Abstract
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.
Highlights
Today, wind energy is considered one of the leading renewable energy sources for electricity generation throughout the globe
MVA flows for each hour of operation, fuel cost, and system losses are collected, and the basis on which VaR and conditional value-at-risk (CVaR) are calculated with 95%
This paper presents a detailed economic risk analysis study of VaR and CVaR with confidences levels of 95% and 99% in the wind and pumped hydroelectric storage (WPHS) integrated system
Summary
Wind energy is considered one of the leading renewable energy sources for electricity generation throughout the globe. CVaR is considered risk exposure due to the uncertainties present in the stochastic model for energy and reserve scheduling of renewable-based microgrid under an energy market environment, which is further solved by using multi-objective mixed-integer linear programming [18]. CVaR is considered a risk evaluation method to avoid over-optimistic solutions in a two-layer adaptive stochastic model for an optimal multi-energy microgrid (wind, PV, thermal, Battery, and capacitor) under a voltage security constraint environment [20]. Risks due to uncertainty associated with solar energy, price, load, and EV’s arriving and departing times in optimal scheduling of heating, power, and hydrogen-based micro grid incorporated with renewable energy sources (RES)–PV and plug-in electric vehicle (PEV) are modeled using CVaR in ref. The system comprises a base-load, wind power, and pumped hydroelectric storage (WPHS) hybrid system
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