Dynamic optimal allocation of energy storage systems integrated within photovoltaic based on a dual timescale dynamics model

Energy storage systems(ESSs)operate as independent market participants and collaborate with photovoltaic(PV)generation units to enhance the flexible power supply capabilities of PV units.However,the dynamic variations in the profitability of ESSs in the electricity market are yet to be fully understood.This study introduces a dual-timescale dynamics model that integrates a spot market clearing(SMC)model into a system dynamics(SD)model to investigate the profit-aware capacity growth of ESSs and compares the profitability of independent energy storage systems(IESSs)with that of an ESS integrated within a PV(PV-ESS).Furthermore,this study aims to ascertain the optimal allocation of the PV-ESS.First,SD and SMC models were set up.Second,the SMC model simulated on an hourly timescale was incorporated into the SD model as a subsystem,a dual-timescale model was constructed.Finally,a development simulation and profitability analysis was conducted from 2022 to 2040 to reveal the dynamic optimal range of PV-ESS allocation.Additionally,negative electricity prices were considered during clearing processes.The simulation results revealed differences in profitability and capacity growth between IESS and PV-ESS,helping grid investors and policymakers to determine the boundaries of ESSs and dynamic optimal allocation of PV-ESSs.

GRU-integrated constrained soft actor-critic learning enabled fully distributed scheduling strategy for residential virtual power plant

In this study,a novel residential virtual power plant(RVPP)scheduling method that leverages a gate recurrent unit(GRU)-integrated deep reinforcement learning(DRL)algorithm is proposed.In the proposed scheme,the GRU-integrated DRL algorithm guides the RVPP to participate effectively in both the day-ahead and real-time markets,lowering the electricity purchase costs and consumption risks for end-users.The Lagrangian relaxation technique is introduced to transform the constrained Markov decision process(CMDP)into an unconstrained optimization problem,which guarantees that the constraints are strictly satisfied without determining the penalty coefficients.Furthermore,to enhance the scalability of the constrained soft actor-critic(CSAC)-based RVPP scheduling approach,a fully distributed scheduling architecture was designed to enable plug-and-play in the residential distributed energy resources(RDER).Case studies performed on the constructed RVPP scenario validated the performance of the proposed methodology in enhancing the responsiveness of the RDER to power tariffs,balancing the supply and demand of the power grid,and ensuring customer comfort.

A Special Issue:“Energy Transition Technology for Emission Peak and Carbon Neutrality”for Global Energy Interconnection

In 2020,China made a solemn commitment to the world:striving to achieve carbon peak before 2030 and carbon neutrality before 2060.The energy industry is the main source of carbon emissions and the key path to achieving the“dual carbon”goals.The revolution in energy production and consumption has already sparked a wave.However,the energy transition still faces challenges such as a high proportion of fossil fuel usage,multiple constraints on clean energy supply,urgent request to improve the carrying capacity and flexible regulation capability of the power system,and rising energy costs for the entire society.To cope with these difficulties and challenges,it is necessary to balance safety and stability,economic efficiency,and clean and low-carbon development three aspects;strengthen energy technology innovation;and deepen institutional and market reform and innovation.Therefore,the editorial department of Global Energy Interconnection has planned the special issue of“Energy Transition Technology for Emission Peak and Carbon Neutrality”.

Missing interpolation model for wind power data based on the improved CEEMDAN method and generative adversarial interpolation network

Randomness and fluctuations in wind power output may cause changes in important parameters(e.g.,grid frequency and voltage),which in turn affect the stable operation of a power system.However,owing to external factors(such as weather),there are often various anomalies in wind power data,such as missing numerical values and unreasonable data.This significantly affects the accuracy of wind power generation predictions and operational decisions.Therefore,developing and applying reliable wind power interpolation methods is important for promoting the sustainable development of the wind power industry.In this study,the causes of abnormal data in wind power generation were first analyzed from a practical perspective.Second,an improved complete ensemble empirical mode decomposition with adaptive noise(ICEEMDAN)method with a generative adversarial interpolation network(GAIN)network was proposed to preprocess wind power generation and interpolate missing wind power generation sub-components.Finally,a complete wind power generation time series was reconstructed.Compared to traditional methods,the proposed ICEEMDAN-GAIN combination interpolation model has a higher interpolation accuracy and can effectively reduce the error impact caused by wind power generation sequence fluctuations.

Power flow calculation for a distribution system with multi-port PETs:an improved AC-DC decoupling iterative method

Recently,power electronic transformers(PETs)have received widespread attention owing to their flexible networking,diverse operating modes,and abundant control objects.In this study,we established a steady-state model of PETs and applied it to the power flow calculation of AC-DC hybrid systems with PETs,considering the topology,power balance,loss,and control characteristics of multi-port PETs.To address new problems caused by the introduction of the PET port and control equations to the power flow calculation,this study proposes an iterative method of AC-DC mixed power flow decoupling based on step optimization,which can achieve AC-DC decoupling and effectively improve convergence.The results show that the proposed algorithm improves the iterative method and overcomes the overcorrection and initial value sensitivity problems of conventional iterative algorithms.

A Method for the Design of UFLS Scheme with Dynamic Correction

This paper presents an adaptive Under Frequency Load Shedding scheme based on Wide Area Measurement System. Due to the lack of enough adaptability to the operation state of the system, the traditional successive approximation under frequency load shedding method will cause excessive cut or undercut problems inevitably. This method consists first in a comprehensive weight index including load characteristics and inertias of generators. Then active-power deficit calculation based on the Low-order Frequency Response Model, concerning the effect of voltage was put forward. Finally, a dynamic correction of the load shedding amount was proposed to modify the scheme. This approach was applied to IEEE-39 system and the simulation results indicated that the proposed method was effective in reducing the load shedding amount as well as the frequency recovery time.

Optimal Planning of AC-DC Hybrid Transmission and Distributed Energy Resource System: Review and Prospects

With the highly-extensive integration of distributed renewable energy resources(DER)into the grid,the power distribution system has changed greatly in the structure,function and operating characteristics.On this ground,An AC-DC hybrid DER system becomes necessary for effective management and control over DER.This paper first summarizes the physical characteristics and morphological evolution of AC-DC hybrid DER system.The impact of these new features on system configuration planning is analyzed with respect to its flexible networking,rich operation control modes,and tight sourcenetwork-load-storage coupling.Then,based on a review of the existing research,problems and technical difficulties are figured out in terms of converter modeling,steady-state analysis,power flow calculation,operating scenarios management,and optimization model solution.In light of the problems and difficulties,a framework for the configuration optimization of AC-DC hybrid DER systems is proposed.At last,the paper provides a prospect of key technologies from six aspects including morphology forecasting,coupling interaction analysis,uncertainty modeling,operation simulation,optimization model solving algorithm and comprehensive scheme evaluation.

Clean Energy Consumption of Power Systems Towards Smart Agriculture: Roadmap, Bottlenecks and Technologies

Over the past decades,both agriculture and power systems have faced serious problems,such as the power supply shortage in agriculture,and difficulties of clean energy consump-tion in the power system.To address and overcome these issues,this paper proposes an idea to combine smart agriculture and clean energy consumption,use surplus clean energy to supply agriculture production,and utilize smart agriculture to support power system with clean energy penetration.A comprehensive review has been conducted to first depict the roadmap of coupling a agriculture-clean energy system,analyze their feasibilities and advantages.The recent technologies and bottlenecks are summa-rized and evaluated for the development of a combined system consisting of smart agriculture production and clean energy consumption.Several case studies are introduced to explore the mutual benefits of agriculture-clean energy systems in both the energy and food industries.

A Two-stage Robust Optimal Allocation Model of Distributed Generation Considering Capacity Curve and Real-time Price Based Demand Response

Demand response, the reactive power output of distributed generation(DG), and network reconfiguration have significant impacts on a DG allocation strategy. In this context, a novel real-time price-based demand response formulation is integrated into the allocation model of DG. The tariff is regulated by the difference between the load and active power of renewable energy. Meanwhile, network reconfiguration and the capacity curve describing the active and reactive power limits of DG are included in the optimization model for promoting the allocation of DG.With these measures, the optimal allocation model of DG is established with the goal of maximizing the net annual profit while guaranteeing the efficient utilization of renewable energy. In addition, the uncertainties of renewable energy are considered on the basis of a two-stage robust optimization method. Finally, the entire optimization model is solved by the column and constraint generation algorithm in the IEEE 33-bus distribution system and a practical 99-bus distribution system. Numerical simulations show that the proposed model is effective in terms of improving both the usage of renewable energy and net annual profit.

Multi-stage sizing approach for development of utility-scale BESS considering dynamic growth of distributed photovoltaic connection

The battery energy storage system(BESS)is regarded as one of the most promising address operational challenges caused by distributed generations.This paper proposes a novel multi-stage sizing model for utility-scale BESS,to optimize the BESS development strategies for distribution networks with increasing penetration levels and growth patterns of dispersed photovoltaic(PV)panels.Particularly,an integrated model is established in order to accommodate dispersed PVs in short-term operation scale while facilitating appropriate profits in long-term planning scale.Clusterwise reduction is adopted to extract the most representative operating scenarios with PVs and BESS integration,which is able to decrease the computing complexity caused by scenario redundancy.The numerical studies on IEEE 69-bus distribution system verify the feasibility of the proposed multi-stage sizing approach for the utility-scale BESS.

Multi-indicator Inference Scheme for Fuzzy Assessment of Power System Transient Stability

A multi-indicator inference scheme is proposed in this paper to achieve an intuitive assessment of post-fault transient stability of power systems.The proposed scheme uses the fuzzy inference technique to classify the stability level as“safe,”“low-risk,”“high-risk,”and“danger.”A multi-criteria quality assessment method is first introduced.Several transient indicators are then proposed as assessment criteria.To select the effective indicators for assessment,correlation mining using univariate regression analysis is performed between each indicator and a critical clearance time(CCT)-based stability index.The fuzzy sets of indicators for different stability levels are then determined according to their correlations with the stability index.The weighting factors of indicators are also allocated according to their regression error in correlation mining.The proposed inference scheme is further demonstrated and its effectiveness is validated in case studies on IEEE 68-bus system and a 756-bus transmission system in China.

Integrated traffic-power simulation framework for electric vehicle charging stations based on cellular automaton

This paper presents a novel integrated framework for simulating the charging load of an electric vehicle charging station. The framework is built based on the cellular automaton model, including five modules: vehicle generation, charging station, lane change, speed update and boundary clear. The proposed framework can effectively stimulate the system dynamics of the traffic system and charging power process in a charging station. Case studies have verified its feasibility and effectiveness.

High-performance predictor for critical unstable generators based on scalable parallelized neural networks

A high-performance predictor for critical unstable generators(CUGs) of power systems is presented in this paper. The predictor is driven by the Map Reduce based parallelized neural networks. Specifically, a group of back propagation neural networks(BPNNs), fed by massive response trajectories data, are efficiently organized and concurrently trained in Hadoop to identify dynamic behavior of individual generator. Rather than simply classifying global stability of power systems, the presented approach is able to distinguish unstable generators accurately with a few cycles of synchronized trajectories after fault clearing,enabling more in-depth emergency awareness based on wide-area implementation. In addition, the technique is of rich scalability due to Hadoop framework, which can be deployed in the control centers as a high-performance computing infrastructure for real-time instability alert.Numerical examples are studied using NPCC 48-machines test system and a realistic power system of China.

A Hybrid Agent-based Model Predictive Control Scheme for Smart Community Energy System with Uncertain DGs and Loads

A multi-agent consensus-based market scheme is proposed for the cooperation of community and multiple microgrids(MGs)in a distributed,economic and hierarchal man-ner.The proposed community-based market framework with frequency regulation(FR)market is formulated as a two-level scheduling problem:the global decision-making process of community agent(CA)to participate in the FR market and the interaction and control process of local MGs to achieve collaboration in response to the global target with efficient pricing rules.Specifically,the model predictive control(MPC)is integrated with the consensus-based theory to allow MG to obtain an economic and reliable dispatch in the presence of uncertainties of distributed generators and loads.Thanks to the distributed nature of the proposed scheme,its robustness to communication issues has been strengthened and a win-win situation for all energy stakeholders can be achieved.The robustness of the proposed scheme is investigated in various conditions,including different implementation strategies,communication topologies,and the level of uncertainties.

A Data-driven Distributionally Robust Operational Model for Urban Integrated Energy Systems

A multi-energy conversion can effectively increase the utilization of renewable energy in the urban integrated energy system(UIES).Meanwhile,the uncertainties of renewable energy resources(e.g.,wind energy)also bring increased challenges to the operation of UIES.In this study,a typical two-stage datadriven distributionally robust operation(DDRO)model based on finite scenarios is proposed for UIES including power,gas and heat networks to obtain a salient strategy from both an economic and robustness perspective.In the first stage,the forecasted information for wind power is especially included to improve the economic aspect of robust decisions.The worst probability distribution for the selected known real-time wind power scenarios can be identified in the second stage where the power differences caused by the real-time uncertainties of wind power can be mitigated by flexible regulation of energy purchasing and coupling units(such as gas turbine,power to gas equipment,electric boiler and gas boiler).Moreover,norm-1 and norm-inf co-constraints are utilized to construct a confidence set for the probability distributions of uncertain wind power.The whole two-stage model is solved by the column-and-constraint generation(CCG)algorithm.Finally,case studies are conducted to show the performance of the proposed model and various approaches.Index Terms-Data-driven methods,distributionally robust optimization,urban integrated energy system,wind power.

Measurement-based Cell-DT Method for Power System Transient Stability Classification

In this paper,a combined Characteristic Ellipsoid(CELL)and Decision Tree(DT)method for fast classifying the transient stability of power systems after a large disturbance is proposed.The proposed two-stage method involves projection of the PMU measurement data after the disturbance on a multidimensional space to form the CELL and then classification of the transient stability using DT which takes the characteristic attributes of CELL under different fault scenarios as input features.The dynamic behaviors after a disturbance for both stable and unstable situations are identified from the variation of the CELL’s shape.The database,composed of the geometrical properties of the CELL such as volume,eccentricity,center and change rate of volume,is used to train a DT for transient stability classification.Case study on a IEEE 39-bus system demonstrates the feasibility of the proposed method.Investigations show that the proposed method requires less information from the system to fast classify the transient stability with high precision.