Abstract
Introducing new technologies in co-generation and tri-generation systems has led to a rapid growth toward the energy hubs (EHs) as an effective way for coupling among various energy types. On the other hand, the energy systems have usually been exposed to uncertain environments due to the presence of renewable energy sources (RESs) and interaction with the electricity markets. Hence, this paper develops a novel optimization framework based on a hybrid information gap decision theory (IGDT) and robust optimization (RO) to handle the optimal self-scheduling of the EH within a medium-term horizon for large consumers. The proposed mixed-integer linear programming (MILP) framework aims to capture the advantages of both the IGDT and RO techniques in dealing with the complicated binary variables and achieving the worst-case realization arisen from wind turbine generation and day-ahead (DA) electricity market uncertainties. The RO optimization approach is presented to model the DA electricity price uncertainty while the uncertainty related to the wind turbine generations is taken into account by the IGDT. Numerical results validate the capability of the model facing uncertainties. The amount of total operation cost of the EH increases by 8.6 % taking into account the worst-case realization of uncertainties through the proposed hybrid IGDT-RO compared to the case considering perfect information. Besides, the results reveal that optimal decisions can be taken by the operator using the proposed hybrid IGDT-RO model.
Highlights
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While there are advantages and disadvantages to these methods, this paper aims at capturing the positive features of the IGDT and RO models to achieve the worst-case realization of the uncertainties within a medium-term horizon
Since the problem deals with the uncertainties of the wind generations and DA electricity prices, the developed model maximizes the DA electricity prices deviation using the RO approach, and the IGDT maximizes the wind generation uncertainty radius when the total operation cost of the energy hub (EH) has been incorporated in the IGDT part
Summary
This material is protected by copyright and other intellectual property rights, and duplication or sale of all or part of any of the repository collections is not permitted, except that material may be duplicated by you for your research use or educational purposes in electronic or print form. The cooperation and management of electric, thermal, and gas units to meet the customers’ demands form the energy hub (EH) [2]. The EH may consist of different sources such as wind turbine (WT), combined heat and power (CHP) plant, photovoltaic (PV), electric vehicles (EV), power to gas (P2G), as well as storage units of electric or thermal energy [3]. The most crucial feature of EH is its capability in converting one type of energy to other types to meet the demands of other types of energy and assure the highest profit or reliability [4]. Different timely topics such as EH planning and sizing, optimal operation of EH, demand response in the EH, the effect of uncertainty on the EH, etc., has become the primary focus
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