A mathematical programming framework for energy planning in services’ sector buildings under uncertainty in load demand: The case of a hospital in Athens

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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“…Empirical method: 13 typical periods, one per month as the average values and one extreme day [7]. 2.…”

Section: Test Casementioning

confidence: 99%

“…Casini et al [6] used 3 typical periods for the thermal demands, one per season (winter, summer and intermediary) and 24 typical periods for electricity sold to the grid. Mavrotas et al [7] used 12 typical periods, using monthly averages. Balachandra and Chandru [8] proposed 9 representative daily load curves for electricity demand.…”

Section: Introductionmentioning

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

128

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“…Loken (2007) presented a literature review about the implementation of MCDM instruments in energy planning problems. Mavrotas et al (2008) modeled the uncertain power demand by using a multi-objective mathematical programming approach. They aimed at minimizing the total cost, and maximizing the demand satisfaction degree.…”

Section: Literature Reviewmentioning

confidence: 99%

A Fuzzy Multi-Criteria Decision Making Methodology Based Upon the Interval Type-2 Fuzzy Sets for Evaluating Renewable Energy Alternatives in Turkey

Balın

Baraçlı

2015

Technological and Economic Development of Economy

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Abstract.Energy is substantial for social development and has an essential role for the survival and development of human as an environmental factor. Due to the serious increase ofthe world's primary energy price and rapid development of Turkey's economy, energy consumption of Turkey has become a major problem. For that purpose, opinions of the decision makers upon the relative importance of selection criteria are determined by using a fuzzy AHP procedure based upon type-2 fuzzy sets, and fuzzy multi-criteria decision-making based upon the interval type-2 TOPSIS method is used to rank the best renewable alternative energy. In this paper, a fuzzy multi criteria decision making (MCDM) methodology was proposed to determine the best renewable energy alternatives for Turkey, because type-2 fuzzy sets provide additional degrees of independence to represent the uncertainty and the fuzziness of real world applications. The results showed that wind was the best alternative for Turkey's energy investments, as being followed subsequently by solar, biomass, geothermal, hydraulic and hydrogen. The results were based upon the opinions of three energy planning experts who ranked the energy alternatives according to the ten criteria we have selected.

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“…The result of the MO optimization is usually not a single solution, but a set of the Pareto optimal solutions, i.e., the solutions that cannot be improved with respect to one objective value without deteriorating at least one of the other values of the objective functions [51].…”

Section: Multiple Objectives Considerationmentioning

confidence: 99%

Multi-Objective Combinatorial Optimization of Trigeneration Plants Based on Metaheuristics

Stojiljković

Blagojević

2014

Energies

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In this paper, a methodology for multi-objective optimization of trigeneration plants is presented. It is primarily applicable to the systems for buildings' energy supply characterized by high load variations on daily, weekly and annual bases, as well as the components applicable for flexible operation. The idea is that this approach should enable high accuracy and flexibility in mathematical modeling, while remaining efficient enough. The optimization problem is structurally decomposed into two new problems. The main problem of synthesis and design optimization is combinatorial and solved with different metaheuristic methods. For each examined combination of the synthesis and design variables, when calculating the values of the objective functions, the inner, mixed integer linear programming operation optimization problem is solved with the branch-and-cut method. The applicability of the exploited metaheuristic methods is demonstrated. This approach is compared with the alternative, superstructure-based approach. The potential for combining them is also examined. The methodology is applied for multi-objective optimization of a trigeneration plant that could be used for the energy supply of a real residential settlement in Niš, Serbia. Here, two objectives are considered: annual total costs and primary energy consumption. Results are obtained in the form of a Pareto chart using the epsilon-constraint method.Energies 2014, 7 8555

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A mathematical programming framework for energy planning in services’ sector buildings under uncertainty in load demand: The case of a hospital in Athens

Cited by 110 publications

References 27 publications

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

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

A Fuzzy Multi-Criteria Decision Making Methodology Based Upon the Interval Type-2 Fuzzy Sets for Evaluating Renewable Energy Alternatives in Turkey

Multi-Objective Combinatorial Optimization of Trigeneration Plants Based on Metaheuristics

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