Publication | Open Access
Day-ahead scheduling of multi-carrier energy systems with multi-type energy storages and wind power
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2018
Year
Distributed Energy SystemEngineeringEnergy EfficiencyIntegrated Energy SystemsMulti-energy SystemDay-ahead SchedulingEnergy OptimizationSystems EngineeringRenewable Energy SystemsElectrical EngineeringLatin Hypercube SamplingComputer EngineeringPower System OptimizationEnergy Storage SystemInteger ProgrammingUnit CommitmentSmart GridEnergy ManagementMulti-type Energy Storages
Integrating large‑scale wind power poses operational challenges for integrated energy systems. This study proposes a day‑ahead scheduling model for integrated energy systems with wind power and multi‑type energy storage within a scenario‑based stochastic programming framework. The model incorporates electricity, natural‑gas, and heating networks, linearizes nonlinear constraints, represents correlated wind uncertainties via Latin hypercube sampling of historical data, and formulates a mixed‑integer linear program that minimizes expected operation cost. Simulation on a modified PJM 5‑bus electricity system with coupled natural‑gas and heating networks shows that multi‑type storage reduces wind curtailment and enhances operational flexibility, validating the proposed approach.
The integration of large-scale wind power brings challenges to the operation of integrated energy systems (IES). In this paper, a day-ahead scheduling model for IES with wind power and multi-type energy storage is proposed in a scenario-based stochastic programming framework. The structure of the IES consists of electricity, natural gas, and heating networks which are all included in the model. Operational constraints for IES incorporating multi-type energy storage devices are also considered. The constraints of the electricity network, natural gas network and heating network are formulated, and nonlinear constraints are linearized. The calculation method for the correlation of wind speed between wind farms based on historical data is proposed. Uncertainties of correlated wind power were represented by creating multiple representative scenarios with different probabilities, and this was done using the Latin hypercube sampling (LHS) method. The stochastic scheduling model is formulated as a mixed integer linear programming (MILP) problem with the objective function of minimizing the total expected operation cost. Numerical results on a modified PJM 5-bus electricity system with a seven-node natural gas system and a six-node heating system validate the proposed model. The results demonstrate that multi-type energy storage devices can help reduce wind power curtailments and improve the operational flexibility of IES.