Hierarchical Energy Management System for Optimising Self-consumption in Building MicroGrids

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Submitted on 24 Jan 2020

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Hierarchical Energy Management System for Optimising Self-consumption in Building MicroGrids

Daniela Yassuda Yamashita, Ionel Vechiu, Jean-Paul Gaubert

To cite this version:

Daniela Yassuda Yamashita, Ionel Vechiu, Jean-Paul Gaubert. Hierarchical Energy Management System for Optimising Self-consumption in Building MicroGrids. Microgrid Summer School, Jul 2019, Belfort, France. �hal-02454915�

Journées SEEDS 11-14 Juin 2019 - Ile d'Oléron

Ministère de lʼEnseignement supérieur, de la Recherche et de lʼInnovation

GT Micro-Réseau

Hierarchical Energy Management System for

Optimising Self-consumption in Building MicroGrids

Three-level hierarchical control structure for a grid- connected building microgrid.

Control Strategy

Aiming to keep adequate

ranges of self-consumption and self-coverage indexes under PV power generation and building power

consumption uncertainties, the EMS was divided into

three modules:

• Upper Model Predictive Control (MPC ₁ )

• Lower Model Predictive Control (MPC ₂ )

• Power Sharing Module with state machine

Data prediction of PV power generation ( 𝑷 _𝑷𝑽 ), building power consumption ( 𝑷 _{𝒄𝒐𝒏𝒔} ) and number

of PEVs plugged to the BMG ( 𝑵 _𝑷𝑬𝑽 )

State of charge of each Plug-in Electric Vehicle (PEV).

(a) Ideal case without any disturbance.(b) Case with error in data prediction.

Power sharing Power sharing

Hierarchical Control MPC. (a) MPC ₁ variables in Daily Market time frame. (b) MPC ₂ variables in Intraday Market time frame. (c) Li-ion batteries SoC, self-consumption and self-sufficiency index in the three days.

Without disturbance With disturbance

Hierarchical MPC Hierarchical MPC

Building MicroGrid Overview

Renewable energy sources are increasingly deployed as

distributed generators,

restructuring the traditional electrical grid toward smart

grids. Their intermittent power generation makes difficult the development of a complete

carbon-free MicroGrid. The

Hierarchical Control structure

has demonstrated very suitable to manage multivariable

systems with different time frames like those of Building MicroGrids (BMG).

D. Y. Yamashita ^1,2 , I. Vechiu ¹ , J. P. Gaubert ²

(1) ESTIA Institute of Technology

(2) Université de Poitiers, Laboratoire d’Informatique et d’Automatique pour les Systèmes (LIAS) – ENSIP

$ Electricity price Energy exchange

Data prediction

P _GRID ^DM

P _GRID ^IM

$

$

Transformer

P PEV1

P PEVn

P _BAT ^IM

P ^IM _{all_PEV} P max&min All_PEV

P _{all_PEV} ^DM P _BAT ^DM

.. . ...

SOC ^DM _y x = {PEV 1 n , BAT }

y = {PEV ^all , BAT }

MPC ¹

PEV ¹

P OWER S HARING

PEV ⁿ

Grid

Aggregator

SOC ^meas _x SOC ^IM _y

G rid s ig n als

MPC ²

B u ild in g M ic ro g rid M a in G rid LC

DC DC

MPPT

DC DC

LC

DC DC

LC

DC DC

C 1

Cn w _pv

w d

Load

DC

AC