Volume 3, Issue 1

As large load disturbances with changing frequency in the vicinity of the inter-area oscillations mode occur in an interconnected power system, a system frequency may be heavily perturbed and oscillatory. To stabilise frequency oscillations, the active power controlled by a superconducting magnetic energy storage (SMES) unit placed in a power system, can be utilised. However, it may not be feasible to locate a SMES unit in every area of a multi-area interconnected power system due to the economic reason. Therefore, it should be advantageous if a SMES unit located in an area is available for frequency stabilisation of other interconnected areas. To implement this concept, a static synchronous series compensator (SSSC), can be applied to coordinate with a SMES unit to stabilise frequency oscillations. On the other hand, variations of system parameters, several load changes etc., cause system uncertainties. To enhance the robustness of frequency stabilisers against uncertainties, this paper focuses on a robust frequency stabilisation by SMES in co-ordination with SSSC. To take system uncertainties into account, the multiplicative uncertainty is included in system modeling. As a result, the system robust stability can be easily guaranteed in terms of the multiplicative stability margin (MSM). The structure of frequency stabiliser equipped with SMES and SSSC is a second-order lead/lag compensator. Based on the proposed optimisation technique, control parameters of frequency stabilisers can be automatically tuned by a tabu search. Consequently, the desired damping ratio of the target inter-area mode and the best MSM are achieved. Simulation studies in a two-area interconnected power system confirm the high robustness of the coordinated SMES with SSSC against several uncertainties.

The objective of this paper is to optimize maintenance scheduling of generating units of a captive thermal plant using intelligent optimization techniques such as genetic algorithm (GA) and simulated annealing (SA). A multi-class maintenance scheduling has been considered to incorporate statutory safety regulations for maintenance of boilers, turbines and generators. Such multi-class maintenance scheduling in terms of mathematical formulations necessitates a planning horizon of five years compared to the conventional one to two year horizon. As the utilities catered by captive power plants are very sensitive to power failure, the reliability of power supply has been evaluated by loss of load probability (LOLP) index. The significant contribution of this paper is to evaluate confidence intervals of LOLP instead of conventional point estimate of LOLP. A point-estimate of LOLP uses expected values and provides no information about the range of values over which LOLP may vary. Some variations from optimum schedules are anticipated while executing maintenance schedules due to different real-life unforeseen exigencies. Such exigencies are considered in terms of near-optimum schedules obtained from SA during the final stages of convergence. LOLP evaluated from such sub-optimum schedules forms the basis to treat LOLP as a random variable for subsequent evaluation of confidence intervals. Case studies pertaining to a captive power plant for an aluminium smelter corroborate that the schedules obtained from two optimization techniques yield the same optimum schedule and thus the efficacy of the proposed algorithm is validated. Also the interval of confidence for LOLP denotes the possible risk in a quantified manner and it is of immense use from perspective of captive power plants intended for quality power.

Concentration of greenhouse gases in the atmosphere is steadily increasing leading to global warming. India is expected to be one of the major contributors to Green House Gases (GHGs) due to increased share of thermal power generation, which is a major contributor to carbon dioxide (CO2) emission. One way to limit these emissions is by implementing some economic instruments like carbon tax or energy tax. This study has been carried out to analyze the impact of carbon tax on the complete Indian power sector network. An Integrated Resource Planning and Analysis (IRPA) model and CPLEX software has been used to carry out the present study.

This article presents a solution model for the unit commitment problem (UCP) using fuzzy logic to address uncertainties in the problem. Hybrid simulated annealing (SA), particle swarm optimization (PSO) and sequential quadratic programming (SQP) technique (hybrid SA-PSO-SQP) is used to schedule the generating units based on the fuzzy logic decisions. The fitness function for the hybrid SA-PSO-SQP is formulated by combining the objective function of UCP and a penalty calculated from the fuzzy logic decisions. Fuzzy decisions are made based on the statistics of the load demand error and spinning reserve maintained at each hour. SA is used to solve the combinatorial sub-problem of the UCP. An improved random perturbation scheme and a simple method for generating initial feasible commitment schedule are proposed for the SA method. The non-linear programming sub-problem of the UCP is solved using the hybrid PSO-SQP technique. Simulation results on a practical Neyveli Thermal Power Station system (NTPS) in India and several example systems validate, the presented UCP model is reasonable by ensuring quality solution with sufficient level of spinning reserve throughout the scheduling horizon for secure operation of the system.

This paper presents novel, simple and efficient method for ranking line outage contingencies from network loadability limit considerations. The method follows conventional optimal multiplier based Newton Raphson power flow. The simulation of line outage contingencies is carried out near pre-contingency critical loading using the above power flow. A salient feature of the method is the use of the pre-contingency power flow Jacobian factors for simulation of all contingencies. Results for two sample test systems have been obtained with the new method and continuation power flow method in order to verify the potential of the former method for practical use.

In this paper we explain how to design intelligent agents able to process the information acquired from interaction with a system to learn a good control policy and show how the methodology can be applied to control some devices aimed to damp electrical power oscillations. The control problem is formalized as a discrete-time optimal control problem and the information acquired from interaction with the system is a set of samples, where each sample is composed of four elements: a state, the action taken while being in this state, the instantaneous reward observed and the successor state of the system. To process this information we consider reinforcement learning algorithms that determine an approximation of the so-called Q-function by mimicking the behavior of the value iteration algorithm. Simulations are first carried on a benchmark power system modeled with two state variables. Then we present a more complex case study on a four-machine power system where the reinforcement learning algorithm controls a Thyristor Controlled Series Capacitor (TCSC) aimed to damp power system oscillations.

In bilateral transaction market, the congestion relief cost should be allocated in an equitable manner among all the market participants contributing to the congestion. Unfortunately in the allocation of the congestion relief cost to the congested lines, which is the first step of cost allocation, the previously proposed shadow price method and the aggregated allocation method both have some shortcomings. To overcome these shortcomings, a cost allocation method based on the Aumann-Shapley value is proposed. By means of the Aumann-Shapley value, a solution concept in the cooperative game with infinitely many players, the proposed method takes all orders of relieving congestion on the lines into consideration and satisfies the consistency of the allocation, thus guarantees equitable allocation of the congestion relief cost to the congested lines. In addition, the calculating procedure is both simple and accurate due to the use of the Gauss Integration method. Test results show that this method can lead to better allocation than do the shadow price method and the aggregated allocation method.