Optimal unit commitment based on second‐order cone programming in high wind power penetration scenarios

Abstract

With the increase in wind power integration in power systems, wind power uncertainty can no longer be neglected in the determination of the day-ahead power generation schedules. Aiming to achieve the best economy of power generation of the system, this study proposes a unit commitment (UC) optimal model for thermal plants, considering the fuel costs required for compensating for the wind power below schedule. The AC power flow equations are included as constraints based on the second-order cone programming (SOCP) method. SOCP is further improved here by considering line loss as constraint to guarantee the validity of its solution. Research shows that generators with lower minimum-output and fuel-cost rates are preferred to be online and that reasonable wind curtailment is beneficial for reducing the generation cost. Finally, AC power flow verification is carried out and the results suggest that the improved SOCP method could model the AC power flow equations correctly. The proposed methodology can be effective in making UC decisions for power grids with high wind power penetration.

A Planned Power Generation for Battery-Assisted Photovoltaic System Using Short-Term Forecast

“…The IEEJ AGC30 model [28] (developed by the Institute of Electrical Engineers of Japan, IEEJ) was adopted as the analytical model for the frequency control simulation. The unit commitment problem to determine the day-ahead generation plan for regulating generators was solved based on our preliminary study [29]. In addition, economic dispatch control has not been implemented and will be considered in future work.…”

Pyramidal approximation for power flow and optimal power flow

“…g PK = S r cos θ K (35) 0 ≤ h PK ≤ S r sin θ K (36) Therefore, the PA-based PFE formulation with the R&F strategy is derived as (5), (6), (8), (11), (31)- (36), and similar counterpart of (30). In the derivation from the benchmark BIM to the proposed PA-based PFEs, the conical approximation (i.e.…”

“…In CP, the globally OPF can be efficiently solved and the solution is not sensitive to the initial estimate. Jabr [34] proposes the conic quadratic format of OPF in meshed networks based on BIM, and Dui and Zhu [35] apply the conic quadratic format to the unit commitment with stochastic wind power integrated. The branch flow model (BFM) is another kind of formulation of PFEs, which is usually adopted in relaxation methods [36].…”

Peaking Compensation Mechanism for Thermal Units and Virtual Peaking Plants Union Promoting Curtailed Wind Power Integration

“…The objective function (10) minimizes the system operating cost C, which consists of coal consumption cost a i (P i,t ) 2 + b i P i,t + c i , start-up cost C i,t , oil cost C oil i,t and pollution charges C ev i,t [42][43][44]. In the process of power system operation optimization, taking no account of the cost of wind power, the system operating cost is mainly the operating cost of thermal units which is primarily composed of coal consumption costs and start-up costs when thermal units provide basic peaking services [45].…”