Intelligent control of PV co-located storage for feeder capacity optimization

Battery energy storage is identified as a strong enabler and a core element of the next generation grid. However, at present the widespread deployment of storage is constrained by the concerns that surround the techno-economic viability. This thesis addresses this issue through optimal integration of storage to improve the efficiency of the electricity grid. A holistic approach to optimal integration includes the development of methodologies for optimal siting, sizing and dispatch coordination of storage

Applying the Herman-Beta probabilistic method to MV feeders

Includes bibliographical references.The assessment of voltage drop in radial feeders is an important element in the process of network design and planning. This task is however not straight forward as the operation of modern power systems is highly influenced by a variety of uncertain and random variables such as stochasticity in load demand and power generation from renewable energy resources. Classic deterministic methods which model load demand and generation with fixed mean values consequently turn out to be inadequate and inaccurate tools for the analysis of power flow in the uncertainty-filled system. Statistically based methods become more suitable for such a task as they account for input variable uncertainties in their calculation of load flow. In the South African context, the Herman Beta algorithm, a probabilistic load flow tool developed by Herman et al. was adopted as the method for voltage assessment in Low Voltage (LV) network. The method was shown to have significant advantages compared with many other probabilistic methods for LV feeders, as investigated by Sellick and Gaunt. Its performance with regards to speed and accuracy is superior to deterministic, numeric probabilistic and other analytical probabilistic methods. The evolving connections of smaller generators, referred to as Distributed Generators (DGs), to the utility grid inspired the extension of the HB algorithm to active LV distribution networks. The HB algorithm was however formulated specifically for LV feeders. The assumptions of purely resistive feeders and unity power factor loads make it unsuitable for the Medium Voltage (MV) distribution network. In South Africa, deterministic methods are still being used for network design in MV distribution networks. This means that the drawbacks of such methods, for example inaccuracy and computational burden with large systems, are characteristic of the quality of network design in MV feeders. The performance of the HB algorithm together with the advantages and superiority of load modelling using the Beta probability density function (Beta pdf) suggested that modifying the input parameters could allow the HB algorithm to be used for voltage calculations on MV networks. This work therefore involves the adaptation of the way the HB algorithm is used, to make it suitable for voltage calculations on MV feeders. The HB algorithm for LV feeders is firstly analysed, coded into MATLAB, tested and then validated. Following this, the input parameters for feeder impedance and load current are modified to include the effects of reactance and non-unity power factor loads, using approximate modelling techniques. For reactance, the modulus or absolute value of the complex impedance is used in place of the resistance, to compensate for the line reactance. The load current is adjusted by inflating it by the power factor. The results of calculations with the HB algorithm are tested against a Monte-Carlo Simulation (MCS) solution of the feeder with an accurate model (full representation of feeder impedance and load power factor). The approach is extended to include shunt capacitor connections and DG in voltage calculations using the HB algorithm and testing the results with MCS. The outcomes of this research are that the approach of adjusting the input parameters of line resistance and load current significantly improves the accuracy of calculations using the HB algorithm for MV feeders. Comparison with the results of MC simulations indicates that the error of voltage calculations on MV feeders will be less than 2% of the 'accurate probabilistic value'. However, it is not possible to predict the error for a particular application

Review on distribution network optimization under uncertainty

With the increase of renewable energy in electricity generation and increased engagement from demand sides, distribution network planning and operation face great challenges in the provision of stable, secure and dedicated service under a high level of uncertainty in network behaviors. Distribution network planning and operation, at the same time, also benefit from the changes of current and future distribution networks in terms of the availability of increased resources, diversity, smartness, controllability and flexibility of the distribution networks. This paper reviews the critical optimization problems faced by distribution planning and operation, including how to cope with these changes, how to integrate an optimization process in a problem-solving framework to efficiently search for optimal strategy and how to optimize sources and flexibilities properly in order to achieve cost-effective operation and provide quality of services as required, among other factors. This paper also discusses the approaches to reduce the heavy computation load when solving large-scale network optimization problems, for instance by integrating the prior knowledge of network configuration in optimization search space. A number of optimization techniques have been reviewed and discussed in the paper. This paper also discusses the changes, challenges and opportunities in future distribution networks, analyzes the possible problems that will be faced by future network planning and operations and discusses the potential strategies to solve these optimization problems

A hybrid algorithm for voltage stability enhancement of distribution systems

This paper presents a hybrid algorithm by applying a hybrid firefly and particle swarm optimization algorithm (HFPSO) to determine the optimal sizing of distributed generation (DG) and distribution static compensator (D-STATCOM) device. A multi-objective function is employed to enhance the voltage stability, voltage profile, and minimize the total power loss of the radial distribution system (RDS). Firstly, the voltage stability index (VSI) is applied to locate the optimal location of DG and D-STATCOM respectively. Secondly, to overcome the sup-optimal operation of existing algorithms, the HFPSO algorithm is utilized to determine the optimal size of both DG and D-STATCOM. Verification of the proposed algorithm has achieved on the standard IEEE 33-bus and Iraqi 65-bus radial distribution systems through simulation using MATLAB. Comprehensive simulation results of four different cases show that the proposed HFPSO demonstrates significant improvements over other existing algorithms in supporting voltage stability and loss reduction in distribution networks. Furthermore, comparisons have achieved to demonstrate the superiority of HFPSO algorithms over other techniques due to its ability to determine the global optimum solution by easy way and speed converge feature

An mi-sdp model for optimal location and sizing of distributed generators in dc grids that guarantees the global optimum

This paper deals with a classical problem in power system analysis regarding the optimal location and sizing of distributed generators (DGs) in direct current (DC) distribution networks using the mathematical optimization. This optimization problem is divided into two sub-problems as follows: the optimal location of DGs is a problem, with those with a binary structure being the first sub-problem; and the optimal sizing of DGs with a nonlinear programming (NLP) structure is the second sub-problem. These problems originate from a general mixed-integer nonlinear programming model (MINLP), which corresponds to an NP-hard optimization problem. It is not possible to provide the global optimum with conventional programming methods. A mixed-integer semidefinite programming (MI-SDP) model is proposed to address this problem, where the binary part is solved via the branch and bound (B&B) methods and the NLP part is solved via convex optimization (i.e., SDP). The main advantage of the proposed MI-SDP model is the possibility of guaranteeing a global optimum solution if each of the nodes in the B&B search is convex, as is ensured by the SDP method. Numerical validations in two test feeders composed of 21 and 69 nodes demonstrate that in all of these problems, the optimal global solution is reached by the MI-SDP approach, compared to the classical metaheuristic and hybrid programming models reported in the literature. All the simulations have been carried out using the MATLAB software with the CVX tool and the Mosek solver

WHALE OPTIMIZATION ALGORITHM FOR OPTIMAL LOCATION AND SIZING OF RENEWABLE DISTRIBUTED GENERATION

Renewable generation is a viable source of clean and smart energy in a modern distribution network. Thus, the synergy between photovoltaic and small-hydropower yields a complementary and uninterruptible power output. However, location and sizing mostly affect operational output. This paper presents a combined Voltage Stability Index estimation (VSI) and Whale Optimization Algorithm (WOA) for the optimal allocation of renewable-based energy sources. The nodes voltage stability index is ranked to signal the whale optimization selection of candidate solution agents at each algorithm iterations. Thereby turning the Distributed Generation (DG) node selection into non-random mode to improve simulation time and performance. The WOA technique is modeled using the hunting activities of whales and analysed on IEEE 33 bus systems. The results confirm the algorithm’s improved performance of 89% voltage improvement and 48.50 power loss reduction for single PV integration. The technique ensures efficient network resource management for improved output

Grid-Connected Renewable Energy Sources

The use of renewable energy sources (RESs) is a need of global society. This editorial, and its associated Special Issue “Grid-Connected Renewable Energy Sources”, offers a compilation of some of the recent advances in the analysis of current power systems that are composed after the high penetration of distributed generation (DG) with different RESs. The focus is on both new control configurations and on novel methodologies for the optimal placement and sizing of DG. The eleven accepted papers certainly provide a good contribution to control deployments and methodologies for the allocation and sizing of DG