Two-Stage Stochastic Optimization Model for Multi-Microgrid Planning

Enrique Gabriel Vera

doi:10.1109/TSG.2022.3211449

Two-Stage Stochastic Optimization Model for Multi-Microgrid Planning

2023-05-01

21

PRE

AI

E

Enrique Gabriel Vera

C

Claudio A. Cañizares *

DOI：10.1109/TSG.2022.3211449

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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 Sao 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.

Keyword:

Planning

Batteries

Stochastic processes

Costs

Uncertainty

Optimization

Solar panels

Multi-microgrids

planning

renewable energy sources

stochastic optimization

uncertainties

Two-Stage Stochastic Optimization Model for Multi-Microgrid Planning

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