Robert T. F. Ah King

Evolutionary multi-objective environmental/economic dispatch: stochastic versus deterministic approaches

Due to the environmental concerns that arise from the emissions produced by fossil-fueled electric power plants, the classical economic dispatch, which operates electric power systems so as to minimize only the total fuel cost, can no longer be considered alone. Thus, by environmental dispatch, emissions can be reduced by dispatch of power generation to minimize emissions. The environmental/economic dispatch problem has been most commonly solved using a deterministic approach. However, power generated, system loads, fuel cost and emission coefficients are subjected to inaccuracies and uncertainties in real-world situations. In this paper, the problem is tackled using both deterministic and stochastic approaches of different complexities. The Nondominated Sorting Genetic Algorithm – II (NSGA-II), an elitist multi-objective evolutionary algorithm capable of finding multiple Pareto-optimal solutions with good diversity in one single run is used for solving the environmental/economic dispatch problem. Simulation results are presented for the standard IEEE 30-bus system.

Stochastic Evolutionary Multiobjective Environmental/Economic Dispatch

Power system operation is subject to many uncertainties since acquired data are subject to inaccuracies due to inaccuracies in the process of measuring and forecasting of input data and changes of unit performance during the period between measuring and operation. In the environmental/economic dispatch problem, both fuel cost and emission are to be simultaneously minimized. In this paper, in order to obtain a solution closer to real-world situations, a constrained Monte Carlo sampling scheme is considered with stochastic decision variables, power system loads and objective functions whereby NSGA-II is used for solving the resulting stochastic environmental/economic dispatch problem. Simulation results presented for the standard IEEE 30-bus system show that the optimized system is reliable if stochastic variables are correlated.

Solving the multiobjective environmental/economic dispatch problem with prohibited operating zones using NSGA-II

A thermal or hydro generating unit may have prohibited operating zones due to physical limitations of power plant components caused, for example, by vibrations in a shaft bearing which are amplified in a certain operating region. In this case, the unit can only operate above or below the prohibited operating zone. The effect of prohibited operating zones on the multiobjective environmental/economic dispatch problem is investigated in this paper. The IEEE 30-bus system with transmission losses has been considered with prohibited operating zones and simulation performed using the elitist Nondominated Sorting Genetic Algorithm (NSGA-II) for two constraint handling methods, namely penalty parameterless and decoder-based approaches. Simulation results show that both methods can equally handle this particular scenario in the environmental/economic dispatch problem. Different number of prohibited operating zones was considered for the six generators in the system yielding discontinuous and non-convex non-dominated fronts.

Integrating distributed energy resources in the electrical grid considering resource variability for reliable power planning

Lately, there has been major development in distributed energy resources (DERs) technologies due to the constraints on traditional power generation. However, due to the volatility of renewable power, the integration of this new type of generation in the existing network is highly problematic when a reliable planning is required. This paper investigates a stochastic planning model to minimise the lifecycle cost of distributed power generation (DG) and reduce power loss in transmission lines under the energy reliability criterion, namely the loss of load probability. In the proposed techniques, two stages are involved: the modelling of DERs stage and optimal placement and capacity of DG stage. Statistical moments including mean and variance are utilised to model the wind and solar power volatility and load uncertainty. Computer simulations based on MATLAB using multiobjective genetic algorithm (GA) are used to find the best siting and sizing of the DERs with multi-system constraints. A real section of the 66 kV transmission network of Mauritius is used to evaluate the optimization process. From the results it can be observed that the proper siting and sizing of DG are important to reduce total system cost and power loss in transmission lines while achieving a reliable planning method.

Comparative application of multi-objective evolutionary algorithms to the voltage and reactive power optimization problem in power systems

This study investigates the applicability of two elitist multi-objective evolutionary algorithms (MOEAs), namely the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and an improved Strength Pareto Evolutionary Algorithm (SPEA2), in the voltage and reactive power optimization problem. The problem has been formulated mathematically as a nonlinear constrained multiobjective optimization problem where the real power loss, the load bus voltage deviations and the installation cost of additional reactive power (VAR) sources are to be minimized simultaneously. To assess the effectiveness of the proposed approach, different combinations of the objectives have been minimized simultaneously. The simulation results showed that the two algorithms were able to generate a whole set of well distributed Pareto-optimal solutions in a single run. Moreover, fuzzy logic theory is employed to extract the best compromise solution over the trade-off curves obtained. Furthermore, a performance analysis showed that SPEA2 found better convergence and spread of solutions than NSGA-II. However, NSGA-II found more extended trade-off curves in some cases and required less computational time than SPEA2.

Short term electrical load forecasting for mauritius using Artificial Neural Networks

The Central Electricity Board is the sole utility responsible for the generation, transmission, distribution and sale of electrical power in Mauritius. The countrys highest peak demand increased from 353.1 MW in 2005 to 367.3 MW in 2006 and corresponding annual consumptions increased from 2014.9 GWh to 2091.1 GWh and these figures are continuously increasing every year. In this paper, different Artificial Neural Network models are proposed for Short Term Load Forecasting (STLF) of the Mauritian electrical load. It is shown that models based on a combined supervised/unsupervised architecture provide better forecasting abilities compared to those relying on supervised architectures only. This is achieved by clustering of data.

A web-based interface for the Gryphon robot

The overall system that has been implemented involves a web site which introduces the study and applications of robotics. The web site also acts as a platform to program and control the Gryphon robot found at the Faculty of Engineering, University of Mauritius. The system is based on the client/server model, with a suitable front-end for viewing and interactive sessions with web users and a back-end relational database for storing and retrieving relevant information at the server side. This paper describes the development of a web-based interface for undergraduate students to learn the basics of robotics. The advantage of this interface is that the students can study robotics, write their programs and run the robot remotely at any time and this flexible learning approach definitely contributes to distance education and e-learning through the World Wide Web.

Multi-contingency transient stability-constrained optimal power flow using multilayer feedforward neural networks

Transient stability-constrained optimal power flow (TSC-OPF) aims at optimising the scheduling of generation with stability constraints to ensure a secure system in the event of contingencies. This paper proposes a new approach based on a critical clearing time (CCT) constraint that replaces the dynamic and transient stability constraints of the TSC-OPF problem. The CCT is computed by a multilayer feedforward neural network (MFNN) trained using Gauss-Newton approximation for Bayesian regularization. In order to ensure a uniform distribution of generated points in the input space to train the neural networks, a Sobol quasi-random sequence is adopted for data generation. The proposed method has the merit of removing the computational burden of dynamic simulation during optimisation. Multi-contingency can simply be handled by adding a CCT constraint for each contingency. Simulation results for the New England 10-machine 39-bus system show that TSC-OPF using MFNN has very fast convergence to optimal operating points with the desired CCT.

Independent component analysis for feature reduction in critical clearing time estimation

Critical clearing time (CCT) is an important parameter used in transient stability assessment of power systems. In this paper, only pre-fault generator voltage magnitudes and active powers are used as inputs to a multilayer feedforward neural network (MFNN) trained using Gauss-Newton approximation for Bayesian regularization to estimate the CCT in an optimal power flow framework. Principal component analysis (PCA) and independent component analysis (ICA) are utilized to reduce the dimension of these features. Simulations performed on the New England 10-machine 39-bus system show that ICA outperforms PCA and gives a smooth degradation of MFNN performance as the number of input features are reduced.

Voltage Stability Maximization by Distribution Network Reconfiguration Using a Hybrid Algorithm

Voltage stability maximization by Distribution Network Reconfiguration (DNR) is the process of finding a network configuration offering the least voltage deviation at the buses. Through the use of an Improved Voltage Deviation Index (IVDI) the stability of the entire distribution network has been studied. Using a hybrid algorithm, the DNR problem was tested on 6 standard test systems: 16, 33, 70, 118, 135 and 880 bus systems. The results showed that DNR using the IVDI can indeed lead to the improvement of the entire system stability.