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Thermostatically controlled loads (TCLs) are regarded as one of the promising resources for suppressing power fluctuations due to renewable energy (RENs). However, due to the great burdens of fully considering each users’ characteristics, it is difficult to achieve unity of the individual optimal decisions and global optimum in demand-side management (DSM). This paper proposes a game-theoretic DSM that can optimize the global power consumption schedule by individual TCL user's optimization. By integrating the prediction of REN outputs into the pricing mechanism, the proposed DSM can guide the users to make their best power consumption schedules along with intermittent REN generations, thus to smooth the tie-line power of microgrids. In this paper, a novel pricing mechanism is firstly developed based on the concave N-person game theory, which is more adaptive and flexible compared with existing game-theoretic DSM. Then, an individual's power consumption optimization and its simplified model are developed considering the constraints of the TCL model and personal preferences. The simplified model can be easily solved and achieve a fast solution in the power consumption game. An implementation framework of the proposed DSM is further developed for practical application. Finally, numerical studies verify the effectiveness of the proposed models and methods. [ABSTRACT FROM AUTHOR]

The commutation failure (CF) is the most common fault in line-commutated high voltage direct current (LCC-HVDC) systems that may lead to the transient overvoltage in the sending-side system. In the worst condition, the CF may lead to large-scale wind turbine tripping. To resolve this problem, the mathematical relationship between the reactive power consumed by the rectifier and DC voltage, DC current is derived. Then, a transient overvoltage calculation method is proposed in this paper. Furthermore, the mechanism of transient overvoltage caused by the CF is analyzed; it is revealed that the reason for three times transient overvoltage is the rapid decrease of the reactive power consumed by the rectifier during the CF and the recovery period from the CF. This paper proposes a constant reactive power control (CRPC) to inhibit transient overvoltage of the sending-side AC system. The proposed CRPC can increase the reactive power consumed by the rectifier, reduce the exchange reactive power between AC and DC systems, and suppress the transient overvoltage. A simulation model in PSCAD serves to verify the proposed CRPC on the transient overvoltage suppression in the situation of different fault types, fault duration, fault severity and short circuit ratio (SCR). [ABSTRACT FROM AUTHOR]

This paper demonstrates that false data injection (FDI) attacks are extremely limited in their ability to cause physical consequences on $N-1$ reliable power systems operating with real-time contingency analysis (RTCA) and security constrained economic dispatch (SCED). Prior work has shown that FDI attacks can be designed via an attacker-defender bi-level linear program (ADBLP) to cause physical overflows after re-dispatch using DCOPF. In this paper, it is shown that attacks designed using DCOPF fail to cause overflows on $N-1$ reliable systems because the system response modeled is inaccurate. An ADBLP that accurately models the system response is proposed to find the worst-case physical consequences, thereby modeling a strong attacker with system level knowledge. Simulation results on the synthetic Texas system with 2000 buses show that even with the new enhanced attacks, for systems operated conservatively due to $N-1$ constraints, the designed attacks only lead to post-contingency overflows. Moreover, the attacker must control a large portion of measurements and physically create a contingency in the system to cause consequences. Therefore, it is conceivable but requires an extremely sophisticated attacker to cause physical consequences on $N-1$ reliable power systems operated with RTCA and SCED. [ABSTRACT FROM AUTHOR]

This paper discusses the impact of discrete secondary controllers on the dynamic response of power systems. The idea of the paper originates from the observation that there is a range of values, from few tens of seconds to few minutes, of the execution cycles of conventional automatic generation control (AGC) that leads to a limit cycle. Below and above this range the system is stable. This is certainly not a problem in practice as the AGC updates the power set points of generating units every few seconds. However, this phenomenon has interesting consequences if one considers real-time electricity markets with short dispatch periods (i.e., 5 minutes) as these markets can be modeled as a sort of AGC. The paper first provides a formal analogy between conventional AGC and real-time electricity markets. Then it shows that the discretization-driven instability exists if the system includes a real-time electricity market modeled as secondary frequency controller. Finally, the paper discusses the impact of the combined effect of high wind generation shares and discrete secondary controllers on power system dynamics. [ABSTRACT FROM AUTHOR]

Time-domain indirect methods based on Lyapunov stability theory and linear matrix inequality techniques (LMIs) have been applied for delay-dependent stability analysis of large-scale load frequency control (LFC) schemes. This paper aims to enhance the numerical tractability of large-scale LMIs by exploiting the special characteristics of the LFC loops. First, in the typical LFC model, only a few delayed states that are directly influenced by transmission delays are distinguished from other normal system states. Hence, an improved reconstruction model is formed, based on which the delay-dependent stability condition is established with the decreased order of the LMIs and decision variables. Then, to further improve the numerical tractability of the developed stability criterion, all weighting matrices required in the augmented Lyapunov functional are enforced to have structural restrictions by proposing an extended symmetry-exploiting technique. Case studies show that the method proposed in this paper significantly improves the calculation efficiency of stability criterion established for multi-area power systems at the cost of only a minor reduction in computational accuracy. [ABSTRACT FROM AUTHOR]

Pricing multi-interval economic dispatch of electric power under operational uncertainty is considered in this two-part paper. Part I investigates dispatch-following incentives of profit-maximizing generators and shows that, under mild conditions, no uniform-pricing scheme for the rolling-window economic dispatch provides dispatch-following incentives that avoid discriminative out-of-the-market uplifts. A nonuniform pricing mechanism, referred to as the temporal locational marginal pricing (TLMP), is proposed. As an extension of the standard locational marginal pricing (LMP), TLMP takes into account both generation and ramping-induced opportunity costs. It eliminates the need for the out-of-the-market uplifts and guarantees full dispatch-following incentives regardless of the accuracy of the demand forecasts used in the dispatch. It is also shown that, under TLMP, a price-taking market participant has incentives to bid truthfully with its marginal cost of generation. Part II of the paper extends the theoretical results developed in Part I to more general network settings. It investigates a broader set of performance measures, including the incentives of the truthful revelation of ramping limits, revenue adequacy of the operator, consumer payments, generator profits, and price volatility under the rolling-window dispatch model with demand forecast errors. [ABSTRACT FROM AUTHOR]

This paper proposes a novel inertia-enhanced distributed control method to complement the inertia of microgrids. The rate of change of frequency (RoCoF) and the rate of change of voltage (RoCoV) are employed in this paper to quantify the frequency inertia and voltage inertia, respectively. Then, a fully distributed algorithm with constrained changing rates is proposed. By bounding the changing rates of frequency and voltage during the consensus control, the algorithm can address the consensus problem while enhancing the inertia of microgrids. Compared with most inertia control methods, the proposed method can utilize the reserve power of scattered DGs to supply inertia. Besides, it performs better under disturbances and delays than conventional distributed control methods. The effectiveness of the proposed method is validated by several cases in MATLAB/Simulation and a hardware experiment. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive scheduling framework for residential demand response (DR) programs considering both the day-ahead and real-time electricity markets. In the first stage, residential customers determine the operating status of their responsive devices such as heating, ventilation, and air conditioning (HVAC) systems and electric water heaters (EWHs), while the distribution system operator (DSO) computes the amount of electricity to be purchased in the day-ahead electricity market. In the second stage, the DSO purchases insufficient (or sells surplus) electricity in the real-time electricity market to maintain the supply-demand balance. Due to its computational complexity and data privacy issues, the proposed model cannot be directly solved in a centralized manner, especially with a large number of uncertain scenarios. Therefore, this paper proposes a combination of stochastic programming (SP) and the alternating direction method of multipliers (ADMM) algorithm, called SP-ADMM, to decompose the original model and then solve each sub-problem in a distributed manner while considering multiple uncertain scenarios. The simulation study is performed on the IEEE 33-bus system including 121 residential houses. The results demonstrate the effectiveness of the proposed approach for large-scale residential DR applications under weather and consumer uncertainties. [ABSTRACT FROM AUTHOR]

Service restoration is one of the critical functions to enable the future self-healing distribution system. To restore the distribution system in a timely and reliable manner, the realistic system operating conditions need to be accurately characterized. In this paper, two main factors that have great impacts on distribution system restoration (DSR) in practice are investigated. First, cold load pickup (CLPU), generally caused by thermostatically controlled loads (TCLs), is a common phenomenon after an outage and shaped by the outage duration. However, the time-dependent behaviors of CLPU are rarely considered in literature. In this paper, the operating state evolution of TCLs after an outage is analyzed to characterize time-dependent CLPU. And the time-dependent CLPU is analytically embedded in DSR to accurately represent the actual behaviors of the restored loads. Second, it is difficult to predict loads that fluctuate during DSR due to the lack of real-time measurement data. Accordingly, a robust DSR based on the information gap decision theory (IGDT) is proposed to address this challenge, fully considering the uncertainty of CLPU. The proposed models are tested in IEEE 13-node and 123-node test feeders. Simulation results demonstrate that the time-dependent CLPU model and the uncertainty modeling of CLPU can accurately capture the actual behaviors of loads with TCLs after an outage, which greatly improves DSR decisions in practice. [ABSTRACT FROM AUTHOR]

This paper studies the characteristics of subsynchronous oscillation (SSO) power propagation in power systems with a large-scale wind farm integration. Based on the instantaneous power theory, a novel definition of SSO power is proposed to characterize its propagation. The SSO power contains both DC power components and AC power components. Utilizing SSO power, SSO propagation is quantitatively studied in single oscillation source systems, and further studied in multiple oscillation sources systems. Theoretical analysis reveals that in addition to the recognized impact of line impedance, power flow also affects SSO power propagation significantly. Hence, propagation impact factor is proposed for the determination of the dominant influencing element. To reveal SSO power propagation paths in a network, shunt coefficients of DC power components and AC power components are expressed respectively. Test cases under different operating conditions and a practical case are carried out to demonstrate analysis and conclusions in this paper. [ABSTRACT FROM AUTHOR]

Since the publication of the original paper on power system stability definitions in 2004, the dynamic behavior of power systems has gradually changed due to the increasing penetration of converter interfaced generation technologies, loads, and transmission devices. In recognition of this change, a Task Force was established in 2016 to re-examine and extend, where appropriate, the classic definitions and classifications of the basic stability terms to incorporate the effects of fast-response power electronic devices. This paper based on an IEEE PES report summarizes the major results of the work of the Task Force and presents extended definitions and classification of power system stability. [ABSTRACT FROM AUTHOR]

This paper investigates the small-signal power oscillations in a VSC-based multi-terminal DC (MTDC) power transmission system. Regarding the DC power oscillation modes (DPOMs), this paper demonstrates that when the master–slave control is adopted for the MTDC power system, there is only one DPOM. In contrast, when the DC voltage droop control is adopted, the DC electric power oscillations (DEPOs) may be multi-modal oscillations as there are multiple DPOMs. Furthermore, a better understanding of DEPOs, which is analogous to low-frequency electric power oscillations (LEPOs) in conventional AC power systems, is obtained, unveiling how the DPOMs determine the power oscillations in AC and MTDC power systems. Moreover, this paper theoretically verifies that the DPOMs determine the behaviors of both DEPOs in MTDC systems and power oscillations between the VSCs (which adopt the DC voltage control or DC voltage droop control) and external AC systems. By analogy to LEPOs, damping torque analysis is applied to examine the stability risk of the MTDC power system. The results indicate that even if VSCs are self-stable, there is a potential risk of growing DEPOs occurring in MTDC power systems under heavy DC loads. [ABSTRACT FROM AUTHOR]

The modern power grid features the high penetration of power converters, which widely employ a phase-locked loop (PLL) for grid synchronization. However, it has been pointed out that PLL can give rise to small-signal instabilities under weak grid conditions. This problem can be potentially resolved by operating the converters in grid-forming mode, namely, without using a PLL. Nonetheless, it has not been theoretically revealed how the placement of grid-forming converters enhances the small-signal stability of power systems integrated with large-scale PLL-based converters. This paper aims at filling this gap. Based on matrix perturbation theory, we explicitly demonstrate that the placement of grid-forming converters is equivalent to increasing the power grid strength and thus improving the small-signal stability of PLL-based converters. Furthermore, we investigate the optimal locations to place grid-forming converters by increasing the smallest eigenvalue of the weighted and Kron-reduced Laplacian matrix of the power network. The analysis in this paper is validated through high-fidelity simulation studies on a modified two-area test system and a modified 39-bus test system. This paper potentially lays the foundation for understanding the interaction between PLL-based (i.e., grid-following) converters and grid-forming converters, and coordinating their placements in future converter-dominated power systems. [ABSTRACT FROM AUTHOR]

Solving nonlinear algebraic equations is a core task in many fields of engineering and the sciences. Iterative methods such as Newton's method have been popular in solving power flow equations. Recently, the non-iterative holomorphic embedding (HE) methods that employ polynomial embedded systems have been proposed to solve power flow equations. It is advantageous for the solution functions obtained by the HE method to possess an algebraic property. The present paper points out that algebraic solution functions can have several desired properties for the HE method to work. This paper also exemplifies that the algebraic property cannot be ensured by the elimination process, which corrects an assertion in the literature. It is hence essential to study required conditions for ensuring the existence of algebraic solution functions. The present paper is devoted to the theoretical foundation of HE methods using a polynomial embedded system and develops a novel general theorem and sufficient conditions to ensure the required algebraic property. Examples and numerical results are presented to better explain the derived theoretical results. [ABSTRACT FROM AUTHOR]

This paper proposes a method to design power system stabilizers (PSSs) based on single-machine infinite-bus system models to mitigate the risk of low-frequency electro- mechanical power oscillations in an N-machine power system. First, models of N fabricated identical-machine power systems are established for the N-machine power system. Analysis in the paper indicates that the electromechanical oscillation mode of fabricated identical-machine power systems with the least damping is of less damping than the electromechanical oscillation modes of N-machine power system. Second, it is proved that models of fabricated identical-machine power systems are dynamically equivalent to the single-machine infinite-bus system models. Finally, it is suggested that the PSSs are designed using single-machine infinite-bus system models to enhance the least damped electromechanical oscillation mode of fabricated identical-machine power systems. This eventually improves the damping of electromechanical oscillation modes of N-machine power system, thus mitigating the risk of low-frequency electromechanical power oscillations. In the paper, the proposed method is demonstrated and evaluated by using three well-known example multi-machine power systems. Effectiveness of PSSs is confirmed by the results of modal computation and simulation. [ABSTRACT FROM AUTHOR]

The recent power systems literature has witnessed an intensified interest in state estimation methods that can handle a vast array of measurements from large-scale networks while maintaining performance that is commensurate with real-time requirements. When measurements are from the Supervisory Control and Data Acquisition (SCADA) system and Phasor Measurement Units (PMUs), an elegant formulation of the weighted least squares problem can be cast in complex variables and solved via the Gauss-Newton (GN) method. This paper presents a Perturbed Gauss-Newton (PGN) method in complex variables that is suitable for real-time tracking of the network's state vector. The proposed PGN method is built around an entirely linear measurement model expressed in function of the complex phasor voltages, and its iterative solution scheme requires one factorization of the coefficient matrix followed by repeated forward/backward substitutions. The complex variable PGN method for state estimation can be therefore seen as the counterpart of the classical implicit bus impedance method for power flow. The paper reports numerical results of the PGN method on large-scale transmission networks having up to 9241 nodes and contrasts the proposed method with the equality constrained GN method in complex variables and a recent two-stage estimator also operating in the complex domain. [ABSTRACT FROM AUTHOR]

This paper presents a multi-period active distribution network planning (ADNP) with distributed generation (DG). The objective of the proposed ADNP is to minimize the total planning cost, subject to both investment and operation constraints. The paper proposes a multi-stage stochastic optimization model to address DG uncertainties over several periods, in which the decisions are made sequentially by only using the present-stage information. A nested decomposition method is proposed which applies the stochastic dual dynamic integer programming (SDDIP) method to address computational intractabilities of the proposed ADNP approach. The presented numerical results and discussions on a 33-bus distribution system and a large-scale 906-bus system verify the effectiveness of the proposed ADNP method and its solution method. [ABSTRACT FROM AUTHOR]

The modern photovoltaic inverter has an active power-reactive power (Watt-VAr) control curve that is part of its management modes. The Watt-VAr curve, whose settings can be remotely modified, regulates the inverter reactive power output in function of its active power. This paper proposes the optimization of inverter segregated linear decision rules (LDRs), starting from the legacy Volt-VAr optimization in distribution networks. As opposed to classical LDRs that are interfaced with Volt-VAr optimization, the segregated LDRs are piecewise linear continuous curves that directly satisfy the reactive power capability of inverters, and their multiple segments can capture the correlation in solar power variability. The paper presents linear programming formulations for optimizing the segregated linear decision rules using stochastic optimization, robust optimization, and distributionally robust optimization. The optimization schemes are demonstrated on both meshed and radial networks, and their performance is validated via Monte-Carlo simulation. [ABSTRACT FROM AUTHOR]

Although distribution grid customers are obliged to share their consumption data with distribution system operators (DSOs), a possible leakage of this data is often disregarded in operational routines of DSOs. This paper introduces a privacy-preserving optimal power flow (OPF) mechanism for distribution grids that secures customer privacy from unauthorised access to OPF solutions, e.g., current and voltage measurements. The mechanism is based on the framework of differential privacy that allows to control the participation risks of individuals in a dataset by applying a carefully calibrated noise to the output of a computation. Unlike existing private mechanisms, this mechanism does not apply the noise to the optimization parameters or its result. Instead, it optimizes OPF variables as affine functions of the random noise, which weakens the correlation between the grid loads and OPF variables. To ensure feasibility of the randomized OPF solution, the mechanism makes use of chance constraints enforced on the grid limits. The mechanism is further extended to control the optimality loss induced by the random noise, as well as the variance of OPF variables. The paper shows that the differentially private OPF solution does not leak customer loads up to specified parameters. [ABSTRACT FROM AUTHOR]

The low-frequency oscillations (LFOs) inherent in power systems will be impacted by the increasing penetration of renewable energy sources (RESs). This paper investigates the impact of virtual synchronous machine (VSM) based RESs on the LFOs in power systems. A detailed two-machine test-bed has been developed to analyze the LFOs which exists when VSMs replace synchronous generators. The characteristics of the LFO modes, and the dominant states have been comprehensively analyzed. Furthermore, this study analyzes the LFO modes which exists in an all-VSM grid. The role of the power system stabilizers in the all-VSM grid has been comprehensively evaluated. The IEEE benchmark two-area four-machine system has been employed to corroborate the results of the small-signal analysis, and observe the transient performance. The analysis in this paper have been performed in MATLAB/SIMULINK environment. [ABSTRACT FROM AUTHOR]

This paper focuses on the issue of network connectedness (NC) in security-constrained optimal transmission switching problems, which is complicated by branch contingencies and corrective line switching. Two criteria are firstly proposed with the principle of preserving NC as much as possible within reasonable limits. By extending the electrical flow based NC constraints, a proposition is derived to associate different cases of NC with the optimum of a linear program, yielding the mathematical formulation of the NC criteria. By Karush-Kuhn-Tucker conditions, this formulation is further transformed into a tractable version which can be incorporated with existing SCOTS models without affecting the applicability of original solution approaches. Finally, case studies on various networks and SCOTS models demonstrate the efficacy of the proposed approach. [ABSTRACT FROM AUTHOR]

It is found from actual data that due to generation dispatch and uncertain renewable generations and loads with complicated correlations, inferring the probabilistic distributions for uncertain inputs is challenging. Many probabilistic power flow approaches have been developed in the literature but their validations using realistic systems and data are lacking. This paper proposes a data-driven probabilistic analysis approach for system risk assessment of the miniWECC system using actual data. The sparse Gaussian process (SGP) is advocated to quantify the impacts of uncertain inputs on voltage security. SGP does not need the probability distribution function of uncertain inputs, can handle correlations and is highly computationally efficient. Results on the miniWECC system using realistic data show that SGP outperforms existing approaches and is able to quantify the voltage violation risks. [ABSTRACT FROM AUTHOR]

This paper discusses battery storage formulations and analyzes the impact of the constraints on the computational performance of security constrained unit commitment (SCUC). Binary variables are in general required due to mutual exclusiveness of charging and discharging modes. We use valid inequalities to improve the SOC constraints. Adding batteries to the MISO day ahead market clearing cases reveals the impact of binary variables and the valid inequalities on SCUC solving time. Warm start and lazy constraint techniques are applied to improve the performance and make the valid inequalities more effective, reducing computation time to acceptable levels for implementation. [ABSTRACT FROM AUTHOR]

In this paper, we propose a data-driven approach for uncertainty moment propagation through the non-linear power system dynamics. The proposed approach relies on the linear representation of a nonlinear system using the Perron-Frobenius & Koopman operators for propagating the moments. Data from non-linear simulations is used to approximate the linear operators by matrices, thereby enabling moment propagation by matrix multiplication. Results for a large-scale system are presented to demonstrate the accuracy and speed-up of the proposed method. [ABSTRACT FROM AUTHOR]

The existing research on voltage-source converter HVDC (VSC-HVDC) system stability mostly focuses on small-signal stability analysis methods. However, when large-signal disturbances occur, the intrinsic nonlinearity of the VSC-HVDC system becomes inevitable so that the small-signal studies are no longer valid. In this paper, a large-signal stability analysis method of the VSC-HVDC system is proposed based on the estimation of domain of attraction (DOA). This method can not only analyze the asymptotic stability of the operating point, but also answer the question of how large deviations from the operating point can be tolerated by the system. By using Takagi-Sugeno (T-S) fuzzy model and Lyapunov stability theorem, the DOA of the VSC-HVDC System at the operating point is estimated, and the large-signal stability of VSC-HVDC System is analyzed. On this foundation, the influence of system parameters on the large-signal stability is studied. The results can be used to guide the adjustment of system parameters to solve the problem of system instability. The time-domain simulations conducted in Matlab/Simulink are used to test the stability of the system under large-signal disturbance in the range of DOA. The simulation results verify the feasibility and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

With the new feature of multi-energy coupling and the advancement of the energy market, Energy Internet (EI) has higher requirements for the efficiency and applicability of integrated energy response. This paper proposes an indirect multi-energy transaction (IMET) to promote multi-energy collaborative optimization in local energy market (LEM) and improve energy utilization through personalized responses from We-Energies (WEs). Firstly, an indirect customer-to-customer multi-energy transaction is modeled for local multi-energy coupling market which can satisfy privacy, preference and autonomy of users. The efficiency of energy matching can be promoted through the participation of conversion devices. In addition, multi-time scale hybrid trading mechanism is constructed with the consideration of the transmission speed of different energy sources. Meanwhile, energy transaction process is built as a Markov decision process (MDP) with deep reinforcement learning algorithm so that the system modeling error can be successfully avoided. Furthermore, a distributed training structure is utilized to obtain more experience for a wider range of scenarios. The results of numerical simulations demonstrate the performance of the proposed method. [ABSTRACT FROM AUTHOR]

The increasing of electric vehicles (EVs) alleviates the faced environmental problems but brings challenges to the optimal operation of transportation network (TN) and distribution network (DN). However, the most of existing research works consider EV charging station assignment and navigation services in the TN separately from charging station power scheduling services in the DN. To overcome this research gap, this paper proposes a collaborative optimal routing and scheduling (CORS) method, providing optimal route to charging stations and designing optimized charging scheduling schemes for each EV. In the order of reporting, whenever an EV reports its charging demand, a CORS optimization model is built and solved so that a specific charging scheme is designed for that EV. Then, the TN and DN status is updated to guide the subsequent EVs operating. The proposed CORS integrates the real-time state of the TN and DN, and effects positive benefits in helping EVs to avoid traffic congestion, improving the utilization level of charging facilities and enhancing charging economy. The combined distributed biased min consensus algorithm and generalized benders decomposition algorithm are adopted to solve the complex nonlinear optimization problem. Through comparing with the existing methods, better effectiveness is verified by simulation results. [ABSTRACT FROM AUTHOR]

This paper addressesthe problem of real-time state estimation in European-type four-wire low-voltage distribution networks with advanced metering infrastructure based on power line communication (PLC). The main drawback of this type of infrastructure is its high latency and a sequential data sampling process. Both factors make it difficult to obtain instantaneous values from the smart meters in a timely and adequate manner to estimate the real-time state of the system. This problem may be solved in the future with the deployment of metering systems based on other technologies such as 5G. However, PLC technology prevails in many countries, and is not likely to be replaced in the medium term. In the meantime, a solution is needed to provide acceptable real-time state estimates in low voltage networks, not to hinder the active management of these assets in the context of smart grids. In this work, a solution is proposed that allows estimating the state of the low voltage network with a reasonable accuracy considering the low quality of the measurements provided by the deployed infrastructure. The system is validated using a portion of a real pilot network located in the north of Spain fed by a transformer station with seven feeders. [ABSTRACT FROM AUTHOR]

In AC microgrids (MGs), the required high control gain for dealing with control faults would reduce the robustness of the secondary control to communication constraints. To tackle with this problem, the subarea physical infrastructure of AC MGs and the two-layer secondary control framework are designed. For the physical infrastructure, the whole AC MG is divided into several areas, where each area is composed of a distributed generator (DG) and several geographical-close loads. Furthermore, by utilizing the advanced metering infrastructure (AMI), loads transmit the local load change information to the DG in the same area. Based on this physical infrastructure, a distributed event-triggered fixed-time fault-tolerant (ET-FTFT) secondary control framework is proposed in this paper to improve the resilience of the secondary control to sensor and actuator faults/attacks, and communication constraints, simultaneously. The adopted two-layer control framework is to decouple the communication constraints and control faults. The event-based strategy is adopted in the upper layer to reduce the communication burden and Zeno behavior can be evaded. Real-time simulations based on the NI-PXI real-time simulator validate the advantages of the distributed ET-FTFT secondary control framework, which are the better robustness to control faults, and the fixed-time convergence to improve the power quality. [ABSTRACT FROM AUTHOR]

Multi-interval or dynamic economic dispatch (D-ED) is the core of various power system management functions. This optimization problem contains many constraints, a small subset of which is sufficient to enclose the D-ED feasible region. This paper presents a topology-aware learning-aided iterative constraint screening algorithm to identify a feasibility outlining subset of network and generating units ramp up/down constraints and create a truncated D-ED problem. We create a colorful image from nodal demand, thermal unit generation cost, and network topology information. Convolutional neural networks are trained for constraint status identification using colorful images corresponding to system operating conditions and transfer learning. Filtering inactive line flow and ramp up/down constraints reduces the optimization problem's size and computational burden, resulting in a reduction in solution time and memory usage. Dropping all inactive branch and ramp constraints may activate some of these originally inactive constraints upon solving the truncated D-ED. A loop is added to form a constraints coefficient matrix iteratively during training dataset preparation and algorithm utilization. This iterative loop guarantees truncated D-ED results feasibility and optimality. Numerical results show the proposed algorithm's effectiveness in constraint status prediction and reducing D-ED size and solution time. [ABSTRACT FROM AUTHOR]

Renewable generation has grown rapidly these years due to its advantages in low environmental impacts. Stand-alone microgrid with renewable generation and energy storage is a promising option in remote areas beyond the reach of an existing power grid. This paper proposes a multiobjective optimization model to co-optimize the sizes of renewable generation and energy storage in stand-alone microgrids, which minimizes the load shedding risk and the total investment cost. The load shedding degree is obtained by an optimal operation problem and measured by shortfall risk, considering the uncertainties of demand and renewable resources. To deal with the inaccurate empirical distribution, distributionally robust shortfall risk is further established under a Wasserstein-metric-based ambiguity set, and an equivalent distributionally robust formulation is obtained. Then a conservative approximation is developed, and the resulting problem appears to be a bi-objective linear program. The Pareto frontier is proven to be piecewise linear, and the analytical expression of the Pareto frontier is derived. Case studies demonstrate the effectiveness and performance of the proposed method. [ABSTRACT FROM AUTHOR]

This paper exploits the decomposition structure of the large-scale hydrothermal generation expansion planning problem with an integrated modified Benders Decomposition and Progressive Hedging approach. We consider detailed and realistic data from the Brazilian power system to represent hourly chronological constraints based on typical days per month and year. Also, we represent the multistage stochastic nature of the optimal hydrothermal operational policy through co-optimized linear decision rules for individual reservoirs. Therefore, we ensure investment decisions compatible with a nonanticipative (implementable) operational policy. To solve the large-scale optimization problem, we propose an improved Benders Decomposition method with multiple instances of the master problem, each of which strengthened by primal cuts and new Benders cuts generated by each master’s trial solution. Additionally, our new approach allows using Progressive Hedging penalization terms for accelerating the convergence of the method. We show that our method is 60% faster than the benchmark. Finally, the consideration of a nonanticipative operational policy can save 7.64% of the total cost (16.18% of the investment costs) and significantly improve spot price profiles. [ABSTRACT FROM AUTHOR]

This paper proposes a market framework to approach economic dispatch as a subproblem in smart distribution systems. Four subproblems are associated with four different submarkets (SMs) in the presented framework. Market participants are eligible to bid in different submarkets based on the type of their distributed generation (DG) unit. To effectively tackle the conflicts among objective functions of different submarkets, the corresponding formulated subproblems are assigned to two different agents, namely distribution system operator (DSO) and a green bank agent (GBA). A communication mechanism is designed within the proposed framework such that DSO and GBA compromise their solutions and find a tradeoff to reasonably address the concerns of the four subproblems within SMs 1-4. The effectiveness of the proposed market framework is validated on a well-known 136-bus distribution system, which is modified to contain wind turbine DG aggregators, photovoltaic DG aggregators, and diesel-engine DG aggregators. [ABSTRACT FROM AUTHOR]

Faults are extreme events that canadversely affect the voltages in islanded microgrids. This paper provides a new data-driven methodology for timely prediction of the post-fault voltage stability in hybrid AC/DC microgrids. The proposed method performs a binary classification with delay constraints by processing sequences of the short-time mean squared deviations using a deep learning system. The deep learning system consists of a bidirectional long short-term memory network whose output is a probabilistic voltage instability indicator. When the value of the indicator is non-zero, persistent voltage disturbances are most likely to occur even after the fault clearance. The proposed method enables the microgrid to carry out remedial or preventive actions, such as event-triggered protection and control of distributed energy resources (DERs), which are advantageous to the resilient operation of the microgrids. Extensive and detailed electromagnetic transient (EMT) simulations of a low-voltage hybrid AC/DC microgrid benchmark are analyzed, and the results confirm the effectiveness of the proposed method for online prediction and fast voltage regulation. [ABSTRACT FROM AUTHOR]

Globally,wind power plants (WPPs) installations are dramatically increasing, leading to a large-scale integration into the power grids. Huge endeavors are devoted to achieving an efficient operation of WPPs. This paper offers a novel chaotic-billiards optimizer (C-BO) algorithm to properly design the fuzzy logic control (FLC) strategy for stability enhancement of grid-integrated WPPs. The C-BO algorithm has distinct features such as simple construction, low numbers of parameters required to design, high convergence speed, and low computational burden. The permanent-magnet synchronous generator (PMSG)-based WPP is interlinked into the electric power grid through power converters. A cascaded FLC approach, which is optimally fine-tuned by the C-BO, is offered as the control methodology for these converters. The C-BO algorithm fine-tunes the gain factors of multiple FLCs at a rapid convergence speed. A simulation-based optimization approach is applied, in which the integrated square error criterion is chosen as an objective function. For the sake of preciseness, the PMSG-based WPP is connected to the IEEE 39-bus New England test system. The effectiveness of the optimal FLC using the C-BO algorithm is compared with that achieved using the water cycle algorithm-based optimal FLC, the genetic algorithm-based optimal FLC, and the marine predator algorithm-based optimal proportional-integral controller, taking into account severe grid disturbances. Moreover, real wind speed measured data captured from Zaafarana Station in Egypt are extensively performed in the system studies. The validity of the offered control approach is widely verified by the simulation analyses using the MATLAB/Simulink environment. [ABSTRACT FROM AUTHOR]

This paper studies the impact of the inherent characteristics on the initial-stage short-circuit current of modular multilevel converter based multi-terminal direct current (MMC-based MTDC) transmission system. A short-circuit current calculation method based on input-output characteristics is proposed. The MMC’s input-output characteristics are factorized into a series combination of a linear and a nonlinear part through the operator approach. The inherent characteristics of the system represent the inherent properties of the MMC-based MTDC system, which are unrelated to the short-circuit fault. Therefore, short-circuit currents are decoupled and quantitative indices are proposed to measure the impact of the system inherent characteristics on the initial-stage short-circuit current. The proposed decoupled short-circuit current calculation method and the quantitative indices are verified in a typical MMC-based MTDC transmission system under different system parameters and structures. The proposed quantitative indices can effectively evaluate the short-circuit current of the MMC-based MTDC system without calculating the short-circuit current, which indicates that they can provide guidance for the planning of DC system structure, determination of operational modes, and selection of system parameters from the perspective of short-circuit current. [ABSTRACT FROM AUTHOR]

In this paper, an approximate aggregated impedance model (AAIM) of a grid-connected wind farm with multiple wind turbine generators (WTGs) for the small-signal stability study is derived. The derivation is conducted under the condition that the WTGs inside the wind farm are of the same type and from the same manufacturer. The AAIM derived can be seen as the impedance model (IM) of a grid-connected aggregated WTG. The IM of the aggregated WTG is the weighted average of the IMs of the individual WTGs in the wind farm and hence is simple to be built. The reactance of the line connecting the aggregated WTG to the external AC grid is the maximum eigenvalue of the network reactance matrix of the grid-connected wind farm. In addition, the derivation justifies the representation of a wind farm by an aggregated WTG in the IM-based small-signal stability analysis. Finally, two example grid-connected wind farms are used to evaluate and demonstrate the effectiveness of the AAIM proposed in the small-signal stability analysis. It is also shown that the computational burden of the stability analysis of a large-scale wind farm could be effectively reduced when the AAIM is used. [ABSTRACT FROM AUTHOR]

With the large-scale wind power integration into the power system, the inertia and damping characteristics of the system have been weakened, which is not conducive to fulfill the grid-friendly operation of wind power. Aiming at this, an adaptive control scheme combining virtual inertia control (VIC) and supplementary damping controller (SDC) is proposed in this paper. Firstly, the mathematical model of wind grid-connected power system with VIC is established. Secondly, the polytopic linear parameter varying (LPV) system is built to describe the uncertainty of system operating points, and the gap metric-based nonlinear evaluation method is used for polytope vertex configuration. Next, Youla-Kucera parametrization are extended to polytopic LPV system to design an adaptive damping control, in which VIC and SDC are coordinated to provide damping and virtual inertia simultaneously. Finally, the proposed adaptive control scheme is compared with several existing control techniques to validate its effectiveness and advantages in suppressing system oscillation in the modified IEEE 39-bus system integrated with 2 wind turbines. [ABSTRACT FROM AUTHOR]

Recently, many distributed fault-tolerant (Di-FT) secondary controls have been designed to improve the robustness of the islanded AC microgrids (MGs) to actuator faults/attacks. However, some problems still exist among the existing research: (a) the convergence speed of the faulty MG's states has not been discussed, which is important for reducing the inter-harmonic component around the fundamental frequency thereby improving the power quality; (b) large computing and sampling resources are required by the relatively high control gain for dealing with the possible serious actuator faults. Therefore, a distributed event-triggered fixed-time (Di-ET-FT) fault-tolerant secondary control of the faulty islanded AC MG is designed in this paper to release the communication burden, save the computing resources, and achieve the fixed-time convergence speed for improving power quality simultaneously. The Di-ET-FT fault-tolerant secondary control has a two-layer control framework, which are the observer layer as the upper layer and the fault-tolerant control layer as the lower layer. Meanwhile, two event-triggered strategies are designed in these two layers to decrease sample/control updating numbers and communication numbers, respectively. Zeno behavior of the Di-ET-FT fault-tolerant secondary control can be evaded effectively by proving that the interevent time is lower bounded by a positive value. The abovementioned advantages of the designed secondary control are validated by the real-time simulation based on NI-PXI simulator. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel control approach for dynamic virtual power plants (DVPPs). In particular, we consider a group of heterogeneous distributed energy resources (DERs) which collectively provide desired dynamic ancillary services such as fast frequency and voltage control. Our control approach relies on an adaptive divide-and-conquer strategy: first, we disaggregate the desired frequency and voltage control specifications of the aggregate DVPP via adaptive dynamic participation matrices (ADPMs) to obtain the desired local behavior for each device. Second, we design local linear parameter-varying (LPV) $\mathcal {H}_\infty$ controllers to optimally match this local behaviors. In the process, the control design also incorporates the physical and engineered limits of each DVPP device. Furthermore, our adaptive control design can properly respond to fluctuating device capacities, and thus include weather-driven DERs into the DVPP setup. Finally, we demonstrate the effectiveness of our control strategy in a case study based on the IEEE nine-bus system. [ABSTRACT FROM AUTHOR]

Optimal Volt/VAR control (VVC) in distribution networks relies on an effective coordination between the conventional utility-owned mechanical devices and the smart residential photovoltaic (PV) inverters. Typically, a central controller carries out a periodic optimization and sends setpoints to the local controller of each device. However, instead of tracking centrally dispatched setpoints, smart PV inverters can cooperate on a much faster timescale to reach optimality within a PV inverter group. To accommodate such PV inverter groups in the VVC architecture, this paper proposes a bi-level optimization framework. The upper-level determines the setpoints of the mechanical devices to minimize the network active power losses, while the lower-level represents the coordinated actions that the inverters take for their own objectives. The interactions between these two levels are captured in the bi-level optimization, which is solved using the Karush-Kuhn-Tucker (KKT) conditions. This framework fully exploits the capabilities of the different types of voltage regulation devices and enables them to cooperatively optimize their goals. Case studies on typical distribution networks with field-recorded data demonstrate the effectiveness and advantages of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper proposes a new uncertainty quantification framework for power flow (PF) and optimal power flow (OPF) considering the nonlinear correlations of uncertain power injections with limited information. The D-Vine copula is leveraged to capture the nonlinear correlations among uncertain power injections from historical data. This is further integrated with the evidence theory and the reformulated quadratic affine form to obtain PF and OPF results. The D-Vine copula and evidence theory allow one to effectively eliminate the explosive-growth joint focal elements of large-scale power system with large numbers of partial known uncertain power injections, leading to significant reduction of computing time. The reformulated quadratic affine form aims at characterizing the PF and OPF outputs with partially known uncertain power injections in a simple form based on Dempster-Shafer structure, yielding further computational efficiency improvement without the loss of accuracy. Comparison results with other alternatives show that the proposed framework leads to more accurate PF and OPF outcomes while achieving high computational efficiency for large-scale power systems with large numbers of nonlinear correlated power injections in presence of limited information. [ABSTRACT FROM AUTHOR]

This paper applies the principles of distribution locational marginal pricing (DLMP) to unbalanced three-phase distribution networks. We first propose a linear model for AC optimal power flow derived through a series of approximation and reformulation techniques. Then a scenario-generation algorithm is proposed to properly model the uncertain parameters in the linear model. Through a proposed No U-Turn sampler (NUTS) based algorithm, probability density functions (PDFs) of DLMPs are calculated. These PDFs provide statistical information about the locational and temporal price risks. By means of applications to two IEEE unbalanced test networks, the numerical results show promising performance for the proposed linear model and the NUTS-based algorithm in creating PDFs of DLMPs. DLMP price densities will be increasingly useful as distribution system operators seek flexible, low risk solutions from embedded generators and aggregators of distributed energy resources. [ABSTRACT FROM AUTHOR]

The time required to restore distribution systems following an extreme event is highly dependent on damage assessment. Waiting for field assessors patrolling the feeders to identify fault locations is a bottleneck in improving restoration efficiency. This paper proposes an optimal service restoration model for resilient distribution systems considering the coordination with damage assessment, as a contribution to earlier studies. The restoration actions such as fault isolation, network reconfiguration, crew mobilization and fault repair are brought forward to the damage assessment stage and the restoration schedules are dynamically updated with the reveal of the damage status. The relationship between fault location, switch status and node status is established to optimize the network topology and guarantee crew operation safety under the condition that the network has multiple faults or unchecked potential faulted areas. Moreover, the crew routing formulations are modified to enable fault isolation and load island reconnection by manual switches during the restoration process. Case studies validate the effectiveness of the proposed model in reducing load shedding and restoration duration. [ABSTRACT FROM AUTHOR]

Energy sharing has the potential to decrease operation costs by exploiting the complementarity of different entities. The further increase of microgrids’ generation capacity in the distribution network could trigger the emergence of MW-level microgrids aggregator (MGA) to participate in the regional energy sharing market (ESM) and sell excess energy. However, the uncertainty of renewable energy generation will cause the failure of energy sharing and an economic loss for the energy sharing market. Therefore, a novel two-sided market with the clearing price and risk management is proposed in this paper. In order to optimize the energy sharing strategy of MGAs, a network-constrained risk-averse two-stage stochastic game model based on DC optimal power flow (OPF) is established. The objective of the MGA is to maximize the revenue in the ESM while the subtransmission network operator (SNO) aims to achieve regional energy self-sufficiency. The sample average approximation (SAA) method is employed to approximate the stochastic Cournot-Nash equilibrium. A distributed market clearing algorithm based on the alternating direction method of multipliers (ADMM) and best response seeking is developed to solve the optimal bidding problem of MGAs and the optimal dispatch problem of the SNO. Furthermore, the existence of the SAA Nash equilibrium is also investigated. Numerical simulations prove that the proposed game model can effectively control the overbidding risk and promote regional energy self-sufficiency. [ABSTRACT FROM AUTHOR]

With the power system operating close to its stability margin, the increased penetration of renewable resources and loads with diverse characteristics, appropriate stability studies, and control decisions have become crucial. Due to the importance of accurate representation of aggregate loads in such studies, research interests for load modeling have increased. This paper puts forward a novel measurement-based load modeling approach that adapts itself based on the complex dynamics exhibited by the installed aggregate loads. The proposed approach primarily utilizes the collected measurements to gain insight regarding the dynamic trend in the measurements and subsequently determines the optimal order of an equivalent system or load model capable of effectively representing the aggregate load dynamics. An advanced signal pre-processing technique is employed, which effectively suppresses the noise and also preserves the transients present in the measurements. Afterward, an adaptive dynamic load model (ADLM) accurately represents the installed aggregate load. Lastly, the Refined Instrumental Variable (RIV) approach estimates the equivalent dynamic load model parameters. The results on a 2 bus and the IEEE 118 bus networks in DIgSILENT Powerfactory and a 7 bus network in Real-Time Digital Simulator (RTDS) reveal the efficacy of the proposed approach. [ABSTRACT FROM AUTHOR]

Voltage regulation and proper load-sharing are two main control objectives of DC microgrids which the consensus-based secondary control can be regarded as a feasible approach to achieve them. Using this approach, in addition to the stable voltage regulation, the load is shared according to the nominal potential of the distributed generation units. Nevertheless, the conventional consensus-based secondary control uses the time-triggered communication paradigm that leads to a high communication burden due to transmitting and receiving unnecessary data. In this paper, to prevent excessive data-exchanging, the mentioned approach is upgraded with a novel event-triggered scheme. The system stability analysis under the suggested event-triggered secondary controller is assessed using the Lyapunov theorem. Furthermore, the Zeno-freeness feature of the proposed solution is mathematically proved after assessing uniformly ultimately bounded stability and extracting the current per-unit and voltage derivatives dynamic as the consensus algorithm’s external inputs. The proposed approach is applied to a DC microgrid, and its performance is validated under different scenarios using Simulink/MATLAB environment. Moreover, the experimental tests prove the merits of the suggested method. [ABSTRACT FROM AUTHOR]

Unit Commitment (UC) is an important problem in power system operations. It is traditionally planned for 24 hours with one-hour time intervals. To accommodate the increasing net-load variability, sub-hourly UC has been suggested for improved system flexibility. Such a problem is larger and more complicated than hourly UC because of the increased number of periods and reduced unit ramping capabilities per period. The computational burden is further exacerbated for systems with large numbers of virtual transactions leading to dense transmission constraint matrices. Consequently, the state-of-the-art and practice method, branch-and-cut (B&C), suffers from poor performance. In this paper, our recent Surrogate Absolute-Value Lagrangian Relaxation (SAVLR) is enhanced by embedding ordinal-optimization concepts for a drastic reduction in subproblem solving time. Rather than formally solving subproblems by using B&C, subproblem solutions satisfying SAVLR's convergence condition are obtained by modifying solutions from previous iterations or solving crude subproblems. All virtual transactions are included in each subproblem to reduce major changes in solutions across iterations. A parallel version is also developed to further reduce the computation time. Testing on MISO's large cases demonstrates that our ordinal-optimization embedded approach obtains near-optimal solutions efficiently, is robust, and provides a new way of solving other MILP problems. [ABSTRACT FROM AUTHOR]

The quantification of heating, ventilation, and air condition (HVAC) system flexibility is critical to the operations of both the grid and buildings in demand response (DR) programs. However, the flexibility quantification is challenging due to the non-linearity and non-convexity of thermal dynamics associated with HVAC components. This paper proposes a novel HVAC flexibility quantification method based on a semidefinite programming (SDP) formulation. The SDP is reformulated from the non-convex problem of HVAC power optimization, and can be solved efficiently in real-time. The physics-based HVAC model is incorporated to ensure the reliability and accuracy of solutions. The quantification results are organized into an HVAC flexibility table that can provide response strategies on adjusting HVAC setpoints in response to the grid signals received. The developed response strategies minimize occupant discomfort while satisfying grid requirements. A case study of a test building model is carried out to illustrate the flexibility quantification framework and compares the performance of two DR strategies. [ABSTRACT FROM AUTHOR]

This paper presents a wind turbine (WT) control strategy to deliver frequency response (FR) that is similar to conventional synchronous generators, i.e., to deliver accurate and efficient FR. This is achieved by the coordination of WT’s rotor speed control (RSC) and pitch angle control (PAC), wherein the RSC is the summation of the proposed deloading power point tracking curves and FR signal; the PAC is kept as adjusting its control law according to the WT rotor speed. Through the above coordination, the strategy pre-reserves a portion of wind power and releasing it during underfrequency events. Compared with existing methods, the strategy has two significant advantages: 1) it does not need the switching of WT controls according to the real-time wind speed (WS), nor the identification of the boundaries among high, medium and low WSs according to the required reserved power, ultimately reducing the complexity of applying the strategy to practice; 2) its active power response during FR period is independent of the deviated rotor speed and is equal to the FR signal, ultimately enabling the WT to provide accurate and efficient FR. The simulations focusing on the grid frequency side demonstrated the advantages of the strategy over existing methods. [ABSTRACT FROM AUTHOR]