Abstract
Areas with hydro power may purchase extra power from the outside power market during dry seasons, which will cause a deviation between the actual and expected power purchase amount due to the inaccurate judgment of the market situation. Because of the uncertainty of price fluctuations, the risk of purchasing power in the real-time market to eliminate this deviation is very high. This paper proposes an innovative trade mode, where the power exchange strategy between multiple areas is adopted through forming an alliance, i.e., one area can use the controllable elements within others, and constructing a monthly and post day-ahead two phase optimization model. The objective function of the monthly stochastic robust optimization considers the power purchase cost to determine the controllable elements dispatch dates for every area in the alliance. Thus, areas can make reasonable dispatch schedules for controllable elements to avoid the resource waste that means more controllable elements are prepared before post day-ahead optimization but less are used after post day-ahead optimization. While the post day-ahead optimization model determines the internal controllable elements dispatch and power exchange amount after the day-ahead market clearing process, users’ satisfaction and dispatch schedule changes for energy storage device are also considered. In order to solve the proposed two phase model, the dual principle and linearization methods are used to convert them into mixed-integer linear programming problems that can be effectively solved by the Cplex solver. The study case verifies the power deviation cost decreases with the power exchange strategy and the important role of energy storage devices.
Highlights
In western China, there are many adjacent areas with hydro power plants that supply the local loads
This paper proposes an innovative trading mode, where a power exchange strategy between multiple areas is adopted through an alliance of areas
After the power market clearing process, the controllable elements dispatch amount, controllable elements exchange amount, and power exchange amount are all obtained through the post day-ahead market optimization, which contains two different energy storage adjustment strategies
Summary
In western China, there are many adjacent areas with hydro power plants that supply the local loads. In [8], a bidding strategy model for brokers participating in the day-ahead market was proposed based on the aggregated household demand response. All these studies mainly use demand response to cut down peak loads and shift them to off-peaks and ignore the feasibility of applying a power exchange strategy, i.e., where one area can use the controllable elements within another area, to eliminate the power purchase deviations occurred in the day-ahead market. In [12], a power exchange strategy was adopted to eliminate the power purchase deviations occurring in a day-ahead market, but only the interruptible load was used as controllable element.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have