48 results found
Zhu J, Meng W, Sun M, et al., 2024, FLLF: A Fast-Lightweight Location Detection Framework for False Data Injection Attacks in Smart Grids, IEEE Transactions on Smart Grid, Vol: 15, Pages: 911-920, ISSN: 1949-3053
This paper proposes a fast-lightweight location detection framework (FLLF) for false data injection attacks (FDIAs). The location detection of false data injection attacks is traditionally realized by computationally intensive neural networks, which can cause large detection delays due to the limited computation power and storage resources of the detection device. By contrast, the proposed method is lightweight and can be deployed on low-cost devices such as embedded devices, significantly improving detection speed while reducing power consumption. The proposed method consists of an efficient FDIA location detection model and an automatic model search algorithm. The model is a lightweight model that enables fast and accurate attack location detection through a combination of the early exiting mechanism and mixed-precision quantization (EE-MPQ). Then, an automatic model search algorithm is designed to enable the EE-MPQ model to match the smart grids with different structures. Finally, it is shown that the proposed method can accurately detect and locate FDIAs through numerical analysis on the IEEE 14-bus and 118-bus power systems.
Wang Y, Qiu D, Wang Y, et al., 2024, Graph Learning-Based Voltage Regulation in Distribution Networks With Multi-Microgrids, IEEE Transactions on Power Systems, Vol: 39, Pages: 1881-1895, ISSN: 0885-8950
Microgrids (MGs), as localized small power systems, can effectively provide voltage regulation services for distribution networks by integrating and managing various distributed energy resources. Existing literature employs model-based optimization approaches to formulate the voltage regulation problem of multi-MGs, which require complete system models. However, this assumption is normally impractical due to time-varying environment and privacy issues. To fill this research gap, this paper suggests a data-driven decentralized framework for the cost-effective voltage regulation of a distribution network with multi-MGs. A novel multi-agent reinforcement learning method featuring an augmented graph convolutional network and a proximal policy optimization algorithm is proposed to solve this problem. Furthermore, the techniques of critical bus and electrical distance enhance the capability of feature extractions from the distribution network, allowing for the decentralized training with privacy preserving. Simulation results based on modified IEEE 33-bus, 69-bus, and 123-bus networks are developed to validate the effectiveness of the proposed method in enabling multi-MGs to provide distribution network voltage regulation.
Huang R, Guo M, Gu C, et al., 2023, Toward Scalable and Efficient Hierarchical Deep Reinforcement Learning for 5G RAN Slicing, IEEE Transactions on Green Communications and Networking, Vol: 7, Pages: 2153-2162
As an emerging and promising network paradigm, network slicing creates multiple logical networks on shared infrastructure to provide services with customized Quality-of-Service (QoS) for heterogeneous devices and applications. However, network complexity and service heterogeneity pose a huge challenge in achieving optimal performance and ensuring stringent QoS requirements. In this paper, we design a hierarchical deep reinforcement learning based 5G radio access network slicing framework to achieve scalable and efficient resource allocation. By decomposing the resource allocation problem into a slice-level task and several user-level tasks, the proposed framework tackles each task with an agent, thus conquering insufficient exploration and achieving scalable management. Knowledge transfer and progressive learning are employed to improve training efficiency and stability, respectively. We apply collaborative training to eliminate distribution mismatch by refining value approximators and policies of agents alternately. Extensive experiments show that the proposed framework can learn effective resource allocation policies stably and efficiently and outperform other methods in network utility maximization and QoS assurance, which improves the network utility by 25% and 8% compared with the random strategy and the ADMM strategy, respectively. Furthermore, we validate that our framework is more robust to changes in network traffic conditions including network congestion.
He Y, Luo F, Sun M, et al., 2023, Privacy-Preserving and Hierarchically Federated Framework for Short-Term Residential Load Forecasting, IEEE Transactions on Smart Grid, Vol: 14, Pages: 4409-4423, ISSN: 1949-3053
Short-term Load Forecasting (STLF) plays a fundamental role in modern energy systems. This paper proposes a privacy-preserving STLF framework for residential energy users. The framework enables users to collaboratively train an STLF model without exchanging their load data. The system works in two stages: (i) a federated user clustering method is developed to divide the users into multiple clusters based on load pattern similarity. Instead of aggregating the users' data to do clustering, the developed method determines the user clusters in a distributed manner. (ii) after the user clustering stage, a hierarchically federated STLF model training is developed, which applies federated learning principles to facilitate intra- and inter-user cluster model training. In the process, the individual users also do not expose their load data. Further, an asynchronous communication mechanism is designed and integrated into the framework, making it high fault-tolerant and adaptable to the residential environment with communication uncertainties. Comprehensive experiments based on a real Australian load dataset are conducted to validate the system. The simulation results show that the proposed framework can effectively train the load forecasting model with a fast coverage rate and achieve up to 37.25% prediction accuracy improvement (in terms of the Mean Absolute Percentage Error metric) compared with the other benchmark methods.
Zhang T, Sun M, Qiu D, et al., 2023, A Bayesian Deep Reinforcement Learning-Based Resilient Control for Multi-Energy Micro-Gird, IEEE Transactions on Power Systems, Vol: 38, Pages: 5057-5072, ISSN: 0885-8950
Aiming at a cleaner future power system, many regimes in the world have proposed their ambitious decarbonizing plan, with increasing penetration of renewable energy sources (RES) playing an alternative role to conventional energy. As a result, power system tends to have less backup capacity and operate near their designed limit, thus exacerbating system vulnerability against extreme events. Under this reality, resilient control for the multi-energy micro-grid is facing the following challenges, which are: 1) the effect from the stochastic uncertainties of RES; 2) the need for a model-free and fast-reacting control scheme under extreme events; and 3) efficient exploration and robust performance with limited extreme events data. To deal with the aforementioned challenges, this paper proposes a novel Bayesian Deep Reinforcement Learning-based resilient control approach for multi-energy micro-grid. In particular, the proposed approach replaces the deterministic network in traditional Reinforcement Learning with a Bayesian probabilistic network in order to obtain an approximation of the value function distribution, which effectively solves the Q-value overestimation issue. Compared with the naive Deep Deterministic Policy Gradient (DDPG) method and optimization method, the effectiveness and importance of employing the Bayesian Reinforcement Learning approach are investigated and illustrated across different operating scenarios. Case studies have shown that by using the Monte Carlo posterior mean of the Bayesian value function distribution instead of a deterministic estimation, the proposed Bayesian Deep Deterministic Policy Gradient (BDDPG) method achieves a near-optimum policy in a more stable process, which verifies the robustness and the practicability of the proposed approach.
Lyu R, Guo H, Zheng K, et al., 2023, Co-Optimizing Bidding and Power Allocation of an EV Aggregator Providing Real-Time Frequency Regulation Service, IEEE Transactions on Smart Grid, Vol: 14, Pages: 4594-4606, ISSN: 1949-3053
The rapidly expanding scale of electric vehicle (EV) fleets and continuously decreasing battery costs are making vehicle-to-grid services a reality. In this paper, we study the interaction between the problems of an EV aggregator's bidding in the regulation market and power allocation (i.e., determining the (dis)charging powers of the EVs in regulation deployment). Although the two problems are coupled, they are often regarded as decoupled and optimized separately for complexity issues. However, failing to consider the coupling of bidding and power allocation can lead to a decline in the profit of the EV aggregator (EVA). In this paper, we propose a framework for co-optimizing EVA bidding and power allocation in the regulation market. The bidding model is formulated as a stochastic programming problem with embedded power allocation in discretized regulation signal scenarios. To meet the solution time requirement for regulation deployment, we further propose a power allocation model that can be solved online. It utilizes the Lagrange multipliers from the bidding problem to ensure that the allocation results correspond to the optimal solution of the bidding problem. The effect of the proposed framework on improving EVA profits and reducing degradation costs is verified in the case study.
Zhang Z, Zuo K, Deng R, et al., 2023, Cybersecurity Analysis of Data-Driven Power System Stability Assessment, IEEE Internet of Things Journal, Vol: 10, Pages: 15723-15735
Machine learning-based intelligent systems enhanced with Internet of Things (IoT) technologies have been widely developed and exploited to enable the real-time stability assessment of a large-scale electricity grid. However, it has been extensively recognized that the IoT-enabled communication network of power systems is vulnerable to cyberattacks. In particular, system operating states, critical attributes that act as input to the data-driven stability assessment, can be manipulated by malicious actors to mislead the system operator into making disastrous decisions and thus cause major blackouts and cascading events. In this article, we explore the vulnerability of the data-driven power system stability assessment, with a special emphasis on decision tree-based stability assessment (DTSA) approaches, and investigate the feasibility of constructing a physics-constrained adversarial attack (PCAA) to undermine the DTSA. The PCAA is formulated as a nonlinear programming problem considering the misclassification constraint, power limits, and bad data detection, computing potential adversarial perturbations that reverse the 'stable/unstable' prediction of the real-time input while remaining invisible/stealthy. Extensive experiments based on the IEEE 68-bus system are conducted to evaluate the impact of PCAAs on predictions of DTSA and their transferability.
Chen Y, Sun M, Chu Z, et al., 2023, Vulnerability and Impact of Machine Learning-Based Inertia Forecasting Under Cost-Oriented Data Integrity Attack, IEEE TRANSACTIONS ON SMART GRID, Vol: 14, Pages: 2275-2287, ISSN: 1949-3053
Zeng L, Sun M, Wan X, et al., 2023, Physics-Constrained Vulnerability Assessment of Deep Reinforcement Learning-Based SCOPF, IEEE TRANSACTIONS ON POWER SYSTEMS, Vol: 38, Pages: 2690-2704, ISSN: 0885-8950
Qiu D, Xue J, Zhang T, et al., 2023, Federated reinforcement learning for smart building joint peer-to-peer energy and carbon allowance trading, Applied Energy, Vol: 333, ISSN: 0306-2619
The multi-energy system (MES), which is regarded as an optimum solution to a high-efficiency, green energy system and a crucial shift towards the future low-carbon energy system, has attracted great attention at the district building level. However, the current exploration of flexible MES operation has been hampered by (1) the increasing penetration of renewable energies and the complicated operation of coupling multi-energy sectors; (2) the privacy concern in the decentralization of the energy system; and (3) the lack of integration of the energy market and carbon emission trading scheme. To address the aforementioned challenges, this paper proposes a joint peer-to-peer energy and carbon allowance trading mechanism for a building community, and then models it as a multi-agent reinforcement learning (MARL) paradigm. In this setting, the flexibility of building local trading and the decarbonization of building energy management can both be fully utilized. To stabilize the training performance, an abstract critic network capturing system dynamics is introduced based on a deep deterministic policy gradient method. The technique of federated learning (FL) is also applied to speed up the training and safeguard the private information of each building in the community. Empirical results on a real-world test case evaluate its superior performance in terms of achieving both economic and environmental benefits, resulting in 5.87% and 8.02% lower total energy and environment costs than the two baseline mechanisms of peer-to-grid energy trading and peer-to-peer energy trading, respectively.
Zhang H, Li R, Chen Y, et al., 2023, Risk-Aware Objective-Based Forecasting in Inertia Management, IEEE Transactions on Power Systems, ISSN: 0885-8950
The objective-based forecasting considers the asymmetric and non-linear impacts of forecasting errors on decision objectives, thus improving the effectiveness of its downstream decision-making process. However, existing objective-based forecasting methods are risk-neutral and not suitable for tasks like power system inertia management and unit commitment, of which decision-makers are usually biased toward risk aversion in practice. To tackle this problem, this paper proposes a generic risk-aware objective-based forecasting method. It enables decision-makers to customize their forecasting with different risk preferences. The equivalence between the proposed method and optimization under uncertainty (stochastic/robust optimization) is established for the first time. Case studies are carried out on a Great Britain 2030 power system with system operational data from National Grid. The results show that the proposed model with deterministic optimization can approximate the performance of stochastic programming or robust optimization at only a fraction of their computational cost.
Zhang Z, Sun M, Deng R, et al., 2023, Physics-Constrained Robustness Evaluation of Intelligent Security Assessment for Power Systems, IEEE TRANSACTIONS ON POWER SYSTEMS, Vol: 38, Pages: 872-884, ISSN: 0885-8950
Kong L, Yang C, Lou S, et al., 2023, Collaborative Extraction of Intervariable Coupling Relationships and Dynamics for Prediction of Silicon Content in Blast Furnaces, IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, Vol: 72, ISSN: 0018-9456
Yu P, Wan C, Sun M, et al., 2022, Distributed Voltage Control of Active Distribution Networks with Global Sensitivity, IEEE Transactions on Power Systems, Vol: 37, Pages: 4214-4228, ISSN: 0885-8950
With the growing penetration of distributed renewable energy, distributed control approaches are widely utilized in voltage control of active distribution networks (ADNs), suffering from limited applicability for control devices or heavy communication burden. This paper develops an innovative distributed voltage control strategy of ADNs with global sensitivities (DVC-GS), which integrates network information from a global optimization view to coordinate energy storages, PV inverters and the OLTC with little communication and computing time. The global sensitivities of voltage violations across all buses with respect to nodal voltage and active/reactive power injection are formulated to quantify the ability of each controllable resource for alleviating voltage violations in the entire ADN. The back-and-forth communication combined with the modified DistFlow model is newly developed for the real-time update of global sensitivities at each bus with high accuracy and no iteration. Then the coordinated control of various devices is implemented on basis of global sensitivities to effectively ensure voltage security of ADNs. Comprehensive simulations based on modified 11-bus and 123-bus systems demonstrate the significant efficiency and superiority of the global sensitivities and DVC-GS.
Qiu D, Wang Y, Zhang T, et al., 2022, Hybrid Multiagent Reinforcement Learning for Electric Vehicle Resilience Control Towards a Low-Carbon Transition, IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, Vol: 18, Pages: 8258-8269, ISSN: 1551-3203
Zeng L, Qiu D, Sun M, 2022, Resilience enhancement of multi-agent reinforcement learning-based demand response against adversarial attacks, Applied Energy, Vol: 324, ISSN: 0306-2619
Demand response improves grid security by adjusting the flexibility of consumers meanwhile maintaining their demand–supply balance in real-time. With the large-scale deployment of distributed digital communication technologies and advanced metering infrastructures, data-driven approaches such as multi-agent reinforcement learning (MARL) are being widely employed to solve demand response problems. Nevertheless, the massive interaction of data inside and outside the demand response management system may lead to severe threats from the perspective of cyber-attacks. The cyber security requirements of MARL-based demand response problems are less discussed in the existing studies. To this end, this paper proposes a robust adversarial multi-agent reinforcement learning framework for demand response (RAMARL-DR) with an enhanced resilience against adversarial attacks. In particular, the proposed RAMARL-DR first constructs an adversary agent that aims to cause the worst-case performance via formulating an adversarial attack; and then adopts periodic alternating robust adversarial training scenarios with the optimal adversary aiming to diminish the severe impacts induced by adversarial attacks. Case studies are conducted based on an OpenAI Gym environment CityLearn, which provides a standard evaluation platform of MARL algorithms for demand response problems. Empirical results indicate that the MARL-based demand response management system is vulnerable when the adversary agent occurs, and its performance can be significantly improved after periodic alternating robust adversarial training. It can be found that the adversary agent can result in a 41.43% higher metric value of Ramping than the no adversary case, whereas the proposed RAMARL-DR can significantly enhance the system resilience with an approximately 38.85% reduction in the ramping of net demand.
Li B, Wan C, Luo F, et al., 2022, A bi-level transactive control model for integrating decision-making and DLMP-pricing in distribution networks, IET GENERATION TRANSMISSION & DISTRIBUTION, Vol: 16, Pages: 3814-3824, ISSN: 1751-8687
Bi J, Luo F, He S, et al., 2022, False Data Injection- and Propagation-Aware Game Theoretical Approach for Microgrids, IEEE Transactions on Smart Grid, Vol: 13, Pages: 3342-3353, ISSN: 1949-3053
Defending microgrids against cyberattacks has been recognized as a significant task in modern energy systems. This paper studies a False Data Injection Attack (FDIA) scenario targeting microgrids, in which the attacker maliciously propagates false data malware in the communication network of a microgrid system, aiming at misleading the microgrid's operation. Firstly, this paper establishes the propagation and attack models for the false data malware; then, the impact of the FDIA is quantified through the formulation of a network-constrained microgrid dispatch model. Based on this, a false data malware propagation-aware game between the attacker and the defender is established and an effective computational approach is developed for solving the proposed game and obtaining the potential Nash equilibrium strategy pair. Extensive numerical simulations are conducted to validate the proposed framework.
Lu R, Bai R, Luo Z, et al., 2022, Deep Reinforcement Learning-Based Demand Response for Smart Facilities Energy Management, IEEE Transactions on Industrial Electronics, Vol: 69, Pages: 8554-8565, ISSN: 0278-0046
This work proposes a novel deep reinforcement learning (DRL)-based demand response algorithm for smart facilities energy management to minimize electricity costs while maintaining a satisfaction index. Specifically, to accommodate the characteristics of the decision-making problem, long short-term memory (LSTM) units are adopted to extract discriminative features from past electricity price sequences and fed into fully connected multi-layer perceptrons (MLPs) with the measured energy and time information, then a deep Q-network is developed to approximate the optimal policy. After that, an experimental setup is constructed to investigate the effectiveness of the proposed DRL-based demand response algorithm to bridge the gap between theoretical studies and practical implementations. Numerical results demonstrate that the proposed algorithm can handle energy management well for multiple smart facilities. Moreover, the proposed algorithm outperforms the model predictive control (MPC) strategy and uncontrolled solution and is close to the theoretical optimal control method.
Zhang J, Wang Y, Sun M, et al., 2022, Two-Stage Bootstrap Sampling for Probabilistic Load Forecasting, IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT, Vol: 69, Pages: 720-728, ISSN: 0018-9391
Wang Y, Jia M, Gao N, et al., 2022, Federated Clustering for Electricity Consumption Pattern Extraction, IEEE Transactions on Smart Grid, Vol: 13, Pages: 2425-2439, ISSN: 1949-3053
The wide popularity of smart meters enables the collection of massive amounts of fine-grained electricity consumption data. Extracting typical electricity consumption patterns from these data supports the retailers in their understanding of consumer behaviors. In this way, diversified services such as personalized price design and demand response targeting can be provided. Various clustering algorithms have been studied for electricity consumption pattern extraction. These methods have to be implemented in a centralized way, assuming that all smart meter data can be accessed. However, smart meter data may belong to different retailers or even consumers themselves who are not willing to share their data. In order to better protect the privacy of the smart meter data owners, this paper proposes two federated learning approaches for electricity consumption pattern extraction, where the k-means clustering algorithm can be trained in a distributed way based on two frequently used strategies, namely model-averaging and gradient-sharing. Numerical experiments on two real-world smart meter datasets are conducted to verify the effectiveness of the proposed method.
Huang DW, Luo F, Bi J, et al., 2022, An Efficient Hybrid IDS Deployment Architecture for Multi-Hop Clustered Wireless Sensor Networks, IEEE Transactions on Information Forensics and Security, Vol: 17, Pages: 2688-2702, ISSN: 1556-6013
Deploying Intrusion Detection Systems (IDSs) is an essential way to enhance the security of Multi-hop Clustered Wireless Sensor Networks (MCWSNs). The conventional IDS deployment architectural designs show limitations in ensuring the security of MCWSNs due to the limited monitoring range of the nodes. This paper proposes an efficient IDS deployment architecture for MCWSNs. The architecture is a hybrid design, in which the cluster heads and the sink collaboratively act as IDS agents to monitor the entire network and perform intrusion detection. Based on the new architecture, this paper proposes a resource allocation model to optimally allocate resources among the IDS agents so that the network's security metric can be maximized. A comprehensive analytical framework is proposed to analyze the optimality of the model's solution, and an efficient computational approach is developed to obtain the optimal resource allocation strategy. Extensive numerical simulation and comparison studies are conducted to validate the proposed method.
Bugaje A-AB, Cremer JL, Sun M, et al., 2021, Selecting decision trees for power system security assessment, ENERGY AND AI, Vol: 6, ISSN: 2666-5468
Zhang T, Sun M, Cremer JL, et al., 2021, A Confidence-Aware Machine Learning Framework for Dynamic Security Assessment, IEEE TRANSACTIONS ON POWER SYSTEMS, Vol: 36, Pages: 3907-3920, ISSN: 0885-8950
Wang Y, Bennani IL, Liu X, et al., 2021, Electricity Consumer Characteristics Identification: A Federated Learning Approach, IEEE TRANSACTIONS ON SMART GRID, Vol: 12, Pages: 3637-3647, ISSN: 1949-3053
Cheng C, Ma G, Zhang Y, et al., 2020, A Deep Learning-Based Remaining Useful Life Prediction Approach for Bearings, IEEE-ASME TRANSACTIONS ON MECHATRONICS, Vol: 25, Pages: 1243-1254, ISSN: 1083-4435
Huyghues-Beaufond N, Tindemans S, Falugi P, et al., 2020, Robust and automatic data cleansing method for short-term load forecasting of distribution feeders, Applied Energy, Vol: 261, Pages: 1-17, ISSN: 0306-2619
Distribution networks are undergoing fundamental changes at medium voltage level. To support growing planning and control decision-making, the need for large numbers of short-term load forecasts has emerged. Data-driven modelling of medium voltage feeders can be affected by (1) data quality issues, namely, large gross errors and missing observations (2) the presence of structural breaks in the data due to occasional network reconfiguration and load transfers. The present work investigates and reports on the effects of advanced data cleansing techniques on forecast accuracy. A hybrid framework to detect and remove outliers in large datasets is proposed; this automatic procedure combines the Tukey labelling rule and the binary segmentation algorithm to cleanse data more efficiently, it is fast and easy to implement. Various approaches for missing value imputation are investigated, including unconditional mean, Hot Deck via k-nearest neighbour and Kalman smoothing. A combination of the automatic detection/removal of outliers and the imputation methods mentioned above are implemented to cleanse time series of 342 medium-voltage feeders. A nested rolling-origin-validation technique is used to evaluate the feed-forward deep neural network models. The proposed data cleansing framework efficiently removes outliers from the data, and the accuracy of forecasts is improved. It is found that Hot Deck (k-NN) imputation performs best in balancing the bias-variance trade-off for short-term forecasting.
Ye Y, Qiu D, Sun M, et al., 2020, Deep reinforcement learning for strategic bidding in electricity markets, IEEE Transactions on Smart Grid, Vol: 11, Pages: 1343-1355, ISSN: 1949-3053
Bi-level optimization and reinforcement learning (RL) constitute the state-of-the-art frameworks for modeling strategic bidding decisions in deregulated electricity markets. However, the former neglects the market participants' physical non-convex operating characteristics, while conventional RL methods require discretization of state and / or action spaces and thus suffer from the curse of dimensionality. This paper proposes a novel deep reinforcement learning (DRL) based methodology, combining a deep deterministic policy gradient (DDPG) method with a prioritized experience replay (PER) strategy. This approach sets up the problem in multi-dimensional continuous state and action spaces, enabling market participants to receive accurate feedback regarding the impact of their bidding decisions on the market clearing outcome, and devise more profitable bidding decisions by exploiting the entire action domain, also accounting for the effect of non-convex operating characteristics. Case studies demonstrate that the proposed methodology achieves a significantly higher profit than the alternative state-of-the-art methods, and exhibits a more favourable computational performance than benchmark RL methods due to the employment of the PER strategy.
Sun M, Zhang T, Wang Y, et al., 2020, Using Bayesian deep learning to capture uncertainty for residential net load forecasting, IEEE Transactions on Power Systems, Vol: 35, Pages: 188-201, ISSN: 0885-8950
Decarbonization of electricity systems drives significant and continued investments in distributed energy sources to support the cost-effective transition to low-carbon energy systems. However, the rapid integration of distributed photovoltaic (PV) generation presents great challenges in obtaining reliable and secure grid operations because of its limited visibility and intermittent nature. Under this reality, net load forecasting is facing unprecedented difficulty in answering the following question: how can we accurately predict the net load while capturing the massive uncertainties arising from distributed PV generation and load, especially in the context of high PV penetration? This paper proposes a novel probabilistic day-ahead net load forecasting method to capture both epistemic uncertainty and aleatoric uncertainty using Bayesian deep learning, which is a new field that combines Bayesian probability theory and deep learning. The proposed methodological framework employs clustering in subprofiles and considers residential rooftop PV outputs as input features to enhance the performance of aggregated net load forecasting. Numerical experiments have been carried out based on fine-grained smart meter data from the Australian grid with separately recorded measurements of rooftop PV generation and loads. The results demonstrate the superior performance of the proposed scheme compared with a series of state-of-theart methods and indicate the importance and effectiveness of subprofile clustering and high PV visibility.
Sun M, Wang Y, Teng F, et al., 2019, Clustering-based residential baseline estimation: a probabilistic perspective, IEEE Transactions on Smart Grid, Vol: 10, Pages: 6014-6028, ISSN: 1949-3061
Demand Response (DR) is one of the most cost-effective solutions for providing flexibility to power systems. The extensive deployment of DR trials and the roll-out of smart meters enable the quantification of consumer responsiveness to price signals via baseline estimation. The traditional deterministic baseline estimation approach can provide only a single value without consideration of uncertainty. This paper proposes a novel probabilistic baseline estimation framework that consists of a daily load profile pool construction stage, a deep learning-based clustering stage, an optimal cluster selection stage, and a quantile regression forests model construction stage. In particular, the concept of a daily load profile pool is introduced, and a deep-learning-based clustering approach is employed to handle a large number of daily patterns to further improve the baseline estimation performance. Case studies have been conducted on fine-grained smart meter data collected from a real dynamic time-of-use (dTOU) tariffs trial of the Low Carbon London (LCL) project. The superior performance of the proposed method is demonstrated based on a series of evaluation metrics regarding both deterministic and probabilistic estimation results.
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