Short-term scheduling of gas-fired CHP plant with thermal storage using optimization algorithm

Accurate production planning is an important aspect of the combined heat and power plants operation on the electricity market. The complexity of production planning and scheduling depends mainly on the scale of the power system. Nowadays, the role of the modern computer systems seems to be crucial and significantly affects on 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. A critical aspect in optimal scheduling of power systems is precisely forecasting of heat demand in district heating network and electricity price 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 mathematical model of cogeneration plant that is integrated 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 the prediction of heat demand in the district heating network. The electricity price forecast is realized using exponential smoothing methods. The presented results show that the developed optimization model is efficient and effective for short-term scheduling of CHP plant with gas turbines and heat accumulator.