A higher share of renewable energy in power generation is one of most ambitious goals for sustainable development under the increasing pressure of climate change. On the deregulated electricity market in Sweden, the consumers are able to choose their electricity from a specific supplier and a specific energy source, which gives them the opportunity to execute their consumer power to have positive impacts on increasing energy efficiency and renewable electricity generation by stopping buying electricity produced from fossil fuels. A new paradigm is thus proposed in this paper which provides a new perspective on purchase of the product electricity. The new paradigm makes electricity a specified product for a specific customer, the electricity audited is never mixed with that not audited, because all electricity which is audited can be traced. The aim of this study is to provide a greater understanding of the new paradigm on the deregulated electricity market. By conducting a willingness to pay survey and several deep interviews, it analyzed the main factors hindering the customers’understanding of the new market dynamics in terms of active choice in ‘green’ electricity and stopping buying fossil electricity. The results and discussions show that the new paradigm could facilitate shedding light on some important implications for strategic decision making in power companies, for policy-makers as well as customers.

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.

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.

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