Microgrids for Improving Manufacturing Energy Efficiency

Thirty-one percent of annual energy consumption in the United States occurs within the industrial sector, where manufacturing processes account for the largest amount of energy consumption and carbon emissions. For this reason, energy efficiency in manufacturing facilities is increasingly important for reducing operating costs and improving profits. Using microgrids to generate local sustainable power should reduce energy consumption from the main utility grid along with energy costs and carbon emissions. Also, microgrids have the potential to serve as reliable energy generators in international locations where the utility grid is often unstable. For this research, a smart microgrid system was designed as part of an innovative load management option to improve energy utilization through active Demand-Side Management (DSM). An intelligent active DSM algorithm was developed to manage the intermittent nature of the microgrid and instantaneous demand of the site loads. The controlling algorithm required two input signals; one from the microgrid indicating the availability of renewable energy and another from the manufacturing process indicating energy use as a percent of peak production. Based on these inputs the algorithm had three modes of operation: normal (business as usual), curtailment (shutting off non-critical loads), and energy storage. The results show that active management of a manufacturing microgrid has the potential for saving energy and money by intelligent scheduling of process loads

Energy Management Strategy of Microgrids Based on Benders Decomposition Method

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. This paper discusses an optimal energy management system for microgrids, taking into account distribution power flow and dynamic loads, in presence of storage units and all associated constraints, aiming to reduce microgrid costs under two grid-connected and islanded modes. Getting the unit commitment, the microgrid energy management problem is introduced as a mixed integer nonlinear problem (MINLP). Since solving MINLP problems is complex and time consuming, a linearization technique is applied for simplification of the problem as a mixed integer linear programming (MILP) problem. Then, the Benders decomposition method is used to reach an efficient and accurate answer. The model proposed is implemented on a 14-bus microgrid including conventional and renewable distributed resources, storage units, and dynamic loads. The results indicated fair and fast performance of the proposed model

Microgrid Disaster Resiliency Analysis: Reducing Costs in Continuity of Operations (COOP) Planning

The electric grid serves a vital role in the supply chain of nearly all industrial and commercial organizations. A Microgrid infrastructure can provide this service and beneficial non-emergency services including a variety of generation/energy sources. To demonstrate the applicability of microgrids for energy resiliency, we present a microgrid resiliency case study for United Parcel Service’s (UPS) three separate shipping facilities. The goal, to enhance energy security, minimize cost and prevent cascading losses within other related business units. The impacts and consequences of which are quantified in this study using a Mean Failure Cost (MFC) risk assessment measure. MFC accounts for the potential loses to identified stakeholders that may result from a set of identified failures due to a set of identified threats. In this case, our study uses a method we call All Hazards Econometric System (AHES). AHES incorporates the cost of COOP using a strategy that considers the payback period of microgrid installation as compared to other energy delivery strategies

Robust Energy Management for Microgrids With High-Penetration Renewables

Due to its reduced communication overhead and robustness to failures, distributed energy management is of paramount importance in smart grids, especially in microgrids, which feature distributed generation (DG) and distributed storage (DS). Distributed economic dispatch for a microgrid with high renewable energy penetration and demand-side management operating in grid-connected mode is considered in this paper. To address the intrinsically stochastic availability of renewable energy sources (RES), a novel power scheduling approach is introduced. The approach involves the actual renewable energy as well as the energy traded with the main grid, so that the supply-demand balance is maintained. The optimal scheduling strategy minimizes the microgrid net cost, which includes DG and DS costs, utility of dispatchable loads, and worst-case transaction cost stemming from the uncertainty in RES. Leveraging the dual decomposition, the optimization problem formulated is solved in a distributed fashion by the local controllers of DG, DS, and dispatchable loads. Numerical results are reported to corroborate the effectiveness of the novel approach.Comment: Short versions were accepted by the IEEE Transactions on Sustainable Energy, and presented in part at the IEEE SmartGridComm 201

Reinforcement Learning for Energy-Storage Systems in Grid-Connected Microgrids: An Investigation of Online vs. Offline Implementation

Grid-connected microgrids consisting of renewable energy sources, battery storage, and load require an appropriate energy management system that controls the battery operation. Traditionally, the operation of the battery is optimised using 24 h of forecasted data of load demand and renewable energy sources (RES) generation using offline optimisation techniques, where the battery actions (charge/discharge/idle) are determined before the start of the day. Reinforcement Learning (RL) has recently been suggested as an alternative to these traditional techniques due to its ability to learn optimal policy online using real data. Two approaches of RL have been suggested in the literature viz. offline and online. In offline RL, the agent learns the optimum policy using predicted generation and load data. Once convergence is achieved, battery commands are dispatched in real time. This method is similar to traditional methods because it relies on forecasted data. In online RL, on the other hand, the agent learns the optimum policy by interacting with the system in real time using real data. This paper investigates the effectiveness of both the approaches. White Gaussian noise with different standard deviations was added to real data to create synthetic predicted data to validate the method. In the first approach, the predicted data were used by an offline RL algorithm. In the second approach, the online RL algorithm interacted with real streaming data in real time, and the agent was trained using real data. When the energy costs of the two approaches were compared, it was found that the online RL provides better results than the offline approach if the difference between real and predicted data is greater than 1.6%

Monitorando Energia Elétrica em Residências no Ambiente de Cidades Inteligentes / Monitoring Electricity in Households in the Smart City Environment

A crescente utilização de energia elétrica nas cidades, atrelado com o gasto desnecessário de energia, vem aumentando os gastos com energia em todo mundo. Muitas vezes os consumidores não tem a noção do porque o gasto com energia elétrica tornou-se tão elevado em alguns meses. A falta de informações do consumo de energia é um causador desse evento. Este trabalho apresenta um projeto em que utiliza um hardware para monitorar a energia elétrica, aplicando técnicas e funcionalidades necessárias em uma cidade inteligente para disponibilizar informações sobre o consumo de energia elétrica em uma residência, entregando um ambiente de monitoramento de energia em tempo real.

A stochastic optimal control solution to the energy management of a microgrid with storage and renewables

The presence of renewable energy generators in a microgrid calls for the usage of storage units so as to smooth the variability in energy production. This work addresses the optimal management of a battery in a microgrid including a wind turbine facility. A Markov chain model is employed to predict the wind power production and the optimal management of the energy storage element is formulated as a stochastic optimal control problem. An approximate dynamic programming approach resting on system abstraction is then proposed for control policy design. Some numerical examples show the effectiveness of the approach

Bidding Strategy for Networked Microgrids in the Day-Ahead Electricity Market

In recent years, microgrids have drawn increasing attention from both academic and industrial sectors due to their enormous potential benefits to the power systems. Microgrids are essentially highly-customized small-scale power systems. Microgrids’ islanding capability enables microgrids to conduct more flexible and energy-efficient operations. Microgrids have proved to be able to provide reliable and environmental-friendly electricity to quality-sensitive or off-grid consumers. In addition, during the grid-connected operation mode, microgrids can also provide support to the utility grid. World-widely continuous microgrid deployments indicate a paradigm shift from traditional centralized large-scale systems toward more distributed and customized small-scale systems. However, microgrids can cause as many problems as it solves. More efforts are needed to address these problems caused by microgrids integration. Considering there will be multiple microgrids in future power systems, the coordination problems between individual microgrids remain to be solved. Aiming at facilitating the promotion of microgrids, this thesis investigates the system-level modeling methods for coordination between multiple microgrids in the context of participating in the market. Firstly, this thesis reviews the background and recent development of microgrid coordination models. Problems of existing studies are identified. Motivated by these problems, the research objectives and structure of this thesis are presented. Secondly, this thesis examines and compares the most common frameworks for optimization under uncertainty. An improved unit commitment model considering uncertain sub-hour wind power ramp behaviors is presented to illustrate the reformulation and solution method of optimization models with uncertainty. Next, the price-maker bidding strategy for collaborative networked microgrids is presented. Multiple microgrids are coordinated as a single dispatchable entity and participate in the market as a price-maker. The market-clearing process is modeled using system residual supply/demand price-quota curves. Multiple uncertainty sources in the bidding model are mitigated with a hybrid stochastic-robust optimization framework. What’s more, this thesis further considers the privacy concerns of individual microgrids in the coordination process. Therefore a privacy-preserving solution method based on Dantzig-Wolfe decomposition is proposed to solve the bidding problem. Both computational and economic performances of the proposed model are compared with the performances of conventional centralized coordination framework. Lastly, this thesis provides suggestions on future research directions of coordination problems among multiple microgrids

Review on Control of DC Microgrids and Multiple Microgrid Clusters

This paper performs an extensive review on control schemes and architectures applied to dc microgrids (MGs). It covers multilayer hierarchical control schemes, coordinated control strategies, plug-and-play operations, stability and active damping aspects, as well as nonlinear control algorithms. Islanding detection, protection, and MG clusters control are also briefly summarized. All the mentioned issues are discussed with the goal of providing control design guidelines for dc MGs. The future research challenges, from the authors' point of view, are also provided in the final concluding part