Optimal Price-maker Trading Strategy of Wind Power Producer Using Virtual Bidding

This paper proposes a stochastic optimization model for generating the optimal price-maker trading strategy for a wind power producer using virtual bidding,which is a kind of financial tool available in most electricity markets of the United States.In the proposed model,virtual bidding is used to improve the wind power producer’s market power in the dayahead(DA)market by trading at multiple buses,which are not limited to the locations of the wind units.The optimal joint wind power and virtual trading strategy is generated by solving a bi-level nonlinear stochastic optimization model.The upperlevel problem maximizes the total expected profit of the wind power and virtual bidding while using the conditional value at risk(CVa R)for risk management.The lower-level problem represents the clearing process of the DA market.By using the Karush-Kuhn-Tucker(KKT)conditions,duality theory,and big-M method,the bi-level nonlinear stochastic model is firstly transferred into an equivalent single-level stochastic mathematical program with the equilibrium constraints(MPEC)model and then a mixed-integer linear programming(MILP)model,which can be solved by existing commercial solvers.To reduce the computational cost of solving the proposed stochastic optimization model for large systems,a method of reducing the number of buses considered for virtual bidding is proposed to simplify the stochastic MPEC model by reducing its decision variables and constraints related to virtual bidding.Case studies are performed to show the effectiveness of the proposed model and the method of reducing the number of buses considered for virtual bidding.The impacts of the transmission limits,wind unit location,risk aversion parameters,wind power volatility,and wind and virtual capacities on the price-maker trading strategy are also studied through case studies.

Prognostic Condition Monitoring for Wind Turbine Drivetrains via Generator Current Analysis

Maintenance costs account for a significant portion of the total cost of electricity generated by wind turbines.Currently in the wind power industry,maintenance is mainly performed on regular schedules or when significant damage occurs in a wind turbine making it inoperable,instead of being determined by the actual condition of the wind turbine.Among the total maintenance costs,approximately 25%~35%is related to regularly scheduled preventive maintenance and 65%~75%to unscheduled corrective maintenance.To reduce the failure rate and level and maintenance costs and improve the availability,reliability,safety,and lifespans of wind turbines,it is desirable to perform condition-based predictive maintenance for wind turbines,which will require a high-fidelity online prognostic condition monitoring system(CMS)for fault diagnosis and prognosis and remaining useful life(RUL)prediction of wind turbines.Most of the existing wind turbine CMSs are based on vibration monitoring and have no or limited capability in fault prognosis and RUL prediction.Compared to vibration monitoring,the prognostic condition monitoring techniques based on generator current signal analysis proposed recently have significant advantages in terms of cost,hardware complexity,implementation,and reliability.This paper discusses the principles and challenges of using generator current signals for prognostic condition monitoring of wind turbine drivetrains and presents an overview of recent advancements in this area.

Hybrid Scenario Generation Method for Stochastic Virtual Bidding in Electricity Market

Stochastic optimization can be used to generate optimal bidding strategies for virtual bidders in which the uncertain electricity prices are represented by using scenarios.This paper proposes a hybrid scenario generation method for electricity price using a seasonal autoregressive integrated moving average(SARIMA)model and historical data.The electricity price spikes are first identified by using an outlier detection method.Then,the historical data are decomposed into base and spike components.Next,the base and spike component scenarios are generated by using the SARIMA-and historical data-based methods,respectively.Finally,the electricity price scenarios are obtained by combining the base and spike component scenarios.Case studies are carried out for a virtual bidder in the PJM electricity market to validate the proposed method.The optimal bidding strategies of the virtual bidder are generated by solving a stochastic optimization problem using the electricity price scenarios generated by the proposed method,the SARIMA method,and a historical data-based method,respectively.Case study results show that the proposed method is better than the SARIMA method in preserving statistical properties of the electricity price in the generated scenarios and is better than the historical data-based method in predicting the future trend of the electricity price and,therefore,can help the virtual bidder earn more profit in the electricity market.