Existing power forecasting models struggle to simultaneously handle high-dimensional,noisy load data while capturing long-term dependencies.This critical limitation necessitates an integrated approach combining dimens...Existing power forecasting models struggle to simultaneously handle high-dimensional,noisy load data while capturing long-term dependencies.This critical limitation necessitates an integrated approach combining dimensionality reduction,temporal modeling,and robust prediction,especially for multi-day forecasting.A novel hybrid model,SLHS-TCN-XGBoost,is proposed for power demand forecasting,leveraging SLHS(dimensionality reduction),TCN(temporal feature learning),and XGBoost(ensemble prediction).Applied to the three-year electricity load dataset of Seoul,South Korea,the model’s MAE,RMSE,and MAPE reached 112.08,148.39,and 2%,respectively,which are significantly reduced in MAE,RMSE,and MAPE by 87.37%,87.35%,and 87.43%relative to the baseline XGBoost model.Performance validation across nine forecast days demonstrates superior accuracy,with MAPE as low as 0.35%and 0.21%on key dates.Statistical Significance tests confirm significant improvements(p<0.05),with the highest MAPE reduction of 98.17%on critical days.Seasonal and temporal error analyses reveal stable performance,particularly in Quarter 3 and Quarter 4(0.5%,0.3%)and nighttime hours(<1%).Robustness tests,including 5-fold cross-validation and Various noise perturbations,confirm the model’s stability and resilience.The SLHS-TCN-XGBoost model offers an efficient and reliable solution for power demand forecasting,with future optimization potential in data preprocessing,algorithm integration,and interpretability.展开更多
Grey theory is a multidisciplinary and generic theory to cope with systems of poor or deficient information. We proposed in this paper an improved grey method (GM) to overcome the disadvantages of the general GM(1,1)....Grey theory is a multidisciplinary and generic theory to cope with systems of poor or deficient information. We proposed in this paper an improved grey method (GM) to overcome the disadvantages of the general GM(1,1). In the improved GM(1,1), a new background value formula is deduced and Markov-chain sign estimation is imbedded into the residual modification model. We tested the efficiency and accuracy of our model by applying it to the power demand forecasting in Taiwan. Experimental results demonstrate the new method has obviously a higher prediction accuracy than the general model.展开更多
The supercritical CO_(2)(S-CO_(2)) Brayton cycle is expected to replace steam cycle in the application of solar power tower system due to the attractive potential to improve efficiency and reduce costs.Since the conce...The supercritical CO_(2)(S-CO_(2)) Brayton cycle is expected to replace steam cycle in the application of solar power tower system due to the attractive potential to improve efficiency and reduce costs.Since the concentrated solar power plant with thermal energy storage is usually located in drought area and used to provide a dispatchable power output,the S-CO_(2) Brayton cycle has to operate under fluctuating ambient temperature and diverse power demand scenarios.In addition,the cycle design condition will directly affect the off-design performance.In this work,the combined effects of design condition,and distributions of ambient temperature and power demand on the cycle operating performance are analyzed,and the off-design performance maps are proposed for the first time.A cycle design method with feedback mechanism of operating performance under varied ambient temperature and power demand is introduced innovatively.Results show that the low design value of compressor inlet temperature is not conductive to efficient operation under low loads and sufficient output under high ambient temperatures.The average yearly efficiency is most affected by the average power demand,while the load cover factor is significantly influenced by the average ambient temperature.With multi-objective optimization,the optimal solution of designed compressor inlet temperature is close to the minimum value of35℃ in Delingha with low ambient temperature,while reaches 44.15℃ in Daggett under the scenario of high ambient temperature,low average power demand,long duration and large value of peak load during the peak temperature period.If the cycle designed with compressor inlet temperature of 35℃ instead of 44.15℃ in Daggett under light industry power demand,the reduction of load cover factor will reach 0.027,but the average yearly efficiency can barely be improved.展开更多
Power demand prediction for buildings at a large scale is required for power grid operation.The bottom-up prediction method using physics-based models is popular,but has some limitations such as a heavy workload on mo...Power demand prediction for buildings at a large scale is required for power grid operation.The bottom-up prediction method using physics-based models is popular,but has some limitations such as a heavy workload on model creation and long computing time.Top-down methods based on data driven models are fast,but less accurate.Considering the similarity of power demand patterns of single buildings and the superiority of generative adversarial network(GAN),this paper proposes a new method(E-GAN),which combines a physics-based model(EnergyPlus)and a data-driven model(GAN),to predict the daily power demand for buildings at a large scale.The new E-GAN method selects a small number of typical buildings and utilizes EnergyPlus models to predict their power demands.Utilizing the prediction for those typical buildings,the GAN then is adopted to forecast the power demands of a large number of buildings.To verify the proposed method,the E-GAN is used to predict 24-hour power demands for a set of residential buildings.The results show that(1)4.3%of physics-based models in each building category are required to ensure the prediction accuracy;(2)compared with the physics-based model,the E-GAN can predict power demand accurately with only 5%error(measured by mean absolute percentage error,MAPE)while using only approximately 9%of the computing time;and(3)compared with data-driven models(e.g.,support vector regression,extreme learning machine,and polynomial regression model),E-GAN demonstrates at least 60%reduction in prediction error measured by MAPE.展开更多
A support vector machine (SVM) forecasting model based on rough set (RS) data preprocess was proposed by combining the rough set attribute reduction and the support vector machine regression algorithm, because there a...A support vector machine (SVM) forecasting model based on rough set (RS) data preprocess was proposed by combining the rough set attribute reduction and the support vector machine regression algorithm, because there are strong complementarities between two models. Firstly, the rough set was used to reduce the condition attributes, then to eliminate the attributes that were redundant for the forecast, Secondly, it adopted the minimum condition attributes obtained by reduction and the corresponding original data to re-form a new training sample, which only kept the important attributes affecting the forecast accuracy. Finally, it studied and trained the SVM with the training samples after reduction, inputted the test samples re-formed by the minimum condition attributes and the corresponding original data, and then got the mapping relationship model between condition attributes and forecast variables after testing it. This model was used to forecast the power supply and demand. The results show that the average absolute error rate of power consumption of the whole society and yearly maximum load are 14.21% and 13.23%, respectively, which indicates that the RS-SVM forecast model has a higher degree of accuracy.展开更多
The paper analyzes the present situation of power supply and demand based on full and accurate data.Although the electricity generation in 2003 will reach the target of the 10"Five-year Plan,but the scale of powe...The paper analyzes the present situation of power supply and demand based on full and accurate data.Although the electricity generation in 2003 will reach the target of the 10"Five-year Plan,but the scale of power sources construction is severely insufficient.The situation of supply and demand will be very pressing in the latter three years of the 10"Five-year Plan.Therefore,an urgent task is to speedily start constructing a batch of medium and large generation projects.展开更多
Based on the analysis on economic situation in China in 2001, the paperdiscusses power supply and demand features nationwide and by regions andprovinces, present estimation of power supply and demand in 2002. In concl...Based on the analysis on economic situation in China in 2001, the paperdiscusses power supply and demand features nationwide and by regions andprovinces, present estimation of power supply and demand in 2002. In conclusion,the paper presents suggestions to overcome difficulties on capital funds andtechniques.[展开更多
In the first half of 2007, the power industry in Shandongprovince continued to maintain a rapid growth momentum.The gross electricity consumption amounted to 121.25 TWh,14.4% higher over that in the same period of las...In the first half of 2007, the power industry in Shandongprovince continued to maintain a rapid growth momentum.The gross electricity consumption amounted to 121.25 TWh,14.4% higher over that in the same period of last year. The totalinstalled capacity reached 53.29 GW. It was expected that bythe end of 2007, the gross electricity consumption in Shan-dong would reach 260 TWh, increasing by 14.4% on ayear-on-year basis; the maximum load would reach 40.展开更多
为实现微网和氢储能电站之间的双赢,构建了一种计及氢储能电站服务并考虑需求响应和热电联供(combined heat and power,CHP)型微网。所建系统将电、热、氢能结合起来,实现三者能量的相互转换,并进一步引入余热回收以有效提高能量利用。...为实现微网和氢储能电站之间的双赢,构建了一种计及氢储能电站服务并考虑需求响应和热电联供(combined heat and power,CHP)型微网。所建系统将电、热、氢能结合起来,实现三者能量的相互转换,并进一步引入余热回收以有效提高能量利用。在此基础上,建立考虑两个优化问题的双层规划模型,上层模型研究氢储能电站优化问题,以氢储能电站总运行成本最低为目标;下层模型分析基于氢储能电站的微网系统优化运行模型,以热电联供型微网总运行成本最低为目标进行优化研究。运用拉格朗日方法并基于KKT条件将下层目标转化为上层的约束条件,运用大M法将非线性规划问题转为混合整数线性规划问题求解。分析了价格型、替代型负荷占比对于系统的影响,选择合适的占比实现系统收益的最大化。结合冬夏两个典型季节的特点,设置了3种场景进行对比分析,结果验证了该模型的可行性和有效性,在合理调整需求响应负荷占比后,降低了运行成本,经济性更好。展开更多
The main problem existing in Guangdong electric power sources is analyzed in this paper. Based on theanalysis on energy-supply features, power demand and the technical and economic performances of various powersource...The main problem existing in Guangdong electric power sources is analyzed in this paper. Based on theanalysis on energy-supply features, power demand and the technical and economic performances of various powersources in Guangdong, the power sources construction scale and its structure are studied and analyzed in detail byusing Generation Expansion Software Package (GESP). The future development of Guangdong electric power sourcesunder the new situation of "Power from West to East" is studied as well.[展开更多
The supply and demand features of China electric power market are elaborated in this paper, based on the data of power production and demand in the first quarter 2001, and the present situation on power supply and dem...The supply and demand features of China electric power market are elaborated in this paper, based on the data of power production and demand in the first quarter 2001, and the present situation on power supply and demand is analyzed from multi aspects.展开更多
Micro-grid plays a vital role in fulfilling the increasing demand by using distributed renewable energy resources. Demand and response technique can be broadly classified under the setup DR deployed (e.g. ISO’s/RTO’...Micro-grid plays a vital role in fulfilling the increasing demand by using distributed renewable energy resources. Demand and response technique can be broadly classified under the setup DR deployed (e.g. ISO’s/RTO’s). Demand response program can be implemented to improve power system quality, reliability and increasing demand. In modern power industry, strategic player can take more benefit from more emphasized DR study in terms of social benefit (uninterrupted power supply to consumers) and economy. This paper proposes the distributed micro-grid control and implemented control setup implemented demand response algorithm, which provides better power system reliability. This paper presents contingencies control demand and response for micro-grid. The main advantage of implementation of demand and response algorithms in Micro-grids provides reliable power supplies to consumers. The proposed micro-grid TCP/IP setup provides a chance to respond the contingencies to recover the shed to active condition. Micro-grid controller implements demand and response algorithm reasonable for managing the demand of the load and intelligent load scheme in case of blackout.展开更多
The current energy supply trajectory in the Association of Southeast Asian Nations(ASEAN)region is not sustainable.Factors such as rising standards of living and demographic patterns,including population growth,lead t...The current energy supply trajectory in the Association of Southeast Asian Nations(ASEAN)region is not sustainable.Factors such as rising standards of living and demographic patterns,including population growth,lead to continuous increase in power demand,which is difficult to meet using limited fossil fuel resources.Thus,a transition toward clean energy sources is needed in the region.While ASEAN member countries are rich in clean energy resources,such resources are located far from demand centers;thus,allocation of clean energy is necessary to increase its utilization.In this study,power demand is forecasted using a combination of prediction methods.A model to evaluate the installed capacity and power exchange potential is proposed to deal with mismatch between the location of the clean energy base and the load center.Furthermore,the concept of cross-regional allocation of clean energy between the ASEAN region,China,and South Asia is presented.A power interconnection scheme among the ASEAN member countries as well as neighboring countries is proposed based on the power exchange potential.The proposed grid interconnection scheme contributes to the utilization of clean energy in the ASEAN region,increasing the proportion of clean energy in the generation mix,which ensures that the region becomes a sustainable and resilient society with a clean and low carbon development route.Furthermore,the proposed power interconnection scheme will generate valuable economic,social,environmental,and resource allocation benefits.展开更多
With certain controllability of various distribution energy resources (DERs) such as battery energy storage system (BESS), demand response (DR) and distributed generations (DGs), virtual power plant (VPP) can suitably...With certain controllability of various distribution energy resources (DERs) such as battery energy storage system (BESS), demand response (DR) and distributed generations (DGs), virtual power plant (VPP) can suitably regulate the powers access to the distribution network. In this paper, an optimal VPP operating problem is used to optimize the charging/discharging schedule of each BESS and the DR scheme with the objective to maximize the benefit by regulating the supplied powers over daily 24 hours. The proposed solution method is composed of an iterative dynamic programming optimal BESS schedule approach and a particle swarm optimization based (PSO-based) DR scheme approach. The two approaches are executed alternatively until the minimum elec-tricity cost of the whole day is obtained. The validity of the proposed method was confirmed with the obviously decreased supplied powers in the peak-load hours and the largely reduced electricity cost.展开更多
由于全球对气候变化的日益关注,可再生能源(Renewable energy sources,RES)在电力系统中的渗透率在过去几年中显著增加。然而,将可再生能源并入电力市场存在很大的问题。风电、光伏等可再生能源出力具有高度不确定性,它们与负荷需求的...由于全球对气候变化的日益关注,可再生能源(Renewable energy sources,RES)在电力系统中的渗透率在过去几年中显著增加。然而,将可再生能源并入电力市场存在很大的问题。风电、光伏等可再生能源出力具有高度不确定性,它们与负荷需求的相关性并不总是得到保证,从而降低了系统的可靠性。为了解决这些问题,虚拟电厂(Virtual power plant,VPP)作为一种创新性解决方案日益受到关注,成为提升电力系统稳定性的重要手段。虚拟电厂通过聚合多种分布式能源资源,提升了系统的灵活性和响应能力,从而减轻了不确定性带来的影响,尽管如此,不确定性仍是虚拟电厂研究中的重要因素。不确定因素可以分为源侧-可再生能源的不确定性、荷侧-负荷需求的不确定性和需求响应的不确定性。在此分类基础上,介绍虚拟电厂的相关概念结构、发展历程以及虚拟电厂中的不确定性;对处理不确定性的方法进行总结和对比分析;分别对上述不确定性分析以及处理其不确定性的方法分类进行描述;最后总结全文并展望未来虚拟电厂不确定性的研究方向。展开更多
基金supported by Mahasarakham University for Piyapatr Busababodhin’s work.Guoqing Chen’s research was supported by Chengdu Jincheng College Green Data Integration Intelligence Research and Innovation Project(No.2025-2027)the High-Quality Development Research Center Project in the Tuojiang River Basin(No.TJGZL2024-07)+1 种基金the Open Fund ofWuhan Gravitation and Solid Earth Tides,National Observation and Research Station(No.WHYWZ202406)the Scientific Research Fund of the Institute of Seismology,CEA,and the National Institute of Natural Hazards,MEM(No.IS202236328).
文摘Existing power forecasting models struggle to simultaneously handle high-dimensional,noisy load data while capturing long-term dependencies.This critical limitation necessitates an integrated approach combining dimensionality reduction,temporal modeling,and robust prediction,especially for multi-day forecasting.A novel hybrid model,SLHS-TCN-XGBoost,is proposed for power demand forecasting,leveraging SLHS(dimensionality reduction),TCN(temporal feature learning),and XGBoost(ensemble prediction).Applied to the three-year electricity load dataset of Seoul,South Korea,the model’s MAE,RMSE,and MAPE reached 112.08,148.39,and 2%,respectively,which are significantly reduced in MAE,RMSE,and MAPE by 87.37%,87.35%,and 87.43%relative to the baseline XGBoost model.Performance validation across nine forecast days demonstrates superior accuracy,with MAPE as low as 0.35%and 0.21%on key dates.Statistical Significance tests confirm significant improvements(p<0.05),with the highest MAPE reduction of 98.17%on critical days.Seasonal and temporal error analyses reveal stable performance,particularly in Quarter 3 and Quarter 4(0.5%,0.3%)and nighttime hours(<1%).Robustness tests,including 5-fold cross-validation and Various noise perturbations,confirm the model’s stability and resilience.The SLHS-TCN-XGBoost model offers an efficient and reliable solution for power demand forecasting,with future optimization potential in data preprocessing,algorithm integration,and interpretability.
文摘Grey theory is a multidisciplinary and generic theory to cope with systems of poor or deficient information. We proposed in this paper an improved grey method (GM) to overcome the disadvantages of the general GM(1,1). In the improved GM(1,1), a new background value formula is deduced and Markov-chain sign estimation is imbedded into the residual modification model. We tested the efficiency and accuracy of our model by applying it to the power demand forecasting in Taiwan. Experimental results demonstrate the new method has obviously a higher prediction accuracy than the general model.
基金supported by Beijing Natural Science Foundation (Grant No.3202014)。
文摘The supercritical CO_(2)(S-CO_(2)) Brayton cycle is expected to replace steam cycle in the application of solar power tower system due to the attractive potential to improve efficiency and reduce costs.Since the concentrated solar power plant with thermal energy storage is usually located in drought area and used to provide a dispatchable power output,the S-CO_(2) Brayton cycle has to operate under fluctuating ambient temperature and diverse power demand scenarios.In addition,the cycle design condition will directly affect the off-design performance.In this work,the combined effects of design condition,and distributions of ambient temperature and power demand on the cycle operating performance are analyzed,and the off-design performance maps are proposed for the first time.A cycle design method with feedback mechanism of operating performance under varied ambient temperature and power demand is introduced innovatively.Results show that the low design value of compressor inlet temperature is not conductive to efficient operation under low loads and sufficient output under high ambient temperatures.The average yearly efficiency is most affected by the average power demand,while the load cover factor is significantly influenced by the average ambient temperature.With multi-objective optimization,the optimal solution of designed compressor inlet temperature is close to the minimum value of35℃ in Delingha with low ambient temperature,while reaches 44.15℃ in Daggett under the scenario of high ambient temperature,low average power demand,long duration and large value of peak load during the peak temperature period.If the cycle designed with compressor inlet temperature of 35℃ instead of 44.15℃ in Daggett under light industry power demand,the reduction of load cover factor will reach 0.027,but the average yearly efficiency can barely be improved.
基金The Chinese team is supported by the National Natural Science Foundation of China(62076150,62173216,61903226)the Taishan Scholar Project of Shandong Province(TSQN201812092)+2 种基金the Key Research and Development Program of Shandong Province(2019GGX101072,2019JZZY010115)the Youth Innovation Technology Project of Higher School in Shandong Province(2019KJN005)the Key Research and Development Program of Shandong Province(2019JZZY010115)。
文摘Power demand prediction for buildings at a large scale is required for power grid operation.The bottom-up prediction method using physics-based models is popular,but has some limitations such as a heavy workload on model creation and long computing time.Top-down methods based on data driven models are fast,but less accurate.Considering the similarity of power demand patterns of single buildings and the superiority of generative adversarial network(GAN),this paper proposes a new method(E-GAN),which combines a physics-based model(EnergyPlus)and a data-driven model(GAN),to predict the daily power demand for buildings at a large scale.The new E-GAN method selects a small number of typical buildings and utilizes EnergyPlus models to predict their power demands.Utilizing the prediction for those typical buildings,the GAN then is adopted to forecast the power demands of a large number of buildings.To verify the proposed method,the E-GAN is used to predict 24-hour power demands for a set of residential buildings.The results show that(1)4.3%of physics-based models in each building category are required to ensure the prediction accuracy;(2)compared with the physics-based model,the E-GAN can predict power demand accurately with only 5%error(measured by mean absolute percentage error,MAPE)while using only approximately 9%of the computing time;and(3)compared with data-driven models(e.g.,support vector regression,extreme learning machine,and polynomial regression model),E-GAN demonstrates at least 60%reduction in prediction error measured by MAPE.
基金Project(70901025) supported by the National Natural Science Foundation of China
文摘A support vector machine (SVM) forecasting model based on rough set (RS) data preprocess was proposed by combining the rough set attribute reduction and the support vector machine regression algorithm, because there are strong complementarities between two models. Firstly, the rough set was used to reduce the condition attributes, then to eliminate the attributes that were redundant for the forecast, Secondly, it adopted the minimum condition attributes obtained by reduction and the corresponding original data to re-form a new training sample, which only kept the important attributes affecting the forecast accuracy. Finally, it studied and trained the SVM with the training samples after reduction, inputted the test samples re-formed by the minimum condition attributes and the corresponding original data, and then got the mapping relationship model between condition attributes and forecast variables after testing it. This model was used to forecast the power supply and demand. The results show that the average absolute error rate of power consumption of the whole society and yearly maximum load are 14.21% and 13.23%, respectively, which indicates that the RS-SVM forecast model has a higher degree of accuracy.
文摘The paper analyzes the present situation of power supply and demand based on full and accurate data.Although the electricity generation in 2003 will reach the target of the 10"Five-year Plan,but the scale of power sources construction is severely insufficient.The situation of supply and demand will be very pressing in the latter three years of the 10"Five-year Plan.Therefore,an urgent task is to speedily start constructing a batch of medium and large generation projects.
文摘Based on the analysis on economic situation in China in 2001, the paperdiscusses power supply and demand features nationwide and by regions andprovinces, present estimation of power supply and demand in 2002. In conclusion,the paper presents suggestions to overcome difficulties on capital funds andtechniques.[
文摘In the first half of 2007, the power industry in Shandongprovince continued to maintain a rapid growth momentum.The gross electricity consumption amounted to 121.25 TWh,14.4% higher over that in the same period of last year. The totalinstalled capacity reached 53.29 GW. It was expected that bythe end of 2007, the gross electricity consumption in Shan-dong would reach 260 TWh, increasing by 14.4% on ayear-on-year basis; the maximum load would reach 40.
文摘The paper analyzes the un certainty on power supply and demandforecast during the 10th Five-year Plan period and sug gests measures to beemp lo ye d.
文摘为实现微网和氢储能电站之间的双赢,构建了一种计及氢储能电站服务并考虑需求响应和热电联供(combined heat and power,CHP)型微网。所建系统将电、热、氢能结合起来,实现三者能量的相互转换,并进一步引入余热回收以有效提高能量利用。在此基础上,建立考虑两个优化问题的双层规划模型,上层模型研究氢储能电站优化问题,以氢储能电站总运行成本最低为目标;下层模型分析基于氢储能电站的微网系统优化运行模型,以热电联供型微网总运行成本最低为目标进行优化研究。运用拉格朗日方法并基于KKT条件将下层目标转化为上层的约束条件,运用大M法将非线性规划问题转为混合整数线性规划问题求解。分析了价格型、替代型负荷占比对于系统的影响,选择合适的占比实现系统收益的最大化。结合冬夏两个典型季节的特点,设置了3种场景进行对比分析,结果验证了该模型的可行性和有效性,在合理调整需求响应负荷占比后,降低了运行成本,经济性更好。
文摘The main problem existing in Guangdong electric power sources is analyzed in this paper. Based on theanalysis on energy-supply features, power demand and the technical and economic performances of various powersources in Guangdong, the power sources construction scale and its structure are studied and analyzed in detail byusing Generation Expansion Software Package (GESP). The future development of Guangdong electric power sourcesunder the new situation of "Power from West to East" is studied as well.[
文摘The supply and demand features of China electric power market are elaborated in this paper, based on the data of power production and demand in the first quarter 2001, and the present situation on power supply and demand is analyzed from multi aspects.
文摘Micro-grid plays a vital role in fulfilling the increasing demand by using distributed renewable energy resources. Demand and response technique can be broadly classified under the setup DR deployed (e.g. ISO’s/RTO’s). Demand response program can be implemented to improve power system quality, reliability and increasing demand. In modern power industry, strategic player can take more benefit from more emphasized DR study in terms of social benefit (uninterrupted power supply to consumers) and economy. This paper proposes the distributed micro-grid control and implemented control setup implemented demand response algorithm, which provides better power system reliability. This paper presents contingencies control demand and response for micro-grid. The main advantage of implementation of demand and response algorithms in Micro-grids provides reliable power supplies to consumers. The proposed micro-grid TCP/IP setup provides a chance to respond the contingencies to recover the shed to active condition. Micro-grid controller implements demand and response algorithm reasonable for managing the demand of the load and intelligent load scheme in case of blackout.
基金supported by the Science and Technology Foundation of GEIG (No.524500180014)
文摘The current energy supply trajectory in the Association of Southeast Asian Nations(ASEAN)region is not sustainable.Factors such as rising standards of living and demographic patterns,including population growth,lead to continuous increase in power demand,which is difficult to meet using limited fossil fuel resources.Thus,a transition toward clean energy sources is needed in the region.While ASEAN member countries are rich in clean energy resources,such resources are located far from demand centers;thus,allocation of clean energy is necessary to increase its utilization.In this study,power demand is forecasted using a combination of prediction methods.A model to evaluate the installed capacity and power exchange potential is proposed to deal with mismatch between the location of the clean energy base and the load center.Furthermore,the concept of cross-regional allocation of clean energy between the ASEAN region,China,and South Asia is presented.A power interconnection scheme among the ASEAN member countries as well as neighboring countries is proposed based on the power exchange potential.The proposed grid interconnection scheme contributes to the utilization of clean energy in the ASEAN region,increasing the proportion of clean energy in the generation mix,which ensures that the region becomes a sustainable and resilient society with a clean and low carbon development route.Furthermore,the proposed power interconnection scheme will generate valuable economic,social,environmental,and resource allocation benefits.
文摘With certain controllability of various distribution energy resources (DERs) such as battery energy storage system (BESS), demand response (DR) and distributed generations (DGs), virtual power plant (VPP) can suitably regulate the powers access to the distribution network. In this paper, an optimal VPP operating problem is used to optimize the charging/discharging schedule of each BESS and the DR scheme with the objective to maximize the benefit by regulating the supplied powers over daily 24 hours. The proposed solution method is composed of an iterative dynamic programming optimal BESS schedule approach and a particle swarm optimization based (PSO-based) DR scheme approach. The two approaches are executed alternatively until the minimum elec-tricity cost of the whole day is obtained. The validity of the proposed method was confirmed with the obviously decreased supplied powers in the peak-load hours and the largely reduced electricity cost.
文摘由于全球对气候变化的日益关注,可再生能源(Renewable energy sources,RES)在电力系统中的渗透率在过去几年中显著增加。然而,将可再生能源并入电力市场存在很大的问题。风电、光伏等可再生能源出力具有高度不确定性,它们与负荷需求的相关性并不总是得到保证,从而降低了系统的可靠性。为了解决这些问题,虚拟电厂(Virtual power plant,VPP)作为一种创新性解决方案日益受到关注,成为提升电力系统稳定性的重要手段。虚拟电厂通过聚合多种分布式能源资源,提升了系统的灵活性和响应能力,从而减轻了不确定性带来的影响,尽管如此,不确定性仍是虚拟电厂研究中的重要因素。不确定因素可以分为源侧-可再生能源的不确定性、荷侧-负荷需求的不确定性和需求响应的不确定性。在此分类基础上,介绍虚拟电厂的相关概念结构、发展历程以及虚拟电厂中的不确定性;对处理不确定性的方法进行总结和对比分析;分别对上述不确定性分析以及处理其不确定性的方法分类进行描述;最后总结全文并展望未来虚拟电厂不确定性的研究方向。
