In Sweden, electricity is purchased on a so-called day-ahead spot market (Nordpool). The electricity is based on a predicted hourly need for the upcoming day [4, 5]. Production and consumption of electricity need to be balanced since it is hard to store electricity [25]. Today, electricity companies struggle to uphold this balance using currently available tools. A potential solution would be to support bidders by visualizing time-series. Then they could identify time-series lacking data crucial to the prediction phase and resolve them. In this thesis, a prototype was implemented consisting of different views/use-cases, aimed at simplifying the bidding process for balance responsible parties (BRPs). The prototype consisted of structured time-series and presents predicted data in a way that makes the decision making easier when placing bids. Results from a study using the prototype with BRPs and professionals showed that the use-cases/views are useful in terms of 1) getting a better structure, 2) identifying incomplete time series, 3) better quality assurance of the time-series and 4) lowering the time-consumption. Additionally, the bidders suggested that the addition of references, in terms of other prediction methods than the one that was used could improve their decision making. / I Sverige köps el på en så kallad day-ahead marknad (Nordpool). Elen är baserad på ett förutsagt timbehov för den kommande dagen [4, 5]. Produktion och konsumtion av el måste balanseras, eftersom det är svårt att lagra el [25]. Idag har elföretag problem med att upprätthålla denna balans med hjälp av nuvarande verktyg. En potentiell lösning skulle vara att stödja budgivare genom att visualisera tidsserier. Då kunde de identifiera tidsserier som saknar data som är avgörande för prediktionsfasen och förse dem med korrekt data. I denna avhandling implementerades en prototyp bestående av olika vyer/användningsfall, som syftar till att förenkla budprocessen för balansansvariga parter (BRP). Prototypen bestod av strukturerade tidsserier och presenterar predikterat data på ett sätt som gör beslutet enklare när de placerar bud. Resultat från en studie med prototypen tillsammans med BRP och yrkesverksamma visade att vyerna/användningsfallen är användbara när det gäller 1) att få en bättre struktur, 2) identifiera ofullständiga tidsserier, 3) bättre kvalitetssäkring av tidsserier och 4) minska tidsförbrukningen. Dessutom föreslog budgivarna att en tillsats av referenser när det kommer till andra prediktioners metoder än den som användas kan förbättra deras beslutsfattande.

The very specific features of the spot prices, especially occurrence of severe jumps, create a spot price risk for retailers who purchase electricity at unregulated highly volatile prices but resell it to consumers at fixed price. Therefore, it is of high im- portance to forecast whether jump is likely to occur during the next hour. However, to the best of our knowledge, such research has not been devoted to the Czech power market yet. Therefore, the aim of this thesis is to forecast the jump occurrence in the Czech day-ahead market. For this purpose we suggest four logit model spec- ifications, each containing various independent variables (for example, electricity demand, outside temperature, lagged price and various dummy variables) where the variable selection is supported by the previous literature and by the characteristic features of the spot prices. Within the in-sample period we compare the suggested models based on the values of pseudo-R squared and Bayesian information criterion. When evaluating the out-of sample performance of suggested models we apply jump prediction accuracy and confidence, but opposed to the previous literature we sug- gest a kind of sensitivity analysis which, to the best of our knowledge, has not be proposed by any other power research. JEL Classification C25, C32, C51,...

Combined heat and power (CHP) is a technology that decreases total fuel consumption and related greenhouse gas emissions by producing both electricity and useful thermal energy from a single energy source. In the industrial and commercial sectors, a typical CHP site relies upon the electricity distribution network for significant periods, i.e., for purchasing power from the grid during periods of high demand or when off-peak electricity tariffs are available. On the other hand, in some cases, a CHP plant is allowed to sell surplus power to the grid during on-peak hours when electricity prices are highest while all operating constraints and local demands are satisfied. Therefore, if the plant is connected with the external grid and allowed to participate in open energy markets in the future, it could yield significant economic benefits by selling/buying power depending on market conditions. This is achieved by solving the power system generation scheduling problem using mathematical programming. In this work, we present the application of mixed-integer nonlinear programming (MINLP) approach for scheduling of a CHP plant in the day-ahead wholesale energy markets. This work employs first principles models to describe the nonlinear dynamics of a CHP plant and its individual components (gas and steam turbines, heat recovery steam generators, and auxiliary boilers). The MINLP framework includes practical constraints such as minimum/maximum power output and steam flow restrictions, minimum up/down times, start-up and shut-down procedures, and fuel limits. We provide case studies involving the Hal C. Weaver power plant complex at the University of Texas at Austin to demonstrate this methodology. The results show that the optimized operating strategies can yield substantial net incomes from electricity sales and purchases. This work also highlights the application of a nonlinear model predictive control scheme to a heavy-duty gas turbine power plant for frequency and temperature control. This scheme is compared to a classical PID/logic based control scheme and is found to provide superior output responses with smaller settling times and less oscillatory behavior in response to disturbances in electric loads. / text

With the aim for higher economical efficiency, considerable and radical changes have occurred in the worldwide electricity sector since the beginning of 1980s. By that time, the electricity sector has been controlled by the state-owned vertically integrated monopolies which manage and control all generation, transmission, distribution and retail activities and the consumers buy electricity with a price set by these monopolies in that system. After the liberalization and restructuring of the electricity power sector, separation and privatization of these activities have been widely seen. The main purpose is to ensure competition in the market where suppliers and consumers compete with each other to sell or buy electricity from the market and the consumers buy the electricity with a price which is based on competition and determined according to sell and purchase bids given by producers and customers rather than a price set by the government. Due to increasing competition in the electricity market, accurate electricity price forecasts have become a very vital need for all market participants. Accurate forecast of electricity price can help suppliers to derive their bidding strategy and optimally design their bilateral agreements in order to maximize their profits and hedge against risks. Consumers need accurate price forecasts for deriving their electricity usage and bidding strategy for minimizing their utilization costs. This thesis presents the determination of system day ahead price (SGOF) at the day ahead market and system marginal price (SMF) at the balancing power market in detail and develops artificial neural network models together with multiple linear regression models to forecast these electricity prices in Turkish electricity market. Also the methods used for price forecasting in the literature are discussed and the comparisons between these methods are presented. A series of historical data from Turkish electricity market is used to understand the characteristics of the market and the necessary input factors which influence the electricity price is determined for creating ANN models for price forecasting in this market. Since the factors influencing SGOF and SMF are different, different ANN models are developed for forecasting these prices. For SGOF forecasting, historical price and load values are enough for accurate forecasting, however, for SMF forecasting the net instruction volume occurred due to real time system imbalances is needed in order to increase the forecasting accuracy.

High performance grid computing is a key enabler of large scale collaborative computational science. With the promise of exascale computing, high performance grid systems are expected to incur electricity bills that grow super-linearly over time. In order to achieve cost effectiveness in these systems, it is essential for the scheduling algorithms to exploit electricity price variations, both in space and time, that are prevalent in the dynamic electricity price markets. Typically, a job submission in the batch queues used in these systems incurs a variable queue waiting time before the resources necessary for its execution become available. In variably-priced electricity markets, the electricity prices fluctuate over discrete intervals of time. Hence, the electricity prices incurred during a job execution will depend on the start and end time of the job. Our thesis consists of two parts. In the first part, we develop a method to predict the start and end time of a job at each system in the grid. In batch queue systems, similar jobs which arrive during similar system queue and processor states, experience similar queue waiting times. We have developed an adaptive algorithm for the prediction of queue waiting times on a parallel system based on spatial clustering of the history of job submissions at the system. We represent each job as a point in a feature space using the job characteristics, queue state and the state of the compute nodes at the time of job submission. For each incoming job, we use an adaptive distance function, which assigns a real valued distance to each history job submission based on its similarity to the incoming job. Using a spatial clustering algorithm and a simple empirical characterization of the system states, we identify an appropriate prediction model for the job from among standard deviation minimization method, ridge regression and k-weighted average. We have evaluated our adaptive prediction framework using historical production workload traces of many supercomputer systems with varying system and job characteristics, including two Top500 systems. Across workloads, our predictions result in up to 22% reduction in the average absolute error and up to 56% reduction in the percentage prediction errors over existing techniques. To predict the execution time of a job, we use a simple model based on the estimate of job runtime provided by the user at the time of job submission. In the second part of the thesis, we have developed a metascheduling algorithm that schedules jobs to the individual batch systems of a grid, to reduce both the electricity prices for the systems and response times for the users. We formulate the metascheduling problem as a Minimum Cost Maximum Flow problem and leverage execution period and electricity price predictions to accurately estimate the cost of job execution at a system. The network simplex algorithm is used to minimize the response time and electricity cost of job execution using an appropriate flow network. Using trace based simulation with real and synthetic workload traces, and real electricity price data sets, we demonstrate our approach on two currently operational grids, XSEDE and NorduGrid. Our experimental setup collectively constitute more than 433K processors spread across 58 compute systems in 17 geographically distributed locations. Experiments show that our approach simultaneously optimizes the total electricity cost and the average response time of the grid, without being unfair to users of the local batch systems. Considering that currently operational HPC systems budget millions of dollars for annual operational costs, our approach which can save $167K in annual electricity bills, compared to a baseline strategy, for one of the grids in our test suite with over 76000 cores, is very relevant for reducing grid operational costs in the coming years.

Electricity markets may become more sensitive to weather conditions because of higher penetration of renewable energy sources and climatic changes. Albania is 100% reliant on hydropower for its domestic energy generation, making this country compelling to investigate as it is highly sensitive to changing weather conditions. We use an ARMA-GARCH model to investigate whether weather and economic factors had a relationship with monthly hydroelectricity prices in the Albanian Energy Market in the period 2013-2018. We find that electricity price is affected by variations in weather and is not utterly robust to extreme hydrological changes. Generally, our dependent variable appears to be particularly influenced by air pressure followed by temperature and rainfall. We also perceive that there is a relationship between economic factors and hydroelectricity prices, where residual supply appears to have a significant negative relationship with our dependent variable. However, we were originally anticipating a higher dependency of electricity prices on weather conditions, due to the inflated hydro-power reliance for electricity production in the Albanian Energy Market. This effect is offset by several factors, where the state monopolized behaviour of the energy sector occupies a predominant influence on our results.

Electricity is an essential good, which can hardly be replaced. It can be produced thanks to a wide rangeof sources, from coal to nuclear, not to mention renewables such as wind and solar. In order to meetdemand at the lowest cost, an optimisation is made on electricity markets between the differentproduction plants. This optimisation mainly relies on the electricity production cost of each technology.In order to include long-term constraints in the short-term optimisation, a so-called use value (oropportunity cost) can be computed and added to the production cost. One long-term constraint thatEDF, the main French electricity producer, is facing is that its gas plants cannot exceed a given numberof operation hours and starts between two maintenances. A specific software, DiMOI, computes usevalues for this double constraint but its parameters needs to be tested in order to improve thecomputation, as it is not thought to work properly.DiMOI relies on dynamic programming and more particularly on an algorithm called Bellman algorithm.The software has been tested with EDF R&D department in order to propose some modellingimprovements. Electricity and gas market prices, together with real plant parameters such as startingcosts, operating costs and yields, were used as inputs for this work, and the results were checkedagainst reality.This study gave some results but they appeared to be invalid. Indeed, an optimisation problem wasdiscovered in DiMOI computing core: on a deterministic context, a study with little degrees of freedomwas giving better profits than a study with more degrees of freedom. This problem origin was notfound precisely with a first investigation, and the R&D team expected the fixing time to be very long.The adaptation of a simpler tool (MaStock) was proposed and made in order to replace DiMOI. Thisproject has thus led to DiMOI giving up and its replacement by MaStock. Time was missing to testcorrectly this tool, and the first study which was made was not completely positive. Further studiesshould be carried out, for instance deterministic ones (using real past data) whose results could becompared to reality.Some complementary studies were made from a fictitious system, in order to study the impact of someparameters when computing use values and operations schedules. The conclusions of these studiesare the little impacts that changes in gas prices and start-up costs parameters have on the global resultsand the importance of an accurate choice in the time periods durations used for the computations.Unfortunately these conclusions might be too specific as they were made on short study periods.Further case studies should be done in order to reach more general conclusions.

<p>In the US, seven regional transmission organizations (RTOs) operate wholesale electricity markets within three largely independent transmission systems, the largest of which includes five RTO regions and many vertically integrated utilities.</p> <p>RTOs operate a day-ahead and a real-time market. In the day-ahead market, generation and demand-side resources are optimally scheduled based on bids and offers for the next day. Those schedules are adjusted according to actual operating conditions in the real-time market. Both markets involve a unit commitment calculation, a mixed integer program that determines which generators will be online, and an economic dispatch calculation, an optimization determines the output of each online generator for every interval and calculates locational marginal prices (LMPs).</p> <p>The use of LMPs for the management of congestion in RTO transmission systems has brought efficiency and transparency to the operation of electric power systems and provides price signals that highlight the need for investment in transmission and generation. Through this work, we aim to extend these efficiency and transparency gains to the coordination across RTOs. Existing market-based inter-regional coordination schemes are limited to incremental changes in real-time markets. </p> <p>We propose a multi-regional unit-commitment that enables coordination in the day-ahead timeframe by applying a distributed approach to approximate a system-wide optimal commitment and dispatch while allowing each region to largely maintain their own rules, model only internal transmission up to the boundary, and keep sensitive financial information confidential. A heuristic algorithm based on an extension of the alternating directions method of multipliers (ADMM) for the mixed integer program is applied to the unit commitment. </p> The proposed coordinated solution was simulated and compared to the ideal single-market scenario and to a representation of the current uncoordinated solution, achieving at least 58% of the maximum potential savings, which, in terms of the annual cost of electric generation in the US, could add up to nearly $7 billion per year. In addition to the coordinated day-ahead solution, we develop a distributed solution for financial transmission rights (FTR) auctions with minimal information sharing across RTOs that constitutes the first known work to provide a viable option for market participants to seamlessly hedge price variability exposure on cross-border transactions.

Renewable cannibalization refers to the phenomenon where the increasing penetration of zeromarginal cost renewable energy sources, such as solar and wind power, leads to a decline in their market value. By extension, this threatens to reduce investment incentives in wind and solar. Based on theory of supply and demand, the extent to which cannibalization is experienced should increase as the penetration of wind and solar increases. Since electricity prices, and therefore cannibalization, are set with considerations to domestic system dynamics, regulations and policies, cannibalization research are typically limited to investigate it for a specific country or region. For this thesis, Spain has been chosen for the case study. Coupling an already high penetration of both wind and solar with ambitious goals for wind and solar capacity expansion, Spain constitutes an interesting case study for cannibalization research. The investigation is centered around two factors, the market capturing price (MCP) and the cannibalization factor (CF). The MCP is the generation weighted electricity price and measures absolute cannibalization, while the CF is the ratio between the MCP and the average electricity price, thus constituting a relative cannibalization measurement. These will be calculated using hourly day-ahead wind and solar forecasts, and day-ahead electricity prices. A time series econometric study is then conducted to quantify the cannibalization effect together with potentially influential factor that, in theory, should be the driving factors behind the cannibalization phenomenon. To investigate dynamic affects across these factors, temporal regressions are conducted. In these regressions, data is isolated in different groups based on their characteristics. Furthermore, carbon pricing and granular guarantees of origin (GOs) are investigated and assessed based on their potential for alleviating the effect of cannibalization.  The study finds that both wind and solar cannibalizes their own market values, and that cannibalization occurs across technologies. The results indicate that there is a negative marginal relation between wind and solar infeed, suggesting the presence of both self-cannibalization and cross-cannibalization effects on their respective MCPs. The same can be said for the CF of solar, for which there is a negative marginal effect with the infeed of wind and solar. These statements hold true across all ranges of wind and solar penetration investigated in the temporal regression analysis. Moreover, the negative relations increase as the penetration range increases, indicating that cannibalization effects are stronger at high renewable penetration. For wind power, this is not entirely the case. Although the regression results yielded a low coefficient of determination (R2), indicating weak explanatory power in the regressions, it is possible to interpret whether the marginal effects are positive or negative. The temporal regression results indicate that there is a positive marginal effect between solar infeed and the CF of wind when the penetration of solar is lower than 10%. Thus, considering the ambitious wind and solar capacity targets of Spain, the economic viability of wind and solar could be threatened. Furthermore, the results from the study indicate that although carbon pricing helps increasing the MCP for both wind and solar by adding an increment to the day-ahead price, it reduces the CF. Furthermore, carbon pricing is a limited tool for alleviating cannibalization, considering that it requires. Thus, once the system is fully decarbonized, carbon pricing is rendered obsolete. On the contrary, the implementation of granular (hourly) GOs provide extra revenue in addition to the revenue from sold electricity. Furthermore, its immediate effect is that it increases the revenue while not impacting the price of electricity. Thus, it helps counteracting both absolute and relative cannibalization effects. / Förnybar kannibalisering hänvisar till fenomenet där en ökande markandspenetration av förnybar energi med noll marginalkostnad, så som vind- och solkraft, leder till en minskning i deras marknadsvärde. Detta hotar i förlängningen att minska incitament för fortsatta investeringar inom vind- och solkraft. Baserat på teorier om utbud och efterfrågan bör graden av kannibalisering öka i takt med att marknadspenetrationen av vind- och solenergi ökar inom ett givet system. Eftersom elpriser, och därmed graden av kannibalisering, beror på nationella dynamiker inom kraftsystemet, föreskrifter och riktlinjer, utförs ofta studier om kannibaliserade effekter inom förnybar energi från fallstudier inom ett land eller en regions system. I denna avhandling har Spanien valt som fallstudie. Med en redan hög grad av förnybart inom elsystemet, samt ambitiösa mål för fortsatt utbyggnad av vind- och solkraft, utgör Spanien en intressant fallstudie för forskning om förnybar kannibalisering.  Undersökningen utgår från två faktorer; det produktionsviktade elpriset (MCP) och kannibaliseringsfaktorn (CF). MCP utgör en absolut faktor och är det produktionsviktade elpriset för en viss teknologi, medan CF utgör ett mått på relativ kannibalisering och beräknas genom att dividera MCP med det genomsnittliga elpriset. Dessa två faktorer beräknas genom att använda timvisa prognoser från dagen-före marknaden för produktion av vind- och solkraft, samt priser från spotmarknaden. Därefter utförs en ekonometrisk studie baserad på tidseriedata för att kvantifiera de kannibaliserande effekterna med avseende på utomstående faktorer som bör kunna ses som drivkrafter bakom kannibaliseringsfenomenet. Dessutom undersöks och bedöms koldioxidprissättning och timvisa ursprungsgarantier baserat på deras förmåga att lindra effekterna av kannibalisering i Spanien. Studien visar att både vind- och solenergi kannibaliserar sina egna marknadsvärden, samt att kannibalisering sker mellan kraftslag. Resultaten indikerar att det finns en negativ marginaleffekt mellan produktion av vind- och solel, vilket tyder på förekomsten av både självkannibaliserande och korskannibaliserande effekter på deras respektive produktionsviktade elpriser (MCP). Samma kan sägas gälla för solkraftens kannibaliseringsfaktor (CF), där det finns en tydlig negativ marginaleffekt gentemot produktion av vind- och solel. Dessa påståenden stämmer över alla intervall av vind- och solpenetration som undersökts i den temporala regressionsanalysen. Dessutom ökar de negativa relationerna desto högre intervall som avses, vilket tyder på att kannibaliseringseffekten är starkare vid hög penetration av förnybar energi. För vindkraft stämmer inte detta helt. Även om regressionsresultaten gav en låg determinationskoefficient (R2), vilket indikerar svag förklarande kraft i regressionerna, är det möjligt att tolka om marginaleffekterna är positiva eller negativa. De temporala regressionsresultaten visar att det finns en positiv marginal- effekt mellan produktionen av solel och CF för vindkraft när solkraft utgör mindre än 10% av den dagliga produktionen. Med tanke på Spaniens ambitiösa mål för vind- och solkapacitet kan den ekonomiska livskraften för vind- och solenergi hotas. Vidare visar resultaten från studien att även om koldioxidprissättning hjälper till att öka MCP för både vind- och solenergi genom att skapa ett tillägg på spotmarknadspriset, minskar det CF. Dessutom är koldioxidprissättning ett begränsat verktyg för att lindra kannibalisering, med tanke på att det kräver. När systemet är fullständigt avkarboniserat blir koldioxidprissättning överflödig. Å andra sidan ger implementeringen av granulära (timvisa) ursprungsgarantier (GOs) extra intäkter utöver intäkterna från såld el. Dessutom ökar det omedelbart intäkterna utan att påverka elpriset. På så sätt hjälper det till att motverka både absoluta och relativa kannibaliseringseffekter.

[pt] A possível mudança no paradigma de formação de preço no Brasil do modelo vigente por custos auditados para o modelo por oferta, com o objetivo de modernizar o Setor Elétrico e buscar práticas que incentivem a competição, implica a necessidade de estudos prévios para auxiliar o processo de transição e a definição do desenho de mercado adequado à realidade brasileira. Nesse sentido, o uso de modelos de equilíbrio, notadamente o Equilíbrio de Nash, desponta como uma poderosa ferramenta ex-ante que permite analisar o comportamento dos competidores para identificar possíveis ineficiências a serem mitigadas. Com esse fim, o presente trabalho modela o processo decisório de ofertas ótimas de um competidor em um mercado de energia elétrica de dia-seguinte de base hidrotérmica por um modelo de otimização binível, possibilitando a identificação do Equilíbrio de Nash do mercado através de um algoritmo baseado em Gauss-Seidel. Adicionalmente, o método é aplicado a dois experimentos numéricos: a um sistema-teste de três barras e a um caso representativo do sistema brasileiro completo, permitindo a análise do comportamento dos competidores a partir da comparação dos resultados com os modelos de Custos Auditados e Equilíbrio Competitivo. Foram observados os impactos das afluências e das cascatas de usinas hidrelétricas de múltiplos proprietários nas receitas e, consequentemente, no comportamento dos competidores. Por fim, foi verificada a ocorrência de competição exclusivamente pelas quantidades, além da prática de retenção de ofertas por parte dos competidores para a modificação do preço de equilíbrio de mercado, aumentando suas receitas. / [en] The potential shift in Brazil s energy pricing paradigm from the current Audited Costs model to the Bid-Bases model, with the aim of modernizing the electricity sector and seeking practices that encourage competition, implies the need for preliminary studies to assist the transition process and define the appropriate market design for the Brazilian reality. In this sense, the use of equilibrium models, notably the Nash Equilibrium, emerges as a powerful ex-ante tool that allows the analysis of competitors behavior to identify possible inefficiencies to be mitigated.To this end, this thesis models the optimal bidding decision process of a competitor in a hydrothermal day-ahead electricity market using a bilevel optimization model, enabling the identification of the Nash Equilibrium of the market through an algorithm based on the Gauss-Seidel. Additionally, the method is applied to two numerical experiments: a three-bus test system and a representative case of the complete Brazilian system, allowing for the analysis of competitors behavior by comparing the results with the Audited Costsand Competitive Equilibrium models. The impacts of water inflows and hydro plants in cascade with distinct ownership on revenues and consequently competitors behavior were observed. Finally, the occurrence of competition exclusively in quantities was verified, as well as the practice of quantity bids retention by competitors in order to modify the market equilibrium price, increasing their revenues.