WP - TPWRS - 2014

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Abstract
The goal of this paper is to describe the approach in designing and constructing wind power laboratory experiments for undergraduate- and graduate-level courses in power and energy systems. These are separated into basic hands-on laboratory and advanced simulation-based experiments. The basic experiments are integrated into an undergraduate course that includes topics such as the steady-state operation of induction machines, fixed-speed, and variable-speed wind turbines. Advanced experiments are integrated into a stand-alone course dedicated to wind energy and power systems. Topics include the modeling of aerodynamic, mechanical, and electrical components for each type of wind turbine along with their steady-state and dynamic operations. The experiments were originally designed at the University of Texas at Austin. Their transferability to a different laboratory platform at the University of Texas Pan American is also discussed.
An ad-hoc probabilistic analytical solution for modeling wind farm power output is proposed in this letter. In accordance with the unit impulse function described in signals and systems engineering, and combined with the output power versus wind speed curve with wake effect consideration and the wind speed probability density function (PDF), the PDF of wind farm power output is deduced analytically. The comparisons with Monte Carlo method are given to demonstrate the validity of the proposed model.
Extreme power fluctuations in wind farms are rare but high-impact events, so proper characterization of these extreme fluctuations would assist with power systems operations planning in a power system with a high penetration of wind power. This work applies extreme value analysis methods to the statistical characterization of wind power ramps with 10-min resolution. The annual maxima series (AMS) method and peaks over threshold (POT) method are used to determine the probability of extreme wind power ramp events in a wind farm.
Maintaining a close balance between power generation and demand is essential for sustaining the quality and reliability of a power system. Currently, due to increased renewable energy generation, frequency deviations and power fluctuations of greater concern are being introduced to the grid, particularly in regions that are weakly interconnected with their surrounding areas, such as small islands. This paper addresses the problem of frequency control in isolated power systems with relevant inclusion of wind power generation. With this aim, we have analyzed the contribution of the demand side to the primary frequency control together with an auxiliary frequency control, which is carried out by variable-speed wind turbines through an additional control loop that synthesizes virtual inertia. We have evaluated both the suitability of these two additional control actions counteracting frequency deviation and their potential reserves and compatibility. The results indicate a substantial improvement in both the dynamic performance and grid frequency stability. Simulations also indicate a decrease in the steady-state frequency error, which may relieve the secondary frequency control.
With rapid increase in wind power penetration into the power grid, wind power forecasting is becoming increasingly important to power system operators and electricity market participants. The majority of the wind forecasting tools available in the literature provide deterministic prediction, but given the variability and uncertainty of wind, such predictions limit the use of the existing tools for decision-making under uncertain conditions. As a result, probabilistic forecasting, which provides information on uncertainty associated with wind power forecasting, is gaining increased attention. This paper presents a novel hybrid intelligent algorithm for deterministic wind power forecasting that utilizes a combination of wavelet transform (WT) and fuzzy ARTMAP (FA) network, which is optimized by using firefly (FF) optimization algorithm. In addition, support vector machine (SVM) classifier is used to minimize the wind power forecast error obtained from WT+FA+FF. The paper also presents a probabilistic wind power forecasting algorithm using quantile regression method. It uses the wind power forecast results obtained from the proposed hybrid deterministic WT+FA+FF+SVM model to evaluate the probabilistic forecasting performance. The performance of the proposed forecasting model is assessed utilizing wind power data from the Cedar Creek wind farm in Colorado.
Increasing levels of wind power integration pose a challenge in system operation, owing to the uncertainty and non-dispatchability of wind generation. The probabilistic nature of wind speed inputs dictates that in an optimization of the system, all output variables will themselves be probabilistic. In order to determine the distributions resulting from system optimization, a probabilistic optimal power flow (POPF) method may be applied. While Monte Carlo (MC) techniques are a traditional approach, recent research into point estimate methods (PEMs) has displayed their capabilities to obtain output distributions while reducing computational burden. Unfortunately both spatial and temporal correlation amongst the input wind speed random variables complicates the application of PEM for solving the POPF. Further complications may arise due to the large number of random input variables present when performing a multi-period POPF. In this paper, a solution is proposed which addresses the correlation amongst input random variables, as well as an input variable truncation approach for addressing the large number of random input variables, such that a PEM can be effectively used to obtain POPF output distributions.
Reducing the fossil fuel consumption of aluminum smelting production, which consumes around 8% of electricity annually, is of great significance for China's energy sector. One possible solution is to integrate wind power as supply for aluminum smelter loads. Based on this background, this paper studies an actual industrial case-an isolated power system for aluminum production with integrated wind penetration levels as high as 30%. However, due to the integration of wind power, frequency stability issues become critical in such a system. Therefore, a demand-side frequency control scheme responding to frequency deviations of the system is proposed in this paper to control the load power of aluminum smelters. The scheme is designed based on the relationship between the DC voltage supply for aluminum smelting loads and the commutation voltage drop which can be adjusted by changing the inductance value of the saturable reactors. Finally, simulations under both N-1 and N-2 contingency scenarios demonstrate the effectiveness of the proposed control scheme considering various wind power outputs.
"In recent grid codes for wind power integration, wind turbines are required to stay connected during grid faults even when the grid voltage drops down to zero; and also to inject reactive current in proportion to the voltage drop. However, a physical fact, instability of grid-connected converters during current injection to very low (close to zero) voltage faults, has been omitted, i.e., failed to be noticed in the previous wind power studies and grid code revisions. In this paper, the instability of grid side converters of wind turbines defined as loss of synchronism (LOS), where the wind turbines lose synchronism with the grid fundamental frequency (e.g., 50 Hz) during very deep voltage sags, is explored with its theory, analyzed and a novel stability solution based on PLL frequency is proposed; and both are verified with power system simulations and by experiments on a grid-connected converter setup."
The integration of wind power requires additional operating reserves to cope with the uncertainty in power system operation. Previous research shows that the uncertainty of the wind power forecast varies with the level of its output.Therefore, allocating reserves dynamically according to the specific distribution of the wind power forecast would benefit system scheduling. This paper presents a statistical model to formulate the conditional distribution of forecast error for multiple wind farms using copula theory. The proposed model is tested using a set of synchronous data of wind power and its day-ahead forecast. It is then utilized in a stochastic unit commitment model to simulate the day-ahead and real-time scheduling of the modified IEEE RTS-79 system integrating wind power. The results show that scheduling reserves dynamically according to the modeled conditional forecast error reduces the probability of reserve deficiency while maintaining the same level of operating costs.
Since wind at the earth's surface has an intrinsically complex and stochastic nature, accurate wind power forecasts are necessary for the safe and economic use of wind energy. In this paper, we investigated a combination of numeric and probabilistic models: a Gaussian process (GP) combined with a numerical weather prediction (NWP) model was applied to wind-power forecasting up to one day ahead. First, the wind-speed data from NWP was corrected by a GP, then, as there is always a defined limit on power generated in a wind turbine due to the turbine controlling strategy, wind power forecasts were realized by modeling the relationship between the corrected wind speed and power output using a censored GP. To validate the proposed approach, three real-world datasets were used for model training and testing. The empirical results were compared with several classical wind forecast models, and based on the mean absolute error (MAE), the proposed model provides around 9% to 14% improvement in forecasting accuracy compared to an artificial neural network (ANN) model, and nearly 17% improvement on a third dataset which is from a newly-built wind farm for which there is a limited amount of training data.
This paper proposes an optimal power flow (OPF) model with flexible AC transmission system (FACTS) devices to minimize wind power spillage. The uncertain wind power production is modeled through a set of scenarios. Once the balancing market is cleared, and the final values of active power productions and consumptions are assigned, the proposed model is used by the system operator to determine optimal reactive power outputs of generating units, voltage magnitude and angles of buses, deployed reserves, and optimal setting of FACTS devices. This system operator tool is formulated as a two-stage stochastic programming model, whose first-stage describes decisions prior to uncertainty realization, and whose second-stage represents the operating conditions involving wind scenarios. Numerical results from a case study based on the IEEE RTS demonstrate the usefulness of the proposed tool.
The ability of load to respond to short-term variations in electricity prices plays an increasingly important role in balancing short-term supply and demand, especially during peak periods and in dealing with fluctuations in renewable energy supplies. However, price responsive load has not been included in standard models for defining the optimal scheduling of generation units in short-term. Here, elasticities are included to adjust the demand profile in response to price changes, including cross-price elasticities that account for load shifts among hours. The resulting peak reductions and valley fill alter the optimal unit commitment. Enhancing demand response also increases the amount of wind power that can be economically injected. Further, wind power uncertainty can be managed at a lower cost by adjusting electricity consumption in case of wind forecast errors, which is another way in which demand response facilitates the integration of intermittent renewables.
Wind power has become one of the most prominent renewable energy applications in the power industry. Its annual installation is continuously setting a record high. Due to the large portion of generation mix within the system, it is desirable to operate wind power under the automatic generation control (AGC). This issue also raises another concern on the modeling of wind farms under AGC. The traditional way of portraying wind farm production in simulation is to use an aggregated wind turbine model that is generally operated in maximum power point tracking (MPPT). This approach is not suitable for wind's participation in AGC. Therefore, more studies should be done to investigate how the individual wind turbines affect the wind farm performance under the set-point operation. This paper first illustrates the controllability of wind turbines under the set-point control. Following that, a comprehensive guidance of wind generator modeling is demonstrated to yield different approaches for developing the wind farm model. Two types of wind farm models are derived and demonstrated to portray the capability of set-point tracking under the intermittent wind condition. Associated simulations are presented and compared to show the different characteristics of these two wind farm models in depicting the set-point operation under AGC.
Accurate and reliable wind power forecasting is essential to power system operation. Given significant uncertainties involved in wind generation, probabilistic interval forecasting provides a unique solution to estimate and quantify the potential impacts and risks facing system operation with wind penetration beforehand. This paper proposes a novel hybrid intelligent algorithm approach to directly formulate optimal prediction intervals of wind power generation based on extreme learning machine and particle swarm optimization. Prediction intervals with associated confidence levels are generated through direct optimization of both the coverage probability and sharpness to ensure the quality. The proposed method does not involve the statistical inference or distribution assumption of forecasting errors needed in most existing methods. Case studies using real wind farm data from Australia have been conducted. Comparing with benchmarks applied, experimental results demonstrate the high efficiency and reliability of the developed approach. It is therefore convinced that the proposed method provides a new generalized framework for probabilistic wind power forecasting with high reliability and flexibility and has a high potential of practical applications in power systems.
Accurate and reliable forecast of wind power is essential to power system operation and control. However, due to the nonstationarity of wind power series, traditional point forecasting can hardly be accurate, leading to increased uncertainties and risks for system operation. This paper proposes an extreme learning machine (ELM)-based probabilistic forecasting method for wind power generation. To account for the uncertainties in the forecasting results, several bootstrap methods have been compared for modeling the regression uncertainty, based on which the pairs bootstrap method is identified with the best performance. Consequently, a new method for prediction intervals formulation based on the ELM and the pairs bootstrap is developed. Wind power forecasting has been conducted in different seasons using the proposed approach with the historical wind power time series as the inputs alone. The results demonstrate that the proposed method is effective for probabilistic forecasting of wind power generation with a high potential for practical applications in power systems.
This paper proposes a decentralized methodology to optimally schedule generating units while simultaneously determining the geographical allocation of the required reserve. We consider an interconnected multi-area power system with cross-border trading in the presence of wind power uncertainty. The multi-area market-clearing model is represented as a two-stage stochastic programming model. The proposed decentralized procedure relies on an augmented Lagrangian algorithm that requiresno central operator intervention but just moderate interchanges of information among neighboring regions. The methodology proposed is illustrated using an example and a realistic case study.
In this paper, the value of intra-day markets in managing wind power uncertainty in competitive electricity markets is analyzed. A competitive electricity market model consisting of a day-ahead market and a number of intra-day markets is considered. Real-time operation adjustment is also taken into account. Stochastic programming is used to model decision making under wind power uncertainty. Numerical simulations based on two test systems are presented.
This paper considers the problem of identifying the optimal investment of a strategic wind power investor that participates in both the day-ahead (DA) and the balancing markets. This investor owns a number of wind power units that jointly with the newly built ones allow it to have a dominant position and to exercise market power in the DA market, behaving as a deviator in the balancing market in which the investor buys/sells its production deviations. The model is formulated as a stochastic complementarity model that can be recast as a mixed-integer linear programming (MILP) model. A static approach is proposed focusing on a future target year, whose uncertainties pertaining to demands, wind power productions, and balancing market prices are precisely described. The proposed model is illustrated using a simple example and two case studies.
Wind energy is present in many countries throughout the world. The main types of wind sales in electricity markets are via regulated tariffs or pool-based markets. Production companies choose cost-effective options for selling wind energy, and some markets, like the Irish electricity market, use regulated tariffs to remunerate wind production. This paper aims to provide some answers to explain what effect wind offers may have in an electricity market if wind power producers participated in the day-ahead market without receiving any premium or aid. A price-maker optimization model is used to detect its effect on prices. The model encompasses energy offers by other technologies using residual demand curves and detailed modeling of wind imbalances. It is observed that wind acting as price-maker reduces electricity prices and the imbalance penalties help the system operator to reduce imbalances. A realistic case study using data from the Irish electricity market illustrates the methodology used comparing the effect of imbalance penalties in the models in terms of profit and total imbalance of the system.
We propose a stochastic generation expansion model, where we represent the long-term uncertainty in the availability and variability in the weekly wind pattern with multiple scenarios. Scenario reduction is conducted to select a representative set of scenarios for the long-term wind power uncertainty. We assume that the short-term wind forecast error induces an additional amount of operating reserves as a predefined fraction of the wind power forecast level. Unit commitment (UC) decisions and constraints for thermal units are incorporated into the expansion model to better capture the impact of wind variability on the operation of the system. To reduce computational complexity, we also consider a simplified economic dispatch (ED) based model with ramping constraints as an alternative to the UC formulation. We find that the differences in optimal expansion decisions between the UC and ED formulations are relatively small. We also conclude that the reduced set of scenarios can adequately represent the long-term wind power uncertainty in the expansion problem. The case studies are based on load and wind power data from the state of Illinois.
This paper proposes a new approach for corrective voltage control (CVC) of power systems in presence of uncertain wind power generation and demand values. The CVC framework deals with the condition that a power system encounters voltage instability as a result of severe contingencies. The uncertainty of wind power generation and demand values is handled using a scenario-based modeling approach. One of the features of the proposed methodology is to consider participation of demand-side resources as an effective control facility that reduces control costs. Active and reactive redispatch of generating units and involuntary load curtailment are employed along with the voluntary demand-side participation (demand response) as control facilities in the proposed CVC approach. The CVC is formulated as a multi-objective optimization problem. The objectives are ensuring a desired loading margin while minimizing the corresponding control cost. This problem is solved using ε-constraint method, and fuzzy satisfying approach is employed to select the best solution from the Pareto optimal set. The proposed control framework is implemented on the IEEE 118-bus system to demonstrate its applicability and effectiveness.
In this paper, short-term forecast of wind farm generation is investigated by applying spatio-temporal analysis to extensive measurement data collected from a large wind farm where multiple classes of wind turbines are installed. Specifically, using the data of the wind turbines' power outputs recorded across two consecutive years, graph-learning based spatio-temporal analysis is carried out to characterize the statistical distribution and quantify the level crossing rate of the wind farm's aggregate power output. Built on these characterizations, finite-state Markov chains are constructed for each epoch of three hours and for each individual month, which accounts for the diurnal non-stationarity and the seasonality of wind farm generation. Short-term distributional forecasts and a point forecast are then derived by using the Markov chains and ramp trend information. The distributional forecast can be utilized to study stochastic unit commitment and economic dispatch problems via a Markovian approach. The developed Markov-chain-based distributional forecasts are compared with existing approaches based on high-order autoregressive models and Markov chains by uniform quantization, and the devised point forecasts are compared with persistence forecasts and high-order autoregressive model-based point forecasts. Numerical test results demonstrate the improved performance of the Markov chains developed by spatio-temporal analysis over existing approaches.
"In this paper, a detailed ancillary service pricing mechanism must be in place to encourage wind farm owners to opt for deloaded operating strategies. The authors do not agree that “over-speeding” de-loading tends to respond poorly in the high wind-speed regime; on the contrary, the WTG response to frequency regulation will be better during high wind speeds due to higher reserve generation available. As demonstrated in this work, it is possible to reduce the stability related issues significantly by carefully selecting the deloading percent and the droop parameter of the wind turbine generator."
This paper proposes an approach for analyzing the impacts of large-scale wind power integration on electricity market equilibria. A pool-based oligopolistic electricity market is considered including a day-ahead market and a number of real-time markets. Wind power is considered within the generation portfolio of the strategic producers, and the uncertainty of wind power production is modeled through a set of plausible scenarios. The strategic behavior of each producer is modeled through a stochastic bilevel model. The resulting nonlinear equilibrium problem with equilibrium constraints (EPEC) is linearized and then solved. Numerical results for a test case with increasing levels of the wind power penetration is provided.
The fault ride-through capability (FRTC) of a full-rated converter wind turbine relies on the operation and control of the grid-side converter. To enhance the FRTC of the wind power generation system (WPGS), this paper investigates anovel controller for the grid-side converter, based on nonlinear adaptive control (NAC). Lumped perturbation terms are defined in the NAC to include all unknown and time-varying dynamics and external disturbances of the WPGS, and can be estimated by designing perturbation observers. The estimate of the perturbation terms is used to compensate the real perturbations and finally achieve an adaptive feedback linearizing control of the original nonlinear system, without requiring the accurate system model and full state measurements. The proposed NAC is an output feedback control and adaptive to parameter uncertainties and unknown nonlinearities of the WPGS, and time-varying external disturbances including grid faults, voltage dips and intermittent wind power inputs. The effectiveness of the proposed NAC is verified by simulation studies and compared with conventional vector controller and feedback linearizing controller, which show that it can provide better FRTC even though the grid voltage levels are far below their nominal values.
This paper proposes a new adaptive control strategy for a wind energy conversion system based on a permanent magnet synchronous generator and a pulse-width modulated current source converter. Most of the studies on wind farms are based on double fed induction technology. Nevertheless, the proposed conversion system is a good alternative due to its high efficiency and reliability. Electrolytic capacitors are not required in this type of converter and the voltage in the DC-link as well as the generated reactive power can be dynamically modified according to the wind velocity, being even negative if required. However, it is challenging from the control and stability standpoint. Capacitive filters placed on the AC side, which are required for safe commutation, can create resonances with the power grid. Reactive power is generated according to the capacity of the converter, the wind velocity and the load profile. The adaptive control strategy uses an adaptive PI which is self-tuned based on a linear approximation of the power system calculated at each sample time. A model reference is also proposed in order to reduce the post-fault voltages. Simulation results demonstrate the advantages of the proposed control.
Although the installed wind generation capacity has grown remarkably over the past decades, percentage of wind energy in electricity supply portfolio is still relatively low. Due to the technical limitations of power system operations, considerable wind generation cannot integrate into the grid but gets curtailed. Among various factors, transmission congestion accounts for a significant portion of wind curtailment. Derived from DC power network, an analytical approach is proposed to efficiently assess the congestion induced wind curtailment sensitivity without iterative simulation. Compared to empirical simulation-based wind curtailment studies, the proposed approach offers the following advantages: 1) computational efficiency, 2) low input information requirement, and 3) robustness against uncertainties. This approach could benefit system operators, wind farm owners as well as wind power investors to better understand the interactions between wind curtailment and power system operations and can further help for curtailment alleviation. Numerical experiments of a modified IEEE 24-bus Reliability Test System (RTS) as well as a practical 5889-bus system are conducted to verify the effectiveness and robustness of the proposed approach.
This paper presents a set of linear control designs for shaping the inter-area oscillation spectrum of a large radial power system through coordinated control of a wind farm and a battery energy system (BES). We consider a continuum representation of the power system with the wind and battery power modeled as point-source forcings. A spectral analysis of the system demonstrates that its oscillation spectrum strongly depends on the locations of these power injections, implying that there are siting locations that produce more favorable spectral responses. However, the ability to site a wind farm or BES at a specific location may be limited by geographic, environmental, economic or other considerations. Our work provides a means to circumvent this problem by designing co-ordinated controllers for the power outputs of the wind farm and the BES by which one can shape the spectral response of the system to a desired response. The design is posed as a parametric optimization problem that minimizes the error between the two spectral responses over a finite range of frequencies. The approach is independent of the locations of the wind farm and the BES, and can be implemented in a decentralized fashion.
This paper studies the feasibility of utilizing the reactive power of grid-connected variable-speed wind generators to enhance the steady-state voltage stability margin of the system. Allowing wind generators to work at maximum reactive power limit may cause the system to operate near the steady-state stability limit, which is undesirable. This necessitates proper coordination of reactive power output of wind generators with other reactive power controllers in the grid. This paper presents a trust region framework for coordinating reactive output of wind generators with other reactive sources for voltage stability enhancement. Case studies on 418-bus equivalent system of Indian southern grid indicates the effectiveness of proposed methodology in enhancing the steady-state voltage stability margin.
This paper presents a novel perturbation observer-based multiloop control method for the integrated control of the doubly-fed induction generator-based wind turbine (DFIG-WT) in a multimachine power system. The DFIG-WT is decoupled into four independent subsystems in accordance with four outputs. A perturbation state is introduced into each subsystem, and the optimal output feedback control of each subsystem is achieved with the estimate of perturbation obtained by a sliding-mode perturbation observer. Thus a four-loop perturbation observer-based control (POC) scheme is adopted to achieve the integrated control of the DFIG-WT. The design of the four-loop POC does not require any accurate system model, and it is robust to external disturbances and parameter variations. Meanwhile, the four-loop POC presents a fast tracking rate and shows the capability of damping the oscillations of output variables. Moreover, interactions between two DFIG-WT-based wind farms and their influence to the stability of power system are also investigated. Simulation studies reveal that superior integrated control of DFIG-WT can be achieved by the four-loop POC under various conditions compared with the conventional vector control scheme.
Synchronous island power systems, such as the combined Ireland and Northern Ireland power system, are facing increasing penetrations of renewable generation. As part of a wider suite of studies, performed in conjunction with the transmission system operators (TSOs) of the All-Island system (AIS), the frequency stability challenges at high and ultra-high wind penetrations were examined. The impact of both largest infeed loss and network fault induced wind turbine active power dips was examined: the latter contingency potentially representing a fundamental change in frequency stability risk. A system non-synchronous penetration (SNSP) ratio was defined to help identify system operational limits. A wide range of system conditions were studied, with results showing that measures such as altering ROCOF protection and enabling emulated inertia measures were most effective in reducing the frequency stability risk of a future Ireland system.
This paper presents a probabilistic optimal power flow (POPF) technique considering the correlations of wind speeds following arbitrary probability distributions based on the point estimation method (PEM). Correlated wind speeds following different distributions are transformed into random variables followingcorrelated normal distributions and then independent normal distributions so that the traditional 2m+1 point estimation method can be applied to solve the probabilistic optimal power flow with wind speed correlations. The IEEE 14-bus system, IEEE 118-bus system and an actual utility system in the southwest of China with additional wind farms are used to demonstrate the effectiveness of the presented method.
We propose a probabilistic methodology to estimate a demand curve for operating reserves, where the curve represents the amount that a system operator is willing to pay for these services. The demand curve is quantified by the cost of unserved energy and the expected loss of load, accounting for uncertainty from generator contingencies, load forecasting errors, and wind power forecasting errors. The methodology addresses two key challenges in electricity market design: integrating wind power more efficiently and improving scarcity pricing. In a case study, we apply the proposed operating reserve strategies in a two-settlement electricity market with centralized unit commitment and economic dispatch and co-optimization of energy and reserves. We compare the proposed probabilistic approach to traditional operating reserve rules. We use the Illinois power system to illustrate the efficiency of the proposed reserve market modeling approach when it is combined with probabilistic wind power forecasting.
This paper examines the feasibility of coordinating variable-speed wind farms with other power system devices, such as flexible ac transmission systems and synchronous generators, to damp low-frequency oscillations. For this purpose, an observer-based state-feedback approach is used to build a power oscillation damping (POD) controller, implemented through coordinated actions of multiple control devices, and able to manage several measurement channels. The wide-area POD controller also presents a time-delay compensation stage to mitigate adverse effects of latency involved in wide-area communication systems. Several practical issues are discussed and analyzed, such as measurement and control selection, model-order reduction, transmission time-delay compensation, impact of the POD control on wind farms, and robustness aspects. The control performance is evaluated and compared with other control schemes using eigenvalue analyses and nonlinear time-domain simulations over a wide range of operating conditions, for example, severe system faults, N - 1 outage contingencies, load/generation shedding, and line tripping.
This study presents an integrated methodology that considers renewable distributed generation (RDG) and demand responses (DR) as options for planning distribution systems in a transition towards low-carbon sustainability. It is assumed that demand responsiveness is enabled by real-time pricing (RTP), and the problem has been formulated as a dynamic two-stage model. It co-optimizes the allocation of renewables [including wind and solar photovoltaic (PV)], non-renewable DG units (gas turbines) and smart metering (SM) simultaneously with network reinforcement for minimizing the total economic and carbon-emission costs over planning horizons. The behavior compliance to RTP is described through a nodal-based DR model, in which the fading effect attended during the load recovery is highlighted. Besides, uncertainties associated with renewable energy generation and price-responsiveness of customers are also taken into account and represented by multiple probabilistic scenarios. The proposed methodology is implemented by employing an efficient hybrid algorithm and applied to a typical distribution test system. The results demonstrate the effectiveness in improving the efficiency of RDG operations and mitigating CO2 footprint of distribution systems, when compared with the conventional planning paradigms.
The authors of [1] practically investigate the limits within which the droop of a wind generator (WG) can be controlled in order for the latter to offer primary frequency control (FC).
This paper proposes an expected value and chance constrained stochastic optimization approach for the unit commitment problem with uncertain wind power output. In the model, the utilization of wind power can be adjusted by changing the utilization rate in the proposed expected value constraint. Meanwhile, the chance constraint is used to restrict the probability of load imbalance. Then a Sample Average Approximation (SAA) method is used to transform the objective function, the expected value constraint, and the chance constraint into sample average reformulations. Furthermore, a combined SAA framework that considers both the expected value and the chance constraints is proposed to construct statistical upper and lower bounds for the optimization problem. Finally, the performance of the proposed algorithm with different utilization rates and different risk levels is tested for a six-bus system. A revised IEEE 118-bus system is also studied to show the scalability of the proposed model and algorithm.
With the increasing penetration of variable renewable generations, independent system operators (ISOs)/regional transmission organizations (RTOs) are faced with new challenges for the secure and economic operation of power systems. This paper proposes an effective approach for deriving robust solutions to the security-constrained unit commitment (SCUC) problem, which considers load and wind uncertainties via interval numbers. Different from most robust optimization-based SCUC approaches in literature which explore robust unit commitment (UC) solutions for immunizing against the worst economic scenario in terms of the highest minimum dispatch cost, the proposed robust SCUC model minimizes operation cost for the base case while guaranteeing that the robust UC and dispatch solutions could be adaptively and securely adjusted in response to uncertain intervals. Thus, the proposed model achieves smaller unit commitment costs while maintaining the solution robustness as compared with literature. In addition, the proposed model describes base case dispatches and corrective actions in uncertain intervals, which is more consistent with the current day-ahead and real-time markets. Furthermore, besides budget constraints used in literature, this paper also considers load and wind variability correlations in constructing uncertain intervals, which would eliminate unlike-to-happen scenarios and further limit the level of conservatism of the robust solution. The proposed robust SCUC model is solved by Benders decomposition, which decomposes the original problem into a master UC problem for the base case and subproblems for the base case network evaluation and the security checking for uncertain intervals. Feasibility cuts are generated and fed back to the master problem for further iterations when violations are identified in subproblems. Numerical case studies on the modified IEEE 118-bus system illustrate the effectiveness of the proposed robust SCUC model for the secure and economic operation of power systems under various uncertainties.
Large-scale offshore wind farms can be integrated with onshore ac grids by the voltage source converter based high voltage direct current (VSC-HVDC) technology. The resulting impact on the security of ac grids can be significant. Therefore, it is important to develop an HVDC model for online stability monitoring of the integrated ac/dc systems. This paper proposes a new HVDC model based on a circuit-theoretic foundation. With the available phasor measurement units (PMUs) at VSC stations, the parameters of the HVDC equivalent model can be identified in real-time by synchronized voltage and current phasor measurements at VSC ac terminals. The proposed HVDC model is applied to online voltage instability detection for integrated ac/dc systems by the Thevenin impedance matching. The HVDC equivalent circuit enables the Thevenin equivalent impedance of the HVDC-connected offshore wind farm to be determineddirectly from the PMU measurements. Numerical simulations are performed on the IEEE 39-bus system with an HVDC-connected offshore wind farm to validate the effectiveness of the proposed HVDC model.
This paper identifies when the transmission network local hosting capacity for a wind harvesting network may be limited because of steady-state bus voltage limits. In addition, the paper addresses how with the wind farm voltage control provision, such constraints may be overcome and the local hosting capacity can be increased. To answer these questions, actual Spanish system data is used on different network models of increasing complexity. Firstly, a simplified model of both transmission network and harvesting network is discussed to show that generally, only buses with low short-circuit power and low or high reactance-resistance ratio may limit local hosting capacity significantly. Secondly, in order to assess how modeling simplifications affect the results, the full model of an actual Spanish harvesting network is considered: the real reactive capability of the harvesting network at the transmission network connection node is computed and the local hosting capacity recalculated. Finally, in the last step, the results of the aforementioned simplified models are validated using the complete model of the Spanish transmission network. In addition, a complementary area hosting capacity analysis is included in order to show the importance of steady-state bus voltage constraints when large amounts of power need to be transported over long distances.
Integration of renewable energy resources into the power system has increased the financial and technical concerns for the market-based transmission expansion planning. This paper proposes a stochastic framework for transmission grid reinforcement studies in a power system with wind generation. A multi-stage multi-objective transmission network expansion planning (TNEP) methodology is developed which considers the investment cost, absorption of private investment and reliability of the system as the objective functions. A non-dominated sorting genetic algorithm (NSGA II) optimization approach is used in combination with a probabilistic optimal power flow (POPF) to determine the Pareto optimal solutions considering the power system uncertainties. Using a compromise-solution method, the best final plan is then realized based on the decision-maker preferences. The proposed methodology is applied to the IEEE 24-bus Reliability Tests System (RTS) to evaluate the feasibility and practicality of the developed planning strategy.
The impact of increasing penetration of converter control-based generators (CCBGs) in a large-scale power system is assessed through a model based small signal stability analysis. Three test bed cases for the years 2010, 2020, and 2022 of the Western Electricity Coordinating Council (WECC) in the United States are used for the analysis. Increasing penetration of wind-based Type 3 and wind-based Type 4 and PV Solar CCBGs is used in the tests. The participation and interaction of CCBGs and synchronous generators in traditional electromechanical interarea modes is analyzed. Two new types of modes dominated by CCBGs are identified. The characteristics of these new modes are described and compared to electromechanical modes in the frequency domain. An examination of the mechanism of the interaction between the CCBG control states and the synchronous generator control states is presented and validated through dynamic simulations. Actual system and forecast load data are used throughout.
This paper presents a novel system configuration for voltage source converter (VSC)-based high-voltage direct current (HVDC) transmission connected to a large-scale offshore wind power plant (WPP). The proposed scheme is reconfigured at the onshore end to achieve shunt and series compensation, which is named as `Unified-VSC-HVDC' (U-VSC-HVDC). A mathematical model of the proposed configuration is derived to determine the rating of the employed series and shunt converters. To achieve a flexible control strategy for balanced and unbalanced fault conditions, the proposed transient management scheme employs positive and negative sequence controllers for the series compensation. The negative sequence voltage components are determined in such a way as to minimize power oscillations caused by asymmetrical faults, and hence to reduce DC link voltage overshoots. A test system comprised of a detailed representation of the proposed configuration is simulated and evaluated using PSCAD/EMTDC. A comprehensive study validates the capability of the proposed configuration and transient management scheme for achieving smooth power transfer and superior transient performance of the electrical grid. Also, it minimizes the possibilities of electrical network propagations in response to symmetrical and asymmetrical grid faults.
A novel fault-tolerant configuration of doubly fed induction generator (DFIG) for wind energy conversion systems (WECSs) is proposed in this paper for the seamless operation during all kinds of grid faults. The proposed configuration is developed by replacing the traditional six-switch grid-side converter (GSC) of DFIG with a nine-switch converter. With the additional three switches, the nine-switch converter can provide six independent output terminals. One set of three output terminals are connected to the grid through interfacing inductors to realize normal GSC operation while, the other set of three output terminals are connected to neutral side of the stator windings to provide fault ride-through (FRT) capability to the DFIG. An appropriate control algorithm is developed for the proposed configuration that: 1) achieves seamless fault ride-through during any kind of grid faults and 2) strictly satisfies new grid codes requirements. The effectiveness of the proposed configuration in riding through different kind of faults is evaluated through detailed simulation studies on a 1.5-MW WECS.
Growth and penetration of renewable energy has been remarkable during the last few years in many power systems around the world. Essentially, there are two major technologies responsible for the growth, namely wind and photovoltaic. The technologies involved in harnessing energy from wind and sun are distinctly different in dynamic characteristics and limitations. Consequently, their influence on stability of power system should not be overlooked. This paper examines the small-disturbance angle stability with high penetration of renewable energy and proposes a methodology to control. The hierarchical principal component analysis, which is a clustering method corresponding to the eigenvalue sensitivity of reactive power control, is utilized to select the renewable generator clusters for different reactive power control scheme. Then, the framework based on structured singular value has been employed, in which the reactive power controls of renewable generator clusters are selected such that the desired robust stability criterion is satisfied. Results obtained in 16-machine 68-bus test system (typically used for small-signal angle stability studies) show the effectiveness of the proposed methodology.
This paper presents a complete wave-to-wire approach to the modeling of wave energy farms. It captures all the main peculiarities of such applications, from the variability of sea waves to the issues related to the grid integration of a multi-MW wave farm, including the hydrodynamic modeling of wave energy converters (WECs). The paper specifically discusses the different levels of control of a wave farm and their integration and coordination. These are crucial to meet the power quality requirements at the point of common coupling (PCC) and ensure the efficiency of the power transfer from the waves to the main electric grid. A specific real-time technique for the centralized control of a wave farm is also proposed, which is exemplified with reference to the PCC voltage control inthe real test case of bimep. Critical cases of weaker grids are also considered to extend the validity of the analysis.
Wind generation variability in an energy-only market such Australian National Electricity Market (NEM) can create significant revenue uncertainties for incumbent generators and substantially increase price risks faced by retailers. This paper presents a Cournot game model to formally analyze how high volatility of wind generation in a concentrated energy-only market can raise the peak/shoulder period (of typically low wind generation) prices to offset the foregone revenue during off-peak periods (of high wind generation). A Monte Carlo simulation around a Cournot game is formulated as an inter-temporal nonlinear optimization problem to assess these issues. The model is implemented for the South Australian zone of the Australian NEM that has experienced high growth in wind generation in recent years. The model results support some of the observed spot pricing behavior in the region in recent years. These findings have significant ramifications for the efficacy of the energy-only market in scenarios with high penetration of intermittent generation.
This paper proposes a new computational intelligence-based control strategy, to enhance the low voltage ride-through capability of grid-connected wind turbines (WTs) with doubly fed induction generators (DFIGs). Grid codes world-wide require that WTs should supply reactive power to the grid during and after the fault, in order to support the grid voltage. The conventional crowbar-based systems that were initially applied in order to protect the rotor-side converter at the occurrence of grid faults, do not fulfill this requirement, as during the connection of the crowbar, the DFIG behaves as a squirrel cage machine, absorbing reactive power from the grid. This drawback led to the design of control systems that eliminate or even avoid the use of the crowbar. In order to conform to the above-mentioned requirement, this paper proposes a coordinated control strategy of the DFIG converters during a grid fault, managing to ride-through the fault without the use of any auxiliary hardware. The coordination of the two controllers is achieved via a fuzzy controller which is properly tuned using genetic algorithms. To validate the proposed control strategy, a case study of a 1.5-MW DFIG supplying a relatively weak electrical system is carried out by simulation.
Several studies have recently reported the capability of doubly-fed induction generator (DFIG) wind farms in improving the dynamic performance of power systems. Since wind farms are more often located far from the system conventional generation centers, local signals do not comprise enough content for damping inter-area oscillations. This paper presents a particle swarm optimization (PSO)-based wide-area damping controller (WADC) for the DFIG wind farms. The proposed controller is designed with a centralized nature on the basis of latest technological development of wide-area measurement system (WAMS). Damping both inter-area and local oscillatory modes are intended in the design process. The most challenging deficiency against WAMS real-time applications is the variable communication latency which can deteriorate the system stability if not properly accounted for in the controller design process. The proposed WADC hence incorporates effective means to compensate for the destructive characteristics of WAMS delayed signals. The PSO technique is applied to normalize and optimize the parameters of WADC. A set of comprehensive case studies are conducted on a 16-bus six-machine test system and the obtained numerical evidences are thoroughly discussed.
With the growing trend of extreme weather events in the Northeast U.S., a region of dense vegetation, evaluating hazard effects of wind storms on power distribution systems becomes increasingly important for disaster preparedness and fast responses in utilities. In this paper, probabilistic wind storm models for the study region have been built by mining 160-year storm events recorded in the National Oceanic and Atmospheric Administration's Atlantic basin hurricane database (HURDAT). Further, wind storms are classified into six categories according to NOAA criteria and IEEE standard to facilitate the evaluation of distribution system responses under different levels of hazards. The impacts of wind storms in all categories are accurately evaluated through a Sequential Monte Carlo method enhanced by a temporal wind storm sampling strategy. Extensive studies for the selected typical distribution system indicate that our models and methods effectively reveal the hazardous effects of wind storms in the study region, leading to useful insights towards building better system hardening schemes.
Integration of renewable energy sources and demand response poses new challenges to system operators as they increase the uncertainty of the power supply and demand. Recently, robust optimization techniques are applied to the unit commitment problem with uncertainty as an alternative to the stochastic programming approaches. However, it remains challenging to solve the robust unit commitment model with full transmission line constraints. In this paper, we propose novel acceleration techniques for solving two-stage robust unit commitment problem with consideration of full transmission line constraints. We use 1) the cutting-plane algorithm for the master problem, which dynamically includes critical transmission line constraints, and 2) column-generation methods, including the branch-and-price-and-cut algorithm and heuristic approaches, for the subproblems, which add only necessary transmission line dual variables on the fly. Computational results for the modified IEEE 118-bus system show that the combination of the cutting-plane algorithm and the heuristic column-generation approach greatly reduces the total solution time of the two-stage robust unit commitment problem.
The increasing penetration of distributed generation (DG) power plants into distribution networks (DNs) causes various issues concerning, e.g., stability, protection equipment, and voltage regulation. Thus, the necessity to develop proper control techniques to allow power delivery to customers in compliance with power quality and reliability standards (PQR) has become a relevant issue in recent years. This paper proposes an optimized distributed control approach based on DN sensitivity analysis and on decentralized reactive/active power regulation capable of maintaining voltage levels within regulatory limits and to offer ancillary services to the DN, such as voltage regulation. At the same time, it tries to minimize DN active power losses and the reactive power exchanged with the DN by the DG units. The validation of the proposed control technique has been conducted through a several number of simulations on a real MV Italian distribution system.
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.
Active Network Management is a philosophy for the operationof distribution networks with high penetrations of renewable distributed generation. Technologies such as energy storage and flexible demand are now beginning to be included in Active Network Management (ANM) schemes. Optimizing the operation of these schemes requires consideration of inter-temporal linkages as well as network power flow effects. Network effects are included in optimal power flow (OPF) solutions but this only optimizes for a single point in time. Dynamic optimal power flow (DOPF) is an extension of OPF to cover multiple time periods. This paper reviews the generic formulation of DOPF before developing a framework for modeling energy technologies with inter-temporal characteristics in an ANM context. The framework includes the optimization of nonfirm connected generation, principles of access for nonfirm generators, energy storage, and flexible demand. Two objectives based on maximizing export and revenue are developed and a case study is used to illustrate the technique. Results show that DOPF is able to successfully schedule these energy technologies. DOPF schedules energy storage and flexible demand to reduce generator curtailment significantly in the case study. Finally, the role of DOPF in analyzing ANM schemes is discussed with reference to extending the optimization framework to include other technologies and objectives.
An affine arithmetic (AA) method is proposed in this paper to solve the optimal power flow (OPF) problem with uncertain generation sources. In the AA-based OPF problem, all the state and control variables are treated in affine form, comprising a center value and the corresponding noise magnitudes, to represent forecast, model error, and other sources of uncertainty without the need to assume a probability density function (pdf). The proposed AA-based OPF problem is used to determine the operating margins of the thermal generators in systems with uncertain wind and solar generation dispatch. The AA-based approach is benchmarked against Monte Carlo simulation (MCS) intervals in order to determine its effectiveness. The proposed technique is tested and demonstrated on the IEEE 30-bus system and also a real 1211-bus European system.
Microgrid is an aggregation of distributed generators (DGs) and energy storage systems (ESS) through corresponding power interface, such as synchronous generators, asynchronous generators and power electronic devices. Without the support from the public grid, the control and management of an autonomous microgrid is more complex due to its poor equivalent system inertia. To investigate microgrid dynamic stability, a small-signal model of a typical microgrid containing asynchronous generator based wind turbine, synchronous diesel generator, power electronic based energy storage and power network is proposed in this paper. The small-signal model of each of the subsystem is established respectively and then the global model is set up in a global reference axil frame. Eigenvalues distributions of the microgrid system under certain steady operating status are identified to indicate the damping of the oscillatory terms and its effect on system stability margin. Eigenvalues loci analysis is also presented which helps identifying the relationship among the dynamic stability, system configuration and operation status, such as the variation of intermittent generations and ESS with different control strategies. The results obtained from the model and eigenvalues analysis are verified through simulations and experiments on a study microgrid system.
The integration of non-dispatchable generation has increased the need for ancillary services to maintain frequency control performance, and the determination of adequacy to ensure reliability has become an important operating concern. This work determines the AGC gain of a single balancing authority interconnection in order for system frequency to comply with NERC control performance standards, ensuring secondary control adequacy. The particular case of wind expansion is considered, and a simulation of ERCOT is presented to evaluate the performance of the formulation.
Environmental factors, such as weather, trees, and animals, are major causes of power outages in electric utility distribution systems. Of these factors, wind and lightning have the most significant impacts. The objective of this paper is to investigate models to estimate wind and lighting related outages. Such estimation models hold the potential for lowering operational costs and reducing customer downtime. This paper proposes an ensemble learning approach based on a boosting algorithm, AdaBoost+, for estimation of weather-caused power outages. Effectiveness of the model is evaluated using actual data, which comprised of weather data and recorded outages for four cities of different sizes in Kansas. The proposed ensemble model is compared with previously presented regression, neural network, and mixture of experts models. The results clearly show that AdaBoost+ estimates outages with greater accuracy than the other models for all four data sets.
Investing in innovative Smart Grids (SGs) technology can reduce overall investments in distribution systems with unpredictable renewable energy sources (RES). Regulators should, therefore, decide what incentives are desirable and design a flexible economic regulatory framework able to encourage distribution system operators (DNOs) to decide for innovation in the SGs. An innovative method that can support the regulators in encouraging RES exploitation by determining the right incentives to favor investments in innovation in an electrical distribution network is proposed in this paper. The method allows stakeholders like regulators, distribution companies and developers evaluating the long-term economic effects of their decisions. The effectiveness of the proposed method, based on non-dominated sorting genetic algorithm (NSGA-II) and on multi-period optimal power flow (MP-OPF), is verified on an 84-bus network.
Recent work suggests that further integration of wind and solar resources into the power grid will increase the demand for regulation and load following. The purpose of this letter is to investigate an important aspect of using energy storages for providing regulation: The short-term energy restoration of such limited energy resources (LERs) coincides with load following operations and could therefore lead to higher load following requirements. We therefore analyze the impact of corresponding scheduling approaches on load following energy and capacity using stochastic simulations. In particular, we compare the load following impact of a basic control strategy proposed by the stakeholders of the Independent System Operator in California (CAISO) to a strategy that attempts to manage the energy level of LERs such that their impact on load following requirements is optimal. Our results show that such smart state of charge management could even reduce the demand for load following while satisfying the full regulation demand in the CAISO control region.
The second part of this two-paper series analyzes the primary frequency response (PFR) market design developed in its companion paper with several case studies. The simulations will show how the scheduling and pricing change depending on whether requirements for PFR are included as well as how the requirements are defined. We first perform simulations on the base case IEEE RTS and show differences in production costs, prices, and amount of PFR when incorporating the PFR constraints. We show how new market designs can affect other linked markets when performing co-optimization. We then test a system with a significant amount of wind power, which does not provide PFR or synchronous inertia, to see how the incorporation of PFR constraints may become more critical on future systems. We then show how pricing can reduce make-whole payments and ensure resources needed for reliability reasons are incentivized. Lastly, we show how resources thatimprove their capabilities can earn additional profit if the improvement is needed ensuring the incentives can work for innovation in PFR capabilities.
A newly proposed oscillation energy analysis method for power system low frequency oscillation is further developed. The oscillation energy flow in a generator and the energy dissipations of the field winding and the damper winding are studied. The oscillation energy flows into the field winding and the damper winding actually correspond to the electric energy transferred to the windings. The average powers of energy dissipation for oscillation modes with different frequencies are decoupled. For an individual mode, the energy dissipation of generator is computed using eigenvector and energy dissipation coefficient is obtained. The real-part of eigenvalue is negatively proportional to the sum of energy dissipation coefficients of all generators. It means the composite damping of the system comes from the energy dissipations of all generators. Especially in the single-machine system, the energy dissipation coefficient is equal to the damping torque coefficient. The consistency of oscillation energy analysis with damping torque analysis and modal analysis is verified. The energy dissipation leads to a new understanding of damping in power system and can be used for quantitative evaluation of generator damping in both offline and online applications.
This paper explores a technique denoted LASCA to solve large scale optimization problems with metaheuristics by reducing the search space dimension with autoassociative neural networks. The technique applies autoencoders as a reversible mapping between the original problem space and a reduced space. A metaheuristic then evolves in the latter, having its objective function assessed in the original space. The technique is illustrated with an application of an Evolutionary Particle Swarm Optimization (EPSO) algorithm to four benchmarking unconstrained optimization functions and to a wind-hydro constrained coordination problem. The new technique allows an improvement in the quality of the solutions attained.
"This paper develops the information fusion based pricing control (IFPC) scheme for the generation-side in the liberalized wholesale electricity market. The market mechanism is described by a feedback system, in which the independent system operator (ISO) interacts with generation companies (GenCOs) via proposing and responding to real-time nodal prices. The linear constrained quadratic optimal tracking problem is formulated; and the solution (nodal price sequence) is repeatedly computed by fusing the recently available information with backward manner and updated over the receding preview horizon. So that the total supply dynamically tracks the demand, the price is stationarized and each GenCO gains the maximal profit for the given price. Numeric results on a 118-bus network validate IFPC's effectiveness and flexibility on incorporating various line rating constraints as well as wind power fluctuation uncertainties. Comparative study demonstrates that IFPC outperforms the negotiated predictive dispatch but elapsing heavier computations. The irrational behavior analysis verifies the convergence to equilibrium; and speculation behavior of GenCOs in the real market is also discussed. Along with the rapid development of smart grid, IFPC is practically enabled by the information and communication technology (ICT); its significance and potentials for renewable energy sources (RES) integration would be realized and exploited."
This paper addresses the problem posed by complex, nonlinear controllers for power system load flows employing multi-terminal voltage source converter (VSC) HVDC systems. More realistic dc grid control strategies can thus be carefully considered in power flow analysis of ac/dc grids. Power flow methods for multi-terminal VSC-HVDC (MTDC) systems are analyzed for different types of dc voltage control techniques and the weaknesses of present methods are addressed. As distributed voltage control is likely to be adopted by practical dc grids, a new generalized algorithm is proposed to solve the power flow problems with various nonlinear voltage droops, and the method to incorporate this algorithm with ac power flow models is also developed. With five sets of voltage characteristics implemented, the proposed scheme is applied to a five-terminal test system and shows satisfactory performance. For a range of wind power variations and converter outages, post-contingency behaviors of the system under the five control scenarios are examined. The impact of these controls on the power flow solutions is assessed.
Power fluctuations generated by most oscillating wave energy converters may have a negative impact on the power quality of the local grid to which the wave farms will be connected. Hence, assessing their impact is an important step in the selection process of a suitable deployment location. However, site-specific grid impact assessment studies are relatively time-consuming and require a high level of detail on the local network. Both of these constraints mean that grid impact studies are usually not performed in the preliminary stages of the site selection process, despite the extremely negative consequences resulting from poor power quality. This paper details a comprehensive study based on a relatively typical wave farm design connected to networks of different strength levels. The study was performed using experimental electrical power time series of an oscillating water column (OWC) device generated under the framework of the European FP7 project “CORES”. Simulations were performed using DIgSILENT power system simulator “PowerFactory”.
Distribution system operators are often challenged by voltage regulation problems, energy losses, and network capacity problems. This paper analyses a real-life 3.9-MVA distribution network in Gujarat State, India. Distributed generation from renewable energy sources like wind and solar, at optimal locations on distribution feeders, may enable energy loss reduction and voltage profile improvement. A methodology is developed and presented for deciding the appropriate location of these embedded renewable generators. Simulations are performed to calculate different scenarios, and the final analysis reveals that the low voltage problem has totally been eliminated on all of the nodes of the distribution network. Complimentary, significant energy loss reductions are also achieved in the distribution, and the network reserve capacity has also increased.
A dynamic positioning (DP) system on a diesel-electric ship applies electric power to keep the positioning and heading of the ship subject to dynamic disturbances due to the winds, waves and other external forces using electric thrusters. Vice versa, position and heading errors can be allowed in order to implement energy storage in the kinetic and potential energy of the ship motion using the DP control system to convert between mechanical and electrical power. New simple formulas are derived in order to relate the dynamic energy storage capacity to the maximum allowed ship position deviation, as a function of the frequency of the requested dynamic energy storage. The benefits of DP dynamic energy storage are found to be reduced diesel-generator maintenance need, reduced fuel consumption and emissions, reduced risk for blackout, and increased operational flexibility allowing power-consuming operations such as drilling and lifting to be safely prioritized over DP for short periods of time.
This letter proposes a new merit order for the dispatch of stochastic production in forward markets (e.g., day-ahead markets). The proposed merit order considers not only the marginal cost of the stochastic generating unit, which is often very low or zero, but also the projected cost of balancing its energy deviations during the real-time operation of the power system. We show, through an illustrative example, that the proposedmerit order leads to increased market efficiency as the penetration of stochastic generation in the electricity market grows.
Rapidly increasing the penetration level of renewable energies has imposed new challenges to the operation of power systems. Inability or inadequacy of these resources in providing inertial and primary frequency responses is one of the important challenges. In this paper, this issue is addressed within the framework of security-constrained unit commitment (SCUC) by adding new constraints representing the system frequency response. A modified system frequency response model is first derived and used to find analytical representation of system minimum frequency in thermal-dominant multi-machine systems. Then, an effective piecewise linearization (PWL) technique is employed to linearize the nonlinear function representing the minimum system frequency, facilitating its integration in the SCUC problem. The problem is formulated as a mixed-integer linear programming (MILP) problem which is solved efficiently by available commercial solvers. The results indicate that the proposed method can be utilized to integrate renewable resources into power systems without violating system frequency limits.
We consider a cluster of interconnected price-responsive demands (e.g., an industrial compound or a university campus) that can be supplied through the main grid and a stochastic distributed energy resource (DER), e.g., a wind plant. Additionally, the cluster of demands owns an energy storage facility. An energy management system (EMS) coordinates the price-responsive demands within the cluster and provides the interface for energy trading between the demands and the suppliers, main grid and DER. The DER and the cluster of demands have a contractual agreement based on a take-or-pay contract. Within this context, we propose an energy management algorithm that allows the cluster of demands to buy, store, and sell energy at suitable times. This algorithm results in maximum utility for the demands. The uncertainty related to both the production level of the DER and the price of the energy obtained from/sold to the main grid is modeled using robust optimization (RO) techniques. Smart grid (SG) technology is used to realize 2-way communication between the EMS and the main grid, and between the EMS and the DER. Communication takes place on an hourly basis. A realistic case study is used to demonstrate the advantages of both the coordination provided by the EMS through the proposed algorithm and the use of SG technology.
This paper proposes a day-ahead stochastic scheduling model in electricity markets. The model considers hourly forecast errors of system loads and variable renewable sources as well as random outages of power system components. A chance-constrained stochastic programming formulation with economic and reliability metrics is presented for the day-ahead scheduling. Reserve requirements and line flow limits are formulated as chance constraints in which power system reliability requirements are to be satisfied with a presumed level of high probability. The chance-constrained stochastic programming formulation is converted into a linear deterministic problem and a decomposition-based method is utilized to solve the day-ahead scheduling problem. Numerical tests are performed and the results are analyzed for a modified 31-bus system and an IEEE 118-bus system. The results show the viability of the proposed formulation for the day-ahead stochastic scheduling. Comparative evaluations of the proposed chance-constrained method and the Monte Carlo simulation (MCS) method are presented in the paper.
"The method proposed in the procedure of the first step of the spectral clustering controlled islanding (SCCI) is actually equivalent to the application of slow coherency. The slow coherency method is very useful for ofiline analysis. However, the following two questions must be answered before slow coherency can be applied to identify suitable generator groups: 1) have the generators lost synchronism, or will they, i.e., is the separation of generator groups necessary? 2) How many generator groups should be formed? This means that there are distinct drawbacks when applying slow coherency online; however, the method can still be adapted to this purpose to a certain extent [1]. We think a better way is using an online algorithm to replace the slow coherency method [2], [3]. The drawbacks and limitations of the first step of the SCCI have been discussed in Section III-A."
We present a stochastic unit commitment model for assessing the impacts of the large-scale integration of renewable energy sources and deferrable demand in power systems in terms of reserve requirements. We analyze three demand response paradigms for assessing the benefits of demand flexibility: the centralized co-optimization of generation and demand by the system operator, demand bids and the coupling of renewable resources with deferrable loads. We motivate coupling as an alternative for overcoming the drawbacks of the two alternative demand response options and we present a dynamic programming algorithm for coordinating deferrable demand with renewable supply. We present simulation results for a model of the Western Electricity Coordinating Council.
The integration of non-dispatchable generation has led to a change in the dynamics of primary frequency response. This change, mostly driven by the limited contribution of non-dispatchable generation to both total system inertia and governor response, may deteriorate the performance of current practices for determining primary reserves to the extend that real time operation reliability may no longer be ensured. This work establishes sufficient conditions for ensuring primary response adequacy through ex-ante dispatch instructions. A simplified dynamic model of primary frequency response (including system inertia, system governors' ramp rates, and dead bands) is developed to formulate a constraint suitable for an OPF framework. A simulation of ERCOT is presented to test the proposed formulation.
This paper proposes a multi-year multi-objective planning algorithm for enabling distribution networks to accommodate high penetrations of plug-in electric vehicles (PEVs) in conjunction with renewable distributed generation (DG). The proposed algorithm includes consideration of uncertainties and will help local distribution companies (LDC) better assess the expected impacts of PEVs on their networks and on proposed renewable DG connections. The goal of the proposed algorithm is to minimize greenhouse gas emissions and system costs during the planning horizon. An approach based on a non-dominated sorting genetic algorithm (NDSGA) is utilized to solve the planning problem of determining the optimal level of PEV penetration as well as the location, size, and year of installation of renewable DG units. The planning problem is defined in terms of multi-objective mixed integer nonlinear programming. The outcomes of the planning problem represent the Pareto frontier, which describes the optimal system solutions, from which the LDC can choose the system operating point, based on its preferences.
Designing future capacity mixes with adequate flexibility requires capturing operating constraints through an embedded unit commitment approximation. Despite significant recent improvements, such simulations still require significant computation times. Here we propose a method, based on clustering units, for approximate unit commitment with dramatic improvements in solution time. This method speeds computation by aggregating similar but non-identical units. This replaces large numbers of binary commitment variables with fewer integers while still capturing individual unit decisions and constraints. We demonstrate the trade-off between accuracy and run-time for different levels of aggregation. A numeric example using an ERCOT-based 205-unit system illustrates that careful aggregation introduces errors