Search Results (35)

Smart-port decarbonisation requires operations-research decision support under day-ahead uncertainty. We present a rolling-horizon Model Predictive Control Energy Management System, formulated as a Mixed-Integer Linear Program with five forecast streams, and benchmark it against a deterministic rule-based controller on an identical configuration. A full-year proof-of-concept at the Port of Ancona (8760 hourly steps over the 2024 Italian Day-Ahead Market, 6.5 MWp PV, 1.0 MWh BESS) combines realised 2024 market, photovoltaic and auxiliary-demand series with a post-AFIR projected cold-ironing demand—the dominant load—and is therefore an operational proof-of-concept rather than a fully metered baseline. The principal MPC outcome is structural: anticipatory dispatch raises the mean BESS state of charge from 13.6% to 46.0% and cuts residence at the minimum SoC from 81% to 6% of hours. The forecasting layer attains sub-7% sMAPE on cold-ironing-loaded demand and 9–18% on the remaining streams (seasonal MASE₂₄ ≤ 0.74 on demand and price streams). At the relay-constrained 0.08 C pilot, the realised savings is 0.44% (€14,463 yr⁻¹; 95% moving-block bootstrap CI [€12,842, €15,742]); benchmarked against an enhanced rule-based controller that is itself permitted price-threshold grid charging, the residual value of predictive optimisation is €5652 yr⁻¹ (0.17%), with the remainder of the gap being the value of enabling grid charging. A C-rate sweep shows the savings doubling to 0.93% at 0.5 C, and a direct 20 MWh/±10 MW simulation yields a €0.57 M yr⁻¹ gross arbitrage savings whose net value, after a realistic battery-degradation penalty, is substantially smaller. Controller-level operational CO₂ rises marginally (+6.2 t, +0.13%), an effect distinct from—and dwarfed by—the system-level cold-ironing decarbonisation. The framework is reproducible in open-source Python (PuLP/HiGHS) from the actual data and is portable to other single-node smart city energy hubs. Full article

Smart-city advanced metering infrastructure enables utility-scale remote analytics, but some forms of under-registration closely resemble lawful changes in demand and are hard to model as anomalies. We study a narrow, physically motivated event family at the single-meter level, namely multiplicative under-registration with unknown onset (a shunt-like attack), in which recorded active energy is approximately scaled by a factor $\alpha <1$ after a change-point while the daily-profile structure and spectral shape remain invariant. We formalize the problem and develop a physics-guided detector family based on weighted daily-profile regression (GLS) and its robust variant (RGLS), with quality-control filters, spectral-consistency checks, and an optional reactive-channel gate, designed to stay selective under confounders such as rooftop photovoltaics, electric-vehicle charging, and heat-pump onsets. On a device-disjoint Low Carbon London benchmark (487 households) the preferred GLS detector attains precision $0.915$, recall $0.978$, and $F_1=0.945$ at $\alpha =0.10$ while keeping the non-theft suspected rate near $1\%$; a cross-dataset check on Open Power System Data with real EV/PV/heat-pump overlays yields zero false alarms on all 72 cases, and Mendeley and WPuQ benchmarks add a second large family and a reactive-channel test. We compare against external baselines (classical change-point detection, Isolation Forest, autoencoder, LSTM, gradient boosting, and a supervised statistical pipeline) on the same protocol: generic anomaly detectors fail on this shape-preserving attack, and supervised models match the detector only in-distribution while, unlike it, failing to transfer to real lawful confounders. All metrics carry bootstrap confidence intervals, and a full reproducibility bundle accompanies the submission. Full article

Smart energy meters are widely deployed in modern distribution networks, extending their role beyond revenue billing to real-time monitoring and data-driven smart city applications. However, conventional legal metrology frameworks rely on periodic recalibration and are not intended for the detection of accuracy drift or unexpected malfunctions between scheduled inspections. In scientific publications, various techniques for remote smart meters’ error surveillance are presented, but experimental verification on real distribution network data remains limited. The objective of this study is to experimentally verify two previously proposed power event-driven methods for remote estimation of active power measurement error in individual consumer meters, using a feeder-level sum meter as a reference instrument. One-second resolution electrical readings were collected from a real low-voltage distribution branch using ESP32-based local adapters communicating via MQTT over Wi-Fi, with SNTP-based clock synchronization for power event correlation. Under optimized detection parameters, the linear regression method achieved 0.20% RMSE and 0.75% maximum absolute error, and the neural network method 0.09% RMSE and 0.31%, confirming suitability for Class 1 m accuracy surveillance. Feasibility analysis of three MQTT-based deployment scenarios demonstrates that binary encoding limits local adapter buffers to 2.8 kB and worst-case daily channel demand to 2000 kB, confirming the practical viability of the proposed architecture. Full article

Accurate solar irradiance forecasting is important for efficient planning of solar energy systems, renewable energy integration, and data-driven energy management in smart cities. This becomes more essential in regions with limited measured data availability and varying climatic conditions, where reliable forecasting can support urban energy planning and smart grid operation. Pakistan faces a scarcity of available solar data and has varying climatic conditions, which makes it ideal for such a study. This study utilizes nine geographically diverse stations to develop a benchmark framework for direct one-step-ahead hourly solar irradiance forecasting. The dataset was subjected to data preprocessing, feature engineering, and multi-model evaluation. A staged approach was adopted for feature selection, starting from a base model comprising three input variables: extraterrestrial radiation, solar zenith angle, and relative humidity. Features were added in an incremental order, which resulted in an optimized four-variable input set through the addition of a lagged clearness index to the base model. The forecasting models evaluated in this study, using these input variables, were ANN, NAR, NARX, LSTM, GRU, SARIMA, and Prophet. Deep learning models outperformed the other considered approaches, with LSTM showing the best overall benchmark performance with an average RMSE of 92.93 W/m², MAE of 66.56 W/m², and R-Squared of 0.872. The performance trends were broadly consistent across the evaluated stations, indicating stable behaviour within the adopted dataset and experimental setup. The study shows that a compact and physically interpretable input feature set, used with recurrent deep learning models, provides an effective solution for hourly solar irradiance forecasting, especially in locations with varying climatic conditions. The proposed benchmark can support smart city applications related to distributed solar generation, energy-aware urban planning, and intelligent operation of renewable-rich power systems. Full article

Urban flexibility research is expanding across buildings, electric vehicles (EVs), distributed energy resources (DERs), storage, positive energy districts (PEDs), digital twins, and interoperability platforms. These strands are often reviewed separately, although urban distribution operators must manage their combined impacts on the same feeders. This paper presents a PRISMA 2020-aligned systematic review with evidence mapping and narrative synthesis of feeder-aware coordination in smart-city electricity systems. Searches of Scopus, Web of Science, IEEE Xplore, ScienceDirect, and citation chasing identified 312 records; 127 studies were included after screening and eligibility assessment, 101 entered the quantitative mapping sample, and 31 formed the deep-synthesis anchor core. Sparse contingency tables were analyzed with Monte-Carlo permutation chi-square tests and bootstrap confidence intervals for Cramér’s V, while ordinal variables were summarized with medians and interquartile ranges. Explicit feeder grounding was concentrated in grid-oriented and EV-oriented studies, whereas many AI/digital-twin and interoperability studies were less often validated against distribution-network operation. Economic and peak-flexibility indicators were reported far more often than interoperability, cybersecurity, or validation-maturity indicators in the anchor core. The synthesis also showed that deployment-oriented work depends on clearer treatment of standards, co-simulation workflows, regulatory instruments, and stakeholder roles. The evidence base is heterogeneous, English-only, and single-coded, so the quantitative results are descriptive rather than population-level. The review contributes a transparent three-layer corpus design (127 included/101 mapped/31 anchor), a domain-specific specialization of SGAM/IEEE 2030 for urban feeder orchestration, an operational digital-twin definition and validation ladder, a retrofittable benchmarking framework, and a practical roadmap for DSOs, municipalities, aggregators, EV operators, building managers, and ICT providers. Full article

Buildings represent 40% of the European Union’s energy consumption and 36% of its greenhouse gas emissions. Nursing homes are among the buildings that consume the most energy. The objective of this study was to make predictive models of Energy Consumption, Energy Costs, and CO₂ Emissions in nursing homes using different variables. To do this, data from 20 public nursing homes located in Extremadura (Spain) during the 2019–2023 period were analyzed. All the buildings were built or renovated between 1995 and 2009; the useful area and the number of residents were in the range of 1332–10,880 m² and 24–254 residents. A statistical analysis was performed using multivariable linear regression. During the research, equations that allow for the estimation of the annual Energy Consumption, Energy Costs and CO₂ Emissions of nursing homes, according to the useful area and number of residents, were found. The R_adj² was 0.9710, 0.9744 and 0.9742, respectively. The quality of the models obtained was contrasted using the mean absolute error (MAE), the relative error (RE) and the root mean square error (RMSE), together with the assessment of multicollinearity through the Variance Inflation Factor (VIF). The findings of this study may prove beneficial for stakeholders within the elder care sector. Full article

With the rapid integration of distributed energy resources, achieving a balance between economic efficiency and environmental sustainability in multi-microgrid (MMG) systems is critical. However, existing studies typically treat microgrid operators as fully compliant entities. They often neglect the “trust-risk” dimension along with potential default behaviors in decentralized markets. This paper proposes a novel co-optimized scheduling model for urban MMG systems, centered on a unified “Social–Economic–Physical” coupling framework. To ensure transaction integrity, a robust reputation evaluation framework is developed using Root Mean Square Error (RMSE), mean absolute error (MAE), plus Dynamic Time Warping (DTW). This framework effectively identifies fraudulent data or contractual breaches. Furthermore, to enhance fairness while promoting decarbonization, the model integrates a dynamic network pricing strategy based on the Shapley value. It works alongside a reputation-weighted reward–penalty step-type carbon trading scheme. The proposed model is formulated as a mixed-integer linear programming (MILP) problem and solved using MATLAB R2025b with CPLEX 12.10. Simulation results demonstrate that the integrated approach significantly optimizes system performance. Total carbon emissions are reduced by 49.6 tons. Meanwhile, revenues for the MMG Alliance, individual microgrids, and shared energy storage operators increase by 4.08% to 33.00%. The proposed framework provides a practical governance solution for Smart City multi-microgrid systems, effectively addressing the “trust-risk” challenge in decentralized urban energy markets. The findings validate that the proposed mechanism effectively fosters a trustworthy trading environment, achieving a “win-win” outcome for economic profitability and urban energy resilience. Full article

The transition to smart cities is accelerating distributed wind and solar deployment. However, their intermittency challenges grid operation, thereby making accurate machine-learning-based prediction of wind speed and global horizontal irradiance (GHI) crucial. This study presents a cost-effective approach that enhances prediction accuracy by extracting additional features from timestamp records for deep learning models used to forecast GHI and wind speed. Unlike conventional methods that require onsite meteorological measurements, the proposed approach uses only date and time information as inputs to multivariate deep neural networks, including recurrent neural networks, gated recurrent units, long short-term memory (LSTM), bidirectional LSTM, and convolutional neural networks. For wind speed prediction, the proposed configuration achieves R² up to 0.9987, with RMSE as low as 0.067 m/s for 3 d ahead forecasting, outperforming univariate baselines and matching models. For GHI forecasting, the time-based configuration attains R² values above 0.9994 in 12 h ahead predictions, with the RMSE reduced to approximately 4.47 W/m², representing a substantial improvement over univariate models. The proposed framework maintains strong performance, particularly under clear and sunny conditions. These results demonstrate that timestamp-engineered features can deliver forecasting accuracy comparable to conventional multivariate meteorological models while significantly reducing infrastructure requirements, making the approach well-suited for scalable smart city energy management. Full article

The global drive toward energy transition and carbon neutrality requires integrated and data-driven approaches for managing buildings and smart cities. Existing urban energy assessment frameworks remain fragmented and often lack multiscale interoperability between building-level models and territorial datasets. At the same time, shallow geothermal energy is emerging as an efficient and renewable solution for sustainable heating and cooling. To address these gaps, this study examines the potential of GeoBIM, the integration of Building Information Modeling (BIM) and Geographic Information Systems (GIS), as a unified framework for multiscale energy analysis and for supporting shallow geothermal applications. A systematic literature review was conducted based on the PRISMA framework, combining a systematic literature review using the Scopus database with the critical examination of representative case studies. The results show that GeoBIM-based modeling improves data quality, enhances thermal performance assessments, and supports the implementation of shallow geothermal systems, including energy piles and district-scale ground-coupled networks. Reported applications demonstrate energy consumption reductions exceeding 40% in certain urban contexts. Several research gaps and challenges were identified, particularly data interoperability issues, lack of standardization, computational complexity, and the need for specialized training. Overall, the review indicates that GeoBIM offers a promising pathway for optimizing resources, supporting informed decision-making, and advancing resilient and sustainable smart buildings and cities. Full article

Floods are among the most destructive natural disasters, posing major challenges to human safety, property, and urban resilience. Effective communication of flood risk is therefore crucial for disaster preparedness and the sustainable management of smart cities. This study explores how interface design elements of flood hazard maps, including interaction modes and legend color schemes, influence users’ risk perception, decision support, and usability. An online questionnaire survey (N = 776) and a controlled 2 × 2 experiment (N = 40) were conducted to assess user comprehension, cognitive load, and behavioral responses when interacting with different visualization formats. Results show that slider-based interaction significantly reduces task completion and map-reading times compared with drop-down menus, enhancing usability and information efficiency. Multicolor legends, although requiring higher cognitive effort, improve users’ risk perception, engagement, and memory of flood-related information. These findings suggest that integrating cognitive principles into interactive design can enhance the effectiveness of digital disaster communication tools. By combining human–computer interaction, visual cognition, and smart governance, this study provides evidence-based design strategies for developing intelligent and user-centered flood hazard mapping systems. The proposed framework contributes to the advancement of smart urban resilience and supports the broader goal of building safer and more sustainable cities. Full article

The increased uptake of Electric Vehicles (EVs) requires the installation of charging stations in parking lots, both to facilitate charging while running daily errands and to support EV owners with no access to home charging. Photovoltaic (PV) generation is ideal for powering up EVs, both for environmental reasons and for the benefit it creates for Charging Point Operators (CPOs). In this paper, we propose a centralised V1G Smart Charging (SC) algorithm for EV parking lots, considering real EV charging dynamics, which minimises both the EV charging costs for their owners and the CPO electricity provision costs or the related CO₂ emissions. We also introduce an innovative SC benefit-splitting algorithm that makes sure SC savings are fairly split between EV owners. Eight scenarios are described, considering costs or emissions minimisation, with and without a PV system. The centralised algorithm is benchmarked against a decentralised one, and tested in an exemplary workplace parking lot in Denmark, that includes includes 12 charging stations and one PV system, owned by the same entity. Reductions of up to 11% in EV charging costs, 67% in electricity provision costs for the CPO, and 8% in CO₂ emissions are achieved by making smart use of a 35 kWp rooftop PV system. Additionally, the SC benefit-splitting algorithm successfully ensures that EV owners save money when adopting SC. Full article

The applicability of Hybrid Energy Storage Systems (HESSs) has been shown in multiple application fields, such as Charging Stations (CSs), grid services, and microgrids. HESSs consist of an integration of two or more single Energy Storage Systems (ESSs) to combine the benefits of each ESS and improve the overall system performance. In this work, we propose a novel power management controller called the Hybrid Controller for the efficient HESS’s charging and discharging, considering the State of Charge (SoC) of the HESS and the dynamic supply and load. The Hybrid Controller optimises the use of the HESS, i.e., minimises the amount of energy drawn from and discharged to the grid, thus utilising and prioritising the provided Photovoltaic (PV) power. The performance of our proposal was assessed via simulation using various evaluation metrics, i.e., Autarky, charge/discharge cycle, and Self-Consumption (SC), where we defined 24 scenarios in different locations in Germany. Full article

The global deployment of fifth-generation (5G) mobile networks, especially in urban cities, is dedicated to accommodating the demand for high data rates and reliable wireless communications. While the latest 5G networks improve service quality, the support for a simultaneous serving of more user devices (UDs) with higher data rates than previous mobile network generations will require a massive installation of different 5G base station (BS) types dominantly in urban cities. Besides contributing to the smart city service improvements, this massive installation of heterogeneous 5G BSs will also contribute to the increase in 5G network energy consumption (EC) and carbon dioxide emissions. Since this increase in installed 5G BSs imposes environmental and economic challenges, this paper analyzes the impact of the continuously rising number of 5G UDs on the energy efficiency (EE) of the radio part of Croatian and Dutch 5G networks as example cases in the period of 2020s. Analyses consider the countries’ rural, suburban, urban, and dense urban UD density areas by utilizing the proposed simulation framework for the EE evaluation of 5G heterogeneous networks (HetNet) valued through standardized mobile networks EE metrics. The study examines four proposed BS installation and operation scenarios for reducing energy costs of 5G networks that differ in optimizing energy consumption via different BS installations, sleep modes, and transmission power scaling techniques. The obtained results indicate that dynamic adaptation of BS deployments and radio resource management during operation according to the increase in the number of UDs and corresponding DVs can enhance 5G HetNet EE. The findings provide valuable insights for mobile network operators looking to optimize 5G network EE in the upcoming decade. Full article

There is a clear European Strategy to transition by 2050 from a fossil fuel-based economy to a completely new system based on renewable energy resources, with electricity as the main energy carrier. Positive Energy Districts (PEDs) are urban areas that produce at least as much energy as their yearly consumption. To meet this objective, they must incorporate distributed generation based on renewable systems within their boundaries. This article considers the fluctuations in electricity prices and local renewable availability and develops a PED model with a centralised energy storage system focused on electricity self-sufficiency and self-consumption. We present a fuzzy logic-based energy management system which optimises the state of charge of the energy storage solution considering local electricity production and loads along with the contracted electric tariff. The methodology is tested in a PED comprising 360 households in Bilbao (a city in the north of Spain), setting various scenarios, including changes in the size of the electric storage, long-term climate change effects, and extreme changes in the price of energy carriers. The study revealed that the assessed PED could reach up to 75.6% self-sufficiency and 76.8% self-consumption, with climate change expected to improve these values. On economic aspects, the return on investment of the proposal ranges from 6 up to 12 years depending on the configuration choice. Also, the case that boosts the economic viability is tight to non-business as usual (BaU), whichever event spiked up the prices or climate change conditions shortens the economic variables. The average bill is around 12.89 EUR/month per house for scenario BaU; meanwhile, a catastrophic event increases the bill by as much as 76.7%. On the other hand, climate crisis events impact energy generation, strengthening this and, as a consequence, slightly reducing the bill by up to 11.47 EUR/month. Full article

Wireless communication infrastructure is a key enabling technology for smart cities. This paper investigates a novel technique to enhance the performance of 5G base stations by addressing the compensation of nonlinear distortions caused by radiofrequency power amplifiers. For this purpose, a sequential digital predistortion approach that uses twin nonlinear two-box structure along with reduced sampling rates in the feedback path is proposed to implement a linearization system. Such a system is shown to have a correction bandwidth that exceeds the bandwidth of the feedback path. This is achieved by synthesizing the predistortion function in two successive characterization iterations. Both characterizations use the same hardware, which has a reduced sampling rate in the feedback path. Hence, the proposed predistorter scheme does not require any additional hardware compared to standard schemes. Moreover, coarse delay alignment is performed while identifying the memory polynomial function in order to further reduce the computational complexity of the proposed system. Experimental results using an inverse Class-F power amplifier demonstrate the ability of the proposed predistorter to achieve a correction bandwidth of 100 MHz with a feedback sampling rate as low as 25 MSa/s. Full article