Power System Scenario Research Articles

Robust Control of Inter-area Oscillations in a Multimachine Network Employing LMI Based Wide Area TCSC Controller

This paper proposes a Linear Matrix Inequality (LMI) based ∞ Η robust controller design method for enhancement of damping of inter-area oscillations in a multimachine power system network. A Four-input, Single-output (FISO) ∞ Η controller is designed for a Thyristor Controlled Series compensator (TCSC) employing Wide Area Measurement (WAM) based stabilizing signals as generator speed. The major concern of signal transmission delay in wide area measurement is overcome here using the novel concept of Synchronized Speed Phasor Measurement (SSPM) with Global Position Satellite System (GPS) technology. The controller design has been carried out based on the ∞ Η mixed-sensitivity formulation in a LMI framework with pole-placement constraint. The small signal stability performance of the controller has been examined employing eigenvalue as well as time domain analysis for different operating scenarios of a 14-area real power system comprising of 24 numbers of generators, 203 buses and 266 lines. The designed controller has been found to be robust against varying generation and load power demand as well as for transmission line outage.

Weight optimisation for iterative distributed model predictive control applied to power networks

This paper presents a weight tuning technique for iterative distributed Model Predictive Control (MPC). Particle Swarm Optimisation (PSO) is used to optimise both the weights associated with disturbance rejection and those associated with achieving consensus between control agents. Unlike centralised MPC, where tuning focuses solely on disturbance rejection performance, iterative distributed MPC practitioners must concern themselves with the trade off between disturbance rejection and the overall communication overhead when tuning weights. This is particularly the case in large scale systems, such as power networks, where typically there will be a large communication overhead associated with control. In this paper a method for simultaneously optimising both the closed loop performance and minimising the communications overhead of iterative distributed MPC systems is proposed. Simulation experiments illustrate the potential of the proposed approach in two different power system scenarios.

Experimental Evaluation of Voltage Positive Feedback Based Anti-Islanding Algorithm: Multi-Inverter Case

The novel scenario of power systems with massive utilization of distributed generation (DG) imposes that islanding detection methods for voltage source inverter (VSI)-interfaced DGs must be evaluated taking into account multi-inverter configurations. This paper presents the experimental validation of an active islanding detection algorithm for multi-inverter systems proposed by the authors in previous work. Field programmable gate array implementation of power control and islanding detection algorithms has been used to test this proposition in a multi-inverter system based on grid-connected insulated gate bipolar transistor VSIs. The proposed islanding detection algorithm offers a very low impact on power quality and rapid and efficient islanding confirmation which is demonstrated by simulation and experimentation. The performance of the algorithm was tested under critical conditions as resonant load with high quality factor and unity power factor and also for cases where load and VSI powers are matched.

Road transport and power system scenarios for Northern Europe in 2030

Increasing focus on sustainability affects all parts of the energy system. The future integration of the power and road transport system due to the introduction of electric drive vehicles influences the economically optimal investments and optimal operation of the power system. This work presents analysis of the optimal configuration and operation of the integrated power and road transport system in Northern Europe, i.e. Denmark, Finland, Germany, Norway, and Sweden using the optimization model, Balmorel, with a transport model extension. A number of scenarios have been set up, including sensitivity on CO 2 and oil prices, inclusion/exclusion of electric drive vehicles, and change in investment possibilities in flexible power plants. Plug-in hybrid electric vehicles are shown to be competitive in all scenarios except the low oil scenarios. The increased electricity consumption for the electric vehicles is covered by wind power in Denmark and Norway and by coal production in Finland and Germany. The competition between wind power and coal is dependent on fuel price and CO 2 price assumptions. Furthermore, introducing the flexibility of electric drive vehicles helps decrease cycling on the remaining power plants. Finally, the electric drive vehicles can replace the use of both heat storage and electric boilers as well as decrease the use of gas turbines.

Performance of IEC 61850-9-2 Process Bus and Corrective Measure for Digital Relaying

International Electrotechnical Commission (IEC) standard 61850 proposes the Ethernet-based communication networks for protection and automation within the power substation. Major manufacturers are currently developing products for the process bus in compliance with IEC 61850 part 9-2. For the successful implementation of the IEC 61850-9-2 process bus, it is important to analyze the performance of time-critical messages for the substation protection and control functions. This paper presents the performance evaluation of the IEC 61850-9-2 process bus for a typical 345 kV/230 kV substation by studying the time-critical sampled value messages delay and loss by using the OPNET simulation tool in the first part of this paper. In the second part, this paper presents a corrective measure to address the issues with the several sampled value messages lost and/or delayed by proposing the sampled value estimation algorithm for any digital substation relaying. Finally, the proposed sampled value estimation algorithm has been examined for various power system scenarios with the help of PSCAD/EMTDC and MATLAB simulation tools.

Modified Shuffled Frog Leaping Algorithm for Solving Economic Load Dispatch Problem

In the recent restructured power system scenario and complex market strategy, operation at absolute minimum cost is no longer the only criterion for dispatching electric power. The economic load dispatch (ELD) problem which accounts for minimization of both generation cost and power loss is itself a multiple conflicting objective function problem. In this paper, a modified shuffled frog-leaping algorithm (MSFLA), which is an improved version of memetic algorithm, is proposed for solving the ELD problem. It is a relatively new evolutionary method where local search is applied during the evolutionary cycle. The idea of memetic algorithm comes from memes, which unlike genes can adapt themselves. The performance of MSFLA has been shown more efficient than traditional evolutionary algorithms for such type of ELD problem. The application and validity of the proposed algorithm are demonstrated for IEEE 30 bus test system as well as a practical power network of 203 bus 264 lines 23 machines system.

An Analytic Model-Based Approach for Power System Alarm Processing Employing Temporal Constraint Network

The alarm-processing problem is to interpret a large number of alarms under stress conditions, such as faults or disturbances, by providing summarized and synthesized information instead of a flood of raw alarm data. Alarm timestamps represent the temporal relationship among event occurrences and consist of rich and useful information for alarm processing. However, the temporal information has not been well utilized in existing alarm-processing methods. The temporal constraint network (TCN) is a type of directed acyclic graph suitable for representing temporal logics. Based on TCN, a new analytic model is developed for alarm processing with temporal information taken into account. Three major modules are included in the developed approach or alarm processor (i.e., alarm selection, event analysis, and result evaluation). In the alarm selection module, reported alarms are divided into related groups. The function of the event analysis module is to find out what events cause the reported alarms and to estimate when these events happen. The result evaluation module is used to identify abnormal or missing alarms. Finally, two alarm-processing scenarios of an actual power system are served for demonstrating the feasibility and efficiency of the developed approach.

Modelling Sustainable Development Scenarios of Croatian Power System

Modelling Sustainable Development Scenarios of Croatian Power SystemThe main objective of power system sustainable development is to provide the security of electricity supply required to underpin economic growth and increase the quality of living while minimizing adverse environmental impacts. New challenges such as deregulation, liberalization of energy markets, increased competition on energy markets, growing demands on security of supply, price insecurities and demand to cut CO2 emissions, are calling for better understanding of electrical systems modelling. Existing models are not sufficient anymore and planners will need to think differently in order to face these challenges. Such a model, on the basis on performed simulations, should enable planner to distinguish between different options and to analyze sustainability of these options. PLEXOS is an electricity market simulation model, used for modeling electrical system in Croatia since 2005. Within this paper, generation expansion scenarios until 2020 developed for Croatian Energy Strategy and modeled in PLEXOS. Development of sustainable Croatian energy scenario was analyzed in the paper - impacts of CO2 emission price and wind generation. Energy Strategy sets goal for 1200 MW from wind power plants in 2020. In order to fully understand its impacts, intermittent nature of electricity generation from wind power plant was modeled. We conclude that electrical system modelling using everyday growing models has proved to be inevitable for sustainable electrical system planning in complex environment in which power plants operate today.

Stochastic congestion management in power markets using efficient scenario approaches

Congestion management in electricity markets is traditionally performed using deterministic values of system parameters assuming a fixed network configuration. In this paper, a stochastic programming framework is proposed for congestion management considering the power system uncertainties comprising outage of generating units and transmission branches. The Forced Outage Rate of equipment is employed in the stochastic programming. Using the Monte Carlo simulation, possible scenarios of power system operating states are generated and a probability is assigned to each scenario. The performance of the ordinary as well as Lattice rank-1 and rank-2 Monte Carlo simulations is evaluated in the proposed congestion management framework. As a tradeoff between computation time and accuracy, scenario reduction based on the standard deviation of accepted scenarios is adopted. The stochastic congestion management solution is obtained by aggregating individual solutions of accepted scenarios. Congestion management using the proposed stochastic framework provides a more realistic solution compared with traditional deterministic solutions. Results of testing the proposed stochastic congestion management on the 24-bus reliability test system indicate the efficiency of the proposed framework.

Integrated evolutionary neural network approach with distributed computing for congestion management

Electric supply industry is facing deregulation all over the world. Under deregulated power supply scenario, power transmission congestion has become more intensified and recurrent, as compared to conventional regulated power system. Congestion may lead to violation of voltage or transmission capacity limits, thus threatens the power system security and reliability. Also the growing congestion may lead to unanticipated divergent electricity pricing. Owing to these facts congestion management has become a crucial issue in the deregulated power system scenario. Fast and precise prediction of nodal congestion prices in real time deregulated/spot power market may enable market participants and system operators to keep pace with the congestion by taking preventive measures like transaction rescheduling, bids (both for supplying and consuming electricity) modification, regulated dispatch of electric power, etc. This paper proposes an integrated evolutionary neural network (ENN) approach to predict nodal congestion prices (NCPs) for congestion management in spot power market. Distributed computing is employed to tackle the heterogeneity of the data in the prediction of NCP values. Developed ENNs have been trained and tested under distributed computing environment, using a message passing paradigm. Proposed hybrid approach for NCP prediction is demonstrated on a 6-bus test power system with and without distributed computing. The proposed approach not only demonstrated the computing efficiency of the developed ENN model over the conventional optimal power flow method but also shows the time saving aspect of distributed computing.

Frequency and fundamental signal measurement algorithms for distributed control and protection applications

Increasing penetration of distributed generation within electricity networks leads to the requirement for cheap, integrated, protection and control systems. To minimise cost, algorithms for the measurement of AC voltage and current waveforms can be implemented on a single microcontroller, which also carries out other protection and control tasks, including communication and data logging. This limits the frame rate of the major algorithms, although analogue to digital converters (ADCs) can be oversampled using peripheral control processors on suitable microcontrollers. Measurement algorithms also have to be tolerant of poor power quality, which may arise within grid-connected or islanded (e.g. emergency, battlefield or marine) power system scenarios. This study presents a 'Clarke-FLL hybrid' architecture, which combines a three-phase Clarke transformation measurement with a frequency-locked loop (FLL). This hybrid contains suitable algorithms for the measurement of frequency, amplitude and phase within dynamic three-phase AC power systems. The Clarke-FLL hybrid is shown to be robust and accurate, with harmonic content up to and above 28% total harmonic distortion (THD), and with the major algorithms executing at only 500 samples per second. This is achieved by careful optimisation and cascaded use of exact-time averaging techniques, which prove to be useful at all stages of the measurements: from DC bias removal through low-sample-rate Fourier analysis to sub-harmonic ripple removal. Platform-independent algorithms for three-phase nodal power flow analysis are benchmarked on three processors, including the Infineon TC1796 microcontroller, on which only 10% of the 2000 mus frame time is required, leaving the remainder free for other algorithms.

Market operations in future Indian restructured power system scenario

Indian power industry is undergoing deregulation at a rapid rate. In this transition phase, it is now essential to design the most appropriate market and suitable pricing schemes for the power sector. In this article, a restructured electricity market model for future Indian power market is explained which is based on the institutional frame work – The Indian Electricity Act 2003. The activities of the three-tier market model and the entities involved in the power trading are discussed. Inter-regional and intra-regional congestion management scheme that provide comparable access to all users of the transmission system is proposed. Method for estimation of transmission charges to wheel electrical energy through transmission utilities is also proposed. The method is tested with a five region eleven zone Indian power system model and the results are illustrated.

On the Assignment of Voltage Control Ancillary Service of Generators in Spain

The aim of the Spanish voltage control ancillary service is the minimization of the transmission losses while keeping the system away from voltage instability. The latter target can be achieved maintaining an adequate voltage profile in normal operating condition and assuring that generators exhibit enough reactive margins that guarantee that the system voltages will remain under acceptable values in case of contingencies. The service has been designed in Spain to be partially mandatory and partially subject to payment based on market-driven rules and performance evaluation. In the annual time scope, the optional reactive offers submitted by the service suppliers are selected analyzing a number of forecasted scenarios. This paper explains the implementation of the Spanish voltage control ancillary service (VCAS) that is provided by generators in the annual time scope. For this purpose, a mixed-integer linear programming optimal power flow (OPF) is used. Reactive bids of generators are modeled within each scenario by modifying the reactive limits of the generators. The use of the proposed OPF in the assignment of the voltage control ancillary service is explained and illustrated using actual scenarios of the Spanish power system

A corrective load shedding scheme to mitigate voltage collapse

Voltage stability is concerned with the ability of a power system to maintain acceptable voltages at all buses. A measure of the power system voltage stability is the distance to the saddle node bifurcation of the power flow equations, which is called the load margin. When the power system load is very high, and/or there exists a large generation-demand imbalance in the power system areas the load margin to the saddle node bifurcation may be too low, and the power system may become close to voltage collapse. In case that active and reactive power generation resources in the importing areas are exhausted, corrective load shedding may become the last option. This paper presents a LP-based optimization load shedding algorithm to improve the load margin. The objective function consists of minimizing the total system demand decrease. First order sensitivities of the load margin with respect to the load to be shed are considered. The performance of the method is illustrated with a scenario of the Spanish power system.

Preventive analysis and solution of overloads in the Spanish electricity market

The technical feasibility of a power generation dispatch in competitive electricity markets consists of not having any overloaded equipment, not only in case of normal operating condition, but also when any contingency established in the security criteria occurs. In addition, bus voltages should be within their limits. This paper describes an optimization method to analyze and solve the transmission overloads that arise in each hourly scenario of the Spanish power system, after the electricity market has been cleared. Overloads are solved in the Spanish market by increasing and decreasing generation of connected units, and by connecting off-line ones. The proposed method comprises three steps: (a) contingency analysis, (b) preventive active dispatch and (c) classification of generation re-dispatch. The performance of the method is illustrated using an actual example of the Spanish electricity market.

Control centers evolve with agent technology

Energy management system (EMS) architectures are deeply influenced by power system and information system scenarios. The computer industry has been evolving continuously, and the power industry, which remained relatively stable for decades, is now undergoing revolutionary changes that require the special attention of EMS developers. The introduction of new players, the decentralization of production and processing of information, and competition will change the way control centers operate and, consequently, their architecture. Distinct areas of computer science have suggested many different approaches to tackle the problems that arise in the new decentralized scenario. Of particular interest, agent technology, proposed in the context of distributed artificial intelligence, is a very promising approach to support the construction of a new generation of EMS in an open environment.

Transportable simulator trains control center operators

The Electric Power Research Institute (EPRI) developed an advanced transportable operator training simulator (OTS) that features improvements over other simulators on the market. The OTS consists of four subsystems: power system modeling, educational, scenario building, and control center modeling. The interaction between the instructor and the OTS is supported by the instructor position man-machine interface (IPMMI) software and hardware. Control of the OTS, data entry into the database, and presentation of data on displays are performed by the IPMMI. All data requests and updates performed by the IPMMI relate to data resident in the power system model (PSM) database. The IPMMI also has the ability to initiate PSM tasks and to transmit program-generated messages to the instructor. OTS implementation and testing at a host utility are described. >