Active Power Dispatch Problem Research Articles

Joint application of multi-object beetle antennae search algorithm and BAS-BP fuel cost forecast network on optimal active power dispatch problems

The multi-object optimal active power dispatch (MOAPD) problem can realize the optimized operation of electric systems by reducing the fuel cost, power loss and exhaust emission. In this paper, the Pareto front (PF) and the best-compromise scheme (BCS) of MOAPD problems are determined by novel multi-object beetle antennae search algorithm (NMBAS). The proposed NMBAS algorithm, which innovatively integrates the advanced ideas of widely-used NSGA-Ⅱ and multi-goal PSO (MPSO) algorithms, achieves better BCS-quality, PF-uniformity and PF-diversity. More notably, further research indicates that several winning elite solutions (WES) with smaller goal values are distributed around the current BCS of NMBAS. Therefore, a brand new BAS-BP fuel cost forecast network with less prediction-error and time-cost is put forward to seek the potential WES schemes. With BP network as main body and modified by beetle antennae search (BAS) algorithm, the BAS-BP fuel cost forecast model achieves more than six higher-quality WES schemes which are superior to the traditional BCS solution. The superiorities of proposed NMBAS algorithm and BAS-BP fuel cost forecast network are intuitively demonstrated by dual-objective and triple-objective MOAPD experiments. In general, the joint application of NMBAS algorithm and BAS-BP forecast network successfully solves the non-differentiable power grid optimization problems.

Gravity Local Search Inspired Particle Swarm Algorithm for Economic Power Dispatch Planning Problem in Small Scale System

This research presents novel Particle swarm optimization inspired by gravitational based search method to solve active power dispatch problem in electrical power system planning. The proposed PSO utilizes the operator of social thinking coupled with search capacity of gravity inspired algorithm to formulate and develop technique for active power dispatch problem to satisfy power demand requirements. Optimal scheduling of generators and system constraints to match load demand and losses is successfully done with proposed method. Total operating cost is minimized satisfying various bounds of system with proposed method. Exploration and convergence efficiency are evaluated to checklist the computational efficiency and robustness of the proposed technique. The suggested technique is tested and evaluated on different test systems comprises three, five, six test systems. Test results are compared with other techniques presented in literature .Investigations shows promising results which further benchmark the effectiveness of proposed method to solve complex optimization non linear problems.

Grey Wolf Optimization for Active Power Dispatch Planning Problem Considering Generator Constraints and Valve Point Effect

This research proposed an application of swarm inspired new meta heuristic algorithm Grey Wolf Optimization to solve active power dispatch problem imposing valve point effect and generator constraints. Grey Wolf optimization is based on mathematical approach whose solution convergence inspired by the leadership hierarchy and hunting mechanism of grey wolves. It explores search space as a multi-level decision mechanism and does not require gradient for search path. This approach converged to global optimal solution in spite of the non linearity added by valve point effect while solving the fitness function. Optimal scheduling of generators to minimize the total operating cost coupled with generator constraints and valve point effect to match load demand is implemented with proposed method and. Exploration, Computation and Convergence power are evaluated to track the computational efficiency of the proposed technique. The presented technique is tested on different test cases comprises three, six and thirteen test systems incorporating valve point effect. Test results are compared with other nature and bio inspired algorithms presented in literature .Analysis shows cut throat results as total operating cost turns out to be minimum as compared to other techniques which infers the effectiveness of proposed method and encourage to further explore the potential of proposed method to solve complex optimization problems in active power dispatch planning area.

Probabilistic economic/environmental power dispatch of power system integrating renewable energy sources

The study of different integration aspects of renewable energy sources (RES) becomes very important to overcome problems caused by their variability or uncertainty. This paper treats the economic environmental power dispatch as a probabilistic multiobjective problem. The operation cost is considered as the sum of deterministic part and probabilistic one. First, the problem is solved based on expected values of generated RES power. Then, using the cumulative distribution function (CDF) of each RES, the CDF of the required reserve to compensate RES power variability is developed. After that, respecting to the reserve contribution of each thermal generator, the probabilistic part of the global generation cost as well as its CDF are developed. In order to solve the proposed multiobjective problem, a new computation approach based on particle swarm is investigated. Finally, the proposed approach is applied to solve the active power dispatch problem of IEEE 30-bus test system in two cases with and without RESs. The simulation results show that the proposed approach allows to get the complete information about the cumulative distribution function of the actual global cost of the system operation.

Probabilistic Economic Emission Dispatch Optimization of Multi-sources Power System

The interest on renewable energy resources is growing and the study of different integration aspects of these resources becomes very important to overcome problems caused by their variability or uncertainty. This paper treats the economic environmental power dispatch as a probabilistic multiobjective problem. The operation cost and green house gas emission functions are considered as the sum of deterministic part and probabilistic one. First, the problem is solved based on expected values of generated wind power then, using the cumulative density function (CDF) of each renewable energy source (RES), the CDF of the required reserve to compensate the RESs variability in order to keep the power balance. Then, respecting to the reserve contribution of each thermal generator, the probabilistic part of the global generation cost as well as its CDF are developed. Finally, the proposed approach is applied to solve the active power dispatch problem of IEEE 30-bus test system in two cases with and without RESs. The simulation results show that this method allows to get the complete information about the cumulative distribution function of the actual global cost of the system operation.

A pseudo spot price of electricity algorithm applied to environmental economic active power dispatch problem

In this study, an environmental economic active power dispatch problem is transformed into an optimization problem with a single-objective function by applying the weighted sum method (WSM) and the conic scalarization method (CSM). A pseudo spot price of electricity algorithm (PSPA) is used to solve the transformed problem. This is demonstrated on an example problem, which is a lossy electrical power system having only thermal units The WSM and CSM methods are applied to the example problem, and the weight factor values of these methods is increased from 0 to 1 by a step size of 0.1. For each possible value of the coefficient, a different total generation cost rate that is to be minimized is obtained, and an optimal solution is also calculated for it (Pareto optimal solutions). Solutions obtained from both methods are not onlycompared with each other, but also compared with other solutions obtained using different methods such as the first order gradient method, the lambda iteration method, nonlinear programming, linear programming, and fuzzy linear programming. In this study, the CSM is used for the first time in the transformation of an environmental economic active power dispatch problem into a single-objective optimization problem. Thus, it is shown that the PSPA can also be used in the solution of the environmental economic power dispatch problem.

Optimal Active Power Dispatch with Small-signal Stability Constraints

Conventional optimal active power dispatch without considering small-signal stability constraints may violate the small-signal stability requirement. This article investigates the optimal active power dispatch problem with small-signal stability constraints. A new optimal active power dispatch model with small-signal stability constraints is built in order to ensure the generation optimization result that can satisfy the given small-signal stability requirement. The successive linear programming method is used to solve the new model. The proposed methodology is validated by two test systems.

Optimal active power dispatch by network flow approach

An optimal active power dispatch problem is formulated as a nonlinear capacitated network flow problem with additional linear constraints. Transmission flow limits and both Kirchhoff laws are taken into account. The problem is solved by a generalized upper bounding technique that takes advantage of the network flow structure of the problem. The proposed approach has potential applications to power system problems such as economic dispatch, load supplying capability, minimum load shedding and generation-transmission reliability. The authors also review the use of transportation models for power system analysis. A detailed illustrative example is presented.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>