Optimal Planning of Grid Reinforcement with Demand Response Control

Abstract

This chapter presents a hybrid methodology based on a local search algorithm and a genetic algorithm, used to address the multi-objective and multistage optimal distribution expansion planning problem. The methodology is conceived to solve optimal network investment problems under the new possibilities enabled by the smart grid, namely the new observability and controllability investments that will be available to enable demand response in the future. The multi-objective methodology is applied to an existing low-voltage electric distribution network under a congestion scenario to yield a Pareto-optimal set of solutions. The solutions are then projected onto the two investment possibilities considered: demand control investments and traditional network asset investments. The projected surface is then analyzed to discuss the merit of demand control with respect to postponing traditional asset investments.

Appendix

The notations used throughout this chapter are listed below:

f _j :

Objective function j

P :

Set of investment projects

O :

Set of orders for project analysis (population)

p _i :

Project i of P

o _k :

Order k of O (individual of the population)

t _i :

Timing of project p _i , $t_{i} \in \left\{ {1,2, \ldots ,T + 1} \right\}$

$\overline{t}$ :

Decision schedule: indexed array of timings t _i for projects p _i

N :

Number of projects

T :

Number of stages of the planning horizon

G :

Graph of the electric distribution network