Coordinated Planning of Power Systems under Uncertain Characteristics Based on the Multilinear Monte Carlo Method

Abstract

The randomness of the power supply side and the load side of comprehensive energy systems is increasingly prominent. It is very difficult to meet demand through traditional planning methods. To solve this problem, this paper explores the coordinated planning of a power system under uncertain characteristics using the multilinear Monte Carlo method. The uncertain characteristic model and probability density function of the system’s power supply side and load side are established. Taking the optimal operating cost and the maximum wind power consumption as the system planning objectives, a system coordination planning scheme is established, and it is solved by multilinear Monte Carlo simulation. The superiority of this method is verified by taking the modified IEEE 39-bus test system as an example. This method can provide a reference for system planning.

Load Day-Ahead Automatic Generation Control Reserve Capacity Demand Prediction Based on the Attention-BiLSTM Network Model Optimized by Improved Whale Algorithm

“…New energy output is characterized by high volatility and randomness; while the penetration of new energy sources in the power system is increasing, the power system needs to prepare more reserve capacity to cope with the volatility of their output [10,11]. But due to the crowding out of the feed-in space of conventional thermal power units [12], the hot reserve resources available for systems are further reduced, thus posing a great challenge to the security and stability of the power system [13][14][15]. In the electricity market, ancillary services provide additional support for source-load power mismatch [16,17].…”