Optimal Dispatch Of Microgrid Research Articles

Energy Optimal Dispatch of Microgrid Based on Energy Storage Level Control

Energy Optimal Dispatch of Microgrid Based on Energy Storage Level Control

Multiobjective optimal dispatch of microgrid based on analytic hierarchy process and quantum particle swarm optimization

Owing to the rapid development of microgrids (MGs) and growing applications of renewable energy resources, multiobjective optimal dispatch of MGs need to be studied in detail. In this study, a multiobjective optimal dispatch model is developed for a standalone MG composed of wind turbines, photovoltaics, diesel engine unit, load, and battery energy storage system. The economic cost, environmental concerns, and power supply consistency are expressed via subobjectives with varying priorities. Then, the analytic hierarchy process algorithm is employed to reasonably specify the weight coefficients of the subobjectives. The quantum particle swarm optimization algorithm is thereafter employed as a solution to achieve optimal dispatch of the MG. Finally, the validity of the proposed model and solution methodology are confirmed by case studies. This study provides reference for mathematical model of multiojective optimization of MG and can be widely used in current research field.

Distributed Control Methods and Impact of Communication Failure in AC Microgrids: A Comparative Review

The objectives of this paper are to review and compare the distributed control methods in AC microgrids and also to identify the impact of communication failure on this type of the controller. The current AC microgrids are distinguished from the traditional power system topologies because of the high penetration of advanced control methods, measurements, sensors, power electronic devices, and communication links. Also, because of the increasing integration of renewable energy sources, control strategy for congestion management, frequency control, and optimal dispatch of microgrids has become more complicated. This paper explains the characteristics and features of distributed control systems and discusses the challenges of these approaches. In addition, a comprehensive review of the advantages and disadvantages of these techniques are explained in detail. On the other hand, the possible challenges, related to communication failure, noise, delay, and packet dropout on the operation of the distributed controller are presented, and several techniques, which reduce the impact of communication failure of the distributed controller, are compared. This comprehensive study on distributed control systems reveals the challenges in and future possible studies on this issue.

Stochastic optimal dispatch of PV/wind/diesel/battery microgrids using state‐space approximate dynamic programming

In the operation of microgrids (MGs), the stochastic production of solar/wind, the discrete variables of photovoltaic (PV) inverter's auxiliary service state and diesel generators’ (DGs’) off–on state generally need to be considered, and a stochastic mixed-integer non-linear non-convex programming (MINNP) model is established for the optimal dispatch of MGs. In this model, the expected value of the sum of DGs’ operation as well as start-up cost, the network-loss cost and the PV inverter's auxiliary service cost, is considered as the objective function. The stochastic MINNP model is transformed into a stochastic mixed-integer second-order cone programming (MISOCP) model to reduce the computational complexity. The state-space approximate dynamic programming algorithm is adopted to solve the stochastic MISOCP model. In the algorithm, based on the approximate value functions of typical states that are computed according to the Markov decision process, solving the optimisation model of multiple periods is executed by solving each period's optimisation model one by one to improve the computational efficiency. Meanwhile, parallel computing is executed to greatly improve the efficiency of the proposed algorithm. Test results on two modified IEEE-33 bus and IEEE-123 bus islanded MGs with PV/wind/diesel/battery demonstrate the correctness and efficiency of the proposed model and algorithm.

CRSO approach for microgrid power dispatching

The optimal dispatch of microgrids is greatly challenged by various uncertainties, introduced by dynamic power fluctuations in loads and variations in renewable energy sources. To depress the over-conservativeness of traditional robust optimisation, this study puts forward a two-stage coupled robust and stochastic optimal (CRSO) dispatching model with mixed integer recourse for the islanded hybrid AC/DC microgrid. The CRSO model comprehensively considers the uncertainties in source–load power and bidirectional converter overhauling, and the stochastic source–load uncertainty sets are constructed by interval partitioning methods. The CRSO model optimises the binary states of the diesel generator and the bidirectional converter in the first stage, and the second stage obtains the flexible operating states and power of each unit. The daily worst-case operation cost is minimised by the nested column-and-constraint generation algorithm. Numerical simulations have validated the rationality and superiority of the developed dispatching model and its solution method.

Optimal Dispatch of Microgrid with Combined Heat and Power System Considering Environmental Cost

With the rapid development of wind power generation and photovoltaic power generation, the phenomenon of wind and solar abandoning becomes more and more serious in the operation of power systems, and the microgrid is a new operating mode of power systems which provides a new consumption mode for wind power generation. With the increasingly close connection among energy resources and people’s increasing awareness of environmental protection, this paper establishes a microgrid optimal scheduling model with a combined heat and power system, in consideration of environmental costs. This model aims at the lowest comprehensive cost, at the same time taking into account the emission reductions of SO2 and NOx, considering the cost of power generated by the micro-generator, environmental cost, the related cost of battery, operation and maintenance cost of wind power, and photovoltaic power generation. The related constraints of thermal balance and power balance are also considered during microgrid system operation. The established model is solved with an improved particle swarm algorithm. At last, taking a microgrid system as an example, the validity and reliability of the proposed model are verified.