考虑气耗特性及一次能源约束的燃机日前发电计划优化

目前燃机日前发电计划通常与实际发电曲线存在较大偏差,不利于提前安排运行方式和进行调峰分析,给电网安全稳定运行带来不利影响。为此,提出考虑气耗特性及一次能源约束的日前发电计划优化策略。燃机电厂每日气量受供气合同约束为固定值,对燃机气耗特性进行线性化逼近,获取燃机有功功率与耗气量的解析关系,依据次日负荷预测数据,按照高峰、腰荷和低谷时段对燃机有功功率进行分割,获取各时段耗气量并累加求和,和值受气量总指标约束,进而建立优化模型目标函数。利用细菌群体趋药性算法求解该模型,获取燃机日前发电计划。夏季高峰和低谷2个实际典型方式算例结果显示,该方法制订的计划曲线与实际发电曲线均方误差均低于10 MW,完全满足调度生产要求,表明了所提方法的正确性和有效性。

Optimization of Day-Ahead Gas Turbine Generation Scheduling with Gas Consumption Characteristics and Primary Energy Constraints in Consideration

Currently day-ahead gas turbine generation scheduling usually deviates a lot from the real generation curves, which brings much trouble to the operation and the peak load regulation of the power system. Thus, an optimization strategy for day-ahead gas turbine generation scheduling with the consideration of the gas consumption characteristics and the constraints of primary energy is proposed in this paper . The total gas supplied to the plant is fixed by the contract. The mathematical relationship between the output of a generator and its gas consumption can be acquired by linear approximation of the gas consumption characteristics. In terms of the day-ahead load forecasting, the generation curves are divided into three parts, i.e., the peak, medium , and low load periods. Then the gas consumed during the three periods can be obtained and summed up. The summed gas consumption is constrained by the total gas supplied. Therefore, an optimization model is built and solved with the bacterial colony chemotaxis algorithm to obtain the day-ahead generation scheduling. The results of two typical cases of summer peak and valley loads show that the mean squared errors between the calculated and the practical curves are less than 10 MW, which definitely meets the requirements of the real operation and proves the correctness and effectiveness of the proposed method.