Optimal reactive power dispatch and distributed generation placement based on a hybrid co‐evolution algorithm and bi‐level programming

Abstract

To improve the economy and stability of distribution networks with a high penetration of distributed generation (DG), active management strategies should be considered during the planning stages and operating stage. This article presents a bi-level model of DG placement and an optimal reactive power dispatch, where the total investment and operation cost are minimized through an optimal allocation of the DG at the upper planning level, and the active power loss and voltage deviation are optimized based on the optimal reactive power output of the DG, static var compensator (SVC), and shunt capacitor bank (SCB). In this study, a period division strategy based on the Grey relation analysis was determined to reduce the switching time of the SCB. The co-evolution algorithm based on the List of Symbols and Abbreviations: α t c and β t c , shape parameters of the distribution functions; δ s , demand curves of load type s; ε, smallest constant used to avoid a zero-division error; ε t,t+1 , Grey absolute correlation degree; Γ, gamma function; λ 1 , penalty function of the node voltage crossing line; ρ, air density; ρ t,t+1 , Grey comprehensive correlation degree; θ nq , voltage phase angle difference of the branch between nodes n and q; θ si , ratio of load type s in the total load of node i; Β, step size control parameter; B nq , susceptance difference of the branch between nodes n and q; C c , carbon dioxide emission cost; C CO2 , carbon emission penalty cost; C q , load cost; C inv , unit investment cost of DG; C inv+ope , investment and operation cost of DG; C ope , unit operating cost of DG; cosθ, power factor; C p , active power loss cost; C p , wind energy utilization coefficient; c t , the Weibull scale parameter; C uep , unit electricity price; D 0 , starting point of zero operator; D 1 , initial valued operator; E f , a fixed-point; E m , greenhouse gas emission intensity; E max , maximum value of solar radiation; f g , current global best fitness values; f i , fitness values of the present sparrow; f w , current global worst fitness values; G nq , conductance difference of the branch between nodes n and q; iter max , maximum number of iterations; J, total number of branches; k c

Distributed Power Grid Connected Panoramic Perception Technology Based on Digital Twin Model

“…In formula (8), y is used to describe the device state signal after denoising; i represents the number of basic mode components; i e is used to describe the basic mode components after threshold processing; n represents the length of the basic mode component. By combining signal reconstruction, a noise-free operating state signal of power equipment can be obtained [20][21]. On this basis, it adopts DT technology to map the collected state signals into virtual and real ones, thereby constructing a dual-star model for the operation status of power equipment.…”

Using the Whale Optimization Algorithm to Solve the Optimal Reactive Power Dispatch Problem

“…Khan et al introduced a modified salp swarm technique to address the ORPD; the technique boosted population variety and broadened the search field to produce high computational accuracy [23]. Chen et al utilized a combination of the co-evolution approach and bi-level planning to resolve the ORPD; this technique exhibited adequate reliability to decrease the active power loss [24]. Hosseini-Hemati et al indicated a superior grey wolf optimization to resolve the ORPD; this technique conveyed excellent dependability to arrive at the finest global solution [25].…”