Energy storage, both short- and long-term, will play a vital role in the energy system of the future. One storage technology that provides high power and capacity and that can be operated without carbon emissions is compressed air energy storage (CAES). However, it is widely assumed that CAES plants are not economically feasible. In this context, a mixed-integer linear programming (MILP) model of the Huntorf CAES plant was developed for revenue maximization when participating in the day-ahead market and the minute-reserve market in Germany. The plant model included various plant variations (increased power and storage capacity, recuperation) and a water electrolyzer to produce hydrogen to be used in the combustion chamber of the CAES plant. The MILP model was applied to four use cases that represent a market-orientated operation of the plant. The objective was the maximization of revenue with regard to price spreads and operating costs. To simulate forecast uncertainties of the market... [more]

Network reconfiguration is an effective way to avoid severe, large-scale power outages and to improve the resilience of active distribution networks (ADNs). Furthermore, the rapid development of distributed energy resources (DERs) provides new perspectives for network reconfiguration. In this paper, the effect of network reconfiguration and DER collaboration on ADN’s resilient restoration are studied. The applications of DERs are fully explored. In order to achieve a better resilient performance, a detailed multiperiod model considering both reconfiguration and multiple DERs is established. Some linearization techniques are used to simplify the proposed model. Then, we build a rolling horizon optimization framework to solve the model. The framework eliminates the adverse effect of prediction errors and speeds up the calculation. By introducing predictions into strategy determination, the framework achieves a better restoration effect than the traditional greedy method. The proposed fra... [more]

We present a new two-level rolling horizon optimization framework applied to a zero-emissions coal-fueled solid-oxide fuel cell power plant with compressed air energy storage for peaking applications. Simulations are performed where the scaled hourly demand for the year 2014 from the Ontario, Canada market is met as closely as possible. It was found that the proposed two-level strategy, by slowly adjusting the SOFC stack power upstream of the storage section, can improve load-following performance by 86% compared to the single-level optimization method proposed previously. A performance analysis indicates that the proposed approach uses the available storage volume to almost its maximum potential, with little improvement possible without changing the system itself. Further improvement to load-following is possible by increasing storage volumes, but with diminishing returns. Using an economically-focused objective function can improve annual revenue generation by as much as 6.5%, but no... [more]