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This paper proposes and implements a model-free optimal strategy to regulate the voltage in distribution systems by effective control of Inverter-Interfaced Distributed Energy Resources (I-DERs) output power. In distribution systems, where the ratio of R/X is considerable, reactive power control is not always an optimum solution for voltage regulation. With the aim of improving voltage regulation, this paper proposes an alternative method to optimally coordinate active and reactive power of I-DERs in the grid. The proposed method does not need to have a system model and phase measurement, which are barriers in control and operation of behind-the-meter assets. The paper formulates the problem of voltage regulation in form of equilibrium-map-with-optimum, without any restriction on system configuration, amount of reverse power flow and losses. Using the extremum seeking control technique and without an explicit model of distribution circuit, the ratio of active and reactive power is coordinated in real time. The amount of exchanged apparent power is also adjusted simultaneously. Simulation results for a typical distribution circuit verify the effectiveness of the proposed approach in improving the voltage regulation performance. The experimental results also show a 20 percent reduction in use of system capacity to provide the desired voltage regulation performance.

Modern distribution systems are confronted by increasing penetration of distributed energy resources, making state estimation a critical application for distribution systems. However, existing state estimation schemes are often time-consuming and therefore, hard to scale up for large systems. In this context, this paper has proposed using a surrogate model to accelerate state estimations. Long-short-term memory (LSTM) recurrent neural networks have been applied to produce a fast yet coarse surrogate of the system states, which captures the temporal correlations between consecutive states. We have further applied an autoencoder to reduce the input size of LSTM networks, thereby shrinking LSTM network size and increasing the scalability of the proposed method. The surrogate states from LSTM are then fed into the forward/backward sweep state estimator as initial values. As a result, the state estimation convergence is accelerated by the LSTM surrogates. The proposed method is tested on IEEE 123-bus and 8500-node three-phase unbalanced test systems. Experimental results show that the proposed LSTM networks significantly reduce the computational time of distribution systems state estimation.

This study, based on a novel control strategy, proposes a sizing method for battery energy storage systems (ESSs), which makes the wind power system more dispatchable. The main objective of the proposed control-based sizing method is to facilitate robust unit commitment by smoothing the output power of wind according to a desired reference. Owing to the energy conversion loss, the controller closely monitors the battery state of charge (SOC) to prevent the battery from being fully discharged during the smoothing procedure. The proposed controller can automatically revise the battery dispatching reference maintaining the SOC constant. In each dispatch interval, model predictive control is used in real time to ensure that the output power follows the generated reference. The ESS is sized according to the simulation results solved by the proposed control strategy. Meanwhile, the performance of the proposed control strategy is validated over actual wind power data.

With the rapidly growing electric vehicle adoption rate and increasing number of public electric vehicle charging stations in recent years, electric vehicle becomes more and more critical in the demand response programs. Development in Vehicle to Grid technology has converted electric vehicle to distributed energy resources. Using the Electric Vehicle Smart Charging Infrastructures on UCLA campus and city of Santa Monica as testbeds, we have collected real-world datasets of electric vehicle usage, based on which, we proposed optimal bi-directional charging control strategies to integrate electric vehicle in commercial and public parking facilities into the power grid as distributed energy resources for demand response programs by two-stage distributed optimization and water-filling algorithm. Driver behavioral uncertainties have been considered in our approach. Specifically, electric vehicle users are clustered by their behavioral patterns using a modified Latent Semantic Analysis. The first-stage optimization is performed to minimize energy cost using day-ahead wholesale energy price with predictions on energy demand and electric vehicle availability which generated by a mixture user model. Decentralized optimization (second-stage) is carried out on the next day in real-time to control individual electric vehicle so that the aggregated load can follow the first-stage optimal profile. As an alternative, a fast converging water filling algorithm is proposed and compared with two-stage optimization. Extensive simulation results show that proposed charging controls can utilize electric vehicle as distributed energy resource to accommodate demand response program while satisfying electric vehicle energy demands and providing significant energy cost savings.

Traditional iterative-based numerical methods for distribution systems state estimation suffer from slow convergence on large systems. Confronted with increasing renewable integration and its fast power variations, distribution systems call for faster state estimations. In this context, a combination of a machine learning surrogate and a numerical method is developed for distribution system state estimations in this paper. First, a deep neural network model is trained as a fast yet coarse surrogate to the iterative state estimator. Then, the surrogate is fed into a forward/backward sweep estimator as initial values for refinement. By starting from a better “guess” using the surrogate model, the iterative method converges much faster. The proposed method is validated on IEEE 123-bus and 8500-node three-phase unbalanced test systems.

Electric vehicle (EV) charging management systems control and schedule EV load according to the measurements of local building load, solar generation, and dynamic electricity price. Within this information network, any data replaced or modified by an attacker will disrupt the EV charging schedule and could cause damage to the electricity grid. Under real circumstances, these measurements are correlated in a way that is not true for false data. This paper examines the relationship of pairwise measures within the system to establish a correlation-invariant network, and a multivariate time-series segmentation method along with a weighted k nearest neighbor (kNN) classifier is proposed to detect the changes in correlations and identify anomalous data within the network.

Significant increase in the installation and penetration of Renewable Energy Resources (RES) has raised intermittency and variability issues in the electric power grid. Solutions based on fast-response energy storage can be costly, especially when dealing with higher renewable energy penetration rate. The rising popularity of electric vehicles (EVs) also brings challenges to the system planning, dispatching and operation. Due to the random dynamic nature of electric vehicle charging and routing, electric vehicle load can be challenging to the power distribution operators and utilities. The bright side is that the load of EV charging is shiftable and can be leveraged to reduce the variability of renewable energy by consuming it locally. In this paper, we proposed a real-time system that incorporates the concepts of prioritization and cryptocurrency, named SMERCOIN, to incentivize electric vehicle users to collectively charge with a renewable energy-friendly schedule. The system implements a ranking scheme by giving charging priority to users with a better renewable energy usage history. By incorporating a blockchain-based cryptocurrency component, the system can incentivize user with monetary and non-monetary means in a flat-rate system. The effectiveness of the system mechanism has been verified by both numerical simulations and experiments. The system experiment has been implemented on campus of the University of California, Los Angeles (UCLA) for 15 months and the results show that the usage of solar energy has increased significantly.

A new method for the control of a battery energy storage system and its implementation on a 25 kW solar system to compensate solar power intermittency and improve distribution grid power quality is presented in this paper. The novelty of the proposed method is to provide a systematic way to optimize the size of the battery capacity for the desired level of solar power smoothing. This goal is achieved by designing a two-stage filter solution. The first stage is a fast response digital finite impulse response (FIR) filter that makes a trade-off between smoothing of the solar output and battery capacity. This paper proposes an optimal design of a minimum-length, low-group-delay FIR filter by employing convex optimization, discrete signal processing, and polynomial stabilization techniques. The new strategy proposed in this paper formulates the design of a length- $N$ low-group-delay FIR filter as a convex second-order cone programming, which guarantees that all the filter zeros are inside the unit circle (minimum-phase). A quasi-convex optimization problem is formulated to minimize the length of the low-group-delay FIR filter. The second-stage filter is designed to level the battery charging load. The effectiveness and performance of the proposed approach is demonstrated by simulation results and also over a real-case implementation.

The trending integrations of Battery Energy Storage System (BESS, stationary battery) and Electric Vehicles (EV, mobile battery) to distribution grids call for advanced Demand Side Management (DSM) technique that addresses the scalability concerns of the system and stochastic availabilities of EVs. Towards this goal, a stochastic DSM is proposed to capture the uncertainties in EVs. Numerical approximation is then used to make the problem tractable. To accelerate the computational speed, the proposed DSM is tightly relaxed to a convex form using second-order cone programming. Furthermore, in light of the continuous increasing problem size, a distributed method with a guaranteed convergence is applied to shift the centralized computational burden to distributed controllers. To verify the proposed DSM, real-life EV data collected on UCLA campus is used to test the proposed DSM in an IEEE benchmark test system. Numerical results demonstrate the correctness and merits of the proposed approach., This paper has been withdrawn because important typos

Energy management in microgrids is typically formulated as an offline optimization problem for day-ahead scheduling by previous studies. Most of these offline approaches assume perfect forecasting of the renewables, the demands, and the market, which is difficult to achieve in practice. Existing online algorithms, on the other hand, oversimplify the microgrid model by only considering the aggregate supply-demand balance while omitting the underlying power distribution network and the associated power flow and system operational constraints. Consequently, such approaches may result in control decisions that violate the real-world constraints. This paper focuses on developing an online energy management strategy (EMS) for real-time operation of microgrids that takes into account the power flow and system operational constraints on a distribution network. We model the online energy management as a stochastic optimal power flow problem and propose an online EMS based on Lyapunov optimization. The proposed online EMS is subsequently applied to a real-microgrid system. The simulation results demonstrate that the performance of the proposed EMS exceeds a greedy algorithm and is close to an optimal offline algorithm. Lastly, the effect of the underlying network structure on energy management is observed and analyzed.

Increasing the number of electric vehicles (EVs) in the transportation sector can change the load pattern in power grids and affect its service quality and operational reliability. As EVs are mostly parked during the day, an EV load management (EVLM) system has been shown effective in mitigating those effects on power grids. The uncertainties in EV load and the computational burden of its coordination in the large-scale EV penetration, however, make EVLM challenging. In this paper, we address the former using the non-parametric diffusion kernel density estimation (DKDE) to estimate the expected charging energy demand and EV availability. To deal with the latter, we formulate EVLM considering the power flow constraints as a consensus problem which is solved by the alternating direction methods of multipliers (ADMM) in a distributed manner. Using real data, we evaluate the performance of DKDE and compare its results with Gaussian kernel density (GKDE). Owing to the smoothing properties of linear diffusion process and optimal bandwidth selection, DKDE is more adaptive to the training dataset and results in more accurate load estimation. Applying the prediction results to IEEE-13 bus system, the effectiveness of the proposed EVLM in nodal voltage improvement and feeder congestion mitigation is validated.

The rapid pace of Electrification in the transportation sector, realized through plug-in electric vehicle (EV) integration, necessitates the smart charging infrastructures. These systems built on real-time data collection and decision making coordinate the charging demand to facilitate high penetration of PEV s in the power grids. Accordingly, the inherent cyber-physical characteristic of smart charging networks makes them susceptible to cyber-physical threats. Due to the lack of a consistent cyber-physical attack assessment in EV networks, this paper aims to propose the vulnerability analysis and risk assessment for the smart charging infrastructures. To this end, we define several potential failure scenarios for the WinSmartEV™ charging system on the UCLA campus and study the impacts of potential cyber-physical attacks. Moreover, we outline a codified methodology and taxonomy for assessing vulnerability and risk of cyber-physical attacks on the EV charging networks in order to create a generalizable and comprehensive solution. The outcome is a framework to prioritize the degree of the vulnerabilities and risks in the EV networks and to develop effective countermeasures.

This paper introduces a scalable framework to coordinate the net load scheduling, sharing, and matching in a neighborhood of residential prosumers connected to the grid. As the prosumers are equipped with smart appliances, photovoltaic panels, and battery energy storage systems, they take advantage of their consumption, generation, and storage flexibilities to exchange energy with neighboring prosumers through negotiating on the amount of energy and its price with an aggregator. The proposed framework comprises two separate multi-objective mixed integer nonlinear programming optimization models for prosumers and the aggregator. Prosumers' objective is to maximize the comfort level and minimize the electricity cost at each instant of time, while aggregator intends to maximize its profit and minimize the grid burden by matching prosumers' supply and demand. The evolutionary nondominated sorting genetic algorithm-III (NSGA-III) is employed to generate a set of feasible nondominated solutions to the optimization problem of each individual prosumer and the aggregator. As a bilateral negotiation between each prosumer and the aggregator results in significant computational and communication overhead, a virtual power plant is introduced as an intermediator on behalf of all prosumers to proceed the negotiation with the aggregator in a privacy-preserving noncooperative environment, where no private information is shared. Hence, an automated negotiation approach is embedded in the framework, which enables the negotiators to reactively negotiate on concurrent power and price using private utility functions and preferences. To converge to an acceptable agreement, the negotiation approach follows an alternating-offer production protocol and a reactive utility value concession strategy. The effectiveness of the framework is evaluated by several economic and environmental assessment metrics through a variety of numerical simulations.

In this paper, a hierarchical distributed method consisting of two iterative procedures is proposed for optimal electric vehicle charging scheduling (EVCS) in the distribution grids. In the proposed method, the distribution system operator (DSO) aims at reducing the grid loss while satisfying the power flow constraints. This is achieved by a consensus-based iterative procedure with the EV aggregators (Aggs) located in the grid buses. The goal of aggregators, which are equipped with the battery energy storage (BES), is to reduce their electricity cost by optimal control of BES and EVs. As Aggs' optimization problem increases dimensionally by increasing the number of EVs, they solved their problem through another iterative procedure with their customers. This procedure is implementable by exploiting the mathematical properties of the problem and rewriting Aggs' optimization problem as the \textit{sharing problem}, which is solved efficiently by the alternating direction method of multipliers (ADMM). To validate the performance, the proposed method is applied to IEEE-13 bus system., This paper has been accepted to IEEE PES General Meeting 2019

This paper investigates the Demand Side Management (DSM) in a commercial building microgrid with solar generation, stationary Battery Energy Management System (BESS) and gridable (V2G) Electric Vehicle (EV) integration. Taking into consideration of a comprehensive pricing model, we first formulate a deterministic DSM as a mixed integer linear programming problem, assuming perfect knowledge of the uncertainties in the system. A two-stage stochastic DSM is further developed that addresses the stochastic nature in solar generation, loads, EV availabilities and EV energy demands. The proposed DSMs are validated with real solar generation, loads, BESS and EV data using sample average approximation. Detailed case studies show that the stochastic DSM outperforms its deterministic counterpart for cost saving for a wide range of prices, though at the expense of higher computational time. Computational results also demonstrate that moderate number of EVs helps to cut down the overall operation cost, which sheds light on the benefit of future large scale EV integration to smart buildings.

Trending integration of distributed energy resources calls for state estimation at the distribution level for providing reliable power systems information. In contrast to transmission systems, distribution systems are sparsely monitored, and consequently difficult to estimate states. To address measurement scarcity problem in distribution systems, this paper proposes a distribution system state estimation framework that relies on robust pseudo-measurement modeling. User-level metering data is used to train gradient boosting tree models for generating pseudo-measurements. A ladder iterative state estimator is then applied on the pseudo-measurements to solve for system states. Simulation studies are performed on the IEEE 13-bus and 123-bus test feeders. Numerical results demonstrate that the proposed state estimation scheme outperform two benchmark approaches in terms of accuracy (error), consistency (error variance) and robustness (high accuracy subject to load changes).

We propose a distributed electric vehicle (EV) charging scheduling to minimize load variance in the distribution grid and reduce EV charging cost. To predict the availability and load demand of the EVs, we use nonparametric diffusion-based kernel density estimator (DKDE) to model the stochasticity of charging load. DKDE is based on smoothing properties of linear diffusion process which is more adaptive to the training dataset and results in optimal bandwidth selection comparing to Gaussian kernel density estimator (GKDE). Then, we formulate the optimal charging problem as a sharing problem which is solved efficiently by alternating direction method of multipliers (ADMM). Using real data numerical simulation, we evaluate DKDE prediction accuracy and verify the EV charging scheduling performance.

This paper proposes a hybrid kernel density estimator (HKDE) that uses both Gaussian- and Diffusion-based KDE (GKDE and DKDE) to predict the stay duration and charging demand of electric vehicles (EVs), which are essential parameters for optimizing EV charging schedule. While DKDE has higher accuracy in general, GKDE tends to result in better estimation for users who charge the EV irregularly. Therefore, the HKDE evaluates and categorizes the charging pattern regularity of a user, and determines which KDE to use by a novelty detection method based on the user's historical data. The estimations are then applied to an optimal EV charging algorithm to minimize load variance in an EV charging infrastructure and reduce EV charging cost. Real data is used for the numerical simulation to show the effectiveness of the proposed approach for predicting EV user behavior and scheduling EV charging load.

Electric vehicles (EVs) are controllable loads from which distribution grid operator can benefit in order to minimize the load profile variations. In this paper, we proposed a hierarchical distributed optimization framework such that EV management system (EVMS), as a part of distribution grid management system, minimizes the load variance of the grid in communication with the EV aggregators which control EV charging load of the distribution system feeders. The hierarchical distributed framework, based on alternative direction method of multipliers (ADMM), increases the scalability of the EV charging infrastructure while decreases computational burden. In our proposed approach, each EV aggregator schedules the EV charging profiles of its feeder in a distributed fashion which avoids sharing the EV owners' desired charging profile information and enables privacy preserving. To show the performance of our approach, we apply it to a case study with 100% EV penetration, including 4 feeders and 60 EVs, and show how the load variance of the system and charging cost of individual EVs decrease.

In this paper, we purpose an autonomous electric vehicle (AEVs) based charging navigation system. It accommodates charging requests by a fleet of AEVs in an integrated manner. We focus on one major problem of implementing such framework: matching between AEVs to electric vehicle charging stations (EVSEs) that minimize total operational cost. The problem is first formulated as a minimum weight perfect matching through creation of a bipartite graph that could be solved by integer programming. We further relax this to linear programming. To demonstrate effectiveness of such formulation, we implement the system with different use cases. We also propose two simple charging strategies that considers various factors, including charging price, current state of charge, and restrictions of power consumption added by utilities. Based on the system, we study their effects on the power grid and AEV owners.

Electric Vehicle (EV) is playing a significant role in the distribution energy management systems since the power consumption level of the EVs is much higher than the other regular home appliances. The randomness of the EV driver behaviors make the optimal charging or discharging scheduling even more difficult due to the uncertain charging session parameters. To minimize the impact of behavioral uncertainties, it is critical to develop effective methods to predict EV load for smart EV energy management. Using the EV smart charging infrastructures on UCLA campus and city of Santa Monica as testbeds, we have collected real-world datasets of EV charging behaviors, based on which we proposed an EV user modeling technique which combines statistical analysis and machine learning approaches. Specifically, unsupervised clustering algorithm, and multilayer perceptron are applied to historical charging record to make the day-ahead EV parking and load prediction. Experimental results with cross-validation show that our model can achieve good performance for charging control scheduling and online EV load forecasting., Comment: 2018 IEEE PES General Meeting

Summary A distributed energy resource (DER) system uses renewable generation and energy storage to provide power and ancillary services directly to users in the distribution system, and benefits the power grid by offering a solution to the energy crisis and mitigating the waste pollution to the environment. A key challenge in implementing a DER system is how to ensure the standardization of its communication network. The goal of this paper is to study and propose a common approach to standardize the communication network in the DER system using IEC 61850 and includes a design and implementation of the communication architecture. Two prototypical testbeds for the DER system have been developed in the SMERC laboratory and at KIER, with the aim to demonstrate a case study using the proposed approach. The testbeds contain various DERs such as photovoltaics, energy management systems, and electric vehicles. The procedure of integrating the IEC 61850 standard includes the system architecture configuration, data set design, test file write-up, and deployment of the gateway/client for communication. In the data set design, the specification of logical nodes, data object names, data attributes and common data classes are explained in detail. The research results and data collected from the current testbeds indicate a quality integration of the IEC 61850 standard with stable power flow and unified communication architecture in the DER system. Copyright © 2015 John Wiley & Sons, Ltd.

With the increasing of electric vehicle (EV) adoption in recent years, the impact of EV charging activities to the power grid becomes more and more significant. In this article, an optimal scheduling algorithm which combines smart EV charging and V2G gird service is developed to integrate EVs into power grid as distributed energy resources, with improved system cost performance. Specifically, an optimization problem is formulated and solved at each EV charging station according to control signal from aggregated control center and user charging behavior prediction by mean estimation and linear regression. The control center collects distributed optimization results and updates the control signal, periodically. The iteration continues until it converges to optimal scheduling. Experimental result shows this algorithm helps fill the valley and shave the peak in electric load profiles within a microgrid, while the energy demand of individual driver can be satisfied., IEEE PES General Meeting 2017

As electric vehicle (EV) adoption is growing year after year, there is no doubt that EVs will occupy a significant portion of transporting vehicle in the near future. Although EVs have benefits for environment, large amount of un-coordinated EV charging will affect the power grid and degrade power quality. To alleviate negative effects of EV charging load and turn them to opportunities, a decentralized real-time control algorithm is developed in this paper to provide optimal scheduling of EV bi-directional charging. To evaluate the performance of the proposed algorithm, numerical simulation is performed based on real-world EV user data, and power flow analysis is carried out to show how the proposed algorithm improve power grid steady state operation. . The results show that the implementation of proposed algorithm can effectively coordinate bi-directional charging by 30% peak load shaving, more than 2% of voltage drop reduction, and 40% transmission line current decrease.

WINSmartEV is a software based plug-in electric vehicle (PEV) monitoring, control, and management system. It not only incorporates intelligence at every level so that charge scheduling can avoid grid bottlenecks, but it also multiplies the number of PEVs that can be plugged into a single circuit. This paper proposes, designs, and executes many upgrades to WINSmartEV. These upgrades include new hardware that makes the level 1 and level 2 chargers faster, more robust, and more scalable. It includes algorithms that provide a more optimal charge scheduling for the level 2 (EVSE) and an enhanced vehicle monitoring/identification module (VMM) system that can automatically identify PEVs and authorize charging.

This research investigates electric vehicle (EV) charging behavior and aims to find the best method for its prediction in order to optimize the EV charging schedule. This paper discusses several commonly used machine learning algorithms to predict charging behavior, including stay duration and energy consumption based on historical charging records. It is noted that prediction error increases along with the rise of data entropy or the decrease of data sparsity. Thus, this paper accounts for both indicators by defining the entropy/sparsity ratio (R). When R is low, support vector regression (SVR) and random forest (RF) regression show better accuracy for stay duration and energy consumption predictions, respectively. While R is high, a diffusion-based kernel density estimator (DKDE) performs better for both predictions. The three methods are assembled as the proposed Ensemble Predicting Algorithm (EPA) to improve predicting performance by decreasing 11 % of the duration and 22 % of the energy consumption prediction errors. The prediction results are then applied to an optimal EV charging scheduling algorithm to minimize load variance while reducing the EV charging cost. A numerical simulation using real charging data is conducted to show the effectiveness of improved predictions and EV load management. The results show that the charging scheduling combined with EPA prediction can reduce 27% of peak load, 10% of load variation, and 4% cost reduction, compared to uncoordinated charging.

Un-coordinated Electric Vehicle (EV) charging behaviors will impact the power grid and degrade power quality. Smart charging system can optimally allocate the energy among EVs and minimize the impact on grid. IEC 61850 is an international standard for distribution & substation automation and intelligence device communication in smart grid. This paper introduces the extension of IEC 61850 with smart EV scheduling algorithms, considering current multiplexing, power sharing strategies and user behaviors. We have developed an IEC 61850 abstract data model for the information exchanged among components of smart EV charging infrastructures, including the EV charging control center, intelligent Electric Vehicle Supply Equipment (EVSE) and the EV users with mobile applications. Real-world EV usage data on UCLA campus is utilized in the simulation experiment, which is based on the predictive control paradigm. The data model is instantiated by a web service on EV control center by converting the raw data streams from EVSEs in various formats, such as JSON or raw string, etc. into a standardized IEC 61850 SCL file, which contains the critical meter data and charging session parameters. The system cost performance, the interoperability and scalability of smart EV charging infrastructure are greatly improved.

With recent years' fast growing penetration of Plug-in Electric Vehicles (PEV), the energy source under demand is gradually transferring from gasoline to electricity. It would be of significant interest to the utility and energy service community to predict charging demand on users' side. In this paper, we find and propose a practical PEV user demand prediction algorithm through user-based charging session data. The analysis is completed with the data collected from the charging stations installed on campus of University of California, Los Angeles (UCLA). We also present a mobile interface for PEV users to interact with the charging system. Initial investigations recommend to use the median value of each individual user's charging history as a personal energy demand value. The SMAPE accuracy of this approach is 0.55 for users with small and large sample sizes.

This paper studies energy management in a Microgrid (MG) with solar generation, Battery Energy Management System (BESS) and gridable (V2G) Electric Vehicles (EVs). A two-stage stochastic optimization method is proposed to capture the intermittent solar generation and random EV user behaviors. It is subsequently formulated as a Mixed Integer Linear Programming (MILP) problem. To evaluate the proposed method, real solar generation, loads, BESS and EV data is used in Sample Average Approximation (SAA). Computational results show the correctness of the proposed method as well as steady and tightly bounded optimality gap. Comparisons demonstrate that the proposed stochastic method outperforms its deterministic counterpart at the expense of higher computational cost. It is also observed that moderate number of EVs helps to reduce the overall operational cost of the MG, which sheds light on future EV integration to the smart grid.

This paper considers a typical solar installations scenario with limited sensing resources. In the literature, there exist either day-ahead solar generation prediction methods with limited accuracy, or high accuracy short timescale methods that are not suitable for applications requiring longer term prediction. We propose a two-tier (global-tier and local-tier) prediction method to improve accuracy for long term (24 hour) solar generation prediction using only the historical power data. In global-tier, we examine two popular heuristic methods: weighted k-Nearest Neighbors (k-NN) and Neural Network (NN). In local-tier, the global-tier results are adaptively updated using real-time analytical residual analysis. The proposed method is validated using the UCLA Microgrid with 35kW of solar generation capacity. Experimental results show that the proposed two-tier prediction method achieves higher accuracy compared to day-ahead predictions while providing the same prediction length. The difference in the overall prediction performance using either weighted k-NN based or NN based in the global-tier are carefully discussed and reasoned. Case studies with a typical sunny day and a cloudy day are carried out to demonstrate the effectiveness of the proposed two-tier predictions.

Un-coordinated Electric Vehicle (EV) charging can create unexpected load in local distribution grid, which may degrade the power quality and system reliability. The uncertainty of EV load, user behaviors and other baseload in distribution grid, is one of challenges that impedes optimal control for EV charging problem. Previous researches did not fully solve this problem due to lack of real-world EV charging data and proper stochastic model to describe these behaviors. In this paper, we propose a new predictive EV scheduling algorithm (PESA) inspired by Model Predictive Control (MPC), which includes a dynamic load estimation module and a predictive optimization module. The user-related EV load and base load are dynamically estimated based on the historical data. At each time interval, the predictive optimization program will be computed for optimal schedules given the estimated parameters. Only the first element from the algorithm outputs will be implemented according to MPC paradigm. Current-multiplexing function in each Electric Vehicle Supply Equipment (EVSE) is considered and accordingly a virtual load is modeled to handle the uncertainties of future EV energy demands. This system is validated by the real-world EV charging data collected on UCLA campus and the experimental results indicate that our proposed model not only reduces load variation up to 40% but also maintains a high level of robustness. Finally, IEC 61850 standard is utilized to standardize the data models involved, which brings significance to more reliable and large-scale implementation.

In this paper, we present a layered architecture and approach for creating a high performance edge server called reader coordinator (RC) for managing variety of automatic identification hardware such as radio frequency identification (RFID) reader, RFID printer, sensor, barcode scanner, or a controllable device (e.g., programmable logic controller). A unique hardware abstraction approach is used to provide device extensibility and manageability. Rule-based device configuration and control which allows the real-time changing of system behavior is presented. Various data encoding schemes used to accommodate data captured by a device and used as the input of rule based data filtration and aggregation are discussed. We discuss a mechanism of building self-governed automation processing in the data capture process. Performance is measured by using virtual reader that generates high volume of data at high speed. The measurement shows the RC is capable of handling such data.

Uncontrolled Electric Vehicle (EV) and Plug-in Hybrid Electric Vehicle (PHEV) charging within a local distribution grid may cause unexpected high load, which further results in power quality degradation. However, coordinating charging behaviors of a number of EVs is a challenging task, which involves not only the deterministic schedule computing but also nondeterministic EV driver behaviors with random arrival time and energy demands. Previous researches in this area rarely consider these random behaviors for real EV users. In this paper, an implementable event-based cost optimal scheduling algorithm (ECSA) is developed, which solves EV scheduling problem by dynamically estimating the stay duration and energy demand for each participating EV user. Datasets, including users' historical charging records and time series meter data collected from Electric Vehicle Supply Equipments (EVSEs) in UCLA campus, are utilized for feature extraction. Based on that, proper inference technique is employed to determine parameters within each charging session. In addition, solar generation integration into EVSEs is also considered in our problem formulation. The proposed approaches are tested and validated by real EV charging schedules of users in UCLA campus. The results from simulation experiment demonstrate that the proposed algorithm has a better performance in cost minimization and load shifting compared to existing equal-sharing scheduling algorithm (ESSA).

A Networked Electric Vehicle (NEV) that can convert energy in the vehicle back to the grid when it has excess has the similar electrical characteristics to stationary energy storage devices. A Vehicle-to-Grid (V2G) system, which is composed of these NEVs, can mitigate the impact of renewable penetration, minimize the grid losses and improve the reliability of electricity supply. The goal of the paper is to propose an innovative V2G system with NEVs including mathematical modeling and practical implementation. More importantly, as one approach, the international unified standard IEC 61850 has been integrated into the system, with the aim to standardize the communication network. In the paper, authors investigate the current state of art in the integration of IEC 61850 into power grid systems, introduce a mathematical modeling of NEV that can realize V2G functionality and develop an explicit integration procedure including steps of system architecture, data set design and communication between gateway and client. The results represent a successful implementation of V2G system with the integration of IEC 61850, which implement a power system test bed with providing stable power quality and unified communication network.

A microgrid can be characterized by its integration of distributed energy resources and controllable loads. Such integration brings unique challenges to the microgrid management and control which can be significantly different from conventional power systems. Therefore, a conventional energy management system (EMS) needs to be re-designed with consideration of the unique characteristics of microgrids. To this end, we propose a microgrid EMS named a microgrid platform (MP). We take into account all the functional requirements of a microgrid EMS (i.e., forecast, optimization, data analysis, and human-machine interface) and address the engineering challenges (i.e., flexibility, extensibility, and interoperability) in the design and development of the MP. Moreover, we deploy the prototype system and conduct experiments to evaluate the microgrid management and control in real-world settings at the UCLA Smart Grid Energy Research Center. Our experimental results demonstrate that the MP is able to manage various devices in the testbed, interact with the external systems, and perform optimal energy scheduling and demand response.

Integration of Electrical Vehicles (EVs) with power grid not only brings new challenges for load management, but also opportunities for distributed storage and generation in distribution network. With the introduction of Vehicle-to-Home (V2H) and Vehicle-to-Grid (V2G), EVs can help stabilize the operation of power grid. This paper proposed and implemented a hybrid V2H/V2G system with commercialized EVs, which is able to support both islanded AC/DC load and the power grid with one single platform. Standard industrial communication protocols are implemented for a seamless respond to remote Demand Respond (DR) signals. Simulation and implementation are carried out to validate the proposed design. Simulation and implementation results showed that the hybrid system is capable of support critical islanded DC/AC load and quickly respond to the remote DR signal for V2G within 1.5kW of power range.

Energy management in microgrids is typically formulated as a non-linear optimization problem. Solving it in a centralized manner not only requires high computational capabilities at the microgrid central controller (MGCC) but may also infringe customer privacy. Existing distributed approaches, on the other hand, assume that all the generations and loads are connected to one bus and ignore the underlying power distribution network and the associated power flows and system operational constraints. Consequently, the schedules produced by those algorithms may violate those constraints and thus are not feasible in practice. Therefore, the focus of this paper is on the design of a distributed energy management strategy (EMS) for the optimal operation of microgrids with consideration of the distribution network and the associated constraints. Specifically, we formulate microgrid energy management as an optimal power flow problem and propose a distributed EMS where the MGCC and the local controllers jointly compute an optimal schedule. As one demonstration, we apply the proposed distributed EMS to a real microgrid in Guangdong Province, China consisting of photovoltaics, wind turbines, diesel generators, and a battery energy storage system. The simulation results demonstrate that the proposed distributed EMS is effective in both islanded and grid-connected mode. It is also shown that the proposed algorithm converges fast.

Integration of Electrical Vehicles (EVs) with power grid not only brings new challenges for load management, but also opportunities for distributed storage and generation. This paper comprehensively models and analyzes distributed Vehicle-to-Grid (V2G) for automatic load sharing with driver preference. In a micro-grid with limited communications, V2G EVs need to decide load sharing based on their own power and voltage profile. A droop based controller taking into account driver preference is proposed in this paper to address the distributed control of EVs. Simulations are designed for three fundamental V2G automatic load sharing scenarios that include all system dynamics of such applications. Simulation results demonstrate that active power sharing is achieved proportionally among V2G EVs with consideration of driver preference. In additional, the results also verify the system stability and reactive power sharing analysis in system modelling, which sheds light on large scale V2G automatic load sharing in more complicated cases.

A Distributed Energy Resources (DER) system, composed of distributed generation and storage units, has been proposed as a promising enhancement to the traditional power grids. One key challenge to implement a DER system, however, places on standardizing the communication network for seamless information exchange. As one approach, this paper focuses on integrating IEC 61850, which is an international unified standard for standardizing the communication network within a substation, into a DER system, using the UCLA SMERC building as test bed. To this end, we discuss a mathematical model of PV generation, present a representative demand profile, and develop a battery charging/discharging control algorithm. Moreover, we demonstrate a procedure to integrate IEC 61850 into the DER system step by step, with configuring the communication network and defining the data structure for the information exchange.

With an increased number of plug-in electric vehicles (PEVs) on the roads, PEV charging infrastructure is gaining an ever-more important role in simultaneously meeting the needs of drivers and those of the local distribution grid. However, the current approach to charging is not well suited to scaling with the PEV market. If PEV adoption continues, charging infrastructure will have to overcome its current shortcomings such as unresponsiveness to grid constraints, low degree of autonomy, and high cost, in order to provide a seamless and configurable interface from the vehicle to the power grid. Among the tasks a charging station will have to accomplish will be PEV identification, charging authorization, dynamic monitoring, and charge control. These will have to be done with a minimum of involvement at a maximum of convenience for a user. The system proposed in this work allows charging stations to become more responsive to grid constraints and gain a degree of networked autonomy by automatically identifying and authorizing vehicles, along with monitoring and controlling all charging activities via an RFID mesh network consisting of charging stations and in-vehicle devices. The proposed system uses a ZigBee mesh network of in-vehicle monitoring devices which simultaneously serve as active RFID tags and remote sensors. The system outlined lays the groundwork for intelligent charge-scheduling by providing access to vehicle’s State of Charge (SOC) data as well as vehicle/driver IDs, allowing a custom charging schedule to be generated for a particular driver and PEV. The approach presented would allow PEV charging to be conducted effectively while observing grid constraints and meeting the needs of PEV drivers.

With the rapid development of wind turbine, photovoltaicand battery technologies, renewable energy resources such as wind and solar become the most common distributedgenerations (DG) that are being integrated into microgrids. One key impediment is to determine the sizes and placements of DGs within which the microgrid can achieve its maximum potential benefits. The objective of the paper is to study and propose an approach to find the optimal sizes and placements of DGs in a microgrid. The authors propose a comprehensive objective function with practical constraints which take all the important factors that will impact the reliability of the power grid into account. To solve the optimization problem, genetic algorithm (GA) is used and compared with a mathematical optimization method nonlinear programming. The proposed model is tested on a real microgrid, i.e. Jeju Island, to evaluate and validate the performances of the approach. The simulation results present the optimal configuration of DGs for Jeju Island power grid. The analysis on results shows that GA maintains a delicate balance between performance and complexity. It is concluded that GA performs better not only in accuracy, stability, but also in computation time.