Data clustering based probabilistic optimal scheduling of an energy hub considering risk-averse

An energy hub (EH) is a multi-carrier energy system supplying various types of energy demands. Optimal management of these systems is a non-linear, non-convex, and complicated problem. This complexity is increased because of the unpredictable renewable generation and consumption patterns. Inattention to the probabilistic nature of the uncertain variables may increase the risk of encountering undesired conditions. The use of accurate and low computational probabilistic assessment methods is very important in this problem. This paper presents risk-constrained stochastic scheduling for an EH considering the uncertainties of renewable generations and load demands. The risk is assessed by the conditional value at risk (CVaR) method. A tradeoff between decrement of the operation and emissions cost and increment of the risk aversion is offered. The proposed method is applied on an energy hub consisting of a wind turbine (WT), photovoltaic (PV) cells, a fuel cell power plant (FCPP), a combined heat and power generation unit (CHP) and plug-in electric vehicles (PEVs). The wind speed, solar irradiation, all types of demands as well as the market prices are considered as uncertain variables. In order to get maximum profit and enhance the consumption curve, electrical, thermal and cooling demand response programs (DRPs) are applied. Uncertainties in input random variables are managed by the efficient k-means data clustering method. The results, which show considerable flexibility in the energy management of the energy hub, are comprehensively discussed. Simulation results indicate that 1.97%, 6.25%, and 10.17% reduction in the operation cost of the proposed EH can be achieved with the integration of the PEVs, FCPP, and DRPs, respectively. Additionally, the risk cost of the EH is improved by 1.95%, 6.2%, and 9, 68% with consideration of the PEVs, FCPP, and DRPs, respectively.

Automated summary

This paper presents a risk-constrained stochastic scheduling method for an energy hub, considering uncertainties of renewable generation and load demands. By managing uncertainties in input random variables using an efficient data clustering method, the method aims to reduce operational costs and risk costs of the energy hub. Simulation results demonstrate significant reductions in operational costs and improvements in risk costs with the integration of various technologies and demand response programs.