Deciphering the electricity-carbon market nexus:Challenges and prospects of electricity-carbon coupling

The power industry's carbon emissions stand out as a primary contributor to the overall carbon dioxide emissions within the energy system under the context of energy Internet.Thus,reducing emissions in the power sector has become crucial for achieving carbon neutrality.However,challenges from the electricity-carbon nexus have surfaced in effectively coordinating and integrating the carbon market with the electricity market.This paper initiates the exploration of such nexus by analysing the current status of major carbon emission trading markets on a global scale.Subsequently,it delves into a comprehensive examination of the coupling between the electricity and carbon markets across three levels:market participants,operational models and market mechanisms.Four key issues are then identified in the electricity-carbon nexus:challenges in decision‐making for market participants,discrepancies in operational timelines,the intricate design of market coupling mechanisms and the spillover effects of market risks.To tackle the above challenges in the electricity-carbon nexus,this paper takes a deep dive into two different models in understanding the nexus including econometric/statistical models and optimisation models,serving as the foundation for understanding the intricacies of electricity-carbon market coupling.This paper concludes with a detailed exploration for future roadmap and research prospects in the electricity-carbon market nexus.

Security Constrained Decentralized Peer-to-Peer Transactive Energy Trading in Distribution Systems

Peer-to-peer(P2P)transactive energy trading offers a promising solution for facilitating the efficient and secure operation of a distribution system consisting of multiple prosumers.One critical but challenging task is how to avoid system network constraints to be violated for the distribution system integrated with extensive P2P transactive energy trades.This paper proposes a security constrained decentralized P2P transactive energy trading framework,which allows direct energy trades among neighboring prosumers in the distribution system with enhanced system efficiency and security in which no conventional intermediary is required.The P2P transactive energy trading problem is formulated based on the Nash Bargaining theory and decomposed into two subproblems,i.e.,an OPF problem(P1)and a payment bargaining problem(P2).A distributed optimization method based on the alternating direction method of multiplier(ADMM)is adopted as a privacy-preserving solution to the formulated security constrained P2P transactive energy trading model with ensured accuracy.Extensive case studies based on a modified 33-bus distribution system are presented to validate the effectiveness of the proposed security constrained decentralized P2P transactive energy trading framework in terms of efficiency improvement,loss reduction,and voltage security enhancement.

Intelligent data attacks against power systems using incomplete network information: a review

With the integration of information technologies, power system operations are increasingly threatened by cyber-attacks. It has even been revealed that an attacker can inject false data into real-time measurements stealthily without knowing the full configuration(e.g., network topology) of a power system. In this paper, we present a comprehensive review on false data injection attacks which utilize barrier conditions, blind identification techniques and data driven approaches to overcome limitations of incomplete network information. We also point out future research topics for facilitating the detection and prevention of such false data attacks.

State identification of home appliance with transient features in residential buildings

Nonintrusive load monitoring(NILM)is crucial for extracting patterns of electricity consumption of household appliance that can guide users9 behavior in using electricity while their privacy is respected.This study proposes an online method based on the transient behavior of individual appliances as well as system steady-state characteristics to estimate the operating states of the appliances.It determines the number of states for each appliance using the density-based spatial clustering of applications with noise(DBSCAN)method and models the transition relationship among different states.The states of the working appliances are identified from aggregated power signals using the Kalman filtering method in the factorial hidden Markov model(FHMM).Thereafter,the identified states are confirmed by the verification of system states,which are the combination of the working states of individual appliances.The verification step involves comparing the total measured power consumption with the total estimated power consumption.The use of transient features can achieve fast state inference and it is suitable for online load disaggregation.The proposed method was tested on a high-resolution data set such as Labeled hlgh-Frequency daTaset for Electricity Disaggregation(LIFTED)and it outperformed other related methods in the literature.

Characterization and Trading of Energy Level and Energy Shift Considering Virtual Power Plant

The capability of shifting the electricity generation or consumption to proper time of the day,also defined as energy shift(ES),is the key factor to ensure the power balance,especially under high penetration of variable renewable energy(VRE).However,the ES is not characterized and traded as an independent product in current market mechanisms.In this letter,the marginal utility of an ES is assessed and leveraged to characterize the effective ES,while a novel market scheme is proposed considering the trading of both ES and energy level(EL).The proposed scheme can well integrate ES producers such as virtual power plants that cannot be rewarded sufficiently to actively participate in the current market because they are principally labeled as EL consumers.Finally,the novel concept and mechanism are illustrated by a numerical study and verified to outperform the existing price schemes on integrating the ES resources and VRE.

Architecture,Control,and Implementation of Networked Microgrids for Future Distribution Systems

Microgrid(MG)is a small-scale,self-sufficient power system that accommodates various distributed energy resources(DERs),controllable loads,and future distribution systems.Networked microgrids(NMGs)are clusters of MGs,which are physically interconnected and functionally coordinated to enhance distribution systems in terms of economics,resilience,and reliability.This paper introduces the architecture and control of NMGs including nanogrid(NG)and MG.To accommodate variable DERs in NMGs,master and distributed control strategies are adopted to manage the high penetration of DERs,where master control focuses on economic operation,while distributed control focuses on reliability and resilience through active power sharing and voltage and frequency regulation.The initial practices of NG,MG,and NMG in the networked Illinois Institute of Technology(IIT)campus microgrid(ICM)and Bronzeville community microgrid(BCM)in the U.S.are presented.The applications of the master and distributed control strategies are illustrated for the networked ICM-BCM to show their benefits to economics,resilience,and reliability.

Coordinated Operation of Concentrating Solar Power Plant and Wind Farm for Frequency Regulation

As a dispatchable renewable energy technology, the fast ramping capability of concentrating solar power (CSP) can be exploited to provide regulation services. However, frequent adjustments in real-time power output of CSP, which stems out of strategies offered by ill-designed market, may affect the durability and the profitability of the CSP plant, especially when it provides fast regulation services in a real-time operation. We propose the coordinated operation of a CSP plant and wind farm by exploiting their complementarity in accuracy and durability for providing frequency regulation. The coordinated operation can respond to regulation signals effectively and achieve a better performance than conventional thermal generators. We further propose an optimal bidding strategy for both energy and frequency regulations for the coordinated operation of CSP plant and wind farm in day-ahead market (DAM). The validity of the coordinated operation model and the proposed bidding strategy is verified by a case study including a base case and sensitivity analyses on several impacting factors in electricity markets.

Multi-time Scale Optimal Power Flow Strategy for Medium-voltage DC Power Grid Considering Different Operation Modes

Direct current(DC)power grids based on flexible high-voltage DC technology have become a common solution of facilitating the large-scale integration of distributed energy resources(DERs)and the construction of advanced urban power grids.In this study,a typical topology analysis is performed for an advanced urban medium-voltage DC(MVDC)distribution network with DERs,including wind,photovoltaic,and electrical energy storage elements.Then,a multi-time scale optimal power flow(OPF)strategy is proposed for the MVDC network in different operation modes,including utility grid-connected and off-grid operation modes.In the utility grid-connected operation mode,the day-ahead optimization objective minimizes both the DER power curtailment and the network power loss.In addition,in the off-grid operation mode,the day-ahead optimization objective prioritizes the satisfaction of loads,and the DER power curtailment and the network power loss are minimized.A dynamic weighting method is employed to transform the multi-objective optimization problem into a quadratically constrained quadratic programming(QCQP)problem,which is solvable via standard methods.During intraday scheduling,the optimization objective gives priority to ensure minimum deviation between the actual and predicted values of the state of charge of the battery,and then seeks to minimize the DER power curtailment and the network power loss.Model predictive control(MPC)is used to correct deviations according to the results of ultra short-term load forecasting.Furthermore,an improved particle swarm optimization(PSO)algorithm is applied for global intraday optimization,which effectively increases the convergence rate to obtain solutions.MATLAB simulation results indicate that the proposed optimization strategy is effective and efficient.

Coordinated Control Strategy for Operation Mode Switching of DC Distribution Networks

Due to the advantages such as low line cost,low transmission loss,and high power supply reliability,DC distribution networks have become the main development trend for future distribution networks.In this paper,a typical DC distribution network with multiple voltage levels is considered as a research object.It is proposed that the interface converters between DC buses with different voltage levels be implemented through the series-parallel combination of full-bridge LLC resonant converters.To realize the decentralized self-discipline control of DC voltage under various working conditions,different slack buses are prepared according to the voltage ranges of the DC buses,and the voltage regulation modes of the DC distribution network are divided into main voltage regulation mode,backup voltage regulation mode,and off-grid voltage droop regulation mode.By introducing a voltage coefficient related to DC voltage deviation as a basis for mode switching,the voltage fluctuations caused by slow switching between control modes in the method of traditional voltage margin control is reduced,facilitating fast and smooth switching between different voltage regulation modes.Finally,a simulation model for DC distribution networks is constructed utilizing MATLAB/Simulink.Simulation results verify the effectiveness and feasibility of the proposed voltage regulation modes and switching methods for DC distribution networks.Finally,an experimental platform is also constructed to verify the feasibility of the mode switching method proposed in this paper.