A CVaR-Robust Risk Aversion Scheduling Model for Virtual Power Plants Connected with Wind-Photovoltaic-Hydropower-Energy Storage Systems, Conventional Gas Turbines and Incentive-Based Demand Responses

Abstract

To make full use of distributed energy resources to meet load demand, this study aggregated wind power plants (WPPs), photovoltaic power generation (PV), small hydropower stations (SHSs), energy storage systems (ESSs), conventional gas turbines (CGTs) and incentive-based demand responses (IBDRs) into a virtual power plant (VPP) with price-based demand response (PBDR). Firstly, a basic scheduling model for the VPP was proposed in this study with the objective of the maximum operation revenue. Secondly, a risk aversion model for the VPP was constructed based on the conditional value at risk (CVaR) method and robust optimization theory considering the operating risk from WPP and PV. Thirdly, a solution methodology was constructed and three cases were considered for comparative analyses. Finally, an independent micro-grid on an industrial park in East China was utilized for an example analysis. The results show the following: (1) the proposed risk aversion scheduling model could cope with the uncertainty risk via a reasonable confidence degree β and robust coefficient Γ. When Γ ≤ 0.85 or Γ ≥ 0.95, a small uncertainty brought great risk, indicating that the risk attitude of the decision maker will affect the scheduling scheme of the VPP, and the decision maker belongs to the risk extreme aversion type. When Γ ∈ (0.85, 0.95), the decision-making scheme was in a stable state, the growth of β lead to the increase of CVaR, but the magnitude was not large. When the prediction error e was higher, the value of CVaR increased more when Γ increased by the same magnitude, which indicates that a lower prediction accuracy will amplify the uncertainty risk. (2) when the capacity ratio of (WPP, PV): ESS was higher than 1.5:1 and the peak-to-valley price gap was higher than 3:1, the values of revenue, VaR, and CVaR changed slower, indicating that both ESS and PBDR can improve the operating revenue, but the capacity scale of ESS and the peak-valley price gap need to be set properly, considering both economic benefits and operating risks. Therefore, the proposed risk aversion model could maximize the utilization of clean energy to obtain higher economic benefits while rationally controlling risks and provide reliable decision support for developing optimal operation plans for the VPP.

Analysis of Wind Turbine Equipment Failure and Intelligent Operation and Maintenance Research

“…However, the literature does not take into account the cost of employing staff and the replacement of parts for wind turbines. Ju et al [135] proposed a price-based demand-response virtual power plant and constructed a risk-averse model based on a conditional value-at-risk approach and robust optimization theory, considering the operational risks of wind power plants and photovoltaic power generation. However, experimental data show that the system is not suitable for simple scheduling scenarios where decision makers must fully account for uncertainty in the factors and require improved prediction accuracy.…”

Review and clustering of optimal energy management problem studies for industrial microgrids

“…Several methodologies and approaches have been stood out which are metaheuristics, 114,115 stochastic programming, 116 game theory, 117 simulation 118 and robust optimization. 119 Han et al 114 Other studies with economic objectives can be summarized as the price elasticity estimation with Monte Carlo analysis 120 and optimization of costs and the thermal comfort by simulation. 121 Figure 4 summarizes the research with the economic objectives.…”

“…The studies can be given by arranging according to their methodologies. Several methodologies and approaches have been stood out which are metaheuristics, 114,115 stochastic programming, 116 game theory, 117 simulation 118 and robust optimization 119 . Han et al 114 applied a hybrid algorithm for the cost‐benefit analysis of an industrial MG which includes the methods of artificial fish swarm, GA, PSO etc.…”

“…Khayatian et al 116 aimed both to maximize the profits of MG, generation, and transmission companies and to minimize short‐term operation costs with stochastic MIP model. Liu et al 117 used Stackelberg game for optimal EM of a grid‐connected MG. Derakhshandeh et al 118 simulated a unit commitment study with the objective of profit maximization of a grid‐connected MG. Ju et al 119 preferred robust optimization approach in a demand response study. They aimed to maximize operation revenue in an islanded MG.…”

Distribution Level Aggregator Platform for DSO Support—Integration of Storage, Demand Response, and Renewables

“…The idea in this paper is block this chain to DSO level since aggregators exist at DSO level too (Asimakopoulou and Hatziargyriou, 2018;Minniti et al, 2018). It requires a platform for aggregator at DSO level, with the integration, monitoring, and control capabilities using different market players (Han et al, 2017;Yazdani-Damavandi et al, 2017;Ju et al, 2018;Pasetti et al, 2018).…”