The primary question related to smart metering deployment is not whether it is a beneficial technology for the consumers or a profitable business for the actors, but the efficiency of cost allocation process and providing incentives for beneficiaries to participate in the process. This study investigates the cost allocation problem as a
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complex challenge that needs to take into account the dynamics of interaction between different actor groups and keeping the track of incentives over time.
Although existing policies such as dynamic pricing policies in the forms of Time-of- Use (TOU) or Real-Time Monitoring (RTM) may lead to positive results for cost and benefit analysis, this study claims keeping the track of incentives over time is a key factor for a successful technology deployment, by balancing short-term and long-term consequences. Maintaining the incentives for all the actors over time has implications for policymaking. For instance, facilitating the emergence of a cooperative strategy based on the potential benefits over time, such as the smart metering tariff, without forcing any proposed cooperation is an important factor for incentivizing retailers as the missing actors in current CBA practices. Without finding collaborative and inclusive strategies, market forces can hardly reach an economically feasible solution.
The simple models proposed in this study try to grasp the primary factors shaping the system behavior. Apparently, there are other factors relevant for the cost allocation process, but they are beyond the scope of this paper. They include information ownership issues, access control, confidentiality and solution scalability among the others that remain out of the focus of this paper. In addition, as discussed in §2.4, institutional environment is an important external factor for analyzing the motivations of different actors. For instance, even in a liberalized market, not all the actors have access to the market (in countries like Switzerland, liberalization means only consumers with consumption higher than a threshold can have access to the market, and as result, households are out of the market). In different institutional contexts, the role of regulator can be different; but in general, it should act to balance the advantages of smart metering roll-out between actors by setting up institutions to protect the consumers against abusive cost recovery and increase social benefits of all the actors affected by technological change. Such broader roles can be reflected in the system structure behind simulation models, while detailed specifications should be customized based on contextual differences.
To sum, this study took a step further in the cost allocation problem by taking a dynamic approach and using simulation models for revealing the interdependencies and feedback structures that make the cost allocation problem a complex task. Paying attention to the endogenous dynamics is a necessary step for the identification of the
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tipping points and critical variables, which help to design more effective scenarios for system intervention.
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3 A Multi-method Approach for Analyzing the Spatial
Diversity of Technological Innovation Systems: The Case
of Smart Grid Development
Keywords: Technological Innovation Systems, Social Network Analysis, Agent-based Modeling, Smart Grid, Spatial Analysis, Complex System Theory, Institutional Analysis
3.1 Introduction
The technological innovation system (TIS) approach has emerged as a key framework in innovation and transition studies (Jacobsson and Bergek, 2011; Markard et al., 2012). It has been devised to analyze the development of new technologies, highlighting the systemic interplay of actors, networks and institutional structures (Carlsson and Stankiewicz, 1991). In contrast to regional or national innovation systems approaches, the TIS framework does not depart from a spatial focus but takes technology as the starting point (Hekkert et al., 2007). As a consequence, the spatial focus of the analysis is a priori undefined as technologies cut across sectoral and spatial boundaries (Bergek et al., 2015).
However, many TIS studies have confined their inquiry to national boundaries, often without even discussing the consequences of such a focus setting. This practice has been criticized in recent years (Binz et al., 2014; Markard et al., 2015) and meanwhile, there are a few studies that explicitly study the spatial dimensions of TIS, primarily by taking a relational approach to space or doing comparative analysis between national networks (Bento and Fontes, 2015; Binz et al., 2014; Coenen et al., 2012; Wieczorek et al., 2015). Another issue with recent interest in studying spatial dimension of TIS development is to find out how contextual factors shape different spatial settings. It means from a theoretical point of view, spatial characteristics of a multinational network can be attributed to the spatial characteristics of the countries that constitute network interactions including the tendency to focus on national initiatives versus international collaborations, state of different TIS functions in the network and how spatial properties change over these functions, as well as the way past innovative activities shape the future developments and contribute to the emergence of new spatial diversity. These
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factors highlight national differences, the maturity of TIS functions and the path- dependency of network development respectively.
National differences can be explained as part of the argument that structural elements of a TIS such as actors, networks and institutions are embedded in the pre- existing structures of a territory. This embedding creates spatial dynamics resulting from structural coupling between a TIS and territorial innovation systems (Bergek et al., 2015). In order to understand the spatial dynamics at the TIS level, understanding the synergies between technologies and institutions at different spatial levels is required. In addition, the contribution of local and regional policies to system development is an important factor in shaping the diversity of spatial configurations at the system level. It means the institutional environment should be decomposed to identify different institutions at different spatial levels.
One way to investigate the impact of spatial properties and institutions of specific national innovation systems on the spatial properties of a TIS as a whole, is through analyzing the spatial diversity of TIS development and how this diversity emerges and changes over time in such a complex system. It means institutions at different levels contribute to the emergence of diverse spatial patterns, and this multi-level institutional environment should be analyzed to understand the impact of institutions at different levels of system development.
Addressing the concepts of diversity of spatial settings and multi-level institutional analysis open up some new questions for spatial analysis of TIS development. How can we investigate the spatial dynamics of TIS development resulting from interactions between heterogeneous actors over time and space? How does the interplay of institutions at different levels (such as EU policies vs. national differences) influence the emergence of spatial patterns in a multi-scalar TIS (e.g. a European TIS)?
Based on these lines of research, this paper takes a first step to analyze the diversity of spatial configurations as an emerging property of complex TIS dynamics. In this respect, spatial diversity of a TIS can be understood by analyzing the patterns of interactions between innovative firms over time and space, influenced and surrounded by the multi-level institutional environment (national, regional, TIS). For this purpose, it combines insights from different perspectives on TIS development with ideas from
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complex systems theory and institutional analysis to develop a multi-method approach for spatial analysis. Focusing on the case of smart grid development in Europe, first we develop a model of Social Network Analysis (SNA) for investigating the emergence of modules or sub-systems with different spatial diversities over time. Then, an Agent- based Model (ABM) is developed to further analyze the insights derived from SNA on the contribution of different countries with different spatial characteristics.
The paper is structured as follows. §3.2 explains the theoretical background of this study. §3.3 describes the data and explains the method developed for social network analysis. §3.4 presents the results, while §3.5 develops an agent-based model to complement the results of social network analysis. §3.6 concludes.