Integration of electric vehicles into electric power system brings both challenges and solutions in the operation of power grids. On the one hand, simultaneously charging a large number of electric ...vehicles causes branch congestion or large voltage drop. Operating the electric vehicles in the discharging mode, on the other hand, introduces the provision of several ancillary services like peak power shaving and spinning reserves. From the electric vehicles operation point of view, thus, the distribution system operators require a real-time monitoring infrastructure to capture the states of electric vehicle chargers and accordingly operate their grids in the safe mode with respect to the power quality standards (e.g., EN 50160). In this context, the real-time smart charging and storage platform of the EU Horizon 2020 “MEISTER” project, based on the information and communication technology, manages the availability of electric vehicles as a potential source of energy in the need of one or more flexibility services demanded by low voltage distribution system operators. In addition to the implemented information and communication technology platform, this paper presents how the smart use of the electric vehicle resources supports the power quality of the distribution system in terms of system voltage support, bidirectional power flow management, harmonic alleviation and power factor control.
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.
As microgrids have gained increasing attention over the last decade, more and more applications have emerged, ranging from islanded remote infrastructures to active building blocks of smart grids. To ...optimally manage the various microgrid assets towards maximum profit, while taking into account reliability and stability, it is essential to properly schedule the overall operation. To that end, this paper presents an optimal scheduling framework for microgrids both for day-ahead and real-time operation. In terms of real-time, this framework evaluates the real-time operation and, based on deviations, it re-optimises the schedule dynamically in order to continuously provide the best possible solution in terms of economic benefit and energy management. To assess the solution, the designed framework has been deployed to a real-life microgrid establishment consisting of residential loads, a PV array and a storage unit. Results demonstrate not only the benefits of the day-ahead optimal scheduling, but also the importance of dynamic re-optimisation when deviations occur between forecasted and real-time values. Given the intermittency of PV generation as well as the stochastic nature of consumption, real-time adaptation leads to significantly improved results.
Unlocking flexibility on the demand side is a prerequisite for balancing supply and demand in distribution networks with high penetration levels of renewable energy sources that lead to high ...volatility in energy prices. The main means of fully gaining access to the untapped flexibility is the application of demand response (DR) schemes through aggregation. Notwithstanding, to extract the utmost of this potential, a combination of performance-, financial-, and technical-related parameters should be considered, a balance rarely identified in the state of the art. The contribution of this work lies in the introduction of a holistic DR framework that refines the DR-related strategies of the aggregator towards optimum flexibility dispatch, while facilitating its cooperation with the distribution system operator (DSO). The backbone of the proposed DR framework is a novel constrained-objective optimisation function which minimises the aggregator’s costs through optimal segmentation of customer groups based on fairness and reliability aspects, while maintaining the distribution balance of the grid. The proposed DR framework is evaluated on a modified IEEE 33-Bus radial distribution system where a real DR event is successfully executed. The flexibility of the most fair, reliable and profitable sources, identified by the developed optimisation function, is dispatched in an interoperable and secure manner without interrupting the normal operation of the distribution grid.
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.
On becoming a commodity, Microgrids (MGs) have started gaining ground in various sizes (e.g., nanogrids, homegrids, etc.) and forms (e.g., local energy communities) leading an exponential growth in ...the respective sector. From demanding deployments such as military bases and hospitals, to tertiary and residential buildings and neighborhoods, MG systems exploit renewable and conventional generation assets, combined with various storage capabilities to deliver a completely new set of business opportunities and services in the context of the Smart Grid. As such systems involve economic, environmental and technical aspects, their performance is quite difficult to evaluate, since there are not any standards that cover all of these aspects, especially during operational stages. Towards allowing an holistic definition of a MG performance, for both design and operational stages, this paper first introduces a complete set of Key Performance Indicators to measure holistically the performance of a MG’s life cycle. Following, focusing on the MG’s day-to-day operation, a data-driven assessment is proposed, based on dynamic metrics, custom made reference models, and smart meter data, in order to be able to extract its operational performance. Two different algorithmic implementations (i.e., Dynamic Time Warping and t-distributed Stochastic Neighbor Embedding) are used to support the methodology proposed, while real-life data are used from a small scale MG to provide the desired proof-of-concept. Both algorithms seem to correctly identify days and periods of not optimal operation, hence presenting promising results for MG performance assessment, that could lead to a MG Performance Classification scheme.
In recent years, the growing use of Intelligent Personal Agents in different human activities and in various domains led the corresponding research to focus on the design and development of agents ...that are not limited to interaction with humans and execution of simple tasks. The latest research efforts have introduced Intelligent Personal Agents that utilize Natural Language Understanding (NLU) modules and Machine Learning (ML) techniques in order to have complex dialogues with humans, execute complex plans of actions and effectively control smart devices. To this aim, this article introduces the second generation of the CERTH Intelligent Personal Agent (CIPA) which is based on the RASA framework and utilizes two machine learning models for NLU and dialogue flow classification. CIPA-Generation B provides a dialogue-story generator that is based on the idea of adjacency pairs and multiple intents, that are classifying complex sentences consisting of two users’ intents into two automatic operations. More importantly, the agent can form a plan of actions for implicit Demand-Response and execute it, based on the user’s request and by utilizing AI Planning methods. The introduced CIPA-Generation B has been deployed and tested in a real-world scenario at Centre’s of Research & Technology Hellas (CERTH) nZEB SmartHome in two different domains, energy and health, for multiple intent recognition and dialogue handling. Furthermore, in the energy domain, a scenario that demonstrates how the agent solves an implicit Demand-Response problem has been applied and evaluated. An experimental study with 36 participants further illustrates the usefulness and acceptance of the developed conversational agent-based system.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
This paper presents a fault-tolerant secondary and adaptive primary microgrid control scheme using a hybrid multi-agent system (MAS), capable of operating either in a semi-centralised or distributed ...manner. The proposed scheme includes a droop-based primary level that considers the microgrid energy reserves in production and storage. The secondary level is responsible for: a) the microgrid units' coordination, b) voltage and frequency restoration and c) calculation of the droop/ reversed-droop coefficients. The suggested architecture is arranged upon a group of dedicated asset agents that collect local measurements, take decisions independently and, collaborate in order to achieve more complex control objectives. Additionally, a supervising agent is added to fulfill secondary level objectives. The hybrid MAS can operate either with or without the supervising agent operational, manifesting fast redistribution of the supervising agent tasks. The proposed hybrid scheme is tested in simulation upon two separate physical microgrids using three scenarios. Additionally, a comparison with conventional control methodologies is performed in order to illustrate further the operation of a hybrid approach. Overall, results show that the proposed control framework exhibits unique characteristics regarding reconfigurability and fault-tolerance, while power quality and improved load sharing are ensured even in case of critical component failure.
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.
Building energy consumption has been declared as an untapped energy source, accounting for 40% of energy consumption worldwide. In this paper, in order to decrease power consumption in an office ...building aiming at peak reduction a Demand Response solution is proposed integrated in a real building pilot site. Consumption profiling, day-ahead forecasting as long as various feedback techniques were implemented taking into consideration occupant thermal comfort aspects. To make the framework more accurate, various variables have been considered, such as weather forecast, along with environmental conditions (in- and outdoor temperature), occupancy and thermal comfort patterns, space occupancy and current operation of the system. A real-life demonstration of the proposed system was implemented in a commercial building in Thessaloniki Greece, integrating field equipment for real-time monitoring and control of several offices. Preliminary field test results of the working framework are included in this work.
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