Abstract
Nowadays, the presence of renewable energy resources (RERs), electric vehicle (EV) penetration, and the implementation of demand response (DR) programs are the main affecting factors in the operational scheduling of a distribution company (DISCO). By the new market participants such as parking lot (PL) owners in the DISCO, a bi-level framework can be created for modeling the distribution network. Therefore, in this paper, a new bi-level model is suggested for DISCO's operational scheduling that involves technical and environmental terms in the objective function. The maximization of the profit of the DISCO owner and the PL owner are the objective functions in each level. These purposes depend on the customers' load, the power purchased from the upstream network, the power exchanged with the PL owner (for the upper-level) and the power exchanged with the DISCO owner, as well as the EV owners (for the lower-level). Linearization of the model is carried out by applying the Karush-Kuhn-Tucker (KKT) condition and Fortuny-Amat and McCarl linearization approach. Furthermore, EVs' and RERs' uncertainties, as well as DR programs are modeled. Also, three types of risk are described including risk-seeker, risk-neutral, and risk-averse (with conditional value-at-risk (CVaR) index). For evaluation of the proposed model, it is applied to the IEEE 15-bus test system. Results show that by charging/discharging schedule of EVs and critical peak pricing program, the DISCO owner gains more profit. Also, the sensitivity analysis allows determining that the EV penetration, nominal power of RERs and customer involvement in the DR program directly affect the DISCO owner's profit.

Abstract
Gas-fired units and power-to-gas facilities provide pivotal backups for power systems with volatile renewable generations. The deepened system interdependence calls for elaborate consideration of network models of both natural gas and power systems, as well as uncertain factors. This paper proposes a data-driven distributionally robust optimization model for the optimal gas-power flow problem with uncertain wind generation. The concept of zonal line pack and line pack reserve are raised to topologically distinguish fuel suppliers of gas-fired units and ensure gas system operating security during reserve deployment. Wind power uncertainty is described by an ambiguity set, i.e., a family of candidate distributions around an empirical distribution in the sense of Wasserstein distance. A convex optimization-based solution procedure is developed, which entails solving only second-order cone programs. Computational results validate the effectiveness of the proposed models and methods.

Abstract
This study relies on a dynamic reliability-based model for distributed energy resources (DER) planning in electric energy distribution networks (EEDN) with the aim of maximizing the profit of EEDN companies by increasing income and reducing costs. Load uncertainty is considered in the proposed planning model and the robust optimization (RO) approach is employed to cope with the uncertainty. The developed methodology is illustrated using real-world voltage-dependent load models, including residential, commercial and industrial types. These load models are used in evaluating the reliability cost and energy selling for customers. The reliability cost is calculated based on the total unsupplied load after an outage. Furthermore, a new modified harmony search algorithm is proposed to solve the formulated robust dynamic DER planning problem. The solution of the proposed optimization model provides the size, location, and power factor of DER. Furthermore, the need for transformers or lines upgrades and the best year for DER installation are other decision variables determined by the model. The effectiveness and capability of the developed model have been demonstrated with the aid of a case study based on a typical EEDN. The obtained results indicate that installing DER in EEDNs can relieve congestion on feeders; therefore, it can mitigate or defer upgrade investment. Moreover, if carefully planned, other benefits of DER integration such as reliability improvement and energy loss reduction can be achieved.

Abstract
The growth of intermittent renewable power generation has been drawing attention to the design of balancing markets. Portugal is an interesting case study because wind generation already accounts for a high fraction of demand (23% in 2012-2016), but still there are no economic incentives for efficient wind forecasting (wind balancing costs are passed to end consumers). We analyze the evolution of the balancing market from 2012 to 2016. Using actual market data, we find wind balancing costs around 2 euros per MWh of generated energy. One main reason for these low costs is the existence of a robust transmission grid, which allows for the compensation of positive with negative wind imbalances across the system. Nevertheless, the results suggest that final consumers could save several million euros per year if wind generators were made responsible for the economic cost of their imbalances, in line with other European markets.

Abstract
As the demand for renewable energy sources energy grows worldwide, small-scale urban wind energy (UWE) has drawn attention as having the potential to significantly contribute to urban electricity demand with environmental and socio-economic benefits. However, there is currently a lack of academic research surrounding realizable UWE potential, especially in the South African context. This study used high-resolution annual wind speed measurements from six locations spanning Cape Town to quantify and analyze the city's UWE potential. Two-parameter Weibull distributions were constructed for each location, and the annual energy production (AEP) was calculated considering the power curves of four commonly used small-scale wind turbines (SWTs). The two Horizontal Axis Wind Turbines (HAWTs) showed higher AEP and capacity factors than Vertical Axis Wind Turbine (VAWT) ones. A diurnal analysis showed that, during summer, an SWT generates the majority of its electricity during the day, which resembles the typical South African electricity demand profile. However, during winter, the electricity is mainly generated in the early hours of the morning, which does not coincide with the typical load demand profile. Finally, the calculation of Levelized Cost of Electricity (LCOE) showed that SWT generation is more expensive, given current electricity market conditions and SWT technology. The study provides a detailed, large-scale and complete assessment of UWE resources of Cape Town, South Africa, the first of its kind at the time of this work.

Abstract
Residential buildings have become an active market participant in future power grid transactions due to the development of smart grid technologies, particularly smart meters. Keeping this in mind, this paper proposes a two-stage stochastic model including day-ahead and real-time local energy markets with the aim of domestic equipment scheduling, which reflects the uncertain mobility pattern of Electric Vehicle (EV) as well as the variability of micro wind turbine generation. The contribution of EV and battery in providing additional flexibility through bi-directional energy trading has been investigated considering deterministic and stochastic EV mobility patterns. Moreover, the smart home is modeled as a price-taker agent in the local market. Hence, different price-based Demand Response (DR) programs can affect its decisions. On this basis, a comprehensive analysis on the participation of a smart home in various price-based DR strategies is carried out with the aim of determining the most effective DR program from smart home owner point of view. The obtained results reveal that the participation of the smart home in Time-of-Use (ToU) pricing scheme not only reduces the operation cost, but also leads to smart home profitability.