Conference PaperPDF Available

Economic performance optimization of a hybrid PV-BESS power generator: a case study la Réunion island

Authors:
  • University La Réunion

Abstract and Figures

This paper proposes an economic performance optimization strategy for a PV plant coupled with a battery energy 10 storage system (BESS). The case study of La Reunion Island, a non-interconnected zone (NIZ) with a high level of renewable 11 energy sources (RES), is considered. This last decade, to reach the ambitious target of electricity autonomy by 2030 set by the 12 local authorities, local and national plans have been launched to promote RES integration that led to a noticeable development of 13 photovoltaic (PV) systems. To avoid a decrease of the grid reliability due to a large integration of intermittent energy sources 14 into a non-interconnected grid, the authorities have introduced new regulatory rules for RES producers. The proposed 15 optimization strategy relies on a these new regulatory rules and takes into account the energy market data, the amount of PV 16 production subject to penalties for imbalance, the batteries and the PV technological characteristics together with a PV 17 production forecast model. The effectiveness and relevance of the proposed strategy are assessed on experimental data collected 18 on a real PV power plant. An economical analysis demonstrates that the proposed optimization strategy is able to fulfill the new 19 regulatory rules requirements while increasing the economic performance of the system.
Content may be subject to copyright.
HAL Id: hal-02355288
https://hal.archives-ouvertes.fr/hal-02355288
Submitted on 8 Nov 2019
HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entic research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diusion de documents
scientiques de niveau recherche, publiés ou non,
émanant des établissements d’enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.
Economic performance optimization of a hybrid
PV-BESS power generator: a case study la Réunion
island
Cédric Damour, Michel Benne, Y Gangate, D Payet, Mickaël Hilairet
To cite this version:
Cédric Damour, Michel Benne, Y Gangate, D Payet, Mickaël Hilairet. Economic performance op-
timization of a hybrid PV-BESS power generator: a case study la Réunion island. International
Conference on Renewable Energy: Generation and Applications, Feb 2016, Belfort, France. �hal-
02355288�
Paper&Submitted&to&ICREGA’16&
&
&
ECONOMIC PERFORMANCE OPTIMIZATION OF A HYBRID PV- BESS 1&
POWER GENERATOR: A CASE STUDY LA REUNION ISLAND 2&
C. Damour1, M. Benne1*, Y. Gangate2, D. Payet2, M. Hilairet3 3&
1&LE2P, EA 4079, Univ. of La Reunion, 15, avenue R. Cassin, CS 92003, 97744 Saint Denis Cedex 9, France 4& 2&LIM, EA 2525, Univ. of La Reunion, PTU, Bât. 2, 2, rue J. Wetzell, 97490 Sainte-Clotilde, France 5&
3&FCLAB, FR CNRS 3539, FEMTO-ST, UMR CNRS 6174, Univ. of Franche-Comté, rue T. Mieg, 90010 Belfort Cedex, France 6&
*michel.benne@univ-reunion.fr 7&
8&
9&
Abstract. This paper proposes an economic performance optimization strategy for a PV plant coupled with a battery energy 10&
storage system (BESS). The case study of La Reunion Island, a non-interconnected zone (NIZ) with a high level of renewable 11&
energy sources (RES), is considered. This last decade, to reach the ambitious target of electricity autonomy by 2030 set by the 12&
local authorities, local and national plans have been launched to promote RES integration that led to a noticeable development of 13&
photovoltaic (PV) systems. To avoid a decrease of the grid reliability due to a large integration of intermittent energy sources 14&
into a non-interconnected grid, the authorities have introduced new regulatory rules for RES producers. The proposed 15&
optimization strategy relies on a these new regulatory rules and takes into account the energy market data, the amount of PV 16&
production subject to penalties for imbalance, the batteries and the PV technological characteristics together with a PV 17&
production forecast model. The effectiveness and relevance of the proposed strategy are assessed on experimental data collected 18&
on a real PV power plant. An economical analysis demonstrates that the proposed optimization strategy is able to fulfill the new 19&
regulatory rules requirements while increasing the economic performance of the system.&20&
21&
1. Introduction 22&
To date, reducing carbon emission has become a major 23&
concerned. Among possible options, increasing shares of 24&
renewable energy sources (RES) such as solar, wind or 25&
biomass resources appears as a promising solution for a 26&
cleaner power generation. High shares of RES may then 27&
become a critical aspect of future energy systems. In this 28&
context, small islands that mainly rely on imported fossil fuels 29&
for energy production are likely to be pioneers in the 30&
development of decarbonized electricity production [1-2]. 31&
32&
In this study, the case of La Reunion Island, a non-33&
interconnected zone (NIZ), is considered. Even if the territory 34&
has a high level of RES, its electricity production remains 35&
strongly based on imported fuels. In this context, local 36&
authorities have set the ambitious objective of reaching 37&
electricity autonomy by 2030. This last decade, to reach this 38&
target, local and national plans have been launched to 39&
promote RES integration [3-4]. Thus, supported by incentive 40&
mechanisms such as tax exemptions, direct subsidies or feed-41&
in tariffs, photovoltaic (PV) systems have experienced a rapid 42&
and noticeable development [5]. However, a large integration 43&
of intermittent sources into a non-interconnected grid raises 44&
critical technical issues due to the uncertainties of the energy 45&
production. The intermittency and unreliability of solar-46&
generated power may reduce the network stability and lead to 47&
load shedding or to the interruption of electric service [6].&To 48&
avoid such situations, the authorities have set a limit of 30% 49&
of intermittent sources in the instantaneous electricity 50&
production and have introduced new regulatory rules for RES 51&
development. Henceforth, RES producers have to declare to 52&
the grid operator, a day in advance, the power profile that will 53&
be injected to the grid. Then, if the power plants do not meet 54&
the submitted schedule for injected power, they face financial 55&
penalties. In La Reunion Island, if mismatches between actual 56&
and scheduled power injection exceed a given tolerance, RES 57&
producers are charged with imbalance penalties. In order to 58&
address the problem related to the intermittency of solar-59&
generated electricity while reducing the amount of PV 60&
production subject to penalties for imbalance, energy storage 61&
systems (ESSs) appears as one of the most relevant option. 62&
Recently, several works related to the applicability, 63&
advantages and disadvantages of various ESS technologies 64&
for RES integration have been reported [7]. As regards PV 65&
power plants coupling with ESS, several works dealing with 66&
technical issues and economic feasibility have been 67&
conducted [8-11]. However, from a regulatory point of view 68&
(incentive schemes and economic feasibility), nearly all 69&
works reported in the literature focus on the determination of 70&
the optimal sizing of the ESS [12-15].&71&
72&
In the case of La Reunion, and according to our best 73&
knowledge, none work has been conducted to optimize the 74&
economical performance of existing hybrid photovoltaic-75&
battery energy storage system (BESS) power generators, 76&
based on the latest regulatory rules. In this paper, an 77&
economical optimization of a hybrid PV-BESS power 78&
generator is developed. The proposed methodology relies on a 79&
metaheuristic optimization algorithm taking into account the 80&
energy market data, the amount of PV-generated energy 81&
subject to penalties for imbalance, the PV and the batteries 82&
Paper&Submitted&to&ICREGA’16&
&
&
technological characteristics together with a PV production 1&
forecast model. To assess the effectiveness and relevance of 2&
the proposed strategy, the economic analyses are performed 3&
on data measured on a real power plant. Indeed, a one-year 4&
experimental data, collected from August 2013 to August 5&
2014 on a 57 kWp PV farm coupled with a 78.5kWh BESS, 6&
are considered. 7&
8&
The rest of this paper is organized as follows. The regulatory 9&
rules applied in La Reunion are presented in Section 2. 10&
Section 3 is dedicated to model design. In this section the PV 11&
production forecast model and the energy storage model are 12&
detailed. The performance of the proposed optimization 13&
strategy in terms of economical efficiency improvement is 14&
demonstrated in Section 4. 15&
16&
Table 1: Nomenclature. 17&
Variable
Description [unity]
𝑃
!"#
Scheduled profile to be injected to the grid [kW]
𝑃
!"#
Power injected to the grid [kW]
𝑃
!"_!& &
Measured PV power [kW]
𝑃
!"_!& &
Forecasted PV power [kW]
𝑃
!"_!"#$& &
Installed PV power capacity [kWp]
𝑃
!"#& &
Storage power [kW] (>0 charge, <0 discharge)
𝑃
!"#_!"
Storage power exchanged with the AC bus
!P!& &
Maximal power in discharge [kW]
P
!& &
Maximal power in charge [kW]
𝑃
!& &
Imbalance power [kW]
E!"#_!"#$& &
Amount of energy dedicated to evening peak [kWh]
𝐸!"_!_!"!#$&
Estimated total energy produced by PV plant [kWh]
α!"#$& &
Parameter to be estimated
β!"#$%&'(&&
Parameter to be estimated
𝐶!"#& &
Maximum usable storage capacity [kWh]
𝑆𝑂𝐶!"#& &
Min. energy storage level [%𝐶!"#]
𝑆𝑂𝐶!"#& &
Max. energy storage level [%𝐶!"#]
η!& &
Efficiency of storage in charge
η!& &
Efficiency of storage in discharge
𝐶!& &
Electricity selling price
𝐶!& &
Electricity buying price
DFR&
Daily fault rate
𝜏!"#$%& &
Cumulated time of faulty condition [minute]
18&
2. Regulatory rules 19&
In NIZ such as La Reunion, the large integration of 20&
intermittent sources raises critical technical issues related to 21&
the reliability of power supply. The reliability of an electrical 22&
grid can be defined by its ability to supply the aggregate 23&
electrical demand and energy requirements of the customers 24&
at all times, while withstanding sudden disturbances such as 25&
unanticipated loss of system elements (e.g. load or production 26&
fluctuations) [16-18]. Currently the sustainability of power 27&
supply in La Reunion is already lower than in Metropolitan 28&
France, with an average power outage duration estimated at 29&
4 h/year/consumer vs 73 min [5]. In this context, and 30&
considering the rapid and important growth of PV systems in 31&
La Reunion the last decade, the authorities have recently 32&
decided to set up new regulatory rules to ensure the reliability 33&
of the power supply. Henceforth, producers have to declare a 34&
one minute based profile that represents the day-ahead 35&
forecasted power to be injected by their plants. If the 36&
mismatches between the actual injected power and the 37&
announced power exceed the admitted tolerance, financial 38&
penalties are applied. According to this regulatory framework, 39&
energy imbalance is calculated with minutely resolution, and 40&
the tolerance band is taken equal to ± 5% of the installed PV 41&
power capacity (𝑃
!"_!"#$ ). 42&
43&
The electricity tariff system relies on peak and off-peak hours. 44&
During peak hours, 7PM to 9PM, the electricity feed-in tariff 45&
is more attractive. However, during this time period, 46&
producers have to guarantee a constant power injection to the 47&
grid comprise between 20 % and 70 % of 𝑃
!"_!"#$ . The 48&
current electricity tariff system applied to PV power 49&
producers in La Reunion, including peak and off-peak feed-in 50&
tariffs, is summarized in Table 2. 51&
52&
Table 2: Summary of tariff system in La Reunion. 53&
Peak hours (7PM to 9PM)
[ ct/kWh]
Off-peak hours
[ ct/kWh]
Selling price (𝐶!)
60
40
Buying price (𝐶!)
40
40
54&
Note that producers have the possibility to buy electricity 55&
from the grid. In some very specific cases, it could be 56&
interesting to buy electricity from the grid during off-peak, 57&
store the energy in an ESS, and sell it back during peak hours. 58&
59&
The producer’s revenue is calculated each minute using the 60&
following expression: 61&
62&
revenue = Pinj CS/60 – Pout Cb/60penalties (1) 63&
64&
where Pinj denotes the power injected to the grid and Pout the 65&
power extracted from the grid. The financial penalties are 66&
calculated as follows: 67&
68&
if Pbid0.05 PPV_peak < Pinj < Pbid + 0.05 PPV_peak then 69&
70&
penalties = 0 71&
72&
elseif Pinj > Pbid + 0.05 PPV_peak then 73&
74&
penalties = Pinj CS/60 75&
76&
else 77&
&78&
𝑝𝑒𝑛𝑎𝑙𝑡𝑖𝑒𝑠 =
𝐶!
60 𝑃
!"#
𝑃
!"#
𝑃
!!!"#$
0.1+
2𝑃
!"#
𝑃
!!!"#$
𝑃
!"#
+𝑃
!"# 0.05𝑃
!"_!"#$ 0.015
𝑃
!"#
𝑃
!!!"#$
where Pbid denotes the day-ahead schedule power profile to be 79&
injected to the grid. 80&
81&
Every time Pinj is outside the tolerance band, the system is 82&
said to be in faulty condition and financial penalties are 83&
applied. 84&
Paper&Submitted&to&ICREGA’16&
&
&
In this work, the economic effectiveness of the system is 1&
assessed using two criteria, which are the revenue and the 2&
daily fault rate (DFR). This last criterion defines the ratio of 3&
time where the system is in faulty condition each day: 4&
5&
DFR = !𝜏!"#$% 1440 6&
7&
With 1440 minutes per day, 𝜏!"#$% represents the cumulated 8&
time of fault condition in minute. 9&
10&
To fulfill the requirements of the new regulatory rule, PV 11&
power producers have to announce a day in advance the 12&
power profile to be injected to the grid, which requires a PV 13&
production forecast model. Besides, regardless of the 14&
accuracy of the PV production forecast model, financial 15&
penalties due to imbalance are unavoidable. Therefore, to 16&
reduce financial penalties and above all take advantage of 17&
peak hours feed-in tariff, the use of ESS appears to be a 18&
relevant option. 19&
20&
3. Model design 21&
PV production forecast model 22&
In the literature, a wide variety of parametric and non-23&
parametric forecast models have been reported [19]. 24&
Parametric models require a wide set of information about the 25&
PV power plant technology and its installation configuration. 26&
Non-parametric models are generally based on weather 27&
forecast models [20]. These limitations make the reliability 28&
and the suitability for “on field” uses of parametric and non-29&
parametric forecast models questionable. 30&
31&
In this study, regarding practical purposes, the widely used 32&
persistence model is chosen to forecast the PV output power 33&
at a minute basis. This is a simple model based on the 34&
assumption that the PV production of today is the same as 35&
yesterday [21]: 36&
37&
𝑃
!"_!𝑡=!𝑃
!"_!𝑡1440 (2) 38&
39&
where 𝑃
!"_! and 𝑃
!"_! denotes respectively the measured and 40&
forecast PV output power at time 𝑡. 41&
42&
Even if this method does not take into account the intra-day 43&
variability of solar irradiance, it represents with a good 44&
accuracy the periodicity and seasonality of weather conditions 45&
(day/night and summer/winter cycles) [13]. Besides, as a low-46&
tech approach compared with irradiance forecast based 47&
strategies, it has the merit of avoiding additional costs, which 48&
cannot be underestimated for small cases applications. 49&
Obviously, the accuracy of the PV production forecast could 50&
be improved using more sophisticated models but at the price 51&
of increasing complexity and computational cost. 52&
53&
Energy storage model 54&
Regarding optimization purposes and according to the 55&
considered time scale (minutes in this study), a simplified 56&
static model is proposed. This model relies on the static 57&
characteristics of the battery (Cf. table 3) and neglects the 58&
transient dynamics of the process. In the sequel, powers are 59&
considered negative (respectively positive) during the 60&
discharge (respectively charge) phase. In this context, the 61&
battery state of charge (SOC) at time t is computed by: 62&
63&
𝑆𝑂𝐶 𝑡=𝑆𝑂𝐶 𝑡𝑡+𝑃
!"# 𝑡𝑡𝐶!"# (3)!!64&
65&
Subjected to constraints on power and capacity: 66&
67&
𝑃
!𝑃
!"# 𝑡𝑃
!!
𝑆𝑂𝐶!"# 𝑆𝑂𝐶 𝑡𝑆𝑂𝐶!"#
(4) 68&
69&
where 𝑃
!"# 𝑡 is the storage power at time t. 𝑃
!<0, !𝑃
! 70&
represents the maximum battery discharging power, and 71&
𝑃
!>0 the maximum battery charging power. 𝑆𝑂𝐶!"# and 72&
𝑆𝑂𝐶!"# are the minimum and maximum battery state of 73&
charge, respectively. 𝐶!"# denotes the maximum usable 74&
storage capacity of the ESS. Note that the battery aging and 75&
self-discharge rate, which obviously affect 𝐶!"#, has not been 76&
considered. Besides, powers are considered constant during 77&
the time interval t;!t+t. In this work, t is equal to one 78&
minute. 79&
80&
The PV, battery, grid, and loads are all connected to an AC 81&
bus. Since the battery is operated on DC, an AC-to-DC 82&
(respectively DC-to-AC) converter is necessary when 83&
charging (respectively discharging) the battery. Therefore, 84&
considering the storage charge and discharge efficiencies (η! 85&
and η! respectively), the storage power exchanged with the 86&
AC bus 𝑃
!"#_!" 𝑡 is expressed as follows: 87&
88&
!!!!!!!!!!P
!"#_!" t!=!P
!"# t!η!!!!!!!!!!if!P
!"# t<0!discharge !!
P
!"!!" t!=!P
!"# t/η!!!!!!!!!!!!!!!if!P
!"# t0!charge (5) 89&
90&
Table 3: Storage system parameter values. 91&
Variable
Value
𝐶!"#
78.5
𝑆𝑂𝐶!"#
20
𝑆𝑂𝐶!"#&
99
η!&
0.9
η!&
0.9
!𝑃
!&
36.1
𝑃
!
17.2
DODmax
80
92&
4. Economic performance optimization 93&
In this work, a minute dispatch strategy for a 57 kWp PV 94&
farm with 78.5 kWh BESS is implemented and an economical 95&
optimization of the dispatch strategy is proposed. BESS has 96&
two main applications: first, compensate PV production 97&
forecast errors during off-peak and thus reduce financial 98&
penalty due to imbalance. Second, inject power to the grid 99&
during peak hours and thus take advantage of the attractive 100&
feed-in tariff. In this context, two parameters are introduced. 101&
The first one, denoted α!"#$, is related to the amount of 102&
Paper&Submitted&to&ICREGA’16&
&
&
energy dedicated to the evening peak hours (E!"#_!"#$), which 1&
have to be stored in the BESS meanwhile, and is written as a 2&
fraction of the estimated total energy produced by the PV 3&
plant (𝐸!"_!_!"!#$): 4&
5&
𝐸!"#_!"#$ =𝛼!"#$!𝐸!"_!_!"!#$ (6) 6&
7&
Here E!"_!_!"!#$ =P
!"_!𝑑𝑡
!""#
!. The second one, denoted 8&
𝛽!"#$%&'( , represents the fraction of the storage capacity that is 9&
dedicated to compensate power imbalance (𝐶
!"#$%&%'() due to 10&
forecast errors: 11&
12&
𝐶
!"#$%&%'( =𝛽!"#$%&'( !𝐶!"# (7) 13&
14&
To assess the effectiveness and relevance of the proposed 15&
strategy, the economic analyses are performed on a one-year 16&
experimental data, collected from August 2013 to August 17&
2014 at La Reunion on a real PV power plant. Due to its high 18&
convergence rate to the true global minimum and its perfect 19&
suitability to practical engineering optimization problems, the 20&
recently developed Modified Cuckoo Search algorithm 21&
proposed by [22] is used as optimization algorithm. 22&
23&
PV/BESS control rules 24&
The power injected to the grid is defined as the sum of the 25&
PV output power and the storage power exchanged with the 26&
AC bus: 27&
28&
𝑃
!"# =𝑃
!"_!+P
!"#_!" (8) 29&
30&
The BESS is used to adjust the PV power plant 𝑃
!"_! to 31&
maintain, as much as possible, the difference between the 32&
day-ahead announcement 𝑃
!"# and the actual power injected 33&
𝑃
!"# to the grid within the tolerance band. If 𝑃
!"_! is above 34&
(respectively below) the tolerance limit, the BESS can be 35&
used, when it is possible, to store (respectively deliver) the 36&
imbalance power 𝑃
!. Depending on whether the measured 37&
output PV is within, below or above the tolerance band, 𝑃
! is 38&
defined as follows: 39&
40&
P
!=0!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!within!
!!!!!!!!!!!P
!!=!𝑃
!!_!𝑃
!"# +0.05!𝑃
!!!"#$ !!!!!!!above!charge
!!!!!!!!!!P
!!=𝑃
!!
_!𝑃
!"# 0.05!𝑃
!!!"#$ !!!!!!!!!!!!!!below!discharge
(9) 41&
42&
Every time P
!0 financial penalties for imbalance are 43&
applied. Therefore, the storage charge/discharge process must 44&
be suitably controlled in order to reduce the DFR and thereby 45&
the amount of financial penalties, while ensuring that there is 46&
enough energy stored in the BESS for the evening peak. In 47&
this aim, a tolerance band control strategy is proposed and a 48&
specific control rules is designed for each zone: above, within 49&
and below the band. 50&
51&
Above the upper limit 52&
When 𝑃
!"_! is above the tolerance band, the BESS is used to 53&
compensate the imbalance power and store the excess of 54&
energy whenever possible. Indeed, the imbalance power 55&
cannot always be compensated. Several conditions related to 56&
operational and technical limits of the BESS have to be 57&
verified (i.e state of charge, charge/discharge rate limits). In 58&
this case, the storage power is computed as follows: 59&
60&
P
!"#_!" =𝑚𝑖𝑛 𝑃
!𝜂!,𝑃
!,𝑃
!"#_!"# 𝜂! (10) 61&
62&
where P
!"#_!"# =SOC!"# SOC tC!"# t 63&
64&
Below the lower limit 65&
When 𝑃
!"_! is below the tolerance band, the imbalance 66&
power due to forecast error can be compensated using the 67&
energy stored in the BESS. However, this energy has to be 68&
manipulated very wisely in order to ensure that enough 69&
energy remains to guarantee peak hours. The storage power is 70&
calculated according to operational and technical limits of the 71&
BESS and subjected to constraints on 𝐸!"#_!"#$ and 72&
𝐶
!"#$%&%'(: 73&
74&
P
!"#_!" =𝑚𝑎𝑥 𝑃
!𝜂!,𝑃
!,𝑃
!"#_!"#𝜂!!!𝑖𝑓!𝑆𝑂𝐶 𝑡>𝐸!"!!"#$ C!"# 75&
𝑜𝑡𝑒𝑟𝑤𝑖𝑠𝑒!P
!"#_!" =0 (11) 76&
77&
where 78&
𝑃
!"#_!"# =𝑚𝑖𝑛 SOC t𝐸!"#_!"#$ C!"# C!"# t,𝐶
!"#$%&%'( t 79&
80&
Within the tolerance band 81&
When 𝑃
!"_! is within the tolerance band, the power 82&
imbalance is equal to zero and there is not need to 83&
absorb/inject power from/to the grid through the BESS: 84&
85&
P
!"#_!" =0 (12) 86&
87&
Economic performance improvement 88&
The economical performance improvement relies on the 89&
estimation of two parameters α!"#$ and 𝛽!"#$%&'(. The effects 90&
of these parameters on the annual revenue and the average 91&
annual DRF are illustrated on Fig. 1 and 2. 92&
93&
As expected, while 𝛽!"#$%&'( is increasing the DRF is 94&
decreasing. Indeed, 𝛽!"#$%&'( is straightforwardly linked to the 95&
fraction of the storage capacity dedicated to compensate 96&
power imbalance due to forecast errors. However, it is 97&
important to highlight that decreasing the DRF and so the 98&
financial penalties do not necessary means increasing the 99&
revenue. In fact, while 𝐶
!"#$%&%'( becomes closer to 𝐶!"#, the 100&
amount of energy that can be stored in the BESS for the 101&
evening peak hours decreases. Since peak hours feed-in tariff 102&
is more attractive than off-peak one, it could be more 103&
interesting to sell more energy during peak hours even if that 104&
means paying more penalties during off-peak due to forecast 105&
error. 106&
As illustrated on Fig. 2, while α!"#$ is increasing the DRF is 107&
increasing, whereas the revenue increasing to a maximum 108&
before decreasing. Which seems indicate that, for a fixed 109&
value of 𝛽!"#$%&'(, there is an optimal amount of energy to 110&
store in the BESS for the evening peak hours. 111&
Paper&Submitted&to&ICREGA’16&
&
&
1&
Fig. 1: Effects of 𝛽!"#$%&'( on the revenue and the DRF (with 2&
α!"#$ =0.3 and calculated over one year) 3&
4&
5&
Fig. 2: Effects of α!"#$ on the revenue and the DRF (with 6&
𝛽!"#$%&'( =0.2 and calculated over one year) 7&
8&
9&
Annual optimization 10&
In a first attempt, the economic performance improvement 11&
strategy consists on finding the optimal values of α!"#$ and 12&
𝛽!"#$%&'( that maximizes the annual revenue. The 13&
optimization goal is to find the optimal set of parameter 14&
p=α!"#$!𝛽!"#$%&!"!
! that maximizes the cost function J: 15&
16&
p=𝑎𝑟𝑔max
!𝐽 (13) 17&
18&
with 𝐽=𝑃
!"
!,!𝐶!60 𝑃
!"#
!,!𝐶!60 !𝑃𝑒𝑛𝑎𝑙𝑡𝑖𝑒𝑠!,!
!""#
!!!
!"#
!!! and 19&
subjected to p𝑝!"#,𝑝!"# ;&𝑝!"# =0!0!;&𝑝!"# =1!0.8!. 20&
21&
The optimization procedure, performed on experimental data 22&
collected from August 31st 2013 to September 1st 2014, leads 23&
to the optimal set of parameter !p=0.2978!0.1387!!, which 24&
results to an annual revenue of 25 449.20 euros and an 25&
average DRF of 13.12%. The revenue and the DRF for each 26&
day are presented in Fig. 3. 27&
28&
29&
30&
Fig. 3: Daily revenue and DRF obtain over one year 31&
32&
It can be noticed that the DFR seems to contain a periodic 33&
component. The analysis of the DFR in the frequency domain 34&
reveals that the frequency component with the higher 35&
magnitude is located at 0.002732 day-1, which corresponds to 36&
a periodicity of 366 days. Moreover, a thorough study of the 37&
DRF reveals a strong and significant correlation between 38&
DRF and the forecast error, with a Pearson’s correlation 39&
coefficient of 82% (p-value < 0.0001). 40&
41&
The analysis of the forecast error reveals the same periodicity 42&
of 366 days, which means that the forecast error is linked to 43&
the season. Indeed, as illustrated on Fig. 4, the forecast error 44&
is higher in summer than in winter. 45&
46&
Fig. 4: Forecast error 47&
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8
2.42
2.44
2.46
2.48
2.5
2.52
2.54
2.56 x 104
ßforecast
Revenue [euros]
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8
9
10
11
12
13
14
15
16
Average DFR [%]
0.2 0.3 0.4 0.5 0.6 0.7 0.8
2.49
2.495
2.5
2.505
2.51
2.515
2.52
2.525
2.53
2.535
2.54x 104
Revenue [euros]
0.2 0.3 0.4 0.5 0.6 0.7 0.8
13.15
13.2
13.25
13.3
13.35
13.4
13.45
13.5
13.55
13.6
13.65
Apeak
Average DFR [%]
0 50 100 150 200 250 300 350
20
0
20
40
60
80
100
120
140
Time [day]
Revenue [euro]
50 100 150 200 250 300 350
0
10
20
30
40
50
Time [day]
DFR [%]
Raw data
Filtered data
50 100 150 200 250 300 350
20
30
40
50
60
70
80
Time [day]
Forecast error [kWh]
Filtered data
Summer (october to april)
Winter (april to october)
Paper&Submitted&to&ICREGA’16&
&
&
This can be mainly explained by the fact that the forecast 1&
model does not take into account the intra-day variability of 2&
solar irradiance and that in La Reunion Island the intra-day 3&
variability is higher in summer. In this context, when the 4&
intra-day variability increases the error modeling increases 5&
too, which leads to an increase of the DFR. In other worlds, 6&
the seasonality of the solar irradiance variability introduces a 7&
seasonal component into the forecast error that is transmitted 8&
to the DFR. 9&
10&
The analysis of the total energy produced by the PV plant 11&
each day (𝐸!"_!_!"!#$) reveals a seasonal component that is 12&
due to the yearly solar irradiance variability (Cf. Fig. 5). The 13&
amount of energy dedicated to the peak hours (𝐸!"#_!"#$), 14&
which could have a strong influence on the revenue, is taken 15&
as a fraction of 𝐸!"_!_!"!#$ using α!"#$ . In this context, the 16&
seasonal component contained into 𝐸!"_!_!"!#$ is transmitted 17&
to 𝐸!"#_!"#$ . A thorough study of intra-day and yearly 18&
irradiance variability in La Reunion can be consulted in [23]. 19&
20&
21&
Fig. 5: Energy produced by the PV plant 22&
23&
Regarding the seasonality of the solar irradiance, and since 24&
𝛽!"#$%&'( and α!"#$ respectively influence the DRF and the 25&
amount of energy dedicated to the peak hours, it is likely that 26&
a seasonal-based or even a daily-based optimization of these 27&
parameters could increase the revenue. 28&
29&
6. Conclusions and prospects 30&
In this work, a minute dispatch strategy for a 57 kWp PV 31&
farm with 78.5 kWh BESS has been simulated, and an 32&
economical optimization of the dispatch strategy has been 33&
developed. This strategy has been designed to fulfill the 34&
requirements of the new regulatory rules set in La Reunion 35&
while optimizing the economic performance of the system. 36&
The BESS is used during off-peak to compensate PV 37&
production forecast error and during peak hours to inject 38&
power to the grid. Therefore, two parameters have been 39&
introduced. The first one is related to amount of energy to 40&
store for the evening peak hours whereas the second one 41&
represents the fraction of the storage capacity that is dedicated 42&
to compensate power imbalance due to forecast errors. The 43&
optimization goal is to find the optimal value of these 44&
parameters that maximizing the revenue while taking into 45&
account the new regulatory rules constraints. The proposed 46&
optimization strategy takes into account the energy market 47&
data, the amount of PV production subject to penalties for 48&
imbalance, the batteries and the PV technological 49&
characteristics together with a PV production forecast model. 50&
The effectiveness and relevance of the proposed strategy have 51&
been assessed on experimental data collected on a real PV 52&
power plant. An economical analysis demonstrated that the 53&
proposed optimization strategy has been able to fulfill the 54&
new regulatory rules requirements while increasing the 55&
economic performance of the system. 56&
Due to the seasonal behavior of solar radiation, it is likely that 57&
economical performance can be further increased using a 58&
seasonal-based optimization approach. Additional works are 59&
currently in progress to study if the revenue can be increased 60&
by taking into account the seasonal component contained in 61&
the forecast error and the total energy produced by the PV 62&
plant. 63&
64&
Acknowledgement 65&
This work is a contribution to the FEDER project 66&
GYSOMATE funded by the European Social Fund and the 67&
Reunion Region. The authors would like to kindly 68&
acknowledge Pr. Jean-Daniel Lan-Sun-Luk for the fruitful 69&
scientific discussions, and M. Patrick Jeanty for the solar 70&
experimental data. 71&
72&
References 73&
[1] Weisser D. On the economics of electricity consumption in 74&
small island developing states: a role for renewable energy 75&
technologies? Energy Policy 2004;32(1):12740. 76&
http://dx.doi.org/10.1016/S0301-4215(03)00047-8. 77&
[2] Guerassimoff G, Maïzi N, Mastère. OSE. Îles et Énergie: un 78&
paysage de contrastes. Les Presses-MINES ParisTech; 2008 [in 79&
french]. 80&
[3] ARER. PETREL - île de la Réunion. Plan économique de 81&
Transition et de Relance via des énergies 100 % Locales île de 82&
la Réunion. Technical Report, ARER; 2009 [in french]. 83&
[4] Praene JP, David M, Sinama F, Morau D, Marc O. Renewable 84&
energy: progressing towards a net zero energy island, the case 85&
of Reunion Island. Renew Sustain Energy Rev 2012;16(1):42686&
42. http://dx.doi.org/10.1016/j.rser.2011.08.007. 87&
[5] Drouineau, M., Assoumou, E., Mazauric, V., Maïzi, N. 88&
Increasing shares of intermittent sources in Reunion Island: 89&
Impacts on the future reliability of power supply. Renewable 90&
and Sustainable Energy Reviews 46, 120128, 2015. 91&
[6] Fesquet F, Juston P, Garzulino I. Impact and limitation of wind 92&
power generation in an island power system. In: IEEE Bologna 93&
power technical conference; 2003. 94&
[7] Beaudin M, Zareipour H, Schellenberglabe A, Rosehart W. 95&
Energy storage for mitigating the variability of renewable 96&
50 100 150 200 250 300 350
0
50
100
150
200
250
300
350
Time [day]
Energy produced by PV plant [kWh]
Paper&Submitted&to&ICREGA’16&
&
&
electricity sources: an updated review. Energy Sustain Dev. 14, 1&
302-14, 2010. 2&
[8] Delfanti M, Falabretti D, Merlo M, Monfredini G. Distributed 3&
generation integration in the electric grid: energy storage 4&
system for frequency control. J Appl Math. 13, 2014. 5&
[9] Weiss T, Schulz D. Development of fluctuating renewable 6&
energy sources and its influence on the future energy storage 7&
needs of selected European Countries. In: 4th International 8&
Youth Conference on Energy (IYCE), Hungary, 6-8; June 2013. 9&
[10] Alam MJE, Muttaqi KM, Sutanto D. Mitigation of rooftop solar 10&
PV impacts and evening peak support by managing available 11&
capacity of distributed energy storage systems. IEEE Trans 12&
Power Syst 28(4), 3874-3884. 2013. 13&
[11] Hill CA, Such MC, Chen D, Gonzalez J, Grady WM. Battery 14&
energy storage for enabling integration of distributed solar 15&
power generation. IEEE Trans Smart Grid 3(2), 850-857, 2012. 16&
[12] Ru, Y., Kleissl, J., Martinez, S. Storage Size Determination for 17&
Grid-Connected Photovoltaic Systems. IEEE Transactions on 18&
Sustainable Energy. 4(1), 68-81, 2011. 19&
[13] Delfanti, M., Falabretti, D., Merlo, M. Energy storage for PV 20&
power plant dispatching. Renewable Energy 80, 61-72, 2015. 21&
[14] Cervone, A., Santini, E., Teodori, S., Romito, D.Z. Impact of 22&
regulatory rules on economic performance of PV power plants. 23&
Renewable Energy 74, 78-86, 2015. 24&
[15] Bridier, L., David, M., Lauret, P. Optimal design of a storage 25&
system coupled with intermittent renewables. Renewable 26&
Energy 67, 2-9, 2014. 27&
28&
[16] European Network of Transmission System Operators for 29&
Electricity. Operation Handbook. Technical Report, European 30&
Network of Transmission System Operators for Electricity; 31&
2004. 32&
[17] Bergen AR, Vittal V. Power system analysis, 2nd ed., 33&
Englewood Cliffs, NJ: Prentice-Hall Series; 2000. 34&
[18] Bornard P, Pavard M, Testud G. Réseaux d'interconnexion et de 35&
transport: fonctionnement (in French). Techniques de 36&
l'ingénieur 2005;(D4091):112. 37&
[19] Perez R, Lorenz E, Pelland S, Beauharnois M, Knowe GV, 38&
Hemker K, et al. Comparison of numerical weather prediction 39&
solar irradiance forecasts in the US, Canada and Europe. Sol 40&
Energy 2013;94:305e26. 41&
[20] Almeida, M.P., Perpinan, O., Narvarte, L. PV power forecast 42&
using a nonparametric PV model. Solar Energy 115, 354368, 43&
2015. 44&
[21] Lorenz E, Scheidsteger T, Hurka J, Heinemann D, Kurz C. 45&
Regional PV power prediction for improved grid integration. 46&
In: Special Issue: 25th EU PVSEC WCPEC-5, Valencia, Spain, 47&
vol. 19(17); 2011. p. 757e71. 48&
[22] Walton, S., Hassan, O., Morgan, K., Brown, M.R. Modified 49&
cuckoo search: A new gradient free optimisation algorithm. 50&
Chaos, Solitons & Fractals 44 , 710718, 2011. 51&
[23] Jeanty P., M. Delsaut, L. Trovalet, H. Ralambondrainy, J.D. 52&
Lan-Sun-Luk, M. Bessafi, P. Charton, J.P. Chabriat. Clustering 53&
daily solar radiation from Reunion Island using data analysis 54&
methods. Renewable Energy and Power Quality Journal 55&
(RE&PQJ) ISSN 2172-038 X, No.11, March 2013. 56&
ResearchGate has not been able to resolve any citations for this publication.
Article
Full-text available
A partitioning of daily solar radiation using Hierarchical clustering on Principal Components method is proposed based on daily distribution of direct fraction in solar irradiance. This t ool, similar to clearness index, is only based on diffuse and global solar radiation measurement, taking in account the local topology. The clustering method is applied on data collected in Reunion island (21 � S, 55 � E), a southern subtropical site. We obtain five daily solar radiations classes as results. The interpretations of the a verage time series signals for each class shows that daily dynamics curves are correlated with local meteorological phenomena. The season distribution of classes is correlated to the intensity of tr ade winds flow. Thus, it helps us to define an approach to predict the sola r radiation for different time horizons and the energy potential of the day.
Article
Full-text available
During the last few years generation from renewable energy sources (RESs) has grown considerably in European electrical networks. Transmission system operators are greatly concerned about the impact of RESs on the operational security and efficiency of their networks and more in general of the ENTSO-E interconnected system. Grid codes are to be revised in order to harmonise the rules regarding the connection of RES power plants. A main issue concerns frequency control: frequency is greatly affected by RESs intermittency and its deviations must be limited as much as possible in order to guarantee a suitable level of power quality. To improve frequency stability, in the future, Grid codes could extend frequency control requirements also to RES units, whereas today they are applied only to conventional power plants. Energy storage systems can be a possible solution to increase the flexibility and performance of RES power plants: they allow generators to modulate their power injections without wasting renewable energy. In this paper, the authors studied the suitability of extending frequency control to RES units integrating them with energy storage systems. In particular, the paper focuses on the impact of frequency control on the storage lifetime by analysing the power charge/discharge in response to real frequency oscillations.
Article
Full-text available
As solar photovoltaic power generation becomes more commonplace, the inherent intermittency of the solar resource poses one of the great challenges to those who would design and implement the next generation smart grid. Specifically, grid-tied solar power generation is a distributed resource whose output can change extremely rapidly, resulting in many issues for the distribution system operator with a large quantity of installed photovoltaic devices. Battery energy storage systems are increasingly being used to help integrate solar power into the grid. These systems are capable of absorbing and delivering both real and reactive power with sub-second response times. With these capabilities, battery energy storage systems can mitigate such issues with solar power generation as ramp rate, frequency, and voltage issues. Beyond these applications focusing on system stability, energy storage control systems can also be integrated with energy markets to make the solar resource more economical. Providing a high-level introduction to this application area, this paper presents an overview of the challenges of integrating solar power to the electricity distribution system, a technical overview of battery energy storage systems, and illustrates a variety of modes of operation for battery energy storage systems in grid-tied solar applications. The real-time control modes discussed include ramp rate control, frequency droop response, power factor correction, solar time-shifting, and output leveling.
Article
Forecasting the AC power output of a PV plant accurately is important both for plant owners and electric system operators. Two main categories of PV modeling are available: the parametric and the nonparametric. In this paper, a methodology using a nonparametric PV model is proposed, using as inputs several forecasts of meteorological variables from a Numerical Weather Forecast model, and actual AC power measurements of PV plants. The methodology was built upon the R environment and uses Quantile Regression Forests as machine learning tool to forecast AC power with a confidence interval. Real data from five PV plants was used to validate the methodology, and results show that daily production is predicted with an absolute cvMBE lower than 1.3%.
Article
Energy from the sun is weather-dependent. In modern electric grids that is a shortcoming; generation (and load) has to be regulated accordingly. This issue is a cornerstone for an effective transition to a renewable-based energy system. Weather forecast algorithms can predict photovoltaic production but, in real life conditions, their reliability is only partially effective with respect to the actual grid operation requirements. In the paper, Energy Storage Systems are adopted to compensate the mismatch between the injections of a photovoltaic power plant and the day-ahead market power schedule: the final goal is to achieve the full programmability of the photovoltaic resource by minimizing energy imbalances, as defined in the Italian regulatory framework, on an hourly basis. In particular, the optimal design of the storage apparatus (nominal power and capacity) is defined according to the regulating performances required. Moreover, three forecast models are tested to evaluate the impact of weather prediction accuracy on the ESS design. Finally, the benefit/cost ratio of the ESS application is assessed according to the main economic and technical parameters (ESS cost, round trip efficiency, lifespan). The analyses are performed on data measured on a real power plant, with hypotheses consistent with the actual Italian scenario. Full paper: http://authors.elsevier.com/a/1QXt33QJ-dKurI
Article
The rapid growth of Renewable Energy Sources (RES) power plants connected to the grid has introduced new problems related to the safe and reliable operation of the electricity network from transmission to distribution sectors. New regulatory rules can promote RES producers to make a commitment on the energy amount that is likely to be supplied to the network. The present paper concerns the analysis of the energy production of a PV power plant from the economic point of view, with reference to the presence of regulatory rules. Costs of penalty and value of energy are compared, in order to evaluate the economic efficiency of the plant. Use of auxiliary energy storage devices is investigated, with the aim to determine the relevant dimensions that increase the economic efficiency of the PV plant. A software instrument that implements these algorithms is described and applied to a case study.
Conference Paper
The 20-20-20 targets of the European Union regarding renewable energies and climate are ambitious but realistic. Since the promotion of renewable energies first started, the share of sustainable energy on the net electricity consumption is rising steadily. Especially fluctuating, non-controllable sources like wind and sun are gaining importance. With an installed amount of non-controllable power that exceeds the yearly peak load, situations can occur with a surplus of energy in the electricity supply system. This surplus is strongly dependent on the share amongst the installed technologies, especially wind and sun. This paper deals with the calculation of short and long term energy storage needs and its dependence on the installed amount of solar energy and wind power. The target countries are Germany, Greece and Spain. In each country the electricity system was modeled and future development scenarios for renewable energies have been investigated. For all countries an equal development of wind and solar power, as defined in the targets of the National governments, is investigated as well as scenarios for a favored development of wind and solar energy respectively. Furthermore the market framework conditions in the target countries are discussed and possibilities to give incentives towards a more regulated support of energies from renewable sources are highlighted.
Article
A high penetration of rooftop solar photovoltaic (PV) resources into low-voltage (LV) distribution networks creates reverse power-flow and voltage-rise problems. This generally occurs when the generation from PV resources substantially exceeds the load demand during high insolation period. This paper has investigated the solar PV impacts and developed a mitigation strategy by an effective use of distributed energy storage systems integrated with solar PV units in LV networks. The storage is used to consume surplus solar PV power locally during PV peak, and the stored energy is utilized in the evening for the peak-load support. A charging/discharging control strategy is developed taking into account the current state of charge (SoC) of the storage and the intended length of charging/discharging period to effectively utilize the available capacity of the storage. The proposed strategy can also mitigate the impact of sudden changes in PV output, due to unstable weather conditions, by putting the storage into a short-term discharge mode. The charging rate is adjusted dynamically to recover the charge drained during the short-term discharge to ensure that the level of SoC is as close to the desired SoC as possible. A comprehensive battery model is used to capture the realistic behavior of the distributed energy storage units in a distribution feeder. The proposed PV impact mitigation strategy is tested on a practical distribution network in Australia and validated through simulations.