Abstract
Variable renewable energy sources introduce significant amounts of short-term uncertainty that should be considered when making investment decisions. In this work, we present a method for representing stochastic power system operation in day-ahead and real-time electricity markets within a capacity expansion model. We use Benders’ cuts and a stochastic rolling-horizon dispatch to represent operational costs in the capacity expansion problem (CEP) and investigate different formulations for the cuts. We test the model on a two-bus case study with wind power, energy storage, and a constrained transmission line. The case study shows that cuts created from the day-ahead problem gives the lowest expected total cost for the stochastic CEP. The stochastic CEP results in 3% lower expected total cost compared to the deterministic CEP capacities evaluated under uncertain operation. The number of required stochastic iterations is efficiently reduced by introducing a deterministic lower bound, while extending the horizon of the operational problem by persistence forecasting leads to reduced operational costs.
Highlights
E need for more flexibility, changes in market structures, and operational rules have been evident in countries which are integrating large amounts of variable renewable energy (VRE) such as Denmark, China, Ireland, and Spain [5, 6]
Compared with the extant literatures, the main contributions of this paper are as follows: (a) We propose an algorithm for representing two-stage stochastic rolling-horizon dispatch in capacity expansion problem (CEP) using Benders’ cuts, where a lower bound for the operational problem is derived from a deterministic model (b) We investigate different approaches for using Benders’ cuts to extract operational values in the context of day-ahead and real-time electricity markets (c) We evaluate the impact of the short-term uncertainty and forecast horizon for operations on optimal investments in a realistic case study e rest of the paper is organized as follows: in Section 2, we describe the investment model with the rolling-horizon operation
We investigate the impact of these challenges on the performance of the Stochastic rolling-horizon dispatch (SRHD)-CEP and the effect of the short-term wind power uncertainty on investments in a two-bus case study
Summary
A common method for solving the CEP is to decompose investments and operation into two different parts [31], a master problem and a subproblem, which is solved by iterating between them until the upper and lower bounds of the problem converge. E cuts in the master problem consist of the optimal objective value of the subproblem, the installed capacities used in the operational model for the current iteration, and the dual of the capacity constraints in equations (14), (15), and (16) summed over all times. E upper bound is the objective of the best solution found so far calculated by summing up the values from the master and subproblem according to the original objective function in equation (1). E lower bound is the best solution that can be found and is the same as the objective of the master problem in equation (11)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
