Copula based self scheduling model for risk averse GenCo in restructured electricity market

2017 ◽  
Author(s):  
Debasmita Panda ◽  
S N Singh ◽  
Vimal Kumar
Keyword(s):  
Electricity Market ◽  
Risk Averse ◽  
Scheduling Model

scholarly journals Long-Term Generation Scheduling for Cascade Hydropower Plants Considering Price Correlation between Multiple Markets

Energies ◽  
2019 ◽  
Vol 12 (12) ◽  
pp. 2239
Author(s):  
Bin Luo ◽  
Shumin Miao ◽  
Chuntian Cheng ◽  
Yi Lei ◽  
Gang Chen ◽  
...  
Keyword(s):  
Electricity Market ◽  
Large Scale ◽  
Copula Function ◽  
Market Environment ◽  
Scheduling Model ◽  
Multiple Markets ◽  
Generation Scheduling ◽  
Hydropower Plants ◽  
Proposed Model

The large-scale cascade hydropower plants in southwestern China now challenge a multi-market environment in the new round of electricity market reform. They not only have to supply the load for the local provincial market, but also need to deliver electricity to the central and eastern load centers in external markets, which makes the generation scheduling much more complicated, with a correlated uncertain market environment. Considering the uncertainty of prices and correlation between multiple markets, this paper has proposed a novel optimization model of long-term generation scheduling for cascade hydropower plants in multiple markets to seek for the maximization of overall benefits. The Copula function is introduced to describe the correlation of stochastic prices between multiple markets. The price scenarios that obey the Copula fitting function are then generated and further reduced by using a scenario reduction strategy that combines hierarchical clustering and inconsistent values. The proposed model is applied to perform the long-term generation scheduling for the Wu River cascade hydropower plants and achieves an increase of 106.93 million yuan of annual income compared with the conventional scheduling model, without considering price scenarios, showing better performance in effectiveness and robustness in multiple markets.

Risk-averse profit-based optimal scheduling of a hydro-chain in the day-ahead electricity market

2007 ◽  
Vol 181 (3) ◽  
pp. 1354-1369 ◽  
Author(s):  
Javier García-González ◽  
Ernesto Parrilla ◽  
Alicia Mateo
Keyword(s):  
Electricity Market ◽  
Optimal Scheduling ◽  
Risk Averse

scholarly journals Robust Day-Ahead Scheduling of Electricity and Natural Gas Systems via a Risk-Averse Adjustable Uncertainty Set Approach

2018 ◽  
Vol 10 (11) ◽  
pp. 3848 ◽  
Author(s):  
Li Yao ◽  
Xiuli Wang ◽  
Tao Qian ◽  
Shixiong Qi ◽  
Chengzhi Zhu
Keyword(s):  
Natural Gas ◽  
Operational Risk ◽  
Test System ◽  
Risk Averse ◽  
Spinning Reserve ◽  
Constraint Generation ◽  
Scheduling Model ◽  
Gas System ◽  
Uncertainty Set ◽  
Natural Gas System

The requirement for energy sustainability drives the development of renewable energy technologies and gas-fired power generation. The increasing installation of gas-fired units significantly intensifies the interdependency between the electricity system and natural gas system. The joint scheduling of electricity and natural gas systems has become an attractive option for improving energy efficiency. This paper proposes a robust day-ahead scheduling model for electricity and natural gas system, which minimizes the total cost including fuel cost, spinning reserve cost and cost of operational risk while ensuring the feasibility for all scenarios within the uncertainty set. Different from the conventional robust optimization with predefined uncertainty set, a new approach with risk-averse adjustable uncertainty set is proposed in this paper to mitigate the conservatism. Furthermore, the Wasserstein–Moment metric is applied to construct ambiguity sets for computing operational risk. The proposed scheduling model is solved by the column-and-constraint generation method. The effectiveness of the proposed approach is tested on a 6-bus test system and a 118-bus system.

scholarly journals A Deep Peak Regulation Auxiliary Service Bidding Strategy for CHP Units Based on a Risk-Averse Model and District Heating Network Energy Storage

Energies ◽  
2019 ◽  
Vol 12 (17) ◽  
pp. 3314 ◽  
Author(s):  
Liang Tian ◽  
Yunlei Xie ◽  
Bo Hu ◽  
Xinping Liu ◽  
Tuoyu Deng ◽  
...  
Keyword(s):  
Energy Storage ◽  
Electricity Market ◽  
Value At Risk ◽  
District Heating ◽  
Expected Profit ◽  
Conditional Value At Risk ◽  
Bidding Strategy ◽  
Energy Market ◽  
Risk Averse ◽  
Service Market

With the advance of China’s power system reform, combined heat and power (CHP) units can participate in multi-energy market. In order to maximize CHP profit in a multi-energy market, a bidding strategy for deep peak regulation auxiliary service of a CHP based on a two-stage stochastic programming risk-averse model and district heating network (DHN) energy storage was proposed. The quotation set of competitors and load uncertainty was modeled with a Latin hypercube sampling (LHS) method. A dynamic queuing method was used to clear the market for the deep peak regulation auxiliary service to determine the bidding capacities of CHPs in the electricity market and the deep peak regulation auxiliary service market, respectively. Finally, the conditional value-at-risk (CVaR) indicator is used to measure the risk brought by the system uncertainty to the CHP, and the quotation coefficient is determined after considering the expected profit and risk profit comprehensively. The results of the example show that the profits produced by simultaneous participation in both electricity market and the deep peak regulation auxiliary service market are increased by approximately 9.5% compared with the profits produced by only participation in a single market. In addition, the use of DHN energy storage led to a profit increase of approximately 4.6%. As the risk aversion coefficient increases, the expected profit will be further reduced.

scholarly journals IGDT-Based Wind–Storage–EVs Hybrid System Robust Optimization Scheduling Model

Energies ◽  
2019 ◽  
Vol 12 (20) ◽  
pp. 3848
Author(s):  
Bo Sun ◽  
Simin Li ◽  
Jingdong Xie ◽  
Xin Sun
Keyword(s):  
Robust Optimization ◽  
Wind Power ◽  
Hybrid System ◽  
Electricity Market ◽  
Optimization Model ◽  
Probabilistic Approach ◽  
System Optimization ◽  
Stable Operation ◽  
Scheduling Model ◽  
Optimization Scheduling

Wind power has features of uncertainty. When wind power producers (WPPs) bid in the day-ahead electricity market, how to deal with the deviation between forecasting output and actual output is one of the important topics in the design of electricity market with WPPs. This paper makes use of a non-probabilistic approach—Information gap decision theory (IGDT)—to model the uncertainty of wind power, and builds a robust optimization scheduling model for wind–storage–electric vehicles(EVs) hybrid system with EV participations, which can make the scheduling plan meet the requirements within the range of wind power fluctuations. The proposed IGDT robust optimization model first transforms the deterministic hybrid system optimization scheduling model into a robust optimization model that can achieve the minimum recovery requirement within the range of wind power output fluctuation, and comprehensively considers each constraint. The results show that the wind–storage–EVs hybrid system has greater operational profits and less impact on the safe and stable operation of power grids when considering the uncertainty of wind power. In addition, the proposed method can provide corresponding robust wind power fluctuation under different expected profits of the decision-maker to the wind–storage–EVs hybrid system.

Sign in / Sign up

Export Citation Format

Share Document