Optimal allocation of distributed generation using a two-stage multi-objective mixed-integer-nonlinear programming

Abstract

SUMMARYCost is one of the most essential factors that influence many decisions taken in the distribution system planning. In general, cost can be defined as everything that should be sacrificed to gain the desired results. This paper proposes a new two-stage methodology for distributed generation (DG) placement as an attractive option for distribution system planner. This method aims to minimize cost and maximize total system benefit (TSB). Optimal placement and size are obtained from total cost minimization mathematical problem which is solved in the first stage. For each DG cost characteristics and for each investment payback time, there is an optimal location and size. Then the optimal DG investment payback time results from the TSB maximization problem, which is solved in the second stage. The various DG technologies offer the opportunity of selecting the right energy solution at the right location. Five types of DG are tested to give system deciders some choices. Different system conditions are simulated to illustrate the effect of DG installation on the distribution system as well as the ability of the proposed methodology. A user-friendly software package has been developed to solve efficiently and quickly the two optimization mathematical problems. The proposed methodology has been tested on IEEE 30-bus test system.

A Multi-objective Optimization for Sizing and Placement of Voltage-controlled Distributed Generation Using Supervised Big Bang–Big Crunch Method

“…Simple conventional iterative search technique along with Newton-Raphson method [22] Multi-objective with weights algorithm for optimal placement of DG considering electricity market price fluctuation…”

“…Multi-objective optimization algorithms were used for optimal sizing and placement of DG units [11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27]; the reviewed literature and their contributions are summarized in Table 1. The big bang-big crunch (BB-BC) optimization method was first presented by Osman and Eksin in [28].…”

Ubicación y dimensionamiento de generación distribuida: Una revisión

“…Son restringidos a la hora de aplicarlos a problemas reales, debido a la alta complejidad que presentan; sin embargo, son eficientes en problemas sencillos. Para solucionar el problema de GD en el SD, los métodos más utilizados son [34]: programación lineal [40], programación no lineal [41], programación dinámica [42], programación entera [43] y programación estocástica [44].…”

Optimal sizing and siting distributed generation resources using a multi objective algorithm

“…The authors in [12] proposed a 2-stage method in order to minimize the costs and increase the benefits of DG installation while sizing and siting. In the 1st stage, the costs were minimized and in the 2nd stage the benefits of installing DG were maximized.…”