Assessing the impact of inertia and reactive power constraints in generation expansion planning

Wogrin, Sonja; Tejada-Arango, Diego A.; Delikaraoglou, Stefanos; Botterud, Audun

doi:10.1016/j.apenergy.2020.115925

Cited by 34 publications

(28 citation statements)

References 28 publications

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“…In this section we present the mathematical formulation of the LEGO model. However, since LEGO was based on a preliminary model version, presented in [18], in this section we merely focus on the modeling novelties. Constraints that have remained the same are briefly mentioned in the appendix for completeness, but are not addressed here.…”

Section: Mathematical Formulation Of the Lego Modelmentioning

confidence: 99%

Section: Objective Function and Standard Constraintsmentioning

confidence: 99%

“…Other standard constraints, that have been previously presented in [18] and therefore do not constitute an original contribution of this paper, are briefly mentioned here and listed in the appendix for completeness. For a detailed explanation of these constraints, the reader is referred to [18]. In particular, the constraints regarding thermal power plant operations are given by (7), and the constraints that are specific for storage technologies are given by (8).…”

Section: Objective Function and Standard Constraintsmentioning

confidence: 99%

“…LEGO is based on preliminary work [18] by the authors, and has been extended to incorporate many novel aspects, such as transmission expansion planning in both a DC-and AC framework, the introduction of DSM, the hydrogen sector, battery degradation, the economic output based on dual variables, and the fact that it is now freely available on Github 12 . To the best of our knowledge, there is no single open-source model available that combines all of the previously mentioned characteristics, which makes LEGO uniquely versatile.…”

Section: Introductionmentioning

confidence: 99%

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The full Low-carbon Expansion Generation Optimization (LEGO) model

Wogrin,

Tejada-Arango,

Bachhiesl

et al. 2021

Preprint

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This paper introduces the full Low-carbon Expansion Generation Optimization (LEGO) model available on Github 1 . LEGO is a mixed integer quadratically constrained optimization problem and has been designed to be a multipurpose tool, like a Swiss army knife, that can be employed to study many different aspects of the energy sector. Ranging from short-term unit commitment to long-term generation and transmission expansion planning. The underlying modeling philosophies are: modularity and flexibility. Its unique temporal structure allows LEGO to function with either chronological hourly data, or all kinds of representative periods. LEGO is also composed of thematic modules that can be added or removed from the model easily via data options depending on the scope of the study. Those modules include: unit commitment constraints; DC-or AC-OPF formulations; battery degradation; rate of change of frequency inertia constraints; demand-side management; or the hydrogen sector. LEGO also provides a plethora of model outputs (both primal and dual), which is the basis for both technical but also economic analyses. To our knowledge there is

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Section: Mathematical Formulation Of the Lego Modelmentioning

confidence: 99%

Section: Objective Function and Standard Constraintsmentioning

confidence: 99%

Section: Objective Function and Standard Constraintsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

The full Low-carbon Expansion Generation Optimization (LEGO) model

Wogrin,

Tejada-Arango,

Bachhiesl

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…However, the primary frequency regulation response was described by a static model, which only reflects the magnitude of the steady-state frequency deviation. S. Wogrin et al [18] combined synchronous inertia and virtual inertia of wind farms to study the influence of inertia in generation expansion planning, but only the minimum inertia demand constraint of the system was considered. In the frequency constrained unit commitment model proposed by Z. Zhang et al [19], the frequency regulation characteristics of the thermal generators and the frequency support from variable renewable energy were considered at the same time, and the analytical formulation for system frequency nadir was derived.…”

Section: Introductionmentioning

confidence: 99%

A Short-Term Optimal Scheduling Model for Wind-Solar-Hydro-Thermal Complementary Generation System Considering Dynamic Frequency Response

et al. 2021

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This paper proposes a model to realize the coordinated optimal dispatch of wind-solar-hydrothermal hybrid power generation system, aiming at minimizing the power generation cost of thermal generators and maximizing the water storage value of hydropower stations at the end of the scheduling periods, while considering the dynamic frequency response of wind/solar/hydro/thermal generators. Considering the virtual inertia and droop control of wind farms and PV stations, the dynamic frequency response model of wind-solar-hydro-thermal multi-energy complementary system is derived and the metrics that evaluate the frequency dynamic characteristics of the generation system are presented. Then the dynamic frequency response constraints are incorporated into the traditional optimal scheduling model and the Mixed Integer Linear Programming (MILP) method is used to solve it. Finally, the validity and applicability of the proposed model are verified by simulation examples.INDEX TERMS Dynamic frequency response of generators, virtual inertia control, droop control, windsolar-hydro-thermal multi-energy complementary system, Mixed Integer Linear Programming (MILP). NOMENCLATURE A. SETSc mt P Active injection power of node m at period t in scenario c. , c ht Q Water discharge volume of hydro generator h at period t in scenario c. , p c ht h Water head of hydro station p h at period t in scenario c.

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The impact of convexity on expansion planning in low-carbon electricity markets

Wogrin

Tejada-Arango

Delikaraoglou

et al. 2022

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Expansion planning models are tools frequently employed to analyze the transition to a carbon-neutral power system. Such models provide estimates for an optimal technology mix and optimal operating decisions, but they are also often used to obtain prices and subsequently calculate profits. This paper analyzes the impact of modeling assumptions on convexity for power system outcomes and, in particular, on investment cost recovery. Through a case study, we find that although there is a long-term equilibrium for producers under convex models, introducing realistic constraints, such as non-convexities/lumpiness of investments, inelastic demand or unit commitment constraints, leads to profitability challenges. We furthermore demonstrate that considering only short-term marginal costs in market-clearing may potentially create a significant missing-money problem caused by a missing-market problem and dual degeneracy in a 100 percent renewable system.

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Assessing the impact of inertia and reactive power constraints in generation expansion planning

Cited by 34 publications

References 28 publications

The full Low-carbon Expansion Generation Optimization (LEGO) model

The full Low-carbon Expansion Generation Optimization (LEGO) model

A Short-Term Optimal Scheduling Model for Wind-Solar-Hydro-Thermal Complementary Generation System Considering Dynamic Frequency Response

The impact of convexity on expansion planning in low-carbon electricity markets

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