This report investigates the effects a demand based tariff would have on private electricity consumers and how it could contribute to a sustainable electrical grid. The fuse tariff that is currently used charges customers for their electricity usage and does not fully reflect the electrical grid load and thereby not the cost for the distribution system operators. A demand based tariff, that charges customers for their power peaks and promotes a change of behaviour better reflects the grid load and could lead to a more even grid distribution and a sustainable development. In light of this issue, two demand based tariffs have been constructed for the energy company Upplands Energi’s private customers’ current electricity consumption. One of the tariffs consists of a higher fixed cost and a lower variable cost and the other consists of a lower fixed cost and a higher variable cost, both leading to the same revenue för Upplands Energi. The two different demand based tariffs have been simulated and analysed, where the one with the lowest fixed cost is recommended since it gives customers higher economic incentives to change their electricity usage behaviour. It is essential that electricity retailers communicate the information of how the demand based tariff works to the customers in order to make them change their electricity behaviour to attain a sustainable grid.

There is a drive to transform the electricity industry in the UK from one based largely on fossil fuels to one based on low or zero carbon sources. The challenge of this transition, enabling a secure and sustainable electricity industry at an acceptable cost to consumers, has been dubbed the Energy Trilemma. Grid-connected electrical energy storage presents a potential solution to this challenge. However, the benefits of storage are split across different sectors of the electricity industry and there are a number of regulatory barriers preventing access to revenue streams. One accessible revenue stream is energy trading or price arbitrage. In current market conditions, arbitrage cannot provide sufficient revenue for electricity storage to cover its capital costs; however, some studies have suggested that with increased penetration of intermittent renewable power, electricity price volatility will increase enabling storage to become commercially viable through price arbitrage alone. This thesis examines the hypothesis that: Increased wind penetration leads to increased commercial opportunities for energy storage through price arbitrage. A linear programme is used to define the optimum operating strategy for a storage device, subject to the constraints of maximum storage capacity, charging and discharging rates, conversion efficiency and self-discharge. Initially, historic electricity prices from the British electricity market are used to investigate the value of storage with a low penetration of intermittent wind power. The results show that revenue is dependent on storage characteristics, with the performance of different technologies varying substantially. Furthermore, revenue is highly dependent on changes in market structure and fuel price variations from one year to the next. The thesis describes the development of a fundamental electricity price model based on the stacked merit order dispatch of thermal generation bidding to produce electricity in a competitive market centred around marginal generation costs. For peaking plant, an exponential uplift in price is applied to represent scarcity of supply. The implications of increasing wind power output are examined using projections of the location and capacity of future wind farms and spatially distributed hind cast wind speed data generated from a mesoscale atmospheric model. The analysis highlights that despite increased value being placed on storage in an energy system with a high penetration of wind power, opportunities for arbitrage are, in fact, reduced. This is a result of an oversupply of electricity on windy days suppressing peak electricity prices and reducing the daily price spread, which arbitrage exploits.

This thesis consists of four self-contained papers related to the Nordic electricity market. Paper [I] examine how the reform of the Nordic electricity markets has affected competition in the electric power supply market, Nord Pool. The question is if the common power market has been competitive or if electric power generators have had market power during the period 1996 -2004. Moreover, since there was a stepwise evolution from national markets to a multinational power market, we also ask how the degree of market power has evolved during this integration process. The results show that electric power generators have had a small, but statistically significant, degree of market power during the whole period.  However, studying the integration effect, i.e. how the market power has been affected by additional countries joining Nord Pool, it show that the degree of market power has been reduced and finally vanished as the market has expanded and more countries joined the collaboration. Paper [II] analyse how the deregulation of the Swedish electricity market has affected the price of electric power and how the change in electric power price, in turn, has affected consumers’ welfare. The result shows that the change in pricing principle of electric power following the deregulation has increased consumer welfare over the period studied (1996-2006), with welfare gains about 100 SEK per customer per year, indicating a three per cent welfare gain for the average customer. Paper [III] study whether (and to what extent) the multinational electricity market integration has affected the price dynamics at the Nordic power exchange. The results shows that a larger electricity market seems to reduce the probability of sudden price jumps, but also that the effect on volatility seem to depend on the characteristics, i.e. production structure, of the integrated markets. In Paper [IV] a two-stage study is conducted to investigate the extent to which shocks in the demand and supply for electricity translate into price jumps, and the extent to which this process is affected by the prevailing market structure. The main findings from the study is that whether demand and supply shocks translate into price jumps largely depends on the prevailing market structure, i.e. on how far the market works from capacity constraints. A notable feature of the empirical analysis is also that the marginal effects from positive demand and negative supply shocks on the jump probabilities are mostly insignificant and of small magnitude.

La posada en marxa del Mercat Ibèric de l'Electricitat va introduir al sector elèctric espanyol un seguit de nous mecanismes de participació que han forçat els agents a renovar les seves polítiques de gestió. D'aquesta nova situació sorgeix l'oportunitat d'estudiar noves estratègies d'oferta a curt termini per a companyies de generació price-taker que participin diàriament al Mercat Ibèric de l'Electricitat. Aquestes estratègies se centraran al mercat diari, ja que és aquí on es negocia un 80% de l'electricitat que es consumeix diàriament a Espanya i on s'integren gran part de la resta de mecanismes de participació. La liberalització dels mercats elèctrics obre a noves tècniques d'optimització els problemes clàssics de gestió de l'energia. En particular, atesa la incertesa que l'existència del mercat ocasiona als preus, les tècniques de programació estocàstiques es converteixen en la forma més natural per abordar aquests problemes. Als mercats elèctrics el preu es fixa horàriament com a resultat d'un procés de casació , és a dir que quan l'agent ha d'efectuar la seva oferta desconeix el preu al qual li vindrà remunerada l'energia. Aquesta incertesa fa imprescindible l'ús de tècniques estadístiques per obtenir informació del mercat i introduir-la als models d'optimització. En aquest aspecte, una de les contribucions d'aquesta tesi és l'estudi dels preus del mercat de l'electricitat a Espanya i el seu modelat mitjançant models factorials. D'altra banda, s'hi es descriuen els nous mecanismes presents al Mercat Ibèric de l'Electricitat que afecten directament la producció física de les unitats. En particular, s'inclou el modelat detallat dels contractes de futurs físics i bilaterals i de la seva inclusió a l'oferta del mercat diari per part de les companyies de generació. Als models presentats, es tenen en compte explícitament les regles del mercat, així com les clàssiques restriccions d'operació de les unitats, tant tèrmiques com de cicle combinat. A més, es deriva i es demostra l'expressió de la funció d'oferta. Per tant, els models construïts són una eina per decidir l'assignació de les unitats, la generació dels contractes de futurs físics i bilaterals a través seu i l'oferta òptima d'una companyia de generació. Un cop s'han cobert aquests objectius, es presenta una millora dels models mitjançant la inclusió de la seqüència de mercats de molt curt termini per tal de modelar la influència que tenen en l'oferta al mercat diari. Aquests mercats es casen just abans i durant el dia en què l'energia ha de ser consumida, i això permetrà veure com la possibilitat d'augmentar els beneficis participant-hi afecta directament les estratègies d'oferta òptima del mercat diari. Els models presentats en aquest treball han estat provats amb dades reals provinents del Mercat Ibèric de l'Electricitat i d'una companyia de generació que hi opera. Els resultats obtinguts són adequats i es discuteixen al llarg del document / La puesta en marcha del Mercado Ibérico de la Electricidad introdujo en el sector eléctrico español una serie de nuevos mecanismos de participación que han forzado a los agentes a renovar sus políticas de gestión. De esta nueva situación surge la oportunidad de estudiar nuevas estrategias de oferta para las compañías de generación. Esta tesis se enmarca en las estrategias de oferta a corto plazo para compañías de generación price-taker que participen diariamente en el Mercado Ibérico de la Electricidad. Estas estrategias se centraran en el mercado diario ya que es donde se negocia un 80% de la electricidad consumida diariamente en España y es donde se integran gran parte del resto de los mecanismos de participación. La liberalización de los mercados eléctricos permite aplicar nuevas técnicas de optimización a los problemas clásicos de gestión de la energía. En concreto, dada la incertidumbre en el precio existente en el mercado, las técnicas de programación estocástica se convierten en la forma más natural para abordar estos problemas. En los mercados eléctricos el precio se fija horariamente como resultado de un proceso de casación, es decir, cuando el agente debe efectuar sus ofertas desconoce el precio al que la energía le será pagada. Esta incertidumbre hace imprescindible el uso de técnicas estadísticas para obtener información del mercado e introducirla en los modelos de optimización. En este aspecto, una de las contribuciones de esta tesis es el estudio del precio de la electricidad en España y su modelado mediante modelos factoriales. Se describen los nuevos mecanismos presentes en el Mercado Ibérico de la Electricidad que afectan directamente a la producción física de las unidades. En particular, se incluye una modelización detallada de los contratos de futuros físicos y bilaterales y su inclusión en la oferta enviada al mercado diario por las compañías de generación. En los modelos presentados se tiene en cuenta explícitamente las reglas del mercado así como las clásicas restricciones de operación de las unidades, tanto térmicas como de ciclo combinado. La expresión de la función de oferta óptima se deriva y se demuestra. Por lo tanto, los modelos construidos son una herramienta para decidir la asignación de unidades, la generación de los contratos de futuros físicos y bilaterales a través de ellas y la oferta óptima de una compañía de generación. Una vez alcanzados estos objetivos, se presenta una mejora del modelo con la inclusión de la secuencia de mercados de muy corto plazo. El objetivo es modelar la influencia que esta tiene en la oferta al mercado diario. Estos mercados se casan justo antes y durante el día en el que la energía va a ser consumida y se verá cómo la posibilidad de aumentar los beneficios participando en ellos afecta a las estrategias de oferta óptima del mercado diario. Los modelos presentados en este trabajo se han probado con datos reales procedentes del Mercado Ibérico de la Electricidad y de una compañía de generación que opera en él. Los resultados obtenidos son adecuados y se discuten a lo largo del documento. / The start-up of the Iberian Electricity Market introduced a set of new mechanisms in the Spanish electricity sector that forced the agents participating in the market to change their management policies. This situation created a great opportunity for studying the bidding strategies of the generation companies in this new framework. This thesis focuses on the short-term bidding strategies of a price-taker generation company that bids daily in the Iberian Electricity Market. We will center our bidding strategies on the day-ahead market because 80% of the electricity that is consumed daily in Spain is negotiated there and also because it is the market where the new mechanisms are integrated. The liberalization of the electricity markets opens the classical problems of energy management to new optimization approaches. Specifically, because of the uncertainty that the market produces in the prices, the stochastic programming techniques have become the most natural way to deal with these problems. Notice that, in deregulated electricity markets the price is hourly fixed through a market clearing procedure, so when the agent must bid its energy it is unaware of the price at which it will be paid. This uncertainty makes it essential to use some statistic techniques in order to obtain the information coming from the markets and to introduce it in the optimization models in a suitable way. In this aspect, one of the main contributions of this thesis has been the study the Spanish electricity price time series and its modeling by means of factor models. In this thesis, the new mechanism introduced by the Iberian Market that affects the physical operation of the units is described. In particular, it considers in great detail the inclusion of the physical futures contracts and the bilateral contracts into the day-ahead market bid of the generation companies. The rules of the market operator have been explicitly taken into account within the mathematical models, along with all the classical operational constraints that affect the thermal and combined cycle units. The expression of the optimal bidding functions are derived and proved. Therefore, the models built in this thesis provide the generation company with the economic dispatch of the committed futures and bilateral contracts, the unit commitment of the units and the optimal bidding strategies for the generation company. Once these main objectives were fulfilled, we improved the previous models with an approach to the modeling of the influence that the sequence of very short markets have on optimal day-ahead bidding. These markets are cleared just before and during the day in which the electricity will be consumed and the opportunity to obtain benefits from them changes the optimal day-ahead bidding strategies of the generation company, as it will be shown in this thesis. The entire models presented in this work have been tested using real data from a generation company and Spanish electricity prices. Suitable results have been obtained and discussed.

This work deals with the electricity market in the Czech republic. First part represents substance of electricity market, next chapters describe the market in terms of laws, Prague energy exchange, elektricity consuption in the Czech republic and in the Europe and elements influencing the price of elektricity.

In the first essay I examine whether the occurrences of the extreme price events display any regularities that can be captured using an econometric model. Here I treat these price events as point processes and apply Hawkes and Poisson autoregressive models to model the dynamics in the intensity of this process. I use load and meteorological information to model the time variation in the intensity of the process. The models are applied to data from the Australian wholesale electricity market, and a forecasting exercise illustrates both the usefulness of these models and their limitations when attempting to forecast the occurrence of extreme price events. In the second essays I explain that in the past doubts have been raised as to whether the pre-dispatch process in Australia Electricity Market is able to give market participants and market operator good and timely quantity and price information. It is the purpose of the second essay to introduce a framework to analyse whether the pre-dispatch process is delivering biased predictions of the actual wholesale spot price outcomes. Here I investigate the bias by comparing the actual wholesale market spot price outcome to pre-dispatch sensitivity prices established the day before dispatch and on the day of dispatch. I observe a significant bias (mainly indicating that the pre-dispatch process tends to underestimate spot price outcomes) and I further establish the seasonality features of the bias across seasons and/or trading periods. I also establish changes in bias across the years in our sample period (1999 to 2007). In the formal setting of an ordered probit model I establish that there are some exogenous variables that are able to explain increased probabilities of over- or under-predictions of the spot price. It transpires that meteorological data, expected pre-dispatch prices and information on past over- and under-predictions contribute significantly to explaining variation in the probabilities for over- and under-predictions. The results allow me to conjecture that some of the bids and re-bids provided by electricity generators are not made in good faith. Finally, the third essay investigates whether information from this pre-dispatch process can be useful when predicting next-day price spikes. In a preliminary analysis I establish the effect of pre-dispatch prices on the quantiles of the spot price distribution. A Quantile regression approach reveals that higher pre-dispatch prices signal only to a certain extent an increased probability of higher spot price outcomes. They also signal a higher uncertainty about the resulting spot price outcomes. I further establish whether the inclusion of information from the pre-dispatch process can significantly improve the predictability of price spikes when these are modelled as a point process (as in the first essay). The models used here are Hawkes and Poisson autoregressive models which allow for time variation (correlated to exogenous information) in the intensity process that governs the occurrence of price spikes. It transpires that the pre-dispatch process of the Australian Electricity Market does not provide any information that can be used in a systematic manner to help predicting on what days price spikes are more likely to occur.

At the start of 2010, the National Energy Regulator of South Africa (NERSA) announced that electricity tariffs would increase at an average rate of 25 percent per year over a three year period (Njobeni, 2010). This raised fears within the economy and specifically within the agricultural sector that these increases would negatively impact the agricultural sector. Various stakeholders within the agricultural sector also raised opinions on what the true impact will be on agricultural production and market prices. The main objective of this study was to quantify the true impact of higher electricity tariffs on production and market prices within the potato industry. The study focused on the potato producing regions of the Sandveld, Limpopo and South Western Free State. On-farm data were collected in an attempt to capture the electricity consumption and costs associated with potato farming in these specific regions. An effort was also made to calculate and capture production costs in these three regions which, together with the collected electricity costs, formed the basis of the analysis. The study applied a supply response model developed by the Bureau for Food and Agricultural Policy to evaluate the impact of increased electricity tariffs on potato production and prices in South Africa. The supply response model used is a standard econometric recursive dynamic model that has the purpose to model policy analysis, iv with short, medium and long term projections on an annual baseline basis (van Tongeren et al, 2000). However, in order to conduct analysis on electricity tariff increases, this supply response model required adaptation and improvement in order to incorporate electricity costs. Before this adjustment, the model applied the producer price / fertilizer price ratio as a proxy for production costs. Since detailed production costs (including electricity costs) were acquired through this study it was now possible to alter this ratio to a producer price / production costs ratio which included the electricity costs. To illustrate the impact of the electricity price increase the electricity cost component in production cost was shocked to reflect an increase at the set rate of an average of 25 percent per annum for the 2010, 2011 and 2012 production years. The results demonstrated that these three regions will see a decrease in hectares planted over the period between 2013 and 2020 as a result of the increased electricity tariffs, but that this decrease in hectares planted will be very small. The Sandveld region had the highest impact as it was calculated that on average, over the period between 2013 and 2020, a total of 35 hectares of potato production will be lost due to this higher electricity tariff. It can further be expected that the market price in the Sandveld region would increase slightly by 52c/10kg over the same period. The South Western Free State region was least effected by the higher electricity tariffs as a mere 1.6 hectares of potato production land could be lost due to the higher electricity tariffs which will lead to an increase of around 36c/10kg in market prices over the period between 2013 and 2020. The study further introduced a cost saving technique that farmers can use to counter the higher electricity tariffs. The majority of farmers consume electricity under the Landrate and Ruraflex tariff structure. It is this Ruraflex tariff structure that farmers can use to their advantage by consuming electricity during specific periods of the day that would result in a lower c/kWh cost. By reviewing the irrigation scheduling and activities of farmers the study established that most farmers pay far too much for electricity since their peak usage are during the periods of the day where higher rates apply. The study calculated that farmers, by applying this technique, could save between R190 and R455 on electricity costs per hectare in the Sandveld region. The study concluded that the impact of higher electricity tariffs on potato production and market prices in the Sandveld, Limpopo and South Western Free State regions are of a small nature which will most likely be absorbed by the farmers. The claims by various industry participants that the potato industry would be adversely negatively impacted were unfounded in this study. Nevertheless, in the event that electricity tariffs continue to increase in the future, farmers have to their disposal a cost saving technique that will aid them in countering some of the negative effects of electricity price hikes. / Dissertation (MCom)--University of Pretoria, 2012. / Agricultural Economics, Extension and Rural Development / unrestricted

Accurate short-term electricity price forecasts are essential to all electricity market participants. Generation companies adopt price forecasts to hedge generation shortage risks; load serving entities use price forecasts to purchase energy with low cost; and trading companies utilize price forecasts to arbitrage between markets. Currently, researches on point forecast mainly focus on exploring periodic patterns of electricity price in time domain. However, frequency domain enables us to identify more information within price data to facilitate forecast. Besides, price spike forecast has not been fully studied in the existing works. Therefore, we propose a short-term electricity price forecast framework that analyzes price data in frequency domain and consider price spike predictions. First, the variational mode decomposition is adopted to decompose price data into multiple band-limited modes. Then, the extended discrete Fourier transform is used to transform the decomposed price mode into frequency domain and perform normal price forecasts. In addition, we utilize the enhanced structure preserving oversampling and synthetic minority oversampling technique to oversample price spike cases to improve price spike forecast accuracy. In addition to point forecasts, market participants also need probabilistic forecasts to quantify prediction uncertainties. However, there are several shortcomings within current researches. Although wide prediction intervals satisfy reliability requirement, the over-width intervals incur market participants to derive conservative decisions. Besides, although electricity price data follow heteroscedasticity distribution, to reduce computation burden, many researchers assume that price data follow normal distribution. Therefore, to handle the above-mentioned deficiencies, we propose an optimal prediction interval method. 1) By considering both reliability and sharpness, we ensure the prediction interval has a narrow width without sacrificing reliability. 2) To avoid distribution assumptions, we utilize the quantile regression to estimate the bounds of prediction intervals. 3) Exploiting the versatile abilities, the extreme learning machine method is adopted to forecast prediction intervals. The effectiveness of proposed point and probabilistic forecast methods are justified by using actual price data from various electricity markets. Comparing with the predictions derived from other researches, numerical results show that our methods could provide accurate and stable forecast results under different market situations.

Wind energy has been growing rapidly during recent years. This paper aims to estimate the impact of wind power generation on the Swedish wholesale electricity price, using monthly time series data over the periods 2000-2016. The error-correction model is used to measure the price effect by including other factors that influence the electricity supply and demand. Thefindings suggest that the impact of changes in wind power production on the wholesale priceof electricity is negative in the short-term. When the wind power production increases by 1%, the wholesale electricity price decreases with 0.08%. Furthermore, the magnitude of the coefficient increases to 0.10% in the long-term.

The electricity price changes depending on the time of the day in most countries. In Sweden there is a spot price that changes every hour while China uses Time of Use (ToU) tariff. To avoid the most expensive hours this degree project investigates the feasibility of using lithium- ion batteries to shift the load of an industrial air compressor. The Depth of Discharge and the State of Charge (SoC) for the battery are analyzed to find the optimal use of the battery. Through simulations in MATLAB the degradation-curve and State of Charge were analyzed, which was further used for economics analysis. The feasibility of the system is evaluated by using payback time and Net Present Value (NPV). Results show that a battery has a slightly longer lifetime when it is working in a SoC of 50-70%, but a larger SoC is more profitable from the perspective of NPV. For the SoC of 0-100%, the NPV is about ~9683 US$. Compared to Sweden, using batteries to shift load is more profitable in China, which is mainly due to the high electricity prices. For the same air compressor, the payback time is 5 and 15 years for the investment of batteries in China and Sweden respectively.