Multi-Objective Economic Dispatch of Cogeneration Unit with Heat Storage Based on Fuzzy Chance Constraint

Abstract

With the increasing expansion of wind power, its impact on economic dispatch of power systems cannot be ignored. Adding a heat storage device to a traditional cogeneration unit can break the thermoelectric coupling constraint of the cogeneration unit and meet the economic and stable operation of a power system. In this paper, an economy-environment coordinated scheduling model with the lowest economic cost and the lowest pollutant emissions is constructed. Economic costs include the cost of conventional thermal power generating units, the operating cost of cogeneration units, and the operating cost of wind power. At the same time, green certificate costs are introduced into the economic costs to improve the absorption of wind power. Pollutant emissions include SO2 and NOx emissions from conventional thermal power units and cogeneration units. The randomness and uncertainty of wind power output are fully considered, and the prediction error of wind power is fuzzy treated according to the fuzzy random theory, and the electric power balance and spinning reserve fuzzy opportunity conditions are established, which are converted into the explicit equivalent. The improved multi-objective particle swarm optimization (MOPSO) was used to solve the model. With this method, the validity of the model is verified by taking a system with 10 machines as an example.

Enhanced flexibility utilization and coordinated dispatch method of energy‐intensive enterprises in power systems under time of use prices

“…Then, considering that the value of confidence level in this paper is larger than 0.5, the original chance constraints () and () can be translated into the clear equivalence class model () and () according to [27–29]. $$$$\begin{equation} \def\eqcellsep{&}\begin{array}{l} (2 - 2\psi )({P}_{L3,t} - {P}_{w2,t}) + (2\psi - 1)({P}_{L4,t} - {P}_{w1,t})\\[11pt] \quad +\, P_{buy,t}^{arc} + P_{buy,t}^{eaf} - \displaystyle\sum_{n = 1}^N {P_{n,t}^G} = 0 \end{array} \end{equation}$$$$ $$$$\begin{equation} \def\eqcellsep{&}\begin{array}{l} (2 - 2\psi )({P}_{L3,t} - {P}_{w2,t}) + (2\psi - 1)({P}_{L4,t} - {P}_{w1,t})\\[11pt] \quad +\, P_{buy,t}^{arc} + P_{buy,t}^{eaf} - \displaystyle\sum_{n = 1}^N {P_{n,t}^{G,\max }} \le 0 \end{array} \end{equation}$$$$…”

“…Then, considering that the value of confidence level in this paper is larger than 0.5, the original chance constraints (34) and (35) can be translated into the clear equivalence class model (36) and (37) according to [27][28][29].…”