Active Support of Power System to Energy Transition

According to the International Renewable Energy Agency,energy transition is the pathway of the transformation of the global energy sector from the current fossil-based energy to renewable-based energy,with zerocarbon emission by the second half of the 21st century.One of the technological areas that is key in ensuring a successful energy transition is sector coupling,in which the characteristics of each sector are further developed and exploited by other energy systems in order to ensure energy ade-quacy and security over both short-and long-time spans.Electric-ity-as one of the most important energy carriers discovered by humans-plays a central role in modern society due to its conve-nience and efficiency in production and conversion to other types of energy.Therefore,it can be envisaged that the power system.

A comprehensive review on deep learning approaches in wind forecasting applications

The effective use of wind energy is an essential part of the sustainable development of human society,in particular,at the recent unprecedented pressure in shaping a low carbon energy environment.Accurate wind resource and power forecasting play a key role in improving the wind penetration.However,it has not been well adopted in the real-world applications due to the strong stochastic characteristics of wind energy.In recent years,the application boost of deep learning methods provides new effective tools in wind forecasting.This paper provides a comprehensive overview of the forecasting models based on deep learning in the field of wind energy.Featured approaches include timeseries-based recurrent neural networks,restricted Boltzmann machines,convolutional neural networks as well as auto-encoder-based approaches.In addition,future development directions of deep-learning-based wind energy forecasting have also been discussed.

A Distributed Transactive Energy Mechanism for Integrating PV and Storage Prosumers in Market Operation

The decreasing cost of solar photovoltaics(PVs)and battery storage systems is driving their adoption in the residential distribution system,where more consumers are becoming prosumers.Accompanying this trend is the potential roll-out of home energy management systems(HEMSs),which provide a means for prosumers to respond to externalities such as energy price,weather,and energy demands.However,the economic operation of prosumers can affect grid security,especially when energy prices are extremely low or high.Therefore,it is paramount to design a framework that can accommodate the interests of the key stakeholders in distribution systems—namely,the network operator,prosumer,and aggregator.In this paper,a novel transactive energy(TE)-based operational framework is proposed.Under this frame-work,aggregators interact with the distribution grid operator through a negotiation process to ensure network security,while at the lower level,prosumers submit their schedule to the aggregator through the HEMS.If network security is at risk,aggregators will send an additional price component representing the cost of security(CoS)to the prosumer to stimulate further response.The simulation results show that the proposed framework can effectively ensure the economic operation of aggregators and prosumers in distribution systems while maintaining grid security.

Pathway Planning of Nuclear Power Development Incorporating Assessment of Nuclear Event Risk

The nuclear event risk(NER)is an important and disputed factor that should be reasonably considered when planning the pathway of nuclear power development(NPD)to assess the benefits and risks of developing nuclear power more objectively.This paper aims to explore the impact of nuclear events on NPD pathway planning.The influence of nuclear events is quantified as a monetary risk component,and an optimization model that incorporates the NER in the objective function is proposed.To optimize the pathway of NPD in the lowcarbon transition course of power supply structure evolution,a simulation model is built to deduce alternative NPD pathways and corresponding power supply evolution scenarios under the constraint of an exogenously assigned carbon emission pathway(CEP);moreover,a method is proposed to describe the CEP by superimposing the maximum carbon emission space and each carbon emission reduction(CER)component,and various CER components are clustered considering the emission reduction characteristics and resource endowments of different power generation technologies.A case study is conducted to explore the impact of NER and its risk valuation uncertainty on NPD pathway planning.The method presented in this paper allows the impact of nuclear events on NPD pathway planning to be quantified and improves the level of coordinated optimization of benefits and risks.

Energy internet or comprehensive energy network?

What needs to be developed from the concept of"Smart Grid"is that:when renewable energy sources are absolutely prevailing in power generation,distributed power generation and distributed energy storage systems are widespread across the grid,and electric vehicle charging loads are prevailing in power load demands,how can the power grid support electric power as a core secondary energy source,undertake the role of a bridge between primary energy and end-use energy,and achieve the coordination and the optimization in macro energy perspective;how to guarantee the security of both macro energy and environment as well as the reliability of electricity.If a new term is needed,it should be Comprehensive Energy Network,not Energy Internet.

Generalized congestion of power systems:insights from the massive blackouts in India

The Indian power system underwent two separate massive blackouts on 30th and 31st of July 2012.These blackouts exposed a series of inherent problems underlying the power system of India,such as its generation planning,transmission planning,grid infrastructure,management systems,primary energy resources,environmental constraints,electricity market,and information technology,which together aggravate the massive blackouts.These factors which affect the adequacy and security of the power supply in a similar manner as the transmission congestion of physical grids does,can be called by a generic term as generalized congestion.Competitive manipulations and“game-playing”between various interested parties further intensifies these congestions.Here,from the perspective of generalized congestions,the paper reflects the inevitability of India’s massive blackouts,discusses occasional effects of trigger events,inherent evolution law of blackouts and the enlightenment on power industry in China,and explores risk control measures to defend blackouts in China.The positive role of strong smart grid in insuring energy and environment security is highlighted.

Transition towards higher penetration of renewables: an overview of interlinked technical, environmental and socio-economic challenges

Investment for renewables has been growing rapidly since the beginning of the new century, and the momentum is expected to sustain in order to mitigate the impact of anthropogenic climate change.Transition towards higher renewable penetration in the power industry will not only confront technical challenges, but also face socio-economic obstacles.The connected between environment and energy systems are also tightened under elevated penetration of renewables.This paper will provide an overview of some important challenges related to technical, environmental and socio-economic aspects at elevated renewable penetration.An integrated analytical framework for interlinked technical, environmental and socio-economic systems will be presented at the end.

Multi-agents modelling of EV purchase willingness based on questionnaires

Traditional experimental economics methods often consume enormous resources of qualified human participants,and the inconsistence of a participant’s decisions among repeated trials prevents investigation from sensitivity analyses.The problem can be solved if computer agents are capable of generating similar behaviors as the given participants in experiments.An experimental economics based analysis method is presented to extract deep information from questionnaire data and emulate any number of participants.Taking the customers’willingness to purchase electric vehicles(EVs)as an example,multi-layer correlation information is extracted from a limited number of questionnaires.Multiagents mimicking the inquired potential customers are modelled through matching the probabilistic distributions of their willingness embedded in the questionnaires.The authenticity of both the model and the algorithmis validated by comparing the agent-based Monte Carlo simulation results with the questionnaire-based deduction results.With the aid of agent models,the effects of minority agents with specific preferences on the results are also discussed.

Power-to-gas technology in energy systems:current status and prospects of potential operation strategies

Regarded as a long-term, large capacity energy storage solution, commercialized power-to-gas(PtG) technology has attracted much research attention in recent years.PtG plants and natural gas-fired power plants can form a close loop between an electric power system and a natural gas network. An interconnected multi-energy system is believed to be a solution to the future efficient and environmental friendly energy systems. However, some crucial issues require in-depth analysis before PtG plants can be economically implemented. This paper discusses current development status and potential application of PtG plants in the future interconnected multi-energy systems, and further analyzes the costs and benefits of PtG plants in different application scenarios. In general, the PtG plants are not economical efficient based on current technologies and costs. But the situation is likely to change with the development of PtG technologies and interconnected operation of gas-electricity energy system.

Optimal operation of electricity, natural gas and heat systems considering integrated demand responses and diversified storage devices

In recent years, the increasing penetration level of renewable generation and combined heat and power(CHP) technology in power systems is leading to significant changes in energy production and consumption patterns. As a result, the integrated planning and optimal operation of a multi-carrier energy(MCE) system have aroused widespread concern for reasonable utilization of multiple energy resources and efficient accommodation of renewable energy sources. In this context, an integrated demand response(IDR) scheme is designed to coordinate the operation of power to gas(P2 G) devices, heat pumps,diversified storage devices and flexible loads within an extended modeling framework of energy hubs. Subsequently, the optimal dispatch of interconnected electricity,natural gas and heat systems is implemented considering the interactions among multiple energy carriers by utilizing the bi-level optimization method. Finally, the proposed method is demonstrated with a 4-bus multi-energy systemand a larger test case comprised of a revised IEEE 118-bus power system and a 20-bus Belgian natural gas system.

Transactive control:a framework for operating power systems characterized by high penetration of distributed energy resources

The increasing number of distributed energy resources connected to power systems raises operational challenges for the network operator, such as introducing grid congestion and voltage deviations in the distribution network level, as well as increasing balancing needs at the whole system level. Control and coordination of a large number of distributed energy assets requires innovative approaches. Transactive control has received much attention due to its decentralized decision-making and transparent characteristics. This paper introduces the concept and main features of transactive control, followed by a literature review and demonstration projects that apply to transactive control. Cases are then presented to illustrate the transactive control framework. At the end, discussions and research directions are presented, for applying transactive control to operating power systems, characterized by a high penetration of distributed energy resources.

Review of Service Restoration for Distribution Networks

With the rapid deployment of the advanced metering infrastructure(AMI)and distribution automation(DA),selfhealing has become a key factor to enhance the resilience of distribution networks.Following a permanent fault occurrence,the distribution network operator(DNO)implements the selfhealing scheme to locate and isolate the fault and to restore power supply to out-of-service portions.As an essential component of self-healing,service restoration has attracted considerable attention.This paper mainly reviews the service restoration approaches of distribution networks,which requires communication systems.The service restoration approaches can be classified as centralized,distributed,and hierarchical approaches according to the communication architecture.In these approaches,different techniques are used to obtain service restoration solutions,including heuristic rules,expert systems,metaheuristic algorithms,graph theory,mathematical programming,and multi-agent systems.Moreover,future research areas of service restoration for distribution networks are discussed.

Decision Support System for Adaptive Restoration Control of Transmission System

The power system restoration control has a higher uncertainty level than the preventive control of cascading failures. In order to ensure the feasibility of the decision support system of restoration control, a decision support framework for adaptive restoration control of transmission system is proposed, which can support the coordinated restoration of multiple partitions, coordinated restoration of units and loads, and coordination of multi-partition decision-making process and actual restoration process. The proposed framework is divided into two layers, global coordination layer and partition optimization layer. The upper layer partitions the transmission system according to the power outage scenario, constantly and dynamically adjusts the partitions during the restoration process, and optimizes the time-space decision-making of inter-partition connectivity. For each partition, the lower layer pre-selects restoration targets according to the estimated restoration income, optimizes the corresponding restoration paths, and evaluates the restoration plans according to the expected net income per unit of power consumption. During the restoration process, if the restoration operation such as energizing the outage branch fails, the current restoration plan will be adaptively switched to the sub-optimal one or re-optimized if necessary. The framework includes two operation modes, i.e., the on-line operation mode and training simulation mode, and provides an information interaction interface for collaborative restoration with related distribution systems. The effectiveness and adaptability of the proposed framework is demonstrated by simulations using the modified IEEE 118-bus system.

A self-learning TLBO based dynamic economic/environmental dispatch considering multiple plug-in electric vehicle loads

Economic and environmental load dispatch aims to determine the amount of electricity generated from power plants to meet load demand while minimizing fossil fuel costs and air pollution emissions subject to operational and licensing requirements.These two scheduling problems are commonly formulated with non-smooth cost functions respectively considering various effects and constraints,such as the valve point effect,power balance and ramprate limits.The expected increase in plug-in electric vehicles is likely to see a significant impact on the power system due to high charging power consumption and significant uncertainty in charging times.In this paper,multiple electric vehicle charging profiles are comparatively integrated into a 24-hour load demand in an economic and environment dispatch model.Self-learning teaching-learning based optimization(TLBO)is employed to solve the non-convex non-linear dispatch problems.Numerical results onwell-known benchmark functions,as well as test systems with different scales of generation units show the significance of the new scheduling method.

Multi-agent modeling and analysis of EV users' travel willingness based on an integrated causal/statistical/behavioral model

An electric vehicle(EV) centred ecosystem has not yet been formed, the existing limited statistic data are far from enough for the analysis of EV users' travel and charge behaviors, which however tends to be affected by many certain and uncertain factors. An experimental economics(EE)-based simulation method can be used to analyze thebehaviors of key participants in a system. However, it is restricted by the system size, experimental site and the number of qualified human participants. Therefore, this method is hard to be adopted for the behavioral analysis of a large number of human participants. In this paper, a new method combining a questionnaire statistics and the EEbased simulation is proposed. The causal relationship is considered in the design of the questionnaires and data extraction, then a multi-agent modeling integration method is introduced in the EE-based simulation, which enables the integration of causal/statistical/behavioral models into the multi-agent framework to reflect the EV users' travel willingness statistically. The generated multi-agents are used to replace human participants in the EE-based simulation in order to evaluate EV users' travel demands in different scenarios, and compare the differences of simulated or measured travel behaviors between potential EV users and internal combustion engine(ICE) vehicle users.

Operational reliability evaluation of restructured power systems with wind power penetration utilizing reliability network equivalent and time-sequential simulation approaches

In the last two decades,the wind power generation has been rapidly and widely developed in many regions and countries for tackling the problems of environmental pollution and sustainability of energy supply.However,the high share of intermittent and fluctuating wind power production has also increased the burden of system operator for securing power system reliability during the operational phase.Moreover,the power system restructuring and deregulation have not only introduced the competition for reducing cost but also changed the strategy of reliability evaluation and management of power systems.The conventional long-term reliability evaluation techniques have been well developed,which have been more focused on planning and expansion rather than operation of power systems.This paper proposes a new technique for evaluating operational reliabilities of restructured power systems with high wind power penetration.The proposed technique is based on the combination of the reliability network equivalent and time-sequential simulation approaches.The operational reliability network equivalents are developed to represent reliability models of wind farms,conventional generation and reserve provides,fast reserve providers and transmission network in restructured power systems.A contingency management schema for real time operation considering its coupling with the dayahead market is proposed.The time-sequential Monte Carlo simulation is used to model the chronological characteristics of corresponding reliability network equivalents.Asimplified method is also developed in the simulation procedures for improving the computational efficiency.The proposed technique can be used to evaluate customers’reliabilities considering high penetration of wind power during the power system operation in the deregulated environment.

Primary energy congestion of power systems

Primary energy has the potential to bring challenges to the reliability,economic,and eco-friendliness of global electric power systems.The concept of electric power security are proposed,including many factors that are not considered in power system reliability analysis,such as coal supply for power system,fuel price for electricity power market,carbon emissions of power generation,and so on.It is broader than power system reliability and security,which means that providing energy in the form of reliable,economical,and eco-friendly electric power.Following an account of existing blackout defence methods and systems,the processes of transforming various kinds of primary energy into electric power and the interactions between them are described,particularly with regard to flows of energy,capital,and information.Factors that limit the liquidity of these flows are defined as“generalized congestion”,and the challenges presented by primary energy are named“primary energy congestion”.China’s dilemma on coal supply for power generation is presented in some detail as an example of primary energy congestion.To cope with impacts of the primary energy on electric power systems,an idea is introduced to extend the scope of existing coordinated blackout defence systems to account for primary energy,which would lead to an integrated tool to provide decision support to power system operators accounting for primary energy congestion.

LSTM-based Energy Management for Electric Vehicle Charging in Commercial-building Prosumers

As typical prosumers,commercial buildings equipped with electric vehicle(EV)charging piles and solar photovoltaic panels require an effective energy management method.However,the conventional optimization-model-based building energy management system faces significant challenges regarding prediction and calculation in online execution.To address this issue,a long short-term memory(LSTM)recurrent neural network(RNN)based machine learning algorithm is proposed in this paper to schedule the charging and discharging of numerous EVs in commercial-building prosumers.Under the proposed system control structure,the LSTM algorithm can be separated into offline and online stages.At the offline stage,the LSTM is used to map states(inputs)to decisions(outputs)based on the network training.At the online stage,once the current state is input,the LSTM can quickly generate a solution without any additional prediction.A preliminary data processing rule and an additional output filtering procedure are designed to improve the decision performance of LSTM network.The simulation results demonstrate that the LSTM algorithm can generate near-optimal solutions in milliseconds and significantly reduce the prediction and calculation pressures compared with the conventional optimization algorithm.

Controlled islanding schemes for interconnected power systems based on coherent generator group identification and wide-area measurements

A reasonable islanding strategy of a power system is the final resort for preventing a cascading failure and/or a large-area blackout from occurrence. In recent years, the applications of wide area measurement systems(WAMS) in emergency control of power systems are increasing. Therefore, a new WAMS-based controlled islanding scheme for interconnected power systems is proposed. First, four similarity indexes associated with the trajectories of generators are defined, and the weights of these four indexes are determined by using the well-developed entropy theory. Then, a coherency identification algorithm based on hierarchical clustering is presented to determine the coherent groups of generators.Secondly, an optimization model for determining controlled islanding schemes based on the coherent groups of generators is developed to seek the optimal cutset. Finally, a 16-generator68-bus power system and a reduced WECC 29-unit 179-bus power system are employed to demonstrate the proposed WAMS-based controlled islanding schemes, and comparisons with existing slow coherency based controlled islanding strategies are also carried out.

Small signal stability analysis of power systems with high penetration of wind power

The integration of large amount of wind power into a power system imposes a new challenge for the secure and economic operation of the system.It is necessary to investigate the impacts of wind power generation on the dynamic behavior of the power system concerned.This paper investigates the impacts of large amount of wind power on small signal stability and the corresponding control strategies to mitigate the negative effects.The concepts of different types of wind turbine generators(WTGs)and the principles of the grid-connected structures of wind power generation systems are first briefly introduced.Then,the state-of-the-art of the studies on the impacts of WTGs on small signal stability as well as potential problems to be studied are clarified.Finally,the control strategies on WTGs to enhance power system damping characteristics are presented.