The purpose of this paper is to develop an optimal bidding strategy for a generation company (GenCo) in the network constrained electricity markets and to analyze the impact of network constraints and opponents bidding behavior on it.
A bi‐level programming (BLP) technique is formulated in which upper level problem represents an individual GenCo payoff maximization and the lower level represents the independent system operator's market clearing problem for minimizing customers' payments. The objective function of BLP problem used for bidding strategy by economic withholding is highly nonlinear, and there are complementarity terms to represent the market clearing. Fuzzy adaptive particle swarm optimization (FAPSO), which is a modern heuristic approach, is applied to obtain the global solution of the proposed BLP problem for single hourly and multi‐hourly market clearings. Opponents' bidding behavior is modeled with probabilistic estimation.
It is very difficult to obtain the global solution of this BLP problem using the deterministic approaches, even for a single hourly market clearing. However, the effectiveness of this new heuristic approach (FAPSO) has been established with four simulation cases on IEEE 30‐bus test system considering multi‐block bidding and multi‐hourly market clearings. The joint effect of network congestion and strategic bidding by opponents offer additional opportunities of increase in payoff of a GenCo.
FAPSO having dynamically adjusted particle swarm optimization inertia weight uses fuzzy evaluation to effectively follow the frequently changing conditions in the successive trading sessions of a real electricity market. This approach is applied to find the optimal bidding strategy of a GenCo competing with five GenCos in IEEE 30‐bus test system.
This paper is possibly the first attempt to evaluate an optimal bidding strategy for a GenCo through economic withholding in a network constrained electricity market using FAPSO.
Bajpai, P. and Niwas Singh, S. (2008), "Strategic bidding in network constrained electricity markets using FAPSO", International Journal of Energy Sector Management, Vol. 2 No. 2, pp. 274-296. https://doi.org/10.1108/17506220810883252Download as .RIS
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