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

HOMEBOTS: Intelligent Decentralized Services for Energy Management

The deregulation of the European energy market, combined with emerging advanced capabilities of information technology, provides strategic opportunities for new knowledge-oriented services on the power grid. HOMEBOTS is the namewe have coined for one of these innovative services: decentralized power load management at the customer side, automatically carried out by a `society' of interactive household, industrial and utility equipment. They act as independent intelligent agents that communicate and negotiate in a computational market economy. The knowledge and competence aspects of this application are discussed, using an improved \ud version of task analysis according to the COMMONKADS knowledge methodology. Illustrated by simulation results, we indicate how customer knowledge can be mobilized to achieve joint goals of cost and energy savings. General implications for knowledge creation and its management are discussed

Catching Cheats: Detecting Strategic Manipulation in Distributed Optimisation of Electric Vehicle Aggregators

Given the rapid rise of electric vehicles (EVs) worldwide, and the ambitious targets set for the near future, the management of large EV fleets must be seen as a priority. Specifically, we study a scenario where EV charging is managed through self-interested EV aggregators who compete in the day-ahead market in order to purchase the electricity needed to meet their clients' requirements. With the aim of reducing electricity costs and lowering the impact on electricity markets, a centralised bidding coordination framework has been proposed in the literature employing a coordinator. In order to improve privacy and limit the need for the coordinator, we propose a reformulation of the coordination framework as a decentralised algorithm, employing the Alternating Direction Method of Multipliers (ADMM). However, given the self-interested nature of the aggregators, they can deviate from the algorithm in order to reduce their energy costs. Hence, we study the strategic manipulation of the ADMM algorithm and, in doing so, describe and analyse different possible attack vectors and propose a mathematical framework to quantify and detect manipulation. Importantly, this detection framework is not limited the considered EV scenario and can be applied to general ADMM algorithms. Finally, we test the proposed decentralised coordination and manipulation detection algorithms in realistic scenarios using real market and driver data from Spain. Our empirical results show that the decentralised algorithm's convergence to the optimal solution can be effectively disrupted by manipulative attacks achieving convergence to a different non-optimal solution which benefits the attacker. With respect to the detection algorithm, results indicate that it achieves very high accuracies and significantly outperforms a naive benchmark

Multi-Agent System Control and Coordination of an Electrical Network

Multi-Agent Systems (MAS) have the potential to solve Active Network Management (ANM) problems arising from an increase in Distributed Energy Resources (DER). The aim of this research is to integrate a MAS into an electrical network emulation for the purpose of implementing ANM. Initially an overview of agents and MAS and how their characteristics can be used to control and coordinate an electrical network is presented. An electrical network comprising a real-time emulated transmission network connected to a live DER network controlled and coordinated by a MAS is then constructed. The MAS is then used to solve a simple ANM problem: the control and coordination of an electrical network in order to maintain frequency within operational limits. The research concludes that a MAS is successful in solving this ANM problem and also that in the future the developed MAS can be applied to other ANM problems. © 2012 IEEE

Systematic categorization of optimization strategies for virtual power plants

Due to the rapid growth in power consumption of domestic and industrial appliances, distributed energy generation units face difficulties in supplying power efficiently. The integration of distributed energy resources (DERs) and energy storage systems (ESSs) provides a solution to these problems using appropriate management schemes to achieve optimal operation. Furthermore, to lessen the uncertainties of distributed energy management systems, a decentralized energy management system named virtual power plant (VPP) plays a significant role. This paper presents a comprehensive review of 65 existing different VPP optimization models, techniques, and algorithms based on their system configuration, parameters, and control schemes. Moreover, the paper categorizes the discussed optimization techniques into seven different types, namely conventional technique, offering model, intelligent technique, price-based unit commitment (PBUC) model, optimal bidding, stochastic technique, and linear programming, to underline the commercial and technical efficacy of VPP at day-ahead scheduling at the electricity market. The uncertainties of market prices, load demand, and power distribution in the VPP system are mentioned and analyzed to maximize the system profits with minimum cost. The outcome of the systematic categorization is believed to be a base for future endeavors in the field of VPP development

An Exchange Mechanism to Coordinate Flexibility in Residential Energy Cooperatives

Energy cooperatives (ECs) such as residential and industrial microgrids have the potential to mitigate increasing fluctuations in renewable electricity generation, but only if their joint response is coordinated. However, the coordination and control of independently operated flexible resources (e.g., storage, demand response) imposes critical challenges arising from the heterogeneity of the resources, conflict of interests, and impact on the grid. Correspondingly, overcoming these challenges with a general and fair yet efficient exchange mechanism that coordinates these distributed resources will accommodate renewable fluctuations on a local level, thereby supporting the energy transition. In this paper, we introduce such an exchange mechanism. It incorporates a payment structure that encourages prosumers to participate in the exchange by increasing their utility above baseline alternatives. The allocation from the proposed mechanism increases the system efficiency (utilitarian social welfare) and distributes profits more fairly (measured by Nash social welfare) than individual flexibility activation. A case study analyzing the mechanism performance and resulting payments in numerical experiments over real demand and generation profiles of the Pecan Street dataset elucidates the efficacy to promote cooperation between co-located flexibilities in residential cooperatives through local exchange.Comment: Accepted in IEEE ICIT 201

Analysis of peer-to-peer electricity trading models in a grid-connected microgrid

The thesis proposed an investigation on the implementation of peer-to-peer (P2P) energy transaction platforms in power systems as a possible energy management solution to deal with distributed generation (DG) and renewable energy sources (RES) penetration. Firstly, a state of the art of the current P2P trading technologies development is provided, reviewing and analysing several projects carried out in this field in recent years and doing a comparison of the models, considering their commonalities, strengths and shortcomings, along with.an overview of the main techniques utilized. In the second stage, the focus shifts on the presentation of the structure of the system used in the case study investigated in the project. A multi agent system (MAS) integrated with a micro grid management platform (μGIM) acts in a grid connected microgrid located in an office building, equipped with solar panels (PVs) to operate energy transactions among different agents (prosumers/consumers). Each agent is represented by a tenant of a zone in the building, which owns a part of the total photovoltaic generation. From the starting point of the English auction model, initially used in the trading platform, two new algorithms have been implemented in the system in an attempt to improve the efficiency of the trading process. The algorithms formulation is based on the analysis of the initial model behaviour and results, and is supported by the state of art provided in the first chapter. A specific simulation platform was used to run the model using consumption data recorded from previous week of monitoring, in order to compare different trading algorithms working on the same consumption/generation profile. The developments obtained from this study proves the capabilities of the P2P energy trading to advantage the end users, allowing them to manage their own energy and pursue their personal goals. They also emphasize that this type of models have still a good improvement margin and with further studies they can represent a key element in the future smart grids and decentralized systems

How to trade electricity flexibility using artificial intelligence - An integrated algorithmic framework

In course of the energy transition, the growing share of Renewable Energy Sources (RES) makes electricity generation more decentralized and intermittent. This increases the relevance of exploiting flexibility potentials that help balancing intermittent RES supply and demand and, thus, contribute to overall system resilience. Digital technologies, in the form of automated trading algorithms, may considerably contribute to flexibility exploitation, as they enable faster and more accurate market interactions. In this paper, we develop an integrated algorithmic framework that finds an optimal trading strategy for flexibility on multiple markets. Hence, our work supports the trading of flexibility in a multi-market environment that results in enhanced market integration and harmonization of economically traded and physically delivered electricity, which finally promotes resilience in highly complex electricity systems