Energy storage and demand response (DR) resources, in combination with intermittent renewable generation, are expected to provide domestic customers with the ability to reducing their electricity consumption. This study highlights the role that an intelligent battery control, in combination with solar generation, could play to increase renewable uptake while reducing customers’ electricity bills without intruding on people's daily life. The optimal performance of a home energy management system (HEMS) is investigated through a range of interventions, leading to different levels of customer weariness and consumption patterns. Thus, the DR is applied with efficient and specific control of domestic appliances through load shifting and curtailment. Regarding the uncertainty associated with the photovoltaic generation, a chance‐constrained (CC) optimal scheduling is considered subject to the operation constraints from each power component in the HEMS. By applying distributionally robust optimisation, the ambiguity set is accurately built for this distributionally robust CC (DRCC) problem without the need for any probability distribution associated with uncertainty. Based on the greatly altered consumption profiles in this study, the proposed DRCC‐HEMS is proven to be optimally effective and computationally efficient while considering uncertainty.

The active distribution network management (ADNM) equipped by active distribution networks (ADNs) can enhance the resilience of the network after failure. This paper proposes a novel comprehensive post-event network restoration model and its chance-constrained variant for ADN that considering the dispatch of distributed generation (DG), energy storage system (ESS), demand response (DR), static var compensator (SVC), and network reconfiguration to fully investigate the potential of the ADNM in network restoration. In this paper, the line failure, bus failure, and the uncertainty from load and DG output forecast error are considered. Specifically, the line and bus failures are modeled by an enhanced fictitious network technique, while the uncertainty of load and DG output forecast is modeled by the chance-constrained optimization. The power flow is described by a linearized DistFlow model, and thus the deterministic and chance-constrained network restoration models are programmed by the mixed-integer linear programming (MILP). The proposed deterministic and chance-constrained restoration models are tested on the IEEE 33 bus system. Results demonstrate the effectiveness of the proposed deterministic and chance-constrained network restoration model.

Despite it is essential to distribution network economics, computing energy loss for most major networks is still a tough task due to the absence of full monitoring. Assume the line current follows a normal distribution, the sum of its square is a linear combination of independent chi-square variables, which follows a generalized noncentral chi-square distribution. Based on this finding, we develop a new probability-based analytical method to estimate distribution network energy losses efficiently. The proposed analytical method requires merely the knowledge of mean and variance of line current as well as the line resistance while provides a closed-form formula of the probability characteristics of energy loss. The method is demonstrated on a three-feeder radial network.

Imbalance-induced energy loss accounts for a significant part of energy loss in low-voltage distribution networks. However, due to the prohibitive cost of full monitoring, high-resolution time-series data of low-voltage feeder currents are usually absent for accurate energy loss computation. This letter presents a novel method to estimate the probability distribution of imbalance-induced energy loss. In the proposed method, only the line resistance and statistical data (mean and covariance) of the phase currents are required to estimate imbalanced networks' energy loss. Furthermore, a Bayesian statistics-based data-driven method is also proposed to get the mean and covariance value of phase current with minimal measurements. Numerical tests on a real three-phase imbalanced circuit show the proposed approach obtains the probability distribution of imbalance-induced energy loss with a low estimating error and low field measurement frequency.

In embedded avionic system, time and space partitioning is introduced to achieve stable and secure performance. Tasks must meet the certain time constraints when executed. Proper scheduling scheme is needed to ensure that the hardware is adequately used and all time and function requirements are met. However, existing partition aware scheduling works for no more than 2 partitions only. In this paper, a novel scheduling scheme of task allocation among multiple partitions is proposed. The scheme is based on balancing load of each partition and preferentially execute tasks that can optimize worst respond time. Experiment proved this scheme can reduce the average worst case respond time on the premise of schedulability, making tasks effectively executed.

Under the background of energy internet and the targets of carbon peak and carbon neutrality, new energy development paths need to be explored. A coordinated optimal operation method of electricity and gas integrated distribution system (EGIDS) considering electricity-gas demand response is proposed. A gradually linearized method is used to deal with the power flow equation constraints of the distribution system, and the second-order cone relaxation method and piecewise linearization method are used to constrain the natural gas power flow equation. So that the nonlinear optimal problem of the EGIDS is transformed into a mixed integer linear programming model. Then the combined electricity-gas demand response model was established and introduced into the EGIDS. A case study is implemented on a modified 51-bus distribution network with a 20-node natural gas network to demonstrate the superiority of the proposed EGIDS model. The case study shows that the method of EGIDS considering combined electricity-gas demand response (CDR) can better improve the economics of the distribution system operation and the accommodation of renewable energy.