ABSTRACT. The Metaverse is emerging as a new layer connecting the digital and the real worlds into a unified environment, offering enhanced opportunities to a huge variety of application fields, with potentials for inclusive accessibility to all. AI plays a key role in enabling the metaverse, by providing humans’ interaction with the metaverse, and allowing a personalized experience in the metaverse. For a trustworthy metaverse, the AI should be trustworthy. AI reliability, safety and security challenges, due to faults affecting the hardware implementing AI and security strategies will be discussed, as well as possible solutions.

ABSTRACT. The presentation takes a forward-looking approach to policy and regulation and asks the question whether the current energy market design is fit for purpose in view of the 2050 horizon. It starts by looking at the changes and uncertainties that affect the energy market, notably technological development and the rapid emergency of batteries, the impact of electrification and decentralisation of generation, geopolitics and security risks as well as the urgent need to improve the competitiveness of European industry. It then raises issues that characterise the functioning of the energy market, including the efficiency of short-term market, and describes the policy responses to the problems that have emerged, e.g. long-term price signals through contract for difference and power-purchase agreements, long lead times of investments, role of market in ensuring energy security, the need to accelerate investments. Furthermore, it looks at areas where the “energy only market” has been complemented by policy and regulatory measures, such as support to generation, capacity remuneration mechanisms for adequacy, grid congestion and prioritisation of connections, role of citizens. The relevant EU legislation includes i.a. Electricity Market Design, Green Deal, Renewables Directive, Industrial Acceleration Act, Grids Package. The presentation concludes with some emerging recommendations on the question whether “more of the same” policy and regulation will lead to accelerated decarbonisation at lowest cost or whether a new way of cooperation at regional or EU levels would deliver better and faster.

ABSTRACT. With the accelerated integration of renewable energy sources, modern electrical power systems are experiencing a shift from synchronous generation and consumption to Inverter-Based Resources (IBRs). This transition poses significant stability and regulatory challenges. This workshop provides a comprehensive deep dive into the operation, control, and regulation of IBRs, with a specialized focus on Battery Energy Storage Systems (BESS).

Attendees will explore advanced BESS control frameworks, specifically comparing Grid-Following (GFL) and Grid-Forming (GFM) topologies. Furthermore, the workshop will address the practical industry application by detailing grid code compliance. We will specifically analyze the European ENTSO-E Requirements for Generators (RfG) and the Finnish grid code (SJV 2024). This session includes step-by-step simulation demonstrations of grid code compliance testing using industry-standard software: PSCAD (EMT domain) and PSS/E (RMS domain).

ABSTRACT. Contemporary analysis and synthesis of materials, devices, systems, and systems-of-systems rely primarily on the standard calculus tracing back to Leibniz and Newton. The associated infinitesimal calculus no longer fully fits the challenges posed by boundary initial value problems in harsh computational environments. Robinson’s non-standard calculus and Shashkov’s conservative finite-difference method were attempts to extend the scope of traditional methods by relaxing the limit process and weakening the geometric constraints, respectively. This tutorial offers an engaging presentation of techniques, grounded in more than four decades of dedicated research, to address a select set of pressing computational problems in the current and near future. The presentation delves into the following topics: i) Diagonalization of fundamental partial differential equations (PDEs) in mathematical physics, leading to algebraic eigenvalue systems in the spectral domain. This includes coupled PDEs that describe thermal-acoustic-electromagnetic multiphysics boundary problems in inhomogeneous and anisotropic media. ii) Novel algebraic and exponential regularization techniques are introduced in response to the challenges associated with the near- and far-field divergences in computations. iii) The theory of wavelets and frames is augmented by embracing quantum physics-inspired multiresolution schemes, including Wannier functions and associated gauge theories, to address multiscale features in many problems in industrial practice. iv) Digital filtering, signal processing, and stochastic processes, and their relationships with multiresolution analysis, are established. v) The presenter’s “Discrete Taylor transform and Inverse Transform, D-TTIT” (IEEE Press, 2024) has been discussed, elaborating novel procedures for calculating mixed derivatives on nonuniform Cartesian computational grids in multiple dimensions. vi) The presenter’s most recent generalization of Lagrange functions to multivariate functions, defined on fully unstructured grid points, promises to attract the attention of researchers and industry professionals alike and open new venues to computational engineering. A carefully compiled compendium of essential algorithms for optimization, learning, and quantum computing will be discussed. The careful integration of techniques from signal processing, mathematical physics, and computer science will be presented in a comprehensive, book-style manuscript for participants. The participants are invited to engage with the presenter not only during the tutorial but also prior to and after the event to ask questions, provide feedback, and foster collaboration.

ABSTRACT. Manufacturing industries can reduce their carbon footprint by aligning production schedules with the availability of renewable electricity (RE). We develop methods for a RE-centric flexible job shop scheduling problem that jointly optimize two conflicting objectives: maximizing on-site renewable usage and minimizing makespan. To solve this NP-hard problem, we propose and compare three approaches: (i) a local weighted greedy heuristic balancing renewable utilization and completion time at each decision, (ii) a global weighted lookahead heuristic that anticipates renewable peaks across the horizon, and (iii) a deep reinforcement learning approach that learns long-term policies. We benchmark these methods against a non-weighted greedy baseline. Renewable profiles are derived from real photovoltaic production at LUT University, and jobs are scheduled based on renewable energy forecasts over the scheduling horizon. The lookahead heuristic improves renewable utilization when forecasts are accurate, while simpler heuristics are more robust to forecast errors. Moreover, the heuristics outperformed the RL approach, due to sparse rewards and combinatorial structures.

ABSTRACT. Smart ports increasingly depend on IoT-enabled digitalization to turn heterogeneous operational processes into measurable, optimizable workflows. Accordingly, this paper first provides a concise review of IoT-enabler technologies for port digital transformation, emphasizing how sensing, connectivity, and data integration capabilities support real-time decisionmaking in port environments. Motivated by the growing emphasis on operational decarbonization reflected in the European Green Deal and the Sustainable Development Goals (SDGs), we then present a data-driven fleet scheduling framework for landside port operations at the Port of HaminaKotka (Mussalo terminal), Finland. The scheduling framework reallocates vehicle operating hours to minimize total CO2eq emissions while preserving the same aggregate workload. The study uses a two-year dataset from 77 port-operating vehicles, analyzed at monthly and annual aggregation levels, to derive vehicle-specific intensity metrics (CO2eq per operating hour) and identify reallocation opportunities. We formulate the scheduling task as a linear programming workload allocation problem that minimizes the weighted sum of assigned hours while keeping total required hours fixed, enforcing fleet-level caps on CO2 and energy use, and respecting per-vehicle operating limits. The optimization is solved using the HiGHS linear programming solver via SciPy’s linprog. Results show that shifting utilization away from high-intensity vehicles and toward more efficient units yields substantial system-level improvements with no reduction in total operating hours: the optimized schedule achieves an approximately 31% reduction in total CO2eq and a simultaneous 9.4% reduction in total energy consumption.

ABSTRACT. The space-air-ground integrated network (SAGIN) has emerged as a prominent network architecture to support ubiquitous coverage and high-rate global connectivity. In this paper, we investigate a SAGIN consisting of uncrewed aerial vehicles (UAVs) that can communicate with ground users or machines and a low-earth orbit satellite via a high-altitude platform acting as a relay. First, we formulate a dual prob- lem with task scheduling and task building in the underlying SAGIN for teleoperated forestry. We then provide a solution to this multilevel combinatorial problem, which minimizes task times, workload imbalances, and the global makespan of task scheduling. Finally, we analyze and compare the performance of three different combinatorial optimizers used in satellite task scheduling problems through simulation results; the deterministic constraint programming-based method outperforms the heuristic and learning-based method.

ABSTRACT. We investigate the entanglement-assisted (EA) capacity-power function of a quantum simultaneous information and power transfer (QSIPT) system operating over a thermal-loss bosonic channel. The transmitter and receiver share pre-distributed two-mode squeezed vacuum (TMSV) entanglement, while the receiver implements information decoding and energy harvesting through a generalized beam-splitter (BS) architecture with adjustable transmissivity. Exploiting the single-letter character of the EA classical capacity for Gaussian channels and the cascade structure of the channel-BS pair, we derive a closed-form expression for the EA capacity-power function in terms of the symplectic eigenvalues of the joint output Gaussian state. We establish that the EA capacity-power function strictly dominates its unassisted counterpart over the entire feasibility interval, with both functions vanishing at the common feasibility horizon at the same asymptotic rate, while the absolute gain remains strictly positive throughout, demonstrating that pre-shared entanglement provides a strict rate advantage at every feasible energy-harvesting operating point.

ABSTRACT. With the accelerated integration of renewable energy sources, modern electrical power systems are experiencing a shift from synchronous generation and consumption to Inverter-Based Resources (IBRs). This transition poses significant stability and regulatory challenges. This workshop provides a comprehensive deep dive into the operation, control, and regulation of IBRs, with a specialized focus on Battery Energy Storage Systems (BESS).

Attendees will explore advanced BESS control frameworks, specifically comparing Grid-Following (GFL) and Grid-Forming (GFM) topologies. Furthermore, the workshop will address the practical industry application by detailing grid code compliance. We will specifically analyze the European ENTSO-E Requirements for Generators (RfG) and the Finnish grid code (SJV 2024). This session includes step-by-step simulation demonstrations of grid code compliance testing using industry-standard software: PSCAD (EMT domain) and PSS/E (RMS domain).

ABSTRACT. Contemporary analysis and synthesis of materials, devices, systems, and systems-of-systems rely primarily on the standard calculus tracing back to Leibniz and Newton. The associated infinitesimal calculus no longer fully fits the challenges posed by boundary initial value problems in harsh computational environments. Robinson’s non-standard calculus and Shashkov’s conservative finite-difference method were attempts to extend the scope of traditional methods by relaxing the limit process and weakening the geometric constraints, respectively. This tutorial offers an engaging presentation of techniques, grounded in more than four decades of dedicated research, to address a select set of pressing computational problems in the current and near future. The presentation delves into the following topics: i) Diagonalization of fundamental partial differential equations (PDEs) in mathematical physics, leading to algebraic eigenvalue systems in the spectral domain. This includes coupled PDEs that describe thermal-acoustic-electromagnetic multiphysics boundary problems in inhomogeneous and anisotropic media. ii) Novel algebraic and exponential regularization techniques are introduced in response to the challenges associated with the near- and far-field divergences in computations. iii) The theory of wavelets and frames is augmented by embracing quantum physics-inspired multiresolution schemes, including Wannier functions and associated gauge theories, to address multiscale features in many problems in industrial practice. iv) Digital filtering, signal processing, and stochastic processes, and their relationships with multiresolution analysis, are established. v) The presenter’s “Discrete Taylor transform and Inverse Transform, D-TTIT” (IEEE Press, 2024) has been discussed, elaborating novel procedures for calculating mixed derivatives on nonuniform Cartesian computational grids in multiple dimensions. vi) The presenter’s most recent generalization of Lagrange functions to multivariate functions, defined on fully unstructured grid points, promises to attract the attention of researchers and industry professionals alike and open new venues to computational engineering. A carefully compiled compendium of essential algorithms for optimization, learning, and quantum computing will be discussed. The careful integration of techniques from signal processing, mathematical physics, and computer science will be presented in a comprehensive, book-style manuscript for participants. The participants are invited to engage with the presenter not only during the tutorial but also prior to and after the event to ask questions, provide feedback, and foster collaboration.

ABSTRACT. Distributed model predictive control (DMPC) enables autonomous agents to coordinate without centralized computation, but its scalability depends on timely and consistent exchange of coordination data.

This paper investigates communication bottlenecks in a Stewart--Wright--Rawlings cooperative DMPC implementation using a 20-agent crossing benchmark designed to stress snapshot freshness and consistency.

Across 40 independent runs, 16 satisfy the primary task criterion, while 24 fail with meter-scale terminal errors.

All runs remain collision-safe, but the coordination-data diagnostic fails in every case because full fresh snapshots are rare and fallback activity is high.

The results reveal a degraded operating regime in which DMPC can sometimes complete the task despite poor nominal coordination availability, followed by an abrupt performance cliff when reused or incomplete snapshots no longer support convergence. The study provides a communication-oriented diagnostic approach for scalable DMPC and motivates Quality-of-Service-aware, neighbor-scoped, and better-instrumented coordination mechanisms.

ABSTRACT. Modern AI systems are increasingly deployed under non-stationary computational, demographic, and operational conditions in which static resource allocation strategies degrade both predictive performance and human-centric properties such as fairness and explainability. This paper presents AURORA-AI, an Adaptive Utility-driven Resource Orchestration framework for Resilient AI that unifies Hamilton-Jacobi-Bellman feedback control, Lyapunov-based stability monitoring, and a fairness-aware composite utility into a single closed-loop policy. The framework continuously redistributes computational budget across a population of heterogeneous AI models so that the global utility, defined jointly over predictive performance, demographic parity, cost, latency, robustness, and interpretability, remains maximised under disruption. The framework is evaluated in a stress-rich discrete-time simulation that concurrently injects demographic bias shocks, gradual concept drift, and abrupt black-swan disruptions, and is compared against five established controllers including Static, Round Robin, Greedy, LinUCB, and a deep reinforcement-learning agent based on Proximal Policy Optimisation. AURORA-AI achieves immediate recovery from the black-swan event compared to eighty-eight time steps for the Static baseline and twenty-two for Proximal Policy Optimisation, lifts the alpha-quantile and the super-quantile by twenty-nine and twenty-five percent respectively, simultaneously reduces the mean and maximum demographic parity gap, and increases the fraction of Lyapunov-stable operating steps. These results indicate that fairness-aware adaptive orchestration grounded in stability theory is a practical and theoretically motivated path toward resilient human-centric AI deployment.

ABSTRACT. This article introduces an energy-centric framework to assess fundamental efficiency limits in networked cyber–physical systems (CPSs). We define Work Capability as the flow of information with the potential to enable useful physical work and introduce the Work Factor as a metric relating this capability to unavoidable energetic dissipation across sensing, combining, and action stages. Unlike conventional metrics focused on signal power or device-level consumption, the proposed formulation links energy dissipation to the function that a CPS is designed to perform. To illustrate the framework, we analyze an autonomous cargo-transport system in which vehicles, platforms, and a control center form a networked decision-making architecture. The results show that Work Factor increases with state-space granularity and sensing uncertainty, revealing a trade-off between precision, robustness, and thermodynamic efficiency. They also show that frequent interventions increase energetic costs, whereas idle-heavy regimes reduce responsiveness. The proposed framework provides a principled basis for designing CPS architectures from functional and energetic requirements.

ABSTRACT. Battery energy storage systems (BESS) paired with rooftop photovoltaics (PVs) can deliver measurable cost sav- ings under time-of-use (TOU) electricity tariffs; however, the relative performance of model-based and model-free dispatch strategies remains insufficiently benchmarked on full-year, real- world commercial datasets. This paper presents a full-year (2023) comparative evaluation of three BESS dispatch approaches using data from a commercial PV installation operating under a TOU tariff. The examined strategies include: (i) a mixed-integer linear programming (MILP) formulation with perfect foresight, providing an oracle performance benchmark under the assumed model; (ii) a model predictive control (MPC) scheme based on a day-ahead persistence forecast, representing a low-complexity deployable approach; and (iii) a soft actor-critic (SAC) deep reinforcement learning agent trained under causal information constraints. The MILP benchmark achieves an annual cost reduc- tion of 24.6% relative to a no-storage baseline. The persistence- based MPC approach recovers 99.2% of this benchmark using only prior-day data. In contrast, the evaluated SAC agent yields an annual cost higher than the no-storage baseline. This outcome is analyzed in the context of known challenges in reinforcement learning for energy systems, including limited observability and reward design. Overall, the results indicate that, for the studied dataset and tariff structure, persistence-based MPC captures nearly all achievable economic benefits under practical deploy- ment constraints, whereas the considered RL configuration does not yield competitive performance under the same information limitation

ABSTRACT. With the accelerated integration of renewable energy sources, modern electrical power systems are experiencing a shift from synchronous generation and consumption to Inverter-Based Resources (IBRs). This transition poses significant stability and regulatory challenges. This workshop provides a comprehensive deep dive into the operation, control, and regulation of IBRs, with a specialized focus on Battery Energy Storage Systems (BESS).

Attendees will explore advanced BESS control frameworks, specifically comparing Grid-Following (GFL) and Grid-Forming (GFM) topologies. Furthermore, the workshop will address the practical industry application by detailing grid code compliance. We will specifically analyze the European ENTSO-E Requirements for Generators (RfG) and the Finnish grid code (SJV 2024). This session includes step-by-step simulation demonstrations of grid code compliance testing using industry-standard software: PSCAD (EMT domain) and PSS/E (RMS domain).

ABSTRACT. Contemporary analysis and synthesis of materials, devices, systems, and systems-of-systems rely primarily on the standard calculus tracing back to Leibniz and Newton. The associated infinitesimal calculus no longer fully fits the challenges posed by boundary initial value problems in harsh computational environments. Robinson’s non-standard calculus and Shashkov’s conservative finite-difference method were attempts to extend the scope of traditional methods by relaxing the limit process and weakening the geometric constraints, respectively. This tutorial offers an engaging presentation of techniques, grounded in more than four decades of dedicated research, to address a select set of pressing computational problems in the current and near future. The presentation delves into the following topics: i) Diagonalization of fundamental partial differential equations (PDEs) in mathematical physics, leading to algebraic eigenvalue systems in the spectral domain. This includes coupled PDEs that describe thermal-acoustic-electromagnetic multiphysics boundary problems in inhomogeneous and anisotropic media. ii) Novel algebraic and exponential regularization techniques are introduced in response to the challenges associated with the near- and far-field divergences in computations. iii) The theory of wavelets and frames is augmented by embracing quantum physics-inspired multiresolution schemes, including Wannier functions and associated gauge theories, to address multiscale features in many problems in industrial practice. iv) Digital filtering, signal processing, and stochastic processes, and their relationships with multiresolution analysis, are established. v) The presenter’s “Discrete Taylor transform and Inverse Transform, D-TTIT” (IEEE Press, 2024) has been discussed, elaborating novel procedures for calculating mixed derivatives on nonuniform Cartesian computational grids in multiple dimensions. vi) The presenter’s most recent generalization of Lagrange functions to multivariate functions, defined on fully unstructured grid points, promises to attract the attention of researchers and industry professionals alike and open new venues to computational engineering. A carefully compiled compendium of essential algorithms for optimization, learning, and quantum computing will be discussed. The careful integration of techniques from signal processing, mathematical physics, and computer science will be presented in a comprehensive, book-style manuscript for participants. The participants are invited to engage with the presenter not only during the tutorial but also prior to and after the event to ask questions, provide feedback, and foster collaboration.

ABSTRACT. Good ideas rarely come from a single brilliant mind in a room. They come from teams that know how to think together. This hands-on session introduces practical design thinking tools for facilitating brainstorming and problem-solving with a team, whether in research, industry, or student projects. Through guided exercises, participants will practice framing problems, generating ideas, and converging on solutions, with examples touching on sustainability among other real-world challenges. The session also highlights how IEEE Student memberships offer a space to apply these skills and turn them into career development opportunities. Participants leave with a toolkit they can use immediately in their own projects or teams.

ABSTRACT. Additive manufacturing (AM) systems exhibit highly dynamic energy consumption patterns, requiring high-resolution monitoring to capture transient behavior. This paper proposes an energy-aware digitalized monitoring infrastructure based on a multi-layer Industrial Internet of Things architecture integrating heterogeneous data acquisition pathways. The framework combines coarse-resolution grid measurements (1 minute) with high-resolution sensing (100 ms) through edge and cloud components, supported by a robust data acquisition pipeline. Energy consumption is estimated using multiple numerical integration methods to assess the impact of sampling resolution. Experimental results show that low-resolution measurements introduce significant estimation bias, with deviations of up to 15%. These findings highlight the importance of multi-resolution monitoring and motivate hybrid sensing architectures for accurate and scalable energy analysis in AM systems.

ABSTRACT. This paper presents a comparative performance analysis of Proportional-Integral (PI) and Proportional-Integral-Derivative (PID) controllers for electric vehicle (EV) speed control evaluated against two standardized reference drive cycles: the Federal Test Procedure (FTP-75) and the Modified Indian Driving Cycle (MIDC). The EV dynamics model incorporates rolling resistance, aerodynamic drag, and drivetrain mechanics based on Newton's second law. Controller performance is quantified using Integral Square Error (ISE), Integral Absolute Error (IAE), and Integral Time Absolute Error (ITAE) metrics, along with speed tracking fidelity and battery State of Charge (SOC) analysis. Results demonstrate that under the MIDC drive cycle, the PID controller achieves an ISE of 31.81 compared to 49.90 for the PI controller, representing a 36.2% improvement. Under the FTP-75 cycle, the performance difference narrows significantly, with the PI controller yielding an ISE of 1842.28 versus 1842.58 for PID, indicating marginal PI superiority. These findings suggest that controller selection for EV applications should be drive-cycle-specific: PID is preferable for low-speed urban Indian driving conditions, while PI offers comparable and computationally simpler control under highway-mixed profiles.

ABSTRACT. The increasing integration of Electric Vehicles (EVs) has imposed a growing harmonic challenge on the power grid. For the AC/DC Power Factor Correction (PFC) of single-phase On-Board Chargers (OBCs), Model Predictive Current Control (MPCC) improves current quality by predicting and tracking the inductor current. However, finite control set MPCC selects switching states, resulting in discrete control actions and a limited optimisation space. Moreover, the MPCC's cost function based on instantaneous current tracking error has limited capability to compensate for low-order harmonic disturbances induced by dead time, control delay, and model parameter mismatch. This paper proposes a duty cycle predictive MPCC incorporating a real-time harmonic estimation reference. The proposed method dynamically estimates the low-order harmonic components of the input current and corrects the MPCC reference current, enabling continuous duty cycle control and targeted suppression of dominant low-order harmonics. Simulation results on a single-phase OBC demonstrate that the proposed duty cycle predictive MPCC reduces the steady current $THD_i$ from 11.47\% to 6.10\% compared with the switching state predictive MPCC. With harmonic reference, the $THD_i$ is reduced to 2.85\%.

ABSTRACT. Traditional cloud-centric power system fault detection is hindered by transmission latencies and high bandwidth costs. To address this, this study introduces an integrated, low-complexity edge computing framework for real-time fault detection and localization. Streaming high- precision data from single-point Phasor Measurement Units (PMUs), the proposed pipeline utilizes Multi-Resolution Analysis (MRA) via the Maximal Overlap Discrete Wavelet Transform (MODWT) to extract critical time-frequency transient features. These features are processed by a 1D Convolutional Neural Network (1D CNN) deployed directly within the Edge AI layer and validated experimentally in MATLAB/Simulink. This framework provides a localized, self-healing capability that minimizes cascading failures and reinforces modern smart grid stability.

ABSTRACT. The integration of distributed energy resources(DERs), storage, and controllable loads into low-voltage microgrids has tightly coupled the operation of the power network with that of its supporting Information and Communication Technology (ICT) infrastructure. In this paper, we propose a co-optimisation framework for hybrid microgrid-enabled ICT environments that jointly minimises electrical operating cost and ICT cost under Ultra-Reliable Low-Latency Communication (URLLC) constraints. We model the cyber-physical system as a pair of coupled directed graphs and formulate a Mixed-Integer Linear Program (MILP) that places microgrid monitoring and control tasks across an Edge-Fog-Cloud continuum served by an SDN-managed 5G fabric. To ensure scalability, we derive a Lagrangian dual decomposition that admits a simple per node tier-selection rule and complement it with a low-complexity greedy heuristic suitable for real-time deployment. Numerical results on a representative microgrid show that the proposed framework reduces communication energy by up to 40% and average end-to-end latency by 43% relative to a centralised cloud only baseline, while keeping URLLC latency margins positive for all critical services.

ABSTRACT. Electric boilers are increasingly relevant for flexible loads in Finland because their electricity consumption can be shifted according to price signals and heating-system conditions. This paper studies how false electricity price signals can affect price-responsive electric boiler load forecasting and hourly cost exposure. Real electric boiler consumption data from Fingrid Open Data and Finnish day-ahead price data from Nord Pool are used to evaluate Gradient Boosting, Random Forest, XGBoost, and Long Short-Term Memory (LSTM) models as forecasting baselines. The baseline validation results show that XGBoost gives the lowest boiler-load forecasting error, while Random Forest gives the lowest price-forecasting error for the selected validation period. False price data injection scenarios are then formulated in which an attacker modifies the price input used by forecasting or scheduling logic without directly controlling the boiler. The study also proposes an anomaly-aware validation layer based on price change and forecast-residual checks. The main contribution is to treat the electricity price signal itself as a cyber-physical attack surface for price-responsive electric boiler operation.

ABSTRACT. This paper presents a distributed battery energy management framework developed for Renewable Energy Communities (RECs) in the context of a European research and deployment project. The proposed cyber-physical optimization framework addresses multiple operational objectives, including CO2 minimization, cost minimization and market participation. The framework is implemented in Python using Pyomo package and solves an optimization problem with a two-stage architecture. The study combines theoretical simulations with real-world deployment evidence, allowing a direct comparison between idealized optimization and operational performance under forecast uncertainty and practical constraints. Unlike a purely conceptual REC study, this work is grounded in an industrial REC with a main prosumer and residential consumer members, enabling validation across both individual and community battery operation. Hence, its novelty lies in the combination of distributed control, multiple use cases, and deployment on actual REC assets.