Top PDF Time-Varying Optimization and Its Application to Power System Operation

Time-Varying Optimization and Its Application to Power System Operation

Time-Varying Optimization and Its Application to Power System Operation

As mentioned in Chapter 1, in future smart grids, the fluctuations and uncertainties introduced by large penetration of renewable generation make the operation of smart grids challenging, while controllable devices provide diverse control capabilities that can be employed to overcome the challenges. Extensive real-time measurement data will also become available by smart meters and other advanced measurement equipment. When time-varying optimization tools are applied for real-time power system operation, these factors should be taken into consideration to tailor the algorithms so that the structures and properties of smart grids can be utilized. We now present the formulation of the time-varying optimal power flow problem. Suppose we have a single-phase power network 1 with a topology represented by a connected graph (N + , E) where N + : = { 0 } ∪ N , N = { 1, 2, . . . , n } and E ⊆ N + × N + . Bus 0 will be the slack bus, and the phase angle of its voltage will be the reference and taken as zero. Let t ∈ [ 0, T ] be an arbitrary time instant. We use v( t ) ∈ R N + and θ ( t ) ∈ R N to denote respectively the vector of voltage magnitudes and the vector of voltage phase angles, so that V i ( t ) : = v i ( t ) e jθ i (t) is the voltage
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Overview of current development in electrical energy storage technologies and the application potential in power system operation

Overview of current development in electrical energy storage technologies and the application potential in power system operation

TES encompasses a variety of technologies that store available heat energy using different approaches in insulated repositories [6,26]. A TES system normally consists of a storage medium in a reservoir/tank, a packaged chiller or built-up refrigeration system, piping, pump(s), and controls. Based on the range of operating temperature, TES can be classified into two groups: low-tempera- ture TES (consisting of aquiferous low-temperature TES and cryo- genic energy storage) and high- temperature TES (including latent (fusion) heat TES, sensible heat TES and concrete thermal storage) [4,163–166]. Aquiferous low-temperature TES normally uses water cooled/iced and reheating processes, which is more suitable for peak shaving and industrial cooling loads [4]. Cryo- genic energy storage employs a cryogen (such as liquid nitrogen or liquid air) to achieve the electrical and thermal energy conver- sion. For instance, Liquid Air Energy Storage (LAES) is attracting attention due to the high expansion ratio from the liquid state to the gaseous state and the high power densities of liquid air com- pared to that of gaseous state of air. Latent heat TES employs Phase Change Materials (PCMs) as the storage media and uses the energy absorption or emission in liquid-solid transition of these PCMs at constant temperature. Concrete thermal storage utilizes concrete or castable ceramics to store heat energy, normally supported by synthetic oil as a heat transfer fluid. The above TES technologies have different features with various applications. For instance, latent heat storage can provide a relatively high storage density with a small dimension reservoir, thus the use of this technology in buildings receives attention [21]. In addition, cryogenic energy storage is expected to be used for future grid power management. The TES system can store large quantities of energy without any major hazards and its daily self-discharge loss is small (0.05–1%); the reservoir offers good energy density and specific energy (80–500 W h/L, 80–250 W h/kg) and the system is economically viable with relatively low capital cost (3–60 $/kW h) [4,10,166– 168]. However, the cycle efficiency of TES systems is normally low (30–60%) [4]. TES has been used in a wide spectrum of Fig. 12. Topology of hydrogen storage and fuel cell.
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Study of Kalman Filtering Techniques and Its Application for Optimal Power System Operation

Study of Kalman Filtering Techniques and Its Application for Optimal Power System Operation

In a mixture of the situations described above, the power system in Fig. 1 can be indicated by total power loss shown in Fig. 7 with concern of only energy years and consumptions. The division between buses and in Fig. 7 can become an arbitrary division in Fig. 1. When the product routine of Fig. 2 is regarded, Fig. 8 reveals the difference of power loss corresponding to the dimension DG at load-concentration-bus 10 and the amount of fill intake at bus 21 in Fig. 1.

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Design and application of Genetic Algorithm for Fuel Cost Minimization to ensure secure operation of Power System

Design and application of Genetic Algorithm for Fuel Cost Minimization to ensure secure operation of Power System

Optimal power flow (OPF) has been widely used in power system operation and planning. In deregulated environment of power sector, it is of increasing importance, for determination of electricity prices and also for congestion management.OPF is a computationally intensive tool when analyzing many generation plants, transmission lines and demands. Finally the engineering constraints and economic objectives for system operations are combined by formulating and solving the optimal power flow problem.OPF is used in economic analysis of the power system as well. Optimal Power Flow (OPF) is a method to find steady state operation point which minimizes generation cost, loss etc. or maximizes social welfare, loadability etc while maintaining an acceptable system performance in terms of limits on generator’s real and reactive powers, line flow limits, output of various compensating devices etc. The OPF problem may also have the formulation of active power generation dispatch (Economic Dispatch Problem, EDP) and reactive power generation dispatch. The main purpose of the EDP is to determine the generation schedule of the electrical energy system that minimizes the total generation and operation cost and does not violate any of the system operating constraints such as line overloading, bus voltage profiles and deviations. On the other hand, the objective of reactive power dispatch is to minimize the active power transmission losses in an electrical system while satisfying all the system operating constraints .The objective function of the OPF can take different forms other than minimizing the generation cost and the losses in the transmission system. The OPF can be used to obtain the settings of the control variables under the steady-state functions of the power system. These control variables may include generator control and transmission system control variables. For generators, the control variable can be generator MW output. For the transmission system, the control variable can be bus voltages of the generator buses, the tap ratio or phase shift angle for transformers, settings of switched shunt or flexible ac transmission system (FACTS) devices.
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Automatic Generation Control in Three-Area Power System Operation by using “Particle Swarm Optimization Technique”

Automatic Generation Control in Three-Area Power System Operation by using “Particle Swarm Optimization Technique”

In this paper, an evolutionary computing approach for determining the optimal values for the proportional integral controller parameters of automatic generation control (AGC). Three area thermal-thermal unequal power systems using the particle swarm optimization technique is presented. The AGC loop controls real power & frequency. Due to rising and falling power demand, the real power balance is harmed; hence frequency gets deviated from nominal value. This necessitates designing of an accurate and fast controller to maintain the system parameters at nominal value. The main purpose of system generation control is to balance the system generation against the load and losses so that the desired frequency and power interchange between neighboring systems are maintained. This work demonstrates the application of PSO method to search efficiently optimal PI controller parameters of AGC. The proposed method had superior features like, stable convergence characteristics, easy implementation and good computational efficiency. The simulation results demonstrate the effectiveness of the designed system in terms of reduced settling time, overshoot and oscillations.
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A New Particle Swarm Optimization Algorithm to Hierarchy Multi-objective Optimization Problems and Its Application in Optimal Operation of Hydropower Stations

A New Particle Swarm Optimization Algorithm to Hierarchy Multi-objective Optimization Problems and Its Application in Optimal Operation of Hydropower Stations

The hydropower stations are important economic and kinetic energy unit in power system, which, on the one hand, can gain capacity benefits through peak load regulation, frequency modulation and undertaking spinning reserve capacity; and, on the other hand, can gain electricity quantity benefit by replacing the thermal power stations [1]. The traditional economic benefits of hydropower systems generally only consider the generation benefit, and rarely include dynamic benefits, such as peak-energy capacity benefits, spinning reserve capacity benefits, etc [2], which is disadvantageous to optimize energy structure and resource allocation, and economically dispatch and safety operate of grid. With the development of power market in our country, the capacity benefits of hydropower system have played a more and more important role. It's an important issue worthy of deep study to change the optimal scheduling model which includes capacity and electricity quantity benefits from the traditional scheduling model which
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Modified particle swarm optimization for economic emission load dispatch of power system operation

Modified particle swarm optimization for economic emission load dispatch of power system operation

Abstract: This paper proposes a modified particle swarm optimization considering time-varying acceleration coefficients for the economic-emission load dispatch (EELD) problem. The new adaptive parameter is introduced to update the particle movements through the modification of the velocity equation of the classical particle swarm optimization (PSO) algorithm. The idea is to enhance the performance and robustness of classical PSO. The price penalty factor method is used to transform the multiobjective EELD problem into a single-objective problem. Then the weighted sum method is applied for finding the Pareto front solution. The best compromise solution for this problem is determined based on the fuzzy ranking approach. The IEEE 30-bus system has been used to validate the effectiveness of the proposed algorithm. It was found that the proposed algorithm can provide better results in terms of best fuel cost, best emissions, convergence characteristics, and robustness compared to the reported results using other optimization algorithms.
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Application of Genetic Algorithm in Power System Optimization with Multi-type FACTS

Application of Genetic Algorithm in Power System Optimization with Multi-type FACTS

The deregulated market model is progressively taking place in power industries all over the world. With such liberalization, a third-party access is provided by separating the generation & transmission of power, and the consumers are empowered to pick from the private utilities as per their own choices for the electricity-buying purpose. However, in order to grab a larger customer-base and survive in the market, these suppliers might cause the commercial rivalry to take an unhealthy turn and resort to the unplanned power exchange through transmission lines. Parallel to restructuring of market, the industry also faces the challenge of satisfying the ever-increasing growth of load demand. All these force to operate some of the transmission lines close to their thermal limits which could result in being overstressed and eventually in congestion. On the other hand, the up gradation of power network by building new infrastructures like transmission lines, substations etc, most of the time, turns out to be not practical due to political, economical & environmental constraints.
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Dynamic Optimization of a Subcritical Steam Power Plant under Time-Varying Power Load

Dynamic Optimization of a Subcritical Steam Power Plant under Time-Varying Power Load

options for efficiency optimization. Model-based, system-scale dynamic simulation and optimization are useful tools in this effort, and the subject of the work presented here. In prior work, a dynamic model validated against steady-state data from a 605 MW subcritical power plant was presented. This power plant model is used as a test-bed for dynamic simulations, in which the coal load is regulated to satisfy a varying power demand. Plant-level control regulates plant load to match an anticipated trajectory of the power demand. The efficiency of the power plant operating at varying load is optimized through a supervisory control architecture that performs set point optimization on the regulatory controllers. Dynamic optimization problems are formulated to search for optimal time-varying input trajectories that satisfy operability and safety constraints during the transition between plant states. An improvement in time-averaged efficiency of up to 1.8% points is shown feasible with corresponding savings in coal consumption of 184.8 tons/day and carbon footprint decrease of 0.035 kg/kWh.
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Cascade Hydroelectric Power System Model and its Application to an Optimal Dispatch Design

Cascade Hydroelectric Power System Model and its Application to an Optimal Dispatch Design

The last years there has been an increase of renewable-based resource penetration into the electric grid that leads to the gradual de-carbonization of the energy supply sector [1]. Apart from a 40% reduction in greenhouse gas emissions and a 30% improvement in Energy Efficiency, the 2030 EU-wide targets set a minimum of at least 27% share of renewable energy resources in the energy consumption. However, renewable resources have unique characteristics and their integration raises several challenges into power systems operation. The variability of the renewable-based generation requires that the system has enough ramping capability to follow the net load variations in different time frames, ranging from seconds to hours. Furthermore, there is an increase in the need for balanc- ing services, larger reserves for the frequency control, as well as better ramping capability. There are several technologies available that may satisfy these needs. However, they are often associated with either additional cost or partial loss of the energy output. In this regard, appropriate use of existing resources, such as hydroelectric power systems, without any extra cost are worth to be investigated. Hydroelectric power systems are fitting candidates since they have good ramping capability and energy storage possibility in form of hydro reservoirs. Thus, they may be used to smooth the output of renewable-based generation and resolve any potential prob- lems caused to the grid due to solar output variability and intermittency.
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Global, non-parametric, non-iterative optimization of time-averaged quantities under small, time-varying forcing: An application to a thermal convection field

Global, non-parametric, non-iterative optimization of time-averaged quantities under small, time-varying forcing: An application to a thermal convection field

That is the reason why non-parametric methods are applied to solve the types of op- timization. However, very little work is available for optimizing time-averaged quantities because of the difficulty of unsteady optimization [28–34]. Srinath and Mittal [28] and Fang and Li [33] applied a continuous adjoint method to obtain the optimal shape of airfoils for maximizing time-averaged lift coefficient or minimizing time-averaged drag coefficient, in conjunction with iterative methods, such as descent or conjugate gradient methods. Simi- larly, iterative algorithms were utilized to maximize time-averaged system energy efficiency [29], and to maximize time-averaged transmission rate [32], and so on.
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Time Varying Acceleration Coefficients with 
		Dominant Social Component 
		particle swarm optimization for interconnected power system

Time Varying Acceleration Coefficients with Dominant Social Component particle swarm optimization for interconnected power system

system that significantly improved the transient response for corresponding load perturbations. C.T.Pan and C.M.Liaw [4] presented an adaptive controller for load frequency control. Aleksandar M.Stankovic et al ., [5] presented a physically motivated augmentation of the standard integral controller in LFC. Janardan Nanda et al ., [6] investigated continuous discrete mode interconnected hydro thermal system using conventional integral and proportional integral controllers (system works in continuous mode, controller works in discrete mode). Seyed Abbas Taher and Reza Hematti [7] presented quantitative feedback theory for load frequency control. I. A. Chidambaram and B. Paramasivam [8] designed a Genetic Algorithm (GA) based controllers with Integral Square Error criterion for the decentralized load frequency control of two area interconnected thermal reheat power systems with and without Redox Flow Batteries (RFB) considering Thyristor Controlled Phase Shifter (TCPS) in the Tie-line. H. A. Shayanfar et al. , [9] described a Multi Input Multi Output (MIMO) design technique based on the Characteristic Loci (CL) method applied to load frequency control of interconnected power system. Wen Tan [10] discussed a unified PID tuning method for load frequency control of interconnected power system. Gayadhar Panda et al ., [11] presented a modified genetic algorithm based optimal selection of integral gain and frequency bias constant for load frequency control of multi area interconnected power system. K. P. Singh Parmar et al. , [12] presented output feedback controller design for two area interconnected power system. Armin Ebrahimi Milani and Babak Mozafari [13] presented new genetic algorithm based method for achieving optimal gains in two area interconnected power system. Serhat Duman and Nuran Yorukeren [14] presented GSA method for determination of optimal PID parameters in two area interconnected power system. Rita Saini et al ., [15] presented Bacterial Foraging Optimization (BFO) method for two area interconnected power system.
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Performance Evaluation and Operation of PMUs in Power System

Performance Evaluation and Operation of PMUs in Power System

positional coordinates of the satellites from which the location of a receiver station on earth could be determined. In addition, the satellites transmit a one- pulse per- second signal, along with an identifier for the signal that can be interpreted by the earth station receivers[5]-[8]. The civilian-use transmission of the time signal is precise to within 1 microsecond, and often in practice is found to be much more accurate. The time pulse is of critical importance to the application considered here. The normal practice is to phaselock a sampling clock to this pulse. The sampling instant would be identified as the pulse number within a one-second interval identified by the GPS time-tag. The exact format for time-tagging is defined in IEEE standard 1344. It should be mentioned that a time standard known as the IRIG-B standard is currently being used by the power industry for time-tagging digital fault recorders and other substation event monitoring systems. However, with standard IRIG-B receivers the synchronization accuracy is of the order of 1 millisecond, which is not enough for precise power system measurement.
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A Review on PSO Models in Power System Operation

A Review on PSO Models in Power System Operation

36 Two of these three introduced techniques, the enhanced general passive congregation (GPAC) PSO and local passive congregation (LPAC) PSO, are based on the global and local-neighborhood variant PSOs, respectively. They are hybridized with the constriction factor approach together with a new operator, reflecting the physical force of passive congregation observed in swarms. The third one is based on a new concept of coordinated aggregation (CA) and simulates how the achievements of particles can be distributed in the swarm affecting its manipulation. Specifically, each particle in the swarm is attracted only by particles with better achievements than its own, with the exception of the particle with the best achievement, which moves randomly as a ―crazy‖ agent. The proposed PSO algorithms as well as the state-of-the-art PSO and the conventional interior-point OPF-based algorithm competed in the optimization problems. The results obtained in IEEE 30-bus and IEEE 118-bus systems indicated an improved performance of LPAC and an excellent performance of CA. The CA achieves the global optimum solution and exhibits better convergence characteristics, regulating the fewest random parameters than others. However, its main drawback remains the computing time.
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Wind Speed Forecasting for Power System Operation

Wind Speed Forecasting for Power System Operation

reserve requirements for integrating wind (Bouffard and Galiana 2008; Doherty and O’Malley 2005). In recent years, many efforts have focused on enhanced day-ahead power system operation using NWP models (Constantinescu et al. 2011; Pappala et al. 2009). To handle potential risks posed by wind generation, advanced dis- patch methods such as robust optimization (Zhao and Zeng 2010) and stochastic optimization (Constantinescu et al. 2011; Wang et al. 2008; Wu et al. 2007; Mei- bom et al. 2011; Papavasiliou et al. 2011) based unit commitment (UC)/economic dispatch (ED) models were proposed and studied. Although there have been many different proposals on what should be an optimal dispatch method in future power systems, actual practice during real-time operations is still a single-stage security- constrained economic dispatch (SCED). Our aim is to assess the economic value brought by the RRSTD model using a well-accepted industry model in real-time power system operations. In other words, the power system dispatch model is as- sumed to be a single-stage SCED. Consequently, we neglect the time step index for decision variables and parameters in the formulation. The mathematical formulation of the single-stage SCED is described as follows with the notation listed in Table 3.2:
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Optimization of Solar Power by varying Tilt Angle/Slope

Optimization of Solar Power by varying Tilt Angle/Slope

HOMER simulates the operation of a system by making energy balance calculations for each of the 8,760 hours (365x24) in a year. Over the last few years, many authors have presented models to Predict solar radiation on inclined surfaces. Some of these models apply to specific cases; some require special measurements and some are limited in their scope. These models use the same method of calculating beam and ground reflected radiation on a tilted surface. The only difference exists in the treatment of the diffuse radiation. The approximation commonly used for converting the diffuse component value for a horizontal surface to that for a tilted one is that sky radiation is isotropically distributed at all times [1–3].
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Neural Networks in power system operation and control

Neural Networks in power system operation and control

Dynamic programming approach is one of the widely employed methods but for a practical-sized system, the fine step size and the large units number often cause the ‘curse of dimensionality'. Main drawbacks of genetic algorithm and taboo search for ED are difficulty to define the fitness function, find the several sub-optimum solutions without guaranty that this solution isn't locally and longer search time. Neural networks and specially the Hopfield model, have a well-demonstrated capability of solving combinational optimization problem. This model has been employed to solve the conventional ED problems for units with continuous or piecewise quadratic fuel cost functions. Because of this network’s capability to consider all constrained limitation such as transmission line loss and transmission capability limitations, penalty factor when we have special units, control the unit’s pollutions and etc., caused increasing the paper proposed recently.
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Study on Application and Operation Optimization of Hydrocyclone for Solid-liquid Separation in Power Plant

Study on Application and Operation Optimization of Hydrocyclone for Solid-liquid Separation in Power Plant

As everyone knows that short-circuit flow is one of the flow characteristics, and also it is one of important rea- sons that coarse particles are into the overflow and the coarse particles and the fine particles are mixed. And the existence of short-circuit flow and the quantity of the flow are directly related to the structure size of the over- flow. The pressure drop will reduce and the production capacity will increase if the diameter of the overflow pipe fills out, but at the same time, it will also increase the separation size of the particles, lead to decline of the sep- aration ability. In general, the diameter of the overflow pipe is equal to D/8-D/2.3 is appropriate . The increase of thickness of the overflow pipe will lead to the small size of separation chamber, cause hydrodynamic loss de- creases, so that the rotating fluid flow more stable, it can reduce the activities scope of short-circuit flow and re- duce the influence of the hydrocyclone performance by short-circuit flow [7].
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The Optimization and Implementation of Collaborative System for Power Grid Operation Mode Calculation

The Optimization and Implementation of Collaborative System for Power Grid Operation Mode Calculation

In the analysis of power system simulation, transient stability analysis curve is a record in the change of each element in the simulation process, the generator power angle and voltage equivalent are shown at the appointed time within the period of time, which reflects the change of state in each time the process of power grid.

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Wind Generation, Power System Operation and Emissions Reduction

Wind Generation, Power System Operation and Emissions Reduction

Abstract— With increasing concern over global climate change, policy makers are promoting renewable energy sources, pre- dominantly wind generation, as a means of meeting emissions reduction targets. Although wind generation does not itself produce any harmful emissions, its effect on power system operation can actually cause an increase in the emissions of conventional plants. A dispatch model was developed which analyses the impact that wind generation has on the operation of conventional plants and the resulting emissions of Carbon Dioxide ( CO 2 ), Sulphur Dioxide ( SO 2 ) and Oxides of Nitrogen ( NO X ). The analysis concentrates on a ‘forecasted’ approach which incorporates wind generation forecasts in the dispatch decisions. It was found that wind generation could be used as a tool for reducing CO 2 emissions but alone it was not effective in curbing SO 2 and NO X emissions.
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