Two-stage robust planning-operation co-optimization of energy hub considering precise energy storage economic model

Recently, the penetration rate of plug-in hybrid electric vehicles (PHEVs) and renewable/distributed energy resources (RERs/DERs) has increased in microgrids (MGs). However, due to the high uncertainties on both the demand (i.e., PHEV) and the supply (i.e., RER) sides, it is an indisputable fact that these components should be coordinated intelligently with the rest of the grid. In this article, a smart charging scheme is proposed for PHEVs that can minimize the energy drawn from the main grid, and this minimizes the dependence of MGs on the main grid by maximizing the use of RERs/DERs. Two scenarios are conducted on the modified version of the IEEE 33-bus test system to verify the effectiveness of the proposed power management strategy for different penetration levels of PHEVs, and the results of the proposed scheme are compared with the results of other, previously reported strategies. The obtained results from offline digital time-domain simulations and software verification indicate that the energy exchanged between the MG and the main grid to charge PHEVs is decreased since the RER/DER generation is increased. Therefore, the obtained results reveal the superiority of the proposed power management strategy over other, previously reported strategies.

Power Management of Microgrids Including PHEVs Based on Maximum Employment of Renewable Energy Resources

A stochastic distribution system planning method considering regulation services and energy storage degradation

To address the shortcomings of the existing linear power flow calculation methods for distribution networks with low versatility and calculation accuracy, a linear power flow calculation method considering distributed generation (DG) is proposed. According to the characteristics of distribution networks and using the Taylor series expansion technology, the nonlinear terms of the basic power flow equation are linearized. The reactive power generated by the non-PV type DG is simplified to a quadratic function of the voltage amplitude. Then, combined with linear approximation and the simplified DG calculation model, the basic power flow equation is simplified into a linear form. Based on the linear power flow equation, a linear power flow calculation method for distribution networks with DG is proposed. The proposed method, which considers various types of DG, has a strong versatility. Finally, the calculation results of typical test systems show that compared with the existing linear power flow calculation methods, the proposed method has a higher calculation accuracy and more versatility and can be used for rapid analysis of distribution networks.

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Linear Power Flow Calculation of Distribution Networks With Distributed Generation

Internet of energy based smart power grids demonstrate high in-feed from renewable energy resources (RESs) and lofty out-feed to energy consumers. Uncertainties evolved by incorporating RESs and time-varying energy consumption present immense challenges to the optimal control of smart power networks. To deal with these challenges, it is important to make the system deterministic by making time-ahead prediction and scheduling of power supply and demand. The present work confers a model of a co-scheduling framework, organizing cost-efficient activation of energy supply entities (ESEs) and load demands in a home area power network (HAPN). It integrates roof-top photovoltaic (PV) panels, diesel energy generator (DE), energy storage devices (ESDs), and smart load demands (SLDs) along with grid-supplied power. The scheduling model is based on mixed-integer linear programming (MILP) framework, incorporates a “min-max” optimization algorithm that reduces the daily energy bills, maintains high comfort level for the energy consumers, and increases the self-sufficiency of the home. The proposed strategy exploits the flexibility in dynamic energy price signals and SLDs of various classes, providing day-ahead cost-optimal scheduling decisions for incorporated energy entities. A linearized component-based model is developed, considering inefficiencies, taking various power phase modes of the SLDs along with the cost of operation, maintenance, and degradation of the equipment. A case study based on numerical analysis determines the particular features of the proposed HAPN model. Simulation results demonstrate the real prospect of our implemented strategy, utilizing a cost-effective optimal blend of distinct energy entities in a smart home.

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Modeling and Optimizing Energy Supply and Demand in Home Area Power Network (HAPN)

In this paper, a multi-stage planning-operation co-optimization model of an active distribution network (ADN) integrated with energy storage systems (ESSs) is proposed, in which the lifetime of batteries is also considered. By adopting piecewise linearized battery lifetime model and approximate power balance constraints based on Kirchhoff’s Current Law and Kirchhoff’s Voltage Law, the problem is reconstructed as a mixed integer linear programming model. This model considers the bi-directional power flows in the ADN, multi-stage planning logical constraints, and the replacement/addition of feeders/ESS. In addition, the cost of construction, operation, power trading, ESS lifetime loss, and lost load are included in the objective function. Moreover, Benders decomposition is utilized to improve the convergence speed of the model, which becomes difficult to solve due to the complex battery lifetime evaluation. Case studies based on a fictitious 18-node ADN demonstrate the effectiveness of the proposed method, which produces economic planning result considering the battery lifetime for an ADN with ESS.

Zhang Yuying

Papers

Planning-Operation Co-Optimization Model of Active Distribution Network With Energy Storage Considering the Lifetime of Batteries