Publications

The aim of this document is to present SiSTEM, a multi-level simulation model of European short-term electricity markets, covering day-ahead and intraday exchanges to balancing activations in real-time, and imbalance settlement. In this model, power companies interact by making offers, notifying their positions to the system operator and impacting the balance of the electric system. The system operator activates balancing energy to restore the balance of the system, using all balancing activation offers, including from balancing reserves. Imbalance settlement implies bidirectional transactions between the system operator and power companies depending on the direction of their imbalance. A simulation of the model is performed by sequentially considering each time step and simulating actors’ decisions. The objective of this model is to understand the problems behind decisions of the actors within the short-term electrical system operation, to provide insights on how these problems can be solved through market design and to see how the decisions are linked together to shape a coherent system. This paper presents different simulation cases of an illustrative system in order to portray main features of the model in a practical and effective manner. In particular, the results show the importance of considering steady-state constraints and notice delays of generation units when looking at short-term issues. Future works could use this model to provide quantitative assessments of short-term market designs.

The integration of renewable energy sources in the electricity production mix has an important impact on the management of the electricity grid, due to their intermittency. In particular, there is a rising need for flexibility, both on the supply and demand sides. This paper assesses the amount of flexibility that could be reserved from a set of flexible residential heat pumps in a given geographical area. It addresses the problem of a load aggregator controlling a set of heat pumps used to provide both space-heating and domestic hot water. The flexibility of the heat pumps is unlocked in order to reduce electricity procurement costs in the day-ahead electricity market, while ensuring the provision of a predefined amount of reserve for real-time grid management. The objective of the paper is two-fold. On the one hand, an aggregation method of large sets of heat pumps based on physics-based models and random sampling techniques is proposed. On the other hand, a combined optimization problem is formulated to determine both the optimal electricity demand profile to be bought on the day-ahead market and the cost associated to the reservation of a defined amount of power. The method is applied to a set of 40000 residential heat pumps in Belgian houses. Results show that these houses can provide up to 100MW of upward reserve for 50% of the current costs. The provision of downward reserve at competitive cost is hampered by significant overconsumption.

This paper addresses the problem of an aggregator controlling residential heat pumps to offer a direct control flexibility service. The service consists of a power modulation, upward or downward, that is activated at a given time period over a fixed number of periods. The service modulation is relative to an optimized baseline that minimizes the energy costs. The load modulation is directly followed by a constrained rebound effect, consisting of a delay time with no deviations from the baseline consumption and a payback time to return to the baseline state. The potential amount of modulation and the constrained rebound effect are computed by solving mixed integer linear problems. Within these problems, the thermal behavior of the building is modeled by an equivalent thermal network made of resistances and lumped capacitances. Simulations are performed for different sets of buildings typical of the Belgian residential building stock and are presented in terms of achievable modulation amplitude, deviations from the baseline and associated costs. A cluster of one hundred ideal buildings, corresponding to retrofitted freestanding houses, is then chosen to investigate the influence of each parameter defined within the service. Results show that with a set of one hundred heat pumps, a load aggregator could expect to harvest mean modulation amplitudes of up to 138 kW for an upward modulation and up to 51 kW for a downward modulation. The obtained values strongly depend on the proposed flexibility service. For example, they can decrease down to 2.6 kW and 0.4 kW, respectively, if no rebound effect is allowed.

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The work presented in this thesis considers the electrical flexibility from the electric load to its usage as a commodity. The conception of the European electrical system has led to a large amount of actors that are impacted by flexibility exchanges. This thesis proposes approaches to assess the impact of exchanging flexibility in the electrical system and analyzes the complex interactions resulting from these exchanges. The modeling techniques used to carry the analysis are optimization, game theory and agent-based modeling. The impacts on different parts of the electrical system are presented: the day-ahead energy market, the secondary reserve and the distribution system. Since flexibility is the base block of this thesis, two methods to obtain flexibility from actual consumption processes are broached: direct control of the loads and dynamic pricing. One chapter provides an example of how flexibility can be obtained by the direct control of a portfolio of heat pumps and another chapter studies the control of electric heaters and boilers via the use of a simple price signal.

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There is a need to clearly state an interaction model that formalizes interactions between actors of the distribution system exchanging flexibility. In previous works we quantitatively evaluated the performance of five interaction models devised with industrial partners using the agent-based testbed DSIMA. Simulation results showed that these interaction models relying on active network management suffer from a lack of coordination between the distribution and the transmission system operator, activating flexibility simultaneously in opposite directions. This paper introduces a new interaction model fixing this issue based on dynamic access bounds to the network changing throughout the day and preventing the activation of flexibility leading to congestions. This new interaction model is implemented in DSIMA and compared to a model restricting the grid users to a very restrictive but safe access range. Results show that this new model allows to safely increase by 55% the amount of distributed generation in the network.

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With the ongoing trend for connected electric appliances, electricity retailers now not only retail electricity bought on the energy market but also control flexible consumption. This control grants to the retailer the possibility of shifting their consumption from one hour of the day to another, influencing the corresponding market prices and consequently their costs. This article studies this system and shows that it can be represented as an atomic splittable flow congestion game with players sending flow in arcs linking a unique source and destination. We focus on games with affine cost functions and define laminar Nash equilibria where the constraints on the minimum and maximal flow that a player must send in a given arc are not binding. We show that the flow sent by a player at a laminar Nash equilibrium does not depend on the demand of other players. In laminar flow, we bound the price of anarchy and the ratio between the maximum and the minimum arc cost. Finally, we propose a simple method based on the property of a laminar Nash equilibrium to compute the price of flexibility to which energy flexibility should be remunerated in electric power systems.

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This paper addresses the problem of an aggregator controlling residential heat pumps to offer a direct control flexibility service. The service is defined by a 15 minute power modulation, upward or downward, followed by a payback of one hour and 15 minutes. The service modulation is relative to an optimized baseline that minimizes the energy costs. The potential amount of modulable power and the payback effect are computed by solving mixed integer linear problems. Within these problems, the building thermal behavior is modeled by an equivalent thermal network made of resistances and lumped capacitances whose parameters are identified from validated models. Simulations are performed on 100 freestanding houses. For an average 4.3 kW heat pump, results show a potential of 1.2 kW upward modulation with a payback of 600 Wh and 150 Wh of overconsumption. A downward modulation of 500 W per house can be achieved with a payback of 420 Wh and 120 Wh of overconsumption.

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To increase their competitiveness, many industrial companies monitor their production process, collecting large amount of measurements. This paper describes a technique using this data to improve the performance of a monitored process. In particular we wish to find a set of rules, i.e. intervals on a reduced number of parameters, for which an output value is maximized. The model-free optimization problem to solve is to find a box, restricted on a limited amount of dimensions, with the maximum mean value of the included points. This article compares a machine learning-based heuristic to the solution computed by a mixed-integer linear program on real-life databases from steel and glass manufacturing. Computational results show that the heuristic obtains comparable solutions to the mixed integer linear approach. However, the exact approach is computationally too expensive to tackle real life databases. Results show that the restriction of five process parameters, on these databases, may improve the quality of the process by 50%.

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This article proposes an open-source testbed to simulate interaction models governing the exchange of flexibility services located within a distribution network. The testbed is an agent-based system in which the distribution system operator, the transmission system operator, producers and retailers make their decisions based on mixed-integer linear programs. This testbed helps to highlight the characteristics of an interaction model, the benefits for the agents and eases the detection of unwanted or abusive behaviors which decreases the welfare. The testbed is implemented in Python and the optimization problems are encoded in the modeling language ZIMPL. A web interface is coupled with an instance generator using macroscopic parameters of the system such as the total power production. This testbed is, therefore, well suited to test the implemented interaction models on various scenarios and to extend the implementation to other models. Five interaction models developed with industrial partners are simulated over a year on a 75-bus test system. Simulations show that interaction models relying on active network management, as they have been developed, lead to substantial welfare even though they suffer from a lack of coordination between the DSO and the TSO. A conservative interaction model restricting grid users to an access range that is computed ahead of time to prevent any congestion, avoids shedding distributed generation but considerably restrains the amount of distributed production.

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To ease the transition towards the future of distribution grid management, regulators must revise the current interaction model, that is, the set of rules guiding the interactions between all the parties of the system. Five interaction models are proposed, three of them considering active network management. This paper evaluates the economic efficiency of each model using macroscopic representation of the system, by opposition to more techniques requiring a complete picture of the system. The interaction models are simulated on the horizon 2015-2030. Results show that for the first five years all the models provide similar economic efficiency. For the remaining ten years, interaction models implementing active network management provide up to a 10% higher economic efficiency.

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We consider a price signal with two settings: off-peak tariff and on-peak tariff. Some loads are connected to specific electricity meters which allow the consumption of power only in off-peak periods. Historically, off-peak periods were located during the night and on-peak periods during the day. Changing the assignment of off-peak periods is an easy method for distribution system operators to access to the flexibility of small consumers. This solution can be implemented quickly as the infrastructure needed already exists in some countries. We propose a mixed-integer linear model to assign optimally the off-peak hours so as to minimize a societal cost. This cost gathers together the cost of electricity, the financial losses due to energy curtailments of photovoltaic installations and the loads' wellbeing. Our model considers automatic tripping of inverters and constraints of the electrical distribution networks. Simulation results show that the new disposition of off-peak hours could reduce significantly the photovoltaic energy curtailed in the summer.

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We propose and analyze a day-ahead reserve market model that handles bids from flexible loads. This pool market model takes into account the fact that a load modulation in one direction must usually be compensated later by a modulation of the same magnitude in the opposite one. Our analysis takes into account the gaming possibilities of producers and retailers, controlling load flexibility, in the day-ahead energy and reserve markets, and in the imbalance settlement. This analysis is carried out by an agent-based approach where, for every round, each actor uses linear programs to maximize its profit according to forecasts of the prices. The procurement of reserve is assumed to be determined, for each period, as a fixed percentage of the total consumption cleared in the energy market for the same period. Results show that the provision of reserve by flexible loads has negligible impact in the energy market prices but strongly decreases the cost for reserve procurement. However, as the rate of flexible loads increases, the system operator has to rely more and more on non-contracted reserve, which may cancel out the benefits made in the reserve procurement.

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Considering a set of points in a multi-dimensional space with an associated real value for each point, we want to find the box with the maximum sum of the values of the included points. This problem has applications in data mining and can be formulated as a mixed-integer linear program. We propose a branch-and-bound algorithm where the bounding is obtained by combinatorial arguments instead of the traditional linear relaxation. Computational experiments show that this approach competes with current state of the art mixed-integer solvers. The algorithm proposed in this paper may be seen as a simple and dependence-free method to solve the box search problem.

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This article studies a decision making problem faced by an aggregator willing to offer a load modulation service to a Transmission System Operator. This service is contracted one day ahead and consists in a load modulation option, which can be called once per day. The option specifies the range of a potential modification on the demand of the loads within a certain time interval. The specific case where the loads can be modeled by a generic tank model is considered. Under this assumption, the problem of maximizing the range of the load modulation service can be formulated as a mixed integer linear programming problem. A novel heuristic-method is proposed to solve this problem in a computationally efficient manner. This method is tested on a set of problems. The results show that this approach can be orders of magnitude faster than CPLEX without significantly degrading the solution accuracy.

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Maybe you are confronted to a large amount of data representative of a year, i.e. data for each quarters of the year. However, you are unwilling to work with all of them and would like to focus only on a few days. But if you select 10 days over the 365, which one would you select? This program provides you these days and their weights.

DSIMA is an open-source testbed to simulate interaction models governing the exchange of flexibility services located within a distribution network. The testbed is an agent-based system in which the distribution system operator, the transmission system operator, producers and retailers takes their decisions based on mixed-integer linear programs. This testbed helps to highlight the characteristics of an interaction model, its benefits for the agents and ease the detection of unwanted or abuse behaviors which decreases the welfare. The testbed is implemented in Python and the optimization problem are encoded in the modeling language ZIMPL. A web interface is coupled with an instance generator using macroscopic parameters of the system such as the total power production. This tested is therefore well suited to test the implemented interaction models on various scenarios and extend the implementation to other models.

Pour effectuer sa tâche, le gestionnaire de réseau dispose de différents services auxiliaires. Une de ses missions les plus importantes est de veiller à ce que toute l’électricité consommée soit produite et inversement. Pour fournir ce service, certaines entreprises commandent à distance un parc de charges constitué, par exemple, de chauffages, voitures électriques, climatisations, ... En ajustant leur consommation totale à la hausse ou à la baisse, action que nous appellerons réglage, ces "agrégateurs" participent à l’équilibre du réseau électrique. Ceci est possible en exploitant une certaine flexibilité des charges. Prenons le cas simple d’une voiture électrique restant sur son socle de charge 10 heures, alors que le temps réellement nécessaire à la recharge totale de la batterie n’est que de 5 heures. Nous choisirons alors les moments pendants lesquels la betterie sera alimentée tout en assurant sa charge complète à l’issue de cette période de 10 heures. Dans ce travail, nous étudions la possibilité de traiter ce problème à l’aide de la théorie de l’optimisation en nombres entiers. Nous examinons comment effectuer une prévision des quantités de réglages que le parc de charges est capable de fournir. Ensuite, des méthodes pour résoudre ce problème sans l’aide d’un solveur d’optimisation linéaire classique sont exposées. Connaitre la quantité maximale que l’on peut garantir est certes utile, mais il faut également être capable de fournir une quantité donnée, évidemment inférieure ou égale. En effet, admettons que nous sommes aptes à effectuer un délestage de 100 MW : quelles décisions prendre pour obtenir un réglage à la baisse de seulement 50 MW ? Une méthode permettant de minimiser la gêne des différentes charges par rapport au réglage semble pertinente et est donc présentée. Finalement, une incertitude inhérente aux données du problème doit être prise en compte. Prenons par exemple une voiture électrique. Il se peut qu’on sache seulement que le niveau de la batterie se situe entre 40% et 50%. Les méthodes présentées sont donc étendues pour prendre en compte ce genre d’incertitudes.

Prix Melchior Salier

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