Replication Academy
Central to this collaboration is the iterative exchange of information and insights between the tools. MultiFASE performs real-time power flow analysis and state estimation, identifying nodes or areas within the grid that may exhibit instability or require optimization. This information is relayed to Optimems, enriching its optimization algorithms with additional constraints derived from the grid's state estimation. By integrating MultiFASE's findings, Optimems can proactively address potential grid instabilities or bottlenecks, ensuring that microgrid operations are optimized while maintaining grid stability and reliability. Simultaneously, the Demand Response tool employs Non-Intrusive Load Monitoring (NILM) techniques to extract appliance usage patterns from user consumption data. Leveraging these patterns, the Demand Response tool identifies opportunities for demand response participation, empowering citizens to modify their energy consumption behaviors and actively engage in energy markets.
Click on any of the topics below to learn more
Our Investment Advisor Tool is your key to systematically evaluating sustainable investment options within the Energy Community (EC) at both the building and community levels. It provides strategic deployment plans, estimated expenses, and anticipated outcomes, all aimed at achieving financial and energy-related economies for users and the EC as a whole.
This platform addresses the need to enhance community performance by promoting energy efficiency. By recognizing that different communities are at varying stages of implementation, our tool offers customized solutions:
For example, one community may need to improve household insulation to enhance comfort, while another may need to electrify cooking or heating systems to maximize local generation benefits.
Designed with accessibility in mind, a survey with baseline questions has been considered for each type of solution to simulate household scenarios and aggregate data at the community level, thereby providing tailored plans. These questions are accessible to all citizens, regardless of their energy literacy levels.
At the end of this process, users receive deployment plans, including the results of simulations with key technical and financial performance indicators such as initial investment, payback period, net present value (NPV), cost savings from energy savings, and CO2 reduction.
The platform involves two main actors: EC members and the EC manager. For EC members, personalized dashboards allow access to individual investment plans. If enough members show interest in a specific investment, such as better insulating windows or rooftop PV, the EC manager is notified to secure better quotes through bulk orders. For EC managers, the platform offers community-level insights and a dedicated dashboard to manage investment plans, aggregate and analyse community information, and make informed decisions.
Our platform empowers both EC members and managers to achieve their full potential through strategic, data-driven investments.
The COMMUNITAS Energy Community Planning Tool (ECPT) simulates different scenarios in terms of energy balances, energy supply costs and GHG emissions for an energy community, to determine the best option for citizens. The ECPT targets being used by a group of citizens, or a managing stakeholder, who are planning to create an energy community. The tool capitalizes on RINA experience with another tool, initially developed in MUSE GRIDS project.
The user interface of the ECPT allows the end user to provide inputs to the tool in terms of energy demand regarding the baseline scenario of the energy community and of scenarios to be simulated for optimization purposes. The input values to be provided are:
For all the data required to characterize the baseline energy demand, the input values shall be provided with hourly resolution, but in case these are not available to the user, annual values can be inserted and algorithms to distribute them to hourly level will automatically be applied by the ECPT.
Based on the above inputs, the ECPT will make available to the end users the results in terms of annual and hourly energy demand/supply, annual GHG emissions and energy supply costs, as well as with the optimal configuration of renewable power plants and energy storage systems that reach the desired optimization.
To do so, the ECPT will run multiple simulations, varying the size of the renewable power plants and of the energy storage systems foreseen in the energy community (PV, wind, solar thermal, biomass, district heating, etc.) with the aim of identifying the best scenario for the configuration of the energy community according to the optimization criteria requested by the user (minimum GHG emissions, minimum fossil energy consumption, minimum energy supply cost, minimum investment).
This platform serves as an aggregator of energy data, presenting information at both household and community levels. Designed to integrate members and visually present results, it shows why meters are essential.
For Community Members:
For EC Managers:
Our platform supports EC managers in overseeing community performance and helps members maximize their energy efficiency. Join us in transforming energy management and achieving a sustainable future together.
Non-Intrusive Load Monitoring (NILM) is a technique for estimating energy consumption on the appliance level. By solely monitoring the total consumption, whether through active power or consumed energy, one can obtain the disaggregated individual consumption of each connected device in the house, commerce or industry.
In the following figure, the pipeline of this tool can be observed. Data acquisition is achieved through meters installed in households, which provide electrical consumption magnitudes. These values are processed to generate new variables that allow for a deeper understanding of consumption. Subsequently, the models are capable of learning from historical data and making inferences about the new data, proceeding with the disaggregation of household devices.
By combining public internet datasets with data received from pilots, generalized models are created using supervised methods, specifically machine learning. Each appliance has its own generalized model, which consists of an artificial intelligence model that can be applied in any household with total consumption data. In other words, the models are trained with Big Data from many different houses, allowing them to extract general information about the activation of the specific appliance and predict connections in any context (Figure 3).
The base of predictive models relies on data from both public datasets and data collected from the monitoring of project pilots. By combining these two sources of information, it's possible to create generalized models that take the total household consumption as input for all of them. However, depending on whether one wants to monitor one appliance or another, the model output will be the individual consumption of the corresponding appliance. Thus, there will be as many machine learning models as there are appliances in the house.
Once the models have been trained, validated, and saved; in order to apply them to a completely new household, it is necessary to have the total energy consumption data of this household and the list of connected appliances to apply each of the models. This way, the disaggregation of the total consumption among all devices can be carried out. In order to clarify the concept of non-intrusive monitoring a little better, we've added the next video.
That said, the data retrieved from the NILM tool can be used to explore the consumption patterns of occupants and thus optimize their consumption. Therefore, following on from the NILM tool developed, a Demand Response tool has been developed in order to explore the energy flexibility of certain user-defined loads.
The operation of the Demand Response Tool is divided into two independent parts. In the first part, consumption on day t is analyzed. This has two objectives: to analyze the number of recommendations (made on day t-1) accepted by the user, in order to understand the level of acceptance; to educate the user by showing, if the recommendations had been followed, the potential reduction in costs associated with consumption. In the second part, the energy flexibility of specific user-defined loads is assessed, and corresponding recommendations for day t+1 are provided.
The second part of the tool is also divided into two stages. The first stage involves processing all the necessary inputs required for optimizing the problem. To determine the optimal schedule, the Demand Response Tool requires the following:
After processing these inputs, the optimization phase can begin. Assisted by a genetic algorithm, the Demand Response Tool will search within 15-minute intervals to determine the optimal timings for activating loads in order to maximize energy savings. For example, it may recommend running the dishwasher at 06:30 and the washing machine at 22:30, leading to a total cost of 3.5€.
To enhance your understanding of what has been discussed, we have provided a video that facilitates comprehension of the Demand Response tool's functionality.
The MultiFASE tool is designed for state estimation of multi-energy systems by utilizing real-time measurements. It currently includes two modules: Electrical Grid State Estimation and District Heating State Estimation. The primary objective of MultiFASE is to calculate the multi-energy states, which encompass the physical quantities at each node, such as electrical system voltage (for electrical grid), temperature, pressure, and mass flow rate (for District Heating) of key energy networks. Additionally, the tool facilitates real-time monitoring and assessment of energy systems while effectively eliminating measurement errors and noise. State estimation aims to calculate all the states of a given network from a set of noisy measurements.
Electrical Grid State Estimation Module
As illustrated in the following figure, the states include the voltages (both magnitudes and angles) at all buses and the currents at all lines. The measurements consist of power flows on the lines (red arrows), power consumption at consumer buses, and the voltage at the slack bus.
In modern distribution systems, particularly those incorporating distributed generation, state estimation plays a pivotal role in integrating new technologies and managing network operations effectively. In this respect, the electrical module of MultiFase facilitating 3-ph state estimation is crucial for several reasons:
The network requirements for the Electrical Grid Module in the MultiFASE framework, within the context of COMMUNITAS, are focused on low voltage network topology, Accuracy information (margin of error/ standard error) for the measuring devices per unit variables, as well as nominal states of the variables.
District Heating State Estimation Module
The goal of state estimation in the context of a District Heating Network, is to calculate all the states of a given network from a set of noisy measurements. As illustrated in next figure, the states include temperatures and pressure at nodes along with mass flow at all pipes. The measurements consist of the temperatures at selected nodes, the pressures at selected nodes, heat consumption and generation at end nodes as well as ambient temperature.
State estimation in district heating networks is essential, as it significantly improves the accuracy of real-time monitoring. The insights derived from this state information can be instrumental in optimizing both the management and operation of the network. Furthermore, the increased granularity in temporal and spatial network state data can facilitate in optimizing the district heating production processes.
The network requirements for the District Heating Module within the MultiFASE framework are: network topology, accuracy information (margin of error/ standard error) for the measuring devices per unit variables, as well as the nominal states of variables for each subsystem (both electrical and heating) during standard operational conditions.
The CERTH team has developed and successfully tested an innovative scheduling framework called the Optimized Energy Management System (OptiMEMS), designed for use in microgrids (MGs) and Virtual Power Plants (VPPs). OptiMEMS combines advanced forecasting, optimization, and real-time supervisory capabilities to deliver efficient and adaptable energy management solutions. Its flexible architecture supports operation in both grid-connected and islanded modes. Key features of the system include:
OptiMEMS has been successfully applied upon a plethora of applications, varying from residential VPPs, EV-based VPPs, and dynamic VPPs supporting widely used standards, such as OpenADR. It is designed to integrate Distributed Energy Resources (DERs) like Renewable Energy Sources (RES), Energy Storage Systems (ESSs), and grid interaction points using the Passive Sign Convention.
For the tool’s operation, the following data are required:
Peer-to-peer, or P2P, energy trading is a new approach to energy systems that lets individuals in a local community buy and sell energy from each other. When a household has extra energy from solar panels or other sources instead of sending surplus energy back to the grid these households can sell their excess energy to their neighbours who need it. These trades benefit both the seller who earn revenue and the buyers who buy cheaper local sustainable energy. P2P trading operates alongside traditional energy systems, providing a flexible and decentralized way to manage energy needs.
Blockchain is a new technology that can be used to implement these P2P systems. Blockchain is a system for securely recording transactions without a central authority. This means when we collaborate on a blockchain, no single person or outside company needs to be in charge, and we can make exchanges of energy and money on an open and collaborative basis.
In conventional systems, especially for things like energy, we rely on a central authority—such as utility companies or grid operators—to manage everything. These authorities control the generation, pricing, and distribution of energy. Consumers need to trust these companies to keep the lights on and make sure everything works smoothly. But this model has limitations. Consumers are passive—they pay their bills, and the energy flows in one direction. Prices are set by the company, and consumers have no say in how energy is managed.
Blockchain changes that. Instead of relying on a central authority, blockchain distributes control across all participants in the network.
WattSwap is a blockchain P2P energy trading platform. To use the wattswap, users need to connect a blockchain wallet which can be used to hold value (like cryptocurrencies) and sign transactions that communicate how they want to use the system. For households with solar panels, they simply need to register their blockchain wallet, and indicate that they will join the WattSwap platform. Then any excess solar energy will be sold at the market price to their neighbours, with their wallet able to claim their payment whenever they desire.
For consumers wishing to buy cheaper, sustainable, local energy, they need to register their blockchain wallet and indicate when they would wish to purchase energy and for what price. For example, they could indicate that they use energy on weekdays at lunchtime and are willing to pay up to half their typical utility price of energy. Or if they wish to support local energy production and guarantee they are using sustainable green energy they could offer a higher price. The WattSwap system then matches in real time bids for energy with the excess solar production provided to the system.
Local energy communities or municipalities can also use WattSwap to reward sustainable behaviour. Households that manage to share the most energy with their neighbours, or who use more local sustainable energy sources will be rewarded with blockchain based tokens called Non-Fungible Tokens, of NFTs. These tokens are exclusive collectable artworks that can be traded on the blockchain with other collectors or displayed to others. They can also be used to unlock exclusive discounts on local services or entry to local events.
The Wattswap blockchain-based P2P energy trading isn’t just about saving money—it’s about rethinking the relationship between consumers, producers, and energy. People can now become active participants in the energy market, rather than passive consumers.