1,001 research outputs found
Optimized Local Control for Active Distribution Grids using Machine Learning Techniques
Modern distribution system operators are facing a changing scenery due to the increasing penetration of distributed energy resources, introducing new challenges to system operation. In order to ensure secure system operation at a low cost, centralized and decentralized operational schemes are used to optimally dispatch these units. This paper proposes a decentralized, real-time, operation scheme for the optimal dispatch of distributed energy resources in the absence of extensive monitoring and communication infrastructure. This scheme uses an offline, centralized, optimal operation algorithm, with historical information, to generate a training dataset consisting of various operating conditions and corresponding distributed energy resources optimal decisions. Then, this dataset is used to design the individual local controllers for each unit with the use of machine learning techniques. The performance of the proposed method is tested on a low-voltage distribution network and is compared against centralized and existing decentralized methods
Kernel-Based Learning for Smart Inverter Control
Distribution grids are currently challenged by frequent voltage excursions
induced by intermittent solar generation. Smart inverters have been advocated
as a fast-responding means to regulate voltage and minimize ohmic losses. Since
optimal inverter coordination may be computationally challenging and preset
local control rules are subpar, the approach of customized control rules
designed in a quasi-static fashion features as a golden middle. Departing from
affine control rules, this work puts forth non-linear inverter control
policies. Drawing analogies to multi-task learning, reactive control is posed
as a kernel-based regression task. Leveraging a linearized grid model and given
anticipated data scenarios, inverter rules are jointly designed at the feeder
level to minimize a convex combination of voltage deviations and ohmic losses
via a linearly-constrained quadratic program. Numerical tests using real-world
data on a benchmark feeder demonstrate that nonlinear control rules driven also
by a few non-local readings can attain near-optimal performance.Comment: Submitted to the 2018 IEEE Global Signal and Information Processing
Conf., Symposium on Smart Energy Infrastructure
Deep Reinforcement Learning for Distribution Network Operation and Electricity Market
The conventional distribution network and electricity market operation have become challenging under complicated network operating conditions, due to emerging distributed electricity generations, coupled energy networks, and new market behaviours. These challenges include increasing dynamics and stochastics, and vast problem dimensions such as control points, measurements, and multiple objectives, etc. Previously the optimization models were often formulated as conventional programming problems and then solved mathematically, which could now become highly time-consuming or sometimes infeasible. On the other hand, with the recent advancement of artificial intelligence technologies, deep reinforcement learning (DRL) algorithms have demonstrated their excellent performances in various control and optimization fields. This indicates a potential alternative to address these challenges.
In this thesis, DRL-based solutions for distribution network operation and electricity market have been investigated and proposed. Firstly, a DRL-based methodology is proposed for Volt/Var Control (VVC) optimization in a large distribution network, to effectively control bus voltages and reduce network power losses. Further, this thesis proposes a multi-agent (MA)DRL-based methodology under a complex regional coordinated VVC framework, and it can address spatial and temporal uncertainties. The DRL algorithm is also improved to adapt to the applications. Then, an integrated energy and heating systems (IEHS) optimization problem is solved by a MADRL-based methodology, where conventionally this could only be solved by simplifications or iterations. Beyond the applications in distribution network operation, a new electricity market service pricing method based on a DRL algorithm is also proposed. This DRL-based method has demonstrated good performance in this virtual storage rental service pricing problem, whereas this bi-level problem could hardly be solved directly due to a non-convex and non-continuous lower-level problem. These proposed methods have demonstrated advantageous performances under comprehensive case studies, and numerical simulation results have validated the effectiveness and high efficiency under different sophisticated operation conditions, solution robustness against temporal and spatial uncertainties, and optimality under large problem dimensions
An overview of grid-edge control with the digital transformation
Distribution networks are evolving to become more responsive with increasing integration of distributed energy resources
(DERs) and digital transformation at the grid edges. This evolution imposes many challenges to the operation of the network,
which then calls for new control and operation paradigms. Among others, a so-called grid-edge control is emerging to
harmonise the coexistence of the grid control system and DER’s autonomous control. This paper provides a comprehensive
overview of the grid-edge control with various control architectures, layers, and strategies. The challenges and opportunities
for such an approach at the grid edge with the integration of DERs and digital transformation are summarised. The potential
solutions to support the network operation by using the inherent controllability of DER and the availability of the digital
transformation at the grid edges are discussed
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