An optimal schedule model of multi-energy hubs network integrating solar energy

Recently, multi-energy systems based on energy hub are introduced because of significant benefits in reducing energy and emission cost. This paper proposed an optimal schedule model of multi-energy hubs networks consisting of energy hubs, renewable sources, and energy storage which are connected by electrical and natural gas distribution networks. In the proposed mixed-integer nonlinear programming model, the objective is to minimize the operation, energy, and emission costs of energy hubs with both renewable sources and storage and energy distribution networks. The proposed schedule framework allows simultaneously selections of optimal operation structure of EHs together with the optimal operation parameters of energy distribution networks and therefore this model can maximize the profit of the entire large-scale multi-energy hubs network. Besides, the operation parameters and energy loss of both electrical and natural gas distribution networks are considered in conjunction with optimal operation of energy hubs and thus guarantee the operation and optimization of the network in all operational scenarios. The IEEE 5-bus test system is utilized to demonstrate the applicability of the proposed model. The simulation results show the feasibility of the proposed model, and demonstrate that the energy hubs, renewable sources, and energy storage in the proposed structure significantly enhance the efficiency of the multi-energy hubs network by reducing not only energy and operation costs but also emission

Risk-Aware Stochastic Scheduling of Hybrid Integrated Energy Systems with 100% Renewables

Recently, ambitious endeavors have been carried out to facilitate the transition from traditional grids to hybrid interconnected energy networks in the form of grid modernization. Align to such efforts, this article aims at developing a novel framework for satisfying techno-economic-environmental goals in the grid modernization process. To this end, a detailed examination is conducted for the optimal exploitation of energy hubs (EHs) equipped with 100% renewables to pursue the environmental goal alongside intending technical and economic constraints. The energy conversion technology is adopted to enable the power-to-gas system for establishing multi-energy interactions among electricity and gas networks. Fully benefiting from renewable units has exposed the system to uncertain fluctuations that necessitate the modeling of uncertainties to achieve near-reality results. Hence, risk-averse and seeker strategies are developed based on robustness and opportunistic modes of the information gap decision theory (IGDT) method to deal with stochastic fluctuations of uncertain parameters. The integrated electricity and gas test system is considered to analyze the applicability of the proposed framework in modeling efficient multi-energy interactions. Given the obtained results, 43.68% more energy cost is reached for EHs when they adopted a robust strategy against uncertainties under the risk-averse strategy. Moreover, the proposed framework procured a rational decision-making model for balancing multi-energy in the hybrid energy grid with 100% renewables

Linearized Stochastic Optimization Framework for Day-Ahead Scheduling of a Biogas-Based Energy Hub Under Uncertainty

Energy hubs (EHs), due to their multiple nature in the production, consumption, and storage of energy, as well as the ability to participate in different energy markets, have made their optimal and profitable scheduling important for operators. Considering the literature review, one of the main motivations of this paper is the use of biogas as a pivotal fuel and through production using biomass in the structure of EHs. Therefore, this paper proposes a linearized optimization framework for optimal scheduling of a biogas-based EH for participation in day-ahead (DA) electricity and thermal energy markets. The proposed EH directly converts local biomass into biogas, thereby providing the fuel to generate electricity and thermal. This EH comprises digester, biogas storage, electric heat pump (EHP), biogas burner CHP and boiler, solar farm, electrical storage, and internal electrical and thermal loads. In this framework, the uncertainties related to solar radiation and the DA price are modeled to generate random scenarios using the Monte-Carlo method. The proposed EH is simulated for numerical studies based on data from Finland’s two selected spring and autumn days. The results show the optimal performance of the EH because it can participate in the electricity and thermal markets by using the biogas produced inside it and providing complete internal loads, and earns a decent income. In the autumn, operating the EH is more economical than in the spring. Moreover, comparative results have shown that eliminating the biogas unit and using natural gas significantly increases the expected costs of EH.© 2021 IEEE. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/fi=vertaisarvioitu|en=peerReviewed