Stochastic optimal scheduling of distributed energy resources with renewables considering economic and environmental aspects. (February 2018)

• Record Type: Journal Article
• Authors: Di Somma, M.; Graditi, G.; Heydarian-Forushani, E.; Shafie-khah, M.; Siano, P.
• Is Part Of: Renewable energy. Volume 116:Part A(2018)
• Page Start: 272
• Page End: 287
• Publication Date: 2018-02
• Subjects: Distributed energy resources -- Stochastic programming -- Uncertainties modeling -- Multi-objective optimization -- Operation scheduling; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.renene.2017.09.074
• Languages: English
• ISSNs: 0960-1481

Abstract

Abstract: A distributed energy resource (DER) system is a multi-input and multi-output energy system consisting of small-scale technologies, which provide electricity and thermal energy close to end-users. In recent years, DER systems have attracted much interest as a promising opportunity with substantial economic and environmental benefits. To reduce energy costs and environmental impact of DER systems, daily operation scheduling is crucial, and presents significant challenges even more under energy demand and supply uncertainty in presence of renewables. The contribution of this paper is to provide a stochastic programming model for the optimal operation scheduling of a DER system with multiple energy devices including renewables, considering economic and environmental aspects. To model uncertain parameters of supply and demand side, 24-h scenarios are generated by using roulette wheel mechanism and Monte Carlo simulation method. A stochastic multi-objective linear programming problem is formulated to find the optimized operation strategies of the DER system to reduce the expected energy costs and CO2 emissions, while satisfying the time-varying user demand. By minimizing a weighted sum of the total energy cost and CO2 emissions, the problem is solved by using branch-and-cut. Numerical results show that the Pareto frontier provides good trade-off solutions for DER system operators based on economic and environmental priorities. The total daily energy cost and CO2 emissions under a stochastic approach result to be lower than those under a deterministic one. Moreover, the operation method provided is found to be efficient in reducing significantly energy costs and CO2 emissions of the DER system, as compared with conventional energy supply systems and combined heat and power systems. In addition, a sensitivity analysis is also carried out to investigate the impact of renewables penetration on the economic and environmental performances of the DER system. Highlights: Stochastic operation scheduling of DER with renewables is presented. Multi-objective optimization is developed considering economic and environmental aspects. Optimal operation scheduling is obtained under energy demand and supply uncertainty. Trade-off solutions are found for operators based on economic and environmental priorities. A significant reduction of energy costs and CO2 emissions is attained through the operation method.