A way to improve energy management is to perform balancing both at the Peer-to-peer (P2P) level and then at the Virtual Microgrid-to-Virtual Microgrid (VMG2VMG) level, while considering the ...intermittency of available Renewable Energy Source (RES). This paper proposes an interdisciplinary analytics-based approach for the formation of VMGs addressing energy balancing. Our approach incorporates Computer Science methods to address an Energy sector problem, utilizing data preprocessing techniques and Machine Learning concepts. It features P2P balancing, where each peer is a prosumer perceived as an individual entity, and Virtual Microgrids (VMGs) as clusters of peers. We conducted several simulations utilizing clustering and binning algorithms for preprocessing energy data. Our approach offers options for generating VMGs of prosumers, prior to using a customized Exhaustive brute-force Balancing Algorithm (EBA). EBA performs balancing at the cluster-to-cluster level, perceived as VMG2VMG balancing. To that end, the study simulates on data from 94 prosumers, and reports outcomes, biases, and prospects for scaling up and expanding this work. Finally, this paper outlines potential ideal usages for the approach, either standalone or integrated with other toolkits and technologies.
Over the past few decades, industry and academia have made great strides to improve aspects related with optimal energy management. These include better ways for efficient energy asset management, ...generating great opportunities for optimization of energy distribution, discomfort minimization, energy production, cost reduction and more. This paper proposes a framework for a multi-objective analysis, acting as a novel tool that offers responses for optimal energy management through a decision support system. The novelty is in the structure of the methodology, since it considers two distinct optimization problems for two actors, consumers and aggregators, with solution being able to completely or partly interact with the other one is in the form of a demand response signal exchange. The overall optimization is formulated by a bi-objective optimization problem for the consumer side, aiming at cost minimization and discomfort reduction, and a single objective optimization problem for the aggregator side aiming at cost minimization. The framework consists of three architectural layers, namely, the consumer, aggregator and decision support system (DSS), forming a tri-layer optimization framework with multiple interacting objects, such as objective functions, variables, constants and constraints. The DSS layer is responsible for decision support by forecasting the day-ahead energy management requirements. The main purpose of this study is to achieve optimal management of energy resources, considering both aggregator and consumer preferences and goals, whilst abiding with real-world system constraints. This is conducted through detailed simulations using real data from a pilot, that is part of Terni Distribution System portfolio.
In 2020, residential sector loads reached 25% of the overall electrical consumption in Europe and it is foreseen to stabilise at 29% by 2050. However, this relatively small increase demands, among ...others, changes in the energy consuming behaviour of households. To achieve this, Demand Response (DR) has been identified as a promising tool for unlocking the hidden flexibility potential of residential consumption. In this work, a holistic incentive-based DR framework aiming towards load shifting is proposed for residential applications. The proposed framework is characterised by several innovative features, mainly the formulation of the optimisation problem, which models user satisfaction and the economic operation of a distributed household portfolio, the customised load forecasting algorithm, which employs an adjusted Gradient Boosting Tree methodology with enhanced feature extraction and, finally, a disaggregation tool, which considers electrical features and time of use information. The DR framework is first validated through simulation to assess the business potential and is then deployed experimentally in real houses in Northern Greece. Results demonstrate that a mean 1.48% relative profit can be achieved via only load shifting of a maximum of three residential appliances, while the experimental application proves the effectiveness of the proposed algorithms in successfully managing the load curves of real houses with several residents. Correlations between market prices and the success of incentive-based load shifting DR programs show how wholesale pricing should be adjusted to ensure the viability of such DR schemes.
Though much attention is dedicated to the development of its research and innovation policy, the European Union constantly struggles to match the level of the strongest innovators in the world. ...Therefore, there is a necessity to analyze the individual efforts and conditions of the 27 member states that might determine their final innovative performance. The results of a scientific literature review showed that there is a growing interest in the usage of artificial intelligence when seeking to improve decision-making processes. Data envelopment analysis, as a branch of computational intelligence methods, has proved to be a reliable tool for innovation efficiency evaluation. Therefore, this paper aimed to apply DEA for the assessment of the European Union’s innovation efficiency from 2000 to 2020, when innovation was measured by patent, trademark, and design applications. The findings showed that the general EU innovation efficiency situation has improved over time, meaning that each programming period was more successful than the previous one. On the other hand, visible disparities were found across the member states, showing that Luxembourg is an absolute innovation efficiency leader, while Greece and Portugal achieved the lowest average efficiency scores. Both the application of the DEA method and the gathered results may act as viable guidelines on how to improve R&I policies and select future investment directions.
Accurately forecasting power generation in photovoltaic (PV) installations is a challenging task, due to the volatile and highly intermittent nature of solar-based renewable energy sources. In recent ...years, several PV power generation forecasting models have been proposed in the relevant literature. However, there is no consensus regarding which models perform better in which cases. Moreover, literature lacks of works presenting detailed experimental evaluations of different types of models on the same data and forecasting conditions. This paper attempts to fill in this gap by presenting a comprehensive benchmarking framework for several analytical, data-based and hybrid models for multi-step short-term PV power generation forecasting. All models were evaluated on the same real PV power generation data, gathered from the realisation of a small scale pilot site in Thessaloniki, Greece. The models predicted PV power generation on multiple horizons, namely for 15 min, 30 min, 60 min, 120 min and 180 min ahead of time. Based on the analysis of the experimental results we identify the cases, in which specific models (or types of models) perform better compared to others, and explain the rationale behind those model performances.
Geographical Islands experience harsh climate conditions resulting in diminished reliability of their grid. IANOS, a project funded by the European Commission, is developing technologies to overcome ...these challenges and allow for the secure decarbonisation of islands. The project will deploy and use storage technologies in two islands: Terceira, in Portugal, and Ameland, in the Netherlands. These include a 100kW/3kWh flywheel; thirty-five 2kW private fuel cells; sixteen 3kW/3kWh electrochemical batteries; two large-scale Battery Energy Storage System (BESS); a biobased saline battery of 120kWh; and twenty-four heat batteries of 3.5kWhth. These technologies, managed by an intelligent Virtual Power Plant (iVPP) also developed within the project, will be used for the maximisation of demand-side self-consumption, as well as for the provision of ancillary services, targeting the maximum economic and environmental benefit for the population and the island’s grid. In this paper, the specifications of each storage technology are described, as well as the impacts on the grid stability. The role of storage technologies in the decarbonisation of geographical islands will be emphasized.
The integration of renewable energy sources into residential power systems, accompanied by the growth of edge computing, presents a dynamic landscape for energy management. In this paper, an ...innovative solution for solar generation forecasting is introduced, effectively addressing issues of data privacy, operational efficiency, scalability, and network optimization. A Federated Learning Gradient Boosting (FedXGB) framework integrated with edge computing is applied, significantly amplifying energy efficiency and sustainability in residential environments. Powered by FogFlow, the orchestration and optimization of IoT ecosystems, particularly within smart homes, are advanced, and data privacy is ensured. Remarkable results are observed, especially in mid-term and long-term solar forecasting, highlighting the framework's transformative potential in reshaping energy management. This innovation holds the promise of revolutionizing how renewable energy is harnessed within homes and communities, paving the way for a more sustainable and efficient future. With this comprehensive approach, the intersection of renewable energy and edge computing takes a significant step forward in enhancing energy management.
Emerging Energy Load Forecasting (ELF) methodologies assist Distribution System Operators (DSOs) and Aggregators. Energy imbalance among consumption and generation could also be managed with high ...prediction accuracy, as well as smart grid applications, like Demand Response (DR) events. This study aims to test several algorithms as a solution for ELF. The proposed methodology utilizes machine/deep learning models for time-series forecasting in the domain of energy consumption. Via result comparison it has been illustrated that Neural Networks (NNs), both artificial NNs such as Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM) recurrent NNs with Extreme Gradient Boosting (XGBoost) were the more accurate ones among other models, showcasing Mean Absolute Error (MAE), R-squared (R 2 ), Root Mean Squared Error (RMSE) and Coefficient Variation of Root Mean Squared Error (CVRMSE) values equal to 1.281, 0.98, 2.238 and 0.147, respectively.
The integration of distributed battery energy storage systems has started to increase in power systems recently, as they can provide multiple services to the system operator, i.e. frequency ...regulation, system peak shaving, backup power etc. Additionally, batteries can be installed even in facilities where the installation of renewable energy sources are impossible, such as apartments within urban areas. Consequently, an aggregator could deploy distributed battery systems to households under his portfolio, utilising them to capitalize on Demand Response services while sharing benefits of electricity cost reduction with them. To enable that, this paper provides an integrated solution for monitoring, scheduling, and controlling a residential battery energy storage system. The proposed system has been realised in the context of inteGRIDy project to a pilot site that consist of 4 different dwellings that are located in Northern Greece. The analysis of the pilot results revealed that battery systems could exploit the variation in electricity price in order to succeed some profit alongside with the provided energy services.
Microgrids are becoming one of the most promising building blocks for smart grids gaining an increasing attention in the last decade. From critical infrastructures such as hospitals, military bases, ...desolated or hard to reach areas to universities, research centers, and other industrial infrastructures, basic microgrid principles have been applied presenting enhanced reliability and added value business services to their operation. Recently, such application principles have been further scaled-down, presenting an even more challenging paradigm at single building infrastructure, the nanogrid. Nevertheless, even in such occasions, optimal operation is restricted by the commercial product services, that aren't always easy to configure or extend towards enabling additional functionalities. This manuscript presents step by step the transformation of a smart home infrastructure into a flexible yet safe nanogrid that allows various control strategies in real-life operation while respecting the fail-safe mechanisms of the deployed commercial assets while introducing additional safety measures. After two years of operation and configuration, by only optimising the energy storage system and evaluating overall performance for almost half a year, promising results demonstrate the untapped potential of such deployments.
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