Although wind energy is volatile, the output of a wind-storage plant is partially dispatchable, making it a promising paradigm on the generation side. A grid-friendly wind-storage plant ought to be able to continuously output the desired power over a certain period of time. This paper proposes a dependable dynamic capacity provision scheme of a wind-storage plant over a daily horizon. It stipulates a minimum number of periods during which the committed capacity must be fulfilled and a maximum mismatch during the remaining periods when the desired power output is not achievable. In the general case, the day-ahead piecewise constant capacity provision results in a two-stage stochastic program formulated as a mixed-integer linear program. Specifically, for constant capacity provision, a decomposition algorithm is developed to determine the global optimal solution, and the complexity grows linearly with the number of scenarios. Given the committed capacity trajectory, the real-time operation problem is modeled as a four-state stochastic dynamic program. The discrete state-action values are derived recursively via the principle of optimality. Real-time dispatch actions are generated by using the action-value tabular leveraging inexact ultra-short-term forecasts. Numerical tests over one year demonstrate that the proposed method successfully fulfills reliable operation on 355 days and achieve an optimality gap of 9.47% compared with the ex-post optimum, which is comparable to model predictive control using exact 2–3-hour-ahead wind power forecasts.

With growing public awareness of decarbonization and increasing penetration of renewable generation, energy storage is in great need. Advanced adiabatic compressed air energy storage (AA-CAES) is capable of producing power, heating and cooling, making it an ideal choice of an environmental-friendly energy hub. This paper proposes an energy and exergy efficiency analysis for an AA-CAES based trigeneration energy hub. Impact of power storage and heat load supply rates on energy output efficiency and total exergy losses are analyzed. Based on the proposed model, optimal configuration of power storage and heat load supply rates can be determined under different purposes. According to basic thermodynamic principles, the proposed method calculates trigeneration capability estimates considering energy grade difference and multi-dimension energy distribution, which can demonstrate more energy conversion properties of the system. Case studies verify that the proposed method can provide various characteristic analyses for an energy hub and its application in actual systems proves computation accuracy. Integrative energy efficiency is improved compared to pursuing maximum electricity-to-electricity efficiency.

With the reduction of cost, large-capacity energy storage unit is playing an increasingly important role in modern power systems. When a merchant energy storage unit participates in the power market, its arbitrage problem can be modeled via a bilevel program. The lower-level problem simulates power market clearing and gives the nodal price, based on which the upper-level problem maximizes the arbitrage profit of energy storage. To solve this bilevel problem, the conventional method replaces the lower level problem with its KKT optimality conditions and further performs linearization. However, because the size of the market clearing problem grows with the scale of the power system and the number of periods, the resulting MILP (mixed-integer linear program) is very challenging to solve. This paper proposes a decomposition method to address the bilevel energy storage arbitrage problem. First, the locational marginal price at the storage connection node is expressed as a piecewise constant function in the storage bidding strategy, so the market clearing problem can be omitted. Then, the storage bidding problem is formulated as a mixed-integer linear program, which contains only a few binary variables. Numeric experiments validate the proposed method is exact and highly efficient.