An Analysis to Identify the Key Factors in Power System Planning: The Case of Mexico
Abstract
:1. Introduction
- The analysis integrates elements to obtain energy shortage, congestion in transmission network, regional reserve margin performance, power plant installation regions that could significantly impact emission reduction, and technologies with higher/lower plant factors.
- Disaggregated data are included for different parameters such as generation cost, availability factors, or regional transmission capacity.
- The case study demonstrates the importance of disaggregating data for the identification of vulnerabilities in decarbonization scenarios of a Power Electrical System.
- The general ED is extended to consider the characteristics of the SEN using four sets of variables and parameters (technologies, generation regions, consumption regions, and time steps).
- The model programming runs under the open-source Python Optimization Modeling (PYOMO) platform, which is freely available in the repository https://github.com/IhanKaydarin/Multi-regional-time-step-and-technology-economic-dispatch, accessed on 30 September 2024.
2. Methodology
- Characterization of electrical power system;
- Mathematical modeling;
- Data collection;
- Model programming;
- Identification of vulnerabilities.
2.1. Characterization of Electrical Power System
2.2. Mathematical Modeling
2.2.1. Assumptions
2.2.2. Objective Function
2.2.3. Restrictions
2.3. Data Collection
2.4. Model Programming
2.5. Identification of Vulnerabilities
- Hourly energy shortage: If any restrictions are unmet, they can be determined. There are diverse reasons why this situation could occur, such as the low availability of intermittent technologies, congestion or limited transmission capacity, or the region’s lack of self-supply capacity. However, it is possible to overcome this inconvenience by proposing an additional technology with the highest generation cost so that when it is dispatched, the quantity, hours, and region of missing energy can be known.
- Hours of congestion on transmission lines: This aspect is obtained by counting the hours in which the energy transmitted from one region to another is equal to or greater than 90% of the link capacity. This calculation is shown in Equation (6).The links with the highest number of hours of congestion represent the regions with the most significant external power dependence, high potential to increase the national transmission network, and areas of opportunity to reduce generation costs.
- Regional reserve margin performance: This factor identifies the regions vulnerable to changes in the generation availability of the power units installed in the region. In addition, those regions that meet the indicative values established in the reliability policy are identified. The regional reserve margin is determined through Equation (7):
- Regions that can install power plants that could significantly reduce emissions must be identified: The plants with the highest emission factor and the highest generation must be identified. However, it is essential to remember that proposing to install a new plant with a low emission factor requires a more exhaustive analysis than those described in this research.
- Technologies with a higher/lower capacity factor: This parameter provides valuable information on the units essential for energy supply and the underutilized plants.
3. Case Study
3.1. Characterization of SEN
3.2. Mathematical Modeling
3.2.1. Assumptions for SEN 2025
- International interconnections were not considered.
- Availability factors were averaged for thermoelectric, combined cycle, coal-fired, turbogas, internal combustion, fluidized bed, geothermal, bioenergy, cogeneration, and nuclear technologies.
- The total installed capacity for each technology in each region was used.
- The generation cost considers the levelized fuel cost, the regional increase in fuel costs, and the operation and maintenance cost.
- Availability factors for thermal technologies were annual averages.
- Seasons of unavailability due to preventive maintenance of the plants were not considered.
3.2.2. Objective Function of SEN 2025
3.2.3. Restrictions of SEN 2025
- In agreement with Equation (2), the first restriction outlined in Equation (9) represents the balance between energy production and demand by consumption region. It assesses the total energy produced across the 9 generation regions using each of the 13 technologies. Parameter (MWh) denotes the energy demand in each region of the SEN for every hour:
- Equation (11) defines the maximum transmission capacity (MWh) between the 9 regions, as indicated by Equation (4). The electricity produced by each generation region and transmitted to each consumption region during each hour must not exceed the maximum capacity of the links connecting these 9 regions:
3.3. Data Collection
4. Results and Discussion
4.1. Results of SEN 2025
4.1.1. Hourly Energy Shortage
4.1.2. Hours of Congestion on Transmission Lines
4.1.3. Regional Reserve Margin Performance
4.1.4. Regions Where Power Plants Are Installed That Could Significantly Impact Reducing Emissions Must Be Identified
4.1.5. Technologies with Higher/Lower Capacity Factor
5. Conclusions
- There was no power shortage during the simulated year, indicating adequate capacity to meet demand.
- The minimum reserve margin was 8% of total capacity, suggesting sufficiency.
- High congestion occurred in the Noreste–Oriental, Norte–Occidental, Noroeste–Occidental, and Noreste–Occidental links, exceeding 5000 h, highlighting an opportunity to diversify power plant capacity.
- Central and Noreste regions had the highest emissions and contributed most to the global emission factor.
- Combined cycle, biomass, geothermal, and nuclear technologies achieved capacity factors of 75% or higher.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Appendix A. Glossary of Terms
Type | Index | Description |
---|---|---|
Sets | i ∈ I | The index for technology |
g ∈ G | The index for generation regions | |
c ∈ C | The index for consumption regions | |
t ∈ T | The index for time step | |
Parameters and variables | x | Energy that technology i installed in generation region g |
sends to consumption region c during hour t (MWh) | ||
c | Generation cost of technology i installed in generation | |
region g to send electricity to consumption region c during | ||
hour t (USD/MWh) | ||
D | Energy demand in each region for each hour (MWh) | |
fd | Availability factor of each technology in each generation | |
region for each hour (fraction) | ||
F | Installed capacity of each technology in each generation | |
region (MW) | ||
MCT | Maximum capacity that each link can transmit | |
between generation and consumption regions (MWh) |
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Parameter/Variable | Sets | Number of Values | |||
---|---|---|---|---|---|
Technologies (13) | Generation Region (9) | Consumption Region (9) | Time Steps (8760) | ||
Cost | X | X | X | X | 9,224,280 |
Demand | X | X | 78,840 | ||
Availability factor | X | X | X | 1,024,920 | |
Installed capacity | X | X | X | 1,024,920 | |
Transmission capacity | X | X | X | 709,560 | |
Energy dispatch | X | X | X | X | 9,224,280 |
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Hernandez-Hurtado, U.; Medina-Marín, J.; Seck-Tuoh-Mora, J.C.; Hernández-Romero, N.; Martin-del-Campo, C. An Analysis to Identify the Key Factors in Power System Planning: The Case of Mexico. Energies 2025, 18, 1316. https://doi.org/10.3390/en18061316
Hernandez-Hurtado U, Medina-Marín J, Seck-Tuoh-Mora JC, Hernández-Romero N, Martin-del-Campo C. An Analysis to Identify the Key Factors in Power System Planning: The Case of Mexico. Energies. 2025; 18(6):1316. https://doi.org/10.3390/en18061316
Chicago/Turabian StyleHernandez-Hurtado, Ulises, Joselito Medina-Marín, Juan Carlos Seck-Tuoh-Mora, Norberto Hernández-Romero, and Cecilia Martin-del-Campo. 2025. "An Analysis to Identify the Key Factors in Power System Planning: The Case of Mexico" Energies 18, no. 6: 1316. https://doi.org/10.3390/en18061316
APA StyleHernandez-Hurtado, U., Medina-Marín, J., Seck-Tuoh-Mora, J. C., Hernández-Romero, N., & Martin-del-Campo, C. (2025). An Analysis to Identify the Key Factors in Power System Planning: The Case of Mexico. Energies, 18(6), 1316. https://doi.org/10.3390/en18061316