Bilevel optimization applied to strategic pricing in competitive electricity markets

Abstract

In this paper, we present a bilevel programming formulation for the problem of strategic bidding under uncertainty in a wholesale energy market (WEM), where the economic remuneration of each generator depends on the ability of its own management to submit price and quantity bids. The leader of the bilevel problem consists of one among a group of competing generators and the follower is the electric system operator. The capability of the agent represented by the leader to affect the market price is considered by the model. We propose two solution approaches for this non-convex problem. The first one is a heuristic procedure whose efficiency is confirmed through comparisons with the optimal solutions for some instances of the problem. These optimal solutions are obtained by the second approach proposed, which consists of a mixed integer reformulation of the bilevel model. The heuristic proposed is also compared to standard solvers for nonlinearly constrained optimization problems. The application of the procedures is illustrated in case studies with configurations derived from the Brazilian power system.

An optimal strong equilibrium solution for cooperative multi-leader-follower Stackelberg Markov chains games

“…Applications were presented into the security domain by ( [9,27,31]) suggesting a upper level that represents defenders trying to minimize risk, and a lower level that represents attackers trying maximizing destruction for a given target. Additionally, an application into energy area was suggested by ( [15]) where the upper level represents the energy provider that minimizes total cost, and the lower level represents the energy consumer that determines the pattern of consumption. There are several applications implemented into different areas: transportation ( [7,10,21]), agriculture ( [16]), network ( [23,22]), management ( [3]), gas ( [13]).…”

A new methodological development for solving linear bilevel integer programming problems in hybrid fuzzy environment

“…Now, both the leader and follower optimize their objective independently under the same set of system constraints defined in (9). Let…”

“…In the context of BLPP, the decisions maintain a hierarchy from leader to the follower. BLPPs have been successfully applied to various hierarchical decision making situations such as traffic planning [3], pricing and fare optimization in the airline industry [4], management of hazardous materials [5], aluminum production process [6], pollution control policy determination [7], tax credits determination for biofuel producers [8], pricing in competitive electricity markets [9], supply chain planning [10], facility location [11], defense problem [12] and so forth. Most of the developments on BLPPs are based on vertex enumeration method [1] and transformation approaches [2] which are effective only for very simple types of problems.…”

Bi-level optimization for a dynamic multiobjective problem

“…Bi-level optimization can be applied in many fields and for different kinds of problems. For example, in chemical engineering, bi-level optimization is exploited to optimize the design and control of processes (Mohideen et al 1996b, Bansal et al 2000a, in electricity markets, it is applied to strategic pricing (Fampa et al 2008), and in supply chain problems, production-distribution interactions are studied (Calvete et al 2011). Bi-level optimization methodology was developed by Mohideen et al (1996b) who proposed an algorithm for solving integrated design and operation problems including dynamic mathematical models, uncertainty parameters, timevarying disturbances and robust stability criteria.…”