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Energy management of hybrid resources has become a critical issue in integrated energy system analysis. In this study, as a self-regulating demand response (DR) management mechanism, deferrable electrolyzers are used as a main controlled resource in a hydrogen-based clean energy hub (CEH), which includes a traditional generation plant (TGP), a low-carbon generation plant (LGP), and wind energy. Based on the hysteresis control model for aggregated electrolyzers, a comfort-constrained optimal energy state regulation (OESR) control strategy is implemented to model the deregulation feature of aggregated electrolyzers. The electrolyzers' population can be integrated as a controlled efficient power plant (EPP) to provide the virtual spinning reserve for CEH. As a flexible and self-regulating participant, the electrolyzer-based EPP is integrated into the hybrid resource constrained optimization model; this reduces the total cost of CEH and carbon emissions and improves the integration of wind energy. Combined with TGP, LGP, and wind energy, the simulation results show that the deployment of aggregated electrolyzers on both the supply and demand sides of the CEH contributes to significant amounts of low-carbon hydrogen. The simulation also illustrates that the DR control strategy has a positive effect on active power and reserve re-dispatch.

Unlike some thermostatically controlled appliances (TCAs) with small capacities, Central Air-conditioner (CAC) has huge potential for demand response because of its large capacity. This paper presents a new CAC control strategy under multiple constraints. The CAC is modeled by three main modules: CAC central unit, water pumps, and temperature simulation of terminal users. The CAC's power consumption is mainly determined by users' load ratio. As the information and communication system have become the central nervous system of the smart grid, big data analysis is of great significance. Assuming that reliable two-way communication systems are preset, an integrated parameter priority list (IPPL) control strategy is used to control and monitor CAC. A new intelligent algorithm, Space Exploration and Unimodal Region Elimination (SEUMRE) algorithm, is introduced for solving the optimization problem of demand response targets generation under multiple constraints with the help of big data analysis. In this paper, influences and constrain factors, such as price and users' comfortable levels are taken into account to satisfy the need of actual situation. Simulation results show that the proposed approach, when comparing with other typical optimization algorithms, yields better performances and efficiency.

Optimal operation of the active distribution networks (ADN) is essential to keep its safety, reliability and economy. With the integration of multiple controllable resources, the distribution networks are facing more challenges in which the optimization strategy is the key. This paper establishes the optimal operation model of the ADN considering a diversity of controllable resources including energy storage devices, distributed generators, voltage regulators and switchable capacitor banks. The objective functions contain reducing the power losses and improving the voltage profiles. To solve the optimization problem, the Kriging model based Improved Surrogate Optimization-Mixed-Integer (ISO-MI) algorithm is proposed in this paper. The Kriging model is applied to approximate the complicated distribution networks, which speeds up the solving process. Finally, the accuracy of the Kriging model is validated and the efficiency among the proposed method, genetic algorithm (GA) and particle swarm optimization (PSO) is compared in an unbalanced IEEE-123 nodes test feeder. The results demonstrate that the proposed method has better performance than GA and PSO.