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Energy management and cost control are inevitable problems that restrict the development of standalone distributed renewable energy systems used in buildings. This paper presents a flexible allocation strategy based on a two-stage fuzzy logic controller to address these problems. Special attention has been paid to the management of stored energy and emergency resource. Decision boundaries are optimised based on actual operating characteristics of units. The fuzzy controller considering decision boundaries is more objective and realizes more accurate real-time energy management with less cost. Sensitivity analysis is conducted for uncertainty factors, which would affect decision boundaries. The effectiveness and advantages of the proposed strategy are verified through a typical hybrid system on an isolated island in the South China Sea. Results indicate that fuzzy controller considering decision boundaries saves 14.15% of the operating cost compared to fuzzy controller without considering decision boundaries, and it saves 20.45% when compared to the fixed priority strategy without flexible allocation. Moreover, its loss of power supply probability is the smallest, which is only 0.72%. A maximum synergy between the power supply and cooling demand is achieved. This novel controller performs better in reliability, economy, and response speed for a real time system. The proposed strategy has a positive effect on practical application of hybrid renewable energy systems for buildings in isolated areas. • The two-stage fuzzy logic controller is designed for flexible energy allocation. • Decision boundaries are optimised based on units' actual characteristics. • Impacts of decision boundaries on system performance are analyzed. • Cuckoo search via levy flights is adopted to find optimal range of battery state. • Sensitivity analysis is carried out to predict future trends of decision boundaries. [ABSTRACT FROM AUTHOR]

Over the last few decades, the use of renewable and clean energy sources has been the core of attention of the researchers. Besides, utilization of the energy hubs as a great innovation for future multi-carrier energy systems is well perceived for integrating intermittent sources of energy into the energy systems, which is surged and bolded in recent years. In this regard, this paper proposes an effective distributed energy management framework for modeling and optimal operation management of clean smart islands based on primal-dual method of multipliers. The primal-dual method of multipliers approach has shown superior performance compared to the alternating method of multipliers for distributed optimization. In this study, two different agents including a smart energy hub and a microgrid comprises of renewable and clean energy sources are considered in the smart island. These two distinct energy systems are assumed to be equipped with communication apparatuses and are intended to negotiate over the energy they need in a proper and completely distributed manner. Results show the effectiveness, accuracy and applicability of the approach for energy communication. • Providing a novel management scheme to properly supply the proposed system. • Incorporating a smart island, a smart microgrid and a SEH in remote areas. • Proposing a fully distributed energy management scheme based PDMM approach. • Investigating the distributed framework which covers demands in a smart island. [ABSTRACT FROM AUTHOR]

This paper proposes a robust co-state predictive model for Pontryagin's Minimum Principle (PMP)-based energy management of plug-in hybrid electric bus (PHEB). The main innovation is that the robust co-state predictive model is only expressed by a simplified formula. Moreover, it is exclusively designed by the Design For Six Sigma (DFSS) method in consideration of noises of driving cycles and stochastic vehicle mass. Because the DFSS strives to minimize the weighted sum of mean and standard deviation of fuel consumption, the proposed strategy can simultaneously improve the fuel economy of the PHEB and its robustness. The DFSS results show that the coefficients of the robust co-state predictive model can be found; the simulation results demonstrate that the proposed strategy has similar fuel economy to dynamic programming (DP); the hardware-in-loop (HIL) results demonstrate that the proposed strategy has good real-time control performance, and can averagely improve the fuel economy by 35.19% compared to a rule-based control strategy. Image 1052 • A robust co-state predictive model is designed by the Design For Six Sigma method. • The predictive model can counter the noises of driving cycles and stochastic vehicle mass. • The predictive model can simultaneously minimize the mean and standard deviation of the fuel consumption. • The robust and real-time control performances of the strategy are verified by hardware-in-loop test. [ABSTRACT FROM AUTHOR]