New Forecasting-based Solutions for Optimal Energy Consumption in

Microgrids with Load Shedding

Case Study: Petroleum Platform

Mohamed Ghaieth Abidi, Moncef Ben Smida and Mohamed Khalgui

LISI Lab, INSAT Institute, University of Carthage, Carthage, Tunisia

Keywords:

Microgrid, High Availability, Forecasting, Weather Condition, Control, Load Shedding.

Abstract:

This paper presents a new control strategy for optimal energy consumption in microgrids based on forecasting

and load shedding method. In islanded mode, only local generation resources are usable. In this mode, the

availability of resources is very inﬂuenced by meteorological factors. In order to achieve a high availability of

energy, microgrid has to satisfy high requirements on intelligent power management. The main objective of

this study is to develop a control based on the forecast of production and the priority of loads to ensure high

availability of electrical power for critical loads. A mathematical model for the proposed strategy is developed.

The simulation of this model on Matlab Simulink for different cases of microgrid topology (sources and loads)

shows clearly a high improvement of degree of availability of electrical power supply distribution in microgrid.

1 INTRODUCTION

Microgrid system is a new concept that aims to inte-

grate decentralized energy sources efﬁciently and re-

liably. A microgrid consists mainly of distributed en-

ergy resources, energy storage devices, ﬂexible loads

and energy controllers. A microgrid can operate in a

connected mode if is interconnected to the main grid,

or in an island mode if is disconnected from. In island

mode, the main constraints to ensure a high avail-

ability are the intermittent behaviour of distributed

generation sources and their possible unavailability

when they are required to product. To improve the

availability, several studies are interested in the use

of hybrid sources and power management strategies

based on balancing between loads demand and pro-

duction sources. In order to achieve a high availabil-

ity of energy and minimize the inﬂuence of intermit-

tent behaviour of sources, an intelligent power man-

agement based on forecasting production and loads

shedding methods should be used. The proposed con-

trol strategy in this paper is based on the using of

the forecast weather information to predict the avail-

ability of renewable sources and the ability of refu-

elling of programmable sources in an island micro-

grid. This method gives to the microgrid management

system the ability to make the right decision about

the achievement refuelling and to choose between us-

ing the energy produced to supply total loads or us-

ing the load shedding method to store the energy in

storage devices for use thereafter in order to supply

main loads. In this paper, a mathematical approach is

presented to show the different relationships between

the different components of microgrid and the inﬂu-

ence of their yields by the meteorological factor (in-

solation and wind). This mathematical approach is

used to show the advantage of the new forecasting

based control strategy in the improvement of avail-

ability. Several simulations on matlab Simulink of

the mathematical model of the proposed strategy are

developed for different cases of microgrid topology

(sources and loads). The simulation results show a

high improvement of availability of electrical power

supply distribution in microgrid. Comparing to the

control strategy without forecasting and load shed-

ding, this new control strategy allows to increase the

availability value from 85% to 100%. This paper is

organized as follows: Section 1 presents the state of

the art of microgrid power availability. The second

Section explains the case study and the problem. Sec-

tion 3 proposes the new control solution for optimal

availability. In Section 4, we evaluate the proposed

solution. Finally, Section 5 summarizes this paper.

2 STATE OF THE ART

Nowadays, many human activities depend critically

289

Abidi M., Ben Smida M. and Khalgui M..

New Forecasting-based Solutions for Optimal Energy Consumption in Microgrids with Load Shedding - Case Study: Petroleum Platform.

DOI: 10.5220/0005244002890296

In Proceedings of the 5th International Conference on Pervasive and Embedded Computing and Communication Systems (PECCS-2015), pages

289-296

ISBN: 978-989-758-084-0

 2015 SCITEPRESS (Science and Technology Publications, Lda.)

on a secure supply of energy. With Growing con-

cern about the availability of primary energy, rising

electricity demand, use of renewable energy sources

such as wind and solar become an obligation (Bhoyar

and Bharatkar, 2013). The new generation of electric

networks should integrate renewable energy into the

electrical grid (Hatziargyriou, 2014). Thus, system

security, environmental protection, quality of electric-

ity, cost of supply and energy efﬁciency should be

considered in new ways to meet the changing needs

in a liberalized market environment. Microgrid is a

contained network of distributed generation sources

and energy storage devices that are connected to the

loads. The generation can be from renewable sources,

that reduce (or cancel) the need to conventional en-

ergy sources. The potential for improving the avail-

ability of Microgrid power supply is one of the main

motivations behind the development and deployment

of microgrids. Because of the importance of the avail-

ability of electrical energy for various applications

and the ﬂuctuating availability of renewable energy

sources, a lot of research are interested in different

kinds of power sources hybridisation and their avail-

ability. The adequate choice of sources is the most

important step to improve availability. The hybridi-

sation can be on the type of sources (renewable or

programmable source, energy storage devices) or on

kind of the same type of source energy storage de-

vices (wind turbine and PV, fast-dynamic storage de-

vices and long-term storage devices, fuel cell, diesel

generator) or on both of them. After an adequate

choice, the sizing of these sources plays a mattering

role in the guarantee of the continuity of service (Lo-

genthiran and Raj, 2010). Many optimizing method-

ologies are proposed to calculate the optimum size of

energy source and storage system considering avail-

ability criterion (Y. Nian and Liu, 2013). Some pa-

pers explores how microgrids availability is impacted

by the different topology design choices for the power

electronic interfaces between the distributed genera-

tion (DG) sources and the rest of the microgrid. This

work focuses on the effect of DC or AC architec-

ture choice, converter design on system availability.

Power management strategy (Wang Haiyan and BiY-

ing, 2011) also have a signiﬁcant impact on the avail-

ability of electrical energy, especially in the case of in-

sufﬁcient production of energy or hardware problem.

Several research works have been interested in the im-

pact of the strategy of control on the different crite-

ria of microgrid power supply (WANG, 2013), espe-

cially the power-quality and the availability of elec-

trical produced energy (Thang, 2012). Whatever the

approaches of control (centralized/decentralized ap-

proaches) and especially in island mode, they require

Figure 1: Petroleum platform in Gulf of Hammamet in

Tunisia.

forecasting of the generation from renewable power

sources, electricity demand (and heat demand in some

cases). Prediction of the evolution of this quantity al-

lows us to face unsafe situations and optimize produc-

tion costs and power supply availability (H.X. Yang

and Burnett, 2003) (V. and K.U, 2014) . Therefore,

forecasting options may have a direct impact on the

economic viability and supply availability of micro-

grids, since they allow them to enhance their compet-

itiveness compared to centralized generation. To have

a wider degree of freedom on control strategy of elec-

trical power supply distribution in microgrid compar-

ing to the control based on forecasting (V. and K.U,

2014) and the control based on load shedding (Lee

and Huang, 2013), we propose a new optimized con-

trol strategy combining these two aspects by taking

in consideration the priority of the load supply. The

basic idea of our proposed strategy is to use the fore-

casted weather information to predict the availability

of renewable sources of refuelling of programmable

sources in an island microgrid. We aim in this paper

to make the right decision about the achievement re-

fuelling and load shedding.

3 CASE STUDY AND PROBLEM

We will describe in this section the Microgrid archi-

tecture and the climate values and we will introduce

the problem.

3.1 Microgrid Architecture

The Microgrid investigated in this paper is an abstrac-

tion of a petroleum platform (Figure 1) islanded lo-

cated on the Tunisian Coast. This Microgrid concep-

tion adopted for this case study is composed of PV

arrays, a wind turbine, a diesel generator with its fuel

tank, a storage device (batteries) and two loads (Fig-

ure 2). The Microgrid is designed as follows:

• Each renewable energy source (PV, Wind turbine)

is sized to be able to generate the electrical power

supply required by both loads and batteries in

favourable weather conditions,

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Figure 2: Microgrid.

• Diesel generator is dimensioned to be able to pro-

duce electrical energy required by the load. The

autonomy of this source is proportional to the

level of fuel in its tank and its produced energy,

• Battery is sized to be able to provide the electri-

cal power supply required by both loads with an

autonomy proportional to its charge level and the

electrical power request by loads. The battery be-

comes another load in her charging phase,

• load 1 is a critical load, it must be always con-

nected to the grid. Load 2 is a secondary load, it

can be disconnected in some cases.

The problem in this case is the intermittent nature

of all the sources of energy in this islanded microgrid.

The availability of sources is relative to the meteoro-

logical terms (Gooding, 2013). Based on sizing solu-

tions, the system - especially the elements of storage

- must be oversized; price and weight are the weak

points of this solution. We can increase the availabil-

ity by hybridisation of sources with other sources that

can operate in the bad times, such as the waves en-

ergy sources. This solution decreases the weight sup-

ported by the platform, but it is very expensive and

this type of energy is still in the process of study and

development. In this case, an adequate management

of energy can ensure or at least improve the availabil-

ity of electrical energy for the critical loads without

needs of new sources and new weight on the platform.

This strategy can minimize the aspect of intermittent

sources of energy by the use of the forecast of the me-

teorological factors and can act accordingly.

3.2 Climate values

The proposed Microgrid management systems must

be enhanced with forecasting technical functionalities

able to predict the power production of distributed

production sources in the next hours or days. To that

purpose, historical measurements database are needed

to generate the forecasting models. In Tunisia, you

can get this data from the National Institute of Mete-

orology

3.3 Problem and Discussion

In the island function mode of a microgrid, the avail-

ability of power supply is very inﬂuenced by the in-

termittent behaviour of its distributed sources. With

a view to improve this availability, the hybridisation

and the oversizing of sources represents a good tech-

nical solution, but this solution is very expensive. In

economical point of view, the solutions based on con-

trol strategy are always the cheapest. This kind of

solution is characterized by its ﬂexibility and ability

to be adapted to the climate change. For this reason,

a microgrid must have an intelligent control strategy

based on forecasting methods that can minimize the

impact of weather conditions on the renewable and

programmable sources availability. The strategy of

control should minimize the impact of the unavail-

ability of renewable sources by increasing the avail-

ability of backup sources (programmable sources and

storage devices).

4 CONTRIBUTION

Our contribution consists in the development of the

new forecasting-based solution for optimal energy

consumption in microgrids with Load Shedding. This

contribution is based on mathematical formulation of

the electrical network structure of Tunisian offshore

petroleum platform existing in the Gulf of Hammamet

with taking into account the weather condition.

4.1 Architecture

A microgrid is composed of Photovoltaic Cells, Wind

turbine, Battery, Diesel Generator and loads. All

the sources are sized so that each of them - when is

available- is able to meet the energy demand loads.

The availability of renewable sources depends on cli-

matic factors (insolation and wind). The availability

of the battery depends on the charge level. The battery

can be charged and discharged through the microgrid.

The diesel generator is available when the tank is not

empty. The tank can be ﬁlled with refuelling in case

of good weather. There are two types of loads in the

microgrid: (i) critical load that should be supplied all

the time, (ii) and uncritical load that can be discon-

nected from network in the case of load shedding.

http://www.meteo.tn

NewForecasting-basedSolutionsforOptimalEnergyConsumptioninMicrogridswithLoadShedding-CaseStudy:

PetroleumPlatform

291

4.2 Optimal Energy Consumption

In this subsection, we will describe the notation and

the problem formulation.

4.2.1 Notation

We denote in the following by:

PV Photovoltaic Generator

WT Wind Turbine

B Battery

GE Diesel Generator

Source

(t) Electrical power exchanged between the

source and the rest of network

Load1

(t) Electrical power consumed by critical load

Load2

(t) Electrical power consumed by uncritical load

En Insolation

Wind speed

charge

Battery charge level

clim

Sea State

Charge

Level of the fuel in the tank of the diesel

generator

clim

Nominal sea State

ψ(t) Electrical power produced by this source

4.2.2 Problem Formulation

In term of availability A(t) (Equation 1), all electrical

energy sources can have two states:

• 1: available energy producer.

• 0: unavailable energy producer.

In its charging phase, the battery acts as a load that

may consume excess production. The battery can

have a third state:

• -1: load state of battery.











(t) = 1; 0

W T

(t) = 1; 0

(t) = 1; 0;−1

(t) = 1; 0

(1)

Only available sources can be connected to the

grid. The state of penetration of sources C(t) to the

network is deﬁned by equation(2):











(t) = d

e − 1

W T

(t) = (d

e − 1).A

(t)

(t) = (d

Charge

e − 1).A

W T

(t).A

(t)

(t) = (d

Charge

e − 1).A

(t).A

W T

(t).A

(t)

(2)

Where:

(t), V

(t), N

charge

(t) and E

clim

(t) are the nominal

values from which the sources are capable of produc-

ing energy.

The electrical power supplied by different sources to

the microgrid is equal to:











(t) = C

(t).ψ

(t)

W T

(t) = C

W T

(t).ψ

W T

(t)

(t) = C

(t).ψ

(t)

(t) = C

(t).ψ

(t)

(3)

The power produced in the microgrid may not be

sufﬁcient to satisfy the totality of power demand for

all the time. For this reason, we should be allocated a

speciﬁed priority for each load. In this study, we have

two levels of priority:

• Load 1: critical load, it must be supplied most of

the time,

• Load 2: uncritical load. It can be disconnected in

the load shedding phase (C

Load2

= 0).

To ensure the availability of power supply, the pro-

duced power must check the following inequation:

(t) +P

W T

(t)) +P

(t)

(t) ≥ P

Load1

(t) +P

Load2

(t).C

Load2

(4)

In case of basic load shedding (without forecast-

ing), the load shedding method takes into considera-

tion only the real-time information about production

and consumption.

Load2

(t) = f (P

(t), P

W T

(t),

charge

, N

charge

, P

Load1

, P

Load2

)

(5)

In the case of a load shedding based on the

forecasting, the uncritical load can be disconnected.

Load2

can be re-written as:

Load2

(t) = β

(t).β

(t)

(6)

With











(t) = (d

∑

i=1

Clim

(i)

n.E

Clim

e − 1).(d

∑

i=1

(i)

n.N

e − 1)

(t) = (d

∑

i=1

(i)

n.E

e − 1).(d

∑

i=1

(i)

n.V

e − 1)

.(d

∑

i=1

Charge

(i)

n.N

Charge

e − 1)

(7)

(t) and β

(t) are predictive probabilistic in-

dex about the state of the diesel generator and battery

availability in the future. These two indexes will be

used in order to optimize the load shedding and the

refuelling system decision.

5 EVALUATION

In this section, we will deal with the power sys-

tem model as well as the simulation result of the

petroleum platform.

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5.1 Power System Modelling

We present in this subsection the photovoltaic gen-

erator, the wind turbine, the diesel generator and the

battery.

5.1.1 Photovoltaic Generator

The behaviour of photovoltaic generator (PVG) can

be presented by the following equation:

(t) = η

PV G

(8)

With:

• A

PV G

) is the PVG surface,

• η

PV G

is the PVG conversion efﬁciency given by:

PV G

= η

[1-β.(Tc-Tcref)]

5.1.2 Wind Turbine

As seen in equation(9), the energy of wind turbine

.ψ

W T

and mechanical output power of wind turbine

W T

(V ) =











V −V

dem

−V

dem

, V

dem

≤ V ≤ V

, V

≤ V ≤ V

max

0, Otherwise

(9)

(t) =

(λ, β).ρ.AV

(10)

With:

• ρ: Air density (Kg/m

• A: Turbine swept area (m

• V: Wind speed (m/s), V

: Nominal wind speed,

Max

: Maximal wind speed, V

dem

: Startup Wind

speed,

• C

(λ, β): Performance coefﬁcient of the turbine,

• λ: Tip speed ratio of the rotor blade tip speed to

wind speed,

• β: Blade pitch angle (deg).

5.1.3 Diesel Generator

The used model is a very simple. If its tank is not

empty, the diesel generator is a generator able to pro-

vide an electrical power P

gen

. After each unit of time

T of operation of generator, the fuel level decreases

by a quantity proportional to T and P

gen

5.1.4 Battery

There are many models to charge and discharge the

batteries. For the problems of sizing, it is enough to

use models that feel the State Of Charge (SOC) of the

battery in a simple manner.

5.2 Simulation Result

In this subsection, a number of simulation results that

highlight the inﬂuence of: (i) the strategy of com-

mand and (ii) the oversizing of backup sources on the

availability of electrical energy are presented and dis-

cussed. We will focus mainly on calculating the avail-

ability of energy: (i) at the level of critical loads, (ii)

at the level of the entire system. We will focus also on

the evolution of the charge level of the battery and the

fuel level in the tank of diesel generator. The instanta-

neous availability may have only two values, 1 in the

case of availability and 0 in the opposite case. The

average Availability A

(t) is the mean value of the

instantaneous availability between time=0 and time=t

(Taylor and Ranganathan, 2013).

(t) =

A(x)dx (11)

The rate of load shedding is also an important index.

It gives us the idea about the availability of electrical

energy at the level of uncritical load and also at the

entire system.

5.2.1 Case 1: Without Forecasting and Load

Shedding

This case does not address the paper’s contribution. It

deals with a simulation without forecasting and load

shedding. Two types of load are considered: both

powered or both unpowered. Note that the refuelling

occurs only in the good weather if the tank is empty.

Table 1 shows the power supply availability index for:

(i) critical loads, (ii) entire system and (iii) the load

shedding rate for uncritical loads. Since there is no

Table 1: Availability values.

Power supply availability for the main loads 0.8517

Power supply availability for all the system 0.8517

Load shedding rate 0%

load shedding, so the power supply availability for

the main loads and the power supply availability for

all the system are equal. The calculation of availabil-

ity at the level of charges has given an availability

rate equals to 85.17 %. That it may be considered

as a mediocre value. Figure 3 presents the evaluation

of the instantaneous and average power availability at

NewForecasting-basedSolutionsforOptimalEnergyConsumptioninMicrogridswithLoadShedding-CaseStudy:

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Figure 3: Instantaneous and average availability.

Figure 4: State of penetration of sources.

Figure 5: State of charge of the battery.

the level of critical load. Figure 4 shows the state of

penetration of sources (to the microgrid) which pro-

vide energy for consumers (Equation 2).

Note that: at each moment, only one source can be

penetrated to the network. We can also see the evo-

lution of the state of charge in the battery on Figure

Figure 6 gives us information about the possibil-

ity of refuelling, the act of refuelling and the evolu-

tion of level of fuel in the tank of the diesel generator.

According to this ﬁgure, we have three times of refu-

elling.

5.2.2 Case 2: Without Forecasting and with

Load Shedding

This case is interested in a simulation that we did

without forecasting but with load shedding, if the sys-

tem is powered by the non-renewable energy sources,

there is automatically a load shedding. Comparing

with the previous value of simulation without load

shedding, we note that the power supply availability

rate for critical loads increases more than 9% but the

power supply availability rate for the entire system is

decreased by 7% (Table 2). Using the strategy of load

Figure 6: Good weather, refuelling and fuel level.

Figure 7: Instantaneous and average availability.

Figure 8: Good weather, refuelling and fuel level.

Table 2: Availability values.

Power supply availability for the main loads 0.9433

Power supply availability for all the system 0.7883

Load shedding rate 36.67%

shedding, we note that the supply system becomes to-

tally unavailable only once (Figure 7). The gain that

we obtain in Case 2 comparing to Case 1 is the im-

provement of power availability at the level of critical

loads. The reader can observe this improvement by

comparing Figure 7 to Figure 3. Table 2 shows that

the application of load shedding without forecasting

desceases the power supply availability of the system

from 0.85 to 0.78.

Despite that the availability rate of the power supply

for critical loads increases, we note that the number of

refuelling act decreases. This presents an economic

gain in term of fuel and its transport (Figure 8).

Figure 9 shows the inﬂuence of load shedding on

the rate of the connected loads.

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Figure 9: Load(pu).

Figure 10: Instantaneous and average availability.

5.2.3 Case 3: With Load Shedding and

Forecasting Only for Refuelling

Case 3 is interesting in the paper’s contribution that

deals with the prediction of the future weather con-

ditions to manage the current energy consumption to

optimize the refuelling process. Using the same pre-

vious strategy of load shedding, we can optimize the

values of availability (Figure 10), if we improve the

refuelling decision system taking into consideration

the present state of tank and the provisional state-

ments of all the sources of microgrid. This fore-

cast helps the system to take the right decision of re-

fuelling: Figure 11 shows three times of refuelling

which improve the availability of the diesel generator.

Thanks to this new solution of Case 3, the power sup-

ply availability is increased from 0.94 to 0.98 (com-

pared to Case 2) thanks to the proposed Equation 7.

Table 3: Availability values.

Power supply availability for the main loads 0.9847

Power supply availability for all the system 0.809

Load shedding rate 36.67%

5.2.4 Case 4: With Forecasting and Load

Shedding

In this case, we present the paper’s contribution that

deals with the forecasting and load shedding. Table 4

and Figure 12 show a full availability of power supply

thanks to the proposed forecasting solution with load

shedding. Figure 13 shows the state of penetration

of sources and Figure 14 shows the state of charge

of the battery. Figure 15 shows the inﬂuence of the

load shedding on the rate of the connected loads. The

reader can observe the gain that we get by applying

Figure 11: Good weather, refuelling and fuel level.

Figure 12: Instantaneous and average availability.

Figure 13: State of penetration of sources.

Figure 14: State of charge of the battery.

Figure 15: Load(pu).

the forecasting with load shedding. Figure 16 gives

us information on the possibility of refuelling, the act

of refuelling and the evolution of the level of the fuel

in the tank of the diesel generator. In Case 4 compared

to Case 3, the power supply availability is increased

from 0.98 to 1 thanks to the proposed Equation 6 and

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Figure 16: Good weather, refuelling and fuel level.

Table 4: Availability values.

Power supply availability for the main loads 1

Power supply availability for all the system 0.809

Load shedding rate 38.12%

5.2.5 Result

According to the results obtained, it can be seen that:

• The type of hybridisation affects the availability.

But when we choose the sources, the reconﬁgura-

tion is costly and takes time,

• Note that the load shedding is a very important

strategy to increase the availability of electric

power in the priority loads, but it decreases the

rate at secondary loads and the system as a whole,

• Load shedding may be based on real-time infor-

mation or forecasts or of course both. The right

choice of this command and forecasting methods

provide a very high availability level of critical de-

vices, which can reach 100%.

6 CONCLUSION

In order to ensure a high availability in the is-

landed and autonomous microgrid, we propose a new

forecasting-based solution for optimal energy man-

agement. The major problem of this solution is the

probabilistic aspect of the forecasting data on which

the strategy is based on to make its decision. The load

shedding increase the availability in the level of crit-

ical loads; but in return, this method decreases the

availability in the level of uncritical loads. Despite

their problems, this strategy provides good results in

a case study. Predictive control strategy can help the

microgrid to improve the power supply availability for

its user by proactive control. Comparing with existing

solutions, our new solution presents several econom-

ical and technical beneﬁts and it can be implemented

easily. The implementation of this proposed solution

and its experimental result will be published nearly.

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