Due to the restructuring of the power system, customers always try to obtain low-cost power efficiently and reliably. As a result, there is a chance to violate the system security limit, or the system may run in risk conditions. In this paper, an economic risk analysis of a power system considering wind and pumped hydroelectric storage (WPHS) hybrid system is presented with the help of meta-heuristic algorithms. The value-at-risk (VaR) and conditional value-at-risk (CVaR) are used as the economic risk analysis tool with two different confidence levels (i.e., 95% and 99%). The VaR and CVaR with higher negative values represent the system in a higher-risk condition. The value of VaR and CVaR on the lower negative side or towards a positive value side indicates a less risky system. The main objective of this work is to minimize the system risk as well as minimize the system generation cost by optimal placement of wind farm and pumped hydro storage systems in the power system. Sequential quadratic programming (SQP), artificial bee colony algorithms (ABC), and moth flame optimization algorithms (MFO) are used to solve optimal power flow problems. The novelty of this paper is that the MFO algorithm is used for the first time in this type of power risk curtailment problem. The IEEE 30 bus system is considered to analyze the system risk with the different confidence levels. The MVA flow of all transmission lines is considered here to calculate the value of VaR and CVaR. The hourly VaR and CVaR values of the hybrid system considering the WPHS system are reported here and the numerical case studies of the hybrid WPHS system demonstrate the effectiveness of the proposed approach. To validate the presented approach, the results obtained by using the MFO algorithm are compared with the SQP and ABC algorithms’ results.

Electricity demand is sharply increasing with the growing population of human beings. Due to financial, social, and political barriers, there are lots of difficulties when building new thermal power plants and transmission lines. To solve this problem, renewable energy sources and flexible AC transmission systems (FACTS) can operate together in a power network. Renewable energy sources can provide additional power to the grid, whereas FACTS devices can increase the thermal limit of existing transmission lines. It is always desirable for an electrical network to operate under stable and secure conditions. The system runs at risk if any abnormality occurs in the generation, transmission, or distribution sections. This paper outlines a strategy for reducing system risks via the optimal operation of wind farms and FACTS devices. Here, a thyristor-controlled series compensator (TCSC) and a unified power flow controller (UPFC) have both been considered for differing the thermal limit of transmission lines. The impact of the wind farm, as well as the combined effect of the wind farm and FACTS devices on system economy, were investigated in this work. Both regulated and deregulated environments have been chosen to verify the proposed approach. Value at risk (VaR) and cumulative value at risk (CVaR) calculations were used to evaluate the system risk. The work was performed on modified IEEE 14 bus and modified IEEE 30-bus systems. A comparative study was carried out using different optimization techniques, i.e., Artificial Gorilla Troops Optimizer Algorithm (AGTO), Honey Badger Algorithm (HBA), and Sequential Quadratic Programming (SQP) to check the effect of renewable integration in the regulated and deregulated power systems in terms of system risk and operating cost.

Power system networks are becoming more complex and decentralized with the foreword of deregulation in the global power sector. In this scenario, an independent system operator (ISO) is responsible for determining the appropriate actions to deliver stable and quality power to the customers connected to the network at the lowest cost without violating the system security limits. Violations of any security limit may result in system risk. The unstable and non-reliable system always has some drawbacks and is not desirable from the consumer’s point of view. A deregulated power market always keeps the consumer on the advantage side by giving stable, reliable, and less costly power. By using risk assessment tools, we identify the fault conditions and we try to minimize the risk by various uses of sequential programming methods. In this paper, a novel power system risk analysis and congestion management approach are introduced with considering meta-heuristic algorithms i.e., Slime Mould Algorithm (SMA) and Artificial Bee Colony Algorithm (ABC) in renewable energy integrated electricity market. The proposed power system risk analysis is constructed with the help of two risk valuation tools named Conditional-Value-at-risk (CVaR) and Value-at-risk (VaR). The higher negative value of VaR and CVaR represents the higher risk system and lower negative value or towards a positive value of VaR and CVaR denotes the less risk or stable system. The projected method has been experienced on the IEEE 14-bus test system and IEEE 30-bus test system to examine the usefulness of the meta-heuristic algorithm in system risk analysis under the deregulated environment. The importance of renewable energy integration in system risk curtailment has also been depicted in this work: basically, to measure the system’s risk, hence enhancing the system’s reliability and societal welfare. As a result, it will benefit both supply and demand-side participants.

Electric Vehicles (EVs) are known to be future mode of transportation because of their environment-friendly nature. The increase in electric vehicle (EV) penetration needs to set up the new charging stations to meet the demand. The EV charging shows the negative impact on distribution system and system failures lead to the unavailability of power to charge EVs. The EVs not charging due to system failure is to be considered but ignored in the previous studies. Incorporating the Vehicle-to-Grid (V2G) technologies into charging station (CS) improves the system reliability. In this paper, solar rooftop PV units are integrated with CSs to overcome the negative impacts of EV charging and further enhance the reliability of the system. To extracts the maximum benefits from the solar PV integrated charging stations (PVCS), optimal placement is done with objective of reliability improvement. EV reliability is evaluated by using a novel index called as expected energy not charged (EENC). The reliability of both distribution system and EVs are considered as objective functions simultaneously, hence, placement problem becomes multi-objective. The optimal placement is done by considering different EV penetration levels. A multi-objective Grasshopper optimization algorithm (MOGOA) is applied to solve the optimal placement problem of PVCS. The EENS value is improved by 6.18% and 13.9% as compared to base case for case 1 and case 2 respectively. The EENC is improved by 11.51% in case 2 as compared to case 1.