A modell of socially optimal hydro reservoir usage - yelding socially optimal outcomes in nordic electricity markets

An extensive electricity transmission network facilitates electricity trading between Finland, Sweden, Norway and Denmark. Currently most of the area's power generation is traded at NordPool, where the trading volumes have steadily increased since the early 1990's, when the exchange was founded. The Nordic electricity is expected to follow the current trend and further integrate with the other European electricity markets. Hydro power is the source for roughly a half of the supply in the Nordic electricity market and most of the hydro is generated in Norway. The dominating role of hydro power distinguishes the Nordic electricity market from most of the other market places. Production of hydro power varies mainly due to hydro reservoirs and demand for electricity. Hydro reservoirs are affected by water inflows that differ each year. The hydro reservoirs explain remarkably the behaviour of the Nordic electricity markets. Therefore among others, Kauppi and Liski (2008) have developed a model that analyzes the behaviour of the markets using hydro reservoirs as explanatory factors. Their model includes, for example, welfare loss due to socially suboptimal hydro reservoir usage, socially optimal electricity price, hydro reservoir storage and thermal reservoir storage; that are referred as outcomes. However, the model does not explain the real market condition but rather an ideal situation. In the model the market is controlled by one agent, i.e. one agent controls all the power generation reserves; it is referred to as a socially optimal strategy. Article by Kauppi and Liski (2008) includes an assumption where an individual agent has a certain fraction of market power, e.g. 20 % or 30 %. In order to maintain the focus of this thesis, this part of their paper is omitted. The goal of this thesis is two-fold. Firstly we expand the results from the socially optimal strategy for years 2006-08, as the earlier study finishes in 2005. The second objective is to improve on the methods from the previous study. This thesis results several outcomes (SPOT-price and welfare loss, etc.) due to socially optimal actions. Welfare loss is interesting as it describes the inefficiency of the market. SPOT-price is an important output for the market participants as it often has an effect on end users' electricity bills. Another function is to modify and try to improve the model by means of using more accurate input data, e.g. by considering pollution trade rights effect on input data. After modifications to the model, new welfare losses are calculated and compared with the same results before the modifications. The hydro reservoir has the higher explanatory significance in the model followed by thermal power. In Nordic markets, thermal power reserves are mostly nuclear power and other thermal sources (coal, natural gas, oil, peat). It can be argued that hydro and thermal reservoirs determine electricity supply. Roughly speaking, the model takes into account electricity demand and supply, and several parameters related to them (water inflow, oil price, etc.), yielding finally the socially optimal outcomes. The author of this thesis is not aware of any similar model being tested before. There have been some other studies that are close to the Kauppi and Liski (2008) model, but those have a somewhat different focus. For example, a specific feature in the model is the focus on long-run capacity usage that differs from the previous studies on short-run market power. The closest study to the model is from California's wholesale electricity markets that, however, uses different methodology. Work is constructed as follows.

Ruthenium-Oxygen Coordination-Driven Self-Assembly of a Ru-8(II) Incomplete Prism: Synthesis, Structure, and Shape-Selective Molecular Recognition Study

Coordination-driven self-assembly of 1,3,5-benzenetricarboxylate (tma; 1) and oxalato-bridged p-cymeneruthenium(II) building block Ru-2(mu-eta(4)-C2O4)(MeOH)(2)(eta(6)-p-cymene)(2)](O3SCF3)(2) (2) affords an unusual octanuclear incomplete prism Ru-8(eta(6)-p-cymene)(8)(tma)(2)(mu-eta(4)-C2O4)(2)(OMe)(4)](O3SCF3)( 2) (3), which exhibits a remarkable shape-selective binding affinity for neutral phenolic compounds via hydrogen-bonding interactions (p-cymene = p-(PrC6H4Me)-Pr-i). Such a binding was confirmed by single-crystal X-ray diffraction analysis using 1,3,5-trihydroxybenzene as an analyte.

Factors Affecting Knowledge Sharing in Strategic Alliances: The Role of Knowledge Sharing as Strategic Control Behavior among Multinational Enterprises

International strategic alliances (ISAs) have become increasingly important for the stability, growth, and long-term viability of modern business organizations. Alliance partnerships as inter-firm cooperative ventures represent an influential mechanism for asserting corporate strategic control among autonomous multinational enterprises. These different cooperative arrangements are made of equity investments or contractually-based partnerships. Different alliance forms represent different approaches that partner firms adopt to control their mutual dependence on the alliance and on other partners. Earlier research shows that the partner characteristics could provide an explanation for alliance strategic behavior and see alliances as alternative forms to markets or hierarchies for addressing specific strategic needs linked to partners’ characteristics and their subsequent strategic motives. These characteristics of the partners’ and subsequent strategic motives are analyzed as knowledge sharing factors and how these influence inter-firm control in alliances within the context of the focal-firm STMicroelectronics and its alliance partners Nokia, Ericsson and IBM. This study underline that as contracts are incomplete, they are therefore required to maintain mutual dependence based control mechanisms in addition to a contract. For example, mutual dependence based control mechanisms could be joint financial investments and the building of an ownership structure between the parties (e.g., JVs). However, the present study clarifies that subsequent inter-firm control is also exercised through inter-firm knowledge sharing. The present study contributes by presenting a dynamic interplay between competitive and cooperative rent seeking behavior. Such coopetition behavior describes the firm's strategic orientation to achieve a dynamic balance between competitive and cooperative strategies. This balance is seen in knowledge sharing based cooperation and competition behavior. Thus this study clarifies coopetition strategies by introducing the role of inter-firm cooperation and the competitive nature of knowledge sharing. Simultaneous cooperative and competitive behavior is also seen as synergetic rent-seeking behavior. Therefore, this study extends the perspective of previous studies on competitive and cooperative seeking behavior.

Image reconstruction from incomplete projections data: A multiresolution modeling approach

Computerized tomography is an imaging technique which produces cross sectional map of an object from its line integrals. Image reconstruction algorithms require collection of line integrals covering the whole measurement range. However, in many practical situations part of projection data is inaccurately measured or not measured at all. In such incomplete projection data situations, conventional image reconstruction algorithms like the convolution back projection algorithm (CBP) and the Fourier reconstruction algorithm, assuming the projection data to be complete, produce degraded images. In this paper, a multiresolution multiscale modeling using the wavelet transform coefficients of projections is proposed for projection completion. The missing coefficients are then predicted based on these models at each scale followed by inverse wavelet transform to obtain the estimated projection data.

Reinforcement learning applications in dynamic pricing of retail markets

In this paper, we investigate the use of reinforcement learning (RL) techniques to the problem of determining dynamic prices in an electronic retail market. As representative models, we consider a single seller market and a two seller market, and formulate the dynamic pricing problem in a setting that easily generalizes to markets with more than two sellers. We first formulate the single seller dynamic pricing problem in the RL framework and solve the problem using the Q-learning algorithm through simulation. Next we model the two seller dynamic pricing problem as a Markovian game and formulate the problem in the RL framework. We solve this problem using actor-critic algorithms through simulation. We believe our approach to solving these problems is a promising way of setting dynamic prices in multi-agent environments. We illustrate the methodology with two illustrative examples of typical retail markets.

Electrochemical capacitors: Technical challenges and prognosis for future markets

Electrochemical capacitors are potential devices that could help bringing about major advances in future energy storage. They are lightweight and their manufacture and disposal has no detrimental effects on the environment. A comprehensive description of fundamental science of electrochemical capacitors is presented. Similarities and differences between electrochemical capacitors and secondary batteries for electrical energy storage are highlighted and various types of electrochemical capacitors are discussed with special reference to lead-carbon hybrid ultracapacitors. Some envisaged applications of electrochemical capacitors are described along with the technical challenges and prognosis for future markets. (C) 2012 Published by Elsevier Ltd.

Dynamic mechanism design for markets with strategic resources

The assignment of tasks to multiple resources becomes an interesting game theoretic problem, when both the task owner and the resources are strategic. In the classical, nonstrategic setting, where the states of the tasks and resources are observable by the controller, this problem is that of finding an optimal policy for a Markov decision process (MDP). When the states are held by strategic agents, the problem of an efficient task allocation extends beyond that of solving an MDP and becomes that of designing a mechanism. Motivated by this fact, we propose a general mechanism which decides on an allocation rule for the tasks and resources and a payment rule to incentivize agents' participation and truthful reports. In contrast to related dynamic strategic control problems studied in recent literature, the problem studied here has interdependent values: the benefit of an allocation to the task owner is not simply a function of the characteristics of the task itself and the allocation, but also of the state of the resources. We introduce a dynamic extension of Mezzetti's two phase mechanism for interdependent valuations. In this changed setting, the proposed dynamic mechanism is efficient, within period ex-post incentive compatible, and within period ex-post individually rational.

Enhanced photoabsorption efficiency of incomplete nanoshells

The rather low scattering or extinction efficiency of small nanoparticles, metallic and otherwise, is significantly enhanced when they are adsorbed on a larger core particle. But the photoabsorption by particles with varying surface area fractions on a larger core particle is found to be limited by saturation. It is found that the core-shell particle can have a lower absorption efficiency than a dielectric core with its surface partially nucleated with absorbing particles-an ``incomplete nanoshell'' particle. We have both numerically and experimentally studied the optical efficiencies of titania (TiO2) nucleated in various degrees on silica (SiO2) nanospheres. We show that optimal surface nucleation over cores of appropriate sizes and optical properties will have a direct impact on the applications exploiting the absorption and scattering properties of such composite particles.

Average Overlap for Clustering Incomplete Data using Symmetric Non-Negative Matrix Factorization

Clustering techniques which can handle incomplete data have become increasingly important due to varied applications in marketing research, medical diagnosis and survey data analysis. Existing techniques cope up with missing values either by using data modification/imputation or by partial distance computation, often unreliable depending on the number of features available. In this paper, we propose a novel approach for clustering data with missing values, which performs the task by Symmetric Non-Negative Matrix Factorization (SNMF) of a complete pair-wise similarity matrix, computed from the given incomplete data. To accomplish this, we define a novel similarity measure based on Average Overlap similarity metric which can effectively handle missing values without modification of data. Further, the similarity measure is more reliable than partial distances and inherently possesses the properties required to perform SNMF. The experimental evaluation on real world datasets demonstrates that the proposed approach is efficient, scalable and shows significantly better performance compared to the existing techniques.