WHICH GRID BALANCING MODEL FOR A MORE AFFORDABLE ENERGY TRANSITION?
HARNESSING FLEXIBILITY IN THE ENERGY TRANSITION:
A COMPARATIVE STUDY OF DIFFERENT MODELS TO BALANCE THE ELECTRICAL GRID
Elia White Paper
Abstract — Wholesale electricity markets, such as the day-ahead auction and intraday trading, do the bulk of the work to balance demand with supply of electricity in each quarter-hour. However, usually some deviations remain - or occur - after the closure of the intraday market, and these deviations need to be solved to keep the grid frequency stable. This paper compares models that are commonly used in Europe to solve these residual deviations and balance the electrical grid, in the context of the energy transition. It argues that while balancing models exclusively relying on the flexibility which is explicitly offered to the grid operator might, at first sight, offer more operational comfort, other models that also leverage implicit reactions of flexible assets to price signals might, in some circumstances, be more efficient due to their ability to unlock broader system flexibility. The study includes a theoretical analysis of the advantages, drawbacks, prerequisite and conditions for success of each model in today’s context. Finally, a practical demonstration of the efficiency of balancing models that leverage implicit reactions of flexible assets to price signals is provided, based on the experience observed the last decade in Belgium.
Keywords: Energy Transition, Grid Balancing, Flexibility, Social Welfare, Real-Time Price
Bosschaerts C., Market Manager at Elia
Hirth L., Director of Neon and associate professor at Hertie school
Roques F., Executive Vice President in the Paris office of Compass Lexecon and Associate Professor at the University Paris Dauphine
Vandenberghe F., Managing director at Onoma Energy Consult and former Chief Officer Customers, Markets and Systems at Elia
Last years have been characterized by a fast-increasing share of renewables in the electricity production mix, and by an important electrification of industrial and residential appliances, all driven by the energy transition and accelerated by the recent energy crisis. These trends are expected to become even more important over the next decade and necessitate the reconsideration of grid balancing strategies.
On the one hand, the massive integration of intermittent renewable production in the electricity system creates a major challenge in terms of grid balancing, since it comes with ever more significant last minute forecast errors, that usually translate into grid imbalances. Today already, the significant installed capacity of (most of the time decentralized and barely controllable) solar - and of wind - production sometimes creates massive and unexpected excess of injection in the grid that grid operators have to face in real-time. This issue is only going to get more
critical as the installed capacity of intermittent renewable production increases at fast pace and will soon exceed the peak load in most European countries.
On the other hand, as a consequence of the rapid electrification of industrial and residential sectors, more and more flexible assets are connected to the grid. The emergence of electrical cars, heat pumps or home batteries, and the electrification of industrial processes, offer a natural solution to counterbalance the new highly volatile nature of the production mix. The flexibility of these emerging new electrical appliances should hence be used to the maximum in order to keep balancing the grid in an efficient way.
There exist two ways for these new electrical appliances to engage their flexibility in the system and participate to grid balancing by quickly increasing or decreasing injection or offtake of electricity 1. They can either offer their flexibility
explicitly to the Transmission System Operator (TSO), who can then decide the exact volume of flexibility it wants to activate to cover the system needs, by sending an explicit activation request to the flexibility provider. Or, they can react implicitly (i.e. outside any explicit bidding process) to price signals that are providing incentives to balance the system, i.e. via the imbalance settlement price. In the context of this study, balancing models allowing and encouraging implicit reactions to price signals up to real-time are called “decentralized balancing models”, whereas balancing models for which explicit participation is the only way to help the system after a certain moment in time are called “centralized balancing models”.
The distinction used here between decentralized and centralized balancing models is not to be confused with the distinction between centralized and decentralized resources: both centralized and decentralized resources - the latter for example via aggregators – can offer their flexibility explicitly to the TSO and hence contribute to a centralized balancing model. The distinction made here is not about the type of resources, but rather about the entities making the decision to activate these resources for grid balancing purposes: either a unique centralized entity (i.e. the TSO only), or several decentralized entities (i.e. not only the TSO but also the operators of flexible assets).
sion is illustrated by the Belgian use case, where a decentralized balancing model has been applied for around 10 years and has demonstrated efficiency over time.
This paper explores the advantages, drawbacks and conditions for success of both types of balancing models, in light of the ongoing evolutions of the electrical system. It concludes that, under certain conditions, decentralized balancing models are better suited to these ongoing changes, allowing to capture more social welfare and to reduce the overall balancing costs. This conclu-
FIGURE 1 : COMPARISON BETWEEN PEAK CONSUMPTION AND INSTALLED SOLAR CAPACITY IN BELGIUM AND GERMANY, AND ILLUSTRATION OF SOLAR BOOM IN BELGIUM (FIGURE EXTRACTED FROM THE ADEQUACY AND FLEXIBILITY STUDY PERFORMED BY ELIA FOR BELGIUM FOR THE PERIOD 2024-2034)
THE THEORETICAL APPEAL OF CENTRALIZED BALANCING MODELS
In centralized balancing models, the TSO takes full control on grid balancing actions as from a given moment in time which is set a few minutes to a few hours before delivery (e.g., in France this gate is set one hour before delivery). After this gate, flexible assets are no longer allowed to react to price signals to help balance the system.
Theoretically speaking, they can then only react to activation requests from the TSO, which is of course only possible when their flexibility is explicitly offered to the system operator.
These centralized balancing models provide the TSO with comprehensive control, making it easier to monitor and manage resource availability and dispatch through explicit bids. This theoretically enables precise balancing, as the TSO can adapt to changes on a very short-term basis (e.g., every 4 seconds for automatic Frequency Restoration Reserve - aFRR).
Benefits of centralized balancing models
1. Tight Control:
The TSO maintains a clear, immediate overview of available flexibility.
2. Firm Participation:
There is a contractual obligation for service providers to deliver activated resources. Activations requested by the TSO are monitored and sometimes penalized for non-compliance.
3. High Precision:
Balancing services with short regulation steps (e.g., aFRR) allow precise control.
These centralized balancing models provide the TSO with comprehensive control, making it easier to monitor and manage resource availability and dispatch through explicit bids.
THE PRACTICAL REALITY OF CENTRALIZED BALANCING MODELS
Despite their theoretical appeal, centralized balancing models have inevitable practical limitations in the context of the energy transition. They often exclude some flexible assets due to stringent technical and reliability requirements, operational complexity, transaction costs and sometimes significant lead times to participate to explicit balancing services. These constraints are mostly required to guarantee the quality of the provided balancing services, but they can prevent smaller or less traditional flexible assets from contributing to balancing the system up to real-time, thereby reducing overall efficiency and likely increasing balancing costs.
Centralized balancing models are therefore well suited to power systems made of conventional assets and few flexible loads, but their ability to deal with the challenges and opportunities brought by the energy transition can be questioned.
Exclusion of assets
1. Technical and Operational Barriers:
Not all assets can (or want to make the necessary investment to) meet the required technical standards or have the necessary setup for participation to balancing services.
2. Complex Bidding Processes:
The complexity and burden of explicit bidding (technical tool chain, 24/7 operation, etc.) deter participation, particularly from smaller entities or non-traditional power sources.
3. Lead Time:
The processes to enter the explicit market may be time-consuming and/or have long lead times, possibly discouraging assets of which the core business is not to be active in the balancing markets.
4. Difficulty to account for non-electrical constraints in explicit bids: Flexible assets of which the core business is far away from electricity markets have to consider many non-electrical constraints to decide whether flexibility can be engaged in the system at a given moment. Those constraints are extremely complex or even impossible to reflect in explicit bids.
5. Inefficient price back-propagation leading to suboptimal dispatch of slow flexible assets : Because of short activation times, only fast-responding assets can offer their flexibility explicitly to the TSO. Flexible assets which are not able to adapt their behavior
within balancing timeframe should engage their flexibility through the Intraday market. However, in centralized balancing models, the main objective of the imbalance settlement price is to recover the balancing costs and not to incentivize Balance Responsible Parties (BRPs) to balance the grid. Therefore, the imbalance price is usually an average and as neutral as possible price signal, which does not represent the costs of the marginal resource activated by the TSO. Besides by forcing the BRPs to close their position before the balancing timeframe (and hence by preventing healthy arbitrage from BRPs until (close to) real-time), the imbalance price cannot back-propagate efficiently to previous timeframes. For these reasons, flexible assets that are too slow to participate to balancing services may not receive correct incentives to engage their flexibility in the system through the Intraday market (i.e. even if their flexibility is cheaper than the marginal resource activated explicitly by the TSO).
The relevance of centralized balancing models in areas with high renewable penetration and explosion of decentralized flexible load can be questioned.
DECENTRALIZED BALANCING MODELS – A PROMIZING ALTERNATIVE IN THE CONTEXT OF THE ENERGY TRANSITION?
In decentralized balancing models, BRPs are allowed and encouraged to take actions at all times (and up to real time) to balance their portfolio. It is only in the balancing timeframe that the TSO undertakes explicit activations of resources to cover the residual imbalances that the market doesn’t manage to solve.
In these models, BRPs are even usually incentivized to deviate from their balanced position to help the system in real-time. Explicit participation in the system during the balancing timeframe (i.e. through explicit balancing bids offered to the TSO) is therefore complemented by the implicit participation (i.e. as a reaction to the imbalance price signal) of the assets that cannot
or are not willing to offer their flexibility explicitly to the TSO. This makes it possible to benefit from the whole flexibility available in the system, even the one which is excluded by centralized balancing models.
However, decentralized balancing models come with additional challenges, both from a design
as from an operational perspective. A condition for the success of these models is to make the explicit and implicit participations in the system co-exist in an efficient way, which is even more important in the current context where more and more decentralized and very quick assets are connected to the grid.
Conditions for success of decentralized balancing models
1. A design optimizing the potential of each flexible assets: despite the fact that an alternative exists, explicit participation in the system should remain sufficiently attractive in order to incentivize flexible assets to offer their flexibility in the service that brings the most value to the system. For instance, unless this market is saturated, a new largescale battery should consider participation in the aFRR market (where it can valorize its quick reaction by following a setpoint that evolves every 4 seconds and where it can valorize its flexibility cross-border) and hence participate to fine balancing of the system, rather than only reacting to prices evolving on a quarter-hourly basis.
2. A good complementarity between implicit reactions and explicit activations: in order for a decentralized balancing model to be efficient, it is important to make sure that the sum of the volumes activated explicitly by the TSO and the implicit reactions of the market to the resulting price signal comes as close as possible to the real-time disturbance that needs to be covered.
In the context of the energy transition, efficient and well-designed decentralized balancing models will be able to harness more flexibility
Prerequisite of decentralized balancing models
To realize these conditions for success, the barriers to offer flexibility explicitly to the system should be lowered to the extent possible, while making sure not to denature or downgrade the product (i.e. for instance, without questioning the reliability of participation, that should be subjected to activation controls and possibly to adequate penalties). At the same time, the design of the price signal should ensure that implicit participation is not fostered for assets that have the ability to participate explicitly in the system. This, combined with the fact that explicit participation comes with inherent benefits such as the possibility to valorize the flexibility across the borders, or the transfer of the activation risks to the TSO, should ensure that implicit participation to the system in real-time does not supplant explicit participation, but rather complement it by addressing its limitations.
On the other hand, the barriers to react to the price signal should also be lowered as far as possible to make sure that every flexible asset which is not able, or not willing, to participate to explicit balancing products finds an accessible alternative to participate in the system. To do so, it is important to make sure that the imbalance price signal is clear, reliable and stable (a.o. over the quarter-hour) and that the necessary information is published close to real-time in order to help BRPs calibrate their implicit reaction in an efficient way. Besides, it should be possible for this imbalance price to easily reach the flexible assets (even when located behind a head meter). This can only be made possible through supply or contract split, allowing the grid user to segregate flexible from non-flexible assets and define for both a different strategy in terms of sourcing and optimization.
Finally, the sensitivity of BRP imbalances to price has to be known by the TSO in order to make sure it activates the right volumes explicitly, taking into account the implicit reactions of the market to the resulting price signal. In a context where more and more decentralized assets can react to the price signal with a quite dynamic pattern (e.g. the implicit reaction of electrical vehicles might be very different when most of them are plugged in during the night or when they are on the road), robust forecasting methods (for system imbalances as well as with respect to price sensitivity curves) and tools play an important role in the success of decentralized balancing models.
The design of the decentralized balancing models should strive to optimize the use of each flexible asset
2.
COMPARISON OF EFFICIENCY OF CENTRALIZED AND DECENTRALIZED BALANCING MODELS IN THE CONTEXT OF THE ENERGY TRANSITION
In order to assess their efficiency, centralized and decentralized balancing models are assessed from a technoeconomic perspective, in a context assuming the emergence of substantial decentralized (even behind the meter) and fast flexibility.
Economic efficiency - Balancing costs and social welfare
In centralized balancing models, any disturbance that occurs after the moment when the TSO takes full control on balancing actions, as represented by the black double arrow on Figure 2 and Figure 3, is covered by explicit activations of resources by the TSO, since actions from BRPs to balance their portfolio or help balance the sys-
tem are no longer allowed (which means that BRP imbalances are supposed to be perfectly inelastic, as represented by the vertical dotted blue line on Figure 2 and Figure 3). The volume Vcb of explicit activations made by the TSO is therefore equal to the black double arrow and the price of the marginal resource dispatched to balance
the system in real-time is equal to ‘Marginal FRR price CB (MPCB)’. If the explicit activations are remunerated with a pay-as-cleared mechanism2, which is the target model for remuneration of aFRR and mFRR in Europe, then the balancing costs amount to Vcb * MPCB.
In decentralized balancing models, actions from BRPs are possible until real-time to balance their portfolio or help balance the system. In these models, if implicit reactions from BRPs are acknowledged, encouraged and efficiently steered through an appropriate price signal, they should perfectly complement the explicit activations requested by the TSO, so that the less expensive resources available in the system are dispatched to cover any imbalance that occurs after the closure of the wholesale markets. This time, the BRP imbalances are no longer inelastic and the BRP price elasticity is represented by the solid
blue line in Figure 2 and Figure 3. In such a model, the volume Vdb is activated explicitly and is complemented by the implicit reaction from the market to the price signal. The price of the marginal resource dispatched to balance the system in real-time is this time equal to ‘Marginal FRR price DB (MPDB)’. If the explicit activations are remunerated with a pay-as-cleared mechanism, the balancing costs amount to Vdb * MPDB.
Both Vdb and MPDB are smaller than Vcb and MPCB. As a consequence, the decentralized balancing models allow reducing the balancing
costs by (Vcb * MPCB - Vdb * MPDB). This reduction of balancing costs is represented by the solid light orange area on Figure 3.
Besides, in well-functioning decentralized balancing models, the resources dispatched by BRPs as a reaction to the price signal are cheaper and likely “greener”3 than the resources activated by the TSO to deliver the volume (Vcb - Vdb). This translates into a creation of social welfare equal to the light blue area in Figure 2.
FIGURE 2 : WELFARE CREATED BY DECENTRALIZED BALANCING MODELS
FIGURE 3 : BALANCING COSTS AVOIDED IN DECENTRALIZED BALANCING MODELS
3. if the costs of carbon are adequately priced into the bids, then the cheapest resources
Economic efficiency – Capacity reservation costs & market distortion
Another consequence of the exclusions of some flexible assets from the centralized balancing models is that it increases the challenge to find the required volumes of flexibility to balance the grid at any moment in time. Figure 4 illustrates the expected increase in system flexibility needs in Belgium for the next decade. If we don’t manage to unlock the flexibility which will naturally emerge with the electrification of industrial and residential appliances, by allowing them to participate to grid balancing through a market
mechanism which better fits the constraints of this emerging flexibility than the explicit balancing services, then we can wonder how we will manage to attract the required additional flexibility volumes in the explicit balancing markets. This would potentially only be possible through the subsidy of additional (probably centralized) flexible assets via the capacity reservation mechanism, which presents several drawbacks. Not only balancing capacity reservation comes with an important cost for the society, but it may
also distort previous markets by preventing the reserved flexibility from participating to these markets and/or imposing some units that would otherwise be out of the market to run in order to create downward flexibility. In that case, the procurement of additional balancing capacity should hence probably be avoided, or at least carefully analyzed and only considered as last resort if decentralized balancing models do not manage to unlock enough flexibility to cover the increasing needs.
Slow Flexibility [MW/5h] Fast Flexibility [MW/15min]
Ramping Flexibility [MW/5min] Total Flexibility [MW/5min]
FIGURE 4 : EXPECTED EVOLUTION OF SYSTEM FLEXIBILITY NEEDS IN BELGIUM (FIGURE EXTRACTED FROM THE ADEQUACY AND FLEXIBILITY STUDY PERFORMED BY ELIA FOR THE PERIOD 2024-2034)
Operational efficiency - Precision of the balancing actions
Another difference between centralized and decentralized balancing models is that centralized balancing models assume that the BRP imbalances are perfectly inelastic whereas, in practice, BRP reactions to prices is inevitable, even when it is legally forbidden. The existence of implicit reactions to prices, even when not encouraged by the TSO, has been experienced by neighboring countries such as Germany and documented in the literature4. This can be explained by at least two reasons:
1. BRP imbalances are settled at the imbalance price which can hardly be totally neutral (i.e. it will inevitably provide some kind of financial incentives to the BRPs). This becomes even truer with the connection to the European balancing platform and the harmonization of imbalance settlement at European level. Of course, withholding from market participants any information that could be used to forecast this incentivizing imbalance price could discourage implicit reactions. However, experience from neighboring countries show that there will always exist some BRPs which, for instance due to their market power or to the size/composition of their portfolio, are able to forecast the imbalance price and are hence incentivized to adjust their position to this price
signal. Withholding information from market participants may therefore create non-level playing field between BRPs and may not be a robust option to prevent implicit reaction.
2. A legal prohibition for BRPs to adapt its position after a given moment is very difficult to enforce. Indeed, in many situations, BRPs with load or with intermittent production in their portfolio could allege that their open position in real-time is unintentional and relates to load/renewables forecast errors.
Applying centralized balancing models that deny the existence of implicit reaction, and hence do not rely on appropriate design and tools to efficiently steer this reaction, therefore causes inefficiencies in the balancing process. For instance,
Operational efficiency – System stability
Since decentralized balancing models encourage implicit reactions up to real-time, they usually come with frequent (e.g. updated each minute) publications of information regarding the way the system is evolving close to real-time. A drawback of this high level of transparency is that, if the publications are not properly managed and the resulting signals sent to the market are not stable enough, they could provide incentives for fluctuating implicit reactions, pos-
sibly leading to intra-quarter hour system oscillations even though the system is correctly balanced in average over the quarter-hour. These oscillations can also create additional balancing costs or even create operational issues if their magnitude becomes too important. If this is the case, they should be avoided, by attaching special importance to the stability of the price signal when designing the imbalance price and its related publications.
4. Systemstützende Bilanzkreis-Bewirtschaftun; L. Hirth, I. Schlecht, J. Mühlenpfordt, A. Eicke; 2023
a TSO denying the existence of implicit reaction or a TSO having poor view on what he considers as ‘parasitic’ and ‘illegal’ implicit reactions might regularly overactivate explicit balancing reserves, making the system switch in the opposite direction and creating additional balancing costs.
Applying centralized balancing models that deny the existence of implicit reaction causes inefficiencies in the balancing process.
THE BELGIAN EXPERIENCE WITH THE DECENTRALIZED BALANCING MODEL
Belgium has started its journey towards a decentralized balancing model more than 10 years ago. It is back in 2013 that Belgium first introduced the possibility for the BRPs to deviate from a balanced portfolio, in real-time, in order to help balance the system. The formula of the imbalance price has then evolved towards a single marginal price, and Elia, the Belgian TSO started publishing more and more close to real-time pieces of information to help BRPs anticipate the imbalance price and hence calibrate their implicit reaction.
This evolution has proved to be effective for the Belgian case so far. This is demonstrated by two indicators. First of all, the evolution of the System Imbalance (SI5) and the Area Control Error (ACE6) has been analyzed all along the journey. Figure 5 shows that the average System Imbalance significantly decreased when the BRPs were first allowed to help balance the system up to real time, and that it then remained stable despite the huge increase in the installed capacity of intermittent renewable production in Belgium, which would be expected to worsen the System Imbalance due to higher forecast errors.
The second indicator assesses the impact of the implicit reactions of one large demand facility located in Belgium7 on the mFRR volumes and mFRR marginal prices of activations performed in the upward direction. This analysis, which has been run on data from 2022, shows that, without implicit reaction from this large demand facility, an overall increase in both the mFRR activated volume and the mFRR marginal price would have occurred in the upward direction. More specifically, in the absence of implicit reaction, the 95th percentiles of the mFRR volumes and prices distributions would increase by (respectively)
50MW and 60€/MWh. This is due to the fact that the demand facility reduced its consumption at moments when the imbalance price got higher, usually reflecting a quite important deficit of injection in the system. By doing so, they reduced the system imbalance and hence also the volume of mFRR to be activated by the system operator. Since this volume is remunerated at the price offered by the marginal activated resource, the reduction in the activated volume also has an impact on the mFRR price paid for this volume, leading to an overall reduction of the mFRR costs.
FIGURE 5 : EVOLUTION OF SI AND ACE AGAINST THE TREND IN RENEWABLE PENETRATION
These figures illustrate more concretely the benefits of the decentralized balancing models from an economical perspective, showing that the balancing costs are already reduced today even if the number of flexible assets that cannot participate explicitly in the system is limited. The added value of decentralized balancing models is expected to significantly increase as more decentralized/demand-side flexibility emerge. In that respect, we can easily extrapolate the added value of decentralized balancing models in a world where the heating and mobility sectors are electrified, and lots of small decentralized assets are able to (further) adapt their consumption to the real-time conditions of the system.
with implicit reaction without implicit reaction
95%-percentile = 866.1 €/MWh
95%-percentile = 927.5 €/MWh
• Overall increase in mFRR Up marginal price
• Increase of 95% percentile ±60€/MWh
price [€/MWh]
with implicit reaction without implicit reaction
95%-percentile = 752.4 €/MWh
95%-percentile = 807.6 €/MWh
• Overall increase in mFRR Up activated volume
• Increase of 95% percentile ±50MWh
FIGURE 6 : OVERALL INCREASE IN MFRR ACTIVATED VOLUME AND MARGINAL PRICE IF NO IMPLICIT REACTION OCCURRED IN BELGIUM
Upward mFRR activated price distribution
Upward mFRR activated price distribution
From an operational perspective, oscillations in the system, triggered by important volatility in the imbalance price publications can sometimes be observed today, as illustrated in Figure 7. Up to now, the magnitude of the oscillations has remained acceptable from a system operation perspective. However, these oscillations could become more concerning when more flexible assets react to the price signal.
For these reasons, the Belgian TSO has decided to bring its decentralized balancing model one step further, by working on five parallel tracks:
1. Continue decreasing the barriers for participation to explicit balancing products (to the extent possible while preserving the nature and added value of these products);
2. Review the imbalance price formula to make it more stable and more representative of the true value of energy over a 15-minutes period (which corresponds to the imbalance settlement period);
3. Start publishing imbalance price forecasts to further help the market to efficiently calibrate its implicit reaction.
4. Facilitate the development of market mechanisms that allow grid users to define different sourcing or optimization strategies for different assets that are located behind the same head meter, e.g. allowing to treat differently the flexible assets and the non-flexible load and hence to valorize its flexibility while securing the supply of the non-flexible load at interesting prices.
5. Develop a decision-making tool8 that helps the TSO to define the volume to be activated explicitly, taking into account the complementary implicit reaction that these explicit activations would trigger.
FIGURE 7 : ILLUSTRATION OF OSCILLATING SYSTEM IN BELGIUM
CONCLUSION
This paper explored the performances of centralized and decentralized balancing models in the context of the energy transition and concluded that, under certain conditions, decentralized balancing models can harness more flexibility, including the one that cannot be offered explicitly to the TSO in the balancing markets. As a result, these models may reduce balancing costs and increase social welfare.
It also highlighted a number of conditions for decentralized balancing models to be (or remain) efficient, both from an economic as from an operational perspective, in the context of the energy transition, and further suggested concrete actions to make sure these conditions are met.
Finally, it illustrated the benefits and risks of decentralized balancing models on the Belgian case. To strengthen the conclusions of this paper, the analysis could be transposed to other European countries implementing different balancing models, in order to assess the trends that they observe in terms of economic and operational balancing efficiency.