Stochastic Economic Dispatch Based Optimal Market Clearing Strategy Considering Flexible Ramping Products Under Wind Power Uncertainties

High penetration level of renewable energy has brought great challenges to operation of power systems,and use of flexible resources(FRs)is becoming increasingly important.Flexibility of power systems can be improved by changing generation arrangements,but the interests of some market participants may be harmed in the process.This study proposes a stochastic economic dispatch model with trading of flexible ramping products(FRPs).To calculate changes in revenue and reasonably compensate units that provide FRs,multisegmented marginal bidding for energy is simulated by linearizing generation cost,and an optimal market clearing strategy for FRPs is developed according to changes in clearing energy and marginal clearing price.Then,the correlation between prediction errors of wind speeds among different wind farms is determined based on a joint distribution function modeled by the copula function,and quasi-Monte Carlo simulation(QMC)is used to generate wind power scenarios.Finally,numerical simulations of modified IEEE-30 and IEEE-118 bus systems is performed with minimum comprehensive cost as the objective function.This verifies the proposed model could effectively deal with wind variability and uncertainty,stabilize the marginal clearing price of the electricity market,and ensure fairness in the market.

Building and Researching the Bidding Model Based on the Costof Power Plant

A bidding model of neural network was presented to pursue the largest benefit according to the policy of separating power plants from network and bidding transaction. This model bases on the cost of power plant and its research object is a power plant in the market. The market clearing price (MCP) can be predicted and an optimized load curve can be decided in this model. The model may provide technical support for the power plant.

Optimal operation of cascaded hydroelectric power plants in the power market

An improved network flow algorithm, which includes the minimum cost network flow and the same period network flow, is proposed to solve the optimization of cascaded hydroelectric power plants in a competitive electricity market. The typical network flow is used to find the feasible flow and add the discharge water to different cascaded hydroelectric power plants at the same step. The same period network flow is used to find the optimal flow and add the power output at a different step. This new algorithm retains the advantages of the typical network flow, such as simplicity and ease of realization. The result of the case analysis indicates that the new algorithm can achieve high calculation precision and can be used to calculate the optimal operation of cascaded hydroelectric power plants.

A new method on hydrothermal scheduling optimization in electric power market

The hydrothermal scheduling in the electric power market becomes difficult because of introducing competition and considering sorts of constraints. An augmented Lagrangian approach is adopted to solve the problem,which adds to the standard Lagrangian function a quadratic penalty term without changing its dual property,and reduces the oscillation in iterations. According to the theory of large system coordination and decomposition,the problem is divided into hydro sub-problem and thermal sub-problem,which are coordinated by updating the Lagrangian multipliers,then the optimal solution is obtained. Our results for a test system show that the augmented Lagrangian approach can make the problem converge into the optimal solution quickly.