Abstract
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.

Abstract
This paper presents an integrated approach for assessing the impact that distributed energy resources (DERs), including intermittent photovoltaic (PV) generation, might have on the reliability performance of power networks. A test distribution system, based on a typical urban MV and LV networks in the UK, is modelled and used to investigate potential benefits of the local renewable generation, demand-manageable loads and coordinated energy storage. The conventional Monte Carlo method is modified to include time-variation of electricity demand profiles and failure rates of network components. Additionally, a theoretical interruption model is employed to assess more accurately the moment in time when interruptions to electricity customers are likely to occur. Accordingly, the impact of the spatio-temporal variation of DERs on reliability performance is quantified in terms of the effect of network outages. The potential benefits from smart grid functionalities are assessed through both system- and customer-oriented reliability indices, with special attention to energy not supplied to customers, as well as frequency and duration of supply interruptions. The paper also discusses deployment of an intelligent energy management system to control local energy generation-storage-demand resources that can resolve uncertainties in renewable-based generation and ensure highly reliable and continuous supply to all connected customers.

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Renewable energy resources are integrated into microgrid through power converters. During abnormal situations like PV intermittency and load variations, the controller plays the most key role. In this paper, a combined control method is proposed and it consists of model predictive control (MPC) and sliding mode control (SMC) for power converters. The voltage source inverter (VSI) is controlled by using MPC, while the DC-DC boost converter is controlled by using SMC. Discrete state space model of interlinking inverter, LC-filter and load is used to predict the future trend of load voltage for each of the eight switching states. On the other hand, the detailed SMC model for boost converter is analyzed for fast convergence rate with finite-time convergence and chattering free signals. A comparison between the proposed control method and the control method based on PID is presented to illustrate the superiority of the proposed method. The performance of the proposed control strategy is verified by the simulation results. The controller performance is analyzed under different scenarios including fluctuating generation and variable loads. Unlike conventional PID controllers, the proposed strategy is simple and robust with a fast-dynamic response.

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