Joint Distribution Network and Renewable Energy Expansion Planning Considering Demand Response and Energy Storage—Part I: Stochastic Programming Model

Abstract

The first part of this two-paper series describes the incorporation of Demand Response (DR) and Energy Storage Systems (ESS) in the joint distribution and generation expansion planning for isolated systems. The role of DR and ESS has recently attracted an increasing interest in power systems. However, previous models have not been completely adapted in order to treat DR and ESS on an equal footing. The model presented includes DR and ESS in the planning of insular distribution systems. Hence, this paper presents a novel model to decide the joint expansion planning of Distributed Generation (DG) and the distribution network considering the impact of ESS and price-dependent DR programs. The problem is formulated as a stochastic-programming-based model driven by the maximization of the net social benefit. The associated deterministic equivalent is formulated as a mixed-integer linear program suitable for commercially available software. The outcomes of the model are the location and size of new generation and storage units and the distribution assets to be installed, reinforced or replaced. In the second companion paper, an insular case study (La Graciosa, Canary Islands, Spain) is provided illustrating the effects of DR and ESS on social welfare.

Load modeling of active low‐voltage consumers and comparative analysis of their impact on distribution system expansion planning

“…15 In this study, the demand forecast techniques are classified according to the planning horizon as very short-term, 16 short-term, 17 medium-term, 18 and long-term load forecasting. 2 The advantage of this approach is the reduction of computational effort, making the planning task more efficient. 2 The advantage of this approach is the reduction of computational effort, making the planning task more efficient.…”

“…19 In models for mediumand long-term studies, such as DS expansion planning, it is common to represent the load in different levels. 2 The advantage of this approach is the reduction of computational effort, making the planning task more efficient. However, the insertion of DGs, DRs, and plug-in electric vehicles (PEVs) in the context of load modeling leads to simplified representations, such as thresholds, insufficient to describe consumer behavior.…”

“…The research work carried out in previous studies 30,31 considers the incorporation of DR and energy storage systems (ESSs) in the joint distribution and generation expansion planning for isolated systems. The model proposed maximizes the net social benefit, includes price-dependent DR programs, and is solved using stochastic programming.…”

“…In this context, it is common practice to represent the LV demand in levels/blocks to perform medium and long-term distribution systems (DS) analyses/studies with minimal computational effort. 2…”

Optimal sizing and operating strategy of a stand‐alone generation–load–storage system: An island case study

“…Maïzi et al 24 developed a specific model to predict the optimal future energy composition for an island in 2030. Asensio et al 25,26 constructed a linear model with demand response and energy storage systems to improve the customer affordability and living costs in an island. Despite the unavoidable compromise on modeling accuracy and complexity in model setup, linear models and mixed integer linear models remain popular choices among researchers on scenario-based microgrid simulation, due to the faster model solving and more informative solution convergence feedback.…”