Abstract

This paper proposes a mathematical model in order to simulate Day-ahead markets of large-scale multi-energy systems with a high share of renewable energy. Furthermore, it analyses the importance of including unit commitment when performing such analysis. The results of the case study, which is performed for the North Sea region, show the influence of massive renewable penetration in the energy sector and increasing electrification of the district heating sector towards 2050, and how this impacts the role of other energy sources, such as thermal and hydro. The penetration of wind and solar is likely to challenge the need for balancing in the system as well as the profitability of thermal units. The degree of influence of the unit commitment approach is found to be dependent on the configuration of the energy system. Overall, including unit commitment constraints with integer variables leads to more realistic behaviour of the units, at the cost of considerably increasing the computational time. Relaxing integer variables significantly reduces the computational time, without highly compromising the accuracy of the results. The proposed model, together with the insights from the study case, can be especially useful for system operators for optimal operational planning.

Highlights

  • Energy systems, including all energy vectors, such as heating, transportation, and agriculture, are converting to electricity-based energy usage, due to climate change and environmental concerns

  • In order to analyse the importance of the unit commitment (UC) modelling approach when modelling the DA market, three different sensitivity cases of UC modelling approaches are studied in the DA optimisations: (1) adding UC constraints with integer commitment variables (UC-Mixed integer programming (MIP)), (2) adding UC constraints with relaxed commitment variables (UC-relaxed mixed integer problem (RMIP)), and

  • Optimal planning of maintenance is solved as a relaxed mixed integer problem (RMIP), including all days of the years, but with 1 every 3 h to reduce the complexity

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Summary

Mathematical Modelling

The methodology used in this paper to simulate the DA market can be divided into four stages: DA optimisation (Section 2.1), VRE simulations (Section 2.2), storage and planned maintenance optimisation (Section 2.3), and stochastic outage simulations (Section 2.4). The optimisations and simulations, except for the VRE simulations, are performed with the energy system model Balmorel [24], an energy system tool, deterministic, open source [25], with a bottom-up approach. It has been traditionally used in order to model the electricity and district heating sectors, it is being developed to increase its capabilities and include more sectors [26]. The regions in the model represent copperplates for the transmission of electricity. The areas in the model represent copperplates for the transmission of heat

Day-Ahead Optimisation
Objective Function
System Constraints
Technological Constraints
Modelling of Renewable Generation Including Fluctuations
Storage and Planned Maintenance Optimisation
Available Units
Yearly Maintenance Requirement
Logical Conditions
Stochastic Outage Simulations
Sensitivity Cases
Unit Commitment Assumptions
Simplifications in Storage and Planned Maintenance Optimisation
Case Study
Results and Discussion
Planned Maintenance
Planned Storage Use
Annual Production
System Costs
Hourly Electricity Balance
Electricity Prices
Curtailment
Average Revenue of Wind and Solar Pv Units
Electricity-Only Thermal Plant Operation
Computational Time
Limitations of the Study
Conclusions
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