RH
R.J. Hennig
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Two concurrent developments lead to challenges for electric distribution networks: On the demand side, there is an increase in the electrification of heating, transportation, and industrial processes, and on the supply side, there is significant growth in the capacity of intermittent electric generation based on wind and solar. Both of these developments lead to a strong growth in the peaks of electricity flows through networks. These networks were often not designed for such large peak flows, and they may be unable to accommodate the desired peak flows with the current infrastructure. As network upgrades have extended lead times and high resource demands, upgrading the network fast enough everywhere is impossible. This leads to the question of what can be done to manage high electricity peak flows, which may overload the network. The peaks can often be flattened on the demand side using the flexibility inherent to the new electrical end-uses. This process is also called congestion management. In this thesis, we investigate different methods for congestion management, with a particular focus on the role of network tariffs and a class of congestion management mechanisms based on limiting network capacity. We analyze how network tariffs and electricity-generation-related end-user prices influence household investments and energy dispatch decisions....
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Two concurrent developments lead to challenges for electric distribution networks: On the demand side, there is an increase in the electrification of heating, transportation, and industrial processes, and on the supply side, there is significant growth in the capacity of intermittent electric generation based on wind and solar. Both of these developments lead to a strong growth in the peaks of electricity flows through networks. These networks were often not designed for such large peak flows, and they may be unable to accommodate the desired peak flows with the current infrastructure. As network upgrades have extended lead times and high resource demands, upgrading the network fast enough everywhere is impossible. This leads to the question of what can be done to manage high electricity peak flows, which may overload the network. The peaks can often be flattened on the demand side using the flexibility inherent to the new electrical end-uses. This process is also called congestion management. In this thesis, we investigate different methods for congestion management, with a particular focus on the role of network tariffs and a class of congestion management mechanisms based on limiting network capacity. We analyze how network tariffs and electricity-generation-related end-user prices influence household investments and energy dispatch decisions....
Electrification of energy end-uses brings an increasing load on electric distribution grids with load peaks that can cause network congestion. However, many new end-uses like electric vehicles, heat pumps, and electrified industrial processes have some flexibility to move their power consumption away from peak times. Congestion management mechanisms can harness this flexibility. This paper investigates congestion management mechanisms based on limited available network capacity for flexible loads during peak times. A case study discusses and investigates real-world examples of such mechanisms from proposals in Germany and the Netherlands. They differ concerning the lead time at which the capacity limitation is announced, with options from near real-time and day-ahead to long-term. These mechanisms are suited to remove network congestion, but there are significant trade-offs concerning the lead time. A shorter lead time leaves more room for using the network during non-congested times but creates a risk of curtailment for end-users, which may come with associated balancing and re-procurement costs. Longer lead times give more certainty on network access conditions but often restrict network usage even when there is no network congestion.
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Electrification of energy end-uses brings an increasing load on electric distribution grids with load peaks that can cause network congestion. However, many new end-uses like electric vehicles, heat pumps, and electrified industrial processes have some flexibility to move their power consumption away from peak times. Congestion management mechanisms can harness this flexibility. This paper investigates congestion management mechanisms based on limited available network capacity for flexible loads during peak times. A case study discusses and investigates real-world examples of such mechanisms from proposals in Germany and the Netherlands. They differ concerning the lead time at which the capacity limitation is announced, with options from near real-time and day-ahead to long-term. These mechanisms are suited to remove network congestion, but there are significant trade-offs concerning the lead time. A shorter lead time leaves more room for using the network during non-congested times but creates a risk of curtailment for end-users, which may come with associated balancing and re-procurement costs. Longer lead times give more certainty on network access conditions but often restrict network usage even when there is no network congestion.
Congestion management in electricity distribution networks
Smart tariffs, local markets and direct control
Increasing peaks from high-power loads such as EVs and heat pumps lead to congestion of electric distribution grids. The inherent flexibility of these loads could be used to resolve congestion events. Possible options for this are smart network tariffs, market-based approaches, and direct control of flexible loads by the network operator. In most instances, these approaches are looked at in isolation, without considering potential connections and trade-offs between them. In this contribution, we aim to bridge this gap by presenting an overarching design framework for congestion management mechanisms. We classify proposals based on design choices and qualitatively discuss their benefits and risks based on an extensive literature analysis. As there is no one-size-fits-all solution, we map possible risks and discuss the pros and cons of different mechanisms for various problem types. We caution against using market-based mechanisms for local congestion, as they can be susceptible to undesired strategic behavior of market actors.
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Increasing peaks from high-power loads such as EVs and heat pumps lead to congestion of electric distribution grids. The inherent flexibility of these loads could be used to resolve congestion events. Possible options for this are smart network tariffs, market-based approaches, and direct control of flexible loads by the network operator. In most instances, these approaches are looked at in isolation, without considering potential connections and trade-offs between them. In this contribution, we aim to bridge this gap by presenting an overarching design framework for congestion management mechanisms. We classify proposals based on design choices and qualitatively discuss their benefits and risks based on an extensive literature analysis. As there is no one-size-fits-all solution, we map possible risks and discuss the pros and cons of different mechanisms for various problem types. We caution against using market-based mechanisms for local congestion, as they can be susceptible to undesired strategic behavior of market actors.
The steady uptake of PV cells and high-power flexible loads such as electric vehicles (EVs) and heat pumps can lead to localized network congestion, if their power consumption or feed-in is not controlled well. One potential way that has been proposed to manage this congestion, are so-called Local Flexibility Markets. It is often argued, that these proposals are theoretically efficient, as they are market-based. However, some of these proposals may suffer from design flaws that allow market participants to obtain undue profits at the expense of the network operator. In this contribution, we discuss which kinds of market failures can occur based on theoretical reasoning and demonstrate them in a toy model. Based on this, we argue for a more careful consideration of congestion management options.
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The steady uptake of PV cells and high-power flexible loads such as electric vehicles (EVs) and heat pumps can lead to localized network congestion, if their power consumption or feed-in is not controlled well. One potential way that has been proposed to manage this congestion, are so-called Local Flexibility Markets. It is often argued, that these proposals are theoretically efficient, as they are market-based. However, some of these proposals may suffer from design flaws that allow market participants to obtain undue profits at the expense of the network operator. In this contribution, we discuss which kinds of market failures can occur based on theoretical reasoning and demonstrate them in a toy model. Based on this, we argue for a more careful consideration of congestion management options.
A higher share of renewables and electric vehicles increase the risk of congestion in electricity distribution systems. New distribution tariff designs have been proposed to prevent congestion. However, most modeling of tariff performance assumes deterministic price information. This paper proposes a method to assess the impact of price uncertainty for network tariffs, using price forecasting scenarios in a simulation model. Electricity price forecasting scenarios are generated by analyzing autoregressive forecasting errors and recursively generating time-series. The scenarios are used as price forecasting inputs in a model case study of tariff performance in a Dutch context. Results show a reduction in congestion frequency and charging costs using forecasts in this model setup, likely by enabling longer time horizons. Highest peaks however are larger when using forecasts for the fixed and capacity-based tariffs. Overall, this method provides insight into performance of new tariffs in electricity grids, incorporating the impact of price uncertainty.
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A higher share of renewables and electric vehicles increase the risk of congestion in electricity distribution systems. New distribution tariff designs have been proposed to prevent congestion. However, most modeling of tariff performance assumes deterministic price information. This paper proposes a method to assess the impact of price uncertainty for network tariffs, using price forecasting scenarios in a simulation model. Electricity price forecasting scenarios are generated by analyzing autoregressive forecasting errors and recursively generating time-series. The scenarios are used as price forecasting inputs in a model case study of tariff performance in a Dutch context. Results show a reduction in congestion frequency and charging costs using forecasts in this model setup, likely by enabling longer time horizons. Highest peaks however are larger when using forecasts for the fixed and capacity-based tariffs. Overall, this method provides insight into performance of new tariffs in electricity grids, incorporating the impact of price uncertainty.
What is a good distribution network tariff?—Developing indicators for performance assessment
Developing indicators for performance assessment
The adoption of distributed energy resources such as PV cells, electric vehicles and batteries in electric grids is increasing steadily. This brings new challenges for distribution networks. The current network tariffs were not designed for these types of usage and, in many cases, they are not adequate anymore. Thus, many new tariff frameworks have been proposed. In this paper, we focus on the question of how to assess whether a given tariff framework fulfills its objectives. We propose to use quantitative indicators for performance assessment. We give examples of indicators for common objectives and demonstrate how they can be derived from a cost-accounting methodology for distribution networks.
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The adoption of distributed energy resources such as PV cells, electric vehicles and batteries in electric grids is increasing steadily. This brings new challenges for distribution networks. The current network tariffs were not designed for these types of usage and, in many cases, they are not adequate anymore. Thus, many new tariff frameworks have been proposed. In this paper, we focus on the question of how to assess whether a given tariff framework fulfills its objectives. We propose to use quantitative indicators for performance assessment. We give examples of indicators for common objectives and demonstrate how they can be derived from a cost-accounting methodology for distribution networks.
Residential distribution networks in Europe are undergoing rapid changes. As high-power flexible loads, such as electric vehicle (EV) chargers, become more prevalent, the risk of network congestion increases. This is exacerbated by tariff structures which do not give incentives to limit simultaneous power consumption. Network charges in this case may not reflect the true costs of usage, as network costs are driven mainly by simultaneous load peaks.We present a systematic assessment of a new class of tariffs that is currently gaining attention in the Netherlands: capacity subscription models. We argue that this tariff structure is more cost-reflective and fair than the current fixed network fee and show how it helps to prevent transformer overloading in a simple simulation model of a neighborhood of 100 households constrained by a LV transformer, where a varying number of EVs are added.
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Residential distribution networks in Europe are undergoing rapid changes. As high-power flexible loads, such as electric vehicle (EV) chargers, become more prevalent, the risk of network congestion increases. This is exacerbated by tariff structures which do not give incentives to limit simultaneous power consumption. Network charges in this case may not reflect the true costs of usage, as network costs are driven mainly by simultaneous load peaks.We present a systematic assessment of a new class of tariffs that is currently gaining attention in the Netherlands: capacity subscription models. We argue that this tariff structure is more cost-reflective and fair than the current fixed network fee and show how it helps to prevent transformer overloading in a simple simulation model of a neighborhood of 100 households constrained by a LV transformer, where a varying number of EVs are added.