In this paper, we propose a novel transactive energy trading (TET) framework to deal with the economic issues in and technical distribution system operation holistic manner. particular, innovatively integrate bilateral mechanism optimal power flow (OPF) technique increase benefits individual participants, meanwhile ensure reliability security of operation. order resolve inherent conflict interests, Nash bargaining theory is used model TET problem, which further decomposed into multiperiod...

This paper proposes a data-driven distributionally robust co-optimization model for the peer-to-peer (P2P) energy trading and network operation of interconnected microgrids (MGs). In particular, three-phase unbalanced MG networks are considered to account implementation practices, emerging soft open point (SOP) technology is used flexible connection multi-MGs. First, management in individual MGs modeled as optimization (DRO) problem considering P2P options various operational constraints....

This paper proposes a bi-level peer-to-peer (P2P) multi-energy trading framework for coupled distribution network (DN) and district heating (DHN). At the lower level, each nodal agent represents its intra-nodal prosumers to optimize local energy scheduling P2P strategies based on modified Nash bargaining theory, distributed algorithm is then adopted enable individual agents make their autonomously only with sharing of information. Once lower-level settled, required submit net loads...

To account for the probability distribution information of uncertain scenarios, this letter proposes a novel interval power flow (NIPF) method based on hybrid sets. Firstly, NIPF model is established by integrating box-ellipsoid sets characterizing uncertainties in (IPF), which solved optimizing-scenario (OSM). Then, multivariate statistical analysis (MSAM) and chance-constrained programming-based (CCPM) are proposed determining optimal radii ellipsoid set side length box under two...

In this paper, we propose a distributed online voltage control algorithm for distribution networks with multiple photovoltaic (PV) systems based on dual-ascent method. Conventional algorithms implement only when the converge. However, our proposed is able to carry out immediately. particular, derive closed-form solution PV controllers locally update active and reactive power set points aiming at minimizing total loss maintaining bus voltages within acceptable ranges. The optimality...

This paper proposes a novel peer-to-peer (P2P) multi-grade energy trading design to encourage demand side flexibility locally absorb the uncertainty of renewable distributed resources (DERs) in distribution networks. In particular, reliability credit (RC) assignment method is developed for customers differentiate grades considering heterogeneity supplying DERs and consumption preferences customers. Later, an innovative P2P model introduced where different types are matched up with...

This paper proposes a central limit theorem-based method (CLTM) to overcome the conservatism of interval DC and AC power flow analysis under uncertainty renewable generation. Interval (IDCPF) models are solved by expressing bus angle active transmission as linear combinations nodal injections, then theorem is applied obtain high-confidence intervals variables. (IACPF) first applying optimizing-scenarios acquire more accurate affine arithmetic forms variables defined according obtained via...

This paper proposes a novel two-stage game-theoretic residential photovoltaic (PV) panels planning framework for distribution grids with potential PV prosumers. One innovative contribution is that location-allocation model integrated the energy sharing mechanism to increase economic benefits prosumers and meanwhile facilitate reasonable installation of panels. The optimization decisions formulated as model. In first stage, we develop Stackelberg game based stochastic bi-level determine...

We study the optimal location planning of renewable distributed generation (RDG) units by taking into account random uncertainties and load demand. In presence uncertainties, problem is naturally a two-stage stochastic mixed integer nonlinear programming problem, which hard to solve efficiently. Instead using traditional sampling methods or robust optimization methods, we propose novel analytical approach in this paper efficiently optimally. particular, expressions are derived for evaluating...

Construction of local nanostructures shins new light on separately modulate electric and thermal transport toward high thermoelectric performance.

In industrial informatics-enabled smart grids, machine learning approaches have exhibited high potential in data-driven electricity theft detection (ETD), whereas none of the existing studies pay sufficient attention to costs manually labeling massive sensing data during preparation. To address this defect, article develops a cost-effective ETD approach that significantly reduces without sacrificing reliability ETD. Specifically, is systematically realized via an intelligent deep active...

The triboelectric properties of the tribolayers are essential factors affecting current density nanogenerators (TENGs). To enhance density, composites have been developed to tune their properties. Previous studies reported enhanced TENG performance with composite materials, primarily based on composition, while chemical interactions between components less analyzed. In this study, we report a novel approach improve by introducing dipole-dipole nylon filter membrane and graphene oxide (GO)...

While many promising data-driven power system transient stability assessment (TSA) studies have been recently reported, very few of them further propose efficient solutions for follow-up control actions, e.g., generator tripping, against potential instability. To address this inadequacy, work develops an integrated monitoring and enhancement (TSMAE) approach that can reliably efficiently handle various emergency situations in real time. First, by introducing the emerging spatial-temporal...

To improve the resilience of distribution networks (DNs) in event extreme natural disasters such as typhoons and rainstorms, it is imperative to efficiently implement service restoration (DSR) restore loads soon possible. In previous studies, DSR has mainly adopted distributed resource model with droop or PQ control. This inhibits exploitation potential generators (DGs) load when DN loses support from upstream transmission network. Thus, this paper proposes a multi-resource collaborative...

The intrinsic uncertainties in the widespread distributed renewable energy resources pose considerable threats to secure and reliable operation of distribution networks (DNs). To fully absorb DN, this paper proposes a novel two-stage hybrid optimization approach for generalized storage systems (DGESSs) by integrating day-ahead optimal scheduling with realtime uncertainty mitigation. First, considering features large population DGESSs, an inner approximation-based aggregation model is...

In this article, a novel machine learning based data-driven pricing method is proposed for sharing rooftop photovoltaic (PV) generation and energy storage in an electrically interconnected residential building cluster (RBC). the studied problem, process modeled by leader-follower Stackelberg game where owner of PV system responsible self-generated operating ES devices. Meanwhile, local electricity consumers RBC choose their consumption with given internal prices. To track stochastic panel...

Abstract Bound states in the continuum (BICs) photonic crystals describe originally leaky Bloch modes that can become bounded when their radiation fields carry topological polarization singularities. However, singularities do not energy to far field, which limits efficiencies of BICs for light emitting applications. Here, we demonstrate a singular laser has channel second Brillouin zone and paired linearly polarized first zone. The presence enables lasing mode with higher quality factor than...