Distributed energy resources in an electricity retailer’s short-term profit optimization

Liberalization of electricity markets has resulted in a competed Nordic electricity market, in which electricity retailers play a key role as electricity suppliers, market intermediaries, and service providers. Although these roles may remain unchanged in the near future, the retailers’ operation may change fundamentally as a result of the emerging smart grid environment. Especially the increasing amount of distributed energy resources (DER), and improving opportunities for their control, are reshaping the operating environment of the retailers. This requires that the retailers’ operation models are developed to match the operating environment, in which the active use of DER plays a major role. Electricity retailers have a clientele, and they operate actively in the electricity markets, which makes them a natural market party to offer new services for end-users aiming at an efficient and market-based use of DER. From the retailer’s point of view, the active use of DER can provide means to adapt the operation to meet the challenges posed by the smart grid environment, and to pursue the ultimate objective of the retailer, which is to maximize the profit of operation. This doctoral dissertation introduces a methodology for the comprehensive use of DER in an electricity retailer’s short-term profit optimization that covers operation in a variety of marketplaces including day-ahead, intra-day, and reserve markets. The analysis results provide data of the key profit-making opportunities and the risks associated with different types of DER use. Therefore, the methodology may serve as an efficient tool for an experienced operator in the planning of the optimal market-based DER use. The key contributions of this doctoral dissertation lie in the analysis and development of the model that allows the retailer to benefit from profit-making opportunities brought by the use of DER in different marketplaces, but also to manage the major risks involved in the active use of DER. In addition, the dissertation introduces an analysis of the economic potential of DER control actions in different marketplaces including the day-ahead Elspot market, balancing power market, and the hourly market of Frequency Containment Reserve for Disturbances (FCR-D).

Optimization of Transportation System by Tailored Logistics Network: A Case Study in Automotive Industry

This thesis aims to redesign the supply chain system in an automotive industry in order to obtain space reduction in the inventory by using tailored logistics network. The redesigning process by tailored supply chain will combine all possible shipment methods including direct shipment, milk-run, milk-run via distribution center and Kanban delivery. The current supply chain system in Nissan goes rather well when the production volume is in moderate level. However, when the production volume is high, there is a capacity problem in the warehouse to accommodate all delivered parts from suppliers. Hence, the optimization of supply chain system is needed in order to obtain efficient logistics process and effective inventory consumption. The study will use primary data for both qualitative and quantitative approach as the research methods. Qualitative data will be collected by conducting interviews with people related to procurement and inventory control. Quantitative data consists of list of suppliers with their condition in several parameters which will be evaluated and analyzed by using scoring method to assign the most suitable transportation network to each suppliers for improvement of inventory reduction in a cost efficient manner.

Content-centered method for search engine optimization

Value of online business has grown to over one trillion USD. This thesis is about search engine optimization, which focus is to increase search engine rankings. Search engine optimization is an important branch of online marketing because the first page of search engine results is generating majority of the search traffic. Current articles about search engine optimization and Google are indicating that with the proper use of quality content, there is potential to improve search engine rankings. However, the existing search engine optimization literature is not noticing content at a sufficient level. To decrease that difference, the content-centered method for search engine optimization is constructed, and content in search engine optimization is studied. This content-centered method consists of three search engine optimization tactics: 1) content, 2) keywords, and 3) links. Two propositions were used for testing these tactics in a real business environment and results are suggesting that the content-centered method is improving search engine rankings. Search engine optimization is constantly changing because Google is adjusting its search algorithm regularly. Still, some long-term trends can be recognized. Google has said that content is growing its importance as a ranking factor in the future. The content-centered method is taking advance of this new trend in search engine optimization to be relevant for years to come.

GIS-based approach for optimization of onshore wind park infrastructure alignment in Finland

Wind power is a rapidly developing, low-emission form of energy production. In Fin-land, the official objective is to increase wind power capacity from the current 1 005 MW up to 3 500–4 000 MW by 2025. By the end of April 2015, the total capacity of all wind power project being planned in Finland had surpassed 11 000 MW. As the amount of projects in Finland is record high, an increasing amount of infrastructure is also being planned and constructed. Traditionally, these planning operations are conducted using manual and labor-intensive work methods that are prone to subjectivity. This study introduces a GIS-based methodology for determining optimal paths to sup-port the planning of onshore wind park infrastructure alignment in Nordanå-Lövböle wind park located on the island of Kemiönsaari in Southwest Finland. The presented methodology utilizes a least-cost path (LCP) algorithm for searching of optimal paths within a high resolution real-world terrain dataset derived from airborne lidar scannings. In addition, planning data is used to provide a realistic planning framework for the anal-ysis. In order to produce realistic results, the physiographic and planning datasets are standardized and weighted according to qualitative suitability assessments by utilizing methods and practices offered by multi-criteria evaluation (MCE). The results are pre-sented as scenarios to correspond various different planning objectives. Finally, the methodology is documented by using tools of Business Process Management (BPM). The results show that the presented methodology can be effectively used to search and identify extensive, 20 to 35 kilometers long networks of paths that correspond to certain optimization objectives in the study area. The utilization of high-resolution terrain data produces a more objective and more detailed path alignment plan. This study demon-strates that the presented methodology can be practically applied to support a wind power infrastructure alignment planning process. The six-phase structure of the method-ology allows straightforward incorporation of different optimization objectives. The methodology responds well to combining quantitative and qualitative data. Additional-ly, the careful documentation presents an example of how the methodology can be eval-uated and developed as a business process. This thesis also shows that more emphasis on the research of algorithm-based, more objective methods for the planning of infrastruc-ture alignment is desirable, as technological development has only recently started to realize the potential of these computational methods.

Content-centered method for search engine optimization

Value of online business has grown to over one trillion USD. This thesis is about search engine optimization, which focus is to increase search engine rankings. Search engine optimization is an important branch of online marketing because the first page of search engine results is generating majority of the search traffic. Current articles about search engine optimization and Google are indicating that with the proper use of quality content, there is potential to improve search engine rankings. However, the existing search engine optimization literature is not noticing content at a sufficient level. To decrease that difference, the content-centered method for search engine optimization is constructed, and content in search engine optimization is studied. This content-centered method consists of three search engine optimization tactics: 1) content, 2) keywords, and 3) links. Two propositions were used for testing these tactics in a real business environment and results are suggesting that the content-centered method is improving search engine rankings. Search engine optimization is constantly changing because Google is adjusting its search algorithm regularly. Still, some long-term trends can be recognized. Google has said that content is growing its importance as a ranking factor in the future. The content-centered method is taking advance of this new trend in search engine optimization to be relevant for years to come.

Histogram filtering as a tool in variational Monte Carlo optimization

Optimization of wave functions in quantum Monte Carlo is a difficult task because the statistical uncertainty inherent to the technique makes the absolute determination of the global minimum difficult. To optimize these wave functions we generate a large number of possible minima using many independently generated Monte Carlo ensembles and perform a conjugate gradient optimization. Then we construct histograms of the resulting nominally optimal parameter sets and "filter" them to identify which parameter sets "go together" to generate a local minimum. We follow with correlated-sampling verification runs to find the global minimum. We illustrate this technique for variance and variational energy optimization for a variety of wave functions for small systellls. For such optimized wave functions we calculate the variational energy and variance as well as various non-differential properties. The optimizations are either on par with or superior to determinations in the literature. Furthermore, we show that this technique is sufficiently robust that for molecules one may determine the optimal geometry at tIle same time as one optimizes the variational energy.

Optimization of the valence energy variance of the CuH molecule

We developed the concept of split-'t to deal with the large molecules (in terms of the number of electrons and nuclear charge Z). This naturally leads to partitioning the local energy into components due to each electron shell. The minimization of the variation of the valence shell local energy is used to optimize a simple two parameter CuH wave function. Molecular properties (spectroscopic constants and the dipole moment) are calculated for the optimized and nearly optimized wave functions using the Variational Quantum Monte Carlo method. Our best results are comparable to those from the single and double configuration interaction (SDCI) method.

Optimization of trial wave functions for use in quantum Monte Carlo with application to LiH

Methods for both partial and full optimization of wavefunction parameters are explored, and these are applied to the LiH molecule. A partial optimization can be easily performed with little difficulty. But to perform a full optimization we must avoid a wrong minimum, and deal with linear-dependency, time step-dependency and ensemble-dependency problems. Five basis sets are examined. The optimized wavefunction with a 3-function set gives a variational energy of -7.998 + 0.005 a.u., which is comparable to that (-7.990 + 0.003) 1 of Reynold's unoptimized \fin ( a double-~ set of eight functions). The optimized wavefunction with a double~ plus 3dz2 set gives ari energy of -8.052 + 0.003 a.u., which is comparable with the fixed-node energy (-8.059 + 0.004)1 of the \fin. The optimized double-~ function itself gives an energy of -8.049 + 0.002 a.u. Each number above was obtained on a Bourrghs 7900 mainframe computer with 14 -15 hrs CPU time.

A sequential analysis of therapist scaffolding and child concept formation in narrative therapy

Narrative therapy is a postmodern therapy that takes the position that people create self-narratives to make sense of their experiences. To date, narrative therapy has compiled virtually no quantitative and very little qualitative research, leaving gaps in almost all areas of process and outcome. White (2006a), one of the therapy's founders, has recently utilized Vygotsky's (1934/1987) theories of the zone of proximal development (ZPD) and concept formation to describe the process of change in narrative therapy with children. In collaboration with the child client, the narrative therapist formalizes therapeutic concepts and submits them to increasing levels of generalization to create a ZPD. This study sought to determine whether the child's development proceeds through the stages of concept formation over the course of a session, and whether therapists' utterances scaffold this movement. A sequential analysis was used due to its unique ability to measure dynamic processes in social interactions. Stages of concept formation and scaffolding were coded over time. A hierarchical log-linear analysis was performed on the sequential data to develop a model of therapist scaffolding and child concept development. This was intended to determine what patterns occur and whether the stated intent of narrative therapy matches its actual process. In accordance with narrative therapy theory, the log-linear analysis produced a final model with interactions between therapist and child utterances, and between both therapist and child utterances and time. Specifically, the child and youth participants in therapy tended to respond to therapist scaffolding at the corresponding level of concept formation. Both children and youth and therapists also tended to move away from earlier and toward later stages of White's scaffolding conversations map as the therapy session advanced. These findings provide support for White's contention that narrative therapists promote child development by scaffolding child concept formation in therapy.

Investigations in flow injection analysis (FIA): conventional FIA determination of chloride in water; sequential injection (SI)-FIA determination of mercury in water

Flow injection analysis (FIA) was applied to the determination of both chloride ion and mercury in water. Conventional FIA was employed for the chloride study. Investigations of the Fe3 +/Hg(SCN)2/CI-,450 nm spectrophotometric system for chloride determination led to the discovery of an absorbance in the 250-260 nm region when Hg(SCN)2 and CI- are combined in solution, in the absence of iron(III). Employing an in-house FIA system, absorbance observed at 254 nm exhibited a linear relation from essentially 0 - 2000 Jlg ml- 1 injected chloride. This linear range spanning three orders of magnitude is superior to the Fe3+/Hg(SCN)2/CI- system currently employed by laboratories worldwide. The detection limit obtainable with the proposed method was determin~d to be 0.16 Jlg ml- 1 and the relative standard deviation was determined to be 3.5 % over the concentration range of 0-200 Jig ml- 1. Other halogen ions were found to interfere with chloride determination at 254 nm whereas cations did not interfere. This system was successfully applied to the determination of chloride ion in laboratory water. Sequential injection (SI)-FIA was employed for mercury determination in water with the PSA Galahad mercury amalgamation, and Merlin mercury fluorescence detection systems. Initial mercury in air determinations involved injections of mercury saturated air directly into the Galahad whereas mercury in water determinations involved solution delivery via peristaltic pump to a gas/liquid separator, after reduction by stannous chloride. A series of changes were made to the internal hardware and valving systems of the Galahad mercury preconcentrator. Sequential injection solution delivery replaced the continuous peristaltic pump system and computer control was implemented to control and integrate all aspects of solution delivery, sample preconcentration and signal processing. Detection limits currently obtainable with this system are 0.1 ng ml-1 HgO.

Particle swarm optimization for two-connected networks with bounded rings

The Two-Connected Network with Bounded Ring (2CNBR) problem is a network design problem addressing the connection of servers to create a survivable network with limited redirections in the event of failures. Particle Swarm Optimization (PSO) is a stochastic population-based optimization technique modeled on the social behaviour of flocking birds or schooling fish. This thesis applies PSO to the 2CNBR problem. As PSO is originally designed to handle a continuous solution space, modification of the algorithm was necessary in order to adapt it for such a highly constrained discrete combinatorial optimization problem. Presented are an indirect transcription scheme for applying PSO to such discrete optimization problems and an oscillating mechanism for averting stagnation.

Bio-inspired optimization & sampling technique for side-chain packing in MCCE

The prediction of proteins' conformation helps to understand their exhibited functions, allows for modeling and allows for the possible synthesis of the studied protein. Our research is focused on a sub-problem of protein folding known as side-chain packing. Its computational complexity has been proven to be NP-Hard. The motivation behind our study is to offer the scientific community a means to obtain faster conformation approximations for small to large proteins over currently available methods. As the size of proteins increases, current techniques become unusable due to the exponential nature of the problem. We investigated the capabilities of a hybrid genetic algorithm / simulated annealing technique to predict the low-energy conformational states of various sized proteins and to generate statistical distributions of the studied proteins' molecular ensemble for pKa predictions. Our algorithm produced errors to experimental results within .acceptable margins and offered considerable speed up depending on the protein and on the rotameric states' resolution used.

(A) Optimization of solid body phase synthesis of RNA using the Cpep chemistry. (B) Synthesis of BODIPY labeled oligonucleotides

(A) Solid phase synthesis of oligonucleotides are well documented and are extensively studied as the demands continue to rise with the development of antisense, anti-gene, RNA interference, and aptamers. Although synthesis of RNA sequences faces many challenges, most notably the choice of the 2' -hydroxy protecting group, modified 2' -O-Cpep protected ribonucleotides were synthesized as alternitive building blocks. Altering phosphitylation procedures to incorporate 3' -N,N-diethyl phosphoramidites enhanced the overall reactivity, thus, increased the coupling efficiency without loss of integrety. Furthermore, technical optimizations of solid phase synthesis cycles were carried out to allow for successful synthesis of a homo UIO sequences with a stepwise coupling efficiency reaching 99% and a final yield of 91 %. (B) Over the past few decades, dipyrrometheneboron difluoride (BODIPY) has gained recognition as one of the most versatile fluorophores. Currently, BODIPY labeling of oligonucleotides are carried out post-synthetically and to date, there lacks a method that allows for direct incorporation of BODIPY into oligonucleotides during solid phase synthesis. Therefore, synthesis of BODIPY derived phosphoramidites will provide an alternative method in obtaining fluorescently labelled oligonucleotides. A method for the synthesis and incorporation of the BODIPY analogues into oligonucleotides by phosphoramidite chemistry-based solid phase DNA synthesis is reported here. Using this approach, BODIPY-labeled TlO homopolymer and ISIS 5132 were successfully synthesized.

Generating Aesthetically Pleasing Images in a Virtual Environment using Particle Swarm Optimization

This research focuses on generating aesthetically pleasing images in virtual environments using the particle swarm optimization (PSO) algorithm. The PSO is a stochastic population based search algorithm that is inspired by the flocking behavior of birds. In this research, we implement swarms of cameras flying through a virtual world in search of an image that is aesthetically pleasing. Virtual world exploration using particle swarm optimization is considered to be a new research area and is of interest to both the scientific and artistic communities. Aesthetic rules such as rule of thirds, subject matter, colour similarity and horizon line are all analyzed together as a multi-objective problem to analyze and solve with rendered images. A new multi-objective PSO algorithm, the sum of ranks PSO, is introduced. It is empirically compared to other single-objective and multi-objective swarm algorithms. An advantage of the sum of ranks PSO is that it is useful for solving high-dimensional problems within the context of this research. Throughout many experiments, we show that our approach is capable of automatically producing images satisfying a variety of supplied aesthetic criteria.

Characterizing Dynamic Optimization Benchmarks for the Comparison of Multi-Modal Tracking Algorithms

Population-based metaheuristics, such as particle swarm optimization (PSO), have been employed to solve many real-world optimization problems. Although it is of- ten sufficient to find a single solution to these problems, there does exist those cases where identifying multiple, diverse solutions can be beneficial or even required. Some of these problems are further complicated by a change in their objective function over time. This type of optimization is referred to as dynamic, multi-modal optimization. Algorithms which exploit multiple optima in a search space are identified as niching algorithms. Although numerous dynamic, niching algorithms have been developed, their performance is often measured solely on their ability to find a single, global optimum. Furthermore, the comparisons often use synthetic benchmarks whose landscape characteristics are generally limited and unknown. This thesis provides a landscape analysis of the dynamic benchmark functions commonly developed for multi-modal optimization. The benchmark analysis results reveal that the mechanisms responsible for dynamism in the current dynamic bench- marks do not significantly affect landscape features, thus suggesting a lack of representation for problems whose landscape features vary over time. This analysis is used in a comparison of current niching algorithms to identify the effects that specific landscape features have on niching performance. Two performance metrics are proposed to measure both the scalability and accuracy of the niching algorithms. The algorithm comparison results demonstrate the algorithms best suited for a variety of dynamic environments. This comparison also examines each of the algorithms in terms of their niching behaviours and analyzing the range and trade-off between scalability and accuracy when tuning the algorithms respective parameters. These results contribute to the understanding of current niching techniques as well as the problem features that ultimately dictate their success.