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Virtual power plants (VPPs) are an effective platform for attracting private investment and customer engagement to speed up the integration of green renewable resources. In this paper, a robust bidding strategy to participate in both energy and ancillary service markets in the wholesale electricity market is proposed for a realistic VPP in Western Australia. The strategy is accurate and fast, so the VPP can bid in a very short time period. To engage customers in the demand management schemes of the VPP, the gamified approach is utilized to make the exercise enjoyable while not compromising their comfort levels. The modelling of revenue, expenses, and profit for the load-following ancillary service (LFAS) is provided, and the effective bidding strategy is developed. The simulation results show a significant improvement in the financial indicators of the VPP when participating in both the LFAS and energy markets. The payback period can be improved by 3 years to the payback period of 6 years and the internal rate of return (IRR) by 7.5% to the IRR of 18% by participating in both markets. The accuracy and speed of the proposed bidding strategy method is evident when compared with a mathematical method. [ABSTRACT FROM AUTHOR]

Virtual Power Plants (VPPs) are efficient structures for attracting private investment, increasing the penetration of renewable energy and reducing the cost of electricity for consumers. It is expected that the number of VPPs will increase rapidly as their financial return is attractive to investors. VPPs will provide added value to consumers, to power systems and to electricity markets by contributing to different services such as the energy and load-following services. One of the capabilities that will become critical in the near future, when large power plants are retired, is grid-forming capability. This review paper introduces the concept of grid-forming VPPs along with their corresponding technologies and their advantages for the new generation of power systems with many connected VPPs. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The current searching schemes in reliability evaluation of radial distribution systems require complicated and extensive codes, that contribute to the majority of the CPU times. This paper proposes a new one-step method operating with a set of binary descriptor matrices, replacing the searching, to calculate load and system reliability indices. In this technique, ‘fuse to branch’ and ‘switch to branch’ are introduced and employed to facilitate the solutions along with the use of bus-injection to branch-current matrices. In this approach, the cross-connects operation, islanding mode of small-scale DGs, and network constraints are also considered as part of the adequacy evaluation. The proposed technique is highly efficient for long-term distribution network planning and optimization where many cross-connects are involved. The proposed technique is applied to RBTS Bus 2 and 4, as well as a realistic 747-node distribution network. Results for the large distribution planning of the 747-node network demonstrate the computational time is reduced from 61 days using the traditional method to 4.6 hours based on the proposed technique. [ABSTRACT FROM AUTHOR]

This paper investigates the risk-oriented multi-area economic dispatch (MAED) problem with high penetration of wind farms (WFs) combined with compressed air energy storage (CAES). The main objective is to help system operators to minimize the operational cost of thermal units and CAES units with an appropriate level of security through optimized WF power generation curtailment strategy and CAES charging/discharging control. In the obtained MAED model, several WFs integrated with CAES units are considered in different generation zones, and the probability to meet demand by available spinning reserve during $N - 1$ security contingency is characterized as a risk function. Furthermore, the contribution of CAES units in providing the system spinning reserve is taken into account in the MAED model. The proposed framework is demonstrated by a case study using the modified IEEE 40-generator system. The numerical results reveal that the proposed method brings a significant advantage to the efficient scheduling of thermal units’ power generation, WF power curtailment, and CAES charging/discharging control in the power system. [ABSTRACT FROM AUTHOR]

Single period economic dispatch cannot handle the intertemporal constraints in multi-period environment. To cope with this issue, the extension of economic dispatch over multiple time intervals (i.e., dynamic economic dispatch) has been introduced that considers the intertemporal constraints between different time intervals. Another issue is determining the most economical generation dispatch that could supply the area demand without violating the tie-line capacity, which cannot be solved by conventional economic dispatch problems. However, this study shows that the most economic schedule of power generation cannot satisfy echo-system expectation; therefore, making a compromise between fuel cost and environmental issues, a hot-button subject in industrialized nations, seems to be crucial. To reach the goals a bi-objective multi-area dynamic economic dispatch approach, which can handle intertemporal and multi-area constraints concurrently, is proposed to assist power system operators more and more. Finally, a hybrid algorithm, namely gray wolf optimizer-particle swarm optimization is introduced to solve the proposed problem and also a set of benchmark problems. By implementing the proposed approach on two small (10-unit, three areas) and large (40-unit, four areas) scale test systems, about 3.1% and 3.3% improvement in generation cost is obtained, respectively compare to the best reported results in the literature. [ABSTRACT FROM AUTHOR]

Penetration of renewable energy sources (RESs) and electrical energy storage (EES) systems in distribution systems is increasing, and it is crucial to investigate their impact on systems’ operation scheme, reliability, and security. In this paper, expected energy not supplied (EENS) and voltage stability index (VSI) of distribution networks are investigated in dynamic balanced and unbalanced distribution network reconfiguration, including RESs and EES systems. Furthermore, due to the high investment cost of the EES systems, the number of charge and discharge is limited, and the state-of-health constraint is included in the underlying problem to prolong the lifetime of these facilities. The optimal charging/discharging scheme for EES systems and optimal distribution network topology are presented in order to optimize the operational costs, and reliability and security indices simultaneously. The proposed strategy is applied to a large-scale 119-bus distribution test network in order to show the economic justification of the proposed approach. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Low-voltage (LV) feeder voltage magnitude and unbalance are often the constraining factors on a feeder's capacity to absorb rooftop photovoltaic (PV) generation. This paper presents a new probabilistic method for voltage management in distribution networks through the placement of distribution static compensators (dSTATCOMs) and on-load tap changers (OLTCs) considering the reactive capability of PV inverters in multiple LV and medium-voltage distribution networks. The method uses a modified particle swarm optimization. In this paper, several scenarios for the placements of multiple dSTATCOMs with and without embedded energy storage systems using both reactive and real power compensation are investigated in combination with an OLTC equipped with independent per-phase tap-changing control. The voltage constraints in the proposed method are statistically defined using three duration curves. These are the voltage unbalance, maximum voltage, and minimum voltage duration curves. The method is comprehensively tested for varying load and PV generation based on data from a real Australian distribution network with considerable unbalance and distributed PV generation. The results show that PV hosting capacity increases where the proposed approach is applied. [ABSTRACT FROM PUBLISHER]

Utilization 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The connection of renewable-based distributed generation (DG) in distribution networks has been increasing over the last few decades, which would result in increased network capacity to handle their uncertainties along with uncertainties associated with demand forecast. Temporary non-network solutions (NNSs) such as demand response (DR) and temporary energy storage system/DG are considered as promising options for handling these uncertainties at a lower cost than network alternatives. In order to manage and treat the risk associated with these uncertainties using NNSs, this paper presents a new risk-managed approach for multi-stage distribution expansion planning (MSDEP) at a lower cost. In this approach, the uncertainty of available DR is also taken into account. The philosophy of the proposed approach is to find the “optimal level of demand” for each year at which the network should be upgraded using network solutions while procuring temporary NNSs to supply the excess demand above this level. A recently developed forward–backward approach is fitted to solve the risk-managed MSDEP model presented here for real sized networks with a manageable computational cost. Simulation results of two case studies, IEEE 13-bus and a realistic 747-bus distribution network, illustrate the effectiveness of the proposed approach. [ABSTRACT FROM PUBLISHER]

This paper presents an efficient noniterative method for distribution state estimation using conditional multivariate complex Gaussian distribution (CMCGD). In the proposed method, the mean and standard deviation (SD) of the state variables is obtained in one step considering load uncertainties, measurement errors, and load correlations. In this method, first the bus voltages, branch currents, and injection currents are represented by MCGD using direct load flow and a linear transformation. Then, the mean and SD of bus voltages, or other states, are calculated using CMCGD and estimation of variance method. The mean and SD of pseudo measurements, as well as spatial correlations between pseudo measurements, are modeled based on the historical data for different levels of load duration curve. The proposed method can handle load uncertainties without using time-consuming approaches such as Monte Carlo. Simulation results of two case studies, six-bus, and a realistic 747-bus distribution network show the effectiveness of the proposed method in terms of speed, accuracy, and quality against the conventional approach. [ABSTRACT FROM PUBLISHER]

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. [ABSTRACT FROM AUTHOR]

To integrate large-scale renewable energy into energy systems, an effective participation from private investors and active customer engagement are essential. Virtual power plants (VPPs) are a very promising approach. To realize this engagement, an efficient monitoring and control system needs to be implemented for the VPP to be flexible, scalable, secure, and cost-effective. In this paper, a realistic VPP in Western Australia is studied, comprising 67 dwellings, including a 810 kW rooftop solar photovoltaic (PV) system, a 700 kWh vanadium redox flow battery (VRFB), a heat pump hot water system (HWS), an electric vehicle (EV) charging station, and demand management mechanisms. The practical and detailed concept design of the monitoring and control system for EEBUS-enabled appliances, and also for the PV and VRFB system, with smart inverters, is proposed. In addition, a practical fog-based storage and computing system is developed to enable the VPP owner to manage the PV, VRFB, and EV charging station for maximizing the benefit to the customers and the VPP owner. Further, the proposed cloud-based applications enable customers to participate in gamified demand response programs for increasing the level of their engagement while satisfying their comfort level. All proposed systems and architecture in this paper have the capability of being implemented fully and relevant references for practical devices are given where necessary. [ABSTRACT FROM AUTHOR]

Achieving the renewable energy integration target will require the extensive engagement of consumers and the private sector in investment and operation of renewable-based energy systems. Virtual power plants are an efficient way to implement this engagement. In this paper, the detailed costs and benefits of implementing a realistic virtual power plant (VPP) in Western Australia, comprising 67 dwellings, are calculated. The VPP is designed to integrate and coordinate rooftop solar photovoltaic panels (PV), vanadium redox flow batteries (VRFB), heat pump hot water systems (HWSs), and demand management mechanisms. An 810-kW rooftop solar PV system is designed and located using the HelioScope software. The charging and the discharging of a 700-kWh VRFB are scheduled for everyday use over a year using an optimization algorithm, to maximize the benefit of it for the VPP owners and for the residents. The use of heat pump HWSs provides a unique opportunity for the residents to save energy and reduce the total cost of electricity along with demand management on some appliances. The cost-and-benefit analysis shows that the cost of energy will be reduced by 24% per dwelling in the context of the VPP. Moreover, the internal rate of return for the VPP owner is at least 11% with a payback period of about 8.5 years, which is a promising financial outcome. [ABSTRACT FROM AUTHOR]

The world is progressing towards a more advanced society where end-consumers have access to local renewable-based generation and advanced forms of information and technology. Hence, it is in a current state of transition between the traditional approach to power generation and distribution, where end-consumers of electricity have typically been inactive in their involvement with energy markets and a new approach that integrates their active participation. This new approach includes the use of distributed energy resources (DER) such as renewable-based generation and demand response (DR), which are being rapidly adopted by end-consumers where incentives are strong. This paper presents the role of the DR aggregator to effectively integrate DER technology as a new source of energy capacity into electricity networks using information communication technology and industry knowledge., Based on DR aggregators, this framework will efficiently facilitate renewable energy integration and customer engagement into the electricity market. To this aim, advantages and disadvantages of DR aggregators are discussed in this paper from political, economic, social, and technological (PEST) points of view. Based on this analysis, a strengths, weaknesses, opportunities, and threats (SWOT) analysis for a typical DR aggregator is presented. [ABSTRACT FROM AUTHOR]