• Energy Research

Many of the most commonly used generation planning models have been formulated in a way that neglects the chronological sequence of demand and the mixed-integer nature of generating units. The generator schedules assumed by these models are inaccurate and become increasingly divorced from real schedules with increasing variability. This paper seeks to characterize and quantify the limitations of these models over a broad set of input parameters. For an illustrative set of test systems, wind capacities and generator types, annual system costs are determined for all combinations of generating units using a unit-commitment model, which captures the chronological behavior of units and a dispatch model which does not. It is seen that the relative performance of the dispatch model is highly system specific but generally degrades with increasing variability. The difference in cost estimates between the models is decomposed into start costs, starts avoidance and average cost estimation error. The impact on least-cost portfolios is shown and finally sensitivities are performed with the addition of hydro and nuclear power to assess their impact.

Variability and uncertainty of wind power generation increase the cost of maintaining the short-term energy balance in power systems. As the share of wind power grows, this cost becomes increasingly important. This thesis examines different options to mitigate such cost increases. More detailed analysis is performed on three of these: flexibility of conventional power plants, smart charging of electric vehicles (EVs), and flexibility in heat generation and use. The analysis has been performed with a stochastic unit commitment model (WILMAR) and a generation planning model (Balmorel). Electric boilers can absorb excess power generation and enable shutdown of combined heat and power (CHP) units during periods of high wind generation and low electricity demand. Heat storages can advance or postpone heat generation and hence affect the operation of electric boilers and CHP units. The availability of heat measures increased the cost optimal share of wind power from 35% to 47% in one of the analysed scenarios. The analysis of EVs revealed that smart charging would be a more important source of flexibility than vehicle-to-grid (V2G), which contributed 23% to the 227 /vehicle/year cost savings when smart charging with V2G was compared with immediate charging. Another result was that electric vehicles may actually reduce the overall CO2 emissions when they enable a higher share of wind power generation. Most studies about wind power integration have not included heat loads or EVs as means to decrease costs induced by wind power variability and uncertainty. While the impact will vary between power systems, the thesis demonstrates that they may bring substantial benefits. In one case, the cost optimal share of wind-generated electricity increased from 35% to 49% when both of these measures were included.

The way in which GHG (greenhouse gas) emissions associated with grid electricity consumption is handled in different LCA (life cycle assessment) studies, varies significantly. Apart from differences in actual research questions, methodological choices and data set selection have a significant impact on the outcomes. These inconsistencies result in difficulties to compare the findings of various LCA studies. This review paper explores the issue from a methodological point of view. The perspectives of ALCA (attributional life cycle assessment) and CLCA (consequential life cycle assessment) are reflected. Finally, the paper summarizes the key issues and provides suggestions on the way forward. The major challenge related to both of the LCA categories is to determine the GHG emissions of the power production technologies under consideration. Furthermore, the specific challenge in ALCA is to determine the appropriate electricity production mix, and in CLCA, to identify the marginal technologies affected and related consequences. Significant uncertainties are involved, particularly in future-related LCAs, and these should not be ignored. Harmonization of the methods and data sets for various purposes is suggested, acknowledging that selections might be subjective.

This paper explores the sensitivity of electricity prices in energy-only markets with large amounts of wind and solar power. After electricity prices have fallen in many energy-only markets in recent years, the topic has been discussed in many studies with different approaches. The approach in this paper is to perform extensive electricity market simulations. The study is based on the North European power system, and it was carried out using a generation planning model to create reasonable capacity mixes for future, and a unit commitment and economic dispatch model to simulate electricity prices. The results show that the amount of base load generation capacity and overcapacity has a very high impact on electricity prices. The share of wind and solar power and the price of CO2 also have a clearly detectable, but less significant, impact.

AbstractAn open-source modelling framework Predicer, standing for Predictive Decider, for multi-market day-ahead market operation purposes is described in this paper. The Predicer model uses scenario-based stochastic optimisation to obtain decision variables and bid matrixes for energy and reserve markets by maximising the risk-adjusted expected value of the profit during the model time frame. The modelled energy system structure is abstract, that is, based on basic elements such as nodes representing different energy types and processes representing flows between nodes. The abstract model structure enables user to construct arbitrary energy systems and define links between assets, commodities, energy markets and reserve markets. Predicer model can include properties such as unit ramp rates, online units, dynamic energy storages, market realisation and market bidding requirements. The aggregation of unit-based energy and reserve opportunities into a virtual power plant allows the asset owner to make optimized portfolio-level bids for different market products. The model scenarios consist of user defined forecasts for market prices, renewable energy supply, energy demand and other system related time series. Predicer is implemented in Julia programming language and uses the JuMP optimisation package.

Power systems require a certain amount of flexibility to meet varying demand and to be able to cope with unexpected events, and this requirement is expected to increase with the emergence of variable power generation. In this paper, we focus on gas engine power plant technology and the beneficial influence its flexible operation can have on a power system. The study introduces the concept of a combined-cycle gas engine power plant (CCGE), which comprises a combination of several gas-fired combustion engines and a steam turbine. The operation of CCGE is then comprehensively analyzed in electricity and reserve production in the South African power system and compared with combined-cycle gas turbine (CCGT) technology. Even though CCGE is a form of technology that has already been commercialized, it is rarely considered as a source of flexibility in the academic research. That is the notion providing the motivation for this study. Our core contribution is to show that the flexibility of CCGE can be valuable in power systems. The methodology is based on the unit-level model of the studied system and the solving of a day-ahead unit commitment problem for each day of the simulated 11-year period. The simulation studies reveal how a CCGE is able to offer system flexibility to follow hourly load variations and capacity to provide reserve power effectively.