Voltage Profile Improvement Index Research Articles

Multi-objective robust optimization allocation for energy storage using a novel confidence gap decision method

With the increasing penetration rate of uncertain wind/photovoltaic power, robust optimization allocation for energy storage becomes more and more important in the distribution network. By introducing chance constraints and the classified probability confidence intervals, and integrating the robust idea of information gap decision theory (IGDT), a novel confidence gap decision (CGD) method based on confidence level driving robust optimization is proposed. Considering the comprehensive optimization objectives of maximizing voltage profile improvement index and minimizing annual investment cost, a multi-objective robust optimization allocation model of energy storage based on CGD is established. The proposed CGD model can not only mitigate the conservativeness of conventional robust optimization, but also overcome the roughness of uncertain set and the subjectivity of objective deviation factor in IGDT model, so that a more reasonable and accurate uncertainty planning can be achieved. Moreover, the chance constraints in CGD model are transformed into the equivalent deterministic constraints according to uncertainty theory, and a new adaptive harmonic aliasing multi-objective compound differential evolution algorithm is proposed to solve above model. Finally, sample applications are applied to demonstrate the advantages of the proposed theory and method.

Multi objective optimal location and sizing of Distributed Generation unit using PSO

In this paper, multi objective approach used for Optimal Location and Sizing (OPS) of Distributed Generation (DG) unit using Particle Swarm Optimization (PSO) algorithm. The objectives used are Voltage Profile Improvement Index (VPII) and Locational Marginal Price (LMP). In order to improve the voltage profile and to lessen the congestion above two objectives are combined and formulate the multi objective optimization (MOO) problem. To solve the MOO problem metaheuristic optimization technique called PSO has been used. It is verified on IEEE 14-bus system to illustrate the effectiveness of the PSO.

Optimal sizing and sitting of EVCS in the distribution system using metaheuristics: A case study

In this study, a new optimal allocation and sizing have been proposed for an Electric Vehicle Charging Station (EVCS) on a Distribution System in Allahabad, India. The main idea is to optimize the EVCS configuration by considering Voltage Profile Improvement Index (VPII), Reactive Power Loss Reduction Index (QLRI), Real Power Loss Reduction Index (PLRI), and the preliminary development cost to get the minimum value of the installation cost and to provide higher quality of parameters for the power grid. For solving the studied nonlinear mixed-integer optimization problem, a new improved metaheuristic, called Balanced Mayfly Algorithm (BMA) is proposed. The modification is established to improve the accuracy and to resolve the exploration issue of the algorithm. The BMA used two modifications including elite mayfly couples and chaos mechanism to resolve these issues as it is possible. After validating the algorithm, it is applied to 30-bus distribution system in Allahabad, India and its results are compared with GAIPSO and basic MA. The results indicated that the voltage shape is smoothened and a reasonable balance between voltage profile and network losses is obtained. The results also show that the suggested method with 18.358 MW active power loss, 73.826 MVar reactive power loss, 10961 s computational burden, and 415 number of charging ports gives superior performance with lesser power losses. The number of CS allocated through GAIPSO and MA does not satisfy the demand of the city’s consumers.

Enhanced Sine–Cosine Algorithm for Optimal Planning of Distribution Network by Incorporating Network Reconfiguration and Distributed Generation

One of the important directions of the current research trend in distribution network planning in the prevailing smart grid scenario, is to explore various possibilities to enhance the performance of these networks without expanding the existing infrastructure. This paper proposes an enhanced sine–cosine algorithm (ESCA) to obtain an optimally planned system by simultaneous incorporation of network reconfiguration (NR) and DG allocation. In the proposed algorithm, the SCA is enhanced with neighborhood search strategy and self-adapting levy mutation strategy to ensure proper balance between exploration and exploitation during different reconfiguration phases. A multi-objective function is formulated considering the reduction of total real power loss and annual operation costs with suitable weights without violating the system operating constraints. The proposed algorithm is successfully experimented on 33- and 69-bus distribution system with four distinct scenarios of NR and DG allocation, and its performance assessment is based on technical (total system active power loss index, overall voltage stability index and voltage profile improvement index), economic (total system operation cost index) and reliability (expected energy not supplied index) indices. As the computation of reliability index adds complexity to the problem, a graph theory-based algorithm is proposed for its accurate calculation. The obtained results showed the effectiveness of ESCA for solving simultaneous NR and DG allocation problem over other competitive algorithms, and its robustness is confirmed through a detailed statistical analysis such as plotting of box plots, normality checking and two nonparametric tests, namely Friedman ANOVA and Wilcoxon signed rank tests.

Multi-Objective Approach for Strategic Incorporation of Solar Energy Source, Battery Storage System, and DSTATCOM in a Smart Grid Environment

In the smart grid environment, there is a worldwide consensus concerning the high penetration of renewable energy resources (RESs) to cater a multitude of technical, economic, and social concerns. In this perspective, a combination of RESs and battery storage system (BSS) in co-ordination with the incorporation of distributed static compensator (DSTATCOM) is a smart, efficient, and viable approach to alleviate the power quality concerns. In this study, a comprehensive strategic model is designed to optimally incorporate RESs, BSS, and DSTATCOM with an aim to maximize technical, economic, and ecological benefit of the system. Logical and innovative formulas, such as benefit cost ratio, network voltage profile improvement index, environmental benefit index, and reliability indices, i.e., expected energy not served are formulated to assess the overall benefits in the present planning framework. A fuzzy-based extended version of nondominant sorting genetic algorithm NSGA II (E_NSGA II) is utilized for the strategic incorporation of the devices taken into account the practical security limits. The proposed multi-objective approach is applied on 69-bus radial distribution system considering the time variant practical load models and the obtained results are compared with other multi-objective algorithms to validate the efficacy and efficiency of the proposed approach.

TECHNO-ECONOMIC ASSESMENT FOR DISTRIBUTED GENERATION PLACEMENT IN DISTRIBUTION SYSTEM

This paper presents a proposed function which is known as techno-economic model for optimal placement of distributed generation (DG) resources in distribution systems in order to minimize the power losses and improve voltage profile. Combined sensitivity factors (CSF), such real power loss reduction index, reactive power loss reduction index, voltage profile improvement index, and life cycle cost, and particle swarm optimization (PSO) are applied to the proposed technique to obtain the best compromise between these costs. Simulation results on IEEE 14-bus test system are presented to demonstrate the usefulness of the proposed procedure.

Optimised planning of distribution network with photovoltaic system, battery storage, and DSTATCOM

In this circumstance of global warming, energy market deregulation, and enormous load growth, distribution network entails a proficient strategy to maintain the reliability and efficiency of the power service. Incorporation of solar photovoltaic (PV) system and battery storage (BS) in coordination with distributed static compensator (DSTATCOM) is a competent and practical approach to alleviate the power quality and reliability concern. In this study, a comprehensive strategic model is presented to optimally deploy PV, BS, and DSTATCOM to maximise voltage profile improvement, reliability, economic, and ecological benefit of the network. An accurate and precise novel voltage profile improvement indicator namely network voltage profile improvement index is proposed. Benefit-cost ratio and environment benefit index are proposed to quantify economic and environmental benefits, respectively. Similarly, reliability indices such as expected energy not served are used to appraise the system reliability. A fuzzy based extended version of NSGA II is utilised for the optimal deployment of the devices considering security limits. The proposed method is tested on 33-bus and 69-bus distribution networks considering time variant practical load models and the obtained results validate the efficacy and efficiency of the proposed method when compared with other multi-objective algorithms.

An Approach to Quantify the Technical Benefits of Distributed Generation

Recent changes in the electric utility infrastructure have created opportunities for many technological innovations, including the employment of distributed generation (DG) to achieve a variety of benefits. After a brief discussion of the benefits, this paper proposes a general approach and a set of indices to assess some of the technical benefits in a quantitative manner. The indices proposed are: 1) voltage profile improvement index; 2) line-loss reduction index; 3) environmental impact reduction index; and 4) DG benefit index. Simulation results obtained using a simple 12-bus test system and a radial system are presented and discussed to illustrate the value and usefulness of the proposed approach.