Optimal scheduling of regional integrated energy system considering multiple uncertainties

Integrated energy system (IES) is an effective way to realize the efficient utilization of energy. Under deregulated electricity market, IES operator gains profits by providing customers with service, including electricity, heat or cooling With deepening market reform, higher penetration rate renewable energy, economic risks embed in IES. Based on this, optimal scheduling model regional considering uncertainties proposed, aiming at maximizing profits. Scenario analysis method has been adopted uncertainties: Markov-Chain-Monte-Carlo (MCMC) sampling method, which a better performance fitting probability distribution, utilized generate scenarios; K-means clustering applied narrow down sets. By replacing parameters deterministic sets, series results can be achieved. The case study shows that storage tank improve benefits about 4.97% converting lower price period and releasing peak hours. Besides, through proposed optimization model, operators have straight understanding venture brought more reliable result formed for reference.