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  4. Stochastic Energy Management of Microgrid with Nodal Pricing
 
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Stochastic Energy Management of Microgrid with Nodal Pricing

Source
Journal of Modern Power Systems and Clean Energy
Author(s)
D., Prudhviraj, Dhanapala
P.B.S., Kiran, P. Bala Sai
N.M., Pindoriya, Naran M.  
DOI
10.35833/MPCE.2018.000519
Volume
8
Issue
1
Start Page
11-04-1900
End Page
110
Abstract
This paper develops a stochastic framework for the energy management of a microgrid to minimize the energy cost from the grid. It considers the uncertainties in solar photovoltaic (PV) generation, load demand, and electricity price. Furthermore, the opportunity of flexible load demand, i.e., the effect of demand response (DR), on the test system is studied. The uncertainties are modeled by using Monte Carlo simulations and the generated scenarios are reduced to improve the computational tractability. In general, microgrid scheduling is implemented by using substation (source node) price as a reference, but that reference price is not the same at all nodes. Therefore, this paper develops the nodal price based energy management in a microgrid to improve the scheduling accuracy. The stochastic energy management framework is formulated as a mixed integer non-linear programming (MINLP). Four case studies are simulated for a modified 15-node radial distribution network integrated with solar PV and battery energy storage system (BESS) to validate the effectiveness of the energy management framework for a microgrid with nodal pricing. � 2021 Elsevier B.V., All rights reserved.
Publication link
https://ieeexplore.ieee.org/ielx7/8685265/8966538/08922949.pdf
Sherpa Url
https://v2.sherpa.ac.uk/id/publication/28292
URI
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092891069&doi=10.35833%2FMPCE.2018.000519&partnerID=40&md5=18977ed581b19a95cf450047964e6d71
https://d8.irins.org/handle/IITG2025/29380
Keywords
Costs
Data storage equipment
Electric energy storage
Energy management
Integer programming
Microgrids
Monte Carlo methods
Nonlinear programming
Solar power generation
Stochastic systems
Battery energy storage systems
Computational tractability
Management frameworks
Mixed-integer nonlinear programming
Radial distribution networks
Scheduling accuracy
Solar photovoltaics
Stochastic framework
Scheduling
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