Pointe de consommation électrique

Une est la consommation la plus élevée d’un réseau électrique pendant une période définie (jour, mois, saison). Elle dépend de la localisation et de la période étudiée et, du fait de la dificulté du stockage de l'électricité, pose des problèmes particuliers aux gestionnaires de réseaux et aux producteurs d’électricité. Généralités Contrairement à d'autres formes d'énergie, l'énergie électrique ne peut généralement pas être stockée telle quelle à grande échelle, chaque kilowatt-heure devant donc être produit au moment de sa consommation. Ce délicat équilibre entre l'offre et la demande doit être maintenu en tout temps pour assurer la fiabilité du service électrique. Par ailleurs, la demande globale d'électricité suit des cycles journaliers, hebdomadaires et saisonniers, qui dépendent d'une combinaison de facteurs, dont la succession du jour et de la nuit, le climat, l'activité économique et les habitudes de vie quotidienne des ménages. On distingue trois principaux types

Multi-period analysis of heat integration measures in industrial clusters

Laurent Gabriel Félix Alexandre Stéphane Bungener, Roman Hackl, François Maréchal, Greta Martha Van Eetvelde

TSA (total site analysis) has shown to be an efficient tool for identifying heat integration measures in industrial clusters, leading to the optimal design of utility systems and energy bill reduction. In order to justify investments, any proposed utility system must be shown to be able to operate in all configurations that an industrial cluster can encounter, especially those relating to varying heat demand. Previous TSAs have generally been carried out using yearly means of heat exchange loads or using scenarios corresponding to specific operation modes of the sites. While these have been useful for designing systems under normal conditions, they are not fit for evaluating minima and peaks in utility demand. Carrying out a TSA on each possible configuration of a cluster is not feasible from a computational and results analysis point of view. A method is therefore proposed to represent the variability of data over long periods in a reduced form in order to carry out engineering studies.A methodology is proposed to identify typical operating periods of an industrial cluster made up of several production units. This algorithm exploits a multi-objective optimisation to identify n periods that delimit typical operating modes or multiple profiles.A TSA was previously carried out on the Stenungsund petrochemical cluster in Sweden, leading to the design of a utility system to significantly reduce the overall energy consumption of the cluster. The solution proposes that a common utility system would decrease the hot utility demand from 124 MWth to 70 MWth. The multi-period analysis methodology is demonstrated by application to this case study in order to identify the resilience of the proposed solution when faced with variations in heat production and consumption. The multi-period analysis of the proposed utility system leads to the identification of a peak utility demand of 88 MWth rather than the previously identified 70 MWth. A Total Site Sensitivity Analysis leads to a better understanding of the contribution of each of the clusters units and feasibility of investments. © 2015 Elsevier Ltd.

2015

Real-Time Optimization of Solid Oxide Fuel Cell Systems

Tafarel de Avila Ferreira

Optimization of industrial processes aims at minimizing operating cost or maximizing economic profit while respecting plant constraints. In process industry, real-time optimization (RTO) is often considered to ensure optimal plant operation and constraint satisfaction. Solid-oxide fuel-cell (SOFC) devices oxidize fuels such as hydrogen and methane to produce electric energy through electrochemical reactions at very high efficiency. To reduce the emission of carbon dioxide and the high cost of non-renewable energies, fuel cells have become popular as an alternative energy source with a wide range of applications. The optimal operation of SOFC systems, while being subjected to changing operating conditions such as variations in the electrical power demand, remains a challenging problem in the field of control and optimization. In addition, these types of devices are characterized by slow dynamics and drifts such as degradation. This motivates the use of real-time optimization for optimizing and controlling the operation of SOFC systems. The most popular RTO technique in industry is the so-called two-step approach. This approach consists in estimating the model parameters based on measurements by solving a parameter estimation problem, and computing the next operating point by solving an optimization problem using the updated model. However, this technique is highly dependent on model accuracy and tends to fail in the presence of structural plant-model mismatch. As an alternative, modifier adaptation has been developed to enforce convergence to the plant optimum even in presence of structural plant-model mismatch. Since plant optimality is often characterized by active constraints, it is possible to use a simpler version of modifier adaptation, called constraint adaptation (CA). This approach corrects the constraints in the model-based optimization problem by means of bias terms computed from the differences between the measured and predicted values of the constraints. The active plant constraintsâi.e. the intersection of the constraints where the plant opti- mum liesâfor each mode of operation is typically known from experience. Therefore, as the optimum of SOFC systems typically lies on active plant constraints, we can apply constraint adaptation. One of the drawbacks of static RTO is the fact that it is necessary to wait until the plant reaches its steady state. Since this may be inappropriate for slow processes, we propose an RTO approach that can speed up the procedure by using transient measurements combined with a dynamic tendency model. In this work, we apply constraint adaptation combined with transient measurements and a dynamic model to experimental SOFC systems. The proposed approach is applied to two SOFC systems: (i) an SOFC system with 6 cells that consists of both hardware and software components; (ii) a commercial SOFC system named BlueGEN. The results obtained are very promising. For both systems, the proposed approach can reach the power demand much faster than the time it takes for the process to settle to steady state. The optimizer is quick at finding and tracking the correct set of active constraints, thus avoiding large constraint violations. In addition, we study the effect of the RTO period on the convergence rate. Finally, we show that CA acts simultaneously as a controller and an optimizer that is able to efficiently detect and track the correct set of active constraints.

EPFL2019

Optimal Operations and Resilient Investments in Steam Networks

Laurent Gabriel Félix Alexandre Stéphane Bungener, François Maréchal, Greta Martha Van Eetvelde

Steam is a key energy vector for industrial sites, most commonly used for process heating and cooling, cogeneration of heat and mechanical power as a motive fluid or for stripping. Steam networks are used to carry steam from producers to consumers and between pressure levels through letdowns and steam turbines. The steam producers (boilers, heat and power cogeneration units, heat exchangers, chemical reactors) should be sized to supply the consumers at nominal operating conditions as well as peak demand. First, this paper proposes an Mixed Integer Linear Programing formulation to optimize the operations of steam networks in normal operating conditions and exceptional demand (when operating reserves fall to zero), through the introduction of load shedding. Optimization of investments based on operational and investment costs are included in the formulation. Though rare, boiler failures can have a heavy impact on steam network operations and costs, leading to undercapacity and unit shutdowns. A method is therefore proposed to simulate steam network operations when facing boiler failures. Key performance indicators are introduced to quantify the network’s resilience. The proposed methods are applied and demonstrated in an industrial case study using industrial data. The results indicate the importance of oversizing key steam producing equipments and the value of industrial symbiosis to increase industrial site resilience.