Adverse environmental impacts of carbon emissions are causing increasing concerns to the general public throughout the world. Electric energy generation from conventional energy sources is considered to be a major contributor to these harmful emissions. High emphasis is therefore being given to green alternatives of energy, such as wind and solar. Wind energy is being perceived as a promising alternative. This source of energy technology and its applications have undergone significant research and development over the past decade. As a result, many modern power systems include a significant portion of power generation from wind energy sources. The impact of wind generation on the overall system performance increases substantially as wind penetration in power systems continues to increase to relatively high levels. It becomes increasingly important to accurately model the wind behavior, the interaction with other wind sources and conventional sources, and incorporate the characteristics of the energy demand in order to carry out a realistic evaluation of system reliability. Power systems with high wind penetrations are often connected to multiple wind farms at different geographic locations. Wind speed correlations between the different wind farms largely affect the total wind power generation characteristics of such systems, and therefore should be an important parameter in the wind modeling process. This paper evaluates the effect of the correlation between multiple wind farms on the adequacy indices of wind-integrated systems. The paper also proposes a simple and appropriate probabilistic analytical model that incorporates wind correlations, and can be used for adequacy evaluation of multiple wind-integrated systems.

Variability of wind power is one of the main concerns of power system operation with significant wind power. Energy storage can be employed in conjunction with wind power to reduce the uncertainty associated with wind power. This study assumes the storage facility being operated by the wind farm operator for the explicit purpose of minimising the risk of wind power commitment. A time‐dependent wind power model is developed in conjunction with the energy storage model for short operating lead times that are conditional on the initial wind and storage conditions. This study presents a probabilistic method to assess the reliability contribution of energy storage using two probabilistic indices: the wind power commitment risk and the unit commitment risk.

With the increased uncertainty in the power system operation due to growing penetration of highly intermittent energy sources such as wind power, the need for the impact assessment of the renewable penetration on system operating risk and the quantification of benefits of using energy storage technologies is more than ever. A recovery-risk-analysis based analytical framework for operating risk assessment of windintegrated bulk power system following a major contingency disturbance is presented in this paper. Two new indices that quantify the recovery risk profile of the bulk power system and its load delivery points following major disturbances are introduced in this work. The indices obtained from the proposed framework quantifies the impact of increasing wind penetration on the system operating risk and the reliability benefits of using fast-responding energy storage system such as flywheel energy storage systems. The proposed methodology is illustrated through several case studies carried out in a test system.

The contribution of solar power in electric power systems has been increasing rapidly due to its environmentally friendly nature. Photovoltaic (PV) systems contain solar cell panels, power electronic converters, high power switching and often transformers. These components collectively play an important role in shaping the reliability of PV systems. Moreover, the power output of PV systems is variable, so it cannot be controlled as easily as conventional generation due to the unpredictable nature of weather conditions. Therefore, solar power has a different influence on generating system reliability compared to conventional power sources. Recently, different PV system designs have been constructed to maximize the output power of PV systems. These different designs are commonly adopted based on the scale of a PV system. Large-scale grid-connected PV systems are generally connected in a centralized or a string structure. Central and string PV schemes are different in terms of connecting the inverter to PV arrays. Micro-inverter systems are recognized as a third PV system topology. It is therefore important to evaluate the reliability contribution of PV systems under these topologies. This work utilizes a probabilistic technique to develop a power output model for a PV generation system. A reliability model is then developed for a PV integrated power system in order to assess the reliability and energy contribution of the solar system to meet overall system demand. The developed model is applied to a small isolated power unit to evaluate system adequacy and capacity level of a PV system considering the three topologies.

Integration of an energy storage system (ESS) into a distribution network not only affects the supply reliability of the customer, but also has distinct reliability implications and consequences to the utility. The reliability value associated with an ESS highly depends on the ownership, market and regulatory structures. This paper presents a probabilistic framework to evaluate the reliability value of ESS to the distribution system considering aforementioned factors. In this regard, a new probabilistic reliability model of ESS is developed and integrated into a sequential Monte Carlo based simulation framework. The developed ESS model consists of the Markov based component model and the mixed integer linear programming based formulation of operating strategies that incorporate different scenarios of ownership, market structures, and the ESS characteristics. The reliability/financial risk performance of the Distribution System Operator (DSO) with ESS under quality regulations are quantified. Furthermore, the developed ESS model is utilized to explore the prospect of investor-owned ESS providing supply recovery and distribution grid capacity services to the DSO. Case studies are conducted on a test distribution network to show the effectiveness of the proposed model. Finally, the paper presents discussions on important considerations for efficient utilization of ESS in active distribution systems.