APEN Multiapartament Microgrid 2015-Libre

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with the availability of storage systems in the Leaf House, their high investment cost made them not

really profitable at the current price conditions for devices and energy purchase.

2014 Elsevier Ltd. All rights reserved.

1. Introduction

Power grids are going to face several challenges, such as the

increasing diffusion of distributed generation technologies [1–3],

many including renewable energy sources [4–7]. Other challenges

come from the integration and connection, at local scale, between

electric and thermal networks (but also electric mobility in the

near future) [8–12]. Moreover, with the adoption of demand side

management (DSM) strategies, final consumers are going to play

an active role in the grid activity [13–19]. In this context, energy

storage technologies have a key role for several reasons. In the first

place, they represent the means to match energy production from

renewable sources and energy demand [20,21]. Even more so in

residential scenarios, since the load profile often describes the typ-

ical energy demand of an employee, who needs hot water mainly

early in the morning and late in the afternoon, with a minimumcontemporary factor with solar production. Thermal energy stor-

age allows to collect renewable energy during day-time and to

use it during night-time. In addition, energy storage devices

enhance the energy self-consumption level achievable by final

users. Thermal energy storages, in particular, help in reducing

the burden on service utilities (natural gas or electricity). Electrical

energy storages double the benefit. On the one hand, it lowers the

burden on the power grid. On the other hand, it reduces, or pre-

vents altogether, depending on circumstances, the bidirectional

flux of energy, from and towards the grid. It compensates for the

main obstacle against renewable energy source widespread, that

is the aleatory nature of renewable energy source availability

[22–24]. The third reason, which is related to thermal storage, if

tailored on purpose, is the ability to increase the energy yield of solar based thermal energy plants [25]. In conclusion, energy sto-

rages improve the flexibility of DSM strategies, enhance the final

user energy demand profile, and also minimize the overall energy

bill [19,26].

In the transition from nowadays power grid technology to the

smart grid technology, microgrids play a fundamental role as small

scale test bench of DSM strategies [27–29].

In this paper we present a residential microgrid, the Leaf House,

which accounts six apartments, a photovoltaic (PV) energy produc-

tion plant, a solar based thermal energy production plant, a geo-

thermal heat pump, a thermal energy storage in the form of a

water tank of 1300 l and two batteries of 5.8 kW h each. The Leaf

House hosts a building automation and monitoring system which

makes it an ideal test field for energy storage systems applications.By recording and collecting the data resulting from the everyday

life of its lodgers, the Leaf House is a living lab that records real life

energy demand profiles. Also the performance of both electrical

and thermal storages are evaluated on real life operating condi-

tions, rather than in simulated ones.

A relevant contribution of this work is the computational

framework aimed at micro-grid design, which serves as a tool to

model and simulate the energy management occurring within

the Leaf House electrical system. It has been used to simulate the

environment behavior over a one-year time horizon, accounting

different storage management strategies and various system

configurations. The suitability of computational tools to monitor,

control and simulate the smart grid behavior in different operating

conditions and at different abstraction levels, has been extensively

shown and commented in literature [30–33]. The proposed

framework is based on the Mixed-Integer Linear Programming

(MILP) paradigm, successfully used for energy management pur-

poses by some of the authors in recent publications [34,35], but

not yet proposed as a design tool in the evaluation of case studies

based on a real life environment such as the Leaf House. The MILP

approach has shown its effectiveness and capability, in dealing

with a large number of constraints, with respect to other computa-

tional intelligence techniques [36–38]. The framework includes,

also, a Neural Network based software for solar power forecasting.

The simulations carried out in this work have provided the means

to evaluate the yearly energy overall cost for each of the addressed

configurations. Therefore, the importance of the electrical and

thermal energy storages within the system has been evaluated,

but also the fact that the Leaf House energy management system

can be improved with the adoption of adequate hardware

modifications and storage management strategies.The paper is organized as follows: Section 2 presents the

microgrid used as reference, along with operational results and

related comments. In Section 3 the energy management simulation

framework and the solar power forecasting algorithm are pre-

sented, whereas Section 4 presents the case study and the scenar-

ios being examined. The simulation results are reported and

discussed in Section 5. Section 6 draws the conclusions of the work.

2. Microgrid under study: the Leaf House building

The Leaf House (see Fig. 1) is one of the six international case

studies selected by the IEA Task 40/ECBCS Annex 52: ‘‘Towards

Net Zero Energy Solar Buildings’’ [39,40]. Built in 2008, the Leaf

House is located in Angeli di Rosora, Ancona, Italy (latitude4328043.16 N, longitude 1304003.65 E, altitude 130 m above sea

level). The site is characterized by a moderate climate: annual

temperature between 5 and37 C; 1688 degree day, mean annual

horizontal solar radiation 302 W/m2. The Leaf House is a three

stories building, hosting three couples of twin flats. Two apart-

ments are occasionally occupied, while the remaining four flats

are occupied by two lodgers each. The building is south oriented

and the ratio between the lengths of the south and east facades

was set to 1.34 to maximize solar gains during the colder season.

The roof, the solar thermal panels and the balcony have been

designed to behave as solar shadings during the hottest months.

Fig. 1. The Leaf House.

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According to [28], a microgrid is essentially an active powergrid, which includes distributed generation, storage systems and

multiple loads at main grid voltage level. From the functional point

of view, the sources within the microgrid are provided with power

electronic interfaces (PEIs) and controls, to coordinate the activity

of each element of the system, in order to guarantee the required

power quality and energy output. The microgrid presents itself to

the main utility power system as a single controlled unit, and

meets local energy needs for reliability and security.

Table 1 lists the main sensors used in the monitoring activity

and their characteristics.

2.1. Electrical energy subsystem

The outline of the residential microgrid is presented in Fig. 2. A

20 kW peak power photovoltaic plant is integrated into the

building and produces the electrical energy needed to supply the

building. The plant is made of 150 m2 of mono crystalline modules,

it is south oriented and it is installed on the 21 sloped roof. It

provides energy not only to the HVAC system but also to the apart-

ments lighting and loads.

Two energy storage systems have been installed in July 2012.

The Leaf House PV power plant is made of three single-phase

circuits, connected in order to obtain a balanced three phase cir-

cuit. Also the six apartments are grouped in pairs, each pair con-

nected to form a single phase circuit. The resulting three circuits

are then connected to form a three phase circuit. The two energy

storage systems serve respectively two couples of flats and they

store energy from two out of the three PV arrays. This choice

was made because just four of the six apartments are regularly

occupied.

The main elements of the system are:

a 5.8 kW h Li-ion battery and its inverter based interface that

manages the charge and discharge policies;

a 6 kWp PV array and the corresponding inverter;

the energy manager that coordinates the storage, the produc-

tion and the demand.

The microgrid is connected to the main grid through a point of

common coupling (PCC) (represented by the circuit breaker CB0 in

Fig. 2). Self-consumption is maximized by storing the energy when

the PV production exceeds the consumption while feeding it

directly to the building otherwise. To maximize the reliability of

the system, the control strategies have been defined following

the policies listed below:

the batteries serve only the apartments; the central HVAC sys-

tem is not supplied by the electrical energy storage system;

during the discharge phase, the battery supplies about the 90%

of the demand, the remaining 10% is always supplied by the

grid, thus avoiding the event of energy being feed back to the

main grid, since it is not allowed by the national law on PV feed-

ing tariffs;

the discharge process is not allowed below the 30% of the nom-inal capacity of the device, the reason being that, below said

value, the discharge process becomes very fast and the readings

Table 1

List of sensors.

Thermal energy

meter

Brunata HG ðQ =S Þ

Precision class: EN1434 class 2

Pt500 sensor temperature

Water flow meter Turbine flow-meter

Nominal flow: 2.5 6 10 m3/h

Signal: 100 l/pulse

Temperature sensors Series: TR 10 Omnigrad M

Accuracy: 3% (range 200 to +600 C)

Precision: 0.2 K (range 200 to +650 C)

Electric meters Beckhoff module KL3403

Mesured values: current (I); voltage (V); effective

power (P);

power factor ðcos/Þ; I, V and P peak values; frequency

Accuracy: 0.5% full scale; (V/I), 1 % of calculated value

Data Acquisition

System

PLC Beckhoff (CX1100)

Operating/storage temperature: 0 to +55 C/25 to

+85 C

Relative humidity: 95%, no condensation

Fig. 2. Electrical scheme.

856 G. Comodi et al. / Applied Energy 137 (2015) 854–866


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of the residual energy level become unreliable, and may lead to

a loss of control over the discharge process; as such, in the eventof the battery being completely discharged, the system may

shut down and may not restart automatically because of the

voltage mismatch between the inverter and the battery; in

addition, it should be reminded that deep discharges tend to

reduce the battery lifetime;

if the battery residual energy level fall at 30%, and the PV pro-

duction is not sufficient to recharge the storage, the battery

manager draws about 100 Watt from the main grid, to supply

the battery and prevent its residual energy level to fall below

30%; the amount of power drawn from the grid has been iden-

tified during the preliminary test phase and covers both the

power needed by the battery control system and the battery self

discharge;

the battery supplies the load when the demand is greater thanthe PV production. However, after the charge phase begins, the

battery does not supply the load until the residual energy level

has risen up to, at least, the 65% of its residual nominal capacity;

this policy is required to limit the frequency of charge and dis-

charge cycles.

Provided that the system under study was part of a scientific

project, the turn-key cost of the electrical storage was (in 2012)

2650 €/kW h, with a yearly O&M cost of 500 € for each battery.

2.2. Thermal energy subsystem

The building hosts a central geothermal heat pump (GHP)

supplying cooling, heating and domestic hot water. The electricpeak-power of the heat pump is 16.6 kW.

Radiant floors provide the apartments with the requested

amount of thermal energy during cold seasons, while removing

the heat in excess during hot ones. The association of the ground

source and radiating floors reduces the gap between the heat

pump condensing and evaporating temperatures, increasing the

heat pump efficiency. Seven flat plane solar thermal collectors

Fig. 3. Thermal scheme.

Table 2

hot water energy demand and supply.

Hot water

demand

(kWh) (A)

Thermal solar

production

(kWh) (B)

Thermal energy

auxiliary source

(kWh) (C)

B= A

(%)

C = A

(%)

2010 2986 3039 1950 102 65

2011 3149 2304 1789 73 57

(a) Summer days.

(b) Winter days.

Fig. 4. Hot water energy demand, thermal energy produced by the solar plant andthermal energy provided by the auxiliary boiler.



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(2.6 m2 each), according to the season, support the production of

domestic hot water; the solar based thermal energy plant is cou-

pled with a 1300 l water tank acting as a thermal energy stor-

age. An auxiliary electric boiler, of about 15 kWt (thermal

kilowatt), also supports the thermal energy production, in the

occurrence of low solar based thermal energy production in win-

ter season. The set-point of the domestic hot water temperature

is 45 C. The temperature of the water supplied to the apart-

ments by the heating system is 30 C during winter season and

18 C during summer season. The amount of heating and cooling

energy, provided to the apartments, is regulated through digital

valves that control the water flow in the radiant floor coils.

The valves are driven by a PWM (Pulse Width Modulation) con-

troller, that acts based on the internal air temperature in each

thermal zone. The outline of the Leaf House thermal circuit is

presented in Fig. 3.

The cost of the 1300 l storage tank was 2000 €, installation

included; the yearly maintenance cost is 100 €.

2.3. Present day energy management: operational results and

preliminary comments

The monitoring activity of the energy parameters of the Leaf

House started in 2010. In the following, we present a summary

of the main operational results, related to both thermal and electri-

cal storage management. Table 2 reports the yearly aggregated

results related to hot water energy demand, and supply, relating

to 2010 and 2011.

The high performance of the solar based thermal energy plant

are due to the system being oversized in order to meet the

winter energy demand. In two years the auxiliary boiler has pro-

vided 3739 kW h of energy. During summer the solar thermal

system has collected more energy than required, therefore part

of said energy has remained unused. That, and the additional

thermal losses, explains the sum of the energy supplied, by both

the solar thermal plant and the auxiliary boiler, exceeding the

100% of yearly hot water demand. Fig. 4 shows the hourly trendof the hot water energy demand, the hourly trend of the thermal

energy produced by the solar plant and the hourly trend of the

thermal energy provided by the auxiliary boiler. In particular,

Fig. 4(a) refers to a ten-days period of summer and Fig. 4(b)

refers to a ten-days period of winter. Fig. 4 highlights the bene-

fits provided by the thermal energy storage:

It allows the solar plant to satisfy almost the 100% of the hot

water summer demand: no energy has been provided by the

auxiliary boiler in summer; indeed, depending on the evaluated

year, the maximum number of consecutive days, during which

the auxiliary boiler operation has not been necessary, ranges

between 77 and 94.

It smoothes the thermal peak-power of the energy supplysystems: the yearly maximum of the hourly average value of

the hot water peak demand of the cluster of apartments is

slightly less than 12 kWt; even so, Fig. 4(b) shows that the

hourly average power provided by the auxiliary boiler is about

1 kWt; the yearly maximum of the hourly average value of the

peak power provided by the auxiliary boiler has been 3.5 kWt.

This ‘‘smoothing effect’’ will most likely lead, during 2014, to

the replacement of the currently installed electric boiler, of

about 15 kWt, with a 2.73 kWe (electrical kilowatt) water–air

heat pump; hypothesizing a HP COP of 3, the thermal energy

production peak-power will drop down to 8.2 kWt (45%).

Assuming a 96% efficiency of the electrical boiler, its replace-

ment with a HP, will furtherly reduce the electrical peak con-

sumption, required by hot water production, from 15.6 kWedown to 2.73 kWe (82.5%).

It is a fundamental component for residential demand side

management; indeed, the load profile describes the typical

energy demand of an employee, who needs hot water mainly

early in the morning and late in the afternoon, with a minimum

contemporary factor with solar production. Thermal energy

storage thus allows to collect renewable energy during day-

time and to use it during night-time.

Data pertaining the electrical energy management have been

collected every 15 min, starting from the installation of the electric

storage system. Table 3 summarizes one-year operational results,

from 1 November 2012 to 31 October 2013, achieved by the two

couples of apartments, each couple taking advantage of a

5.8 kW h lithium ion battery.

Data reported in Table 3 show the role played by the electrical

energy storage in the maximization of renewable energy self-

consumption, in the reduction of the amount of energy feed back

to the main grid and, therefore, in the increase of self-sufficiency

of the microgrid. Since the management policies of the two electri-

cal energy storages are the same, the slight differences between

the results achieved by the two couples of apartments are mainly

due to the different load profiles. Thanks to the electrical energy

storage, the percentage of energy being self-consumed (column g

in Table 3) is 58.1% for the first couple of apartments and 63.5%

for the second one. The actual number of hours, during which full

self-sufficiency has been achieved, are 3163 and 3356, respec-

tively. Without electrical energy storage, the figures of self-con-

sumption would had been 30.4% and 34.6% respectively, that is

the direct consumption of the electricity produced by the PV plant

(column f). The availability of batteries allows to store about the

17.5% of the electricity produced by the PV plant (column h). It is

worth noting that, since the batteries have been managed to store

the PV electricity exceeding the apartments electricity demand, the

energy being stored (column b) would have been fed back into the

main grid. Actually, not all the energy transferred to the batteries

has been produced by the PV system. Given the previously

described electric storage management strategy, if the batteryresidual energy level drop to 30% of its nominal capacity, and no

renewable energy is available, the battery manager constantly

draws about 100 W from the main grid, to prevent the residual

energy level of the battery from dropping lower. Because of this,

about 14% of electricity transferred to the batteries comes from

the main grid, thus reducing down to about 15% the increase in

exploitable PV electricity that can be achieved thanks to the elec-

trical energy storage. In column i, of Table 3, it is reported the

yearly average efficiency of the two batteries: 63.7% for the one

coupled with the 1–2 apartments and 65.3% for the other one.

These efficiency figures describe a much less performing device

with respect to the manufacturer certification, which guarantees

a nominal level of efficiency up to 95%. The analysis of recorded

data has pointed out also, a significant efficiency drop, when theresidual energy level of the batteries rise from 97% to 100% of its

nominal capacity, during recharge. Tests are currently being

carried out to improve the overall performance of the electrical

storage system, by modifying the charging conditions: an increase

of the battery efficiency up to 85–90% is expected.

Fig. 5 shows the trend of energy consumption, PV production,

energy being charged and discharged and the state of charge of

the storage for the battery coupled with apartments 1–2. As stated

above, data have been acquired every 15 min. In particular,

Fig. 5(a) shows the trend for a 8-days period, while Fig. 5(b) focuses

on a two-days period, characterized by both a sunny and a vari-

able-weather day. The trend in Figs. 5(a) and (b) reflects the batter-

ies management strategies previously described. The electrical

energy storage, as well as the thermal energy one, has proven itseffectiveness as DSM tool. Even more so in the particular case of



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the Leaf House, whose electric load profiles describe the typical

demand of an employee who needs the electricity the most when

PV production is low or missing. During daytime, PV production

supplies the base-load of the apartments, whereas the surplus of

renewable electricity is stored in the battery. When PV production

is low or missing, the battery supplies the appliances with electric-

ity until the residual energy level drop to 30% of its nominal capac-

ity. When this threshold is reached, the battery manager starts to

draw power from the main grid (100 W) until the PV production

begins anew. From this point onwards, the trend of the energy

being stored results from the trend of PV production minus the

amount of energy consumed by the two apartments.

3. Energy management simulation framework

The suggested framework is meant to simulate the behavior of

the energy management subsystem at a high level of abstraction.

The aim is to focus on the energy fluxes and their balance, so that

the energy management subsystem can be tailored and evaluated

early in time, during the design process. Moreover, as a design tool

that focuses on the energy flows, the devised framework does not

require to simulate the system from a physical point of view. In

other words there is no need to account the evolution, over time,

of low level parameters such as the voltages of electrical storage

devices, or the temperatures within thermal storage devices. Once

the most appropriate configuration of the structure has been

selected, the proposed approach can be complemented with a

more accurate modeling of the system, if a deeper insight of its

performance is required.

3.1. The structure of the model and the MILP problem

The proposed model has been devised as a set of blocks and ele-

ments. The blocks model the substructures within the main energy

management subsystem. The elements model the single devices

that compose each block. Elements can be arranged together to

model a block, by defining the constraints that bind together the

variables of chosen elements. Depending on the constraints, differ-ent structures, or even different policies, can be evaluated through

simulation.

In the current work, a real life microgrid is used as a reference,

namely the Leaf House. In this environment it is possible to iden-

tify an electrical management subsystem, that can be partitioned

in three independent blocks. Each block involves two apartments,

out of the six within the building, and a photovoltaic plant. Addi-

tionally, two blocks also include an electrical storage device each.

Within the management of thermal resources, it is possible to

identify hot water production, heating and cooling. Although

heating and cooling are provided by the same heat pump, which

operates alternatively as a cooler or heater, in the current work,

to improve the robustness and flexibility of the framework, heating

and cooling are modeled as independent blocks. Hot water produc-tion is modeled as an additional independent block.

In total, therefore, six blocks have been modeled. By arranging

the elements within each block, and by organizing the blocks

within the management subsystem, the performance of 8 alterna-

tive configurations have been evaluated. For each configuration a

dedicated routine has been created, while maintaining the same

parametrization for the model components and the same energy

production and load demand profiles.

Although the resulting model can be easily extended to account

non linear constraints, in the current scenario only linear con-

straints have been adopted. Therefore the simulation problem

has been addressed as a MILP problem. On purpose, since the con-

straints take the form of a block matrix, Mathworks MatLab is

adopted as simulation environment. The solver, namely SCIP, has

been adopted to complement said environment. On purpose, the

Opti Toolbox 2.05 [41] has been used as interface between the sol-

ver and the MatLab environment. The framework is hosted on a

Laptop PC, based on the Intel Core i7 CPU series, with 8 GB of

RAM, and running on the Microsoft Windows 8 64-bit OS.

3.2. Energy management model

The model operates on a single time slot at a time. The objective

function to be minimized is the sum of the costs of energy pur-

chased and energy surplus. The aim is to avoid either unnecessary

purchase and, at times, unnecessary sale of electricity, thus pro-

moting storage, since the sale price is usually much lower than

the purchase price. The variable modeling the energy fluxes withinthe environment can be addressed as following:

Table 3

Operational results from 1 November 2012 to 31 October 2013.

a b c d e f g h i l m

Cons.

(kWh)

Energy charged

(kWh)

Energy dischar.

(kWh)

PV Prod.

(kWh)

self-cons. from

PV (kWh)

e/a (%) (c + e)/a (%) b/d (%) c /b (%) Autonomy

from grid (h)

Nighttime

charge (%)

Ap.1–2 2720 1183 753 6635 827 30.4 58.1 17.8 63.7 3163 14.4

Ap.3–4 2844 1257 821 7179 984 34.6 63.5 17.5 65.3 3356 13.9

(a) 8-days overview.

(b) 2-days focus.

Fig. 5. Trend of electric parameters monitored.



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EP a: amount of energy purchased to supply the entire structure;

ES a: amount of energy surplus resulting from the entire

structure;

E p: the energy purchase price;

EP i: energy required by the i-th electrical block with i ¼ 1; 2; 3;

Eg i: energy required by the i-th electrical storage (to guard

against over discharge) with i ¼ 1; 2;

EP

hw: electricity consumption of the water boiler;

EP he: electricity consumption of the heat pump during heating

phases;

EP re: electricity consumption of the heat pump during cooling

phases;

ES i: energy production surplus provided by the i-th electrical

block with i ¼ 1; 2; 3.

EP th: electricity demand of the thermal blocks not covered by

the available electricity surplus;

Capiðt þ 1Þ: residual capacity level (of the i-th electrical storage)

at the end of the t -th time slot;

Capiðt Þ: residual capacity level (of the i-th electrical storage) at

the beginning of the t -th time slot;

Chiðt Þ: charged energy amount (of the i-th electrical storage)

during the t -th time slot;

Diiðt Þ: discharged energy amount (of the i-th electrical storage)

during the t -th time slot;

Dei: demanded energy amount (of the i-th electrical block);

PV i: energy production of the photovoltaic panel (of the i-th

electrical block);

Cheff : charge efficiency of each storage device;

Dieff : discharge efficiency of each storage device;

S di: self discharge of each storage device;

Ch g : hourly energy amount, provided by the main grid, to

compensate the self discharge;

bg i: binary guard that equals zero if Capiðt Þ is higher than 30%;

Ephw: heat production of the water boiler;

Chhw: heat collected by the water tank;

Dihw: heat lost as the water leave the tank;

S hw: heat provided by the thermal solar panel; Dehw: heat demand corresponding to the hot water being used;

Caphwðt þ 1Þ: energy level of the water tank at the end of the t -

th time slot;

Caphwðt Þ: energy level of the water tank at the beginning of the

t -th time slot;

Ephe: heat production of the heat pump;

Chhe: heat collected by the heater storage;

Dihe: heat output of the heater storage;

Dehe: heat demand of the environment;

Capheðt þ 1Þ: energy level of the heat storage at the end of the

t -th time slot;

Capheðt Þ: energy level of the heat storage at the beginning of the

t -th time slot;

Epre: chill production of the heat pump; Chre: chill collected by the chill storage;

Dire: chill output of the chill storage;

Dere: chill demand of the environment;

Capreðt þ 1Þ: energy level of the chill storage at the end of the

t -th time slot;

Capreðt Þ: energy level of the chill storage at the beginning of the

t -th time slot.

Therefore the objective function can be written as:

Q ¼ ðEP a þ ES aÞ E p ð1Þ

meaning that either energy purchase and the sale of energy surplus

are not desirable, and thus they are accounted as a cost. In other

words the objective function is used to model a policy rather thanthe actual energy cost. Of course, depending on the policy of choice,

the objective function can be restricted not to account the energy

surplus as a cost.

Almost every configuration adopted in the current work is

composed of three electrical blocks and three thermal blocks. Since

the thermal blocks require electrical energy to provide the thermal

management two alternatives are possible.

On the one hand, if the model is configured not to supply the

electricity surplus to the thermal blocks, the energy amount to

be purchased can be computed as:

EP a ¼ EP 1 þ EP 2 þ EP 3 þ Eg 1 þ Eg 2 þ EP hw þ EP he þ EP re ð2Þ

which results in the energy surplus, computed as:

ES a ¼ ES 1 þ ES 2 þ ES 3 ð3Þ

being either stored or discarded.

On the other hand, if the model is configured to route the elec-

tricity surplus to the thermal blocks, Eqs. (2) and (3) can be revised

in the following manner:

EP a ¼ EP 1 þ EP 2 þ EP 3 þ Eg 1 þ Eg 2 þ EP th ð4Þ

EP th ES a ¼ EP hw þ EP he þ EP re ðES 1 þ ES 2 þ ES 3Þ ð5Þ

In this case, the variable EP th has been introduced to account the

electricity demand of the thermal blocks that is not covered by

the available electricity surplus. Conversely, ES a represents the elec-

tricity surplus that still exceeds the needs of the whole structure.

Concerning the electrical blocks, the amount of electricity to be

purchased, and the electricity surplus, are bound to production and

demand according to the following:

EP i ES i Chi þ Dii ¼ Dei PV i ð6Þ

meaning that purchase or sale, while accounting the energy man-

aged by the storage, must match the difference among production

and demand.

The electrical storage devices are modeled, each, by computing

its residual capacity level as:

Capiðt þ 1Þ ¼ Capiðt Þ þ Chiðt Þ Cheff Diiðt Þ Dieff þ S di þ Ch g

Cheff bg iðt Þ ð7Þ

thus accounting charge and discharge efficiency as well as self

discharge. Also, the model includes a charge level guard. If the bat-

tery residual energy level drops to 30%, a charger, that compensates

for self discharge, is enabled so that the battery depletion due to self

discharge is prevented.

A few constraints have also been devised to model the original

policy used to manage the storage devices:

Chiðt Þ ¼ 0; if PV iðt Þ 6 Deiðt Þ

PV iðt Þ Deiðt Þ; if PV iðt Þ > Deiðt Þ

ð8Þ

Diiðt Þ ¼0; if Capiðt Þ < 65

0:9 ðDeiðt Þ PV iðt ÞÞ; if PV iðt Þ < Deiðt Þ

Capiðt ÞP 65%

8><>: ð9Þ

For instance, Eq. (8) prevents purchased electricity from being

stored, whereas Eq. (9) prevents storage device from being deeply

discharged. These constraints are intended to achieve a conserva-

tive management of the storage, and they are paired with an expli-

cit avoidance of energy sale. Thus they are complemented with the

objective function presented in Eq. (1). Conversely, to adopt a more

aggressive or, which is the same, a less conservative policy, said

constrains are not applied. In this case, also, Eq. (1) does not

account the energy surplus amount.

Similarly to the electrical blocks, with regard to the hot water

management, the balance between consumption and productionhas been modeled:



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Ephw Chhw þ Dihw ¼ Dehw S hw ð10Þ

thus accounting hot water production and demand, and the man-

agement of a hot water tank.

The energy capacity of the water tank can be computed as:

Caphwðt þ 1Þ ¼ Caphwðt Þ þ Chhwðt Þ Dihwðt Þ ð11Þ

assuming that heat loss over time from the storage is negligible.

In the case of the heating block and the refrigeration block, themodel is almost identical, the only exception being the lack of

energy production from renewable sources, and the use of the heat

pump in place of the water boiler. For instance, being the heat

balance in the form Ephe Chhe þ Dihe ¼ Dehe, the integration of hot

water and heat production has been modeled as a linear combina-

tion of said balance with Eq. (10), in placeof two separate equations.

3.3. Solar power forecasting

In this paper an on-line learning procedure is proposed, to

predict the PV output and the Solar Thermal power output. On pur-

pose, Radial Basis Function Networks (RBFNs) have been used.

These networks have been widely used for nonlinear system iden-

tification [42] because they have the ability both to approximatecomplex nonlinear mappings, directly from input–output data,

with a simple topological structure that avoids lengthy calcula-

tions, and to reveal how learning proceeds in an explicit manner

[43]. The proposed on-line learning algorithm is based on the

Extended Minimal Resource Allocating Network (EMRAN)

technique, that adds hidden neurons to the network, based on

the innovation of each new RBFN input pattern which arrives

sequentially. As stated in [42], to obtain a more parsimonious net-

work topology, a pruning strategy is introduced. This strategy

detects and removes, as learning progresses, those hidden neurons

which provide little contribution to the network output. If an

observation has no novelty then, the existing parameters of the

network are adjusted by an Extended Kalman Filter (EKF). In this

paper the performance of the filter is improved by an on-lineadjustment of the noise statistics, obtained by a suitably defined

estimation algorithm; the proposed Adaptive Extended Kalman Fil-

ter (AEKF) is able to adaptively estimate the unknown statistical

parameters [44,45]. To minimize the computational effort, in

real-time implementation, a ‘‘winner neuron’’ strategy is incorpo-

rate in the learning algorithm [42,45]. In this work the proposed

RBFN-based prediction algorithm is used, because it has the

capability to adapt on-line as operating conditions vary (i.e. night

and day and season succeed). Also these Networks do not need a

training dataset and they can be used in different case studies

without a training stage. The EMRAN estimation algorithm

enhanced by the AEKF is called EMRANAEKF algorithm [44], and

is shown in Fig. 6. In this work the input data consists of a tapped

delay line of 10 samples of the past data, each sample correspond-ing to a one-hour time interval. The forecast is aimed to predict

external temperature, solar irradiation and photovoltaic power

data, so the input dimension is 10 3. The output data is a ‘‘one-

hour ahead’’ forecast of solar irradiation and photovoltaic power

production.

4. Case study and scenarios

As mentioned above, the suggested framework is intended as a

design aid, meant to evaluate the performance of different energy

management alternative systems, which are to be integrated in the

microgrid context of choice. It follows that, given the needs and

habits of the user, the designer can identify the best performing

solution early in time, without the need of an in depth analysisof each candidate solution. To evaluate the performance of the

framework as a design tool, a real life energy management system

has been analyzed in order to identify its building blocks. Thereaf-

ter the identified blocks have been modeled and thus 8 different

configurations, with growing complexity, have been derived from

the original structure.

In the first configuration, only production and demand of

electricity, hot water, heating and cooling has been evaluated. In

this case, therefore, no storage device has been included. The set-

up is composed of three electrical blocks, a hot water block, a heat-

ing block and a cooling one, but noneof themis ableto store energy.

The second configuration differs from the first one, by the addition

of three thermal energy storage devices, which pertain hot water,

heating and cooling respectively. The third configuration, on the

other hand, differs from the first one by the addition of two electri-

cal energy storage devices. Thus, two electrical blocks are able to

store energy, whereas the remaining electrical block, and the three

thermal ones do not provide any form of storage. The fourth config-

uration includes two electrical energy storage devices and three

thermal energy storage devices. The four configurations already

described have, as common traits, that the building blocks areinde-

pendent from each other, meaning that the energy surplus from

each block is not routedto the others. Also, when theelectrical stor-

age is used, a management policy, based on the constraints pre-

sented in Eqs. (8) and (9), is used. As said, this policy is meant to

prevent the storage from being recharged from the main grid, and

from being discharged if the residual capacity is below 65%. Also,

the fourth configuration presented matches the actual set up of

the real environment used as a reference.

Fig. 6. Flow chart of the EMRANAEKF algorithm.



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To evaluate additional improvements, few incremental changes

have been applied. From configuration 5 onwards, for instance, the

constraints presented in Eqs. (8) and (9) have been removed. Thus,

configuration 5 differs from configuration 4 in the electrical stor-

age management policy. In configuration 6, in addition, the man-

agement of hot water and heating has been integrated. Then, in

configuration 7, the integration of electrical and thermal manage-

ment is included, as presented in Eq. (5). Notably, the three electri-

cal blocks remain independent in each configuration. As such, the

surplus from an electrical block is never routed to another electri-

cal block, therefore, even in configuration 7 only the heat pump

and the water boiler can take advantage of the electricity surplus.

Since in the configuration 7 each electrical block remain indepen-

dent from the others, as a mean to evaluate a further integrated

design, the configuration 8 is also proposed. In the case of config-

uration 8 the electrical subsystem has been modeled as a single

phase circuit, meaning that the solar energy production is the

sum of the production of the three panels, the energy demand is

the demand of the 6 apartments, and the electrical storages, com-

bined, support the whole demand and production of the building.

As such, in configuration 8, electricity demand and production

are managed at building level rather than at electrical block level.

The proposed configurations are briefly listed as follows.

Configurations under test

1. storage devices not used

2. thermal storage

3. electrical storage with a conservative electrical storage

management policy

4. thermal and electrical storage with a conservative electri-

cal storage management policy

5. thermal and electrical storage

6. thermal and electrical storage with the integrated produc-

tion of hot water and heat

7. thermal and electrical storage with the integration of ther-

mal and electrical management8. thermal and electrical storage with the integration of ther-

mal and electrical management at building level

The first test is based on historical data. The input is composed

of the electrical and thermal demand, the latter involving hot

water, heating and cooling needs. Additionally, photovoltaic

energy production, thermal solar energy production and residual

stored energy are assigned to the system. For instance the simula-

tion covers an entire year spanning from 1 November 2012 to 31

October 2013. For each hour a simulation step is carried out.

Test 1 algorithm

1. the simulation routine receives the input data for the t -th

slot and simulates the energy management;

2. the simulation routine computes the energy fluxes at the t -

th time slot, and the residual stored energy at the end of

the same time slot;

3. the energy fluxes are recorded along with their costs and

the residual stored energy;

4. the input set of the ðt þ 1Þ-th time slot is composed and a

new simulation step is carried out.

Also, at the beginning of the first step, the thermal storage

devices are fully charged, the electrical storage devices are at 50%

of their nominal capacitance level.

After the simulation described, to evaluate if the suggested

framework is also able to plan the activity of the storage early intime, an additional test has been carried out. In this case the histor-

ical data, as regards photovoltaic energy production and thermal

solar energy production, has been replaced with its forecast coun-

terpart. For instance, since the forecast is ‘‘hour ahead’’, the test

requires two simulations per hour.

According to what stated above, the same framework can be of

use, not only to evaluate the system performance ahead of time,

but also to operate an actual resource management. In the fore-

casting case study then, since an offline planning is carried out, it

is also possible to search for an optimal scheduling scheme.

Test 2 algorithm

1. at the t -th time slot, the energy management, based on

forecast data, is carried out;

2. the planned storage activity (charge and discharge), at the

t -th time slot, is then recorded, along with the expected

costs;

3. the recorded storage activity is assigned, as an input, along

with historical data, to execute the planned management;

4. the resulting energy cost, at the t -th time slot, is computed,

and recorded along with the residual stored energy;

5. the process is repeated for the next time slot.

Notably, the distinction of the planning phase and the execution

phase it is not simply limited to the way the framework is used. In

order to separate properly the two phases, even the simulator con-

figuration shall differ from the manager set-up, so that planning

and execution may not overlap. In regard of this, it is possible to

conclude that a policy is an essential element of a decisional

process, and thus its role, within the management process, is clear.

Conversely, however, its role during the execution process is

dubious, since the execution of a plan shall be carried out without

question. If that is not the case, in fact, the execution of the plan,

and its management, overlap, leading to management conflicts.

For these reasons, in order to distinguish clearly the management

phase and the execution phase, the constraints presented in Eqs.(8) and (9), are applied to the configurations 3 and 4 during the

management process, but they are removed during the execution

phase.

In both tests, concerning the details of electrical configuration,

three electrical blocks have been modeled, each of them featuring

a photovoltaic panel of about 6 kW of peak power, and two of them

including an electrical storage device of 5.8 kW h each. Concerning

the thermal configuration, a hot water block has been modeled

including a solar thermal panel, an electrical water boiler of about

15 kW and a water tank of 1300 l. The water tank has been mod-

eled assuming a DT of about 34 C between the temperature of

the output water and the temperature of the input water, thus

50 kW h of maximum stored energy has been assumed for the

water tank. Relating to the heating and cooling blocks, a heat pumpof about 16.6 kW has been used. The water storage, of about 400 l,

has been modeled assuming a DT of about 10 C, leading to roughly

7 kW h of maximum stored energy. For simplicity sake, two sepa-

rated storage devices and heat pumps have been assumed, one set

for heating and the other for cooling, thus separating the heating

management from the cooling management. As regards the elec-

tricity price, a two tiered tariff has been assumed, distinguishing

peak hours from 8 a.m. to 7 p.m., from off peak hours. In particular

the peak hours tariff amounts to 0.138 €, whereas the off peak

hours tariff amounts to 0.129 €.

5. Results and comments

In the first test, the energy management is simulated, basedon historical data, for each of the proposed configurations. The



10/13

performance is summarized in Table 4, which reports, in each

column, the results of the corresponding configurations.

The reported amounts present, in order, the cost of the energy

purchased to supply the electrical blocks and the cost of the energy

purchased to supply the thermal blocks. Next, the overall energy

purchased amount, and surplus are reported. At last, the residual

energy stored within each storage device is presented.

The comparison of the results, within the synoptic table is quite

straightforward. In the 1st configuration, the storage devices are

not included, therefore the energy surplus is discarded immedi-

ately. As a consequence, the system is forced to purchase

additional energy in order to match the demand, either electrical

or thermal, each time the local production is lacking.

In the 2nd configuration, on the other hand, the availability of

the thermal storage grants the system the ability to preserve the

thermal energy surplus thus lowering the electrical needs of the

thermal system. In this case, however, since the solar collector only

support the hot water production, the savings are quite limited and

amount to 38 € per year.

In the 3rd configuration, the storage devices support the activity

of two, out of the three electrical blocks. Due to the higher energy

demand and production, therefore, an enhanced performance is

possible, with respect to the 2nd configuration. In particular

130 € can be saved with respect to the 1st configuration.

Also, since each block operates independently from the others,

when both thermal and electrical storage devices are used, the sav-

ing from both the hot water block and the electrical blocks concurs

to reduce the overall energy bill, as shown from the results

obtained by the 4th configuration.

The 5th configuration proves to be a little less performing than

the 4th one. In this case, in fact, the savings provided by the addi-

tion of the electrical storage amount to 125 €, with respect to the

reference case, represented by the 1st configuration. This is likely

due to the fact that, without accounting the energy surplus in

the objective function (Eq. (1)), the manager is not forced to store

the energy surplus, thus achieving a sub optimal management of

the energy surplus. On the other hand however, the absence of the constraints, described in Eqs. (8) and (9), also grants the energy

manager with two additional degree of freedom, thus improving

the responsiveness of the storage management. Additional tests,

not shown here for the sake of conciseness, reveal in fact that, by

accounting the energy surplus within the objective function, an

additional saving of about 20 € can be achieved.

The 6th configuration shows that the integration of hot water

production and heating can greatly enhance the efficiency of the

system. In fact, while the solar collector may support the heat pro-

duction, thus reducing the energy required to heat the building, the

heat pump can provide the extra heat required to support hot

water production in place of the water boiler. In results, roughly

488 € can be saved with respect to the 5th configuration due to a

more efficient management of thermal resources. The overallsavings, therefore, rise up to about 650 € if compared against the

performance of the 1st configuration.

In the same perspective, the integration of electrical and ther-

mal management, with the ability to route the electricity surplus

to the heat pump and the water boiler, further improve the ability

of the system to exploit the renewable resources. For instance, the

7th configuration, achieves an additional reduction of the overall

bill, of about 245 €. If compared against the results of the 1st

configuration, the purchased energy amount is practically halved,

whereas the energy surplus is lowered to roughly the 60% of its ori-

ginal amount.Similar results are achieved if the 8th configuration is

used. When the electrical energy is managed from the building

standpoint, a different allocation of the resources may be possible.

As such, with respect to the 7th configuration, a further reductionof the energy cost due to the electrical block is achieved, saving

about 52 €. At the same time however, since the energy surplus

routed to the heat pump is also lowered, the energy cost due to

the thermal blocks increase slightly, requiring an extra of about

9.05 €. The overall saving, therefore, amounts to 43 €. In this case,

then, the algorithm is able to promote the energy purchase during

the off peak hours, thus achieving a lower energy bill in spite of an

increased amount of purchased energy. This management strategy

entails an enhanced self-consumption of the renewable energy

produced by the PV system, as confirmed by the decrease of the

surplus energy amount.

While the results proposed in Table 4 are collected at the end of

a simulation that cover an entire year time span, partial results

have been collected during the entire simulation process. In Figs. 7

and 8 respectively, the energy cost due to the electrical blocks and

the energy cost due to the thermal blocks are proposed.

At a glance, although the plots depict the same situation pre-

sented within Table 4, they also reveal that the actual results

are consistent through the entire simulation. In addition, it is

possible to assess in which way the costs increase through the

year, and thus the ability of each configuration to save energy.

As such, in Fig. 7 it is possible to observe that the energy

demand of the electrical blocks remains roughly the same

through the year, and therefore the cost increase, over time, is

almost linear.

With respect to the energy demand of the thermal blocks in

Fig. 8, in the other hand, it is possible to observe four seasonal

changes, since the simulated time interval begins in November.

In the first quarter of the plot, which roughly correspond to the

winter season, the energy cost and demand increase with a sus-

tained rate, due to the heat demand of the building. In the second

quarter of the plot, that is the spring season, nor heating nor

cooling are required, thus the cost remains almost constant. In

summer, again, the cost over time shows a fast rate of growth that

decreases in the last quarter. Also, the less efficient the configura-

tion, the higher the cost rise over time.

In relation to the case of the scheduling approach, the robust-

ness to data uncertainty has been also evaluated. In this case, infact, the storage activity is planned by means of the forecast data.

This means that the prediction error may affect the performance of

the system, in the different addressed configurations.

The performed tests, in particular, have shown two distinct

trends in connection with the management of electrical and ther-

mal storage devices. In relation to the energy demand due to the

thermal blocks, in particular, only minor changes, with respect to

the previous test, can be highlighted. The electrical management

however has shown a significant difference.

The 1st configuration, since it lacks any form of storage, has

achieved the same results shown in the previous test. The 2nd

configuration, although to a smaller degree with respect to the

previous test, is still able to improve its performance with respect

to the 1st one. The 3rd configuration, conversely not only has notprovided the expected benefits, but also worsened the situation

with respect to the reference case, although to a small degree.

As a result, in the 4th configuration, by integrating both

electrical and thermal storage, the overall performance remain

almost unchanged with respect to the reference case. In the 5th

configuration, the adoption of a less conservative management pol-

icy, partially ease the problems originating from the prediction

error.

In the 6th configuration, the cost, due to the energy demand of

the thermal block, remains almost unchanged with respect to the

previous test, thus confirming that the thermal storage subsystem

is fairly robust against prediction errors. This result is likely con-

nected to the fact that a negligible thermal loss has been assumed

to model the thermal storage, whereas a quite significant self discharge has been applied to the electrical storage. As such, the



11/13

prediction uncertainty affects the activity time of the storage and

therefore influences the storage management strategy.

From this perspective, the results achieved by the 7th configu-

ration appear quite clear. The thermal blocks, in fact, remainalmost unaffected by the prediction error, and no significant differ-

ence, with respect to the previous test, is present in relation to the

energy cost due to the thermal blocks. The energy cost due to the

electrical blocks, on the other hand, shows a further increase with

respect to the 6th configuration. Since the energy surplus is to be

routed towards the thermal blocks, the management of the electri-

cal storage requires an increased accuracy, and thus the process

becomes less robust against prediction errors. On the other hand,

the 8th configuration seems to be the most robust among the

others under test. Indeed, differently from the 7th configuration,

the energy cost saving due to the thermal blocks is not paired with

an increased energy cost due to the electrical blocks. Rather,

although limited, even the energy cost due to the electrical blocks

is reduced. In the light of this, 8th configuration allows achieving

the best performance within the proposed set of tests.

In conclusion, from the first test set it is possible to observe that

the ability to store both the thermal and the electrical energy usu-

ally improves the performance of the energy management, even

more so if the inclusion of storage devices is complemented with

the integration of the different blocks within the system. In partic-

ular, the comparison of the 7th and 8th configuration against the

4th configuration, which is characterized by the same constraints

for storage activity actually adopted in the Leaf House, suggests

that there is room for new design choices within the energy man-

agement subsystem, and that these choices are likely to improve

the related efficiency. The second test set results highlight that

while the thermal energy management shows a fair amount of

robustness against the error prediction, the management of the

electrical storage can be improved based on this perspective.

Therefore, future efforts will be targeted on this direction. For

instance, the comparison of the 5th configuration against the 4th

one, suggests that an appropriate choice of the management policy,

in dependence on the expected liability of the forecaster on hourly

basis, may solve the issue. Nevertheless, the last two configura-

tions, proposing an integrated management of the electrical and

thermal storage capabilities, still allow to achieve a significant

reduction of the annual energy cost with respect to other

approaches.

5.1. Economic considerations

Energy management strategies, investigated in previous sec-

tions, shall not be evaluated in terms of operational savings only.

Indeed, the investment cost often represents a significant barrier

against energy storage installation. In the previous section, simula-

tions have demonstrated that the higher the electrical and thermalstorage integration level, the bigger the amount of microgrid self-

consumption and the annual energy saving. Nevertheless, this

result would be obtained at the expense of a high investment cost.

In Tables 4,5 we present both the capital and maintenance costs of

energy storage systems installed in the Leaf House, in order to

assess the energy savings obtained while accounting the increase

of investment cost required by each configuration proposed. The

comparison between investment costs and achievable savings

shows that the energy storages being proposed are not profitable

at present time. In the case of thermal storage, the high investment

Fig. 7. Energ y cost due to the electrical blocks. The identification number is based

on the entries listed in Section 4.

Fig. 8. Energy cost due to the electrical blocks. The identification number is based

on the entries listed in Section 4.

Table 4

Energy management, based on historical data, for all addressed system configurations. The identification number is based on the entries listed in Section 4.

Configurations

1 2 3 4 5 6 7 8

Energy cost due to electrical blocks ( €) 618.85 618.85 488.50 488.50 493.42 493.42 493.03 441.08

Energy cost due to thermal blocks ( €) 1356.69 1318.76 1356.69 1318.76 1318.76 830.59 585.25 594.30

Energy overall cost ( €) 1975.54 1937.61 1845,15 1807,26 1812.18 1324.01 1078.28 1035.38

Purchased energy amount (kW h) 14,663 14,682 13,964 13,681 13,715 9874 7426 7926

Surplus energy amount (kW h) 20,630 20,630 17,778 17,778 17,396 14,936 13,666 11,181

Residual electrical energy (W h) 0 0 3334 3334 3396 3396 3396 6267

Residual thermal energy (W h) 0 1750 0 1750 1750 1750 1750 1750

Residual refrigerating energy (W h) 0 1750 0 1750 1750 1750 1750 1750

Residual hot water energy (W h) 0 12,500 0 12,500 12,500 12,500 12,500 12,500

Electrical storage invest cost ( €) 0 0 30,740 30,740 30,740 30,740 30,740 30,740

Thermal storage invest cost ( €) 0 2000 0 2000 2000 2000 2000 2000

Total invest cost ( €) 0 2000 30,740 32,740 32,740 32,740 32,740 32,740

Electrical storage maintenance yearly fee ( €) 0 0 1000 1000 1000 1000 1000 1000

Thermal storage maintenance yearly fee ( €) 0 100 0 100 100 100 100 100

Storage maintenance total yearly fee ( €) 0 100 1000 1100 1100 1100 1100 1100



12/13

cost results from the storage being over-sized. Indeed, since the

Leaf House has been designed accounting the Annex 52 of the

EBC Research Programme, annex by the title ‘‘Towards Net ZeroEnergy solar buildings’’, the thermal storage has been designed in

order to supply most of hot water demand with solar thermal

panels also during winter season. Nevertheless, configurations 6,

7 and 8 show that the use of the thermal storage, integrated in

the heating system management, would be a profitable solution,

with no additional cost with respect to the present investment cost

conditions. On the contrary, the involvement of electrical storage

does not seem to guarantee high return of investment rates in

the addressed simulated case studies, due to the high purchase

and maintenance costs for each device. On purpose, a deep analysis

of the economic sustainability of the storage systems installed in

the Leaf House, involving the computational framework as design

tool, is left to future investigations.

6. Conclusions

In the first part of the paper we presented the operational

results of a residential microgrid, the Leaf House, composed by

six apartments, equipped with a 20 kWp PV plant, a solar thermal

plant, a geothermal heat pump, a hot water thermal storage of

1300 l and two 5.8 kW h batteries, each serving a couple of

apartments. Thermal energy storage has been demonstrated to

be a fundamental component for residential demand side manage-

ment. Indeed, it has allowed both to smooth thermal peak-power

of the energy supply systems and to collect renewable energy

during day-time to use it during night-time. The latter feature is

particularly useful since the hot water load profile of the occupants

had a minimum contemporary factor with solar production. Fur-thermore, the thermal storage has allowed to satisfy almost the

entire hot water summer demand through solar thermal energy

production. The two electrical storages have allowed to maximize

the self-consumption of renewable PV energy, thus reducing the

amount of electricity fed back to the main grid and, consequently,

increasing the self-sufficiency of the microgrid. Thanks to the elec-

trical energy storage system the percentages of self-consumed

energy have been 58.1% (first battery) and 63.5% (second battery).

The resulting number of hours during which the microgrid has

been fully self-sufficiency have been 3163 and 3356, respectively.

In the second part of the paper a computational framework

aimed at performance evaluation of the energy system of interest,

has been presented, including suitable energy management strate-

gies therein. The framework is indeed featured by the model of diverse energy blocks composing the energy system of interest,

i.e. the Leaf House, the solar power forecasting algorithm and the

MILP based optimization technique for energy management. From

this perspective, the framework can play the role of a design tool toassess the performance of alternative management solutions. On

purpose, 8 different system configurations, with growing complex-

ity, have been derived from original structure and tested through

simulation.

We have carried out two kind of tests: the first one has been

based on historical data, whereas second one has been relying on

forecasts of photovoltaic energy production and solar based ther-

mal energy production. Both kind of test sets have allowed to

gauge the gain provided by the storage devices within the Leaf

House energy system, and also to show to what extent an higher

electrical and thermal storage integration enables a deeper micro-

grid self-consumption, also allowing to achieve a significant annual

energy cost reduction. Despite the energy cost reduction achiev-

able with the availability of storage systems in the Leaf House,

their high investment cost made them not really profitable at the

current price conditions for devices and energy purchase. In partic-

ular, while the thermal energy storage can be profitable when used

also for the heating system management, the batteries seem to be

still too high-priced to be competitive in the residential market.

Future works will be targeted to deeply analyze the economic

sustainability of the Leaf House in different operating scenarios.

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Table 5

Energy management, based on forecast data, for all addressed system configurations. The identification number is based on the entries listed in Section 4.

Configurations

1 2 3 4 5 6 7 8

Energy cost due to electrical blocks ( €) 618.85 618.85 623.99 623.99 620.38 620.38 655.20 614.93

Energy cost due to thermal blocks ( €) 1356.69 1349.76 1356.69 1349.76 1349.76 830.59 588.25 616.87

Energy overall cost ( €) 1975.54 1968.61 1980.68 1973,75 1970.14 1450.97 1243.45 1231.8

Purchased energy amount (kW h) 14,946 14,895 14,979 14,928 14,891 10,976 9470 9384

Surplus energy amount (kW h) 20,630 20,630 18,791 18,791 18,382 18,382 13,453 11,164

Residual electrical energy (W h) 0 0 10,455 10,455 10,455 10,455 10,441 13,340

Residual thermal energy (W h) 0 1750 0 1750 1750 1750 1750 1750

Residual refrigerating energy (W h) 0 1750 0 1750 1750 1750 1750 1750

Residual hot water energy (W h) 0 12,500 0 12,500 12,500 12,500 12,500 12,500

Electrical storage invest cost ( €) 0 0 30,740 30,740 30,740 30,740 30,740 30,740

Thermal storage invest cost ( €) 0 2000 0 2000 2000 2000 2000 2000

Total invest cost ( €) 0 2000 30,740 32,740 32,740 32,740 32,740 32,740

Electrical storage maintenance yearly fee ( €) 0 0 1000 1000 1000 1000 1000 1000

Thermal storage maintenance yearly fee ( €) 0 100 0 100 100 100 100 100

Storage maintenance total yearly fee ( €) 0 100 1000 1100 1100 1100 1100 1100


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