Sanjib Ganguly

Multi-objective particle swarm optimization based on fuzzy-Pareto-dominance for possibilistic planning of electrical distribution systems incorporating distributed generation

This paper presents a multi-objective planning approach for electrical distribution systems under uncertainty in load demand incorporating distributed generation (DG). Both radial and meshed systems are considered. The overall influence of load demand uncertainty on planned networks is investigated in detail. Uncertainty in load demand is possibilistically modeled using a fuzzy triangular number. The two objectives in system planning are: (i) minimization of total installation and operational costs, and (ii) minimization of the risk factor. The risk factor is a function of the contingency load-loss index (CLLI), which measures load loss under contingencies, and the degree of network constraints violations. CLLI minimization improves network reliability. The network variables optimized are: (i) the network structure type (radial or meshed), (ii) the number of feeders and their routes, and (iii) the number and location of sectionalizing switches. The optimization tool is a multi-objective particle swarm optimization (MOPSO) variant that uses heuristic selection and assignment of leaders or guides for efficient identification of non-dominated solutions. The optimal number, location, and size of the DG units are determined in another planning stage. Performance comparisons between the planning approaches with possibilistic and deterministic load models highlight the relative merits and demerits. The advantages of networks obtained using the proposed planning approach in the context of DG integration are described. The proposed planning approach is validated using three typical distribution systems.

Multi-objective planning of electrical distribution systems incorporating sectionalizing switches and tie-lines using particle swarm optimization

Abstract A multi-objective planning approach for electrical distribution systems using particle swarm optimization is presented in this paper. In this planning, the number of feeders and their routes, number and locations of sectionalizing switches, and number and locations of tie-lines of a distribution system are optimized. The multiple objectives to determine optimal values for these planning variables are: (i) minimization of total installation and operational cost and (ii) maximization of network reliability. The planning optimization is performed in two steps. In the first step, the distribution network structure, i.e., number of feeders, their routes, and number and locations of sectionalizing switches are determined. In the second step, the optimum number and locations of tie-lines are determined. Both the objectives are minimized simultaneously to obtain a set of non-dominated solutions in the first step of optimization. The solution strategy used for the first step optimization is the Strength Pareto Evolutionary Algorithm-2 (SPEA2) based multi-objective particle swarm optimization (SPEA2–MOPSO). In the second step, the solutions/networks obtained from the previous step are further optimized by placement of tie-lines. SPEA2-based binary MOPSO (SPEA2–BMOPSO) is used in the second step of optimization. The proposed planning algorithm is tested and evaluated on different practical distribution systems.

Distributed Generation Allocation on Radial Distribution Networks Under Uncertainties of Load and Generation Using Genetic Algorithm

This paper presents a distribution generation (DG) allocation strategy for radial distribution networks under uncertainties of load and generation using adaptive genetic algorithm (GA). The uncertainties of load and generation are modeled using fuzzy-based approach. The optimal locations for DG integration and the optimal amount of generation for these locations are determined by minimizing the network power loss and maximum node voltage deviation. Since GA is a metaheuristic algorithm, the results of multiple runs are taken and the statistical variations for locations and generations for DG units are shown. The locations and sizes for DG units obtained with fuzzy-based approach are found to be different than those obtained with deterministic approach. The results obtained with fuzzy-based approach are found to be comparatively efficient in working with future load growth. The proposed approach is demonstrated on the IEEE 33-node test network and a 52-node Indian practical distribution network.

Mono- and multi-objective planning of electrical distribution networks using particle swarm optimization

This paper presents a comprehensive study on mono- and multi-objective approaches for electrical distribution network design using particle swarm optimization (PSO). Specifically, two distribution network design problems, i.e., static and expansion planning, are solved using PSO. The network planning involves optimization of both network topology and branch conductor sizes. Both the planning problems are used to illustrate mono- and multi-objective optimization of distribution networks. Firstly, three PSO variants, i.e., PSO with inertia weight (PSO-IW), PSO with constriction factor (PSO-CF), and comprehensive learning PSO, are evaluated on a mono-objective (minimization of total cost of installation and energy loss) static planning problem. A novel encoding/decoding technique is devised to represent the network as a particle in PSO. Also, a heuristics based branch conductor size selection algorithm has been developed and used. Statistical tests performed to compare the performances of the three PSO variants reveal that the PSO-CF exhibits relatively better performance. Subsequently, the PSO-CF is applied for mono-objective expansion planning and multi-objective static and expansion planning problems. In the multi-objective planning with two conflicting objectives (total cost of installation and energy loss, and total non-delivered energy), the Pareto-optimality principle based tradeoff is done using the strength Pareto evolutionary algorithm-2. The efficiency of PSO for distribution system planning problem, in general, is demonstrated through different examples.

Multi-Objective Planning for Reactive Power Compensation of Radial Distribution Networks With Unified Power Quality Conditioner Allocation Using Particle Swarm Optimization

This paper presents a particle swarm optimization (PSO)-based multi-objective planning algorithm for reactive power compensation of radial distribution networks with unified power quality conditioner (UPQC) allocation. A UPQC consists of a series and a shunt inverter. The UPQC model based on phase angle control (UPQC-PAC) is used. In UPQC-PAC, the series inverter injects a voltage with controllable phase angle in such a way that the voltage magnitude at load end remains unchanged. Due to the phase angle shift, the series inverter participates in load reactive power compensation along with the shunt inverter during healthy operating condition. In the proposed approach, the optimal location, the optimal reactive power compensation required at the location, and the optimal design parameters of UPQC are determined by minimizing three objective functions: 1) the rating of UPQC, 2) network power loss, and 3) percentage of nodes with undervoltage problem. These objectives are simultaneously minimized to obtain a set of non-dominated solutions using multi-objective PSO (MOPSO). The performances of two MOPSO variants are compared and the better one is used in all subsequent studies. A load flow algorithm including the UPQC-PAC model is devised. The performance of the proposed algorithm is validated with different case studies.

Impact of Unified Power-Quality Conditioner Allocation on Line Loading, Losses, and Voltage Stability of Radial Distribution Systems

This paper presents an investigative study on the impact of unified power-quality conditioner (UPQC) allocation on radial distribution systems. A design approach for UPQC, called sag-based design for phase-angle control for UPQC (UPQC-SPAC) is proposed. The phase-angle shifting of the load voltage required to mitigate a given value of voltage sag is determined and the same is used during a healthy operating condition in order to provide the reactive power compensation of a distribution network. To study the impact of the UPQC-SPAC allocation on distribution systems, it is placed at each node, except the substation node, one at a time. The load-flow algorithm for radial distribution systems is suitably modified to incorporate the UPQC-SPAC model. The simulation results show that a significant amount of power-loss reduction, under voltage mitigation, and the enhancement of voltage stability margin can be obtained with an appropriate placement of the UPQC-SPAC in a distribution network. The performance comparison of the UPQC-SPAC with one previously reported design approach shows that it is more efficient in undervoltage mitigation. An appropriate allocation of the UPQC-SPAC is also found to be beneficial for the networks with distributed-generation units.

Simple heuristics-based selection of guides for multi-objective PSO with an application to electrical distribution system planning

In multi-objective particle swarm optimization (MOPSO), a proper selection of local guides significantly influences detection of non-dominated solutions in the objective/solution space and, hence, the convergence characteristics towards the Pareto-optimal set. This paper presents an algorithm based on simple heuristics for selection of local guides in MOPSO, named as HSG-MOPSO (Heuristics-based-Selection-of-Guides in MOPSO). In the HSG-MOPSO, the set of potential guides (in a PSO iteration) consists of the non-dominated solutions (which are normally stored in an elite archive) and some specifically chosen dominated solutions. Thus, there are two types of local guides in the HSG-MOPSO, i.e., non-dominated and dominated guides; they are named so as to signify whether the chosen guide is a non-dominated or a dominated solution. In any iteration, a guide, from the set of available guides, is suitably selected for each population member. Some specified proportion of the current population members follow their respective nearest non-dominated guides and the rest follow their respective nearest dominated guides. The proposed HSG-MOPSO is firstly evaluated on a number of multi-objective benchmark problems along with investigations on the controlling parameters of the guide selection algorithm. The performance of the proposed method is compared with those of two well-known guide selection methods for evolutionary multi-objective optimization, namely the Sigma method and the Strength Pareto Evolutionary Algorithm-2 (SPEA2) implemented in PSO framework. Finally, the HSG-MOPSO is evaluated on a more involved real world problem, i.e., multi-objective planning of electrical distribution system. Simulation results are reported and analyzed to illustrate the viability of the proposed guide selection method for MOPSO.

Fuzzy-Pareto-dominance driven possibilistic model based planning of electrical distribution systems using multi-objective particle swarm optimization

This paper presents a fuzzy-Pareto dominance driven possibilistic model based planning of electrical distribution systems using multi-objective particle swarm optimization (MOPSO). This multi-objective planning model captures the possibilistic variations of the system loads using a fuzzy triangular number. The MOPSO based on the Pareto-optimality principle is used to obtain a set of non-dominated solutions representing different network structures under uncertainties in load demands and these non-dominated solutions are stored in an elite archive of limited size. Normally, choosing the candidate non-dominated solutions to be retained in the elite archive while maintaining the quality of the Pareto-approximation front as well as maintaining the diversity of solutions on this front is very much computationally demanding. In this paper, the principles of fuzzy Pareto-dominance are used to find out and rank the non-dominated solutions on the Pareto-approximation front. This ranking in turn is used to maintain the elite archive of limited size by discarding the lower ranked solutions. The two planning objectives are: (i) minimization of total installation and operational cost and (ii) minimization of risk factor. The risk factor is defined as a function of an index called contingency-load-loss index (CLLI), which captures the effect of load loss under contingencies, and the degree of network constraint violations. The minimization of the CLLI improves network reliability. The network variables that are optimized are: (i) number of feeders and their routes, and (ii) number and locations of sectionalizing switches. An MOPSO (developed by the authors), based on a novel technique for the selection and assignment of leaders/guides for efficient search of non-dominated solutions, is used as the optimization tool. The proposed planning approach is validated on a typical 100-node distribution system. Performance comparisons between the planning approaches with the possibilistic and deterministic load models are provided highlighting the relative merits and demerits. It is also verified that the proposed solution ranking scheme based on the fuzzy-Pareto dominance is very much better from both quality and computational burden point of view in comparison with the other well-known archive truncation techniques based on clustering and solution density measurement etc.

Multi-objective Expansion Planning of Electrical Distribution Networks Using Comprehensive Learning Particle Swarm Optimization

In this paper, a Pareto-basedmulti-objective optimization algorithmusing Comprehensive Learning Particle Swarm Optimization (CLPSO) is proposed for expansion planning of electrical distribution networks. The two conflicting objectives are: installation and operational cost, and fault/failure cost. A novel cost-biased particle encoding/decoding scheme, along with heuristics-based conductor size selection, for CLPSO is proposed to obtain optimum network topology. Simultaneous optimization of network topology, reserve-branch installation and conductor sizes are the key features of the proposed algorithm. A set of non-dominated solutions, capable of providing the utility with enough design choices, can be obtained by this planning algorithm. Results on a practical power system are presented along with statistical hypothesis tests to validate the proposed algorithm.

Multi-objective planning of electrical distribution systems incorporating shunt capacitor banks

The paper presents a multi-objective planning approach for electrical distribution systems incorporating shunt capacitor banks. In this planning, the number of feeders, feeder routes, and number, locations, and rating of shunt capacitor banks for a distribution system are determined using a multi-objective optimization approach. The objectives considered for this optimization are: (i) total investment cost minimization, (ii) network reliability maximization, (iii) minimization of network power loss, and (iv) minimization of node voltage deviation. The last three objectives are aggregated into a single objective, named as network performance metric. This objective and the first objective are simultaneously optimized to obtain a set of non-dominated solutions. The solution algorithm is Multi-objective particle swarm optimization (MOPSO). Three different MOPSO variants are used and their performances are compared. The result of this planning approach is compared with that of the planning without capacitor banks. The proposed planning approach is validated on a 54-node distribution system planning problem.