Management and Control of Domestic Smart Grid Technology

Emerging new technologies like distributed generation, distributed storage, and demand-side load management will change the way we consume and produce energy. These techniques enable the possibility to reduce the greenhouse effect and improve grid stability by optimizing energy streams. By smartly applying future energy production, consumption, and storage techniques, a more energy-efficient electricity supply chain can be achieved. In this paper a three-step control methodology is proposed to manage the cooperation between these technologies, focused on domestic energy streams. In this approach, (global) objectives like peak shaving or forming a virtual power plant can be achieved without harming the comfort of residents. As shown in this work, using good predictions, in advance planning and real-time control of domestic appliances, a better matching of demand and supply can be achieved.\ud \u

Measurement-based network clustering for active distribution systems

©2019 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 presents a network clustering (NC) method for active distribution networks (ADNs). Following the outage of a section of an ADN, the method identifies and forms an optimum cluster of microgrids within the section. The optimum cluster is determined from a set of candidate microgrid clusters by estimating the following metrics: total power loss, voltage deviations, and minimum load shedding. To compute these metrics, equivalent circuits of the clusters are estimated using measured data provided by phasor measurement units (PMUs). Hence, the proposed NC method determines the optimum microgrid cluster without requiring information about the network’s topology and its components. The proposed method is tested by simulating a study network in a real-time simulator coupled to physical PMUs and a prototype algorithm implementation, also executing in real time.Peer ReviewedPostprint (author's final draft

Mixed-integer-linear-programming-based energy management system for hybrid PV-wind-battery microgrids: Modeling, design, and experimental verification

© 2017 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 worksMicrogrids are energy systems that aggregate distributed energy resources, loads, and power electronics devices in a stable and balanced way. They rely on energy management systems to schedule optimally the distributed energy resources. Conventionally, many scheduling problems have been solved by using complex algorithms that, even so, do not consider the operation of the distributed energy resources. This paper presents the modeling and design of a modular energy management system and its integration to a grid-connected battery-based microgrid. The scheduling model is a power generation-side strategy, defined as a general mixed-integer linear programming by taking into account two stages for proper charging of the storage units. This model is considered as a deterministic problem that aims to minimize operating costs and promote self-consumption based on 24-hour ahead forecast data. The operation of the microgrid is complemented with a supervisory control stage that compensates any mismatch between the offline scheduling process and the real time microgrid operation. The proposal has been tested experimentally in a hybrid microgrid at the Microgrid Research Laboratory, Aalborg University.Peer ReviewedPostprint (author's final draft

A Novel Microgrid Demand-Side Management System for Manufacturing Facilities

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 manufacturing process that had approximately 20 kW of peak demand was matched with a solar photovoltaic array that had a peak output of approximately 3 KW. An innovative Demand-Side Management (DSM) strategy was developed to manage the process loads as part of this smart microgrid system. The DSM algorithm managed the intermittent nature of the microgrid and the instantaneous demand of the manufacturing process. The control algorithm required three input signals; one from the microgrid indicating the availability of renewable energy, another from the manufacturing process indicating energy use as a percent of peak production, and historical data for renewable sources and facility demand. 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 a real-time management of a manufacturing process with a microgrid will reduce electrical consumption and peak demand. The renewable energy system for this research was rated to provide up to 13% of the total manufacturing capacity. With actively managing the process loads with the DSM program alone, electrical consumption from the utility grid was reduced by 17% on average. An additional 24% reduction was accomplished when the microgrid and DSM program was enabled together, resulting in a total reduction of 37%. On average, peak demand was reduced by 6%, but due to the intermittency of the renewable source and the billing structure for peak demand, only a 1% reduction was obtained. During a billing period, it only takes one day when solar irradiance is poor to affect the demand reduction capabilities. To achieve further demand reduction, energy storage should be introduced and integrated

A rolling horizon rescheduling strategy for flexible energy in a microgrid

In this work an Energy Management System (EMS) prototype for an isolated renewable-based microgrid is presented. The proposed management model not only considers the management of energy sources (generation) but also includes the possibility of flexible timing of energy consumptions (demand management) by modelling controllable and uncontrollable loads. The EMS consists of two stages: first a deterministic management model is formulated and subsequently is integrated into a rolling horizon control strategy, in which the actions on microgrid devices respond to an optimization criterion related to the estimation of the future system behaviour that is continually predicted by updatable forecasts in order to reduce uncertainty in both, production capacity and energy demand. Finally, this contribution presents and discusses a case study where the results of the operation with and without optimal demand management for the same group of loads are evaluated.Postprint (published version

A Novel Microgrid Demand-Side Management System for Manufacturing Facilities

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 manufacturing process that had approximately 20 kW of peak demand was matched with a solar photovoltaic array that had a peak output of approximately 3 KW. An innovative Demand-Side Management (DSM) strategy was developed to manage the process loads as part of this smart microgrid system. The DSM algorithm managed the intermittent nature of the microgrid and the instantaneous demand of the manufacturing process. The control algorithm required three input signals; one from the microgrid indicating the availability of renewable energy, another from the manufacturing process indicating energy use as a percent of peak production, and historical data for renewable sources and facility demand. 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 a real-time management of a manufacturing process with a microgrid will reduce electrical consumption and peak demand. The renewable energy system for this research was rated to provide up to 13% of the total manufacturing capacity. With actively managing the process loads with the DSM program alone, electrical consumption from the utility grid was reduced by 17% on average. An additional 24% reduction was accomplished when the microgrid and DSM program was enabled together, resulting in a total reduction of 37%. On average, peak demand was reduced by 6%, but due to the intermittency of the renewable source and the billing structure for peak demand, only a 1% reduction was obtained. During a billing period, it only takes one day when solar irradiance is poor to affect the demand reduction capabilities. To achieve further demand reduction, energy storage should be introduced and integrated

A simple recurrent neural network for solution of linear programming: Application to a Microgrid

The aim of this paper is to present a simple new class of recurrent neural networks, which solves linear programming. It is considered as a sliding mode control problem, where the network structure is based on the Karush-Kuhn-Tucker (KKT) optimality conditions, and the KKT multipliers are the control inputs to be implemented with finite time stabilizing terms based on the unit control, instead of common used activation functions. Thus, the main feature of the proposed network is the fixed number of parameters despite of the optimization problem dimension, which means, the network can be easily scaled from a small to a higher dimension problem. The applicability of the proposed scheme is tested on real-time optimization of an electrical Microgrid prototype.Consejo Nacional de Ciencia y Tecnologí