The increase of renewable non-programmable production and the necessity to locally self-consume the produced energy led to utilize ever more storage systems. To correctly utilize storage systems, an opportune management method has to be utilized. This paper implements a multi-period management method for storage devices, using different management strategies. The method aims to minimize the total absorbed and supplied energy or the peak power exchanged with the grid. The results show the effectiveness of the method in diminishing the energy exchanged with the grid and also the possibility to optimize the performance of the storage device.

1. A multi-period management method at user
level for storage systems using artificial
neural network forecasts
G. Belli, A. Burgio, D. Menniti, A. Pinnarelli,
N. Sorrentino, P. Vizza

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SUMMARY
 Framework
 Forecasting model
PV Forecasting Model
Load Forecasting Model
 Storage Management Method
Description Method
The University of Calabria case study
Simulation
Minimization of exchanged energy
Minimization of grid Power Peaks
 Conclusion and future aim

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The well-known tendency to produce on
site the necessary energy to the end user
by means of small size plants, generally
from non-programmable renewable
sources, led to a rapid increase of installed
photovoltaic power.
The PV power non-programmability can reduce the economic and environmental benefits.
To increase the possibility to self-consume the produced energy and increase the profit for the
user, storage systems should be utilized, furthermore generation and load forecasting systems
would be useful.
In addition, with the new Italian Electricity Market tools, 48 hours to 7 days ahead PV and load
forecasts could be necessary.
Worth noting that storage systems, particularly battery devices, have a limited lifetime, related to
their charge and discharge cycles. For this reason, it is opportune to manage storage systems with a
specific strategy that ensure them a long lifetime.
FRAMEWORK

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Energy
Energy
Money
Money
Energy
Energy
It would be interesting to observe the benefits
for a single user equipped with a PV
generation system and a storage system.
FRAMEWORK
A goal for the user should be to make “himself-sustainable”, satisfying his total energy demand by
means of local energy production, minimizing the exchanges of energy with the grid.
First of all
PV and Load Forecasting Models are necessary

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Many forecasting methods are able to obtain high levels of accuracy with limited percentage
errors. In such methods, accurate meteorological data are utilized; but such meteorological data
are not always available.
Therefore, a “practical” PV power
forecast method is implemented.
This method is a single-step
method, which predicts the
production 2/7 days ahead, using
weather forecasts directly from the
web. The method implements an
ANN that predicts the power of a
PV plant, starting from definite
inputs.
PV FORECASTING MODEL

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• 5 input neurons (meteorological
condition of the considered hour h,
meteorological condition of the hour h+1,
meteorological condition of the hour h-1,
the hourly irradiance, the considered
hour);
• 30 hidden layer neurons;
• 1 output neuron (hourly forecasted
power production).
The implemented Multi Layer Perceptron (MLP) Artificial Neural
Network (ANN) consists of:
PV FORECASTING MODEL

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Another two important evaluated parameters are the maximum absolute error and the MAE (Mean
Absolute Error): such parameters are respectively 6.7% and 2.6% of rated PV power plant.
The MAPE (Mean Absolute Percentage Error) is equal to 9.8%. The percentage error becomes
minimum (less than 1%) for the hours of higher production.
PV FORECASTING MODEL

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LOAD FORECASTING MODEL
The load forecasting model implemented in
this paper utilizes accessible data and it is a
‘‘simple and practical’’ model; such a model
predicts also individual and aggregate loads.
Although its simplicity the results
demonstrate the good performances of the
model.
Load forecast is utilized for several purposes, specially for islanded operation, because it is
necessary to guarantee grid stability, in addition to allow programmable generation system to
work at a maximum efficient point.

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• 7 input neurons (month, day, day type, hour,
daily maximum temperature, daily minimum
temperature and daily average temperature);
• 30 hidden layer neurons;
• 1 output neuron (hourly forecasted power
consumption).
The implemented ANN consists of:
LOAD FORECASTING MODEL

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The load forecasting model has been tested on real data of a typical user. The forecasted and
the real load profiles are shown, for three days (Thursday, Friday and Saturday). The obtained
Mean Absolute Error (MAE), compared to the rated power of the considered user’s contract is
less than 6%, that is an acceptable error for the purpose of the method.
LOAD FORECASTING MODEL

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Nomenclature
𝑃𝑔 𝑡
𝑑 Grid Transferred Power; (time t, day d)
𝑃𝐿 𝑡
𝑑 Load Power; (time t, day d)
𝑃𝑆 𝑡
𝑑 Storage Transferred Power; (time t, day d)
𝐸𝑆 𝑡
𝑑 Storage Energy Level; (time t, day d)
𝐸𝑆 𝑚𝑖𝑛
, 𝐸𝑆 𝑚𝑎𝑥
Minimum and maximum Storage Energy Level
𝑃𝑆 𝑚𝑖𝑛
, 𝑃𝑆 𝑚𝑎𝑥
Minimum and maximum Storage Power
𝐸𝑆 𝐼𝑛𝑖𝑡
Initial Energy stored
STORAGE MANAGEMENT METHOD
The main equations which describe the
MPSM method are as follow:
𝑂𝐹: min
𝑡=1
𝑑=1
𝐷
𝑇
𝑓(𝑃𝑔 𝑡
𝑑
) (1)
s.t.
𝑃𝑔 𝑡
𝑑
= 𝑃𝐿 𝑡
𝑑
− 𝑃𝑃𝑉 𝑡
𝑑
+ 𝑃𝑆 𝑡
𝑑
(2)
𝐸𝑆 𝑡+1
𝑑
= 𝐸𝑆 𝑡
𝑑
+ 𝑃𝑆 𝑡
𝑑
∗ 𝑡 (3)
𝐸𝑆 𝑚𝑖𝑛
≤ 𝐸𝑆 𝑡
𝑑
≤ 𝐸𝑆 𝑚𝑎𝑥
(4)
𝑃𝑆 𝑚𝑖𝑛
≤ 𝑃𝑆 𝑡
𝑑
≤ 𝑃𝑆 𝑚𝑎𝑥
(5)
𝑠𝑖𝑔𝑛 𝑃𝑆 𝑡
𝑑
= 𝑠𝑖𝑔𝑛(𝑃𝑃𝑉 𝑡
𝑑
− 𝑃𝐿 𝑡
𝑑
) (6)
𝐸𝑆 𝑡=1
𝑑=1
= 𝐸𝑆 𝐼𝑛𝑖𝑡
(7)
“Real Time” management method
If 𝑃𝐿 𝑡
𝑑
< 𝑃𝑃𝑉 𝑡
𝑑
→ 𝑃𝑆 𝑡
𝑑
≥ 0
If 𝑃𝐿 𝑡
𝑑
≥ 𝑃𝑃𝑉 𝑡
𝑑
→ 𝑃𝑆 𝑡
𝑑
≤ 0
If 𝑃𝐿 𝑡
𝑑
< 𝑃𝑃𝑉 𝑡
𝑑
→ 𝑃𝑆 𝑡
𝑑
≤𝑃𝑃𝑉 𝑡
𝑑
− 𝑃𝐿 𝑡
𝑑
If 𝑃𝐿 𝑡
𝑑
≥ 𝑃𝑃𝑉 𝑡
𝑑
→ 𝑃𝑆 𝑡
𝑑
≥𝑃𝑃𝑉 𝑡
𝑑
− 𝑃𝐿 𝑡
𝑑

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The considered building has a maximum
power consumption of 25 kW and the
installed PV plants power is 45 kW.
The test considers a time period of 7 days,
from the 10th to 16th October 2015.
After determining load and PV power
profiles, it is necessary to size storage
system for the required function.
The considered prosumer is a build of University of Calabria: this is a business user, equipped
with photovoltaic plant.
STORAGE MANAGEMENT METHOD

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The considered PV power is 45 kW, that is the rated PV power of the installed PV plants.
Storage capacity will be the smallest between the resulting
average daily energy purchased and supplied to the grid. This
analysis is carried out for profiles of a typical day.
After calculating storage capacity, an overestimation to be
conservative will be necessary: an increase of 20% will be
considered. In addition, in order to safeguard the storage
useful life, a residual state of charge (SOC) of 40% has to be
considered as a further increase of the estimated storage
capacity.
Storage capacity is calculated to supply loads and limit the
exchange of energy with the grid.
STORAGE MANAGEMENT METHOD

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In this case, the daily purchased energy is almost 170 kWh, whereas the energy supplied
to the grid is 80 kWh; so the storage would have a capacity of 80 kWh, and considering
the further increase the obtained storage capacity is about 140 kWh.
Purchased
170 kWh
Supplied
80 kWh
STORAGE MANAGEMENT METHOD

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Simulation
The first group of simulation aims to
minimize the total exchange of energy with
the grid, trying to make the prosumer self-
sustainable and to avoid congestions on the
grid.
While the second group of simulation aims
to minimize only the peaks of power during
the day, trying to reduce the costs of energy
supply and to avoid worthless oversize of
the generation plants.
The overall energy exchanged between the user and the grid without using a storage device
is equal to 1.68 MWh, where 1.13 MWh is the energy purchased by the user and 0.55 MWh
is the energy supplied to the grid.
STORAGE MANAGEMENT METHOD

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Simulation
Total exchange of energy with the energy grid minimization.
For the first group of simulations, when none management method is utilized, the total
energy exchange with the grid decreases until 0.69 MWh, where 0.60 MWh is the purchased
energy by the user and 0.09 MWh supplied to the grid. If the storage is managed by MPSM
method, the total energy exchanged with the grid is equal to 0.68 MWh. Respect the
previous case, the difference is very limited.
STORAGE MANAGEMENT METHOD
-30
-20
-10
0
10
20
30
Real Time management method
MPSM method

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Although this difference is only 0.01 MWh, the positive effect of the MPSM method consists in
the possibility to maximize the performance of the storage system. Indeed, using a “real time”
operation strategy, the charge and discharge cycles are not optimized because they are partial
cycles, while with MPSM method, the storage executes full cycles of charge and discharge.
STORAGE MANAGEMENT METHOD
Storage Cycles Optimization

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For the second group of simulations, while the power exchanged in “real time” reaches 23 kW,
if the MPSM method is utilized, the power exchanged reaches about 8 kW.
Minimization the peaks of power exchanged with the grid
STORAGE MANAGEMENT METHOD

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OPTIMIZED RATED PV POWER
Moreover a simulation using the
optimized rated PV Power is carried
out; Similarly to the previous
subsection the storage system is
properly sized and the obtained
capacity is about 240 kWh.
139 kWh
136 kWh
STORAGE MANAGEMENT METHOD
The obtained result of the total energy
exchanged with the grid is equal to 0.41
MWh, where 0.25 MWh is the
purchased energy by the user and 0.16
MWh is the energy supplied to the grid.
This simulation shows that before to use
the MPSM method, optimal PV and
storage sizing is necessary.

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CONCLUSION AND FUTURE AIMS
• The paper shows the importance of an opportune management method for storage
devices. In fact, the positive effect resulting from the use of storage systems, particularly
in relation to non-programmable resources, can be increased if an appropriate
management strategy is utilized.
• One of the feature of the implemented method is its multi-periodicity. In fact, if the
management is made on more days, there are more data input and the storage can be
managed in a better way.
• First of all, the method minimizes the total energy exchanged with the grid to make users
self-sustainable. This implies the use of opportune PV and load forecast models. Secondly
the method is also utilized to reduce the peak of power exchanged with the grid.
• Moreover, it is underlined that an accurate sizing of PV and storage systems is necessary
before to implement and utilize a management strategy.
• It would be interesting evaluate the behavior of the method if a schedulable load is
adopted, otherwise if an additional programmable source is adopted.

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Thanks
for your attention
Any question?
mail: pasquale.vizza@unical.it