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

The focus of this thesis is to explore a new mechanism to give added incentive to invest in new capacities in deregulated electricity markets. There is a lot of concern in energy markets, regarding lack of sufficient private sector investment in new capacities to generate electricity. Although some markets are using mechanisms to reward these investments directly, e.g., by governmental subsidies for renewable sources such as wind or solar, there is not much theory to guide the process of setting the reward levels. The proposed mechanism involves a long term planning model, maximizing the social welfare measured as consumers’ plus producers’ surplus, by choosing new generation capacities which, along with still existing capacities, can meet demand. Much previous research in electricity capacity planning has also solved optimization models, usually with continuous variables only, in linear or non-linear programs. However, these approaches can be misleading when capacity additions must either be zero or a large size, e.g., the building of a nuclear reactor or a large wind farm. Therefore, this research includes binary variables for the building of large new facilities in the optimization problem, i.e. the model becomes a mixed integer linear or nonlinear program. It is well known that, when binary variables are included in such a model, the resulting commodity prices may give insufficient incentive for private investment in the optimal new capacities. The new mechanism is intended to overcome this difficulty with a capacity price in addition to the commodity price: an auxiliary mathematical program calculates the minimum capacity price that is necessary to ensure that all firms investing in new capacities are satisfied with their profit levels. In order to test the applicability of this approach, the result of the suggested model is compared with the Ontario Integrated Power System Plan (IPSP), which recommends new generation capacities, based on historical data and costs of different sources of electricity generation for the next 20 years given a fixed forecast of demand.

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.

<p>Unforeseen changes in production or consumption in power systems lead to changes in grid frequency. This can cause damages to the system, or to frequency sensitive equipment at the consumers. The system operator (SO) is the responsible for balancing production and consumption in the system. The regulating market is the market place where the SO can sell or purchase electricity in order to balance unforeseen events. Producers acting on the regulating market must be able to change their production levels fast (within minutes) when required. Hydropower is therefore suitable for trading on the regulating market because of its flexibility in power production. This thesis describes models that hydropower owners can use to generate optimal bidding strategies when the regulating market is considered.</p><p>When planning for trading on the market, the prices are not known. Therefore, the prices are considered as stochastic variables. The planning problems in this thesis are based on multi-stage stochastic optimization, where the uncertain power prices are represented by scenario trees. The scenario trees are generated by simulation of price scenarios, which is achieved by using a model based on ARIMA and Markov processes. Two optimization models are presented in this thesis:</p><p>* Model for generation of optimal bidding strategies for the regulating market.</p><p>* Model for generation of optimal bidding strategies for the spot market when trading on the regulating market is considered.</p><p>The described models are applied in a case study with real data from the Nordic power system.</p><p>Conclusions of the thesis are that the proposed approaches of modelling prices and generation of bidding strategies are possible to use, and that the models produces reasonable data when applied to real data.</p> / <p>Oväntade produktions- eller konsumtionsändringar i kraftsystem leder till ändringar i nätfrekvens. Detta kan orsaka skador på systemet eller på frekvenskänslig utrustning hos konsumenterna. Systemoperatören (SO) är den ansvarige för att balansera produktion och konsumtion i kraftsystemet. Till sin hjälp har SO reglermarknaden, som är den handelsplats där SO köper eller säljer el för att balansera oväntade händelser i systemet. Producenter som agerar på reglermarknaden måste snabbt (inom minuter) kunna ändra sina produktionsnivåer om så behövs. Vattenkraft är därför lämplig för handel på reglermarknaden på grund av dess flexibilitet i kraftproduktion. Denna avhandling beskriver modeller som vattenkraftägare kan använda för generering av optimala budstrategier då reglermarknaden beaktas.</p><p>När en producents planering för handel på marknaden utförs är marknadspriserna okända. Dessa är därför betraktade som stokastiska variabler. Planeringmodellerna som presenteras i denna avhandling är baserade på multi-periodisk stokastisk programmering, där de osäkra marknadspriserna är representerade av ett scenarieträd. Scenarierna i trädet genereras genom simulering av marknadspriser. En prismodell, baserad på ARIMA- och Markovprocesser, har därför utvecklats. Två olika optimeringsmodeller presenteras i denna avhandling:</p><p>* Model för generering av optimala budstrategier för reglermarknaden.</p><p>* Model för generering av optimala budstrategier för spotmarknaden då handel på reglermarknaden beaktas.</p><p>Modellerna tillämpas i en studie där data från den nordiska elmarknaden appliceras. Slutsatser i avhandlingen är att de föreslagna ansatserna för modellering av priser och generering av budstrategier är möjliga att anvÄanda, samt att modellerna producerar rimliga resultat när applicerade på verkliga data.</p>

As an energy-importing developing country, Turkey depends heavily on imported petroleum and natural gas. The increase in the global petroleum price has affected the Turkish economy adversely in the last decade. Renewable energy is an important alternative in reducing Turkey’s energy dependency. Turkey’s strategies are improving domestic production and diversifying energy sources for the security of supply. New investments, especially in renewables, have been chosen to achieve these objectives. As a model for Turkey, Texas is the leader in non-hydroelectric renewable energy production in the U.S. and has one of the world’s most competitive electricity markets. However, wind generation creates unique challenges for the Electric Reliability Council of Texas (ERCOT), the transmission system operator of Texas. The market environment has forced the Public Utility Commission of Texas (PUCT) to develop unique deregulated energy markets. In 2005, the Texas Legislature passed Senate Bill 20, in part to break the deadlock between transmission and wind generation development. This legislation instructed the PUCT to establish Competitive Renewable Energy Zones (CREZs) throughout the State, and to designate new transmission projects to serve these zones. In this context, first of all, the electricity market development in Turkey is introduced in terms of renewable energy, especially wind power. Next, considering wind power, the progress in the Texas electricity market is investigated. Subsequently, we examine the development of CREZs in Texas from a regulatory perspective and discuss Texas’ policy initiatives, including the designation of CREZs. Finally, we review the impact of wind power on the primary electricity market of Texas and evaluate market conditions and barriers to renewable energy use in Turkey in order to extract suggestions. This experience may be particularly instructive to Turkey, which has a similar market structure on the supply and transmission sides. This study suggests ways that Turkey might handle renewable applications in combination with existing transmission constraints. / text

Turkey wants to encourage renewable electricity generation to reduce dependence on imported natural gas and meet its highly growing power demand. The government’s objective is to increase the share of renewable resources in electricity generation to at least 30 percent by 2023, and the specific target for the installed wind energy capacity is 20 GW by that date. Fortunately, Turkey has an enormous wind energy potential to exploit for electricity generation. When from “good” to “outstanding” wind clusters are taken into account, the overall technical wind power generation capacity in Turkey is calculated to be nearly 48 GW. In this context, this thesis investigates whether policy instruments in the Turkish regulatory frame contribute to economic viability for wind power projects or not. The financial results point out that an electricity price of 7.3 USD cent/kWh, which is the guaranteed price for wind power generation by current regulations, does not make a typical onshore wind power plant located in a “good” windy resource economically viable. However, when locally produced wing blades and turbine towers are used in the project, the purchase price increases to 8.7 USD cent/kWh, and the project becomes economically viable. As a result, the local content element introduced in recent regulations promotes wind energy investments and helps government to reach its renewable target for 2023. / text

This thesis tries to answer the question of whether there is a single electricity market in the European Union. Although some further steps were taken in terms of market integration, this study shows that it is still not possible to talk about a single electricity market. The attempts to create a single electricity market demonstrate the tensions between supranational and national decision making in a vital issue area of energy. States have been reluctant to transfer their sovereignty in energy policy making as they deemed this area vital to their economic and security interests. This study argues that intergovernmental premises, still explain the reluctance of the member states in this context better. The thesis incorporates two case studies / United Kingdom and Turkey. The first case illustrates that though UK is at the forefront of the other member states in adopting the EU electricity directives, it has still resisted transferring its right of control over its sector to the supranational authorities. Also, the British Case shows that the liberalisation process has some negative consequences. Turkish case will be an evidence for that the model of UK is not appropriate for Turkey in the restructuring process due to the differences between the two states in terms of laws and regulations, institutional capabilities and domestic market conditions. This thesis proposes that Turkey shouldn&rsquo / t disregard its conditions for the sake of EU membership while developing policies in a strategically important area where member states abstain from devolving their rights to the supranational authorities.

Electricity price forecasting is an important task for electricity market participants since the very beginning of the deregulation. Accurate forecasting is essential for designing bidding strategy, risk management, and market operation. However, due to the compli-cated factors affecting electricity prices, there are more uncertainties in electricity price forecasting and hence more complex than demand forecasting. This makes accurate price forecasting very difficult. In the last decade, several methods have been developed in order to fully capture the peculiarities of electricity price dynamics, from classic econometric time series models, e.g., autoregressive moving average (ARMA) model, generalized autoregressive conditional heteroscedasticity (GARCH) model to modern machine learning based techniques such as artificial neural networks (ANN) and sup-port vector machine (SVM). In spite of all models proposed in the literature, there is still no clear consensus about which model is substantively outperforming others. Therefore, when a single method is used, decision-makers are facing the risk of not choosing the best one. On the other hand, the prediction of electricity market prices still involves large errors. If decision-makers take the prediction result on faith, prediction errors could exposure them to serious financial risks. Based on these findings, it can conclude that (1) systematic methodologies and implementations which can efficiently address model selection uncertainty in price forecasting require an investigation; (2) more powerful and robust price forecasting models are still needed to reduce the fore-cast errors; and (3) In addition, the emphasis of price forecasting should shift away from point forecast to uncertainty around the forecast. Unfortunately, most researches in this area have been devoted to finding the single “best” estimates rather than dealing with the uncertainty in model selection and quantifying the predictive uncertainty. In this thesis the research focus is on: (1) finding methodologies and efficient imple-mentations to deal with the uncertainty in model selection; (2) developing more power-ful machine learning based approaches to model electricity spot prices and further im-proving the accuracy of electricity market price forecast; and (3) incorporating uncer-tainty estimation into the application of price forecasting. The thesis makes three main contributions to the study of this topic. Firstly, it proposes linear, nonlinear forecast combination frameworks to deal with model selection prob-lem; secondly, it introduces two novel models: support vector machine based nonlinear generalized autoregressive conditional heteroscedasticity model (SVM-GARCH) and extreme learning machine (ELM) to the price forecasting and furthermore gives a series of bootstrap-based interval construction procedures to quantify the prediction uncer-tainty. Finally, it proposes a more robust interval forecasting approach which is based on quantile regression to electricity price forecasting literature. The effectiveness and efficiency of the proposed approaches have been tested based on real market data of Australian National Electricity Market (NEM).

Electricity price forecasting is an important task for electricity market participants since the very beginning of the deregulation. Accurate forecasting is essential for designing bidding strategy, risk management, and market operation. However, due to the compli-cated factors affecting electricity prices, there are more uncertainties in electricity price forecasting and hence more complex than demand forecasting. This makes accurate price forecasting very difficult. In the last decade, several methods have been developed in order to fully capture the peculiarities of electricity price dynamics, from classic econometric time series models, e.g., autoregressive moving average (ARMA) model, generalized autoregressive conditional heteroscedasticity (GARCH) model to modern machine learning based techniques such as artificial neural networks (ANN) and sup-port vector machine (SVM). In spite of all models proposed in the literature, there is still no clear consensus about which model is substantively outperforming others. Therefore, when a single method is used, decision-makers are facing the risk of not choosing the best one. On the other hand, the prediction of electricity market prices still involves large errors. If decision-makers take the prediction result on faith, prediction errors could exposure them to serious financial risks. Based on these findings, it can conclude that (1) systematic methodologies and implementations which can efficiently address model selection uncertainty in price forecasting require an investigation; (2) more powerful and robust price forecasting models are still needed to reduce the fore-cast errors; and (3) In addition, the emphasis of price forecasting should shift away from point forecast to uncertainty around the forecast. Unfortunately, most researches in this area have been devoted to finding the single “best” estimates rather than dealing with the uncertainty in model selection and quantifying the predictive uncertainty. In this thesis the research focus is on: (1) finding methodologies and efficient imple-mentations to deal with the uncertainty in model selection; (2) developing more power-ful machine learning based approaches to model electricity spot prices and further im-proving the accuracy of electricity market price forecast; and (3) incorporating uncer-tainty estimation into the application of price forecasting. The thesis makes three main contributions to the study of this topic. Firstly, it proposes linear, nonlinear forecast combination frameworks to deal with model selection prob-lem; secondly, it introduces two novel models: support vector machine based nonlinear generalized autoregressive conditional heteroscedasticity model (SVM-GARCH) and extreme learning machine (ELM) to the price forecasting and furthermore gives a series of bootstrap-based interval construction procedures to quantify the prediction uncer-tainty. Finally, it proposes a more robust interval forecasting approach which is based on quantile regression to electricity price forecasting literature. The effectiveness and efficiency of the proposed approaches have been tested based on real market data of Australian National Electricity Market (NEM).