Energy Hub
M. Rashidinejad; S. Dorahaki; S. S. Zadsar; M.R. Salehizadeh
Abstract
Background and Objectives: The smart energy hub framework encompasses physical assets such as thermal storage, boiler, wind turbine, PV panel, water storage and, water desalination unit to ensure continuity of electricity, water, thermal, and gas provision in the case of unexpected outages in the upstream ...
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Background and Objectives: The smart energy hub framework encompasses physical assets such as thermal storage, boiler, wind turbine, PV panel, water storage and, water desalination unit to ensure continuity of electricity, water, thermal, and gas provision in the case of unexpected outages in the upstream networks. In this regard, the smart energy hub as an integrated structure provides a suitable platform for energy supply. Considering the drinking water resources in the smart hub structure can cause operational efficiency improvement. Methods: This paper proposes an integrated scheduling model for energy and water supply. To address the issue of increasing operational flexibility, a set of new technologies such as Compressed Air Energy Storage (CAES) and Power-to-Gas (P2G) system are provided. Also, the energy price is modeled as an uncertain parameter using a robust optimization approach. The proposed model is established as a Mixed Integer Linear Function (MILP). The mentioned model is implemented using the CPLEX solver in GAMS software. The proposed model is simulated in different scenarios in the energy hub and the optimization results are compared with each other to validate the proposed method. Results: The results show that using CAES technology and the P2G system can lead to reducing the operating costs to a desirable level. Moreover, the impact of the P2G unit on the operation cost is more than the CAES unit.Conclusion: The energy hub operator should tradeoff between robustness and operation cost of the system. The obtained results ensured that the proposed methodology was robust, optimal, and economical for energy hub schedules.
Energy Hub
H. Hosseinnejad; S. Galvani; P. Alemi
Abstract
Background and Objectives: Different energy demand calls the need for utilizing Energy Hub Systems (EHS), but the economic dispatch issue has become complicated due to uncertainty in demand. So, scenario generation and reduction techniques are used to considering the uncertainty of the EH demand. Dependent ...
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Background and Objectives: Different energy demand calls the need for utilizing Energy Hub Systems (EHS), but the economic dispatch issue has become complicated due to uncertainty in demand. So, scenario generation and reduction techniques are used to considering the uncertainty of the EH demand. Dependent on the amount of fuel used, each system has various generation costs. Configuration selection stands as a challenging dilemma in the EHS designing besides economic problems. In this paper, the optimal EHS operation along with configuration issue is tackled.Methods: To do so, two EHS types are investigated to evaluate the configuration effect besides energy prices simultaneously change. Typically, the effect of the Demand Response (DR) feature is rarely considered in EHSs management which considered in this paper. Also, Metaheuristic Automatic Data Clustering (MADC) is used to reduce the decision-making problem dimension instead of using human decision makers in the subject of cluster center numbers and considering uncertainty. The "Shannon's Entropy" and the "TOPSIS" methods are also used in the decision-making. The study is carried out in MATLAB© and GAMS©.Results: In addition to minimizing the computational burden, the proposed EHS not only serves an enhancement in benefit by reducing the cost but also provides a semi-flat load curve in peak period by employing Emergency Demand Response Program (EDRP) and Time of Use (TOU).Conclusion: The results show that significant computational burden reduction is possible in the field of demand data by using automatic clustering method without human interference. In addition to the proposed configuration's results betterment, the approach demonstrated EH's configuration effect could consider as important as other features in the presence of DRPs for reaching desires of EHs customers which rarely considered. Also, "Shannon's Entropy" and the "TOPSIS" methods integration could select the best DRP scenario without human interference. The results of this study are encouraging and warrant further analysis and researches.
