Mohammad Javad Sanjari

Bio: Mohammad Javad Sanjari is an academic researcher from Griffith University. The author has contributed to research in topics: Electric power system & Microgrid. The author has an hindex of 20, co-authored 73 publications receiving 1155 citations. Previous affiliations of Mohammad Javad Sanjari include Nanyang Technological University & University of Auckland.

An Optimal Dispatch Algorithm for Managing Residential Distributed Energy Resources

Abstract: This paper deals with a residential hybrid thermal/ electrical grid-connected home energy system, including a fuel-cell with combined heat and power (CHP) and a battery as energy storage system (ESS). A day-ahead scheduling algorithm for managing different resources is developed to generate an efficient look-up table that determines an optimal operation schedule for the distributed energy resources at each time interval, so that the operation cost of a smart house is minimized. The impact of the electricity tariff and the efficiency of the energy storage system are considered when optimizing the operation schedules.

Multiple distributed generation units allocation in distribution network for loss reduction based on a combination of analytical and genetic algorithm methods

Abstract: Many methods have been proposed to determine the optimal location and capacities of distributed generation (DG) units to reach the lowest value for system losses. In this study, the combination of analytical and genetic algorithm methods is used for optimal allocation of multiple DGs in a distribution network to minimise the system losses. This combination guarantees the convergence accuracy and speed in multiple DG units allocation. In this study, the DGs active power, power factor, and location are simultaneously considered during distribution network losses minimisation. The utility will dictate only the maximum DG power generation if the DG is installed by DG owner. However, both of the size and the location of DG will be determined by the utility if the DG is installed by it. The proposed method is applied to 33-bus and 69-bus test distribution systems. Simulation results show that the proposed method results in lower losses compared with the other methods.

Probabilistic Forecast of PV Power Generation Based on Higher Order Markov Chain

Abstract: This paper presents a method to forecast the probability distribution function (PDF) of the generated power of PV systems based on the higher order Markov chain (HMC). Since the output power of the PV system is highly influenced by ambient temperature and solar irradiance, they are used as important features to classify different operating conditions of the PV system. The classification procedure is carried out by applying the pattern discovery method on the historical data of the mentioned variables. An HMC is developed based on the categorized historical data of PV power in each operating point. The 15-min ahead PDF of the PV output power is forecasted through the Gaussian mixture method (GMM) by combining several distribution functions and by using the coefficients defined based on parameters of the HMC-based model. In order to verify the proposed method, the genetic algorithm is applied to minimize a well-defined objective function to achieve the optimal GMM coefficients. Numerical tests using real data demonstrate that the forecast results follow the real probability distribution of the PV power well under different weather conditions.

Optimal placement and sizing of battery energy storage system for losses reduction using whale optimization algorithm

Abstract: This paper proposes an approach for optimal placement and sizing of battery energy storage system (BESS) to reduce the power losses in the distribution grid. A meta-heuristic optimization algorithm known as Whale Optimization Algorithm (WOA) is introduced to perform the optimization. In this paper, two different approaches are presented to achieve the optimal allocation of the BESS. The first approach is to obtain the optimal location and sizing in two steps while the second approach optimizes both location and sizing simultaneously. The performance of the proposed technique has been validated by comparing with two other algorithms namely firefly algorithm and particle swarm optimization. The results show that WOA has outstanding performance in attaining the optimal location and sizing of BESS in the distribution network for power losses reduction.

Optimal Overcurrent Relay Coordination in Interconnected Networks by Using Fuzzy-Based GA Method

Abstract: A new objective function (OF) has been proposed for mathematical formulation of directional overcurrent (OC) relay coordination in interconnected networks. The fuzzy-based genetic algorithm (GA) is applied to optimize the proposed OF for optimal coordination of OC relays. The defined fuzzy rules update the weighting factors of OF during the simulation. The miscoordination problem of OC relays is solved while decreasing the operating time of the relays. The proposed method is implemented in three different networks and the simulation results have been compared with previous studies in order to illustrate the accuracy and efficiency of the proposed method to coordinate the directional OC relays with both discrete and continuous time setting multipliers. The results have also been compared with the results of other optimization methods. Considering the coefficients of OC characteristic curves as the OF variables ensures that the proposed method has no limitation for the types of characteristic curves which will be utilized. Presenting the new term in OF, the performance of the proposed method has not been affected by the size of the networks.

Metaheuristic research: a comprehensive survey

Abstract: Because of successful implementations and high intensity, metaheuristic research has been extensively reported in literature, which covers algorithms, applications, comparisons, and analysis. Though, little has been evidenced on insightful analysis of metaheuristic performance issues, and it is still a “black box” that why certain metaheuristics perform better on specific optimization problems and not as good on others. The performance related analyses performed on algorithms are mostly quantitative via performance validation metrics like mean error, standard deviation, and co-relations have been used. Moreover, the performance tests are often performed on specific benchmark functions—few studies are those which involve real data from scientific or engineering optimization problems. In order to draw a comprehensive picture of metaheuristic research, this paper performs a survey of metaheuristic research in literature which consists of 1222 publications from year 1983 to 2016 (33 years). Based on the collected evidence, this paper addresses four dimensions of metaheuristic research: introduction of new algorithms, modifications and hybrids, comparisons and analysis, and research gaps and future directions. The objective is to highlight potential open questions and critical issues raised in literature. The work provides guidance for future research to be conducted more meaningfully that can serve for the good of this area of research.