Currently, to meet the requirements of modern power systems as fully and efficiently as possible, the electricity markets have diversified greatly. Under these conditions, it becomes difficult for a producer to determine the structure of transactions that is financially optimal. Starting from the operational rules of the power systems that have shaped the electricity markets structure, the objective of this paper is to develop an electricity market simulator model that includes the basics of a best practice guide for producers that compete on various electricity markets to carry out the trading activities and enhance their financial results. The market simulator model considers both the bilateral long-or mid-term agreements and short-term offers on day-ahead, ancillary services and balancing markets providing the entire trading scenario and associated cash-flow and risks. Its significance consists in assisting the producer to plan its resources and create projections by performing multiple trading scenarios and selecting the best one. Thus, this paper proposes to uncover constraints and business rules for a simulator model assisting the market players to access the electricity markets and select the best option using Multiple-Criterial Decision-Making (MCDM) methods (Electre, Topsis, Analytical Hierarchy Process) or the weighted Euclidean distance. The simulations comprise four trading scenarios for different types of producers (gas or fossil-powered generators) generating 100 MW, that are ordered by independent criteria. The results obtained with MCDM and the proposed method showed that they indicated the same scenario as the best trading option based on the type of the producer. INDEX TERMS electricity market trading simulator, cost/revenue allocation, transaction risk.
The generic term of electricity market is in fact a very complex sector of activity, being the result of the interaction of several markets, such as: the bilateral contracts market, the day-ahead market, the balancing market, intra-day market, and the market for ancillary services. In these circumstances, an electricity supplier needs to address the setting of the selling electricity price to the end consumers. The basic objectives to be pursued are as follows: return on sale for a period of at least one year, maintaining competition on the market, use of a user-friendly price system, financial insurance against fluctuations in the market, and ensuring a cash-flow corresponding to the good functioning of the firm. Considering these objectives, in this paper, we propose a data framework to handle electricity consumption data and a procedure to set the electricity price in competitive environment of the electricity markets. In this regard, a case study for an office building that also includes a restaurant is depicted.
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