arXiv subjects

Hafiz Majid Hussain

Publications and source records attributed to Hafiz Majid Hussain.

9 recordsLinked to original sources

arXiv2026

Emission-Aware Optimization of Container Handling Vehicles: A HaminaKotka Port Case Study

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 decision-making 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 CO2eq 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.

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arXiv2026

Comparative Evaluation of MILP, MPC, and Reinforcement Learning for Commercial Battery Dispatch Under Time-of-Use Tariffs

Battery energy storage systems (BESS) paired with rooftop photovoltaics (PVs) can deliver measurable cost savings 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 reduction 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 deployment constraints, whereas the considered RL configuration does not yield competitive performance under the same information limitations.

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arXiv2026

Cognitive Energy Management: Concept,Framework, and Demonstration in Smart Port Energy Systems

Modern energy management systems, even within advanced energy internet (EI) infrastructures, remain fundamentally reactive, optimization-bound, and incapable of reasoning about context, intent, or uncertainty. While the EI paradigm has established a powerful cyber-physical architecture for interconnecting distributed energy resources via software-defined packetized networks, the question of how such systems should think, adapt, and govern energy decisions intelligently remains an open challenge. This paper introduces cognitive energy management (CEM); a new conceptual framework that addresses this gap by redefining how energy systems perceive, reason, learn, and act within complex operational environments. Grounded in the EI cyber-physical foundation, CEM extends beyond conventional optimization by embedding goal-directed reasoning and continuous adaptation into the energy management loop, positioning itself as the cognitive governance layer of EI-based infrastructures. We formally define CEM, distinguish it from rule-based and optimization-based paradigms through structured comparison, and articulate its core architectural layers. To demonstrate the framework's practical value, we develop a toy problem grounded in smart port energy management; one of the most operationally demanding EI node environments in modern infrastructure. Specifically, we model a predictive vessel turnaround scenario in which a CEM-enabled system plans energy procurement, storage pre-charging, and load scheduling across a six-hour operational horizon. The demonstration illustrates how CEM moves the EI beyond feasibility-seeking toward intelligent, anticipatory energy governance.

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arXiv2025

Assessing the Technical and Environmental Impacts of Energy Management Systems in Smart Ports

A vital strategy for ports to mitigate the environmental impact of the maritime industry, while complying with frameworks such as the European Green Deal and the Sustainable Development Goals (SDGs), entails the systematic implementation of comprehensive energy management solutions. This paper provides a baseline evaluation of the energy management systems (EMSs) implementation and their impact on energy consumption, carbon emissions, and operational costs in smart ports. Initially, we provide a systematic review of the literature focusing on case studies from prominent ports, including Hamburg, Genoa, Jurong, and Shanghai Yangshan Phase IV. The analysis emphasises key aspects such as energy efficiency, reductions in emissions, and the minimization of operational costs. Subsequently, we formulate an optimisation model to simulate load dispatch, carbon emission reduction, and transport scheduling. Results indicate that EMS deployment reduces annual energy consumption and carbon emissions significantly by approximately 7%-8% and 11%-12% respectively, while achieving substantial cost savings of 30%. The study also identifies critical challenges, including system integration, data quality issues, cybersecurity risks, and the need for standardization. These findings provide valuable insights for port authorities and policymakers, supporting the transition toward more sustainable and efficient port operations.

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arXiv2025

Pricing Mechanisms versus Non-Pricing Mechanisms for Demand Side Management in Microgrids

In this paper, we compare pricing and non-pricing mechanisms for implementing demand-side management (DSM) mechanisms in a neighborhood in Helsinki, Finland. We compare load steering based on peak load-reduction using the profile steering method, and load steering based on market price signals, in terms of peak loads, losses, and device profiles. We found that there are significant differences between the two methods; the peak-load reduction control strategies contribute to reducing peak power and improving power flow stability, while strategies primarily based on prices result in higher peaks and increased grid losses. Our results highlight the need to potentially move away from market-price-based DSM to DSM incentivization and control strategies that are based on peak load reductions and other system requirements.

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arXiv2025

Crossover-BPSO Driven Multi-Agent Technology for Managing Local Energy Systems

This article presents a new hybrid algorithm, crossover binary particle swarm optimization (crBPSO), for allocating resources in local energy systems via multi-agent (MA) technology. Initially, a hierarchical MA-based architecture in a grid-connected local energy setup is presented. In this architecture, task specific agents operate in a master-slave manner. Where, the master runs a well-formulated optimization routine aiming at minimizing costs of energy procurement, battery degradation, and load scheduling delay. The slaves update the master on their current status and receive optimal action plans accordingly. Simulation results demonstrate that the proposed algorithm outperforms selected existing ones by 21\% in terms average energy system costs while satisfying customers' energy demand and maintaining the required quality of service.

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arXiv2021

Home Energy Management Systems: Operation and Resilience of Heuristics against Cyberattacks

Internet of Things (IoT) and advanced communication technologies have demonstrated great potential to manage residential energy resources by enabling demand-side management (DSM). Home energy management systems (HEMSs) can automatically control electricity production and usage inside homes using DSM techniques. These HEMSs will wirelessly collect information from hardware installed in the power system and in homes with the objective to intelligently and efficiently optimize electricity usage and minimize costs. However, HEMSs can be vulnerable to cyberattacks that target the electricity pricing model. The cyberattacker manipulates the pricing information collected by a customer's HEMS to misguide its algorithms toward non-optimal solutions. The customer's electricity bill increases, and additional peaks are created without being detected by the system operator. This article introduces demand-response (DR)-based DSM in HEMSs and discusses DR optimization using heuristic algorithms. Moreover, it discusses the possibilities and impacts of cyberattacks, their effectiveness, and the degree of resilience of heuristic algorithms against cyberattacks. This article also opens research questions and shows prospective directions.

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arXiv2021

Packetized Energy Management Controller for Residential Consumers

In this paper, we investigate the management of energy storage control and load scheduling in scenarios considering a grid-connected photovoltaic (PV) system using packetized energy management. The aim is to reduce an average aggregated system cost through the proposed \textit{packetized energy management controller} considering household energy consumption, procurement price, load scheduling delays, PV self-sufficiency via generated renewable energy and battery degradation. The proposed approach solves the joint optimization problem using established heuristics, namely genetic algorithm (GA), binary particle swarm optimization (BPSO), and differential evolution (DE). Additionally, the performances of heuristic algorithms are also compared in terms of the effectiveness of load scheduling with delay constraints, packetized energy transactions, and battery degradation cost. Case studies have been provided to demonstrate and extensively evaluate the algorithms. The numerical results show that the proposed packetized energy management controller can considerably reduce the aggregated average system cost up to 4.7\%, 5.14\%, and 1.35\% by GA, BPSO, and DE, respectively while meeting the packetized energy demand and scheduling delays requirements.

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arXiv2020

A Heuristics-based Home Energy Management System for Demand Response

The so-called Internet of Things (IoT) and advanced communication technologies have already demonstrated a great potential to manage residential energy resources via demand-side management. This work presents a home energy management system in that focused on the energy reallocation problem where consumers shall shift their energy consumption patterns away from peak periods and/or high electricity prices. Our solution differentiates residential loads into two categories: (i) fixed power appliances and (ii) flexible ones. Therefrom, we formulate our problem as a constraint optimization problem, which is non-linear and cannot be mathematically solved in closed-form. We then employ and compare two well-known heuristics, the genetic algorithm (GA) and the harmony search algorithm (HSA), to minimize electricity expense and peak to average ratio. These two approaches are compared to the case where no reallocation happens. Our numerical results show that both methods; GAand HSA can effectively reduce the electricity cost by 0.9%, 3.98%, and PAR by 15%, 5.8%, respectively

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