2020
DOI: 10.1109/oajpe.2020.3029134
|View full text |Cite
|
Sign up to set email alerts
|

Complementarity, Not Optimization, is the Language of Markets

Abstract: Each market agent (producer or consumer) in a power market pursues its own objective, typically to maximize its own profit. As such, the specific behavior of each agent in the market is conveniently formulated as a bi-level optimization problem whose upper-level problem represents the profit seeking behavior of the agent and whose lower-level problem represents the clearing of the market. The objective function and the constraints of this bi-level problem depend on the agent's own decision variables and on tho… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

0
8
0

Year Published

2021
2021
2024
2024

Publication Types

Select...
7
1
1
1

Relationship

0
10

Authors

Journals

citations
Cited by 29 publications
(8 citation statements)
references
References 22 publications
(48 reference statements)
0
8
0
Order By: Relevance
“…The optimality of the optimization problem (1) always lies in the extrema of the constraint set [23], namely the intercepts of binding constraints. Therefore, solving problem (1) is equal to solving a system of linear equations, which means the optimum of problem (1) can be represented by ( 3…”
Section: A Constructing Marginal Patterns Library Algorithm Ce: Compr...mentioning
confidence: 99%
“…The optimality of the optimization problem (1) always lies in the extrema of the constraint set [23], namely the intercepts of binding constraints. Therefore, solving problem (1) is equal to solving a system of linear equations, which means the optimum of problem (1) can be represented by ( 3…”
Section: A Constructing Marginal Patterns Library Algorithm Ce: Compr...mentioning
confidence: 99%
“…3 are bi-level optimization problems, these cannot be solved using off-the-shelf solvers, and require reformulation to single-level equivalents. Duality theory and KKT conditions are the two primary approaches to convert multi-level optimization problems into single-level equivalents [23], however, the former approach introduces extensive nonlinearities in the reformulation [24]. To avoid recasting the highly nonlinear problem (NLP) into a mixed-integer NLP, we use KKT conditions in this paper to reformulate the SLSF games into their single-level equivalents.…”
Section: Solution Techniques For Stackelberg Gamesmentioning
confidence: 99%
“…This is an area where significantly more work is needed to bring planning and market outcomes in alignment to better understand why these outcomes often diverge and design market mechanism to reduce the gap. As (Conejo & Ruiz, 2020) discusses that the appropriate representation of the decisions of all market agents, which is beyond the purview of conventional optimization approach and is most appropriately represented through equilibrium models -an issue we discuss at length in a later section of this paper. As many developing countries embark on wholesale market developments together with renewable policies, analytical exploration of these markets is a critical need to ensure markets can deliver the desired clean generation portfolio and lower cost.…”
Section: Can An Incumbent Electricity Market Under a Deep Decarboniza...mentioning
confidence: 99%