Showing papers in "IEEE Transactions on Smart Grid in 2023"
TL;DR: In this paper , a generalized up-to-date review of NILM approaches including a high-level taxonomy of the different methods is provided, and previously published results are grouped based on the experimental setup which allows direct comparison.
Abstract: The rapid development of technology in the electrical energy sector within the last 20 years has led to growing electric power needs through the increased number of electrical appliances and automation of tasks. In parallel the global climate protection goals, energy conservation and efficient energy management arise interest for reduction of the overall energy consumption. These requirements have led to the recent adoption of smart-meters and smart-grids, as well as to the rise of Load Monitoring (LM) using energy disaggregation, also referred to as Non-Intrusive Load Monitoring (NILM), which enables appliance-specific energy monitoring by only observing the aggregated energy consumption of a household. The real-time information on appliance level can be used to get deeper insights in the origin of energy consumption and to make optimization, strategic load scheduling and demand management feasible. The three main contributions are as follows: First, a generalized up-to-date review of NILM approaches including a high-level taxonomy of NILM methodologies is provided. Second, previously published results are grouped based on the experimental setup which allows direct comparison. Third, the article is accompanied by a software implementation of the described NILM approaches.
32 citations
23 citations
TL;DR: In this paper , a two-stage alternating procedure embedded with sequential equivalent techniques is proposed for distributed autonomous dispatch of integrated electricity-heat systems (IEHSs), where a feasible initial point is obtained in the first stage, and the total costs are minimized thereafter.
Abstract: The constant mass flow assumption has dominated distributed dispatch of integrated electricity-heat systems (IEHSs), which ensures the simplicity of decomposition while incurring opportunity costs. In contrast, a heat operation strategy with variable flow and variable temperature (VF-VT) enhances flexibility and optimality. However, VF-VT renders the IEHS dispatch problem into a mixed-integer nonlinear bi-level nested program, which leaves a critical yet unresolved challenge for distributed autonomous dispatch. Therefore, this paper proposes a two-stage alternating procedure embedded with sequential equivalent techniques. A feasible initial point is obtained in the first stage, and the total costs are minimized thereafter. In each iteration, the heat sector optimizes both hydraulic and thermal states based on a surrogate model, and submits the heat equivalent to the electricity sector; the electricity sector solves the reduced IEHS dispatch problem and then updates the boundary. The feasibility is proved theoretically, while numerical tests validate the effectiveness.
20 citations
TL;DR: In this article , a new P2P electricity trading framework with distribution network security constraints considered using the generalized fast dual ascent method is presented, and numerical results indicate that the proposed model could guarantee secure operation of the distribution system with peer-to-peer energy trading, and the solution method enjoys good convergence performance.
Abstract: The wide deployment of renewable energy resources, combined with a more proactive demand-side management, is inducing a new paradigm in both power system operation and electricity market trading, which especially boosts the emergence of the peer-to-peer (P2P) market. A more flexible local market mechanism is highly desirable in response to fast changes in renewable power generation at the distribution network level. Moreover, large-scale implementation of P2P energy trading inevitably affects the secure and economic operation of the distribution network. This paper presents a new P2P electricity trading framework with distribution network security constraints considered using the generalized fast dual ascent method. First, an event-driven local P2P market framework is presented to facilitate short-term or immediate local energy transactions. Then, the sensitivity analysis of nodal voltage and network loss with respect to nodal power injections is used to evaluate the impacts of P2P transactions on the distribution network, which ensures the secure operation of the distribution system. Thereby, the external operational constraints are internalized, and the cost of P2P energy trading can be appropriately allocated in an endogenous way. Moreover, a generalized fast dual ascent method is employed to implement distributed market-clearing efficiently. Finally, numerical results indicate that the proposed model could guarantee secure operation of the distribution system with P2P energy trading, and the solution method enjoys good convergence performance.
17 citations
TL;DR: In this article , a multi-energy trading framework for a hybrid-renewable-to-H2 provider (HP) to coordinate the interaction and trading of electricity and H2 while promoting the efficient accommodation of renewable energy resources (RESs).
Abstract: This paper proposes a multi-energy trading framework for a hybrid-renewable-to-H2 provider (HP) to coordinate the interaction and trading of electricity and H2 while promoting the efficient accommodation of renewable energy resources (RESs). In this framework, the HP can harvest hybrid RESs for green H2 production based on electrochemical effects of biomass electrolysis, and procure stacked profits from both the electricity and H2 markets by the flexibility of electricity-H2 conversion. A Vickrey auction-based pricing mechanism is developed to determine the trading price and quantity of H2 while eliciting truthful offers and bids in a competitive H2 market. Then, a single-leader-multiple-follower Stackelberg game with an iterative solution algorithm is formulated to capture the interactions between the H2 auctioneer and hydrogen fueling stations (HFSs) for achieving the win-win goal. Furthermore, a hybrid-renewable-to-H2 production and control method is proposed for the HP to raise the production efficiency of green H2 and suppress large fluctuations in electrolysis current caused by RES uncertainties. Comparative studies have validated the superiority of the proposed methodology on economic performance and RES accommodation.
14 citations
TL;DR: In this paper , a new P2P electricity trading framework with distribution network security constraints considered using the generalized fast dual ascent method is presented, and numerical results indicate that the proposed model could guarantee secure operation of the distribution system with peer-to-peer energy trading, and the solution method enjoys good convergence performance.
Abstract: The wide deployment of renewable energy resources, combined with a more proactive demand-side management, is inducing a new paradigm in both power system operation and electricity market trading, which especially boosts the emergence of the peer-to-peer (P2P) market. A more flexible local market mechanism is highly desirable in response to fast changes in renewable power generation at the distribution network level. Moreover, large-scale implementation of P2P energy trading inevitably affects the secure and economic operation of the distribution network. This paper presents a new P2P electricity trading framework with distribution network security constraints considered using the generalized fast dual ascent method. First, an event-driven local P2P market framework is presented to facilitate short-term or immediate local energy transactions. Then, the sensitivity analysis of nodal voltage and network loss with respect to nodal power injections is used to evaluate the impacts of P2P transactions on the distribution network, which ensures the secure operation of the distribution system. Thereby, the external operational constraints are internalized, and the cost of P2P energy trading can be appropriately allocated in an endogenous way. Moreover, a generalized fast dual ascent method is employed to implement distributed market-clearing efficiently. Finally, numerical results indicate that the proposed model could guarantee secure operation of the distribution system with P2P energy trading, and the solution method enjoys good convergence performance.
14 citations
TL;DR: In this paper , an Internet-service based energy prosumer model of IDCs is formulated, and fuzzy parameters are utilized to characterize the uncertainties of the forecasted workloads in advance of the dispatch for IDCs.
Abstract: In some emerging Internet data center (IDC) projects, the waste heat dissipated from IT facilities is being captured and supplied to the district heating systems for energy efficiency concerns. From the perspective of electricity consumption and heat production, IDCs can be regarded as energy prosumers, whose spatial and temporal flexibility of energy behaviors is of great significance for the operation of integrated electricity-heat systems (IEHSs). To explore the potential benefits, the required modeling and dispatching techniques are investigated in this paper. Firstly, an Internet-service based energy prosumer model of IDCs is formulated. Especially, the heat production capability is represented considering the complete dynamic heat flow including the heat dissipation, exchange, recovery, and upgrading. And fuzzy parameters are utilized to characterize the uncertainties of the forecasted workloads in advance of the dispatch for IDCs. Moreover, an integrated operation framework for the IDC-IEHS is constructed. Specifically, a hierarchical dispatch scheme is further proposed to achieve privacy-constrained cooperative scheduling between the energy system and IDCs. In the scheme, the energy requirements for IDCs are obtained from the integrated optimal heat and power flow calculation where the temporal scale difference between the electric power and heating systems is considered. The obtained energy requirements are then regarded as the target values for the workload scheduling of IDCs. A deviation correction mechanism is designed to achieve the overall optimality of the IDC-IEHS. Finally, the hydraulic-thermal decomposition and trapezoid fuzzy value equivalence methods are used to solve the optimization problems. Simulation results demonstrate that exploiting IDCs as energy prosumers can promote the accommodation of renewables and reduce system cost in the IEHS, as well as improving the self energy efficiency of IDCs.
13 citations
TL;DR: In this paper , a revised dynamic consensus algorithm is proposed to coordinate distributed inverters for Volt-Var control in real-time, where the information delivery between agents is modelled by stochastic state transition processes among finite numbers of virtual nodes so as to quantitatively depict the random time delay and packet dropout in a discrete way.
Abstract: Coordinated Volt-Var control methods have demonstrated their techno-economic feasibility in voltage regulation of photovoltaic (PV) rich distribution systems. However, fast fluctuating PV power and imperfect communication networks may significantly challenge the effectiveness of these methods. In this paper, a revised dynamic consensus algorithm is proposed to coordinate distributed inverters for Volt-Var control in real time. With this proposed method, Var saturation and overvoltage issues which tend to occur at downstream buses of PV rich distribution systems are significantly mitigated. To quantitatively analyse the algorithm performance in imperfect communication environments, the information delivery between agents is modelled by stochastic state transition processes among finite numbers of virtual nodes so as to quantitatively depict the random time delay and packet dropout in a discrete way. On this basis, the state transition process of the whole system is further depicted by a series of row-stochastic matrices, and the ergodic theory is used to analytically derive the algorithm tracking error in an imperfect communication environment. Our proposed method can also be extended to more complex applications, where both Var compensation and PV curtailment (or EV dispatch) are available for system voltage control. Simulation results verify the superiority of our method over traditional ones.
12 citations
TL;DR: In this paper , an Internet-service based energy prosumer model of IDCs is formulated, and fuzzy parameters are utilized to characterize the uncertainties of the forecasted workloads in advance of the dispatch for IDCs.
Abstract: In some emerging Internet data center (IDC) projects, the waste heat dissipated from IT facilities is being captured and supplied to the district heating systems for energy efficiency concerns. From the perspective of electricity consumption and heat production, IDCs can be regarded as energy prosumers, whose spatial and temporal flexibility of energy behaviors is of great significance for the operation of integrated electricity-heat systems (IEHSs). To explore the potential benefits, the required modeling and dispatching techniques are investigated in this paper. Firstly, an Internet-service based energy prosumer model of IDCs is formulated. Especially, the heat production capability is represented considering the complete dynamic heat flow including the heat dissipation, exchange, recovery, and upgrading. And fuzzy parameters are utilized to characterize the uncertainties of the forecasted workloads in advance of the dispatch for IDCs. Moreover, an integrated operation framework for the IDC-IEHS is constructed. Specifically, a hierarchical dispatch scheme is further proposed to achieve privacy-constrained cooperative scheduling between the energy system and IDCs. In the scheme, the energy requirements for IDCs are obtained from the integrated optimal heat and power flow calculation where the temporal scale difference between the electric power and heating systems is considered. The obtained energy requirements are then regarded as the target values for the workload scheduling of IDCs. A deviation correction mechanism is designed to achieve the overall optimality of the IDC-IEHS. Finally, the hydraulic-thermal decomposition and trapezoid fuzzy value equivalence methods are used to solve the optimization problems. Simulation results demonstrate that exploiting IDCs as energy prosumers can promote the accommodation of renewables and reduce system cost in the IEHS, as well as improving the self energy efficiency of IDCs.
12 citations
TL;DR: In this paper , a novel market-clearing model is proposed to facilitate energy and flexibility transactions through coordinating the flexibility providers in both transmission and distribution networks, which is formulated as a bi-level optimization model.
Abstract: Active distribution networks (ADNs) with distributed generators (DGs) can provide flexibility for the upstream grid by participating in energy and flexibility markets. In this paper, a novel market-clearing model is proposed to facilitate energy and flexibility transactions through coordinating the flexibility providers in both transmission and distribution networks. The energy and flexibility market-clearing problem is formulated as a bi-level optimization model. The upper-level models the transmission-level joint energy and flexibility market clearings while the lower-level represents the distribution-level ADN market clearings. In the proposed model, the locational marginal prices (LMP) at the coupling bus will affect the dispatch of DGs and power demands of the ADNs. In turn, the power demands of the ADNs and the offers submitted by DGs will affect energy and flexibility market-clearing results and the LMPs as well. Karush-Kuhn-Tucker (KKT) conditions and the duality theory are used to transform the proposed nonlinear bi-level model to a single-level mathematical program with equilibrium constraints (MPEC) model. Case studies verify the effectiveness of the proposed model.
11 citations
TL;DR: In this paper , a peer-to-peer multi-grade energy trading design is proposed to encourage demand side flexibility to locally absorb the uncertainty of renewable distributed energy resources (DERs) in distribution networks.
Abstract: This paper proposes a novel peer-to-peer (P2P) multi-grade energy trading design to encourage demand side flexibility to locally absorb the uncertainty of renewable distributed energy resources (DERs) in distribution networks. In particular, a reliability credit (RC) assignment method is developed for customers to differentiate the energy grades considering the heterogeneity in energy supplying reliability of DERs and the consumption preferences of customers. Later, an innovative P2P multi-grade energy trading model is introduced where different types of demand are matched up with the corresponding grades of energy. The market clearing is modeled as a social welfare maximization problem considering the net utilities of customers and the profits of DER producers. Furthermore, a fast decentralized pricing algorithm is developed that achieves the maximum of social welfare and also preserves the privacy of individual participants. To substantially improve the computational efficiency, a fast solution method is devised for customers to solve the optimal energy procurement combination problem, and closed-form solutions are derived for DER producers to allocate energy optimally. Numerical tests on a 69-node distribution feeder validate the superiority of the proposed P2P multi-grade energy trading scheme compared to the benchmark scheme. The high efficiency and scalability of the proposed fast pricing algorithm are corroborated by the simulation on the IEEE 8500-node distribution system.
TL;DR: In this paper , a two-stage alternating procedure embedded with sequential equivalent techniques is proposed for distributed autonomous dispatch of integrated electricity-heat systems (IEHSs), where a feasible initial point is obtained in the first stage, and the total costs are minimized thereafter.
Abstract: The constant mass flow assumption has dominated distributed dispatch of integrated electricity-heat systems (IEHSs), which ensures the simplicity of decomposition while incurring opportunity costs. In contrast, a heat operation strategy with variable flow and variable temperature (VF-VT) enhances flexibility and optimality. However, VF-VT renders the IEHS dispatch problem into a mixed-integer nonlinear bi-level nested program, which leaves a critical yet unresolved challenge for distributed autonomous dispatch. Therefore, this paper proposes a two-stage alternating procedure embedded with sequential equivalent techniques. A feasible initial point is obtained in the first stage, and the total costs are minimized thereafter. In each iteration, the heat sector optimizes both hydraulic and thermal states based on a surrogate model, and submits the heat equivalent to the electricity sector; the electricity sector solves the reduced IEHS dispatch problem and then updates the boundary. The feasibility is proved theoretically, while numerical tests validate the effectiveness.
TL;DR: In this article , a multi-energy trading framework for a hybrid-renewable-to-H2 provider (HP) to coordinate the interaction and trading of electricity and H2 while promoting the efficient accommodation of renewable energy resources (RESs).
Abstract: This paper proposes a multi-energy trading framework for a hybrid-renewable-to-H2 provider (HP) to coordinate the interaction and trading of electricity and H2 while promoting the efficient accommodation of renewable energy resources (RESs). In this framework, the HP can harvest hybrid RESs for green H2 production based on electrochemical effects of biomass electrolysis, and procure stacked profits from both the electricity and H2 markets by the flexibility of electricity-H2 conversion. A Vickrey auction-based pricing mechanism is developed to determine the trading price and quantity of H2 while eliciting truthful offers and bids in a competitive H2 market. Then, a single-leader-multiple-follower Stackelberg game with an iterative solution algorithm is formulated to capture the interactions between the H2 auctioneer and hydrogen fueling stations (HFSs) for achieving the win-win goal. Furthermore, a hybrid-renewable-to-H2 production and control method is proposed for the HP to raise the production efficiency of green H2 and suppress large fluctuations in electrolysis current caused by RES uncertainties. Comparative studies have validated the superiority of the proposed methodology on economic performance and RES accommodation.
TL;DR: In this paper , the authors considered the real-time optimization scheduling problem in BSCS, including battery charging, swapping and truck routing, and solved it using multi-agent deep reinforcement learning (MADRL) algorithms.
Abstract: Battery swapping-charging systems (BSCSs) can provide better battery swapping services for electric vehicles (EVs) in large cities. In BSCSs, EV batteries can be centrally charged at battery charging stations (BCSs) and then dispatched via delivery trucks to battery swapping stations (BSSs) to support local EVs. This paper considers the real-time optimization scheduling problem in BSCS, including battery charging, swapping and truck routing. We model this real-time scheduling problem as a decentralized partially observable Markov decision process (Dec-POMDP) and solve it using multi-agent deep reinforcement learning (MADRL) algorithms. The joint scheduling process of trucks and BCSs has many dynamic hard constraints between them that cannot be solved using the existing MADRL algorithms. To this end, we combine MADRL with binary integer programming (BLP) and propose the Value Decomposition Network (VDN)-BLP algorithm to solve the problem with constraints. We also combine actor-critic architecture and local search with VDN-BLP to substantially improve computational efficiency with little performance loss. Simulation results based on historical battery swapping data in Sanya City verify the effectiveness of the proposed method.
TL;DR: In this article , a peer-to-peer multi-grade energy trading design is proposed to encourage demand side flexibility to locally absorb the uncertainty of renewable distributed energy resources (DERs) in distribution networks.
Abstract: This paper proposes a novel peer-to-peer (P2P) multi-grade energy trading design to encourage demand side flexibility to locally absorb the uncertainty of renewable distributed energy resources (DERs) in distribution networks. In particular, a reliability credit (RC) assignment method is developed for customers to differentiate the energy grades considering the heterogeneity in energy supplying reliability of DERs and the consumption preferences of customers. Later, an innovative P2P multi-grade energy trading model is introduced where different types of demand are matched up with the corresponding grades of energy. The market clearing is modeled as a social welfare maximization problem considering the net utilities of customers and the profits of DER producers. Furthermore, a fast decentralized pricing algorithm is developed that achieves the maximum of social welfare and also preserves the privacy of individual participants. To substantially improve the computational efficiency, a fast solution method is devised for customers to solve the optimal energy procurement combination problem, and closed-form solutions are derived for DER producers to allocate energy optimally. Numerical tests on a 69-node distribution feeder validate the superiority of the proposed P2P multi-grade energy trading scheme compared to the benchmark scheme. The high efficiency and scalability of the proposed fast pricing algorithm are corroborated by the simulation on the IEEE 8500-node distribution system.
TL;DR: In this article , a generalized up-to-date review of NILM approaches including a high-level taxonomy is provided, and previously published results are grouped based on the experimental setup which allows direct comparison.
Abstract: The rapid development of technology in the electrical energy sector within the last 20 years has led to growing electric power needs through the increased number of electrical appliances and automation of tasks. In parallel the global climate protection goals, energy conservation and efficient energy management arise interest for reduction of the overall energy consumption. These requirements have led to the recent adoption of smart-meters and smart-grids, as well as to the rise of Load Monitoring (LM) using energy disaggregation, also referred to as Non-Intrusive Load Monitoring (NILM), which enables appliance-specific energy monitoring by only observing the aggregated energy consumption of a household. The real-time information on appliance level can be used to get deeper insights in the origin of energy consumption and to make optimization, strategic load scheduling and demand management feasible. The three main contributions are as follows: First, a generalized up-to-date review of NILM approaches including a high-level taxonomy of NILM methodologies is provided. Second, previously published results are grouped based on the experimental setup which allows direct comparison. Third, the article is accompanied by a software implementation of the described NILM approaches.
TL;DR: In this article , two voltage optimization algorithms are developed to evaluate the CVR benefit by considering two types of smart inverter functions: aggregated reactive power control and autonomous volt/VAR control.
Abstract: This paper studies the coordinated use of smart inverters with legacy voltage regulating devices to help increase the energy savings from conservation voltage reduction (CVR) in distribution systems with high photovoltaic penetration. Two voltage optimization algorithms are developed to evaluate the CVR benefit by considering two types of smart inverter functions: aggregated reactive power control and autonomous volt/VAR control. In each algorithm, smart inverters are coordinately controlled with the operation of legacy voltage regulating devices including load tap changers and capacitor banks. Both algorithms are tested on representative utility distribution system models. Simulation results demonstrate that the proposed algorithms can achieve around 1.8-3.6% energy savings when only using legacy voltage regulating devices and an additional 0.3-0.9% when adding smart inverters.
TL;DR: In this paper , an underfrequency load shedding (UFLS) scheme based on the comprehensive weight of loads is proposed, which evaluates the multiple attributes of loads and the user preference for load attributes based on entropy method with high adaptability (EMHA) and the analytical hierarchy process (AHP) to obtain the objective and subjective weights of loads.
Abstract: With the rapid development of microgrids, the types of users and loads in microgrids are increasing. Comprehensively reflecting the value of loads in load shedding decisions is one of the main challenges for the intelligent control of an islanded microgrid. However, the existing load shedding schemes ignore the user’s subjective preference for multiple attributes of loads, which are insufficient for reflecting the comprehensive value of loads. To address the aforementioned problem, an underfrequency load shedding (UFLS) scheme based on the comprehensive weight of loads is proposed. This scheme evaluates the multiple attributes of loads and the user’s preference for load attributes based on the entropy method with high adaptability (EMHA) and the analytical hierarchy process (AHP) to obtain the objective and subjective weights of loads. Then, the comprehensive weight of loads is calculated based on the relative entropy to determine the sequence of load shedding. Furthermore, the optimal objective function of the comprehensive cost of load shedding is constructed, and gray wolf optimization (GWO) is used to solve the objective function and obtain the optimal load shedding scheme. Ultimately, the effectiveness of the proposed scheme is verified based on a modified standard IEEE 37-bus system.
TL;DR: In this article , an attentive transfer framework was proposed to ensemble the GNN models trained from source domains and the graph neural network (GNN) model trained on the target domain.
Abstract: The accurate short-term electric load forecasting (STLF) is critical for the safety and economical operation of modern electric power systems. Recently, the graph neural network (GNN) has been applied in STLF and achieved impressive success via utilizing spatial dependency between residential households to improve STLF. However, GNN based forecasting models require a large amount of training data to learn reliable forecasting models. For a newly built residential neighbourhood, the historical electric load data might be insufficient for the training of GNNs. Meanwhile, we can learn GNN based models on other areas, referred to as the source domains, with abundant data. In this paper, we propose to reuse the knowledge learned on the source domains to assist the model learning for an area that only a limited amount of data is available, referred to as the target domain. Specifically, we propose an attentive transfer framework to ensemble the GNN models trained from source domains and the GNN model trained on the target domain. The proposed framework can dynamically assign weights to different GNN based models based on the input data. Extensive experiments have been conducted on real-world datasets and shown the effectiveness of the proposed framework on different scenarios.
TL;DR: In this article , a novel market-clearing model is proposed to facilitate energy and flexibility transactions through coordinating the flexibility providers in both transmission and distribution networks, which is formulated as a bi-level optimization model.
Abstract: Active distribution networks (ADNs) with distributed generators (DGs) can provide flexibility for the upstream grid by participating in energy and flexibility markets. In this paper, a novel market-clearing model is proposed to facilitate energy and flexibility transactions through coordinating the flexibility providers in both transmission and distribution networks. The energy and flexibility market-clearing problem is formulated as a bi-level optimization model. The upper-level models the transmission-level joint energy and flexibility market clearings while the lower-level represents the distribution-level ADN market clearings. In the proposed model, the locational marginal prices (LMP) at the coupling bus will affect the dispatch of DGs and power demands of the ADNs. In turn, the power demands of the ADNs and the offers submitted by DGs will affect energy and flexibility market-clearing results and the LMPs as well. Karush-Kuhn-Tucker (KKT) conditions and the duality theory are used to transform the proposed nonlinear bi-level model to a single-level mathematical program with equilibrium constraints (MPEC) model. Case studies verify the effectiveness of the proposed model.
TL;DR: In this paper , the authors present a robust battery optimization formulation that sidesteps the need for battery complementarity constraints and integers and prove analytically that the formulation guarantees that all energy constraints are satisfied which ensures that the optimized battery dispatch is physically realizable.
Abstract: The non-convex complementarity constraints present a fundamental computational challenge in energy constrained optimization problems. In this work, we present a new, linear, and robust battery optimization formulation that sidesteps the need for battery complementarity constraints and integers and prove analytically that the formulation guarantees that all energy constraints are satisfied which ensures that the optimized battery dispatch is physically realizable. In addition, we bound the worst-case model mismatch and discuss conservativeness. Simulation results further illustrate the effectiveness of this approach.
TL;DR: In this paper , a day-ahead microgrid energy management framework with demand response aggregator as an intermediate coordinator is developed to maximize the social welfare of the microgrid system, with considering the privacy of end-users and the uncertainty of renewable energies.
Abstract: Microgrids, integrating user-side demand response and zero marginal cost renewable energies, are potential components for future smart grids to reduce carbon emissions and improve power system resilience. In this paper, a day-ahead microgrid energy management framework with demand response aggregator as an intermediate coordinator is developed. The corresponding scheduling strategy is obtained to maximize the social welfare of the microgrid system, with considering the privacy of end-users and the uncertainty of renewable energies. To this end, firstly, a accelerated distributed optimization method based on Alternating Direction Method of Multipliers, named as FAST-PP-ADMM, is developed to protect the end-users privacy and improve the scalability of the microgrid system. Secondly, a data-driven risk-adjusted uncertain set is constructed with a distributionally robust chance-constraints model to characterize the forecast error of renewable energies. Based on the constructed uncertain set, a two-stage robust microgrid-side energy management model is solved by using the column-and-constraint generation (C&CG) method. Finally, the effectiveness of the proposed energy management framework and scheduling strategy is verified by simulations.
TL;DR: In this paper , a homomorphically encrypted consensus algorithm is developed in the absence of a third party to achieve optimal power distribution with the least cost while preventing sensitive information leakage during the entire communication process.
Abstract: This paper is concerned with the privacy-preserving distributed economic dispatch problem (ED) of microgrids. A homomorphically encrypted consensus algorithm is developed in the absence of a third party to achieve optimal power distribution with the least cost while preventing sensitive information leakage during the entire communication process. For ease of data encryption, a novel estimator-like dynamic quantizer is first constructed, where the information to be transmitted is converted into a series of finite-level codewords. Then, a sufficient condition is derived, taking advantage of mathematical induction and the properties of matrix norms, to ensure that the quantization output is unsaturated and exact consensus is reached. Furthermore, by means of the additive homomorphic property of the Paillier algorithm to embed secrecy in pairwise interaction dynamics, the confidential communication strategy is adopted to ensure that the distributed algorithm converges to the optimal value without disclosing private or sensitive state information of agents. Finally, case studies are provided to illustrate the feasibility and validity of the adopted privacy-preserving ED scheme in IEEE 39-bus power systems.
TL;DR: In this article , an event-triggered distributed cooperative secure secondary control for islanded cyber-physical inverter-based ac microgrids (MGs) under the energy-limited denial of service (DoS) attacks is addressed.
Abstract: This paper addresses the event-triggered distributed cooperative secure secondary control for islanded cyber-physical inverter-based ac microgrids (MGs) under the energy-limited denial of service (DoS) attacks. The DoS attack refers to the prevention of information exchange among Distributed Energy Resources (DERs) in the secondary control level. In this paper, an event-triggered mechanism (ETM) is employed to improve communication efficiency and reduce control command updates. Based on the last successful local and neighboring transmission attempt, an estimator is proposed which is only activated over attack periods. In addition, this study investigates the contribution of both DERs and Distributed Energy Storage Systems (DESS) in ac MGs. Finally, the performance of the proposed control scheme is evaluated by an offline digital time-domain simulation on a test MG system through different scenarios in MATLAB/Simulink environment. Also, the effectiveness and accuracy of the controller are verified by comparison with several previous studies.
TL;DR: In this article , a nested market clearing algorithm was proposed to obtain optimal trading results in both inter- and intra-microgrid markets, where the objective uncertainty and risk preference inside micro-grids were modeled uniformly, based on which the alternating direction multiplier method was employed to solve this problem while preserving privacy.
Abstract: With the high penetration of renewable energy resources, energy prosumers and microgrids can trade energy directly with each other in a hierarchical way. This paper develops a nested market clearing algorithm that alternately produces optimal trading results in both inter- and intra-microgrid markets. The inter-microgrid market refers to the energy exchange among microgrids in distribution network. The intra-microgrid market is the energy sharing among prosumers inside microgrids. We use a distributionally robust optimization approach that is based on prospect theory (PT) to account for the complexity of prosumers’ behavior. The prosumers’ irrational behavior refers to the incomplete rationality in the market. The objective uncertainty and risk preference inside microgrids are modeled uniformly, based on which the alternating direction multiplier method (ADMM) algorithm is employed to solve this problem while preserving privacy. By filtering the marginal energy prosumers in each microgrid, the privacy of microgrids in the inter-microgrid market is preserved as well. Moreover, we prove that the proposed nested market clearing algorithm can reach the global Nash equilibrium. Numerical simulations demonstrate that the condensed equilibrium representation (CER) successfully selects energy prosumers from an intra-microgrid market to participate in an inter-microgrid market. The impact of risk preference parameters on the total utility is also analyzed.
TL;DR: In this article , a day-ahead microgrid energy management framework with demand response aggregator as an intermediate coordinator is developed to maximize the social welfare of the microgrid system, with considering the privacy of end-users and the uncertainty of renewable energies.
Abstract: Microgrids, integrating user-side demand response and zero marginal cost renewable energies, are potential components for future smart grids to reduce carbon emissions and improve power system resilience. In this paper, a day-ahead microgrid energy management framework with demand response aggregator as an intermediate coordinator is developed. The corresponding scheduling strategy is obtained to maximize the social welfare of the microgrid system, with considering the privacy of end-users and the uncertainty of renewable energies. To this end, firstly, a accelerated distributed optimization method based on Alternating Direction Method of Multipliers, named as FAST-PP-ADMM, is developed to protect the end-users privacy and improve the scalability of the microgrid system. Secondly, a data-driven risk-adjusted uncertain set is constructed with a distributionally robust chance-constraints model to characterize the forecast error of renewable energies. Based on the constructed uncertain set, a two-stage robust microgrid-side energy management model is solved by using the column-and-constraint generation (C&CG) method. Finally, the effectiveness of the proposed energy management framework and scheduling strategy is verified by simulations.
TL;DR: Wang et al. as mentioned in this paper proposed an ensemble learning-based anomaly detector trained only on benign data to detect unseen attacks (traditional and evasion) by sequentially combining an attentive autoencoder, convolutional-recurrent, and feed forward neural networks.
Abstract: Existing machine learning-based detectors of electricity theft cyberattacks are trained to detect only simple traditional types of cyberattacks while neglecting complex ones like evasion attacks. This paper analyzes the robustness of electricity theft detectors against evasion attacks. Such attacks decrease the reported electricity reading values and fool the electricity theft detectors by injecting adversarial samples. We propose strong evasion attacks that fool the benchmark detectors by iteratively generating adversarial samples based on an electricity reading and its neighboring readings. We study the impact of evasion attacks using white, gray, and black-box settings based on the attacker’s knowledge about the detector’s parameters or datasets. Our investigations revealed that the performance degradation of benchmark detectors is up to 35.8%, 26.9%, and 22.2% in white, gray, and black-box settings, respectively. To enhance the detection robustness, we propose an ensemble learning-based anomaly detector trained only on benign data to detect unseen attacks (traditional and evasion) by sequentially combining an attentive autoencoder, convolutional-recurrent, and feed forward neural networks. The proposed model offers a stable detection performance where the average degradation is only $0.7 - 3\%$ , $0.9 - 2.1\%$ , and $0.4 - 1.7\%$ in white, gray, and black-box settings, respectively, with maximum adversarial sample injection levels.
TL;DR: Wang et al. as discussed by the authors proposed an ensemble learning-based anomaly detector trained only on benign data to detect unseen attacks (traditional and evasion) by sequentially combining an attentive autoencoder, convolutional-recurrent, and feed forward neural networks.
Abstract: Existing machine learning-based detectors of electricity theft cyberattacks are trained to detect only simple traditional types of cyberattacks while neglecting complex ones like evasion attacks. This paper analyzes the robustness of electricity theft detectors against evasion attacks. Such attacks decrease the reported electricity reading values and fool the electricity theft detectors by injecting adversarial samples. We propose strong evasion attacks that fool the benchmark detectors by iteratively generating adversarial samples based on an electricity reading and its neighboring readings. We study the impact of evasion attacks using white, gray, and black-box settings based on the attacker’s knowledge about the detector’s parameters or datasets. Our investigations revealed that the performance degradation of benchmark detectors is up to 35.8%, 26.9%, and 22.2% in white, gray, and black-box settings, respectively. To enhance the detection robustness, we propose an ensemble learning-based anomaly detector trained only on benign data to detect unseen attacks (traditional and evasion) by sequentially combining an attentive autoencoder, convolutional-recurrent, and feed forward neural networks. The proposed model offers a stable detection performance where the average degradation is only $0.7 - 3\%$ , $0.9 - 2.1\%$ , and $0.4 - 1.7\%$ in white, gray, and black-box settings, respectively, with maximum adversarial sample injection levels.
TL;DR: In this paper , a data-driven joint chance-constrained game of renewable energy aggregators is proposed for the optimal bidding problem under the network constraints and the risk-averse chance constraint.
Abstract: The local market can promote more self-generated electricity consumed locally. However, the unstable and intermittent generation of renewable energy sources brings uncertainty and overbidding risk to the local market. We proposed a competitive local energy market (LEM) where renewable energy aggregators (REAs) can submit bids instead of acting as price takers. A data-driven joint chance-constrained game of REAs is proposed for the optimal bidding problem under the network constraints and the risk-averse chance constraint. We consider the case that the maximum outputs of REAs are random variables whose probability distributions are unknown, but the decision-maker has access to finite samples. Each player’s strategy set is defined in a data-driven distributionally robust framework with the Wasserstein ambiguity set. The exact reformulation of the distributionally robust chance constraint and the corresponding inner approximation version are derived. We prove the existence of the Nash equilibrium in the proposed distributionally robust chance-constrained game. Finally, an efficient best response seeking algorithm is developed to find the normalized Nash equilibrium, and the convergence proof of the proposed algorithm is also provided. Numerical simulations demonstrate that the proposed game model can effectively increase the revenue of reliable players and reduce the overbidding rate of REAs.
TL;DR: In this article , a novel energy scheduling regime is proposed for the electricity-hydrogen-heat IES clusters under a peer-to-peer (P2P) electricity and heat energy transaction structure.
Abstract: Surplus electricity energy from renewable sources can be efficiently utilized by converting it into other forms of energy in the integrated energy system (IES). Local electricity energy sharing can be realized through effective dispatching and independent transaction between producers and consumers. This features the merits on energy consumption and operational cost reduction. In this paper, a novel energy scheduling regime is proposed for the electricity-hydrogen-heat IES clusters under a peer-to-peer (P2P) electricity and heat energy transaction structure. To instantly control the robustness of the scheduling decisions, a weighted MPC strategy is newly proposed for online decision-makings. Furthermore, an adaptive alternating direction method of multipliers (ADMM) with varying penalties algorithm that can effectively preserve prosumer privacy is proposed, which enables the operators to coordinate various parties in the multi-energy microgrids. Extensive case studies based on a typical testing system are carried out, of which the simulation results demonstrate the effectiveness of the proposed scheduling regime and suggest potential for practical application.