Application of CVaR risk aversion approach in the dynamical scheduling optimization model for virtual power plant connected with wind-photovoltaic-energy storage system with uncertainties and demand response

Tan, Zhongfu; Wang, Guan; Ju, Liu; Tan, Qian; Yang, Wenhai

doi:10.1016/j.energy.2017.02.063

Cited by 111 publications

(59 citation statements)

References 36 publications

(28 reference statements)

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“… ΔL PB , t and ΔQ t are the load changed by PBDR. According to the theory of microeconomics, PBDR is described by the demand‐price elasticity e , take ΔL PB , t as an example, the detailed calculation method could be described as follows:

Δ L_{italicPB, t} = L_{t}^{0} \times ({}, e_{tt} \times ([], P_{t} - P_{t}^{0}) / P_{t}^{0} + \sum_{\begin{matrix} left \\ s = 1 \\ s \neq t \end{matrix}}^{24} e_{st} \times ([], P_{s} - P_{s}^{0}) / P_{s}^{0}) .

According to Equation , the load is mainly influenced from two aspects, namely, itself ( e st , self‐elasticity, only demand curtailment can occur in this way) and other time periods ( e st , cross‐elasticity, demand shifting can also occur in this way) …”

Section: Basic Scheduling Model For Vepmentioning

confidence: 99%

“…In the proposed VEP, five uncertainty factors exist, namely, g WPP , t , g PV , t , Q SC , t , L t , and Q t . How to simulate the uncertainty factors and develop the optimal strategies for VEP is great important for decision makers. Just like the analysis in Section , the natural wind speed and the solar irradiance could be described by the probability distribution function . Similarly, the Rayleigh distribution function could be also used to simulate the heating output of SC.…”

Section: Cvar‐robust‐based Scheduling Model For Vepmentioning

confidence: 99%

“…According to Equation 20, the load is mainly influenced from two aspects, namely, itself (e st , self-elasticity, only demand curtailment can occur in this way) and other time periods (e st , cross-elasticity, demand shifting can also occur in this way). 28…”

Section: Energy Balance Constraintsmentioning

confidence: 99%

“…Just like the analysis in Section 2.2.1, the natural wind speed and the solar irradiance could be described by the probability distribution function. 28 Similarly, the Rayleigh distribution function could be also used to simulate the heating output of SC. If the load prediction deviation L e t could be described by the Normal distribution function ( 20 then the distribution functions for load…”

Section: Uncertainty Analysismentioning

confidence: 99%

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A dynamic risk aversion model for virtual energy plant considering uncertainties and demand response

Tan²,

Wang

2019

Int J Energy Res

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Summary To full use clean energy to meet load demand of electrical and thermal, the paper proposed a novel concept of virtual energy plant (VEP) including wind power plant (WPP), photovoltaic power generation (PV), combined heat and power generation (CHP), solar collectors (SC), electric boiler (EB), heat storage tank (HSK), and incentive‐based demand response (IBDR). Firstly, the basic structure of VEP is designed, including three subsystems, namely, electricity, heating, and energy storage. Then, a basic scheduling model is constructed under the objective of maximizing operating revenue without considering uncertainty. Thirdly, the conditional value at risk (CVaR) method and the robust optimization theory are used to handle the uncertainty factors in objective functions and condition constraints, and the risk aversion scheduling model is proposed. Finally, industrial park group in northern China are chosen, for example, analysis results show (1) VEP could convert the abandoned clean energy, use HSK to store heating energy during the valley load period, and supply heating energy in the peak period to obtain the excess economic benefits. (2) Lower‐prediction accuracy will amplify the uncertainty risk, when the robust β∈[0.8,0.825]&(0.925,1], the increase of confidence level β will lead to larger increase in CVaR. Especially when β∈(0.925,1), decision makers are extremely disgusted with the risks brought by the uncertainty factors, and correspondingly, the output of clean energy becomes minimum. (3) When the capacity ratio of HSK, EB and the electricity price of peak, valley are lower than 3, the values of revenue, VaR and CVaR change faster, but the ratios are larger than 3, the values change slower, which indicates that the scale of HSK capacity needs to be properly controlled to optimize the use of clean energy, and price‐based demand response could improve the operation profit while controlling risk properly. In general, the proposed scheduling model can maximize the use of clean energy to obtain economic benefits while rationally controlling risks.

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Δ L_{italicPB, t} = L_{t}^{0} \times ({}, e_{tt} \times ([], P_{t} - P_{t}^{0}) / P_{t}^{0} + \sum_{\begin{matrix} left \\ s = 1 \\ s \neq t \end{matrix}}^{24} e_{st} \times ([], P_{s} - P_{s}^{0}) / P_{s}^{0}) .

Section: Basic Scheduling Model For Vepmentioning

confidence: 99%

Section: Cvar‐robust‐based Scheduling Model For Vepmentioning

confidence: 99%

Section: Energy Balance Constraintsmentioning

confidence: 99%

Section: Uncertainty Analysismentioning

confidence: 99%

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A dynamic risk aversion model for virtual energy plant considering uncertainties and demand response

Tan²,

Wang

2019

Int J Energy Res

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“…Cassell University in Germany has created the largest HES pilot project to combine wind turbines, solar systems, biogas power stations and hydropower stations [7]. In 2009, Danish Electric Vehicles considered the uncertainty of large-scale wind farms in the project of accessing smart grids, and the intelligent charging and discharging of electric vehicles was managed by HES technology [8]. In 2008, a distributed energy station was put into operation in Guangdong University City in China.…”

mentioning

confidence: 99%

A Multi-objective Scheduling Optimization Model for Hybrid Energy System Connected with Wind-Photovoltaic-Conventional Gas Turbines, CHP Considering Heating Storage Mechanism

Wang

et al. 2019

Energies

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In order to meet the user's electricity demand and make full use of distributed energy, a hybrid energy system (HES) was proposed and designed, including wind turbines (WTs), photovoltaic (PV) power generation, conventional gas turbines (CGTs), incentive-based demand response (IBDR), combined heat and power (CHP) and regenerative electric (RE) boilers. Then, the collaborative operation problem of HES is discussed. First, the paper describes the HES' basic structure and presents the output model of power sources and heating sources. Next, the maximum operating income and minimum load fluctuation are taken as the objective function, and a multi-objective model of HES scheduling is proposed. Then an algorithm for solving the model is proposed that comprises two steps: processing the objective functions and constraints into linear equations and determining the optimal weight of the objective functions. The selected simulation system is a microgrid located on an eastern island of China to comparatively analyze the influence of RE-heating storage (RE-HS) and price-based demand response (PBDR) on HES operation in relation to four cases. By analyzing the results, the following three conclusions are drawn: (1) HES can comprehensively utilize a variety of distributed energy sources to meet load demand. In particular, RE technology can convert the abandoned energy of WT and PV into heat during the valley load time, to meet the load demand combined with CHP; (2) The proposed multi-objective scheduling model of HES operation not only considers the maximum operating income but also considers the minimum load fluctuation, thus achieving the optimal balancing operation; (3) RE-HS and PBDR have a synergistic optimization effect, and when RE-HS and PBDR are both applied, an HES can achieve optimal operation results. Overall, the proposed decision method is highly effective and applicable, and decision makers could utilize this method to design an optimal HES operation strategy according to their own actual conditions. Energies 2019, 12, 425 2 of 28 structure of power generation systems is increasing, especially wind and solar photovoltaic power generation. In particular, with the development of regenerative electric (RE) boiler technology, clean energy is no longer only used for power generation. It has also been gradually extended to heating sectors to form a thermal-electrical coupled operation mode. Meanwhile, combined heat and power (CHP) units are rapidly being more widely adopted due to the advantages of energy saving and environment friendliness [1]. However, due to the disadvantages of distributed energy sources, such as small capacity, intermittent availability, and dispersivity, they cannot be used in the electricity market alone, while the characteristics of CHP power generation closely combine with the supply of heating reduce the flexibility [2]. Therefore, the effective management of wind turbines (WTs), photovoltaic (PV) systems, and other distributed energy sources as well as CHP systems is urgently required to mee...

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Optimal operational scheduling of a virtual power plant participating in day‐ahead market with consideration of emission and battery degradation cost

Akkaş

Çam

2020

Int Trans Electr Energ Syst

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Application of CVaR risk aversion approach in the dynamical scheduling optimization model for virtual power plant connected with wind-photovoltaic-energy storage system with uncertainties and demand response

Cited by 111 publications

References 36 publications

A dynamic risk aversion model for virtual energy plant considering uncertainties and demand response

A dynamic risk aversion model for virtual energy plant considering uncertainties and demand response

A Multi-objective Scheduling Optimization Model for Hybrid Energy System Connected with Wind-Photovoltaic-Conventional Gas Turbines, CHP Considering Heating Storage Mechanism

Optimal operational scheduling of a virtual power plant participating in day‐ahead market with consideration of emission and battery degradation cost

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