This paper proposes a coordinated optimization of a geothermal-solar-wind multi-energy microgrid (MEM) considering energy quality. In this MEM, geothermal, solar, and wind energy are converted and conditioned for electricity, thermal energy, and gas productions, in which multi-energy complementarities are fully exploited based on electrolytic thermos-electrochemical effects. Exergy is introduced to understand and quantify the quality of energy carriers, which is formulated as an objective function to reduce the exergy loss rate. The aforementioned issue is thus addressed through the implementation of coordinated optimization to achieve a trade-off between two conflicting objectives. Simulation results on a high-renewable MEM reveal the effectiveness and superiority on flexibility and overall economic enhancements.

This article proposes a cascaded multilevel battery energy storage based parallel dynamic voltage compensator (DVC) for medium voltage industrial distribution systems. In this parallel DVC, fast switch is series-connected for voltage sag detection, and cascaded multilevel battery energy storage is parallel-connected for voltage support during the voltage sag period. According to the grid-side voltage sags or not, corresponding circuits and three-layer control strategies are developed. While the feedforward proportional–integral control method is adopted in the outer layer to track the ideal compensation voltage when the grid-side voltage sags, the ideal compensation current when the grid-side voltage does not sag is obtained through battery charging current as well as reactive power, harmonics, and negative sequence load current components. In order to cope with parameter differences in cascaded multilevel battery energy storage, the middle layer interphase and the inner layer intermodular state of charge equalization control are adopted in two scenarios. Furthermore, the parallel DVC is quantitatively compared with series DVC in the aspect of required battery capacity, and their advantages and disadvantages are systematically analyzed. Case studies are performed to verify the effectiveness and superiority of the proposed methodology on voltage support.

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 multienergy 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 multicarrier energy supplies based on the concept of energy hub but also exchanges energy with interconnected microgrids and via the transactive market. The multimicrogrid scheduling is a challenging optimization problem due to its severe constraints and strong couplings. A multimicrogrid multienergy 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 multimicrogrid system. The proposed method has been fully tested and benchmarked on the different scaled multimicrogrid system over a 24-h 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 multimicrogrid problems.