The synthesis problem of decentralized energy systems is strongly NP-hard

Goderbauer, Sebastian; Comis, Martin; Willamowski, Felix J. L.

doi:10.1016/j.compchemeng.2019.02.002

Cited by 34 publications

(17 citation statements)

References 25 publications

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“…Energy sector related issues, which are often NP‐hard (Goderbauer et al. 2019), seem to be the most frequently represented subjects in publications on CO, with the keywords power and energy . The topic of power is mainly about the above‐mentioned smart grid (e.g., Meskina et al., 2018) applications or optimal power flow (e.g., Abido, 2002) calculations.…”

Section: Resultsmentioning

confidence: 99%

Research trends in combinatorial optimization

Weinand

Sörensen

Segundo

et al. 2021

Int Trans Operational Res

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Real‐world problems are becoming highly complex and therefore have to be solved with combinatorial optimization (CO) techniques. Motivated by the strong increase in publications on CO, 8393 articles from this research field are subjected to a bibliometric analysis. The corpus of literature is examined using mathematical methods and a novel algorithm for keyword analysis. In addition to the most relevant countries, organizations, and authors as well as their collaborations, the most pertinent CO problems, solution methods, and application areas are presented. Publications on CO focus mainly on the development or enhancement of metaheuristics like genetic algorithms. The increasingly problem‐oriented studies deal particularly with real‐world applications within the energy sector, production sector, or data management, which are of increasing relevance due to various global developments. The demonstration of global research trends in CO can support researchers in identifying the relevant issues regarding this expanding and transforming research area.

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Section: Resultsmentioning

confidence: 99%

Research trends in combinatorial optimization

Weinand

Sörensen

Segundo

et al. 2021

Int Trans Operational Res

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“…ESIP problems can be very challenging to solve, they are usually NP‐hard. In Reference 101, authors show that the synthesis problem (ie, the investment problem) of decentralized energy systems is strongly NP‐hard. Some papers reviewed focus on computational issues and propose alternative approaches.…”

Section: Survey Of Optimization Methods For Energy System Planningmentioning

confidence: 99%

Techno‐economic planning of local energy systems through optimization models: a survey of current methods

Cuisinier

Bourasseau

Ruby

et al. 2020

Intl J of Energy Research

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Summary Energy system planning is a difficult task, since it involves long‐term decision‐makings with multiple dimensions: technical, economic, social, ecological, and political. The rise of distributed and multi‐energy systems including new technologies has further increased this complexity. Techno‐economic studies based on optimization models have recently received much attention in the literature. They are essential to grasp energy systems complexity and provide decision support. This work targets methodological issues related to local systems while comparing them with methods used at larger scales. First, a new framework providing a comprehensive vision of added values and limits of optimization models is presented. Then, the main methodological trends as well as several undertaken research paths are identified based on the analysis of more than 60 research papers. The review results are summarized in a complete and concise table. Finally, future research topics are discussed including the operational facets of investment optimization models in case of high intermittent energy shares, flexibility issues and long‐term operational decisions. The results provide useful information to modellers or researchers that look for appropriate and state‐of‐the‐art optimization methods, or aim to deepen current research paths. Hence, they will facilitate the planning and development of energy systems for the future.

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“…10 10 ). Goderbauer et al [44] recently formalised the complexity of MINLP energy system problems as N P − Hard but did not discuss MILP problems. In fact, model complexity is rarely formalised.…”

Section: Side Note On Tractability Of Optimisation Modelsmentioning

confidence: 99%

District energy system optimisation under uncertain demand: Handling data-driven stochastic profiles

Pickering

Choudhary

2019

Applied Energy

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Current district energy optimisation depends on perfect foresight. However, we rarely know how the future will transpire when undertaking infrastructure planning. A key uncertainty that has yet to be studied in this context is building-level energy demand. Energy demand varies stochastically on a daily basis, owing to activities and weather. Yet, most current district optimisation models consider only the average demand. Studies that incorporate demand uncertainty ignore the temporal autocorrelation of energy demand, or require a detailed engineering model for which there is no validation against real consumption data. In this paper, we propose a new 3-step methodology for handling demand uncertainty in mixed integer linear programming models of district energy systems. The three steps are: scenario generation, scenario reduction, and scenario optimisation. Our proposed framework is data-centric, based on sampling of historic demand data using multidimensional search spaces. 500 scenarios are generated from the historical demand of multiple buildings, requiring historical data to be nonparametrically sampled whilst maintaining interdependence of hourly demand in a day. Using scenario reduction, we are able to select a subset of scenarios that best represent the probability distribution of our large number of initial scenarios. The scenario optimisation step constitutes minimising the cost of technology investment and operation, where all realisations of demand from the reduced scenarios are probabilistically weighted in the objective function. We applied these three steps to a real district development in Cambridge, UK, and an illustrative district in Bangalore, India.Our results show that the technology investment portfolios derived from our 3-step methodology are more robust in meeting large possible variations in demand than any model optimised independently with a single demand scenario. This increased robustness comes at a higher monetary cost of investment. However, the high investment cost is lower than the highest possible cost when each of the initial 500 scenarios is optimised independently. In both our case studies, building level energy systems are always more robust than district level ones, a result which disagrees with many existing studies. The outcomes enable better examination of district energy systems. In addition, our methodology is compiled as an open-source code that can be applied to optimise existing and future energy masterplans of districts.

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The synthesis problem of decentralized energy systems is strongly NP-hard

Cited by 34 publications

References 25 publications

Research trends in combinatorial optimization

Research trends in combinatorial optimization

Techno‐economic planning of local energy systems through optimization models: a survey of current methods

District energy system optimisation under uncertain demand: Handling data-driven stochastic profiles

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