Optimization Of Spinning Reserve Capacity Of Power System With New Energy

The configuration of the spinning reserve(SR) capacity is an important measure for the system to resist risks, which is closely related to the operation reliability of the system,and it also affects the system stability and economic decision-making. Spinning reserve capacity is generally determined by traditional deterministic method which satisfies the system operation reliability as the primary task, and the relationship between the spinning reserve capacity and the uncertain factors is seldom analyzed. However, with the increasing scale of the grid-connected new energy, it brings great challenge to the safe operation of the system because its output is uncertain and intermittent, how to ensure the reliability and make the economy optimal is worth of studying.Firstly, taking the effects of uncertainty factors on the reserve optimization into account, in this paper, the conventional modeling method is used to model conventional unit fault outage, wind power output, photovoltaic power output and load which consider the forecast deviation. On this basis, discretization of the probability density function of the output of the four kinds of uncertain factors, establishes probabilistic sequence based on sequence operation theory and form an integrated wind power and photovoltaic serialization model which can intuitive reflect the risk level in the system.Then, a unit commitment model considering expected customer interruption cost and reserve cost is proposed. The proposed model which adds expected customer interruption cost and reserve cost into the unit commitment objective function, can realize the mutual restraint between risk and cost, and achieve the coordination of system reliability and economy. According to the model established in this paper, the unit commitment problem is decomposed into the inner and outer layers, the paper puts forward a hybrid intelligent algorithm of discrete particle swarm optimization and bacterial colony chemotaxis, the outer layer which optimize unit state using the discrete particle swarm optimization algorithm, the inner layer about load and spinning reserve economic distribution by bacterial colony chemotaxis, hybrid algorithm improves the optimization speed and the optimal degree.Finally, considering the actual operating condition of the system, the calculation method of stochastic DC power flow based on the operation of a sequence is detailed. In order to reduce and limit the more limited probability of system transmission line, a unit output dynamic adjustment strategy based on electrical distance partition is proposed, as a constraint in unit commitment optimization model is introduced, while adding transmission capacity constraints in the model. The example analysis show that the dynamic adjustment strategy proposed in the paper can effectively reduce the optimization time and the probability of line flow limit, make the research more in line with the actual needs, and do have significant meaning in optimization and scheduling of power system with new energy.