As an essential part of the future national energy system, hydrogen energy has the advantages of clean, long-time scale energy storage and good complementary characteristics to electrical power, making it an important player in the low-carbon transformation of energies. However, the long timescale storage characteristics and tradability of hydrogen energy are rarely considered in existing capacity allocation optimization methods for hydrogen integrated energy systems(HIES). Meanwhile, the impact of the physical characteristics of the hydrogen storage equipment is rarely considered in the construction of HIES. Therefore, this paper proposes a new capacity allocation method for HIES in industrial park considering hydrogen trading and long-term hydrogen storage. The proposed capacity allocation optimization method is a bilevel mixed-integer linear programming model, which is solved by the reconfiguration decomposition algorithm. The nonlinear constraint problem due to the physical characteristics of the hydrogen storage device is solved by the Big-M method and the binary method. The proposed method can effectively improve the economy of HIES and reduce the cost of hydrogen production. Meanwhile, the reconstruction decomposition algorithm can effectively solve the bilevel mixed integer programing model. Case studies demonstrate that the proposed method can reduce hydrogen production economics by 28%. Considering hydrogen trading, the total investment and operating cost of HIES is reduced by 25%, while long-term hydrogen storage can reduce the cost of hydrogen production for HIES and reduce the impact of hydrogen trading fluctuations.

The multi-energy system is a promising energy-efficient technology to supply electric and thermal energy to end-users simultaneously, which can realize the energy cascade utilization. However, it is challenging to optimize the operation of multi-energy systems due to their inherent structural complexity, as well as the highly coupled nature of multiple energy flows and the uncertainty of renewable energy generation. This paper proposed a collaborative demand-controlled operation strategy for a multi-energy system, which consists of an upper-level model and a lower-level model. In the upper-level model, a robust linear optimization method is adopted to optimize the system operation in a day-ahead stage. In the lower-level model, a stochastic rolling optimization method is applied to achieve a dynamic adjustment to cope with the fluctuation in both renewable electricity generation and electric load. The multiple energy demand-controlled strategy is also applied in the optimal operation strategy to achieve load shifting and to create flexibility in energy demand despite the “source-load” imbalance power fluctuation. A case study is carried out and simulation results verify the effectiveness and correctness of the proposed model of the coordinated operation framework.

A multi-energy system can supply both electric and thermal energy simultaneously to the end-users to achieve a high energy use-efficiency. However conventional operational strategies for multi-energy systems, in the existing works, do not consider renewable energy generation plants and CCHP plants as belonging to different interests. To cope with this problem, this article studies a cooperative operation framework model for a Wind-Solar-CCHP Multi-energy system. Instead of the conventional non-cooperative solution-based method, the optimal operation scheduling problem of a multi-energy system is described as an optimization problem of Nash Bargaining. Then the optimization problem of Nash Bargaining for the multi-energy system decomposed into two subproblems: social welfare maximization problem (P1) and energy trading payment problem (P2). The optimal energy transaction profiles can be obtained by solving the P1 problem and the problem of payment of energy trading between PV, WT plant agents and the CCHP plant agent can be obtained by solving the P2 problem. Both problems P1 and P2 can be formulated into mixed-integer linear programming problems and solved by the alternating directional multiplier method. Numerical studies demonstrate the correctness and effectiveness of the proposed cooperative operation framework and distributed algorithm for solving the two subproblems. Moreover, the bargaining-based cooperative operation method can further induce the multi-energy system to improve the operating profit with the reduced feed-in tariff of distributed generation.

The combined cooling, heating and power (CCHP) system is a promising energy-efficient technology to realize energy cascade utilization. With the integration of photovoltaic/thermal panels and thermal energy storage, the comprehensive performance of the system can be further improved. However, the performance is also affected by the operation strategy. This paper proposes an effective operation strategy to deal with the energy flow of the system well to achieve a better performance. The mathematical model of a CCHP system hybridized with photovoltaic/thermal panels and thermal energy storage is established in this paper. The optimal size of key components of the CCHP system is determined by the particle swarm optimization (PSO) algorithm. Case studies of four scenarios of a residential zone in Beijing are conducted to verify the effectiveness of the system structure and efficiency of the proposed method. The results show that by adding photovoltaic/thermal (PV/T) panels and thermal energy storage, the economic and energetic benefits can be effectively improved and the proposed operation strategy is effective.

The regional energy system (RES) is a system that consumes multiple forms of energy in the region and achieves coordinated and efficient utilization of energy resources. The RES is composed of multiple micro energy systems (MESs); however, due to the mismatch of energy resources and different energy consumption within each MES, a large amount of clean energy is wasted, and each MES has to acquire extra energy. This significantly increases operation costs and contributes to environmental pollution. One of the promising ways to solve this problem is to deploy an energy storage system in the RES, which can make use of its advantages to transfer energy in space-time and fulfill the demand for loads in different periods, and conduct unified energy management for each MES in the RES. Nevertheless, a large number of users are deterred by the high investment in energy storage devices. A shared energy storage system (SESS) can allow multi-MESs to share one energy storage system, and meet the energy storage needs of different systems, to reduce the capital investment of energy storage systems and realize efficient consumption of clean energy. Taking multiple MESs as the object, this paper proposes a model and collaborative optimal strategy of energy management for the RES to accomplish high utilization of clean energy, environmental friendliness, and economy. First, the paper analyzes the internal energy supply characteristics of the RES and develops a model of the RES with an SESS. Then, the paper poses the management concept of load integration and unified energy distribution by using the operational information of each subsystem. An optimal operation strategy is established to minimize daily operation costs and achieve economic, environmentally friendly, and efficient operation of the RES. Third, by setting up scenarios such as no energy storage system and an independent energy storage system (IESS) of each MES and SESS, a case of a science and education park in Guangzhou, China, is illustrated for experiments. Numerical experiment results show that with an SESS built by the investor in the RES and applying the mentioned energy management strategy, the utilization of clean energy can be 100%, the operation costs can be reduced by up to 9.78%, the pollutant emission can be reduced by 3.92%, and the peak-to-valley difference can be decreased by 20.03%. Finally, the influence of energy storage service fees and electricity tariffs on daily operation costs is discussed, and the operation suggestions of the SESS are proposed. It validates the effectiveness of the proposed strategy.