Showing 3 results for Energy Market
H. Rajabi Mashhadi, J. Khorasani,
Volume 9, Issue 1 (3-2013)
Abstract
Strategic bidding in joint energy and spinning reserve markets is a challenging task from the viewpoint of generation companies (GenCos). In this paper, the interaction between energy and spinning reserve markets is modeled considering a joint probability density function for the prices of these markets. Considering pay-as-bid pricing mechanism, the bidding problem is formulated and solved as a classic optimization problem. The results show that the contribution of a GenCo in each market strongly depends on its production cost and its level of risk-aversion. Furthermore, if reserve bid acceptance is considered subjected to winning in the energy market, it can affect the strategic bidding behavior.
M. Farshad, J. Sadeh, H. Rajabi Mashhadi,
Volume 9, Issue 2 (6-2013)
Abstract
This paper presents a novel solution method for joint energy and Spinning Reserve (SR) dispatch problem. In systems in which the Lost Opportunity Cost (LOC) should be paid to generators, if the LOC is not considered in the dispatch problem, the results may differ from the truly optimum solution. Since the LOC is a non-differentiable function, including it in the formulation makes the problem solving process to be time-consuming and improper for real time applications. Here, the joint energy and SR dispatch problem considering the LOC in the objective function is reformulated as a Linear Programming (LP) problem which its solving process is computationally efficient. Also, with reliance on the performance of LP problem solving process, an iterative algorithm is proposed to overcome the self-referential difficulty arising from dependence of the LOC on the final solution. The IEEE 30-bus test system is used to examine the proposed solution method.
H. Bakhshandeh, A. Akbari Foroud,
Volume 12, Issue 1 (3-2016)
Abstract
This paper addresses the possibility of capacity withholding by energy producers, who seek to increase the market price and their own profits. The energy market is simulated as an iterative game, where each state game corresponds to an hourly energy auction with uniform pricing mechanism. The producers are modeled as agents that interact with their environment through reinforcement learning (RL) algorithm. Each producer submits step-wise offer curves, which include the quantity-price pairs, to independent system operator (ISO) under incomplete information. An experimental change is employed in the producer's profit maximization model that causes the iterative algorithm converge to a withholding bidding value. The producer can withhold the energy of his own generating unit in a continuous range of its available capacity. The RL relation is developed to prevent from becoming invalid in certain situations. The results on a small test system demonstrate the emergence of the capacity withholding by the producers and its effect on the market price.
