Multiyear stochastic wind generation investment planning with demand response in distribution system using improved water evaporation optimization

This article proposes a multiyear distributed generation (DG) investment planning model with the coordination of demand response for making the decision of investment under uncertainty. The aim is to reduce the net present value related to the energy purchasing cost from the grid, investment cost including operation, and maintenance cost, emission penalty cost and energy losses cost. In this article, two investment planning models are developed with a total planning horizon of 20 years: (i) static investment planning model and (ii) dynamic investment planning model. In the static investment, DG is installed in first year, whereas for dynamic investment model, time period of DG installation is considered flexible. The impact of real-time demand response is also analyzed for both cases. The optimal distribution network investment planning problem, which includes multiple DG and demand response, is a complicated combinatorial optimization problem that traditional optimization methods are unable to tackle efficiently. Therefore, an improved water evaporation optimization algorithm is proposed and compared with the genetic algorithm, basic water evaporation algorithm, and particle swarm optimizationin this article. Both DG investment models are applied to the standard 33-bus radial distribution network.

Automated summary

This article proposes a multiyear investment planning model for distributed generation, considering uncertainty and coordination with demand response. The aim is to reduce various costs related to energy purchasing, investment, emission penalties, and energy losses. Two investment planning models, static and dynamic, are developed with the analysis of real-time demand response. An improved water evaporation optimization algorithm is proposed to solve the complex optimization problem. The models are applied to a standard distribution network.