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The increasing penetration of photovoltaic (PV) systems in low-voltage residential feeders has elevated the need for grid support at the distribution level to prevent violations of local voltage constraints. In this article, a coordinated reactive power support (RPS) methodology is presented that utilizes the demand-side flexibilities of the end user to keep local voltage levels within allowed levels. A cloud-based architecture is implemented to optimally coordinate consumers’ reactive power capable demand-side resources such as electric vehicles, solar PV systems, flexible home appliances, etc., considering their varying characteristics, ratings, and purposes. An optimization-based two-stage device scheduling and management model is presented for the cloud server that schedules consumers’ devices in day ahead for cost minimization, and optimally allocates the required RPS in real time among the candidate devices based on priority. Two device prioritization strategies are proposed that consider the reliability of reactive power capable consumer devices and management complexity, thereby, allowing consumers to either enhance the candidate devices’ lifetime or reduce the management complexity while participating in grid support. The proposed RPS methodology is validated using simulation studies, and an experimental setup is established to verify the viability of the proposed cloud-based coordination system for RPS. Case studies indicate that the proposed method can effectively prevent overvoltage situations by using coordinated RPS from consumers’ devices while maximizing their reliability. Results also indicate that the proposed methodology is economically more viable than state-of-the-art voltage control strategies. [ABSTRACT FROM AUTHOR]

Economic dispatch is a critical problem in operation of power grids. A consensus-based algorithm was recently proposed to solve the economic dispatch problem in a distributed manner. In this paper, we propose a novel secure scheme for the consensus-based economic dispatch algorithm using the Paillier cryptosystem. This secure scheme ensures that not only the network transmitted information is protected from external malicious party but also the privacy information of each node remains intact. The proposed secure scheme has two features. First, it relies on the solution to the so-called structural consensus problem with time-varying network weights. Second, it contains a strategy for transmitting encrypted information and generating network weights with randomness, as well as treatment of the practical issues like quantization error and computation overflow/underflow. The performance in terms of cost optimization and privacy-preserving is verified by rigorous theoretical analysis and numerical simulation. [ABSTRACT FROM AUTHOR]

In this article, the impact of prediction errors on the performance of a domestic power demand management is thoroughly investigated. Initially, real-time peak power demand management system using battery energy storage systems (BESSs), electric vehicles (EVs), and photovoltaics (PV) systems is designed and modeled. The model uses real-time load demand of consumers and their roof-top PV power generation capability, and the charging–discharging constraints of BESSs and EVs to provide a coordinated response for peak power demand management. Afterward, this real-time power demand management system is modeled using autoregressive moving average and artificial neural networks-based prediction techniques. The predicted values are used to provide a day-ahead peak power demand management decision. However, any significant error in the prediction process results in an incorrect energy sharing by the energy management system. In this research, two different customers connected to a real-power distribution network with realistic load pattern and uncertainty are used to investigate the impact of this prediction error on the efficacy of an energy management system. The study shows that in some cases the prediction error can be more than 300%. The average capacity of energy support due to this prediction error can go up to 0.9 kWh, which increases battery charging–discharging cycles, hence reducing battery life and increasing energy cost. It also investigates a possible relationship between environmental conditions (solar insolation, temperature, and humidity) and consumers’ power demand. Considering the weather conditions, a day-ahead uncertainty detection technique is proposed for providing an improved power demand management. [ABSTRACT FROM AUTHOR]

This article investigates and proposes an efficient control design to address the degradation in the mechanical speed of a traction machine drive (TMD) in an electric vehicle (EV) caused by thermal effects during its operation. Variations in the operating as well as ambient temperature cause unexpected uncertainties in TMD parameters such as stator and rotor resistances, which results in significant degradation in EV's speed performance capability. To mitigate this problem, an output feedback robust linear parameter varying (LPV) controller-observer set is designed using H $_{\infty }$ control theory that enhances the EV's speed performance in field-oriented control (FOC) frame. The internal stability of the closed-loop control and the $L_{2}$ gain bound are ensured by linear matrix inequalities. The performance of the proposed control technique is compared with that of conventional FOC, sliding mode control (SMC) and higher order sliding mode control (HOSMC) to validate its efficacy and advantages. The robustness of the proposed control technique is tested for an EV operation against the Worldwide Harmonised Light Vehicles Test Procedure (WLTP) Class 3 driving cycle. The nonlinear MATLAB simulation results guarantee the effectiveness of the proposed controller-observer set. These results are verified experimentally on an induction machine drive setup. [ABSTRACT FROM AUTHOR]

This paper presents a new methodology to extract the unknown parameters of a single-diode photovoltaic (PV) cell model. The first contribution of this paper is the development and implementation of a new version of the wind-driven optimization algorithm, called an adaptive wind-driven optimization (AWDO) algorithm. The advantages of the AWDO algorithm are: 1) accurate extraction of the global values of the optimized PV parameters in changing weather conditions, which is achieved by building solutions from random operations; and 2) capability of handling the given complex multi-modal and multi-dimensional optimization problems. The second contribution is the identification of a generalization model to generalize the extracted parameters of a single-diode PV cell model. That provides an ability of the proposed methodology to work with any I–V characteristic curve of PV cells and at any weather condition on a 15-min basis. To validate the proposed methodology, it has been tested for 1307 I–V characteristic curves of a PV module at various weather conditions on a 15-min basis. Additionally, its accuracy and computational efficiency are verified and compared with five well-known existing extraction methods: Villalva's model, particle swarm optimization, biogeography-based optimization, Gang's model, and bacterial foraging optimization by both simulation and outdoor measurements. The results show that the AWDO algorithm can provide the extracted five parameters with an acceptable range of accuracy and faster than the aforementioned models. Therefore, the proposed methodology (AWDO based on Chenlo's model) can be confidently recommended as a reliable, feasible, valuable, and fast optimization algorithm for parameter extraction of a single-diode PV cell model. [ABSTRACT FROM AUTHOR]

Proper management of building loads and distributed energy resources (DER) can offer grid assistance services in transactive energy (TE) frameworks besides providing cost savings for the consumer. However, most TE models require building loads and DER units to be managed by external entities (e.g., aggregators), and in some cases, consumers need to provide critical information related to their electricity demand and usage, which hampers their privacy. This article introduces a transactive energy management framework for the buildings in a residential neighborhood to address grid overloading and cost optimization of the buildings. The decentralized coordination for the energy management system is realized by using a multiagent system architecture, which provides the consumers with full decision-making authority and preserves their privacy. A new event-triggered transactive market algorithm is developed, where the buildings trade energy to maximize profits, while the regional grid operator procures energy-supply flexibility of active consumers to prevent transformer overloading. A two-stage energy management system is developed for the residential buildings that schedules building loads and DER units in day-ahead stage to minimize cost and inconveniences for the consumer while participating in the real-time transactive market to maximize profits. An optimal bidding model is developed for the buildings that incorporates the degradation of residential storage devices for energy trading. Case studies and analyses with actual Australian building data and electricity tariff structures indicate the efficacy of the proposed methodology for effective mitigation of transformer overloading at a negligible cost compared to transformer replacement cost. Results also indicate that the proposed system can provide 15–20% cost savings for the consumers while minimizing their inconveniences and degradation of storage devices. [ABSTRACT FROM AUTHOR]

In this paper, an improved vehicle-to-microgrid (V2M) framework is proposed for a commercial locality operating as a hybrid ac–dc microgrid, which optimally coordinates electric-vehicle (EV) storages in a distributed manner. An aggregator model is proposed that solves the economic dispatch problem of parked EV storages in a centralized fashion and generates power references in real time for the designed EV storage controllers. Unlike the conventional EV storage controller, the proposed optimization-incorporated distributed EV storage controller can switch from decentralized to distributed control mode or vice versa based on situations and utilizes a sparse vehicle-to-vehicle communication network. The power flow between the ac and dc subgrids is managed by interlinking converters (IC). The IC control structure is augmented by combining voltage- and power-based droop-control schemes in the power control loop. This modification enables simultaneous ac and dc bus voltage regulations. Case studies are carried out to validate the efficacy of the developed framework with a real commercial network and loads. The results exhibit robust performance of the overall system for generation-demand variability, transitions between islanded and grid-tied conditions, and user-preferred EV disconnections and time delay. [ABSTRACT FROM AUTHOR]

Contrary to reliability analysis in power systems with the main mission on safely and securely withstanding credible contingencies in day-to-day operations, resilience assessments are centered on high-impact low probability (HILP) events in the grid. This paper proposes an autonomous load restoration architecture founded on IEC 61850-8-1 GOOSE communication protocol to engender an enhanced feeder-level resilience in active power distribution grids. Different from the past research on outage management solutions, most of which 1) are not resilience-driven; 2) are reactive solutions to local single-fault events; and 3) do not address both network built-in flexibilities and flexible resources. The proposed solution harnesses 1) the imported power and flexibility from the neighboring networks; 2) distributed energy resources; and 3) vehicle to grid capacity of electric vehicles aggregations to enhance the feeder-level resourcefulness for agile response and recovery. Through real-time self-reconfiguration strategies, the suggested solution is capable of coping both single and subsequent outage events, and will engender a heightened resilience before and during the contingency period. Moreover, a resilience evaluation framework, which quantifies the contribution of all resources involved in service restoration, is developed. Real-time performance of the designed architecture is evaluated on a real-world power distribution grid using a real-time hardware-in-the-loop platform. Numerical case studies through a number of diverse scenarios demonstrate the efficacy of the proposed restoration solution in practicing an enhanced resilience in power distribution systems in response to HILP scenarios. [ABSTRACT FROM AUTHOR]

The fluctuation of meteorological data causes random changes in photovoltaic (PV) performance, which may negatively affect the stability and reliability of the electrical grid. This paper proposes a new ultra-short-term offline hybrid prediction model for PV I–V characteristic curves based on the dynamic characteristics of the meteorological data on a 15-min basis. The proposed hybrid prediction model is a combination of the random forests (RFs) prediction technique and the ant-lion optimizer (ALO). ALO is used to optimize the hyper-parameters of the RFs model, which aims to improve its performance in terms of accuracy and computational time. The performance of the proposed hybrid prediction model is compared with that of conventional RFs, RFs-iteration, generalized regression neural network (GRNN), GRNN-iteration, GRNN-ALO, a cascade-forward neural network (CFNN), CFNN-iteration, CFNN-ALO, feed-forward neural network (FFNN), FFNN-iteration, and FFNN-ALO models. The result shows that the I–V characteristic-curve prediction accuracy, in terms of the root-mean-squared error, mean bias error, and mean absolute percentage error of the proposed model are 0.0091 A, 0.0028 A, and 0.1392%, respectively, with an accuracy of 99.86%. Moreover, the optimization, training, and testing times are 162.15, 10.1919, and 0.1237 s, respectively. Therefore, the proposed model performs better than the aforementioned models and the other existing models in the literature. Accordingly, the proposed hybrid (RFs-ALO) offline model can significantly improve the accuracy of PV performance prediction, especially in grid-connected PV system applications. [ABSTRACT FROM AUTHOR]