Peer-to-peer and community-based markets: A comprehensive review

The advent of more proactive consumers, the so-called "prosumers", with production and storage capabilities, is empowering the consumers and bringing new opportunities and challenges to the operation of power systems in a market environment. Recently, a novel proposal for the design and operation of electricity markets has emerged: these so-called peer-to-peer (P2P) electricity markets conceptually allow the prosumers to directly share their electrical energy and investment. Such P2P markets rely on a consumer-centric and bottom-up perspective by giving the opportunity to consumers to freely choose the way they are to source their electric energy. A community can also be formed by prosumers who want to collaborate, or in terms of operational energy management. This paper contributes with an overview of these new P2P markets that starts with the motivation, challenges, market designs moving to the potential future developments in this field, providing recommendations while considering a test-case

Overload Prevention in an Autonomous Microgrid using Battery Storage Units

A new control strategy for smooth transition of a battery storage unit (BSU) is proposed in this paper to prevent overloading in an autonomous hybrid microgrid. The BSU is controlled to come online to prevent overloading to the distributed generators (DGs) in the autonomous microgrid and to go offline when the load demand is less than the total rating of the DGs in the microgrid. The microgrid can contain either inertial DG or non–inertial DGs, which are controlled in a frequency droop. The sensing of switching on and switching off of the BSU depends on the frequency signal, which is developed in the paper. The proposed strategy is validated through PSCAD/EMTDC simulation studies

Ancillary Services in Hybrid AC/DC Low Voltage Distribution Networks

In the last decade, distribution systems are experiencing a drastic transformation with the advent of new technologies. In fact, distribution networks are no longer passive systems, considering the current integration rates of new agents such as distributed generation, electrical vehicles and energy storage, which are greatly influencing the way these systems are operated. In addition, the intrinsic DC nature of these components, interfaced to the AC system through power electronics converters, is unlocking the possibility for new distribution topologies based on AC/DC networks. This paper analyzes the evolution of AC distribution systems, the advantages of AC/DC hybrid arrangements and the active role that the new distributed agents may play in the upcoming decarbonized paradigm by providing different ancillary services.Ministerio de Economía y Competitividad ENE2017-84813-RUnión Europea (Programa Horizonte 2020) 76409

Characterization of non-intentional emissions from distributed energy resources up to 500 kHz: A case study in Spain

Narrow Band Power Line Communications (NB-PLC) systems are currently used for smart metering and power quality monitoring as a part of the Smart Grid (SG) concept. However, non-intentional emissions generated by the devices connected to the grid may sometimes disturb the communications and isolate metering equipment. Though some research works have been recently developed to characterize these emissions, most of them have been limited to frequencies below 150 kHz and they are mainly focused on in-house electronic appliances and lightning devices. As NB-PLC can also be allocated in higher frequencies up to 500 kHz, there is still a lack of analysis in this frequency range, especially for emissions from Distributed Energy Resources (DERs). The identification and characterization of the emissions is essential to develop solutions that avoid a negative impact on the proper performance of NB-PLC. In this work, the non-intentional emissions of different types of DERs composing a representative microgrid have been measured in the 35–500 kHz frequency range and analyzed both in time and frequency domains. Different working conditions and coupling and commutation procedures to mains are considered in the analysis. Results are then compared to the limits recommended by regulatory bodies for spurious emissions from communication systems in this frequency band, as no specific limits for DERs have been established. Field measurements show clear differences in the characteristics of non-intentional emissions for different devices, working conditions and coupling procedures and for frequencies below and above 150 kHz. Results of this study demonstrate that a further characterization of the potential emissions from the different types of DERs connected to the grid is required in order to guarantee current and future applications based on NB-PLC.This work has been financially supported in part by the Basque Government (Elkartek program)

A Review on Application of Artificial Intelligence Techniques in Microgrids

A microgrid can be formed by the integration of different components such as loads, renewable/conventional units, and energy storage systems in a local area. Microgrids with the advantages of being flexible, environmentally friendly, and self-sufficient can improve the power system performance metrics such as resiliency and reliability. However, design and implementation of microgrids are always faced with different challenges considering the uncertainties associated with loads and renewable energy resources (RERs), sudden load variations, energy management of several energy resources, etc. Therefore, it is required to employ such rapid and accurate methods, as artificial intelligence (AI) techniques, to address these challenges and improve the MG's efficiency, stability, security, and reliability. Utilization of AI helps to develop systems as intelligent as humans to learn, decide, and solve problems. This paper presents a review on different applications of AI-based techniques in microgrids such as energy management, load and generation forecasting, protection, power electronics control, and cyber security. Different AI tasks such as regression and classification in microgrids are discussed using methods including machine learning, artificial neural networks, fuzzy logic, support vector machines, etc. The advantages, limitation, and future trends of AI applications in microgrids are discussed.©2022 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.fi=vertaisarvioitu|en=peerReviewed