This paper proposes a concept of generalized energy storage (GES) to facilitate the integration of large-scale heterogeneous flexible resources with electric/thermal energy storage capacity to participate in multiple markets. First, a generalized state variable referred to as degree of satisfaction (DoS) is defined, and dynamic models with a unified form are derived for different types of GESs. Second, a real-time market-based coordination framework is proposed to facilitate control, and ensure user privacy and device security. Demand curves of different GESs are then developed based on DoS to express their demand urgencies as well as flexibilities. Furthermore, a low-dimensional aggregate dynamic model of a GES cluster is derived thanks to the DoS-equality control feature provided by the design of demand curve. At last, an optimization model for a large-scale GESs to participate in both the energy market and regulation market is established based on the aggregate model. Simulations results demonstrate that the optimization algorithm could effectively reduce the total cost of an aggregator. Additionally, the proposed coordination method has high tracking accuracy and could well satisfy users' diversified power demand.

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Summary As essential components in the energy hub, the multi‐energy devices are diverse, and their working conditions are complex. The relevant energy efficiency level of devices in the energy hub is also an important factor for the long‐term operation of the energy systems. The device efficiency is always considered constant in modeling the energy hub. However, it is necessary to study the efficiency variation and optimization of devices under off‐design working conditions. In this paper, we propose a coordinated dispatch method of the multi‐energy system considering the device off‐design characteristics in the energy hub. The energy efficiency optimization is included in the dispatch process. Then, we use the analysis target cascade (ATC) to obtain the optimal solution. The method is tested in the system, including distribution network, natural gas network, and energy hub. Compared to the traditional method, the proposed method can describe the operation of the whole system more authentically and thereby prove the effectiveness of energy efficiency optimization. The corresponding nonlinear constraints are linearized by piecewise linearization. Besides, we discuss the results of energy efficiency optimization at different load levels and capacity combinations, which is of guiding significance to optimize system working conditions.

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