Abstract

Flexibility requirements are becoming more relevant in power system planning due to the integration of variable Renewable Energy Sources (vRES). In order to consider these requirements Generation Expansion Planning (GEP) models have recently incorporated Unit Commitment (UC) constraints, using traditional energy-based formulations. However, recent studies have shown that energy-based UC formulations overestimate the actual flexibility of the system. Instead, power-based UC models overcome these problems by correctly modeling ramping constraints and operating reserves. This paper proposes a power-based GEP-UC model that improves the existing models. The proposed model optimizes investment decisions on vRES, Energy Storage Systems (ESS), and thermal technologies. In addition, it includes real-time flexibility requirements, and the flexibility provided by ESS, as well as other UC constraints, e.g., minimum up/down times, startup and shutdown power trajectories, network constraints. The results show that power-based model uses the installed investments more effectively than the energy-based models because it more accurately represents flexibility capabilities and system requirements. For instance, the power-based model obtains less investment (6–12%) and yet it uses more efficiently this investment because operating cost is also lower (2–8%) in a real-time validation. We also propose a semi-relaxed power-based GEP-UC model, which is at least 10 times faster than its full-integer version and without significantly losing accuracy in the results (less than 0.2% error).

Highlights

  • G ENERATION Expansion Planning (GEP) is a classic long-term problem in power systems that aims at determining the optimal generation technology mix [1]

  • 2) In order to improve how this extended problem can be addressed, we propose a semi-relaxed version of the power-based GEP-Unit Commitment (UC) model, which aims at reducing the computational burden without losing accuracy in the results

  • The total coal capacity is higher in the proposed power-based formulation (PB) model, the actual total coal production is lower (7%) than the one in the classic energy-based using SU/SD trajectories (EBs) model, see Table IV

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Summary

Parameters

Power flow limit on transmission line l [MW]. Time interval limit of startup segment k [h]. Initial capacity for technology j; [# units] for g, and [MW] for s and v

Continuous non-negative variables eωjt
INTRODUCTION
GENERATION EXPANSION MODEL FORMULATIONS
Energy-Based Formulation
Power-Based Formulation
SYSTEM FLEXIBILITY EVALUATION
CASE STUDIES
Modified IEEE 118-Bus System
Stylized Dutch System
CONCLUSION
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