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Load profile classification is very important in load forecast, planning and management. Although customers are generally grouped by utilities into residential, commercial classes and respective subclasses, there is a lack of systematic framework that can be used to characterize different classes with signatures that are both human-readable and machine-readable. The work presented in this paper attempts to formulate the theoretical framework for customer classification using the annual load profiles. This paper demonstrates how to extract characteristic attributes in frequency domain (CAFD) and use these CAFDs to formulate a hierarchy of load profiles that can be used as the systematic framework for customer load classification. As signatures for customer classes and subclasses, the CAFDs are obtained by using a data mining method called CART (classification and regression tree). The paper presents a load profile classification test to establish the efficacy of the proposed approach which is significant improvement over current practices that provide mostly qualitative labeling. [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]

The control of reactive power exchange between grids of different voltage levels has always been a concern for system operators. With production moving from the transmission to the distribution level, its importance increases. This paper proposes a novel approach to estimate reactive power capability of the grid as a whole. A linearized analytical model for an estimation of available reactive power exchange at the interface between two grids has been developed. The maximum estimation error for the scenarios we tested was only 2%. The model gives the relation between important grid parameters and the supported reactive power. The conclusions drawn from the model are confirmed on typical Swedish distribution network with scattered wind power and small industry consumers. Common scenarios in development of distribution grids are applied to show relevant parameters influence. One studied scenario is replacement of overhead lines with cables. It is shown that this particular change enhances the reactive power capability of the grid which is directly seen from the analytical analysis without running any optimal power flow. The analytical model proposed in this paper gives fundamental understanding of the reactive power capability of radial distribution grids. [ABSTRACT FROM AUTHOR]

This paper presents a nonlinear dynamic model of grid-connected combined heat and power (CHP) system that can effectively simulate the thermoelectric interactions and examine the impact of the CHP on the power grid. Based on the theorems of mass balance and energy balance, this paper studies and analyses the principles and dynamic characteristics of the main CHP components, including gas internal combustion engine, synchronous generator, waste heat exchanger, water storage tank, exhaust-heat boiler, and gas fired boiler. Then, the nonlinear time-domain models for each component are built against input and output parameters in order to facilitate the study of CHP operation under various conditions. Subsequently, a simulation method that can deal with the coupling of all models with different time constants is proposed. Finally, dynamic simulation analysis of the developed CHP model is carried out on the MATLAB/Simulink. Extensive demonstration results show that the developed models are accurate to represent the thermoelectric interactions in the gird-connected CHP and capture the impact of these gird-connected CHPs on the power grid. This study is particularly beneficial to the design of new control strategies for CHPs to maximise its efficiency and stability value to the grid. [ABSTRACT FROM AUTHOR]

Risk of blackouts is more than expected and must be considered in the power system studies. The interest of this paper is to minimize the blackouts risk during minimizing the costs in probabilistic transmission expansion planning (TEP). In this paper, a long-term reliability analysis, which was developed by self-organized criticality theory, is used in a multi-objective optimization to include risk of blackouts in TEP. The objective functions are costs and expected energy not supplied. Also, a new formulation for calculating transmission operating cost as a part of cost function is proposed. The problem is solved by a multi-objective particle swarm optimization to find an optimal Pareto set from which the decision-maker can choose the most desirable one, regarding existing budget. The proposed method is applied to the IEEE 118-bus test system to investigate its applicability and scalability. The results are compared with security-constrained TEP, which uses famous deterministic criterion known as N-1. The proposed approach produces more efficient plans for suppressing cascading blackouts with less network investment. Moreover, the results provide motivation for considering the risk of cascading blackouts in TEP, which afford substantial saving to society. [ABSTRACT FROM AUTHOR]

The feeder reconfiguration problem chooses the on/off status of the switches in a distribution network in order to minimize a certain cost such as power loss. It is a mixed-integer nonlinear program and, hence, hard to solve. In this paper, we propose a heuristic algorithm that is based on the recently developed convex relaxation of the ac optimal power flow problem. The algorithm is computationally efficient and scales linearly with the number of redundant lines. It requires neither parameter tuning nor initialization for different networks. It successfully computes an optimal configuration on all four networks we have tested. Moreover, we have proved that the algorithm solves the feeder reconfiguration problem optimally under certain conditions for the case where only a single redundant line needs to be opened. We also propose a more computationally efficient algorithm and show that it incurs a loss in optimality of less than 3% on the four test networks. [ABSTRACT FROM PUBLISHER]

To respond to volatility and congestion in the power grid, demand response (DR) mechanisms allow for shaping the load compared to a base load profile. When tapping on a large population of heterogeneous appliances as a DR resource, the challenge is in modeling the dimensions available for control. Such models need to strike the right balance between accuracy of the model and tractability. The goal of this paper is to provide a medium-grained stochastic hybrid model to represent a population of appliances that belong to two classes: deferrable or thermostatically controlled loads. We preserve quantized information regarding individual load constraints, while discarding information about the identity of appliance owners. The advantages of our proposed population model are 1) it allows us to model and control load in a scalable fashion, useful for ex-ante planning by an aggregator or for real-time load control; 2) it allows for the preservation of the privacy of end-use customers that own submetered or directly controlled appliances. [ABSTRACT FROM PUBLISHER]

As climate concerns, low natural gas prices, and renewable technologies increase the electric power sector's dependence on natural gas-fired power plants, operational and investment models for gas and electric power systems will need to incorporate the interdependencies between these two systems to accurately capture the impacts of one on the other. Currently, few hybrid gas–electricity models exist. This paper reviews the state of the art for hybrid gas–electricity models and presents a new model and case study to illustrate a few potential coupling effects between gas and electric power systems. Specifically, the proposed model analyzes the optimal operation of gas-fired power plants in a competitive electricity market taking into consideration gas purchases, gas capacity contracting, and residual demand uncertainty for the generation company due to renewable energy sources. [ABSTRACT FROM PUBLISHER]

Electric power systems are facing major challenges because of the increase in penetration of energy resources (ERs). This paper focuses on composite system reliability based planning for ERs, and presents novel clustering techniques based approaches to determine the optimal location, size and year of installation of ERs in the system. The K-means clustering and Fuzzy C-means clustering techniques are applied to the set of reliability indices, Load Not Served per Interruption (LNSI), which are determined using nodal minimal cut sets. The nodal minimal cut sets are obtained using an optimal power flow (OPF) based approach in this paper. Once the optimal sizes and locations of ERs are obtained, the earliest year of penetration is determined using an adequacy check algorithm. Detailed studies presented considering the IEEE RTS demonstrate the applicability of the proposed technique. [ABSTRACT FROM PUBLISHER]

This paper provides an overview of hyperloop technologies, including a brief discussion of key components of a hyperloop system that determine the electric power requirements as a function of time. At-scale hyperloop systems do not yet exist, so a model was used to generate a set of load profiles for conceptual hyperloop realizations at four locations in the United States: two systems in California and one each in Colorado and Ohio. In all four cases, the modeled hyperloop load profiles included pulses in both active and reactive power. Grid modeling was performed to estimate the grid impacts and for discussing grid integration challenges. The paper discusses how energy storage systems might be used to eliminate the pulsating load characteristics or significantly reduce it to accommodate current grid planning guidelines. [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 for generators under the locational marginal pricing (LMP) and temporal locational marginal pricing (TLMP) policies. Extending the theoretical results developed in Part I, Part II evaluates a broader set of performance measures under a general network model. For networks with power flow constraints, TLMP is shown to have an energy-congestion-ramping price decomposition. Under the one-shot dispatch and pricing model, this decomposition leads to a nonnegative merchandising surplus equal to the sum of congestion and ramping surpluses. It is also shown that, comparing with LMP, TLMP imposes a penalty on generators with limited ramping capabilities, thus giving incentives for generators to reveal their ramping limits truthfully and improve their ramping capacities. Several benchmark pricing mechanisms are evaluated under the rolling-window dispatch and pricing models. The performance measures considered are the level of out-of-the-market uplifts, the revenue adequacy of the system operator, consumer payment, generator profit, level of discriminative payment, and price volatility. [ABSTRACT FROM AUTHOR]

This paper is devoted to the description of a new approach for large-scale electric power systems (EPS) simulation, so-called hybrid simulation approach. The design and research of EPS require solving a large number of non-trivial tasks. Due to the well-known EPS specificity, mathematical modeling is the only way to proceed. According to the hybrid approach, several simulation techniques (analogue, digital, and physical) are used to achieve the best results. An analogue part provides the elimination of methodological integration error, which appears in a case of numerical methods usage. A digital part allows the realization of control and parameter changing algorithms on software level. In a physical part, the connection and switching of modeled elements are provided in a way as in real EPS. In addition, the paper presents hardware features of the simulator—hybrid real-time power system simulator (HRTSim), which is an alternative to the digital simulators currently used. The important part of this paper is the result of comparative analysis of transient processes oscillograms obtained by HRTSim and real disturbance recorders in Tomsk region power system, as well as HRTSim and real-time digital simulator. [ABSTRACT FROM AUTHOR]

To encourage and support innovation, synthetic electric grids are fictional, designed systems that mimic the complexity of actual electric grids but contain no confidential information. Synthetic grid design is driven by the requirement to match wide variety of metrics derived from statistics of actual grids. In order to scale these systems to 10,000 buses or more, robust reactive power planning is needed, accounting for power flow convergence issues. This paper addresses reactive power planning and power flow convergence in the context of large synthetic power grids. The iterative algorithm presented by this paper supplements a synthetic transmission network that has been validated by a dc power flow with a realistic set of voltage control devices to meet a specified voltage profile, even with the constraints of difficult power flow convergence for large systems. The algorithm is illustrated with an example new synthetic 10,000 bus system, geographically situated in the western United States, which is publicly available and useful for a variety of research studies. An analysis is shown validating the synthetic system with actual grid characteristics. [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]

This paper presents a methodological framework to evaluate the resilience, with the primary focus on performance degradation, of levee-protected electric power networks to flooding in a changing climate. To this end, a multi-disciplinary framework is established by integrating climate science, hydrology, and electric power network analysis. The framework quantifies the effect of climate change on flood hazard levels in a levee-protected area and the subsequent changes in the resilience of the electric power network. In the first step, the changes in the exposure of levee-protected regions to flooding hazard in a warming climate are calculated. Then, the probabilities of failure of power network components due to flooding under current and projected future climate are determined. Finally, the power system resilience index is used to assess the system resiliency for pre-flooding (baseline), historic flooding, and two projected future flooding scenarios. For demonstration, the proposed framework is applied to a levee-protected area in Northern California. The IEEE 118-bus standard test system is employed to represent the power network in the study area. Results reveal that climate change can considerably decrease the system resilience of the levee-protected electric power network. The findings of this study can contribute towards more resilient power network systems under a changing climate. [ABSTRACT FROM AUTHOR]

This paper proposes a stochastic day-ahead scheduling of electric power systems with flexible resources for managing the variability of renewable energy sources (RES). The flexible resources include thermal units with up/down ramping capability, energy storage, and hourly demand response (DR). The Monte Carlo simulation (MCS) is used in this paper for simulating random outages of generation units and transmission lines as well as representing hourly forecast errors of loads and RES. Numerical tests are conducted for a 6-bus system and a modified IEEE 118-bus system and the results demonstrate the benefits of applying demand response as a viable option for managing the RES variability in the least-cost stochastic power system operations. [ABSTRACT FROM PUBLISHER]

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]

In this paper we address some shortcomings in existing nonlinear dynamic models of power system experiencing symmetrical and unsymmetrical faults in their transmission/distribution networks. The shortcomings relate to: 1) approximations relating to stator dynamics, internal voltage of the positive sequence network and the dynamics of the negative and zero sequence networks, and 2) existing models are time -variant, which may not be suitable for system studies such as stability and fault detection studies that require time invariant model. The approximations outlined in 1) adversely affect the modeling accuracy, especially that of the system response during the period immediately after the occurrence of faults. We present in this paper a full fault-dependent time-invariant dynamical model of power systems experiencing symmetrical and unsymmetrical faults in their transmission/distribution networks and verify it through simulation studies mimicking real life scenarios. IEEE 30 bus power system is considered as the study case. The simulation results are compared to Matlab's “SimPower” and PSCAD/EMTDC software packages. [ABSTRACT FROM AUTHOR]

This paper proposes a generalized approach of the per unit normalization, named complex per unit normalization (cpu), to improve the performance of fast decoupled power flow methods applied to emerging distribution networks. The proposed approach takes into account the changes envisaged and also already faced by distribution systems, such as high penetration of generation sources and more interconnection between feeders, while considering the typical characteristics of distribution systems, as the high R/X ratios. These characteristics impose difficulties on the performance of both backward-forward sweep and decoupled-based power flow methods. The cpu concept is centred on the use of a complex volt-ampere base, which overcomes the numerical problems raised by the high R/X ratios of distribution feeders. As a consequence, decoupled power flow methods can be efficiently applied to distribution system analysis. The performance of the proposed technique and the simplicity of adapting it to existing power flow programs are addressed in the paper. Different distribution network configurations and load conditions have been used to illustrate and evaluate the use of cpu. [ABSTRACT FROM PUBLISHER]

In this paper, a joint scheduling scheme for the electric supply and demand of Home Energy Management System (HEMS) and Building Energy Management System (BEMS) via energy management in houses and buildings is presented. To reflect ongoing policy, the Korea Electric Power COoperation (KEPCO) tariff is employed to formulate the optimization problem in terms of the sequential management procedure of prediction, supply control and demand control. As a result, an optimal electric supply pattern to produce the minimum energy cost can be obtained under a given curtailment. From simulation, the proposed scheduling scheme demonstrates greater reliability and adaptation for the interruptible load (IL) than the day-ahead method. [ABSTRACT FROM AUTHOR]

An integrated energy system (IES) represents coordinated operations of constrained natural gas (NG), electric power, and thermal systems in three distinctly different time scales. Microturbines (MTs) are increasingly utilized in IES as suppliers of combined cooling, heating and power (CCHP) units, which optimize the delivery and the usage of interdependent NG, electric power, and thermal energy sectors in a distributed IES. In this paper, a dynamic IES model is proposed based on the singular perturbation theory (SPT), which denotes the dynamic interactions of the three energy systems. The dynamic IES model representing a boundary layer system (BLS) is solved and analyzed in three time-scales. The simulation results demonstrate the validity of the proposed dynamic IES model in three time-scales, highlighting the necessity of incorporating the dynamic characteristics of NG, electric power, and thermal energy in dynamic analyses of distributed IES. [ABSTRACT FROM AUTHOR]

A large part of the electric power demand of commercial ports is particularly flexible. Nowadays, the most significant flexible loads of a commercial port are the refrigerated containers, known as reefers, and the shore-side electric power supplies for ships. Moreover, ports usually have a great potential for local energy generation from renewable energy sources. The real-time control of the numerous port flexible loads is difficult to be achieved with conventional control systems. Multi-agent systems (MAS) have been proved to be very efficient in complex systems and, as it is shown in this paper, they can also be effective for large ports. In this paper, a real-time distributed demand response system using MAS with hierarchical structure is proposed for extensively electrified large ports. The proposed control system ensures real-time limitation of ship emissions, and the reduction of operation cost and fast power variations caused by local wind parks. To achieve the above, the electric power demands of in-port reefers and shore-to-ship power supplies are suitably controlled. A fuzzy-logic-based system is exploited to estimate reefers’ desired utilization factor and integrated to the developed MAS. The efficiency of the proposed method is evaluated by detailed simulations of a realistic case study of a large commercial port. [ABSTRACT FROM AUTHOR]

This paper proposes a method to obtain the optimal placement of wind turbines (WTs) in an offshore wind farm (WF). The optimization objective is to minimize the levelized average cost per net electric power generated by a WF with a fixed number of WTs while the distance between WTs is not less than the allowed minimal distance in the far wake region. The WT wake losses have been taken into account, with the Frandsen–Gaussian (F-G) wake model and the optimization problem is subsequently solved by the hybrid grey wolf optimization (HGWO) algorithm. Synthesis methods that contain a special WT ranking strategy for multiple WTs are described in detail. Both the F-G model and Jensen's model are applied in the offshore WF optimization simulation platform for comparison. Simulation results demonstrate that the F-G model is more consistent with real wakes and thus the optimization result is more accurate than the commonly used Jensen's model. [ABSTRACT FROM AUTHOR]

This paper presents a suite of analytics that are proposed to be embedded in next-generation smart sensors in electric power grids. The proposed analytics take the electrical signals as the input and unlock the full potential in signal processing and machine learning for real-time event detection and classification. Meanwhile, a robust synchrophasor estimation mechanism is housed within the proposed sensor technology that will be triggered following a detected event and guides on the adaptive selection of the best-fit (most accurate) synchrophasor estimation algorithms at all times. Embedding such analytics within the sensor and closer to where the waveforms are captured, the proposed distributed intelligence solution technology mitigates the potential risks to communication failures and latencies as well as malicious cyber threats. Our experiments demonstrate that the introduced scheme achieves improved quality of measurements with a promising event detection and classification accuracy, collectively resulting in enhanced online situational awareness in modern power grids. [ABSTRACT FROM AUTHOR]

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This paper deals with the market-bidding problem of a cluster of price-responsive consumers of electricity. We develop an inverse optimization scheme that, recast as a bilevel programming problem, uses price-consumption data to estimate the complex market bid that best captures the price-response of the cluster. The complex market bid is defined as a series of marginal utility functions plus some constraints on demand, such as maximum pick-up and drop-off rates. The proposed modeling approach also leverages information on exogenous factors that may influence the consumption behavior of the cluster, e.g., weather conditions and calendar effects. We test the proposed methodology for a particular application: forecasting the power consumption of a small aggregation of households that took part in the Olympic Peninsula project. Results show that the price-sensitive consumption of the cluster of flexible loads can be largely captured in the form of a complex market bid, so that this could be ultimately used for the cluster to participate in the wholesale electricity market. [ABSTRACT FROM PUBLISHER]

This paper applies a known stochastic approximation method to solve a two-phase optimal power dispatch problem under load uncertainty using a novel realistic model for random loads. The stochastic power dispatch problem is solved with the Robbins–Monro method applied with the Kiefer–Wolfowitz procedure with random directions. The constraints of this optimization problem have been investigated and considered by truncated algorithms. Two numerical examples are presented for illustration of the significant improvement obtained with the proposed method. In the first example, the optimizable cost of the system can be reduced by 1.6% under 2.68% load variation. In the second example, the results show that the possibility of voltage violations is reduced from 49.5% to 0.01%. All the improvements are compared with deterministic optimal solutions validated with scenario-based Monte Carlo simulations. The method is 60 times faster than scenario-based Monte Carlo for similar accuracy for the IEEE 30-bus test system. [ABSTRACT FROM PUBLISHER]

Hydropower scheduling in day-ahead electricity markets is complex due to uncertainty in the electricity price. Internal cascade dependency of hydro power plants can also increase this complexity. One way to overcome this complexity is to replace the original hydropower system by an equivalent system, which provides simulation results sufficiently close to the ones of the original system. This paper presents a method to obtain multi-station equivalent models using a bilevel optimization problem, where the objective is to minimize the difference in outcomes between the original and the equivalent models. This bilevel problem is then transformed into a single-level optimization problem that can be solved using standard optimization techniques. Finally, the errors between the simulation results of the original and equivalent hydropower models are computed and analyzed for a Swedish system to show the accuracy of different multi-station equivalents. [ABSTRACT FROM PUBLISHER]

This paper introduces a novel idea for adding virtual inertia in power systems by controlling the frequency swing dynamics through voltage channel. The purpose is to develop a frequency-based supplementary VAR modulation to assist governor action during power imbalance events. In this aim, we propose a two-band power system stabilizer, tuned for very low-frequency common swing mode, to adaptively adjust reference voltage of a Synchronous Condenser (SC) and modulate its reactive power on a second-by-second basis. The feasibility of this idea is supported by theoretical and simulation evidences. From theoretical side, we formulate a computational procedure to measure the degree of control impact, accompanied by sensitivity analyses around varying operating points. On the simulation side, we perform extensive studies on four well-known IEEE multi-machine test systems. The results show that VAR modulation by SC has a considerable impact on the minimum post-contingency frequency (frequency nadir), even more than so-called load modulation methods in some cases, which in fact has not been given enough attention in the past. In addition, we show that the proposed method can aid governors to improve primary frequency response particularly in low inertia power systems by reducing post-event frequency settling time and bias. [ABSTRACT FROM PUBLISHER]

This paper proposes a unified power flow algorithm based on the Newton–Raphson (NR) method to obtain a feasible solution for hybrid AC/DC grids incorporating voltage source converters (VSCs). The power flow model of hybrid AC/DC grids is composed of an AC grid model, a VSC station model, and a DC grid model. Detailed converter losses are involved in VSC station modeling. In the DC grid model, a unified dc control expression is applied to adapt to different control methods. To obtain sparse Jacobian matrix, the mismatch equations and unknown variables are classified into three categories corresponding to AC, VSC, and DC models. Then the proposed algorithm achieved quadratic convergence with the NR method. Once the solution is infeasible (variable out-of-limit) or diverging, the constrained load flow (CLF) method is applied automatically to achieve a feasible solution by revising the references of ac/dc grids. Simulations were carried out for the hybrid AC/DC test system composed of two AC grids and two DC grids. The results under different DC voltage control modes show that the proposed algorithm is able to obtain a feasible solution with high accuracy and convergence speed. [ABSTRACT FROM PUBLISHER]

Large-scale blackouts and extreme weather events in recent decades raise the concern for improving the resilience of electric power infrastructures. Distribution service restoration (DSR), a fundamental application in outage management systems, provides restoration solutions for system operators when power outages happen. As distribution generators (DGs) and remotely controllable devices are increasingly installed in distribution systems, an advanced DSR framework is needed to perform optimally coordinated restoration that can achieve maximal restoration performance. This paper introduces a DSR modeling framework, which can generate optimal switching sequences and estimated time of restoration in the presence of remotely controllable switches, manually operated switches, and dispatchable DGs. Two mathematical models, a variable time step model and a fixed time step model, are presented and compared. The proposed models are formulated as a mixed-integer linear programming model, and their effectiveness is evaluated via the IEEE 123 node test feeder. [ABSTRACT FROM AUTHOR]

Linear power flow models are widely used in power systems to simplify the nonlinear power flow equations. The DC power flow model is one of the representatives. There are many other linear power flow models that improve the DC power flow model with the inclusion of $\boldsymbol{Q}$ and $\boldsymbol{v}$. However, existing linear models are derived based on empirical mathematical approximation without a general methodology guidance. In this paper, we found that the fundamental difference among different linear power flow models lies in the formulation of “independent variables.” Based on this finding, a general formulation of linear power flow models is proposed. The linearization error is theoretically analyzed. In particular, the case when $\boldsymbol{ \theta }$ and $\boldsymbol{v^{\scriptscriptstyle k}}$ are regarded as independent variables is thoroughly investigated. Method for finding the linear power flow with the minimum error is presented. The formulation of the independent variables associated with the minimum linearization error is determined by the distribution of state variables $\boldsymbol{v}$ and $\boldsymbol{\theta }$. It is shown that the linearization error when $\boldsymbol{v^{\scriptscriptstyle 2}}$ is regarded as an independent variable is normally smaller than that for $\boldsymbol{v}$ because of the special properties of the distribution of $\boldsymbol{v}$ in power grids. [ABSTRACT FROM AUTHOR]

This paper proposes a multi-timescale volt/var optimization for the optimal dispatch of battery energy storage system in smart distribution grids. It aims to coordinate the substation on-load tap changer operation on slow-timescale (hourly basis) with the photovoltaic inverters and battery storage operations on fast-timescale (15 min basis). This coordination is achieved by using two-stage stochastic programming and implemented via model predictive control. The power loss and energy purchase cost are reduced while maintaining voltages within limits. The forecasting uncertainties are modeled by generating a large number of random scenarios and then subsequently reducing scenario numbers to establish a tradeoff between computational burden and accuracy of the solution. The mixed-integer second-order cone program (MISOCP) is formulated with reduced scenarios, which achieves global optimum. Simulation results demonstrate the effectiveness of proposed MISOCP model in keeping the voltages within limits under forecasting uncertainties. [ABSTRACT FROM AUTHOR]

Due to growing environmental concerns, wind power has taken a larger share of the generation market. While the increase in wind generation provides diversification of the supply, it also undermines system reliability by introducing uncertainty in power supply and decreasing the maneuverability of the grid in terms of VAR absorbance and provision. To restore the reliability of high wind penetrated systems, this paper proposes hierarchical voltage control (HVC). The proposed pattern generalization methodology is based on principle component analysis and a three-layer feedback architecture utilizing online state estimation. The proposed control includes both steady-state and dynamic constraints. Governed by an optimization trained with cases representing operating snapshots and solved via an active-set algorithm adopting stepwise linearization, the proposed HVC provides efficiency and adaptability. As a result, the proposed control provides adequate system support under a wide range of contingencies and operating conditions to enhance grid reliability. Its capability to regulate yearly round operations in real time is validated using a reduced Western Electricity Coordinating Council power system model. [ABSTRACT FROM AUTHOR]

This paper introduces a two-step methodology for online identification of the participation of generators in power system oscillatory modes, based on measured responses. The dominant modes in generator measured responses are initially identified using a mode identification technique and then introduced, in the next step, as input into a clustering algorithm. Critical groups of generators that exhibit poorly or negatively damped oscillations are identified, in order to enable corrective control actions and stabilize the system. The uncertainties associated with the operation of modern power systems with renewable energy sources (RESs) are investigated as well as the impact of the dynamic behavior of power electronic interfaced RESs. [ABSTRACT FROM AUTHOR]

Electric power grids regularly experience uncertain fluctuations from load demands and renewables, which poses a risk of violating operational limits designed to safeguard the system. In this paper, we consider the robust AC OPF problem that minimizes the generation cost while requiring a certain level of system security in the presence of uncertainty. The robust AC OPF problem requires that the system satisfy operational limits for all uncertainty realizations within a specified uncertainty set. Guaranteeing robustness is particularly challenging due to the non-convex, nonlinear AC power flow equations, which may not always have a solution. In this work, we extend a previously developed convex restriction to a robust convex restriction, which is a convex inner approximation of the non-convex feasible region of the AC OPF problem that accounts for uncertainty in the power injections. We then use the robust convex restriction in an algorithm that obtains robust solutions to AC OPF problems by solving a sequence of convex optimization problems. We demonstrate our algorithm and its ability to control robustness versus operating cost trade-offs using PGLib test cases. [ABSTRACT FROM AUTHOR]

This paper considers future distribution networks featuring inverter-interfaced photovoltaic (PV) systems, and addresses the synthesis of feedback controllers that seek real- and reactive-power inverter setpoints corresponding to AC optimal power flow (OPF) solutions. The objective is to bridge the temporal gap between long-term system optimization and real-time inverter control, and enable seamless PV-owner participation without compromising system efficiency and stability. The design of the controllers is grounded on a dual \epsilon-subgradient method, while semidefinite programming relaxations are advocated to bypass the non-convexity of AC OPF formulations. Global convergence of inverter output powers is analytically established for diminishing stepsize rules for cases where: i) computational limits dictate asynchronous updates of the controller signals, and ii) inverter reference inputs may be updated at a faster rate than the power-output settling time. [ABSTRACT FROM PUBLISHER]

In liberalized electricity markets, it is essential and of great importance to develop a fair transmission cost allocation scheme that is able to reflect the “extent of use” of the transmission facilities as much as possible. In this paper, the transmission capacity is divided into four components: capacity used in normal conditions (CN), capacity reserved for contingencies (CC), capacity reserved for future use (CF) and invalid capacity (IC). CN and CC are determined by the power flow in the normal and contingency conditions of the system operation snapshot, respectively. The CF is determined by the maximum power flow in contingency conditions over a certain period (say yearly). The cost corresponding to IC would not be allocated to network users. Based on capacity usage identification, differential cost allocation methods are designed according to the functions of the transmission capacity. The cost corresponding to the capacity usage of the transmission asset under normal and contingency conditions is allocated separately. Case studies in a 3-bus system and the IEEE 30-bus system validate the good performance of the proposed structural transmission cost allocation scheme. [ABSTRACT FROM PUBLISHER]

In this paper, a novel solution method is proposed for the optimal topology control problem in AC framework with N-1 reliability. The resultant new topology of the proposed method is guaranteed to yield a better objective function value than the global solution of the problem with the original topology. In order to prove our statement, semi-definite programming relaxation is formulated to find a lower bound to the objective function value of the problem. The proposed method is favorable for parallelization to increase the computational efficiency, and the parallel computing flowchart is presented. Computational time of the algorithm for IEEE test cases is reported. An in-house power market simulator is developed to simulate a market environment complete with optimal topology control mechanism. Simulations held by participating human subjects are analyzed under the subtitles of the total operating cost, real power losses, and locational marginal price (LMP) variance. A simple example is illustrated to show that the resultant network topology may increase the real power losses while still providing a lower operating system cost than that of the original topology. [ABSTRACT FROM PUBLISHER]

This paper discusses the need for short circuit analysis in real-time applications of modern distribution networks and presents a short circuit tool that builds on recent advances in Fortescue-based current injection power flow. The proposed short circuit computation (SCC) method is fundamentally based on the symmetrical components transformation of three-phase, two-phase, and one-phase systems. Unlike the classical symmetrical components SCC method that postulates a structurally symmetrical three-phase pre-fault network with balanced loading, the proposed method accounts for multiphase networks that are comprised of three-phase, two-phase, and one-phase network parts; given a pre-fault power flow solution, it requires a maximum of three current injection iterations to compute the short circuit current flow in the entire network. Numerical results show that the Fortescue SCC approach with multiphase lines exhibits significant computational performance improvement on large-scale networks as compared to classical SCC in phase coordinates. [ABSTRACT FROM PUBLISHER]

Convex relaxations of non-convex optimal power flow (OPF) problems have recently attracted significant interest. While existing relaxations globally solve many OPF problems, there are practical problems for which existing relaxations fail to yield physically meaningful solutions. This paper applies moment relaxations to solve many of these OPF problems. The moment relaxations are developed from the Lasserre hierarchy for solving generalized moment problems. Increasing the relaxation order in this hierarchy results in “tighter” relaxations at the computational cost of larger semidefinite programs. Low-order moment relaxations are capable of globally solving many small OPF problems for which existing relaxations fail. By exploiting sparsity and only applying the higher-order relaxation to specific buses, global solutions to larger problems are computationally tractable through the use of an iterative algorithm informed by a heuristic for choosing where to apply the higher-order constraints. With standard semidefinite programming solvers, the algorithm globally solves many test systems with up to 300 buses for which the existing semidefinite relaxation fails to yield globally optimal solutions. [ABSTRACT FROM PUBLISHER]

The renewable energy-based hydrogen production can lead to the integrated electric power and hydrogen system (IPHS) and offer a pathway to a sustainable energy utilization. Hydrogen is mainly transported via hydrogen tube trailers (HTs), making the hydrogen energy system (HES) operation quite different from those of other energy technologies. This paper proposes an optimal IPHS operation strategy which utilizes HT for transportation. The proposed strategy coordinates hydrogen generation, transportation, and storage stages considering constrained operations of electric power system (EPS), transportation system, and variable renewable energy. The proposed solution method is based on the alternating direction method of multipliers (ADMM) in which HES and EPS constraints are managed individually and the respective solutions are coordinated accordingly. The case studies using the modified IEEE-RTS79 have verified the validity of the proposed IPHS model and its solution method and confirmed the necessity of considering HES in enhancing the EPS operation. The synergies between EPS and HES are studied via numerical examples and the impact of the flexibilities in hydrogen generation, transportation and demand are highlighted. [ABSTRACT FROM AUTHOR]

The potential for cascading failure in power systems adds substantially to overall reliability risk. Monte Carlo sampling can be used with a power system model to estimate this impact, but doing so is computationally expensive. This paper presents a new approach to estimating the risk of large cascading blackouts triggered by multiple contingencies. The method uses a search algorithm (Random Chemistry) to identify blackout-causing contingencies, and then combines the results with outage probabilities to estimate overall risk. Comparing this approach with Monte Carlo sampling for two test cases (the IEEE RTS-96 and a 2383-bus model of the Polish system) illustrates that the new approach is at least two orders of magnitude faster than Monte Carlo, without introducing measurable bias. Moreover, the approach enables one to compute the sensitivity of overall blackout risk to individual component-failure probabilities in the initiating contingency, allowing one to quickly identify low-cost strategies for reducing risk. By computing the sensitivity of risk to individual initial outage probabilities for the Polish system, we found that reducing three line-outage probabilities by 50% would reduce cascading failure risk by 33%. Finally, we used the method to estimate changes in risk as a function of load. Surprisingly, this calculation illustrates that risk can sometimes decrease as load increases. [ABSTRACT FROM AUTHOR]

This paper deals with wind power offering strategies in day-ahead markets. A new plan is proposed in which a wind power producer participates in the day-ahead market while employing demand response (DR) to smooth its power variations. In this context, a new DR scheme is presented through which the wind power producer is able to achieve DR by establishing various DR agreements with DR aggregators. The proposed offering plan involves two stages: the first stage clears on the day-ahead market. The wind power producer decides on day-ahead offers as well as DR agreements with the aggregator. The second stage takes place on the balancing market. In a successive approach, the wind power producer determines its energy trading for each period until the whole day is covered. Additionally, proper DR agreements for each period are confirmed here. The proposed plan is formulated in a stochastic programming approach, where its validity is assessed on a case of the Nordic market. [ABSTRACT FROM PUBLISHER]

Currently, demand response resources can sell load reductions in wholesale energy markets. However, paying for load reductions ultimately results in an unbalanced market, where the amount of resources sold (megawatts) is less than the amount of resources bought (megawatts and “negawatts”). To resolve this imbalance, the ISO must allocate the cost of compensating demand response to those buyers who benefit from reduced LMPs. Current cost allocation methods are quite broad and based on each energy buyer's share of the total load. In an uncongested network, this results in a “fair” allocation of costs, i.e., an allocation proportional to the benefits that each party accrues. However, in a congested network, this is no longer the case, as price separation occurs between nodes. In this paper, we therefore propose a cost allocation method based on LMP sensitivity that accounts for the effect of congestion on the distribution of benefits between nodes with different LMPs. Since this sensitivity-based method only takes into account the cost allocation per node, we also propose a means of allocating costs between individual load serving entities (LSEs) at a single node. Due to this refinement, LSEs are rewarded according to their individual contribution to demand response. Finally, we define a fairness index to evaluate the performance of the proposed method as compared to a load-based allocation. We find that when load reductions are small (1%–3% total load), the fairness index of the proposed method is very close to zero, indicating almost identical benefit to cost ratios for all market participants. Although the fairness index increases with increasing load reductions, results show that even for larger load reductions, the fairness index is still lower for the proposed method than for the load-based allocation method. [ABSTRACT FROM PUBLISHER]

Economic transmission switching has been proposed as a new control paradigm to improve the economics of electric power systems. In practice, the transmission switching operation itself is a disruptive action to the system. Frequently switching lines into or out of service can create undesirable effects on the security and reliability of power systems and may require new investments in the automation and control systems. In this paper, we formulate an economic seasonal transmission switching model where transmission switching occurs once at the beginning of a time period (season) and then the transmission topology remains unchanged during that period. The proposed seasonal transmission switching model is a large-scale mixed integer programming problem. The objective of the optimization model is to minimize the total energy generation cost over the season subject to loads and N-1 reliability requirements. We develop a novel decomposition method that decomposes the seasonal problem into one-hour problems which are then solved efficiently. We demonstrate our model and the decomposition approach on the 14-bus, 39-bus, and 118-bus power systems and show potential cost savings in each case. [ABSTRACT FROM PUBLISHER]

Security enhancement of distribution networks are constrained with multiple causes including limited availability of network resources and high penetration of intermittent distributed generation. In that context, this paper proposes a new methodology to enhance the security of power supply in a distribution network by strategically integrating plug-in hybrid electric vehicle (PHEV) micro grids based on the nodal criticality. The nodal criticality is assessed by integrating operational uncertainties of events into samples of Monte Carlo simulation and then classifying load interruptions on the basis of their magnitudes and frequencies. The load shedding due to criticality of system stress is classified into arrays of clusters on the basis of magnitudes of interrupted loads. The critical clusters that provide the largest disturbances to nodal loads are used as the reference capacities of PHEV micro grids to mitigate impacts. Case studies are performed by applying the methodology into a realistic model of a distribution network. Results depict that the proposed methodology can improve the system-wide security of supply. There are some nodes of which the security of supply can be improved significantly. The levels of improvement in security of supply of nodes are not consistent and some nodes can also receive marginal improvements. [ABSTRACT FROM PUBLISHER]

The increasing coupling between natural gas and electricity systems by gas-fired generation units brings new challenges to system analysis, such as pressure variations due to consumption perturbations of generation units. The emerging issues require revolutionary modeling and analysis techniques. This paper proposes a novel model to quantify gas pressure variations due to gas-fired power unit ramping and the impact of constraints from natural gas pressure change on ramp rates of gas-fired plants. By utilizing Laplace transform to resolve the governing equations of gas networks, the proposed model can significantly reduce modeling complexity and computational burden. The dynamic behaviors in time scale in s-domain and spatial partial differential equations are transformed into finite difference equations. By introducing the concept of transfer matrices, the relation between states at each node of gas systems can be expressed by transfer parameter matrices. Additionally, a simplified model is introduced to simply the analysis. The explicit expressions of nodal pressure variations in response to demand change are very convenient for analyzing system dynamic performance under disturbances, identifying the most influential factors. The new models are extensively demonstrated on three natural gas networks and benchmarked with traditional simulation approaches. Results illustrate that they produce very close results with the simulation approach, particularly when gas pipelines are long and enter steady states. [ABSTRACT FROM PUBLISHER]

Reactive power reserves are important for grid stability and have a variety patterns depending on fault location as power systems become larger and complex. This paper presents the clustered effective reactive reserve (CEQR), an indicator that is suitable for identifying the risk of dynamic voltage stabitlity in terms of power system operation. This indicator provides the following features: i) It calculate the local realistic reactive reserve to recognize dynamically changing system conditions; ii)it derive clustered areas to identify valid regions for system control. The proposed method uses the sensitivity between dynamic reactive resources or load buses and a particular bus to obtain the correlation. And since this indicator is calculated using only the system topology, current and maximum outputs of the generators, there is less computational burden. This study verified the features of clustered effective reactive reserve (CEQR) by analyzing the practicable dynamic voltage collapse scenarios in the Korean electric power (KEPCO) system. In addition, as a countermeasure against the dynamic voltage collapse, the control performance of blocking tap changer scheme using the proposed indicator is described. [ABSTRACT FROM AUTHOR]

The object of this paper is to demonstrate the performance of a Ramp-Rejecting electric Power Prefilter (RRPP), which is a modification of the Conventional electric Power Prefilter (CPP) for a speed-PSS. The RRPP can markedly reduce the undesirable swings in generator voltage and reactive power output due to ramping of turbine power. Like the Dual Input Prefilter (DIP) of the integral-of-accelerating-power (IAP) PSS the RRPP (i) significantly attenuates the torsional modes; and (ii) enhances modal damping. Significantly, the RRPP does not require an externally-estimated rotor-speed input signal - which is an important requirement of the DIP. For example, using generator terminal variables for the DIP it is difficult to accurately estimate a rotor-speed input that covers a wide range of operating conditions and modal frequencies. [ABSTRACT FROM AUTHOR]