Demand profile analysis is very crucial in the evolution of intelligent demand side management and power dispatch planning practices. This is so, because not only a prior knowledge of the demand fluctuation is necessary, but also the sector of the demand causing the fluctuations. In this paper, a detailed demand profile analysis of Nigeria's annual electricity demand is presented using systems optimization approach of MESSAGE model. Since the MESSAGE load evaluation screen does not incorporate a module for the evaluation of peak to offpeak demand ratio, an extension of the model is proposed for this evaluation. The simulation results for the services, industrial and residential demand subsectors as well as the total demand were within expected pattern, with the services sector presenting the highest peak to off-peak demand ratio of 2.137. I. INTRODUCTIONDemand profile analysis normally involve load forecast and load characterization. Recently, systems engineering models such as ENPEP, MAED have been widely deployed for load analysis [1,2]. While these models are quite exquisite for demand projections, their extensive data requirement for a detailed demand characterization makes their deployment a daunting task. Here, an attempt has been made to carry out the demand characterization using MESSAGE systems optimization model, rather than the traditional MAED-el module being used by the Nigerian country study team. MAED-el converts the given demand to load curves using appropriate load modulation coefficients, which requires extensive data definition and computations [3,4].MESSAGE stands for a Model for Energy Supply Strategy, Alternatives and their General Environmental Impact. It is a dynamic linear programming model designed and developed at IIASA for the optimization of energy supply and utilization. The data structures have been designed to a databank based on keywords, which simplifies automated data processing. In the mathematical formulation, the multiobjective options has been employed, while the reference point optimization method, adapted to dynamic modeling into a reference trajectory optimization method, is implemented [5]. In demand characterization it offers the advantage of utilizing ratios of demand curve data, rather than absolute values. However, the results are based on real time slices as against load duration curve used in MAED, MARKAL and WASP [1,3].
Voltage instability has been identified as the most critical factor responsible for poor power quality in electric power systems. The high losses experienced at the distribution level of these systems has become a major concern to power system operators, with about 10-13% of the total generation being dissipated as heat. Maintaining the system voltage within an acceptable limit will go a long way in reducing these losses and enhancing the overall system operational capability. The objective of this paper is to improve the voltage magnitude and reduce overall power losses in an existing 50-bus radial distribution feeder via the allocation of Distribution Static Compensator (DSTATCOM) using an established bacterial foraging algorithm (BFA) based model. The application of the swarm-based meta-heuristic model is extended to a three-quarter (75%) loading condition of the standard IEEE 33-bus test network and then, employed on the 50-bus Canteen feeder for both normal (100%) and three-quarter (75%) loading conditions. Comprehensive analysis was performed for both networks and the results were compared with their respective base-case scenarios. The final results of the evaluation obtained through simulation showed appreciable reduction in power losses and improvement in overall voltage profile with the allocation of DSTATCOM in both networks using the BFA based model. Voltage improvement in the region of 20.04% and active power loss reduction of 24.86% were recorded for three-quarter loading of the IEEE test network. For the 50-bus Canteen feeder, an overall voltage profile improvement of 6.13% and active power loss reduction of 22.84% were achieved for normal loading condition, whereas 2.99% and 19.71% improvement in total voltage profile and active power loss respectively were attained under three-quarter loading condition.
This paper proposes the implementation of a Bacterial Foraging Algorithm (BFA) based approach for optimal positioning and sizing of Universal Power Quality Conditioner (UPQC) in a radial distribution network. The objective here is to demonstrate the strength and capability of the approach in the placement of UPQC in not just standard test networks, but also in practical power distribution networks. A simple and direct power flow computation is performed to determine the network base-case scenario. Based on the power flow outcome, the network bus voltage deviation VD is formulated and combined with the total active power loss PLoaa(Total) to form a multi-objective function needed by the BFA in order to improve voltage stability and minimize losses, while at the same time maintaining the network constraints. The BFA approach is implemented on a practical 50-bus Canteen Feeder for steady-state normal loading condition. The performance of the technique on the standard IEEE 33-bus test network as established in an earlier literature is once again reported in this paper, after which the results obtained from the practical distribution feeder analysis is presented. Simulation outcomes from MATLAB R2017a virtual environment showed that the installation of UPQC in the practical distribution feeder using the BFA method has improved the overall feeder voltage profile and reduce the active power loss by 9.78 and 26.21% respectively as compared to the network base-case scenario.
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