Binary Particle Swarm Optimization for Scheduling MG Integrated Virtual Power Plant Toward Energy Saving

Abstract

This paper introduces a novel optimal schedule controller to manage renewable energy resources (RESs) in virtual power plant (VPP) using binary particle swarm optimization (BPSO) algorithm. It is crucial to minimize the costs giving priority for sustainable resources use instead of purchasing from the national grid. The effectiveness of the proposed approach is examined by the IEEE 14 bus system containing microgrids (MGs) integrated with RESs in the form of VPP. Real load demand recorded is used to model and simulate the test case studies of the system for 24 h in Perlis, Malaysia. Moreover, weather data collected from the Malaysian Meteorological Department such as wind, solar, fuel, and battery status data are used in the BPSO to find the best ON and OFF schedules. The results found that the developed BPSO algorithm is robust in reducing energy consumption and emissions of the VPP. This study contributes to the development of an optimization algorithm for an optimal scheduling controller of MG integrated VPP in order to reduce carbon emissions and manage sustainable energy. Finally, a comparative analysis of the optimal algorithms over conventional justifies the use of RESs integration and validates the developed BPSO for sustainable energy management and emissions reduction. INDEX TERMS Virtual power plant, microgrid, energy management, carbon reduction, scheduling controller, optimization.

A BPSO-Based Method for Optimal Voltage Sag Monitor Placement Considering Uncertainties of Transition Resistance

“…Well-known meta-heuristics algorithms include Simulated Annealing, Genetic Algorithm, Ant Colony Optimization, Particle Swarm Optimization (PSO) and etc., which have all been exploited to solve the placement problem [11], [25]- [27]. Of all the population-based meta-heuristics, PSO is easy to implement, which has less dependent empirical parameters and fast convergence rate [27], [28]. But PSO is not suitable for solving discrete optimal problems.…”

“…The Sag Global Observability Index (SGOI) defined in this paper refers to the probability of the event that voltage sag(s) can be recorded by monitoring system, under the condition that a fault of a ''random'' type at a ''random'' fault position has caused voltage sag(s), that is, the conditional probability of {Y 1 = 1} given by {Y 1 ≤ 2}. As can be seen, compared with the expression of SLOI in (28), SGOI cancels the limitation of the certainties of fault position and fault type, which indicates that not only the uncertainties of transition resistance but also that of fault positions and fault types are considered. According to the full probability equation and condition probability equation, SGOI can be expressed as (29) where λ j equal to P(Y 2 = j) represents the probability of a fault event at position j, and ω t equal to P(Y 3 = t) represents the probability of a type-t fault event.…”

“…Compared with equations (28) and (29), it can be seen that SGOI can be regarded as the average value of all SLOIs corresponding to each certain fault position and fault type with λ j and ω t as weights. Besides, it is worthwhile to note that if n f is used to represent the total number of faults randomly occurring in the entire network over a period of time, SGOI can be also expressed as n f…”

“…The local observability index SLOI t j of the monitoring system (buses 4, 5, and 26) related to each fault position is calculated for each fault type individually, according to (28) and Table 6. The value-frequency distribution histogram of SLOIs for each fault type is shown in Fig.…”

Backtracking Search Algorithm Based Fuzzy Charging-Discharging Controller for Battery Storage System in Microgrid Applications

“…Some other optimization algorithm such as gravitational search algorithm (GSA), firefly algorithm (FA), NARX etc. are available to solve the aforementioned issues [23]. The drawbacks of these techniques are easy trapping in local.…”