The operating modes directly influence the system performance of combined cooling, heating and power (CCHP) systems. To improve the comprehensive operational performance, in this paper, based on two typical operating modes, i.e., following the electric load (FEL) and following the thermal load (FTL), a novel optimal operational strategy depending on an integrated performance criterion (IPC) is proposed. Using this strategy, the whole operating space of the CCHP system can be divided into several regions by one to three border surfaces determined by energy requirements and the IPC. Then the operating point of the CCHP system is located in a corresponding operating mode region to achieve improved IPC. The IPC simultaneously considers the primary energy consumption, the operational cost and the carbon dioxide emissions. The carbon tax is also included in the operational cost. A hypothetical hotel building is modeled as the reference system to evaluate the proposed strategy. Simulation results show that, for the same CCHP system of the hypothetical hotel, the IPC with the proposed strategy is better than that with the traditional strategy. The proposed strategy can reflect and balance the influences of energy requirements, energy prices and emissions effectively.

Virtual power plant (VPP) has gradually become a key technology to the increasing penetration of renewable energy. The uncertainty and variability of renewable energy and load demand pose significant challenges to the efficiency and stability of VPP's operation. In this paper, a data-driven two-stage stochastic robust optimization (SRO) scheduling model is proposed for a VPP considering wind power, solar power, load demand, and market price uncertainties. The objective is to maximize the profit of the VPP in the energy market and minimize the total system cost of the VPP in the reserve market under the worst-case realization of the uncertainties. The Dirichlet process mixture model (DPMM) and variational inference algorithm are employed for constructing the data-driven uncertainty ambiguity set considering the correlations among multiple uncertainties. The tailored column-and-constraint generation algorithm is developed to solve the SRO model iteratively by reformulating the second stage with the application of the Karush-Kuhn-Tucker conditions. Results from a case study illustrate the effectiveness and superiority of the proposed model.

This article considers the consensus problem of multiple Euler–Lagrange systems subject to unavailable velocity information and limited communication resources. First, a new event-triggered communication scheme, based on designing a virtual system for each agent, is proposed to achieve the coordination task without velocity information. The event-triggering conditions only rely on the states of virtual systems and allow agents to transmit virtual states only at some discrete time instants. Under the proposed strategy, the control objective can be accomplished with lower communication and measurement costs. Second, we consider the time delay effects in each information channel with event-triggered communication. For the proposed event-triggered scheme, the Zeno triggering is excluded. Finally, two simulation examples with six two-linked robot manipulator arms are given to demonstrate the effectiveness of the proposed scheme.

The irradiation forecasting technology is important for the effective utilization of solar power. Existing irradiation forecasting methods have achieved excellent performance with a massive amount of data in a centralized way. However, concerns about privacy protection and data security, which may arise in the process of data collection and transmission from distributed points to the centralized server, pose challenges to current forecasting methods. In this paper, a novel federated probabilistic forecasting scheme of solar irradiation is proposed based on deep learning, variational Bayesian inference, and federated learning. In this scheme, the training data are stored and computed in local IoT devices, only forecasting models are shared. Two real-world datasets from SolarGIS and NSRDB, and one benchmark dataset of Folsom are used to verify the feasibility and performance of the federated-based scheme. Comprehensive case studies are conducted to analyze the performance of the proposed scheme in multi-horizon. And the effects of its components are evaluated. Compared with other state-of-the-art probabilistic centralized models, when data can be shared, the proposed scheme achieves competitive forecasting performance on the basis of data privacy protection; when data-sharing is unavailable, due to the cooperative nature inherent (model-sharing) of federated learning, the performance advantage of the proposed scheme is more obvious.