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How to determine the business case behind congestion management?

It is expected that as the share of renewable energy increases, Distribution System Operators (DSOs) will have to deal with occasional peak loads that far exceed their current grid capacity [1]. Congestion management could help DSOs solve this peak load challenge. For that reason new markets are being developed, such as the Universal Smart Energy Framework (USEF), which brings together flexibility such as Storage and Demand Response. By purchasing this flexibility, DSOs could reduce the investment in costly hardware/network upgrades. This article presents a few important considerations and guidelines for DSOs to assess whether acquiring flexible resources in such a flexibility market is suitable to mitigate congestion efficiently.

Three stages to congestion

The cost of solving congestions (i.e. peak loads) can take various forms which are characterized by a short and long term dimension. DSOs should make a rational decision to select the most economically efficient option depending on the magnitude, duration, and frequency of the congestion issues.

We roughly identify three stages of stress for the DSO (Figure 1). The first stage is OK, the volumes of energy are within the nominal capacity of the hardware. There is no grid congestion and no extra strain on the hardware. In this situation no flexibility is needed.


Figure 1 Three stages of congestion

The next stage is overheating. The grid runs the risk of increased degradation, but there is no acute outage concern. In the short term, a degree of overheating can be acceptable for a DSO as long as the components do not heat up too much and too long. Which in the case of a power transformer can be 20-30% during a short period, taking into account ambient temperature. As components of the grid are pushed pass their threshold, their lifespan will be compromised.

The final stage is Power outage, in which the grid is pushed past its physical limits and malfunctions. In order to assess the cost of solving such issues, two timescales need to be taken into account. On the one hand, the short term acute cost of a power outage, and the cost of a structural long term solution to prevent the outage in the future on the other hand.

Short term business case

For the short term the DSO can look at:

  • The cost of overheating; Which results in an accelerated write-off period for hardware [2]. Costs for a new transformer for instance, can range from 10k€ to 50k€, depending on the capacity.
  • The cost of an outage; The compensation that the DSO is required to pay each connection for an outage depending on the duration of the outage. For households in the Netherlands it starts at 35 euros after 4 hours [3]. This compensation stays the same up to an 8 hour outage. For each additional 4 hours of outage, there is an additional 20 euros. For businesses the compensation fees are higher [4].
  • The Value of Lost Load (VOLL); An estimation of the willingness to pay to prevent a power outage. There is no single value for VOLL as this depends on the economic activity that is compromised by the power outage and the perception of the consumer. For residential consumers the willingness-to-pay has been estimated to be between 1 €/kWh and 10 €/kWh according to a range of studies across Europe [5]. The VOLL approach is highly contested, which is why DSOs and regulators often use predetermined compensations as discussed above.

Traditionally, DSOs weigh these costs above with the investment cost of new hardware if the predicted load will exceed the nomimal capacity. However, due to the current trends in electricity markets and technologies, it is possible to consider flexibility. The required volume can be calculated by taking the delta between the load prediction and the nominal capacity. The DSO will have to weigh the Total Flex Costs against the Alternative costs. The Total Flex Costs is the sum of the cheapest Flexprice offerings in the flexibility market needed to cover the flex demand. This is similar to the way total power demand is first covered by the cheapest production assets in most current electricity markets, the so-called merit order [6]. The Flexprice is multiplied by the sum of the predicted Volume and the Safety margin. The Safety margin can be set by the DSO to compensate for the forecasting error [7]. The granularity of the forecast plays a large role in the Total Flex Costs as well.  Granularity influences the Duration of the flex request which in turn poses a direct multiplication factor for the Total Flex Costs (Figure 2).

Figure 2 Determining the maximum acceptable price for flexibility in the short term

Long term business case

The long term dimension of the flexibility business case for DSOs can be found in the cost of increasing the hardware capacity. An important component is expected to be the estimated Utilisation of the hardware (Figure 3). The higher the utilisation rate, the lower the relative long term costs of the hardware capacity.

Figure 3 Determining the maximum acceptable price for flexibility in the long term


This article provides a condensed view on the economics behind congestion management. Other factors that a DSO should consider (among others) are;

  • the ability to absorb large volumes of decentralized generation,
  • the long planning period associated with capacity increases,
  • the costs involved in predicting congestion and taking part in a flexibility market,
  • and the affect that the age of the assets has on long-term investment needs.

There may also be other non-economic considerations, such as the reputational damage resulting from an outage or the DSO’s reliability targets.

Congestion management can provide a solution to hardware degradation, or even power outages in the short term. To understand if congestion management is the right strategy for grid balancing, a good view on the long term development of capacity needs and potential utilisation on your grid is required. How do production and consumption patterns change once electric vehicles are adopted by the masses? What will be the impact of renewable energy generation? The ways to provide an answer to these questions will be addressed in a follow-up article, to be published on the Sia Partners Energy blog soon.

About the authors

Diederik KuipersConsultant Energy & Utilities

Diederik is passionate about energy markets and innovation and has a background in energy finance, electricity market modelling, market regulations and market interactions. He has been involved in a Smart Grid Demo project at a DSO and in 2016 he won the Topsector Energie’s Energy Innovation Talks, with a novel idea for integrating flexibility in the standard consumer contract.

Robin BrouwerJr Consultant Energy & Utilities

Robin gained experience through various projects in the energy industry at the Eneco Group, as well as internships at a production company and shipyard in the Qatar Ras Laffan. During his most recent project, he became even more passionate about solving tomorrow’s energy challenges. While researching the value of aggregated flexibility services provided by residential batteries.


[1] https://www.rijksoverheid.nl/binaries/rijksoverheid/documenten/rapporten/2016/01/18/frontier-economics-2015-scenarios-for-the-dutch-electricity-supply-system/frontier-economics-2015-scenarios-for-the-dutch-electricity-supply-system.pdf

[2] Haque describes the methodology for calculating the cost of overheating in detail in paragraph 3.3 https://pure.tue.nl/ws/files/76305173/20170927_Haque.pdf

[3] https://www.enexis.nl/consument/storingen-en-onderhoud/storingen/vergoedingen

[4] https://www.enexis.nl/zakelijk/storingen-en-onderhoud/schade-en-compensatie/compensatie

[5] http://www.innoenergy.com/wp-content/uploads/2016/03/INSIGHT_E_RREB_2_FINAL_2.pdf

[6] https://www.powerworld.com/WebHelp/Content/MainDocumentation_HTML/Generator_Economic_Merit_Order_Dispatch.htm

[7] Even a good prediction of load flow will be too low 50% of the time. By adding a safety margin, the DSO can increase the likelihood that the purchased volumes will be sufficient to cover the peak load.

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