Showing papers in "IEEE Transactions on Smart Grid in 2019"
TL;DR: The proposed LSTM approach outperforms the other listed rival algorithms in the task of short-term load forecasting for individual residential households and is comprehensively compared to various benchmarks including the state-of-the-arts in the field of load forecasting.
Abstract: As the power system is facing a transition toward a more intelligent, flexible, and interactive system with higher penetration of renewable energy generation, load forecasting, especially short-term load forecasting for individual electric customers plays an increasingly essential role in the future grid planning and operation. Other than aggregated residential load in a large scale, forecasting an electric load of a single energy user is fairly challenging due to the high volatility and uncertainty involved. In this paper, we propose a long short-term memory (LSTM) recurrent neural network-based framework, which is the latest and one of the most popular techniques of deep learning, to tackle this tricky issue. The proposed framework is tested on a publicly available set of real residential smart meter data, of which the performance is comprehensively compared to various benchmarks including the state-of-the-arts in the field of load forecasting. As a result, the proposed LSTM approach outperforms the other listed rival algorithms in the task of short-term load forecasting for individual residential households.
1,415 citations
TL;DR: A systematic analysis of harmonic stability in the future power-electronic-based power systems reveals that the linearized models of ac–dc converters can be generalized to the harmonic transfer function, which is mathematically derived from linear time-periodic system theory.
Abstract: The large-scale integration of power electronic-based systems poses new challenges to the stability and power quality of modern power grids. The wide timescale and frequency-coupling dynamics of electronic power converters tend to bring in harmonic instability in the form of resonances or abnormal harmonics in a wide frequency range. This paper provides a systematic analysis of harmonic stability in the future power-electronic-based power systems. The basic concept and phenomena of harmonic stability are elaborated first. It is pointed out that the harmonic stability is a breed of small-signal stability problems, featuring the waveform distortions at the frequencies above and below the fundamental frequency of the system. The linearized models of converters and system analysis methods are then discussed. It reveals that the linearized models of ac–dc converters can be generalized to the harmonic transfer function, which is mathematically derived from linear time-periodic system theory. Lastly, future challenges on the system modeling and analysis of harmonic stability in large-scale power electronic based power grids are summarized.
703 citations
TL;DR: An application-oriented review of smart meter data analytics identifies the key application areas as load analysis, load forecasting, and load management and reviews the techniques and methodologies adopted or developed to address each application.
Abstract: The widespread popularity of smart meters enables an immense amount of fine-grained electricity consumption data to be collected. Meanwhile, the deregulation of the power industry, particularly on the delivery side, has continuously been moving forward worldwide. How to employ massive smart meter data to promote and enhance the efficiency and sustainability of the power grid is a pressing issue. To date, substantial works have been conducted on smart meter data analytics. To provide a comprehensive overview of the current research and to identify challenges for future research, this paper conducts an application-oriented review of smart meter data analytics. Following the three stages of analytics, namely, descriptive, predictive, and prescriptive analytics, we identify the key application areas as load analysis, load forecasting, and load management. We also review the techniques and methodologies adopted or developed to address each application. In addition, we also discuss some research trends, such as big data issues, novel machine learning technologies, new business models, the transition of energy systems, and data privacy and security.
621 citations
TL;DR: This paper proposes a methodology based on sensitivity analysis to assess the impact of P2P transactions on the network and to guarantee an exchange of energy that does not violate network constraints, which is tested on a typical U.K. low-voltage network.
Abstract: The increasing uptake of distributed energy resources in distribution systems and the rapid advance of technology have established new scenarios in the operation of low-voltage networks In particular, recent trends in cryptocurrencies and blockchain have led to a proliferation of peer-to-peer (P2P) energy trading schemes, which allow the exchange of energy between the neighbors without any intervention of a conventional intermediary in the transactions Nevertheless, far too little attention has been paid to the technical constraints of the network under this scenario A major challenge to implementing P2P energy trading is ensuring network constraints are not violated during the energy exchange This paper proposes a methodology based on sensitivity analysis to assess the impact of P2P transactions on the network and to guarantee an exchange of energy that does not violate network constraints The proposed method is tested on a typical UK low-voltage network The results show that our method ensures that energy is exchanged between users under the P2P scheme without violating the network constraints, and that users can still capture the economic benefits of the P2P architecture
427 citations
TL;DR: This paper proposes bilateral contract networks as a new scalable market design for peer-to-peer energy trading, consisting of energy contracts offered between generators with fuel-based sources, suppliers acting as intermediaries and consumers with inflexible loads, time-coupled flexible loads and/or renewable sources.
Abstract: This paper proposes bilateral contract networks as a new scalable market design for peer-to-peer energy trading Coordinating small-scale distributed energy resources to shape overall demand could offer significant value to power systems, by alleviating the need for investments in upstream generation and transmission infrastructure, increasing network efficiency and increasing energy security However, incentivising coordination between the owners of large-scale and small-scale energy resources at different levels of the power system remains an unsolved challenge This paper introduces real-time and forward markets, consisting of energy contracts offered between generators with fuel-based sources, suppliers acting as intermediaries and consumers with inflexible loads, time-coupled flexible loads and/or renewable sources For each type of agent, utility-maximising preferences for real-time contracts and forward contracts are derived It is shown that these preferences satisfy full substitutability conditions essential for establishing the existence of a stable outcome—an agreed network of contracts specifying energy trades and prices, which agents do not wish to mutually deviate from Important characteristics of energy trading are incorporated, including upstream–downstream energy balance and forward market uncertainty Full substitutability ensures a distributed price-adjustment process can be used, which only requires local agent decisions and agent-to-agent communication between trading partners
391 citations
TL;DR: In this article, the benefits of using deep reinforcement learning (RL) to perform on-line optimization of schedules for building energy management systems are explored. But, the authors do not consider the impact of different types of data.
Abstract: Unprecedented high volumes of data are becoming available with the growth of the advanced metering infrastructure. These are expected to benefit planning and operation of the future power systems and to help customers transition from a passive to an active role. In this paper, we explore for the first time in the smart grid context the benefits of using deep reinforcement learning, a hybrid type of methods that combines reinforcement learning with deep learning, to perform on-line optimization of schedules for building energy management systems. The learning procedure was explored using two methods, Deep Q-learning and deep policy gradient, both of which have been extended to perform multiple actions simultaneously. The proposed approach was validated on the large-scale Pecan Street Inc. database. This highly dimensional database includes information about photovoltaic power generation, electric vehicles and buildings appliances. Moreover, these on-line energy scheduling strategies could be used to provide real-time feedback to consumers to encourage more efficient use of electricity.
345 citations
TL;DR: It is found that SSTs are less efficient than low-frequency transformers (LFTs), yet their prospective prices are significantly higher, and four essential challenges in detail are discussed, distilled into an applicability flowchart for SST technology.
Abstract: Solid-state transformers (SSTs) are power electronic converters that provide isolation between a medium-voltage and a low-voltage (LV) system using medium-frequency transformers. The power electronic stages enable full-range control of the terminal voltages and currents and hence of the active and reactive power flows. Thus, SSTs are envisioned as key components of a smart grid. Various SST concepts have been proposed and analyzed in literature concerning technical aspects. However, several issues could potentially limit the applicability of SSTs in distribution grids. Therefore, this paper discusses four essential challenges in detail. It is found that SSTs are less efficient than low-frequency transformers (LFTs), yet their prospective prices are significantly higher. Furthermore, SSTs are not compatible with the protection schemes employed in today’s LV grids, i.e., they are not drop-in replacements for LFTs. The limited voltage control range typically required in distribution grids can be provided by competing solutions, which do not involve power electronics (e.g., LFTs with tap changers), or by hybrid transformers, where the comparably inefficient power electronic stage processes only a fraction of the total power. Finally, potential application scenarios of SSTs (ac-dc, dc-dc, weight/space limited applications) are discussed. All considerations are distilled into an applicability flowchart for SST technology.
308 citations
TL;DR: This paper presents a comprehensive discussion on how blockchain technology can be used to enhance the robustness and security of the power grid, by using meters as nodes in a distributed network which encapsulates meter measurements as blocks.
Abstract: The cyber security of modern power systems has drawn increasing attention in both academia and industry. Many detection and defense methods for cyber-attacks have therefore been proposed to enhance robustness of modern power systems. In this paper, we propose a new, distributed blockchain-based protection framework to enhance the self-defensive capability of modern power systems against cyber-attacks. We present a comprehensive discussion on how blockchain technology can be used to enhance the robustness and security of the power grid, by using meters as nodes in a distributed network which encapsulates meter measurements as blocks. Effectiveness of the proposed protection framework is demonstrated via simulation experiments on the IEEE-118 benchmark system.
305 citations
TL;DR: A model-free approach based on deep reinforcement learning is proposed to determine the optimal strategy for charging strategy due to the existence of randomness in traffic conditions, user's commuting behavior, and the pricing process of the utility.
Abstract: Driven by the recent advances in electric vehicle (EV) technologies, EVs have become important for smart grid economy. When EVs participate in demand response program which has real-time pricing signals, the charging cost can be greatly reduced by taking full advantage of these pricing signals. However, it is challenging to determine an optimal charging strategy due to the existence of randomness in traffic conditions, user’s commuting behavior, and the pricing process of the utility. Conventional model-based approaches require a model of forecast on the uncertainty and optimization for the scheduling process. In this paper, we formulate this scheduling problem as a Markov Decision Process (MDP) with unknown transition probability. A model-free approach based on deep reinforcement learning is proposed to determine the optimal strategy for this problem. The proposed approach can adaptively learn the transition probability and does not require any system model information. The architecture of the proposed approach contains two networks: a representation network to extract discriminative features from the electricity prices and a Q network to approximate the optimal action-value function. Numerous experimental results demonstrate the effectiveness of the proposed approach.
277 citations
TL;DR: An updated version of the existing six theft cases to mimic real-world theft patterns and applies them to the dataset for numerical evaluation of the proposed algorithm.
Abstract: For the smart grid energy theft identification, this letter introduces a gradient boosting theft detector (GBTD) based on the three latest gradient boosting classifiers (GBCs): 1) extreme gradient boosting; 2) categorical boosting; and 3) light gradient boosting method. While most of existing machine learning (ML) algorithms just focus on fine tuning the hyperparameters of the classifiers, our ML algorithm, GBTD, focuses on the feature engineering-based preprocessing to improve detection performance as well as time-complexity. GBTD improves both detection rate and false positive rate (FPR) of those GBCs by generating stochastic features like standard deviation, mean, minimum, and maximum value of daily electricity usage. GBTD also reduces the classifier complexity with weighted feature-importance-based extraction techniques. Emphasis has been laid upon the practical application of the proposed ML for theft detection by minimizing FPR and reducing data storage space and improving time-complexity of the GBTD classifiers. Additionally, this letter proposes an updated version of the existing six theft cases to mimic real-world theft patterns and applies them to the dataset for numerical evaluation of the proposed algorithm.
257 citations
TL;DR: It is shown that transient instability can occur to the droop-controlled VSC when its current is saturated under large disturbances and a stability enhanced P-f droop control is proposed to deal with this instability problem.
Abstract: In the modern power grid, distributed generators are widely connected to the grid via voltage source converters (VSCs). Analyzing the transient stability of VSCs under large disturbances is useful for maintaining the security of the grid. However, this topic is very little studied. In this paper, the transient stability behavior of the droop-controlled VSC is theoretically explained. In particular, it is shown that transient instability can occur to the droop-controlled VSC when its current is saturated under large disturbances. In addition, the dynamics of the VSC under voltage sags that could incur instability problem is elaborately studied to consider special non-fault disturbances. To deal with this instability problem, a stability enhanced P-f droop control is proposed. Simulations and hardware-in-the-loop experiments verify the validity of the transient stability analysis and the effectiveness of the proposed control scheme.
TL;DR: A composite nonlinear controller is proposed for stabilizing dc/dc boost converter feeding CPLs by integrating a nonlinear disturbance observer (NDO)-based feedforward compensation with backstepping design algorithm with strictly guaranteed large signal stability.
Abstract: Transportation electrification involves the wide utilization of power electronics based dc distribution networks and the integration of a large amount of power electronic loads. These power electronic loads, when tightly controlled, behave as constant power loads (CPLs) and may cause system instability when interacting with their source converters. In this paper, a composite nonlinear controller is proposed for stabilizing dc/dc boost converter feeding CPLs by integrating a nonlinear disturbance observer (NDO)-based feedforward compensation with backstepping design algorithm. First, the model is transformed into the Brunovsky’s canonical form using the exact feedback linearization technique, to handle the nonlinearity introduced by the CPL. Second, the NDO technique is adopted to estimate the load power variation within a fast dynamic response, serving as a feedforward compensation to increase the accuracy of output voltage regulation. Then a nonlinear controller is developed by following the step-by-step backstepping algorithm with strictly guaranteed large signal stability. The proposed controller not only ensures global stability under large variation of the CPL but also features fast dynamic response with accurate tracking over wide operating range. Both simulations and experiments are conducted to verify the proposed strategy.
TL;DR: An intelligent fault detection scheme for microgrid based on wavelet transform and deep neural networks that can provide significantly better fault type classification accuracy and can also detect the locations of faults, which are unavailable in previous work.
Abstract: Fault detection is essential in microgrid control and operation, as it enables the system to perform fast fault isolation and recovery. The adoption of inverter-interfaced distributed generation in microgrids makes traditional fault detection schemes inappropriate due to their dependence on significant fault currents. In this paper, we devise an intelligent fault detection scheme for microgrid based on wavelet transform and deep neural networks. The proposed scheme aims to provide fast fault type, phase, and location information for microgrid protection and service recovery. In the scheme, branch current measurements sampled by protective relays are pre-processed by discrete wavelet transform to extract statistical features. Then all available data is input into deep neural networks to develop fault information. Compared with previous work, the proposed scheme can provide significantly better fault type classification accuracy. Moreover, the scheme can also detect the locations of faults, which are unavailable in previous work. To evaluate the performance of the proposed fault detection scheme, we conduct a comprehensive evaluation study on the CERTS microgrid and IEEE 34-bus system. The simulation results demonstrate the efficacy of the proposed scheme in terms of detection accuracy, computation time, and robustness against measurement uncertainty.
TL;DR: A two-stage mixed-integer linear programming approach for district level MES planning considering distributed renewable energy integration and a case study based on the MES in Beijing's new subsidiary administrative center is conducted using the proposed approach.
Abstract: Multi-energy systems (MESs) contribute to increasing energy utilization efficiency and renewable energy accommodation by coupling multiple energy sectors. The preferable characteristic of MESs raises the need for optimizing the configuration of MESs across multiple energy sectors at the planning stage. Based on the energy hub (EH) model, this research presents a two-stage mixed-integer linear programming approach for district level MES planning considering distributed renewable energy integration. The approach models an MES as a directed acyclic graph with multiple layers. The proposed EH configuration planning procedure includes two stages: 1) optimizing what equipment (e.g., energy converters, distributed renewable energy sources and storages) should be invested in for each layer and 2) optimizing the connection relationships between the invested equipment in each two adjacent layers. The proposed approach is able to optimize both the equipment selection and the MES configuration. It can be applied to both expansion planning and initial planning of MESs from scratch. An illustrative example of planning a typical MES is given. A sensitivity analysis is performed to show the impacts of load profiles, energy prices and equipment parameters on the optimal MES configuration. A case study based on the MES in Beijing’s new subsidiary administrative center is conducted using the proposed approach.
TL;DR: Experimental results demonstrate that the proposed hour-ahead DR algorithm can handle energy management for multiple appliances, minimize user energy bills, and dissatisfaction costs, and help the user to significantly reduce its electricity cost compared with a benchmark without DR.
Abstract: Ever-changing variables in the electricity market require energy management systems (EMSs) to make optimal real-time decisions adaptively. Demand response (DR) is the latest approach being used to accelerate the efficiency and stability of power systems. This paper proposes an hour-ahead DR algorithm for home EMSs. To deal with the uncertainty in future prices, a steady price prediction model based on artificial neural network is presented. In cooperation with forecasted future prices, multi-agent reinforcement learning is adopted to make optimal decisions for different home appliances in a decentralized manner. To verify the performance of the proposed energy management scheme, simulations are conducted with non-shiftable, shiftable, and controllable loads. Experimental results demonstrate that the proposed DR algorithm can handle energy management for multiple appliances, minimize user energy bills, and dissatisfaction costs, and help the user to significantly reduce its electricity cost compared with a benchmark without DR.
TL;DR: In this paper, a model for forecasting short-term electric load based on deep residual networks is presented, which is able to integrate domain knowledge and researchers' understanding of the task by virtue of different neural network building blocks.
Abstract: We present in this paper a model for forecasting short-term electric load based on deep residual networks. The proposed model is able to integrate domain knowledge and researchers’ understanding of the task by virtue of different neural network building blocks. Specifically, a modified deep residual network is formulated to improve the forecast results. Further, a two-stage ensemble strategy is used to enhance the generalization capability of the proposed model. We also apply the proposed model to probabilistic load forecasting using Monte Carlo dropout. Three public datasets are used to prove the effectiveness of the proposed model. Multiple test cases and comparison with existing models show that the proposed model provides accurate load forecasting results and has high generalization capability.
TL;DR: A novel mixed-integer programming model that resolves different timescales of MPS dispatch and DS operation, coupling of road and power networks, etc., is formulated to optimize dynamic dispatch of M PSs.
Abstract: Mobile power sources (MPSs), including electric vehicle fleets, truck-mounted mobile energy storage systems, and mobile emergency generators, have great potential to enhance distribution system (DS) resilience against extreme weather events. However, their dispatch is not well investigated. This paper implements resilient routing and scheduling of MPSs via a two-stage framework. In the first stage, i.e., before the event, MPSs are pre-positioned in the DS to enable rapid pre-restoration, in order to enhance survivability of the electricity supply to critical loads. DS network is also proactively reconfigured into a less impacted or stressed state. A two-stage robust optimization model is constructed and solved by the column-and-constraint generation algorithm to derive first-stage decisions. In the second stage, i.e., after the event, MPSs are dynamically dispatched in the DS to coordinate with conventional restoration efforts, so as to enhance system recovery. A novel mixed-integer programming model that resolves different timescales of MPS dispatch and DS operation, coupling of road and power networks, etc., is formulated to optimize dynamic dispatch of MPSs. Case studies conducted on IEEE 33-node and 123-node test systems demonstrate the proposed method’s effectiveness in routing and scheduling MPSs for DS resilience enhancement.
TL;DR: A joint post-disaster restoration scheme for TESS and generation scheduling in microgrids and network reconfigurations is proposed to minimize the total system cost, including customer interruption cost, generation cost, and TESS related costs.
Abstract: Transportable energy storage systems (TESSs) have great potential to enhance resilience of distribution systems (DSs) against large area blackouts. A joint post-disaster restoration scheme for TESS and generation scheduling in microgrids (MGs) and network reconfigurations is proposed to minimize the total system cost, including customer interruption cost, generation cost, and TESS related costs. A temporal–spatial TESS model which is related to both transportation networks and DSs is proposed to represent the difference between TESS and ESS in terms of flexibility and cost reduction of ESS sharing among MGs. The proposed restoration problem is formulated as a mixed-integer linear programming with considering various network and TESS constraints. The proposed model and scheme are tested in a modified 33-bus test system with three MGs and four TESSs. The results verify that a distribution system with TESS is more resilient compared with conventional ESS because of the benefit from total cost reduction.
TL;DR: The proposed ADPED algorithm can be adaptive to both day-ahead and intra-day operation under uncertainty and can make full use of historical prediction error distribution to reduce the influence of inaccurate forecast on the system operation.
Abstract: This paper proposes an approximate dynamic programming (ADP)-based approach for the economic dispatch (ED) of microgrid with distributed generations. The time-variant renewable generation, electricity price, and the power demand are considered as stochastic variables in this paper. An ADP-based ED (ADPED) algorithm is proposed to optimally operate the microgrid under these uncertainties. To deal with the uncertainties, Monte Carlo method is adopted to sample the training scenarios to give empirical knowledge to ADPED. The piecewise linear function (PLF) approximation with improved slope updating strategy is employed for the proposed method. With sufficient information extracted from these scenarios and embedded in the PLF function, the proposed ADPED algorithm can not only be used in day-ahead scheduling but also the intra-day optimization process. The algorithm can make full use of historical prediction error distribution to reduce the influence of inaccurate forecast on the system operation. Numerical simulations demonstrate the effectiveness of the proposed approach. The near-optimal decision obtained by ADPED is very close to the global optimality. And it can be adaptive to both day-ahead and intra-day operation under uncertainty.
TL;DR: Transient angle stability of a VSG is investigated by Lyapunov’s direct method and an enhanced control strategy is presented to improve the transient angle stability by adjusting the reference power.
Abstract: With an increasing number of distributed energy resources integrated into the power system, inverters need to take on the corresponding responsibility for the security and stability of the system. Virtual synchronous generators (VSGs) are proposed to mimic dynamic characteristics of traditional rotational synchronous generators (RSGs) to compensate for the loss of inertia and reserve capacity. Similar to RSGs, VSGs will experience transient angle instability under certain conditions, which likely threatens the system security. In this paper, transient angle stability of a VSG is investigated by Lyapunov’s direct method. The deteriorative effect of reactive power control loop on transient angle stability is first analyzed and then voltage variation is incorporated into an approximate Lyapunov’s direct method. In this method, the inverter internal voltage is treated as a parameter rather than a state variable. Moreover, the influence of different parameters on transient angle stability is studied. Finally, an enhanced control strategy is presented to improve the transient angle stability by adjusting the reference power. Numerical simulation results are presented to validate the effectiveness of the proposed method and the enhanced control.
TL;DR: The results indicate that the proposed algorithm can reduce the operational cost and at the same time provide higher tolerability toward uncertainties.
Abstract: A four-stage intelligent optimization and control algorithm for an electric vehicle (EV) bidirectional charging station equipped with photovoltaic generation and fixed battery energy storage and integrated with a commercial building is proposed in this paper. The proposed algorithm aims at maximally reducing the customer satisfaction-involved operational cost considering the potential uncertainties, while balancing the real-time supply and demand by adjusting the optimally scheduled charging/discharging of EV mobile/local battery storage, grid supply, and deferrable load. The chance-constrained optimization objective has been stated in stages: 1) stage I, optimization of day-ahead energy management schedules; 2) stage II, multitiered EV charging price update and optimization of discharging participation bonus; 3) stage III, optimization of hour-ahead energy management schedules; and 4) stage IV, real-time control. Such algorithm provides more resilience for unpredictable conditions, provides more incentives for EV users to participate, and better coordinates the integrated system including the building load to reliably serve the customers while lessening cost. Case studies are implemented and the comparison analysis is performed in terms of the use and benefit of each design feature of the algorithm. The results indicate that the proposed algorithm can reduce the operational cost and at the same time provide higher tolerability toward uncertainties.
TL;DR: In this article, a resilient scheme for disaster recovery logistics to co-optimize DS restoration with the dispatch of repair crews (RCs) and mobile power sources (MPSs) is proposed.
Abstract: Repair crews (RCs) and mobile power sources (MPSs) are critical resources for distribution system (DS) outage management after a natural disaster. However, their logistics have not been well investigated. We propose a resilient scheme for disaster recovery logistics to co-optimize DS restoration with the dispatch of RCs and MPSs. A novel co-optimization model is formulated to route RCs and MPSs in the transportation network, schedule them in the DS, and reconfigure the DS for microgrid formation coordinately, etc. The model incorporates different timescales of DS restoration and RC/MPS dispatch, the coupling of transportation and power networks, etc. To ensure radiality of the DS with variable physical structure and MPS allocation, we also model topology constraints based on the concept of spanning forest. The model is convexified equivalently and linearized into a mixed-integer linear programming. To reduce its computation time, preprocessing methods are proposed to pre-assign a minimal set of repair tasks to depots and reduce the number of candidate nodes for MPS connection. Resilient recovery strategies thus are generated to enhance service restoration, especially by dynamic formation of microgrids that are powered by MPSs and topologized by repair actions of RCs and network reconfiguration of the DS. Case studies demonstrate the proposed methodology.
TL;DR: The simulation results show that the proposed energy sharing strategy is economically beneficial for the energy buildings, computationally efficient, and is promising to facilitate a sustainable regional building cluster.
Abstract: Efficient building energy management is essential for energy saving and green society. This paper investigates sustainable energy management for an energy building cluster with distributed transaction. The building cluster consists of several types of energy buildings, e.g., office, industrial, and commercial buildings. We first formulate utility functions for the buildings of consuming energy based on the characteristics of their controllable loads. Then a two-stage energy sharing strategy is presented. In the first stage, the total social energy cost is minimized through finding the optimal energy sharing profiles in a distributed way. In the second stage, the clearing for mutual energy sharing is modeled as a non-cooperative game, and the existence of the equilibrium of the game is illustrated and a relaxation-based algorithm is introduced to search for the equilibrium. Moreover, a real-time model for each building to overcome real-time uncertainties, such as renewable energy generation and base loads is provided. The simulation results show that the proposed energy sharing strategy is economically beneficial for the energy buildings, computationally efficient, and is promising to facilitate a sustainable regional building cluster.
TL;DR: This work explores sharing economy opportunities in the electricity sector, and describes equilibrium prices for shared storage in a spot market, and shows that under a mild alignment condition, a Nash equilibrium exists, it is unique, and it supports the social welfare.
Abstract: The sharing economy has upset the market for housing and transportation services. Homeowners can rent out their property when they are away on vacation, car owners can offer ridesharing services. These sharing economy business models are based on monetizing under-utilized infrastructure. They are enabled by peer-to-peer platforms that match eager sellers with willing buyers. Are there compelling sharing economy opportunities in the electricity sector? What products or services can be shared in tomorrow’s smart grid? We begin by exploring sharing economy opportunities in the electricity sector, and discuss regulatory and technical obstacles to these opportunities. We then study the specific problem of a collection of firms sharing their electricity storage. We characterize equilibrium prices for shared storage in a spot market. We formulate storage investment decisions of the firms as a non-convex non-cooperative game. We show that under a mild alignment condition, a Nash equilibrium exists, it is unique, and it supports the social welfare. We discuss technology platforms necessary for the physical exchange of power, and market platforms necessary to trade electricity storage. We close with synthetic examples to illustrate our ideas.
TL;DR: A standardized matrix modeling method based on the concept of EH to build the coupling matrix automatically for multiple energy systems to improve the overall efficiency of the energy system is proposed.
Abstract: Multiple energy systems (MESs) bring together the electric power, heat, natural gas, and other systems to improve the overall efficiency of the energy system. An energy hub (EH) models an MES as a device with multiple ports using a matrix coupling the inputs and outputs. This paper proposes a standardized matrix modeling method based on the concept of EH to build the coupling matrix automatically. The components and the structure of MES are first defined using graph theory. Then, the matrices describing the topology of the MES and the characteristics of the energy converters are developed. On this basis, the energy flow equations are formulated. Gaussian elimination can then be applied to obtain the coupling matrix and analyze the degree of freedom of the EH. A standard data structure for basic information on the MES is proposed to facilitate computerized modeling. Further, extension modeling of energy storage and demand response is also discussed. Finally, a case study of a modified tri-generation system is conducted to illustrate the proposed method.
TL;DR: A methodology for non-technical loss detection using supervised learning that uses all the information the smart meters record to obtain an in-depth analysis of the customer’s consumption behavior and shows that extreme gradient boosted trees outperform the rest of the classifiers.
Abstract: Non-technical electricity losses due to anomalies or frauds are accountable for important revenue losses in power utilities. Recent advances have been made in this area, fostered by the roll-out of smart meters. In this paper, we propose a methodology for non-technical loss detection using supervised learning. The methodology has been developed and tested on real smart meter data of all the industrial and commercial customers of Endesa. This methodology uses all the information the smart meters record (energy consumption, alarms and electrical magnitudes) to obtain an in-depth analysis of the customer’s consumption behavior. It also uses auxiliary databases to provide additional information regarding the geographical location and technological characteristics of each smart meter. The model has been trained, validated and tested on the results of approximately 57 000 on-field inspections. It is currently in use in a non-technical loss detection campaign for big customers. Several state-of-the-art classifiers have been tested. The results show that extreme gradient boosted trees outperform the rest of the classifiers.
TL;DR: A mathematic program with equilibrium constraints (MPEC) model is proposed to study the strategic behaviors of a profit-driven EH in the electricity and heating markets under the background of energy system integration.
Abstract: Integration of electricity and heat distribution networks offers extra flexibility to system operation and improves energy efficiency. The energy hub (EH) plays an important role in energy production, conversion and storage in such coupled infrastructures. This paper provides a new outlook and thorough mathematical tool for studying the integrated energy system from a deregulated market perspective. A mathematic program with equilibrium constraints (MPEC) model is proposed to study the strategic behaviors of a profit-driven EH in the electricity and heating markets under the background of energy system integration. In the upper level, the EH submits bids of prices and quantities to a distribution power market and a heating market. In the lower level, these two markets are cleared and energy contracts between the EH and two energy markets are determined. Network constraints of physical systems are explicitly represented by an optimal power flow problem and an optimal thermal flow problem. The proposed MPEC formulation is approximated by a mixed-integer linear program via performing integer disjunctions on the complementarity and slackness conditions and binary expansion technique on the bilinear product terms. Case studies demonstrate the effectiveness of the proposed model and method.
TL;DR: A robust generalized maximum-likelihood unscented Kalman filter (GM-UKF) is developed that can detect bad phasor measurement unit measurements and incorrect state predictions, and filter out unknown Gaussian and non-Gaussian noises through the generalized maximum likelihood-estimator.
Abstract: Due to the communication channel noises, GPS synchronization process, changing environment temperature and different operating conditions of the system, the statistics of the system process and measurement noises may be unknown and they may not follow Gaussian distributions. As a result, the traditional Kalman filter-based dynamic state estimators may provide strongly biased state estimates. To address these issues, this paper develops a robust generalized maximum-likelihood unscented Kalman filter (GM-UKF). The statistical linearization approach is presented to derive a compact batch-mode regression form by processing the predicted state vector and the received measurements simultaneously. This regression form enhances the data redundancy and allows us to detect bad phasor measurement unit measurements and incorrect state predictions, and filter out unknown Gaussian and non-Gaussian noises through the generalized maximum likelihood-estimator. The latter minimizes a convex Huber function with weights calculated via the projection statistics (PS). Particularly, the PS is applied to a proposed 2-dimensional matrix that consists of temporally correlated innovation vectors and predicted states. Finally, the total influence function is used to derive the error covariance matrix of the GM-UKF state estimates, yielding the robust state prediction at the next time instant. Extensive simulations carried out on the IEEE 39-bus test system demonstrate the effectiveness and robustness of the proposed method.
TL;DR: A combined method using the battery energy management of plug-in electric vehicles (PEVs) and the active power curtailment of PV arrays is proposed to regulate voltage in LVDNs with high penetration level of PV resources.
Abstract: The rapid growth of rooftop photovoltaic (PV) arrays installed in residential houses leads to serious voltage quality problems in low voltage distribution networks (LVDNs). In this paper, a combined method using the battery energy management of plug-in electric vehicles (PEVs) and the active power curtailment of PV arrays is proposed to regulate voltage in LVDNs with high penetration level of PV resources. A distributed control strategy composed of two consensus algorithms is used to reach an effective utilization of limited storage capacity of PEV battery considering its power/capacity and state of charge. A consensus control algorithm is also developed to fairly share the required power curtailment among PVs during overvoltage periods. The main objective is to mitigate the voltage rise due to the reverse power flow and to compensate the voltage drop resulting from the peak load. Overall, the proposed algorithm contributes to a coordinated charging/discharging control of PEVs battery which provides a maximum utilization of available storage capacity throughout the network. In addition, the coordinated operation minimizes the required active power which is going to be curtailed from PV arrays. The effectiveness of the proposed control scheme is investigated on a typical three-phase four-wire LVDN in presence of PV resources and PEVs.
TL;DR: In this article, a review of the literature on state estimation in power systems is presented, including mathematical problem formulation, application of pseudo-measurements, metering instrument placement, network topology issues, impacts of renewable penetration, and cyber-security.
Abstract: This paper presents a review of the literature on state estimation (SE) in power systems. While covering works related to SE in transmission systems, the main focus of this paper is distribution system SE (DSSE). The critical topics of DSSE, including mathematical problem formulation, application of pseudo-measurements, metering instrument placement, network topology issues, impacts of renewable penetration, and cyber-security are discussed. Both conventional and modern data-driven and probabilistic techniques have been reviewed. This paper can provide researchers and utility engineers with insights into the technical achievements, barriers, and future research directions of DSSE.