AS4.0, An Ancillary Services Framework for TSOs and DSOs Interaction

By S. Ali Pourmousavi, Giulia De Zotti, Juan M. Morales, and Henrik Madsen

Due to the increasing share of intermittent energy resources in the generation portfolio, conventional power plants with synchronous generators are forced to retire because of low energy prices and strict environmental policies. These resources are the main sources of regulation services in the network, such as inertia, primary and secondary frequency regulation. The increase in renewable generation coinciding with the retirement of conventional generators, raises concerns over the Ancillary Services (AS) provision in the future, as one of the main challenges in a grid with a large renewable penetration. Therefore, it is necessary to seek out cheaper and readily available flexibility resources for the system operation. Traditionally, AS used to be required only by the Transmission System Operator (TSO) at the high-voltage level. However, large penetration of rooftop solar PV in low-voltage networks is causing unprecedented issues for Distribution System Operators (DSOs), e.g., overvoltage, reverse power flows, and congestion. As a result, a comprehensive AS mechanism is needed to enhance TSOs and DSOs operation at different voltage levels so that the new dynamics and uncertainties can be handled in a secure, reliable and cost-effective manner.

To address AS requirements in the future grid, several solutions have been proposed, such as local energy trading algorithms, storage, new market products, regulation services from renewable generation, and demand response programs. While each of the proposed solutions offers enhancement over the existing AS schemes, and a combination of these solutions might be the ultimate answer to a 100% renewable grid, they are different from the technical and economical points of view. Most of the proposed solutions, e.g., Peer-to-Peer (P2P), are using slow bidding-clearing processes, which require numerous complex entities, and intensive communication and computation power. While storage (electrochemical, mechanical, heat and water) is a natural solution to the problem, they are still expensive. Furthermore, market-based solutions, e.g., flexible ramp products (FRP) in California Independent System Operator, might lead to inefficiency in the energy market, high transaction costs and the lack of a tractable market-clearing framework with thousands of new participants. Finally, the renewable-provided AS highly depends on the generation forecast, and leads to green energy spillage and opportunity costs for the owners, as is shown in Hornsdale wind farm 2 trial in Australia. Most importantly, none of the proposed methods effectively solves the AS requirements of the DSOs. To address these caveats, we hypothesised a novel AS mechanism, called AS4.0 that unifies AS provision for all system operators at different voltage levels to fulfil their needs simultaneously. The solution is based on demand flexibility (i.e., behind-the-meter generation, storage, and demand response) and time-varying prices through one-way communication links. No real-time feedback from the end-users is required in this framework. Therefore, it is cheaper and faster compared to other solutions.

The AS4.0 leverages one of the most promising smart grid technologies, i.e., home energy management systems (HEMSs). According to a report from Research and Market group, the global HEMSs’ market was worth US$ 1.3 Billion in 2017 and is projected to reach US$ 3.7 Billion by 2023, exhibiting a compound annual growth rate of around 19% during 2018-2023. In the AS4.0 mechanism, individual consumers decide how to react to the dynamic prices received from different system operators (a decision that is best made locally considering internal generation, curtailable/shiftable loads, and storage.) This way, the AS4.0 avoids interfering with the end-user decisions and respects the privacy of consumers by not requiring real-time feedback. In order to avoid conflicts among system operators and price discrimination, a coordinating entity is considered in the AS4.0. So far, we have developed an optimisation-based algorithm to estimate consumers’ reaction to price signals at the TSO level considering the given characteristics of AS4.0. In addition, we implemented a multi-timescale dynamic model of TSO-DSO interaction to verify the operation of the mechanism in different scenarios. The goal was to investigate whether multiple system operators are able to fulfil their needs from demand flexibility simultaneously. Furthermore, a simple algorithm is developed to ensure that the consumers are not discriminated by receiving different price signals.

In order to deploy the AS4.0 mechanism in practice, it is required to assess the performance of the power system operation under such a mechanism in the first step. To do so, a combined TSO-DSO dynamic simulation model is needed along with price generation algorithms at different voltage levels. In addition, the operation of the coordinating entity is the key, which shall be further investigated and appropriate rule-based/optimal algorithms should be developed. Upon successful completion of the simulation studies, the next step is to verify the applicability of the framework in an islanded microgrid, such as the Faroe Islands or Bruny Island in Tasmania, Australia. When the performance of the AS4.0 is verified, the following step is to implement it in an electricity market as a residual AS provision tool. In this case, the AS4.0 will be used along with the existing AS market to only fulfil unexpected requirements or shortage in other sources. Finally, the possibility to extend AS4.0 from that residual role to a fully competitive technology is to be examined.

The AS4.0 framework offers a unified approach to make the most of demand flexibility and as such, is expected to disruptively enter the AS market. Under the AS4.0 framework, DSOs can overcome the operational issues to allow for more rooftop PVs in the low-voltage network without requiring system upgrade. Simultaneously, flexible load demand will be used by TSOs to manage a higher level of large-scale intermittent generation without stability and reliability concerns. One of the major concerns about AS4.0, however, is the uncertain nature of demand flexibility in response to time-varying prices in the absence of real-time feedback. It is of paramount importance because a model of aggregated consumers’ behaviour is necessary to generate appropriate price signals. In addition, most of the demand flexibility resources are shiftable loads and their rebound effect should be accounted for in a price-response estimation algorithm. Finally, it is expected that the AS4.0 mechanism will pave the way to a grid with bigger share of renewable generation in the most cost-effective manner.


S. Ali Pourmousavi

S. Ali Pourmousavi (S’07, M’15) received the B.Sc., M.Sc., and PhD degrees with honours in 2005, 2008, and 2014, respectively, in electrical engineering. He worked for California ISO, NEC Laboratories America Inc., and Denmark Technical University (DTU) from 2014 to 2017. He is currently a research fellow at the University of Queensland (UQ), Australia. His current research interests include battery and its integration to the grid for different applications, control-based ancillary services, and demand response.

Giulia De Zotti

Giulia De Zotti received a B.S. degree in energy engineering in 2012 and a M.S. degree in electrical engineering in 2015 from the University of Padua, Italy. From 2014 to 2015, she investigated the water-energy nexus in the Chinese water supply sector at Tsinghua University. Since 2016, she has been with the Department of Applied Mathematics and Computer Science at DTU, Denmark, where she is working as a PhD student for the SmartNet project. Her current research interests include control-based approaches in smart grid, local electricity market design and ancillary services provision through demand response.

Juan M. Morales

Juan M. Morales (S’07-M’11-SM’16) received the Ingeniero Industrial degree from the University of Malaga, Malaga, Spain, in 2006, and a PhD degree in Electrical Engineering from the University of Castilla-La Mancha, Ciudad Real, Spain, in 2010. He is currently an associate professor in the Department of Applied Mathematics at the University of Malaga in Spain. His research interests are in the fields of power systems economics, operations and planning; energy analytics and optimization; smart grids; decision making under uncertainty, and electricity markets.

Henrik Madsen

Henrik Madsen got a PhD in Statistics at the Technical University of Denmark in 1986. He was appointed Ass. Prof. in Statistics in 1986, Assoc. Prof. in 1989, and Professor in Mathematical Statistics in 1999. In 2017 he was appointed Professor II at NTNU in Trondheim. His main research interest is related to the analysis and modelling of stochastic dynamics systems. This includes signal processing, time series analysis, identification, estimation, grey box modelling, prediction, optimisation and control. The applications are mostly related to energy systems, smart grids, wind and solar power, environmental systems, bioinformatics, process modelling and finance. He has got several awards. Lately, in June 2016, he has been appointed Knight of the Order of Dannebrog by Her Majesty the Queen of Denmark, and he was appointed Doctor HC at Lund University in June 2017. He has authored or co-authored approximately 500 papers and 12 books.

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