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Optimal Policy Identification: Insights from the German Electricity Market
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... Last not least, the role of the demand side eects is not clear. Is it benecial for the knowledge discovery process in general and the GPT adoption in particular that society starts favoring a certain product development as it was the case, e.g., for nuclear power plants in the 1950s (Cowan, 1990) or renewable energy generation in the last two decades (Herrmann and Savin, 2016)? In both cases, the policy maker was providing large subsidies to discover a product with certain characteristics, while actual choice among dierent technological trajectories were left to innovating rms. ...
... Thus, any change in the rank of priorities can reect either changes in preferences or institutions. For example, policy instruments introduced in the German energy sector made it protable to concentrate on the renewable energy technologies (above all, wind and solar) (Herrmann and Savin, 2016 ). the product type, a critical decision with respect to the preferred technological trajectory had to be taken (in this case between light water, heavy water and gas graphite). Given that typically [...] when a technology is introduced its future payos are not well known (Cowan, 1990, p. 544), the choice has been made mainly based on knowledge accumulated by the U.S. Navy adopting the light water for submarine propulsion. ...
The realisation of the smart grids and markets vision constitutes a substantial transition of electricity systems affecting multiple stakeholders and creating various technical, social, economic, political, and environmental challenges. These need to be considered adequately in decision support tools for agents in electricity systems. Agent-based modelling and simulation as a flexible and rich modelling framework can serve as a testbed for analysing new paradigms in the field of smart grids, such as demand response, distributed generation, distribution grid modelling, and efficient market integration. While so far wholesale electricity markets have been the focus of agent-based modelling and simulation, this paper provides a detailed review of literature using such techniques for analysing smart grids from a systems perspective. For that purpose, a general classification of applying agent-based modelling and simulation techniques to electricity systems is provided. The literature review of specifically using agent-based modelling and simulation for analysing smart grids shows that, although being still a limited field of research, quite different applications are identified with the number of contributions having increased in recent years. Agent-based modelling and simulation can deliver specific insights in how different agents in a smart grid would interact and which effects would occur on a global level. Thereby, the approach can deliver valuable input for decision processes of stakeholders and policy making. Future research could feature more focused analyses of storage systems, local market concepts, interactions with centralised markets, and the role of intermediaries.
Policy makers constantly face optimal control problems: what controls allow them to achieve certain targets in, e.g., GDP growth or inflation? Conventionally this is done by applying certain linear-quadratic optimization algorithms to dynamic econometric models. Several algorithms extend this baseline framework to nonlinear stochastic problems. However, those algorithms are limited in a variety of ways including, most importantly, their restriction to local best solutions only and the symmetry of objective function. The contribution of the current study is that we adopt Differential Evolution (DE) in the context of nonlinear stochastic optimal control problems, thus ensuring better convergence to a global optimum and explicitly considering parameter uncertainty by evaluating the expected objective function. The latter is done by minimizing the median over a set of multiple Monte Carlo draws of uncertain parameters and by separately evaluating the random parameter draws looking particularly at extreme cases. Comparing DE with more traditional methods, which
make use of linear-quadratic optimization, in two economic models, we find that the solutions obtained for expected and ex-post functions differ consistently raising doubts about the optimality of ex-post solutions. We claim that this research is aimed to broaden the range of decision support information used by policy makers when choosing an optimal strategy under much more realistic conditions.
Technological change and gains in efficiency of renewable power generation technologies are to a large extent driven by governmental support. Various policies that can broadly be categorized as technology push, demand pull or systemic constitute a policy mix for renewable energies. Our goal is to gain insights on the influence of this policy mix on the intensity and organization of inventive activities within the technological innovation systems for wind power and photovoltaic in Germany since the 1980s. We examine the effect of different instruments on the size and structure of co-inventor networks based on patent data. Our results indicate notable differences between the technologies: The network size for wind power is driven by technology push and systemic instruments, while in photovoltaic demand pull is decisive for network growth. The instruments complement each other and form a consistent policy mix. The structure of the networks is driven by demand pull for both technologies. Systemic instruments increase interaction especially in the wind power network and are complementary to demand pull in fostering collaboration.
Computational optimization methods can be broadly classified into two groups: classical methods—which require and exploit specific functional forms of objective function and constraints—and heuristics. Those latter methods impose few, if any, restrictions on models, at the price of being more computationally demanding. But because of the growth of computing capacity over the last decades, those methods are now perfectly practical tools for everyday use. Yet, instead of realizing the advantages of heuristics, users still cling to classical methods. We discuss the reasons for this nonacceptance of heuristics, and argue that the choice of numerical‐optimization techniques is as much driven by the culture of the user—field of work and educational background—as by the quality of the method. In particular, we argue that many of the alleged shortcomings of heuristics could be overcome if researchers stopped treating optimization as a mathematical, exact discipline; instead, they should consider it a practical, computational tool. WIREs Comput Stat 2014, 6:352–358. doi: 10.1002/wics.1312
This article is categorized under: Algorithms and Computational Methods > Stochastic Optimization
This paper presents a power quality analysis on the effects of high penetration of electric vehicle charging stations (EVCSs) and renewable energy based generators (REGs) which include wind turbines, grid-connected photovoltaic and fuel cell power generation units on the 16-bus test distribution system. All data on EVCS, wind farm, photovoltaic and fuel cell units as well as weather conditions were collected from various manufacturers and the Malaysian Meteorological Department. The system is modeled and simulated using the MATLAB/Simulink software and the effects of the REGs and EVCS technologies on system performances were investigated for various penetration levels and at different loading and weather conditions. Simulation results indicated that the presence of high penetration EVCS and REGs can cause severe power quality problems such as frequency and voltage fluctuations, voltage drop, harmonic distortion and power factor reduction. (C) 2013 The Authors. Published by Elsevier Ltd.
Traditional economic theory could not explain, much less predict, the near collapse of the financial system and its long-lasting effects on the global economy. Since the 2008 crisis, there has been increasing interest in using ideas from complexity theory to make sense of economic and financial markets. Concepts, such as tipping points, networks, contagion, feedback, and resilience have entered the financial and regulatory lexicon, but actual use of complexity models and results remains at an early stage. Recent insights and techniques offer potential for better monitoring and management of highly interconnected economic and financial systems and, thus, may help anticipate and manage future crises
Of the large industrial countries, Germany is clearly leading with regard to new renewable energy sources, occupying the first rank in terms of installed wind energy capacity, and the second rank in photovoltaics. This capacity is not due to an exceptional natural resource base but to policy, despite the fact that this policy was conducted in a rather lukewarm fashion until 1997. This led to a remarkable expansion of this sector. The red-green coalition, in office since 1998, developed the vision of achieving 50 percent and more of electricity generated from RES by 2050, a goal that seems well accepted by the public but not by the established energy interests or the leaders of the conservative-liberal opposition, even though the cost appears to be comparatively modest.
The article gives a historical account of German RES-E policy since 1974 and focuses in particular on the evolution of “feed-in” legislation from 1990 to 2004. The first 15 years of RES-E policy after 1974 were devoted to R&D. Market creation measures only came into effect after 1988; of these, the Feed-In Law was the most important. During the 1990s, it barely managed to survive, but significant improvement occurred after the 1998 election; the new majority greatly strengthened RES-E support, particularly for photovoltaics and biomass. However, this legislation is still not fully accepted on both the domestic and the EU levels.
Reaching a better understanding of the policies and politics of transitions presents a main
agenda item in the emerging field of sustainability transitions. One important requirement
for these transitions, such as the move towards a decarbonized energy system, is
the redirection and acceleration of technological change, for which policies play a key
role. Several studies of policies supporting environmental technological change have argued
for the need to combine different policy instruments in so-called policy mixes.
However, existing policy mix studies often fall short of reflecting the complexity and dynamics
of actual policy mixes and the underlying politics of (re)designing them. In this
paper we take a first step towards a more comprehensive, interdisciplinary policy mix
concept for environmental technological change based on a review of the bodies of literature
on innovation studies, environmental economics and policy analysis. The concept
introduces a clear terminology and consists of the three building blocks elements, processes
and characteristics, which can be delineated by several dimensions. Throughout the
paper, we illustrate the concept using the example of the policy mix for fostering the
transition of the German energy system to renewable power generation technologies.
We argue that the proposed concept provides an integrating analytical framework for
empirical studies analyzing the impact of the policy mix on environmental technological
change and sustainability transitions more broadly. Finally, we derive policy implications
and suggest avenues for future research.
The creation of an innovation niche depends on the interaction of three mechanisms involving: converging expectations, networking among the innovation actors, and learning about the novelty through efficient knowledge creation and diffusion. Such mechanisms define the key characteristics of a network affirms (i.e. the innovation niche), and the interaction among them guides the development and diffusion of a new technology. In this paper, we propose an agent-based model to investigate the dynamics characterising such interactions and the role that policy intervention can have in governing the niche development process. Specifically, we consider and assess the impact of two policy actions: (1) increasing actors' expectations towards the new technology by means of information spreading and (2) providing subsidies aimed at stimulating technological switch. Our results confirm the importance of policy intervention and show the dominance of information spreading activities over subsidies. The former policy action, in fact, preserves a broad consensus around the new technology, a fact which turned out to be fundamental in order to promote efficient knowledge diffusion and the effective use of individual and network resources.