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Hydrogen filling stations can provide hydrogen by consuming renewable electricity, and are an important part of the electric-hydrogen coupling system. An appropriate pricing mechanism for renewable-dominated hydrogen stations is urgently required. In this paper, we propose a dynamic pricing mechanism for such hydrogen stations enhanced by blockchain technology. First, a two-way decentralized trading mode is constructed in the electricity/hydrogen energy markets. Second, a series of propensity factors are integrated into the two-way pricing mechanism, which is a mathematical characterization of various real-time information in the system. Blockchain cross-chain interoperability technology can ensure seamless and effective performance of the proposed pricing mechanism. A case study is presented, and our simulation results show that our method has obvious advantages in improving trading profit and renewable energy utilization efficiency over the method of a constant energy price. Finally, we developed a double-chain blockchain decentralized application to illustrate how the system can implement a two-way dynamic pricing mechanism in an electric-hydrogen coupling system.

With the increase of the penetration rate of photovoltaic (PV) power plant in the power system, PV power fluctuation has become one of the important factors affecting the power quality. The energy storage system (ESS) is an effective way to smooth short-term PV power fluctuation and has been widely used. The control strategy is a key factor that will influence the smoothing effect and size of ESS. In this paper, by using ESS to smooth PV power fluctuation, we proposed a novel control strategy that can regulate the state of charge (SoC) of the battery and calculate the output power of ESS based on the super short-term prediction of PV power. Besides, the rolling prediction theory is introduced into the control strategy, which makes the regulating process be implemented at every sampling moment. Based on this control strategy, an optimal sizing model of ESS is built to analyze the economy of the combined system. Finally, actual data of a PV power plant are used for simulation and verification. The results demonstrate that the proposed strategy can smooth the PV power fluctuation effectively. The total cost and the power of exceeding limitation both are reduced.

Energy trading in the multi-energy market is affected by many uncertainties, especially the fluctuation of renewable energy sources and intermittent demand behavior of customers. The real-time energy management can effectively solve the impact of various uncertainties, ensure the instantaneous balance of energy and improve trading returns. A bidding strategy for multi-energy market is presented, in which reserve price adjustment and dynamic compensation mechanism is innovatively integrated into adaptive learning process. All energy trading participants conduct adaptive learning bidding adjustment based on real-time information in order to obtain higher transaction rate and transaction income. Meanwhile, adding dynamic compensation to the quoted price of fossil energy increases the consumption rate of renewable energy and reduces the emissions of pollutants. Furthermore, blockchain technology can ensure the seamless and effective performance of the presented bidding strategy. In the case study, the results show that our bidding strategy has an obvious advantage in social welfare and allocation efficiency than existing bidding strategies. Moreover, the problem of environmental pollution can be solved to a certain extent through flexible dynamic compensation. Finally, a decentralized application of blockchain is developed to demonstrate how the system could realize real-time energy management and dynamic trading in practice.