Search

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]

Rapid improvement in efficiency of renewable energy sources (RESs), increased concerns about carbon emission, substantial power-flow losses, and the emergence of smart grid have all contributed to increased interest toward customers’ contribution in electricity generation in RES-based prosumer nano-grids (PNGs). Such contributions within a PNG can be effectively controlled using dynamic pricing tariffs (DPTs) that hold customers accountable for their electric power exchange behaviors. Prosumer customers optimize their internal power grid based on utility-provided DPT and expect to surpass the break-even point within an anticipated period of time. However, their expected payback period may not be met, as the DPT changes over time as more customers join the market. Therefore, the joint design of PNG and DPT is necessary to give the customers the opportunity to revisit their investment plans, and, at the same time, the utility the opportunity to adjust the pricing scheme. We propose an iterative decentralized three-level optimization scheme to achieve both objectives. The proposed scheme can be applied to a large system for planning of optimal PNGs, while providing a reliable payback period on investment for customers that are involved. The effectiveness of the proposed scheme is verified numerically using a large historical data set. [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]

The increasing penetration of Renewable Energy Sources (RES) and, more in general, of converter connected power generation is progressively reducing the overall inertia of the electricity system introducing new challenges in the network Frequency Support (FS). Therefore, FS strategies for RES are becoming an important aspect to be developed in order to en-sure the secure operation of the electricity power grid. The aim of the present paper is to propose an innovative approach for the inertial emulation of Wind Turbine Generators (WTGs) in order to ensure a positive effect on the system frequency avoiding unstable operations of the WTG and reducing the negative impact of the rotor speed recovery on the secondary frequency drop. Moreover, the paper provides a detailed definition of the triggering logics adopted to activate and deactivate the FS making it suitable for implementation in available industrial controllers. The proposed controller is firstly tested on a simplified configuration to show its enhanced performances with respect to previously developed approaches. Then, simulations are carried out in a more realistic network to assess the positive impact of the controller on the system frequency. Results highlighted a 40% reduction of the system Rate of Change of Frequency while the frequency deviation at the transient nadir is improved by 35%. [ABSTRACT FROM AUTHOR]

The integration of variable renewable energy (VRE) generation, i.e., wind power and solar photovoltaic, brings significant uncertainty for the power system operation. Different with VRE techniques, concentrating solar power (CSP) is an appealing renewable generation technology due to its dispatch ability through the use of thermal energy storage and is thus expected to play a significant role in high renewable energy penetrated power systems. In this paper, we propose a look-ahead stochastic unit commitment model to operate power systems with CSP under high renewable energy penetration. It has a three-stage structure. The first stage optimizes the operational decisions in a day-ahead framework based on forecasts; the second stage minimizes the expected generation cost for possible realizations in the real time; and the third stage accounts for look-ahead operation in future operating days. This paper has a dual purpose: first, exploring how CSP plants operate in high renewable penetrated power systems; and second, analyzing the benefits of CSP in accommodating VRE generation. A case study on a modified IEEE RTS-79 system with actual solar and wind power data is provided to validate the proposed method. [ABSTRACT FROM AUTHOR]

Distributed generation (DG) has been rapidly integrated into microgrids. However, uncertain power generation of intermittent DG such as wind turbines brings challenges to the DG planning problem. This paper proposes a novel probability-weighted robust optimization (PRO) method to allocate DG units including microturbines and wind turbines in microgrids, aiming to maximize the total profit over a long-term planning horizon. First, probability-weighted uncertainty sets are proposed to model uncertainties including wind turbine output and load demand during a long-term planning horizon which can fully cover the uncertainty spectrum and accurately represent the uncertainty probability distribution. Then, the PRO method optimizes DG sizing and locating under the worst uncertainty cases considering their occurrence probabilities. Therefore, the planning decision is robust against any possible uncertainty realization. Besides, a modified column-and-constraint generation algorithm is developed to solve the PRO problem. Simulation results show that the DG planning obtained by the proposed method can achieve full operating robustness. [ABSTRACT FROM AUTHOR]

To reduce the computational burden of capacity expansion models, power system operations are commonly accounted for in these models using representative time periods of the planning horizon such as hours, days, or weeks. However, the validity of these time-period aggregation approaches to determine the capacity expansion plan of future power systems is arguable, as they fail to capture properly the mid-terms dynamics of renewable power generation and to model accurately the operation of electricity storage. In this paper, we propose a new time-period clustering method that overcomes the aforementioned drawbacks by maintaining the chronology of the input time series throughout the whole planning horizon. Thus, the proposed method can correctly assess the economic value of combining renewable power generation with interday storage devices. Numerical results from a test case based on the European electricity network show that our method provides more efficient capacity expansion plans than existing methods while requiring similar computational needs. [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]

Uncertainty of the wind power output is the main influence factor for wind power utilization in the bulk power system. Because of the large capacity and the load characteristic, energy-intensive load holds great potentials to share the burden of wind power fluctuation smoothing. In this paper, a direct control scheme of the energy-intensive load power is proposed to smooth the wind power fluctuation. Based on the characteristic of the energy-intensive load production technology, the feasibility of energy-intensive load control and its influence on the production are discussed. A feedback control is designed to regulate the saturable reactor and transformer tap changer when the power support from the bulk power system is limited. Regulation capacity is analyzed quantitatively based on the energy-intensive load equivalent circuit model. A grid-connected energy-intensive enterprise driven by self-generation wind power plants and thermal power plants is studied as an example case. By the proposed energy-intensive load control scheme, power fluctuation of the wind farms can be smoothed with the minimum control cost. Besides, the regulation requirement of the bulk power system is eased correspondingly so that the utilization capacity of the power system can be improved significantly. Simulation is done on the real-time digital simulator to verify the effectiveness of the proposed control method. [ABSTRACT FROM AUTHOR]

Deploying high penetration of variable renewables represents a critical pathway for deep decarbonizing the power sector. The conflict between their temporal variability and limited system flexibility has been largely ignored currently at planning stage. Here, we present a novel capacity expansion model optimizing investment decisions and full-year, hourly power balances simultaneously, with considerations of storage technologies and policy constraints, such as carbon tax and renewable portfolio standards (RPS). Based on a computational efficient modeling formulation, all flexibility constrains (ramping, reserve, minimum output, minimal online/offline time) for the 8760-h duration are incorporated. The proposed model is applied to the northwestern grid of China to examine the optimal composition and distribution of power investments with a wide range of renewable targets. Results indicate that the cost can increase moderately toward 45% of RPS, when properly designing the generation portfolio: prioritizing wind investments, distributing renewable investments more evenly, and deploying more flexible midsize coal and gas units. Reaching higher penetrations of renewables is expensive and the reductions of storage costs are critically important for an affordable low-carbon future. RPS or carbon taxes to reach a same target of emission reduction in China will result in similar overall costs but different generation mixes. [ABSTRACT FROM AUTHOR]

With the ever-increasing penetration level of renewable generation sources, a modern power system is facing more inevitable uncertainties that could lead to weakly damped oscillations. Detecting coherency among synchronous generators is one of the key steps of situational awareness for a given power system with a very high level of renewable penetration. In this paper, a wide-area measurement system (WAMS) based coherency detection algorithm employing the kernel principal component analysis (KPCA) and clustering analysis based on affinity propagation (AP) is proposed for a power system with extensive penetration of renewable generation sources. First, several trajectory similarity indexes are presented for determining the similarity between the trajectories of any two generators in the center of inertia coordinate. Second, a KPCA-based method is presented to integrate the trajectory similarity indexes for addressing the correlations among multiple indexes. Next, the AP-based clustering analysis method is utilized to detect the coherency among synchronous generators without the need of prespecifying the number of clusters. Finally, Southern China power system and a part of northern China power system with Zhangbei wind farms included, both with very high levels of renewable generation penetration, are utilized to demonstrate the proposed WAMS-based coherency detection methodology, and the application to actual Guangdong power system in south China to verify the applicability and practicality. [ABSTRACT FROM AUTHOR]

The unit commitment is still a widely studied problem, especially when more renewable energy of stochastic character is being added to power systems. This paper proposes a model for weekly planning of systems involving hydro, wind, and thermal energy under a stochastic perspective. The proposed model follows the endogenous reserve determination criterion to achieve a simultaneous optimization of energy and reserves considering wind uncertainty, forced outages of equipment, a dc Flow model of the network, and cascaded head sensitive hydro systems, among others. The paper also develops a practical solution methodology based on outer approximation and benders decomposition. Testing has been conducted over four systems, and computational results demonstrate that the proposed model and solution are useful and effective. [ABSTRACT FROM AUTHOR]

Higher levels of renewable electricity generation increase uncertainty in power system operation. To ensure secure system operation, new tools that account for this uncertainty are required. In this paper, we adopt a chance-constrained AC optimal power flow formulation, which guarantees that generation, power flows, and voltages remain within their bounds with a predefined probability. We then discuss different chance-constraint reformulations and solution approaches for the problem. We first describe an analytical reformulation based on partial linearization, which enables us to obtain a tractable representation of the optimization problem. We then provide an efficient algorithm based on an iterative solution scheme which alternates between solving a deterministic AC optimal power flow problem and assessing the impact of uncertainty. The flexibility of the iterative scheme enables not only scalable implementations, but also alternative chance-constraint reformulations. In particular, we suggest two sample-based reformulations that do not require any approximation or relaxation of the AC power flow equations. In a case study based on four different IEEE systems, we assess the performance of the method, and demonstrate scalability of the iterative scheme. We further show that the analytical reformulation accurately and efficiently enforces chance constraints in both in- and out-of-sample tests, and that the analytical reformulations outperforms the two alternative, sample-based chance constraint reformulations. [ABSTRACT FROM PUBLISHER]

This paper presents a new generation adjustment method for achieving economical operation of power systems. Uncertainty in the loads and renewable energy sources is inevitable in power systems. The participation factor, which can be calculated using the incremental cost, is generally used to manage such unexpected variations. The incremental cost considers only the generators; however, owing to line loss, the location of the variation may also affect the operation performance. Herein, we introduce a bus-dependent participation factor (BDPF) to precisely reflect the line loss. The BDPF is calculated based on the generation-load incremental cost (GLIC), which is the cost variation for a specific generator and load. The GLIC can fairly reflect line loss changes; in addition, the second-order loss sensitivity is used to improve the accuracy of the BDPF. Using the BDPF, power generation can be instantly adjusted under unexpected variations, after which re-adjustment can be performed, if operation constraint violations exist. The proposed method is tested on the IEEE 14-bus and 118-bus systems, and the simulation results validate its effectiveness. [ABSTRACT FROM AUTHOR]

Knowledge of the impact of uncertain inputs is valuable, especially in power systems with large amounts of stochastic renewable generations. A global sensitivity analysis (GSA) can determine the impact of input uncertainties on the output quantity of interest in a certain physical or mathematical model. The GSA has not been widely employed in power systems due to the prohibitively computational burden. In this paper, it is demonstrated that, via the implementation of a basis-adaptive sparse polynomial chaos expansion, a GSA can be applied to the power system with numerous uncertain inputs. The performance of the proposed method is tested on both the IEEE 13-bus test feeder and the IEEE 123-node test system, in presence of a large amount of independent or correlated uncertain inputs. The possible application of a GSA on the basis of the basis-adaptive sparse polynomial chaos expansion in power systems are discussed in terms of various sensitivities. The findings cannot only be used to rank the most influential input uncertainties with respect to a specific output, such as variances of the nodal power, but also to identify the most sensitive or robust electrical variables such as the bus voltage with respect to input uncertainties. [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]

The problem of multi-area interchange scheduling under system uncertainty is considered in this paper. A new scheduling technique is proposed for a multi-proxy bus system based on stochastic optimization that captures uncertainty in renewable generation and stochastic load. In particular, the proposed algorithm iteratively optimizes interface flows using multidimensional demand and supply functions. Optimality and convergence are guaranteed for both synchronous and asynchronous scheduling under nominal assumptions. [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 article describes a new publicly available version of the IEEE 118–bus test system, named NREL-118. The database is based on the transmission representation (buses and lines) of the IEEE 118-bus test system, with a reconfigured generation representation using three regions of the US Western Interconnection from the latest Western Electricity Coordination Council (WECC) 2024 Common Case [Transmission expansion planning home and GridView WECC database]. Time-synchronous hourly load, wind, and solar time series are provided for one year. The public database presented and described in this manuscript will allow researchers to model a test power system using detailed transmission, generation, load, wind, and solar data. This database includes key additional features that add to the current IEEE 118-bus test model, such as the inclusion of ten generation technologies with different heat rate functions, minimum stable levels and ramping rates, GHG emissions rates, regulation and contingency reserves, and hourly time series data for one full year for load, wind, and solar generation. [ABSTRACT FROM PUBLISHER]

This paper establishes a generic severity function that can be used to produce power system security risk profiles. It is illustrated by analyzing the impacts of system load attributes on the small-disturbance rotor angle stability of a power system. The load attributes contributing to the oscillatory modes can be considered as inherent uncertain variables within power systems and include load power variations, load composition, and load model parameters. Uncertainty in the renewable power generation is also incorporated in the probabilistic modelling and risk assessment to demonstrate the flexibility of the approach. A novel approach is proposed to select the severity functions to logically represent small-disturbance security margin. The risk profile of a power system has been presented considering the probability density functions of power system critical modal damping and a selected set of severity functions. The analysis techniques developed are illustrated with a modified version of the 68-bus NETS-NYPS power system with a high amount of renewable power penetration. The relative importance of the load attributes and the impact of these attributes on stability boundaries have been identified at varying risk levels with respect to their contribution to small-disturbance stability. [ABSTRACT FROM PUBLISHER]

The paper introduces a probabilistic framework for online identification of post fault dynamic behavior of power systems with renewable generation. The framework is based on decision trees and hierarchical clustering and incorporates uncertainties associated with network operating conditions, topology changes, faults, and renewable generation. In addition to identifying unstable generator groups, the developed clustering methodology also facilitates identification of the sequence in which the groups lose synchronism. The framework is illustrated on a modified version of the IEEE 68 bus test network incorporating significant portion of renewable generation. [ABSTRACT FROM PUBLISHER]

This paper presents a bi-level model for distribution network and renewable energy expansion planning under a demand response (DR) framework. The role of DR has recently attracted an increasing interest in power systems. However, previous models have not been completely adapted in order to treat DR on an equal footing. The target of the distribution network and generation planner, modeled through the upper-level problem, is to minimize generation and network investment cost while meeting the demand. This upper-level problem is constrained by the lower-level problem, stressing the importance of integrating DR to time-varying prices into those investment models. The objective function considered for the lower level is the minimization of overall payment faced by the consumers. Using the Karush-Kuhn-Tucker complementarity constraints, the proposed bi-level model is recast as a mixed-integer linear programming problem, which is solvable using efficient off-the-shelf branch-and-cut solvers. Detailed results from an insular case study (La Graciosa, Canary Islands, Spain) are presented. [ABSTRACT FROM PUBLISHER]

Virtual inertia is known as an inevitable part of the modern power systems with high penetration of renewable energy. Recent trend of research is oriented in different methods of emulating the inertia to increase the sustainability of the system. In the case of dynamic performance of power systems especially in Automatic Generation Control (AGC) issue, there are concerns considering the matter of virtual inertia. This paper proposes an approach for analyzing the dynamic effects of virtual inertia in two-area AC/DC interconnected AGC power systems. Derivative control technique is used for higher level control application of inertia emulation. This method of inertia emulation is developed for two-area AGC system, which is connected by parallel AC/DC transmission systems. Based on the proposed technique, the dynamic effect of inertia emulated by storage devices for frequency and active power control are evaluated. The effects of frequency measurement delay and phase-locked loop effect are also considered by introducing a second-order function. Simulations performed by MATLAB software demonstrate how virtual inertia emulation can effectively improve the performance of the power system. A detailed eigenvalue analysis is also performed to support the positive effects of the proposed method. [ABSTRACT FROM PUBLISHER]

Due to the significant improvements of power generation technologies and the trend of replacing traditional power plants with renewable generation resources, the generation portfolio will experience dramatic changes in the near future. The uncertainty and variability of renewable energy and their sitting call for strategic and economic plans for expanding the transmission capacities. In this study, we develop a data-driven two-stage stochastic transmission expansion planning with uncertainties. In the proposed approach, purely by learning from the historical data, we first construct a confidence set for the unknown distribution of the uncertain parameters. Then, we develop a two-stage data-driven transmission expansion framework, by considering the worst-case distribution within the constructed confidence set, so as to provide a reliable while economic transmission planning decision. Furthermore, to tackle the model complexity, we propose a decomposition framework embedded with Benders’ and Column-and-Constraint generation methods. We implement our approach on 6-bus and 118-bus systems to test its effectiveness. Finally, we show as the amount of historical data grows, the conservativeness of the model decreases. [ABSTRACT FROM PUBLISHER]

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]

The recent rise of electricity generation based on renewable energy sources increases the demand for transmission capacity. Capacity expansion via the upgrade of transmission line capacity, e.g., by conversion to a high-voltage direct current (HVDC) line, is an attractive option. In this paper, it is shown that if the upgrade to HVDC is applied systematically to selected transmission lines across the grid, a hybrid architecture is obtained that enables an efficient and globally optimal solution of the optimal power flow (OPF) problem. More precisely, for conventional meshed AC transmission grids the OPF problem is nonconvex and in general NP-hard, rendering it hard to solve. We prove that after the upgrade to the proposed hybrid architecture, the same mesh topology facilitates an exact convex relaxation of the OPF problem, enabling its globally optimal solution with efficient polynomial time algorithms. This OPF method is then employed in simulations, which demonstrate that the hybrid architecture can increase the effective transmission capacity and substantially reduce the generation costs, even compared to the AC grid with optimal transmission switching. [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 proposes a torque limit-based inertial control scheme of a doubly-fed induction generator (DFIG) that supports the frequency control of a power system. If a frequency deviation occurs, the proposed scheme aims to release a large amount of kinetic energy (KE) stored in the rotating masses of a DFIG to raise the frequency nadir (FN). Upon detecting the event, the scheme instantly increases its output to the torque limit and then reduces the output with the rotor speed so that it converges to the stable operating range. To restore the rotor speed while causing a small second frequency dip (SFD), after the rotor speed converges the power reference is reduced by a small amount and maintained until it meets the reference for maximum power point tracking control. The test results demonstrate that the scheme can improve the FN and maximum rate of change of frequency while causing a small SFD in any wind conditions and in a power system that has a high penetration of wind power, and thus the scheme helps maintain the required level of system reliability. The scheme releases the KE from 2.9 times to 3.7 times the Hydro-Québec requirement depending on the power reference. [ABSTRACT FROM PUBLISHER]

The European Union is targeting a 20% share of energy from renewable resources by 2020, and this increase is in turn expected to lead to operational challenges that will require various congestion management actions by system operators. In this paper, we deal with topology control of the transmission network as a congestion management resource and evaluate the impacts of topology control on the European electricity network. To do this, we co-optimize unit commitment and transmission switching over 24 h, and we use a decomposition scheme to tackle the resulting large-scale problem. Our analysis is conducted on different scenarios of load and renewable power generation. We find that topology control results in significant cost savings within Europe which tend to be inversely related to net load. The robustness of our results is supported by an extensive sensitivity analysis. [ABSTRACT FROM AUTHOR]

Distribution microgrids are being challenged by reverse power flows and voltage fluctuations due to renewable generation, demand response, and electric vehicles. Advances in photovoltaic (PV) inverters offer new opportunities for reactive power management provided PV owners have the right investment incentives. In this context, reactive power compensation is considered here as an ancillary service. Accounting for the increasing time-variability of distributed generation and demand, a stochastic reactive power compensation scheme is developed. Given uncertain active power injections, an online reactive control scheme is devised. This scheme is distribution-free and relies solely on power injection data. Reactive injections are updated using the Lagrange multipliers of a second-order cone program. Numerical tests on an industrial 47-bus microgrid and the residential IEEE 123-bus feeder corroborate the reactive power management efficiency of the novel stochastic scheme over its deterministic alternative, as well as its capability to track variations in solar generation and household demand. [ABSTRACT FROM PUBLISHER]

Distribution systems are naturally unbalanced as a consequence of unbalanced loads and the electrical network itself, where the use of single-phase or two-phase sections is not uncommon. In addition, a detailed representation of a typical distribution system can have tens of thousands of nodes, depending on the area selected for study and whether a three-phase representation is employed. This contrasts with Transmission systems, where typical sizes in terms of number of nodes are much smaller and where a balanced (single-phase) representation is sufficient in most situations. Thus, Transmission and Distribution systems have traditionally been studied separately, but this approach is becoming increasingly inadequate for various reasons. This paper then proposes a methodology that integrates Transmission and Distribution systems into a common electrical model. At the core of the methodology there are 1) single- and three-phase models for each component (branches, transformers, loads, etc.) and 2) interchangeable balanced and unbalanced network equivalents that allow for analyzing specific areas of a given complete network. Potential gains brought about by the proposed methodology are illustrated through its application in a real-world electric system. [ABSTRACT FROM AUTHOR]

This paper investigates the impact of the complementarity, connection technology and network control strategy of offshore wind generation on the probabilistic small-signal stability of power systems. The cumulant-based theory is firstly applied to assess the variances of both the complementarity of combined wind power and the probabilistic small-signal stability of power systems. These variances are later employed as two indices to explain how the different complementarity levels, connection topologies and DC network control schemes of offshore wind generation affects the probabilistic distribution of critical eigenvalue and thus the probabilistic small-signal stability. In the paper, an example of 16-machine 5-area power system with three grid-connected offshore wind farms is presented. The index of probabilistic small-signal stability is computed and then compared under two complementarity conditions, two typical HVDC transmission systems through which the offshore wind farms are connected, and two DC grid control schemes, respectively. It is demonstrated that complementarity can effectively enhance probabilistic stability and multi-terminal HVDC may have either beneficial or detrimental impact on small-signal stability depending on the control pattern and setting when compared with point-to-point topology. [ABSTRACT FROM AUTHOR]

In order to increase the utilization of renewable energy sources and to reduce the need of generator-provided ancillary services and inefficient peaking generation, buildings are progressively transforming into smarter electricity consumers and active participants in power system operations. In this work, an R-C thermodynamic model of building that strongly relates to the power consumption of heating ventilation air-conditioning system (HVAC) is firstly introduced. To offer the aggregated flexible HVAC power to the grid as regulating power, we propose a building-aggregator-grid contract framework and formulate a robust model predictive control (MPC) algorithm which both maximizes the profit of aggregator and minimizes the payment of each participating building to optimally declare power flexibility. Lastly, simulation results on a group of real and hypothetical buildings validate the feasibility of the proposed method. [ABSTRACT FROM AUTHOR]

As intermittent energy resources become more significant in power production, traditional capacity planning may be insufficient to ensure reliable system operation. A system planner must ensure that flexibility solutions are available to respond to large and uncertain ramping events. These solutions may be operational, such as improved unit commitment and dispatch, curtailment of renewables, or demand response; procurement based, such as new fast ramping resources or batteries; or involve market reform. This paper outlines a new methodology for modeling the economic tradeoffs in implementing flexibility solutions for integrating renewables. The proposed model includes both a stochastic treatment of system states to account for a wide range of operating conditions and an adapted production simulation methodology that weighs the cost of reliability and subhourly flexibility violations against the cost of the operational flexibility solutions available to mitigate them. The model's functionality is demonstrated with a case study of California at a 50% RPS in 2030. The model tests the value of 1088 MW of generic flexible units, relative to the same capacity of must-run resources, finding an expected annual value of \$347\pm 42 million/yr. Potential applications of the model for resource planning and procurement are also discussed. [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]

As a result of subsidies and technological maturity, renewable electricity producers have grown in some jurisdictions to clearly dominant positions in the market. Under this context, we propose an offering strategy for a wind power producer with market power that participates in the day-ahead market as a price-maker, and in the balancing market as a deviator. Uncertainty pertaining to wind power production and balancing market price is represented through a set of correlated scenarios. The proposed model is a stochastic mathematical program with equilibrium constraints (MPEC) that can be recast as a tractable mixed-integer linear programming (MILP) problem, which is solvable using available optimization software. Results from an illustrative example and two case studies show the effectiveness of the proposed model. [ABSTRACT FROM PUBLISHER]

We propose a probabilistic framework to design an N-1 secure day-ahead dispatch and determine the minimum cost reserves for power systems with wind power generation. We also identify a reserve strategy according to which we deploy the reserves in real-time operation, which serves as a corrective control action. To achieve this, we formulate a stochastic optimization program with chance constraints, which encode the probability of satisfying the transmission capacity constraints of the lines and the generation limits. To incorporate a reserve decision scheme, we take into account the steady-state behavior of the secondary frequency controller and, hence, consider the deployed reserves to be a linear function of the total generation-load mismatch. The overall problem results in a chance constrained bilinear program. To achieve tractability, we propose a convex reformulation and a heuristic algorithm, whereas to deal with the chance constraint we use a scenario-based-approach and an approach that considers only the quantiles of the stationary distribution of the wind power error. To quantify the effectiveness of the proposed methodologies and compare them in terms of cost and performance, we use the IEEE 30-bus network and carry out Monte Carlo simulations, corresponding to different wind power realizations generated by a Markov chain-based model. [ABSTRACT FROM PUBLISHER]

Ramps events are a significant source of uncertainty in wind power generation. Developing statistical models from historical data for wind power ramps is important for designing intelligent distribution and market mechanisms for a future electric grid. This requires robust detection schemes for identifying wind ramps in data. In this paper, we propose an optimal detection technique for identifying wind ramps for large time series. The technique relies on defining a family of scoring functions associated with any rule for defining ramps on an interval of the time series. A dynamic programming recursion is then used to find all such ramp events. Identified wind ramps are used to propose a new stochastic framework to characterize wind ramps. Extensive statistical analysis is performed based on this framework, characterizing ramping duration and rates as well as other key features needed for evaluating the impact of wind ramps in the operation of the power system. In particular, evaluation of new ancillary services and wind ramp forecasting can benefit from the proposed approach. [ABSTRACT FROM PUBLISHER]

In addition to long-existing load uncertainty on power systems, continuously increasing renewable energy injections (such as wind and solar) have further made the power grid more volatile and uncertain. Stochastic and recently introduced robust optimization approaches have been studied to provide the day-ahead unit commitment decision with the consideration of real-time load and supply uncertainties. In this paper, we introduce an innovative minimax regret unit commitment model aiming to minimize the maximum regret of the day-ahead decision from the actual realization of the uncertain real-time wind power generation. Our approach will ensure the robustness of the unit commitment decision considering the inherent uncertainty in wind generation. Meanwhile, our approach will provide a system operator a clear picture in terms of the maximum regret value among all possible scenarios. A Benders' decomposition algorithm is developed to solve the problem. Finally, our extensive case studies compare the performances of three different approaches (robust optimization, minimax regret, and stochastic optimization) and verify the effectiveness of our proposed algorithm. [ABSTRACT FROM PUBLISHER]

We consider the problem of a wind power producer trading energy in short-term electricity markets. The producer is a price-taker in the day-ahead market, but a price-maker in the balancing market, and aims at optimizing its expected revenues from these market floors. The problem is formulated as a mathematical program with equilibrium constraints (MPEC) and cast as a mixed-integer linear program (MILP), which can be solved employing off-the-shelf optimization software. The optimal bid is shown to deliver significantly improved performance compared to traditional bids such as the conditional mean or median forecast of wind power distribution. Finally, sensitivity analyses are carried out to assess the impact on the offering strategy of the producer's penetration in the market, of the correlation between wind power production and residual system deviation, and of the shape of the forecast distribution of wind power production. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a distributed recharging rate control algorithm which combines the objectives of regulating frequency and improving the utilization of electric generators. An incentive policy \kappa is created that encourages electric vehicles (EVs) to demand energy when non-EV demand is low and electric generators are underutilized. EVs also act as frequency regulators which can control their participation role by modifying their respective payment rate wi. The proposed distributed recharging rate control algorithm can realize a Demand Management solution for EVs and does not require explicit real time communication from the electric generators or between the recharging sockets. A mechanism is presented that can trade off an ideal incentive policy with its approximation and enable the integration of the proposed controller with legacy protection system. Simulation is used to assess the algorithm and to highlight its embedded characteristics. [ABSTRACT FROM PUBLISHER]

To describe the impact of uncertainties, such as fluctuation of bus loads and intermittent behavior of renewable generations, on the available load supply capability (ALSC) of distribution system accurately and comprehensively, this paper defines a series of meaningful indices for the probabilistic evaluation of ALSC. An efficient simulation method, Latin hypercube sampling-based Monte Carlo simulation (LHS-MCS), combined with step-varied repeated power flow method is proposed to compute the defined indices. Compared with simple random sampling-based Monte Carlo simulation (SRS-MCS), LHS-MCS is found to be more suitable for the probabilistic evaluation of ALSC. It can achieve more accurate and stable ALSC indices under relatively small sample sizes. The calculation speed of LHS-MCS is comparable with that of SRS-MCS under the same sample sizes, and the required CPU time of LHS-MCS is far less than SRS-MCS under the same calculation accuracy. Case studies carried out on the modified Baran & Wu 33-bus and the modified IEEE 123-bus distribution systems verify the feasibility of the defined indices and high performance of the proposed method. [ABSTRACT FROM PUBLISHER]

Power systems around the world have set ambitious targets for renewable energy integration. Several jurisdictions incentivize renewables such as the Feed-in Tariff in Ontario, Canada. These incentives shall eventually run out and renewables would have to competitively sell energy into electricity markets, overcoming uncertainty and variability in their output. [ABSTRACT FROM PUBLISHER]

This paper introduces an accurate and computationally efficient reduced-order dynamic equivalent of a Type-4–based wind power plant (WPP) for the analysis of electromagnetic transients (EMTs) in the power system external to the WPP. The proposed model significantly reduces the computational resources and the simulation run time while preserving the WPP response fidelity in the desired frequency range, e.g., 0 to 50 kHz. The proposed WPP equivalent model is composed of two parts: 1) a frequency-dependent equivalent model which represents the WPP passive component in the entire frequency range and 2) a dynamic equivalent model that represents the WPP supervisory control and the aggregated low-frequency dynamics of wind-turbine generator (WTG) units. The latter can be constructed from the: 1) dynamic generic models; 2) vendor-specific models; or 3) user-defined models. The proposed model is incorporated as a software module in the PSCAD/EMTDC environment, and its efficiency and accuracy are verified based on comparing the results with those of a detailed model. [ABSTRACT FROM PUBLISHER]

When deciding on wind power investments, three major issues arise: the production variability and uncertainty of wind facilities, the eventual future decline in wind power investment costs, and the significant financial risk involved in such investment decisions. Recognizing the above important issues, this paper proposes a risk-constrained multi-stage stochastic programming model to make optimal investment decisions on wind power facilities along a multi-stage horizon. The proposed model is illustrated using a clarifying example and a case study. [ABSTRACT FROM PUBLISHER]

Although wave power installations are currently limited in number, the capacity value of wave power is of interest in power system planning. The capacity value of wind power has been studied in previous work, but the value for wave power is not known. Continuous time series characterizing the wave energy resource at multiple sites are generated from raw wave data. These time series are analyzed with a forced outage rate imposed for individual devices, as may be encountered by real wave energy farms. The capacity value of wave power at various sites is evaluated with reference to 500 MW of installed capacity. Portfolios are created, allocating wave power capacity to individual sites, with the impact of geographical aggregation being examined. The inter-annual variation for each portfolio is then determined in order to examine the robustness of the results. Comparison is also made between sites off the west coasts of Ireland and Portugal. The implications for the power system resulting from an increase in generation adequacy are discussed. [ABSTRACT FROM PUBLISHER]