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Highlights • A day-ahead scheduling strategy is proposed with detailed device modeling. • The effects of multiple thermal storage devices on flexibility are fully utilized. • The unit commitment constraints in practical operation are well considered. • The optimal scheduling model is transformed into a MILP formulation by linearization. Abstract The operation of community integrated energy systems (CIESs) is challenging because it is necessary to couple energy production, conversion and consumption on the end-user side. Multiple energy demands need to be fed simultaneously with the goal of economy and reliability. This paper formulates the optimal scheduling problem based on a real community integrated energy system in China. The system has multiple chillers (i.e. three ground source heat pumps, two conventional water-cooled chillers and two double-duty chillers) and two types of thermal storage devices (i.e. two cold water tanks and an ice-storage tank). Based on detailed device modeling, a community integrated energy system operation strategy considering unit commitment is proposed, and then is transformed into a mixed-integer linear programming model by linearization of nonlinear items. Case studies are conducted based on the data for a typical day in summer. The results show that the proposed strategy can utilize the flexibility of the energy storage devices and realize an economic and reliable operation of the community integrated energy system by coordinating various energy devices. The strategy can also reduce the startup/shutdown frequency of chillers significantly. [ABSTRACT FROM AUTHOR]

• The flexibilities of multiple links are utilized with economic performance improved. • The adaption to uncertainties is enhanced by the MPC-based rolling optimization. • The scheduling strategy with reservation margins is robust for real-time errors. Community integrated energy system (CIES) couples multiple energy forms and links, it is important to cope with the inadequacy of flexibility for realizing efficient utilization and reliable supply. Based on the detailed modeling of central energy station, district heating network (DHN) and building loads, a unified scheduling model is established by fully considering the coupling characteristics of the sources, the networks, and the loads. Meanwhile, the flexibilities from adjustable capacity of thermal storage device and the indoor temperature on the demand side, as well as the energy storage of DHN, are utilized to improve the economy. Furthermore, a model predictive control (MPC) based robust scheduling strategy is proposed to maintain and utilize CIES flexibility for enhancing the uncertainty adaptability. The scheduling framework consists of rolling optimization and robust constraint generation. The rolling optimization schedules the system with a better adaptation to flexibility demands, and the robust constraints generate adjustable margin for building demands by modifying the upper/lower and ramping limits. After the linearization of nonlinear terms in the model, case studies are conducted based on the data of a typical day in summer. The results show that the unified source-network-load scheduling strategy can give full play to the flexibilities of different energy links with a better operation economy. Additionally, the MPC-based optimization can well adapt to the rolling forecasting uncertainties, and the schedule generated by advance reserving means has stronger robustness for real-time errors. [ABSTRACT FROM AUTHOR]

Independent microgrids (MGs) consisting of diesel generator (DG), photovoltaic (PV), and energy storage system (ESS) are becoming a cost effective solution for the power supply in remote areas. However, besides PV intermittence, limited reserve and time-consuming replenishment of diesel fuel in remote areas make it challenging to guarantee long-period reliable power supply. In this paper, a risk-aware energy scheduling and stochastic optimization method is proposed to enhance long-period operational reliability of independent MGs. The possible extreme scenarios in the future are considered in an energy scheduling optimization model (ESOM). Based on energy forecast results of PV and loads for the next 7 days, ESOM maximizes the reliable power supply probability by optimizing energy scheduling strategies and reserve requirement for future operational risk simultaneously. Subsequently, a day-ahead stochastic optimization model is established to determine optimal power scheduling strategies of DG, PV, ESS, and flexible loads. The conditional value at risk (CVaR) is used to address the operation risk caused by uncertainties of PV and loads. Compared with traditional day-ahead optimization methods by numerous simulations, the proposed method has less expected load losses and PV curtailment, as well as less total supply-demand deviation. The resistance for future operational risks of independent MGs is therefore significantly enhanced. • A risk-aware dynamic optimization method is proposed for independent microgrids. • Future operational risks are considered in the energy scheduling model. • Uncertainties are considered in the CVaR-based day-ahead optimization model. • The long-period operational reliability is significantly enhanced. [ABSTRACT FROM AUTHOR]