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In interconnected power systems, operated by several system operators, the participating areas are strongly dependent on their neighbors. In order to identify the economically efficient generation dispatch in each area, a distributed multi-area optimal power flow (OPF) must be solved. Additionally, installation of an increasing number of HVDC lines to deal with increased power flows from renewable generation is expected to change the power system operation paradigm. The controllability introduced by the HVDC lines should be considered and incorporated in the OPF algorithm. In this paper we introduce a formulation for the distributed solution of the OPF problem in multi-area systems consisting of both HVAC and HVDC lines. We show the applicability of this formulation on two different operating schemes for HVDC grids and we compare their performance with a central solution for the mixed HVAC/HVDC grid. The proposed formulations are based on a linearized solution of the OPF problem. The only data to be exchanged between the areas pertains to the border nodes. [ABSTRACT FROM AUTHOR]

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]

This paper proposes a control-oriented energy function (COEF) based on passivity for transient stability control (TSC) of power systems and investigates the feasibility of an iterative process using the COEF for determining generation shedding (GS) requirements for real-time TSC, in particular, for severe cases when autoreclosing fails due to permanent faults. The COEF for TSC set up in this paper gives directly the GS requirement without calculations of stability region and Critical Clearing Time, and exempts all modeling assumptions except for nonnegative damping the generator models. The simulation with the full planning model of Western Electricity Coordinating Council system shows that the passivity-based COEF for TSC offers a concise, practical, and effective index for GS determination for transient stability control of power systems, which is particularly suitable for the Remedial Action Schemes that BC Hydro has been using. [ABSTRACT FROM AUTHOR]

The paper addresses the problem of determining the maximum distributed energy resource (DER) capacity that can be connected to low-voltage (LV) networks without creating voltage-rise problems. That maximum capacity depends on the network characteristics and on the candidate sites for DER connection. When sites are multiple and LV networks are poorly characterized, such maximum capacity cannot be determined with power-flow or other admittance-matrix-based approaches. This paper proposes an approach to estimate voltage rise caused by DER under reduced network characterization. Such approach is built upon customary voltage and impedance measurements to be made at the sites of DER injection and gives sufficiently accurate results under most practical situations. The paper validates the accuracy of the proposed approach against exact power-flow results for well-characterized networks and load situations and provides guidelines for DER connection based on maximum capacity nodal allocation. [ABSTRACT FROM PUBLISHER]

This paper presents a new security-constrained multiobjective optimal dispatch (SC-MOOD) framework for an economic and reliable operation of microgrids. The framework is developed based on a computationally effective multiobjective optimization technique, Pareto concavity elimination transformation (PaCcET). The new method considers grid steady-state and dynamic constraints while solving the optimal dispatch in both grid-connected and islanded modes. The constraints consist of power balance, voltage magnitude, line flows, power generation, frequency, and voltage transients. The proposed framework finds the most economic operating solutions to not only minimize the generation cost but also minimize the reliability cost. In this paper, the PaCcET is utilized to solve the SC-MOOD. The PaCcET uses an extraordinary transformation to first transfer all the points from a multiobjective space to a transformed objective space. It then solves a linear combination of transformed objectives using a single-objective optimizer to find all the nondominated points of the original multiobjective space. The performance of the new framework is verified using the simulation results in a microgrid with several distributed energy resources in both grid-connected and islanded modes. The results are then compared with two multiobjective optimization techniques, nondominated sorting genetic algorithm II and multiobjective particle swarm optimization, as two well-known benchmarks. [ABSTRACT FROM AUTHOR]

Recurrence quantification analysis (RQA) is a nonlinear data analysis technique that can quantify the number and duration of a dynamical system of being in nearly the same area in phase space trajectory. This paper proposes three applications of RQA to analyse power system dynamics. These are denoising of PMU data, localization of power system events, and transient stability contingency ranking of power system. All three applications have been demonstrated using a multimachine test system. [ABSTRACT FROM AUTHOR]

In this paper, dynamic performance of automatic generation control (AGC) of two area thermal–thermal power system is determined through the steam chest and reheater constant trajectories. In earlier AGC studies, steam chest and reheater time constants denoted as T\text{SC} and T\text{RH} are considered to be constant, irrespective of plant generation schedules. However, it is discovered that T\text{SC} and T\text{RH} follow the specific trajectories with the plant generation schedules. These time constant trajectories are determined by the power output control strategies being employed to change the plant generation schedules. These control strategies are the different combinations of steam pressure versus flow rate curves used to operate the plant at different generation schedules. In this paper, time constant versus generation schedule trajectories of T\text{SC} and T\text{RH} have been derived for different power output control strategies. Furthermore, these time constant trajectories have been utilized to investigate the dynamic performance of two area reheat thermal power system. [ABSTRACT FROM AUTHOR]

This paper demonstrates that machine learning can be used to create effective algorithm selectors that select between power system control algorithms depending on the state of a network, achieving better performance than always using the same algorithm for every state. Also presented is a novel method for creating algorithm selectors that consider two objectives. The method is used to develop algorithm selectors for power flow management algorithms on versions of the IEEE 14- and 57-bus networks, and a network derived from a real distribution network. The selectors choose from within a diverse set of power flow management algorithms, including those based on constraint satisfaction, optimal power flow, power flow sensitivity factors, and linear programming. The network state-based algorithm selectors offer performance benefits over always using the same power flow management algorithm for every state, in terms of minimizing the number of overloads while also minimizing the curtailment applied to generators. [ABSTRACT FROM AUTHOR]

Due to uncertainties in generation and load, optimal decision making in electrical energy markets is a complicated and challenging task. Participating agents in the market have to estimate optimal bidding strategies based on incomplete public information and private assessment of the future state of the market, to maximize their expected profit at different time scales. In this paper, we present an agent-based model to address the problem of short-term strategic bidding of conventional generation companies (GenCos) in a power pool. Based on the proposed model, each GenCo agent develops a private probabilistic model of the market (using dynamic Bayesian networks), employs an online learning algorithm to train the model (sparse Bayesian learning), and infers the future state of the market to estimate the optimal bidding function. We show that by using this multiagent framework, the agents will be able to predict and adapt to approximate Nash equilibrium of the market through time using local reasoning and incomplete publicly available data. The model is implemented in MATLAB and is tested on four test case systems: two generic systems with 5 and 15 GenCo agents, and two IEEE benchmarks (9-bus and 30-bus systems). Both the day-ahead (DA) and hour-ahead (HA) bidding schemes are implemented. The results show a drop in market power in the 15-agent system compared to 5-agent system, along with a Pareto superior equilibrium in the HA scheme compared to the DA scheme, which corroborates the validity of the proposed decision making model. Also, the simulations show that introduction of an HA decision making stage as an uncertainty containment tool, leads to a more stable and less volatile price signal in the market, which consequently results in flatter and improved profit curves for the GenCos. [ABSTRACT FROM PUBLISHER]

Currently, power system planning practices are undergoing various transformations in an attempt to integrate efficiently significant amounts of low-carbon power generation technologies. At the heart of this efficient integration lies the need to plan for and exploit the available flexibility in power systems. In the past, the emphasis was on planning the capacity (i.e., power) of operating reserve requirements, in the form of categorized reserve types. Such approach was suitable for traditional power systems exhibiting low variability and uncertainty. The concept of power system flexibility is emerging as a way to emphasize the need to also consider the ramping capability of operating reserve needed to accommodate high variability and uncertainty arising from renewable energy integration. Prior considerations of power system flexibility, however, are found to be inadequate to handle energy-limited power system resources like energy storage assets and demand response. Hence, this paper sets to consider systematically energy limitations of operating reserve by proposing energy-based operating reserve definitions. We demonstrate the benefits of the new reserve definitions, using a receding-horizon economic dispatch integrating energy storage. [ABSTRACT FROM PUBLISHER]

This paper presents a new approach for reliability evaluation of composite power systems by combining Monte Carlo simulation and multi label k-nearest neighbor (MLKNN) algorithm. MLKNN is a classification technique in which target vector of each instance is assigned into multiple classes. In this paper, MLKNN is used to classify states (failure or success at system or bus level) of a complete power system without requiring optimal power flow (OPF) analysis, except in the training phase. As a result, the computational burden to perform OPF is reduced dramatically. For illustration, the proposed method is applied to the IEEE 30 BUS Test System and IEEE Reliability Test System. The obtained results from various case studies demonstrate that MLKNN based reliability evaluation provides promising results in both classification accuracy and computation time in evaluating the composite power system reliability. [ABSTRACT FROM AUTHOR]

The co-optimization of security-constrained unit commitment and dispatchable transmission network is a large-scale and computationally complex optimization problem that brings big challenges for the traditional centralized and/or master–slave based decomposition approaches. This paper proposes a fully parallel approach to achieve an efficient and fast solution while considering post-contingency corrective actions. In this paper, the original co-optimization problem is decomposed into six major solution modules for unit commitment, optimal power flow, and transmission switching in base case and contingencies, respectively. Moreover, each major solution module is further decomposed by unit, period, dispatchable line, and/or contingency to make the proposed approach favorable in a fully parallel computing environment. The effectiveness and efficiency of the proposed fully parallel approach are justified using numerical cases on a high-performance computing (HPC) platform. [ABSTRACT FROM AUTHOR]

In this paper, a past-aware state estimation (PASE) method (for static state estimation) is proposed for power distribution systems, which takes previous estimates into account to improve the accuracy of the current one, using an Ensemble Kalman Filter (EnKF). Fewer phasor measurements units are needed to achieve the same estimation error target than snapshot-based methods. Furthermore, contrary to existing methods, the proposed approach does not embed power flow equations into the state estimator, thus making it a versatile technique. The theoretical formulation of the EnKF-based PASE presented in the paper has been validated considering a 33-bus distribution system and using power consumption traces from real households. [ABSTRACT FROM AUTHOR]

The development of medium-/long-term studies for power-system operation and planning under the uncertainty of renewable generation is a key challenge faced by power-system agents worldwide. There is a vast literature on stochastic optimization models devoted to addressing the relevant issues on both operation and planning applications. Notwithstanding, few papers focus on addressing the gaps within the subject of joint scenario generation despite the high sensibility of stochastic optimization models with regard to their input scenarios. Characterizing wind power generation (WPG) stochastic processes to devise time- and spatial-dependent scenarios, based on simulation procedures, for time horizons of one to a few years is a difficult task. Multiple regimes and non-Gaussian distributions are two of the main issues that significantly change the risk described through generated scenarios. In this paper, a new methodology to simulate long-term joint scenarios for multivariate WPG time series is presented. The proposed framework, known as generalized auto regressive score models, is derived based on a new class of time-series model with time-varying parameters and an arbitrary non-Gaussian distribution. Our case study shows, based on real data from the Brazilian power system, that the proposed methodology is capable of producing scenarios with coherent temporal and spatial dependence that are needed in power system studies. [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]

Recent years have witnessed significant interest in convex relaxations of the power flows, with several papers showing that the second-order cone relaxation is tight for tree networks under various conditions on loads or voltages. This paper shows that ac-feasibility, i.e., to find whether some generator dispatch can satisfy a given demand, is NP-hard for tree networks. [ABSTRACT FROM AUTHOR]

This paper proposes an efficient solution approach based on Benders’ decomposition to solve a network-constrained ac unit commitment problem under uncertainty. The wind power production is the only source of uncertainty considered in this paper, which is modeled through a suitable set of scenarios. The proposed model is formulated as a two-stage stochastic programming problem, whose first-stage refers to the day-ahead market, and whose second-stage represents real-time operation. The proposed Benders’ approach allows decomposing the original problem, which is mixed-integer nonlinear and generally intractable, into a mixed-integer linear master problem and a set of nonlinear, but continuous subproblems, one per scenario. In addition, to temporally decompose the proposed ac unit commitment problem, a heuristic technique is used to relax the inter-temporal ramping constraints of the generating units. Numerical results from a case study based on the IEEE one-area reliability test system (RTS) demonstrate the usefulness of the proposed approach. [ABSTRACT FROM AUTHOR]

Emerging smart-grid-enabling technologies will allow an unprecedented degree of observability and control at all levels in a power system. Combined with flexible demand and storage, they could allow procuring energy at minimum cost and environmental impact. That however presupposes real-time coordination of demand of individual households and industries down at the distribution level, with generation at the transmission level. This is closely related with the balancing market economic dispatch (ED) problem, which currently does not take into account distribution network constraints and flexible demand characteristics. Still, assuming a suitably modified form of that problem was available, due to both computational and communications requirements, its centralized solution in its full detail would not be tractable. While there is currently a wealth of literature dealing with distributed optimization applications in power systems, it typically focuses on smaller parts of the overall energy management problem (e.g., transmission area synchronization or electric vehicles management) often without considering its full scale or establishing any association with energy market mechanisms. The target of this paper is twofold: 1) identify a flexible demand and distribution network inclusive formulation for ED and 2) propose a solution method. [ABSTRACT FROM AUTHOR]

This paper presents a method that in real-time determines remedial actions, which restore stable operation with respect to aperiodic small signal rotor angle stability (ASSRAS) when insecure or unstable operation has been detected. An ASSRAS assessment method is used to monitor the stability boundary for each generator in real-time. The ASSRAS boundary represents the condition when a generator reaches the maximum steady state active power injection. The proposed control method exploits analytically derived expressions for the ASSRAS boundary and other characteristic curves in the injection impedance plane to determine an active power redispatch among selected generators to restore stable and secure operation. Since the method is purely based on analytically derived expression, the computation of the remedial actions is fast and well suited for real-time operation. The method was tested on the IEEE 14-bus and the Nordic32 test systems where results show that the method can efficiently determine the required active power redispatch to avoid an imminent instability. [ABSTRACT FROM AUTHOR]

This paper proposes a novel comprehensive operation and self-healing strategy for a distribution system with both dispatchable and nondispatchable distributed generators (DGs). In the normal operation mode, the control objective of the system is to minimize the operation costs and maximize the revenues. A rolling-horizon optimization method is used to schedule the outputs of dispatchable DGs based on forecasts. In the self-healing mode, the on-outage portion of the distribution system will be optimally sectionalized into networked self-supplied microgrids (MGs) so as to provide reliable power supply to the maximum loads continuously. The outputs of the dispatchable DGs will be rescheduled accordingly too. In order to take into account the uncertainties of DG outputs and load consumptions, we formulate the problems as a stochastic program. A scenario reduction method is applied to achieve a tradeoff between the accuracy of the solution and the computational burden. A modified IEEE 123-node distribution system is used as a test system. The results of case studies demonstrate the effectiveness of the proposed methodology. [ABSTRACT FROM PUBLISHER]

This paper is to evaluate the presented model in part I for bidding strategy of virtual power plant (VPP) with centralized control in a joint market of energy and spinning reserve service. Two test VPPs are introduced and different scenarios are considered for markets prices. At first, the participation of VPP in only energy market is studied. Then, the spinning reserve market is taken into consideration and the bids of VPP in a joint market of energy and spinning reserve service is studied under different scenarios of markets prices and the results are analyzed. In all cases, the results show the effectiveness and the quality of the procedure and validate the proposed model. [ABSTRACT FROM PUBLISHER]

This paper addresses the problem of a multiyear security constrained hybrid generation-transmission expansion planning. It is assumed that the overall generation requirements of a network are known along the planning horizon, but their allocations are unknown. Moreover the fuel cost throughout the net- work is not uniform. By allocating the overall generation capacity among the grid nodes, and determining the new transmission element additions along the planning horizon, the overall cost of the system is minimized. The problem is formulated as a mixed integer nonlinear programming problem, which for a large-scale system is very difficult to solve. In this paper a new constructive heuristic approach is proposed, so that the problem can be readily solved. To assess the capabilities of the proposed approach, two networks are studied: the Garver test grid as a small grid and the Iranian power grid as a large-scale grid. [ABSTRACT FROM AUTHOR]

This paper analyzes the benefits of dynamic line rating (DLR) in the system with high penetration of wind generation. A probabilistic forecasting model for the line ratings is incorporated into a two-stage stochastic optimization model. The scheduling model, for the first time, considers the uncertainty associated with wind generation, line ratings, and line outages to co-optimize the energy production and reserve holding levels in the scheduling stage as well as the redispatch actions in the real-time operation stage. Therefore, the benefits of higher utilization of line capacity can be explicitly balanced against the costs of increased holding and utilization of reserve services due to the forecasting error. The computational burden driven by the modeling of multiple sources of uncertainty is tackled by applying an efficient filtering approach. The case studies demonstrate the benefits of the DLR in supporting cost-effective integration of high penetration of wind generation into the existing network. We also highlight the importance of simultaneously considering the multiple sources of uncertainty in understanding the benefits of DLR. Furthermore, this paper analyzes the impact of different operational strategies, the coordination among multiple flexible technologies, and the installed capacity of wind generation on the benefits of DLR. [ABSTRACT FROM PUBLISHER]

This paper aims at providing a novel techno-economic flexibility index to quantify the relative flexibility of different conventional generation technologies. The proposed measure takes into account a comprehensive set of technical flexibility characteristics of generation units including minimum stable generation level, operating range, minimum up/down times, and ramp up/down capability. The impact of various technical parameters on the flexibility restriction is determined based on the additional costs imposed to the system operator in order to satisfy that constraint. Afterward, the calculated flexibility index is incorporated into day-ahead market clearing procedure as a new objective function beside operation cost using a multiobjective decision-making approach. Simulation results show that the need of different flexibility levels changes the optimal generation dispatch and results in different operation costs. Furthermore, the impact of two emerging flexible options, i.e., bulk energy storages and a typical demand response program in the flexibility contribution of other generation technologies is also investigated. The results reveal that the integration of emerging flexible options frequently changes the flexibility share of conventional units and can provide the required flexibility level at a lower cost. [ABSTRACT FROM AUTHOR]

A basic principle of electricity market design is that cost allocation should follow cost causation as closely as possible. As renewable resources play a larger role in electricity generation, system operators have become increasingly concerned about costs related to variability in net load. While variable generation resources have attracted a great deal of attention along these lines, load is a larger source of variability in most systems. This paper introduces a Shapley value framework to assess the total system cost attributable to electricity market participants that either exacerbate or alleviate net load variability. Furthermore, this paper investigates the extent to which these costs are adequately reflected in wholesale market prices. Numerical tests indicate that the cost of variability on the hourly time scale is in most cases internalized by market-integrated generators and buyers, mitigating concerns that variability imposes a socialized cost from ramping or cycling of thermal generators. [ABSTRACT FROM PUBLISHER]

As electric power system operators shift from conventional energy to renewable energy sources, power distribution systems will experience increasing fluctuations in supply. These fluctuations present the need to not only design online decentralized power allocation algorithms, but also characterize how effective they are given fast-changing consumer demand and generation. In this paper, we present an online decentralized dual descent (OD3) power allocation algorithm and determine (in the worst case) how much of observed social welfare can be explained by fluctuations in generation capacity and consumer demand. Convergence properties and performance guarantees of the OD3 algorithm are analyzed by characterizing the difference between the online decision and the optimal decision. We demonstrate validity and accuracy of the theoretical results in the paper through numerical experiments using real power generation data. [ABSTRACT FROM PUBLISHER]

This paper presents a new method to evaluate the reliability of a power system with high penetration of wind generation, considering the impact of not only the intermittence but also the low inertia characteristic of wind power. As wind generation gradually replaces conventional generation, the system stability and the reliability are negatively affected. Some of the measures employed to deal with the challenges resulting from increasing wind penetration include operating wind generators at lower levels than their available output and providing inertia so that wind generation is able to contribute to system frequency regulation. Apart from these measures, another factor that limits the amount of wind power that can be absorbed into the grid is the imposition of the frequency standard and this also affects the reliability of the system in the presence of wind penetration. The reliability evaluation approach proposed in this paper is developed using discrete convolution and implemented on an IEEE RTS-79 system with a suitable modification. Power system reliability with and without considering the impacts of wind intermittence and low inertia are compared to show the effectiveness of the proposed method. [ABSTRACT FROM PUBLISHER]

This paper develops an equilibrium-based model for ex ante analysis of capacity withholding under financial virtual divestiture in a day-ahead market, including one dominant producer and a number of small producers considered as a price-taking competitive fringe. We model the offering problem of the dominant producer as a bilevel optimization problem. In the upper-level problem, the dominant producer derives optimal strategy of physical capacity withholding restricted by operational limits of its own generating units and conditions stated in the financial virtual divestiture. In the lower-level problem, market operator clears the day-ahead market on an hourly basis. We recast the bilevel optimization problem as a mathematical programming with equilibrium constraints problem and convert it to a mixed integer linear programming problem. We show how the application of the financial virtual divestiture may mitigate market power in the day-ahead market, through a case study. The numerical results demonstrate that the dominant producer can exploit technological diversification of its own generating units to exercise market power, even in the presence of this market power mitigation policy. [ABSTRACT FROM PUBLISHER]

Due to rapid expansions of renewable energy (RE) generations, it becomes more important to assess the feasibility of power system operation under limited controllable resources. Especially, exact evaluation of the system reserve for preserving system security is required under erroneous RE output predictions. This paper proposes a method to evaluate the size of the feasible region of power system operation in control space for the examination of the effective system reserve margin under uncertainties. Predicted RE and demands with their confidence intervals (CIs) are specified to formulate a problem for the evaluation of the size of the worst-case feasible region, where positive size implies feasibility, while negative, infeasibility. The method computes the degree of system security, which is referred to as “Robust Power System Security” in this paper. The problem is formulated as bi-level optimization, which is linearized and transformed into the mixed integer linear programming (MILP) problem. This is a new approach in the treatment of uncertainties. We use DC power flow and linear constrained dynamic economic dispatch problem to demonstrate the effectiveness of the proposed method. The proposed approach is useful in power system planning in analyzing the feasibility of dynamic real-time operation in future circumstance. [ABSTRACT FROM PUBLISHER]

The increasing penetration of variable energy resources has led to more uncertainties in power systems. Flexible Ramping Products (FRP) have been adopted by several electricity markets to manage the uncertainties. We reveal that neglected line congestion for FRP may not only cause infeasibility, but also result in a failure of cost recovery. To address the deliverability issues on FRP, this paper proposes a new concept, Deliverable Robust Ramping Products (DRRP), in real-time markets. The DRRP includes generation ramping reserve and generation capacity reserve. The DRRP is deliverable and immunized against any predefined uncertainty. It also fully addresses the bid cost recovery issue caused by the line congestion in existing FRPs. The prices of DRRP are derived within the Affine Adjustable Robust Optimization (AARO) framework. These prices can be used to identify valuable reserves among available reserves and quantify the values of flexible resources that provide reserves. This paper also proposes a general approach to obtaining the time-decoupled prices for DRRP and generation, which can be used for the market settlement of the first interval only in real-time markets. Simulations on a 3-bus system and the IEEE 118-bus system are performed to illustrate the concept of DRRP and the advantages of DRRP compared to existing FRP. [ABSTRACT FROM PUBLISHER]

This paper proposes a new method to generate synthetic load and generation profiles that can be customized to simulate different groups of consumers or energy production technologies. The method relies upon generic individual profiles and specific aggregate profiles to set up a discrete-time non-stationary Markov process that realistically reproduces high-resolution daily load volatility and time-dependency. The framework to set up the process and the details for its parameterization are provided in the paper for arbitrary load-discretization and time-resolution. Three examples with real data and 15 min resolution are used to discuss the limitations of the method proposed and the accuracy of its results. [ABSTRACT FROM PUBLISHER]

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]

The problem of optimally sizing hybrid energy storage systems (HESS) installed in electric railway systems, considering the effect of regenerative braking is studied in this paper. HESSs combine traditional batteries and newly developed ultracapacitors, taking advantage of the high energy capacity of batteries and of the flexibility and ability to capture high power density of ultracapacitors. A novel mixed integer linear programming formulation that includes the counting of battery cycles is presented. Some particularities of battery operation are included in the model, like the dependence of its performance on the number of cycles and the depth of discharge (DOD). Results are reported first for a illustrative case study, allowing us to perform sensitivity studies for several parameters; results are also reported and for a larger case, closely resembling the problem faced by railway energy systems planners and operators. [ABSTRACT FROM PUBLISHER]

A hierarchical decentralized network reconfiguration approach to minimize power losses for smart distribution systems was presented in Part I. In this paper, the proposed approach is applied to four test distribution systems to examine its performance. A demonstration system consisting of the test system and distributed intelligent agents is built using MATLAB/Simulink to illustrate how the decentralized approach is implemented. Simulation results of the proposed approach are then compared with results of a centralized implementation and the harmony search method. It is shown that the decentralized approach can achieve similar results as other methods with significantly reduced computation time. The impacts of time-varying loads and faults are also studied through dynamic network reconfiguration on the 118-bus test system. Simulation results illustrate that dynamic network reconfiguration with time-ahead planning can determine the optimal configuration for each operating period to significantly reduce system energy losses. The enhanced performance of the hierarchical decentralized approach is clearly established. [ABSTRACT FROM PUBLISHER]

This paper explores the impact that different network access arrangements have on the capacity factor of non-firm wind generator connections to distribution networks. Active network management (ANM) projects are increasing in application in distribution networks as the need to integrate distributed generation becomes a more serious challenge. An important aspect of ANM is the contractual arrangement of the participating generation developer and network operator parties. These are sometimes referred to as “Principles of Access” (PoA) and deals with the rights of the generator when curtailment of generation output is required to maintain the network within its limits. Currently, a Last in First Off (LIFO) PoA is implemented in most U.K. ANM deployments and is the prevailing PoA in the Orkney ANM scheme. The Orkney network is used in this paper as a case study to analyze different PoA and compare the renewable energy capacity factor (CF) which result from these arrangements. The archival value of this paper is in the application of optimal power flow (OPF) to the PoA analysis challenge, in the quantification of the impact of previously identified PoA and in the conclusions drawn on the implications of different PoA choices for ANM schemes at the current early stage of ANM deployment. [ABSTRACT FROM PUBLISHER]

This paper presents a procedure for estimating the optimum allocation of distributed generation (DG) units in large distribution systems by using a multicore computing environment. The main goal of this work is to explore the application of a refined parallel Monte Carlo method for energy loss minimization in three-phase distribution systems when considering the intermittent nature of some distributed resources and the time varying shape of the loads. The procedure has been implemented in an open environment in which OpenDSS is driven from MATLAB. Some examples illustrate the scope of the proposed procedure. [ABSTRACT FROM AUTHOR]

Solar energy is one of the most important and widely utilized renewable energy resource. Despite the many attractive features of solar-based renewables, the wide scale integration of solar generation into electric power systems presents significant challenges to network planners and operators, mainly due to the intermittent nature of solar energy. Most published work in this area is, however, focused on short-term forecasting studies used for unit commitment and energy market decisions with very little attention paid to solar irradiance probability density estimation needed for network planning and design studies. In this paper, a reliable nonparametric model of solar irradiance probability density is proposed based on the application of local linear regression in tandem with a root transformation method, introduced here for the first time. The performance of the proposed estimator is assessed via comparisons with the parametric Beta distribution (conventionally employed to model solar irradiance probability density) and two nonparametric kernel density estimation models; using the Kolmogorov–Smirnov (K–S) goodness-of-fit test, coefficient of determination ($R^2$), and a number of error metrics. Results confirm the suitability and accuracy of the proposed method for solar irradiance probability density estimation. [ABSTRACT FROM AUTHOR]

Coordinated preheating can potentially enhance the flexibility of the electricity system, thereby driving significant economic savings. In this context, this paper proposes a novel game-theory based preheating coordination scheme aimed at guaranteeing the effectiveness of collective preheating performed by a large population of households. By adopting an innovative two-phase iterative algorithm, individual households autonomously schedule their heating power allocation to seek for savings in energy bills while smartly maintaining thermal comfort. Meanwhile, the total electricity generation cost decreases at each iteration and converges to an equilibrium solution asymptotically. Compared to the centralised optimization-based energy management control methods, the proposed algorithm effectively reduces the information exchange while significantly reduces computational complexity. The simulation results indicate that the proposed coordination strategy can drive 12.30% cost saving when all households participate in the preheating scheme whereas 11.35% cost increase will be incurred if coordination is absent. Overall, the proposed preheating control algorithm simultaneously benefits both individual households and the whole system by intelligently linking local behaviour with global interests. [ABSTRACT FROM AUTHOR]

This paper proposes a decision tree (DT)-based systematic approach for cooperative online power system dynamic security assessment (DSA) and preventive control. This approach adopts a new methodology that trains two contingency-oriented DTs on a daily basis by the databases generated from power system simulations. Fed with real-time wide-area measurements, one DT of measurable variables is employed for online DSA to identify potential security issues, and the other DT of controllable variables provides online decision support on preventive control strategies against those issues. A cost-effective algorithm is adopted in this proposed approach to optimize the trajectory of preventive control. The paper also proposes an importance sampling algorithm on database preparation for efficient DT training for power systems with high penetration of wind power and distributed generation. The performance of the approach is demonstrated on a 400-bus, 200-line operational model of western Danish power system. [ABSTRACT FROM AUTHOR]

Mixed integer linear programming (MILP) based formulations and solution methods for short-term hydro generation scheduling (HGS) have been widely adopted by researchers, hydropower producers, and system operators in recent years. This approach calls for the nonlinear forebay level, tailrace level, penstock loss, and hydropower production functions to be replaced with their piecewise linear approximations. However, the effects of the linearization of the nonlinear functions and related constraints on solution feasibility have not been fully discussed in the literature. In this paper, the issues concerning solution feasibility are discussed in detail and a method is presented to ensure that the solution obtained based on the approximated MILP formulation remains feasible for the original nonlinear formulation. Furthermore, it is found that the real number water delay can be handled in the formulation without destroying the linear structure of the water balance constraints. Numerical testing results show that the method presented in this paper is effective. [ABSTRACT FROM PUBLISHER]

This paper proposes an enhanced optimization formulation to help determine the type of power generation mix that can meet a given carbon emission target at the minimum cost. Compared to the previous studies, the model proposed in this paper takes account of the emission cost at operational level and explores its impacts on the long-term emission target oriented generation planning innovatively. Meanwhile, the model is able to take account of the integer variables and nonlinearity of the operational cost together with network constraints and renewable generation expansion in one long-term generation planning model. The problem is solved by an innovative discrete gradient search method, and a new concept, emission reduction cost (ERC), is developed, which helps determine which generation technology is the most cost efficient in emission reduction during different stages of generation expansion. A case study on a modified IEEE 30-bus system is presented to demonstrate the application of this model and the value of considering short-term emission costs and the network constraints on the long-term generation expansion. The results and sensitivity analysis are provided to show that a higher short-term financial pressure can help realize the emission target at a lower total cost (investment and operational costs). Optimization without considering it may overestimate the total cost required for the generation mix restructuring. Additionally, a comparative study shows that optimization without considering network constraints may underestimate the total cost required for realizing the specified emission reduction target. [ABSTRACT FROM PUBLISHER]

This paper presents our study results on the correlated assignment of generation, load, or connection buses in a given grid topology and the development of an optimized search algorithm to improve the proposed synthetic grid model, called RT-nestedSmallWorld. A numerical measure, called “bus type entropy”, was proposed in an initial study on this subject to characterize the correlation of bus type assignments in realistic grids. In this paper, its definition has been redefined and improved with the help of some newly obtained data of realistic grids so that the scaling property of a real-world grid's entropy value versus the network size can be effectively captured with a curve-fitting approach. With the help of the derived scaling function, we will be able to determine an appropriate target entropy value that a correlated bus type assignment should assume in a specific N-bus grid network. Therefore, our previously proposed synthetic power grid modeling has been enhanced with a direct search procedure for the best bus type assignments in a specific N-bus grid topology, saving the mandatory, but most of the time unattainable, requirement of a set of realistic grid data with a comparable network size for identifying the search target. Finally, the performance of the proposed approach is examined based on some available grid data including the IEEE test cases, the NYISO-2935, the ERCOT-5633, and the WECC-16994 systems. [ABSTRACT FROM PUBLISHER]

The screening curve method (SCM) is an intuitive and fast model that estimates the least-cost generation mix for generation planning purposes. It calculates an economically adapted generation mix for a target load duration curve within a few seconds, but lacks many detailed considerations of the generation system. In order to improve SCM, many developments have been made in recent years. However, one of the biggest limitations had always been that the SCM assumed all capacity to be new with no existing capacity. This drawback made SCM less useful compared to other generation planning models. In this paper, we develop a direct way to model existing capacity in SCM. The proposed method is studied in this paper and is illustrated with an example. Finally, an ERCOT year 2030 case is simulated. [ABSTRACT FROM AUTHOR]

This paper discusses the modeling and analysis of a carbon capture technology-based resource scheduling/unit commitment (UC) methodology in carbon/emission-constrained environment. In carbon markets, the overall generation from conventional/fossil-fueled thermal plants is constrained over the total cost which is the sum of generation, capture cost/emission avoidance cost. Therefore, in this paper, generalized/uniform performance indices affecting the UC schedule are derived. Also, a commitment/scheduling methodology based on capture and fuel cost is devised rather than the existing penalty cost methodology as in the modernized markets everything was dealt in monetary value. Furthermore, the impact of type of resource used, i.e., coal rank is also considered to evaluate the sensitivity of scheduling decisions and financial influence of carbon capture technology. Along with performance indices, correction factors are also proposed to justify the effect of resource/coal rank in the process of optimal generation allocation. The effect of correction factors and type of coal is observed to be predominant at lower capture efficiencies compared to higher capture efficiency. The effectiveness of the proposed method over the penalty method is reflected in reduced generation cost and emission avoidance cost when compared to the penalty-based methodology. [ABSTRACT FROM AUTHOR]

This paper proposes a distributionally robust optimization model for solving unit commitment (UC) problems considering volatile wind power generation. The uncertainty of wind power is captured by an ambiguity set that defines a family of wind power distributions, and the expected total cost under the worst-case distribution is minimized. Compared with stochastic programming, this method may have less dependence on the data of exact probability distributions. It should also outperform the conventional robust optimization methods because some distribution information can be incorporated into the ambiguity sets to generate less conservative results. In this paper, the UC model is formulated based on the typical two-stage framework, where the UC decisions are determined in a here-and-now manner, and the economic dispatch decisions are assumed to be wait-and-see, made after the observation of wind power outcomes. For computational tractability, the wait-and-see decisions are addressed by linear decision rule approximation, assuming that the economic dispatch decisions affinely depend on uncertain parameters as well as auxiliary random variables introduced to describe distributional characteristics of wind power generation. It is shown in case studies that this decision rule model tends to provide a tight approximation to the original two-stage problem, and the performance of UC solutions may be greatly improved by incorporating information on wind power distributions into the robust model. [ABSTRACT FROM AUTHOR]

The first of this two-paper series addresses a practical day-ahead auction model, where generation revenue constraints are explicitly incorporated in the problem formulation, as routinely done in several national electricity markets across Europe. The revenue-constrained market-clearing procedure includes the effect of the transmission network, inter-temporal constraints associated with generation scheduling, demand-side bidding, and marginal pricing. This auction design is an instance of price-based market clearing which features two major complicating factors. First, locational marginal prices become decision variables of the optimization process. In addition, producer revenues are formulated as bilinear and highly nonconvex products of power outputs and market-clearing prices. The resulting problem is formulated as a mixed-integer nonlinear bilevel program with bilinear terms for which available solution techniques rely on heuristics, approximations, or modeling simplifications. This paper presents a novel and exact methodology whereby the original problem is recast as an equivalent single-level mixed-integer linear program. As a consequence, finite convergence to optimality is guaranteed and the use of standard commercial software is allowed. The proposed transformation is based on duality theory of linear programming, Karush-Kuhn-Tucker optimality conditions, and integer algebra results. In the second part of this two-paper series, numerical results from several case studies illustrate the effective performance of the proposed solution approach. [ABSTRACT FROM AUTHOR]

The first part of this two-part paper developed the framework for characterizing the feasibility regions of power systems in the demand space. This characterization, however, leads to the generation of a large number of extraneous constraints as an unwelcome byproduct. This shortcoming motivates the second part of this paper series, whose objective is the achievement of a minimal representation for loadability sets through an offline process. Thus, in this paper, we set forth to eliminate efficiently redundant constraints with the proposal of an enhanced umbrella constraint discovery (E-UCD) problem formulation. The use of E-UCD in this paper is fourfold: 1) It serves to identify redundant line flow constraints not potentially shaping the feasibility regions of power systems in the generation-demand space. 2) It serves to determine the maximum number of line flow limits that could ever become active simultaneously in a given power system. 3) It pinpoints the generators who have the ability to become pivotal in relieving network congestion. 4) It is used to identify redundant constraints generated, while generation dispatch variables are projected from the generation-demand space onto the demand space. Experiments are carried out on the standard IEEE test systems to show that the minimal representations of loadability sets contain a reasonable number of constraints. Thus, the application of loadability sets to operation and planning problems will result in computational savings. [ABSTRACT FROM AUTHOR]

Promoting net zero energy buildings (NZEB) is among the key carbon emissions reduction approaches widely adopted by policymakers in recent years in the U.S. and the EU countries (see work of Marszal ). Due to the relatively higher cost of electricity generation from renewable energy (RE), federal, state, and local governments offer various financial incentive programs to promote NZEB. This paper presents a model-based framework for the policymakers to design suitable incentive programs. The model is a mixed integer program (MIP) that is particularized for wind and solar energy sources. The MIP model finds an optimal design for a NZEB. The optimal design and the cost of NZEB is then used to design incentive programs. The incentive programs considered includes loans, production tax credit, and net metering, among others. A time-of-day pricing is assumed to be in effect. The model is implemented on commercial buildings in Tampa, Florida, U.S.A. For a given region, the framework provides policymakers two reports, 1) a set of optimal portfolios of incentives for different classes of commercial buildings (based on credit rating, expected return on investment (ROI), and building type), 2) for a specified portfolio of incentives, determines which classes of commercial buildings will be willing to invest in RE. [ABSTRACT FROM AUTHOR]

Wind power is the source of electrical energy that has grown more over the last years, with annual rate in installed capacity around 20%. Therefore, it is important to optimize the production efficiency of wind farms. In a wind farm, the electrical energy is collected at a central substation from different wind turbines placed nearby. This paper addresses the optimal design of the cable network interconnecting the turbines to the substation aiming to minimize not only the infrastructure cost but also the cost of the energy losses in the cables. Although this problem is nonlinear, different integer linear programming models are proposed considering the wind farm technical constraints. The models are applied to three real cases Portuguese wind farms. The computational results show that the proposed models are able to compute the optimal solutions for all cases. [ABSTRACT FROM PUBLISHER]

Electric vehicles (EVs), similar to other types of flexible loads, can be controlled to provide additional flexibility to power system operators, which is needed to transition from primarily fossil fuel based electricity generation to more renewable generation. Existing electricity markets already provide economic incentives to offer flexibility. So-called aggregators in charge of controlling EV charging could take advantage of these incentives. However, random driving behavior and area-specific market rules complicate the market participation of EV aggregators in energy and reserve markets. The goal of this paper is to investigate the design and performance of a system that would enable EV aggregators to participate in wholesale electricity markets. We consider a certain type of market environment, which includes an intraday energy market and pay-as-bid reserve markets that require exact reserve delivery and have long operating intervals. We therefore propose a novel approach for concurrent market participation that can deal with these constraints, even when faced with highly uncertain reserve activation and EV behavior. [ABSTRACT FROM PUBLISHER]