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• A novel multi-stage scheduling method is presented for a multi-energy system. • Robustness and nonanticipativity are essential for multi-energy system operation security. • Proposed proposition accurately describes the relationship between hydropower outputs and system security. • Established models effectively guarantee both operation security and economy of the multi-energy systems. This paper explores a new multi-stage robust scheduling method for cascade hydropower system considering nonanticipativity. In the existing scheduling methods, the nonanticipativity and robustness of solutions are not considered simultaneously, which may result in infeasibility in the actual operation of energy system. And, due to concerns about operation security, the available reserve of the hydropower system are not fully utilized. To this end, this paper proposes a novel multi-stage robust scheduling method for cascade hydropower system to guarantee the feasibility of scheduling solutions and increase the utilization of hydropower reserves. Specifically, first, an important proposition is presented to accurately describe the relationship between the hydropower output and the operation security of electricity system, indeed, the nonanticipativity and robustness are considered against the uncertainties of loads and renewable outputs. Second, based on this proposition, a pre-scheduling model of cascade hydropower is established to ensure the security of the whole energy system against the uncertainties of natural inflows. Last, within the safe ranges, a real-time economic dispatch model is solved to obtain final scheduling decisions that guarantee both operation security and economy of energy system. Numerical tests implemented on a real energy system verify the effectiveness of proposed method. [ABSTRACT FROM AUTHOR]

• A novel look-ahead risk-constrained framework is proposed. • Day-ahead and look-ahead scheduling strategies of the energy hub are proposed. • The proposed framework can effectively reduce the operational cost of the energy hub. • Demand response programs increase system flexibility and reduce operational cost. Energy hubs or multi-energy systems facilitate an improvement in their efficiency and flexibility for different energy supplies. However, the application of energy hubs results in scheduling challenges for the entire system due to the mutual impact of various energy forms and the increase in the number of uncertain variables. One of the parameters of an energy storage system, which is an indispensable component of an energy hub, is the state-of-charge at the end of the first day of scheduling. This parameter is crucial because its final state represents the initial state-of-charge for the second day, which has an effect on the operational cost of the second day. Based on a look-ahead risk-constrained technique, this work investigates the optimal scheduling of an energy hub for two days with the aim of minimizing the total operational cost. The state-of-charges of an energy storage system at the end of the first day is optimized by considering the scheduling result on the second day. The uncertainties of the two days are demonstrated via various scenarios. Additionally, a demand response program is adopted to reduce the total operational cost and increase the system flexibility. The results validate the feasibility of using look-ahead risk-constrained scheduling in an energy hub and indicate that a reduction in the total operational cost is achieved. [ABSTRACT FROM AUTHOR]

Data clustering is an instrumental tool in the area of energy resource management. One problem with conventional clustering is that it does not take the final use of the clustered data into account, which may lead to a very suboptimal use of energy or computational resources. When clustered data are used by a decision-making entity, it turns out that significant gains can be obtained by tailoring the clustering scheme to the final task performed by the decision-making entity. The key to having good final performance is to automatically extract the important attributes of the data space that are inherently relevant to the subsequent decision-making entity, and partition the data space based on these attributes instead of partitioning the data space based on predefined conventional metrics. For this purpose, we formulate the framework of decision-making oriented clustering and propose an algorithm providing a decision-based partition of the data space and good representative decisions. By applying this novel framework and algorithm to a typical problem of real-time pricing and that of power consumption scheduling, we obtain several insightful analytical results such as the expression of the best representative price profiles for real-time pricing and a very significant reduction in terms of required clusters to perform power consumption scheduling as shown by our simulations. • General framework for designing clustering methods in energy systems optimization problems. • Novel clustering method (Decision-Making Oriented Clustering) by taking the use of clustered data in energy system optimization problems into account. • Automatically extract the features relevant to the optimization problems. • Application of the new framework to two important problems: the pricing problem and the power consumption scheduling. • Significant performance improvement compared to existing clustering methods. [ABSTRACT FROM AUTHOR]

• This paper proposes an optimal scheduling strategy for interconnected microgrids based on P2P trading. • The proposed optimization model considers the grid structure of microgrids and uses the robust optimization method to express uncertainty. • The incentive mechanism based on Nash bargaining is used to encourage the energy trading and realize fair benefit sharing. • Alternating direction method of multipliers (ADMM) is used to achieve the decentralized solution of the proposed model. Based on the peer-to-peer (P2P) trading framework of interconnected microgrids, this paper proposes an optimal scheduling strategy for interconnected microgrids considering the uncertainty of wind power, to minimize the operation cost of the individual microgrid and obtain profits through active energy trading with other microgrids. The proposed optimization model considers the grid structure of microgrids and uses the robust optimization (RO) method to express uncertainty. The incentive mechanism based on Nash bargaining is used to encourage the individual microgrid to trade energy actively and realize fair benefit sharing. In order to protect the privacy of the individual microgrid, the alternating direction method of multipliers (ADMM) is used to achieve the decentralized solution of the proposed model. Simulation analysis based on four interconnected microgrids shows that each microgrid can ultimately make profits in the trading, verifying the effectiveness and fairness of the proposed method. [ABSTRACT FROM AUTHOR]