Electricity trading based on distribution locational marginal price

Department of Finance and Education of Guangdong Province; Education Department of Guangdong Province; Brunel University Londo

Maximal Information Coefficient Based Residential Photovoltaic Power Generation Disaggregation

Due to policy support, low cost and easy applicability, distribution photovoltaic systems (DPVSs) are increasingly popular among residential community. However, small-scale DPVSs of less than 10 kWp are always installed behind the meter (BTM), which results in the invisible of the photovoltaic (PV) power generation. Only access of composite power data can result in non-optimal distribution network control and optimization, leading to a series of energy management problems. In order to solve the aforementioned problems, this paper proposes a BTM composite power disaggregation method focusing on small-scale DPVSs, with only composite power data of residential users in a community, without relying on weather data and models assumption. Considering that community users’ DPVSs usually exhibit approximate output characteristics, neighboring composite power is used to extract PV power generation information as mutual proxies. After obtaining approximate PV proxy data by subtracting composite power of inter-users, a grid search algorithm guided by Maximal Information Coefficient (MIC) is performed to obtain final PV power generation disaggregation results. The proposed method is evaluated using data gathered from residential customers located in Ithaca, New York and Austin, Texas in real-life scenarios. Testing results show that our proposed method achieve considerable disaggregation accuracy in the absence of solar radiation and temperature data as compared to other state-of-art methods

Robustness Evaluation of Extended and Unscented Kalman Filter for Battery State of Charge Estimation

OAPA In this paper, the robustness of model-based state observers including extended Kalman filter (EKF) and unscented Kalman filter (UKF) for state of charge (SOC) estimation of a lithium-ion battery against unknown initial SOC, current noise, and temperature effects is investigated. To more comprehensively evaluate the performance of EKF and UKF, two battery models including the first-order resistor-capacitor (RC) equivalent circuit and combined model are considered. A novel method is proposed to identify the parameters of the equivalent circuit model. The performance of SOC estimation is evaluated by employing measurement data from a commercial lithium-ion battery cell. The experiment results show that UKF generally outperforms EKF in terms of estimation accuracy and convergence rate for each battery model. However, the advantages of UKF over EKF with the combined model is not as significant as with the equivalent circuit model. Both EKF and UKF demonstrate strong robustness against current noise. The updates of model parameters corresponding to operational temperatures generally improve the estimation accuracy of EKF and UKF for both models

Fault Diagnosis Approach of Main Drive Chain in Wind Turbine Based on Data Fusion

This article belongs to the Special Issue Electrification of Smart Cities.Copyright: © 2021 by the authors. The construction and operation of wind turbines have become an important part of the development of smart cities. However, the fault of the main drive chain often causes the outage of wind turbines, which has a serious impact on the normal operation of wind turbines in smart cities. In order to overcome the shortcomings of the commonly used main drive chain fault diagnosis method that only uses a single data source, a fault feature extraction and fault diagnosis approach based on data source fusion is proposed. By fusing two data sources, the supervisory control and data acquisition (SCADA) real-time monitoring system data and the main drive chain vibration monitoring data, the fault features of the main drive chain are jointly extracted, and an intelligent fault diagnosis model for the main drive chain in wind turbine based on data fusion is established. The diagnosis results of actual cases certify that the fault diagnosis model based on the fusion of two data sources is able to locate faults of the main drive chain in the wind turbine accurately and provide solid technical support for the high-efficient operation and maintenance of wind turbines.China Southern Power Grid (Research Program of Digital Grid Research Institute, Grant YTYZW20010)

Energy management considering multiple power markets and microgrid storage

Data availability statement: The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.Copyright © 2023 Yan, Li, Lai, Zhao, Zobaa, Lai and Shi. Storage response highly influences the operational cost of the microgrid. Power market has provided a selection of tariffs which increases difficulties for customer choice. New market entry and traditional tariffs exist together to form a complex market environment. Facing this situation, this paper establishes a complex microgrid market environment with four types of tariffs. By modelling response of electric storage and cold storage in microgrid, a non-linear mixed integer optimization problem is formulated. Numerical studies are implemented for model verification and market performance analysis. Results show that behavior trade-off occurs on different market entries on total cost optimization for microgrid operation.National Natural Science Foundation of China (62206062); Guangdong Basic and Applied Basic Research Foundation (2021A1515010742)

Modeling, Oscillation Analysis and Distributed Stabilization Control of Autonomous PV-based Microgrids

Driven by rising energy demand and the goal of carbon neutrality, renewable energy generations (REGs), especially photovoltaic (PV) generations, are widely used in the urban power energy systems. While the intelligent control of microgrids (MG) brings economic and efficient operation, its potential stability problem cannot be ignored. To date, most of the research on modeling, analyzing and enhancing the stability of MG usually assume the DC-link as an ideal voltage source. However, this practice of ignoring the dynamics of DC-link may omit the latent oscillation phenomena of autonomous PV-based MG. First, this paper establishes a complete dynamic model of autonomous PV-based MG including PV panels and DC-link. Different from previous conclusions of idealizing DC-link dynamics, participation factor analysis finds the potential impact of DC-link dynamics on system dynamic performance, and different influence factors including critical control parameters and non-linear V-I output characteristic of PV array are considered to further reveal oscillation mechanisms. Second, based on the average consensus algorithm, a distributed stabilization controller with strong robustness is proposed to enhance stability of the PV-based MG, which does not affect the steady-state performance of the system. Finally, the correctness of all theoretical analysis and the effectiveness of the proposed controller are verified by time domain simulation and hardware-in-loop tests.10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 51907031

Short-Term Wind Power Prediction Based on Data Decomposition and Combined Deep Neural Network

A hybrid short-term wind power prediction model based on data decomposition and combined deep neural network is proposed with the inclusion of the characteristics of fluctuation and randomness of nonlinear signals, such as wind speed and wind power. Firstly, the variational mode decomposition (VMD) is used to decompose the wind speed and wind power sequences in the input data to reduce the noise in the original signal. Secondly, the decomposed wind speed and wind power sub-sequences are reconstructed into new data sets with other related features as the input of the combined deep neural network, and the input data are further studied for the implied features by convolutional neural network (CNN), which should be passed into the long and short-term memory neural network (LSTM) as input for prediction. At the same time, the improved particle swarm optimization algorithm (IPSO) is adopted to optimize the parameters of each prediction model. By superimposing each predicted sub-sequence, the predicting wind power could be obtained. Simulations based on a short-term power prediction in different months with huge weather differences is carried out for a wind farm in Guangdong, China. The simulated results validate that the proposed model has a high prediction accuracy and generalization ability.Guangdong-Guangxi Joint Foundation of China: Research on Distributed Optimal Control of Renewable Multi-microgrids based on the Integration of Multi-Agent Dynamic Game and Prediction Mechanism [Project Number 2021A1515410009]

Line Outage Distribution Factors of a Linearized AC model with Reactive Power and Voltage Magnitude for Resilience-Constrained Economic Dispatch

Data Access Statement: This study is a re-analysis of existing data from MATPOWER (https://matpower.org) and modified datasets from MATPOWER are provided in the Appendix of this paper. Appendix: The data of the modified 30-bus system includes the data of bus, branch and generator is shown in Table A.1, Table A.2, Table A.3, respectively. The data of the modified 118-bus system includes the data of bus, branch and generator is shown in Table A.4, Table A.5, Table A.6, respectively.Copyright © 2022 The Author(s). Research on preventive generation dispatch schemes for resilient power system operation is often based on the DC power flow model, ignoring the influence of reactive power and voltage magnitude. This paper presents an in-depth study of a linearized AC power flow (LAC) model with reactive power and voltage magnitude to derive sensitivity factors, including shift factors and line outage distribution factors, for pre-contingency and post-contingency power flow calculations for N-k contingencies. Based on the derived sensitivity factors, a resilience-constrained economic dispatch (LAC-RCED) strategy is developed, which considers the security constraints of N-1 contingency for all lines and N-2 contingency for the affected lines, as well as optimization objectives to improve the power flow distribution in the transmission system. To deal with the computational difficulties associated with the N-k contingency constraints, an iterative contingency filtering algorithm based on the derived line outage distribution factor is proposed for contingency screening and creating security constraints for violated contingency scenarios. In the case study, the accuracy of the power flow solution obtained from the derived sensitivity factors is investigated by comparison with the AC model. The proposed LAC-RCED model and the iterative contingency filtering algorithm are tested on the IEEE 30-bus and 118-bus systems.Education Department of Guangdong Province, China: New and Integrated Energy System Theory and Technology Research Group [Project Number 2016KCXTD022]; National Natural Science Foundation of China (51907031); Brunel University London BRIEF Funding

A Novel Power Market Mechanism Based on Blockchain for Electric Vehicle Charging Stations

© 2021 by the authors. This work presents a novel blockchain-based energy trading mechanism for electric vehicles consisting of day-ahead and real-time markets. In the day-ahead market, electric vehicle users submit their bidding price to participate in the double auction mechanism. Subsequently, the smart match mechanism will be conducted by the charging system operator, to meet both personal interests and social benefits. After clearing the trading result, the charging system operator uploads the trading contract made in the day-ahead market to the blockchain. In the real-time market, the charging system operator checks the trading status and submits the updated trading results to the blockchain. This mechanism encourages participants in the double auction to pursue higher interests, in addition to rationally utilize the energy unmatched in the auction and to achieve the improvement of social welfare. Case studies are used to demonstrate the effectiveness of the proposed model. For buyers and sellers who successfully participate in the day-ahead market, the total profit increase for buyer and seller are 22.79% and 53.54%, respectively, as compared to without energy trading. With consideration of social welfare in the smart match mechanism, the peak load reduces from 182 to 146.5 kW, which is a 19.5% improvement.Department of Finance and Education of Guangdong Prov- ince 2016 [202]: Key Discipline Construction Program, China; the Education Department of Guang- dong Province: New and Integrated Energy System Theory and Technology Research Group [Pro- ject Number 2016KCXTD022]; Brunel University London BRIEF Funding; National Natural Science Foundation of China (51907031)

Robust N-k security-constrained optimal power flow incorporating preventive and corrective generation dispatch to improve power system reliability

Education Department of Guangdong Province: New and Integrated Energy System Theory and Technology Research Group [Project Number 2016KCXTD022]; National Natural Science Foundation of China (51907031); The National Natural Science Foundation of China 51907031; Guangdong Basic and Applied Basic Research Foundation (Guangdong-Guangxi Joint Foundation) 2021A1515410009; China Scholarship Council; Brunel University London BRIEF Funding