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Multistage Robust Look-Ahead Unit Commitment with Probabilistic Forecasting in Multi-Carrier Energy Systems

The combined operations of power distribution network (PDN) and district heating network (DHN) can enhance the flexibility and improve the overall energy efficiency of power systems. This article implements a rolling look-ahead unit commitment scheme in a combined PDN and DHN to exploit the operational flexibility of rapid-response combined heat and power (CHP) units under significantly variable renewable energy source (RES) power output. The scheme is formulated as a multistage distributionally robust (DR) unit commitment model that respects the non-anticipativity of decision variables for sequential revelations of uncertainties. In contrast to the moment-based ambiguity sets employed in conventional DR models, the proposed framework constructs an ambiguity set based on probabilistic forecasts. In this regard, a compatibility is achieved between DR approaches and probabilistic forecasts by incorporating comprehensive distribution information of RES power output stemming from probabilistic forecasts into DR models. The computational challenge associated with the proposed multistage DR model is addressed by applying linear decision rules. Moreover, a new constraint reformulation approach is utilized to increase the computational tractability. The proposed model will ultimately cast into a tractable mixed-integer linear programming problem. The effectiveness of the proposed method in capturing a comprehensive distribution of RES power output and reducing the the combined system operation cost is demonstrated by case studies carried out on the Barry Island multi-carrier energy system. Numerical results also validate the proposed model's improved computational performance.
- Hohai University China (People's Republic of)
- Illinois Institute of Technology United States
- Hohai University China (People's Republic of)
- Illinois Institute of Technology United States
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).33 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 10% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 1%
