Real-Time Energy Monitoring in Renewable Energy Communities Using an IoT System

Real-Time Monitoring of Energy Contributions in Renewable Energy Communities Through an IoT Measurement System

This paper presents an IoT-based monitoring system designed to measure energy exchanges within Renewable Energy Communities (RECs). The proposed system utilizes embedded devices functioning as IoT power meters, which communicate using LoRaWAN technology and the MQTT protocol. Members of the energy community can monitor energy flows in real time, allowing them to stay informed about potential penalties and adopt behaviors that optimize incentives linked to the self-consumption of generated energy. Furthermore, incentive schemes can be implemented that take advantage of storage units. A crucial aspect of this setup is the accurate measurement of energy terms eligible for incentives, which is a challenging task, especially with storage units. The concept of negative energy input is employed to identify the energy absorbed from the network to be fed back into it when necessary. This paper proposes the use of distributed power meters to identify various energy contributions relevant for incentive calculations, such as negative energy input, produced and withdrawn energy, and self-consumed energy. A case study involving resources from a Renewable Energy Community is presented, demonstrating the advantages of this proposal.

Keywords

Renewable Energy Communities; Energy Monitoring; Energy Contributions; Smart Power Meter; IoT Measurements

1. Introduction

Renewable Energy Communities (RECs) provide an innovative model for energy generation and consumption that emphasizes local control, community participation, and sustainability. These communities consist of individuals or organizations that collaborate to invest in and manage renewable energy sources, including solar panels, wind turbines, or biomass systems. Typically rooted in specific geographic areas, RECs enable members to actively engage in and benefit from local energy initiatives. By leveraging renewable energy, these communities aim to reduce carbon emissions and mitigate environmental impacts. Resource pooling and benefit sharing often lead to lower costs and improved energy independence. Governance within these communities typically follows democratic principles, allowing collective decision-making among members. The European Union has strongly supported the development of RECs through the Clean Energy for All Europeans package, ensuring their participation in energy markets alongside larger entities.

The advantages of RECs include:
– Enhanced energy efficiency: Localized energy management reduces waste and optimizes consumption.
– Lower energy costs: Shared investments and collaborative management lead to cost savings for participants.
– Improved energy security: Local energy generation diminishes reliance on external supplies.
– Strengthened community ties: RECs foster social cohesion and a shared sense of purpose.

Providing REC members with insights into energy value offers significant benefits. For instance, participants can monitor real-time risks of penalties and potential incentives accrued at the end of the month. Access to real-time energy flow data encourages behaviors that optimize energy usage, maximizing incentives.

Consider a scenario where the REC informs members about an energy surplus during a specific time period. Instead of feeding this surplus into the grid, members could prioritize self-consumption, such as scheduling the charging of electric vehicles. This approach generates both economic and environmental advantages.

This research aims to develop a low-cost distributed REC monitoring system, where smart power meters are installed at key points in the plant to monitor energy trends in real time. It is important to note that ‘real-time’ in this context refers to soft real-time, where timing constraints are important but not critical.

The energy quantities monitored in a REC vary depending on the actual configuration of the REC and the self-consumption incentive scheme among its members. The energy exchanges need to be considered differently based on the actual location of loads and resources. In terms of storage units (SUs), the concept of Negative Energy Input (NEI) is introduced to quantify the portion of energy exchanged by the SU that is eligible for remuneration. The Italian Regulatory Authority for Energy, Networks, and Environment (ARERA) has defined NEI in Resolution 109/2021/R/eel as the sum of the electrical energy withdrawn from the network and subsequently fed back into the grid.

This paper’s contributions focus on proposing distributed power meters capable of identifying energy contributions significant for estimating incentive calculations, such as negative energy input, produced energy, and withdrawn energy. The monitoring system is designed to measure the energy exchanges of REC members, with a particular emphasis on cost limitation. It involves deploying multiple sensor nodes within the REC, making cost a critical factor in selecting hardware components, transmission technologies, and software processing.

The proposed system employs embedded devices that act as IoT power meters, communicating via Long Range Wide Area Network (LoRaWAN) technology and using the Message Queuing Telemetry Transport (MQTT) protocol. Compared to previous work, this system includes significant improvements such as the use of a 0.2% accuracy power meter by STMicroelectronics, detailed models of REC operation strategies, and new analytical results.

2. Measurement of Energy Exchanges in an REC

To estimate energy exchanges, it is necessary to separate different energy contributions associated with production, storage, self-consumption, and withdrawal. The placement of resources in the network is essential for accurately quantifying the energy quota eligible for incentives. This section details the REC under study, discusses a possible operation strategy for its resources, and explains how this strategy can be implemented by measuring energy exchanges under various configurations of SUs.

2.1 REC Configuration and Operation Strategy

The REC under study consists of members with at least one of the following: a consumption unit (CU), a production system (PS) based on renewable energy sources (RES), such as photovoltaic (PV) systems, and a storage unit (SU). During charging, the SU absorbs power from both the PS and the grid. During discharging, the SU supplies energy to the CU and feeds energy back into the grid. A proper control strategy for the SUs is necessary to maximize shared energy while ensuring that stored energy is properly balanced and a specified power is provided to the network.

The SU control strategy is defined in detail. During charging, the energy charged by the SU is calculated as follows:
[ E_{a,t,i} = \eta_{ch} (E_{a,PSU,t,i} + E_{a,SU,t,i}) ]

During discharging, the energy discharged by the SU into the grid is represented by:
[ E_{p,t,i} = \frac{1}{\eta_{dch}} (E_{p,SU,t,i} + E_{p,SUn,t,i} + E_{p,CU,t,i}) ]

At each time ( t ), for each battery ( i ), the total energy stored in the SU is given by:
[ E_{t,i} = E_{0,i} + \sum_{\tau=1}^{t} E_{a,\tau,i} – E_{p,\tau,i} ]

Constraints must be imposed to limit the stored energy within acceptable ranges:
[ E_{min,i} \leq E_{t,i} \leq E_{max,i} ]

Additionally, constraints must be applied to the SU’s charging and discharging powers, which cannot exceed the SU’s rated power:
[ \frac{1}{\eta_{ch}} E_{t+1,i} – E_{t,i} \Delta t \leq P_{SU,i} ]

Shared energy is defined as:
[ E_{shared,t} = \min { E_{a,rec,t}, E_{f,rec,t} } ]

Where ( E_{a,rec,t} ) is the energy absorbed by the REC’s members and ( E_{f,rec,t} ) is the energy fed into the grid, excluding previously withdrawn energy.

2.2 Measurement of Energy Exchanges

The SU measurements relate to the “Transmission, Dispatching, Development, and Grid Security Code,” which outlines procedures for the connection, management, planning, development, and maintenance of the Italian national transmission grid. The NEI is calculated based on various configurations that involve different combinations of PS, CU, and SU.

This research focuses on configurations where the measurement of energy from the PS can be separated from that discharged by the SU, allowing for accurate energy contributions estimation.

3. IoT Monitoring System

The monitoring system is made up of four power meters arranged at specific points in the system. These meters must meet the metrological characteristics required by the ARERA Resolution, providing updates every fifteen minutes. The monitoring system employs a single board power meter to enhance accuracy and reduce costs.

The developed smart meter includes three sections: measurement, control, and transmission. The measurement section utilizes the STPM34 by STMicroelectronics, which is capable of high-accuracy power and energy measurements. The microcontroller performs tasks such as configuring the power meter, reading measurements, and forwarding data to the transmission unit.

The transmission unit employs a LoRa SX1272 transceiver, chosen for its efficient communication capabilities. The MQTT protocol is utilized for data management, allowing for flexible and scalable communication among users within the community.

4. Measurement Example in a LV Distribution Network

An example simulation of the energy exchanges in a Low Voltage (LV) distribution network was conducted using Matlab. The LV network includes multiple buses, with specific members connected to designated buses. The optimization procedure discussed earlier was applied to evaluate power exchanges between REC members and the network.

5. Conclusions

This paper presents an IoT system for monitoring energy exchanges within a renewable energy community. The system is based on smart power meters that transmit various energy measures through SPI communication, with a microcontroller managing data readings and transmission. The system’s architecture supports real-time updates of energy measurements and enables users to visualize and optimize energy use within the community.

The estimated cost of a single node is approximately EUR 120, with plans to reduce costs to about EUR 50 through industrialization. The research also addresses the placement of energy meters to ensure accurate measurement and focuses on the NEI, a crucial parameter for incentives in energy communities. A dashboard was developed using Node-RED software to visualize measurements and energy exchanges. Future research will continue to enhance the monitoring system and its integration within actual RECs.