The transition to renewable energy sources challenges the operation and stability of the electric power system. Wind and solar power generation are volatile and uncertain, and energy sources may be located far away from the centers of the load. High Voltage Direct Current (HVDC) lines enable long-distance power transmission at low losses, both within and between different synchronous power grids. HVDC interconnectors between different synchronous areas can be used to balance volatile generation by leveraging their fast control behavior, but rapid switching may also disturb the power balance. In this article, we analyze the interaction of HVDC interconnector operation and load-frequency control in different European power grids from operational data. We use explainable machine learning to disentangle the various influences affecting the two systems, identify the key influences, and quantify the interrelations in a consistent way. Our results reveal two different types of interaction: Market-based HVDC flows introduce deterministic frequency deviations and thus increase control needs. Control-based HVDC flows mitigate frequency deviations on one side as desired but generally disturb frequency on the other side. The analysis further provides quantitative estimates for the control laws and operation strategies of individual HVDC links, for which there is little public information. Furthermore, we quantify the importance of HVDC links for the frequency dynamics, which is particularly large in the British grid.

Nowadays, Non-Intrusive Load Monitoring techniques are sufficiently accurate to provide valuable insights to the end-users and improve their electricity behaviours. Indeed, previous works show that commonly used appliances (fridge, dishwasher, washing machine) can be easily disaggregated thanks to their abundance of electrical features. Nevertheless, there are still many ON/OFF devices (e.g. heaters, kettles, air conditioners, hair dryers) that present very poor power signatures, preventing their disaggregation with traditional algorithms. In this work, we propose a new online clustering method exploiting both operational features (peak power, duration) and external features (time of use, day of week, weekday/weekend) in order to recognize ON/OFF devices. The proposed algorithm is intended to support an existing disaggregation algorithm that is already able to classify at least 80% of the total energy consumption of the house. Thanks to our approach, we improved the performance of our existing disaggreation algorithm from 80% to 87% of the total energy consumption in the monitored houses. In particular, we found that 85% of the clusters were identified by only using operational features, while external features allowed us to identify the remaining 15% of the clusters. The algorithm needs to collect on average less than 40 operations to find a cluster, which demonstrates its applicability in the real world.

With rising electricity demand through digitization and innovation, the urgency of climate change mitigation, and the recent geopolitical crisis, stakeholders in developing countries face the complex task to build reliable, affordable, and low-emission energy systems. Information inaccessibility, data unavailability, and scarce local expertise are major challenges for planning and transitioning to decentralized solutions. Motivated by the calls for more solution-oriented research regarding sustainability, we design, develop, and evaluate the web-based decision support system NESSI4Dweb+ that is tailored to the needs and capabilities of various stakeholders in developing countries. NESSI4Dweb+ is open access and considers location-specific circumstances to facilitate multi-energy planning. Its applicability is demonstrated with a case study of a representative rural village in southern Madagascar and evaluated through seven interviews with experts and stakeholders. We show that NESSI4Dweb+ can support the achievement of the United Nations Sustainable Development Goals and enable the very prerequisite of digitization: reliable electrification.

Decision (support) systems are a particularly important type of information system to energy informatics. A key challenge in energy informatics is that electricity supply must be in balance with demand at all times. More volatile renewable energy sources increase the relevance of electricity network balancing, i.e., imbalance settlement. Typically, electricity distribution network operators bought balancing power from external service providers (Buy option). Interestingly, however, more local energy resources help smarter electricity networks develop a Make option, as in our real-world evaluation. Choosing the better decision alternative within the relevant timeframes challenges human decision-making capabilities. Therefore, this research proposes a model-based decision system to improve the operators’ decisions concerning Make or Buy under various levels of data quality represented by availability, granularity, and timeliness. The study reports savings up to 40% of costs for imbalance settlement supporting ambitious development efforts by the municipality we study in our real-world evaluation.

There have recently been intensive efforts aimed at addressing the challenges of environmental degradation and climate change through the applied innovative solutions of AI, IoT, and Big Data. Given the synergistic potential of these advanced technologies, their convergence is being embraced and leveraged by smart cities in an attempt to make progress toward reaching the environmental targets of sustainable development goals under what has been termed “environmentally sustainable smart cities.” This new paradigm of urbanism represents a significant research gap in and of itself. To fill this gap, this study explores the key research trends and driving factors of environmentally sustainable smart cities and maps their thematic evolution. Further, it examines the fragmentation, amalgamation, and transition of their underlying models of urbanism as well as their converging AI, IoT, and Big Data technologies and solutions. It employs and combines bibliometric analysis and evidence synthesis methods. A total of 2,574 documents were collected from the Web of Science database and compartmentalized into three sub-periods: 1991–2015, 2016–2019, and 2020–2021. The results show that environmentally sustainable smart cities are a rapidly growing trend that markedly escalated during the second and third periods—due to the acceleration of the digitalization and decarbonization agendas—thanks to COVID-19 and the rapid advancement of data-driven technologies. The analysis also reveals that, while the overall priority research topics have been dynamic over time—some AI models and techniques and environmental sustainability areas have received more attention than others. The evidence synthesized indicates that the increasing criticism of the fragmentation of smart cities and sustainable cities, the widespread diffusion of the SDGs agenda, and the dominance of advanced ICT have significantly impacted the materialization of environmentally sustainable smart cities, thereby influencing the landscape and dynamics of smart cities. It also suggests that the convergence of AI, IoT, and Big Data technologies provides new approaches to tackling the challenges of environmental sustainability. However, these technologies involve environmental costs and pose ethical risks and regulatory conundrums. The findings can inform scholars and practitioners of the emerging data-driven technology solutions of smart cities, as well as assist policymakers in designing and implementing responsive environmental policies.

It is to be expected that there will be a shift toward electromobility with regard to private passenger cars in the coming years. This will oblige the respective power grid providers to upgrade their networks in future years. So that grid operators can plan and operate their grids to meet future needs, they have to have as complete information as possible about the loads they will be required to handle. Depending on voltage level, geographic location, general grid load, and spread of e-mobility, the situation will vary. The assumption explored in this paper is that external factors influence the distribution of EV chargers. As a second task, the impact on the power grid is simulated by means of various scenarios on the basis of this identified distribution, with the focus on low voltage (LV) grids. Sociodemographic data is used as a geographic grid to determine potential distribution. For this, machine learning methods from the field of “Species Distribution Modeling” are applied for a prospective distribution concept. Using this distribution model, the results of simulation of power grid utilization reveal vulnerabilities scattered around the networks. It is shown that e-mobility will, in the future, present a challenge for power grid operators, for which solution concepts are needed.

The strong interdependence between power systems and information and communication technologies (ICT) makes cyber-physical energy systems susceptible to new disturbances. State estimation (SE) is a vital part of energy management systems, for several monitoring, management, and control services. Failure of SE service leads to loss of situational awareness, which in turn has a detrimental impact on the grid operation. Therefore, it is essential to maintain the performance of SE service. Modern technologies such as virtualization are key drivers to provide the flexibility to reallocate, reconfigure, and manage services as a countermeasure to mitigate the impact of disturbances. This paper introduces the virtualization of SE service as a potential approach to maintain its performance in the case of disturbances. Following a review of existing approaches for maintaining the performance of the SE service, a description of the proposed approach is provided. The benefits of virtualization of SE service are demonstrated via a simulation test platform with an ICT-enriched CIGRE MV benchmark grid.

Electrification of the transportation sector provides several advantages in favor of climate protection and a shared economy. At the same time, the rapid growth of electric vehicles also demands innovative solutions to mitigate risks to the low-voltage network due to unpredictable charging patterns of electric vehicles. This article conceptualizes a stochastic reinforcement learning agent that learns the optimal policy for regulating the charging power. The optimization objective intends to reduce charging time, thus charging faster while minimizing the expected voltage violations in the distribution network. The problem is formulated as a two-stage optimization routine where the stochastic policy gradient agent predicts the boundary condition of the inner non-linear optimization problem. The results confirm the performance of the proposed architecture to control the charging power as intended. The article also provides extensive theoretical background and directions for future research in this discipline.

We introduce the Solar Farm Cable Layout Problem (SoFaCLaP), a novel graph-theoretic optimization problem. SoFaCLaP formalizes the task of finding a cost-optimal cable layout in a solar farm where PV string positions are already determined but the positions of other components such as transformers can be picked from a set of candidate positions. The problem statement incorporates a network flow model in which the flow value of a connection represents the number of strings that are (indirectly) connected to a transformer via this connection. A mixed-integer linear program (MILP) formulation is proposed that uses binary variables to indicate which of several available cable types is chosen for each connection. We propose a framework to randomly generate benchmark instances to evaluate any algorithmic approach to SoFaCLaP. In particular, we generate a set of instances based on real-world solar farm characteristics. With an extensive evaluation of the MILP formulation on those instances we establish mixed-integer linear programming as a baseline for future algorithmic approaches to finding solar farm cable layouts.

Heat pumps are at the heart of the transition to sustainable heating in buildings. Yet, minor installation and setting errors add to unnoticed performance drops over the system’s lifetime. With the advent of smart meters that constantly measure electricity consumption, data patterns of heat pumps have become available, even for the many not connected to the Internet. These data hold the potential to monitor heat pumps continuously, identify issues, and thus assist energy consultants and heat pump owners in lifting hidden conservation potential. Yet, research and practice lack an overview of specific problems that could help in this task. In a mixed-method approach, this study investigated 228 protocols of on-site heat pump inspections in Switzerland and found 47 problem classes with varying frequencies. Based on this empirical data and expert interviews, a classification scheme for heat pump issues is proposed and validated. It uncovers the cause of problems, how and by whom they can be recognised and solved, and potential benefits. The work demonstrates that (i) several problems are likely to create smart meter patterns and that (ii) heat pump owners could be involved in the problem recognition and solving process if they get guidance (i.e. simple rules and instructions). Finally, this study discusses implications for developing information systems to automate and assist the recognition and solving of problems. Such information systems may raise not only the attention of heat pump owners but also trigger desired actions (i.e. request consultancy, inspect heat pump themselves).

Correction : Energy Informatics 2021, 4(Suppl 3):21 http://doi.org/10.1186/s42162-021-00181-5 In the original (Wolgast et al. 2021) publication of the article, 2 symbols were erroneously omitted during the publication process. The incorrect and correct information is shown in this correction article. Incorrect Table 1 shows that the average attacker profit increases drastically by about 323.2 on average and the market share increased by about 39.5 compared to the baseline Correct Table 1 shows that the average attacker profit increases drastically by about 323.2% on average and the market share increased by about 39.5% compared to the baselineWolgast T, Veith EMSP, Nieße A (2021) Towards reinforcement learning for vulnerability analysis in power-economic systems. Energy Inform 4(Suppl 3):21. http://doi.org/10.1186/s42162-021-00181-5Article Google Scholar Download referencesAuthors and AffiliationsUniversity of Oldenburg, Ammerländer Heerstraße 114-118, Oldenburg, GermanyThomas Wolgast & Astrid NießeOFFIS-Institute for Information Technology, Escherweg 2, Oldenburg, GermanyThomas Wolgast, Eric M. S. P. Veith & Astrid NießeAuthorsThomas WolgastView author publicationsYou can also search for this author in PubMed Google ScholarEric M. S. P. VeithView author publicationsYou can also search for this author in PubMed Google ScholarAstrid NießeView author publicationsYou can also search for this author in PubMed Google ScholarCorresponding authorCorrespondence to Thomas Wolgast.Publisher's NoteSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. Reprints and PermissionsCite this articleWolgast, T., Veith, E.M.S.P. & Nieße, A. Publisher Correction: Towards reinforcement learning for vulnerability analysis in power-economic systems. Energy Inform 6, 11 (2023). http://doi.org/10.1186/s42162-023-00264-5Download citationPublished: 09 May 2023DOI: http://doi.org/10.1186/s42162-023-00264-5Share this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy to clipboard Provided by the Springer Nature SharedIt content-sharing initiative

Demand response (DR) management systems are a potentially growing market due to their ability to maximize energy savings by allowing customers to manage their energy consumption at times of peak demand in response to financial incentives from the electricity supplier. Successful execution of a demand response program requires an effective management system where the home energy management system (HEMS) is a promising solution nowadays. HEMS is developed to manage energy use in households and to conduct the management of energy supply, either from the grid or the alternative energy sources like solar or wind power plants. With the increase of vehicle electrification, in order to achieve a more reliable and efficient smart grid (SG), cooperation between electric vehicles (EVs) and residential systems is required. This cooperation could involve not only vehicle to grid (V2G) operation but a vehicle to home (V2H) too. V2H operation is used to transfer the power and relevant data between EVs and residential systems. This paper provides an efficient HEMS enhanced by smart scheduling and an optimally designed charging and discharging strategy for plugged-in electric vehicles (PEVs). The proposed design uses a fuzzy logic controller (FLC) for smart scheduling and to take the charging (from the grid)/discharging (supply the household appliances) decision without compromising the driving needs. Simulations are presented to demonstrate how the proposed strategies can help to reduce electricity costs by 19.28% and 14.27% with 30% and 80% state of charge (SOC) of the PEV respectively compared to the case where G2V operation only used along with the photovoltaic (PV) production, improve energy utilization by smoothing the energy consumption profile and satisfy the user’s needs by ensuring enough EV battery SOC for each planned trip.

Increasing shares of volatile generation and non-steerable demand raise the need for automated control of the Energy Systems (ESs). Various solutions for management and schedule-based control of energy facilities exist today. However, the amount and diversity of applications lead to a multitude of different automated energy management solutions. Different optimization algorithms have proven more or less effective for energy management. The multitude of optimization algorithms and energy management solutions require flexible, modular, and scalable integrations. We present a novel Optimization Service (OS) for easily integrating optimization algorithms while evaluating candidate solutions in the context of ESs applications. We propose an adapter-based architecture using metadata and domain knowledge to bridge between clients, e.g. smart grid applications and optimization algorithms. The architecture interfaces different clients with optimizers in a flexible and modular way. The clients provide metadata-based descriptions of optimization jobs translated by OS. OS then interacts with optimizers and evaluates candidate solutions. A consistent definition of interfaces for clients and optimization algorithms facilitates the modular evaluation of candidate solutions. OS’s separation of client and optimization algorithms increases scalability by managing computational resources independently. We evaluate the presented architecture for scheduling a so-called Energy Hub (EH) as a test case describing a simulation scenario of a renewable EH embedded in grid scenarios from an industrial area in Karlsruhe, Germany. OS utilizes an Evolutionary Algorithm (EA) to optimize schedules for cost and strain on the electrical grid. The use case exemplifies OS’s advantages in a proof-of-concept evaluation.

The transition to a low-carbon society will completely change the structure of energy systems from a standalone hierarchical centralised vision to cooperative and distributed Multi-Energy Systems. The analysis of these complex systems requires the collaboration of researchers from different disciplines in the energy, ICT, social, economic, and political sectors. Combining such disparate disciplines into a single tool for modeling and analyzing such a complex environment as a Multi-Energy System requires tremendous effort. Researchers have overcome this effort by using co-simulation techniques that give the possibility of integrating existing domain-specific simulators in a single environment. Co-simulation frameworks, such as Mosaik and HELICS, have been developed to ease such integration. In this context, an additional challenge is the different temporal and spatial scales that are involved in the real world and that must be addressed during co-simulation. In particular, the huge number of heterogeneous actors populating the system makes it difficult to represent the system as a whole. In this paper, we propose a comparison of the scalability performance of two major co-simulation frameworks (i.e. HELICS and Mosaik) and a particular implementation of a well-known multi-agent systems library (i.e. AIOMAS). After describing a generic co-simulation framework infrastructure and its related challenges in managing a distributed co-simulation environment, the three selected frameworks are introduced and compared with each other to highlight their principal structure. Then, the scalability problem of co-simulation frameworks is introduced presenting four benchmark configurations to test their ability to scale in terms of a number of running instances. To carry out this comparison, a simplified multi-model energy scenario was used as a common testing environment. This work helps to understand which of the three frameworks and four configurations to select depending on the scenario to analyse. Experimental results show that a Multi-processing configuration of HELICS reaches the best performance in terms of KPIs defined to assess the scalability among the co-simulation frameworks.

In the past decade, the global distribution of energy resources has expanded significantly. The increasing number of prosumers creates the prospect for a more decentralized and accessible energy market, where the peer-to-peer energy trading paradigm emerges. This paper proposes a methodology to optimize the participation in peer-to-peer markets based on the double-auction trading mechanism. This novel methodology is based on two reinforcement learning algorithms, used separately, to optimize the amount of energy to be transacted and the price to pay/charge for the purchase/sale of energy. The proposed methodology uses a competitive approach, and that is why all agents seek the best result for themselves, which in this case means reducing as much as possible the costs related to the purchase of energy, or if we are talking about sellers, maximizing profits. The proposed methodology was integrated into an agent-based ecosystem where there is a direct connection with agents, thus allowing application to real contexts in a more efficient way. To test the methodology, a case study was carried out in an energy community of 50 players, where each of the proposed models were used in 20 different players, and 10 were left without training. The players with training managed, over the course of a week, to save 44.65 EUR when compared to a week of peer-to-peer without training, a positive result, while the players who were left without training increased costs by 17.07 EUR.

Worldwide, buildings consume about 40 percent of the overall energy resources and contribute to an average of 30 percent of the global carbon emission. Hence, technologies for improving the energy efficiency of buildings play an essential role in the global fight against climate change. The CELSIUS project aims to improve the energy efficiency and indoor climate of medium to large sized commercial and public buildings by developing an integrated system solution that consists of (1) an IoT-enabled and cloud-based platform for monitoring and diagnostics of building energy performance and indoor climate quality, (2) a middleware software platform for cost-effective large-scale deployment of wireless sensors and gateways, and (3) an IoT network management platform for cost-efficient life-cycle maintenance of sensors and gateways. The integrated system solution will be deployed and demonstrated in a 6000 m2 building in Aarhus, Denmark, and an 18,000 m2 building in Kuala Lumpur, Malaysia. By choosing buildings located in different climate zones on different continents allows the developed system solution to be tested under realistic conditions for the international export market.

The diffusion of domotics solutions and of smart appliances and meters enables the monitoring of energy consumption at a very fine level and the development of forecasting and diagnostic applications. Anomaly detection (AD) in energy consumption data streams helps identify data points or intervals in which the behavior of an appliance deviates from normality and may prevent energy losses and break downs. Many statistical and learning approaches have been applied to the task, but the need remains of comparing their performances with data sets of different characteristics. This paper focuses on anomaly detection on quasi-periodic energy consumption data series and contrasts 12 statistical and machine learning algorithms tested in 144 different configurations on 3 data sets containing the power consumption signals of fridges. The assessment also evaluates the impact of the length of the series used for training and of the size of the sliding window employed to detect the anomalies. The generalization ability of the top five methods is also evaluated by applying them to an appliance different from that used for training. The results show that classical machine learning methods (Isolation Forest, One-Class SVM and Local Outlier Factor) outperform the best neural methods (GRU/LSTM autoencoder and multistep methods) and generalize better when applied to detect the anomalies of an appliance different from the one used for training.

Accurate day-ahead load forecasting is an important task in smart energy communities, as it enables improved energy management and operation of flexibilities. Smart meter data from individual households within the communities can be used to improve such forecasts. In this study, we introduce a novel hybrid bi-directional LSTM-XGBoost model for energy community load forecasting that separately forecasts the general load pattern and peak loads, which are later combined to a holistic forecasting model. The hybrid model outperforms traditional energy community load forecasting based on standard load profiles as well as LSTM-based forecasts. Furthermore, we show that the accuracy of energy community day-ahead forecasts can be significantly improved by using smart meter data as additional input features.

Buildings are responsible for a significant fraction of the overall electrical load. Given the increasing penetration of renewables into the generation mix, it is important to make building loads flexible, to better match the variability in generation. Of course, building loads can be made arbitrarily flexible using sufficient stationary storage, but this comes at considerable cost. In this paper, we investigate how to reduce this cost by exploiting electric vehicle (EV) charging control for unidirectional and bidirectional charging. Specifically, we design a model-predictive control algorithm to reshape building load to match a specified load shape. In realistic settings and for two use cases, we investigate the degree to which the amount of stationary storage is reduced using EV charging control. In both cases, we find that our controller reduces the need for stationary storage compared to existing solutions. Moreover, bidirectional EV charging control substantially reduces the required amount of stationary storage.

During the last decades, numerous simulation tools have been proposed to faithfully reproduce the different entities of the grid together with the inclusion of new elements that make the grid “smart”. Often, these domain-specific simulators have been then coupled with co-simulation platforms to test new scenarios. In parallel, agent-oriented approaches have been introduced to test distributed control strategies and include social and behavioural aspects typical of the consumer side. Rarely, simulators of the physical systems have been coupled with these innovative techniques, especially when social and psychological aspects have been considered. In order to ease the re-usability of these simulators, avoiding re-coding everything from scratch, we propose a hierarchical and modular agent-oriented framework to test new residential strategies in the energy context. If needed, the presented work enables the user to select the desired level of details of the agent-based framework to match the corresponding physical system without effort to test very different scenarios. Moreover, it allows adding on top of the physical data, behavioural aspects. To this end, the characteristics of the framework are first introduced and then different scenarios are described to demonstrate the flexibility of the proposed work: (i) a first stand-alone scenario with two hierarchy levels, (ii) a second co-simulation scenario with a photovoltaic panel simulator and (iii) a third stand-alone scenario with three hierarchy levels. Results demonstrate the flexibility and ease of use of the framework, allowing us to compare several scenarios and couple new simulators to build a more and more complex environment. The framework is in the early stages of its development. However, thanks to its properties in the future it could be extended to include new actors, such as industries, to get the full picture.