Clean Technologies and Environmental Policy

, Volume 17, Issue 5, pp 1171–1193 | Cite as

A power grand composite curves approach for analysis and adaptive operation of renewable energy smart grids

  • Damian Giaouris
  • Athanasios I. Papadopoulos
  • Spyros Voutetakis
  • Simira Papadopoulou
  • Panos Seferlis
Original Paper
First Online:
Received:
Accepted:

Abstract

This work proposes the use of the power grand composite curves (PGCC) method to identify energy recovery targets in renewable energy smart grids and to adaptively adjust their operation in short-term energy requirements through appropriately selected power management strategies (PMS). A PMS is the sequence of decisions offering efficient utilization of resources and equipment to meet specific targets. The aim is to identify the appropriate PMS within recurrent subsequent time intervals that efficiently serves the desired operating goals in view of operating variability. This is approached by predicting the PGCC for a time horizon extending into the future. Subsequently, the PGCC is appropriately shifted to set a target for the minimum energy inventory needed by the end of the current interval for which decisions about the system operation are sought in order to satisfy the system operating goals. The target energy inventory is guaranteed in the current interval by selecting the PMS that best matches the identified target. A formal mathematical framework is presented, associating Pinch analysis with PMS within a generic model considering numerous structural and temporal grid interactions. The proposed method is implemented on an actual hybrid smart grid considering multiple RES-based energy generation and storage options.

Keywords

Smart grids Energy management Power pinch analysis Grand composite curves 

Abbreviations

List of symbols

\({\text{AEEND}}^{{l,q_{k} }}\)

Available excess electricity for next interval for accumulator l under PMS q k

BAT

Battery

BF

Low-pressure (buffer) storage tanks

Cl

Capacity of accumulator l

CMP

Compressor

DSL

Diesel generator

EL

Electrolyzer

\(F_{n}^{{{\text{In}},j}}\)

Input flow at resource n, in state j

\(F_{m \to n}^{{{\text{Out}},j}}\)

Output flow from resource m to resource n, in state j

FC

Fuel cell

FT

Long-term storage tank

f

Function that defines a PMS

H

Overall time span

H2HP

Hydrogen in high pressure

H2LP

Hydrogen in low pressure

H2O

Water

HRES

Hybrid renewable energy system

hk

PMS in interval k

L

Logical operator

\(L_{m \to n}^{i}\)

Logical operator for the variable \(\varepsilon_{m \to n}^{i} \left( t \right)\)

\(L_{m \to n}^{{{\text{SOAcc}}^{l} }}\)

Logical operator for the accumulator l

LD

Load

Lo

Lower desired limit

\({\text{Lo}}_{m \to n}^{{{\text{SOAcc}}^{l} }}\)

Lower desired limit for accumulator l

\({\text{MOES}}^{{l,q_{k} }}\)

Maximum outsourced energy supply for accumulator l under PMS q k

Nc

Control horizon

Nlm

Number of Lo type limits

Np

Prediction horizon

\({\text{OES}}^{{l,q_{k} }}\)

Outsourced energy supply required for accumulator l under PMS q k

P(t)

Net power in the system, i.e., power produced by RES—power demanded by LD

PCC

Power composite curve

PGCC

Power grand composite curve

PMS

Power management strategy

POW

Electrical power

PV

Photovoltaic panels

pk

PMS in interval k

Q

Set of all available PMS

qk

PMS in interval k

RES

Renewable energy sources

Rs

Set of resources

\(r_{m \to n}^{{{\text{SOAcc}}^{l} }}\)

Parameter associated with temporal conditions in accumulator l

\({\text{SF}}_{n}^{j}\)

External input at resource n, in state j

SOAccl

State of accumulator l

\({\text{SOAcc}}_{ \hbox{min} }^{l}\)

Initial value of SOAcc l that produces the minimum value of SOAcc l

\({\text{SOAcc}}^{{l,q_{k} }}\)

State of accumulator l under PMS q k

\({\text{SOAcc}}_{\text{req}}^{{l,q_{k} }}\)

Initial value of SOAcc l that produces the minimum value of SOAcc l equal to Lo

\({\text{SOAcc}}_{\text{TAR}}^{l}\)

Target value for SOAcc l

St

Set of states

T

End of time interval

th

Time duration of hysteresis zone

tLo

Instant when \({\text{SOAcc}}^{{l,q_{k} }}\) reaches the value of the limit Lo

tmin

Instant when \({\text{SOAcc}}^{{l,q_{k} }}\) reaches the minimum value of \({\text{SOAcc}}^{{l,q_{k} }}\) in interval k

t0

Beginning of time interval under study

t−

Previous time instant

Up

Upper operating limit

\({\text{Up}}_{m \to n}^{{{\text{SOAcc}}^{l} }}\)

Upper operating limit for accumulator l

WG

Wind generator

WT

Water tank

Greek symbols

az

Weights used in Eqs. (11) and (12a, b)

ΔT

Duration of time interval

\(\varepsilon_{m \to n}\)

Binary variable that defines the connection of resource m to resource n

\(\rho_{m \to n}^{{{\text{SOAcc}}^{l} }}\)

Binary variable associated with temporal conditions in accumulator l

Subscripts/Superscripts

Acc

Accumulator

Avl

Available

Conv

Converter

Gen

General

In

Input

j

State of a converter or accumulator

k

Time interval

l

Accumulators as part of the set of resources

Mat

Materials

max

Maximum

min

Minimum

n, m

Resources (converters or accumulators) indicating the type of equipment employed to perform conversion and accumulation tasks \(m,n \in {\text{Rs}},m \ne n\)

Nrg

Energy

OFF

Deactivated converter

ON

Activated converter

Out

Output

Req

Required

TAR

Target

z

Limit number, \(z \in \left[ {1,N_{lm} } \right]\)

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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Damian Giaouris
    • 1
  • Athanasios I. Papadopoulos
    • 1
  • Spyros Voutetakis
    • 1
  • Simira Papadopoulou
    • 1
    • 2
  • Panos Seferlis
    • 1
    • 3
  1. 1.Chemical Process Engineering and Energy Resources Institute, Centre for Research and Technology HellasThessalonikiGreece
  2. 2.Department of Automation EngineeringAlexander Technological Educational Institute of ThessalonikiThessalonikiGreece
  3. 3.Department of Mechanical EngineeringAristotle University of ThessalonikiThessalonikiGreece

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