MIP‐and‐refine matheuristic for smart grid energy management

Fischetti, Matteo; Sartor, Giorgio; Zanette, Arrigo

doi:10.1111/itor.12034

Cited by 17 publications

(5 citation statements)

References 7 publications

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“…These are also known as "model-based heuristics". One example in the assessment of energy networks is Fischetti et al (2015), who present a new matheuristic that combines MIP optimisation and a greedy algorithm to solve the smart grid energy management problem. The proposed application addresses the demand side energy management problem by solving a scheduling problem involving multiple appliances with different operational constraints, user preferences, renewable energy sources, and batteries.…”

Section: Matheuristicsmentioning

confidence: 99%

Using Optimisation and Machine Learning to Validate the Value of Infrastructure Investments

Flores¹,

Banerjee²,

Mathews³

2019

Preprint

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When stakeholders commit to building infrastructure as part of strategic, long-term planning, the final facilities are not normally amenable to modification after completion. A consequence of this is that users are forced to operatewithin the original specifications for, at least, as long as it takes to carry out major refurbishments or retrofitting, and even then, the constraints imposed by the original layout may be inescapable.On one hand, the original infrastructure plans enhance (or limit) the users' ability to operate efficiently for years to come. As time passes and the payback period approaches, changing operating conditions and unforeseen bottlenecks in the original blueprint can, at best, affect the economic returns and, at worst, defeat the purpose of the whole project (see, for example, Castellon airport in Spain, which was built but is grossly underutilised), producing unanticipated economical, social and political repercussions. On the other hand, managers and operators (that is, those living with the consequences of the strategic planning) have some leeway to compensate for miscalculations by means of their tacticaland operational planning.In this chapter, we explore the use of quantitative techniques to, first, amend bottlenecks and uncertain market and operating conditions that affect the performance of infrastructure investments (the tactic and operational levels), and second, validate the effectiveness of the original infrastructure design (the strategic level) under these changing conditions.More specifically, we present a rail scheduling case study where we combine demand forecasting using Machine Learning techniques and formal Operations Research methods to assess and maximise the value of already-existing infrastructure.Rail scheduling is a typical optimisation problem popular in the literature, but its potential value is bounded not only by its technical properties and specifications (how good the algorithm is) but also by the accuracy of data feeding the algorithm. Such data is critical in specifying the demand thata facility will experience in the future, and the costs that will be incurred to operate it. The use of intensive data analytics and appropriate Machine Learning techniques can resolve this and provide a substantial competitive edge forinvestors and operators of rail inter-modal terminals.We anticipate that Machine Learning algorithms that predict future demand, coupled with optimisation techniques that streamline operations of facilities, can be integrated to create tools that help policy makers and terminal operators maximise the value of their current infrastructure, while meeting ever-changing demand.

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Section: Matheuristicsmentioning

confidence: 99%

Using Optimisation and Machine Learning to Validate the Value of Infrastructure Investments

Flores¹,

Banerjee²,

Mathews³

2019

Preprint

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“…The characteristics of the energy losses while discharging the battery lead to nonlinearities in the model, which are tackled by using problem-specific heuristics and piecewise linear approximations. In contrast, previously used models did either not consider losses at all [11] or only constant losses [12]. One of several objective functions can be chosen: Minimization of energy expenses, minimization of the peak power transmission rate, or approximation of a given purchase profile.…”

Section: Optimization Of Network Site's Battery Load Schedulesmentioning

confidence: 99%

Telecommunication Networks as Smart Energy Consumers in Space and Time

Lange

Schlenk

Schlosser

et al. 2016

JGE

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Telecommunication networks and power grids are parallel hierarchical maintenance structures with system-wide reach. National telecommunication carriers figure among the top energy consumers in developed national economies and, thus, it is important to improve the network energy efficiencyin particular in the light of the increasing user and usage demands. One promising option is to exploit traffic dynamics by introducing load-adaptive operation modes leading -besides internal energy efficiency improvementsto a systemic electricity consumer with spatially and temporally fluctuating electricity demands.New ways of coupling both of the two network types open up the possibility of an optimized joint control of the 'energy' and 'transport' dimensions of the telecommunication network. Due to the high overall electricity demands of telecommunication networks, their new flexibility can be assumed to be of a relevant scale, leading to optimized overall energy consumption. To fill out such a role, networks have to be made amenable to control signals of different nature. Thus, the hitherto static and unidirectional powering of the telecommunication network is replaced by feedback options and overall load variability in space and time.In the project DESI, funded by a German federal grant and conducted by a topically wide-ranging consortium, the different coupling mechanisms such as load-adaptive network operation mode or distributed energy storage capability were studied in detail along the entire information and communication technology services delivery chain. This article summarizes theoretical and practical project results and gives insights on their expected future relevance. Project results showed that the coupling and joint optimization leads to an overall optimized energy efficient and reliable telecommunication network operation at the same time offering valuable stability services to power grids.

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“…As already mentioned, our approach can be cast into the Large Neighborhood Search paradigm, and in particular in the MIP-and-refine framework recently investigated in [8], and works as shown in Figure 3.…”

Section: The Overall Approachmentioning

confidence: 99%

Proximity search heuristics for wind farm optimal layout

Fischetti

Monaci

2015

J Heuristics

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MIP‐and‐refine matheuristic for smart grid energy management

Cited by 17 publications

References 7 publications

Using Optimisation and Machine Learning to Validate the Value of Infrastructure Investments

Using Optimisation and Machine Learning to Validate the Value of Infrastructure Investments

Telecommunication Networks as Smart Energy Consumers in Space and Time

Proximity search heuristics for wind farm optimal layout

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