Methods for multi-objective investment and operating optimization of complex energy systems

Fazlollahi, Samira; Mandel, Pierre; Becker, Gwénaëlle; Maréchal, François

doi:10.1016/j.energy.2012.02.046

Cited by 151 publications

(74 citation statements)

References 35 publications

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“…SOWE method has the ability to address this aspect, which has never been addressed explicitly in the literature. ICC can also be used to generate automatically the ordered set of solutions (Fazlollahi et al, 2012). This allows comparing the solutions with regard to different criteria, which has not been taken into account in the objective function.…”

Section: An Improved Linear Programming Approach For Simultaneous Optmentioning

confidence: 99%

An Improved Linear Programming Approach for Simultaneous Optimization of Water and Energy

Kermani

Périn-Levasseur

Benali

et al. 2014

Computer Aided Chemical Engineering

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An optimization method based on Mixed Integer Linear Programming (MILP) has been developed for simultaneous optimization of water and energy (SOWE) in industrial processes. The superstructure integrates process thermal streams and optimizes the consumption of water while maximizing internal heat recovery to reduce thermal utility consumption. In this paper, additional concepts have been implemented in the superstructure to target the issues of the pulp and paper processes. Non-Isothermal Mixing (NIM) has been considered at different locations in order to reduce the number of thermal streams and decrease the investment cost by avoiding unnecessary investment on heat exchangers. The concepts of restricted matches and water tanks have been added to the superstructure to adapt it to the pulp and paper case studies. The Integer-Cut Constraint (ICC) technique has been combined with the MILP model to generate systematically a set of optimal solutions to support the decision-making for cost-effective configurations.

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Section: An Improved Linear Programming Approach For Simultaneous Optmentioning

confidence: 99%

An Improved Linear Programming Approach for Simultaneous Optimization of Water and Energy

Kermani

Périn-Levasseur

Benali

et al. 2014

Computer Aided Chemical Engineering

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“…The application of the ✏-constraints algorithm for multi-objective optimization of urban energy systems has been reviewed in [13]. The second objective, max {Quality}, is therefore defined as a set of constraints with an upper limit of ✏ a j .…”

Section: Typical Periods' Selection Algorithmmentioning

confidence: 99%

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

Fazlollahi

Bungener

Mandel

et al. 2014

Computers & Chemical Engineering

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127

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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 ✏-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 su cient to accurately represent an entire equipments' lifetime.Keywords: Typical periods, District energy systems, Mixed Integer Linear Programming, Evolutionary algorithm, Multi-objective optimization, Cluster analysis, k means algorithm IntroductionMulti period mixed integer linear programming (MILP) is an e↵ective method for designing distributed energy systems [1,2]. It provides guidance for choosing optimal system configurations for minimizing costs and environmental impacts. The evaluation of the district energy system performance requires the estimation of the investment and of the corresponding operating costs. The calculation of the operating costs should consider the hourly, daily and seasonal variations of energy demand and the contribution of each production unit. It is therefore necessary to extend the optimization to the multi-period model. Due to the high number of variables, such a detailed description of the system requires excessive computational resources for solving the MILP optimization model.Using the typical periods provides an e cient alternative for reducing the number of variables. The notion of the limited typical periods relies upon the assumption that a year can be accurately represented by a limited set of periods. The term period describes a portion of time of a certain duration. It can be a set of days, weeks, working days or weekends, defined by a sequence of time steps, over the life time of the equipment.The problem of selecting typical operating periods based on the energy demand variations has been approached in di↵erent ways. Maréchal et al. [3] proposed an evolutionary algorithm optimization approach to select typical production scenarios for an industrial cluster. Ortiga et al.[4] developed a graphical method to select a reduced number of periods that reproduce the heating and cooling load duration curves...

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“…Once the heat load profiles are generated, the whole energy system has to be defined under consideration of spatial and operational aspects with an iterative multi-criteria-optimization [128]. Thereby, the concept of distributed thermal energy storages is an important part of the optimal storage facility planning.…”

Section: Planning Of Decentralized Storage Facilitiesmentioning

confidence: 99%

GIS-Based Planning and Modeling for Renewable Energy: Challenges and Future Research Avenues

Resch

Sagl

Törnros

et al. 2014

IJGI

121

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Abstract:In the face of the broad political call for an -energy turnaround‖, we are currently witnessing three essential trends with regard to energy infrastructure planning, energy generation and storage: from planned production towards fluctuating production on the basis of renewable energy sources, from centralized generation towards decentralized generation and from expensive energy carriers towards cost-free energy carriers. These changes necessitate considerable modifications of the energy infrastructure. Even though most of these modifications are inherently motivated by geospatial questions and challenges, the integration of energy system models and Geographic Information Systems (GIS) is still in its infancy. This paper analyzes the shortcomings of previous approaches in using GIS in renewable energy-related projects, extracts distinct challenges from these previous efforts and, finally, defines a set of core future research avenues for GIS-based energy infrastructure planning with a focus on the use of renewable energy. These future OPEN ACCESS ISPRS Int. J. Geo-Inf. 2014, 3 663 research avenues comprise the availability base data and their -geospatial awareness‖, the development of a generic and unified data model, the usage of volunteered geographic information (VGI) and crowdsourced data in analysis processes, the integration of 3D building models and 3D data analysis, the incorporation of network topologies into GIS, the harmonization of the heterogeneous views on aggregation issues in the fields of energy and GIS, fine-grained energy demand estimation from freely-available data sources, decentralized storage facility planning, the investigation of GIS-based public participation mechanisms, the transition from purely structural to operational planning, data privacy aspects and, finally, the development of a new dynamic power market design.Keywords: integration of GIS and energy system models; GIS and renewable energy; GIS-based energy infrastructure planning; future research challenges; fluctuating renewables; structural planning of local energy systems; operation optimization

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Methods for multi-objective investment and operating optimization of complex energy systems

Cited by 151 publications

References 35 publications

An Improved Linear Programming Approach for Simultaneous Optimization of Water and Energy

An Improved Linear Programming Approach for Simultaneous Optimization of Water and Energy

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

GIS-Based Planning and Modeling for Renewable Energy: Challenges and Future Research Avenues

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