Combined Heat and Power Stochastic Dynamic Economic Dispatch Using Particle Swarm Optimization Considering Load and Wind Power Uncertainties

Due to the increased cost of energy sources and related environmental problems, systems with higher efficiency such as combined heat and power (CHP) units are getting more popular. Renewable energy sources can be another alternative solution for the above mentioned problems. Scheduling of renewable-based systems are getting more complicated due to the intermittent behavior of these sources. In this chapter, a stochastic programming framework is utilized to model uncertainties in dynamic economic dispatch (DED) problem of CHP based systems integrating wind energy. Forecast errors of electrical load and wind power are assumed as the two sources of uncertainty. A heuristic method called particle swarm optimization (PSO) is used to attain optimal solution of the problem due to non-linearity, non-convexity, and complexity of the problem. The stochastic programming provides more comprehensive and realistic viewpoint about dispatch problem by considering a variety of most probable scenarios compared to a single scenario.