In the restructured electricity industry, the engineering aspects of planning need to be reformulated even though the goal to attain remains substantially the same, requiring various objectives to be simultaneously accomplished to achieve the optimality of the power system development and operation. In many cases, these objectives contradict each other and cannot be handled by conventional single optimization techniques. In this paper, a multiobjective formulation for the siting and sizing of DG resources into existing distribution networks is proposed. The methodology adopted permits the planner to decide the best compromise between cost of network upgrading, cost of power losses, cost of energy not supplied, and cost of energy required by the served customers. The implemented technique is based on a genetic algorithm and an /spl epsiv/-constrained method that allows obtaining a set of noninferior solutions. Application examples are presented to demonstrate the effectiveness of the proposed procedure.

A multiobjective evolutionary algorithm for the sizing and siting of distributed generation

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

The goal of this chapter is to give fundamental knowledge on solving multi-objective optimization problems. The focus is on the intelligent metaheuristic approaches (evolutionary algorithms or swarm-based techniques). The focus is on techniques for efficient generation of the Pareto frontier. A general formulation of MO optimization is given in this chapter, the Pareto optimality concepts introduced, and solution approaches with examples of MO problems in the power systems field are given

Pareto Multi Objective Optimization

In this paper, we analyze the N-1 reliable dc optimal dispatch with transmission switching The model is a mixed integer program (MIP) with binary variables representing the state of the transmission element (line or transformer) and the model can be used for planning and/or operations We then attempt to find solutions to this problem using the IEEE 118-bus and the RTS 96 system test cases The IEEE 118-bus test case is analyzed at varying load levels Using simple heuristics, we demonstrate that these networks can be operated to satisfy N-1 standards while cutting costs by incorporating transmission switching into the dispatch In some cases, the percent savings from transmission switching was higher with an N-1 DCOPF formulation than with a DCOPF formulation

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Optimal Transmission Switching With Contingency Analysis

A procedure for solving the power capacitor placement problem is presented. The objective is to determine the minimum investment required to satisfy suitable reactive constraints. Due to the discrete nature of reactive compensation devices, optimal capacitor placement leads to a nonlinear programming problem with mixed (discrete and continuous) variables. It is solved with an iterative algorithm based on successive linearizations of the original nonlinear model. The mixed integer linear programming problem to be solved at each iteration of the procedure is tackled by applying both a deterministic method (branch and bound) and genetic algorithm techniques. A hybrid procedure, aiming to exploit the best features of both algorithms is also considered. The proposed procedures are tested and compared with reference to a small CIGRE system and two actual networks derived from the Italian transmission and distribution system.

Optimal capacitor placement using deterministic and genetic algorithms

Emission constrained dynamic dispatch

A gradient projection algorithm is presented for the dynamic dispatch of thermal generations, based upon an active set strategy and exploiting second order information in determining the descent directions. The algorithm is adapted to the model of the dynamic dispatch problem which consists of the minimisation of a nonlinear objective function subject to quadratic equality constraints and linear inequality constraints. The envisaged algorithm can be applied to the dispatching of thermal units during the steep load pick-up and drop-down periods over short time intervals, and can also be used in the day-before scheduling after the unit commitment has been performed. Efficiency is demonstrated by tests on large-scale networks representative of the Italian EHV system. The limited CPU time requirements also suggest a possible on-line application in determining the constrained participation factors to be used in the Automatic Generation Control function.

Fast and efficient gradient projection algorithm for dynamic generation dispatching

The (very) short term reactive power scheduling function, to be adopted by ENEL Spa, takes into account the voltage stability requirements in a preventive application of the security function. In this environment the procedure determines the voltage collapse distance of the global system and of the areas controlled by the secondary voltage regulation (SVR) both in short (24 hours ahead) and in a very short term (few hours or fractions of hour ahead). The procedure also schedules the control actions to be taken in emergency states in a preventive way. Area or system-wise indicators, based on nodal sensitivities and/or eigen (singular) value analysis, provide effective measures of the margins of the system with respect to the risk of voltage collapse and the related corrective actions. Applications of the procedure to the EHV network and to a subtransmission area of the ENEL system are presented in the paper.

System-area operating margin assessment and security enhancement against voltage collapse

The paper deals with the possible use of multiobjective methodologies in order to improve the knowledge of the power system during the planning and the operation stage. The possible use of several objective functions, the methods used to take into account the different (noninferior) solutions and the procedures that can be adopted to compare the noninferior working points are described and illustrated by some practical examples. The results are relevant to a 64 bus CIGRE test system and show the possibility to obtain useful information for the decision maker.

P. Marannino

Papers

Optimal capacitor placement using deterministic and genetic algorithms

Emission constrained dynamic dispatch

Fast and efficient gradient projection algorithm for dynamic generation dispatching

System-area operating margin assessment and security enhancement against voltage collapse

Multiobjective optimization techniques applied to modern power systems