Optimization of Photovoltaic and Wind Generation Systems for Autonomous Microgrids With PEV-Parking Lots

Abstract

Lately, the integration of renewable energy sources (e.g. photovoltaic and wind generation systems) has been raised into microgrids interconnected with plug-in electric vehicles (PEV). Such intermittent generation and charging/discharging PEV profiles are challenging to ensure the secure and optimal operation of microgrids. In this paper, an optimization approach is proposed to determine the optimal locations and sizes of photovoltaic and wind generation systems in microgrids with PEV-parking lots. The developed approach addresses 1) the uncertainty of generation profiles of photovoltaic and wind generation systems and loads, 2) the DSTATCOM feature of photovoltaic and wind generation systems, and 3) microgrid constraints. The feasible PEV conditions are also considered, i.e. initial and preset conditions of their state of charge (SOC), arriving and departing times, and various controlled/uncontrolled charging schemes. To solve the planning model, we have developed a bi-level metaheuristic-based approach. The upper-level and lower-level optimization tasks are to optimize the decision variables of renewable energy sources and PEV, respectively. Both energy losses through the lines and energy from the main grid are considered as sub-objectives to be minimized. Various simulations and study cases are performed to assess the effectiveness of the proposed approach. The outcomes show the efficacy of the proposed approach to simultaneously plan renewable energy sources and manage PEV to form an autonomous microgrid.

Optimal day‐ahead scheduling in micro‐grid with renewable based DGs and smart charging station of EVs using an enhanced manta‐ray foraging optimisation

“…The main characteristic of the EV is its state of charge (SOC), where the SOC of the EV rises or declines based on G2V and V2G functionalities. Therefore, the SOC of the EV can be updated at each time segment as follows [9]:…”

“…The main characteristic of the EV is its state of charge (SOC), where the SOC of the EV rises or declines based on G2V and V2G functionalities. Therefore, the SOC of the EV can be updated at each time segment as follows [9]: $$\begin{eqnarray} {\rm{SOC}}_i^t &=& {\rm{\;SOC}}_i^{t - 1} + {\eta _{{\rm{CH}},{\rm{i}}}}P_{{\rm{CH}},{\rm{i}}}^t\Delta t\delta \nonumber\\ &&-\, \frac{{\Delta t\gamma P_{{\rm{DC}},i}^t}}{{{\eta _{{\rm{DC}},i}}}},{\rm{\;}}i \in {\rm{\;}}{N_{{\rm{E}},}}{\rm{\;}}t{\rm{\;}} \in T \end{eqnarray}$$where $$P_{{\rm{CH}},i}^t$$ and $$P_{{\rm{DC}},i}^t$$represent the charging and discharging powers of i th EV at time t , respectively; δ and γ belong to {0,1}; it is worth to mention that the EV cannot charge and discharge simultaneously, hence, δ. γ = 0; η CH,I is the charging efficiency, while η DC,i denotes the efficiency of the discharging of the i th EV. $${N_{{\rm{E}}\;}}$$and T are the sets of the EVs and time segments, respectively.…”

Comprehensive Review on Renewable Energy Sources in Egypt—Current Status, Grid Codes and Future Vision

“…The South African National Energy Regulator has affirmed the "GC connection for renewable energy power plants associated to the Transmission or Distribution electrical Systems" [20]. On the other hand, the growth of wind power generation increases, its penetration and its commitment to the overall power supply increases [21], [22]. The installed capacity of wind power by 2019 was 650.8 GW globally, including 59.7 GW added in the same year [23], [24].…”

A Machine Learning-Based Model for Stability Prediction of Decentralized Power Grid Linked with Renewable Energy Resources

“…ML techniques have widely used in the domain of education [13,14], software measurement [15][16][17], decision support system [18,19], social sciences [20,21], healthcare [22][23][24], and disease diagnosis [9,25]. Numerous computational methods were used in the renewable energy domain [26][27][28][29][30]. These include the prediction of the decentralized grid using a decision tree (DT) [11] and Hybrid Kernel Ridge Regression-Extreme Gradient Boosting (KRR-XGBoost) for distributed power systems [31].…”