# Designing of Cost-Effective and Low-Carbon Multi-Energy Nanogrids for Residential Applications

^{1}

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## Abstract

**:**

^{2}located in Italy was considered as the residential end-user. Results show the effectiveness of the model for providing good balancing solutions for end-users based on economic and energetic priorities. Moreover, it was found that the MEN operating in grid-connected mode showed economic and environmental performances much better than those found for the configuration operating in islanded mode.

## 1. Introduction

#### 1.1. Motivation

_{2}emissions in the EU. As a result, the topic of energy efficiency in buildings has assumed central importance in EU energy and environment policy-making. The goal is new buildings being nearly zero-energy buildings, i.e., buildings that have a very high energy performance. The energy performance of a building is determined based on the annual energy that is consumed in order to meet the different needs associated with its typical use and reflects the heating and cooling energy needs to maintain the envisaged temperature conditions of the building, as well as the domestic hot water needs. Furthermore, the low amount of energy required should be covered to a very significant extent by energy from RES produced on-site or nearby.

- -
- effectively integrate RES at local level;
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- demonstrate the economic benefits of hybrid multi-energy systems implementation at local level;
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- reduce primary energy consumption by giving priority to green energy sources and low-carbon solution technologies;
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- increase management efficiency by dynamically matching local electricity and thermal generation and consumption;
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- stimulate the development of a leading-edge market for energy-efficient technologies with new business models.

#### 1.2. Literature Review

_{2}emissions, were the main goals of the proposed multi-objective model, which was solved through the weighted-sum method, and the best possible solution was selected by employing the fuzzy satisfying approach. An MILP approach was used to model the proposed cost-emission operation problem of the hybrid system. In [6], a combined model of multi-objective home energy management and a battery storage system with multiple residential consumers was proposed for the minimization of the total aggregated energy bill and total system peak load. In [7], a decision support tool was established for energy storage selection to find preferable energy storage technologies for a specific application, adopting a multi-objective optimization approach based on an augmented ε-constraint method, to account for technical, economic, and environmental objectives. In [8], a multicarrier energy hub system with the objective of minimizing the economy cost and the CO

_{2}emissions of a residential building without sacrificing household comfort and increasing the exploitation of renewable energy in daily life was proposed. The energy hub combined the electrical grid and natural gas network, a gas boiler, a heat pump, a PV plant, and a photovoltaic/thermal (PV/T) system. In addition, to increase the overall performance of the system, a battery energy storage system was integrated. To evaluate the optimal capacity of each energy hub component, an optimization scheduling process was proposed and the optimization problem was solved with the YALMIP platform in MATLAB environment.

#### 1.3. Aims and Contribution

^{2}located in the Italian climatic zone E, in the city of Turin, is considered as the residential end-user. To identify the hourly space heating and cooling profiles of the user, the thermal behavior of the building is simulated using the dynamic simulation software TRNSYS, whereas the electricity hourly profiles are built up considering the number of occupants, the use of appliances, and the lighting systems, and the domestic hot water hourly profiles are estimated based on the number of occupants. Two scenarios are investigated, where the MEN operates in grid-connected and islanded modes in terms of electricity supply. In both the analyzed scenarios, results show that the Pareto frontiers provide good balancing solutions for the end-user based on economic and energetic priorities. Moreover, the MEN operating in grid-connected mode shows economic and environmental performances much better than those found for the MEN operating in islanded mode.

## 2. System Description

## 3. Optimal Design Model

#### 3.1. Objective Functions

_{MEN}of the MEN, consisting of the sum of the following functions [16]:

_{MEN}, consisting of the sum of the following functions:

#### 3.2. Decision Variables

#### 3.3. Constraints

#### 3.3.1. Design Constraints for Energy Technologies in the Multi-Energy Nanogrid

_{i}, was equal to 1 if the technology was chosen to be part of the MEN configuration.

#### 3.3.2. Operation Constraints for Energy Technologies in the Multi-Energy Nanogrid

_{µCHP}

_{,d,hr}is equal to 1.

#### Operation Constraints for Generation Technologies

#### Operation Constraints for Conversion Technologies

#### Operation Constraints for Storage Technologies

#### 3.3.3. Energy Balances Constraints

#### 3.4. Multi-Objective Optimization Method

## 4. Italian Case Study

#### 4.1. Energy Demand

^{2}, and net height equal to 3.0 m, located in the Italian climatic zone E, in the city of Turin.

^{2}/K, respectively. For the windows, the glazing and frame transmittance were set equal to 6.56 and 1.53 W/m

^{2}/K, respectively [25], and the area of each window was defined as the 12.5% of the useful area of the zone where the window is located [26]. For each thermal zone, the air exchange rate was assumed to be equal to 0.28 h

^{−1}[27]. According to the Italian Law [25], for the climatic zone E, the duration of the heating season goes from 15 October to 15 April. As a consequence, for each thermal zone, the indoor air temperature was controlled during this time interval by setting the set-point temperature for daytime (6.30 a.m. to 11 p.m.) and night-time heating at 21 °C and 15 °C, respectively. Moreover, for each thermal zone, a set-point temperature equal to 26 °C was set to control the indoor temperature during the cooling season, from June to August. Heat coming from occupants, household appliances, and lighting systems were assumed to contribute to the internal gains of the building. In detail, the number of the occupants was fixed at 5 and the sensible heat coming from each one was assumed to be equal to 75.0 W, considering light work/typing as the degree of activity, according to the Standard ISO 7730 [26]. The number of occupants and occupants-related sensible heat gain as a function of the time are shown in Figure 5.

#### 4.2. Solar Irradiance Profiles

^{2}.

#### 4.3. Techno-Economic Information of Energy Technologies

#### 4.4. Other Input Data

^{3}, whereas the time-of-day electricity price was assumed to vary between 0.123 and 0.152 €/kWh. The reference electrical efficiency of the Italian thermoelectric park used to evaluate the primary energy associated with the electricity taken from the grid in Equation (6) was set to 0.488 [35].

## 5. Results

- Scenario 1: MEN operating in grid-connected mode;
- Scenario 2: MEN operating in islanded mode in terms of electricity supply.

#### 5.1. Scenario 1: Multi-Energy Nanogrid Operating in Grid-Connected Mode

#### 5.1.1. Optimized System Configurations on the Pareto Frontier in Scenario 1

#### 5.1.2. Operation Strategies of the Multi-Energy Nanogrid in Scenario 1

#### 5.2. Scenario 2: Multi-Energy Nanogrid Operating in Islanded Mode

#### 5.2.1. Optimized System Configurations on the Pareto Frontier in Scenario 2

#### 5.2.2. Operation Strategies of the Multi-Energy Nanogrid in Scenario 2

## 6. Conclusions

^{2}located in the Italian climatic zone E, in the city of Turin, was considered as the residential end-user. Two scenarios were investigated, where the MEN operated in grid-connected and islanded modes. In both the analyzed scenarios, results showed that the Pareto frontiers provided good balancing solutions for end-users based on economic and energetic priorities. Moreover, the MEN operating in grid-connected mode showed economic and environmental performances much better than those found for the MEN operating in islanded mode. It was found that under energetic optimization, the total annual fossil primary energy obtained in the islanded mode increased by 60.3% as compared with the value obtained in the grid-connected mode. A similar worsening situation was found for the economic performances of the MEN under the economic optimization, where the total annual cost obtained in the islanded mode increased by 16.6% compared with that obtained in the grid-connected mode. When also considering the fixed costs of connection to the electricity grid and of meter transport and management, which were valid only for the grid-connected configuration, the results were more convenient than the islanded one. In fact, under this assumption, the total annual cost of the islanded MEN would increase by 14.4% compared with the new value obtained in grid-connected mode.

## Author Contributions

## Funding

## Conflicts of Interest

## Abbreviations

Decision variables | |

A | PV installed area (m^{2}) |

C | cost function (€) |

C_{d,hr} | cooling rate (kW) |

E_{d,hr} | power (kW) |

Fobj | objective function |

g_{d,hr} | generation level of technology (kW) |

G_{d,hr} | natural gas volumetric flow rate (Nm^{3}/h) |

H_{d,hr} | heating rate (kW) |

PE | primary energy input function (kWh) |

S | designed size of technology (kW)–(kWh) |

SOC | battery state-of-charge |

x | binary decision variable |

Parameters | |

A^{max} | available area for PV installation (m^{2}) |

c | constant in Equation (26) (kWh/€) |

C_{c} | specific capital cost of technology (€/kW)–(€/kWh)–(€/m^{2}) |

COP | coefficient of performance |

CRF | capital recovery factor of technology |

D_{t} | length of the time interval (h) |

I_{d,hr} | total solar irradiance (kW/m^{2}) |

LHV_{gas} | lower heat value of natural gas (kWh/Nm^{3}) |

N | lifetime of technology (years) |

OM | specific O&M cost of technology (€/kWh) |

P_{e,hr} | electricity price (€/kWh) |

P_{gas} | natural gas price (€/Nm^{3}) |

r | interest rate |

S^{max} | maximum size of the technology available in the market (kW) |

S^{min} | minimum size of the technology available in the market (kW) |

H | efficiency of technology |

Φ | storage loss fraction |

ω | weight in Equation (26) |

Superscript/Subscripts | |

AB | auxiliary boiler |

AChil | absorption chiller |

Bat | battery |

Ch | charging |

µCHP | micro-CHP |

D | day |

dem | demand |

Disch | discharging |

HM | heating mode |

Hr | hour |

i | index of energy technology |

In | input |

INV | investment |

max | maximum |

min | minimum |

O&M | operation and maintenance |

Out | output |

PG | power grid |

PV | photovoltaic |

ref | reference |

SC | space cooling |

Sto | stored |

TES | thermal energy storage |

Th | thermal |

Acronyms | |

µCHP | micro combined heat and power |

MILP | mixed-integer linear programming |

MEN | multi energy nanogrid |

O&M | operation and maintenance |

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**Figure 1.**Scheme of the pre-defined superstructure of the multi-energy nanogrid (MEN) with the energy technologies proposed to be part of the optimal configuration.

**Figure 6.**Hourly mean energy rate demand of the end-user: (

**a**) a representative cold season day; (

**b**) a representative cold mid-season day; (

**c**) a representative hot mid-season day; and (

**d**) a representative hot season day.

**Figure 9.**Operation strategies of optimized MEN configurations at points a and b in the four season days for electricity.

**Figure 10.**Operation strategies of optimized MEN configurations at points a and b in the four season days for heat.

**Figure 11.**Operation strategies of optimized MEN configurations at points a and b in the four season days for cooling.

**Figure 13.**Operation strategies of optimized MEN configurations at points a′ and b′ in the four season days for electricity.

**Figure 14.**Operation strategies of optimized MEN configurations at points a′ and b′ in the four season days for heating.

**Figure 15.**Operation strategies of optimized MEN configurations at points a′ and b′ in the four season days for cooling.

Energy Technology | Minimum Size (kW) | Specific Capital Cost | O&M Costs (€/kWh) | Efficiency | Lifetime | |
---|---|---|---|---|---|---|

El | Th | |||||

µCHP (ICE as prime mover) | 1.0 | 1500 €/kW | 0.0024 | 0.28 | 0.65 | 20 |

Auxiliary boiler | 10 | 100 €/kW | 0.015 | 0.8 | 15 | |

PV | - | 2000 Eur/kW_{p} | 0.005 | 0.14 | 30 | |

Reversible heat pump | 5.0 | 460 €/kW | 0.0025 | COP^{HM} = 3.5COP ^{CM} = 3.0 | 20 | |

Absorption chiller | 1.0 | 510 €/kW | 0.001 | 0.8 | 20 | |

Battery | - | 400 €/kWh | 0.005 | η^{Ch} = 0.75η ^{Disch} = 0.75 | 5 | |

TES | - | 20 €/kWh | 0.0014 | φ_{TES} = 0.05 | 20 |

ω Value | 0 | 0.1 | 0.2 | 0.3 | 0.4 | 0.5 | 0.6 | 0.7 | 0.8 | 0.9 | 1 |
---|---|---|---|---|---|---|---|---|---|---|---|

Optimized Sizes of Energy Technologies in the MEN | |||||||||||

µCHP (kW_{e}) | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 |

Auxiliary boiler (kW_{th}) | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |

PV (m^{2}) | 100 | 100 | 100 | 100 | 71 | 46 | 43 | 43 | 41 | 34 | 20 |

Reversible heat pump (kW_{th}) | 9.5 | 9.5 | 9.4 | 8.3 | 7.8 | 7.4 | 7.2 | 7.3 | 7.3 | 6.6 | 6.6 |

Absorption chiller (kW_{th}) | 1.8 | 1.8 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0 | 0 | 0 | 0 |

Battery (kWh_{e}) | 27.0 | 27.0 | 27.0 | 26.7 | 18.1 | 10.8 | 10.0 | 10.0 | 10.0 | 10.0 | 10.0 |

TES (Heat) (kWh_{th}) | 27.4 | 27.4 | 19.6 | 17.7 | 23.5 | 20.4 | 17.2 | 17.8 | 17.3 | 17.9 | 15.5 |

TES (Cooling) (kWh_{th}) | 45.4 | 45.4 | 54.1 | 47.7 | 39.5 | 32.4 | 33.8 | 34.7 | 31.5 | 25.8 | 11.4 |

ω Value | 0 | 0.1 | 0.2 | 0.3 | 0.4 | 0.5 | 0.6 | 0.7 | 0.8 | 0.9 | 1 |
---|---|---|---|---|---|---|---|---|---|---|---|

Optimized Sizes of Energy Technologies in the MEN | |||||||||||

µCHP (kW_{e}) | 1.7 | 1.7 | 1.7 | 1.7 | 1.6 | 1.9 | 2.0 | 2.0 | 1.9 | 2.1 | 2.1 |

Auxiliary boiler (kW_{th}) | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |

PV (m^{2}) | 97 | 97 | 97 | 97 | 94 | 56 | 44 | 39 | 40 | 37 | 22 |

Reversible heat pump (kW_{th}) | 11.0 | 11.0 | 11.0 | 11.0 | 9.1 | 7.9 | 7.2 | 6.5 | 6.1 | 6.1 | 4.4 |

Absorption chiller (kW_{th}) | 2.8 | 2.8 | 2.8 | 2.8 | 2.0 | 2.5 | 3.2 | 3.4 | 2.8 | 1.9 | 2.7 |

Battery (kWh_{e}) | 27.8 | 27.8 | 27.8 | 27.8 | 27 | 13.8 | 10.3 | 10.0 | 10.0 | 10.0 | 10.0 |

TES (Heat) (kWh_{th}) | 25.7 | 25.7 | 25.7 | 25.7 | 19.4 | 25.5 | 14.2 | 23.3 | 25.6 | 12.2 | 20.9 |

TES (Cooling) (kWh_{th}) | 53.5 | 53.5 | 53.5 | 53.5 | 48.5 | 38.2 | 38.1 | 31.5 | 34.5 | 31.0 | 22.3 |

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## Share and Cite

**MDPI and ACS Style**

Di Somma, M.; Caliano, M.; Graditi, G.; Pinnarelli, A.; Menniti, D.; Sorrentino, N.; Barone, G.
Designing of Cost-Effective and Low-Carbon Multi-Energy Nanogrids for Residential Applications. *Inventions* **2020**, *5*, 7.
https://doi.org/10.3390/inventions5010007

**AMA Style**

Di Somma M, Caliano M, Graditi G, Pinnarelli A, Menniti D, Sorrentino N, Barone G.
Designing of Cost-Effective and Low-Carbon Multi-Energy Nanogrids for Residential Applications. *Inventions*. 2020; 5(1):7.
https://doi.org/10.3390/inventions5010007

**Chicago/Turabian Style**

Di Somma, Marialaura, Martina Caliano, Giorgio Graditi, Anna Pinnarelli, Daniele Menniti, Nicola Sorrentino, and Giuseppe Barone.
2020. "Designing of Cost-Effective and Low-Carbon Multi-Energy Nanogrids for Residential Applications" *Inventions* 5, no. 1: 7.
https://doi.org/10.3390/inventions5010007