Optimization of energy and economic scheduling of a hybrid energy plant by using a dynamic programming approach

An optimized energy and economic scheduling of hybrid energy plants can lead to a significant reduction of primary energy consumption and operational costs. Various optimization methods, with their own advantages and limitations, have been proposed in the literature. However, the scheduling optimization of complex hybrid energy plants that are composed of renewable, conventional and storage energy technologies is still an area which demands contribution. Dynamic programming has proved to be a powerful approach because of its ability to solve a variety of optimization problems with nonlinear objective functions and constraints, as well as to find global optimal solutions. Thus, this paper goes beyond previous analysis available in the literature by developing a novel methodology based on dynamic programming for the optimization of the energy and economic scheduling of hybrid energy plants. The hybrid energy plant considered in this paper includes renewable energy systems, fossil fuel energy systems and energy storage technologies. The actual fluctuation of the electricity prices is also considered in this work. The optimal scheduling was identified by considering the minimization of primary energy consumption or operational costs, as well as a hybrid scenario for meeting thermal, cooling and electrical energy demands of the user. Hybrid scenarios of minimizing both primary energy consumption and operational costs weighted by two different weight coefficients α and β, are also evaluated. The validity and capability of the optimization methodology is demonstrated by considering two case studies. The first case is a commercial building and the second case regards a University campus. Compared to commonly-used operation strategies, the energy scheduling optimization (α = 1 and β = 0) by means of dynamic programming allows a primary energy saving between 3.8% and 8.3% for the first case study and a saving between 0.5% and 17.4% for the second case study. Moreover, the economic scheduling optimization (α = 0 and β = 1) enables operational cost reduction in the range 11.7%–25.1% for the first case study and in the range 4.3%–14% for the second case study. For both case studies, the economic scheduling optimization shows that fulfilling the user energy demands by a combined heat and power is economically more convenient than importing electricity from the grid. Finally, unlike the operation strategies used as benchmarks, the dynamic programming methodology is flexible and able to solve scheduling optimization problems under different optimization constraints and can also allow customized hybrid solutions.