Hp Phuong Nguyen

A Game Theory Strategy to Integrate Distributed Agent-Based Functions in Smart Grids

The increasing incorporation of renewable energy sources and the emergence of new forms and patterns of electricity consumption are contributing to the upsurge in the complexity of power grids. A bottom-up-agent-based approach is able to handle the new environment, such that the system reliability can be maintained and costs reduced. However, this approach leads to possible conflicting interests between maintaining secure grid operation and the market requirements. This paper proposes a strategy to solve the conflicting interests in order to achieve overall optimal performance in the electricity supply system. The method is based on a cooperative game theory to optimally allocate resources from all (local) actors, i.e., network operators, active producers, and consumers. Via this approach, agent-based functions, for facilitating both network services and energy markets, can be integrated and coordinated. Simulations are performed to verify the proposed concept on a medium voltage 30-bus test network. Results show the effectiveness of the approach in optimally harmonizing functions of power routing and matching.

Smart Power Router: A Flexible Agent-Based Converter Interface in Active Distribution Networks

Due to the large-scale implementation of distributed generation, the power delivery system is changing gradually from a “vertically” to a “horizontally” controlled and operated structure. This transition has prompted the emergence of the active distribution network (ADN) concept as an efficient and flexible distribution system to deal with various challenging issues. This paper addresses a multiagent system (MAS) as a suitable technology to manage autonomous control actions and perform the coordination in an ADN. In this agent-based ADN a smart power router is implemented, which can flexibly integrate network areas and optimally manage power flows. Operational and control functions of the power router has been investigated in a laboratory experiment. In this lab setup, a configuration of the power router is established in a combination of a three-inverter system and an agent. The experiments show that the ADN can operate in an efficient and flexible way with the support of the power router interface. The control function of the inverters and the operation of MAS are thoroughly verified.

Coordination of voltage regulation in Active Networks

This paper give an introduction about the Active Network concept that can be managed by a multi-agent system (MAS). Voltage regulation, one of Active Networks services, is then presented. The autonomous voltage control within each feeder (Cell) can be deployed by a combination of active and reactive power supports of distributed generators (DG). The coordination voltage control defines the optimal tap setting of the on-load tap changer (OLTC) while comparing amounts of control actions in each Cell. The test results show that the voltage regulation in Active Network can help to integrate more DGs and mitigate voltage violation effectively. The optimal solution can be reached within a small number of calculation iterations.

Social interaction interface for performance analysis of smart grids

Socio-economical and technological developments have prompted electric power systems to move forward to an era of Smart Grids. This mainstream concept has a strong interdisciplinary nature by using state-of-the-art technologies in the fields of Information and Communication Technology (ICT), power electronics, and control systems. Modeling and simulation are fundamental steps to accomplish possible applications of this complex and integrated scheme. However, most of the existing simulation platforms, either commercial or free and open source packages, hardly adapt to this requirements of Smart Grids. This paper presents an on-going framework which integrates open source software, OpenDSS, to provide different web services for end-users. This application aims to create a robust computation platform to support customers in making decisions. Different network functions can be verified in this assessment context tool. Working as a social network, this program will empower end-users with an interaction interface to make them fully aware of Smart Grid applications.

Comparison of machine learning methods for estimating energy consumption in buildings

The increasing number of decentralized renewable energy sources together with the grow in overall electricity consumption introduce many new challenges related to dimensioning of grid assets and supply-demand balancing. Approximately 40% of the total energy consumption is used to cover the needs of commercial and office buildings. To improve the design of the energy infrastructure and the efficient deployment of resources, new paradigms have to be thought up. Such new paradigms need automated methods to dynamically predict the energy consumption in buildings. At the same time these methods should be easily expandable to higher levels of aggregation such as neighborhoods and the power grid. Predicting energy consumption for a building is complex due to many influencing factors, such as weather conditions, performance and settings of heating and cooling systems, and the number of people present. In this paper, we investigate a newly developed stochastic model for time series prediction of energy consumption, namely the Conditional Restricted Boltzmann Machine (CRBM), and evaluate its performance in the context of building automation systems. The assessment is made on a real dataset consisting of 7 weeks of hourly resolution electricity consumption collected from a Dutch office building. The results showed that for the energy prediction problem solved here, CRBM outperforms Artificial Neural Networks (ANNs), and Hidden Markov Models (HMMs).

Power flow management in active networks

This paper proposes a new method to manage the active power in the distribution systems, a function under the framework of the active network (AN) concept. An application of the graph theory is introduced to cope with the optimal power generation (DGs/Cells dispatch) and interarea power flows. The algorithm is implemented in a distributed way supported by the multi-agent system (MAS) technology. Simulations show how the method works in cases of optimal operation, congestion management, and power generation cost change.

Local market framework for exploiting flexibility from the end users

This paper introduces a decentralized implicit interaction framework for trading flexibility available from proactive end users (prosumers) in an economically-efficient way. The proposed framework consists of two mechanisms: ahead planning via markets and real-time dispatching. The ahead, market-based planning includes two mechanisms, day-ahead and intra-day, which are operated by a local flexibility market operator. Each local market seeks to adjust the energy programs before they will be forwarded to the wholesale energy market such that, if accepted, the programs will result in no congestion issues in the distribution grid. The real-time dispatching consists of a set of control actions that are determined and implemented by the DSO to resolve a network congestion issue, should the market-based planning fail. The second part of the paper focuses on establishing a strategy for the DSO to procure the flexibility it needs from the above-mentioned day-ahead and intra-day markets, as well as through real-time dispatching, at the lowest possible cost. To this end, a hierarchical, bi-level optimization problem is proposed, which is mathematically proven to yield the optimal bidding strategy (including prices and volumes) for the DSO.

Congestion management in smart distribution network

The accelerating use of Distributed Energy Resources (DERs) and new forms of loads connected in the Medium Voltage (MV) and Low Voltage (LV) networks are posing a great challenge for the Distribution System Operators (DSOs) in the near future. The bidirectional and uncertain flow of power may result in congestions at certain points in the distribution network. Consequently, assets are overloaded; voltage deviations can occur and cascading failures may take place. Therefore, the DSOs are compelled to investigate and optimize their asset investment cost by introducing smart grid functionalities in order to mitigate investments. Out of a number of alternatives, congestion management is one of the most promising strategies to deal with the network issues. Congestion management schemes have traditionally been treated in the transmission system level. But with the widespread use of Distributed Generators (DGs) and expected severe loading conditions, the management procedure will have to be applied in the distribution network as well. This paper discusses the need and possibility of congestion management in a smart distribution network.

Optimizing the energy exchange between the Smart Grid and Building Systems

Various Smart Grid (SG) technologies and concepts are currently under investigation, driven by the goals of energy transition policies towards future sustainable, reliable and affordable electricity supply systems. This paper presents an approach for modeling the interaction between the Smart Grid and Building Energy Management Systems (SG-BEMS), using Multi Agent Systems control. The interaction consists of three layers: the smart building, the neighborhood, and the distribution grid. It enables the continuous bidirectional flow of energy and information between SG and BEMS. The proposed framework combines optimization techniques inspired by dynamic game theory and stochastic optimization algorithms. The goal of the optimization is to increase the overall performance, while keeping a good level of comfort for people in the built environment.

Real-time congestion management in active distribution network based on dynamic thermal overloading cost

The rapid proliferation of distributed energy resources (DERs) leads to capacity challenges, i.e. network congestions, in the low-voltage (LV) distribution networks. Different types of control strategies are being developed to tackle the challenges with direct switching actions such as load shedding or power curtailment. Alternatively, demand flexibility from the large number of DERs is being considered as a potential approach by influencing the individual end-users with various demand response (DR) programs. However, most of the DR-based solutions focus on scheduling phase, thus having a limitation to handle network issues in real-time grid operation. In order to improve DRs capability, besides a proper incentive scheme for involved actors, the DR-based approach needs to integrate network constraints and quantify this real-time information in its control process. In this paper, a novel method for real-time congestion management is proposed, which focuses on resolving the congestion problem at the MV/LV transformer. Detail models for different loads and thermal overloading of the MV/LV transformer are developed to realize the benefits of the demand flexibility. The overall performance of the integrated approach for the congestion management has been verified by a simulation with a typical LV network of the Netherlands.