Market Equilibrium Based on Cloud-edge Collaboration

Market participants can only bid with lagged information disclosure under the existing market mechanism,which can lead to information asymmetry and irrational market behavior,thus influencing market efficiency.To promote rational bidding behavior of market participants and improve market efficiency,a novel electricity market mechanism based on cloudedge collaboration is proposed in this paper.Critical market information,called residual demand curve,is published to market participants in real-time on the cloud side,while participants on the edge side are allowed to adjust their bids according to the information disclosure prior to closure gate.The proposed mechanism can encourage rational bids in an incentive-compatible way through the process of dynamic equilibrium while protecting participants’privacy.This paper further formulates the mathematical model of market equilibrium to simulate the process of each market participant’s strategic bidding behavior towards equilibrium.A case study based on the IEEE 30-bus system shows the proposed market mechanism can effectively guide bidding behavior of market participants,while condensing exchanged information and protecting privacy of participants.

Carbon-neutral pathway to mitigating transport-power grid cross-sector effects

Dear Editor,The growing demand for transportation energy has brought increasing challenges to reducing greenhouse gas emissions.Currently,many countries or regions have proposed solutions to achieve carbon-neutral transportation such as the rapid expansion of the global electric vehicle(EV)market.However,these benefits are not free.Before the goal of decarbonization in electricity is achieved,the“pseudo net zero emissions”effect of the transportation sector will inevitably be accompanied by a quiet shifting of carbon responsibility.1 This shift is undoubtedly fatal for both net zero emissions at the national level and carbon responsibility at the industry level;however,the corresponding effects have not yet been clarified.

Soft Open Point Planning in Renewable-dominated Distribution Grids with Building Thermal Storage

With an increasing integration of intermittent distributed energy resources(DERs),the consequent voltage excursion and thermal overloading issues limit the self-sufficiency of distribution networks(DNs).The concept of soft open point(SOP)has been proposed as a promising solution to improve the hosting capacity of DNs.In this paper,considering the ability of building thermal storage(BTS)to increase the penetration of renewable energy in DNs,we provide an optimal planning framework for SOP and DER.The optimal planning model is aimed at minimizing the investment and operational costs while respecting various constraints,including the self-sufficiency requirement of the DN,SOP,building thermal storage capacity and DER operations,etc.A steady-state SOP model is formulated and linearized to be incorporated into the planning framework.To make full use of the BTS flexibility provided by ubiquitous buildings,a differential equation model for building thermal dynamics is formulated.A hybrid stochastic/robust optimization approach is adopted to depict the uncertainties in renewable energy and market prices.IEEE 33-bus feeder and a realistic DN in the metropolitan area of Caracas are tested to validate the effectiveness of the proposed framework and method.Case studies show that SOP/BTS plays a complementary and coordinated coupling role in the thermo-electric system,thereby effectively improving the hosting capacity and self-sufficiency of DNs.

Sharing Economy in Local Energy Markets

With an increase in the electrification of end-use sectors,various resources on the demand side provide great flexibility potential for system operation,which also leads to problems such as the strong randomness of power consumption behavior,the low utilization rate of flexible resources,and difficulties in cost recovery.With the core idea of“access over ownership”,the concept of the sharing economy has gained substantial popularity in the local energy market in recent years.Thus,we provide an overview of the potential market design for the sharing economy in local energy markets(LEMs)and conduct a detailed review of research related to local energy sharing,enabling technologies,and potential practices.This paper can provide a useful reference and insights for the activation of demand-side flexibility potential.Hopefully,this paper can also provide novel insights into the development and further integration of the sharing economy in LEMs.

Exploiting Flexibility of Integrated Demand Response to Alleviate Power Flow Violation During Line Tripping Contingency

Multi-energy integrations provide great opportunities for economic and efficient resource utilization. In the meantime, power system operation requires enough flexible resources to deal with contingencies such as transmission line tripping. Besides economic benefits, this paper focuses on the security benefits that can be provided by multi-energy integrations. This paper first proposes an operation scheme to coordinate multiple energy production and local system consumption considering transmission networks. The integrated flexibility model, constructed by the feasible region of integrated demand response(IDR), is then formulated to aggregate and describe local flexibility. Combined with system security constraints, a multi-energy system operation model is formulated to schedule multiple energy production, transmission, and consumption. The effects of local system flexibility on alleviating power flow violations during N-1 line tripping contingencies are then analyzed through a multi-energy system case. The results show that local system flexibility can not only reduce the system operation costs, but also reduce the probability of power flow congestion or violations by approximately 68.8% during N-1 line tripping contingencies.

An Efficient Method for Identifying the Inactive Transmission Constraints in a Network-Constrained Unit Commitment

Network-constrained unit commitment(NCUC)is one of the most widely used applications in power system and electricity market operations.According to empirical evidence,some of the transmission constraints in a NCUC are inactive.Identifying and eliminating these inactive constraints can improve the efficiency.In this paper,an efficient method is first proposed for identifying the inactive transmission constraints.The physical and economic insights of NCUC are carefully considered and utilized.Both the generating costs and power transfer distribution factor(PTDF)are considered.Not only redundant constraints but also non-binding constraints can be identified via the proposed method.An acceleration method that combines relaxation-based neighborhood search and improved relaxation inducement is proposed for further reducing the computation time.The case study shows that the proposed method can significantly reduce the number of transmission constraints and substantially improve the efficiency of NCUC without impacting the optimality.

新型冠状病毒肺炎疫情对中国2020年碳排放的影响

中国的二氧化碳减排工作一直承受着国际社会的压力.新型冠状病毒肺炎疫情(COVID-19,简称"新冠疫情")对全球碳排放产生了显著影响.准确估算新冠疫情对中国二氧化碳排放量及其排放趋势的影响,对评估中国对全球变化的贡献有重要意义.本研究系统构建了基于活动数据的中国省级日碳排放数据库,对涵盖了化石燃料燃烧和水泥生产造成的所有人为源二氧化碳排放进行定量化的动态评估.本研究发现中国各省的碳排放在新冠疫情期间受到不同程度的影响.江苏、湖北和浙江三省的碳排放受疫情影响最严重,其碳排放减少量分别占总排放减少量的19.4%、17.0%和12.5%.青海省和西藏自治区受疫情的影响最小.从排放部门方面看,电力部门是多数省份减排量的主要贡献源,对于湖北、重庆、青海和福建等省份,工业部门是碳减排的主要贡献源.对于北京、天津这样的大城市以及四川和黑龙江两省,新冠疫情对交通部门的减排作用最显著.从疫情对碳排放的时间影响看,江苏省的碳排放在2020年1和2月份下降最多,3月份之后,湖北省的碳排放减少量最为突出.而宁夏回族自治区、新疆维吾尔自治区、福建省、陕西省和云南省受疫情影响较小的地区3月份的碳排放已经率先恢复到2019年同期水平.进入4月份,除湖北省的碳排放相比2019年同期下降4.9 Mt二氧化碳外,其他省份均接近或已经恢复到2019年同期水平.

Incentive mechanism for sharing distributed energy resources

To improve the controllability and utilization of distributed energy resources(DERs),distribution-level electricity markets based on consumers’bidding and offers have been proposed.However,the transaction costs will dramatically increase with the rapid development of DERs.Therefore,in this paper,we develop an energy sharing scheme that allows users to share DERs with neighbors,and design a novel incentive mechanism for benefit allocation without users’bidding on electricity prices.In the energy sharing scheme,an aggregator organizes a number of electricity users,and trades with the connected power grid.The aggregator is aimed at minimizing the total costs by matching the surplus energy from DERs and electrical loads.A novel index,termed as sharing contribution rate(SCR),is presented to evaluate different users’contributions to the energy sharing.Then,based on users’SCRs,an efficient benefit allocation mechanism is implemented to determine the aggregator’s payment to users that incentivize their participation in energy sharing.To avoid users’bidding,we propose a decentralized framework for the energy sharing and incentive mechanism.Case studies based on real-world datasets demonstrate that the aggregator and users can benefit from the energy sharing scheme,and the incentive mechanism allocates the benefits according to users’contributions.

An Efficient Method for Estimating Capability Curve of Virtual Power Plant

To incorporate the operating constraints of a virtual power plant(VPP)in transmission-level operation and market clearing,the concept of the VPP capability curve(VPP-CC)is proposed which explicitly characterizes the allowable range of active and reactive power outputs of a VPP.A two-step projection-based calculation framework is proposed to approximate the VPP-CC by the convex hull of critical points on its perimeter.The output of the proposed algorithm is concise and can be easily incorporated in the existing system operation and market clearing.Case studies based on the IEEE 33 and 123 test feeders show the computational efficiency of the proposed method outperforms existing methods by 4~7 times.Additionally,many fewer inequalities are needed to depict the VPP-CC while achieving the comparative approximation accuracy compared to sampling-based methods,which will relieve the communication and computation burden.

Transmission network expansion planning with embedded constraints of short circuit currents and N-1 security

An approach of transmission network expan-sion planning with embedded constraints of short circuit currents and N-1 security is proposed in this paper.The problem brought on by the strong nonlinearity property of short circuit currents is solved with a linearization method based on the DC power flow.The model can be converted to a mixed-integer linear programming problem,realizing the optimization of planning model that considers the constraints of linearized short circuit currents and N-1 security.To compensate the error caused by the assump-tions of DC power flow,the compensation factor is pro-posed.With this factor,an iterative algorithm that can compensate the linearization error is then presented.The case study based on the IEEE 118-bus system shows that the proposed model and approach can be utilized to:opti-mize the construction strategy of transmission lines;ensure the N-1 security of the network;and effectively limit the short circuit currents of the system.

Month ahead average daily electricity price profile forecasting based on a hybrid nonlinear regression and SVM model:an ERCOT case study

With the deregulation of the electric power industry, electricity price forecasting plays an increasingly important role in electricity markets, especially for retailors and investment decision making. Month ahead average daily electricity price profile forecasting is proposed for the first time in this paper. A hybrid nonlinear regression and support vector machine(SVM) model is proposed. Offpeak hours, peak hours in peak months and peak hours in off-peak months are distinguished and different methods are designed to improve the forecast accuracy. A nonlinear regression model with deviation compensation is proposed to forecast the prices of off-peak hours and peak hours in off-peak months. SVM is adopted to forecast the prices of peak hours in peak months. Case studies based on data from ERCOT validate the effectiveness of the proposed hybrid method.

Texas Electric Power Crisis of 2021 Warns of a New Blackout Mechanism

The Texas electric power crisis that occurred in February 2021 has drawn great attention internationally due to its severity and for not having been foreseen.In this rapid communication,we classify the 2021 Texas electric power crisis as an energy insufficiency-caused power crisis,which alerts of a new blackout mechanism.Different from capacity insufficiencycaused power crises in the past,the Texas electric power crisis of 2021 directly resulted from the long-duration extreme cold weather as well as fundamentally from the insufficiency of sustainable supply capability of energy.We begin this paper with a brief retrospect of the event and its consequences.Definitions of energy/capacity insufficiency-caused power crises are given,as well as an overview of the supply and demand during the event,based on realistic operation data.Quantitative simulations are then conducted to reveal the underlying reasons for the power crisis and reveal how to better prepare for the future.Finally,several insights and suggestions on handling the new mode of blackout in the future are proposed and discussed.

Review of Learning-assisted Power System Optimization

With dramatic breakthroughs in recent years,machine learning is showing great potential to upgrade the toolbox for power system optimization.Understanding the strength and limitation of machine learning approaches is crucial to decide when and how to deploy them to boost the optimization performance.This paper pays special attention to the coordination between machine learning approaches and optimization models,and carefully evaluates how such data-driven analysis may improve the rule-based optimization.The typical references are selected and categorized into four groups:the boundary parameter improvement,the optimization option selection,the surrogate model,and the hybrid model.This taxonomy provides a novel perspective to elaborate the latest research progress and development.We further compare the design patterns of different categories,and discuss several key challenges and opportunities as well.Deep integration between machine learning approaches and optimization models is expected to become the most promising technical trend.

Implications of COVID-19 for the Electricity Industry: A Comprehensive Review

As COVID-19 sweeps through the whole world,human activities have been changed significantly.Under such circumstances,the electricity sector is deeply affected and faced with great challenges.This paper provides a comprehensive review of the impacts that the pandemic has caused on the electricity sector.Electricity demand has dropped sharply as governments around the world executed lockdown restrictions,while the load composition and daily load profile have also changed.The share of renewable generation has increased against the decline of the total electricity generation.Changed power balance situation and increased uncertainty of demand have posed higher pressure on system operators,along with voltage violation issue and challenges for system maintenance and management.The electricity market is also substantially influenced,while longterm investment in clean energy is expected to be stable.The externality such as emission reduction is also discussed.