https://www.econstor.eu/bitstream/10419/244023/1/1693711680.pdf

Comprehensive overview of optimizing PV-DG allocation in power system and solar energy resource potential assessments

Dynamic distribution network reconfiguration (DNR) algorithms perform hourly status changes of remotely controllable switches to improve distribution system performance The problem is typically solved by physical model-based control algorithms, which not only rely on accurate network parameters but also lack scalability To address these limitations, this paper develops a data-driven batch-constrained reinforcement learning (RL) algorithm for the dynamic DNR problem The proposed RL algorithm learns the network reconfiguration control policy from a finite historical operational dataset without interacting with the distribution network The numerical study results on three distribution networks show that the proposed algorithm not only outperforms state-of-the-art RL algorithms but also improves the behavior control policy, which generated the historical operational data The proposed algorithm is also very scalable and can find a desirable network reconfiguration solution in real-time

Batch-Constrained Reinforcement Learning for Dynamic Distribution Network Reconfiguration

This paper presents a methodology for sustainable operation of the distribution network with optimal placement and sizing of dispatchable DGs and shunt capacitors. The objective of this work is aimed at reducing the feeder current drawn from the grid and minimizing the annual energy losses along with real power loss reduction and improvement of voltage profile. An average hourly variation of load demand profile is considered. Sensitivity analysis based on voltage stability index is used to obtain the location of dispatchable DGs and shunt capacitors. The size of the dispatchable DGs is optimized using GA optimization tool. Annual cost savings is computed to show the economic benefits of the operation. Effectiveness of the proposed methodology is demonstrated on a 33 node distribution network. The results demonstrate the effectiveness of the proposed methodology over other existing methods available in the literature.

Operation of distribution network with optimal placement and sizing of dispatchable DGs and shunt capacitors

Contingency screening for transient stability of large-scale, strongly nonlinear, interconnected power systems is one of the most computationally challenging parts of Dynamic Security Assessment and requires huge resources to perform time-domain simulations-based assessment. To reduce computational cost of time-domain simulations, direct energy methods have been extensively developed. However, these methods, as well as other existing methods, still rely on time-consuming numerical integration of the fault-on dynamics. This task is computationally hard, since possibly thousands of contingencies need to be scanned and thousands of accompanied fault-on dynamics simulations need to be performed and stored on a regular basis. In this paper, we introduce a novel framework to eliminate the need for fault-on dynamics simulations in contingency screening. This simulation-free framework is based on bounding the fault-on dynamics and extending the recently introduced Lyapunov Function Family approach for transient stability analysis of structure-preserving model. In turn, a lower bound of the critical clearing time is obtained by solving convex optimization problems without relying on any time-domain simulations. A comprehensive analysis is carried out to validate this novel technique on a number of IEEE test cases.

Toward Simulation-Free Estimation of Critical Clearing Time

Optimal integration of distributed generation resources in active distribution networks for techno-economic benefits

Distribution networks reconfiguration is among the main tasks in distribution management system. The networks reconfiguration is determined by opening and closing switches devices. This reconfiguration can be static or dynamic. In static reconfiguration, the loads are assumed to be constant, while in dynamic reconfiguration loads are assumed to vary according to time. In this work, a dynamic reconfiguration considering the load variation was proposed. A minimum spanning tree (MST) based on Kruskal's algorithm has been applied to find the optimal reconfiguration under multi times, with minimizing the total power loss as objective function. This algorithm was tested on IEEE (33-nodes, 84-nodes) and validated on Algerian distribution network (116 node), using MATLAB Software.

Optimum dynamic distribution network reconfiguration using minimum spanning tree algorithm

The integration of shunts capacitor in the distribution system can have significant effects on the behavior of the system. An improper insertion of this equipment can cause negative impacts, the solution of these impacts requires an optimal location of shunt capacitors with adequate power injected. The main objective of this study is to determine the optimal placement and size so that the maximum annual benefit due to peak power and reduce energy loss, compared to the cost of installation be minimal. So it is necessary to use optimization techniques that we used the Particle Swarm Optimization (PSO) algorithm to solve this problem. The technique has been validated and tested on different types of distribution network including three IEEE standard networks (10 bus, 15 bus and 34 bus) and applied on a real network of Algerian distribution system.

Optimal of shunt capacitor placement and size in Algerian distribution network using particle swarm optimization

The present paper describes a multi-objective optimization approach to determine the optimal placement and sizing of Distributed Generation (DG) units in radial distribution systems. The main objectives are to minimize the active power loss and to enhance the voltage stability. Non-dominated Sorting Genetic Algorithms II (NSGA-II) is used to obtain the set of Pareto optimal solutions, which form a numerous set of non-dominated solutions and will be employed by the Decision Maker (power system operator) to select the best compromise solution. A detailed performance analysis is applied on 12, 33, 69 and 85 bus systems to illustrate the effectiveness of the proposed method.

Optimal DG Placement and Sizing in Radial Distribution Systems Using NSGA-II for Power Loss Minimization and Voltage Stability Enhancement

This paper proposes a technique for shedding against voltage collapse (Under Voltage Load Shedding or UVLS) which is based on the optimal power flow procedure OPF with transient stability constraints (Transient Stability Constraints Optimal Power Flow TSC-OPF). To solve this problem we used a meta-heuristic approach to learn the method of Genetic Algorithms. The objective of this method is to get after shedding tensions in the necessary limits to ensure that the electrical network will be in operating point away from the point of voltage collapse. The developed algorithm has been tested on the IEEE 30 bus network model.

Genetic Algorithms based Optimal Load Shedding with Transient Stability Constraints

This paper present multi-objective optimization for optimal multi DG location and sizes in radial networks. The objectives fitness considered are minimize active power losses and maximize voltage stability. The hybrid technique based on NSGA-II and Fuzzy logic is used to determine optimal solutions. The proposed technique is tested on diversesizes of IEEE distribution network (10bus, 33bus and 85bus) and validated on Algerian distribution network (116bus).

Mustafa Mosbah

Papers

Optimum dynamic distribution network reconfiguration using minimum spanning tree algorithm

Optimal of shunt capacitor placement and size in Algerian distribution network using particle swarm optimization

Optimal DG Placement and Sizing in Radial Distribution Systems Using NSGA-II for Power Loss Minimization and Voltage Stability Enhancement

Genetic Algorithms based Optimal Load Shedding with Transient Stability Constraints

Multi-objective optimization for optimal multi DG placement and sizes in distribution network based on NSGA-II and fuzzy logic combination