This paper proposes a distributed multi-period multi-energy operational model for the multi-carrier energy system. In this model, energy hubs function as distributed decision-makers and feature the synergistic interactions of generation, delivery, and consumption of coupled electrical, heating, and natural gas energy networks. The multi-period multi-energy scheduling is a challenging optimization problem due to its strong couplings and inherent nonconvexities within the multi-energy networks. The original problem is thus reformulated as a mixed integer second-order cone programming (MISOCP) and subsequently solved with a sequential second-order cone programming (SOCP) approach to guarantee a satisfactory convergence performance. Furthermore, a fully-distributed consensus-based alternating direction method of multipliers (ADMM) approach with only neighboring information exchange required is developed to optimize the multi-energy flows while considering the local energy-autonomy of heterogeneous energy hubs. The proposed methodology is performed and benchmarked on a four-hub urban multi-energy system over a 24 hourly scheduling periods. Simulation results demonstrated the superiority of the proposed scheme in system operational economy and renewable energy utilization, and also verify the effectiveness of the proposed distributed approach.

This paper proposes a multi-source multi-product framework for coupled multi-carrier energy supplies with a biogas-solar-wind hybrid renewable system. In this framework, the biogas-solar-wind complementarities are fully exploited based on digesting thermodynamic effects for the synergetic interactions of electricity, gas and heating energy flows, and a coupling matrix is formulated for the modeling of production, conversion, storage, and consumption of different energy carriers. The multi-energy complementarity of biogas-solar-wind renewable portfolio can be utilized to facilitate the mitigation of renewable intermittency and the efficient utilization of batteries, and a multi-carrier generation scheduling scheme is further presented to dynamically optimize dispatch factors in the coupling matrix for energy-efficient con-version and storage, while different energy demands of end-users are satisfied. The proposed methodology has been fully tested and benchmarked on a stand-alone Microgrid over a 24-hour scheduling horizon. Comparative results demonstrate that the proposed scheme can lower the battery charging/discharging actions as well as the degradation cost, and also confirm its capability to accommodate high penetration of variable renewables.

This paper proposes an integrated model of power-to-ammonia (P2A) to exploit the inherent operational dispatchability of nitrogen-ammonia (N ₂ -NH ₃ ) cycles for high-renewable multi-energy systems. In this model, the steady-state electrolytic process is mathematically formulated into a thermodynamic system based on thermo-electrochemical effects, and the long-term degradation process of P2A is transformed as the short-term degradation cost to characterize its cost-efficiency. Furthermore, the enhanced utilization of P2A is explored to form a renewable energy hub for coupled multi-energy supplies, and a coupling matrix is formulated for the optimal synergies of electrical, ammonia and thermal energy carriers. An iterative solution approach is further developed to schedule the hub-internal multi-energy conversion and storage devices for high-efficiency utilization of available hybrid solar-wind renewables. Numerical studies on a stand-alone microgrid over a 24-hour scheduling periods are presented to confirm the effectiveness and superiority of the proposed methodology over regular battery and power-to-gas (P2G) storages on system operational economy and renewable energy accommodation.

This article proposes a peer-to-peer transactive multiresource trading framework for multiple multienergy microgrids. In this framework, the interconnected microgrids not only fulfil the multienergy demands of with local hybrid biogas-solar-wind renewables, but also proactively trade their available multienergy and communication resources with each other for delivering secured and high quality of services. The multimicrogrid multienergy and communication trading is an intractable optimization problem because of their inherent strong couplings of multiple resources and independent decision-makings. The original problem is thus formulated as a Nash bargaining problem and further decomposed into the subsequent social multiresource allocation subproblem and payoff allocation subproblem. Furthermore, fully-distributed alternating direction method of multipliers approaches with only limited trading information shared are developed to co-optimize the communication and energy flows while taking into account the local resource-autonomy of heterogeneous microgrids. The proposed methodology is implemented and benchmarked on a three-microgrid system over a 24-h scheduling periods. Numerical results show the superiority of the proposed scheme in system operational economy and resource utilization, and also demonstrate the effectiveness of the proposed distributed approach.

This paper proposes a distributed multi-energy management framework for the coordinated operation of interconnected biogas-solar-wind microgrids. In this framework, each microgrid not only schedules its local hybrid biogas-solar-wind renewables for coupled multi-carrier energy supplies based on the concept of energy hub, but also exchanges energy with interconnected microgrids and via the transactive market. The multi-microgrid scheduling is a challenging optimization problem due to its severe constraints and strong couplings. A multi-microgrid multi-energy coupling matrix is thus formulated to model and exploit the inherent biogas-solar-wind energy couplings among electricity, gas and heat flows. Furthermore, a distributed stochastic optimal scheduling scheme with minimum information exchange overhead is proposed to dynamically optimize energy conversion and storage devices in the multi-microgrid system. The proposed method has been fully tested and benchmarked on different scaled multi-microgrid system over a 24-hour scheduling horizon. Comparative results demonstrated that the proposed approach can reduce the system operating cost and enhance the system energy-efficiency, and also confirm its scalability in solving large-scale multi-microgrid problems.

This paper proposes a mixed generation portfolio model of hybrid energy generating station (HEGS) for standby emergency power supply (EPS). The HEGS functions as a mobile and transportable reserve power source for critical loads in case of electricity outages. In the proposed model, various renewable and non-renewable energy sources with different mobility, energy density, and power density characteristics are integrated to form a sufficient and reliable hybrid energy EPS system, and the uncertainties of demand load, wind speed, solar radiation intensity, as well as solar cell temperature are modeled as interval numbers to formulate the optimal sizing problem of HEGS under uncertainty into a deterministic combinatorial optimization model. Furthermore, the multiobjective generation portfolio model of EPS configuration is also designed to generate Pareto frontier between implementation cost reduction and mobility improvement. Four generic evaluation metrics are formulated to evaluate the resulting generation-mix schemes of HEGS with case study demonstrated the effectiveness and superiority of the proposed model.