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This work studies the modeling of false data injection attacks (FDIAs) on IEGSs. First, we introduce a static state estimation model and bad data detection method for IEGSs. Then, we develop FDIAs on IEGSs with complete network topology and parameter information. Next, we develop FDIAs on IEGSs when intruders have only local network topology and parameter information of an IEGS. Lastly, we explore FDIAs on IEGSs when intruders have only local network topology information of an IEGS., Comment: 13 pages

The variability caused by the proliferation of distributed energy resources (DERs) and the significant growth in unbalanced three-phase loads pose unprecedented challenges to distribution network operations. This paper focuses on how a distribution system operator (DSO), taking over the distribution grid and market operations, would develop a risk-aware flexibility market to mitigate uncertainties in an unbalanced three-phase power distribution network. First, a distributionally robust chance constraint (DRCC) method is devised to solve the unbalanced three-phase optimal power flow using a semidefinite programming (SDP) model. The DSO can apply the proposed solution to jointly clear energy and flexibility markets. Then, the DRCC model accuracy is improved by an information-sharing mechanism characterized by spatially-correlated uncertainties in the distribution grid. Further, a novel system-wide response function is derived to make the DRCC model tractable. Using the duality theory, the paper further investigates the physical composition of the DSO's cleared flexibility prices to guide the unbalanced distribution network operation. Finally, the effectiveness of the risk-aware flexibility market is verified in a modified three-phase IEEE 34-node system. Results demonstrate that the flexibility market can quantify the impact of spatially correlated uncertainties and facilitate the utilization of flexible resources to mitigate uncertainties across the network.

The advances in deep learning (DL) techniques have the potential to deliver transformative technological breakthroughs to numerous complex tasks in modern power systems that suffer from increasing uncertainty and nonlinearity. However, the vulnerability of DL has yet to be thoroughly explored in power system tasks under various physical constraints. This work, for the first time, proposes a novel physics-constrained backdoor poisoning attack, which embeds the undetectable attack signal into the learned model and only performs the attack when it encounters the corresponding signal. The paper illustrates the proposed attack on the real-time fault line localization application. Furthermore, the simulation results on the 68-bus power system demonstrate that DL-based fault line localization methods are not robust to our proposed attack, indicating that backdoor poisoning attacks pose real threats to DL implementations in power systems. The proposed attack pipeline can be easily generalized to other power system tasks.

Existing methods of non-intrusive load monitoring (NILM) in literatures generally suffer from high computational complexity and/or low accuracy in identifying working household appliances. This paper proposes an event-driven Factorial Hidden Markov model (eFHMM) for multiple appliances with multiple states in a household, aiming for low computational complexity and high load disaggregation accuracy. The proposed eFHMM decreases the computational complexity to be linear to the event number, which ensures online load disaggregation. Furthermore, the eFHMM is solved in two stages, where the first stage identifies state-changing appliance using transient signatures and the second stage confirms the inferred states using steady-state signatures. The combination of transient and steady-state signatures, which are extracted from transient and steady periods segmented by detected events, enhances the uniqueness of each state transition and associated appliances, which ensures accurate load disaggregation. The event-driven two-stage NILM solution, termed as eFHMM-TS, is naturally fit into an edge-cloud framework, which makes possible the real-world application of NILM. The proposed eFHMM-TS method is validated on the LIFTED and synD datasets. Results demonstrate that the eFHMM-TS method outperforms other methods and can be applied in practice., Comment: 14 pages, 17 figures

Event detection is the first step in event-based non-intrusive load monitoring (NILM) and it can provide useful transient information to identify appliances. However, existing event detection methods with fixed parameters may fail in case of unpredictable and complicated residential load changes such as high fluctuation, long transition, and near simultaneity. This paper proposes a dynamic time-window approach to deal with these highly complex load variations. Specifically, a window with adaptive margins, multi-timescale window screening, and adaptive threshold (WAMMA) method is proposed to detect events in aggregated home appliance load data with high sampling rate (>1Hz). The proposed method accurately captures the transient process by adaptively tuning parameters including window width, margin width, and change threshold. Furthermore, representative transient and steady-state load signatures are extracted and, for the first time, quantified from transient and steady periods segmented by detected events. Case studies on a 20Hz dataset, the 50Hz LIFTED dataset, and the 60Hz BLUED dataset show that the proposed method can robustly outperform other state-of-art event detection methods. This paper also shows that the extracted load signatures can improve NILM accuracy and help develop other applications such as load reconstruction to generate realistic load data for NILM research., Comment: 10 pages, 18 figures

Load forecasting is one of the most important and studied topics in modern power systems. Most of the existing researches on day-ahead load forecasting try to build a good model to improve the forecasting accuracy. The forecasted load is then used as the input to generation scheduling with the ultimate goal of minimizing the cost of generation schedules. However, existing day-ahead load forecasting models do not consider this ultimate goal at the training/forecasting stage. This paper proposes a task-based day-ahead load forecasting model labeled as LfEdNet that combines two individual layers in one model, including a load forecasting layer based on deep neural network (Lf layer) and a day-ahead stochastic economic dispatch (SED) layer (Ed layer). The training of LfEdNet aims to minimize the cost of the day-ahead SED in the Ed layer by updating the parameters of the Lf layer. Sequential quadratic programming (SQP) is used to solve the day-ahead SED in the Ed layer. The test results demonstrate that the forecasted results produced by LfEdNet can lead to lower cost of day-ahead SED while maintaining a relatively high forecasting accuracy., Comment: 8 pages

Uncertainty in renewable energy generation and load consumption is a great challenge for microgrid operation, especially in islanded mode as the microgrid may be small in size and has limited flexible resources. In this paper, a multi-timescale, two-stage robust unit commitment and economic dispatch model is proposed to optimize the microgrid operation. The first stage is a combination of day-ahead hourly and real-time sub-hourly model, which means the day-ahead dispatch result must also satisfy the real-time condition at the same time. The second stage is to verify the feasibility of the day-ahead dispatch result in worst-case condition considering high-level uncertainty in renewable energy dispatch and load consumptions. In the proposed model, battery energy storage system (BESS) and solar PV units are integrated as a combined solar-storage system. The BESS plays an essential role to balance the variable output of solar PV units, which keeps the combined solar-storage system output unchanged on an hourly basis. In this way, it largely neutralizes the impact of solar uncertainty and makes the microgrid operation grid friendly. Furthermore, in order to enhance the flexibility and resilience of the microgrid, both BESS and thermal units provide regulating reserve to manage solar and load uncertainty. The model has been tested in a controlled hardware in loop (CHIL) environment for the Bronzeville Community Microgrid system in Chicago. The simulation results show that the proposed model works effectively in managing the uncertainty in solar PV and load and can provide a flexible dispatch in both grid-connected and islanded modes., Comment: A multi-timescale two-stage robust grid-friendly dispatch model for microgrid operation is proposed

The issue of estimating the detailed appliance level load consumption has received considerable attention. This paper first presents a Labelled hIgh-Frequency daTaset for Electricity Disaggregation (LIFTED), which can be used for research on nonintrusive load monitoring (NILM). This dataset consists of one-week detailed appliance-level electricity usage information including voltage, current, active power, and reactive power for a single apartment in the United States, down-sampled at 50Hz. This paper also proposes an efficient Lossless Coding considering Precision (LCP) algorithm on data compression. This algorithm considers both the precision requirements of practical applications on load datasets and the unique characteristics of household appliance-level load datasets. The LCP algorithm is tested on the LIFTED dataset and the results demonstrate that LCP can achieve a higher compression ratio compared to several existing algorithms.

Non-Intrusive Load Monitoring (NILM) is a practical method to provide appliance-level electricity consumption information. Event detection, as an important part of event-based NILM methods, has a direct impact on the accuracy of the ultimate load disaggregation results in the entire NILM framework. This paper presents a hybrid event detection approach for relatively complex household load datasets that include appliances with long transients, high fluctuations, and/or near-simultaneous actions. The proposed approach includes a base algorithm based on moving average change with time limit, and two auxiliary algorithms based on derivative analysis and filtering analysis. The structure, steps, and working principle of this approach are described in detail. The proposed approach does not require additional information about household appliances, nor does it require any training sets. Case studies on different datasets are conducted to evaluate the performance of the proposed approach in comparison with several existing approaches including log likelihood ratio detector with maxima (LLD-Max) approach, active window-based (AWB) approach, and generalized likelihood ratio (GLR) approach. Results show that the proposed approach works well in detecting events in complex household load datasets and performs better than the existing approaches., Comment: 12 pages

Non-Intrusive Load Monitoring (NILM) is an important application to monitor household appliance activities and provide related information to house owner or/and utility company via a single sensor installed at the electrical entry of the house. It can be used for different purposes in residential and industrial sectors. Thus, an increasing number of new algorithms have been developed in recent years. In these algorithms, researchers either use existing public datasets or collect their own data which causes such problems as insufficiency of electrical parameters, missing of ground-truth data, absence of many appliances, and lack of appliance information. To solve these problems, this paper presents a model-based platform for NILM system development, namely Functional Intrusive Load Monitor (FILM). By using this platform, the state transitions and activities of all the involved appliances can be preset by researchers, and multiple electrical parameters such as harmonics and power factor can be monitored or calculated. This platform will help researchers save the time of collecting experimental data, utilize precise control of individual appliance activities, and develop load signatures of devices. This paper describes the steps, structure, and requirements of building this platform. Case study is presented to help understand this platform.

Though considerable effort has been devoted to exploiting generation-side and demand-side operational flexibility in order to cope with uncertain renewable generations, grid-side operational flexibility has not been fully investigated. In this review, we define grid-side flexibility as the ability of a power network to deploy its flexibility resources to cope with the changes of power system state, particularly due to variation of renewable generation. Starting with a survey on the metrics of operational flexibility, we explain the definition from both physical and mathematical point of views. Then conceptual examples are presented to demonstrate the impacts of grid-side flexibility graphically, providing a geometric interpretation for a better understanding of the concepts. Afterwards the formulations and solution approaches in terms of grid-side flexibility in power system operation and planning are reviewed, based on which future research directions and challenges are outlined.

Unified Power Flow Controller (UPFC) is recognized as the most powerful flexible AC transmission systems (FACTS) device for power system operation. This paper addresses how UPFC explores the transmission flexibility and facilitates the integration of uncertain and volatile wind power generation. To this end, a comprehensive unit commitment (UC) model with UPFC and uncertain wind power generation is proposed. Then, some metrics are introduced to evaluate the impacts of UPFC on the reliability, security and economy of power system operation. Further, different dispatch strategies of UPFC are compared to provide helpful guidances on making full use of UPFC to hedge against uncertainties. In addition, facing the challenging mixed-integer non-linear non-convex problems, approximate models are proposed to provide a starting point to solve the problems efficiently. All these models are easy to adapt to other types of FACTS devices. Illustrative numerical results are provided.

Two critical issues have arisen in transmission expansion planning with the rapid growth of wind power generation. First, severe power ramping events in daily operation due to the high variability of wind power generation pose great challenges to multi-year planning decision making. Second, the long construction periods of transmission lines may not be able to keep pace with the fast growing uncertainty due to the increasing integration of renewable energy generation. To address such issues, we propose a comprehensive robust planning model considering different resources, namely, transmission lines, generators, and FACTS devices. Various factors are taken into account, including flexibility requirement, construction period, and cost. We construct the hourly net load ramping uncertainty (HLRU) set to characterize the variation of hourly net load including wind power generation, and the annual net load duration curve uncertainty (LDCU) set for the uncertainty of normal annual net load duration curve. This results in a two-stage robust optimization model with two different types of uncertainty sets, which are decoupled into two different sets of subproblems to make the entire solution process tractable. Numerical simulations with real-world data show that the proposed model and solution method are effective to coordinate different flexible resources, rendering robust expansion planning strategies.

Bilevel optimization has been widely used in decision-making process. However, there still lacks an efficient algorithm to determine an optimal solution of a bilevel optimization problem, especially for a large-size problem. To bridge the gap, this paper proposes an efficient decomposition algorithm for a general bilevel linear programming(GBLP). The simulation results on large-size testing system demonstrate its correctness and efficiency., Comment: The authors found crucial errors in the decomposition method

The increasing penetration of renewable energy in recent years has led to more uncertainties in power systems. In order to maintain system reliability and security, electricity market operators need to keep certain reserves in the Security-Constrained Economic Dispatch (SCED) problems. A new concept, deliverable generation ramping reserve, is proposed in this paper. The prices of generation ramping reserves and generation capacity reserves are derived in the Affine Adjustable Robust Optimization framework. With the help of these prices, the valuable reserves can be identified among the available reserves. These prices provide crucial information on the values of reserve resources, which are critical for the long-term flexibility investment. The market equilibrium based on these prices is analyzed. Simulations on a 3-bus system and the IEEE 118-bus system are performed to illustrate the concept of ramping reserve price and capacity reserve price. The impacts of the reserve credit on market participants are discussed., Comment: We presented related content in 2014 IEEE Power and Energy Society General Meeting

The increasing penetration of Renewable Energy Sources (RES) requires more Flexibility Resources (FR), generally thermal units and storages, must be kept in the system to accommodate the uncertainties from RES. The challenge is how the system can survive when the RES level is very high. In this paper, RESs are considered as full-role market participants. They can bid in the day-ahead market, and the powers they deliver to the market are controllable up to their maximum available powers. Therefore, RESs are effectively dispatchable and can function as FR providers. To integrate dispatchable renewables, a two-stage robust Unit Commitment (UC) and dispatch model is established. In the first stage, a base UC and dispatch is determined. In the second stage, all FRs including RESs are used to accommodate the uncertainties, which is a Mixed-Integer Programming (MIP) problem. It is proved that the solution to the max-min problem can be identified directly whether the strong duality holds or not for the inner minimization problem. The solution robustness is guaranteed by including only one extra scenario. Numerical results show the effectiveness of the proposed model and its advantages over the traditional robust UC model with high level RES penetration.

The increasing penetration of renewable energy in recent years has led to more uncertainties in power systems. These uncertainties have to be accommodated by flexible re- sources (i.e. upward and downward generation reserves). In this paper, a novel concept, Uncertainty Marginal Price (UMP), is proposed to price both the uncertainty and reserve. At the same time, the energy is priced at Locational Marginal Price (LMP). A novel market clearing mechanism is proposed to credit the gener- ation and reserve and to charge the load and uncertainty within the Robust Unit Commitment (RUC) in the Day-ahead market. We derive the UMPs and LMPs in the robust optimization framework. UMP helps allocate the cost of generation reserves to uncertainty sources. We prove that the proposed market clearing mechanism leads to partial market equilibrium. We find that transmission reserves must be kept explicitly in addition to generation reserves for uncertainty accommodation. We prove that transmission reserves for ramping delivery may lead to Financial Transmission Right (FTR) underfunding in existing markets. The FTR underfunding can be covered by congestion fund collected from uncertainty payment in the proposed market clearing mechanism. Simulations on a six-bus system and the IEEE 118-bus system are performed to illustrate the new concepts and the market clearing mechanism., Comment: to appear in IEEE Transactions on Power Systems, IEEE link: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7524711. arXiv admin note: text overlap with arXiv:1507.01167

In Part II of this two-part paper, we analyze the marginal prices derived in Part I of this two-part paper within a robust optimization framework. The load and generation are priced at Locational Marginal Price (LMP) while the uncertainty and generation reserve are priced at Uncertainty Marginal Price(UMP). The Financial Transmission Right (FTR) underfunding is demonstrated when there is transmission reserve. A comparison between traditional reserve price and UMP is presented. We also discuss the incentives for market participants within the new market scheme.

Power systems become more prone to cyber-attacks due to the high integration of information technologies. In this paper, we demonstrate that the outages of some lines can be masked by injecting false data into a set of measurements. The success of the topology attack can be guaranteed by making that: 1)the injected false data obeys KCL and KVL to avoid being detected by the bad data detection program in the state estimation; 2)the residual is increased such that the line outage cannot be detected by PMU data. A quadratic programming problem is set up to determine the optimal attack vector that can maximize the residual of the outaged line. The IEEE 39-bus system is used to demonstrate the masking scheme.