Novel Design of Artificial Ecosystem Optimizer for Large-scale Optimal Reactive Power Dispatch Problem with application to Algerian Electricity Grid (original) (raw)

Optimization of reactive power dispatch (ORPD) problem is a key factor for stable and secure operation of the electric power systems. In this paper, a newly explored nature-inspired optimization through artificial ecosystem optimization (AEO) algorithm is proposed to cope with ORPD problem in large-scale and practical power systems. ORPD is a well-known highly complex combinatorial optimization task with nonlinear characteristics and its complexity increases as number of decision variables increase, which makes it hard to be solved using conventional optimization techniques. However, it can be efficiently resolved by using nature-inspired optimization algorithms. AEO algorithm is a recently invented optimizer inspired by the energy flocking behavior in a natural ecosystem including in non-living elements such as sunlight, water and air. The main merit of this optimizer its high flexibility that leading to achieve accurate balance between exploration and exploitation abilities. Another attractive property of AEO is that it does not have specific control parameters to be adjusted. In this work, 3-objectives version of ORPD problem are considered involving active power losses minimization and voltage deviation (VD), and Voltage stability index (VSI). The proposed optimizer was examined on medium-and large-scale IEEE test systems, including 30-bus, 118-bus, 300-bus and Algerian electricity grid DZA 114-bus (220/60 kV). The results of AEO algorithm are compared with well-known existing optimization techniques and results of comparison shown that the proposed algorithm performs better than other algorithms for all examined power systems. Consequently, we confirm the effectiveness of the introducing AEO algorithm to relieve the over losses problem, enhance power system performance, and meet solutions feasibility. One-way analysis of variance (ANOVA) has 2 been employed to evaluate the performance and consistency of the proposed AEO algorithm in solving ORPD problem. Keywords: Artificial ecosystem optimization algorithm, optimal reactive power dispatch, real power loss, voltage deviation, voltage stability index, Large-scale test system. List of Symbols / loss P VD The total power losses/voltage deviation VSI Voltage stability index ij  The voltage angle difference between i and bus j / ji Gi V  The phase angle of term ji F / voltage magnitude for generator at bus i , PV PQ NN The number of PV and PQ buses respectively k G Conductance of k th branch connected between bus i and j , ,/ PQ i j L N V V V Voltage magnitude of bus i and j/Voltage magnitude for load bus i / ij i YS The elements of bus admittance matrix/apparent power flow of branch i ,, / D i D i PQ The active/reactive, load consumption at bus i / Gi Gi PQ The active/reactive power generation at bus i ,, , PQ PQ L N L N PQ The active and reactive power at each load bus max min , ii VV The maximum and minimum bus voltage magnitude at bus i min max , Gi Gi QQ The minimum and maximum value of power generation at bus i max min / kk TT The maximum/minimum tap ratio of k th tap changing transformer min max , Ci Ci QQ The minimum and maximum VAR injection limits of shunt capacitor banks max i S The maximum apparent power flow limit of branch i / NB NTL The number of buses in the test system/number of transmission lines / NLB NG The number of load buses/The number of generators buses / NT NC The number of the transformer taps /number of shunt capacitor banks ,, V Q l    The penalty factors lim i X The limit value of the dependent variables lim lim lim , , and i i i V Q S max min / ii XX The maximum/minimum limit of state variables