MAGPIE Smart Energy Systems Simulator demo

Introduction

Smart Energy Systems in Ports refer to integrated solutions that simulate the grid operation,[NV2.1] recognize the areas prone to congestion[NV3.1], and support the maritime sector’s transition to renewable energy. As ports electrify operations, managing energy flows becomes increasingly complex. Smart energy systems leverage real-time data, predictive modelling, and decision support tools to identify flexibility needs and anticipate congestion risks. These tools enable ports to plan infrastructure investments more effectively, improve energy reliability, and align with long-term sustainability goals. However, successful implementation requires overcoming challenges such as stringent data sharing regulations, limited access to network data, and coordination across diverse stakeholders. This factsheet outlines the value proposition, applicability, technical solution, and implementation roadmap for smart energy systems in ports.

Value proposition

A decision support tool enables better planning by identifying flexibility needs and congestion risks
It facilitates scenario analysis and informed infrastructure planning, helping ports avoid the costs associated with grid congestion, such as renewable energy curtailment, equipment overloading, operational delays, or penalties for exceeding grid capacity limits
It also helps reduce unnecessary investments and improve overall operational efficiency.

Problem: Ports face increasing electricity demand and congestion risks due to electrification and energy transition plans. Data sharing regulations[NV4.1][FK4.2], lack of stakeholder coordination, and limited access to network data[NV5.1][FK5.2] hinder effective planning.

Port applicability

• Large Container Ports/Hub Ports: Highly applicable due to complex energy needs and infrastructure planning requirements. These ports benefit from congestion avoidance and strategic investment planning. • Ferry Terminals: Moderately applicable; while not explicitly mentioned, terminals with increasing electrification may benefit from demand forecasting and grid simulation. • Industrial Ports: Strong applicability, especially where electricity use is high and coordination with network operators is essential. • Smaller/Regional Ports: Applicable if they face grid limitations or plan for electrification. The tool’s scalability makes it adaptable to smaller contexts. • Other: Applicable to innovation corridors and ports undergoing energy transition. Relevant where transparency on grid development and future energy plans is needed.

Detailed description of the solution

Software Tool
Accepts standardized datasets [NV7.1](demand, price, weather) and supports scenario modelling. The software takes as input .csv and/or .pkl files containing demand, price, and weather data, along with the number of scenarios to be generated and the month for which these scenarios are created, as well as the grid configuration. It is developed in a Python environment and integrates Pandapower modeling, power flow analysis, K-Nearest Neighbors Regression (KNN), and Monte Carlo simulations to evaluate the grid’s operation and performance under various conditions, both in its current state and in future expansion scenarios.
Data Integration
Requires access to electricity network data, regional measurements, and governance structures to make realistic simulations.[NV8.1] However, the model can also be executed using any other open-source historical or synthetic dataset.
Scalability
The model is designed to be highly flexible across different time horizons and grid configurations. Its structure follows a functional design in Python, where each component, data input, model execution, and output generation, is modular and independent. The model operates based on user-provided inputs in standardized formats (e.g., demand, price, and weather data), allowing it to run seamlessly with any new dataset as long as it follows the required input structure. This modular design ensures that the framework is not hard-coded or limited to a specific case study, but can be easily adapted to other networks, time periods, or operating conditions, making it scalable and reusable.
Governance
Depends on who owns the network data (port or external entity).
Use Case
The Port of Sines has several planned future expansions; however, in this study, we only considered those that are close to implementation, with clearly defined locations and technical specifications.

Impact

Impact level per aspect
Impact	Level	Remark
Energy Efficiency	Medium impact	Provides analytical guidance that supports more economical and efficient energy use and infrastructure planning.
GHG emissions	Medium impact	No direct CO₂ reductions, but supports transition from grey to green electricity.
Safety	Medium impact	Indirectly improves safety by reducing grid stress and investment risks.
Circularity	Medium impact	The following points can be considered as some indirect relevant aspects: o Supporting informed decision-making that leads to more efficient and economical use of existing grid infrastructure, reducing the need for unnecessary reinforcements or overinvestments. o Promoting adaptive and long-term planning, which aligns with the principles of circularity by making better use of what already exists before creating new capacity.
Port City	Medium impact	Identify probable congestion problems in different operating conditions, improves planning transparency, and supports sustainable development.
Nature and Community Impact	Medium impact	Indirect benefits through better infrastructure
Avoided investment costs and improved decision-making capacity	Medium impact

Port characteristics

• Need: Access to electricity network data, regional measurements, and clarity on energy transition plans. • Affects: Infrastructure planning, stakeholder coordination, and investment decisions.

Barriers and enablers

Enablers

Non-technologicalEnabler
Governance structures, internal coordination, and standardized data formats.
TechnologyEnabler
Flexible tool design, scalability, and compatibility with existing datasets.

Barriers

Non-technologicalBarrier
Data sharing regulations, missing stakeholders, trust in externally developed tools. Data ownership: In many cases, port operators do not own the operational data; instead, it is managed by the Distribution System Operator (DSO). This ownership structure often complicates data access and sharing processes. To evaluate the grid’s performance under future load demand growth scenarios, access to accurate historical load demand data is essential. However, obtaining real and detailed consumption data from certain customers is often restricted due to privacy and confidentiality constraints.
TechnologyBarrier
TRL/time to market, access to customer time series data. Data availability. Data sharing platforms: There is a need for secure and reliable platforms capable of supporting large volumes of data while ensuring data integrity, confidentiality, and accessibility for authorized stakeholders.

How to implement?

Step 1
Data Collection and Preparation – Gathering and structuring demand, price, and weather data for model input.
Step 2
Grid Structure Modeling – Developing a flexible grid representation adaptable to different configurations and scales.
Step 3
Future Extensions and Developments Integration – Incorporating planned grid expansions and technical updates.
Step 4
Scenario Generation Module Development – Creating a scalable tool for generating time- and condition-based scenarios.
Step 5
Grid Assessment Module Development – Building an analytical engine to evaluate grid performance under diverse operating conditions.
Step 6
KPI Definition and Calculation – Establishing and computing key performance indicators to assess scalability, efficiency, and resilience.

Implementation Interdependencies

Requires cooperation between ports, network operators, and data providers. The port must have access to measurement data of target region, access to electricity network data, data governance possibilities, clarity on future energy transition plans and transparency on grid development.

Required involved stakeholders

Network operator/owner
Central actor responsible for managing electricity infrastructure and data.
Port Authority
Service provider and affected party in case of congestion.
Terminal Operators
May benefit from optimized energy planning.
Shipping Lines
Indirectly affected by port energy reliability.
Technology Providers
Develop and maintain the decision support tool.
Engineering & Construction Firms
May use tool outputs for infrastructure planning.
National & International Regulators
Oversee data sharing and energy regulations.
Financial Institutions/Investors
Benefit from reduced investment risks.
Research & Development Institutions
Support tool development and validation.
Local Government & Community Groups
Indirectly benefit from improved planning.

Knowledge base and references

Canvas – demo 2 presentation
Smart energy system simulator infographic: www.magpie-ports.eu/wp-content/uploads/2026/06/demo-2-smart-energy-systems-simulator-1.png (opens in new tab)