A common electricity market in Europe will in all probability lead to a levelling out of the electricity price, which implies that Swedish consumers will face higher electricity prices with a European structure. This new market situation will force industry and energy suppliers to take new essential measures as actors in a deregulated European electricity market. In this thesis it is shown how over 30 Swedish small and medium-sized industries can reduce their use of electricity by about 50%. When scaling up the result to include all Swedish industry, the measures will lead to a significant reduction in global CO2 emissions, and a situation where Sweden will have a net export of electricity. Changing industrial energy use towards increased use of district heating will consequently affect the local energy suppliers. As a local energy supplier invests in CHP and co-operates on heat with an industry that has altered its energy use, the system cost will be halved. Considering higher European electricity prices, the benefits will be even higher with possibilities to reduce global emission with over 350%. In Sweden where district heating is very well established, heat driven absorption technology is especially favourable since it will lead to cost effective electricity production and increased utilization time for a CHP plant. Vapour compression chillers have been compared with heat driven absorption cooling for a local energy utility with a district cooling network and for industries in a Swedish municipality with CHP. The results show that the higher the share of absorption technology is, in comparison to compression chillers, the lower the production cost will be for producing cooling. This thesis illustrates measures for Swedish industry and energy suppliers in a fully deregulated European electricity market that will shift the energy systems in the direction of cost-effectiveness and resource effectiveness. The thesis also shows that the benefits of the measures will increase even more when accounting with electricity prices with a higher European structures. To methodically change the use of electricity would be an economical way to increase the competitiveness of Swedish plant in relation to other European plants. Taking advantage of these particularly Swedish conditions will contribute to the creation of lean resource systems, and as a result help the whole EU region to meet its commitment under the Kyoto Protocol. Altering industrial energy use towards less electricity and energy dependence will be a competitive alternative to new electricity production and help secure energy supply in the European Union.

Since the liberalisation of the energy market in Europe in the early 1990s, much opportunity to trade electricity as a commodity has arisen. One significant consequence of this movement is that market prices have become more volatile instead of its tradition constant rate of supply. Spot price markets have also been introduced, affecting the demand of electricity as companies now have the option to not only produce their own supply but also purchase this commodity from the market. Following the liberalisation of the energy market, hence creating a greater demand for trading of electricity and other types of energy, various types of options related to the sales, storage and transmission of electricity have consequently been introduced. Particularly, swing options are popular in the electricity market. As we know, swing-type derivatives are given in various forms and are mainly traded as over-the-counter (OTC) contracts at energy exchanges. These options offer flexibility with respect to timing and quantity. Traditionally, the Geometric Brownian Motion (GBM) model is a very popular and standard approach for modelling the risk neutral price dynamics of underlyings. However, a limitation of this model is that it has very few degrees of freedom, as it does not capture the complex behaviour of electricity prices. In short the GBM model is inefficient in the pricing of options involving electricity. Other models have subsequently been used to bridge this inadequacy, e.g. spot price models, futures price models, etc. To model risk-neutral commodity prices, there are basically two different methodologies, namely spot and futures or so-called term structure models. As swing options are usually written on spot prices, by which we mean the current price at which a particular commodity can be bought or sold at a specified time and place, it is important for us to examine these models in order to more accurately inculcate their effect on the pricing of swing options. Monte Carlo simulation is also a widely used approach for the pricing of swing options in the electricity market. Theoretically, Monte Carlo valuation relies on risk neutral valuation and the technique used is to simulate as many (random) price paths of the underlying(s) as possible, and then to average the calculated payoff for each path, discounted to today's prices, giving the value of the desired derivative. Monte Carlo methods are particularly useful in the valuation of derivatives with multiple sources of uncertainty or complicated features, like our electricity swing options in question. However, they are generally too slow to be considered a competitive form of valuation, if any analytical techniques of valuation exist. In other words, the Monte Carlo approach is, in a sense, a method of last resort. In this thesis, we aim to examine a numerical method involved in the pricing of swing options in the electricity market. We will consider an existing and widely accepted electricity price process model, use the finite volume method to formulate a numerical scheme in order to calibrate the prices of swing options and make a comparison with numerical solutions obtained using the theta-scheme. Further contributions of this thesis include a comparison of results and also a brief discussion of other possible methods.

One of the key steps of the liberalization of the Turkish electricity market has been the estab- lishment of PMUM (Turkish day ahead electricity market). The aim of this study is to explore the dynamics of electricity prices observed in this market and their relation with temperature observed in Turkey. The electricity price process is studied as a univariate process and the same process is studied along with temperature together as a two-dimensional process. We give a fairly complete model of temperature. We observe that the electricity prices in Turkey exhibit many of the features that similar prices exhibit in other world markets. In particular, Turkish day ahead prices are seasonal / every year the price seems to follow a path similar to the one years preceding it. To simplify our analysis we focus our study to a 35 day pe- riod where every year the prices show a relatively simple behavior. We study the effects of the fluctuations in temperature in this period on the fluctutations in the day ahead electricity price.

The effects of the deregulation of the electricity market 1996 in Sweden on short-term hydropower regulation are unknown. This report investigates patterns in subdaily regulation in the Ångerman River Basin during the period 1993-2011. Differences in subdaily flows and zero flow events between the periods 1993-1995 and 1996-2011 were studied by analyzing hourly data from 8 regulated and 8 unregulated locations with four subdaily flow variation indices. No correlations between the market deregulation and the regulation intensity in the Ångerman River basin were detected. The number of days natural ranges of variability were exceeded and the magnitude of subdaily variation were significantly higher at regulated locations. Zero flow events increased substantially when the periods 1993-2007 and 2008-2011 were compared. Significant correlations were detected in the summer periods between dry years and high magnitudes for the indices that measured variation in volume and low magnitudes for subdaily flow reversals. Zero flow events in the summer periods increased during dry years if the period 2008-2011 were excluded. The major conclusions are that the deregulation of the electricity market has not affected the subdaily regulation of the Ångerman River and that the regulated sites show highly unnatural subdaily variations. Regulation intensity and zero flows events increases during summertime in dry years and the latter also increased substantially after 2007. The current regulation regime is considered harmful for riverine ecosystems and the high and increasing levels of subdaily flow alteration shows the urgent need of national directives for subdaily hydropower operation.

The objective of the study is to analyse the conditions for connection of residential buildings in heat sparse areas to district heating systems in order to increase electricity production in municipal combined heat and power plants. The European electricity market has been assumed to be fully deregulated. The relation between connection of heat sparse areas, increased electricity and heat production as well as electricity prices, fuel prices and emissions rights is investigated. The results of the study show that there is potential to expand the district heating market to areas with lower heat concentrations in the cities of Gavle, Sandviken and Borlange in Sweden, with both economic and environmental benefits. The expansion provides a substantial heat demand of approximately 181 GWh/year, which results in an electricity power production of approximately 43 GWh/year. Since the detached and stand-alone houses in the studied heat sparse areas have been heated either by oil boiler or by direct electricity, connection to district heating also provides a substantial reduction in emissions of CO2. The largest reductions in CO2 emissions are found to be 211 ktonnes/year assuming coal-fired condensing power as marginal electricity production. Connection of heat sparse areas to district heating decrease the system costs and provide a profitability by approximately 22 million EURO/year for the studied municipalities if the price of electricity is at a European level, i.e. 110 EURO/MWh. Sensitivity analysis shows, among other things, that a strong relation exists between the price of electricity and the profitability of connecting heat sparse areas to district heating systems. / Original Publication:Shahnaz Amiri and Bahram Moshfegh, Possibilities and consequences of deregulation of the European electricity market for connection of heat sparse areas to district heating systems, 2010, Applied Energy, (87), 7, 2401-2410.http://dx.doi.org/10.1016/j.apenergy.2010.02.002Copyright: Elsevier Science B.V., Amsterdam.http://www.elsevier.com/

Under deregulated electric market, electricity price is no longer set by the monopoly utility company rather it responds to the market and operating conditions. Offering the right amount of electricity at the right time with the right bidding price has become the key for utility companies pursuing maximum profits under deregulated electricity market. Therefore, electricity market clearing price (MCP) forecasting became essential for decision making, scheduling and bidding strategy planning purposes. However, forecasting electricity MCP is a very difficult problem due to uncertainties associated with input variables.<p> Neural network based approach promises to be an effective forecasting tool in an environment with high degree of non-linearity and uncertainty. Although there are several techniques available for short-term MCP forecasting, very little has been done to do mid-term MCP forecasting. Two new artificial neural networks have been proposed and reported in this thesis that can be utilized to forecast mid-term daily peak and mid-term hourly electricity MCP. The proposed neural networks can simulate the electricity MCP with electricity hourly demand, electricity daily peak demand, natural gas price and precipitation as input variables. Two situations have been considered; electricity MCP forecasting under real deregulated electric market and electricity MCP forecasting under deregulated electric market with perfect competition. The PJM interconnect system has been utilized for numerical results. Techniques have been developed to overcome difficulties in training the neural network and improve the training results.

In the European Union, there is a driver for a change in the electricity system. The trend is to make the system more environmental friendly and improve the markets functionality. This driver often refers to the development towards a smart grid. In order to accelerate innovation in smart grid and technology application, pilot projects need to be deployed. This master thesis has been done as a part of the Stockholm Royal Seaport urban development project that is a pilot project for smart grid on distribution grid level. The aim of this report is to apply market concept and identify regulatory bottlenecks for smart grid. This report has applied market concept and identified several bottlenecks for two aspects of smart grid. The aspects are integration of distributed energy resources in medium and low voltage level and a changing customer behavior. A changing customer behavior contains both demand response and the implementation of electric vehicles. A state-of-art review on feasible solutions that improve the competition and demand side management of electricity market in smart grid and provide incentives to implement smart grid functions has been performed. The emphasis in the market aspect is on how that new actors like aggregators will enter the market and how the dynamic price can reach consumers. The emphasis in the regulatory aspect is on how regulations promote the application of smart grid supporting technologies for both the DSO and the network users. A case study has been performed for EU countries with a deeper look at Sweden. The case study investigates how far that the current regulations have reached on the way to smart grids. A state-of-art review on conclusion papers of pilot projects has been carried out. However, many pilot projects are still ongoing and not included in the review. The result shows there is still a lack of regulatory incentive to promote smart grid development and supporting market structures. Bottlenecks identified for smart grid services in the Swedish electricity market and regulation are related to four areas. These are the metering system, dynamic consumer price, active distributed units with the possibility to provide services to the system and incentives to the DSO to use new smart grid solutions in the work to enable fast and efficient connection of distributed generation. / Stockholm royal seaport project

Under deregulated electric market, electricity price is no longer set by the monopoly utility company rather it responds to the market and operating conditions. Offering the right amount of electricity at the right time with the right bidding price has become the key for utility companies pursuing maximum profits under deregulated electricity market. Therefore, electricity market clearing price (MCP) forecasting became essential for decision making, scheduling and bidding strategy planning purposes. However, forecasting electricity MCP is a very difficult problem due to uncertainties associated with input variables.<p> Neural network based approach promises to be an effective forecasting tool in an environment with high degree of non-linearity and uncertainty. Although there are several techniques available for short-term MCP forecasting, very little has been done to do mid-term MCP forecasting. Two new artificial neural networks have been proposed and reported in this thesis that can be utilized to forecast mid-term daily peak and mid-term hourly electricity MCP. The proposed neural networks can simulate the electricity MCP with electricity hourly demand, electricity daily peak demand, natural gas price and precipitation as input variables. Two situations have been considered; electricity MCP forecasting under real deregulated electric market and electricity MCP forecasting under deregulated electric market with perfect competition. The PJM interconnect system has been utilized for numerical results. Techniques have been developed to overcome difficulties in training the neural network and improve the training results.

When consumers are able to buy electricity on an hourly instead of monthly basis, the demand side flexibility is likely to increase. One way to lower the cost of electricity is to move consumption from peak price hours to low price hours, a sort of inter-temporal substitution were the net energy use is unaffected. By simulating one example of inter-temporal substitution in the Swedish spot market during 2008-2010, we show that the general welfare effects are small in terms of a more efficient energy production, but that the transfer of resources from producers to consumers is large. Whether the welfare effect is positive or negative is highly dependent on future electricity prices, the introduction of renewable energy resources, and the price of the new technology needed for the demand side regulation. If 2010 is used as a reference case, the results from our specific case concludes that a natural investment equilibrium is reached when approximately 150 000 households invest in the proposed demand side regulation technology. Using the same reference year, we see that if 70 000 households participates the Net Present Welfare benefit is around 10% of the necessary investment cost; to be compared with the transfer of benefits from producers to consumers which estimates roughly 2100% of the necessary investment cost. We argue that this imbalance in potential welfare benefits between producers and consumers might slow down the process of increasing the general welfare.

Turkish Electricity Market is modeled as a non-cooperative game with complete information in order to simulate the behavior of market participants and analyze their possible strategies. Player strategies are represented with multipliers in a discrete strategy set. Different market scenarios are tested through different game settings. As the novelty of this thesis, similar market participants are clustered and treated as single players in order to apply game theory in an efficient way. Generators are clustered using Agglomerative Hierarchical Clustering and Square Sum of Deviations is used as the proximity measure. The game is constructed with three players that reflect the main characteristics of the market participants. Clusters and game scenarios are constructed using the real market data of the Turkish Electricity Market at four different time points in 2008 and results are compared. Clustering results reflect the actual installed capacity distribution based on the main companies and fuel types in Turkish Electricity Market. According to four games of clustered electricity generators, when there is not enough competition in the market, dominant player is advised to submit bids with lower price for energy surplus cases and offers with higher price for energy deficit cases. However, when there is competition in the market, players are advised to submit offers with lower price in order to take a share of the limited demand for up-regulation.