학술논문

Two-Stage Stochastic Optimization Model for Multi-Microgrid Planning
Document Type
Periodical
Source
IEEE Transactions on Smart Grid IEEE Trans. Smart Grid Smart Grid, IEEE Transactions on. 14(3):1723-1735 May, 2023
Subject
Communication, Networking and Broadcast Technologies
Computing and Processing
Power, Energy and Industry Applications
Planning
Batteries
Stochastic processes
Costs
Uncertainty
Optimization
Solar panels
Multi-microgrids
planning
renewable energy sources
stochastic optimization
uncertainties
Language
ISSN
1949-3053
1949-3061
Abstract
This paper presents a Two Stage stochastic Programming (TSSP) model for the planning of Multi-Microgrids (MMGs) in Active Distribution Networks (ADNs). The model aims to minimize the total costs while benefiting from interconnections of Microgrids (MGs), considering uncertainties associated with electricity demand and Renewable Energy Sources (RESs). The associated uncertainties are analyzed using Geometric Brownian Motion (GBM) and probability distribution functions (pdfs). The model includes long-term purchase decisions and short-term operational constraints, using Geographical information Systems (GIS) to realistically estimate rooftop solar limits. The planning model is used to study the feasibility of implementing an MMG system consisting of 4 individual Microgrids (MGs) at an ADN in a municipality in the state of São Paulo, Brazil. The results show that the TSSP model tends to be less conservative than the deterministic planning model, which is based on simple and pessimistic reserve constraints, while performing faster than a simple Stochastic Linear Programming (SLP) algorithm, with higher accuracy.