A medium/long-term auction-based coalition-forming model for a virtual power plant based on stochastic programming

Author(s):  
Mehran Jafari ◽  
Asghar Akbari Foroud
Electronics ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 641
Author(s):  
Michał Jasiński

Analysis of the connection between different units that operate in the same area assures always interesting results. During this investigation, the concerned area was a virtual power plant (VPP) that operates in Poland. The main distributed resources included in the VPP are a 1.25 MW hydropower plant and an associated 0.5 MW energy storage system. The mentioned VPP was a source of synchronic, long-term, multipoint power quality (PQ) data. Then, for five related measurement points, the conclusion about the relation in point of PQ was performed using correlation analysis, the global index approach, and cluster analysis. Global indicators were applied in place of PQ parameters to reduce the amount of analyzed data and to check the correlation between phase values. For such a big dataset, the occurrence of outliers is certain, and outliers may affect the correlation results. Thus, to find and exclude them, cluster analysis (k-means algorithm, Chebyshev distance) was applied. Finally, the correlation between PQ global indicators of different measurement points was performed. It assured general information about VPP units’ relation in point of PQ. Under the investigation, both Pearson’s and Spearman’s rank correlation coefficients were considered.


Energy ◽  
2014 ◽  
Vol 73 ◽  
pp. 958-967 ◽  
Author(s):  
Mohammad Amin Tajeddini ◽  
Ashkan Rahimi-Kian ◽  
Alireza Soroudi

2013 ◽  
Vol 105 ◽  
pp. 282-292 ◽  
Author(s):  
Hrvoje Pandžić ◽  
Juan M. Morales ◽  
Antonio J. Conejo ◽  
Igor Kuzle

2021 ◽  
Vol 256 ◽  
pp. 02006
Author(s):  
Zhendong Du ◽  
Di Wu ◽  
Hua Bai ◽  
Zhengyong Wang ◽  
Yizong Guo ◽  
...  

In order to better manage demand response resources of user side and reduce short-term load forecasting error, a short-term load forecasting method considering demand response in virtual power plant mode is proposed. Firstly, the demand response mechanism of the virtual power plant is analyzed. Taking the maximum profit of the virtual power plant as the goal, considering the user’s energy consumption habits, self built photovoltaic, energy storage behavior and thermal electric coupling, the optimization model is established for each type of demand response resources. The CPLEX solver is called to solve the mixed integer linear programming problem after the model transformation, and the sub signals of each resource participating in the demand response are obtained. Then, based on this model, a long-term and short-term memory network model considering demand response signals is established to predict load power iteratively. At the same time, the long-term and short-term memory network model considering the demand response signals effectively makes up for the shortcomings of the traditional forecasting model without considering the demand response, and is more accurate in predicting the future trend of load change.


Energies ◽  
2019 ◽  
Vol 12 (5) ◽  
pp. 952 ◽  
Author(s):  
Jichun Liu ◽  
Jianhua Li ◽  
Yue Xiang ◽  
Xin Zhang ◽  
Wanxiao Jiang

Due to an imminent fossil energy crisis and environmental pollution, renewable energy, such as photovoltaics, has been vigorously developing. However, the output of photovoltaic energy has strong volatility and intermittency. Thus, the photovoltaic generation system cannot constantly meet the load demand. To address this problem, a virtual power plant with hydro-photovoltaic-thermal generation is proposed in this paper. This virtual power plant utilizes the complementary characteristics of the output of the power sources to ensure a smooth and stable total output curve, and the power supply quality of the virtual power plant is improved. Further, the nonlinear operating cost model of the virtual power plant, with output changing over time, is established on the weighted output of hydro, photovoltaic, and thermal power; then, the corresponding marginal cost model of the virtual power plant is obtained. In the electricity market, three typical mid- to long-term electricity decomposition methods based on average, tracking load and spot price are constructed, and the spot price is predicted by the auto regressive moving average model (ARIMA) model, while the relationship between the spot price and the marginal cost of the virtual power plant is obtained; the marginal cost could also be adjusted based on the ARIMA model. Based on above factors, the sizing model of the virtual power plant is established, considering investment and complementary benefits. Finally, a case study is undertaken, where the sizing scheme for the increasing local load in the typical scenarios of the planning year and the corresponding annual rate of return are obtained. Sensitivity analysis of the influence for the above factors on the sizing of the virtual power plant is carried out. The optimal ratio of mid- to long-term electricity and its decomposition methods, as well as the capacity of the virtual power plant and the sizing ratio of hydropower, photovoltaic, and thermal power are obtained.


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