Robust Control for Optimized Islanded and Grid-Connected Operation of Solar/Wind/Battery Hybrid Energy

Abstract

Wind and solar energy systems are among the most promising renewable energy technologies for electric power generations. Hybrid renewable energy systems (HRES) enable the incorporation of more than one renewable technology, allowing increased reliability and efficiency. Nevertheless, the introduction of variable generation sources in concurrence with the existing system load demand necessitates maintaining the power balance between the components of the HRES. Additionally, the efficiency of the hybrid power supply system is drastically affected by the number of converters interfacing its components. Therefore, to improve the performance of the HRES, this paper proposes a robust sliding mode control strategy for both standalone and grid-connected operation. The control strategy achieves maximum power point tracking for both the renewable energy sources and stabilizes the DC-bus and load voltages irrespective of the disturbances, change in load demand, variations of irradiance level, temperature, and wind speed ensuring an efficient energy management. Furthermore, the solar PV system is directly linked to the DC-bus obviating the need for redundant interfacing boost converters with decreased costs and reduced system losses. Lyapunov candidate function is used to prove the asymptotic stability and the convergence of the entire system. The robustness of the proposed control strategy is tested and validated under various conditions of HRES, demonstrating its efficacy and performance under various conditions of the HRES.

Mitigating Uncertainty Problems of Renewable Energy Resources Through Efficient Integration of Hybrid Solar PV/Wind Systems Into Power Networks

“…The wind/PV microgrid is an example of this kind of microgrid [42]. Two or more power generation methods are incorporated into HRES [43,44]. By properly utilizing the available natural resources, such HRES reduces the intermittent nature of individual RESs, improving overall operational efficiency (OOE) [45] and optimizing capital investments (OCI) [46].…”

“…Electric energy is produced by the generator using the extracted energy. The power coefficient pi C affects the wind turbines aerodynamic power[43,104,105].…”

Data-Driven Optimal Battery Storage Sizing for Grid-Connected Hybrid Distributed Generations Considering Solar and Wind Uncertainty

“…For better approximations and more reliable results, more historical data is needed to access the true distribution of the data, i.e., the normal distribution, mean, variance, standard deviation, and probabilities of specific events that behave periodically [7]. Similarly, high penetration of renewable sources means more and more historical data is needed to deal with the uncertain profile of those renewable sources [8][9][10]. Handling this data distribution is performed in [11] using a method proposed to adjust the parameter of the distribution in distributionally robust optimization.…”

“…The Equation ( 6) represents the power balance between the generation and demand, as p t k,t is the power generated by the k th DG unit at time t, p s l,t is the power generated by the l th solar PV unit at time t, p w j,t is the power generated by the j th wind-farm unit at time t, p d b,t is battery discharge by the battery storage connected to b th bus at time t, p c b,t is battery charge (acting as a load) by the battery storage connected to b th bus at time t and D b,t is the load demand at b th bus in time t, p cur b,t is the power surplus/loss at bus b in time t, if it is negative then there is power surplus at bus b in time t and if positive then it is load shedding at bus b in time t. The dc power flow model confines the power transmission below the line capacity LC g in inequalities (7) and ( 8) [47]. The maximum and minimum capacity of the DG units is defined in (9). In (10), solar PV generation is less than the maximum capacity of the solar PV unit l at time t and (11) defines that the wind-farm generation is less than the maximum capacity of the wind-farm unit j at time t. The ramp-up RU k and ramp-down RD k limits of DG units are enforced in (12) and (13).…”

Distributed voltage unbalance mitigation in islanded microgrid using moth flame optimization and firebug swarm optimization

“…A cost-effective way to reduce voltage unbalance is to install a parallel compensator, but doing so obviously raises investment costs as well. Grid integrated functioning of multiple sources was demonstrated by Maaruf et al [20]. Higher amount of power is achieved for both RES using the control scheme, and the load and DC-bus voltages are stabilized regardless of disturbances, changes in requirement, differences in radiance, wind velocity, resulting in effective organization.…”