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ABSTRACT
Generation Expansion Planning (GEP) is a challenge in electrical power systems because the size of the generating unit is large in scale, non-linear, long-term, and discontinuous. The existing GEP models use an array of methodological techniques. These models, however, primarily focus on the type of generation unit to be installed and when to be installed so as to reduce pollution and overall costs. They do not focus on the optimal location for installation. This research work proposes an IEEE bus-30 and IEEE bus-14 merged bus systems to fulfil the electrical load demand during the 5th and 10th years of planning. In stage 1, the GEP problem is resolved using Black Widow Optimization (BWO). In stage 2, the optimal location for generating units in the proposed bus system is resolved using a Chimp Optimization Algorithm (ChoA). The best location reduces the objective function (real power loss) and satisfies the voltage and power flow limits of the electrical power system. The performance of the proposed model is compared to that of existing optimization models. The results demonstrate that the proposed work reduces costs and provides flexible operations with reduced real power loss.
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Disclosure statement
No potential conflict of interest was reported by the author(s).
Nomenclature
| CoF | = | total cost, $ |
| Ct,j | = | maintenance cost of the unit type j, $ |
| C(Pt) | = | capital cost of the newly added candidate unit in year t, $ |
| ecj | = | emission coefficient of power plant type j |
| FMj | = | fuel mix ratio of the power plant type j |
| F(Tt) | = | total fuel cost including existing and candidate unit in year t, $ |
| fd/fj | = | flexibility coefficient of load/Power plant type |
| ft,j | = | fuel cost of the unit type j in year t. |
| ht,j | = | annual operating hours of unit type j in tth year |
| It,j | = | investment cost of the candidate unit type j in year t, $ |
| i | = | number of buses |
| i, j | = | from bus number, to bus number |
| j | = | candidate plant type |
| M(Pt) | = | maintenance and operational cost of existing and candidate unit in year t, $ |
| NPQ | = | number of load buses |
| Pt | = | vector of N dimension of newly introduced unit in year t |
| Ploss | = | real power (P) loss in the line |
| Qloss | = | reactive power (Q) loss in the line |
| q | = | 8.5% is fixed for Discount rate |
| Rmin/Rmax | = | maximum reserve margin/minimum reserve margin |
| Rij | = | line resistance |
| S(Pt) | = | subsidy cost of the introduced candidate unit in year t, $ |
| t | = | planning year |
| Tt | = | overall capacity of existing units at year t |
| Tc | = | tie line power value |
| Umax | = | upper plant construction limit of tth year. |
| Xij | = | line reactance |
| Yj | = | expected life time of the unit in year |
| ΔPt,j | = | number of newly added candidate unit of type j in year t |
| ɛj | = | power plant efficiency of the unit type j |
| ɛ | = | reliability criteria LOLP |
| = | emission reduction percentage |