The accurate prediction of photovoltaic (PV) power generation is significant to ensure the economic and safe operation of power systems. To this end, the paper establishes a new digital twin (DT) empowered PV power prediction framework that is capable of ensuring reliable data transmission and employing the DT to achieve high accuracy of power prediction. With this framework, considering potential data contamination in the collected PV data, a generative adversarial network is employed to restore the historical dataset, which offers a prerequisite to ensure accurate mapping from the physical space to the digital space. Further, a new DT-empowered PV power prediction method is proposed. Therein, we model a DT that encompasses a digital physical model for reflecting the physical operation mechanism and a neural network model (i.e., a parallel network of convolution and bidirectional long short-term memory model) for capturing the hidden spatiotemporal features. The proposed method enables the use of the DT to take advantages of the digital physical model and the neural network model, resulting in enhanced prediction accuracy. Finally, a real dataset is conducted to assess the effectiveness of the proposed method.
{"title":"Digital Twin Empowered PV Power Prediction","authors":"Xiaoyu Zhang;Yushuai Li;Tianyi Li;Yonghao Gui;Qiuye Sun;David Wenzhong Gao","doi":"10.35833/MPCE.2023.000351","DOIUrl":"https://doi.org/10.35833/MPCE.2023.000351","url":null,"abstract":"The accurate prediction of photovoltaic (PV) power generation is significant to ensure the economic and safe operation of power systems. To this end, the paper establishes a new digital twin (DT) empowered PV power prediction framework that is capable of ensuring reliable data transmission and employing the DT to achieve high accuracy of power prediction. With this framework, considering potential data contamination in the collected PV data, a generative adversarial network is employed to restore the historical dataset, which offers a prerequisite to ensure accurate mapping from the physical space to the digital space. Further, a new DT-empowered PV power prediction method is proposed. Therein, we model a DT that encompasses a digital physical model for reflecting the physical operation mechanism and a neural network model (i.e., a parallel network of convolution and bidirectional long short-term memory model) for capturing the hidden spatiotemporal features. The proposed method enables the use of the DT to take advantages of the digital physical model and the neural network model, resulting in enhanced prediction accuracy. Finally, a real dataset is conducted to assess the effectiveness of the proposed method.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":"12 5","pages":"1472-1483"},"PeriodicalIF":5.7,"publicationDate":"2023-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10322689","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142324327","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In recent years, reinforcement learning (RL) has emerged as a solution for model-free dynamic programming problem that cannot be effectively solved by traditional optimization methods. It has gradually been applied in the fields such as economic dispatch of power systems due to its strong self-learning and self-optimizing capabilities. However, existing economic scheduling methods based on RL ignore security risks that the agent may bring during exploration, which poses a risk of issuing instructions that threaten the safe operation of power system. Therefore, we propose an improved proximal policy optimization algorithm for sequential security-constrained optimal power flow (SCOPF) based on expert knowledge and safety layer to determine active power dispatch strategy, voltage optimization scheme of the units, and charging/discharging dispatch of energy storage systems. The expert experience is introduced to improve the ability to enforce constraints such as power balance in training process while guiding agent to effectively improve the utilization rate of renewable energy. Additionally, to avoid line overload, we add a safety layer at the end of the policy network by introducing transmission constraints to avoid dangerous actions and tackle sequential SCOPF problem. Simulation results on an improved IEEE 118-bus system verify the effectiveness of the proposed algorithm.
{"title":"Improved Proximal Policy Optimization Algorithm for Sequential Security-Constrained Optimal Power Flow Based on Expert Knowledge and Safety Layer","authors":"Yanbo Chen;Qintao Du;Honghai Liu;Liangcheng Cheng;Muhammad Shahzad Younis","doi":"10.35833/MPCE.2023.000232","DOIUrl":"https://doi.org/10.35833/MPCE.2023.000232","url":null,"abstract":"In recent years, reinforcement learning (RL) has emerged as a solution for model-free dynamic programming problem that cannot be effectively solved by traditional optimization methods. It has gradually been applied in the fields such as economic dispatch of power systems due to its strong self-learning and self-optimizing capabilities. However, existing economic scheduling methods based on RL ignore security risks that the agent may bring during exploration, which poses a risk of issuing instructions that threaten the safe operation of power system. Therefore, we propose an improved proximal policy optimization algorithm for sequential security-constrained optimal power flow (SCOPF) based on expert knowledge and safety layer to determine active power dispatch strategy, voltage optimization scheme of the units, and charging/discharging dispatch of energy storage systems. The expert experience is introduced to improve the ability to enforce constraints such as power balance in training process while guiding agent to effectively improve the utilization rate of renewable energy. Additionally, to avoid line overload, we add a safety layer at the end of the policy network by introducing transmission constraints to avoid dangerous actions and tackle sequential SCOPF problem. Simulation results on an improved IEEE 118-bus system verify the effectiveness of the proposed algorithm.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":"12 3","pages":"742-753"},"PeriodicalIF":6.3,"publicationDate":"2023-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10316539","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141091102","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-13DOI: 10.35833/MPCE.2023.000190
Mohammad Kazem Bakhshizadeh;Sujay Ghosh;Guangya Yang;Łukasz Kocewiak
As the proportion of converter-interfaced renewable energy resources in the power system is increasing, the strength of the power grid at the connection point of wind turbine generators (WTGs) is gradually weakening. Existing research has shown that when connected with the weak grid, the stability of the traditional grid-following controlled converters will deteriorate, and unstable phenomena such as oscillation are prone to arise. Due to the limitations of linear analysis that cannot sufficiently capture the stability phenomena, transient stability must be investigated. So far, standalone time-domain simulations or analytical Lyapunov stability criteria have been used to investigate transient stability. However, the time-domain simulations have proven to be computationally too heavy, while analytical methods are difficult to formulate for larger systems, require many modelling assumptions, and are often conservative in estimating the stability boundary. This paper proposes and demonstrates an innovative approach to estimating the transient stability boundary via combining the linear Lyapunov function and the reverse-time trajectory technique. The proposed methodology eliminates the need of time-consuming simulations and the conservative nature of Lyapunov functions. This study brings out the clear distinction between the stability boundaries with different post-fault active current ramp rate controls. At the same time, it provides a new perspective on critical clearing time for wind turbine systems. The stability boundary is verified using time-domain simulation studies.
{"title":"Transient Stability Analysis of Grid-Connected Converters in Wind Turbine Systems Based on Linear Lyapunov Function and Reverse-Time Trajectory","authors":"Mohammad Kazem Bakhshizadeh;Sujay Ghosh;Guangya Yang;Łukasz Kocewiak","doi":"10.35833/MPCE.2023.000190","DOIUrl":"https://doi.org/10.35833/MPCE.2023.000190","url":null,"abstract":"As the proportion of converter-interfaced renewable energy resources in the power system is increasing, the strength of the power grid at the connection point of wind turbine generators (WTGs) is gradually weakening. Existing research has shown that when connected with the weak grid, the stability of the traditional grid-following controlled converters will deteriorate, and unstable phenomena such as oscillation are prone to arise. Due to the limitations of linear analysis that cannot sufficiently capture the stability phenomena, transient stability must be investigated. So far, standalone time-domain simulations or analytical Lyapunov stability criteria have been used to investigate transient stability. However, the time-domain simulations have proven to be computationally too heavy, while analytical methods are difficult to formulate for larger systems, require many modelling assumptions, and are often conservative in estimating the stability boundary. This paper proposes and demonstrates an innovative approach to estimating the transient stability boundary via combining the linear Lyapunov function and the reverse-time trajectory technique. The proposed methodology eliminates the need of time-consuming simulations and the conservative nature of Lyapunov functions. This study brings out the clear distinction between the stability boundaries with different post-fault active current ramp rate controls. At the same time, it provides a new perspective on critical clearing time for wind turbine systems. The stability boundary is verified using time-domain simulation studies.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":"12 3","pages":"782-790"},"PeriodicalIF":6.3,"publicationDate":"2023-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10316538","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141091099","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In the realm of microgrid (MG), the distributed load frequency control (LFC) system has proven to be highly susceptible to the negative effects of false data injection attacks (FDIAs). Considering the significant responsibility of the distributed LFC system for maintaining frequency stability within the MG, this paper proposes a detection and defense method against unobservable FDIAs in the distributed LFC system. Firstly, the method integrates a bi-directional long short-term memory (BiLSTM) neural network and an improved whale optimization algorithm (IWOA) into the LFC controller to detect and counteract FDIAs. Secondly, to enable the BiLSTM neural network to proficiently detect multiple types of FDIAs with utmost precision, the model employs a historical MG dataset comprising the frequency and power variances. Finally, the IWOA is utilized to optimize the proportional-integral-derivative (PID) controller parameters to counteract the negative impacts of FDIAs. The proposed detection and defense method is validated by building the distributed LFC system in Simulink.
{"title":"Detection and Defense Method Against False Data Injection Attacks for Distributed Load Frequency Control System in Microgrid","authors":"Zhixun Zhang;Jianqiang Hu;Jianquan Lu;Jie Yu;Jinde Cao;Ardak Kashkynbayev","doi":"10.35833/MPCE.2023.000400","DOIUrl":"https://doi.org/10.35833/MPCE.2023.000400","url":null,"abstract":"In the realm of microgrid (MG), the distributed load frequency control (LFC) system has proven to be highly susceptible to the negative effects of false data injection attacks (FDIAs). Considering the significant responsibility of the distributed LFC system for maintaining frequency stability within the MG, this paper proposes a detection and defense method against unobservable FDIAs in the distributed LFC system. Firstly, the method integrates a bi-directional long short-term memory (BiLSTM) neural network and an improved whale optimization algorithm (IWOA) into the LFC controller to detect and counteract FDIAs. Secondly, to enable the BiLSTM neural network to proficiently detect multiple types of FDIAs with utmost precision, the model employs a historical MG dataset comprising the frequency and power variances. Finally, the IWOA is utilized to optimize the proportional-integral-derivative (PID) controller parameters to counteract the negative impacts of FDIAs. The proposed detection and defense method is validated by building the distributed LFC system in Simulink.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":"12 3","pages":"913-924"},"PeriodicalIF":6.3,"publicationDate":"2023-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10316540","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141091166","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-10DOI: 10.35833/MPCE.2023.000175
Izudin Džafić;Rabih A. Jabr
The partial differential equation (PDE) solution of the telegrapher is a promising fault location method among time-domain and model-based techniques. Recent research works have shown that the leap-frog process is superior to other explicit methods for the PDE solution. However, its implementation is challenged by determining the initial conditions in time and the boundary conditions in space. This letter proposes two implicit solution methods for determining the initial conditions and an analytical way to obtain the boundary conditions founded on the signal decomposition. The results show that the proposal gives fault location accuracy superior to the existing leap-frog scheme, particularly in the presence of harmonics.
{"title":"Improved Leap-frog Method for Time-domain Fault Location","authors":"Izudin Džafić;Rabih A. Jabr","doi":"10.35833/MPCE.2023.000175","DOIUrl":"https://doi.org/10.35833/MPCE.2023.000175","url":null,"abstract":"The partial differential equation (PDE) solution of the telegrapher is a promising fault location method among time-domain and model-based techniques. Recent research works have shown that the leap-frog process is superior to other explicit methods for the PDE solution. However, its implementation is challenged by determining the initial conditions in time and the boundary conditions in space. This letter proposes two implicit solution methods for determining the initial conditions and an analytical way to obtain the boundary conditions founded on the signal decomposition. The results show that the proposal gives fault location accuracy superior to the existing leap-frog scheme, particularly in the presence of harmonics.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":"12 2","pages":"670-674"},"PeriodicalIF":6.3,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10315080","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140291065","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-01DOI: 10.35833/MPCE.2023.000414
Kun Li;Jiakun Fang;Xiaomeng Ai;Shichang Cui;Rongkang Zhao;Jinyu Wen
Base station (BS) backup batteries (BSBBs), with their dispatchable capacity, are potential demand-side resources for future power systems. To enhance the power supply reliability and post-contingency frequency security of power systems, we propose a two-stage stochastic unit commitment (UC) model incorporating operational reserve and post-contingency frequency support provisions from massive BSBBs in cellular networks, in which the minimum backup energy demand is considered to ensure BS power supply reliability. The energy, operational reserve, and frequency support ancillary services are co-optimized to handle the power balance and post-contingency frequency security in both forecasted and stochastic variable renewable energy (VRE) scenarios. Furthermore, we propose a dedicated and scalable distributed optimization framework to enable autonomous optimizations for both dispatching center (DC) and BSBBs. The BS model parameters are stored and processed locally, while only the values of BS decision variables are required to upload to DC under the proposed distributed optimization framework, which safeguards BS privacy effectively. Case studies on a modified IEEE 14-bus system demonstrate the effectiveness of the proposed method in promoting VRE accommodation, ensuring post-contingency frequency security, enhancing operational economics, and fully utilizing BSBBs' energy and power capacity. Besides, the proposed distributed optimization framework has been validated to converge to a feasible solution with near-optimal performance within limited iterations. Additionally, numerical results on the Guangdong 500 kV provincial power system in China verify the scalability and practicality of the proposed distributed optimization framework.
{"title":"Distributed Stochastic Scheduling of Massive Backup Batteries in Cellular Networks for Operational Reserve and Frequency Support Ancillary Services","authors":"Kun Li;Jiakun Fang;Xiaomeng Ai;Shichang Cui;Rongkang Zhao;Jinyu Wen","doi":"10.35833/MPCE.2023.000414","DOIUrl":"https://doi.org/10.35833/MPCE.2023.000414","url":null,"abstract":"Base station (BS) backup batteries (BSBBs), with their dispatchable capacity, are potential demand-side resources for future power systems. To enhance the power supply reliability and post-contingency frequency security of power systems, we propose a two-stage stochastic unit commitment (UC) model incorporating operational reserve and post-contingency frequency support provisions from massive BSBBs in cellular networks, in which the minimum backup energy demand is considered to ensure BS power supply reliability. The energy, operational reserve, and frequency support ancillary services are co-optimized to handle the power balance and post-contingency frequency security in both forecasted and stochastic variable renewable energy (VRE) scenarios. Furthermore, we propose a dedicated and scalable distributed optimization framework to enable autonomous optimizations for both dispatching center (DC) and BSBBs. The BS model parameters are stored and processed locally, while only the values of BS decision variables are required to upload to DC under the proposed distributed optimization framework, which safeguards BS privacy effectively. Case studies on a modified IEEE 14-bus system demonstrate the effectiveness of the proposed method in promoting VRE accommodation, ensuring post-contingency frequency security, enhancing operational economics, and fully utilizing BSBBs' energy and power capacity. Besides, the proposed distributed optimization framework has been validated to converge to a feasible solution with near-optimal performance within limited iterations. Additionally, numerical results on the Guangdong 500 kV provincial power system in China verify the scalability and practicality of the proposed distributed optimization framework.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":"12 2","pages":"393-404"},"PeriodicalIF":6.3,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10304597","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140291196","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-01DOI: 10.35833/MPCE.2023.000464
Hamid Rezaie;Cheuk Hei Chung;Nima Safari
Wind power prediction interval (WPPI) models in the literature have predominantly been developed for and tested on specific case studies. However, wind behavior and characteristics can vary significantly across regions. Thus, a prediction model that performs well in one case might underperform in another. To address this shortcoming, this paper proposes an ensemble WPPI framework that integrates multiple WPPI models with distinct characteristics to improve robustness. Another important and often overlooked factor is the role of probabilistic wind power prediction (WPP) in quantifying wind power uncertainty, which should be handled by operating reserve. Operating reserve in WPPI frameworks enhances the efficacy of WPP. In this regard, the proposed framework employs a novel bi-layer optimization approach that takes both WPPI quality and reserve requirements into account. Comprehensive analysis with different real-world datasets and various benchmark models validates the quality of the obtained WPPIs while resulting in more optimal reserve requirements.
{"title":"Ensemble Wind Power Prediction Interval with Optimal Reserve Requirement","authors":"Hamid Rezaie;Cheuk Hei Chung;Nima Safari","doi":"10.35833/MPCE.2023.000464","DOIUrl":"https://doi.org/10.35833/MPCE.2023.000464","url":null,"abstract":"Wind power prediction interval (WPPI) models in the literature have predominantly been developed for and tested on specific case studies. However, wind behavior and characteristics can vary significantly across regions. Thus, a prediction model that performs well in one case might underperform in another. To address this shortcoming, this paper proposes an ensemble WPPI framework that integrates multiple WPPI models with distinct characteristics to improve robustness. Another important and often overlooked factor is the role of probabilistic wind power prediction (WPP) in quantifying wind power uncertainty, which should be handled by operating reserve. Operating reserve in WPPI frameworks enhances the efficacy of WPP. In this regard, the proposed framework employs a novel bi-layer optimization approach that takes both WPPI quality and reserve requirements into account. Comprehensive analysis with different real-world datasets and various benchmark models validates the quality of the obtained WPPIs while resulting in more optimal reserve requirements.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":"12 1","pages":"65-76"},"PeriodicalIF":6.3,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10304598","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139572873","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Demand response transactions between electric consumers, load aggregators, and the distribution network manager based on the “combination of price and incentive” are feasible and efficient. However, the incentive payment of demand response is quantified based on private information, which gives the electric consumers and load aggregators the possibility of defrauding illegitimate interests by declaring false information. This paper proposes a method based on Vickrey-Clark-Groves (VCG) theory to prevent electric consumers and load aggregators from taking illegitimate interests through deceptive declaration in the demand response transactions. Firstly, a demand response transaction framework with the price-and-incentive combined mode is established to illustrate the deceptive behavior in the demand response transaction. Then, the idea for eradicating deceptive declarations based on VCG theory is given, and a detailed VCG-based mathematical model is constructed following the demand response transaction framework. Further, the proofs of incentive compatibility, individual rationality, cost minimization, and budget balance of the proposed VCG-based method are given. Finally, a modified IEEE 33-node system and a modified IEEE 123-node system are used to illustrate and validate the proposed method.
{"title":"Vickrey-Clark-Groves-Based Method for Eradicating Deceptive Behaviors in Demand Response Transactions","authors":"Yingjun Wu;Chengjun Liu;Zhiwei Lin;Zhaorui Chen;Runrun Chen;Yuyang Chen","doi":"10.35833/MPCE.2023.000157","DOIUrl":"10.35833/MPCE.2023.000157","url":null,"abstract":"Demand response transactions between electric consumers, load aggregators, and the distribution network manager based on the “combination of price and incentive” are feasible and efficient. However, the incentive payment of demand response is quantified based on private information, which gives the electric consumers and load aggregators the possibility of defrauding illegitimate interests by declaring false information. This paper proposes a method based on Vickrey-Clark-Groves (VCG) theory to prevent electric consumers and load aggregators from taking illegitimate interests through deceptive declaration in the demand response transactions. Firstly, a demand response transaction framework with the price-and-incentive combined mode is established to illustrate the deceptive behavior in the demand response transaction. Then, the idea for eradicating deceptive declarations based on VCG theory is given, and a detailed VCG-based mathematical model is constructed following the demand response transaction framework. Further, the proofs of incentive compatibility, individual rationality, cost minimization, and budget balance of the proposed VCG-based method are given. Finally, a modified IEEE 33-node system and a modified IEEE 123-node system are used to illustrate and validate the proposed method.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":"12 4","pages":"1260-1271"},"PeriodicalIF":5.7,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10304596","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141769491","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-13DOI: 10.35833/MPCE.2023.000230
Yanhong Luo;Haowei Hao;Dongsheng Yang;Bowen Zhou
In this paper, a novel multi-objective optimization model of integrated energy systems (IESs) is proposed based on the ladder-type carbon emission trading mechanism and refined load demand response strategies. First, the carbon emission trading mechanism is introduced into the optimal scheduling of IESs, and a ladder-type carbon emission cost calculation model based on rewards and penalties is established to strictly control the carbon emissions of the system. Then, according to different response characteristics of electric load and heating load, a refined load demand response model is built based on the price elasticity matrix and substitutability of energy supply mode. On these basis, a multi-objective optimization model of IESs is established, which aims to minimize the total operating cost and the renewable energy source (RES) curtailment. Finally, based on typical case studies, the simulation results show that the proposed model can effectively improve the economic benefits of IESs and the utilization efficiency of RESs.
{"title":"Multi-Objective Optimization of Integrated Energy Systems Considering Ladder-Type Carbon Emission Trading and Refined Load Demand Response","authors":"Yanhong Luo;Haowei Hao;Dongsheng Yang;Bowen Zhou","doi":"10.35833/MPCE.2023.000230","DOIUrl":"https://doi.org/10.35833/MPCE.2023.000230","url":null,"abstract":"In this paper, a novel multi-objective optimization model of integrated energy systems (IESs) is proposed based on the ladder-type carbon emission trading mechanism and refined load demand response strategies. First, the carbon emission trading mechanism is introduced into the optimal scheduling of IESs, and a ladder-type carbon emission cost calculation model based on rewards and penalties is established to strictly control the carbon emissions of the system. Then, according to different response characteristics of electric load and heating load, a refined load demand response model is built based on the price elasticity matrix and substitutability of energy supply mode. On these basis, a multi-objective optimization model of IESs is established, which aims to minimize the total operating cost and the renewable energy source (RES) curtailment. Finally, based on typical case studies, the simulation results show that the proposed model can effectively improve the economic benefits of IESs and the utilization efficiency of RESs.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":"12 3","pages":"828-839"},"PeriodicalIF":6.3,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10285629","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141091138","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper proposes a distributed robust optimal dispatch model to enhance information security and interaction among the operators in the regional integrated energy system (RIES). Our model regards the distribution network and each energy hub (EH) as independent operators and employs robust optimization to improve operational security caused by wind and photovoltaic (PV) power output uncertainties, with only deterministic information exchanged across boundaries. This paper also adopts the alternating direction method of multipliers (ADMM) algorithm to facilitate secure information interaction among multiple RIES operators, maximizing the benefit for each subject. Furthermore, the traditional ADMM algorithm with fixed step size is modified to be adaptive, addressing issues of redundant interactions caused by suboptimal initial step size settings. A case study validates the effectiveness of the proposed model, demonstrating the superiority of the ADMM algorithm with adaptive step size and the economic benefits of the distributed robust optimal dispatch model over the distributed stochastic optimal dispatch model.
{"title":"Distributed Robust Optimal Dispatch of Regional Integrated Energy Systems Based on ADMM Algorithm with Adaptive Step Size","authors":"Zhoujun Ma;Yizhou Zhou;Yuping Zheng;Li Yang;Zhinong Wei","doi":"10.35833/MPCE.2023.000204","DOIUrl":"https://doi.org/10.35833/MPCE.2023.000204","url":null,"abstract":"This paper proposes a distributed robust optimal dispatch model to enhance information security and interaction among the operators in the regional integrated energy system (RIES). Our model regards the distribution network and each energy hub (EH) as independent operators and employs robust optimization to improve operational security caused by wind and photovoltaic (PV) power output uncertainties, with only deterministic information exchanged across boundaries. This paper also adopts the alternating direction method of multipliers (ADMM) algorithm to facilitate secure information interaction among multiple RIES operators, maximizing the benefit for each subject. Furthermore, the traditional ADMM algorithm with fixed step size is modified to be adaptive, addressing issues of redundant interactions caused by suboptimal initial step size settings. A case study validates the effectiveness of the proposed model, demonstrating the superiority of the ADMM algorithm with adaptive step size and the economic benefits of the distributed robust optimal dispatch model over the distributed stochastic optimal dispatch model.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":"12 3","pages":"852-862"},"PeriodicalIF":6.3,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10285628","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141091157","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：