Online Optimization in Power Systems With High Penetration of Renewable Generation:Advances and Prospects

Traditionally,offline optimization of power systems is acceptable due to the largely predictable loads and reliable generation.The increasing penetration of fluctuating renewable generation and internet-of-things devices allowing for fine-grained controllability of loads have led to the diminishing applicability of offline optimization in the power systems domain,and have redirected attention to online optimization methods.However,online optimization is a broad topic that can be applied in and motivated by different settings,operated on different time scales,and built on different theoretical foundations.This paper reviews the various types of online optimization techniques used in the power systems domain and aims to make clear the distinction between the most common techniques used.In particular,we introduce and compare four distinct techniques used covering the breadth of online optimization techniques used in the power systems domain,i.e.,optimization-guided dynamic control,feedback optimization for single-period problems,Lyapunov-based optimization,and online convex optimization techniques for multi-period problems.Lastly,we recommend some potential future directions for online optimization in the power systems domain.

Two-Stage Robust Optimization Under Decision Dependent Uncertainty

In the conventional robust optimization(RO)context,the uncertainty is regarded as residing in a predetermined and fixed uncertainty set.In many applications,however,uncertainties are affected by decisions,making the current RO framework inapplicable.This paper investigates a class of two-stage RO problems that involve decision-dependent uncertainties.We introduce a class of polyhedral uncertainty sets whose right-hand-side vector has a dependency on the here-and-now decisions and seek to derive the exact optimal wait-and-see decisions for the second-stage problem.A novel iterative algorithm based on the Benders dual decomposition is proposed where advanced optimality cuts and feasibility cuts are designed to incorporate the uncertainty-decision coupling.The computational tractability,robust feasibility and optimality,and convergence performance of the proposed algorithm are guaranteed with theoretical proof.Four motivating application examples that feature the decision-dependent uncertainties are provided.Finally,the proposed solution methodology is verified by conducting case studies on the pre-disaster highway investment problem.

New framework of low-carbon city development of China:Underground space based integrated energy systems

Cities play a vital role in social development,which contribute to more than 70%of global carbon emission.Low-carbon city construction and decarbonization of the energy sector are the critical strategies to cope with the increasingly serious climate change problems,and low-carbon technologies have attracted extensive attention.However,the potential of such technologies to reduce carbon emissions is constrained by various factors,such as space,operational environment,and safety concerns.As an essential territorial natural resource,underground space can provide large-scale and stable space support for existing low-carbon technologies.Integrating underground space and low-carbon technologies could be a promising approach towards carbon neutrality,and hence,warrants further exploration.First,a comprehensive review of the existing low-carbon technologies including the technical bottlenecks is presented.Second,the features of underground space and its low carbon potential are summarized.Moreover,a framework for the underground space based integrated energy system is proposed,including system configuration,operational mechanisms,and the resulting benefits.Finally,the research prospect and key challenges required to be settled are highlighted.

Continuous Fe^(3+)releasing enabled complete reconstruction of Ni-based MOF for promoted water oxidation in industrial alkali

Most oxygen evolution reaction(OER)electrocatalysts show poor stability under industrial alkaline conditions(20–30 wt.%KOH).Therefore,it is essential to develop stable,efficient,and low-cost OER catalysts for industrial water electrolysis.Herein,we present a straightforward approach for the complete electrochemical reconstruction of Ni-BDC,a Ni-based metal-organic framework,for OER.This method involves the continuous release of Fe^(3+)from Fe foam counter electrode in a high-concentration(6.0 M,25 wt.%)KOH solution.The continuously Fe^(3+)releasing not only realizes in situ Fe^(3+)doping,but also introduces abundant defects in the obtained catalyst during cyclic voltammetry activation,thereby accelerating the electrochemical reconstruction.The reconstructed OER catalyst(Fe-doped nickel hydroxide/oxyhydroxide nanosheets supported on Ni foam,Fe-NiO_(x)(OH)y/NF)manifests a low overpotential of 217 mV at 10 mA cm^(-2)and 263 m V at 100 m A cm^(-2)in 1.0 M KOH.Noteworthy,the Fe-NiO_(x)(OH)_(y)/NF also demonstrates high stability in 30 wt.%KOH.This strategy of regulating the electrochemical reconstruction process sheds light on the construction of stable and efficient OER catalysts for industrial water electrolysis.

Online distributed tracking of generalized Nash equilibrium on physical networks——Closing the Loop via Measurement Feedback

In generalized Nash equilibrium(GNE)seeking problems over physical networks such as power grids,the enforcement of network constraints and time-varying environment may bring high computational costs.Developing online algorithms is recognized as a promising method to cope with this challenge,where the task of computing system states is replaced by directly using measured values from the physical network.In this paper,we propose an online distributed algorithm via measurement feedback to track the GNE in a time-varying networked resource sharing market.Regarding that some system states are not measurable and measurement noise always exists,a dynamic state estimator is incorporated based on a Kalman filter,rendering a closed-loop dynamics of measurement-feedback driven online algorithm.We prove that,with a fixed step size,this online algorithm converges to a neighborhood of the GNE in expectation.Numerical simulations validate the theoretical results.