A Stochastic Bi-Level Decision-Making Framework for a Load-Serving Entity in Day-Ahead and Balancing Markets

This paper investigates a stochastic bi‐level scheduling model for decision‐
making of a load‐serving entity (LSE) in competitive day‐ahead (DA) and regulating markets with uncertainties. In this model, LSE as the main interacting
player of the market sells electricity to end‐use customers and plug‐in electric
vehicles (PEVs) to maximize its expected profit. Therefore, a two‐level
decision‐making process with different objectives is considered to solve the
problem. In one level, the objective is to maximize the LSE's profit by optimally
scheduling of responsive loads and PEVs charging/discharging process, while
in the other level, the payments of the customers and PEV owners should be
minimized in a competitive market. In the proposed decision‐making process,
to model the uncertainties, market prices, required energy of customers and
PEVs, and the rival LSEs' prices are considered as random variables. The bi‐
level stochastic problem is then converted into a linear single‐level stochastic
model with equilibrium constraints by using Karush‐Kuhn‐Tucker (KKT) optimality conditions as well as duality theory. A case study is implemented to
indicate the applicability of the intended model. The applicability of the proposed model is tested on Nordic market and the results show that in a competitive market, the LSE can increase its revenue and attract more demand loads and PEV owners by offering more moderate prices.