Seeker optimization algorithm:a novel stochastic search algorithm for global numerical optimization

A novel heuristic search algorithm called seeker op- timization algorithm （SOA） is proposed for the real-parameter optimization. The proposed SOA is based on simulating the act of human searching. In the SOA, search direction is based on empir- ical gradients by evaluating the response to the position changes, while step length is based on uncertainty reasoning by using a simple fuzzy rule. The effectiveness of the SOA is evaluated by using a challenging set of typically complex functions in compari- son to differential evolution （DE） and three modified particle swarm optimization （PSO） algorithms. The simulation results show that the performance of the SOA is superior or comparable to that of the other algorithms.

Preparation and Characterization of Waterborne Polyurethane/Cellulose Nanocrystal Composite Membrane from Recycling Waste Paper

Cellulose plays a key role in abundant organic natural materials meeting the increasing demand for green and biocompatible products.The highly crystalline nanoscale component of cellulose nanocrystals has recently attracted great attention due to the versatile performance as filler or matrix in producing functional materials.In this work,we prepared the waterborne polyurethane via a prepolymer process,and obtained cellulose and cellulose nanocrystals from waste paper via a facile acid hydrolysis process.After that,the cellulose nanocrystals were assembled into film and mixed with polyurethane to prepare flexible polyurethane/cellulose nanocrystals composite membrane with different soaking time.The correlation between the bulk structure and applied properties including thermal resistance and mechanical property was investigated by using Fourier transform infrared spectroscopy(FTIR),X-ray diffraction(XRD),X-ray photoelectron spectroscopy(XPS),scanning electron microscopy(SEM),thermogravimetric analysis(TGA),differential scanning calorimetry(DSC)and folding test.The structure analysis indicates that cellulose nanocrystals prepared from used paper have a quality similar to that of commercial cellulose.Meanwhile,the cellulose nanocrystals have been mixed with polyurethane uniformly.Polyurethane can significantly benefit to the thermal resistance and mechanical property of the cellulose nanocrystals film.The polyurethane/cellulose nanocrystals composite membrane present good flexibility and may hold a significantly potential application as visual and flexible material.

An Internet of Energy Things Based on Wireless LPWAN

Under intense environmental pressure, the global energy sector is promoting the integration of renewable energy into interconnected energy systems. The demand-side management （DSM） of energy systems has drawn considerable industrial and academic attention in attempts to form new flexibilities to respond to variations in renewable energy inputs to the system. However, many DSM concepts are still in the experimental demonstration phase. One of the obstacles to DSM usage is that the current information infrastructure was mainly designed for centralized systems, and does not meet DSM requirements. To overcome this barrier, this paper proposes a novel information infrastructure named the lnternet of Energy Things （IoET） in order to make DSM practicable by basing it on the latest wireless communication technology： the low-power wide-area network （LPWAN）. The primary advantage of LPWAN over general packet radio service （GPRS） and area Internet of Things （loT） is its wide-area coverage, which comes with minimum power consumption and maintenance costs. Against this background, this paper briefly reviews the representative LPWAN tech- nologies of narrow-band Internet of Things （NB-IoT） and Long Range （LORa） technology, and compares them with GPRS and area IoT technology. Next, a wireless-to-cloud architecture is proposed for the IoET, based on the main technical features of LPWAN. Finally, this paper looks forward to the potential of IoET in various DSM application scenarios.

Resilient power grid for smart city

Modern power grid has a fundamental role in the operation of smart cities.However,high impact low probability extreme events bring severe challenges to the security of urban power grid.With an increasing focus on these threats,the resilience of urban power grid has become a prior topic for a modern smart city.A resilient power grid can resist,adapt to,and timely recover from disruptions.It has four characteristics,namely anticipation,absorption,adaptation,and recovery.This paper aims to systematically investigate the development of resilient power grid for smart city.Firstly,this paper makes a review on the high impact low probability extreme events categories that influence power grid,which can be divided into extreme weather and natural disaster,human-made malicious attacks,and social crisis.Then,resilience evaluation frameworks and quantification metrics are discussed.In addition,various existing resilience enhancement strategies,which are based on microgrids,active distribution networks,integrated and multi energy systems,distributed energy resources and flexible resources,cyber-physical systems,and some resilience enhancement methods,including probabilistic forecasting and analysis,artificial intelligence driven methods,and other cutting-edge technologies are summarized.Finally,this paper presents some further possible directions and developments for urban power grid resilience research,which focus on power-electronized urban distribution network,flexible distributed resource aggregation,cyber-physical-social systems,multi-energy systems,intelligent electrical transportation and artificial intelligence and Big Data technology.

Key Technologies and Prospects for Electric Vehicles Within Emerging Power Systems: Insights from Five Aspects

The energy revolution requires coordination in energy consumption, supply, storage and institutional systems.Renewable energy generation technologies, along with their associated costs, are already fully equipped for large-scale promotion.However, energy storage remains a bottleneck, and solutions areneeded through the use of electric vehicles, which traditionallyplay the role of energy consumption in power systems. Toclarify the key technologies and institutions that support EVsas terminals for energy use, storage, and feedback, the CSEEJPES forum assembled renowned experts and scholars in relevantfields to deliver keynote reports and engage in discussions ontopics such as vehicle–grid integration technology, advancedsolid-state battery technology, high-performance electric motortechnology, and institutional innovation in the industry chain.These experts also provided prospects for energy storage andutilization technologies capable of decarbonizing new powersystems.

A Least-squares-based Iterative Method with Better Convergence for PF/OPF in Integrated Transmission and Distribution Networks

The limitations of the conventional master-slavesplitting(MSS)method,which is commonly applied to power flow and optimal power flow in integrated transmission and distribution(I-T&D)networks,are first analyzed.Considering that the MSS method suffers from a slow convergence rate or even divergence under some circumstances,a least-squares-based iterative(LSI)method is proposed.Compared with the MSS method,the LSI method modifies the iterative variables in each iteration by solving a least-squares problem with the information in previous iterations.A practical implementation and a parameter tuning strategy for the LSI method are discussed.Furthermore,a LSI-PF method is proposed to solve I-T&D power flow and a LSIheterogeneous decomposition(LSI-HGD)method is proposed to solve optimal power flow.Numerical experiments demonstrate that the proposed LSI-PF and LSI-HGD methods can achieve the same accuracy as the benchmark methods.Meanwhile,these LSI methods,with appropriate settings,significantly enhance the convergence and efficiency of conventional methods.Also,in some cases,where conventional methods diverge,these LSI methods can still converge.

Coupon-based Demand Response for Consumers Facing Flat-rate Retail Pricing

Even though smart meters have been widely used in power systems around the world,many consumers are still finding it hard to participate in demand response(DR)due to flat-rate retail pricing policy.To address this issue,this paper proposes a coupon-based demand response(CDR)scheme to achieve equivalent dynamic retail prices to inspire consumers’inherent elasticity.First,a security-constrained unit commitment optimization model is developed in the day-ahead market to obtain coupon rewards,which are then broadcast to consumers to motivate them to reschedule their power consumption behaviors.To evaluate the adjustment value of consumers’power consumption,a collective utility function is proposed to formulate the relationship between power quantity and coupon rewards.On this basis,the security-constrained economic dispatch model is developed in the intra-day market to reschedule generating units’output power according to real-time load demands and fluctuating renewable energies.After the operation interval,a settlement method is developed to quantify consumers’electricity fees and coupon benefits on a monthly basis.The proposed CDR scheme avoids real-time iterative bidding process and effectively decreases the difficulty of massive,small consumers participating in DR.The proposed CDR is implemented in a realistic DR project in China to verify consumers’energy cost and renewables’curtailment can both be decreased.

Constraint Learning-based Optimal Power Dispatch for Active Distribution Networks with Extremely Imbalanced Data

Transition towards carbon-neutral power systems has necessitated optimization of power dispatch in active distribution networks(ADNs)to facilitate integration of distributed renewable generation.Due to unavailability of network topology and line impedance in many distribution networks,physical model-based methods may not be applicable to their operations.To tackle this challenge,some studies have proposed constraint learning,which replicates physical models by training a neural network to evaluate feasibility of a decision(i.e.,whether a decision satisfies all critical constraints or not).To ensure accuracy of this trained neural network,training set should contain sufficient feasible and infeasible samples.However,since ADNs are mostly operated in a normal status,only very few historical samples are infeasible.Thus,the historical dataset is highly imbalanced,which poses a significant obstacle to neural network training.To address this issue,we propose an enhanced constraint learning method.First,it leverages constraint learning to train a neural network as surrogate of ADN's model.Then,it introduces Synthetic Minority Oversampling Technique to generate infeasible samples to mitigate imbalance of historical dataset.By incorporating historical and synthetic samples into the training set,we can significantly improve accuracy of neural network.Furthermore,we establish a trust region to constrain and thereafter enhance reliability of the solution.Simulations confirm the benefits of the proposed method in achieving desirable optimality and feasibility while maintaining low computational complexity.

Photovoltaic and Solar Power Forecasting for Smart Grid Energy Management

Due to the challenge of climate and energy crisis,renewable energy generation including solar generation has experienced significant growth.Increasingly high penetration level of photovoltaic(PV)generation arises in smart grid.Solar power is intermittent and variable,as the solar source at the ground level is highly dependent on cloud cover variability,atmospheric aerosol levels,and other atmosphere parameters.The inherent variability of large-scale solar generation introduces significant challenges to smart grid energy management.Accurate forecasting of solar power/irradiance is critical to secure economic operation of the smart grid.This paper provides a comprehensive review of the theoretical forecasting methodologies for both solar resource and PV power.Applications of solar forecasting in energy management of smart grid are also investigated in detail.

Modeling and Operation of the Power-to-Gas System for Renewables Integration: A Review

Power-to-Gas(P2G)plays an important role in enhancing large-scale renewable energy integration in power systems.As an emerging inter-disciplinary subject,P2G technology requires knowledge in electrochemistry,electrical engineering,thermodynamic engineering,chemical engineering and system engineering.Aiming at P2G modeling and operational problems concerning the research field of power systems and the energy internet,this paper briefly reviews the main technologies and application potentials of the P2G system,and makes systematic summaries of major progresses related to P2G’s integration into the power grid in a bottom-top manner,including the modeling of high/room-temperature electrolysis cells,steady-state/dynamic optimization control of the P2G system,P2G’s integrated model and operational strategies at the grid level.In the final part of this paper,suggestions are put forward on future research directions of P2G systems from the aspects of modeling and operational optimization.

Situation Awareness of Active Distribution Network:Roadmap,Technologies,and Bottlenecks

With the rapid development of local generation and demand response,the active distribution network(ADN),which aggregates and manages miscellaneous distributed resources,has moved from theory to practice.Secure and optimal operations now require an advanced situation awareness(SA)system so that operators are aware of operation states and potential risks.Current solutions in distribution supervisory control and data acquisition(DSCADA)as well as the distribution automation system(DAS)generally are not able to meet the technology requirements of SA.In this paper,the authors’participation in the project of developing an SA system as the basic component of a practical active distribution management system(ADMS)deployed in Beijing,China,is presented.This paper reviews the ADN’s development roadmap by illustrating the changes that are made in elements,topology,structure,and control scheme.Taking into consideration these hardware changes,a systematic framework is proposed for the main components and the functional hierarchy of an SA system for the ADN.The SA system’s implementation bottlenecks are also presented,including,but not limited to issues in big data platform,distribution forecasting,and security evaluation.Potential technology solutions are also provided.

Distributed Photovoltaic Generation in the Electricity Market:Status,Mode and Strategy

With the increasingly serious climate change and energy crisis,photovoltaic(PV)generation,as one of the most important renewable energy resources,has experienced dramatic growth worldwide due to its environmental friendliness.How-ever,the uncertainty and intermittency of PV bring inevitable challenges to power systems.With the rapid development of distributed PV and the continuous evolution of the electricity market,increasingly high penetration levels of distributed PV generation have led to a series of problems in power system operations,such as voltage fluctuation,frequency deviation,etc.The market participation of distributed PV needs to be solved.Reasonable market participation form,market mechanism and bidding strategies are vital to the development of distributed PV in the electricity market.This paper comprehensively reviews the development and impacts of distributed PV in the electricity market and discusses the relevant market modes and bidding strategies in detail.

Additive manufacturing of CNTs/PLA composites and the correlation between microstructure and functional properties

Owing to the facile,low cost,rapid,personalization characters,3D printing method has been one of the most attractive additive manufacturing processes in medicine,airplane,packaging and printing areas.In this work,a series of carbon nanotubes/polylactic acid(CNTs/PLA) composites were prepared through the combination of molten co-extrusion and 3D printing processes.The orientation and dispersion of CNTs in PLA matrix were investigated to explore the impact of 3D printing process on the morphology of CNTs/PLA composites via transmission electron microscopy,field emission scanning electron microscopy and Raman spectroscopy.X-ray diffractometer,differential scanning calorimetry,and thermal gravity analysis were employed to study the crystal structure and thermal properties of the composites.In addition,the electrical conductivity of the prepared specimen revealed that the orientation of CNTs in PLA might enhance the conductivity of the composite.It was found that 3D printing process was beneficial to increasing the purity of CNTs,electrical conductivity and mechanical properties of CNTs/PLA composites.

Mixed Deep Reinforcement Learning Considering Discrete-continuous Hybrid Action Space for Smart Home Energy Management

This paper develops deep reinforcement learning(DRL)algorithms for optimizing the operation of home energy system which consists of photovoltaic(PV)panels,battery energy storage system,and household appliances.Model-free DRL algorithms can efficiently handle the difficulty of energy system modeling and uncertainty of PV generation.However,discretecontinuous hybrid action space of the considered home energy system challenges existing DRL algorithms for either discrete actions or continuous actions.Thus,a mixed deep reinforcement learning(MDRL)algorithm is proposed,which integrates deep Q-learning(DQL)algorithm and deep deterministic policy gradient(DDPG)algorithm.The DQL algorithm deals with discrete actions,while the DDPG algorithm handles continuous actions.The MDRL algorithm learns optimal strategy by trialand-error interactions with the environment.However,unsafe actions,which violate system constraints,can give rise to great cost.To handle such problem,a safe-MDRL algorithm is further proposed.Simulation studies demonstrate that the proposed MDRL algorithm can efficiently handle the challenge from discrete-continuous hybrid action space for home energy management.The proposed MDRL algorithm reduces the operation cost while maintaining the human thermal comfort by comparing with benchmark algorithms on the test dataset.Moreover,the safe-MDRL algorithm greatly reduces the loss of thermal comfort in the learning stage by the proposed MDRL algorithm.

Parametric Problems in Power System Analysis:Recent Applications of Polynomial Approximation Based on Galerkin Method

In power systems, there are many uncertainty factors such as power outputs of distributed generations and fluctuations of loads. It is very beneficial to power system analysis to acquire an explicit function describing the relationship between these factors(namely parameters) and power system states(or performances). This problem, termed as parametric problem(PP) in this paper, can be solved by Galerkin method,which is a powerful and mathematically rigorous method aiming to seek an accurate explicit approximate function by projection techniques. This paper provides a review of the applications of polynomial approximation based on Galerkin method in power system PPs as well as stochastic problems. First, the fundamentals of polynomial approximation and Galerkin method are introduced. Then, the process of solving three types of typical PPs by polynomial approximation based on Galerkin method is elaborated. Finally, some application examples as well as several potential applications of power system PPs solved by Galerkin method are presented, namely the probabilistic power flow, approximation of static voltage stability region boundary, and parametric time-domain dynamic simulation.

Optimal transmission switching to eliminate voltage violations during light-load periods using decomposition approach

With the rapid load increase in some countries such as China, power grids are becoming more strongly interconnected, and the differences between peak and valley loads are also increasing. As a result, some bulk power systems are facing high voltage limit violations during light-load periods. This paper proposes to utilize transmission switching(TS) to eliminate voltage violations. The TS problem is formed as a mixed-integer nonlinear program(MINLP) with AC power flow constraints and binary variables. The proposed MINLP problem is non-deterministic polynomial hard.To efficiently solve the problem, a decomposition approach is developed. This approach decomposes the original problem into a mixedinteger linear programming master problem and an AC optimal power flow slave problem that is used to check the AC feasibility. Prevention of islanding is also taken into consideration to ensure the feasibility of the TS results.The modified IEEE 39-bus and IEEE 57-bus test systems are used to demonstrate the applicability and effectiveness of the proposed method.

Identification of Critical Hidden Failure Line Based on State-failure-network

The hidden failures generally exist in power systems and could give rise to cascading failures.Identification of hidden failures is challenging due to very low occurrence probabilities.This paper proposes a state-failure-network(SF-network)method to overcome the difficulty.The SF-network is formed by searching the failures and states guided by risk estimation indices,in which only the failures and states contributing to the blackout risks are searched and duplicated searches are avoided.Therefore,sufficient hidden failures can be obtained with acceptable computations.Based on the state and failure value calculations in the SF-network,the hidden failure critical component indices can be obtained to quantify the criticalities of the lines.The proposed SF-network method is superior to common sampling based methods in risk estimation accuracy.Besides,the state and failure value calculations in the SF-network used to re-estimate the risks after deployment of measures against hidden failures need shorter time in comparison with other risk re-estimation methods.The IEEE 14-bus and 118-bus systems are used to validate the method.

Global optimal polynomial approximation for parametric problems in power systems

The influence of parameters on system states for parametric problems in power systems is to be evaluated.These parameters could be renewable generation outputs,load factor, etc. Polynomial approximation has been applied to express the nonlinear relationship between system states and parameters, governed by the nonlinear and implicit equations. Usually, sampling-based methods are applied, e.g., data fitting methods and sensitivity methods,etc. However, the accuracy and stability of these methods are not guaranteed. This paper proposes an innovative method based on Galerkin method, providing global optimal approximation. Compared to traditional methods, this method enjoys high accuracy and stability. IEEE 9-bus system is used to illustrate its effectiveness, and two additional studies including a 1648-bus system are performed to show its applications to power system analysis.

Utilization of Energy Storage and Hydrogen in Power and Energy Systems:Viewpoints from Five Aspects

Decarbonizing power systems is crucial to mitigating climate change impacts and achieving carbon neutrality.Increasing renewable energy supply can reduce greenhouse gas emissions and accelerate the decarbonization process.However,renewable energy sources(RESs)such as wind and solar power are characterized by intermittency and often non-dispatchability,significantly challenging their high-level integration into power systems.Energy storage is acknowledged as a vital indispensable solution for mitigating the intermittency of renewables such as wind and solar power and boosting the penetrations of renewables.In the CSEE JPES Forum,five well-known experts were invited to give keynote speeches,and the participating experts and scholars had comprehensive exchanges and discussions on energy storage technologies.Specifically,the views on the design,control,performance,and applications of new energy storage technologies,such as the fuel cell vehicle,water electrolysis,and flow battery,in the coordination and operation of power and energy systems were analyzed.The experts also provided experience that could be used to develop energy storage for constructing and decarbonizing new power systems.

Collusion Potential Assessment in Electricity Markets Considering Generation Flexibility

The collusion among various generating units has been a problematic issue affecting the fairness and transparency of electricity markets.Therefore,it is of great significance to assess the potential of such collusion in the electricity market.However,the previous assessment studies primarily focused on the bidding behaviors of collusive generating units,without considering the influences of generation flexibility,such as ramp rates.In this paper,a novel assessment method is proposed to evaluate the collusion potential in the electricity market considering generation flexibility.First,a bi-level optimization model is developed to simulate the collusive strategies of dif-ferent generating units,including the withholding of generation capacities and ramp rates,as well as the uplifting of minimum outputs and bidding prices.In the upper-level problem,collusive generating units optimize their offering strategies to optimize the generation profits without violating the regulatory laws.The lower-level problem is a day-ahead economic dispatch model which minimizes the dispatching costs.Based on the optimal collusive strategies determined by the bi-level model,a framework is then proposed to assess the collusion potential in electricity markets.Moreover,price-based and profit-based indices are proposed to quantitatively evaluate the collusion potential of different generating units.Finally,the proposed assessment method is validated on a modified IEEE 39-node system.The numerical results demonstrated that generation flexibility can be exploited collusively for making excessive profits,particularly during load peaks and valleys.Index Terms-Collusionpotential,economic cdispatch,generation flexibility,ramp rates,strategic withholding.