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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]

Voltage unbalance and magnitude violations under normal operating conditions have become main power quality problems in many low voltage (LV) distribution networks. Maintaining the voltage level in an LV network within the standard limits is the main constraining factor in increasing the network hosting ability for rooftop photovoltaic (PV). This study presents a new effective method for voltage management in unbalanced distribution networks through the implementation of optimal residential demand response (DR) and on-load tap changers (OLTCs). The proposed method minimises the compensation costs of voltage management (cost of DR and network loss), while prioritises the consumer consumption preferences for minimising their comfort level violations. A modified particle swarm optimisation algorithm (MPSO) is utilised to identify the optimal switching combination of household appliances and OLTC tap positions for the network voltage management. The proposed method is comprehensively examined on a real three-phase four-wire Australian LV network with considerable unbalanced and distributed generations. Several scenarios are investigated for improving the network voltage magnitude and unbalance considering individual and coordinated operations of DR and OLTCs (three phase tap control and independent phase tap control). Simulation results show that the coordinated approach of DR and OLTC, especially, DR integrated with OLTC independent phase tap control effectively improves the network voltage and increases the PV hosting capacity. [ABSTRACT FROM AUTHOR]

Until now, microgrids were assumed to operate either in grid-connected or islanded modes. As a third alternative, neighboring microgrids may interconnect to support each other during faults in a section of one MG, or in the course of overloading. They may also interconnect to reduce the cost of electricity generation in their systems. Thereby, a microgrid will experience a significant transformation in its structure, when coupled with one or more neighboring microgrids. Before the formation of the system of coupled microgrids, the stability of the new system is vital to be cautiously examined to intercept the transformation, if instability is to occur. An eigenanalysis-based small signal stability evaluation technique is developed in this paper which can be used to allow/deny the interconnection of the microgrids. The analysis also defines the suitable range of control parameters for the energy resources of the CMG to guarantee the stability of the new system, if it was determined unstable. Numerical analyses, realized in MATLAB, are performed for the interconnection of a few sample microgrids. The accuracy of the developed technique is validated by comparing its results with the time-domain performance of the similar system, realized in PSCAD/EMTDC. [ABSTRACT FROM AUTHOR]

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]

Abstract: This paper presents an analytical method to analyze the effect of X to R ratio as well as impedance value of branches on observability of a network based on un-decoupled formulation of state estimation (SE) and null space of matrices. The results showed that the X to R ratio of branches had no effect on the observability of networks. In addition, it was shown that observability of some networks was affected by impedance values while some others were not affected. In addition, for branch observability analysis of radial network, a simple and quick method is developed. Illustrative examples of the network under transmission and distribution voltages demonstrate the effectiveness of the proposed methods. [Copyright &y& Elsevier]

• Dynamically reconfiguring the structure of multi-microgrid systems to enhance the penetration of distributed energy resources. • Vehicle-to-grid concept is deployed to capture the intermittency of renewable resources and load demand. • To make the model more accurate, the uncertainty of loads and renewables is handled by using stochastic programming. • A mathematical-based convex optimization model is established to reap the benefits of CPLEX and GUROBI as solvers. • Results illustrate that both technical and economic aspects of multi-microgrid system improved significantly. On the one hand, the inherent intermittency in demands and renewable energy sources (RES) frequently bring challenges such as overload or surplus generation within microgrids. On the other hand, electric vehicle aggregations (EVAs) have garnered substantial attention as a pivotal strategy to address climate change and serve as a sustainable substitute for petroleum-based vehicles. However, the uncoordinated deployment of EVAs within microgrids, especially in the face of the intermittent nature of RES, poses a potential threat to the secure operation of microgrid systems. To tackle the mentioned issues, this research concentrates on interconnecting a group of scattered microgrids to create a multi-microgrid system. In more detail, by developing an energy management strategy to reconfigure the interconnections among microgrids, the efficient exchange of power among these multi-microgrid systems is facilitated, addressing the variability in load demands amidst the stochastic generation patterns of RESs. Besides, grid-to-vehicle (G2V) and vehicle-to-grid (V2G) concepts of EVAs are synchronized within the reconfigurable microgrid structure to enhance the flexibility of the model. To evaluate the model under realistic situations, a scenario-based method is also employed to reflect the effects of uncertainties on the model. The proposed approach, characterized by its mathematical convexity, allows for employing efficient solvers like CPLEX, ensuring the attainment of a feasible global solution within a finite timeframe. The effectiveness of the proposed method is demonstrated through its implementation on a modified 33-bus test system operated as multi-microgrid system. The results show the effectiveness of the proposed approach as a promising tool for optimizing the operation of reconfigurable multi-microgrid systems in the presence of EVAs, leading to operational cost reduction and voltage profile enhancement. [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]

• End-users' investments in DERs is introduced to the distribution network planning. • A planning model is presented that considers neighborhood energy trading schemes. • The model helps to reduce the network stress by optimum design of network charge. The widespread adoption of small-scale distributed energy resources (DERs) amongst energy users has drastically changed the operation of distribution networks. To date, there has not been a consolidated model to incorporate the investment decisions of the end-users in the distribution network planning. The contribution of this paper is a distribution network planning model for the utility which considers the neighborhood energy trading (NET) as a platform for end-users to directly exchange energy between them. The proposed mixed-integer second-order cone programming (MISOCP) problem provides the optimal decisions for line and transformer upgrades, as well as for photovoltaic (PV) and battery in end-users' premises. Moreover, it indicates a fair allocation of network charges among the participants to NET schemes. The simulation results on the IEEE-33 bus test system confirm the effectiveness of the proposed model in lowering the total cost of the planning and the operation. This platform can be used by government-owned utilities as a guide to avoid sunk investments while motivating the increased installation of renewable distributed generation and storage units by end-users. [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]

Highlights • Updating pseudo data considering the impact of spatial-temporal correlation. • Studying the impact of load aggregation and PV penetration on spatial-temporal correlation. • Developing a non-iterative computationally efficient DSSE algorithm. • Proposing a time series state estimation framework suitable for unbalanced LV distribution networks. Abstract The increasing complexity of distribution networks calls for advancement in distribution system state estimation (DSSE) to monitor the operating conditions more accurately. A sufficient number of measurement devices is imperative for a reliable and accurate state estimation. The limitation on the measurement devices is generally tackled with using the so-called pseudo measured data. However, the errors in pseudo data by current techniques are quite high leading to a poor DSSE. As customer loads in distribution networks show high cross-correlation in various locations and over successive time steps, it is plausible that deploying the spatial-temporal dependencies can improve the pseudo data accuracy and estimation. Although the role of spatial dependencies in DSSE has been addressed in the literature, one can hardly find an efficient DSSE framework capable of incorporating temporal dependencies present in customer loads. Consequently, to obtain a more efficient and accurate state estimation, we propose a new non-iterative DSSE framework to involve spatial-temporal dependencies together. The spatial-temporal dependencies are modeled by conditional multivariate complex Gaussian distributions and are studied for both static and real-time state estimations, where information at preceding time steps are employed to increase the accuracy of DSSE. The efficiency of the proposed approach is verified based on the quality and accuracy of the indices, standard deviation and computational time. The method applied to a combination of residential and industrial customer loads in three different balanced medium voltage (MV), and one unbalanced low voltage (LV) distribution case studies for evaluations. [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]

• Coordinating conventional Volt-VAr devices with distributed energy resources. • Incorporating demand response program into the Volt-VAr control problem. • Utilizing ε-constraint approach to generate Pareto optimal solutions. • Using IGDT to develop a robust decision-making approach. • Developing a mixed-integer linear programing framework. Distributed energy resources (DERs), such as renewable energy sources (RESs) and energy storage systems (ESSs), are inevitable parts of future power grids. However, the high penetration of RESs may cause voltage violation issues in distribution levels. To this end, the application of the Volt-VAr management (VVM) scheme is highlighted. This paper aims to maintain voltage levels within standard ranges with optimal coordination of different resources at minimum operational costs and voltage deviation. In more detail, this article develops a new VVM in distribution networks to effectively coordinate novel smart grid technologies such as PV inverters, time of use demand response program (TOU-DRP), energy storage systems (ESSs) with traditional devices, including on-line tap changer transformer (OLTC) and switchable capacitor banks (SCBs). Because the coordinated VVM is a bi-objective model that minimizes both operating cost and voltage deviation, it is proposed to use the ε-constraint technique to identify optimal Pareto solutions. To handle the uncertainty related to RESs and load demands, a robust technique based on information gap decision theory (IGDT) is also utilized. The formulation is configured as a mixed-integer linear program (MILP) and solved by CPLEX. It was implemented on the 118-bus distribution network under various conditions. The simulation results clearly show that the coordination of DERs and traditional devices has a significant impact on maintaining voltage levels within permissible ranges while minimizing operational costs. A higher degree of uncertainty radius can also be obtained if DRP participates in the VVM scheme. [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 optimal operation of active distribution systems in the presence of private renewable-based entities is one of the primary challenges of future power networks. In this regard, developing a practical framework to deal with this kind of issue is essential. Hence, in this paper, a novel bi-level stochastic programming approach is presented for optimal energy and reserve scheduling of the active distribution system in the presence of different eco-efficient autonomous players. In the proposed model, the distribution system operator, as a leader, attempts to minimize its total operating costs. At the same time, the renewable-based microgrid owner, as an independent follower, tends to maximize its profit from exchanging energy and reserve with the distribution system operator. The suggested scheme is a non-linear bi-level problem which is transformed into a non-linear single-level problem through Karush–Kuhn–Tucker conditions. In order to find the global optima, the non-linear single-level problem is linearized by utilizing the Big-M method. Finally, to investigate the effectiveness of the provided model, it is tested on the modified IEEE 15-Bus active distribution system under different cases and scenarios. Obtained results indicate that the operation cost of the distribution system operator can be reduced up to 134.09$, from 10710.11$ to 10576.02$, and the profit of the microgrid owner can be increased significantly 906.93$, from 659.455$ to 1566.39$, by considering both environmentally friendly units, IPL and CAES. • Optimal operation of the Active Distribution System in the presence of a private renewable-based Microgrid is addressed. • A stochastic bi-level programming approach is proposed for joint energy and reserve scheduling of the considered system. • Impacts of Intelligent Parking Lot and Compressed Air Energy Storage are investigated on optimal operation of the system. • A new framework is provided for the participation of the system's independent players in separate electricity markets. [ABSTRACT FROM AUTHOR]