Comparison of flexibility options to improve the value of variable power generation

Kiviluoma, Juha; Rinne, Erkka; Helistö, Niina

doi:10.1080/14786451.2017.1357554

Cited by 28 publications

(35 citation statements)

References 17 publications

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“…Depending on the model objectives, it can be a change in parameters or constraints. Examples of the options for the procedure are: When there is a difference between the planning and operational model results, add an artificial variable cost to the investment options in the planning model and run the planning model again (Kiviluoma et al, ). Run a range of cases using the planning model, each having a different artificial variable cost in the investment options. Run the operational model with true variable costs for each of the cases.…”

Section: Modeling Approachesmentioning

confidence: 99%

“…Run the operational model with true variable costs for each of the cases. Sum fixed costs from the planning model and variable costs from the operational model and find the case with the lowest costs (Kiviluoma et al, ). Solve the planning and operational problems separately in a built‐in iterative procedure using Benders decomposition. Optimality cuts produced by the operational model are included in the planning problem at each iteration before performing the investment cost calculation.…”

Section: Modeling Approachesmentioning

confidence: 99%

“…Rather unexpectedly, in most cases the combination of investment and operational models led to lower costs than the investment model alone (Kiviluoma et al, ). This was explained to be partly due to the selection of the representative weeks.…”

Section: Review Findings: Comparison Of Modeling Approachesmentioning

confidence: 99%

“…A similar finding related to the selection of representative periods and decreasing costs with a higher number of representative days was observed by Frew and Jacobson () with investment model only (see Section 4.2). Yet, Kiviluoma et al () concluded that the most significant factor affecting the system costs and the estimated benefits of increased flexibility was the ability of the models to capture the impact of flexibility measures. Since the GEP model omitted several factors that influenced the benefits of flexibility, it gave less‐reliable results than the operational model.…”

Section: Review Findings: Comparison Of Modeling Approachesmentioning

confidence: 99%

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Including operational aspects in the planning of power systems with large amounts of variable generation: A review of modeling approaches

Helistö

Kiviluoma

Holttinen

et al. 2019

WIREs Energy & Environment

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In the past, power system planning was based on meeting the load duration curve at minimum cost. The increasing share of variable generation (VG) makes operational constraints more important in the planning problem, and there is more and more interest in considering aspects such as sufficient ramping capability, sufficient reserve procurement, power system stability, storage behavior, and the integration of other energy sectors often through demand response assets. In VG integration studies, several methods have been applied to combine the planning and operational timescales. We present a four-level categorization for the modeling methods, in order of increasing complexity: (1a) investment model only, (1b) operational model only, (2) unidirectionally soft-linked investment and operational models, (3a) bidirectionally soft-linked investment and operational models, (3b) operational model with an investment update algorithm, and (4) co-optimization of investments and operation. The review shows that using a low temporal resolution or only few representative days will not suffice in order to determine the optimal generation portfolio. In addition, considering operational effects proves to be important in order to get a more optimal generation portfolio and more realistic estimations of system costs. However, operational details appear to be less significant than the temporal representation. Furthermore, the benefits and impacts of more advanced modeling techniques on the resulting generation capacity mix significantly depend on the system properties. Thus, the choice of the model should depend on the purpose of the study as well as on system characteristics. generation expansion planning, integration of renewable energy sources, operational constraints, power system planning, temporal representation | INTRODUCTIONPower systems can play a crucial role in decarbonizing energy systems as they can relatively easily integrate large amounts of renewable generation. However, the increasing amount of wind and solar power, as well as other forms of variable generation (VG), has a strong impact on the operation of power systems through the variability and uncertainty of wind speed (Wu et al.,

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Section: Modeling Approachesmentioning

confidence: 99%

Section: Modeling Approachesmentioning

confidence: 99%

Section: Review Findings: Comparison Of Modeling Approachesmentioning

confidence: 99%

Section: Review Findings: Comparison Of Modeling Approachesmentioning

confidence: 99%

See 2 more Smart Citations

Including operational aspects in the planning of power systems with large amounts of variable generation: A review of modeling approaches

Helistö

Kiviluoma

Holttinen

et al. 2019

WIREs Energy & Environment

Self Cite

View full text Add to dashboard Cite

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“…They have stated that large‐scale electric boilers are particularly promising due to their ability to absorb “excess” electricity. Kiviluoma et al investigate a range of what they denote flexibility options (which is a broader term than variation management strategies and address uncertainty as well as variability) in a north European context and further strengthen the conclusion that application of power‐to‐heat can enhance the value of wind power and add that similar benefits can be achieved through transmission capacity expansion.…”

Section: Introductionmentioning

confidence: 99%

A comparison of variation management strategies for wind power integration in different electricity system contexts

Göransson

Johnsson

2018

Wind Energy

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Variation management strategies improve the capability of the electricity system to meet variations both in the electricity demand and in the generation that relies on variable energy sources. In this work, we introduce a new, functionality‐based, categorization of variation management strategies: shifting (eg, batteries), absorbing (eg, power‐to‐gas), and complementing (dispatchable generation, including reservoir hydropower) strategies. A dispatch model with European coverage (EU‐27 plus Norway and Switzerland) is applied to compare the benefits of shifting and absorbing strategies on wind integration in regions with different amounts of complementing strategies in place. The benefits are measured in terms of the wind value factor, wind owner revenue, and average short‐term generation cost. The results of the modeling show that the reduction in average short‐term generation cost and the increase in revenue earned by the wind owner from shifting strategies, such as the use of batteries, are more substantial at low wind shares than at high wind shares. The opposite situation is found for absorbing strategies, such as power‐to‐gas, which are found to be more efficient at reducing the average generation cost and increasing profit for the wind owner as the wind share increases. In regions that have access to complementing strategies in the form of reservoir hydropower, variation management has a weak ability to reduce the average short‐term generation cost, although it can increase significantly the revenue accrued by the wind power owner.

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Assessing the Operational Flexibility in Power Systems with Energy Storage Integration

Tziovani

Savva

Asprou

et al. 2020

Lecture Notes in Electrical Engineering

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Increased level of flexibility is essential in power systems with high penetration of renewable energy sources in order to maintain the balance between the demand and generation. Actually, the flexibility provided by energy storage systems and flexible conventional resources (i.e., generating units) can play a vital role in the compensation of the renewable energy sources variability. In this paper, the flexibility of the conventional generating units is quantified and incorporated in a unit commitment model in order to evaluate the impact of different system flexibility levels on the optimal generation dispatch and on the operational cost of the power system. An emerging flexible option such as the battery storage is included in the unit commitment formulation, evaluating the flexibility contribution of the storage and its effect on the system operational cost. In this paper, the flexibility of a real power system is assessed while the unit commitment problem is formulated as a mixed-integer linear program. The results show that the integration of a storage unit in the power generation portfolio provides a significant amount of flexibility and reduces the system operational cost due to the peak shaving and valley filling.

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Comparison of flexibility options to improve the value of variable power generation

Cited by 28 publications

References 17 publications

Including operational aspects in the planning of power systems with large amounts of variable generation: A review of modeling approaches

Including operational aspects in the planning of power systems with large amounts of variable generation: A review of modeling approaches

A comparison of variation management strategies for wind power integration in different electricity system contexts

Assessing the Operational Flexibility in Power Systems with Energy Storage Integration

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