Abstract
In view of uncertainties caused by large-scale wind power integration, energy storage system (ESS) is being considered to stabilize the fluctuation of wind power. In this paper, the influence of ESS on power system operation with wind power is analyzed in detail, and an economic dispatch (ED) model with wind power and ESS is proposed based on scenario set. First, the initial scenario set of wind power output is generated by the Monte Carlo sampling. To overcome the shortcoming of heavy dependence on the initial clustering centers, which usually leads to unstable clustering results, the k-means clustering is improved by combining self-organizing feature map neural network and particle swarm optimization (PSO). Then, the initial scenario set is reduced based on this improved k-means clustering method. Finally, an ED model solved by PSO is used to minimize the comprehensive power generation cost based on the reduced scenario set. Taking IEEE-39 bus system as an example, the scenario-set-based ED model is implemented in this paper. The simulation results show that, when solving the ED problem with wind power and ESS, the proposed method considering scenario reduction makes not only the clustering index better, but also the results of ED more reasonable.
Highlights
In recent years, with the depletion of traditional fossil energy resources and the aggravation of environmental pollution, renewable energy has developed rapidly with its advantages of cleanness and freedom from pollution
In [19], Latin hypercube sampling (LHS) was used to generate the initial scenario set of the wind power output, and an improved k-medoids clustering based on particle swarm optimization (PSO) was used to reduce the scenario set, which effectively improved the k-medoids clustering, which had the shortcoming of unstable clustering results
In this paper, an economic dispatch (ED) model with wind power and energy storage system (ESS) based on a scenario set is established, and the influence of ESS on the optimal operation of power system with wind power is analyzed in detail
Summary
State of generator j at period t State of battery i at period t in scenario s, 1 represents the state of charge, −1 represents the state of discharge Flag of whether the battery i completes a single charge/discharge at period t in scenario s Active power output of generator j at period t in scenario s Continuous operating time/downtime of generator j at period t Wind power forecast error at period t in scenario s Actual output of wind power at period t in scenario s Planned output of wind power at period t in scenario s Wind power forecasting value at period t Charging/discharging power of battery i at period t in scenario s, it is positive when charging, negative when discharging Expected power of load shedding at period t in scenario s Expected power of wind power curtailment at period t in scenario s Wind power output that the system can absorb when the thermal power units provide all upward/downward spinning reserve at period t in scenario s Wind power output that the system can absorb when the energy storage reaches the charging/discharging power limit at period t in scenario s Active load of system at period t Minimum/maximum active power output of generator j at period t Spinning reserve for load fluctuation at period t Spinning reserve for wind power fluctuation at period t Maximum downward/upward ramp rate of generator j at period t Power flow of line l at period t in scenario s Minimum/maximum charging power of battery i at period t Minimum/maximum discharging power of battery i at period t
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