Most power market structures have been developed and implemented without being tested, causing major problems such as shortages and blackouts. The main cause for these problems is the inability of some markets to provide adequate stimulus for new generation investments. The installed generation capacity goes through boom and bust cycles, exposing consumers to potential shortages during long bust periods. With the realisation that the power market has a strong interaction with generation investment, a System Dynamics (SD) model is developed to study how the market interacts with generation expansion. The SD model also allows for market structures and policies to be evaluated before being implemented. It can be an important tool in ensuring that generation expansion is done optimally without the expense of energy security. New Zealand’s generation capacity is no exception to the boom and bust trend. Since the commencement of the New Zealand Energy Market (NZEM) in October 1996, energy shortages occurred in the winters of 2001, 2003 and 2008. As a case study, an SD model is developed to study the NZEM. The results show that under some forecasted scenarios, New Zealand is susceptible to future energy shortages due to boom and bust cycles in the generation capacity.

I denna uppsats skapar vi en modell för forwardkurvan på den nordiska elmarknaden. Vårt mål är attskapa en modell som är lättanvänd och inte kräver kunskaper om dynamiken hos elpriset. Vår modellär baserad på en modell av Benth, Koekebakker & Ollmar (2007) men justeras med avseende på vårakrav om en lättanvänd modell. Vi validerar vår modell och nner att den har möjlighet att föutspåpriset på ett kvartlaskontrakt med en felmarginal på 1.1 procent. Vi lyckas förutspå en säsongsvariationutan att specikt ange en funktion för säsongsvariationen.

The advent of electricity market deregulation has placed great emphasis on the availability of information, the analysis of this information, and the subsequent decision-making to optimize system operation in a competitive environment. This creates a need for better ways of correlating the market activity with the physical grid operating states in real time and sharing such information among market participants. Choices of command and control actions may result in different financial consequences for market participants and severely impact their profits. This work provides a solution, the Intelligent Economic Alarm Processor to be implemented in a control center to assist the grid operator in rapidly identifying the faulted sections and market operation management. The task of fault section estimation is difficult when multiple faults, failures of protection devices, and false data are involved. A Fuzzy Reasoning Petri-nets approach has been proposed to tackle the complexities. In this approach, the fuzzy reasoning starting from protection system status data and ending with estimation of faulted power system section is formulated by Petri-nets. The reasoning process is implemented by matrix operations. Next, in order to better feed the FRPN model with more accurate inputs, the failure rates of the protections devices are analyzed. A new approach to assess the circuit breaker’s life cycle or deterioration stages using its control circuit data is introduced. Unlike the traditional “mean time” criteria, the deterioration stages have been mathematically defined by setting up the limits of various performance indices. The model can be automatically updated as the new real-time condition-based data become available to assess the CB’s operation performance using probability distributions. The economic alarm processor module is discussed in the end. This processor firstly analyzes the fault severity based on the information retrieved from the fault section estimation module, and gives the changes in the LMPs, total generation cost, congestion revenue etc. with electricity market schedules and trends. Then some suggested restorative actions are given to optimize the overall system benefit. When market participants receive such information in advance, they make estimation about the system operator's restorative action and their competitors' reaction to it.

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.

There are many companies operating in the current electricity market and new companies are still forming. This gives every consumer an option to choose a company selling electricity. Electricity dealers can even buy or sell electricity from abroad. Although this situation seems to be natural, it's very complicated to achieve. It's necessary to follow a lot of rules and business processes to be able to establish a functional liberalized market. Because of the amount of business processes it's obvious that liberalized market depends first of all on information systems. However, to develop an information system is usually big investment and it is necessary to find some way to cut the information systems prices. This thesis introduces an effective way of system development for the electricity market domain. This method is introduced as Model Driven Development (MDD) methodology. The idea promoted by MDD is to use models to describe programs and automation code generators to transform models into source code. Model creation is obviously much faster than writing source code, which makes software development more effective. In the category of supporting MDD tools, Damas Energy platform is introduced. The main goal of this thesis is to analyze the usability of Damas Energy with MDD and to show which method to choose when developing information systems for the electricity market domain.

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.

Production of electric energy is continuously affected by many factors. Therefore, tools for predicting the future production are needed. In turn, the production affects the electric energy price, which is set on electric energy exchanges. This thesis is intended to find out if the software SDDP can be used for hydrothermal power production simulations in the Nord pool area. By building a simplified model of the electric energy production in Sweden with a focus on hydro, thermal and wind power, the intention is to see how the model is affected by different conditions. The investigated conditions are several; higher and lower water inflows to the hydro power reservoirs; different amounts of installed wind power production; different price levels of emission allowances for CO2. By using the simulation software SDDP, more wind power was seen to lower the electric energy prices, as well as reduce the need of transmission of power from the northern to the southern parts of Sweden. In the simulation, Sweden was divided into four areas, connected where the main bottlenecks in the power grid are located. Water inflows to the reservoirs are crucial in the model. Actual inflow data can be bought from SMHI. However, due to the limited thesis budget, estimations were constructed instead. The estimations were difficult to make and turned out to be too high. Consequently, no reliable evaluation of the SDDP software could be done using this data.

Nowadays, electricity markets are becoming more deregulated, especially development of smart grid and introduction of renewable energy promote regulations of energy markets. On the other hand, the uncertainties of new energy sources and market participants’ bidding bring more challenges to power system operation and transmission system planning. These problems motivate us to study spot price (also called locational marginal pricing) of electricity markets, the strategic bidding of wind power producer as an independent power producer into power market, transmission expansion planning considering wind power investment, and analysis of the maximum loadability of a power grid. The work on probabilistic spot pricing for a utility grid includes renewable wind power generation in a deregulated environment, taking into account both the uncertainty of load forecasting and the randomness of wind speed. Based on the forecasted normal-distributed load and Weibull-distributed wind speed, probabilistic optimal power flow is formulated by including spinning reserve cost associated with wind power plants and emission cost in addition to conventional thermal power plant cost model. Simulations show that the integration of wind power can effectively decrease spot price, also increase the risk of overvoltage. Based on the concept of loacational marginal pricing which is determined by a marketclearing algorithm, further research is conducted on optimal offering strategies for wind power producers participating in a day-ahead market employing a stochastic market-clearing algoivrithm. The proposed procedure to drive strategic offers relies on a stochastic bilevel model: the upper level problem represents the profit maximization of the strategic wind power producer, while the lower level one represents the marketing clearing and the corresponding price formulation aiming to co-optimize both energy and reserve. Thirdly, to improve wind power integration, we propose a bilevel problem incorporating two-stage stochastic programming for transmission expansion planning to accommodate large-scale wind power investments in electricity markets. The model integrates cooptimizations of energy and reserve to deal with uncertainties of wind power production. In the upper level problem, the objective of independent system operator (ISO) modelling transmission investments under uncertain environments is to minimize the transmission and wind power investment cost, and the expected load shedding cost. The lower level problem is composed of a two stage stochastic programming problem for energy schedule and reserve dispatch simultaneously. Case studies are carried out for illustrating the effectiveness of the proposed model. The above market-clearing or power system operation is based on direct current optimal power flow (DC-OPF) model which is a linear problem without reactive power constraints. Power system maximum loadability is a crucial index to determine voltage stability. The fourth work in this thesis proposes a Lagrange semi-definite programming (SDP) method to solve the non-linear and non-convex optimization alternating current (AC) problem of the maximum loadability of security constrained power system. Simulation results from the IEEE three-bus system and IEEE 24-bus Reliability Test System (RTS) show that the proposed method is able to obtain the global optimal solution for the maximum loadability problem. Lastly, we summarize the conclusions from studies on the above mentioned optimization problems of electric power market under modern grid, as well as the influence of wind power integration on power system reliability, and transmission expansion planning, as well as the operations of electricity markets. Meanwhile, we also present some open questions on the related research, such as non-convex constraints in the lower-level problem of a bilevel problem, and integrating N-1 security criterion of transmission planning. / Graduate / lei.ming296@gmail.com

Drastic changes have taken place in UK electricity policy over recent years as government has sought to address the challenges associated with energy security, affordability and commitments to reduce carbon emissions. This study investigates the underlying policy changes between the year 2000 and 2012, particularly the Electricity Market Reform, as the most fundamental transformation in the British power market since liberalisation, almost three decades ago. It illustrates that although this policy had revised the long legacy of market-based and technology neutral electricity policymaking, it was yet to be claimed as a wholesale paradigmatic shift, because, as of 2012, it still suffered from a form of paradigm ambivalence and socio-technical lock-in. Furthermore, this research identifies an accumulative process of policy change explaining how a complex set of dynamics transformed the UK electricity policy mix. The thesis relies empirically on conducting 53 semi-structured interviews as well as scrutinising policy documents and relevant secondary studies. The thesis draws relevant approaches within policy studies that attend to address continuity and change in policy frameworks, in particular the Advocacy Coalition Framework (Sabatier 1999) and Policy Paradigm (Hall 1993) perspectives. The study contributes to this literature in three distinctive ways. First, it questions the adequacy of existing frameworks for conceptualising policy change in ‘large-technical’ and ‘techno-centric’ subsystems, such as electricity policy. In return, it introduces technology preference, as a policy component capturing the socio-technical elements of electricity policymaking. Second, to explain why and how such significant changes had been undergone, it forms a bridge between the characteristics of policy change and the extent that existing policies are perceived as irreconcilable policy failures. By this, it, albeit, moves beyond the conventional typology of change drivers in policy literature. Third, this research extends the emerging concept of negotiated agreement and policy compromise as a pathway to evolutionary changes (Sabatier & Weible 2007). Inspired by Institutional Change theory (Mahoney & Thelen 2010), it proposes that compromised policies are often at the risk of policy reversibility and retrenchment, subject to any shift in the contextual conditions they have originated in. Overall, the thesis provides an understanding of one of the very complex and contemporary cases for studying policy change theories.

Renewable Energy is by nature intermittent and matching the supply of energy to specific time dependent demand poses huge challenges. Energy storage is a useful tool in handling this temporal disparity, although except for regions very suitable for pumped hydroelectric storage schemes, it suffers from being technically difficult to implement and costly as a result. This study investigates the potential benefits offered by various scales of energy storage to different types of renewable energy generation. It also explores the economic drivers behind energy storage operating as part of an electricity spot market. A stochastic optimisation algorithm for determining the maximum possible arbitrage revenue available to energy storage devices is presented and schedule of operation of storage acting in this manner is analysed. The schedule of operation for maximising the revenue is compared to the schedule of operation for minimising the fuel cost to the network and it is demonstrated that because prices are more volatile than the demand which drives them, storage devices do not always act to decrease the fuel cost to the network. It is shown that storage behaving in the right manner can offer significant benefits to electricity systems, and increases the usage of base-load generation, reducing peak electricity demands and the need for expensive peaking plants. The value of storage also increases as the penetration of renewable energy generation increases, although the current electricity market framework is perhaps not the best way to encourage this behaviour. Advanced Adiabatic Compressed Air Energy Storage (AA-CAES) is also identified as a theoretical storage option which deserves further scrutiny. Using thermodynamic modelling the efficiency of this type of system is estimated in the range of 63-67%, and we suggest that this may be increased closer to 73% by using direct contact heat exchangers rather than indirect contact heat exchangers (and a separate thermal fluid), as described in the currently available literature. However, dealing with large pressure ranges (leading to large variations in pressure ratios) encountered in the expansion process is a problematic area which will have to be resolved before this type of system can be constructed with “off-the-shelf” components. Some small scale experiments are used to gain valuable insights into a AA-CAES system. While these suffer from a very low overall efficiency, they highlight the effect of variable pressure ratio on expander efficiency. We conclude that AA-CAES is thermodynamically sound and will be achieved one of two ways: either through the construction of expanders that can work with high efficiency over large pressure ratios, or by resolving the engineering issues with maintaining a constant storage pressure.