Spatial Approach on The Isolated Island Variable Renewable Energy Based Electricity Planning

2022 International Electrical Engineering Congress (iEECON) | 978-1-6654-0206-4/22/$31.00 ©2022 IEEE | DOI: 10.1109/iEECON53204.2022.9741607
Spatial Approach on The Isolated Island Variable
Renewable Energy Based Electricity Planning
B.W. Yudha
Electrical Engineering Department
School of Electrical Engineering,
Telkom University
Bandung, Indonesia
bayuwirayudha1999@gmail.com
F.N. Nursalam
Electrical Engineering Department
School of Electrical Engineering,
Telkom University
Bandung, Indonesia
nadhilah48@gmail.com
S. Sasmono
Electrical Engineering Department
School of Electrical Engineering,
Telkom University
Quadran Energi Rekayasa
Bandung, Indonesia
ssasmono@telkomuniversity.ac.id
W. Priharti
Electrical Engineering Department
School of Electrical Engineering,
Telkom University
Bandung, Indonesia
wpriharti@telkomuniversity.ac.id
Y.T. Asidda
Electrical Engineering Department
School of Electrical Engineering,
Telkom University
Bandung, Indonesia
hanatresnaa@gmail.com
Abstract—The isolated island which will be electrified by
variable renewable energy needs an optimal electricity
planning. Since the solar irradiation or wind velocity is sitespecific, then spatial approach is proposed to meet the
objectives. The proposed spatial approach combines spatial
demand forecasting and optimal site of the variable renewable
energy power plant. The spatial demand forecasting is adapted
Lee Willis concept in electricity distribution planning. The
spatial matrix score is used to aid the placement of a power
plant.. Another electricity grid can be developed based on
combination of the center spatial load and optimal site of
variable renewable energy power plant (Abstract)
Keywords—electricity planning, isolated island, spatial
demand forecasting, variable renewable energy power plant
I.
1
INTRODUCTION
The variable renewable energy source for the power plant
is site-specific. Whilst the center of load will be lying in a
specific area. The electricity planning has become a challenge
as the variable renewable energy does not meet the center of
load. The challenge becomes obvious when facing secluded
area, such as small islands, due to the highest probability
source of variable renewable energy locates far from the
limited center of load. The spatial approach may give a
solution for electricity planning with massive penetration of
variable renewable energy sources in small islands.
Since it was first introduced by Van Wormer in 1954 at
paper entitled "Some Aspects of the Distribution Area Load
Geometry" which was published in Power Apparatus and
Systems Volume 73, No. 2, 1954, page 1343-1349, the spatial
approach has been continue developed by Lee Willis in
electrical demand forecasting for the electrical distribution
system. It was then followed by another researcher [4]. Most
of the researchers used the spatial approach for spatial demand
forecasting by developing a methodology. The model was
developed with different variations for short-term demand
forecasting by Carreno [1] and Chouw [2] as examples. Tao
Hong and Jain introduced another methodology for long-term
spatial demand forecasting [3] [6]. Furthermore, S.Sasmono
proposed a spatial approach for transmission system planning
[5].
Referring to its ability to provide optimal electricity
planning in limited islands, the spatial approach is proposed
as a methodology to overcome challenging issue where the
site-specific variable renewable energy resources reaches offgrid island with limited load center. The research shows the
methodology will give an optimal planning as the target of
modern power system planning.
II.
PROBLEM AND METHOD
In the isolated island, the load is dispersed. Even there are
small spots as load center. On the other hand, the renewable
energy variable is a site specific which depends on the energy
resources, such as solar irradiation or wind velocity. The
optimal power system planning should consider the location
of not only the center of load, but also the power plant.
Basically, the power plant should be located near the center
of load in order to minimize loss as well as to keep the voltage
level of the buses. However, the solar irradiation or wind
velocity will be different in different sites. The variable
renewable energy power plant should be located in the site
where the energy resources is maximum. The problem is the
location of the power plant may locate far from load center or
dispersed load. Thus, it will result in high loss, and the
voltage level will be out of allowable range.
It is known that there is an insufficient amount of primary
energy resource in the secluded island. Therefore, the solar
energy and wind velocity can be the only local resources for
electricity generation. Since the load is dispersed and the
maximum energy resources can be found far from load, then
the spatial approach can be implemented to produce optimal
power system planning in the secluded island.
On the proposing methods, the spatial approach carries
out into 4 stages as follows: (1). Spatial demand forecasting,
(2). Develop matrix of spatial load and spatial energy
resources, (3). Basic design for variable renewable energy
generation and (4). Grid modelling.
A. Spatial demand forecasting
Spatial demand forecasting predicts a small area in which
the utility area is divided into small rectangles (grid).
Furthermore, the electricity consumption of each small
rectangle area is analyzed. The demand forecasting in small
island is limited to the island’s size. Thus, the spatial demand
forecasting should follow the Gompertz equation as follows.
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2
=
(1)
Where, y is load in time t, others is parameters. Therefore,
the evolution of the island’s land use inside small rectangles
in period becomes the parameter. The information regarding
the evolution of the island is basically obtained from the
government’s document which is a qualitative data. Thus,
spatial demand forecasting requires qualitative analysis.
Several documents that indicate the direction of development
in the observed area of spatial were collected and analyzed.
From the qualitative analysis, the evolution or development
of the island can be interpretated for period of time.
The clustering method was used to classify the type of
utility area inside the small rectangle. Each cluster has a
symbol or number regarding their type. Each type needs to be
separated to tell the difference between clusters such as the
difference type of electrical consumption as shown in Table 1.
The symbol or number used in land use evolution shows the
infrastructure development step by step.
TABLE I.
III.
CASE STUDY
Case study of this research was conducted in Rengit island,
Bangka Belitung, Indonesia. Rengit island is one of small
islands in Belitung which the electricity has not been received,
although it is only 12 km from Suge power plant in Belitung
Island. Therefore, local people use petrol and generator to
provide their electricity needs, although the petrol price is
quite expensive for locals. The National Electricity Company
(PLN) planned to provide electricity to the island through
submarine cable from Belitung main island to Rengit island.
The option to use submarine cable seems to be viable.
However, Rengit island also has potential to build renewable
energy power plant. The island has wind velocity (m/s) of 4.5
m/s at the height (m) of 100 meter and direct normal
irradiation (kWh/m2) 1038.2 kWh/m2. The Rengit Island is
shown in Fig. 1. In the proposed concept, the Solar PV has
planned built electrifying Rengit Island based on the spatial
approach. In this case study, the optimal variable renewable
energy-based electricity planning becomes the objective of the
spatial approach planning.
ELECTRICITY CONSUMPTION CLASSIFICATION
Land Use
Demand Category
Number
Restricted Area
-
0
Low
1
Residential
Medium
2
High
3
Low
4
Tourism
Industry
Medium
5
High
6
Low
7
Medium
8
High
9
Validation of spatial demand forecasting model is done
by comparing the real data from government who already
forecast the maximum demand for a year. The forecasted
demand is also validated to meet the requirements to be
considered as reasonable.
B. Develop Spatial Matrix
Besides the energy consumption, the cluster can be used
as the symbol of the land use evolution and symbol of
irradiation, wind velocity, or another renewable energy
resources inside the rectangle area. The matrix is developed
to determine the optimal cluster where the load and the
energy resources are maximum. The optimal cluster should
be located on variable renewable energy power plant.
Fig. 1. Rengit Island
IV.
SIMULATION RESULTS
A. Spatial Demand Forecasting
Based on spatial approach proposed by Lee Wilis, The
Rengit Island can be divided into 8 grids that is defined as
cluster. Therefore, there are 8 spatial clusters in such island
which is shown in Fig. 2.
C. Grid Modelling
The basic design for VRE generation should be developed
based on the power and energy balance equation as follows:
∑
=∑
+∑
(2)
Where, P = generations, L=load and l = losses. The losses in
the basic design are the assumption of losses target.
Whilst the grid model should consider the location of the
center load in isolated island. The electricity infrastructure
will be built based on the location of center load as well as
the location of power plant.
Fig. 2. Rengit island cluster based on infrastructure development
According to Government Development Plan from 2022 to
2027, the development of each cluster is shown in Fig. 3.
Cluster 1 and 4 will be the starting point of the
developmentby establishing the residential area.
Since the Rengit Island has a potential to be tourist
destination, then the tourist area will be developed at both
cluster 5 in 2023 and cluster 7 in 2025. At least the small
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3
density industrial area will be developed at both cluster 6 in
2024 and cluster 8 in 2025.
Furthermore, the electricity consumption classification in
Table 1 is used to define the electricity demand status in every
year forecast for each cluster. The data is shown in Fig. 4.
=
0,85 1 − −0,0614 , !"
1,36 !"
$ %&$'$
TABLE III.
(3)
RENGIT ISLAND DEMAND PROJECTION
Year
Demand (Watt)
2022
52320
2023
175461
2024
393624
2025
623946
2026
697988
2027
859148
Fig. 3. Land use prediction with GIS software
Fig. 5. Electricity Demand Forecasting 2022-2027
Fig. 4. Rengit island cluster based on electricity consumption
The standard electricity used in demand forecasting is shown
in Table 2. Thus, the electricity demand can be forecasted.
However, the validation should be done in existing year.
Since the electricity demand on the existing year is 50 kW,
then the electricity demand forecasting 2022 – 2027 is shown
in Table III
TABLE II.
Land Use
Residential
Fishery Industry
Tourist
Destination
ELECTRICITY CONSUMPTION STANDARD
Demand Category
Load (Watt/Ha)
Lighting
12,000
Lighting
12,000
Cooling
87,000
Lighting
12,000
Cooling
70,000
Toilet
10,000
The Monte Carlo simulation is carried out to consider
uncertainty in the demand forecasting. The certainty level of
the simulation is 98 % and the results are shown in Table IV
and Fig. 6. The certainty approach gives a range of
forecasting in the maximum, average, and minimum
scenario.
TABLE IV.
RENGIT ISLAND DEMAND PROJECTION WITH MONTE CARLO
Year
Minimum (Watt)
Maximum (Watt)
Mean (Watt)
2022
27818,61
71800,73
47106,44
2023
109830,87
322709,82
203113,16
2024
218618,17
658273,14
417264,08
202
338900,05
871908,74
604546,52
2026
440526,19
977975,48
724410,61
2027
525902,59
1012852,33
787250,6
According to spatial characteristics which must follow the
Gompertz Equation, the 2nd validation should be done to
determine the electricity demand equation and the maximum
demand in the island. The maximum demand is total demand
when all the spatial clusters is electrified. The elctricity
demand equation and the maximum demand is shown in the
following equation:
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4
Fig. 6. Demand projection graph in range number
B. Spatial Matrix
The matrix is developed based on 2 parameters, namely
solar
irradiation
and
spatial
load,
and
the
prediction/forecasting in 2022. The spatial matrix score is
shown in Table V. As shown in the Table V, the highest score
is in Cluster 1. Therefore, the Solar PV will locate in Cluster
1.
TABLE V.
Parameter
Irradiation
(kWh/m2)
Score
Load Need
(kW)
Score
Total Score
SPATIAL MATRIX SCORE
Cluster 1
Cluster 2
Cluster 3
Cluster 4
1034.2
1036.9
1032.8
1027.0
3
4
2
1
35
3
16
1
4
7
2
6
3
5
1
2
C. Grid Modelling
According to spatial load center, which is shown in Fig,
7, 2 feeders are needed to deliver electricity production from
the Solar PV to the load. The single line diagram of the grid
which considers the spatial condition of Rengit is shown in
Fig. 8.
Fig. 8. Single Line Diagram of Rengit Off-Grid System
V.
The optimal planning of isolated island in which the
electricity will be generated should consider the location of
the power plant and the center of load.
The solar irradiation Is an important aspect to determine
the optimal location of Solar PV as variable renewable energy
power plant. The research shows that the combination of
spatial approach in both spatial demand forecasting and
optimum location of Solar PV based on the highest irradiation
will result in the optimum electricity planning for isolated
island.
The robust result of the optimal electricity planning needs
grid impact simulation. It will show whether the grid model
is able to give a good grid performance to meet the grid code
requirements.
REFERENCES
[1]
[2]
[3]
[4]
[5]
Fig. 7. Spatial infrastructure
CONCLUSIONS
[6]
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Automacao, Vol 21 No 4, pp 379 – 387, 2010.
Chouw, Mo-Yuen, Zhu, Jinxiang, Tram, Hahn “Application of
FuzzyMulti-Objective DecisionMaking in Spatial Load Forecasting“,
IEEE Transactionon Power System Vol 13 No. 3, pp1185 – 1189,
1998.
Jain, Amit, Jain, M. Babita, “A Novel Hybrid Method for Short Term
Load Forecasting using Fuzzy Logic and New Particle Swarm
Optimization
(NPSO)”,
16th
National
Power
System Conference, pp 132 – 138, 2010.
Lee Wilis,“Spatial Electric Load Forecasting 2nd edition”, Marcel
Dekker Inc, New York, pp 1 – 5, 2002
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Planning”, International Journal on Electrical Engineering and
Informatics - Volume 7, Number 2, June 2015, pp 193-206
Tao Hong, “Long-Term Spatial Load Forecasting”, Thesis Manuscript,
North Carolina State University, 13 – 70, 2008.
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