Presence Of Wind Power Research Articles

Load frequency predictive control for power systems concerning wind turbine and communication delay

AbstractThis article addresses a model predictive control (MPC) technique for load frequency control (LFC) system in the presence of wind power, communication delay, and denial‐of‐service (DoS) attack. In this article, communication delay is incorporated into a single area control error transmission for simplicity, wind power and load disturbance are regarded as Lipschitz nonlinear terms, as for the randomly occurring DoS attack, it is modeled as Bernoulli processes with known conditional probability. Thinking all these adverse factors to stability and the limitation of input constraint synthetically, the stability of LFC system can be guaranteed by delay‐dependent Lyapunov function lemma and a state feedback MPC controller is designed to solve the LFC problems by minimizing the infinite‐horizon objective function. Although some scholars have studied the performance degradation and instability of LFC system caused by cyber attack and/or communication delay and some very nice results have been addressed, limited works have considered the MPC approach to deal with both the problems of cyber attack and communication delay which explicitly considers the physical constraints. In addition, the delay‐dependent Lyapunov function is adopted to deal with the problem of communication delay, which results in less conservatism of the presented method. Finally, the optimization problem with input constraint is solved and proven to be recursive feasibility, and the closed‐loop system turns out to be stable. The reasonability and validity of the provided strategy is verified through several groups of simulation experiments. It illustrates that the proposed control method can keep the system frequency steady in the standard range in spite of various attack conditions.

A demand side response scheme for enhancing power system security in the presence of wind power

One of the major concerns associated with the wind power integration is its anti-peaking characteristic which increases the required ramping of the conventional generators and potentially threatens power system security. Recently, demand side response has become one of the likely successful solutions that can be employed in overcoming the negative impact of the anti-peaking characteristic of the daily net load curve. This paper presents a demand side response framework in which the load shifting strategy is effectively applied to improve the daily load profile of the system in the presence of wind power. The size of the load to be shifted is determined such that the system security in terms of voltage magnitudes and voltage stability margins is preserved for the normal state and prospective emergency states. To stimulate the demand side response candidates to shift their load and support the utilities to realize a particular daily load shape, an incentive reimbursement pattern based on the participants’ submitted sizes has been proposed. The mathematical formulation of the problem is stated as a nonlinear mixed integer programming problem. The problem is solved using Benders decomposition technique where the intact problem is split into a master problem and several smaller nonlinear problems solved individually by the conventional methods. The performance of proposed approach has been evaluated by its application on the IEEE 57- and 118-bus systems.

Optimal Allocation of Static Var Compensators in Electric Power Systems

In the current age, power systems contain many modern elements, one example being Flexible AC Transmission System (FACTS) devices, which play an important role in enhancing the static and dynamic performance of the systems. However, due to the high costs of FACTS devices, the location, type, and value of the reactive power of these devices must be optimized to maximize their resulting benefits. In this paper, the problem of optimal power flow for the minimization of power losses is considered for a power system with or without a FACTS controller, such as a Static Var Compensator (SVC) device The impact of location and SVC reactive power values on power system losses are considered in power systems with and without the presence of wind power. Furthermore, constant and variable load are considered. The mentioned investigation is realized on both IEEE 9 and IEEE 30 test bus systems. Optimal SVC allocation are performed in program GAMS using CONOPT solver. For constant load data, the obtained results of an optimal SVC allocation and the minimal value of power losses are compared with known solutions from the literature. It is shown that the CONOPT solver is useful for finding the optimal location of SVC devices in a power system with or without the presence of wind energy. The comparison of results obtained using CONOPT solver and four metaheuristic method for minimization of power system losses are also investigated and presented.

Optimal distribution systems operation in the presence of wind power by coordinating network reconfiguration and demand response

This paper discusses the optimal distribution system operation in the presence of wind power by coordinating network reconfiguration and demand response method. Under the specific demand response program, the paper proposes an optimal control method of coordinating thermostatically controlled load (TCL) and battery energy storage (BES). The TCL of the demand response can be controlled in the premise of not hurting customer comfort. The risk of exceeding thermal customer comfort limit is measured by conditional value-at-risk (CVaR) index. Under the requirement of timely wind power balancing, the hourly reconfiguration plan is adopted. Because of the stochastic characteristics of wind power, this is a risk-constrained optimization problem with the objective of minimizing operation cost. Due to the time interdependence of wind power, a Markov Decision Process (MDP) formulation in combination with dynamic programming is adopted to solve the multi-stage stochastic optimization problem. The results show that coordinating reconfiguration and demand response method could significantly contribute to compensating for the forecast error of wind power, therefore, bringing considerable economic savings on operation cost in real-time electricity market.

Electricity price driven active distribution network planning considering uncertain wind power and electricity price

This paper introduces a new computational framework to account for uncertainties in active distribution network (ADN) planning in the presence of wind power, energy storage and electricity price. A key challenge when dealing with wind power and electricity price is the inaccuracy of forecasting methods because of the uncertainty of distributed power such as wind power, and the different demand response providers. We propose a risk aversion planning model for ADN with respect to uncertainties of wind power and electricity price. The proposed model needs neither sampling different scenario nor to calculate multi-objective while guaranteeing a quantifiable risk level by a conditional expectation. Then, an electricity price driven ADN planning has been discussed, and the problem of selecting custom electricity contracts and finding the optimal procurement strategy of meeting contract obligations under spot price uncertainty is considered. Finally, simulation studies are carried out for several IEEE test systems to validate the effectiveness of the proposed model in obtaining the optimal trade-off solution between profit and risk.

Stochastic risk-constrained scheduling of smart energy hub in the presence of wind power and demand response

Energy hub represents a coupling among different energy networks and plays an undeniable role as the interface between energy producers and consumers. Therefore, energy hub creates a great opportunity to achieve more efficient and reliable energy systems. Considering rapid progress of wind energy in power system generation, it is crucial to treat with this resource as a part of future energy infrastructures. This paper attempts to develop a general stochastic optimization and modeling framework for solving the wind integrated smart energy hub (SEH) scheduling problem. The electrical and thermal loads of the energy hub have been served in presence of demand response (DR) programs. In the proposed DR program, the amount of responsive load can change during operation time slots of SEH. Stochastic programming method is used to deal with the impact of uncertainties related to wind power generation and load forecasting on the scheduling problem of SEH. The Monte Carlo sampling approach is used to generate the wind power and the customer demand scenarios. The scenario reduction method is also introduced to make this problem tractable. Furthermore, an appropriate risk measurement, the conditional value-at-risk (CVaR) methodology, is incorporated with the model to mitigate the risk of expected cost due to market price and load forecast volatilities.

A bi-layer optimization based temporal and spatial scheduling for large-scale electric vehicles

Electric vehicle (EV) is a promising, environmental friendly technique for its potential to reduce the using of fossil fuels. Massive EVs pose both opportunities and challenges for power systems, especially with the growing amount of wind-power integration. This paper investigates the problem of collaborative optimization scheduling of generators, EVs and wind power. A novel bi-layer optimization of transmission and distribution system is proposed to solve the scheduling problem of EVs charging and discharging load from respective time and space domain in the presence of wind-power. The upper layer optimization in transmission grid coordinates EVs with thermal generators, base load, with the consideration of wind power, to optimize load periods of EVs in the time domain. The lower layer optimization in distribution grid then spatially schedules the location of EVs load. Based on a power system benchmark with a 10-unit transmission grid and an IEEE 33-bus distribution grid, the performance of the proposed bi-layer optimization strategy is evaluated. The impacts of electricity price profile, EVs penetration and EVs load location are analyzed. Simulation results show that the proposed bi-layer optimization strategy can accommodate wind power and improve both the economics of grid operation and benefits of EV users by scheduling EVs charging and discharging temporally and spatially. Also, the results have shown that the location of EVs charging and discharging load is critical for the distribution network planning.

Market clearing with probabilistic spinning reserve considering wind uncertainty and electric vehicles

Summary One of the aspects of smart/modern grids is to provide conditions for greater penetration of renewable energy resources such as wind power generation units to the grid. Uncertainties associated with the production of these resources require higher amount of reserve to ensure system security and reliability. On the other hand, by development and deployment of electric vehicles in the near future, it is expected that large fleets of electric vehicles will constitute a significant share of electricity demand. These vehicles are equipped with rechargeable and dischargeable batteries that enable them to provide ancillary services by coordination with the system operator and their participation in energy and reserve markets. In this paper, a market clearing model, which also quantifies optimal spinning reserve, at the presence of wind power and participation of electric vehicles in the energy and reserve markets is proposed. Test results for the IEEE-Reliability Test System (RTS) illustrate that electric vehicles participation in these markets can counterbalance the adverse effects of increasing power demand because of their charging while being able to manage renewable resources uncertainties and hence improve system reliability. Copyright © 2015 John Wiley & Sons, Ltd.

Optimal power flow in the presence of wind power using modified cuckoo search

This study proposes a solution to the optimal power flow (OPF) in power systems incorporating wind power. The wind generated electricity's cost modelling is presented which owes to the stochastic nature of wind speed based on the Weibull probability density function. The wind associated costs comprise of (i) reserve cost arising out of overestimation of wind farm output and (ii) penalty cost for not using total available wind power which are included in the conventional OPF. Furthermore, the Q-V formulation of induction generator is deduced which is incorporated through a modification in the conventional load flow solution. A modified cuckoo search optimisation technique is employed and compared with modified particle swarm optimisation for solving OPF with wind power involved. A modified IEEE 30-bus and IEEE 57-bus systems are used as test systems and it is observed that the cuckoo search performs better than particle swarm optimisation.

Optimal generation dispatch incorporating wind power and responsive loads: A chance-constrained framework

This paper presents a novel probabilistic framework for solving the security constrained generation dispatch problem in the presence of wind power and responsive loads. The proposed framework is capable of decreasing the probability or preventing any constraint violations in real time for different combinations of uncertain variables while minimizing the desired objective functions. The presented framework is formulated as a chance constrained problem using the point estimate method to calculate the probabilistic characteristics of output variables. The objective functions which are studied in this work are total operation costs, emissions, and security indexes. Also a probabilistic multi-objective framework is developed to optimize all objective functions concurrently. To study the effect of responsive loads, two different cases are studied neglecting and regarding demand response resources. The proposed method is studied on the IEEE 30-bus system and the results are presented and compared with results obtained from the deterministic generation dispatch framework.

A Dynamic Mechanism for Wholesale Energy Market: Stability and Robustness

In this paper, a dynamic model of the wholesale energy market that captures the effect of uncertainties of renewable energy sources and real-time pricing with demand response is derived. Beginning with a framework that includes real-time pricing as an underlying state, an attempt is made in this model to capture the dynamic interactions between generation, demand, locational marginal price (LMP), and congestion price near the equilibrium of the optimal dispatch. Conditions under which stability of the market can be guaranteed are derived. Modeling the effect of renewable energy resources (RERs) and demand response as perturbations, robust stability of the energy market model in the presence of such perturbations is discussed. Numerical studies of an IEEE 30-bus are reported to illustrate the effect of transmission lines constraints on the wholesale market stability in the presence of wind power.

Impact of Wind Power on the Gaseous Pollutants Emissions in Electrical Systems Operating under Constraints

An original nonsequential Monte Carlo simulation tool is developed. It permits to compute the optimal dispatch of classical thermal generation in order to minimize pollutants emissions in presence of wind power and under operating constraints.

Non-sequential Monte Carlo simulation tool in order to minimize gaseous pollutants emissions in presence of fluctuating wind power

In this paper, an original non-sequential Monte Carlo simulation tool is developed. This tool permits to compute the optimal dispatch of classical (coal, oil, etc.) thermal generation in order to minimize polluting gases (NOx, CO2, etc.) emissions in presence of wind power and under constraints. These constraints include, e.g., the maximal generation cost or the ability of the electrical system to cover the load, … In comparison with existing analytical tools that are based on restrictive hypotheses when it comes to wind power modelling (generally represented by a single entirely correlated global wind park), unexpected outages of conventional parks or fluctuating representation of the load, the use of Monte Carlo simulation allows to remove all those limitations. Indeed, thanks to the developed tool, the optimal dispatch of classical thermal generation can be reached under several load conditions. Well-known reliability indices can also be computed and, moreover, following the wind speed sampling that is used, entirely correlated, independent or more accurate correlation level between wind parks can be considered. Finally, it is thought that the proposed solution can be a useful tool for electrical system operators in order to dispatch the polluting thermal units under cost, reliability, emissions, fluctuating wind power and unexpected outages constraints.

Development of a Monte Carlo simulation tool in order to minimize polluting gases emissions in presence of wind power under cost and load covering constraints

Development of a Monte Carlo simulation tool in order to minimize polluting gases emissions in presence of wind power under cost and load covering constraints

Integration of large-scale wind farm projects including system reliability analysis

This study intends to develop a comprehensive procedure for evaluating locational value of a wind farm project incorporating reduction in transmission system losses, load delivery point interruption cost and operating cost of generating units. The energy extracted from the wind farm in normal operation condition is considered to replace the energy from fossil-fueled conventional units. In addition, composite system reliability analysis in the presence of wind power is carried out to evaluate total costs associated with curtailed energy at different load points as well as generation of generating units in contingency conditions. System reliability analysis related to large-scale wind farms, along with operating cost and transmission losses analysis, can assist policy makers to prioritise wind farm projects based on the total benefits of wind power including reliability benefits and savings in fossil-fueled energy sources.

Analysis of Pricing and Volumes in Selective Capacity Markets

The requirements concerning reliability in power supply are high. This paper treats the issue of system adequacy, i.e., there should be enough installed capacity to meet the load with an acceptable reliability. The challenge in a liberalized market is that the utilization time of rarely used peak units is so low that they require extremely high prices in order to be profitable. This has led to different methods, including the creation of capacity markets. The aim of this paper is to analyze the connection between peak prices, system reliability, required amount of subsidized capacity, i.e., the volume of the capacity market. In the studied market setup, only some selected units, so-called “reserve plants”, receive a capacity payment. The paper also provides an example concerning change of capacity market volume in presence of wind power.

A Reliability Model of Large Wind Farms for Power System Adequacy Studies

In this paper, an analytical approach for the reliability modeling of large wind farms is presented. A systematic method based on frequency and duration approach is utilized to model a wind farm like a multistate conventional unit, where the probability, frequency of occurrence, and departure rate of each state can be obtained using the regional wind regime of wind farm and wind turbine characteristics. The proposed method is capable of finding both annual frequency and average time of load curtailment analytically in the presence of wind power. A wind farm in the northern region of Iran with the wind speed registration of one year is studied in this paper. To accommodate time-varying patterns of wind speed, reliability analysis considering seasonal patterns of wind speed is also carried out. The results show that seasonal patterns significantly affect the reliability indexes. A reliability analysis is also performed using a load profile similar to that of Iran power network. It will be shown that the coincidence of high-load-demand and high-wind-speed periods makes the North Iran wind farm projects highly attractive from a reliability point of view.

Impacts of wind power generation and CO 2 emission constraints on the future choice of fuels and technologies in the power sector of Vietnam

This paper examines the impacts of wind power generation on the future choice of fuels and technologies in the power sector of Vietnam. The study covers a time frame of 20 yr from 2005 to 2025 and the MARKAL model has been chosen to be adaptable to this specific task. The results of the study show that on a simple cost base, power generated from wind is not yet competitive with that of fossil fuel-based power plants. In order to make wind energy competitive, either carbon tax or an emission reduction target on the system must be imposed. The presence of wind power would affect not only the change in generation mix from coal-based power plants to wind turbines but also an increase in the capacity of other technologies which emit less carbon dioxide. It thus helps reduce fossil fuel requirement and consequently enhances energy security for the country. The study also shows that wind turbine in Vietnam could be a potential CDM project for annex I party countries.