Energy hub system (EHS) incorporating multiple energy carriers, storage and renewables can efficiently coordinate various energy resources to optimally satisfy energy demand. However, the intermittency of renewable generation poses great challenges on optimal EHS operation.

This paper applies optimal power flow (OPF) to evaluate and maximize network benefits of demand-side management (DSM). The benefits are quantified in terms of the ability of demand-responsive loads to relieve upstream network constraints and provide ancillary services, such as operating reserve. The study incorporates detailed information on the load structure and composition, and allows the potential network benefits, which could be obtained through management of different load types, to be quantified and compared. It is demonstrated that the actual network location of demand-manageable load has an important influence on the effectiveness of the applied DSM scheme, since the characteristics of the loads and their interconnecting networks vary from one location to another. Consequently, some network locations are more favorable for implementation of DSM, and OPF can be applied to determine the optimal allocation of demand-side resources. The effectiveness of the presented approach is assessed using a time-sequential OPF applied to typical radial and meshed U.K. distribution networks. The results of the analysis suggest that network operators could not just participate in, but also encourage and add value to the implementation of specific DSM schemes at the optimum network locations in order to maximize the total benefit from DSM.

Power-to-gas (P2G) can convert excessive renewable energy into hydrogen via electrolysis, which can then be transported by natural gas systems to bypass constrained electricity systems. However, the injection of hydrogen could impact gas quality since gas composition fundamentally changes, adversely effecting the combustion, safety and lifespan of appliances. This paper develops a new gas quality management scheme for hydrogen injection into natural gas systems produced from excessive wind power. It introduces four gas quality indices for the integrated electricity and gas system (IEGS) measuring gas quality, considering the coordinated operation of tightly coupled infrastructures. To maintain gas quality under an acceptable range, the gas mixture of nitrogen and liquid petroleum gas with hydrogen is adopted to address the gas quality violation caused by hydrogen injection. A distributionally robust optimization (DRO) modelled by Kullback-Leibler (KL) divergence-based ambiguity set is applied to flexibly control the robustness to capture wind uncertainty. Case studies demonstrate that wind power is maximally utilized and gas mixture is effectively managed, thus improving both gas quality and performance of IEGS. The work can benefit system operators with i) ensured gas quality under hydrogen injection ii) low system operation cost and iii) high renewable energy penetration

This paper addresses some general issues related to the difficulties and uncertainties during the assessment and characterization of micro and small-scale wind-based generation in urban areas. This paper proposes four generic wind turbine models, which could be used for the analysis and selection of optimal wind turbines in target applications, as they accurately represent the range of wind turbines currently available on the market. The analysis in this paper compares results for the expected annual energy outputs and cost-benefit analysis obtained using steady state and dynamic wind turbine models of actual and generic micro/small wind turbines, applying both low-resolution and high-resolution measurements of available wind energy resources. The presented results demonstrate the significance of the accurate assessment of wind resources in urban areas, as well as the importance of the correct modeling of wind turbine characteristics. It is shown that substantial errors, typically overestimating wind turbine performance, are obtained if standard assumptions and current recommendations are used for the analysis of wind turbine performance.

This paper develops a novel two-stage coordinated volt-pressure optimization (VPO) for integrated energy systems (IES) networked with energy hubs considering renewable energy sources. The promising power-to-gas (P2G) facilities are used for improving the interdependency of the IES. The proposed VPO contains the traditional volt-VAR optimization functionality to mitigate the voltage deviation while ensuring a satisfying gas quality due to the hydrogen mixture. In addition to the conventional voltage regulating devices, i.e., on-load tap changers and capacitor banks, P2G converter and gas storage are used to address the voltage fluctuation problem caused by renewable penetration. Moreover, an effective two-stage distributionally robust optimization (DRO) based on Wasserstein metric is utilized to capture the renewable uncertainty with tractable robust counterpart reformulations. The Wasserstein-metric based ambiguity set enables to provide additional flexibility hedging against renewable uncertainty. Extensive case studies are conducted in a modified IEEE 33-bus system connected with a 20-node gas system. The proposed VPO provides a voltage-regulated economic operation scheme with gas quality ensured that contributes to high-quality but low-cost multi-energy supply to customers.

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.