In this paper, we focus on solving the integrated energy and exiramp procurement problem in the day-ahead market. The problem of energy and ramp procurement could be perfectly analyzed through Stackelberg concept because of its hierarchical nature of decision-making process. Such a circumstance was modeled via a bi-level programming in which suppliers acted as leaders and the Independent System Operator (ISO) was the follower. The ISO intended to minimize energy and spinning reserve procurement cost, and the suppliers aimed to maximize their pro t. To solve the proposed model, a fuzzy max-min approach was applied to maximizing the utilities of players. The objectives and dynamic o ers of suppliers, determined with regard to the market clearing prices, were reformulated through fuzzy utility functions. The proposed approach is an e ective and simple alternative to the Karush Kuhn Tucker (KKT) method, especially for non-convex problems at lower levels.
It is evident that developing more accurate forecasting methods is the pillar of building robust multi-energy systems (MES). In this context, long-term forecasting is also indispensable to have a robust expansion planning program for modern power systems. While very short-term and short-term forecasting are usually represented with point estimation, this approach is highly unreliable in medium-term and long-term forecasting due to inherent uncertainty in predictors like weather variable in long terms. Accordingly, long-term forecasting is usually represented by probabilistic forecasting values which are based on probabilistic functions. In this paper, a self-organizing mixture network (SOMN) is developed to estimate the probability density function (PDF) of peak load in long-term horizons considering the most important drivers of seasonal similarity, population, gross domestic product (GDP), and electricity price. The proposed methodology is applied to forecast the PDF of annual and seasonal peak load in Queensland Australia.
Summary
In this paper, a novel trilevel programming model for energy, reserve, and flexiramp procurement problem in the day‐ahead market is proposed. The conventional and wind generating units are the strategic decision makers (DMs) in the upper level, which maximize their profit. In the middle level, a large industry and a demand response (DR) aggregator maximize their profit according to the market's status. Finally, the independent system operator (ISO) minimizes its total cost in the lower level. To solve the proposed trilevel model, a fuzzy max‐min technique for multilevel programming problem (MLPP) is proposed. The proposed model is applied to the 24‐bus IEEE test system to demonstrate the benefits of implementing the proposed model in electricity markets.
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