188 research outputs found

    Distributed Control Methods for Integrating Renewable Generations and ICT Systems

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    With increased energy demand and decreased fossil fuels usages, the penetration of distributed generators (DGs) attracts more and more attention. Currently centralized control approaches can no longer meet real-time requirements for future power system. A proper decentralized control strategy needs to be proposed in order to enhance system voltage stability, reduce system power loss and increase operational security. This thesis has three key contributions: Firstly, a decentralized coordinated reactive power control strategy is proposed to tackle voltage fluctuation issues due to the uncertainty of output of DG. Case study shows results of coordinated control methods which can regulate the voltage level effectively whilst also enlarging the total reactive power capability to reduce the possibility of active power curtailment. Subsequently, the communication system time-delay is considered when analyzing the impact of voltage regulation. Secondly, a consensus distributed alternating direction multiplier method (ADMM) algorithm is improved to solve the optimal power ow (OPF) problem. Both synchronous and asynchronous algorithms are proposed to study the performance of convergence rate. Four different strategies are proposed to mitigate the impact of time-delay. Simulation results show that the optimization of reactive power allocation can minimize system power loss effectively and the proposed weighted autoregressive (AR) strategies can achieve an effective convergence result. Thirdly, a neighboring monitoring scheme based on the reputation rating is proposed to detect and mitigate the potential false data injection attack. The simulation results show that the predictive value can effectively replace the manipulated data. The convergence results based on the predictive value can be very close to the results of normal case without cyber attack

    Advanced Control and Optimization for Future Grid with Energy Storage Devices

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    In the future grid environment, more sustainable resources will be increasing steadily. Their inherent unpredictable and intermittent characteristics will inevitably cause adverse impacts on the system static, dynamic and economic performance simultaneously. In this context, energy storage (ES) devices have been receiving growing attention because of their significant falling prices. Therefore, how to utilize these ES to help alleviate the problem of renewable energy (RE) sources integration has become more and more attractive. In my thesis, I will try to resolve some of the related problems from several perspectives. First of all, a comprehensive Future Australian transmission network simulation platform is constructed in the software DIgSILENT. Then in-depth research has been done on the aspect of frequency controller design. Based on mathematical reasoning, an advanced robust H∞ Load Frequency Controller (LFC) is developed, which can be used to assist the power system to maintain a stable frequency when accommodating more renewables. Afterwards, I develop a power system sensitivity analysis based-Enhanced Optimal Distributed Consensus Algorithm (EODCA). In the following study, a Modified Consensus Alternating Direction Method of Multipliers (MC-ADMM) is proposed, with this approach it can be verified that the convergence speed is notably accelerated even for complex large dimensional systems. Overall, in the Master thesis, I successfully provide several novel and practical solutions, algorithms and methodologies in regards to tackling both the frequency, voltage and the power flow issues in a future grid with the assistance of energy storage devices. The scientific control and optimal dispatch of these facilities could provide us with a promising approach to mitigate the potential threats that the intermittent renewables posed on the power system in the following decades

    Distributed Adaptive Primal Algorithm for P2P-ETS over Unreliable Communication Links

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    Algorithms for distributed coordination and control are increasingly being used in smart grid applications including peer-to-peer energy trading and sharing to improve reliability and efficiency of the power system. However, for realistic deployment of these algorithms, their designs should take into account the suboptimal conditions of the communication network, in particular the communication links that connect the energy trading entities in the energy network. This study proposes a distributed adaptive primal (DAP) routing algorithm to facilitate communication and coordination among proactive prosumers in an energy network over imperfect communication links. The proposed technique employs a multi-commodity flow optimization scheme in its formulation with the objective to minimize both the communication delay and loss of energy transactional messages due to suboptimal network conditions. Taking into account realistic constraints relating to network delay and communication link capacity between the peers, the DAP routing algorithm is used to evaluate network performance using various figures of merit such as probability of signal loss, message delay, congestion and different network topologies. Further, we address the link communication delay problem by redirecting traffic from congested links to less utilized ones. The results show that the proposed routing algorithm is robust to packet loss on the communication links with a 20% reduction in delay compared with hop-by-hop adaptive link state routing algorith
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