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Two-fluid model quantifies traffic performance on a network by studying the interaction between moving and stopped vehicles in the traffic stream. The broad objective of this study is to investigate the presence and possible impact of measurement and modeling errors (non-ergodicity and endogeneity) on two-fluid model estimates under mixed traffic conditions. This objective is pursued based on travel time and running time data obtained from six-lane roads in Chennai city. To address non-ergodicity, a suitable temporal disaggregation scheme is proposed. An orthogonal regression model is applied that accounts for measurement errors in running times and trip times. To handle endogeneity, omitted variable bias correction is applied by adding two variables. The proposed non-ergodic model, correcting for both measurement error and endogeneity, provides a better fit and more accurate estimates than conventional model. These measurement and modeling errors also affect critical traffic flow parameters and level of service benchmarks. [ABSTRACT FROM AUTHOR]

The amplitude Variations with Offset (AVO) technique is extensively used in hydrocarbon exploration and production for de-risking of the prospect before drilling. The interpretation of AVO results brings more confidence in the presence or absence of hydrocarbon. The AVO and Direct Hydrocarbon Indicator (DHI) analysis have their own inherent limitations. Therefore, to ensure the presence of true AVO in seismic, other attribute supports are crucial. The present prospect in this study was deposited as a channel-levee complex in the shelf-slope of the Krishna-Godavari basin. There is evidence of false AVO within this complex and thus additional attributes were used in this study to support the AVO analysis. The conventional AVO analysis shows the presence of Class-III AVO for a probable reservoir in seismic. However, it is unable to ensure the presence of hydrocarbon in the stacked reservoir unit. The spectral AVO technique was used to check AVO response at low frequencies for laminated and stacked reservoir units. Additionally, seismic attenuation and dispersion attribute studies were incorporated into the de-risking analysis. The drill result showed two distinct reservoir intervals in this prospect, which validated the predrill interpretation. This integrated approach added significant value in de-risking the prospect using AVO and other seismic attributes in complex geologic setups. [ABSTRACT FROM AUTHOR]

Renewable energies (REs) such as photovoltaic generation (PV) have been gaining attention in distribution systems. Recently, houses with PV and battery systems, as well as electric vehicles (EV) are expected to contribute to not only the suppression of global warming but also reducing electricity bill on the consumer side. However, there are numerous challenges with the introduction of REs at the demand side such as the actual output of REs often deviating from the forecasted output, which causes fluctuation of the power flow and this is challenging for the distribution or transmission system operator. For this challenge, it is expected that smart grid technology using controllable loads such as a fixed battery or EV battery, can suppress fluctuation of power flow. This paper presents a decision method of optimal scheduling of controllable loads to suppress the fluctuation of power flow by PV output in the smart home. An optimization method to cope with uncertainties such as variability of PV power and effective forecasting methods are considered in the proposed scheme. In order to decrease the expected operational cost and to validate the robustness for the uncertainty's optimization approach, statistical analysis is executed for the optimal scheduling scheme. From the optimization results, the proposed methodology suppressed the fluctuation of power flow in the smart home and also minimized the consumer operational cost. [ABSTRACT FROM AUTHOR]

The main focus of the proposed framework is to examine the importance of electricity interconnections with a high share of intermittent Renewable Energy (RE) sources and attempts to link the gap between planning model and operation with considering realistic operating details. Therefore optimal Unit Commitment (UC) is considered to analyze how operational aspects are appropriately done over the planning period. More specifically, a Mixed Integer Linear Programming (MILP) model is developed to address the specific challenges of the underlying UC problem. For modelling purposes, demand forecast, applicable RE potentials and the cost of RE technologies are estimated. To reduce the expenses and improve system stability, energy storage systems (pump storage hydro and thermal energy storage) are considered as well. For optimal UC, a typical day (24 h) is employed to determine the capacity expansion and daily operational planning. Each selected day expresses a part of the year (e.g., a season). Incorporation of short-term decisions into the long-term planning framework can strengthen the accuracy of the decisions and guaranty the stability of power networks. The proposed approach can provide valuable insights into the appropriate energy strategies followed by the investors and policymakers at a national and regional level. [ABSTRACT FROM AUTHOR]

Imbalance (on-line energy gap between contracted supply and actual demand, and associated cost) reduction is going to be a crucial service for a Power Producer and Supplier (PPS) in the deregulated energy market. PPS requires forward market interactions to procure energy as precisely as possible in order to reduce imbalance energy. This paper presents, 1) (off-line) an effective demand aggregation based strategy for creating a number of balancing groups that leads to higher predictability of group-wise aggregated demand, 2) (on-line) a robust energy storage scheduling that minimizes the imbalance energy and cost of a particular balancing group considering the demand prediction uncertainty. The group formation is performed by a Probabilistic Programming approach using Bayesian Markov Chain Monte Carlo (MCMC) method after applied on the historical demand statistics. Apart from the group formation, the aggregation strategy (with the help of Bayesian Inference) also clears out the upper-limit of the required storage capacity for a formed group, fraction of which is to be utilized in on-line operation. For on-line operation, a robust energy storage scheduling method is proposed that minimizes expected imbalance energy and cost (a non-linear function of imbalance energy) while incorporating the demand uncertainty of a particular group. The proposed methods are applied on the real apartment buildings' demand data in Tokyo, Japan. Simulation results are presented to verify the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

This study introduces a demand‐side distributed and secured energy commitment framework and operations for a power producer and supplier (PPS) in deregulated environment. Due to the diversity of geographical location as well as customers' energy profile coupled with high number of customers, managing energy transactions and resulting energy exchanges are challenging for a PPS. The envisioned PPS maintains several aggregators (e.g. microgrids), named as sub service provider (SSP) that manage customers/subscribers under their domains. The SSPs act as agents that perform local energy matching (inside their domains) and distributed energy matching within SSPs to determine the energy commitment. The goal of the distributed energy matching is to reduce the involvement of external energy supplier (e.g. Utility) while providing a platform to demand side players to be a part of energy transaction. A distributed assignment problem is designed that requires minimum and aggregated information exchange (hence, secured) and solved by linear programming that provides the distributed matching decision. The communicative burden among SSPs due to the exchange of energy information is reduced by applying an adaptive coalition formation method. The simulations are conducted by implementing a synchronous distributed matching algorithm while showing the effectiveness of the proposed framework. [ABSTRACT FROM AUTHOR]

In the last decade, there has been much effort to reduce the supply voltage of electronic circuits due to the demand for portable and battery-powered equipment. Since a low-voltage operating circuit becomes necessary, the current-mode technique is ideally suited for this purpose more than the voltage-mode one. In this paper, performance of multi output current controlled current differencing transconductance amplifier (MOCCCDTA) is evaluated using 180nm, 90nm and 45nm CMOS technology. It is found that the 45nm CMOS-based MOCCCDTA provides highest frequency i.e. 33GHz. Further a Universal biquad filter has been designed using a single MOCCCDTA as an active element and two capacitors. Filter offers high frequency in GHz. Tunability of all the filter outputs with respect to a bias current has been analyzed. The tunability of the filter circuit for Bluetooth applications is also shown in this work. The performances of MOCCCDTA circuit and Universal biquad filter are illustrated by HSPICE. The simulation results are found to be in agreement with the theoretical predictions. [ABSTRACT FROM AUTHOR]

This study presents a fuzzy logic‐based strategy to solve thermal generation scheduling [namely unit commitment (UC)] problem integrated with an equivalent solar‐battery system using quantum inspired evolutionary algorithm. Solar‐battery system is included with this model as a measure of green‐house effect. The crisp formulations of UC are modified utilising fuzzy logics, because of the inherent intermittency involved in solar energy integration and other uncertain variables. An evolutionary algorithm based on the concept and principle of quantum computation is applied to solve the resultant fuzzy logic‐based UC problem. Conventional quantum evolutionary algorithm (QEA) is modified by incorporating a hierarchy‐group oriented scheme to deal with the non‐linear and multi‐peak nature of the problem. QEA is further advanced facilitating some genetic algorithm operators and a new binary differential operator along with rotation operator with a redefined rotational angle look‐up table. The probabilities of using such operators on individual solution(s) are fuzzified by defining membership function based on associated fitness of that individual. The fitness function is then determined by combining the objective function, penalty function and the aggregated fuzzy membership function. The proposed fuzzy logic‐based QEA (FLQEA) is applied to UC problem in two different scaled power systems. Provided simulation results will show the effectiveness of FLQEA. [ABSTRACT FROM AUTHOR]

This paper presents an intelligent economic operation of smart grid environment facilitating an advanced quantum evolutionary method. The proposed method models the wind generation (WG) and photovoltaic (PV) generation as renewable power generation sources as a measure of global warming effect. Thermal generators (TGs) are included in this model to provide the maximum amount of energy to meet consumers' demand. On the other hand, plug-in hybrid electric vehicles (PHEVs) are capable of reducing CO_2, NO_x, and gradually becoming an integral part of smart-grid infrastructure. Such integration introduces uncertainties into the system that are addressed by fuzzy-logic-based formulations. Demanded load, wind speed, solar radiation, and number of involved PHEVs are taken under fuzzy formulations. An intelligent quantum inspired evolutionary algorithm (IQEA) is proposed and applied in this model to perform the intelligent economic scheduling operation concerning scheduling and dispatching TG, WG, PV, and PHEV. IQEA features intelligent operators such as sophisticated rotation operator, differential operator, etc. The method is tested on a hypothetical power system with 10 thermal units, equivalent number of PHEVs, equivalent solar and wind farm. The simulation results will show the effectiveness of IQEA that provides excellent operational resource scheduling while reducing the production cost and emission. [ABSTRACT FROM PUBLISHER]

This paper presents an efficient strategy to solve the thermal economic load dispatch (ELD) problem by considering several aspects of ELD. ELD performs an important role in the economical operation of power system, which essentially involves nonlinearity according to the characteristics of the generators. The complexity is amplified when the generators' prohibited zones and valve-point effect are considered, which makes ELD a nonconvex and nonsmooth problem. The strategy employs a mechanism involving a quantum mechanics-inspired particle swarm optimization (QMPSO). The conventional PSO is modified by integrating quantum mechanical theory which redefines the particles' positions and velocities in a dynamic manner and therefore explores more search space. The QMPSO employs a multipopulation-based scheme which ensures particle movement and avoids premature convergence at the same time. Moreover, in order to diversify the particles, a dynamic mutation operator is introduced in the proposed method. Such features deliver a fine balance between the local and global searching abilities. Simulations are carried out by considering several cases of thermal units of varying combinations of system configurations such as with and without the valve point, with and without network loss, and for one or several hours of load demand. The results are quite promising and effective compared with several benchmark methods. © 2012 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]

This paper presents a fuzzy-logic-based binary particle swarm optimization (BPSO) method for solving short-term thermal unit commitment problem integrated with an equivalent wind-battery system. As a renewable power source, wind power is injected stochastically with the model. To handle the uncertainty and intermittency due to the wind power integration, the trivial crisp problem formulations are modified by introducing fuzzy logic. Moreover, since it is also forecast, load demand along with the spinning reserve and production cost are taken under fuzzification. The potential solutions are distributed among several clusters based on a clustering scheme which exploits their associated fitness values. The fitness value is functionalized by combining the objective function, penalty function, and the aggregated fuzzy membership function. After clustering, each solution is updated according to the velocity and position BPSO refinement functions. The clustering scheme inherently introduces multipopulation-based search space exploration in PSO. Therefore, this algorithm allows the particles to explore a larger search space in the problem domain by diversification of particles. Simulation results are provided to show the effectiveness of the proposed method by scrutinizing two different power systems. © 2012 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]

In the framework of the competitive electricity markets, electricity price forecasting is important for market participants in a deregulated electricity market. Rather than forecasting the value, market participants are sometimes more interested interval of the peak electricity price forecasting. Forecasting the peak price is essential for estimating the uncertainty involved in the price and thus is highly useful for making generation bidding strategies and investment decisions. The choice of the forecasting model becomes the important influence factor how to improve price forecasting accuracy. This paper proposes new approach to reduce the prediction error at occurrence time of the peak electricity price, and aims to enhance the accuracy of the next day electricity price forecasting. In the proposed method, the weekly variation data is used for input factors of the ANN at occurrence time of the peak electricity price in order to catch the price variation. Moreover, learning data for the ANN is selected by rough sets theory at occurrence time of the peak electricity price. This method is examined by using the data of the PJM electricity market. From the simulation results, it is observed that the proposed method provides a more accurate and effective forecasting, which helpful for suitable bidding strategy and risk management tool for market participants in a deregulated electricity market. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a modified particle swarm optimization (MPSO) for constrained economic load dispatch (ELD) problem. Real cost functions are more complex than conventional second order cost functions when multi-fuel operations, valve-point effects, accurate curve fitting, etc., are considering in deregulated changing market. The proposed modified particle swarm optimization (PSO) consists of problem dependent variable number of promising values (in velocity vector), unit vector and error–iteration dependent step length. It reliably and accurately tracks a continuously changing solution of the complex cost function and no extra concentration/effort is needed for the complex higher order cost polynomials in ELD. Constraint management is incorporated in the modified PSO. The modified PSO has balance between local and global searching abilities, and an appropriate fitness function helps to converge it quickly. To avoid the method to be frozen, stagnated/idle particles are reset. Sensitivity of the higher order cost polynomials is also analyzed visually to realize the importance of the higher order cost polynomials for the optimization of ELD. Finally, benchmark data sets and methods are used to show the effectiveness of the proposed method. [Copyright &y& Elsevier]

This paper presents a determination methodology for finding optimal operation schedules of thermal units (namely unit commitment) integrated with an energy storage system (ESS) to minimize total operating costs. A generic ESS formulation along with a method for solving unit commitment (UC) of thermal units with ESS is proposed to serve this purpose. The problem of unit commitment with an ESS is solved using the Priority List method. Intelligent Genetic algorithm (GA) is included in the algorithm for generating new and potential solutions. The proposed method consists of two steps. The first step is to determine the schedule of ESS and the schedule of thermal units. The second step is to dispatch the hourly output of thermal units and the ESS which comply a minimized total production cost. The proposed method is applied to a power system with ten thermal units and a large ESS. The presented simulation results show that the schedule of thermal units with an ESS of a particular life cycle, achieved by the proposed method, minimizes the operating cost. The discussion regarding the determination of schedule thermal units (TU) along with the integrated ESS may interest many types of ESS due to their generalized formulations. [ABSTRACT FROM AUTHOR]

Recently, given the increased integration of renewables and growing uncertainty in demand, the wholesale market price has become highly volatile. Energy communities connected to the main electricity grid may be exposed to this increasing price volatility. Additionally, they may also be exposed to local network congestions, resulting in price spikes. Motivated by this problem, in this paper, we present a coordination mechanism between entities at the distribution grid to reduce price volatility. The mechanism relies on the concept of duality theory in mathematical programming through which explicit constraints can be imposed on the local electricity price. Constraining the dual variable related to price enables the quantification of the demand-side flexibility required to guarantee a certain price limit. We illustrate our approach with a case study of a congested distribution grid and an energy storage system as the source of the required demand-side flexibility. Through detailed simulations, we determine the optimal size and operation of the storage system required to constrain prices. An economic evaluation of the case study shows that the business case for providing the contracted flexibility with the storage system depends strongly on the chosen price limit. [ABSTRACT FROM AUTHOR]

With the development of distributed energy resources (DERs) and advancements in technology, microgrids (MGs) appear primed to become an even more integral part of the future distribution grid. In order to transition to the smart grid of the future, MGs must be properly managed and controlled. This paper proposes a microgrid energy management system (MGEMS) based on a hybrid control algorithm that combines Transactive Control (TC) and Model Predictive Control (MPC) for an efficient management of DERs in prosumer-centric networked MGs. A locally installed home energy management system (HEMS) determines a charge schedule for the battery electric vehicle (BEV) and a charge–discharge schedule for the solar photovoltaic (PV) and battery energy storage system (BESS) to reduce residential customers' operation cost and to improve their overall savings. The proposed networked MGEMS strategy was implemented in IEEE 33-bus test system and evaluated under different BEV and PV-BESS penetration scenarios to study the potential impact that large amounts of BEV and PV-BESS systems can have on the distribution system and how different pricing mechanisms can mitigate these impacts. Test results indicate that our proposed MGEMS strategy shows potential to reduce peak load and power losses as well as to enhance customers' savings. [ABSTRACT FROM AUTHOR]

Like in most developing countries, meeting the load demand and reduction in transmission grid bottlenecks remains a significant challenge for the power sector in Sierra Leone. In recent years, research attention has shifted to demand response (DR) programs geared towards improving the supply availability and quality of energy markets in developed countries. However, very few studies have discussed the implementation of suitable DR programs for developing countries, especially when utilizing renewable energy (RE) resources. In this paper, using the Freetown's peak load demand data and the price elasticity concept, the interruptible demand response (DR) program has been considered for maximum demand index (MDI) customers. Economic analysis of the energy consumption, customer incentives, benefits, penalties and the impact on the load demand are analyzed, with optimally designed energy management for grid-integrated battery energy storage system (BESS) and photovoltaic (PV)-hybrid system using the genetic algorithm (GA). Five scenarios are considered to confirm the effectiveness and robustness of the proposed scheme. The results show the economic superiority of the proposed DR program's approach for both customers and supplier benefits. Moreover, RE inclusion proved to be a practical approach over the project lifespan, compared to the diesel generation alternative. [ABSTRACT FROM AUTHOR]

Combating climate change issues resulting from excessive use of fossil fuels comes with huge initial costs, thereby posing difficult challenges for the least developed countries in Sub-Saharan Africa (SSA) to invest in renewable energy alternatives, especially with rapid industrialization. However, designing renewable energy systems usually hinges on different economic and environmental criteria. This paper used the Multi-Objective Particle Swarm Optimization (MOPSO) technique to optimally size ten grid-connected hybrid blocks selected amongst Photo-Voltaic (PV) panels, onshore wind turbines, biomass combustion plant using sugarcane bagasse, Battery Energy Storage System (BESS), and Diesel Generation (DG) system as backup power, to reduce the supply deficit in Sierra Leone. Resource assessment using well-known methods was done for PV, wind, and biomass for proposed plant sites in Kabala District in Northern and Kenema District in Southern Sierra Leone. Long term analysis was done for the ten hybrid blocks projected over 20 years whilst ensuring the following objectives: minimizing the Deficiency of Power Supply Probability (DPSP), Diesel Energy Fraction (DEF), Life Cycle Costs (LCC), and carbon dioxide (CO 2 ) emissions. Capacity factors of 27.41 % and 31.6 % obtained for PV and wind, respectively, indicate that Kabala district is the most feasible location for PV and wind farm installations. The optimum results obtained are compared across selected blocks for DPSP values of 0–50% to determine the most economical and environmentally friendly alternative that policy makers in Sierra Leone and the region could apply to similar cases. [ABSTRACT FROM AUTHOR]