The transition toward sustainable energy systems poses a most prominent societal challenge for decades to come. We demonstrate that the alignment framework provides a set of rich instruments for exploring this field of research. It allows us to disentangle the complex interrelations between the technologies and institutions required to provide expected services and safeguard the critical functions at the core of network infrastructures. Since the energy transition requires structural changes in the technological architecture and the macro-institutions, this chapter focuses on this most generic layer of analysis. In order to illustrate our approach, we compare and analyze three archetypes, namely, traditional, contemporary, and future energy systems. Using a comparative static approach, we identify (in)compatibilities between the technological and institutional characteristics of different coordination arrangements when it comes to safeguarding the critical functions throughout the energy transition. We show that the alignment framework provides an innovative approach for understanding and analyzing these complex changes. This analysis of future energy systems represents an important step toward understanding the consequences of the energy transition and the possible evolution of existing energy systems.

In this chapter, we specify the nature of network infrastructures from our alignment perspective. We first pay attention to the expected services that network infrastructures intend to provide: they are the backbones of the economy and deliver services essential to its citizens. We show how the infrastructures and the services they are expected to deliver are embedded in societal values. We then discuss the two dimensions of network infrastructures, the technological and the institutional dimensions, and analyze the characteristic of complementarity that underlies their components. Complementarities require tight coordination. Furthermore, we discuss in this chapter the core of our argument: the modalities providing technological coordination, on the one hand, and institutional coordination, on the other hand, should be well aligned; otherwise, the fulfillment of critical functions is endangered. We need to better understand how network infrastructures operate and under which conditions they can achieve the expected performance. We focus on the interdependencies between the technological and the institutional dimensions; on the critical functions as requirements for the system to provide the expected services; and on the necessity to align the coordination arrangements in both dimensions, in order to fulfill these critical functions. Otherwise, expected services cannot be delivered.

Notwithstanding their specificities, different network infrastructures share a fundamental property: they are embedded in and part of general institutional settings. In this chapter, we focus on this institutional dimension. The main point we make is that institutions are composed of different layers. Identifying and characterizing these layers is both challenging and essential for better understanding the alignment (or misalignment) between institutions and technologies that conditions the performance of specific infrastructures. It is challenging because the usual representations of institutions tend to aggregate and mix or even revise many distinct components such as firms, parliaments, courts, etc. It is essential because it is through the different layers that rights are defined, allocated, implemented, and monitored, thus providing the scaffolding of network infrastructures. A central hypothesis underlying the analysis provided in this chapter is that these infrastructures are socio-technological systems; although subject to physical laws through their technological dimension, their development and usage are framed by human-made rules and rights.

This book is about network infrastructures. We consider network infrastructures as socio-technological systems characterized by the interdependence and complementarity of two dimensions: institutions and technology. Relying on a combination of nodes and links, these infrastructures require coordination along both dimensions in order to fulfill functions identified as “critical.” Critical functions determine the capacity of a network to deliver expected services in line with societal values. Thus understood, network infrastructures cover a wide range of sectors, from energy, water and sanitation, urban transportation, to telecoms and the internet. These networks provide the backbone of economic as well as social activities. The key argument underlying our analysis is that alignment between the two dimensions, institutions and technology, is central to the fulfillment of the performance expected from these networks. Misalignment can generate discrepancies or gaps challenging the integrity of a network and its capacity to meet its goal. This introduction posits our core hypotheses and concepts, and draws a general picture of the theoretical as well as empirical content developed in the coming chapters.

In analyzing the existing and future transportation system in general, and the testing and deployment of automated and self-driving vehicles in particular, this chapter demonstrates that the application of our framework provides a good understanding of the interdependencies between the technological and institutional dimensions at stake. An analysis of both the vertical coordination between the layers along these two dimensions, respectively, and the horizontal alignment between them offers in-depth insights about the complexity of the transportation network and the conditions to be met if the expected services are to be delivered. The changes in the technological architecture, with the introduction of technological designs and operation of automated vehicles, and their interdependence with macro-institutional values, in particular safety but also security, privacy, and efficiency, offer a rich opportunity to analyze the structural complexities at stake. In this chapter, we focus on the layer of transactions: transactions between car manufacturers and their suppliers, between car manufacturers and the providers of the transportation services, and between these providers and their customers. The importance of the alignment between technical operations and micro-institutions is illustrated by the fatal accident involving an automated test car on March 19, 2018 in a street in Tempe, Arizona.

This chapter assesses factors of alignment between institutions and the technology of network infrastructures, and how to achieve or restore alignment. This is a significant challenge, since institutions and technologies are “two worlds apart” that need to be brought together. This is accomplished in three steps. First, we specify how technology and institutions are interrelated at three layers of analysis: structure, governance, and transactions. By connecting these three layers with the services to be provided, we are able to identify the conditions to be fulfilled within each layer, in order for the critical functions to be safeguarded. Second, we focus on characterizing different problems of coordination that develop either within the technological dimension, or within the institutional dimension. Our underlying argument is that modalities of coordination adopted to solve these problems may partially differ, depending on whether we are looking at the technological side or the institutional side, but that they ultimately need to share compatible characteristics if alignment is to be reached and the critical functions satisfied. Third, when disturbances of different orders challenge the existing arrangements, we provide indications as to how alignment can be reached, or reestablished, at the three layers we have identified.

A surrogate model, also known as a response surface model or metamodel, is an approximate model of a functional output that represents a “curve fit” to some underlying data. The goal of a surrogate model is to build a model that is much faster to compute than the original function, but that still retains sufficient accuracy away from known data points.

General nonlinear optimization problems are difficult to solve. Depending on particular optimization algorithm, they may require tuning parameters, providing derivatives, adjusting scaling, and trying multiple starting points. Convex optimization problems do not have any of those issues and are thus easier to solve. The challenge is that these problems must meet strict requirements. Even for candidate problems with the potential to be convex, significant experience is usually needed to recognize and utilize techniques that reformulate the problems into an appropriate form.

Engineering design optimization problems are rarely unconstrained. In this chapter, we explain how to solve constrained problems. The methods in this chapter build on the gradient-based unconstrained methods fromand also assume smooth functions. We first introduce the optimality conditions for a constrained optimization problem and then focus on three main methods for handling constraints: penalty methods, sequential quadratic programming (SQP), and interior-point methods.