Distributed multi-agent algorithm for residential energy management in smart grids

Distributed renewable power generators, such as solar cells and wind turbines are difficult to predict, making the demand-supply problem more complex than in the traditional energy production scenario. They also introduce bidirectional energy flows in the low-voltage power grid, possibly causing voltage violations and grid instabilities. In this article we describe a distributed algorithm for residential energy management in smart power grids. This algorithm consists of a market-oriented multi-agent system using virtual energy prices, levels of renewable energy in the real-time production mix, and historical price information, to achieve a shifting of loads to periods with a high production of renewable energy. Evaluations in our smart grid simulator for three scenarios show that the designed algorithm is capable of improving the self consumption of renewable energy in a residential area and reducing the average and peak loads for externally supplied power

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

Demand response performance and uncertainty: A systematic literature review

The present review has been carried out, resorting to the PRISMA methodology, analyzing 218 published articles. A comprehensive analysis has been conducted regarding the consumer's role in the energy market. Moreover, the methods used to address demand response uncertainty and the strategies used to enhance performance and motivate participation have been reviewed. The authors find that participants will be willing to change their consumption pattern and behavior given that they have a complete awareness of the market environment, seeking the optimal decision. The authors also find that a contextual solution, giving the right signals according to the different behaviors and to the different types of participants in the DR event, can improve the performance of consumers' participation, providing a reliable response. DR is a mean of demand-side management, so both these concepts are addressed in the present paper. Finally, the pathways for future research are discussed.This article is a result of the project RETINA (NORTE-01-0145- FEDER-000062), supported by Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF). We also acknowledge the work facilities and equipment provided by GECAD research center (UIDB/00760/2020) to the project team, and grants CEECIND/02887/2017 and SFRH/BD/144200/2019.info:eu-repo/semantics/publishedVersio

A distributed load scheduling mechanism for micro grids

Several protocols have recently been defined for smart grids that enable the communication between electric devices and energy management systems. While these protocols and architectures can already be applied in different fields of micro grids, it is still not clear how the distributed resources and constraints of such electrical grids can be managed in an optimum way. In order to achieve a reduction in electricity costs and maximizing investments made in renewable sources, an optimization mechanism should be used to perform load scheduling, considering different variables such as forecasted power generation curve from renewable sources, different tariffs' rates, electric circuit constraints, user restrictions and correspondent comfort levels. Given these considerations, this work defines and evaluates a distributed micro grid resource management architecture and protocol which is able to optimize load scheduling while considering all the mentioned restrictions and parameters. The proposed architecture was implemented on a multi-agent simulator and the performed tests show that significant reductions in electricity cost can be achieved using this methodology. © 2014 IEEE

A review of community electrical energy systems

This paper is aimed at new entrants into the field of electrical community energy. It reviews some of the work that is underway into community electrical energy projects. This review includes a summary of key issues and components which need consideration including some or all of the following; demand side management, energy storage (including vehicle to grid) and renewable generation. The paper looks further into the energy management schemes of these projects and summarises previously published methodology in the area

A Multi-Agent Energy Trading Competition

The energy sector will undergo fundamental changes over the next ten years. Prices for fossil energy resources are continuously increasing, there is an urgent need to reduce CO2 emissions, and the United States and European Union are strongly motivated to become more independent from foreign energy imports. These factors will lead to installation of large numbers of distributed renewable energy generators, which are often intermittent in nature. This trend conflicts with the current power grid control infrastructure and strategies, where a few centralized control centers manage a limited number of large power plants such that their output meets the energy demands in real time. As the proportion of distributed and intermittent generation capacity increases, this task becomes much harder, especially as the local and regional distribution grids where renewable energy generators are usually installed are currently virtually unmanaged, lack real time metering and are not built to cope with power flow inversions (yet). All this is about to change, and so the control strategies must be adapted accordingly. While the hierarchical command-and-control approach served well in a world with a few large scale generation facilities and many small consumers, a more flexible, decentralized, and self-organizing control infrastructure will have to be developed that can be actively managed to balance both the large grid as a whole, as well as the many lower voltage sub-grids. We propose a competitive simulation test bed to stimulate research and development of electronic agents that help manage these tasks. Participants in the competition will develop intelligent agents that are responsible to level energy supply from generators with energy demand from consumers. The competition is designed to closely model reality by bootstrapping the simulation environment with real historic load, generation, and weather data. The simulation environment will provide a low-risk platform that combines simulated markets and real-world data to develop solutions that can be applied to help building the self-organizing intelligent energy grid of the future

Smart Grid Communications: Overview of Research Challenges, Solutions, and Standardization Activities

Optimization of energy consumption in future intelligent energy networks (or Smart Grids) will be based on grid-integrated near-real-time communications between various grid elements in generation, transmission, distribution and loads. This paper discusses some of the challenges and opportunities of communications research in the areas of smart grid and smart metering. In particular, we focus on some of the key communications challenges for realizing interoperable and future-proof smart grid/metering networks, smart grid security and privacy, and how some of the existing networking technologies can be applied to energy management. Finally, we also discuss the coordinated standardization efforts in Europe to harmonize communications standards and protocols.Comment: To be published in IEEE Communications Surveys and Tutorial

Demand Side Management of Electric Vehicles in Smart Grids: A survey on strategies, challenges, modeling, and optimization

The shift of transportation technology from internal combustion engine (ICE) based vehicles to electricvehicles (EVs) in recent times due to their lower emissions, fuel costs, and greater efficiency hasbrought EV technology to the forefront of the electric power distribution systems due to theirability to interact with the grid through vehicle-to-grid (V2G) infrastructure. The greater adoptionof EVs presents an ideal use-case scenario of EVs acting as power dispatch, storage, and ancillaryservice-providing units. This EV aspect can be utilized more in the current smart grid (SG) scenarioby incorporating demand-side management (DSM) through EV integration. The integration of EVswith DSM techniques is hurdled with various issues and challenges addressed throughout thisliterature review. The various research conducted on EV-DSM programs has been surveyed. This reviewarticle focuses on the issues, solutions, and challenges, with suggestions on modeling the charginginfrastructure to suit DSM applications, and optimization aspects of EV-DSM are addressed separatelyto enhance the EV-DSM operation. Gaps in current research and possible research directions have beendiscussed extensively to present a comprehensive insight into the current status of DSM programsemployed with EV integration. This extensive review of EV-DSM will facilitate all the researchersto initiate research for superior and efficient energy management and EV scheduling strategies andmitigate the issues faced by system uncertainty modeling, variations, and constraints