Real-Time Advanced Energy-Efficient Management of an Active Radial Distribution Network

This article elucidates a new real-time optimization framework for advanced energy-efficient management of active radial distribution networks. The proposed energy management process leverages the benefits of simultaneous deployment of online direct load control (DLC) and conservation voltage reduct...

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Bibliographic Details
Published in:IEEE systems journal Vol. 16; no. 3; pp. 3602 - 3612
Main Authors: Paul, Subho, Padhy, Narayana Prasad
Format: Journal Article
Language:English
Published: New York IEEE 01.09.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Active distribution network
Batteries
conservation voltage reduction (CVR)
direct load control (DLC)
Electric power demand
Energy distribution
Energy management
Greedy algorithms
Integer programming
Linear programming
Load modeling
Lyapunov optimization
Manganese
Mixed integer
Optimization
Peak load
Queuing theory
Radial distribution
Reactive power
Real time
Real-time systems
Stochastic processes
successive mixed integer linear programming (s-MILP)
Voltage reduction
ISSN:1932-8184, 1937-9234
Online Access:Get full text
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Summary:This article elucidates a new real-time optimization framework for advanced energy-efficient management of active radial distribution networks. The proposed energy management process leverages the benefits of simultaneous deployment of online direct load control (DLC) and conservation voltage reduction (CVR) for decreasing peak energy demand. Initially the proposed problem is designed as a time-coupled stochastic mixed integer nonconvex programming (MINCP) to accommodate long-term offline beneficial aspects in real-time optimization framework, which is later simplified using merger of Queueing theory and Lyapunov optimization. A successive mixed integer linear programming (s-MILP) solution approach is proposed for accurate and fast convergence of the revised MINCP framework. The efficacy of the developed strategy is evaluated after comparing with two-benchmark energy management models (viz. offline and online greedy algorithm) by demonstrating on modified IEEE 69-bus test system. Further scalability of the proposed approach is validated by testing on large distribution networks.
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ISSN:1932-8184
1937-9234
DOI:10.1109/JSYST.2021.3113038