Increasing carbon dioxide levels and the fear of irreversible climate change has prompted policy makers to implement renewable portfolio standards. These renewable portfolio standards are meant to encourage the adoption of renewable energy technologies thereby reducing carbon emissions associated with fossil fuel-fired electricity generation. The ability to efficiently adopt and utilize high levels of renewable energy technology, such as wind power, depends upon the composition of the extant generation withinthe grid. Western Canadian electric grids are poised to integrate high levels of wind and although Alberta has sufficient and, at times, an excess supply of electricity, it does not have the inherent generator flexibility required to mirror the variability of its wind generation. British Columbia, with its large reservoir storage capacities and rapid ramping hydroelectric generation could easily provide the firming services required by Alberta; however, the two grids are connected only by a small, constrained intertie. We use a simulation model to assess the economic impacts of high wind penetrations in the Alberta grid under various balancing protocols. We find that adding wind capacity to the system impacts grid reliability, increasing the frequency of system imbalances and unscheduled intertie flow. In order for British Columbia to be viable firming resource, it must have sufficient generation capability to meet and exceed the province’s electricity self-sufficiency requirements. We use a linear programming model to evaluate the province’s ability to meet domestic load under various water and trade conditions. We then examine the effects of drought and wind penetration on the interconnected Alberta – British Columbia system given differing interconnection sizes. / Graduate

Participants in deregulated electric power markets compete for financial transmission rights (FTRs) to hedge against losses due to transmission congestion by submitting bids to the independent system operator (ISO). The ISO obtains an FTR allocation, that maximizes sales revenue while satisfying simultaneous feasibility. This FTR allocation remains in place for a length of time during which the participants compete in the energy market to maximize their total payoff from both FTR and energy markets. Energy markets (bi-lateral, day ahead, real time) continue until the the end of the current FTR period, at which time the participants can choose to modify their FTR holdings for the next FTR period. As in any noncooperative game, finding Nash equilibrium bidding strategies is of critical importance to the participants in both FTR and energy markets. In this research, a two-tier matrix game theoretic modeling approach is developed that can be used to obtain equilibrium bidding behavior of the participants in both FTR and energy markets considering the total payoff from FTR and energy. The matrix game model presents a significant deviation from the bilevel optimization approach commonly used to model FTR and energy allocation problems. A reinforcement learning (RL) algorithm is also developed which uses a simulation model and a value maximization approach to obtain the equilibrium bidding strategies in each market. The model and the RL based solution approach allow consideration of multi-dimensional bids (for both FTR and energy markets), network contingencies, varying demands, and many participants. The value iteration based RL algorithm obtains pure strategy Nash equilibrium for FTR and energy allocation. A sample network with three buses and four participants is considered for demonstrating the viability of the game theoretic model for FTR market. A PJM network example with five buses, five generators and three loads is also considered to analyze equilibrium bidding behavior in joint FTR and energy markets. Several numerical experiments on the sample networks are conducted using the approach of statistical design of experiments (DOE) to assess impacts of variations of bid and network parameters on the market outcomes like participant payoffs and equilibrium strategies.

In electricity markets of modern electric power systems, many generation companies, as major market participants, aim to maximize their profits by supplying the electrical load in a competitive manner. This thesis is devoted to investigate the price based unit commitment problem which is used to optimize generation schedules of these companies in deregulated electricity markets. The solution algorithm developed is based on Dynamic Programming and Lagrange Relaxation methods and solves the optimization problem for a generation company having many generating units with different cost characteristics. Moreover, unit constraints including ramp-rate limits, minimum ON/OFF times, generation capacities of individual units and system constraints such as total energy limits, reserve requirements are taken into account in the problem formulation. The verification of the algorithm has been carried out by comparing the results of some sample cases with those in the literature. The effectiveness of the algorithm has been tested on several test systems. Finally, the possible utilization of the method by a generation company in Turkish Electricity Market to develop bidding strategies is also examined based on some case studies.

Aspartokinase is the first enzyme of the aspartate family amino acids biosynthetic pathway. Cephamycin C is a &amp / #946 / -lactam antibiotic produced as a secondary metabolite via the enzymatic reactions in the lysine branch of this pathway in Streptomyces clavuligerus. The aspartokinase activity of S. clavuligerus is under concerted feedback inhibition by two of the end product amino acids, lysine plus threonine. It is also known that carbon flow through the lysine branch of the aspartate pathway is rate limiting step in the formation of cephamycin C. Therefore, genetic alterations in the regulatory regions of the aspartokinase enzyme are expected to lead to an increased cephamycin C production. The aim of this study was to obtain S. clavuligerus mutants that possess aspartokinase enzyme insensitive to feedback inhibition by lysine and threonine, identification of the mutation(s) accounting for the resistance being the ultimate goal. For this aim, chemical mutagenesis was employed to increase random mutation rate and a population of lysine anti-metabolite resistant S. clavuligerus mutants that can grow in the presence of S-(2-aminoethyl)-L-cysteine was obtained. The mutants were screened for their aspartokinase insensitivity via enzyme assays and one mutant exhibiting the highest level of deregulation was assessed for its cephamycin C production. The results revealed a 2-fold increase in specific production of the antibiotic. As a member of &amp / #946 / -lactam class antibiotics, cephamycin C has an importance in medicine. Therefore, the mutant strain obtained might be a candidate for industrial production of the compound.

With a significant number of states in the U.S. and countries around the world trading electricity in restructured markets, a sizeable proportion of capacity expansion in the future will have to take place in market-based environments. However, since a majority of the literature on capacity expansion is focused on regulated market structures, there is a critical need for comprehensive capacity expansion models targeting restructured markets. In this research, we develop a two-level game-theoretic model, and a novel solution algorithm that incorporates risk due to volatilities in profit (via CVaR), to obtain multi-period, multi-player capacity expansion plans. To solve the matrix games that arise in the generation expansion planning (GEP) model, we first develop a novel value function approximation based reinforcement learning (RL) algorithm. Currently there exist only mathematical programming based solution approaches for two player games and the N-player extensions in literature still have several unresolved computational issues. Therefore, there is a critical void in literature for finding solutions of N-player matrix games. The RL-based approach we develop in this research presents itself as a viable computational alternative. The solution approach for matrix games will also serve a much broader purpose of being able to solve a larger class of problems known as stochastic games. This RL-based algorithm is used in our two-tier game-theoretic approach for obtaining generation expansion strategies. Our unique contributions to the GEP literature include the explicit consideration of risk due to volatilities in profit and individual risk preference of generators. We also consider transmission constraints, multi-year planning horizon, and multiple generation technologies. The applicability of the twotier model is demonstrated using a sample power network from PowerWorld software. A detailed analysis of the model is performed, which examines the results with respect to the nature of Nash equilibrium solutions obtained, nodal prices, factors affecting nodal prices, potential for market power, and variations in risk preferences of investors. Future research directions include the incorporation of comprehensive cap-and-trade and renewable portfolio standards components in the GEP model.

Generators bid into the deregulated electricity market in order to get committed & dispatched for meeting demands. In order to maximize their revenues & minimize the cost, systematic planning of the resources and analyzing the results is crucial to the success of any generation utility. UPLAN Network Power Model provides a convenient way to model & simulate the different expected conditions related to transmission, fuel costs & other variables which are of significant importance for generation planning and also allows us to analyze the way the output variables like capacity factors of generators, prices for Energy and Ancillary Services are affected by them. Based on a very simple model, this report describes the typical approach to UPLAN based resources planning & analyzes the significance of the results. Before that it also tried to understand the way UPLAN works for a very simple three bus model by stepwise introduction of complexity & analysis of results of the simulation runs. A few other issues like the Power Purchase Agreements, Congestion & Congestion Revenue Rights & the way Electricity is traded in the Deregulated Market are also presented. / text

The Texas ERCOT market is one of the most open, deregulated electricity markets in the world. This open market brought electricity costs down for Texas residents and businesses, creating a much more competitive economic climate. However, these low prices currently generate insufficient revenue for generators to finance construction of new or replacement generation assets. In the instance of combined cycle advanced natural gas, the Independent Market Monitor 2012 annual report estimated that a plant needed to generate 2.5 times as much as revenue it did in 2012 to incent new generation. This author argues that while the gap is still significant, the continuous changes to the ERCOT market since its inception make an historical examination like that used by the IMM less accurate. New market rules such as price caps or changes in fuel markets through new technologies like hydraulic fracturing create a very different valuation gap than a model based on historical activity alone. This analysis attempts to get a more accurate approximation of the gap through the use of publicly traded futures contracts for natural gas and electricity. Electricity futures reflect market expectations of revenue based on current and future market rules. Gas futures reflect price expectations in light of market changes like fracturing, potential LNG exports, and other changes. Financial positions can be maintained in both markets to give a fixed rate of return. Using this method, one can create a very conservative valuation model that still more accurately reflects market sentiment. This thesis starts with a brief history of ERCOT deregulation from the early 2000s to present in order to clarify for the reader the changes that have taken place in the market. It then demonstrates the futures-valuation model using an advanced combined cycle power plant as an example. / text

In this master thesis the potential value of using RFID technology in train depots has been investigated. Train depots are a crucial part of the railway system as safe and timely traffic relies on the maintenance activities preformed in them. The Swedish passenger train traffic is expected to increase significantly in the coming years and the ability to expand the existing train depots is limited. Optimal utilization of depots, achieved by efficient and effective operations, is therefore crucial. Coordinating the activities within depots is a complex activity due to the limited resources, inflexible infrastructure and the need for continuous monitoring due to unforeseen events. The complexity is further increased with the large number of independent actors. Due to EU-directives all member nations have to liberalize their passenger railway markets, enabling new actors to enter the market. Sweden's passenger railway market was fully deregulated in 2010. For this thesis a case study on one of the largest depots in Sweden has been conducted to examine how RFID technology can enhance the efficiency and effectiveness of its depot operations. The most important factors for achieving this is to facilitate an efficient flow of vehicles between service stations and efficient and accurate flow of communication between the many actors. Based on this, three key objectives were identified for achieving effective and efficient depot operations; communication, coordination and transparency. The benefits of RFID have been analyzed within the areas; asset tracking, maintenance and safety, and opportunities for achieving the key objectives were identified within each of these areas. The findings show that RFID enables for better utilization of resources, which allows more trains to use the depot. Also, cost savings can be captured by automating manual tasks. The effectiveness of the maintenance operations can be improved by elimination of non-value adding activities and the risk of mistakes due to the human factor. It was also discovered that the transparent data that RFID provides can improve stakeholder relations and that it could be used for identification of problem areas. Conclusively, the findings of the study show that effectiveness and efficiency of train depots can be improved with the use of RFID to meet the future demands of an increased passenger train traffic. / I den här uppsatsen har nyttan av att använda RFID i tågdepåer analyserats. Tågdepåer är en viktig del av järnvägssystemet då majoriteten av allt tågunderhåll sker där. Den svenska tågtrafiken förväntas att öka markant framöver men möjligheten att bygga ut de befintliga tågdepåerna är begränsad på grund av den knappa tillgången till mark i anslutning till depåerna. För att hantera den ökade tågtrafiken är det därför viktigt att verksamheten i depåer sker så effektivt som möjligt för att uppnå optimalt resursutnyttjande. Att koordinera flödet av fordon genom depån är en komplex uppgift på grund av de begränsade resurserna och den stela infrastrukturen. Ytterligare en faktor som bidrar till komplexiteten är det stora antalet aktörer som är verksamma i depån. Till följd av att den svenska tågmarknaden avreglerades så har antalet självständiga aktörer på marknaden ökat, vilket gör det svårt att samordna aktiviteterna inom depåer. I enlighet med rådande EU-direktiv kommer alla medlemsländer under de närmaste åren att avreglera sina marknader för persontåg, vilket tillåter nya aktörer att gå in på marknaden. I den här uppsatsen har en fallstudie av en av Sveriges största tågdepåer genomförts för att undersöka hur verksamheten inom depån kan effektiviseras med hjälp av RFID. För att uppnå en effektiv depåverksamhet är det viktigt att säkerställa effektiv kommunikation mellan aktörer och förflyttning av tåg. I enlighet med detta har tre nyckelfaktorer identifierats för att uppnå effektivitet i depån; koordination, kommunikation och transparens. Vidare har värdet av att implementera RFID analyserats inom tre områden; lokalisering av fordon, underhåll och säkerhet. Inom samtliga områden har möjligheter att uppnå de tre nyckelfaktorerna identifierats. Resultatet visar att kostnadsbesparingar kan uppnås genom bättre resursutnyttjande och automatisering av manuella uppgifter. Bättre resursutnyttjande gör det även möjligt för fler fordon att använda depåns faciliteter. Effektivitetsökningar kan uppnås genom att ickevärdeskapande aktiviteter elimineras och fel orsakade av den mänskliga faktorn reduceras. Resultatet visar även att relationerna mellan olika aktörer kan förbättras eftersom mer precis information delas, vilket kan förhindra uppkomsten av oenigheter. Datan från RFID kan också användas för att identifiera problemområden och ligga till underlag för analys av framtida projekt. Sammanfattningsvis visar resultatet av studien att effektiviteten i tågdepåer kan förbättras med hjälp av RFID för att kunna hantera en framtida ökning av tågtrafiken.

With the development of artificial intelligence techniques and increased installation of smart meters in recent years, time series analysis using historical data in the energy domain becomes applicable. In this thesis, microdata analysis approaches are used, which consist of data acquisition, data processing, data analysis and data modelling, aiming to address two research problems in the energy domain. The first research problem is short-term electricity price forecasting of a deregulated market and the second one is anomaly detection of heat energy usage in district heating substations. As a result of electricity market deregulation, third party suppliers can enter the market and consumers are free to choose electricity suppliers, which leads to a more transparent and competitive market. Accurate short-term electricity price forecasting is crucial to the market participants in terms of maximizing profits, risk management and other short-term market operations. Literature review is performed aiming to identify the suitable methods. It is concluded that long short-term memory (LSTM) based methods are superior to other methods for time series analysis. Since the gating mechanisms of long short-term memory alleviate the problem of gradient vanishing. Another conclusion form the literature is that hybrid approach that consists of two or more artificial intelligence algorithms complimenting each other is more effective to solve complex real world problem. Based on the conclusions derived, a hybrid approach based on bidirectional LSTM (BDLSTM) and Catboost is proposed for short-term electricity price forecasting of NordPool. Performance of support vector regression (SVR), ARIMA, ensemble tree, multi-layer perception (MLP), gated recurrent unit (GRU), BDLSTM and LSTM are evaluated. Experiment results show that BDLSTM outperforms the other models in terms of Mean percentage error (MAPE), root mean square error (RMSE) and mean absolute error (MAE). Statistics show that market shares of district heating have increased steadily in the past five decades. District heating shares approximately 55% of the heat supply market in Sweden. Therefore, energy efficiency of district heating systems is of great interest to energy stakeholders. Anomalies are rare observations deviated significantly from the majority of the data, and such suspicious observations are important indicators of potential faults. To reduce the financial loss and improve energy efficiency, detecting anomalies from meter readings is essential. Another type of neural network architecture, LSTM variational autoencoder (LSTMVAE) combined with a heat signature model is proposed for anomaly detection using the dataset from an anonymous substation in Sweden. Results show that the proposed method outperforms other two baseline models LSTM, LSTM autoencoder (LSTMAE) in terms of F1 score and AUC. In this thesis, various approaches based on neural networks are explored to solve different time series data analysis in the energy domain, aiming for supporting decision makings of market participants to maximize profits, enhancing risk managements and improving energy efficiency. Although, two problems domains are covered, methods reviewed and applied in the thesis can be tailored for other energy time series analysis problems as well.

The growth of electricity markets around the world has introduced new challenges in which one of the challenges is the uncertainty that has become a structural element in this new environment. Market players have to deal with it to guarantee the appropriate power system planning and operation as well as its own economical liquidity. Under an open access environment in a deregulated power system, transmission management holds a vital role in supporting transactions between suppliers and customers. Nevertheless, a transmission network has some constraints that should be addressed in order to ensure sufficient control to maintain the security level of a power system while maximizing market efficiency. The most obvious drawback of transmission constraints is a congestion problem that becomes an obstacle of perfect competition among the market participants since it can influence spot market pricing. The system becomes congested when the supplier and customer agree to produce and consume a particular amount of electric power, but this can cause the transmission network to exceed its thermal limits. Congestion can cause the market players to exercise market power that can result in price volatility beyond the marginal costs. Thus, it is important to manage congestion efficiently in the design of a power market. One mechanism that has direct correlation with transmission management is market clearing price (MCP). Under an open access environment, energy prices throughout the network will be different and measured based on transmission constraint and network losses. When network losses are ignored and there is no congestion on the transmission lines, the power price will be the same at all nodes. This is known as uniform marginal pricing (UMP). However, as the power flow violates transmission constraints, redispatching generating units is required and this will cause the price at every node to vary. This phenomenon is defined as locational marginal pricing (LMP). Therefore, the market clearing price has a strong relationship with transmission management, which is needed to be assessed in order to obtain an efficient and transparent price but satisfying all market participants. This project investigates an alternative solution to the dispatch mechanism, and then formulates a new Locational Marginal Price scheme using optimization technique that may well control congestion as the main issue. The model will vary and be improved, to be distilled into energy price, congestion revenue, cost of losses, as well as transmission usage tariff. The objective of the project is to support developing standard market design (SMD) in managing transmission systems which promotes economic efficiency, lowers delivered energy costs, maintains power system reliability and mitigates exercising market power.