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The need to optimize the energy consumption of commercial buildings– responsible for over 40% of U.S. energy consumption–has recently gained significant attention due to the call for energy efficiency. Moreover, the ability to participate in the retail electricity markets through proactive demand-side participation has recently led to the development of an economic model predictive control (EMPC) for these buildings’ Heating, Ventilation, and Air Conditioning (HVAC) systems. The objective of this paper is to develop a price-responsive operational model for buildings’ HVAC systems while considering inflexible loads and other distributed energy resources (DERs), including photovoltaic (PV) generation and battery storage systems. A Nonlinear Economic Model Predictive Controller (NL-EMPC) is presented to minimize the net cost of energy usage by the buildings’ flexible loads, i.e., HVAC systems while satisfying the comfort-level of buildings’ occupants. To improve the computational efficiency of the HVAC system controller, we propose a linearized economic model predictive controller (L-EMPC). The L-EMPC is a novel approximate linearized model for the NL-EMPC and is based on the feedback linearization technique. The proposed approach results in a controller for the building with the reduced complexity that accurately approximates the original nonlinear plant dynamics with its economic constraints. The efficiency of the proposed EMPC controllers are evaluated using several simulation case studies.

Electricity theft is the third largest form of theft in the United States. It not only leads to significant revenue losses, but also creates the risk of fires and fatal electrical shocks. In the past, utilities have fought electricity theft by sending field operation groups to conduct physical inspections of electrical equipment based on suspicious activity reported by the public. However, the recent rapid penetration of advanced metering infrastructure makes it possible to detect electricity theft by analyzing the information gathered from smart meters. In this paper, we develop a physically inspired data driven model to detect electricity theft with smart meter data. The main advantage of the proposed model is that it only leverages the electricity usage and voltage data from smart meters instead of unreliable parameter and topology information of the secondary network. Hence, a speedy and widespread adoption of the proposed model is feasible. We show that a modified linear regression model accurately captures the physical relationship between electricity usage and voltage magnitude on the Kron-reduced distribution secondaries. Our results show that electricity theft on a distribution secondary will lead to negative and positive residuals from the regression for dishonest and honest customers, respectively. The proposed model is validated with real-world smart-meter data. The results show that the model is effective in identifying electricity theft cases.

To facilitate the integration of distributed energy resources and improve existing operational strategies, power distribution systems have seen a rapid proliferation of deep reinforcement learning (DRL) based applications. DRL approach is well suited for dynamic, complex, and uncertain operational environments such as power distribution systems. This paper reviews the rapidly growing body of literature that develops applications of reinforcement learning in power distribution systems. These applications include active grid management, energy management system, retail electricity market, and demand response. This paper also summarizes the challenges of deploying DRL based solutions in distribution systems such as safety, robustness, interpretability, and sample efficiency. Finally, the research opportunities that can be pursued to address the challenges are provided.

Distribution grid planning, control, and optimization require accurate estimation of solar photovoltaic (PV) generation and electric load in the system. Most of the small residential solar PV systems are installed behind-the-meter making only the net load readings available to the utilities. This paper presents an unsupervised framework for joint disaggregation of the net load readings of a group of customers into the solar PV generation and electric load. Our algorithm synergistically combines a physical PV system performance model for individual solar PV generation estimation with a statistical model for joint load estimation. The electric loads for a group of customers are estimated jointly by a mixed hidden Markov model (MHMM) which enables modeling the general load consumption behavior present in all customers while acknowledging the individual differences. At the same time, the model can capture the change in load patterns over a time period by the hidden Markov states. The proposed algorithm is also capable of estimating the key technical parameters of the solar PV systems. Our proposed method is evaluated using the net load, electric load, and solar PV generation data gathered from residential customers located in Austin, Texas. Testing results show that our proposed method reduces the mean squared error of state-of-the-art net-load disaggregation algorithms by 67%.

The maximum diversified demand is an important factor to consider when utilities design new distribution systems. To estimate the maximum diversified demand, engineers need to make an estimate of the diversity factor (DF). In practice, electricity utility companies usually estimate the DF using DF tables, in which the DF changes with the number of customers. However, besides the number of customers, DF also depends on many other factors, such as customer type, weather, demographics, and socioeconomic conditions. Ignoring these factors, DF tables have limited accuracy. In addition, engineers cannot interpret or understand how various factors affect the DF. In this paper, by leveraging supervised machine learning algorithms, we build comprehensive DF prediction models that take a variety of factors into account. These models show high prediction accuracy and interpretabilty when applied to real-world distribution feeders. Using the interpretation method called SHapley Additive exPlanations, we quantity the importance of different features in determining DFs. Finally, we offer more insights into how various factors affect DFs.

Accurate estimates of network parameters are essential for advanced control and monitoring in power distribution systems. The existing methods for parameter estimation either assume a simple single-phase network model or require widespread installation of micro-phasor measurement units (micro-PMUs), which are cost prohibitive. In this paper, we propose a parameter estimation approach, which considers three-phase series impedance and only leverages readily available smart meter measurements. We first build a physical model based on the linearized three-phase power flow manifold, which links the network parameters with the smart meter measurements. The parameter estimation problem is then formulated as a maximum likelihood estimation (MLE) problem. We prove that the correct network parameters yield the highest likelihood value. A stochastic gradient descent (SGD) method with early stopping is then adopted to solve the MLE problem. Comprehensive numerical tests show that the proposed algorithm improves the accuracy of the network parameters.

Covered conductors are widely adopted in the medium to low voltage systems to prevent faults and ignitions from events such vegetation contacting with distribution lines and conductors slapping together. However, such events could cause partial discharge in deteriorated insulation system of covered conductors and ultimately lead to failure and ignition. To prevent power outages and wildfires, it is crucial to detect partial discharges of overhead power lines and perform predictive maintenance. In this paper, we develop advanced machine learning algorithms to detect partial discharge by using measurements from high frequency voltage sensors. Our innovative approach synergistically combines the merits of spectrogram feature extraction and deep convolutional neural networks. The proposed algorithms are validated using real-world noisy voltage measurements. Compared to the benchmark, our approach achieves notably better performance. Furthermore, the classification results from the neural networks are interpreted with an occlusion map, which identifies suspicious time intervals when partial discharges occur.

Volt-VAR control (VVC) is a critical application in active distribution network management system to reduce network losses and improve voltage profile. To remove dependency on inaccurate and incomplete network models and enhance resiliency against communication or controller failure, we propose consensus multi-agent deep reinforcement learning algorithm to solve the VVC problem. The VVC problem is formulated as a networked multi-agent Markov decision process, which is solved using the maximum entropy reinforcement learning framework and a novel communication-efficient consensus strategy. The proposed algorithm allows individual agents to learn a group control policy using local rewards. Numerical studies on IEEE distribution test feeders show that our proposed algorithm matches the performance of single-agent reinforcement learning benchmark. In addition, the proposed algorithm is shown to be communication efficient and resilient., 9 pages, 5 figures

Volt-VAR control is critical to keeping distribution network voltages within allowable range, minimizing losses, and reducing wear and tear of voltage regulating devices. To deal with incomplete and inaccurate distribution network models, we propose a safe off-policy deep reinforcement learning algorithm to solve Volt-VAR control problems in a model-free manner. The Volt-VAR control problem is formulated as a constrained Markov decision process with discrete action space, and solved by our proposed constrained soft actor-critic algorithm. Our proposed reinforcement learning algorithm achieves scalability, sample efficiency, and constraint satisfaction by synergistically combining the merits of the maximum-entropy framework, the method of multiplier, a device-decoupled neural network structure, and an ordinal encoding scheme. Comprehensive numerical studies with the IEEE distribution test feeders show that our proposed algorithm outperforms the existing reinforcement learning algorithms and conventional optimization-based approaches on a large feeder.

Poor vegetation management around power lines can cause severe fires that lead to tremendous economic losses, environmental degradation, and fatalities. The early discovery of a fire's presence is the key to avoiding catastrophic damages. In this paper, we propose a hybrid fire detection framework based on a deep convolutional neural network (CNN) and a pixel-based fire detector to automatically detect both the presence of fire and its scale and position information. The pre-trained deep CNN serve as a binary classifier to detect the presence of fire. The pixel-based fire detector is designed to find the fire pixels in the video frames, which indicate the scale and location of the fire. Case studies are carried out on six real-world videos to validate the proposed framework. It is shown that the proposed approach can effectively detect fire and locate the fire pixels in the testing fire videos.

Dynamic distribution network reconfiguration (DNR) algorithms perform hourly status changes of remotely controllable switches to improve distribution system performance. The problem is typically solved by physical model-based control algorithms, which not only rely on accurate network parameters but also lack scalability. To address these limitations, this paper develops a data-driven batch-constrained reinforcement learning (RL) algorithm for the dynamic DNR problem. The proposed RL algorithm learns the network reconfiguration control policy from a finite historical operational dataset without interacting with the distribution network. The numerical study results on three distribution networks show that the proposed algorithm not only outperforms state-of-the-art RL algorithms but also improves the behavior control policy, which generated the historical operational data. The proposed algorithm is also very scalable and can find a desirable network reconfiguration solution in real-time., Comment: 13 pages, 6 figures

© 2018 John Wiley & Sons, Ltd. Accurate and reliable reservoir inflow forecast is instrumental to the efficient operation of the hydroelectric power systems. It has been discovered that natural and anthropogenic aerosols have a great influence on meteorological variables such as temperature, snow water equivalent, and precipitation, which in turn impact the reservoir inflow. Therefore, it is imperative for us to quantify the impact of aerosols on reservoir inflow and to incorporate the aerosol models into future reservoir inflow forecasting models. In this paper, a comprehensive framework was developed to quantify the impact of aerosols on reservoir inflow by integrating the Weather Research and Forecasting model with Chemistry (WRF-Chem) and a dynamic regression model. The statistical dynamic regression model produces forecasts for reservoir inflow based on the meteorological output variables from the WRF-Chem model. The case study was performed on the Florence Lake and Lake Thomas Alva Edison of the Big Creek Hydroelectric Project in the San Joaquin Region. The simulation results show that the presence of aerosols results in a significant reduction of annual reservoir inflow by 4–14%. In the summer, aerosols reduce precipitation, snow water equivalent, and snowmelt that leads to a reduction in inflow by 11–26%. In the spring, aerosols increase temperature and snowmelt which leads to an increase in inflow by 0.6–2%. Aerosols significantly reduce the amount of inflow in the summer when the marginal value of water is extremely high and slightly increase the inflow in the spring when the run-off risk is high. In summary, the presence of aerosols is detrimental to the optimal utilization of hydroelectric power systems.

A version of the WRF-Chem model with fully coupled aerosol–meteorology–snowpack is employed to investigate the impacts of various aerosol sources on precipitation and snowpack in California. In particular, the impacts of locally emitted anthropogenic and dust aerosols, and aerosols transported from outside California are studied. We differentiate three pathways of aerosol effects: aerosol–radiation interaction (ARI), aerosol–snow interaction (ASI), and aerosol–cloud interaction (ACI). The convection-permitting model simulations show that precipitation, snow water equivalent (SWE), and surface air temperature averaged over the whole domain (34–42∘ N, 117–124∘ W, not including ocean points) are reduced when aerosols are included, therefore reducing large biases in these variables due to the absence of aerosol effects in the model. Aerosols affect California water resources through the warming of mountaintops and the reduction of precipitation; however, different aerosol sources play different roles in changing surface temperature, precipitation, and snowpack in California by means of various weights of the three pathways. ARI by all aerosols mainly cools the surface, leading to slightly increased SWE over the mountains. Locally emitted dust aerosols warm the surface of mountaintops through ASI, in which the reduced snow albedo associated with dusty snow leads to more surface absorption of solar radiation and reduced SWE. Transported aerosols and local anthropogenic aerosols play a dominant role in increasing nonprecipitating clouds but reducing precipitation through ACI, leading to reduced SWE and runoff on the Sierra Nevada, as well as the warming of mountaintops associated with decreased SWE and hence lower surface albedo. The average changes in surface temperature from October 2012 to June 2013 are about −0.19 and 0.22 K for the whole domain and over mountaintops, respectively. Overall, the averaged reduction during October to June is about 7 % for precipitation, 3 % for SWE, and 7 % for surface runoff for the whole domain, while the corresponding numbers are 12, 10, and 10 % for the mountaintops. The reduction in SWE is more significant in a dry year, with 9 % for the whole domain and 16 % for the mountaintops. The maximum reduction of ∼ 20 % in precipitation occurs in May and is associated with the maximum aerosol loading, leading to the largest decrease in SWE and surface runoff over that period. It is also found that dust aerosols can cause early snowmelt on the mountaintops and reduced surface runoff after April.

High degree of unbalance in electric distribution feeders can significantly affect power quality, damage electrical equipment, and result in tripping of protective devices. If not properly managed, integration of new data center and distributed energy resources into the power distribution network will exacerbate the problem. This paper proposes a new paradigm which coordinates the operation of data center and distributed energy resources to reduce phase unbalance and improve the reliability and efficiency of electric distribution networks. The coordination scheme is implemented within the framework of a distribution system operator managed electricity market. The proposed phase balancing algorithm with data center is validated using a modified IEEE distribution test feeder. The simulation results show the proposed data center and distributed energy resources coordination scheme not only significantly reduces the degree of unbalance of distribution feeders but also results in sizable reduction in data center electricity costs.

In this paper, an improved multiperiod risk-limiting dispatch (IMRLD) is proposed as an operational method in power systems with high percentage renewables integration. The basic risk-limiting dispatch (BRLD) is chosen as an operational paradigm to address the uncertainty of renewables in this paper due to its three good features. In this paper, the BRLD is extended to the IMRLD so that it satisfies the fundamental operational requirements in the power industry. In order to solve the IMRLD problem, the convexity of the IMRLD is verified. A theorem is stated and proved to transform the IMRLD into a piece-wise linear optimization problem that can be efficiently solved. In addition, the locational marginal price of the IMRLD is derived to analyze the effect of renewables integration on the marginal operational cost. Finally, two numerical tests are conducted to validate the IMRLD.

Energy storage systems are well poised to mitigate uncertainties of renewable generation outputs. Grid-scale energy storage projects are major investments which call for rigorous valuation and risk analysis. This paper provides a stochastic energy storage valuation framework in wholesale power markets which considers all key revenue streams simultaneously. As part of this framework, an operational optimization model is developed to determine the energy storage system's optimal dispatch sequences. A future curve model is built to capture the volatilities of electricity prices. In addition, a frequency regulation service price forecasting model is developed. Simulation results with a realistic battery storage system reveal that the majority of the market revenues comes from frequency regulation services. Simulation results also show that both round-trip efficiency and power-to-energy ratio are crucial to the cost effectiveness of energy storage systems.

A large amount of energy is wasted through inefficient operation of heating, ventilation, and air conditioning (HVAC) system due to the lack of reliable building occupancy measurement and prediction. To mitigate this problem, an innovative change-point logistic regression model is developed to provide an accurate forecast of building occupancy. A novel building HVAC control algorithm is then developed by embedding the occupancy prediction model into the model predictive control (MPC) framework. The occupancy-based MPC algorithm tries to minimize building electricity consumption and maximize building occupants’ comfort at the same time. A penalty factor is introduced which allows building occupants to determine the optimal trade-off between comfort and energy efficiency. Numerical simulation results show that the proposed HVAC control strategy with real-time occupancy prediction not only reduces electricity consumption but also improves building occupants’ comfort.

The rapid adoption of cloud storage and computing services led to unprecedented growth of data centers in the world. As bulk energy consumers, large-scale data centers in the U.S. rack up billions in electricity costs annually. Fortunately, the operational flexibility of data centers can be leveraged to provide valuable frequency regulation services in smart grids to mitigate the indeterminacy of the renewable generation resources. Specifically, this paper aims to leverage computational flexibility provided by servers, such as dynamic voltage frequency scaling and dummy loads. This paper develops a comprehensive framework for data center’s frequency regulation service provision in both hour-ahead market and real-time operations. A risk constrained hour-ahead bidding strategy along with a real-time data center power consumption control algorithm are developed to minimize electricity bills and the total response time of the requests. The introduction of dummy load, realistic bi-linear server power consumption model, and probabilistic forecast of electricity and frequency regulation service prices enable the data center to accurately follow frequency regulation signals, while reducing the financial risks associated with electricity market participation. The simulation results show that the proposed frequency regulation provision framework results not only in significant cost reduction for data centers, but also limits degradation in quality of service. Meanwhile, the stability and reliability of a power grid will be improved by the frequency regulation service provision.

This paper develops a machine learning framework for algorithmic trading with virtual bids in electricity markets. In the proposed algorithmic trading strategy, a budget and risk constrained portfolio optimization problem is solved, which selects the virtual transactions to be executed. In order to maximize the expected net earnings of the virtual bid portfolio, a mixture density network model is developed to provide robust and accurate forecasts for electricity price spread between day-ahead and real-time market. By leveraging a coherent risk measure and historical price samples, the risk-constrained portfolio optimization problem is solved efficiently. Backcasting results based on market data from ISO New England show that our proposed mixture density network based trading strategy consistently outperforms the benchmark online learning approach.

Owing to the call for energy efficiency, the need to optimize the energy consumption of commercial buildings-- responsible for over 40% of US energy consumption--has recently gained significant attention. Moreover, the ability to participate in the retail electricity markets through proactive demand-side participation has recently led to development of economic model predictive control (EMPC) for building's Heating, Ventilation, and Air Conditioning (HVAC) system. The objective of this paper is to develop a price-sensitive operational model for building's HVAC systems while considering inflexible loads and other distributed energy resources (DERs) such as photovoltaic (PV) generation and battery storage for the buildings. A Nonlinear Economic Model Predictive Controller (NL-EMPC) is presented to minimize the net cost of energy usage by building's HVAC system while satisfying the comfort-level of building's occupants. The efficiency of the proposed NL-EMPC controller is evaluated using several simulation case studies., arXiv admin note: text overlap with arXiv:1906.00352

This paper considers the subject of information losses arising from the finite datasets used in the training of neural classifiers. It proves a relationship between such losses as the product of the expected total variation of the estimated neural model with the information about the feature space contained in the hidden representation of that model. It then bounds this expected total variation as a function of the size of randomly sampled datasets in a fairly general setting, and without bringing in any additional dependence on model complexity. It ultimately obtains bounds on information losses that are less sensitive to input compression and in general much smaller than existing bounds. The paper then uses these bounds to explain some recent experimental findings of information compression in neural networks which cannot be explained by previous work. Finally, the paper shows that not only are these bounds much smaller than existing ones, but that they also correspond well with experiments., To be published in IEEE TNNLS

This paper considers the problem of Phase Identification in power distribution systems. In particular, it focuses on improving supervised learning accuracies by focusing on exploiting some of the problem's information theoretic properties. This focus, along with recent advances in Information Theoretic Machine Learning (ITML), helps us to create two new techniques. The first transforms a bound on information losses into a data selection technique. This is important because phase identification data labels are difficult to obtain in practice. The second interprets the properties of distribution systems in the terms of ITML. This allows us to obtain an improvement in the representation learned by any classifier applied to the problem. We tested these two techniques experimentally on real datasets and have found that they yield phenomenal performance in every case. In the most extreme case, they improve phase identification accuracy from $51.7\%$ to $97.3\%$. Furthermore, since many problems share the physical properties of phase identification exploited in this paper, the techniques can be applied to a wide range of similar problems., Comment: To be published in IEEE Transactions on Smart Grid

Accurate phase connectivity information is essential for advanced monitoring and control applications in power distribution systems. The existing data-driven approaches for phase identification lack precise physical interpretation and theoretical performance guarantee. Their performance generally deteriorates as the complexity of the network, the number of phase connections, and the level of load balance increase. In this paper, by linearizing the three-phase power flow manifold, we develop a physical model, which links the phase connections to the smart meter measurements. The phase identification problem is first formulated as a maximum likelihood estimation problem and then reformulated as a maximum marginal likelihood estimation problem. We prove that the correct phase connection achieves the highest log likelihood values for both problems. An efficient solution method is proposed by decomposing the original problem into subproblems with a binary least-squares formulation. The numerical tests on a comprehensive set of distribution circuits show that our proposed method yields very high accuracy on both radial and meshed distribution circuits with a combination of single-phase, two-phase, and three-phase loads. The proposed algorithm is robust with respect to inaccurate feeder models and incomplete measurements. It also outperforms the existing methods on complex circuits., Comment: Several updates in this version. First, more comprehensive and difficult numerical tests are added, in which we compare our method with existing methods on different test feeders, with missing measurements and erroneous models. Second, we clarify and re-write theoretical derivations and assumptions so that it is easier to understand

Two communication-efficient distributed algorithms are proposed to solve a system of linear equations $Ax=b$ with Laplacian sparse $A$ . A system of linear equations with Laplacian sparse $A$ can be found in many applications, e.g., the power flow problems and other network flow problems. The first algorithm is based on the gradient descent method in optimization and the agents only share two parts of the system state instead of that of the whole system state, which saves significant communication. The two parts shared by every agent through a communication link are the state information of its own and its neighbor connected by the communication link. The second method is obtained from an approximation to the Newton method, which converges faster. It requires twice as much communication as the first one but is still communication-efficient due to the low dimension of each part shared between agents. The convergence at a linear rate of both methods is proved.

With big thermal storage capacity and controllable loads such as the heating ventilation and air conditioning systems, buildings have great potential in providing demand response services to the smart grid. However, uncoordinated energy management of a large number of buildings in a distribution feeder can push power distribution systems into the emergency states where operating constraints are not completely satisfied. In this paper, we propose a bi-level building load aggregation methodology to coordinate the operations of heterogeneous smart buildings of a distribution feeder. The proposed methodology not only reduces the electricity costs of buildings but also guarantees that all the distribution operating constraints such as the distribution line thermal limit, phase imbalance, and transformer capacity limit are satisfied.

Distribution network reconfiguration algorithms change the status of sectionalizing and tie switches to reduce network line losses, relieve network overloads, minimize loss of load, or increase hosting capacity for distributed energy resources. Most of the existing work adopt centralized or hierarchical approaches to solve the network reconfiguration problem. This paper proposes a distributed scheme for network reconfiguration. In the distributed approach, the switches are represented by edge computing agents who communicate and work collectively with their neighbors to find the optimal reconfiguration solution. This scheme breaks the complex computation tasks required by centralized algorithms into much smaller ones. It also relieves the communication and data sharing burden via neighbor-to-neighbor communication. Simulation results on a 16-bus distribution test feeder demonstrate that the quality of the distributed solution is comparable to that of the centralized approach.

This paper presents a method for the predictive maintenance of distribution transformers. That is, a method of predicting which transformers are most likely to fail soon. Once predicted, such transformers may be subject to maintenance or replacement. This practice reduces the costs and increases the reliability of power distribution systems. The practice is common in transmission systems. In that domain, physical methods such as dissolved gas analysis see fantastic results. Data-driven techniques utilizing DGA data are also popular. But such methods are cost prohibitive for distribution systems. Instead, this paper proposes to utilize a data driven framework for the task which only uses readily available data. Such data include the transformers' specification, loading, location, and weather-related information. Such data inspire the use of two suitable machine learning algorithms. The first is random forests. The second is the Random Undersampling with AdaBoost (RUSBoost) algorithm. These algorithms are tested on over 700,000 distribution transformers in Southern California. This test finds that both algorithms outperform the current state of practice. Further, it finds that the RUSBoost algorithm performs better than the Random Forest.

Conservation voltage reduction (CVR) can effectively reduce electricity consumption and peak demand by keeping the customer voltages in the lower half of the permissible range. To facilitate widespread adoption of CVR, a reliable and robust CVR performance evaluation methodology is in critical need. However, it is difficult to accurately estimate the load reduction impact of CVR in practice. The data quality issues in supervisory control and data acquisition and advanced metering infrastructure make it challenging to distinguish a few percentage of load reduction from measurement errors and bad data. This paper develops a multilevel robust regression model within the framework of statistical experimental design to address the data quality issues. The proposed model is capable of providing robust and reliable estimates of load and voltage reduction from CVR at both distribution feeder and substation levels. The effectiveness of the proposed methodology is validated with field CVR demonstration data provided by a major California electric utility.

In order to develop policies to mitigate the impacts of climate change on energy consumption, it is imperative to understand and quantify the impacts of climate change and socioeconomic development on residential electric load. This paper develops a feed-forward neural network to model the complex relationships among socioeconomic factors, weather, distributed renewable generation, and electric load at the census block group level. The influence of different explanatory variables on electric load is quantified through the layer-wise relevance propagation method. A case study with 4,000 census block groups in southern California is conducted. The results show that temperature, housing units, and solar PV systems have the highest influence on net electric load. The scenario analysis reveals that net electric load of disadvantaged communities are much more sensitive to rising temperature than the non-disadvantaged ones. Hence, they are much more vulnerable to climate change.

This paper proposes the risk-limiting unit commitment (RLUC) as the operational method to address the uncertainties in the smart grid with intelligent periphery (GRIP). Three key requirements are identified for the RLUC in GRIP. The first one requires the RLUC to be modeled as a multistage multiperiod unit commitment problem considering power trades, operational constraints, and operational risks. The second one requires the RLUC considering the conditional prediction to achieve a globally optimal solution. It is addressed by using conditional probability in a scenario-based form. The last one requires the risk index in the RLUC to be both valid and computationally friendly, and it is tackled by the utilization of a coherent risk index and the mathematical proof of a risk chain theorem. Finally, the comprehensive RLUC in GRIP satisfying all the three requirements is solved by an equivalent transformation into a mixed integer piecewise linear programming problem. Case studies on a nine-bus system, a realistic provincial power system, and a regional power grid in China demonstrate the advantages of the proposed RLUC in GRIP.

Advanced spatio-temporal electric load modeling and accurate spatio-temporal load forecast are essential to both short-term operation and long-term planning of power systems. This paper explores the spatio-temporal dependencies of electric load time series. The Southern California feeder load data show that feeders which are spatially close to each other share a more similar load pattern than those located further apart. This finding motivates us to develop the vector autoregressive model and the extended dynamic spatio-temporal model to emulate the spatio-temporal correlations of the real-world electric load time series. The testing results show that both models effectively capture the spatio-temporal patterns in the real-world electric load time series. Compared to the traditional vector autoregressive model, the proposed extended dynamic spatio-temporal model not only provides more accurate spatio-temporal electric load forecast but also obtains a parsimonious description of the high dimensional dataset.

The widespread adoption of battery energy storage systems (BESSs) has been hindered by the uncertainty of their financial value. In past research, this value has been estimated by optimizing the system’s actions over the course of the battery’s lifetime. However, these estimates did not consider the fact that battery actions decrease the lifetime itself. This paper uses realistic battery cycle degradation to re-evaluate BESS profitability and attempts to increase profits by mitigating this degradation. For this purpose, this paper develops an approximate linear model of degradation suitable for co-optimization with the set of battery actions. It is shown through simulation that 29.1% of the storage system’s value is lost because of cycle degradation. However, co-optimization through the approximate model reduces this loss to just 3.3%.

Accurate network and phase connectivity models are crucial to distribution system analytics, operations and planning. Although network connectivity information is mostly reliable, phase connectivity data is typically missing or erroneous. In this paper, an innovative phase identification algorithm is developed by clustering of voltage time series gathered from smart meters. The feature-based clustering approach is adopted where principal component analysis is first carried out to extract feature vectors from the raw time series. A constrained k-means clustering algorithm is then executed to separate customers/smart meters into various phase connectivity groups. The algorithm is applied on a real distribution feeder in Southern California Edison's service territory. The accuracy of the proposed algorithm is over 90%.

This paper presents a three-phase iterative direct current optimal power flow (DCOPF) algorithm with fictitious nodal demand. Power losses and realistic distribution system operating constraints such as line flow limits and phase imbalance limits are carefully modeled in the DCOPF formulation. The definition of locational marginal prices (LMPs) is extended to three-phase distribution systems. The three-phase LMP decomposition is derived based on the Lagrangian function. The proposed algorithm is implemented in an IEEE test case and compared with three-phase alternating current optimal power flow (ACOPF) algorithm. The simulation results show that the proposed DCOPF algorithm is effective in coordinating the operations of distributed energy resources (DERs) and managing phase imbalance and thermal overloading. The proposed iterative three-phase DCOPF algorithm provides not only a computationally efficient solution but also a good approximation to the ACOPF solution.

To improve forecasting accuracy for baseline load and load impact from demand response resources, this paper develops three innovative statistical models. These models are regression spline fixed effect model, fixed effect change point model and mixed effect change point model. The models developed are applied to forecast baseline load and load impact from air conditioning cycling demand response program in Southern California. All three forecasting models yield accurate forecasts for baseline load and load impact from demand response events. Noticeable rebound effect from demand response events are observed from the dataset.

Buildings account for nearly 40 percent of the total energy consumption in the United States. As a critical step toward smart cities, it is essential to intelligently manage and coordinate the building operations to improve the efficiency and reliability of overall energy system. With the advent of smart meters and two-way communication systems, various energy consumptions from smart buildings can now be coordinated across the smart grid together with other energy loads and power plants. In this paper, we propose a comprehensive framework to integrate the operations of smart buildings into the energy scheduling of bulk power system through proactive building demand participation . This new scheme enables buildings to proactively express and communicate their energy consumption preferences to smart grid operators rather than passively receive and react to market signals and instructions such as time varying electricity prices. The proposed scheme is implemented in a simulation environment. The experiment results show that the proactive demand response scheme can achieve up to 10 percent system generation cost reduction and 20 percent building operation cost reduction compared with passive demand response scheme. The results also demonstrate that the system cost savings increase significantly with more flexible load installed and higher percentage of proactive customers participation level in the power network.

To promote the installations of solar photovoltaic (PV) systems efficiently, it is important to quantify the impact of government incentive programs and solar PV system life-cycle costs on customer adoption. In this paper, a model for commercial solar PV adoption is developed with explanatory variables such as government incentive programs and solar PV system installation costs. The adoption model is built on top of the Generalized Bass diffusion framework. The model is applied to forecast the commercial solar PV adoption in Southern California. Asymptotic standard errors of the parameter estimates are calculated to verify the significance of the explanatory variables. Empirical results show that decreasing solar PV system installation costs and government incentive programs are the main forces that drove the growth of commercial solar PV adoption. In the case of Southern California, we also discover that government incentive programs and PV system installation costs have a much higher impact on large com...