Stéphane Laurent Bungener

Multi-objectives, multi-period optimization of district energy systems: I. Selection of typical operating periods

Abstract The long term optimization of a district energy system is a computationally demanding task due to the large number of data points representing the energy demand profiles. In order to reduce the number of data points and therefore the computational load of the optimization model, this paper presents a systematic procedure to reduce a complete data set of the energy demand profiles into a limited number of typical periods, which adequately preserve significant characteristics of the yearly profiles. The proposed method is based on the use of a k-means clustering algorithm assisted by an ϵ-constraints optimization technique. The proposed typical periods allow us to achieve the accurate representation of the yearly consumption profiles, while significantly reducing the number of data points. The work goes one step further by breaking up each representative period into a smaller number of segments. This has the advantage of further reducing the complexity of the problem while respecting peak demands in order to properly size the system. Two case studies are discussed to demonstrate the proposed method. The results illustrate that a limited number of typical periods is sufficient to accurately represent an entire equipments’ lifetime.

Multi-Objective, Multi-Period Optimization of Renewable Technologies and storage system Using Evolutionary Algorithms and Mixed Integer Linear Programming (MILP)

Abstract In the present work a systematic procedure, including process design and integration techniques, for sizing and operation optimization of a poly-generation plant integrated with heat storage systems is presented. The storage system is used to balance energy demand fluctuation during 24 hours of a day. Adding thermal storage capacity allows for better utilization of equipments and avoiding over estimation of installed capacity. The integration of heat storage systems with polygeneration technologies in a multi objective and multi-period optimization model is the novelty of this work.

A Methodology for Creating Sequential Multi-Period Base- Case Scenarios for Large Data Sets

Key performance indicators in engineering problems include but are not limited to financial, operational, management and environmental factors, which are significantly affected by aspects such as seasonality, fouling, economic climate, production rates, supply and demand. The search for an optimal solution to a problem must take into consideration this variability, otherwise running the risk of critical dimensioning or cost estimation errors. Testing solutions using full data sets covering large periods of time can be a computational challenge, and the analysis of results complicated. For the feasibility of such a study, it is therefore necessary to reduce the large data sets to a number of base case scenarios, which simultaneously reduce the number of data points to be handled while still representing the variability of the system. A novel method is therefore developed to address this problem. This method offers a way of designing an index of sequential periods common to each production level, which when averaged accurately represent periods of nominal values for each level. The method exploits a multi-objective evolutionary algorithm, minimising the standard deviation of the base cases compared to the real data as well as respecting crucial null value periods. Null value periods are typically found in turnarounds or supply and demand problems and are usually incorrectly represented in other methods. Lastly, the resulting base cases are sequential periods, which is important when dealing with scheduling, shutdown or storage problems. The method is tested using anonymised data and is compared to previously existing methods, with results showing improvement in the performance of the base cases with respect to the objective functions.

Virtual sector profiles for innovation sharing in process industry : sector 01: chemicals

Production data in process industry are proprietary to a company since they are key to the process design and technology expertise. However, data confidentiality restrains industry from sharing results and advancing developments in and across process sectors. Using virtual profiles that simulate the typical operating modes of a given process industry offers an elegant solution for a company to share information with the outside world. This paper proposes a generic methodology to create sector blueprints and applies it to the chemicals industry. It details the profile of a typical chemical site based on essential units and realistic data gathered from existing refineries and chemical plants.

Optimal Operations and Resilient Investments in Steam Networks

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.