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Event When Speaker Title Presentation Material
MESW01 7th January 2019
11:30 to 12:30
Warren Powell A Unified Framework for Stochastic Optimization in Energy
A Unified Framework for Stochastic Optimization in Energy
Warren B. Powell
Dept. of Operations Research and Financial Engineering
Princeton University

Energy systems offer a variety of forms of uncertainty that have to be accommodated to ensure a reliable source of power. The modeling of these sequential decision problems under uncertainty has lacked the kind of canonical framework long enjoyed by deterministic problems. I will introduce a modeling framework that is completely general, which involves three mathematical challenges: 1) machine learning (there are up to five classes of learning problems), 2) uncertainty modeling, and 3) designing policies, which are functions for making decisions. There are two fundamental strategies for creating policies, each of which further divides into two subclasses, creating four classes of policies. These four (meta)classes of policies are universal, in that any method used to solve a sequential decision problem will be drawn from this set. The four classes are illustrated in the context of several applications in energy systems. An energy storage application is then used to demonstrate that each of the four classes of policies might be best depending on the characteristics of the data.


MESW01 7th January 2019
13:30 to 14:30
Andrew Haslett Creating user value through mathematics – the challenge of hidden states
Energy systems are large complex, and expensive, often significantly regulated and delivered through policy interventions. They are paid for through user charges, supported by taxation. Moving away from fossil fuels creates new systems challenges to meet user needs. Understanding these needs is an important element in meeting them, as the supply paradigm changes.Energy use is incidental to daily life – visiting friends, going to work, eating, keeping clean and comfortable etc are the things that matter to users. Control has a critical part to play in managing large loads such as vehicle charging or space heating. How do we understand the user intent behind the energy use and represent it in our mathematics?There are two kinds of hidden states in energy use systems – physical and psychological states. Control systems have direct impact on physical states, and psychological states through the user experience. This talk considers the functional needs of the user, their need for agency and the transaction cost from their perspective of engaging with the control systems for their house or car. It does not consider consumer segmentations on functional, affective and symbolic dimensions. Although in principle, hidden physical states can be discovered by measurement, often this is not practical.Control of heating systems and vehicle charging should aim to deliver satisfied users at lowest system cost. Although the talk will not cover in detail how to estimate current supply side state and forecast its co-evolution with aggregate demand over the control period, it will consider in general terms how this might interact with user needs and willingness to pay.The talk will not cover mathematical solutions to the challenges. It is intended to illuminate the real-world problems that will require advanced mathematics to deliver effective solutions.
MESW01 7th January 2019
14:30 to 15:30
Patrick Panciatici (R)evolution of large electrical systems: Needs and Challenges
Historical power systems are emblematic examples of system of systems. Now, we are living a major evolution and perhaps a revolution in electrical grids. The term “smart grid” is used everywhere without a very precise definition. The concept of cyber-physical System of Systems seems a good framework to capture the essence of this (r)evolution.
The “cyber” layer is going to play a key role in the system reliability. Indeed, more and more controls are embedded in subsystems which become “intelligent” and partially autonomous. The system behavior will be imposed by the interactions between these “intelligent” agents driven by local pieces of software rather than by physical laws.
Different examples showing this trend will be presented from synchronization of inverters to aggregated game for demand response management.
We must pay attention; possible negative impacts could occur when a certain critical level of penetration of new devices and processes will be reached. It is not easy, we must review our historical approach in order to specify behaviors which were yesterday imposed by physical laws and didn’t need any specification and which will be tomorrow defined by software in local controls.
MESW01 7th January 2019
16:00 to 17:00
Hung-po Chao Electricity market reform to enhance the energy market pricing mechanism: observations from PJM
Electricity market reform to enhance the energy and reserve pricing mechanism: Observations from PJM

by Hung-po Chao

Abstract

For more than 20 years, the PJM wholesale markets have successfully worked to promote competition, produce stable energy prices and attract competitive resource investments to ensure efficient and reliable operations. However, in recent years, the PJM markets have been undergoing a significant transition. While such transitions have also occurred elsewhere, each has resulted in some unique challenges.
This paper examines issues regarding efficient price formation in the energy and reserve markets under non-convex. In principle, with non-convexity, no market clearing prices exist without side payments. In a pool-based wholesale electricity market, one of the greatest challenges unmatched in scale and complexity is that in the day-ahead and real-time markets, after running a mixed integer programming model for solving a security constrained economic commitment and dispatch problem to determine the market allocations, a pricing model is employed to determine the market clearing prices and side payments in ways that must promote economic efficiency, consistent incentives and revenue sufficiency.

One of the most severe limitations of the current pricing mechanism (locational marginal pricing or LMP) is that LMP is not incentive compatible. This limitation has caused adverse effects in operations and investments. Building on the classic Lagrangian dual formulation, this paper extends the existing pricing method in a way that is dominant strategy incentive compatible in a competitive market with a large number of independent suppliers, and like a Vickery-Clark-Grove mechanism, truthful revelation would become a dominant strategy. The convex hull pricing method or called the extended LMP, is a well-known case which yields the minimum uplift. Moreover, integer relaxation is a computationally practical implementation that ensures incentive compatibility producing generally good, and often exact, approximations to ELMP solutions if the cost functions are homogeneous of degree one.

As market continues to evolve with flattening demand growth, flattening supply curves with low marginal costs and penetration of renewable resources with zero marginal cost, non-convex conditions will become growingly important. A key advantage of enhanced pricing mechanism is that it would form price signals in ways that would foster economic efficiency in operations and investments, demand participation and market innovation.
MESW01 8th January 2019
09:00 to 10:00
Ben Godfrey Market design for flexible distribution networks
MESW01 8th January 2019
10:00 to 11:00
Gilles Louppe Automated parameter inference and data modelling with deep learning
MESW01 8th January 2019
11:30 to 12:30
Javad Lavaei Computational Methods for Nonlinear Power Operational Problems: Convex Reformulations and Near-Linear Time Algorithms
Co-authors: Somayeh Sojoudi (UC Berkeley), Richard Zhang (UC Berkeley), Salar Fattahi (UC Berkeley), Igor Molybog (UC Berkeley), Ming Jin (UC Berkeley), SangWoo Park (UC Berkeley)In this talk, we will study a set of nonlinear power optimization and decision-making problems, namely power flow, optimal power flow, state estimation and topology error detection. We will propose different conic relaxation and approximation techniques to solve these nonconvex problems. We will prove that such conic problems could be solved in near linear time due to intrinsic properties of real-world power networks. We will offer case studies on systems with as high as 14,000 nodes.
MESW01 8th January 2019
13:30 to 14:30
Juan Miguel Morales González Electricity demand forecasting and bidding via data-driven inverse optimization
A method to predict the aggregate demand of a cluster of price-responsive consumers of electricity is discussed in this presentation. The price-response of the aggregation is modelled by an optimization problem whose defining parameters represent a series of marginal utility curves, and minimum and maximum consumption limits. These parameters are, in turn, estimated from observational data using an approach inspired from duality theory. The resulting estimation problem is nonconvex, which makes it very hard to solve. In order to obtain good parameter estimates in a reasonable amount of time, we divide the estimation problem into a feasibility problem and an optimality problem. Furthermore, the feasibility problem includes a penalty term that is statistically adjusted by cross validation. The proposed methodology is data-driven and leverages information from regressors, such as time and weather variables, to account for changes in the parameter estimates. The estimated price-response model is used to forecast the power load of a group of heating, ventilation and air conditioning systems, with positive results. We also show how this method can be easily extended to be used for demand-side bidding in electricity markets.
MESW01 8th January 2019
14:30 to 15:30
Anna Scaglione On modeling dispatchable loads in grid operation: the good, the bad and the ugly
The past ten years of research had produced a variety of models for managing flexible loads, paving the way for addressing congestion in the grid by enabling a more efficient dynamic pricing of electricity.  However, real change has been hard to come by in practice. The goal of this talk is to review such models, highlighting the difference between distributed algorithms, that seek to decompose the problem,  and aggregate representations that map large populations of flexible loads onto spinning reserves. The objective is to highlight the challenges that exist in transforming and remaining compatible with established retail and wholesale market practices and legacy systems and how new abstractions may be necessary to rip the benefits of flexible load.
MESW01 8th January 2019
16:00 to 17:00
Jean-Yves Le Boudec Real-time operation of micro-grids
Co-authors: Paolone, Marione (EPFL), Reyes, Lorenzo (EPFL), Bernstein, Andrey (NREL), Wang, Cong (EPFL)

Very large amounts of renewable electricity generation, combined with a large penetration of plug-in electric vehicles, may cause considerable stress to electrical grids and to the system of spinning reserves. This problem can be solved by controlling the huge number of electrical resources that are located in distribution grids, such as thermal loads, stationary batteries, charging stations and curtailable power generators. However, this poses a number of new challenges in terms of online computation and scalability. In this talk we discuss how these challenges are solved by COMMELEC, a system of real-time software agents developed at EPFL and deployed in several grids. We also introduce elements of a theory about how uncertainty on power injections affects the controllability of the grid.

Slides available at http://icawww1.epfl.ch/PS_files/MESW01-LEB-2019.pdf
MESW01 8th January 2019
17:00 to 18:00
Poster session
MESW01 9th January 2019
09:00 to 10:00
Rene Aid Optimal Electricity Demand Response Contracting
Co-authors: Dylan Possamaï (Columbia University), Nizar Touzi (Ecole Polytechnique)We address the moral hazard underlying demand response contracts in electricity markets, we formulate the interaction problem between producer and the consumer by means of a Principal-Agent problem. The producer, acting as the Principal, is subject to the generation costs related to the level and the volatility of generation, thus accounting for the limited flexibility of production. Based on the continuous-time consumption of the Agent, representing a single consumer, she sends an incentive compensation in order to encourage him to reduce his average consumption and to improve his responsiveness defined as the volatility of his consumption. We provide closed-form expression for the optimal contract that maximizes the utility of the principal in the case of linear energy valuation. We provide rationality for the form of the observed demand-response contracts, that is a fixed premium for enrolment and a proportional price for the energy consumed. However, we show that the pre mium should be an increasing function of the duration of the demand response event. Further, we show that optimal contracting allows the system to bear more risk as the resulting consumption volatility may increase, but the corresponding risk is now optimally shared between the two actors. We calibrate of our model to publicly available data of the London demand-response trial, and we infer that a significant increase of responsiveness can be expected by the implementation of the control of the consumption volatility. We find that the responsiveness control would lead a significant increase of the value of the producer. We examine the stability of our explicit optimal contract by performing appropriate sensitivity analysis, and show that the linear approximation of the energy value function of the consumer provides a robust approximation of the optimal contract.
MESW01 9th January 2019
10:00 to 11:00
Lang Tong Towards seemless operation: a new look at interface scheduling and market operation
MESW01 9th January 2019
11:30 to 12:30
Anthony Papavasiliou Transmission capacity allocation in zonal electricity markets

We propose a novel framework for modelling zonal electricity markets, based on projecting the constraints of the nodal network onto the space of the zonal aggregation of the network. The framework avoids circular definitions and discretionary parameters, which are recurrent in the implementation and study of zonal markets. Using this framework, we model and analyze two zonal market designs currently present in Europe: flow-based market coupling (FBMC) and available-transfer-capacity market coupling (ATCMC). We develop cutting-plane algorithms for simulating FBMC and ATCMC while accounting for robustness of imports/exports to single element failures, and we conduct numerical simulations of FBMC and ATCMC for a realistic instance of the Central Western European system under 768,000 different operating conditions. We find that FBMC and ATCMC are unable to anticipate congestion of branches interconnecting zones and branches within zones, and that both zonal designs achieve similar overall cost efficiencies 0.5% difference in favour of FBMC), while a nodal market design largely outperforms both of them (5.9% better than FBMC). These findings raise the question of whether it is worth for more European countries to switch from ATCMC to FBMC, instead of advancing directly towards a nodal design.

MESW01 10th January 2019
09:00 to 10:00
Florian Doerfler Real-time feedback optimization on the power flow manifold
I will focus on online optimization of AC power systems in closed loop. In contrast to the conventional approach where an optimal power flow solution is computed offline and online controllers enforce these set-points, our objective is to design an adaptive feedback controller that steers the system robustly and in real time to the optimal operating point. Our methodological approach is based on online algorithms for manifold optimization that can be applied in feedback with real-time measurements and actuation. We treat the power flow equations as implicit constraints that are naturally enforced by the physics and hence give rise to the power flow manifold. Based on our theoretical results for this type of optimization problems, we propose a projected gradient descent scheme on the power flow manifold. In detailed simulation case studies we validate the performance of our algorithm and show that it reliably tracks the time-varying optimum of the underlying AC optimal power flow problem.

Co-authors: Adrian Hauswirth (ETH Zurich), Saverio Bolognani (ETH Zurich), Gabriela Hug (ETH Zurich)
MESW01 10th January 2019
10:00 to 11:00
Andrea Simonetto Time-Varying Optimization: Algorithms and Applications in Power Systems
Continuously varying optimization programs have appeared as a natural extension of time-invariant ones when the cost function, the constraints, or both, depend on a time parameter and change continuously in time. This setting captures relevant control, signal processing, and machine learning problems. Recently, running and prediction-correction methods have been put forward to set up iterative algorithms that sample the continuously-varying optimization program at discrete time steps track the optimizer(s) trajectory while it evolves in time up to an asymptotical error bound.In this talk, we will review current state-of-the-art algorithms in time-varying optimization, with a special emphasis on applications in power grids. We will touch upon time-varying AC optimal power flow problems, real-time optimization of aggregations of distributed energy resources, as well as dynamic distribution state estimation.
MESW01 10th January 2019
11:30 to 12:30
Emiliano Dall'anese Feedback-based online algorithms for time-varying optimization: theory and applications in power systems
The talk focuses on the synthesis and analysis of online algorithmic solutions to control systems or networked systems based on performance objectives and engineering constraints that may evolve over time. Particular emphasis is given to applications in power systems operations and control. The time-varying optimization formalism is leveraged to model optimal operational trajectories of the systems, as well as explicit local and network-level constraints. The design of the algorithms then capitalizes on an online implementation of primal-dual projected-gradient methods; the gradient steps are, however, suitably modified to accommodate actionable feedback in the form of measurements from the network -- hence, the term feedback-based online optimization. By virtue of this approach, the resultant running algorithms can cope with model mismatches in the algebraic representation of the system states and outputs, they avoid pervasive measurements of exogenous inputs, and they naturally lend themselves to a distributed implementation. Under suitable assumptions, Q-linear convergence to optimal solutions of a time-varying convex problem is shown. On the other hand, under a generalization of the Mangasarian-Fromovitz constraint qualification, sufficient conditions are derived for the running algorithm to track a Karush-Kuhn-Tucker point of a time-varying nonconvex problem. Examples of applications in power systems will be provided.

Joint work with: A. Simonetto, Y. Tang, A. Bernstein, and S. Low.
MESW01 10th January 2019
13:30 to 14:30
Andy Sun A new distributed algorithm for nonconvex network flow problems with convergence guarantees
MESW01 10th January 2019
14:30 to 15:30
Pär Holmberg Central- versus Self-Dispatch in Electricity Markets

In centralized markets, producers submit detailed cost data to the day-ahead market, and the market operator decides how much should be produced in each plant. This differs from decentralized markets that rely on self-commitment and where producers send less detailed cost information to the operator of the day-ahead market. Ideally centralized electricity markets would be more effective, as they consider more detailed information, such as start-up costs and no-load costs. On the other hand, the bidding format is rather simplified and does not allow producers to express all details in their costs. Moreover, due to uplift payments, producers have incentives to exaggerate their costs. As of today, US has centralized wholesale electricity markets, while most of Europe has decentralized wholesale electricity markets. The main problem with centralized markets in US is that they do not provide intra-day prices which can be used to continuously up-date the dispatch when the forecast for renewable output changes. Intra-day markets are more flexible and better adapted to deal with renewable power in decentralized markets. Iterative intra-day trading in a decentralized market can also be used to sort out coordination problems related to non-convexities in the production. The downside of this is that increased possibilities to coordinate increase the risk of getting collusive outcomes. Decentralized day-ahead markets in Europe can mainly be improved by considering network constraints in more detail.

MESW01 10th January 2019
16:00 to 17:00
Sean Meyn Irrational Agents and the Power Grid
For decades power systems academics have proclaimed the need for real time prices to create a more efficient grid.   The rationale is economics 101: proper price signals will lead to an efficient outcome.   In this talk we will review a bit of economics 101;  in particular, the definition of efficiency.   We will see that the theory supports the real-time price paradigm, provided we impose a particular model of rationality.    It is argued however that this standard model of consumer utility does not match reality:   the products of interest to the various "agents" are complex functions of time.  The product of interest to a typical consumer is only loosely related to electric power -- the quantity associated with price signals.   There is good news:  an efficient outcome is easy to describe, and we have the control technology to achieve it.  We need supporting market designs that respect dynamics and the impact of fixed costs that are inherent in power systems engineering, recognizing that we need incentives on many time-scales.     Most likely the needed economic theory will be based on an emerging theory of efficient and robust contract design.
MESW01 10th January 2019
17:00 to 18:00
Orcun Karaca Core-Selecting Mechanisms in Electricity Markets
Previous work on electricity market auctions considers the pay-as-bid and the locational marginal pricing (LMP) mechanisms. In both mechanisms, generators can bid strategically to influence their profits since these mechanisms do not incentivize truthful bidding. As an alternative, under the Vickrey-Clarke-Groves mechanism, truthful bidding is the dominant-strategy Nash equilibrium. Despite having this theoretical virtue, coalitions of participants can influence the auction outcome to obtain higher collective profit. In this talk, we characterize the exact class of coalition-proof mechanisms as the core-selecting mechanisms. In addition to being coalition-proof, we show that these mechanisms generalize the economic rationale of the LMP mechanism. Namely, these mechanisms are the exact class of mechanisms that ensure the existence of a competitive equilibrium in linear/nonlinear prices. This implies that the LMP mechanism is also core-selecting, and hence coalition-proof. In contrast to the LMP mechanism, core-selecting mechanisms exist for a broad class of electricity markets, such as ones involving nonconvex costs and nonconvex constraint sets. In addition, they can approximate truthfulness without the price-taking assumption of the LMP mechanism. Finally, we show that they are also budget-balanced. Our results are verified with case studies based on optimal power flow test systems and the Swiss reserve market.
MESW01 11th January 2019
09:00 to 10:00
Clemence Alasseur A mean-field game model for management of distributed storages for the power system
We consider a stylized model for a power network with distributed local power generation and storage. This system is modeled as a network connection of a large number of nodes, where each node is characterized by a local electricity consumption, has a local electricity production (e.g. photovoltaic panels), and manages a local storage device. Depending on its instantaneous consumption and production rate as well as its storage management decision, each node may either buy or sell electricity, impacting the electricity spot price. The objective at each node is to minimize energy and storage costs by optimally controlling the storage device. In a non-cooperative game setting, we are led to the analysis of a non-zero sum stochastic game with N players where the interaction takes place through the spot price mechanism. For an infinite number of agents, our model corresponds to an Extended Mean-Field Game (EMFG). We are able to compare this solution to the optimal strategy of a central planner and in a linear quadratic setting, we obtain and explicit solution to the EMFG and we show that it provides an approximate Nash-equilibrium for N-player game.
MESW01 11th January 2019
10:00 to 11:00
Simon Tindemans Optimal dispatch of heterogeneous batteries to maximise security of supply
Co-authors: Michael Evans (Imperial College London), David Angeli (Imperial College London). We consider the problem of dispatching a fleet of heterogeneous batteries (i.e. energy-constrained generators) to prevent or minimise power shortage scenarios. In the general case on which nothing is known about future power requirements, three significant results are derived. First, a greedy policy exists that uniquely maximises the time until the fleet is first unable to supply demand. This policy implicitly establishes a `feasible set’ of power requests that can be satisfied by the fleet. Second, an analytical transformation is presented that expresses this feasible set in a graphical form instead of a procedural form (i.e. by invoking the policy). The graphical representation also provides a measure of the flexibility penalty due to heterogeneity. Third, it is shown that the policy can be extended to handle scenarios with unavoidable power shortages, in which case it minimises the energy not supplied. The fact that the greedy policy results in best-case security-of- supply performance suggests it is suitable to be used as a reference policy for battery dispatch within system adequacy studies. We present a discrete time algorithm that is tailored for this use case and show results for a Great Britain case study.
MESW01 11th January 2019
11:30 to 12:30
Meng Wang High-dimensional data analytics using low-dimensional models in power systems
Phasor Measurement Units and smart meters provide fine-grained measurements to enhance the system visibility to the operators and reduce blackouts. The recent wealth of data is revolutionizing the conventional model-based power system monitoring and control to a modern data-driven counterpart. One recent research interest is to develop computationally efficient data-driven methods to convert data into information.

This first part of the talk discusses our proposed missing data recovery and error correction methods for synchrophasor data. The low data quality currently prevents the implementation of synchrophasor-data-based real-time monitoring and control. This second half of the talk discusses our proposed privacy-preserving data collection framework for smart meters. We developed load pattern extraction methods from highly noisy and quantized smart meter data such that the estimated load pattern is only accurate for the operator, and the information is obfuscated to a cyber intruder with partial measurements. The common theme of the two projects is to exploit the intrinsic low-dimensional structures in the data to develop fast algorithms for nonconvex problems with analytical performance guarantees.
MESW01 11th January 2019
13:30 to 14:30
Pierre Gaillard Bandit algorithms for power consumption control
We are interesting in optimizing price signals sent by an electricity provider to its customers so has to modify their electricity consumption. We formulate this problem as a sequential problem in which the electricity provider send signals and sequentially observes corresponding feedback. The mathematical theory of bandits will be adapted to this exploration - exploitation problem.

This is a joint work with Margaux Brégère, Gilles Stoltz and Yannig Goude
MESW01 11th January 2019
14:30 to 15:30
John Moriarty, Ana Busic, Steven Low, Louis Wehenkel, Pierre Pinson Wrap-up discussion session
OFBW42 23rd January 2019
10:00 to 10:10
Jane Leeks, David Abrahams Welcome and Introduction
OFBW42 23rd January 2019
10:10 to 10:20
John Moriarty Outline and Summary of INI Research Programme - Mathematics of Energy Systems
OFBW42 23rd January 2019
10:20 to 10:55
Andy Philpott Planning a 100% Renewable Electricity System
OFBW42 23rd January 2019
10:55 to 11:30
James Cruise Modelling Flexibility for Future Networks
OFBW42 23rd January 2019
11:50 to 12:25
Damian Giaouris Decarbonisation, Decentralisation and Digitisation of Energy through Hybrid Storage Systems
OFBW42 23rd January 2019
12:25 to 12:30
Patrick Panciatici (R)evolution of Large Electrical Systems
OFBW42 23rd January 2019
12:30 to 12:35
Michael Coulon Wind Park Valuation and Risk Management in Germany
OFBW42 23rd January 2019
12:35 to 12:40
Valentin Courgeau Continuous-time Modelling of Energy Networks
OFBW42 23rd January 2019
12:40 to 12:45
Paulina Rowinska Blowing in the Wind
OFBW42 23rd January 2019
12:45 to 12:50
Thomas Marge Integrated Offshore Wind Farm Design: Optimising Micrositing and Cable Layout Simultaneously
OFBW42 23rd January 2019
12:50 to 12:55
Tommaso Nesti Emergent Failures and Cascades in Power Grids: A Large Deviations Approach
OFBW42 23rd January 2019
14:00 to 14:30
Cathy McClay Industry Talk: The Electricity System Operator Perspective
OFBW42 23rd January 2019
14:30 to 15:00
Alastair Davies Renewable Energy Focus: Calculating the Carbon Content of Power Imports
OFBW42 23rd January 2019
15:00 to 15:30
Jonas Ströjby An Industry Perspective on Managing Flexibility
OFBW42 23rd January 2019
15:50 to 16:20
Iain Miller Future Energy Systems Challenges - a Distribution Operator Perspective
OFBW42 23rd January 2019
16:20 to 17:00
David Newbery Panel Discussion - can we/should we do 100% Renewables?
MES 11th February 2019
13:30 to 16:00
Research Tracks - Pierre Pinson and Yanning Goude
MES 20th February 2019
15:00 to 16:00
Said Hamadene Zero-sum optimal switching games motivated by energy applications
\author{S.Hamadene, LMM, Le Mans University, France.\\ Jww: R.Martyr (QMUL) and J.Moriary (QMUL).} \begin{abstract} In this paper we study continuous-time two-player zero-sum optimal switching games on a finite horizon. Using the theory of doubly reflected backward stochastic differential equations (DRBSDE) with interconnected barriers, we show that this game has a value and an equilibrium in the players' switching controls. \end{abstract}
MES 25th February 2019
09:30 to 12:00
Stephen Haben Data Analytics for Short Term Operation
MES 25th February 2019
14:00 to 16:00
Assisting control room operators with artificial intelligence : Antoine Marot
MES 26th February 2019
09:30 to 10:30
Pierre Gaillard Forecasting electricity consumption by aggregating forecasts
MES 26th February 2019
10:40 to 12:00
Robust and online learning with the BOA algorithm : Olivier Wintenberger
MES 27th February 2019
09:30 to 12:00
Qiwei Yao Multiple forecasting based on time series PCAs
MES 27th February 2019
13:30 to 16:00
Jethro Browell Aspect of High Dimensional Energy Modelling and Forecasting
MES 28th February 2019
09:30 to 10:30
Generalised additive models for electricity demand forecasting : Matteo Fasiolo
MES 28th February 2019
10:30 to 12:00
Florian Ziel Marginal copula scores for multivariate foecasting evaluation
MES 28th February 2019
13:30 to 17:00
Hierarchical probabilistic forecasting of electricity demand with smart meter data : Souhaib Ben Taieb
MES 4th March 2019
10:00 to 12:00
Janusz Bialek Power System Operation
MES 4th March 2019
14:00 to 16:00
Janusz Bialek Power System Modeling
MESW02 18th March 2019
09:45 to 10:45
David Newbery Market and regulatory design for renewables dominated systems

A low-carbon electricity system has a dominance of plant with high fixed costs and low variable costs, backed up with flexible controllable generation. In a liberalised market all required new investment will need to cover its full cost, necessitating payments for capacity, energy and quality of service to deliver reliability (long-term adequacy) and security of supply (short term resilience to shocks). Their value varies over time and space. Current liberalised markets lack futures markets and proper spatial signals to guide investment. Networks have always had high fixed, very low variable costs and long-run marginal costs well below average costs, creating challenges for setting network tariffs. Consumers need to pay for these generation and network services in ways that are efficient, equitable and acceptable, in a system that offers a greater range of scale and location (voltage level) of options for delivering the three services of capacity, energy and quality of servic e, requiring more careful tariff design than hitherto. ICT can help deliver but cannot avoid the tensions between efficiency, equity and acceptability. The talk will identify the challenges, the theoretical solutions drawing on the mature subject of public economics, and possible solutions.

MESW02 18th March 2019
11:15 to 12:00
Natalia Fabra Competition among Renewables

 

We model strategic behavior of renewable suppliers when competing in electricity auctions. We introduce renewables' intermittency by assuming that firms' available capacities are random and private information. In equilibrium, bid functions are a smooth decreasing function of firms' realized capacities. Thus, at times when there is more renewables' availability, supply functions shift outwards and downwards, leading to reductions in the market price. An increase in correlation between firms' available capacities strengthens competition non-monotonically. Keywords: electricity, competition, auctions.

Natalia Fabra and Gerard Llobet (CEMFi) 

 

MESW02 18th March 2019
12:00 to 12:45
Bert Willems Investments in flexible generation capacity in an energy-only market

A de-carbonized energy system will require sufficient investments in flexible generation capacity. The goal of this paper is to study how market design affects investments in flexible generation capacity.  If markets are perfectly competitive, production costs are convex, and demand is perfectly predictable, then independent energy-only market (one for each hour of operation) will lead to efficient market outcomes both in the short-run (market operation) and in the long-run (investment levels and levels of flexibility). We will relax the assumption of convex production costs and predictable demand and analyze first whether small competitive investors have the right incentives to invest in flexible generation. In a second step we intend to derive equilibrium investment levels. 

MESW02 18th March 2019
13:45 to 14:30
Richard Green Optimal storage, investment and management under uncertainty - It is costly to avoid outages!

 

We show how electricity storage is operated optimally when the load net of renewable output is uncertain. We estimate a diurnal Markov-process representation of this residual load in Germany in 2011 to 2015 on an hourly basis and design a simple dynamic stochastic electricity system model with non-intermittent generation technologies and storage. We derive the optimal storage, generator output and capacity levels.  If storage capacity replaces some generation capacity, the optimal storage strategy must balance arbitrage (between periods of high and low marginal cost) against precautionary storage to ensure energy is available throughout a long peak in net demand. We then solve the model numerically under realistic conditions and compare the results to perfect foresight findings. We show that a perfect foresight model would over-estimate the cost-saving potential of energy storage by 18%, as it takes up arbitrage opportunities that ignore the need for precautionary storage.
Joint work with Joachim Geske.

 

MESW02 18th March 2019
14:30 to 15:15
Pär Holmberg Capacity mechanisms and the technology mix in competitive electricity markets

The paper is co-authored with Robert Ritz at Judge Business School and EPRG, University of Cambridge.

Capacity mechanisms are playing a growing role as part of electricity market design in Europe, North America and other jurisdictions. Yet their role remains hotly debated with some electricity systems retaining an "energy-only" market design without apparent need for capacity payments. In this paper, we introduce a new model of a capacity mechanism in a market with a continuum of generation technologies. We consider two policy instruments: a wholesale price cap and a capacity payment (or procured capacity volume). We show that some combinations of policy instruments will result in socially optimal market investments. Changing capacity payments and the price cap will only influence investments in peak generation plants. Investments improve system reliability, which is a public good. We find that capacity payments can be used to internalize this externality. We also find that capacity payments can be used to mitigate market power for a given social welfare level.

MESW02 18th March 2019
15:45 to 16:30
Eddie Anderson The role of submodularity in capacity auctions

 

 

We consider a capacity auction in which multiple supplying firms offer bids, and an agent, the buyer, selects which bids to accept. Payments may depend on the set of bids accepted. We consider the role played by a submodularity property for the social welfare function in terms of the set of participating firms. We show how submodularity leads to good properties for the equilibrium. We illustrate this through discussion of a case where the buyer faces uncertain demand and there are separate costs incurred by the suppliers for making capacity available (reservation costs) and for delivering against the required demand (execution costs). We demonstrate that when marginal costs are constant the submodularity property holds, and in equilibrium each supplier makes a profit equal to their marginal contribution and the overall expected welfare is maximized. Eddie Anderson (Lusheng Shao and Bo Cheng)

 

 

MESW02 18th March 2019
16:30 to 17:15
Hannes Weigt Long Term Electricity Market Design: Pricing Quality?

 

 

Future electricity market design needs to address a set of challenges to provide for efficient investment, supply and consumption decisions. The objective of this paper is to sketch those problems and challenges embedded along the electricity value chain and identify how they are altered when shifting from a pre-dominantly centralized nuclear and fossil system towards a system with a mix of central and decentralized elements as well as a high share of intermittent renewable generation. Of particular concern is the mismatch of incentive structures (i.e. between highly dynamic wholesale markets setting incentives for large scale investors and average tariff structures on the end user side setting incentives for prosumers) as well as the security of supply dimension (i.e. shifting from a public good character within regulated systems towards a potential private good in a system characterized by smart control structures). Based on those structures we aim to derive potential market and price setting designs suited for a future electricity market. Frank C. Krysiak and Hannes Weigt

 

 

MESW02 19th March 2019
09:00 to 10:00
Richard O'Neill Incentives, Regulation and Analysis for Investments in ISO Markets
The presentation will examine the history of US Independent System Operator (ISO) markets, mistakes that were made and corrected, how they are regulated, and how theoretical sustainable economic efficiency can be achieved.  In addition, we examine how efficient transmission planning in theory is achieved and the problems associated with implementing it.  Lastly, we will address the critical role optimization software and approximations must play to achieve these objectives.
MESW02 19th March 2019
10:00 to 10:45
Sarah Ryan Assessing Potential Benefits of Increased Gas-Electric Coordination by Stochastic Optimization
Natural gas is the single largest fuel source for electricity generation in the US. Its share of electric energy generated is expected to increase through 2050 while the shares attributed to coal and renewable sources approximately swap their values.  However, electric power generation accounts for only about a third of gas consumption and this fraction is expected to remain nearly constant. Despite some recent regulatory changes, the markets for gas and electricity are independent and not well coordinated. This lack of coordination combined with uncertainty associated with variable renewable generation creates risks for both the generators that procure gas by interruptible contracts and the system operators charged with maintaining both reliability and low wholesale electricity prices. We formulate stochastic programming models for daily unit commitment and dispatch with uncertain wind generation to represent operation both within the current uncoordinated markets and under a hypothetical integration of the gas and electricity systems.  Comparison of cost and reliability metrics across models allows an estimation of the potential benefits of increased coordination between the two systems.

Sarah Ryan and Dan Hu
MESW02 19th March 2019
11:15 to 12:00
Ramteen Sioshansi Merchant Storage Investment in a Restructured Electricity Industry
Restructuring and liberalisation of the electricity industry creates opportunities for investment in energy storage, which could be undertaken by a profit-maximising merchant storage operator. Because such a firm is concerned solely with maximising its own profit, the resulting storage-investment decision may be socially suboptimal (or detrimental). This paper develops a bi-level model of an imperfectly competitive electricity market. The modelling framework assumes electricity-generation and storage-operations decisions at the lower level and storage investment at the upper level. Our analytical results demonstrate that a relatively high (low) amount of market power in the generation sector leads to low (high) storage-capacity investment by the profit-maximising storage operator relative to a welfare maximiser. This can result in net social welfare losses with a profit-maximising storage operator compared to a no-storage case. Moreover, there are guaranteed to be net social welfare losses with a profit-maximising storage operator if the generation sector is sufficiently competitive. Using a charge on generation ramping between off- and on-peak periods, we induce the profit-maximising storage operator to invest in the same level of storage capacity as the welfaremaximising firm. Such a ramping charge can increase social welfare above the levels that are attained with a welfare-maximising storage operator.

Afzal S. Siddiqui, Ramteen Sioshansid and Antonio J. Conejo
MESW02 19th March 2019
12:00 to 12:45
Ben Hobbs Sources and Implications of Inaccuracies in Capacity Credit Calculations: A Static Analysis of Electric Generation Capacity Markets

A static (single shot) model of capacity investments for thermal and renewable resources under energy and capacity markets as well as energy price caps is considered. Under assumptions of risk neutrality and continuous capacity investment variables, it is proven that there exists a capacity (in $/MW capacity credit/yr) and a set of capacity credits by generation type (MW credit/MW nameplate capacity) that supports the most efficient mix of capacity types. However, capacity credits are often set for political reasons, fail to reflect dynamics of levels of penetration upon the marginal contribution of capacity to reliability, or are estimated considering inadequate sample sizes of variable energy output. Through market simulations for an ERCOT-like system, the genmix and efficiency impacts of credits deviating from first-best levels are calculated for various scenarios of erroneous credits. (Co-author: Cynthia Bothwell, US Dept. Energy, Office of Energy Efficiency & Renewable Energy)

MESW02 19th March 2019
13:45 to 14:30
Claudia Sagastizabal A Two-Stage Model for Planning Energy Investment under Uncertainty
We consider risk-averse stochastic programming models for the Generation and Expansion Planning(GEP) problem with investment decisions in the first stage and generation variables of recourse, decided in a second stage. The resulting problem is coupled both along scenarios and along power plants. To achieve decomposition, we combine the Progressive Hedging approach in [1] with a suitable duplication of variables [2]. The resulting nonsmooth dual function can then be solved with an inexact dual proximal bundle method, as in [3]. The procedure defines a primal-dual sequence that, under reasonable assumptions, is shown to converge to a primal-dual solution of the original problem.

Claudia Sagastizabal and F. Atenas

MESW02 19th March 2019
14:30 to 15:15
Anthony Papavasiliou Market Design Considerations for Scarcity Pricing: A Stochastic Equilibrium Framework
We develop a stochastic equilibrium framework for analyzing variations of two-settlement system in which energy and reserve capacity is traded in a day-ahead market followed by a real-time market. The framework is aimed at analyzing the impact of various short-term market design decisions on the remuneration of reserve capacity under operating reserve demand curves. The proposed framework accounts for risk aversion, real-time uncertainty, and a relaxed representation of unit commitment decisions. The framework can be used for analyzing the implication of various market design choices on the back-propagation of real-time prices. These choices include (i) the presence or absence of a real-time reserve capacity market, (ii) the simultaneous or sequential clearing of reserves and energy in day-ahead markets, and (iii) the presence or absence of virtual bidding. We propose a decomposition heuristic for solving the resulting non-convex equilibrium problem. We apply our framework for the analysis of market design choices on the remuneration of reserves in the Belgian electricity market.
MESW02 19th March 2019
15:45 to 16:30
Jong Shi Pang Two-stage Stochastic Programming with Linearly Bi-parameterized Quadratic Recourse
This paper studies the class of two-stage stochastic programs (SP) with a linearly bi-parameterized recourse function defined by a convex quadratic program. A distinguishing feature of this new class of stochastic programs is that the objective function in the second stage is linearly parameterized by the first-stage decision variable, in addition to the standard linear parameterization in the constraints. Inspired by a recent result that establishes the difference-of-convexity (dc) property of such a recourse function, we analyze the almost-sure subsequential convergence of a successive sample average approximation (SAA) approach combined with the difference-of-convex algorithm (DCA) for computing a directional derivative based stationary solution of the overall non- convex stochastic program. Under a basic setup, the analysis is divided into two main cases: one, the problem admits an explicit, computationally viable dc decomposition with a differentiable con- cave component, based on which the discretized convex subproblems to be solved iteratively can be readily defined; and two, an implicit bivariate convex-concave property can be identified via a certain smoothing of the recourse function. The first case includes a strictly convex second-stage objective and a few special instances where the second-stage recourse is convex but not strictly convex. A general convex second-stage recourse function belongs to the second main case; this case requires the introduction of the notion of a generalized critical point to which the almost-sure subsequential convergence of the combined SAA and DCA is established. Overall, this research provides the first step in the investigation of this class of two-stage SPs that seemingly has not been, until now, the object of a focused study in the vast literature of computational two-stage stochastic programming.
MESW02 19th March 2019
16:30 to 17:15
Luiz Augusto Barroso Harmonizing energy planning and market mechanisms to ensure supply adequacy in electricity markets
 The objective of this talk is twofold: we first describe multi-stage stochastic optimization models with a very detailed representation of the system components that have been used to carry out realistic generation-transmission planning studies in large systems with strong renewable penetration. The optimization framework of these models allows not only to extract meaningful outputs - such as probabilistic-reserve needs, cost of flexibility - as well as they can be used to factor planning outcomes in auctions for long-term reliability products to ensure supply adequacy. We will show that attributes of generation plants can be factored in the procurement process of these products via primal (price-based) or dual (quantity-based) outcomes of the optimization-based framework for the planning studies.
MESW02 20th March 2019
09:00 to 10:00
Sonja Wogrin Hierarchical optimisation and equilibrium problems in electricity systems: challenges and status quo
This talk starts off with a brief introduction to hierarchical optimisation and equilibrium problems, and continues with a broad overview of different applications of these types of problems within the electricity system (such as transmission and generation expansion planning, strategic bidding in markets and many more). We then establish the status quo of the existing models and analyse: what level of technical detail existing models cover; what are corresponding CPU times, model size and complexity; what are the current methods available to solve the arising problems, and, raise the discussion of what gets lost in translation. With the former analysis in mind, we outline the existing lack in the literature, or better yet the existing need to represent complex technical realities in our models. Complex realities such as: the AC optimal power flow, or unit commitment constraints. We then try to establish the link between the technical nature, and the corresponding arising mathematical difficulties in the hierarchical models, discussing issues such as non-convexities and non-existence of solutions. The talk concludes by pointing out some of the most recent methodological advances in these directions, and what still remains to be done.
MESW02 20th March 2019
10:00 to 10:45
Chris Dent Capacity markets, risk modelling and decision support
This talk will introduce the mathematical and statistical questions associated with running a capacity market. It will then summarise recent research on the modelling of demand and wind generation for capacity adequacy risk assessment in Great Britain, and on decision criteria for determining the volume to procure. Finally, it will describe a new theory of how to run a capacity auction including all resources including storage on a common basis. All these topics will be illustrated using examples based on the GB system.
MESW02 20th March 2019
11:15 to 12:00
Stein-Erik Fleten The Effect of Capacity Payments on Peaking Generator Availability in PJM
We study the effects of capacity payments on the strategic decisions of plant managers for peaking units in the PJM Interconnection. We achieve this through a structural estimation of maintenance and switching costs between the operational state, the standby state and retirement of generating units, using annual data from 2001-2016. The empirical data shows less switching between states after the introduction of capacity remunerations in 2007. We find that the role of peaking units has changed, with the units being dispatched more often. In the counterfactual analysis, we find a clear connection between the level of capacity payments and switching. We conclude that the current level of capacity payments in PJM incentivizes peaking units to stay in the operational state.

Stein-Erik Fleten (with Benjamin Fram, Magne Ledsaak, Sigurd Mehl, Ola Røssum, Carl Ullrich)
MESW02 20th March 2019
12:00 to 12:45
Asgeir Tomasgard Analysing effects of short- and long-term uncertainty on capacity expansion in European electricity markets
The EMPIRE model is a European multiscale power market model with investments towards 2050 as well as representative hours. It is well suited to capture operational uncertainty in generation from intermittent energy sources like wind and sun. In the short-run  horizon, typical uncertainty  is in load, intermittent generation and inflows to hydro reservoirs. Short-run sources of flexibility are regulated hydropower, storages, fossil generators and demand response. In the long run uncertain factors include  learning curves, policy uncertainty, long-term commodity process and demand trends. In this paper we add long-term uncertainty to the formulation. The resulting models are large scale stochastic multi-stage recourse models with hundred of millions of variables. We present both solution methods and analysis of the most important factors. In the short-run  horizon, typical uncertainty  is in load, intermittent generation and inflows to hydro reservoirs. Short-run sources of flexibility are regulated hydropower, storages, fossil generators and demand response. In the long run uncertain factors include  learning curves, policy uncertainty, long-term commodity process and demand trends. We look at the European level and analyze questions like to  what extent the demand response potential can facilitate an optimal transition to an European low emission power system and how does long-term uncertainty affect the investments in renewables.
MESW02 21st March 2019
09:00 to 10:00
Yves Smeers Risk premium and stochastic equilibrium in generation capacity expansion models
Investment in new capacities is most often based on a Weighted Average Cost of Capital where the cost of equities is derived from the Capital Asset Pricing Model (the cost of debt following a different logic). While a single corporate discount rate was most often used in the past, differentiated discount rates reflecting elements such as technology and country risks are now more used. These introduce undesirable arbitrage phenomena in standard capacity expansion model interpreted as market simulation models. We discuss three types of differentiated discounting namely the standard (often based on CAPM) exogenous discount rate, the more general stochastic discount rate and the endogenous discount rate derived from risk functions possibly with hedging instruments. The three approaches are cast in a unifying risk premium equilibrium formulation where arbitrage phenomena are eliminated. The models are of the complementarity form and can be handled through splitting algorithms. We report numerical results for medium size problems adequate for industrial use.  Convergence is based on an (often implicit) assumption of monotonicity that is not necessarily satisfied for endogenous discount rates. We briefly discuss the different equilibrium that can arise when this assumption is not satisfied.
MESW02 21st March 2019
10:00 to 10:45
Danny Ralph Risky Capacity Equilibrium Models for Risk Averse Investment Equilibria with Incomplete Markets
"Risky Capacity Equilibrium Problems” incorporate (i) risk averse investment in power plants, (ii) financial trading to hedge those investments, and (iii) strategic production in a stochastic spot market. These models concatenate short-term electricity market (perfect competition or Cournot) with long-term investments (risk neutral or risk averse behaviour in different risk trading settings). We focus on incomplete financial markets, when not all risks can be traded, using results on “Risky Design Equilibrium Problems” and standard Nash game techniques to show existence of equilibria. Numerical results show the impact of incompleteness on equilibrium capacity and spot prices.
MESW02 21st March 2019
11:15 to 12:00
Michael Ferris Modelling 100 percent renewable electricity
Michael Ferris and Andy Philpott
MESW02 21st March 2019
12:00 to 12:45
Gauthier de Maere d'Aertyrcke Valuation of floating price contract formulae for financial renewable PPAs
We propose a valuation methodology based on risk measure to compute the risk premium to cover new structure of financial renewable PPA, where as opposed to traditional unit contingent PPA, a part of merchant risk remains in the hand of the asset developer. Those new contract structures are currently emerging  on the market:  Offtaker are increasingly showing interest to share the market risk, as in systems with high RES share, the PPA might become a very poor hedge of their electricity bill (due to RES cannibalization effect). We specifically focus on contract formulae based on floating price, where the financial transfer to the Offtaker is not based on the price captured by the renewable asset but rather on a floating price, defined as the average of spot prices over a predefined time duration (e.g. yearly, monthly or daily basis).  We then assess several risk mitigation strategies to lower the risk premium, namely physical (well balanced portfolio of wind, PV and battery) and/or financial (power derivatives trading).
MESW02 21st March 2019
13:45 to 14:30
Jalal Kazempour Distributionally robust chance-constrained generation expansion planning
This talk addresses a centralized generation expansion planning problem, accounting for both long- and short-term uncertainties. The long-term uncertainty (demand growth) is modeled via a set of scenarios, while the short-term uncertainty (wind power) is considered using moment-based ambiguity sets. In the expansion stage, the optimal units to be built are selected among discrete options. In the operational stage, a detailed representation of unit commitment constraints is considered. To make this problem tractable, we solve it in linear decision rules, and use a tight relaxation approach to covexify the unit commitment constraints. The resulting model is a distributionally robust chance-constrained optimization, which eventually recasts as a mixed-integer second-order cone program. We consider the IEEE 118-bus test system as a case study, and explore the performance of the proposed model using an out-of-sample analysis. 
MESW02 21st March 2019
14:30 to 15:15
Jacob Mays Asymmetric Risk and Fuel Neutrality in Capacity Markets
This paper calls into question the fuel neutrality of capacity mechanisms implemented in liberalized electricity markets. The argument relies on two assumptions likely satisfied in practice, first that investors are risk averse and second that markets in risk are incomplete. For the analysis, we develop a heuristic algorithm to solve large-scale stochastic equilibrium models describing a competitive market with incomplete risk trading. Introduction of a capacity mechanism has an asymmetric effect on the risk profile of different generation technologies, tilting the resource mix toward those with lower fixed costs and higher operating costs. One implication of this result is that current market structures may be ill-suited to financing low-carbon resources, the most scalable of which have high fixed costs and near-zero operating costs. Development of new risk trading mechanisms to replace or complement current capacity obligations could lead to more efficient outcomes. 

Jacob Mays, David Morton and Richard O'Neill
MESW02 21st March 2019
15:45 to 16:30
Golbon Zakeri Stochastic auctions to accommodate the future flexi-grid
We will discuss the design and performance of some stochastic auctions designed to accommodate flexibility and hence penetration of intermittent renewable energy generation. We will examine these auctions in the context of competitive markets as well as oligopolies. We will also shed some light on using such mechanisms in markets where the participants are risk averse. The talk is based on the following literature. 
MESW02 21st March 2019
16:30 to 17:15
Michael Hintermüller Generalized Nash Equilibrium Problems with Application to Spot Markets with Gas Transport
A class of noncooperative Nash equilibrium problems is presented, in which the feasible set of each player is perturbed by the decisions of their competitors via a convex constraint. In addition, for every vector of decisions, a common “state” variable is given by the solution of an affine linear equation. The resulting problem is therefore a generalized Nash equilibrium problem (GNEP). The existence of an equilibrium for this problem is demonstrated, and first-order optimality conditions are derived under a constraint qualification. An approximation scheme is proposed, which involves the solution of a parameter-dependent sequence of standard Nash equilibrium problems. An associated path-following strategy based on the Nikaido–Isoda function is then proposed. Function space- based numerics for parabolic GNEPs and a spot-market model are developed.
MESW02 22nd March 2019
09:00 to 10:00
Andrew Wright Societal objectives and their impact on the electricity system of the future
Electricity is an essential to people’s well-being and the lifeblood of a modern economy. The societal objectives around electricity reflect the wider values of society, and these values precondition the way the electricity system is structured and governed, both today and in the future. The electricity system is changing rapidly, but societal values are usually more enduring, and will shape the ways in which the system develops. The challenge of the energy system of the future is use technology to meet the goal of decarbonisation, without undermining the reasonable expectations of society for an essential public service. This talk will look at these societal objectives, how they shape the electricity system of today and the risks and opportunities in the transition to a low carbon future.
MESW02 22nd March 2019
10:00 to 10:45
Goran Strbac Energy system decarbonisation: informing reform of energy market and regulatory framework
There is a growing need and opportunity for new emerging technologies and systems to provide flexibility in supporting a cost-effective transition to a lower carbon energy system. Key results of the comprehensive analysis carried out will be presented highlighting the benefits of emerging technologies and system in providing grid support, various balancing services, system security services and option value services to deal with uncertainty. This will underline the barriers related to the present market and regulatory framework that may prevent the realization of the quantified system value of flexibility and thus weaken the business case for investment in corresponding technologies and systems. Developing fully cost-reflective markets and effective regulatory frameworks will therefore be necessary to ensure that the commercial incentives for investing and operating flexible technologies are aligned with the societal benefits. The importance of the whole-system approach to future market design, strategic versus incremental investment, consumer participation and decentralisation will be discussed and a roadmap for market evolution will be presented, highlighting the importance of aligning the market design with the decarbonisation objectives.
MESW02 22nd March 2019
11:15 to 12:00
Afzal Siddiqui Strategic Storage Use in a Hydro-Thermal Power System with Carbon Constraints
The Northeast Power Coordinating Council (NPCC) comprises American states and Canadian provinces marked by a significant penetration of variable renewable energy sources (VRES) and hydropower production. Major demand centres in New England, New York, Ontario, and Québec that are subject to stringent to stringent caps on CO2 emissions are included in the NPCC. For example, the Regional Greenhouse Gas Initiative (RGGI) mandates a 30% reduction in CO2 emissions from power plants by 2030 relative to 2020 levels, which affects generation in New England and New York. Likewise, Québec participates in the Western Climate Initiative (WCI), which aims to reduce CO2 emissions by approximately 40% by 2030 relative to 1990 levels and included Ontario until recently. Both RGGI and WCI create cap-and-trade (C&T) systems for CO2 emissions in which the shadow price on the binding CO2 emission constraint is the permit price that generators incur as an additional cost for their CO2 emissions. While support schemes such as feed-in tariffs and the C&T system have induced an increase in VRES generation, they have also enhanced the role of energy storage, viz., by hydro reservoirs especially in Québec. In a perfectly competitive power system, storage capacity would be deployed in a socially optimal way to smooth out the fluctuations in uncontrollable VRES output. However, given the persistence of market power in the electricity industry, hydro reservoirs may be used in a strategic manner to the benefit of their proprietors. Consequently, incentives for VRES and social welfare may be detrimentally affected by such exertion of market power. In order to investigate the extent of these distortions in the NPCC and to propose policies for their mitigation, we develop a bottom-up equilibrium model to quantify the welfare losses from the strategic use of hydropower reservoirs and to assess counterfactual CO2 emission caps.
Co-authors: Sébastien Debia and Pierre-Olivier Pineau
MESW02 22nd March 2019
12:00 to 12:45
Duncan Burt Risk and Optimization in Future Network Planning
TThe future of power network use is highly uncertainty, with the arrival of renewables and new patterns of power use, creating large uncertainties, risks and opportunities for the optimisation of future investment and development.  National Grid runs an annual Network Options Assessment which applies a risk-based economics approach to optimise which investments are progressed on an annual basis. his involves modelling a complex power grid and the timing and choice of many potential investment options, totalling multiple billions of pounds over the next 30 years, to deliver an efficient and secure network. This talk sets out the challenges and key factors involved in our current optimisation process and seeks views on alternative approaches.
MESW02 22nd March 2019
13:45 to 14:30
Amy Wilson Accounting for uncertainty when using computer models as decision-support tools in energy system planning
Computer models are widely used as decision-support tools for planning energy systems in both industry and government. These computer models are often computationally intensive and have high-dimensional input spaces, making it difficult to quantify the impact that different sources of uncertainty have on model output. Without a complete picture of the effect of these uncertainties it is difficult to take planning decisions that are robust in the real-world. This presentation will discuss methodology for accounting for uncertainties in computationally intensive energy planning models. Both input uncertainty and uncertainty in the structure of the model itself will be considered. An emulator, or statistical model of the underlying computer model, will be used to quantify uncertainty in areas of the input space where it has not been possible to make model runs. This emulator will be combined with a description of the uncertainty over the input space and a description of the structural error to quantify uncertainty in model outputs. Several real-world examples in energy planning will be discussed, including the modelling of wholesale electricity prices and making decisions about renewable support schemes.
MESW02 22nd March 2019
14:30 to 15:15
Jean-Paul Watson On the Rigorous Evaluation of Stochastic Approaches to Power Systems Operations

While there is significant recent research on stochastic optimization approaches to power systems operations, e.g., unit commitment and economic dispatch, there are still major impediments to their adoption in practice. In our experience, developed over years of attempting to deploy such approaches, one key issue is accurate evaluation of any proposed approach, relative to existing deterministic operational methodologies. In this talk, we discuss the challenges in such evaluation, and report on a novel methodology addressing what we feel to be deficiencies with current approaches. In the talk, we focus on issues relating to data availability and segmentation, probabilistic scenario generation, and the impact of scenarios on operational performance.

MESW02 22nd March 2019
15:45 to 16:30
Andy Philpott Long-term generation capacity expansion models in JuDGE
We consider a multistage stochastic programming model of capacity expansion for renewable energy in New Zealand. The model is solved using the JuDGE package in Julia that exploits a Dantzig-Wolfe decomposition of the problem. 

Andy Philpott and Anthony Downward
MES 9th April 2019
16:00 to 17:00
Claudia Sagastizabal Kirk Fellow Lecture: Three Character Archetypes in Energy Optimisation
The management of energy systems, particularly when transitioning to sustainable sources of electricity, requires a successful blending of interdisciplinary work. We discuss how the frontier between pure and applied maths becomes somewhat artificial when dealing with price signals in energy optimisation. The presentation is organised as a story with three main characters, Isaac Newton, Joseph-Louis Lagrange, and Jean-Jacques Moreau.




MES 24th April 2019
10:00 to 12:00
Research track on mechanism design at the INI
MESW03 29th April 2019
10:00 to 11:00
Janusz Bialek Mathematics for energy systems: an engineer’s view
The presentation will contain a personal view of an engineer on the complex interactions between mathematicians and engineers, exploring the myths, tensions, and modes of collaboration. It will also present a personal view on the trends and themes shaping power system research over the last 40 years and how they involved mathematicians. The presentation will end up by showing an example of unresolved research problems that call for mathematics: making use of wealth of information provided by Phasor Measurement Units.
MESW03 29th April 2019
11:30 to 12:30
Adilson Motter Power Grids: Failures and Opportunities
A fundamental problem in electrical grids is that the same connections that give a network its functionality can promote the spread of failures that would otherwise remain confined. Understanding the resulting cascading failures has been hindered by the lack of realistic large-scale modeling that can account for variable system conditions. In this presentation, I will discuss our full-scale modeling of the U.S.-South Canada power-grid network, which allows us to characterize the set of network components that are vulnerable to cascading failures. I will show that the vulnerable set consists of a small but topologically central portion of the network and that large cascades are disproportionately more likely to be triggered by initial failures close to this set. These results elucidate aspects of the origins and causes of cascading failures, and point to opportunities for failure mitigation in connection with grid design and operation.
MESW03 29th April 2019
13:30 to 14:30
Anupama Kowli SMART Planning and Operations of Grids With Renewables and Storage a.k.a. SPOReS
This talk will introduce the SPOReS project started under the Mission Innovation Smart Grids challenge in India. The goal of SPOReS is to facilitate planning for reliability and affordability along with balancing of grid supply with storage, flexible loads and power electronic controllers. This talk will present two specific deliverables of SPOReS. On the planning front, a graph-based approach for enhancing the reliability of distribution systems by installing new tie-lines will be introduced. The problem is cast as an edge-connectivity augmentation problem. We will discuss the associated solution approaches, ways to factor in practical considerations and metrics based on network topology that capture the reliability impacts of the specific interventions. On the operational front, solutions for load management will be discussed to facilitate demand response and community energy sharing.
MESW03 29th April 2019
14:30 to 15:30
Veronika Grimm Generation and network investment in electricity markets: using multilevel optimization to assess the role of the market design

The talk will present a multilevel optimization approach that allows to analyze the long-run impact of electricity market design on investment and production decisions. The presentation will give an overview over various recent contributions that focus on electricity markets that operate under a zonal pricing market design, as it is established in Europe. We apply the approach to the German electricity market as an example to demonstrate the impact of (i) the establishment of additional price zones, (ii) regionally differentiated network fees (iii) market-driven RES curtailment on key performance indicators such as welfare, economic rents, generation mix and locations, network investment, or electricity prices in the long run. We also show how the model can be used to endogenously determine an optimal configuration of price zones on a network, or to assess the long run impact of structural (regulatory) uncertainty about the future market design. The talk will present work with various coauthors which will be mentioned along the presentation.

MESW03 29th April 2019
16:00 to 17:00
Eddie Anderson Rothschild Lecture: What do we agree on when we disagree? Forward contracts with private forecasts
Forward contracts are used for hedging purposes when firms operate in a spot market. What will happen when firms have different views on the future distribution of prices and are risk averse? We discuss different ways in which two firms may agree on a bilateral forward contract: either through direct negotiation using the ideas of a Nash bargaining solution, or through a broker. We discuss a type of equilibrium in which each firm offers a supply function linking quantities and prices, and the clearing price and quantity for the forward contracts are determined from the intersection. Each firm may also be able to use the offer of the other firm to augment its own information about the future price.




MESW03 30th April 2019
09:00 to 10:00
Shmuel Oren Mobilizing Grid Flexibility for Renewables Integration through Topology Control and Dynamic Thermal Ratings
(Joint work with Jiaying Shi)
The rapid penetration of renewable resources into the electricity supply mix poses challenges for the efficient dispatch of resources due to the inherent uncertainty and variability of such resources. Hence, in order to accommodate large amounts of renewables in is necessary to account for their output uncertainty and mobilize the flexibility of the system embedded in conventional generation, demand side resources and the transmission grid. In this talk we formulate a stochastic unit commitment optimization in which we expand the traditional recourse actions that are available to mitigate the adverse effect of renewables variability. In particular we include in these recourse action, topology control through transmission switching and dynamic line ratings that account for the heating and cooling of transmission lines. We will demonstrate the potential gains from such recourse actions through test cases and discuss heuristic approaches for alleviating the computational burden resulting from such a formulation.
MESW03 30th April 2019
10:00 to 11:00
John Simpson-Porco Optimal Steady-State Control with Application to Secondary Frequency Control of Power Systems
The optimal steady-state control problem is that of designing a feedback controller for a multivariable system which regulates selected system variables to the solution of a constrained optimization problem, despite parametric modelling uncertainty and unmeasured exogenous disturbances. For example, an instance of this problem occurs in the design of secondary frequency control systems, where control resources should be optimally used subject to the elimination of frequency deviations and restoration of inter-area tie-line flows. We present a constructive design framework that reduces the OSS control problem to a more classical control problem of output regulation (tracking and disturbance rejection). Robustness issues which arise in the constructive procedure are discussed, leading to a solution of the OSS control problem for the case where the plant is LTI with structured parametric uncertainty and unknown disturbances are constant in time. We apply the results to frequency control of power systems show that the OSS control framework recovers several recent decentralized and distributed secondary frequency controllers from the literature.

Collaborators: Liam S. P. Lawrence (UWaterloo) and Enrique Mallada (JHU)
MESW03 30th April 2019
11:30 to 12:30
Johanna Mathieu Optimal Power Flow with Stochastic Reserves
My research seeks to improve the sustainability of electric power systems by developing methods to support the integration of renewable energy sources while maintaining system reliability. In this talk I will describe our efforts to develop approaches to dispatch power systems with reserves provided by stochastic resources like residential air conditioners. We formulate the problem as a chance constrained optimal power flow problem with uncertainty in renewable energy production, load consumption, the capacity of load-based reserves. Solving such problems is challenging because we do not have good models of the uncertainties.  I will describe our work exploring of a variety of solution approaches including distributionally robust optimization, which gives us reliable but costly solutions. I will also describe a novel distributionally robust approach that allow us to leverage structural information about the uncertainty to reduce the conservatism of standard approaches.
MESW03 30th April 2019
13:30 to 14:30
Claudia D’Ambrosio Smart Grids Observability using Bilevel Programming
Monitoring an electrical network is an important and challenging task. Phasor measurement units (PMU) are devices that can be used for state estimation of this network. We consider a PMU placement problem and propose two new approaches to model this problem, which take into account a propagation rule based on Ohm’s and Kirchoff’s laws. First, we describe the natural binary linear programming model based on an iterative observability process. Then, we remove the iteration by reformulating its fixed point conditions to a bilevel program. We propose two methods to solve such a problem. The first is based on the observation that the integrality constraint of lower level problem can be relaxed so as to derive a single level reformulation by replacing such a problem with its dual. The second is a tailored cutting plane algorithm. We show through computational results that the tailored cutting plane method is much more effective than the others on a set of instances taken from the literature.
Joint work with Sonia Toubaline, Pierre-Louis Poirion, and Leo Liberti
MESW03 30th April 2019
14:30 to 15:30
Claudia Sagastizabal Nonsmooth Optimization put to good use in energy problems
We examine problems in managing a mix of power plants generating electricity, using optimization modeling. The modeling includes definition of goals, such as minimizing carbon footprints, maximizing revenue, or reducing risk of blackouts. We discuss how to exploit structural properties of nonsmooth objective functions.
MESW03 30th April 2019
16:00 to 17:00
Marija Ilic Dynamic monitoring and decision systems (dymonds) framework for data-enabled integration in complex electric energy systems
In this talk we introduce a unifying Dynamic Monitoring and Decision Systems (DyMonDS) framework that is based on a multi-layered modeling for aggregation and minimal coordination of interactions between the layers of complex electric energy systems. Using this approach, distributed control and optimization problems are formulated so that: (1) the low-level decision makers optimize cost of local interactions while accounting for their heterogeneous technologies, as well as for their social and risk preferences; and, (2) the higher layer aggregators and coordinators optimize the cost of all interactions at their levels to enable cooperative control. The interactions of each layer are abstracted by using unifying energy state space and the Lagrange coefficients associated with the general physical laws. This sets the bases for both nonlinear control of power electronically-switched automation and for market design formulation. Potential benefits (such as enhanced reliability, resiliency, and efficiency) from integrating flexible technologies, storage, and control, in particular, are illustrated on simple IEEE test systems.
OFBW47 1st May 2019
10:00 to 10:10
Jane Leeks, David Abrahams Welcome and Introduction
OFBW47 1st May 2019
10:10 to 10:20
Bert Zwart Outline and Summary of INI Research Programme 'Mathematics of Energy Systems'
OFBW47 1st May 2019
10:20 to 10:55
Florentina Paraschiv Econometrics of Intraday Electricity Prices
OFBW47 1st May 2019
10:55 to 11:30
Pierre Pinson Analytics and Forecasting for Renewable Energy Generation
OFBW47 1st May 2019
11:50 to 12:25
James Cruise Moving Energy through Time: Storage and Demand Side Response
OFBW47 1st May 2019
12:25 to 13:00
John Moriarty, Louis Wehenkel Towards Optimal Operation and Maintenance of Electric Power Grids under Uncertainties
OFBW47 1st May 2019
14:00 to 14:35
Michael Ferris Computation in Markets with Risk
OFBW47 1st May 2019
14:35 to 15:05
Shane Slater Energy Storage and Decarbonised Energy Systems
OFBW47 1st May 2019
15:30 to 16:00
Farina Farrier Open Networks Project - Laying the foundations of a Smart Grid in GB
OFBW47 1st May 2019
16:00 to 16:30
Carol Choi The Value of Data
OFBW47 1st May 2019
16:20 to 16:45
Discussion and Questions
MESW03 2nd May 2019
09:00 to 10:00
Mahnoosh Alizadeh Mobility-Aware Load Management Algorithms for Electric Vehicles
In this talk, we will discuss pricing mechanisms and/or routing algorithms that can be used to manage the charging demand of a population of electric vehicles. After presenting an overview of some recent results, two main papers will be discussed. In the first part of the talk, I will present on the design of optimal pricing and routing policies to provide differentiated services in a network of fast charging stations, where customers have different values of time and energy demands. The second part of the talk is focused on residential EV demand management under real-time pricing programs. We will discuss the application of multi-armed bandit based algorithms for price design, where customers' price response is modeled as a stochastic system with unknown parameters. Given the presence of reliability constraints in power systems, we will discuss the application of well-known bandit heuristics in constrained environments and discuss the effects of reliability constraints on the regret guarantees of the algorithms.
MESW03 2nd May 2019
10:00 to 11:00
Na Li The Role of Prediction And Real-time Learning in Online Optimization and Control
Uncertainties place a key challenge in transforming the grid into one with a large renewable energy integration and active users' participation. The volatile renewable generation constantly introduces disturbance to the grid operation; while the stochastic user behavior makes it difficult to use demand as a reliable resource for balancing real-time supply-demand. This talk will present two lines of work to address these issues. Firstly, we will study how to use a short term prediction, e.g., renewable energy, to improve the performance for online optimization and control, e.g, economic dispatch with ramping cost. Then, we will study how to use real-time learning to design residential demand response programs for achieving grid reliability while balancing the exploitation and exploration of the uncertain user behavior.
MESW03 2nd May 2019
11:30 to 12:30
David Hill Exploring fundamental limits in future grids with mathematics, computation and data-based methods

The talk will present results for computing stability limits using a variety of approaches each with advantages and disadvantages arising from the models used. Mathematics favours simplified equation-based models; computation favours detailed physics-based models and newer data-based methods sometimes claim to be actually ‘model-free’. As systems become more complex in likely scenarios for grids out to 2050and become more ever-changing there should be a new discussion about the merits of the associated methods. Results obtained in future grid and high renewable projects in Sydney and Hong Kong respectively will be used for illustration.

MESW03 2nd May 2019
13:30 to 14:30
Enrique Mallada Embracing Low Inertia for Power System Frequency Control: A Dynamic Droop Approach
Co-Authors: Yan Jiang, Richard Pates, and Fernando Paganini

Abstract: The transition into renewable energy sources -with limited or no inertia- is seen as potentially threatening to classical methods for achieving grid synchronization. A widely embraced approach to mitigate this problem is to mimic inertial response using grid-connected inverters. That is, introduce virtual inertia to restore the stiffness that the system used to enjoy. In this talk, we seek to challenge this approach and advocate towards taking advantage of the system’s low inertia to restore frequency steady state without incurring in excessive control efforts. With this aim in mind, we develop an analysis and design framework for inverter-based frequency control. We define several performance metrics of practical relevance for power engineers that contemplate system disturbances and measurement noise, and systematically evaluate the performance of standard control strategies, such as virtual inertia and droop control. Our analysis unveils the relatively limited role of inertia on improving performance as well as the inability of droop control to enhance performance without incurring in considerable steady-state control efforts. To solve this problem, we propose a novel dynamic droop control (iDroop) for grid-connected inverters -exploiting classical lead/lag compensation from control theory- that can significantly outperform existing solutions with comparable -and in many cases significantly smaller- control efforts.
MESW03 2nd May 2019
14:30 to 15:30
Ana Busic Balancing the Grid through Distributed Control of Flexible Loads
A control scheme that unifies feedforward and feedback control for a large collection of heterogeneous loads is proposed. The feedforward part capitalizes on the knowledge of grid-event forecasts, such as predicted ramps in solar and wind energy and the approximate periodicity of the demand cycle. The slower time-scale feedforward control strategy utilizes a model predictive control (MPC) framework designed on the basis of two pieces of information: aggregate power consumption from each class of loads and the state of charge surrogate that is a part of the leaky battery model. Feedback control is performed at a faster time scale in order to reject real-time disturbances. The control architecture incorporates the decentralized load-level stochastic control design, which ensures that consumer QoS constraints are respected.
MESW03 2nd May 2019
16:00 to 17:00
Steven Low Time-varying nonconvex optimization with application to OPF
Optimal power flow (OPF) problems are fundamental for power system operations.
They are nonconvex and, in future applications, time-varying. We present
a first-order proximal primal-dual algorithm and a second-order algorithm
for general time-varying nonconvex optimization and bound their tracking
performance. We incorporate real-time feedback in our algorithms for
applications to time-varying OPF problems, and illustrate their tracking
performance numerically.

(Joint work with Yujie Tang, Caltech, Emiliano Dall’Anese, U of Colorado,
Andrey Berstein, NREL)
MESW03 3rd May 2019
09:00 to 10:00
Laura Diaz Anadon Innovation in energy technologies and the role of governments
Over the past 20 years there has been a growing amount of empirical research trying to understand the extent to which technology innovation in energy technologies can contribute to mitigating climate change. This presentation will focus on what we know regarding the role of governments in promoting such innovation in low-carbon energy technologies, with a particular emphasis on the United States and China, the largest greenhouse gas emitters. She will discuss some of the main challenges of building and resourcing effective, empowered institutions able to tackle climate change more rapidly. Her presentation will also discuss how research could help overcome some of these challenges.
MESW03 3rd May 2019
10:00 to 11:00
Chris Dent Statistical modelling for planning and policy applications
This presents joint thinking with a number of colleagues, key among them being Amy Wilson, Stan Zachary, Michael Goldstein, Henry Wynn, Jim Smith and Peter Challenor. The contributions of various students and postdocs are also acknowledged, and credited in the presentation where relevant.
MESW03 3rd May 2019
11:30 to 12:30
Florian Doerfler Data-Enabled Predictive Control of Autonomous Energy Systems
We consider the problem of optimal and constrained control for unknown systems. A novel data-enabled predictive control (DeePC) algorithm is presented that computes optimal and safe control policies using real-time feedback driving the unknown system along a desired trajectory while satisfying system constraints. Using a finite number of data samples from the unknown system, our proposed algorithm uses a behavioral systems theory approach to learn a non-parametric system model used to predict future trajectories. We show that, in the case of deterministic linear time-invariant systems, the DeePC algorithm is equivalent to the widely adopted Model Predictive Control (MPC), but it generally outperforms subsequent system identification and model-based control. To cope with nonlinear and stochastic systems, we propose salient regularizations to the DeePC algorithm. Using techniques from distributionally robust stochastic optimization, we prove that these regularization indeed robustify DeePC against corrupted data. We illustrate our results with nonlinear and noisy simulation case studies from aerial robotics, power electronics, and power systems.
MESW03 3rd May 2019
13:30 to 14:30
Sean Meyn State Space Collapse in Resource Allocation for Demand Dispatch
The term demand dispatch refers to the creation of virtual energy storage from deferrable loads. The key to success is automation: an appropriate distributed control architecture ensures that bounds on quality of service (QoS) are met and simultaneously ensures that the loads provide aggregate grid services comparable to a large battery system. A question addressed in our 2018 CDC paper is how to control a large collection of heterogeneous loads. This is in part a resource allocation problem, since different classes of loads are more valuable for different services. The evolution of QoS for each class of loads is modeled via a state of charge surrogate, which is a part of the leaky battery model for the load classes. The goal of this paper is to unveil the structure of the optimal solution and investigate short term market implications. The following conclusions are obtained:
(i) Optimal power deviation for each of the M 2 load classes evolves in a two-dimensional manifold.
(ii) Marginal cost for each load class evolves in a two-dimensional subspace: spanned by a co-state process and its derivative.
(iii) The preceding conclusions are applied to construct a dynamic competitive equilibrium model, in which the consumer utility is the negative of the cost of deviation from ideal QoS. It is found that a competitive equilibrium exists, and that the resulting price signals are very different than what would be obtained based on the standard assumption that the utility is with respect to power consumption. It is argued that price signals are not useful for control of the grid since they are inherently open loop. However, the analysis may inform the creation of heuristics for payments within the context of contracts for services with consumers.
MESW03 3rd May 2019
14:30 to 15:30
Panel
University of Cambridge Research Councils UK
    Clay Mathematics Institute London Mathematical Society NM Rothschild and Sons