Integration of renewable energy sources is generally initiated by incentives and the overall goal of the European Union is to approach the zero-emission generation goal. Unfortunately, passive integration of these sources close to the consumers could lead to considerable over investments necessitated by the critical enhancements on the distribution grid level. Above all, the design of all renewable energy systems and global energy policies must be put together in view of the latest strategic goal; at least 50% of energy production needs to be in the hands of final consumers. This also implies that a share of operational flexibility, curtailing the above limitations, will emanate from the distribution level via integration of technologies with the capacity to respond to various price signals.
A microgrid is a set of consumers, energy storages, and distributed generation coordinated to realize reliable supply to final consumers and exchanging a predetermined amount of energy with the rest of the system via a point of common coupling. However, scheduling the operation of a microgrid is prone to imprecise projection of the local renewable energy system or demand. If these imbalances are offset on the local level, microgrids can become flexibles nodes with the capacity to provide multiple flexibility services to the upstream system, therefore allowing for larger integration of renewable energy systems.
Ninoslav Holjevac, Tomislav Capuder and Igor Kuzle from the University of Zagreb in Croatia in collaboration with Ning Zhang and Chongqing Kang from Tsinghua University in China provided relevant contributions in the quantification of flexibility capacities of multi-energy microgrid. They developed an adaptive receding horizon Mixed Integer Liner Programming optimization model. The study focused on defining the impacts of various compositions of Multi-energy Microgrid and modeling aspects and approximations. Their research work is published in Applied Energy.
The authors presented an extensive multi-energy microgrid model that incorporated flow of various energy vectors including electricity, heating, cooling, and fossil fuel. The proposed model is linear meaning that it guarantees the optimality of results. The authors implemented the model to track the operation of various Multi-energy Microgrid arrangements via defined flexibility indicators for both on-grid and off-grid operation modes. In addition, the authors analyzed the impact of efficiency modeling.
The outcomes indicated that there was a considerable operational difference in flexibility and cost indicators when comparing various multi-energy microgrid arrangements consisting of varying production units. The efficiency modelling aspects affected both the process of developed receding horizon corrective control and the final operational points of the production units.
The authors observed that in the view of the multi-energy microgrid arrangement, the combination of distributed and centralized arrangements gave the best performance. Concerning the coupling of energy vectors, adding separate energy vectors increased flexibility by reducing the total cost, curtailed renewable energy, and wasted energy. The total energy usage on an annual scale were similar irrespective to the efficiency mode implemented.
The outcomes for various efficiency modes were different owing to frequent unit cycling in variable efficiency modeling scenarios. This was in view of the daily corrective Receding Horizon Model Predictive Control algorithm. Additional details concerning the interaction between the energy vectors will be analyzed in future work by the research team and addition of electric vehicles along with their inherent stochastic behavior will also be investigated.
Ninoslav Holjevac, Tomislav Capuder, Ning Zhang, Igor Kuzle, Chongqing Kang. Corrective receding horizon scheduling of flexible distributed multi-energy microgrids. Applied energy. Volume 207, 1 December 2017, Pages 176-194.
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