Luc Girardin

Energy integration of industrial sites with heat exchange restrictions

Abstract Process integration methods aim at identifying options for heat recovery and optimal energy conversion in industrial processes. By applying pinch analysis methods, the first step is to calculate the maximum heat recovery between cold and hot streams. The second step consists in designing the corresponding heat recovery exchanger network, based on a fixed list of streams. For the heat cascade, it is assumed that any heat exchange between cold and hot streams is possible, but due to industrial constraints, in many cases, this assumption cannot be accepted in practice and it is necessary to impose restricted matches. This introduces energy penalties, which could be reduced by using intermediate heat transfer systems. This paper introduces a targeting method including industrial constraints to ensure feasible solutions for the heat exchanger network. Intermediate heat transfer systems are integrated so that restricted heat exchanges become possible and heat recovery penalties, created by those constraints, can be reduced. The problem is formulated as a MILP (mixed integer linear programming) problem, which considers not only restricted matches but also the optimal integration of the energy conversion system, like heat pumping and combined heat and power production. The application of the method will be illustrated by an industrial example from the pulp and paper industry. The extension of the method to study the heat integration of semi batch processes will be discussed.

Clustering Urban Areas for Optimizing the Design and the Operation of District Energy Systems

Abstract Solving the MILP model for optimizing the design and operating strategy of district energy systems (DES) is a computationally demanding task due to the large number of subsystems (i.e. resources, conversion technologies, buildings and networks) and corresponding decision variables. In order to reduce the number of decision variables and therefore the computational load of the problem, this paper presents a systematic procedure to represent an urban area with a macroscopic view as a set of “integrated zones”. The integrated zone is an area where consumers, resources and energy conversion technologies are integrated. This is obtained by developing aggregated district integration models based on GIS data and applying k-means clustering techniques. By using the proposed method, the regional DES is partitioned into limited number of integrated zones. The selected zones allow us to achieve accurate representation of the whole district while significantly reducing the number of decision variables for which more detailed optimization methods can be applied.

Methodology for the design of industrial hydrogen networks and the optimal placement of purification units using multi-objective optimisation techniques

Abstract The optimal design of hydrogen networks aims at minimising the consumption of fresh hydrogen by improving recycling and reuse of process hydrogen. To solve this problem, a new graphical representation has been developed to characterize the minimum hydrogen requirement, and make a preliminary selection of the compatible purification units. From this preliminary analysis, a multi-objective optimisation method is applied in order to define the best hydrogen network and the proper integration of purification units. The proposed method decomposes the problem into two sub-problems: a mixed integer linear programming for network design at the lower level and an evolutionary algorithm strategy to solve the optimal design of the purification units at the upper level.

On the use of process integration techniques to generate optimal steam cycle configurations for the power plant industry

The integration of steam cycles in the power plant industry aims at optimizing the conversion of the available heat into mechanical power. The proposed method uses process integration techniques and a multi-objective optimisation procedure to generate optimal steam cycle configurations which can be ordered by complexity-costs and efficiency. The interest of this methods is to allow the integration of complex heat exchanger network layout, providing systematically alternative arrangements using an effective solving method.

Contribution of Model Predictive Control in the Integration of Renewable Energy Sources within the Built Environment

Integrating intermittent renewable energy sources has renders the power network operator task of balancing electricity generation and consumption increasingly challenging. Aside from heavily investing in additional storage capacities, an interesting solution might be the use predictive control methods to shift controllable loads towards production periods. Therefore, this paper introduces a systematic approach to provide a preliminary evaluation of the thermo-economic impact of model predictive control (MPC) when being applied to modern and complex building energy systems (BES). The proposed method applies an e-constraint multi-objective optimization to generate a large panel of different BES configurations and their respective operating strategies. The problem formulation relies on a holistic BES framework to satisfy the different building service requirements using a mixed integer linear programming technique. In order to illustrate the contribution of MPC, different applications on the single and multi-dwelling level are presented and analysed. The results suggest that MPC can facilitate the integration of renewable energy sources within the built environment by adjusting the heating and cooling demand to the fluctuating renewable generation, increasing the share of self-consumption by up to 27% while decreasing the operating expenses by up to 3% on the single building level. Finally, a preliminary assessment of the national-wide potential is performed by means of an extended implementation on the Swiss building stock.

Multi-period multi-time optimisation of CO2 based district energy systems

Abstract Energy system design should take into account the hourly, daily and seasonal variations of both the energy demand and the considered utilities, and therefore requires a multi-time-resolution problem formulation. Multi-period / multi-time optimization is needed when a multi-time (e.g. hourly) optimization is performed inside another multi-period (e.g. typical day) optimization. However, optimizations over large temporal or spatial horizons tend to become computationally expensive, due to the large number of variables and constraints indexed over the times and over the periods. Employing typical operating periods (e.g. a number of typical operating days during the year) offers an interesting solution for problem size reduction. A variety of data clustering algorithms have been proposed in literature in order to select the best typical periods for different applications. This work uses a MILP formulation of a k-medoids based algorithm (PAM) in order to obtain typical operating periods which pass energy from one period to another, in view of performing long term energy storage. The algorithm is used coupled with an optimization of a CO2 based district energy network in a typical urban center. The intra-daily resolution allows the exploration of short term energy storage in the form of batteries located in the medium and low voltage grid. Coupled with the seasonal resolution, it offers a better understanding of the impact of daily storage on the long term storage and on the total energy requirement. The results show that implementing short-term energy storage leads to reductions of 2% in the size of the long term storage tank and 7.5 - 7.8% in the size of the main energy providers (PV panels).