Abstract
As a main flexible resource, energy storage helps smooth the volatility of renewable generation and reshape the load profile. This paper aims to characterize the impact of energy storage unit on the economic operation of distribution systems in a geometric manner that is convenient for visualization. Posed as a multi-parametric linear programming problem, the optimal operation cost is explicitly expressed as a convex piecewise linear function in the MW/MWh parameter of the energy storage unit. Based on duality theory, a dual linear programming based algorithm is proposed to calculate an approximate optimal value function (OVF) and critical regions, circumventing the difficulty of degeneracy, a common challenge in the existing multi-parametric linear programming solvers. When the uncertainty of renewable generation is considered, the expected OVF can be readily established based on OVFs in the individual scenarios, which is scalable in the number of scenarios. The OVF delivers abundant sensitivity information that is useful in energy storage sizing. Leveraging the OVFs, a robust stochastic optimization model is proposed to determine the optimal MW-MWh size of the storage unit subject to a given budget, which gives rise to a simple linear program. Case study provides a clear sketch of the outcome of the proposed method, and suggests that the optimal energy-power ratio of an energy storage unit is between 5 and 6 from the economical perspective.
Highlights
T HE penetration of renewable generation in distribution systems is growing rapidly [1]
The stochastic energy storage units (ESUs) planning approach is applied in the Western Electricity Coordinating Council (WECC) interconnected system [8], showing its ability in large-scale instances
This paper proposes a multi-parametric linear programming model to quantify the economic impact of energy storage unit on distribution system operation
Summary
T HE penetration of renewable generation in distribution systems is growing rapidly [1]. The volatility of renewable generation and the increasing peak-valley gap imposes great challenges on the operation of distribution systems, where the dispatchable resource is rare. The biggest challenge for sizing ESU is the volatility of renewable power To address this issue, a stochastic optimization (SO) model is suggested in [5], [6] for planning a stand-alone power supply system. A stochastic model predictive control method is developed in [9] to consider receding horizon operation of ESU and ultra-short-term wind power forecast. In this way, the dynamic nature of power system dispatch is captured. The model entails the consideration of all horizons over the entire planning horizon in all scenarios at the same time, imposing a big challenge for computation
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