# Storage System for Energy Communities

Adriana Mar, Pedro Pereira, João Martins

► To cite this version:

Adriana Mar, Pedro Pereira, João Martins. Storage System for Energy Communities. 14th Doctoral Conference on Computing, Electrical and Industrial Systems (DoCEIS), Jul 2023, Monte Da Caparica, Portugal. pp.38-51, 10.1007/978-3-031-36007-7_3. hal-05473063

HAL Id: hal-05473063

https://inria.hal.science/hal-05473063v1

Submitted on 23 Jan 2026

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons CC BY 4.0 - Attribution - International License
<figure><img src="image_2.png" /></figure>

ifip

International Federation for Information Processing

This document is the original author manuscript of a paper submitted to an IFIP
conference proceedings or other IFIP publication by Springer Nature. As such, there
may be some differences in the official published version of the paper. Such
differences, if any, are usually due to reformatting during preparation for publication or
minor corrections made by the author(s) during final proofreading of the publication
manuscript.
# Storage System for Energy Communities

Adriana Mar(✉), Pedro Pereira, and João Martins

NOVA School of Science and Technology, Center of Technology and Systems (UNINOVA-CTS) and Associated Lab of Intelligent Systems (LASI), NOVA University Lisbon, Caparica, Portugal
am.jesus@campus.fct.unl.pt

**Abstract.** Energy communities play a critical role as stakeholders in modern power grids. However, operating such communities can be challenging due to the complexities, uncertainties, and conflicting objectives involved. The purpose of this paper is to showcase how the integration of a storage system into an energy community can contribute to maintaining the well-being of each community member during grid faults. The study models two distinct energy communities, taking into account non-controllable and controllable devices in each home as sources of consumption and power supply sources such as PV systems installed in community homes, as well as power from the main grid or storage system.

**Keywords:** Energy Community · Storage Systems · Genetic Algorithms · Energy Efficiency

## 1 Introduction

This paper aims to study the energy storage systems applied to the Energy Community concept in order to make this reality more reliable together with the use of renewable energy production, in this case, Photovoltaic systems.

Also, thinking in nearly Zero Energy Buildings (nZEB) or Net Zero Energy Buildings (NZEB), one of the possibilities to use the produced energy that it is not used at the moment is to store it and use it later. Considering this, it is important the study of this type of systems to know which one is better for each case, either to apply on a building or to use on an Energy Community.

### 1.1 Energy Community

Energy Communities (EnC), mostly microgrid based, are considered to be a key stakeholder of modern electrical power grids. Operating a microgrid based EnC is a challenging topic due to the involved uncertainties, complexities and often conflicting objectives. Furthermore, as any other grid, they are also subject to operating faults. In order to improve their resilience, EnC flexibility can provide enhance support to the community as a whole.
The European Commission defines EnC as groups consisting of citizens, Individuals and entities, including social entrepreneurs, public authorities, and community organizations, who play an active role in the generation, trade, distribution, and consumption of sustainable energy [1, 2]. Moreover, with the advancement and wider accessibility of renewable energy, citizens have emerged as significant contributors to the energy system, known as prosumers. Typically, a community comprises individuals who share something in common and can actively participate in energy production or consumption as users. As renewable energy issues have become more widespread and accessible, citizens are now recognized as active players in the system and are referred to as prosumers. Essentially, a community is a social group that shares something in common, with a group of users who can actively participate in either energy consumption or production [3, 4].

EnCs are created by integrating energy conversion, transmission, and consumption at a community level. Within the energy context, EnCs can address various relevant issues, such as balancing energy flow, decreasing peak load during high-demand periods, implementing territorial energy planning, connecting different energy sources, and mitigating environmental impacts. [5]. On the other hand, when a fault occurs in an Electrical Power Grid (EPG), a power decrease or a power outage can occur, EnC can be a solution not only to maintain the power supply inside the community as well as to improve the resilience of EPG as an all. This can be achieved considering the energy flexibility of each house as well as of the entire community. The concept of energy flexibility consists on the energy management of appliances of each house inside the EnC in order to change their working time to balance the power, due to a decrease of power.

In this work, the term Energy Community refers to a group of users interconnected by the Low Voltage (LV) grid, as it is possible to observe in Fig. 1. On an EnC, besides the possibility of storage devices, each house can have renewable energy production as well as electrical devices that are possible to control. Instead of using the production for their own consumption, the user can storage and share it with the EnC in order to be used when it's necessary. On the other hand, considering some consumption devices' energy flexibility, it is possible to manage the community's energy to improve the LV grid resilience when a change occurs in the main grid.

<figure><img src="image_3.png" /><figcaption>Fig. 1. Energy community scheme.</figcaption></figure>
The literature identifies various types of energy communities. Some include Net Zero Energy Buildings (NZEBs) [6]; Positive Energy Buildings (PEBs) [7]; individual homes equipped with controllable devices and renewable energy production capabilities, which are grouped together within a community to leverage their collective flexibility. [8]; prosumers; or even plain consumers [9].

As technology and solutions for NZEBs continue to evolve, the concept has progressed to Positive Energy Buildings (PEBs). A building is deemed a PEB if its exported energy during a defined period (usually a year) is greater than its imported energy [10]. Additionally, as shown in Fig. 1 a group of buildings that actively manage their energy consumption and flow, maintain a positive annual energy balance, and interact with the larger energy system are known as Positive Energy Blocks (PEBlocks) or Positive Energy Districts (PEDs) [11–14].

## 1.2 Energy Storage Systems

Energy storage consists on a process of converting electrical energy into different forms of energy that can be stored for converting again into electrical energy when necessary. There are many different storage technologies which can be catalogue in different ways, such as the type of storage (mechanical, electromechanical, electrical, chemical or thermal), the storage duration (short-term or long-term storage), capital cost, capacity, efficiency or environmental impact [15]. In this paper, following the literature review, the technologies will be categorized into storage types, as shown in Fig. 2.

<figure><img src="image_4.png" /><figcaption>Fig. 2. Energy storage technologies</figcaption></figure>

The choice of which storage method should be used depends on a group of factors like the amount of energy or the power to be stored, the time of storage (short-term or long-term), portability, energy efficiency, costs among others. Flywheels or supercapacitors are used in short-term applications, on the other hand CAES or flux batteries are suitable for peak-hour load leveling when high energy storage is required. Different characteristics of the storage methods presented above are compared in Table 1.
<table><caption>**Table 1.** Comparison of technical characteristics of energy storage systems.</caption><thead><tr><th></th><th></th><th>Efficiency</th><th>Response Time</th><th>Power Capacity</th><th>Lifetime</th><th>Discharge time</th><th>Capital Cost</th></tr></thead><tbody><tr><td rowspan="3">Mechanical Batteries</td><td>PHS [16–22]</td><td>65–85%</td><td>&lt;1 min</td><td>100–10000 MW</td><td>30–60 years</td><td>Hours - days</td><td>500–4600 $/kW</td></tr><tr><td>CAES [23–25]</td><td>70–80%</td><td>Sec - min</td><td>100–300 MW</td><td>~40 years</td><td>Hours - days</td><td>400–800 $/kW</td></tr><tr><td>FES [22, 26–30]</td><td>90–95%</td><td>Very fast (&lt;ms)</td><td>&lt;250 kW</td><td>15–20 years</td><td>seconds</td><td>100–350 €/kW</td></tr><tr><td rowspan="4">Electrochemical Batteries</td><td>Lead Acid</td><td>60–78%</td><td>&lt;5 ms</td><td>0–20 MW</td><td>3–15 years</td><td>Seconds - hours</td><td>300–600 $/kW</td></tr><tr><td>Lithium ion</td><td>85–97%</td><td>&lt;5 ms</td><td>0.1–50 MW</td><td>5–15 years</td><td>Minutes - hours</td><td>1200–4000 $/kW</td></tr><tr><td>Sodium-based</td><td>75–90%</td><td>&lt;5 ms</td><td>0.05–10 MW</td><td>10–15 years</td><td>Seconds - hours</td><td>1000–3000 $/kW</td></tr><tr><td>Nickel-based</td><td>60–70%</td><td>&lt;5 ms</td><td>0–40 MW</td><td>10–20 years</td><td>Seconds - hours</td><td>500–1500 $/kW</td></tr><tr><td rowspan="2">Flow Batteries</td><td>Redox Flow</td><td>75–85%</td><td>&lt;5 ms</td><td>0.3–15 MW</td><td>5–20 years</td><td>Seconds - 10 h</td><td>600–1500 $/kW</td></tr><tr><td>Hybrid Flow</td><td>60–80%</td><td>&lt;5 ms</td><td>0.05–10 MW</td><td>5–20 years</td><td>Seconds - 10 h</td><td>400–2500 $/kW</td></tr><tr><td>Chemical Batteries</td><td></td><td>20–50%</td><td>&lt;5ms</td><td>0.001–50 MW</td><td>5–20 years</td><td>Minutes - hours</td><td>500–10k $/kW</td></tr><tr><td>Hydrogen Fuel Cell</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>

## 1.3 Community Energy Storage Systems

Storage devices are suitable to absorb surplus generation to periods where demand is higher than the available power. Community energy storage (CES) systems are getting more attention as a suitable solution of innovation for sustainable energy transition. For Parra et. al, a CES is located on the consumption level, given a positive impact to end users and network operator [31]. More focused on community engagement, Van der Stelt, mentioned CES as a system located on the consumption level but with the capability to perform multiple applications in order to manage demand and supply, having positive impacts for both, consumers and DSOs [32]. Also, for Van Oost Koirala, CES is defined as “an energy storage system with community ownership and governance for generating collective socio-economic benefits such as higher penetration and self-consumption of renewables” [33]. Koirala also refers that CES systems will reduce dependence on fossil fuels, energy bills and increase local economy. Moreover, Barbour and Parra concluded that CES is a more effective system than residential energy storage and presents benefits from the economic point of view, reducing the life-cycle cost of energy storage by 37%
when compared the use of CES with individual household application [34, 35]. Koirala,
[33], presented different CES configurations that are described below:

*   **Shared residential energy storage (SRES):** In this configuration each user can have his own energy storage, up to 20kWh, in their own premises and the energy can be shared among the community users, using the local physical grid.

*   **Shared local energy storage (SLES):** The local configuration energy storage as a capacity of tens to hundreds of kWh and it is installed in the local neighborhood, is shared by physical grid and has community ownership. This type of CES configuration provides multiple benefits namely higher flexibility and energy security.

*   **Shared virtual energy storage (SVES):** this type of community is different from the other two mentioned since this is a virtual community, i.e., the energy storage is installed at different locations, inside or outside of EnC, and have independent ownership and governance. The energy storage is aggregated and virtually shared through the main grid considering the market design and regulations.

## 1.4 Storage Systems for Energy Community Connected with Cyber Physical Spaces

A connected cyber-physical space refers to a network of devices, systems, and spaces that are interconnected and can communicate with each other. These spaces can include buildings, transportation systems, and energy systems. By connecting these spaces, we can create an intelligent and responsive energy ecosystem that optimizes energy usage and reduces waste.

A storage system for an energy community is an important component of this ecosystem. It can help to balance the supply and demand of energy within the community by storing excess energy during times of low demand and releasing it during times of high demand. This can help to reduce the need for energy from external sources, which can be expensive and environmentally harmful.

When integrated with connected cyber-physical spaces, a storage system can become even more effective. For example, sensors within buildings can communicate with the storage system to determine when energy demand is highest and when it is lowest. The storage system can then release energy during times of high demand to help reduce strain on the grid and save energy costs. Additionally, electric vehicles can be charged using energy from the storage system, which can help to further reduce demand from external sources.

# 2 Storage System for Energy Community

This chapter presents the introduction of a storage system on an Energy community as well as the use of Genetic Algorithm to improve the usage of renewable energy during the day.

## 2.1 Energy Community Description

For the sake of simplicity, in Fig. 1 Energy community scheme,a general Energy Community (EnC) is depicted, comprising N dwellings that are equipped with controllable
household electrical devices. Among these dwellings, **n** have renewable energy supply equipment in the form of PV systems, while **y** has storage systems. The EnC modeling framework can be divided into three layers: the physical layer, the modeling layer, and the device layer. The physical layer provides a comprehensive description of the community, including the number of houses and occupants, as well as the equipment (i.e., appliances), storage, and supply infrastructure for each home.

The modeling layer is responsible for parameterizing all data collected from the physical layer and modeling the renewable energy sources, storage devices, and household appliances. Finally, the device layer controls the EnC system and its flexibility. The demand, storage, and supply of each residence, as well as the entire EnC, are managed through the device layer. These three layers are illustrated in Fig. 3.

<figure><img src="image_5.png" /><figcaption>Fig. 3. EnC modelling layers.</figcaption></figure>

The framework of this EnC takes into account a 24-h active power chart that includes both demand and generation, as described by Eqs. (1) and (2) respectively. In these equations, (d) represents demand power and (g) represents generated power.

$$d^N \equiv [d^N(1) \cdots d^N(t)], t \in [0-24] \qquad (1)$$

$$g^N \equiv [g^N(1) \cdots g^N(t)], t \in [0-24] \qquad (2)$$

$N$ denotes the number of the considered EnC houses and $t$ the time.

The community's total demand ($T_d$) and PV total generation ($T_g$) are given by Eqs. (3) and (4), respectively.

$$T_d = \sum_{i=1}^{N} (d(i)) \qquad (3)$$
$$T_g = \sum_{i=1}^{N} (g(i)) \qquad (4)$$

In the device layer, the supply and demand load of each house within the community, as well as the overall EnC, will be managed, taking into account their flexibility.

## 2.2 Genetic Algorithms Applied to Energy Community Flexibility

The cost function, presented in Eq. 7 will minimize a combination of n considerations. In this specific case will minimize the combination between two cases:

1. $X_A$, formulated in Eq. 5, that sums the difference between the initial time chosen by house users to start their appliances and the starting time changed by the algorithm.

2. $X_B$, formulated in Eq. 6, presents the difference between the power consumption and the power available inside the solar curve, when the difference is minimized, it means that the power used by photovoltaic is maximized.

For different simulations and different scenarios each of these cases, $X_A$ and $X_B$ will have different weights, combining in 100%. More cases can be added to cost function depending on what it is intended to minimize.

$$ \left\{ \begin{array}{l} X_A = \sum \left| A p_{\text{initial}}(h_n, a_n) - A p_{\text{final}}(h_n, a_n) \right| \\ A p_{\text{initial}}(h_n, a_n) = \text{hour}_{\text{initial}}(h_n, a_n) * 60 + \text{minute}_{\text{initial}}(h_n, a_n), \text{inminutes} \\ A p_{\text{final}}(h_n, a_n) = \text{hour}_{\text{final}}(h_n, a_n) * 60 + \text{minute}_{\text{final}}(h_n, a_n), \text{inminutes} \end{array} \right. \qquad (5) $$

$$X_B = \left| \left( \sum \text{Power}_{\text{available}}(t) \right) - \left( \sum \text{Power}_{\text{consumption}}(t) \right) \right| \qquad (6)$$

$$f_{\text{cost}} = \min((X_A * w_a) + (X_B * w_b) + \dots + (X_n * w_n)) \qquad (7)$$

Considering:

$h_n$ - represents the house number

$a_n$ - represents appliance number

$t$ - represents solar window, $t \in [10 \text{ am}; 6 \text{ pm}]$

$w_a, w_b, \dots, w_n$ represents the weight of each category (%)

For the simulation of this work two combinations of weights were considered:

$$f_{\text{cost1}} = \min((X_A * 80\%) + (X_B * 20\%))$$

$$f_{\text{cost2}} = \min((X_A * 30\%) + (X_B * 70\%))$$

This will allow to understand different behaviors on the energy community regarding the energy flexibility and the use of photovoltaic system considering the wellbeing of the users.
## 3 Simulation

For this work, was considered one energy community for simulation with thirteen houses, consumption devices, PV system with the characteristics presented in Table 2 and storage system inside the community.

Taking into account the system, to test different possibilities for the community level, is considered two scenarios with two distinct weights:

Scenario I a) – in this scenario the PV generation is the only available power inside the community as well as the storage energy and is considered $f_{cost1}$.

Scenario I b) – in this scenario the PV generation is the only available power inside the community as well as the storage energy and is considered $f_{cost2}$.

<table><caption>**Table 2.** PV model & Inverter parameters used for the reference system with 4kWp.</caption><thead><tr><th></th><th>Parameters</th><th>Value</th><th>Unit</th></tr></thead><tbody><tr><td rowspan="7">PV</td><td>A</td><td>1,60</td><td>m<sup>2</sup></td></tr><tr><td>P<sub>MPPT</sub></td><td>285</td><td>W<sub>p</sub></td></tr><tr><td>T<sub>c,NOCT</sub></td><td>46</td><td>°C</td></tr><tr><td>G<sub>NOCT</sub></td><td>800</td><td>Wm<sup>-2</sup></td></tr><tr><td>α</td><td>-0.003</td><td>°C<sup>-1</sup></td></tr><tr><td>T<sub>a,NOCT</sub></td><td>20</td><td>°C</td></tr><tr><td>T<sub>c,STC</sub></td><td>25</td><td>°C</td></tr><tr><td></td><td>P<sub>p</sub></td><td>4000</td><td>W</td></tr><tr><td rowspan="2">Sunny Tripower 4.0<br>Inverter</td><td>η<sub>inv</sub></td><td>97.1</td><td>%</td></tr><tr><td>P<sub>AC</sub></td><td>4000</td><td>W</td></tr></tbody></table>

Initially, the algorithm characterizes the load and production balance of each house and the EnC by utilizing the available information. This includes considering the type of fault scenario that may occur and incorporating user input regarding power consumption, along with the available power production. After that, the genetic algorithm runs in order to find the best solution taking into consideration the population size among other definitions of the algorithm.

To support the simulations presented in this work, ambient temperature data (T_amb) and solar radiation (G) data were acquired from the photovoltaic geographical information system (PVGIS). The data was collected with a 15-min resolution and the location point used was the NOVA School of Science and Technology (380 39' 36" N/90 12' 11" W). Regarding household devices, the noncontrollable devices' load is based on Richardson's model [36], that considers if it is a week or weekend day and the number of people living in the dwelling. Since noncontrollable devices are used, the load for each house is considered static. Meanwhile, only event-based controllable devices were used for this study, and their characteristics are presented in Table 3. For simplicity, a maximum of three appliances were assumed to be operational per day, specifically
a washing machine, dryer machine, and dishwasher. Users can select the starting time (hour:minute) and indicate if the house has a functioning PV panel using a dedicated application interface.

<table><caption>**Table 3.** Electrical appliance considered for this work.</caption><thead><tr><th>Appliance</th><th>Cycle duration [min]</th><th>Mean cycle power [W]</th><th>Standby power [W]</th><th>Controllable</th></tr></thead><tbody><tr><td>Lights</td><td>Usage Dependent</td><td>Usage Dependent</td><td>0</td><td>No</td></tr><tr><td>Washing Machine [House 1; House 2; House 3]</td><td>137</td><td>401</td><td>1</td><td>Yes</td></tr><tr><td>Dryer Machine [House 1; House 2; House 3]</td><td>59</td><td>[1900; 2333; 2000]</td><td>1</td><td>Yes</td></tr><tr><td>Dishwasher [House 1; House 2; House 3]</td><td>59</td><td>[2000; 1859; 2150]</td><td>0</td><td>Yes</td></tr></tbody></table>

## 3.1 Scenario I – Total Cutoff from Main Grid, Community 1

This energy community consists of thirteen houses, seven of which have PV system. The community also has an energy storage system with a maximum power of 3kW. For this scenario, the initial working hours considered for simulation are presented on Fig. 4 as well as the houses with and without PV system.

On Fig. 4 it is possible to analyze the total house load consumption for community taking into account appliances initial working hours, as well as the total PV load for this community. Regardless the appliances considered, the consumption load also contains lights load during all day that is on or off and it is not considered for the GA optimization.

Considering Fig. 4, it is possible to understand that a considerable quantity of solar energy is wasted since the appliances are working out of the solar hours. On Fig. 5, genetic algorithm is used for optimization considering $f_{cost1}$.

Analysing Fig. 5 and Table 4 it is possible to understand that all of the appliances were moved to a starting hour inside of the solar hours window and only the lights remained turned on night hours. It is also possible to discuss that an energy storage with 3kW it is not enough for a thirteen houses energy community since at 6pm it is possible to see that battery is fully charged and it is not capable to maintain lights on for the entire period.

Considering Fig. 6, together with Table 4, it is possible to understand that considering $f_{cost2}$, the appliances were all moved in for working times inside solar window time as in scenario I a) of community, but as expected the total difference of minutes from the initial time working for the appliances is bigger than in scenario I a) since the $X_A$ factor have a lower weight on cost function.
<figure><img src="image_6.png" /><figcaption>Fig. 4. Total house load for energy community.</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>Fig. 5. Total house load after Genetic Algorithm, scenario I a).</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>Fig. 6. Total house load after Genetic Algorithm, scenario I b).</figcaption></figure>
<table><caption>**Table 4.** Resume of GA solutions for community.</caption><thead><tr><th>Community</th><th>Home</th><th>Washing Machine</th><th>Dryer Machine</th><th>Dishwasher</th><th>PV</th></tr></thead><tbody><tr><td rowspan="13">1</td><td>1</td><td>8:00 am</td><td>X</td><td>9:30 pm</td><td></td></tr><tr><td>a)</td><td>1:48 pm</td><td>X</td><td>4:03 pm</td><td>√</td></tr><tr><td>b)</td><td>10:08 am</td><td>X</td><td>11:04 am</td><td></td></tr><tr><td>2</td><td>8:30 am</td><td>10:30 am</td><td>2:00 pm</td><td>√</td></tr><tr><td>a)</td><td>9:39 am</td><td>10:28 am</td><td>2:36 pm</td><td></td></tr><tr><td>b)</td><td>10:39 am</td><td>3:25 pm</td><td>3:14 pm</td><td></td></tr><tr><td>3</td><td>3:15 pm</td><td>X</td><td>10:00 pm</td><td></td></tr><tr><td>a)</td><td>10:19 am</td><td>X</td><td>12:12 pm</td><td>√</td></tr><tr><td>b)</td><td>10:37 am</td><td>X</td><td>11:44 am</td><td></td></tr><tr><td>4</td><td>3:45 pm</td><td>5:30 pm</td><td>6:00 pm</td><td></td></tr><tr><td>a)</td><td>1:47 pm</td><td>10:43 am</td><td>11:41 am</td><td>X</td></tr><tr><td>b)</td><td>2:09 pm</td><td>11:24 am</td><td>11:10 am</td><td></td></tr><tr><td>5</td><td>6:00 pm</td><td>8:00 pm</td><td>9:30 pm</td><td></td></tr><tr><td>a)</td><td>3:39 pm</td><td>11:53 am</td><td>11:44 am</td><td>√</td></tr><tr><td>b)</td><td>12:08 am</td><td>10:19 am</td><td>1:35 pm</td><td></td></tr><tr><td>6</td><td>7:00 am</td><td>X</td><td>9:30 pm</td><td></td></tr><tr><td>a)</td><td>9:53 am</td><td>X</td><td>10:33 am</td><td>X</td></tr><tr><td>b)</td><td>2:31 pm</td><td>X</td><td>10:52 am</td><td></td></tr><tr><td>7</td><td>6:30 am</td><td>X</td><td>2:30 pm</td><td></td></tr><tr><td>a)</td><td>2:08 pm</td><td>X</td><td>1:28 pm</td><td>√</td></tr><tr><td>b)</td><td>10:36 am</td><td>X</td><td>10:57 am</td><td></td></tr><tr><td>8</td><td>7:00 pm</td><td>X</td><td>X</td><td></td></tr><tr><td>a)</td><td>11:41 am</td><td>X</td><td>X</td><td>X</td></tr><tr><td>b)</td><td>1:39 pm</td><td>X</td><td>X</td><td></td></tr><tr><td>9</td><td>9:00 am</td><td>X</td><td>9:00 pm</td><td></td></tr><tr><td>a)</td><td>2:10 pm</td><td>X</td><td>2:27 pm</td><td>√</td></tr><tr><td>b)</td><td>11:37 am</td><td>X</td><td>2:31 pm</td><td></td></tr><tr><td>10</td><td>6:00 pm</td><td>X</td><td>1:30 pm</td><td></td></tr><tr><td>a)</td><td>9:52 am</td><td>X</td><td>11:19 am</td><td>X</td></tr><tr><td>b)</td><td>1:10 pm</td><td>X</td><td>2:11 pm</td><td></td></tr><tr><td>11</td><td>10:00 am</td><td>12:00 pm</td><td>8:30 pm</td><td></td></tr><tr><td>a)</td><td>1:17 pm</td><td>2:32 pm</td><td>2:38 pm</td><td>√</td></tr><tr><td>b)</td><td>1:55 pm</td><td>3:08 pm</td><td>2:38 pm</td><td></td></tr><tr><td>12</td><td>7:00 am</td><td>9:00 pm</td><td>X</td><td></td></tr><tr><td>a)</td><td>3:26 pm</td><td>12:28 pm</td><td>X</td><td>√</td></tr><tr><td>b)</td><td>12:37 pm</td><td>11:24 am</td><td>X</td><td></td></tr><tr><td>13</td><td>9:00 am</td><td>11:30 am</td><td>X</td><td></td></tr><tr><td>a)</td><td>2:49 pm</td><td>10:43 am</td><td>X</td><td>X</td></tr><tr><td>b)</td><td>11:51 am</td><td>2:20 pm</td><td>X</td><td></td></tr></tbody></table>

<table><caption>**Table 5.** Simulations' summary</caption><thead><tr><th>Scenario</th><th></th><th>Simulation Time (minutes)</th><th>Total difference of minutes (minutes)</th><th>Number of appliances inside solar curve (10 am – 6 pm)</th><th>Number of Houses inside the community</th></tr></thead><tbody><tr><td rowspan="2">I</td><td>a)</td><td>182</td><td>8718</td><td>29/29</td><td rowspan="2">13</td></tr><tr><td>b)</td><td>383</td><td>9247</td><td>29/29</td></tr></tbody></table>
# 4 Conclusions

Regarding the results obtained and showed on Sect. 4, it is concluded that for the use of an energy community completely disconnected from the main grid, an energy storage system is needed since some of the appliances can be moved and optimized for the solar time window but others cannot so it is important to have an appropriate ESS for the community not only to storage the unused energy produced by PV system but also to maintain the EnC working during night time.

For this specific study, it is possible to conclude that ESS was not enough to fulfill the necessities of the community since from community 1 a lot of produced energy was wasted. Also, on community 2 in spite of being a smaller one, the ESS it is not enough to fulfill the necessities from 8pm onwards.

To analyze the different scenarios, 3 metrics were considered, and are presented on Table 5:

- Simulation Time;

- Total difference of minutes (in minutes) from the initial working time for the final working time changed by the GA;

- Number of appliances inside solar curve.

Considering both factors, $X_A$ and $X_B$, it is possible to affirm that both scenarios meet the metrics. For scenario I a) where the weight was higher for the minimization of total difference time from the initial working time of the appliances for the final working time. As it is possible to analyze on Table 5, for scenario I a) of community 1 the time was 8718 min versus 92447 min for scenario I b) of community 1. Regarding the number of appliances working inside of solar time window both scenarios presented the total number of appliances, 29, working on the solar time window.

With these simulations was possible to understand that houses energy flexibility can be used not only to maintain the users' wellbeing when a fault occurs or it is necessary to change the energy flow, but also to consider better energy price markets, to improve the resilience of the grid, or even to consider the connection of electrical vehicles to community's grid.

**Acknowledgments.** This research was funded by the Portuguese FCT program, Center of Technology and Systems (CTS) UIDB/00066/2020 / UIDP/00066/2020.

# References

1. Azarova, V., Cohen, J., Friedl, C., Reichl, J.: Designing local renewable energy communities to increase social acceptance: evidence from a choice experiment in Austria, Germany, Italy, and Switzerland. Energy Policy **132**, 1176–1183 (2019). https://doi.org/10.1016/J.ENPOL.2019.06.067

2. Cohen, J., Moeltner, K., Reichl, A., Schmidthaler, M.: An empirical analysis of local opposition to new transmission lines across the EU-27. Energy J. **37**, 59–82 (2016). https://doi.org/10.5547/01956574.37.3.jcoh
3. Huang, Z., Yu, H., Peng, Z., Feng, Y.: Planning community energy system in the industry 4.0 era: Achievements, challenges and a potential solution. https://www.sciencedirect.com/science/article/pii/S1364032117304768 (2017). https://doi.org/10.1016/j.rser.2017.04.004

4. IEC Technical Committee 1 (Terminology): IEC 60050 - International Electrotechnical Vocabulary - Welcome. http://www.electropedia.org/. Accessed 10 Feb 2020

5. Ceglia, F., Esposito, P., Maurizio, S.: Smart energy community and collective awareness: a systematic scientific and normative review. In: Business Management Theories and Practices in a Dynamic Competitive Environment, pp. 139–149. EuroMed Press (2019)

6. Visa, I., Duta, A. (eds.): CSE 2017. SPE, Springer, Cham (2018). https://doi.org/10.1007/978-3-319-63215-5

7. Paci, S., Bertoldi, D.: Enabling Positive Energy Districts across Europe : energy efficiency couples renewable energy (2020). https://doi.org/10.2760/452028

8. Pontes Luz, G., Amaro E Silva, R.: Modeling Energy Communities with Collective Photo-voltaic Self-Consumption: Synergies between a Small City and a Winery in Portugal (2021). https://doi.org/10.3390/en14020323

9. Sarfarazi, S., Deissenroth-Uhrg, M., Bertsch, V.: Aggregation of households in community energy systems: an analysis from actors⇔ and market perspectives. Energies **13** (2020). https://doi.org/10.3390/en13195154

10. Rehman, H., Reda, F., Paiho, S., Hasan, A.: Towards positive energy communities at high latitudes. Energy Convers. Manag. **196**, 175–195 (2019). https://doi.org/10.1016/j.enconman.2019.06.005

11. Ala-Juusela, M., Crosbie, T., Hukkalainen, M.: Defining and operationalising the concept of an energy positive neighbourhood. Energy Convers. Manag. **125**, 133–140 (2016). https://doi.org/10.1016/j.enconman.2016.05.052

12. Walker, S., Labeodan, T., Maassen, W., Zeiler, W.: A review study of the current research on energy hub for energy positive neighborhoods. In: Energy Procedia, pp. 727–732. Elsevier, Amsterdam, the Netherlands (2017). https://doi.org/10.1016/j.egypro.2017.07.387

13. Bartholmes, J.: Smart cities and communities. https://ec.europa.eu/inea/sites/inea/files/4._smart_cities_and_communities_j.bartholmes_k.maniatis.pdf (2017)

14. Bartholmes, J.: Smart Cities and Communities SCC1 - 2018. 24 (2017)

15. Argyrou, M.C., Christodoulides, P., Kalogirou, S.A.: Energy storage for electricity generation and related processes: technologies appraisal and grid scale applications (2018). https://doi.org/10.1016/j.rser.2018.06.044

16. Kousksou, T., Bruel, P., Jamil, A., El Rhafiki, T., Zeraouli, Y.: Energy storage: applications and challenges (2014). https://doi.org/10.1016/j.solmat.2013.08.015

17. Poullikkas, A.: Optimization analysis for pumped energy storage systems in small isolated power systems. Open Access J. J. Power Technol. **93**, 78–89 (2013)

18. Hadjipaschalis, I., Poullikkas, A., Efthimiou, V.: Overview of current and future energy storage technologies for electric power applications (2009). https://doi.org/10.1016/j.rser.2008.09.028

19. Denholm, P., Kulcinski, G.L.: Life cycle energy requirements and greenhouse gas emissions from large scale energy storage systems. Energy Convers. Manag. **45**, 2153–2172 (2004). https://doi.org/10.1016/j.enconman.2003.10.014

20. Kaldellis, J.K., Zafirakis, D.: Optimum energy storage techniques for the improvement of renewable energy sources-based electricity generation economic efficiency. Energy **32**, 2295–2305 (2007). https://doi.org/10.1016/j.energy.2007.07.009

21. Denholm, P., Holloway, T.: Improved accounting of emissions from utility energy storage system operation (2005). https://pubs.acs.org/sharingguidelines. https://doi.org/10.1021/es0505898
22. Mahlia, T.M.I., Saktisahdan, T.J., Jannifar, A., Hasan, M.H., Matseelar, H.S.C.: A review of available methods and development on energy storage. Technol. Update (2014). https://doi.org/10.1016/j.rser.2014.01.068

23. Chen, H., Cong, T.N., Yang, W., Tan, C., Li, Y., Ding, Y.: Progress in electrical energy storage system: a critical review (2009). https://doi.org/10.1016/j.pnsc.2008.07.014

24. Denholm, P., Ela, E., Kirby, B., Milligan, M.: The role of energy storage with renewable electricity generation. In: Energy Storage: Issues and Applications, pp. 1–58 (2011)

25. Bilgili, M., Ozbek, A., Sahin, B., Kahraman, A.: An overview of renewable electric power capacity and progress in new technologies in the world (2015). https://doi.org/10.1016/j.rser.2015.04.148

26. Mousavi G, S.M., Faraji, F., Majazi, A., Al-Haddad, K.: A comprehensive review of Flywheel Energy Storage System technology (2017). https://doi.org/10.1016/j.rser.2016.09.060

27. Sebastián, R., Peña Alzola, R.: Flywheel energy storage systems: Review and simulation for an isolated wind power system (2012). https://doi.org/10.1016/j.rser.2012.08.008

28. Bolund, B., Bernhoff, H., Leijon, M.: Flywheel energy and power storage systems (2007). https://doi.org/10.1016/j.rser.2005.01.004

29. Liu, H., Jiang, J.: Flywheel energy storage-an upswing technology for energy sustainability. Energy Build. 39, 599–604 (2007). https://doi.org/10.1016/j.enbuild.2006.10.001

30. Evans, A., Strezov, V., Evans, T.J.: Assessment of utility energy storage options for increased renewable energy penetration (2012). https://doi.org/10.1016/j.rser.2012.03.048

31. Parra, D., Norman, S.A., Walker, G.S., Gillott, M.: Optimum community energy storage system for demand load shifting. Appl. Energy. 174, 130–143 (2016). https://doi.org/10.1016/j.apenergy.2016.04.082

32. van der Stelt, S., AlSkaif, T., van Sark, W.: Techno-economic analysis of household and community energy storage for residential prosumers with smart appliances. Appl. Energy. 209, 266–276 (2018). https://doi.org/10.1016/j.apenergy.2017.10.096

33. Koirala, B.P., van Oost, E., van der Windt, H.: Community energy storage: A responsible innovation towards a sustainable energy system? Appl. Energy. 231, 570–585 (2018). https://doi.org/10.1016/j.apenergy.2018.09.163

34. Parra, D., Gillott, M., Norman, S.A., Walker, G.S.: Optimum community energy storage system for PV energy time-shift. Appl. Energy. 137, 576–587 (2015). https://doi.org/10.1016/j.apenergy.2014.08.060

35. Barbour, E., Parra, D., Awwad, Z., González, M.C.: Community energy storage: A smart choice for the smart grid? Appl. Energy. 212, 489–497 (2018). https://doi.org/10.1016/j.ape-nergy.2017.12.056

36. Richardson, I., Thomson, M., Infield, D.: A high-resolution domestic building occupancy model for energy demand simulations. Energy Build. 40, 1560–1566 (2008). https://doi.org/10.1016/j.enbuild.2008.02.006