MANUFACTURER'S UNIFORM PRICING AND CHANNEL CHOICE WITH A RETAIL PRICE MARKUP COMMITMENT STRATEGY

The paper studies channel choice decisions in a multi-channel supply chain under a strategy where there is an ex-ante commitment made on the retail price markup. The market demand is uncertain and dependent on the price and sales efforts. The results show that in any channel structure, when making order decisions the retailer only examines the price ratio and the fluctuation size of random demand, rather than the channel cost and the retailer＇s marketing efficiency. When the retail price rises, the manufacturer is willing to increase inventory quantity for direct sales, because the manufacturer＇s profit margin is higher in direct channel. The increase in demand fluctuation only affects the degree of channel preference but doesn＇t change the manufacturer＇s channel choice. No matter in which level the price ratio is, when the sales efficiency of retail channel is not high or the demand proportion of direct channel is low, the manufacturer and the retailer will be both apt to choose a dual-channel structure. Then adding a direct channel becomes a marketing strategy, rather than a competitor of the retail channel, and helps the supply chain win more market demand.

Selective Learning for Strategic Bidding in Uniform Pricing Electricity Spot Market

In an electricity market,power producers’profits are determined by the market price instead of the regulated price.Therefore,the producers should be cautious in strategic bidding,for which prediction-based approaches are widely used and have been proved effective for many areas.However,in a uniform pricing market,the market environment is so complicated,which is primarily due to the complexity of the participants’interaction,that even the strategies based on machine learning algorithms,which are generally considered as outstanding nonlinear prediction methods,may sometimes lead to unsatisfactory results.Therefore,a selective learning scheme for strategic bidding is proposed to ensure greater effectiveness.The proposed scheme is based on an ensemble technique,where several machine learning algorithms serve as the underlying algorithms to predict the price and generate a bidding recommendation.As the clearing iteration progresses,the most fitting ones will be chosen to dominate the bidding strategy.Considering the characteristics of the electricity market,the prediction method used in the selective learning scheme is modified to achieve higher accuracy.Simulation studies are presented to demonstrate the effectiveness of the proposed scheme,which leads to more reasonable bidding behaviors and higher profits.

Nodal, zonal, or uniform electricity pricing： how to deal withnetwork congestion

In this paper, the main contributions to congestion management and electricity pricing, i.e., nodal, zonal, and uniform electricity pricing, are surveyed. The key electricity market concepts are structured and a formal model framework is proposed for electricity transportation, production, and consumption in the context of limited transmission networks and competitive, welfare maximizing electricity markets. In addition, the main results of existing short-run and long-run congestion management studies are explicitly summarized. In particular, the important interconnection between short-run network management approaches and optimal long-run investments in both generation facilities and network lines are highlighted.

Review of Methods to Calculate Congestion Cost Allocation in Deregulated Electricity Market

With maturing deregulated environment for electricity market, cost of transmission congestion becomes a major issue for power system operation. Uniform Marginal Price and Locational Marginal Price (LMP) are the two practical pricing schemes on energy pricing and congestion cost allocation, which are based on different mechanisms. In this paper, these two pricing schemes are introduced in detail respectively. Also, the modified IEEE-14-bus system is used as a test system to calculate the allocated congestion cost by using these two pricing schemes.