The scope of this study is the optimal siting and sizing of distributedgeneration within a power distribution network considering uncertainties. A probabilistic power flow (PPF)-embedded genetic algorithm (GA)-based ...
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The scope of this study is the optimal siting and sizing of distributedgeneration within a power distribution network considering uncertainties. A probabilistic power flow (PPF)-embedded genetic algorithm (GA)-based approach is proposed in order to solve the optimisation problem that is modelled mathematically under a chance constrained programming framework. Point estimate method (PEM) is proposed for the solution of the involved PPF problem. The uncertainties considered include: (i) the future load growth in the power distribution system, (ii) the wind generation, (iii) the output power of photovoltaics, (iv) the fuel costs and (v) the electricity prices. Based on some candidate schemes of different distributedgeneration types and sizes, placed on specific candidate buses of the network, GA is applied in order to find the optimal plan. The proposed GA with embedded PEM (GA-PEM) is applied on the IEEE 33-bus network by considering several scenarios and is compared with the method of GA with embedded Monte Carlo simulation (GA-MCS). The main conclusions of this comparison are: (i) the proposed GA-PEM is seven times faster than GA-MCS, and (ii) both methods provide almost identical results.
This paper proposes an adaptive weight particle swarm optimization (APSO) for solving optimaldistributedgeneration (DG) placement. APSO has ability to control velocity of particles. The objective is to minimize the ...
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ISBN:
(纸本)9788995003893
This paper proposes an adaptive weight particle swarm optimization (APSO) for solving optimaldistributedgeneration (DG) placement. APSO has ability to control velocity of particles. The objective is to minimize the real power loss within acceptable voltage limits. Four types of DG are considered including DG supplying real power only, DG supplying reactive power only, DG supplying real power and consume reactive power, DG supplying real power and reactive power, representing photovoltaic, synchronous condenser, wind turbines, and hydro power, respectively. The test systems include 33-bus and 69-bus radial distribution systems. With a given number of DGs in each type, APSO could find the optimal sizes and locations of multi-DG which result in less total power system loss than basic particle swarm optimization (BPSO) and repetitive load flow. Moreover, if the number of DG increases from one to three, the total power loss will decrease for all types.
This paper examines the impact of different penetration schemes to the optimal distributed generation placement problem for loss minimisation. The four variables of the problem are presented and a concept based on deg...
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This paper examines the impact of different penetration schemes to the optimal distributed generation placement problem for loss minimisation. The four variables of the problem are presented and a concept based on degrees of freedom (DoF), representing the number of the variables that undergo any kind of limitation during the solution process, is introduced. Four commonly utilised penetration schemes subject to various limitations are examined and compared with a fifth penetration scheme, which is unconstrained and is proposed as the optimal one. All schemes are implemented under a local-particle swarm optimisation-variant algorithm and applied on the IEEE 33 and IEEE 118 bus systems. The results indicate that the proposed penetration scheme with four DoF provides the optimal solution both in terms of loss minimisation and voltage profile improvement.
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