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
This paper investigates the optimal allocation of Spinning Reserve (SR) for power systems in the presence of Renewable Energy Sources (RES) and Electrical Energy Storage (EES) devices. This is done in order to reduce the system's dependency on thermal generation units and the decrease total daily operational cost. A Security Constrained Unit Commitment (SCUC) model for a typical power system was used, which includes thermal and renewable generation units and EES devices in the form of batteries. In the proposed model, the hourly operation strategy is determined by adopting a predetermined level of SR. In order to optimize SR requirements, the Independent System Operator (ISO) runs the SCUC problem and determines the minimum SR that should be provided by generation units and EES devices. The simulation results illustrate that by optimizing the operation of batteries, the ISO can effectively reduce the required capacity of thermal units. Therefore, optimal SR allocation under RES uncertainty is determined in this study.

Abstract
This paper focuses on the long-term planning of power systems considering the impacts of Electrical Energy Storage Devices (ESSD) as well as Demand Response Programs (DRPs). The proposed model incorporates a two-stage optimization strategy in order to reduce the computational burden of the nonlinear problem. The upper-level of optimization model includes investment decision variables (long-term planning) while in the lower-level, the optimal operation of the model for short-term horizon has been addressed. In the operational stage, the optimal scheduling of power system in the presence of suggested ESSD size and location from the upper level is evaluated. Moreover, the Time-of-Use (ToU) Demand Response (DR) pricing scheme has been applied in the operational stage to evaluate its capability to reduce the total operating costs. The simulation results on the standard 6-bus test system validates the applicability of the proposed two-stage optimization model and illustrates that the optimal sizing and location of ESSDs along with DRP implementation could effectively reduce the total systems costs and improve the power system load factor.

Abstract
There is a growing need for increased flexibility in modern power systems. Traditionally, this flexibility has been provided by supply-side technologies. There has been an increase in the research surrounding flexibility services provided by demand-side actors and technologies, especially flexibility services provided by prosumers (those customers who both produce and consume electricity). This work gathers 1183 peer-reviewed journal articles concerning the topic and uses them to identify the current state of the art. This body of literature was analysed with two leading textual and scientometric analysis tools, SAS (c) Visual Text Analytics and VOSviewer, in order to provide a detailed understanding of the current state-of-the-art research on prosumer flexibility. Trends, key ideas, opportunities and challenges were identified and discussed.

Abstract
The integration of new technologies at the residential level such as energy storage systems, electric vehicles, solar photovoltaic generation and mini wind turbines triggered the appearance of a new agent in the power systems called prosumers. This agent has the potential to provide new forms of flexibility and cost-effective solutions. However, associated with these new solutions there are also a number of problems that affect these solutions, particularly network constraints. This work presents an analysis not only on the benefits of utilizing the prosumer's flexibility but also to the problems associated with the operation and optimization of the network. A new model is presented that considers energy transactions between prosumers in the neighborhood and between them and the network using on a stochastic framework, in order to account for a set of uncertainties in the form of scenarios associated with the availability of various resources and technologies. The results show the economic benefit of energy transactions between prosumers resulting in more flexibility for the system while highlighting the effect of network restrictions and potential problems associated with them.

Abstract
There is an ongoing paradigm shift occurring in the electricity sector. In particular, previously passive consumers are now becoming active prosumers and they can now offer important and cost-effective new forms of flexibility and demand response potential to the electricity sector and this can translate into system-wide operational and economic benefits. This work focuses on developing a model where prosumers participate in demand response programs through varying tariff schemes, and the model also quantifies the benefits of this flexibility and cost-reductions. This work includes transactive energy trading between various prosumers, the grid and the neighborhood. A stochastic tool is developed for this analysis, which also allows the quantification of the collective behavior so that the periods with the greatest demand response potential can be identified. Numerical results indicate that the optimization of energy transactions amongst the prosumers, and including the grid, leads to considerable cost reductions as well as introducing additional flexibility in the presence of demand response mechanisms.