To determine the feasibility of bilateral contracts, the independent system operator needs to estimate the transfer capability of the system. The estimated available transfer capability (ATC) values are updated on open access same-time information system by day by day or on hourly basis. This work proposes a fast and effective online technique for dynamic ATC (DATC) estimation. The impact of 2 reserve margins such as capacity benefit margin and transmission reserve margin is introduced with DATC estimation. In this proposed method support vector machine (SVM) is used to estimate the ATC values. The SVM is one of the effective machines learning algorithm in solving the regression problem. The parameters of the SVM are tuned by differential evolution algorithm to get accurate prediction of DATC values. The proposed method is applied to New England 10 machine 39-bus dynamic system for estimating DATC values for different cases. Severe lines are identified using contingency screening analysis. The test results are compared with artificial neural network technique. The study has been carried out for different cases such as normal, line outage and stressed condition. The SVM technique gives better results than artificial neural network technique. KEYWORDS capacity benefit margin, differential evolution algorithm, dynamic available transfer capability, support vector machine, transmission reserve margin

No abstract

No abstract

Summary Due to continuous growth in the demand for electricity with unmatched generation and transmission capacity expansion, the resource management and rescheduling of load without affecting the welfare of the market participants are the major concerns of the power market. As the demand changes continuously, the peak load consumers are unaware of the bidding cost and penalty. The Artificial Neural Network (ANN) based online Demand Response (DR) connectivity scheme is proposed for the smart power networks to obtain the equilibrium demand. The optimally rescheduled load, percentage increase of peak load, and time are considered the ANN input. Bidding cost and penalty of the peak load consumer are considered as the output. The data required to develop the ANN are generated using the Genetic Algorithm (GA) to maximize social welfare as the objective. The optimum load curtailment is taken as the decision variable. In this proposed method, the Curtailment Index (CI) is calculated and incorporated to utilize DR connectivity properly. This adopted method is tested with IEEE 30 bus system, and the GA results for CI and bidding cost have been compared with Particle Swarm Optimization (PSO) methodology. The ANN predicted bidding cost results are compared with GA optimized bidding cost. The result shows the accuracy of ANN for online DR techniques with minimum testing Mean Square Error (MSE) value of 1.72 × 10−3 and the training period of 45.98 seconds.

No abstract

No abstract

Nowadays, the DC distribution system has been suggested, as a replacement for the AC power distribution system with electric propulsion. This idea signifies a fresh approach of issuing energy for low-voltage installations. It can be used for any electrical application up to 20 MW and works at a nominal voltage of 1000 V DC. The DC distribution system is just an extension of the multiple DC links that previously available in all propulsion and thruster drives, which typically comprise more than 80% of the electrical power consumption on electric propulsion vessels. A fault detection and islanding scheme for DC grid connected PV system is presented in this paper. Unlike traditional ac distribution systems, protection has been challenging for dc systems. The goals of this paper are to classify and detect the fault in the PV system as well as DC grid and to isolate the faulted section so that the system keeps operating without disabling the entire system. The results show the measured values of power at PV panel and DC grid side under different fault condition, which indicates the type of fault that occurs in the system.

No abstract