As more renewable resources are added into the grid and environmental regulations are imposed to reduce emissions, there will be dramatic changes in the generation portfolio. Assessing the impact of these changes is important for policy makers, market participants, and general public to understand trends in the electricity market. This paper addresses this issue by analyzing how the ERCOT market is affected by CO2 penalty and wind penetration. In order to assess the future power system, the study model should represent the long term dynamics of various factors to find out how investment decisions are made economically in a competitive market with appropriate assumptions. Another important aspect is the short term market dynamics from real operation of power system. For this study, AURORAxmp, a commercially available market simulator, is utilized to capture both long term and short term dynamics. This study runs 5 different scenarios: two base cases with and without CO2 price, 20%, 27%, and 33% wind penetration level. The result shows that, increasing wind penetration reduces production and capacity of both coal and gas units, electricity market prices, and amount of emissions. However, increasing wind penetration has greater impacts on a decrease in generation from thermal units than reduction in thermal capacity, resulting in 11.4% capacity value of wind power. The study also confirms that CO2 price impacts capacity and generation of coal (negatively) and gas (positively) units in opposite ways, and reduces emission, but increases power prices and generation cost. Especially, the impact on retirement of coal units is noticeable. Almost half of the current coal capacity (19 GW), 9,390 MW, is retired by 2040 in this study. / text

Delays on electrical engineering projects are a phenomenon at Eskom distribution due to a wide range of causes. These project delays result in Eskom to contravene with Electricity Regulation Act 4 of 2006 in terms of providing efficient, effective and sustainable operation of electricity supply infrastructure, promoting the use of renewable energy sources and energy efficiency as well as to facilitate universal access to electricity for South African consumers (Gazette, 2006). Eskom strives to comply with the Electricity Regulation Act by initiating and implementing strengthening projects, refurbishment (reliability) projects, direct customer projects, infills projects and electrification projects (Eskom, 2014).The severe delays experienced in the delivery of electrical distribution projects have a negative impact on South African economic growth and population. This research study adopted interactive management methodology for the identification of project delay factors in Eskom distribution projects through the use of the idea writing technique, nominal group technique, and interpretive structural modelling technique. The interactive management methodology allows a group of people collaboratively to develop a structure that defines the relationship among the system elements. Using interactive management approach, a total of one hundred and twelve project delay factors were reduced to twenty six significant project delays which formed part of interpretive structural modelling. This research study revealed the hierarchical model illustrating interrelationships between the twenty six identified project delay factors. The research study identified three root causes of delays in electrical distribution projects at Eskom Northern Cape Operating Unit, namely "poor communication", "poor planning", and "project scheduling not properly done". The three identified root causes can be used as critical points for eradicating delays in electrical distribution projects at Eskom Northern Cape Operating Unit. The research study found that a total of ten out of twenty six project delay factors were unique to electrical distribution projects at Eskom Northern Cape Operating Unit.

Water is the root of life and the engine that drives agriculture, industry, economy and services. The demand for water often necessitates desalination, particularly in arid coastal environments where there are several desalination technologies in use today such as Multi-Effect Distillation (MED) and Reverse Osmosis (RO). The key utility requirement for technologies such as desalination and population in general include energy in one form or another. Therefore, desalination and co-generation are often integrated. Another key utility is electricity which is generated from either renewable or non-renewable sources. The demands for water and electricity change over time and are subject to uncertainty. In this dissertation, a country-wide large-scale energy and water cogeneration planning model for Kuwait was proposed and solved. Five different plant technologies where the planning horizon used was set to 37 years starting in year 2014 and until 2050. A Mixed Integer Mathematical programming model was proposed and formulated using General Algebraic Modeling System (GAMS), the resulting model was solved using the CPLEX solver engine. In this research obtained detailed data on the consumption on water and energy in Kuwait and performed time series analysis of the population growth and individual behavior of water and energy consumption and novel method to represent cogeneration plants was implemented in the proposed mathematical programming model. ix A modeling framework that involves a data spreadsheet and a proprietary model was implemented. The data spreadsheet and the model were formulated as a template that can receive data from different applications. In addition, automation using Visual Basic for Application (VBA) was made to the data spreadsheets such that the data is sent to the model template, Gams-Cylix, and are written back to the spreadsheet. An analysis was made between oil-based plants, natural gas (NG) plants, and solar-based plants for co-generation. It was found that for water production solar-based plants can supply 50 percent or more of the demand during after period 2020 and after implementation and for electric power generation solar plants are limited. The results indicate the preferred technology for energy generation was NG-RO. With the implementation of solar based plants the electric power load is distributed among the technologies. NG-RO plants are more scalable and therefore were expanded to cope with the future demand. The percentage of the electric power supplied by solar plant was below 35 percent across the planning horizon. By the end of the planning horizon the percentage of electric power supplied by solar base plants was nearly 20 percent. Near 70 percent of the electric power was supplied by NG RO by period 2050. Other technologies had a representation of less than 10 percent by the end of the planning horizon.

Semiconductor industry, especially IC manafacture has been one of the most important high-tech industries in Taiwan since 1970¡¦s. The first professional wafer Fab, called ¡§wafer labor¡¨, was build up in Taiwan and has currently gained more than 60% market share in the wafer market of the world. This kind of industry is usually characterized as ¡§capital intensity¡¨, ¡§technology intensity¡¨ and ¡§short productive life cycle¡¨. Along with the innovation of technology and the internationalization of industry, more and more enterprises have been engaged in the fierce and intensive competition in all senses. Consequently, all of them have to possibly renew and modify their products, facilities and technologies with their limited resources so as to find their own way of making profits constantly. A correct investment decision not only contributes to the constant growth of enterprises, but also helps push up the industrial competitive capacity. Therefore, in the current project, I will try to work out an appropriate method for making investment policy. My concern will basically focuses on the distribution proportion of capital by arguing that the capacity expansion expenditures and research & development expenses are supposed to be included in the capital expenditures of wafer labor industry. However, with regard to the appropriateness of this project, I will also take the so-called ¡§dynamic complexity¡¨ into consideration, which is usually characterized by the phenomena of ¡§Information feedback¡¨, ¡§Time delay¡¨ and ¡§Non-linearity¡¨. If the capital is limited, the relationship between capacity expansion expenditures and research & development expenses are supposed to be definied as a ¡§trade-off¡¨ relationship. The increase of one side will lead to the decrease of the other side and finally leads to in the reduction in profits. Due to the capacity of System Dynamics for sloving the problem of dynamic complexity (Forrester, 1961), I will adopt System Dynamics as the main research method in the current project and to work out an appropriate method for making wafer labor industrial policy. By taking the capital distribution into consideration, I will try to construct a possibly appropriate investment model and herewith make some remarks or suggestions for the investment policy. Some research results will be displayed in the current project: 1. In any case, there is a certain distribution proportion which optimizes the entire profits. The task is to simulate an appropriate one respectively for different scenarios. 2. The increase in the proportion of research & development expenses might lead to the reduction of profits. 3. By using this model, the enterprise can find out the most appropriate policy for distributing the capital and achieving their maximal profit.

No description available.

Climate change is posing significant threats to human beings. Deploying renewable energy sources in electricity generation is widely accepted as an indispensable part of the solution to climate change. The vast potential lies in variable renewable energy sources, such as solar and wind. However, the intermittency of variable renewable energy sources is limiting their deployment. Investing in grid-scale battery systems is a potential solution to this problem. This thesis investigates this potential for the Nordic power sector, taking into account its distinct features, e.g., the vast amount of hydro reservoirs. Taking the capacities of variable renewable energy and battery systems as endogenous variables while considering the salient characteristics of the Nordic power sector, this thesis answers the following research question: How could variable renewable energy investment be supported by complementary investment in grid-scale battery systems to maximise social welfare? Answering this question helps solve the unaddressed research problem of assessing endogenous investment decisions in the capacities of variable renewable energy generation and grid-scale battery systems in the Nordic power market. This, in turn, helps informed decision-making in renewable-energy and energystorage investments to facilitate the realisation of the climate goal. Two experiments are conducted: one with ordinary BESS costs estimation and another assuming a lower-end level of costs. The data come from documents from official statistics (e.g., those from Nord Pool) and peer-reviewed publications. A quadratic programming optimisation model is used to maximise social welfare. Problem instances are then solved using the CPLEX solver implemented in the GAMS software. The results suggest that investing in BESS capacity helps to promote VRE adoption and VRE generation. Consequently, CO2 emission is reduced, and consumer surplus is improved, whereas the total social welfare remains unaffected.

In this dissertation, we study several stochastic capacity expansion models in the presence of permanent, spot market, and contract capacity for acquisition. Using a scenario tree approach to handle the data uncertainty of the problems, we develop multi-stage stochastic integer programming formulations for these models. First, we study multi-period single resource stochastic capacity expansion problems, where different sources of capacity are available to the decision maker. We develop efficient algorithms that can solve these models to optimality in polynomial time. Second, we study multi-period stochastic network capacity expansion problems with different sources for capacity. The proposed models are NP-hard multi-stage stochastic integer programs and we develop an efficient, asymptotically convergent approximation algorithm to solve them. Third, we consider some decomposition algorithms to solve the proposed multi-stage stochastic network capacity expansion problem. We propose an enhanced Benders' decomposition algorithm to solve the problem, and a Benders' decomposition-based heuristic algorithm to find tight bounds for it. Finally, we extend the stochastic network capacity expansion model by imposing budget restriction on permanent capacity acquisition cost. We design a Lagrangian relaxation algorithm to solve the model, including heuristic methods to find tight upper bounds for it. / Thesis / Doctor of Philosophy (PhD)

Governments in many counties are taking measures to promote electric vehicles. An important strategy is to build enough charging infrastructures so as to alleviate drivers’ range anxieties. To help the governments make plans about the public charging network, we propose a multi-stage stochastic integer programming model to determine the locations and capacities of charging facilities over finite planning horizons. We use the logit choice model to estimate drivers’ random choices towards different charging stations nearby. The objective of the model is to minimize the expected total cost of installing and operating the charging facilities. Two simple algorithms are designed to solve this model, an approximation algorithm and a heuristic algorithm. A branch-and-price algorithm is also designed for this model, and some implementation details and improvement methods are explained. We do some numerical experiments to test the efficiency of these algorithms. Each algorithm has advantages over the CPLEX MIP solver in terms of solution time or solution quality. A case study of Oakville is presented to demonstrate the process of designing an electric vehicle public charging network using this model in Canada. / Thesis / Master of Science (MSc)

The study carried out in this master thesis is part of a larger project led by Energynautics GmbH focusing on renewable energy development in the Caribbean. One of the Caribbean states, consisting of multiple islands, has set a target of 30 % of renewable energy in the power sector by 2030. The first objective of the thesis is to develop optimal generation capacity expansion plans for two different islands of this country, utilizing solar PV generation, which is the only available renewable energy resource. To achieve this objective, three main tasks are identified. The first is the development of an optimal generation capacity expansion plan for the next three years using the optimization tool HOMER Energy. At the beginning only diesel generation is present on the islands. For each study case year, the installed capacity of PV and BESS is optimized and enabling technologies such as curtailment (controllability of PV) and grid-forming inverters are deployed. The second task focuses on the development of a new dispatch strategy, improving on the black box dispatch algorithms built into HOMER. The dispatch strategy minimises the cost of electricity generation and is based on a rolling 48 hours forecasts of the load and PV. It is implemented in MATLAB and linked to HOMER via the built-in MATLAB interface. As HOMER is focused on generation expansion and dispatch and inherently neglects the grid, a grid study is required to assess the stability of the network. This study is the last task of the thesis and is limited to determined steady-state voltage and the asset loading on one of the studied islands through load flow simulations in DIgSILENT PowerFactory. It is shown that there are no major issues even at high PV shares, however, grid performance can be improved if the PV unit is equipped with reactive power capability to control the voltage. A study on the impact of the Q(U)- control and the PQ-capability of the PV and BESS inverters is performed. / Studien som genomförts i detta examensarbete är en del av ett större projekt vilket leds av Energynautics GmbH med fokus på utveckling av förnybar energi i Karibien. En av de Karibiska staterna, bestående av flera öar, har ett mål på 30 % förnybar energi i elkraftssektorn innan 2030. Första syftet med examensarbetet är att utveckla optimala utbyggnadsplaner för produktionskapaciteten för två olika öar i detta land, med användning av solcellsproduktion, vilket är den enda tillgängliga förnybara energikällan. Den första uppgiften är utvecklingen av en optimal utbyggnadsplan för produktionskapaciteten för de kommande tre åren med optimeringsverktyget HOMER Energy. Från början fanns det bara dieselgeneratorer på öarna. För varje studerat år optimeras den installerade kapaciteten av PV och BESS samt aktivering av möjliggörande teknologier som begränsning av PV-produktion och grid-forming växelriktare. Den andra uppgiften fokuserar på utvecklingen av en ny driftsstrategi, förbättring av den basala driftsalgoritm som är inbyggd i HOMER. Driftsstrategin minimerar kostnaden av elproduktionen och är baserad på en 48 timmars prognos av laster och PV. Den är implementerad i MATLAB och kopplad till HOMER via det inbyggda MATLABgränssnittet. Eftersom HOMER fokuserar på produktionsutbyggnad och drift och i praktiken försummar elnätet, krävs en studie av elnätet för att utvärdera stabiliteten av elnätet. Studien av denna sista uppgift i examensarbetet är begränsad till att bestämma spänningen vid jämnviktsläge och den utvärderade lasten på en av de studerade öarna genom belastningsfördelningsberäkning i DIgSILENT PowerFactory. Det visade sig att det inte fanns några stora problem även med stora andelar PV, men elnätets prestanda kan förbättras om PV-omriktarna är utrustade med reaktiv effektstyrning som kontrollerar spänningen. En studie avinverkan från Q(U)-styrning och PQ-kapacitet av PV- och BESS-växelriktare har utförts.

Cette thèse aborde les facettes dynamiques des principes fondamentaux du problème de l'appariement stable plusieurs-à-un. Nous menons notre étude dans le contexte du choix de l'école et de l'appariement entre les hôpitaux et les résidents. Dans la première étude, en utilisant le modèle résident-hôpital, nous étudions la complexité de calcul de l'optimisation des variations de capacité des hôpitaux afin de maximiser les résultats pour les résidents, tout en respectant les contraintes de stabilité et de budget. Nos résultats révèlent que le problème de décision est NP-complet et que le problème d'optimisation est inapproximable, même dans le cas de préférences strictes et d'allocations de capacités disjointes. Ces résultats posent des défis importants aux décideurs qui cherchent des solutions efficaces aux problèmes urgents du monde réel. Dans la seconde étude, en utilisant le modèle du choix de l'école, nous explorons l'optimisation conjointe de l'augmentation des capacités scolaires et de la réalisation d'appariements stables optimaux pour les étudiants au sein d'un marché élargi. Nous concevons une formulation innovante de programmation mathématique qui modélise la stabilité et l'expansion des capacités, et nous développons une méthode efficace de plan de coupe pour la résoudre. Des données réelles issues du système chilien de choix d'école valident l'impact potentiel de la planification de la capacité dans des conditions de stabilité. Dans la troisième étude, nous nous penchons sur la stabilité de l'appariement dans le cadre de priorités dynamiques, en nous concentrant principalement sur le choix de l'école. Nous introduisons un modèle qui tient compte des priorités des frères et sœurs, ce qui nécessite de nouveaux concepts de stabilité. Notre recherche identifie des scénarios où des appariements stables existent, accompagnés de mécanismes en temps polynomial pour leur découverte. Cependant, dans certains cas, nous prouvons également que la recherche d'un appariement stable de cardinalité maximale est NP-difficile sous des priorités dynamiques, ce qui met en lumière les défis liés à ces problèmes d'appariement. Collectivement, cette recherche contribue à une meilleure compréhension des capacités et des priorités dynamiques dans les scénarios d'appariement stable et ouvre de nouvelles questions et de nouvelles voies pour relever les défis d'allocation complexes dans le monde réel. / This research addresses the dynamic facets in the fundamentals of the many-to-one stable matching problem. We conduct our study in the context of school choice and hospital-resident matching. In the first study, using the resident-hospital model, we investigate the computational complexity of optimizing hospital capacity variations to maximize resident outcomes, while respecting stability and budget constraints. Our findings reveal the NP-completeness of the decision problem and the inapproximability of the optimization problem, even under strict preferences and disjoint capacity allocations. These results pose significant challenges for policymakers seeking efficient solutions to pressing real-world issues. In the second study, using the school choice model, we explore the joint optimization of increasing school capacities and achieving student-optimal stable matchings within an expanded market. We devise an innovative mathematical programming formulation that models stability and capacity expansion, and we develop an effective cutting-plane method to solve it. Real-world data from the Chilean school choice system validates the potential impact of capacity planning under stability conditions. In the third study, we delve into stable matching under dynamic priorities, primarily focusing on school choice. We introduce a model that accounts for sibling priorities, necessitating novel stability concepts. Our research identifies scenarios where stable matchings exist, accompanied by polynomial-time mechanisms for their discovery. However, in some cases, we also prove the NP-hardness of finding a maximum cardinality stable matching under dynamic priorities, shedding light on challenges related to these matching problems. Collectively, this research contributes to a deeper understanding of dynamic capacities and priorities within stable matching scenarios and opens new questions and new avenues for tackling complex allocation challenges in real-world settings.