Uncertainty sources affect the planning and operation of energy systems. Different system operators need proper alternatives to cope with these uncertainties and improve the operation of their systems from technical and economical viewpoints. This thesis focuses on the risk-averse planning, operation, and coordination of energy systems including the transmission systems, distribution systems, and stand-alone renewable energy-based microgrids. We develop the existing uncertainty modeling methods and propose new mathematical models, pricing strategies, and operational coordination frameworks to enhance the ability of system operators to cope with uncertainties in the real-time operation of the energy systems and the electricity markets.  

From the uncertainty modeling viewpoint, when it comes to planning and operation of power systems with high penetration of renewable energy, since enough flexibility sources may not be available to cope with the uncertainties in the real-time operation, effective uncertainty sources need to be predicted accurately in the planning stage. Consequently, Bayesian statistics and a stochastic-probabilistic method based on Metropolis-coupled Markov chain Monte Carlo simulation are developed to predict the stochastic behavior of uncertainty sources in different energy systems. We utilized our proposed methods to model the stochastic behavior of wind speed, solar radiation, the water flow of a river, electrical load consumption, the behavior of electric vehicle customers, and the harmonic hosting capacity calculation in different case studies. A novel data classification and curve fitting methods are also proposed for deriving appropriate probability distribution functions (PDFs) based on long-term historical data. We consider demand response programs (DRPs), renewable energy sources, and the dynamic line rating as the embedded resources to prepare flexibility services in the ancillary service market. When it comes to utilizing DRPs, the uncertainty in customers' participation and responsiveness profoundly affects the real-time operation of power systems. Therefore, the risk associated with the utilization of uncertain DR is investigated. Moreover, we evaluate the eligibility conditions for risk-averse utilization of DRPs and apply the risk management cost to the pricing policy of DRPs. 

There are several flexibility service buyers in the power system that aim to activate flexibility services based on their objectives. Consequently, there are conflicts between the interest of different buyers that affect the system operation and pay-off mechanism in the electricity market. Accordingly, proper mathematical structures, coordination frameworks, decomposition techniques, and pay-off mechanisms are needed to be introduced to enhance the coordination between different buyers of the flexibility services. Therefore, we propose a look-ahead multi-interval framework for the TSO-DSO operational coordination problem. We develop the logic-based Benders decomposition technique for our large-scale optimization problem, which is a bilevel mixed-integer linear programming (MILP) problem. 

Finally, the results verify that the proposed uncertainty modeling techniques positively affect the planning and operation of different energy systems, especially stand-alone renewable energy-based microgrids. It is shown that the uncertainty of DRPs highly affected the operation of the power system and the ancillary service market. The ramping capability of reserves is introduced as an eligibility condition for risk-averse utilization of DRPs. Dynamic line rating can be used as a reliable flexibility source in the real-time operation of the power system. Furthermore, the results show that the proposed TSO-DSO coordination scheme can properly manage the conflict between the objectives of different flexibility service buyers. Finally, the Logic-based Benders decomposition (LBBD) can properly solve a large-scale bilevel MILP problem. The LBBD method also improves the execution time of MILP problems.

On the road to reducing global warming, the use of renewable energy sources and efficient use of electricity are among the key aspects. The increasing number of energy efficient appliances such as LED lamps, booming demand for electric vehicles (EVs), and growing penetration of distributed energy resources such as photovoltaic (PV) systems in low-voltage (LV) networks are expected to affect the power quality in the entire electric power system and specifically in the LV network, where such new devices are connected. Waveform distortion, mainly expressed by the harmonic components, is one of the topics within power quality that is highly affected by the introduction of such new devices. However, there is a lack of publications discussing the existing level of voltage harmonics in LV networks or addressing the origin and transfer of harmonics in LV and MV distribution networks. This highlights the need for more research in this field.

To evaluate the ability of the network to host new sources of harmonics, the existing harmonic voltage and current levels as well as the impact of these new sources on those levels should be investigated. Harmonic levels are determined by emission from harmonic sources, the propagation from other harmonic sources, and the aggregation between the contributions from different sources. Studies on harmonic emission from a variety of different individual devices under different conditions have already been carried out. However, limited knowledge is available about the harmonic aggregation and propagation in LV networks. This study aims to improve the understanding about the behaviour of harmonics in LV networks covering both aggregation and propagation.

In the first part of this work, the impact of the MV network and remote LV loads on the harmonic voltage in the LV network are examined. Simulation results have revealed that for frequencies below the resonant frequency of the local LV network the harmonic voltage levels mainly are determined by aggregated emission of the whole distribution network (both LV and MV) rather than by the emission from local LV loads. Furthermore, a graphical method is introduced for harmonic propagation studies, using measurements but without the need for accurate synchronized measurements.

In the second part of this work, the aggregated emission from a group of EV fast chargers is examined. A stochastic method, based on Bayesian statistics and harmonic correlation, was used to include uncertainties in harmonic hosting capacity calculation for an EV charging station equipped with fast chargers. The impact of MV network and remote LV loads on harmonic hosting capacity is investigated. It is also shown that harmonic hosting capacity studies are needed; and details of the distribution network must be included to get an accurate estimation of the harmonic hosting capacity.

Finally, an alternative method for time aggregation of harmonic phase angle is proposed in this work.

In general, this work contributes to reducing the research gaps recognized in harmonic analysis in the LV networks considering propagation and aggregation utilizing both simulation and measurement.