Bidding Models Analysis on Ship Repair Projects (Friedman and Ackoff & Sasieni Models)

The existence of a ship project carried out with a tender system by the LPSE allows all shipyard industries to bid on the project, this causes the chances of winning to become smaller, the determination of the tender price greatly determines the size of the profit that can be obtained and the percentage of the possibility of winning the project in a shipping industry. Therefore, the strategy of determining the bid price is very important. The statistical method used is multi discrete distribution, and multi normal distribution, while the bidding model uses Friedman (1956) and Ackoff & Sasieni (1968) models. The results obtained the best bid price strategy to win an auction or tender is the model that produces the lowest optimum mark-up, namely the Friedman model with multi normal distribution, while for Ackoff & Sasieni it produces a higher bid than the Friedman model except in certain company conditions.

Demand Bidding Optimization for an Aggregator with a Genetic Algorithm

Demand response (DR) is an effective solution used to maintain the reliability of power systems. Although numerous demand bidding models were designed to balance the demand and supply of electricity, these works focused on optimizing the DR supply curve of aggregator and the associated clearing prices. Limited researches were done to investigate the interaction between each aggregator and its customers to ensure the delivery of promised load curtailments. In this paper, a closed demand bidding model is envisioned to bridge the aforementioned gap by facilitating the internal DR trading between the aggregator and its large contract customers. The customers can submit their own bid as a pairs of bidding price and quantity of load curtailment in hourly basis when demand bidding is needed. A purchase optimization scheme is then designed to minimize the total bidding purchase cost. Given the presence of various load curtailment constraints, the demand bidding model considered is highly nonlinear. A modified genetic algorithm incorporated with efficient encoding scheme and adaptive bid declination strategy is therefore proposed to solve this problem effectively. Extensive simulation shows that the proposed purchase optimization scheme can minimize the total cost of demand bidding and it is computationally feasible for real applications.

The Power Plant Bidding Strategy in Contract Market

Owing to the fact that the power can not be stored directly and the supply must meet the demand in real time, the price of electricity is more volatile than other commodities. In order to hedge the risk, the power plant can sign the power sale contracts with big customers by the promissory price. Using the Bayesian equilibrium theory, this paper establishes the bidding models on two power plants competing for selling the electricity to the big customer. The computing result shows that the power plant’s optimal bidding strategy equals to the mean of the competitor’s ceiling bidding price and its own marginal cost.