Power system deregulation and the shortage of transmission capacities have led to increased interest in distributed generation (DG) sources. Proper location of DGs in power systems is important for obtaining their maximum potential benefits. This paper presents analytical methods to determine the optimal location to place a DG in radial as well as networked systems to minimize the power loss of the system. Simulation results are given to verify the proposed analytical approaches.

Analytical approaches for optimal placement of distributed generation sources in power systems

This paper presents a new multiobjective evolutionary algorithm for environmental/economic power dispatch (EED) problem. The EED problem is formulated as a nonlinear constrained multiobjective optimization problem. A new strength Pareto evolutionary algorithm (SPEA) based approach is proposed to handle the EED as a true multiobjective optimization problem with competing and noncommensurable objectives. The proposed approach employs a diversity-preserving mechanism to overcome the premature convergence and search bias problems. A hierarchical clustering algorithm is also imposed to provide the decision maker with a representative and manageable Pareto-optimal set. Moreover, fuzzy set theory is employed to extract the best compromise nondominated solution. Several optimization runs of the proposed approach have been carried out on a standard test system. The results demonstrate the capabilities of the proposed approach to generate well-distributed Pareto-optimal solutions of the multiobjective EED problem in one single run. The comparison with the classical techniques demonstrates the superiority of the proposed approach and confirms its potential to solve the multiobjective EED problem. In addition, the extension of the proposed approach to include more objectives is a straightforward process.

Environmental/economic power dispatch using multiobjective evolutionary algorithms

The potential and effectiveness of the newly developed Pareto-based multiobjective evolutionary algorithms (MOEA) for solving a real-world power system multiobjective nonlinear optimization problem are comprehensively discussed and evaluated in this paper. Specifically, nondominated sorting genetic algorithm, niched Pareto genetic algorithm, and strength Pareto evolutionary algorithm (SPEA) have been developed and successfully applied to an environmental/economic electric power dispatch problem. A new procedure for quality measure is proposed in this paper in order to evaluate different techniques. A feasibility check procedure has been developed and superimposed on MOEA to restrict the search to the feasible region of the problem space. A hierarchical clustering algorithm is also imposed to provide the power system operator with a representative and manageable Pareto-optimal set. Moreover, an approach based on fuzzy set theory is developed to extract one of the Pareto-optimal solutions as the best compromise one. These multiobjective evolutionary algorithms have been individually examined and applied to the standard IEEE 30-bus six-generator test system. Several optimization runs have been carried out on different cases of problem complexity. The results of MOEA have been compared to those reported in the literature. The results confirm the potential and effectiveness of MOEA compared to the traditional multiobjective optimization techniques. In addition, the results demonstrate the superiority of the SPEA as a promising multiobjective evolutionary algorithm to solve different power system multiobjective optimization problems.

Multiobjective evolutionary algorithms for electric power dispatch problem

Summary form only given. Power system deregulation and the shortage of transmission capacities have led to increased interest in distributed generation (DG) sources. Proper location of DGs in power systems is important for obtaining their maximum potential benefits. This paper presents analytical methods to determine the optimal location to place a DG in radial as well as networked systems to minimize the power loss of the system. Simulation results are given to verify the proposed analytical approaches.

An operationally "secure" power system is one with low probability of blackout or equipment damage. The power system control processes needed to maintain a designated security level at minimum operating cost are extremely complicated. They increasingly depend upon on-line computer security analysis and optimization. This on-line technology is still relatively new, with enormous further potential. Since security and optimality are normally conflicting requirements of power system control, it is inappropriate to treat them separately. Therefore, they are slowly becoming coalesced into a unified hierarchical mathematical problem formulation: one that is, however, far too complex to afford anything but an approximate, near-optimal solution. The practical validity of this unifying trend relies on being able to incorporate all significant security constraints within the process. The main two current computational tools in this field are contingency analysis and special operations-oriented versions of optimal power flow (OPF). Contingency analysis identifies potential emergencies through extensive "what if?." simulations on the power system network. OPF is a major extension to the conventional dispatch calculation. It can respect system static security limits, and can schedule reactive as well as active power. Moreover, the advanced versions of OPF include or interface with contingency analysis. This paper reviews present formulations and methods, and tries to point out areas of difficulty that constitute the main challenges for successful practical on-line implementations over the coming years.

Security analysis and optimization

An efficient algorithm is proposed to obtain the optimal power flow in power system operation and planning phases by multiobjective optimization. The algorithm makes it possible to treat the optimal dispatch problems with multiple performance indices and to include tradeoff relations between selected indices. The effect of uncertain factors pertaining to power systems can also be taken into account. The effectiveness of the approach is illustrated with numerical examples of the IEEE 30-node, 6-generator system. >

Multiobjective optimal generation dispatch based on probability security criteria

Dynamic generation rescheduling by multi-criteria optimization in power system emergency control

This paper deals with the formulation and its computational algorithm for overload release operation by generation rescheduling, line switching and also load shedding. The power dispatch problem is formulated as a multi-stage decision process with multi-criteria. First, performance index is set to minimize the maximum line overload to be relieved immediately after the overload occurrence. Next, after the initial optimization completed, operations are taken to suppress multiple overloads within a given upper bound. Subsequent operations are taken in the quickest way to restore shedded load. A multi-stage formulation has such an advantage that a dynamic generation scheduling problem which is cast into a linear programming framework to take advantage of sparse linear programming techniques. The advantages of an LP approach are complete computational reliability and very high speed, making it suitable for practical operation and also study-mode purpose. Numerical examples in a testsystem are included to demonstrate the effectiveness of the approach which are shown to have reliability for power system operation.

S.H. Bae

Papers

Multiobjective optimal generation dispatch based on probability security criteria

Dynamic generation rescheduling by multi-criteria optimization in power system emergency control

Dynamic generation rescheduling by multi-criteria optimization in power system emergency control