An efficient two-stage algorithm for decentralized scheduling of micro-CHP units

Abstract

In this paper we present an efficient two-stage hierarchical decomposition algorithm aiming at determining economically improved operation schedules for residential proton exchange membrane fuel cell microcombined heat and power (PEMFC micro-CHP) units and optimizing local charging of electric vehicles (EV) in the same household. Based on an individual short-term load forecasting (STLF) approach (imperfect forecast) for households implemented as an adaptive network-based fuzzy inference system (ANFIS), a mixed-integer linear program (MILP) and a two-stage greedy algorithm are used for determining optimized schedules based on a rolling-window approach. The results of the case study performed for eight variants in exemplary German households reveal that with both the MILP and the algorithmic approach, significant economic savings can be achieved compared to the standard heat-led strategy. Compared to the MILP, however, the two-stage algorithm has the additional advantage of a reduced computing time of only about 151 . Deviations from the MILP solutions are mostly smaller than 3 percent regarding the annual supply costs. Moreover, the comparison between the use of perfect and imperfect demand forecasts quantifies additional average losses due to forecasting errors of 2 percent and 3.3 percent at the maximum. Altogether, the algorithmic approach Postprint of the article "An efficient two-stage algorithm for decentralized scheduling of micro-CHP units" In European Journal of Operational Research, Volume 245, Issue 3, Pages 862-874, https://doi.org/10.1016/j.ejor.2015 3 seems to be convincing for real applications in households due to its good results, high reliability, easy implementation, and short computing times. The combination of a micro-CHP unit and an EV is highly synergetic.

Self-consumption through power-to-heat and storage for enhanced PV integration in decentralised energy systems

“…They apply a non-dominated sorting genetic algorithm as metaheuristic to an illustrative building and evaluate the potential for the reduction of fuel consumption through including electrical and thermal energy storage in the system. Jochem et al (2015) and, similarly, Kia et al (2017) optimise the day ahead scheduling of CHP units with electrical and thermal storage. While Jochem et al (2015) focus on decentralised micro-CHP at the household level and find significant potential to self-consume the CHP's electricity output to more than 50%, Kia et al (2017) evaluate the CHP's added value to avoid costs imposed by security constraints in two alternative IEEE electricity networks.…”

Meeting the Modeling Needs of Future Energy Systems

“… Power‐to‐heat applications (e.g., heat pumps, night‐storage heating, combined heat‐power units, and electric boilers): Power‐to‐heat approaches have a high energy demand (mostly higher than the overall residual electricity demand) that is flexible owing to heat storage . Hence, shifting of heat production seems to be reasonable, at least during the winter season Electric vehicles: Similarly, the energy demand of an average electric vehicle is comparable to the overall electricity consumption of a household.…”

Optimal combined long-term facility design and short-term operational strategy for CHP capacity investments

“…Within the mathematical programming approaches, mixed integer programming (MIP) [40], mixed integer linear programming (MILP) [33,41,42,43,44], or mixed integer nonlinear programming (MINLP) methods [45] are used extensively in the literature for deterministic optimal capacity and configuration of energy systems. Enterprise-wide dynamic optimization seeks similar objectives but with the use of large-scale nonlinear programming (NLP) solvers [46].…”