilized. Then, the shortage or surplus of the MGs power should be submitted to a central EMS in the secondary level. In order to validate the proposed control structure, a test system is simulated and optimized based on multiperiod imperialist competition algorithm. The obtained results clearly show that the proposed BLCS is effective in achieving optimal dispatch of generation resources in systems with multiple MGs.
136 citations
TL;DR: A risk-constrained scenario-based stochastic programming framework is proposed using the conditional value at risk method to address various uncertainties in a microgrid that includes renewable energy, diesel generators, battery storage, and various loads.
Abstract: This paper presents a novel energy-management method for a microgrid that includes renewable energy, diesel generators, battery storage, and various loads. We assume that the microgrid takes part in a pool market and responds actively to the electricity price to maximize its profit by scheduling its controllable resources. To address various uncertainties, a risk-constrained scenario-based stochastic programming framework is proposed using the conditional value at risk method. The designed model is solved by two levels of stochastic optimization methods. One level of optimization is to submit optimal hourly bids to the day-ahead market under the forecast data. The other level of optimization is to determine the optimal scheduling using the scenario-based stochastic data of the uncertain resources. The proposed energy management system is not only beneficial for the microgrid and customers, but also applies the microgrid aggregator and virtual power plant. The results are shown to prove the validity of the proposed framework.
TL;DR: The proposed strategy of the management system capitalizes on the power of binary particle swarm optimization algorithm to minimize the energy cost and carbon dioxide and pollutant emissions while maximizing thePower of the available renewable energy resources.
TL;DR: In this article, the authors proposed a method to enhance resiliency of micro-grids through survivability, where the objective is to minimize the amount of critical load shed for the duration the micro-grid is in islanded mode following a disturbance event.
TL;DR: In this paper, a model predictive control (MPC)-based home energy management system for residential microgrid (RM) is proposed, in which all related information such as the time-varying information of the load demand, electricity price and renewable energy generations, are all taken into account.
Abstract: This study proposes a model predictive control (MPC)-based home energy management system for residential microgrid (RM) in which all related information such as the time-varying information of the load demand, electricity price and renewable energy generations, are all taken into account. A novel finite-horizon mixed-integer linear programming problem is iteratively formulated to investigate the optimal control actions of the RM under an MPC framework. Three case studies are conducted to discuss the technical and economic impacts of the responsive electrical and thermal loads, plug-in hybrid electric vehicles, and electrical and thermal energy storage units. Moreover, a sensitivity analysis is performed to demonstrate the superiority of the proposed approach when forecasts of related information are imperfect.
TL;DR: In this article, an advanced real-time energy management system (RT-EMS) for microgrid (MG) systems is presented, which capitalizes on the power of Genetic Algorithms (GAs) to minimize the energy cost and carbon dioxide emissions while maximizing the available renewable energy resources.
TL;DR: In this paper, an energy management system for a PV system coupled with battery energy storage, which maximizes the daily economic benefits while curtailing the power injection to the grid in such a way that helps to mitigate overvoltage problems caused by reverse power flow, is proposed.
TL;DR: An energy management system for stand-alone microgrid composed of diesel generators, wind turbine generator, biomass generator and an ESS (energy storage system) is proposed in this paper, where different operation objectives are achieved by a hierarchical control structure with different time scales.
TL;DR: This paper elaborates on the design of an efficient algorithm for the EMS (energy management system) inside a residential energy hub, and shows that there exists a competitive equilibrium for the energy hubs.
TL;DR: In this paper, the authors present an IoT-based communication framework with a common information model to facilitate the development of a demand response (DR) energy management system for industrial customers, which takes advantage of integrated energy supply networks to deploy DR energy management in an industrial facility.
TL;DR: In this paper, a mathematical model for the optimal energy management of a residential building and a centralized energy management system (CEMS) framework for off-grid operation is presented, where the optimal decisions are determined in real-time by considering these models with realistic parameter settings and customer preferences.
TL;DR: In this article, a reinforcement learning-based real-time energy management system for plug-in hybrid electric vehicles (PHEVs) is proposed to address the trade-off between realtime performance and optimal energy savings.
Abstract: Plug-in hybrid electric vehicles (PHEVs) show great promise in reducing transportation-related fossil fuel consumption and greenhouse gas emissions. Designing an efficient energy management system (EMS) for PHEVs to achieve better fuel economy has been an active research topic for decades. Most of the advanced systems rely either on a priori knowledge of future driving conditions to achieve the optimal but not real-time solution (e.g., using a dynamic programming strategy) or on only current driving situations to achieve a real-time but nonoptimal solution (e.g., rule-based strategy). This paper proposes a reinforcement learning–based real-time EMS for PHEVs to address the trade-off between real-time performance and optimal energy savings. The proposed model can optimize the power-split control in real time while learning the optimal decisions from historical driving cycles. A case study on a real-world commute trip shows that about a 12% fuel saving can be achieved without considering charging opportunit...
TL;DR: In this article, an expert heuristic approach based on multi-period imperialist competition algorithm is applied to implement an energy management system for optimization purposes to minimize the total generation cost with a fast calculation time.
Abstract: Summary
Microgrid (MG) constitutes non-dispatchable resources and responsive loads, which can serve as a basic tool to reach desired objectives while distributing electricity more effectively, economically, and securely. However, high penetration of distributed generations into the grid leads to fundamental and critical challenges to ensure a reliable power system operation. This paper presents a general formulation of optimum operation strategy with the objective of cost optimization plan and demand response regulation. MG energy management problem can be formulated as an optimization problem in order to minimize the cost-related to generation resources and responsive loads. An expert heuristic approach based on multi-period imperialist competition algorithm is applied to implement an energy management system for optimization purposes. A comparison is carried out between the proposed algorithm and classical techniques, including particle swarm optimization and a modified conventional energy management system algorithms. An artificial neural network combined with Markov-chain approach is used to predict non-dispatchable power generation and load demand under uncertainty conditions. The proposed algorithm is evaluated experimentally on an MG testbed, and the obtained results demonstrate the efficiency of the proposed algorithm to minimize the total generation cost with a fast calculation time, which makes it useful for real-time applications. Copyright © 2015 John Wiley & Sons, Ltd.
TL;DR: An energy management system for stand-alone microgrid consisting of a wind turbine, a diesel generator, an energy storage system (ESS), and a sea water desalination system is proposed and a real-time rolling horizon energy management method based on hour-ahead wind speed forecast is presented.
Abstract: An energy management system for stand-alone microgrid consisting of a wind turbine (WT) generator, a diesel generator, an energy storage system (ESS), and a sea water desalination system is proposed in this paper. The coordinated control of the distributed generations and ESS is researched with two operation modes. Then, a real-time rolling horizon energy management method is presented based on hour-ahead wind speed forecast. The operation mode of the microgrid system and the reference output power of WT generator are determined according to the forecasted wind speed and state of charge of the ESS, which can achieve the goal of maximizing utilization of wind energy and minimizing utilization of diesel generator on the basis of system stable operation. The proposed energy management method has been tested on the real-time digital simulator system. The results clearly verify the effectiveness of the proposed method.
TL;DR: This paper focuses on the development of a theory of remote microgrid operation and its application in the context of a distributed microgrid system.
TL;DR: The proposed REM-S architecture is presented, which is based on a hybrid centralized-decentralized concept and developed according to SG architecture model framework.
Abstract: The modern railway system is a massive grid connected complex system with distributed active loads (trains), sources (particularly distributed renewable sources), and storage (wayside or on-board storage systems). Its energy management therefore requires the concepts and techniques used for managing energy in the smart grid (SG). Accordingly, the new railway energy management system (REM-S) is developed to integrate on-board, wayside, and coordination services. REM-S is driven by the idea that regeneration, loads, storage, and volatile distributed energy resources should be coordinated dynamically to achieve optimal energy usage. This paper presents the proposed REM-S architecture, which is based on a hybrid centralized–decentralized concept and developed according to SG architecture model framework.
TL;DR: An efficient web based energy management system for Campuses which manages in an energy efficient way the Campus buildings and spaces of public use, monitors the energy load and performs energy analysis per building and for the Campus as a whole.
TL;DR: A novel energy management system for the optimized operation of the energy sources of a grid-connected hybrid renewable energy system with battery and hydrogen system, solved using the Particle Swarm Optimization (PSO) method and achieves reasonable operating costs, efficiency and degradation of the devices.
TL;DR: In this paper, a multi-agent system based distributed EMS (energy management system) is proposed to perform optimal energy allocation and management for grids comprising of renewables, storage and distributed generation, where the main objectives of the proposed algorithm are to maintain power balance in the system and to ensure long cycle life for storage units by controlling their SOC (state of charge).
TL;DR: In this paper, a linear weighted summation algorithm based on variable weights is proposed to solve the multi-objective optimisation problem to obtain current reference of supercapacitor and battery.
Abstract: Battery/supercapacitor (SC) hybrid energy storage system (HESS) is an effective way to suppress the power fluctuation of photovoltaic (PV) power generation system during radiation change. This study focuses on the power sharing between different energy storage components with two optimisation objectives: energy loss and state of charge of SC. First, the topology of HESS and its connection with PV system are analysed. Second, the control targets of the HESS have been organised as optimisation objectives. A linear weighted summation algorithm based on variable weights is proposed to solve the multi-objective optimisation problem to obtain current reference of SC and battery. Third, the energy management system of the HESS is designed based on proposed algorithms. Simulation and experiment results verified the proposed algorithms and control strategy.
TL;DR: In this paper, a modular building energy management system (BEMS) is presented, which is capable of handling the energy flows in the building and across all energy carriers as well as the interdependencies between devices, while keeping a unitized approach towards devices and the optimization of their operation.