Abstract
The multi-energy system (MES) provides a good environment for the local consumption of renewable energy such as wind and solar power because of its high operational flexibility. In the MES, the hybrid energy storage system (HESS) composed of the battery and thermal storage tank plays an important role in enhancing reliability, economics, and operational flexibility. Hence, determining the optimal size of HESS in the MES is a critical problem but has not received enough attention. In light of this problem, this paper focuses on the optimal HESS planning problem in the community MES (CMES) under diverse uncertainties. Firstly, a two-stage stochastic planning model is proposed for the CMES to coordinate the optimal long-term HESS allocation and the short-term system operation. The thermal inertia in the heating network, space heating demand, and domestic hot water demand is utilized to reduce both the planning and operational cost. Secondly, a deterministic equivalence is proposed for the two-stage planning model to convert it into a mixed-integer linear programming model, which is then solved by off-the-shelf solvers. Finally, simulation results verify the effectiveness of the proposed method. The results reveal that the HESS can enhance the operational flexibility of the CMES but only needs a very few investment costs and prove that the thermal inertia in the CMES can reduce the investment cost of HESS, the fuel, and the operational maintenance cost.
Highlights
With the development of the distributed renewable energy generation technology such as wind turbines and photovoltaic cells, the modern power system is becoming more and more green and sustainable [1], [2]
There are space heating demand and domestic hot water demand, and the heat load keeps at a high level
There is only domestic hot water demand in the community, and the heat load keeps at a moderate level
Summary
With the development of the distributed renewable energy generation technology such as wind turbines and photovoltaic cells, the modern power system is becoming more and more green and sustainable [1], [2]. Owing to the inherent intermittent, fluctuation, and randomness of renewable energy sources (RES), it is still a hard problem to promote the local consumption of RES [3], [4]. Of energy flow, such as electricity, natural gas, and heat, which meets the different energy demands of users and has considerable operational flexibility because of the energy complementary [5], [6]. The MES is believed to an excellent choice to promote the local consumption of RES and improve energy efficiency [7], [8]. In [9], a large-scale nonlinear optimization model is proposed for the electrical and heating systems to enhance the flexibility of power systems, in which a decompositioncoordination algorithm is proposed for solving the model. In [10], a coordinated operation model with a linearized
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