Smart Microgrids: Overview and Outlook

The idea of changing our energy system from a hierarchical design into a set of nearly independent microgrids becomes feasible with the availability of small renewable energy generators. The smart microgrid concept comes with several challenges in research and engineering targeting load balancing, pricing, consumer integration and home automation. In this paper we first provide an overview on these challenges and present approaches that target the problems identified. While there exist promising algorithms for the particular field, we see a missing integration which specifically targets smart microgrids. Therefore, we propose an architecture that integrates the presented approaches and defines interfaces between the identified components such as generators, storage, smart and \dq{dumb} devices.Comment: presented at the GI Informatik 2012, Braunschweig Germany, Smart Grid Worksho

Self-organizing Coordination of Multi-Agent Microgrid Networks

abstract: This work introduces self-organizing techniques to reduce the complexity and burden of coordinating distributed energy resources (DERs) and microgrids that are rapidly increasing in scale globally. Technical and financial evaluations completed for power customers and for utilities identify how disruptions are occurring in conventional energy business models. Analyses completed for Chicago, Seattle, and Phoenix demonstrate site-specific and generalizable findings. Results indicate that net metering had a significant effect on the optimal amount of solar photovoltaics (PV) for households to install and how utilities could recover lost revenue through increasing energy rates or monthly fees. System-wide ramp rate requirements also increased as solar PV penetration increased. These issues are resolved using a generalizable, scalable transactive energy framework for microgrids to enable coordination and automation of DERs and microgrids to ensure cost effective use of energy for all stakeholders. This technique is demonstrated on a 3-node and 9-node network of microgrid nodes with various amounts of load, solar, and storage. Results found that enabling trading could achieve cost savings for all individual nodes and for the network up to 5.4%. Trading behaviors are expressed using an exponential valuation curve that quantifies the reputation of trading partners using historical interactions between nodes for compatibility, familiarity, and acceptance of trades. The same 9-node network configuration is used with varying levels of connectivity, resulting in up to 71% cost savings for individual nodes and up to 13% cost savings for the network as a whole. The effect of a trading fee is also explored to understand how electricity utilities may gain revenue from electricity traded directly between customers. If a utility imposed a trading fee to recoup lost revenue then trading is financially infeasible for agents, but could be feasible if only trying to recoup cost of distribution charges. These scientific findings conclude with a brief discussion of physical deployment opportunities.Dissertation/ThesisDoctoral Dissertation Systems Engineering 201

Towards the next generation of smart grids: semantic and holonic multi-agent management of distributed energy resources

The energy landscape is experiencing accelerating change; centralized energy systems are being decarbonized, and transitioning towards distributed energy systems, facilitated by advances in power system management and information and communication technologies. This paper elaborates on these generations of energy systems by critically reviewing relevant authoritative literature. This includes a discussion of modern concepts such as ‘smart grid’, ‘microgrid’, ‘virtual power plant’ and ‘multi-energy system’, and the relationships between them, as well as the trends towards distributed intelligence and interoperability. Each of these emerging urban energy concepts holds merit when applied within a centralized grid paradigm, but very little research applies these approaches within the emerging energy landscape typified by a high penetration of distributed energy resources, prosumers (consumers and producers), interoperability, and big data. Given the ongoing boom in these fields, this will lead to new challenges and opportunities as the status-quo of energy systems changes dramatically. We argue that a new generation of holonic energy systems is required to orchestrate the interplay between these dense, diverse and distributed energy components. The paper therefore contributes a description of holonic energy systems and the implicit research required towards sustainability and resilience in the imminent energy landscape. This promotes the systemic features of autonomy, belonging, connectivity, diversity and emergence, and balances global and local system objectives, through adaptive control topologies and demand responsive energy management. Future research avenues are identified to support this transition regarding interoperability, secure distributed control and a system of systems approach

Modelling Of A Microgrid Using Z Notation

A Microgrid is a group of electrical sources and connected loads that operate energy grids in grid-connected or islanded mode. Microgrid usage has increased recently due to improved technology andthe effectiveness of renewable energy sources. To produce a balanced and stable power supply frommicrogrids and meet the load demand is a challenging research area in both the electrical engineering and software engineering fields. This work presents a formal model for representing the microgrid system to prevent failure or inconsistencies in the power generation and usage. A methodology for creating a formal model for a microgrid is a critical approach to overcoming the challenges of microgrid management and is examined in this work. The work was studied in two parts. The first part assessed the microgrid’s existing class diagram that is then transformed into a precise representation in the Z notation. The Z notation is a mathematical specification language used for describing system properties, and to reason about possible refinements of a design. The second part involved verifying and validation of the microgrid system through the creation of a structured specification using Z. The research addressed class diagram faults in model-based testing. Hence, the class diagrams are analyzed, recreated, and then designed using the formal notation in an iterative process, resulting in a precise description of the microgrid structure in a formal, unambiguous, and effective manner. This description can then be analyzed to determine the correctness of the UML description that will be used to design a microgrid power management system

Design and implementation of multiprotocol framework for residential prosumer incorporation in flexibility markets

The growth of distributed renewable energy in the electrical grid presents challenges to its stability and quality. To address this at the local level, flexibility energy strategies emerge as an innovative technique. However, managing these strategies in residential areas becomes complex due to the unique characteristics of each prosumer. A major challenge lies in managing communication among diverse devices with different protocols. To address these issues, a comprehensive framework is designed and implemented to facilitate prosumers' integration in flexibility strategies, addressing communication at various levels. The effectiveness of the proposed framework is demonstrated through its implementation in a real smart home environment with diverse devices. The framework enables seamless integration and communication between IoT devices and IEC 61,850-compliant power devices. This research presents a novel approach to address the challenges of managing flexibility strategies in residential areas, providing a practical solution for prosumers to actively participate in optimizing energy consumption and enhancing the stability and quality of the electricity system amidst the growing integration of distributed renewable energy.</p

Smart Microgrids: Optimizing Local Resources toward Increased Efficiency and a More Sustainable Growth

Smart microgrids are a possibility to reduce complexity by performing local optimization of power production, consumption and storage. We do not envision smart microgrids to be island solutions but rather to be integrated into a larger network of microgrids that form the future energy grid. Operating and controlling a smart microgrid involves optimization for using locally generated energy and to provide feedback to the user when and how to use devices. This chapter shows how these issues can be addressed starting with measuring and modeling energy consumption patterns by collecting an energy consumption dataset at device level. The open dataset allows to extract typical usage patterns and subsequently to model test scenarios for energy management algorithms. Section 3 discusses means for analyzing measured data and for providing detailed feedback about energy consumption to increase customers’ energy awareness. Section 4 shows how renewable energy sources can be integrated in a smart microgrid and how energy production can be accurately predicted. Section 5 introduces a self-organizing local energy system that autonomously coordinates production and consumption via an agent-based energy auction system. The final section discusses how the proposed methods contribute to sustainable growth and gives an outlook to future research

Design and Implementation of a True Decentralized Autonomous Control Architecture for Microgrids

Microgrids can serve as an integral part of the future power distribution systems. Most microgrids are currently managed by centralized controllers. There are two major concerns associated with the centralized controllers. One is that the single controller can become performance and reliability bottleneck for the entire system and its failure can bring the entire system down. The second concern is the communication delays that can degrade the system performance. As a solution, a true decentralized control architecture for microgrids is developed and presented. Distributing the control functions to local agents decreases the possibility of network congestion, and leads to the mitigation of long distance transmission of critical commands. Decentralization will also enhance the reliability of the system since the single point of failure is eliminated. In the proposed architecture, primary and secondary microgrid controls layers are combined into one physical layer. Tertiary control is performed by the controller located at the grid point of connection. Each decentralized controller is responsible of multicasting its status and local measurements, creating a general awareness of the microgrid status among all decentralized controllers. The proof-of concept implementation provides a practical evidence of the successful mitigation of the drawback of control command transmission over the network. A Failure Management Unit comprises failure detection mechanisms and a recovery algorithm is proposed and applied to a microgrid case study. Coordination between controllers during the recovery period requires low-bandwidth communications, which has no significant overhead on the communication infrastructure. The proof-of-concept of the true decentralization of microgrid control architecture is implemented using Hardware-in-the-Loop platform. The test results show a robust detection and recovery outcome during a system failure. System test results show the robustness of the proposed architecture for microgrid energy management and control scenarios