考虑碳交易的多能互补虚拟电厂优化调度运行策略

Optimal dispatching operation strategy of multi-energy complementaryvirtual power plant considering carbon trading

我国地形复杂,小气候众多,可再生能源出力曲线难以预测,日益成熟的市场规则和地域性气候的差异很难支持我国的电力市场规律、安全的运行。为此,文章提出一种多能互补的源网荷储一体化虚拟电厂的优化调度策略。提出在日前市场之前增加考虑可再生能源渗透率的可再生能源日前市场,让可再生能源和负荷达成双边交易,来分担可再生能源参与日前市场时不确定性带来的风险。同时对虚拟电厂参与日前市场的全过程进行建模,在日前市场中增加了出清模型来优化市场出清电价和优化各源侧的申报电价。考虑负荷波动,以及渗透到日前市场的可再生能源发电量的波动性,提出考虑备用出清模型来补偿各源侧为应对这些不确定性预留备用而产生的机会成本,并将这类成本按照“谁产生的成本,谁负责”的原则进行分摊,还原成本来源情况。通过搭建虚拟电厂内部碳交易模型,缓解发电侧碳排放交易市场的履约压力。文章采用粒子群优化算法(PSO)对模型进行优化求解,并通过IEEE 30节点算例验证所述方法的有效性。结果表明,该多能互补模型能有效应对在日前市场中不同风光发电情况和渗透率的备用需求变化,最终达到收益最大。

In China,with the complex terrain and the numerous micro-climates,the renewable energy output curve is difficult to predict.The increasingly mature market rules and regional climate differences are difficult to support the regular and safe operation of the power market in China.Therefore,this paper proposes an optimal dispatching strategy for a virtual power plant with multi-energy complementary sources included power supply,networks,loads and storage.Firstly,it is proposed to increase the renewable energy day-ahead market considering the penetration rate of renewable energy,so that renewable energy and load can enter into bilateral transactions to share the risks brought by the uncertainty of renewable energy participation in the day-ahead market.Meanwhile,the whole process of virtual power plant participating in the day-ahead market is modeled,and the clearing model is added to the day-ahead market to optimize the market clearing price and the declared price of each source side.Secondly,considering the load fluctuation and the volatility of the renewable energy power generation that permeates the day-ahead market,a standby clearing model is proposed to compensate for the opportunity costs incurred by each source side to deal with these uncertainties.Besides,such costs shall be apportioned according to the principle of"who produces the costs,who is responsible"to restore the source of costs.Finally,a carbon trading model inside the virtual power plant is built to relieve the pressure on the performance of the carbon emission trading market on the power generation side.Particle swarm optimization(PSO)algorithm is used to solve the model.The IEEE 30-node numerical example is used to verify the effectiveness of the method described in this paper.The results show that the multi-energy complementary model can effectively cope with the change of reserve demand under different wind power photovoltaic generation conditions and penetration rates in the day-ahead market,and finally achieve the maximum benefit.