Accurate production planning is an important aspect of the combined heat and power plants operating on the electricity market. The complexity of production planning and scheduling depends mainly on the scale of the power system. Nowadays, the role of modern computer systems seems to be crucial and significantly affects optimal production planning in CHP plants. The production scheduling process must take into account the relationship between the production of heat and electricity in cogeneration units. An important aspect of optimal scheduling of power systems is precisely forecasting heat demand in district heating networks and electricity prices on the market.

In this paper, an optimization-based model for short-term scheduling of gas-fired CHP plant with heat accumulator is presented. The optimization model consists of a detailed simulation model of a cogeneration plant which is combined with an evolutionary algorithm. The optimization objective is to maximize the total gross margin for the day-ahead horizon of the CHP operation. An artificial neural network model is used for predicting heat demand in the district heating network. Different forecast models were tested for the electricity price forecast – extreme learning machines, multi-layer perceptron, auto-ARIMA, and triple exponential smoothing methods. The presented results show that the developed computer-based tool is efficient and effective for short-term scheduling of CHP plant with gas turbines and heat accumulator.

准确的生产计划是在电力市场上运行的热电厂的重要组成部分。生产计划和调度的复杂性主要取决于电力系统的规模。如今，现代计算机系统的作用似乎至关重要，并极大地影响了热电联产厂的最佳生产计划。生产计划过程必须考虑到热电联产机组中的热电生产之间的关系。电力系统优化调度的重要方面是精确预测区域供热网络的热需求和市场上的电价。

本文提出了一种基于蓄热的燃气热电联产电厂短期调度优化模型。优化模型由热电联产厂的详细仿真模型组成，该模型与进化算法结合在一起。优化目标是使热电联产业务日日范围内的总毛利率最大化。人工神经网络模型用于预测区域供热网络的热需求。针对电价预测测试了不同的预测模型-极限学习机，多层感知器，自动ARIMA和三重指数平滑方法。结果表明，开发的基于计算机的工具对于燃气轮机和蓄热器的热电联产电厂的短期调度是有效的。