Data-Intensive Computing in Smart Microgrids

Microgrids have recently emerged as the building block of a smart grid, combining distributed renewable energy sources, energy storage devices, and load management in order to improve power system reliability, enhance sustainable development, and reduce carbon emissions. At the same time, rapid advancements in sensor and metering technologies, wireless and network communication, as well as cloud and fog computing are leading to the collection and accumulation of large amounts of data (e.g., device status data, energy generation data, consumption data). The application of big data analysis techniques (e.g., forecasting, classification, clustering) on such data can optimize the power generation and operation in real time by accurately predicting electricity demands, discovering electricity consumption patterns, and developing dynamic pricing mechanisms. An efficient and intelligent analysis of the data will enable smart microgrids to detect and recover from failures quickly, respond to electricity demand swiftly, supply more reliable and economical energy, and enable customers to have more control over their energy use. Overall, data-intensive analytics can provide effective and efficient decision support for all of the producers, operators, customers, and regulators in smart microgrids, in order to achieve holistic smart energy management, including energy generation, transmission, distribution, and demand-side management. This book contains an assortment of relevant novel research contributions that provide real-world applications of data-intensive analytics in smart grids and contribute to the dissemination of new ideas in this area

Optimal and Secure Electricity Market Framework for Market Operation of Multi-Microgrid Systems

Traditional power systems were typically based on bulk energy services by large utility companies. However, microgrids and distributed generations have changed the structure of modern power systems as well as electricity markets. Therefore, restructured electricity markets are needed to address energy transactions in modern power systems. In this dissertation, we developed a hierarchical and decentralized electricity market framework for multi-microgrid systems, which clears energy transactions through three market levels; Day-Ahead-Market (DAM), Hour-Ahead-Market (HAM) and Real-Time-Market (RTM). In this market, energy trades are possible between all participants within the microgrids as well as inter-microgrids transactions. In this approach, we developed a game-theoretic-based double auction mechanism for energy transactions in the DAM, while HAM and RTM are cleared by an optimization algorithm and reverse action mechanism, respectively. For data exchange among market players, we developed a secure data-centric communication approach using the Data Distribution Service. Results demonstrated that this electricity market could significantly reduce the energy price and dependency of the multi-microgrid area on the external grid. Furthermore, we developed and verified a hierarchical blockchain-based energy transaction framework for a multi-microgrid system. This framework has a unique structure, which makes it possible to check the feasibility of energy transactions from the power system point of view by evaluating transmission system constraints. The blockchain ledger summarization, microgrid equivalent model development, and market players’ security and privacy enhancement are new approaches to this framework. The research in this dissertation also addresses some ancillary services in power markets such as an optimal power routing in unbalanced microgrids, where we developed a multi-objective optimization model and verified its ability to minimize the power imbalance factor, active power losses and voltage deviation in an unbalanced microgrid. Moreover, we developed an adaptive real-time congestion management algorithm to mitigate congestions in transmission systems using dynamic thermal ratings of transmission lines. Results indicated that the developed algorithm is cost-effective, fast, and reliable for real-time congestion management cases. Finally, we completed research about the communication framework and security algorithm for IEC 61850 Routable GOOSE messages and developed an advanced protection scheme as its application in modern power systems

A nearly zero-energy microgrid testbed laboratory: Centralized control strategy based on SCADA system

Currently, despite the use of renewable energy sources (RESs), distribution networks are facing problems, such as complexity and low productivity. Emerging microgrids (MGs) with RESs based on supervisory control and data acquisition (SCADA) are an effective solution to control, manage, and finally deal with these challenges. The development and success of MGs is highly dependent on the use of power electronic interfaces. The use of these interfaces is directly related to the progress of SCADA systems and communication infrastructures. The use of SCADA systems for the control and operation of MGs and active distribution networks promotes productivity and efficiency. This paper presents a real MG case study called the LAMBDA MG testbed laboratory, which has been implemented in the electrical department of the Sapienza University of Rome with a centralized energy management system (CEMS). The real-time results of the SCADA system show that a CEMS can create proper energy balance in a LAMBDA MG testbed and, consequently, minimize the exchange power of the LAMBDA MG and main grid

Implementation of Solar Irradiance Forecasting Using Markov Switching Model and Energy Management System

Photovoltaic (PV) systems integration is increasingly being used to reduce fuel consumption in diesel-based remote microgrids. However, uncertainty and low correlation of PV power availability with load reduce the benefits of PV integration. These challenges can be handled by introducing reserve, which however leads to increased operational cost. Solar irradiance forecasting helps to reduce reserve requirement, thereby improving the utilization of PV energy. In this thesis, a new solar irradiance forecasting method for remote microgrids based on the Markov Switching Model (MSM) is presented. This method uses locally available data to predict one-day-ahead solar irradiance for scheduling energy resources in remote microgrids. The model considers the past solar irradiance data, the Clear Sky Irradiance (CSI), and the Fourier basis functions to create linear models for three regimes or states: high, medium, and low energy regimes for a day corresponding to sunny, mildly cloudy, and extremely cloudy days, respectively. The case study for Brookings, SD, discussed in this thesis, resulted in an average Mean Absolute Percentage Error (MAPE) of 31.8% for five years, 2001 to 2005, with higher errors during summer months than during winter months. The solar irradiance forecasting method was implemented in OPAL-RT real-time digital simulator using PV panels as sensors. For forecasting irradiance, the first four hours of irradiance data in the morning are required. These data were measured using the solar panels rather than pyranometers as the sensors . A case study for real-time irradiance forecasting in Brookings on June 9, 2015 showed RMSE and MAPE of 131.08W=m2 and 45.45%, respectively. The improvement of renewable integration is the future and present prospects for power utilization. Microgrids experience several constraints such as integration of intermittent renewable sources, costlier reliability improvements, restricted expansion of the microgrid system, growth in load, etc. Hence, more research in this field of study is required and a complete laboratory scale microgrid testbed is needed for experimenting different types of microgrid topologies and for studying the coordination of individual components with a well-defined energy management scheme. In this thesis, the development of a laboratory scale single-phase microgrid testbed along with the implementation of microgrid’s Energy Management System (EMS) are discussed. The testbed was developed using central controller and Commercial Off-The-Shelf (COTS) equipment. The EMS comprised of double layers: schedule layer and real-time dispatch layer. A case study conducted for the implementation of the EMS showed that the difference in the scheduled and the dispatched powers were handled by the generator and the energy storage system themselves

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%

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

Optimal Economic Schedule for a Network of Microgrids With Hybrid Energy Storage System Using Distributed Model Predictive Control

Artículo Open Access en el sitio web el editor. Pago por publicar en abierto.In this paper, an optimal procedure for the economic schedule of a network of interconnected microgrids with hybrid energy storage system is carried out through a control algorithm based on distributed model predictive control (DMPC). The algorithm is specifically designed according to the criterion of improving the cost function of each microgrid acting as a single system through the network mode operation. The algorithm allows maximum economical benefit of the microgrids, minimizing the degradation causes of each storage system, and fulfilling the different system constraints. In order to capture both continuous/discrete dynamics and switching between different operating conditions, the plant is modeled with the framework of mixed logic dynamic. The DMPC problem is solved with the use of mixed integer linear programming using a piecewise formulation, in order to linearize a mixed integer quadratic programming problem.Ministerio de Economía, Industria y Competitivadad DPI2016-78338-RComisión Europea 0076-AGERAR-6-