Sishaj P. Simon

Artificial Bee Colony Algorithm for Economic Load Dispatch Problem with Non-smooth Cost Functions

Abstract With the depletion of coal and increasing fuel prices, a proper schedule of available generating units may save millions of dollars per year in production cost. In this article, the artificial bee colony algorithm and optimization technique based on the foraging behavior of honeybees is proposed for solving economic load dispatch problems with non-smooth cost functions exhibiting valve-point effect, prohibited operating zones, multiple fuel options, and ramp rate limits. The effectiveness of the proposed algorithm is demonstrated on test cases consisting of 10, 13, 15, and 40 generating units with non-linearities incorporated in their cost functions. The results of the proposed technique are compared with that of other techniques reported in the literature. The results substantiate that the proposed algorithm is capable of yielding quality solution.

Multi-objective scheduling problem: Hybrid approach using fuzzy assisted cuckoo search algorithm

Abstract This article proposes a hybrid cuckoo search algorithm (CSA) integrated with fuzzy system for solving multi-objective unit commitment problem (MOUCP). The power system stresses the need for economic, non-polluting and reliable operation. Hence three conflicting functions such as fuel cost, emission and reliability level of the system are considered. CSA mimics the breeding behavior of cuckoos, where each individual searches the most suitable nest to lay an egg (compromise solution) in order to maximize the egg’s survival rate and achieve the best habitat society. Fuzzy set theory is used to create the fuzzy membership search domain where it consists of all possible compromise solutions. CSA searches the best compromise solution within the fuzzy search domain simultaneously tuning the fuzzy design boundary variables. Tuning of fuzzy design variables eliminate the requirement of expertise needed for setting these variables. On solving MOUCP, the proposed binary coded CSA finds the ON/OFF status of the generating units while the real coded CSA solves economic dispatch problem (EDP) and also tunes the fuzzy design boundary variables. The proposed methodology is tested and validated for both the single and multi-objective optimization problems. The effectiveness of the proposed technique is demonstrated on 6, 10, 26 and 40 unit test systems by comparing its performance with other methods reported in the literature.

Enhanced Energy Output From a PV System Under Partial Shaded Conditions Through Artificial Bee Colony

For the maximum utilization of solar energy, photovoltaic (PV) power generation systems are operated at the maximum power point (MPP) under varying atmospheric conditions, and MPP tracking (MPPT) is generally achieved using several conventional methods. However, when partial shading occurs in a PV system, the resultant power-voltage (P-V) curve exhibits multiple peaks and traditional methods that need not guarantee convergence to true MPP always. This paper proposes an artificial bee colony (ABC) algorithm for global MPP (GMPP) tracking under conditions of in-homogenous insolation. The formulation of the problem, application of the ABC algorithm, and the results are analyzed in this paper. The numerical simulations carried out on two different PV configurations under different shading patterns strongly suggest that the proposed method is far superior to existing MPPT alternatives. Experimental results are also provided to validate the new dispensation.

Nodal ant colony optimization for solving profit based unit commitment problem for GENCOs

This paper proposes a nodal ant colony optimization (NACO) technique to solve profit based unit commitment problem (PBUCP). Generation companies (GENCOs) in a competitive restructured power market, schedule their generators with an objective to maximize their own profit without any regard for system social benefit. Power and reserve prices become important factors in decision process. Ant colony optimization that mimics the behavior of ants foraging activities is suitably implemented to search the UCP search space. Here a search space consisting of optimal combination of binary nodes for unit ON/OFF status is represented for the movement of the ants to maintain good exploration and exploitation search capabilities. The proposed model help GENCOs to make decisions on the quantity of power and reserve that must be put up for sale in the markets and also to schedule generators in order to receive the maximum profit. The effectiveness of the proposed technique for PBUCP is validated on 10 and 36 generating unit systems available in the literature. NACO yields an increase of profit, greater than 1.5%, in comparison with the basic ACO, Muller method and hybrid LR-GA.

Emission constrained economic dispatch with valve-point effect using particle swarm optimization

This paper presents particle swarm optimization (PSO) technique to solve economic dispatch of valve-point loaded generating units considering emission constraint. This problem has gained recent attention due to the deregulation of power industry and environmental regulations. Minimizing operating cost can no longer be the only criterion for dispatching electric power due to increasing concern over the environmental consideration. In this paper, fuel cost and NOx emission functions are considered and formulated as a single objective optimization problem. Based on the literature survey, it could be found that cost function is taken as a quadratic function and solved for emission economic dispatch. Here, in cost function a sine term is added to model the valve-point effect and then solved using PSO algorithm. The objective function is highly non-linear and the proposed method is validated with IEEE 30-bus system. The results obtained demonstrate the effectiveness of the proposed method for solving the environmental constrained economic dispatch problem.

Development of an Improved P&O Algorithm Assisted Through a Colony of Foraging Ants for MPPT in PV System

The perturb and observe (P&O) algorithm is a simple and efficient technique, and is one of the most commonly employed maximum power point (MPP) tracking (MPPT) schemes for photovoltaic (PV) power-generation systems. However, under partially shaded conditions (PSCs), P&O method miserably fails to recognize global MPP (GMPP) and gets trapped in one of the local MPPs (LMPPs). This paper proposes ant-colony-based search in the initial stages of tracking followed by P&O method. In such a hybrid approach, the global search ability of ant-colony optimization (ACO) and local search capability of P&O method are integrated to yield faster and efficient convergence. A theoretical analysis of the static and dynamic convergence behavior of the proposed algorithm is presented together with computed and measured results.

A spiking neural network (SNN) forecast engine for short-term electrical load forecasting

Abstract Short-term load forecasting (STLF) is one of the planning strategies adopted in the daily power system operation and control. All though many forecasting models have been developed through the years, the uncertainties present in the load profile significantly degrade the performance of these models. The uncertainties are mainly due to the sensitivity of the load demand with varying weather conditions, consumption pattern during month and day of the year. Therefore, the effect of these weather variables on the load consumption pattern is discussed. Based on the literature survey, artificial neural networks (ANN) models are found to be an alternative to classical statistical methods in terms of accuracy of the forecasted results. However, handling of bulk volumes of historical data and forecasting accuracy is still a major challenge. The development of third generation neural networks such as spike train models which are closer to their biological counterparts is recently emerging as a robust model. So, this paper presents a load forecasting system known as the SNNSTLF (spiking neural network short-term load forecaster). The proposed model has been tested on the database obtained from the Australian Energy Market Operator (AEMO) website for Victoria State.

Profit based unit commitment for GENCOs using parallel NACO in a distributed cluster

Abstract Deregulation process has created an intense competition with the participation of many generating companies (GENCOs) in a power market. Wholesale transactions (bids and offer) have to be cleared and settled in a shorter duration. Therefore, this necessitates for the system operator to quick and smarter decisions. In this problem formulation, profit based unit commitment (PBUC) problem aims in maximizing the profit of GENCOs. However demand satisfaction is not an obligation. Here, parallel nodal ant colony optimization (PNACO) approach mimicking ants intelligence is used in the decision on committing generating units. The sub problem economic dispatch (ED) is carried out using parallel artificial bee colony (PABC) approach mimicking foraging behavior of bees. Profit based unit commitment (PBUC) must be obtained in less time even though there is a possible increase in generating units. Nowadays, as computing resources are available in plenty, effective utilization will be advantageous for reducing the time complexity for a large scale power system solution. The proposed approach uses a cluster of computers performing parallel operations in a distributed environment for obtaining the PBUC solution. The time complexity and the solution quality with respect to the number of processors in the cluster are thoroughly investigated. The effectiveness of the proposed approach for PBUC is first validated on a standard 10 unit system available in the literature and then analysis for computational efficiency using 1000 generating units, which is a duplicate form of standard 10 unit system.

Optimal Deviation Based Firefly Algorithm Tuned Fuzzy Design for Multi-Objective UCP

Restructuring of power system stresses the need for economic and reliable generation of power. Therefore generating units should be committed considering fuel cost and reliability level of the system. This necessitates the need for multi-objectives to be met in a unit commitment problem (UCP). Since the above objectives are conflicting in nature, a novel methodology employing optimal deviation based firefly algorithm tuned fuzzy membership function is applied to multi-objective unit commitment problem (MOUCP). The ON/OFF status of the generating units is obtained by binary coded FF whereas the sub-problem economic dispatch (ED) is obtained by real coded FF. Here the conflicting functions are formulated as a single objective function using fuzzy weighted optimal deviation. The fuzzy membership design variables are tuned using real coded FF; thereby the requirement of expertise for setting these variables are eliminated. The proposed methodology is validated on 100-unit system, IEEE RTS 24-bus system, IEEE 118-bus system and a practical Taiwan Power (Taipower) 38-unit system over a 24-h period. Effective strategy on scheduling spinning reserve is demonstrated by comparing its performance with other methods reported in the literature.

Profit based unit commitment: A parallel ABC approach using a workstation cluster

This paper proposes a parallel artificial bee colony (PABC) approach for committing generating units thereby maximizing the profit of generation companies. Profit based unit commitment (PBUC) must be obtained in a short time even though there is an increase in generating units. Nowadays, computing resources are available in plenty, and effective utilization of these resources will be advantageous for reducing the time complexity for a large scale power system. Here, the message passing interface based technique is used in the PABC algorithm in distributed and shared memory models. The time complexity and the solution quality with respect to the number of processors in a cluster are thoroughly analyzed. PABC for PBUC is tested for a power system ranging from 10 to 1000 generating units. Also the PABC is validated for economic dispatch and the unit commitment problem in a traditional power system on 40 and 10 unit systems, respectively.