All posts by Panagiotis Moutis

News, Recent & Upcoming Talks

Upcoming Seminars & some recent ones

November 2023

With the start of the 2023-24 academic semester at my new academic home, the Dept. of EE at the CCNY of the CUNY,  I had little time to announce in advance and thank the hosts for inviting me to offer talks and seminars at their institutions. That been said, I owe some gratitude and will also give some heads-up for next events that you can catch me at. Here we go!

Starting from the upcoming seminars, I am delighted beyond what words can describe to be returning to Greece and Cyprus to present my recent works at the Dept. of ECE at the University of Patras on Tuesday Jan. 9th, at the Dept. of ECE at the University of Cyprus on Friday Jan. 12th, at the Dept. of ECE at the Aristotle University of Thessaloniki on an. 16th and, last but not least, at my alma mater the Dept. of ECE at the National Technical University of Athens (date TBD). I want to wholeheartedly thank Professors Alexandridis, Papadaskalopoulos, Aristidou, Panteli, Papagiannis and Hatziargyriou for hosting me. I look forward to meeting with old classmates and revisiting the halls in which we worked together in the mid 2010s.

As for past events, in mid May as one of my last acts at Carnegie Mellon University, I had the distinct honor to deliver a Charge to the 2021-23 Graduates of the Energy Science, Technology & Policy MSc program. Emotions overwhelmed me and I will forever carry with me the love and trust that the Program Director Prof. Paul Salvador and the numerous MSc students put in me, and my educational and mentoring efforts. Lastly, in July, at the 2023 IEEE PES General Meeting in Orlando, I contributed to three panel sessions going over results from and aspirations for my recent and earlier works on Digital Twins for grid components, Wavelet Synopses for timeseries data, and Microgrids for exurban residential communities. Many thanks to Harry Konstantinou, Masoud Nazari and Di Shi for inviting me to these highly engaging sessions.

 

 

 

 

Joining EE at CCNY in Aug. '23, News

Joining the Dept. of Electrical Engineering at CCNY in Aug. ’23

May 2023

I am ecstatic to announce that on Aug. 1st I will be joining the Dept. of Electrical Engineering within the Grove School of Engineering at the City College of New York (CCNY), as Assistant Professor. It is amazing to join an Institution with 175 years of history, founded as the first tuition-free college in the US (until 1976), and which still strives to provide wider access to higher education for all. Two units within CCNY have been named after notable alumni, who decisively redefined their course with their donations and leadership: the Colin Powell School for Civic and Global Leadership (after the first African American Sec. of State) and the Grove School of Engineering (after one of Intel’s founding members & CEO, Andrew Grove). At CCNY, I will establish the Digitalized Electric Grid Innovations, Developments & Applications Laboratory (DEgIDAL). DEgiDAL will focus on how data-sets of historical records and real-time synchronized measurements can inform renewable energy pricing, grid stability and protection, and equitable access to electricity of high power quality for all.

I am grateful to everyone at Carnegie Mellon University for 6 wonderful years as postdoc and special faculty, and for enabling me to take on important roles within the US energy space. My postdoc advisors Profs. Gabriela Hug and Soummya Kar trained me thoroughly in power system optimization. Prof. Jay Whitacre involved me in breakthrough research on battery storage planning. Profs. Jay Apt, Paul Salvador, Barry Rawn, Sevin Yeltekin and Willem van Hoeve inspired me and supported me in developing and teaching 5 courses, and in advising more than a dozen MSc students from the College of Engineering and the Tepper School of Business.

Undoubtedly, this immensely joyful milestone would have been impossible without the strong Electrical & Computer Engineering foundations I received at my alma mater, the National Technical University of Athens (NTUA), Greece. In the form of gratitude I will refer specifically to two exquisite people that defined my path at NTUA. Firstly, my PhD+MSc advisor, the tireless and all-round power systems scholar Prof. Nikos Hatziargyriou, taught me most of what I know and was the ‘charge’ of my academic journey across the Atlantic. Secondly, Prof. Timos Sellis, a bright beacon of databases’ expertise, was the one who infused me with the passion for artificial intelligence and machine learning through data mining.

Lastly, I want to thank Drs. Giannis Bourmpakis, Kyri Baker, Constance Crozier, Jeff Wischkaemper, Mads Almassalkhi and Javad Mohammadi, and Profs. Fran Li, David Infield, Barry Rand, Luigi Vanfretti, Costa Samaras and Antonio Conejo for advising and encouraging me in the past couple of years of my faculty job search. The process was tough, sometimes dubious (if not outright scandalous in a few cases), but the support from these people kept me going!

In a few months I will be recruiting for 3 fully-funded PhD positions to join DEgIDAL. Stay tuned & reach out.

Respice.  Adspice.  Prospice.

 

Jan '23 IEEE Publications' Roles Updates, News

IEEE Publications’ roles updates Jan. ’23

January 2023

Prof. Fran Li, the EiC of the IEEE Open Access Journal of Power & Energy (OAJPE), kindly informed me I have been awarded the Outstanding Associate Editor (AE) recognition for 2022. It is always exciting to realize you are doing your job well, let alone in a nascent publication with exciting trajectory, immense potential and a respectful and strong Open Access policy. I would have not received this recognition without the expert Reviewers that accept my invitations and contribute their thoughtful and in-depth comments on the manuscripts submitted to the IEEE OAJPE. To my dear Reviewers, thank you for putting up with me and taking on my assignments!

Another very exciting development is that I have been nominated by the IEEE Young Professionals (YP) for the position of the YP representative with voting rights at the IEEE Publication Services and Products Board (PSPB). This is a special honor and, also, acknowledgement of all my efforts to improve and enhance the quality of scientific publications, especially in my field of power and energy systems. Beyond my own personal experience, I have been lucky that many friends and colleagues who are Authors, Reviewers and Associate Editors across multiple publications have trusted me with their concerns and ideas. I plan to make the best of this opportunity and all input I have received during my 2023 term at the PSPB, aiming for positive and valuable changes.

In the context of both these updates, please, do not hesitate to contact me with your availability to review papers in the scope of your expertise and also tell me of any concerns and ideas you got for the improvement of publications. I will treat all input as confidential and I am thankful in advance for your interest!

2023 IEEE ISGT NA panel & contest, News

Panel at 2023 IEEE ISGT North America & “Ask the Panelists” Contest

December 2022

With the Inflation Reduction Act in the US and similar incentivizing initiatives all over the world, the clean energy transition is – more or less (and hopefully!) – set on long-term and fast(er) tracks. In this context, the roles and impact of grid modernization, its digitalization and the broader space of (what we call) the “smart grid” become rather interesting. This is because the electricity sector has never – practically – suffered from lack of capital. So one may ask why would the recently introduced frameworks matter and justify expectations for significant changes?

With the support of the IEEE Smart Grid, I invited some good friends, colleagues and alumni of my courses at CMU for a panel at the 2023 IEEE North America Innovative Smart Grid Technologies (ISGT) conference. Together we will probe the new electricity sector landscape and answer some challenging questions about how the decarbonization of this space must rely on a range of solutions, including, among many others, infrastructure planning, energy security, non-wire alternatives and policy per se. I am grateful to my panelists Clare Callahan (Deloitte & CMU alumni), Doug Houseman (Burns McDonnell), Damir Novosel (Quanta Technology) and Rob Gramlich (Grid Strategies LLC, ex-FERC, ex-PJM & others) for joining me in this 1.5 hours endeavor on Tuesday, Jan. 17th at 12:30 pm ET! Special thanks go out to Hannah Morrey Brown (Burns McDonnell) & Shay Bahramirad (Quanta Technology) whom I had initially invited  as panelists, but needed to kindly defer to colleagues.

But wait… There’s more!

With the support of the Climate Change AI (CCAI) Initiative we are organizing a contest for questions for our panelists. The top-5 submitters (judged by CCAI Power & Energy Community Leads) will win complementary remote live access to the panel, during which they may ask their questions themselves! We are particularly interested in receiving questions from junior researchers and young professionals. The Contest will run until January 10th 23:59 AoE. The link to the contest is here. Best of luck to all of you!

 

Keynote speech NJIT ECE Students' Conference Dec. 9, News

Keynote Speech at North Jersey Research Student Conference 2022

November 2022

I am honored and humbled by my dear friend’s, Dr. Philip Pong‘s, invitation to offer the keynote speech at the 1st edition of the North Jersey Research Student Conference. The Conference will be held on December 9th at the Dept. of Electrical & Computer Engineering, at the New Jersey Institute of Technology. I am particularly happy, as this initiative aims first and foremost to promote and encourage students’ research at all levels within the dept. of ECE at NJIT; postdocs, PhD, MSc and undergraduate students will be presenting their most recent research results in the aims of exploring collaborations and impactful outreach!

My keynote speech will be on my recent work on digital twins for near real time sensing and monitoring of – mostly – distribution grid components (transformers and overhead conductors) for security and power quality. I will also be sitting in the committee of the Awards & Certificates of Merit of the presented students’ works.

I hope and wish that more and more ECE departments will organize similar conferences to empower their students and inspire them to engage and collaborate across seniority.

 

Newcastle University Optimization Group DT AC OPF Webinar Nov. 21, News

Webinar on Decision Trees for AC OPF at Newcastle University Optimization Group

November 2022

It is always a joy to join (albeit remotely) the co-organizer of the Newcastle University Optimization Group Webinars, my old student, the very hard-working and inspiring researcher Dr. Ilias Sarantakos. On, Nov. 21st at 14:00 – 15:00 (London time) I will be presenting my recently published paper at the IET Generation, Transmission & Distribution journal on the solution of the AC OPF with the machine learning tool of top-down heuristically inducted binary decision trees (hiBDT). I strongly urge you to register and follow the Group’s webinar series here. They also neatly keep recordings of the webinars they have previously organized here; great resource and a nice overview of recent developments in the space.

I will be discussing the theoretical guarantees (and some apparent implications) of a feasible space search with hiBDT recursively tightening the bounds/intervals of control variables towards a global optimum. The IEEE PES Task Force’s Power Grid library will be briefly presented as a crucial benchmark in assessing AC OPF solvers, relaxations, etc. The efficiency of the proposed hiBDT method will be posed as an open issue requiring considerations of “hot starting” and how to effectively search the AC OPF feasible space. Lastly, the recursive variable bound tightening with hiBDT that progressively improves the dispatch cost will be discussed as a feature of the method to robustly price the commitment of renewables unaffected by their volatility.

IET GTD paper on AC OPF machine learning solver, News

IET GTD Paper on Solving the AC Optimal Power Flow with Machine Learning

October 2022

I am elated to report the acceptance of my recent work at the IET Generation, Transmission & Distribution (GTD) open access (OA) journal of the Wiley publications! Before unpacking my paper with the (kind of long) title “Stochasticity Agnostic Solution to the AC Optimal Power Flow by Recursive Bound Tightening with Top-Down Heuristically Inducted Binary Decision Trees” (link here), let me rejoice in the fact that the IET GTD is, according to Scholar Google, the highest ranking Q1 (in Control & Systems Eng., Electrical & Electronic Eng., Energy Eng. & Power Technology) OA journal on Power Engineering. I have been an avid proponent of OA and Associate and Senior Editor in 2 such publications. Going through the process as an Author myself has reassured me that my heart is in the right place with OA! Read more about my position on OA here.

The AC Optimal Power Flow (OPF) is a non-convex optimization problem for resources or performance metrics within an electrical grid (see formulation above). The non-convexity of the AC OPF is due to the grid physics of power flows that introduce a non-convex equality constraint to the  optimization formulation. Since the AC OPF per se is – almost – never the end-problem we wish to solve, the sizes of problems encapsulating AC OPF are typically orders of magnitude larger than that and may include additional non-convexities, e.g. integer variables. The most typical approach used by several researchers in the recent years is the convex relaxation of the AC OPF to a larger dimension and – provided some conditions are met – projection of the relaxed solution back to the feasible space of the original  AC OPF problem. Much fewer approaches have considered machine learning for solving the AC OPF and even fewer of those come with guarantees of global optimality. The overall framework here described clearly means that the AC OPF cannot be solved in times that make sense for system operators and other electricity stakeholders when making decisions about how to operate and plan the electrical grid functions.

My paper introduces 2 perspectives to solving the AC OPF per se and another 1 in accounting for volatile resources, such as renewables like wind generators and photovoltaics.  But first of all, let me describe in a few words how the solver works (assuming we wish to minimize an objective function of energy cost). It starts by sampling the whole feasible space of the AC OPF instance. The samples are, essentially, random feasible dispatches for said problem. Then the method labels half of the samples as False, if the objective function is greater than the median cost of the samples, and True otherwise. Then a heuristically inducted binary decision tree (BDT) is trained with these samples and tightens/shrinks the feasible space (clearly towards the region of the feasible space with costs below the noted median). The method is recursively employed until the feasible space has tightened/shrunk around the global optimum (see “tightening” steps (a)-(e) in the the graph above).

Solving the AC OPF – Point 1
I prove that the method converges to the global optimum via Bayesian inference. Most typically, AC OPF solvers (and relaxations building upon them) pursue to solve the dual problem, relying on strong duality conditions that indicate that the solution of the primal and the dual problems are the same. I take a different path… I prove that a Bayes classifier for the global minimum in a small vicinity around it exists. This is true, due to the fact that the feasible space has non-random characteristics and I also explicitly label the global optimum (and some epsilon around it) as such, while the rest of the said vicinity as non-globally-optimum. From that point, iteratively, I can expand to the whole feasible space by properly labeling/splitting the vicinities of the feasible space as optimal/sub-optimal.

The existence of these Bayes classifiers means also the existence of the heuristically inducted top-down binary decision trees (BDTs) for the same classifications of optima/non-optima. The second part of the proof here could not have been possible if it had not been for Guy Blanc‘s, Jane Lange‘s & Li-Yang Tan‘s recent work on heuristically inducted BDTs. In 2020, Guy et al proved that if there exists a minimum (in the sense BDT-size; nothing to do with the underlying optimization problem here) classifier for a given function, then there exists a non-optimally-sized BDT for that same function. I am pointing this out, because the first time I have used a draft/reduced version of this method (back in 2010), I could not explain why/how the BDT would consistently converge to an optimum… Well, now I know – thanks Guy, Jane and Li-Yang!

Solving the AC OPF – Point 2
Even though it kind of stems from Point 1, it is a remark that I wish to separately mention here. The steps of tightening the AC OPF feasible space towards the global optimum seem unaffected by the size of the grid, but follow the number of the decision variables. In the way I have structured the solver, the decision variables are the active and reactive power set-points of the generating resources. This can be particularly valuable when larger grids with fewer resources must be optimized. Given how typical solvers descend along gradients of a feasible space, the size of the latter will affect that descent. Hence, larger grids, meaning greater numbers of voltage angles, magnitudes and flows, will be slowing down the solving descent. In the AC OPF solution I propose, the size of the grid does not matter, provided that the BDT training samples are feasible and adequate. This is particularly interesting, because we can consider how to accelerate this solution via the control intervals of generators, which are typically not continuous and cannot take any value between minimum and maximum (looking for the exact NERC rule and I will cite it here).

Effect of Volatile Resources in Solving the AC OPF
Typically, renewable energy has negligible, if not zero, operating costs, hence, gets dispatched by priority in any electricity market. However, the exact amount of available renewable energy is usually impossible to determine ahead of time and only offered within some forecasted confidence intervals (see forecasting figure below). This means that as the proposed AC OPF solver converges to the global optimum by the successive median cost of the sampled feasible space, the commitment of renewable energy within the AC OPF increases towards the global optimum at a decreasing confidence. In other words, renewable energy will be committed within the AC OPF at lower set-points corresponding to higher forecasting confidence in the first few iterations of the method (higher objective function costs) and at higher set-points of limited confidence when the process converges to global minimum.

As in my last paper, I had the joy to work with another CMU MSc student, who put in some coding work for me; Parth joined me along for the ride on the paper authorship, too! It was fun and very rewarding to substantially advance my past work on machine-learning-driven optimization of electrical grids with volatile renewables, gain better understanding of the methodology and its characteristics, test it rigorously for performance and get it published OA. Next steps are determining appropriate BDT training sizes and focusing on the stochasticity aspect of this AC OPF solver. Feel free to reach out for any ideas for extensions or collaborations; I will be glad to see more applications of this tool!

Blog, Teaching Power Engineering

How I teach Power Engineering

September 2022

Past student (Nick Alexander) presenting his project in one of my courses (2019).

My first contribution to teaching was back in 2008 as an assistant to Prof. Korres and on the graduate-level subject on machine learning (ML) applications within power system control centers. The subject was more of a review on research ideas and publications exploring if and how ML could be useful in real-time operation of electrical grids, as also the planning of their infrastructure. As a junior PhD student under Prof. Hatziargyriou at that time I was just then dipping my toes in the vast sea of research on artificial intelligence (AI) in power systems. Helping teach that course offered some valuable insights.

Graph from this Verge article: https://www.theverge.com/2018/12/12/18136929/artificial-intelligence-ai-index-report-2018-machine-learning-global-progress-research

In those years, ML and AI were very appealing in the mainstream engineering education and research (buildings, components, power systems, circuits, etc), but were themselves going through some introspection within the computer science community. There are several articles pointing out to the relative plateauing of R&D on AI & ML in the later 2005-10 period, followed by exponential growth afterwards. Nevertheless, more traditional engineering fields were warming up to their “digitalization” and reinventing themselves as smart: smart grids, smart buildings, smart materials and so on. Even though this latter push was strong within research circles and several professional initiatives started popping up, in practice, AI & ML applications were taking baby-steps. The tide changed after bold start-ups adopted their value en masse in the mid 2010s.

Looking back to the period of 2005-10 through the lens of  the technology shifts of the later years, made me realize something ‘big’ about higher education teaching. We do not teach to only educate or provide necessary skill-sets; we teach to seed a vision. And I am very cautious with my words here. I do not proclaim that university teaching must be ‘persistently’ forward-thinking. I do not imply that the fundamentals should be in any way glossed over.  I do not believe that there is such a thing as either redundant or rudimentary knowledge. What I am saying though is that higher-ed teachers have a unique challenge: we must read the tea leaves or crystal balls of our “craft” and prepare students of what is to come, even though no one else might be seeing this yet.

Image taken from GSMArena article https://www.gsmarena.com/asymco_pricing_doesnt_affect_smartphone_adoption_in_the_us-news-8982.php

Think about the example of the course I mentioned earlier and the ‘smartening up’ of many economy sectors. The actual computer science field was kind of taking a breather on AI and ML at that time, yet all other engineering fields were bracing confidently about a tech boom relying exactly on AI and ML! If we seek to dissect this paradox a bit, we will see that there were – actually – no tea leaves or crystal balls necessary. Computers and mobile devices were becoming widely available and used a ton by consumers, practically giving educators the low-hanging fruit of inspiration about advanced computing tools.

Image taken from this Economist article https://www.economist.com/graphic-detail/2017/01/16/china-powers-ahead-with-a-new-direct-current-infrastructure

Fast forward to 2018, when I developed, proposed to CMU and started teaching a course, then titled, “Optimization Modeling in Power Systems”. I was reading my “crystal ball” and realizing that the ever present discussion about the failing and ageing grids (first pointed out in the 1990s) was actually spilling dangerously into reality. Add another 3-4 years to that and here we are in 2021 and 2022 witnessing the passing of multi-billion dollar bills that will expand the US electrical grid, several start-ups monetize efficiency for end-customers globally, while China has already taken strides ahead in its high-voltage transmission system backbone. Optimizing all electrical grid operations and planning, which used to be the expertise of engineers with PhDs, is now expected by the MSc graduates of energy engineering programs. My personal story here is that in 2018 and the next couple of years, the “Optimization Modeling in Power Systems” course was attended by barely 5 students – mostly in their PhDs; now I teach a roster of 16 students – more than half of whom are MSc students.

What I mean to point out with the above story is that power and energy engineering education is not straight-forward, since it links to multiple other subjects and fields. I am sad to attest to statements of scholars I admire saying that “power engineering is not science”.  And yet here we are, following a “hunker down pandemic” (which should have reduced energy demand) and a 6-month localized war (which should have not hurt international energy security), which are crippling multiple economies by threatening electricity markets everywhere. Even more ironically, most of these electricity markets could have already had energy independence, had they built out their grids and/or made them much more efficient. Put simply, energy and power educators have to look deep and wide in their research and expertise, and aspire to new subjects and angles (decentralized? autonomous? self-sufficient? resilient?). The energy and power work force is crumbling, several places around the world are still not electrified consistently (let alone have energy security), and energy dependencies are heavy and stretch the globe.

I conclude this blog with a final thought. The energy sector comprises entities and processes that are complex, large and slow to adapt, while the challenges they are faced with and the tools to address them are fast and with much impact. This means that the teaching aspirations of power and energy engineering faculty must be also realistic and delivered with confidence and persistence. As instructors in this field, we must develop the necessary intuition and retain adequate humility in keeping our ear to the ground for the right signs of change. And this last thing is not easy; personally, I have acquired these “skills” with years of practicing engineering and humbling disappointment across several projects I have worked on. Others before and around me are trying through broad involvement in committees, initiatives and working groups. Whatever the way, educating the next generations of power and energy engineers is an urgent duty hanging already over our heads, especially, if we wish to be honest to our vision for the clean energy transition.

Appointed Energy & Power Co-Lead CCAI, News

Power & Energy Community Co-Lead at the CCAI

Aug. 2022

It gives me immense joy to announce that I have joined the core team of the Climate Change AI (CCAI) organization. I will be serving the role of Power & Energy Community Co-Lead, alongside the tireless, devoted and unimaginably energetic CCAI’s co-founder, Dr. Priya Donti,

CCAI has identified climate change as the humankind’s most imminent existential threat, affecting primarily those who are underprivileged – communities of color, impoverished, without access to advanced technology and modern infrastructure. Within CCAI it is also understood that the climate change is a multi-faceted problem, at varying scales and with unique nuances across different sectors. In this sense, Artificial Intelligence (AI) and the computational tools, approaches and frameworks it brings to the table represent the means to implement the coordinated and demanding efforts required to address this threat. However, with AI’s meteoric rise, its adverse impacts come also into scope within a world that attempts strides towards a fairer, more inclusive and more equitable environment and society. CCAI seeks to be the organization that brings together researchers, engineers, entrepreneurs, policy and decision makers, and all stakeholders from the public and private sectors to promptly and consistently create the community, educate the society, inform the infrastructure planning and serve as the global forum that will put AI to the service of climate change mitigation for the sake of each and every life on the planet.

My first couple of weeks within CCAI have been absolutely amazing. The organization has shown immense attention to detail, there are multiple outcomes of hard and coordinated work, the processes are defined clearly and the roles are crisp. Having being part of many volunteer and non-profit organizations for the past 15 years of my work on renewable energy research, CCAI is among the very few that have been so meticulously designed and operated. Combining this with the CCAI’s community’s passion for the cause, the members’ unwavering work ethic since the organization was first rolled out in 2019 and all the plans already in motion across multiple venues, I am certain that CCAI heads out to build and inspire many great accomplishments in this space! If you are reading these lines, you should definitely reach out and engage with the CCAI.

I want to thank from the bottom of my heart Dr. Priya Donti, Marcus Voss, Raphaela Kotsch, Dr. Kasia Tokarska, Dr. Evan Sherwin and the many other CCAI team members for the warm welcome. Let’s do this!

Inertia and Frequency Control by RES at UNIFI Sep. 19, News

Inertia Emulation & Frequency Control at the UNIFI Fall 2022 Seminar Series

August 2022

I am sincerely grateful to the many wonderful colleagues at the UNIFI consortium for having me present my older work on methods to emulate inertia and perform frequency control with wind and photovoltaic generators. My seminar is scheduled on Monday Sep. 19th at 4 pm ET, as part of the Fall 2022 Seminar Series (more information here).

The UNIFI project aims to conduct advanced research, design testing and develop standards on grid-forming inverters. Inverters are the power electronics devices that enable the efficient connection of many renewables to the electrical grids. With the gradual replacement of conventional units by renewables, the roles of the former pass on to the latter. “Grid-forming” is the functionality necessary to establish and maintain a standardized three-phase alternating current that serves some load demand. Inverters have been typically able to perform “grid-forming” at limited off-grid scales, but are now expected to expand it at the level of large interconnected grids.

Typical stages of Load-Frequency Control (LFC) in power systems

In my seminar I will focus mostly on the side of the sources, specifically wind and photovoltaic energy. Traditionally, these generators have been operated on strategies of maximum power absorption. Even though these strategies optimize the use of renewable sources, they are inflexible when load demand varies or the generation from other resources fluctuates. In fact, due to electricity physics any generation-demand imbalance, reflects to changes in the electrical frequency of the alternating currents; this change of frequency can control generators to respond to the load-generation imbalances. With wind and photovoltaic generators at maximum power absorption, responding to frequency signals that requires them to contribute additional power is impossible; hence, the requirement to procure reserves arises. I will review the methods, challenges, some results of real-world testing and expand on how grid-forming functionalities might be affected by inertia emulation and frequency control by wind and photovoltaic units.