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Clustering historical electricity consumption data is very important for creating representative demand profiles for the planning and operation of the power grids. This paper investigates a multi-dimensional framework for data clustering, which takes scattering and separation metrics, as well as the number of clusters into account. A combination of wavelet mutation with the Invasive Weed Optimization (IWO) method for clustering features is proposed. One notable advantage of the IWO method over other metaheuristic optimization algorithms is its ability to dynamically adapt the number of weed colonies during the search process, resulting in improved exploration and exploitation of the search space. The proposed strategy is applied to cluster the electricity consumption data from a large municipal government center in Perth, Western Australia. The suggested method is then evaluated by comparing it with the well-known method in the literature, namely, the k-means technique. After the data clustering, the obtained results are implemented in the design of a multi-microgrid system under two different scenarios of cooperative and noncooperative modes. To evaluate the performance of the proposed method, the proposed method is implemented on the operational planning of a real multi-microgrid distribution system in Western Australia using linear programming to take the advantage of the mathematical-based solvers. After performing some investigations, the cooperative mechanism, where the microgrids have participated in supplying the demand of microgrids was found to yield to greater operational and investment cost minimimzation. In terms of numerical comparison, the total cost in the cooperative model is 6.5% lower than that in a non-cooperative situation.

An uninterruptible power supply (UPS) is commended unit or even necessary for consumers’ electronic devices in the individual, industry, and critical categories to protect them against power shortage for loads. This article proposes a family of single-phase inverters for UPS applications requiring significant fault tolerance. The main inverter comprises two unidirectional switches, eight bidirectional switches, and six DC voltage sources to produce a fifteen-level voltage waveform across the load terminals. The proposed topology can tolerate failures of various semiconductor switches and voltage sources, preserving voltage levels at the output. The core structure of the proposed inverter is first explained, followed by a comparison with the existing fifteen-level inverters in terms of several parameters to demonstrate its superiorities. This article presents several fault-tolerant strategies that can be implemented using the proposed topology to facilitate the realization of reliable UPS systems for critical applications. The effectiveness of the suggested schemes is verified through experimental evaluations under various conditions.

Distribution system state estimation (DSSE) is a critical analysis tool for active distribution networks (DNs). Unlike weighted least squares techniques, which are static DSSE methods, the augmented complex Kalman filter (ACKF) is a novel technique that considers the system’s dynamic behavior. Currently, most DNs integrate a large number of unmonitored residential photovoltaic (PV) generations. Existing unmeasured PV sources violate the white noise assumption in Kalman and least-squares-based estimators, causing the estimator to be biased. Because the one-step difference of aggregated customer demand is characterized as white noise, the suggested PV estimation technique based on the differencing strategy is used to decouple PV from the measured load. Using the specified contribution factors, the new online pseudo current injections are generated. In addition, the estimator’s accuracy is improved by using a new PV-scaling-aided ACKF approach based on the PV separation strategy. For validation purposes, this method is applied to real DN case studies. This study also makes use of an actual dataset to illustrate the efficacy of the proposed technique. The proposed technique outperforms the existing snapshot and dynamic DSSE techniques, and significant improvements are achieved in terms of accuracy and computational cost.

Energy storage-embedded soft open point (ESOP) is an innovative electronic device that incorporates energy storage capabilities and is designed to interconnect two or three neighboring distribution feeders. Its primary functions include active power control, reactive power compensation, and grid voltage regulation. On the other hand, distribution grids have been equipped with other distributed energy resources (DERs) such as smart PV inverters, renewable energy sources (solar and wind), and so on. Therefore, this article proposes a novel management scheme for the mentioned devices in distribution grids based on a convex formulation to take powerful solvers like CPLEX into account. Since there are a set of uncertainties associated with the model, including renewable resources, loads, and market price, a hybrid framework is developed based on the unscented transformation (UT) method and information gap decision theory (IGDT). In other words, the uncertainties of renewable resources and loads are captured by the UT method which could provide a correlation among uncertain parameters, while the IGDT method is employed to model the uncertainty associated with the market price. The hybridization of these methods assists in reducing the discrepancy between the actual and predicted values of uncertain parameters. The model is applied to a 33-bus grid, considering various case studies. The proposed method results in a substantial operational cost reduction of approximately 26.82 %. Additionally, it ensures a flatter voltage profile across the network, with the minimum voltage reaching 0.985 p.u.

In this article, an augmented complex Kalman filter (ACKF) is proposed for distribution networks as a noniterative forecasting-aided state estimator. Although most of the distribution state estimator (DSE) algorithms deal with real and imaginary parts of distribution networks’ states independently, the proposed algorithm in this article considers the states as complex values. In the case of real-time DSE and the presence of a large number of customer loads in the system, employing DSEs in one single estimation layer is not computationally efficient. Consequently, our proposed method performs in several estimation layers hierarchically as a multilayer DSE based on an ACKF (DSE-MACKF). In the proposed method, a distribution network can be divided into one main area and several subareas. The aggregated loads in each subarea act like a big customer load in the main area. Customer load aggregation results in lower variability and higher spatial-temporal correlation. This increases the accuracy of the estimated states in the main estimation layer. Additionally, the proposed method is formulated to include unbalanced loads in low-voltage radial distribution networks. This approach is applied to two real distribution networks for comparison and evaluation. The effectiveness of the proposed method is discussed using several criteria such as computational time, standard deviation, and maximum and average voltage error. The performance of the proposed method has been assessed against the weighted least square estimation method, while the computational time and the average voltage error have been decreased from 14 to 2 s and 1.13% to 0.23%, respectively.

Increasing the number of active consumers in distribution networks necessitates transforming the current control, monitoring, and protection schemes. However, on one hand, installing high-frequency measurement devices and fast communication platforms in low-voltage (LV) distribution networks is not cost effective and scalable. On the other hand, the fault detection approaches, which can provide acceptable accuracy by relying only on low-frequency measured data (with 1–30-min sampling rates), are not developed yet. Currently, the overcurrent fault detectors work mainly based on fixed current thresholds, which makes them inefficient in a system with high-distributed-energy resources. This is due to high volatility and uncertainty in the measured profile of the current. In this article, a data-driven fault detection framework with dynamic fault current thresholds is proposed. The motivation here is to develop a framework that can locally detect and isolate faults within the LV distribution networks without requiring high-frequency sampling meters. The proposed model is based on quantile regression as a statistical method to generate the quantiles of distributions of the current measurements. Two different fault current thresholds are formulated for instantaneous and definite time fault detection schemes. The thresholds are dynamically predicted for each next time step. The proposed framework is evaluated using data from a real distribution network with 169 houses. The results suggest that the proposed model is very promising for LV residential distribution networks.

This paper proposes a metaheuristic optimisation algorithm named enhanced breeding swarms (EBS), which combines the strengths of particle swarm optimisation (PSO) with those of genetic algorithm (GA). In addition, EBS introduces three modifications to the original breeding swarms to improve the performance and the accuracy of the optimisation algorithm. These modifications are applied on the acceptance criteria based on the improved glowworm swarm optimisation, velocity impact factor, and the mutation operator. The EBS algorithm is tested and compared against GA, PSO, and original BS algorithms, using unrotated and rotated six recognised optimisation benchmark functions. Results indicate that the EBS outperforms GA, PSO, and BS in most cases in terms of accuracy and speed of convergence, especially when the dimension of optimisation increases. As an application of the proposed EBS algorithm, a load flow analysis on a 6-bus network is performed, and the comparison results against another heuristic algorithm and the Newton-Raphson are reported.

Energy storage systems (ESSs) can help to reduce the intermittency and uncertainty of renewable energy supplies in power systems. ESSs are critical components of renewable-rich standalone microgrids (SMGs) to balance power generation and load demand, which is referred to as reliability. To achieve the same level of reliability as conventional power systems for renewable-based SMGs, significant investment in ESSs is required. However, due to the high investment costs of ESSs, the installation of large ESSs will not result in an affordable solution for achieving renewable SMG at the required reliability. As a result, this paper proposes a new sharing concept for ESS, namely energy storage as a service (ESaaS), to be implemented across microgrids as a low-cost alternative for improving reliability. In the proposed ESaaS concept, microgrids can use ESS from an ESS provider as required for different timeframes such as monthly, weekly, or daily, depending on the renewable resources and load profile characteristics. In this paper, the use of ESaaS is investigated over a range of timeframes for a 100 % renewable-based SMG with photovoltaic (PV), wind turbine (WT), and ESS. The SMG reliability is evaluated using Monte Carlo simulation both before and after the ESaaS strategy has been implemented. To evaluate the ESaaS affordability in improving the reliability of an SMG, this paper proposes the criteria of marginal cost of reliability, which indicates the rate of additional investment amount per percentage of reliability improvement. The marginal cost of reliability combines the economic and technical aspects of ESaaS in one simple criterion for effective decision-making among investment strategies such as different timeframes of ESaaS or permanent ESS. The simulation results show the ESaaS based on daily contract results in a lower marginal cost of reliability for the case study. To validate the effectiveness of the proposed ESaaS approach using marginal cost of reliability, the levelized cost of electricity (LCOE) is also calculated for different strategies of reliability improvement. The results confirm that the lowest LCOE is obtained using the strategy that provides the lowest marginal cost of reliability for the case study. In addition, a sensitivity analysis is performed to assess the difference in marginal cost of reliability under various uncertainties associated with the installed capacity of PV and WT, and the cost of utilising the ESaaS.

This paper forms a framework for allocating a central electric energy storage (EES) in discrete communities, forming as segmentations along the rural feeders, where the installation of cross connects are not economic or even feasible. In this framework, EESs that centrally can be installed in each community are owned and operated by an aggregator/retailer, trading bidirectional energy transactions with both the grid and the customers. The objective is to find optimum investment in storage capacity in rural feeders for minimum annual energy purchase cost using energy arbitrage opportunities, while maintaining an allowable level of system average interruption duration index (SAIDI). As a part of EES sizing and siting optimization in this approach, annual hourly network data are utilized for charge/discharge scheduling and constraint, and for system reliability assessment—using the k-means clustering technique. This method is applied to a rural network in Queensland, Australia, and the results are examined to show the effectiveness of this method.

With increasing photovoltaic (PV) penetration in low-voltage (LV) distribution networks, voltage violation during the peak PV generation period is one of the main power quality concerns. To minimize the PV power curtailment caused by voltage violations, this paper proposes a new approach utilizing one dynamic voltage restorer (DVR) at the secondary of an existing urban distribution transformer as a continuous voltage compensator with an accompanying control algorithm. The voltage compensation algorithm controls the DVR in real-time to prevent over-voltage and under-voltage at all network nodes, thereby maximizing PV injection and preventing equipment damage. The controller uses the equivalent line impedance of network to estimate the average voltage of load points without the need for any communication links for measuring load voltages. In this approach, the fixed tap changer of the distribution transformer is also optimally adjusted to minimize the DVR rating using a proposed offline optimization method. Simulation results on IEEE LV test feeder prove the ability of the DVR in maintaining all network node voltages within the allowable range in peak demand and peak PV generation periods with the benefit of injecting only a small amount of active and reactive power to the system, in contrast to alternative algorithms.

ABSTRACTUtilization of light-emitting diodes and compact fluorescent lamps instead of the traditional incandescent ones in residential applications are strongly recommended by the Australian Building Codes and is very attractive for households because of their higher energy and luminous efficiency. However, they are mostly non-linear loads, injecting harmonic currents and drawing reactive power, because of the ballast and power electronic circuits. The accumulated impact of thousands of them can be very adverse on maintaining power quality and losses in a grid. Due to an increase in the emergence of such lamps from different brands and in various ratings, a detailed study of these lamps is required to identify the potential adverse effects. Through this research, the currently available residential lamps on the Australian market are evaluated from utility and consumer-oriented perspectives. First, they are juxtaposed from cost and illumination and lifespan perspectives, as significant criteria for the consumers when purchasing lamps. Then, using detailed laboratory measurements, they are compared from power quality criteria such as current harmonics and total harmonic distortion, as well as other aspects such as their fundamental and non-fundamental components of apparent power consumption, stabilisation time, deviation of actual power consumption from rated power and power factor, which are important for the utilities. The compliance of lamps with the relevant standards is evaluated where applicable.

Time of Use (TOU) pricing is a cost-reflective electricity pricing scheme; it has proven to be an effective approach for reducing peak electricity demand in the residential sector around the world, especially in developed countries. The implementation of TOU pricing in low and lower-middle income economies is less appealing than in other settings. This is mainly because a traditional TOU pricing scheme may increase the cost of electricity for low income consumers. The lack of a suitable TOU pricing strategy for these countries results in high peak demand, poor utilization of network infrastructure and, consequently, higher electricity prices than necessary. The purpose of this study is to analyse and propose a TOU pricing scheme for the residential sector that will be suitable for countries with a high percentage of low income household consumers. In this study, Bangladesh will be used as an exemplar of a lower-to-middle income developing country. In Bangladesh, the residential sector is responsible for half the country's total electricity consumption, and constitutes an even greater proportion of the peak demand. Residential consumers currently pay inclining block usage rates that provide no financial incentive for them to shift their electricity usage from peak to non-peak periods. The proposed TOU pricing scheme is a combination of the traditional TOU and inclining block usage pricing schemes, based on a realistic load shifting capacity that is applicable to Bangladesh, and to other similar developing countries. Analysis of this pricing system for different income levels of residential consumers shows that the proposed scheme effectively reduces the peak demand, while ensuring minimum impact on consumer monthly energy bills and comfort levels.

•This research concentrates on presenting an effective tool for managing electric vehicle fleet in hybrid AC-DC microgrids under the high penetration of renewable energy sources (RESs).•The scheduling of electric vehicle fleet is modeled from the viewpoint of distribution system operators to minimize the operational cost.•The uncertainty of load and RESs are modeled by an efficient mechanism, named information-gap decision theory (IGDT) from the perspective of risk-averse and risk-seeker operators.•The proposed approach is formulated as mixed integer linear programing (MILP) and implemented on 69-bus and 94-bus systems, operated as hybrid AC-DC microgrids.

The K-means algorithm is one of the most popular techniques in clustering. Nevertheless, the performance of the K-means algorithm depends highly on initial cluster centers and converges to local minima. This paper proposes a hybrid evolutionary programming based clustering algorithm, called PSO-SA, by combining particle swarm optimization (PSO) and simulated annealing (SA). The basic idea is to search around the global solution by SA and to increase the information exchange among particles using a mutation operator to escape local optima. Three datasets, Iris, Wisconsin Breast Cancer, and Ripley’s Glass, have been considered to show the effectiveness of the proposed clustering algorithm in providing optimal clusters. The simulation results show that the PSO-SA clustering algorithm not only has a better response but also converges more quickly than the K-means, PSO, and SA algorithms.