Variable Power Demand Research Articles

New direct reactive power control based fuzzy and modulated hysteresis method for micro-grid applications under real wind speed

ABSTRACT This paper presents a fixed-switching frequency of direct reactive power control, called modulated hysteresis-direct reactive power control (MH-DRPC), for a variable speed wind turbine-based doubly fed induction generator (DFIG). Combination of two techniques, i.e. reactive power control-based fuzzy logic control and modulated hysteresis (MH) direct torque control, is the principle of the proposed strategy. The main advantage of this methodology is that the switching frequency of the converter switches is constant, where the major drawback of the classical DRPC is eliminated (the variable switching frequency). This new methodology aims to achieve sinusoidal forms of generated AC current with a constant frequency, ensure minimal current total harmonic distortion (THD), and minimum output voltage ripple irrespective of the wind speed fluctuations. This research work is aimed primarily at analyzing the performances of the new MH-DRPC, taking into account certain constraints that reflect the true working of wind turbine generators, such as the random wind speed behavior and variable reactive power demand. This allows analyzing all operation modes of DFIG, with an emphasis on synchronous and over-speed modes. A comparison with a classical DRPC is made to underline the effectiveness of the suggested approach. The MPPT algorithm and the pitch angle control are invested to optimize the captured wind power. Simulations of the proposed method are made under Matlab/Simulink. The simulation results revealed significant improvements over the classical DRPC in the following characteristics: a significant reduction in current THD supplying the AC grid (98.29%) and a good dynamic response with a significant reduction of ripples of electromagnetic torque (66.67%), rotor flux (42.67%), generated current frequency (96.70%), local reactive power compensation (50.23%), and generated active power (26.34%). In addition, the suggested MH-DRPC technique is extremely robust to machine parametric changes and random variations in references command.

CFD simulation of an integrated PCM-based thermal energy storage within a nuclear power plant connected to a grid with constant or variable power demand

The current numerical study investigates the integration of a phase change material (PCM)-based thermal energy storage (TES) system within a nuclear power plant (NPP) to enhance the capability of such proposed plant to better follow the variations of power grid demand especially in countries with a high share of nuclear in their energy mix. This study focuses on the transient behavior of the PCM during the simultaneous charging and discharging (SCD) operation mode during one day. For this, an enthalpy-porosity method is used to simulate the SCD operation mode of PCM within an axisymmetric vertical triplex tube. Hot and cold heat transfer fluids (HTFs) are simultaneously co-circulating within the TES for downward-inner heating and upward-outer cooling. Constant and variable loads are considered to study the load-following ability of the coupled system. During the SCD mode with a constant load, results show that an equilibrium between melting and solidification (charging and discharging) is established and the liquid fraction (20%) is not changing with time. Under the variable load, the suggested TES system is able to follow the daily variation of the grid demand and, hence, fill the gap between reactor supplied energy and grid power demand with the reactor constantly operated at full rated capacity. During the variable load, the liquid fraction within PCM is altering from solid to liquid phases and vice versa depending on the deviations of the grid as expected.

Combined Economic Emission Dispatch of Microgrid with the Incorporation of Renewable Energy Sources Using Improved Mayfly Optimization Algorithm

Electricity can be provided to small-scale communities like commercial areas and villages through microgrid, one of the small-scale, advanced, and independent electricity systems out of the grid. Microgrid is an appropriate choice for specific purposes reducing emission and generation cost and increasing efficiency, reliability, and the utilization of renewable energy sources. The main objective of this paper is to elucidate the combined economic emission dispatch CEED problem in the microgrid to attain optimal generation cost. A combined cost optimization approach is examined to minimize operational cost and emission levels while satisfying the load demand of the microgrid. With this background, the authors proposed a novel improved mayfly algorithm incorporating Levy flight to resolve the combined economic emission dispatch problem encountered in microgrids. The islanded mode microgrid test system considered in this study comprises thermal power, solar-powered, and wind power generating units. The simulation results were considered for 24 hours with varying power demands. The minimization of total cost and emission is attained for four different scenarios. Optimization results obtained for all scenarios using IMA give a comparatively better reduction in system cost than MA and other optimization algorithms considered revealing the efficacy of IMA taken for comparison with the same data. The proposed IMA algorithm can solve the CEED problem in a grid-connected microgrid.

Multi-energy flow cooperative dispatch for supply-demand balance of distributed power grid with liquid air energy storage system

It is of great help for alleviating energy shortage and decreasing carbon emission to increase the proportion of renewable energy in energy structure. However, the volatility of renewable energy causes a mismatching for multi-energy flow in distributed micro-grid. To settle this problem, a standalone liquid air energy storage system coupled with cold energy of liquefied natural gas is proposed. A double-parameter regulation method is proposed to make the compressor cope with volatile wind power, and make the expander to meet the variable power demand. To improve the simulation precision, the dynamic behavior of packed bed is investigated and the link between the transient temperature of packed bed and system performance is achieved. The results indicate that the hourly power supply-demand in micro-grid gets balance by employing LAES, and the daily energy storage reaches 285 MWh which is more than enough for the energy demand in peak time (200 MWh). Moreover, the energy storage and supply gets balanced and the round trip efficiency reaches a stable value (63%) in the 8th day. In economic analysis, the short dynamic payback year (5 years) and high net present value (80 million dollars) indicate high economic feasibility. Finally, the proposed system obtains the optimal thermal energy storage density (96 kWh/m3), the minimum work consumption (643 kJ/kg), and the highest round trip efficiency (0.63) compared to those in previous literature.

A Hierarchical Strategy for Multi-Objective Optimization of Distribution Network Considering DGs and V2G-Enabled EVs Integration

As an important part of smart grid construction, the distribution network (DN) optimization problem has always attracted great attention, especially under the background that large-scale penetration of distributed generators (DGs) and electric vehicles (EVs) into building cluster poses both opportunities and challenges to the energy management. This research presents a hierarchical optimization strategy, for improving the safe and economical operation of DN considering the DGs and EVs integration. In Stage 1, the MPPT control model of DGs is designed to obtain the best energy conversion efficiency. In Stage 2, load models of EVs and battery energy storage system (BESS) under coordinate charging/discharging stimulated by a time-of-use incentive mechanism are established respectively, to achieve a load curve with a minimized peak-to-valley difference (PVD). In Stage 3, aiming for the best compromise between the active power loss and node voltage excursion, daily optimal scheduling of the static Var compensator (SVC) capacitors is dynamically worked out according to the varying power demand, as the solution for the defined multi-objective optimization problem. For enhancing the convergence speed, an advanced genetic algorithm with elite preservation strategy is employed. The proposed hierarchical strategy is demonstrated on an IEEE 33-node DN test case, and the simulation results show that first, the MPPT control ensures the maximum power outputs of DGs; next, power supply pressure could be relieved by the load shifting effects of the coordinated vehicle-to-grid (V2G) service and BESS configuration, reflected in the decreased load peak from 4,370.1 to 3,424.99 kW, and the optimized PVD from 1763.8 to 703.8 kW; meanwhile, via applicable power planning of the SVC components, optimized power loss and voltage quality can both be achieved, proving the feasibility of the optimization strategy, which promotes the economic and reliable operation of the DN system.

Distribution Network Reconfiguration Considering Voltage and Current Unbalance Indexes and Variable Demand Solved through a Selective Bio-Inspired Metaheuristic

Operation of distribution networks involves a series of criteria that should be met, aiming for the correct and optimal behavior of such systems. Some of the major drawbacks found when studying these networks is the real losses related to them. To overcome this problem, distribution network reconfiguration (DNR) is an efficient tool due to the low costs involved in its implementation. The majority of studies regarding this subject treat the problem by considering networks only as three-phase balanced, modeled as single-phase grids with fixed power demand, which is far from representing the characteristics of real networks (e.g., unbalanced loads, variable power and unbalance indexes). Due to the combinatorial nature of the problem, metaheuristic techniques are powerful tools for the inclusion of such characteristics. In this sense, this paper proposes a study of DNR considering balanced and unbalanced systems with variable power demand. An analysis of the direct influence of voltage unbalance index (VUI) and current unbalance index (CUI) is carried out for unbalanced cases. To solve the DNR problem, a selective bio-inspired metaheuristic based on micro bats’ behavior named the selective bat algorithm (SBAT) is used together with the EPRI-OpenDSS software (California, US, EPRI). Tests are initially conducted on balanced systems, aiming to validate the technique proposed for both demands and state their differences, and then they are conducted on unbalanced systems to study the influence of VUI and CUI in the DNR solution.

Hybrid chaotic approaches to solve profit based unit commitment with plug-in electric vehicle and renewable energy sources in winter and summer

Recently power sectors have faced lots of challenges for huge penetration growths for renewable energy sources (RES) and effects of charging as well as discharging nature of plug-in electric vehicles (PEVs) to fulfill time varying power demands. The alteration of transport system as well as low carbon energy require not only for the efficiency measurements with large scale acceptance of the clean technology, but also the strategies for new energy managing to incorporate those innovations efficiently in present infrastructure. Thus development of PEVs are needed. Due to development of RES the fossils fuel consumption with greenhouse gas emission can be reduced. In the proposed research, authors have effectively given the combination of chaotic maps with Harris Hawks Optimizer, Sine Cosine Algorithm and Slime Mould Algorithm advancement strategy and assessed execution of the proposed improved technique for standard benchmark of PBUCP with considering effect of PEVs and RES in summer and winter are checked for small type to large type test systems i.e. 10-, 40- and 100-generating unit system are considered and profit of each are evaluated as outcome of this research.

Improved generalised integrator for optimal dispatch of SyRG‐based pico‐hydro‐PV system

The fluctuating outputs from renewable energy sources (RESs) and variable load power demand deteriorate the current quality, thus an improved generalised integrator with a frequency-locked loop (IGI-FLL) control strategy is used, which is not only easy but simple to attain a quick and precise fundamental extraction. IGI-FLL control method is utilised with all-pass filter and is capable to accomplish enhanced harmonics, noise and DC offset rejection ability while estimating fundamental component of the load current. In addition, the FLL structure is used to increase the frequency immunity against voltage variations. It provides improved steady-state and dynamic performances with flexibility in parameter tuning in comparison to SOGI-FLL algorithm. It also presents a cost-effective and robust genetic algorithm (GA)-based optimisation to provide optimum battery charging and discharging power while satisfying the state-of-charge (SOC) limits under RES uncertainty. In this islanded microgrid, a Synchronous Reluctance Generator for pico-hydro and photovoltaic array is used so that this hybrid system utilises maximum solar PV array power, matches the load, or else charges the battery energy storage while maintaining power balance in the microgrid. Comparative outcomes authenticate the efficacy of the algorithm to handle the issue of RES uncertainty and load unbalance in an islanded microgrid.

Energy management system and supervision in a micro-grid using artificial neural network technique

Nowadays, the combination of conventional and renewable energy sources such as solar energy is one of the most widespread solutions to surmount the challenge of the climate and energy crisis. In the presence of random behavior of photovoltaic systems and variable power demand by consumers, energy management is a real challenge. In this paper, we propose a new energy management technique based on artificial neural networks in a smart grid. This will ensure the continuous supply of electricity to the consumer in the presence of random operation in energy consumption and generation. The global system is modeled and simulated under the MATLAB/Simulink tool.

Cost Analysis for CO2 Reduction Pathways

It has been widely acknowledged that addressing the issue of climate change should focus on reducing, and ideally eliminating, the influx of CO2 to the atmosphere. This has led to the emergence of various CO2-reducing technologies, which differ in their economic and environmental performances. Considering only one side of the problem may be misleading, so this work introduces a novel high-level cost analysis methodology which assesses the different CO2 reduction options based on CO2 marginal abatement cost - MAC (an indicator that integrates environmental and economic performances). The study contributes to the existing literature by accounting for the impact of the temporal variations in power demand and renewable energy sources on the MAC of the considered options. The proposed approach is demonstrated through an analysis that explores integrated pathways for CO2 reduction through CCUS technologies and renewable energy technologies. Energy storage is considered for the intermittent renewable energy options. The results for the different scenarios are combined on a Mini-MAC curve, a recently developed cost analysis tool for planning CO2 reduction pathways, in which significant insights on the economics of the CO2 reduction are demonstrated and analyzed.

Development of deep learning artificial neural networks models to predict temperature and power demand variation for demand response application in cold storage

Food warehouses and cold rooms have a significant potential for Demand Response (DR) application (stopping or reducing the power of fans and compressors of the refrigeration system) due to thermal inertia of food products. However, as air and food temperature might increase beyond acceptable limits during DR periods, DR needs to be carefully applied in order to respect the food temperature regulation and to maintain quality and safety of the products. It is thus important to predict the system behaviour in case of DR application in order to evaluate its potential impacts and to decide if DR can be performed or not. Four deep learning artificial neural networks (ANN) models, traditional Long Short-Term Memory LSTM, stacked LSTM, bidirectional LSTM and convolutional LSTM, were developed to predict future temperature and power demand perturbations due to the application of DR in cold storage. The aims of this work are: first, to assess the performance of those models in predicting the system behaviours, in particular the sudden variations during and after DR applications, and second, to identify the impact of data availability (number of sensors, their positions) and data characteristic (quality, quantity and DR patterns) on the prediction performance. The results have shown the high potential of deep learning ANN models in supporting DR application in cold storage.

A unified benchmark for security and reliability assessment of the integrated chemical plant, natural gas and power transmission networks

This work presents a simulation framework to investigate the rigorous transient behavior of integrated systems comprising natural gas and power transmission networks, and a chemical plant whose feedstock is natural gas. This framework entails dynamic models for the gas transmission network and the SynGas plant, and a continuous-time AC-power flow formulation with dispatchable loads. It addresses the following key challenges: (i) analyzing energy and chemical system interdependencies, and their impacts on each other's supply reliability and security; (ii) providing an environment conducive to settling a critical question of how to prioritize the natural gas consumption as fuels of power plants or feedstocks of chemical plants based on sustainability and resiliency criteria. The framework provides an integrated benchmark enabling users to realistically study various planning, scheduling, and operation problems of these integrated systems such as process control, fault diagnosis, optimization, and environmental damage analyses. Effectively coordinating natural gas, power, and chemical industries, the benchmark outclasses conventional energy test problems in analyzing presented reliability and security scenarios since such traditional works solely focused on integrated gas and power systems. Results evidence the effects of nonlinear behavior of chemical plants on reliability and security of both natural gas and power transmission networks, in an integrated manner. Moreover, scenarios on power demand variations, compressor failure occurrences, and gas storage utilizations are enacted; according to the results, the unified benchmark lends insight into crucial information such as spatiotemporal characteristics of disturbances and their propagation throughout the integrated systems and the required period and strategy to damp such disturbances. Such information facilitates reliability and security analyses of the integrated systems and enables the coordination of chemical plants with traditional gas and power networks, particularly in multiple load shedding events which requires supply prioritization of natural gas to power and chemical plants.

Power and Energy Rating Considerations in Integration of Flow Battery with Solar PV and Residential Load

Integration of renewable energy sources such as solar photovoltaic (PV) generation with variable power demand systems like residential electricity consumption requires the use of a high efficiency electrical energy system such as a battery. In the present study, such integration has been studied using vanadium redox flow battery (VRFB) as the energy storage system with specific focus on the sizing of the power and energy storage capacities of the system components. Experiments have been carried out with a seven-day simulated solar insolation and residential load characteristics using a 1 kW VRFB stack and variable amounts of electrolyte volume. Several scenarios have been simulated using power and energy scale factors. Battery response, in terms of its power, state of charge and efficiency, has been monitored in each run. Results show that the stack power rating should be based on peak charging characteristics while the volume of electrolyte should be based on the expected daily energy discharge through the battery. The PV source itself should be sized at about 25% more energy rating than the average daily load. The ability to design a VRFB with a high power-to-energy ratio makes it particularly attractive for PV-load integration.

Optimal Energy Management Strategy for a DC Linked Hydro–PV–Wind Renewable Energy System for Hydroelectric Power Generation Optimization

The goal of this article is to create an intelligent energy management system that will control the stand-alone microgrid and power flow of a grid associated that includes Battery Energy Storage System, Fuel Cell, Wind Turbine, Diesel Generator, Photovoltaic, and a Hydro Power Plant. Storage systems are required for high dependability, while control systems are required for the system's optimum and steady functioning. The control, operation, and planning of both energy demand and production are all part of energy management. By controlling unpredictable power and providing an appropriate control algorithm for the entire system, the suggested energy management strategy is designed to handle diverse variations in power demand and supply. Under the TOU Tariff, the problem is presented as a discrete time multi-objective optimization method to minimize grid imported energy costs. It also maximizes earnings from surplus RE sales to the grid at a pre-determined RE feed-in tariff. Simulations were run using SIMULINK/MATLAB to validate and evaluate the suggested energy management approach under various power demand and power supply scenarios. The simulations indicate that the proposed energy management can fulfill demand at all times utilizing unreliable renewables like wind, solar, and hydroelectric power plants, as well as hydrogen fuel cells and batteries, without affecting load supply or power quality.

Techno-economic analysis of long-duration energy storage and flexible power generation technologies to support high-variable renewable energy grids

Techno-economic analysis of long-duration energy storage and flexible power generation technologies to support high-variable renewable energy grids

Recurrent Neural Network Based Short-Term Load Forecast with Spline Bases and Real-Time Adaptation

Short-term load forecast (STLF) plays an important role in power system operations. This paper proposes a spline bases-assisted Recurrent Neural Network (RNN) for STLF with a semi-parametric model being adopted to determine the suitable spline bases for constructing the RNN model. To reduce the exposure to real-time uncertainties, interpolation is achieved by an adapted mean adjustment and exponentially weighted moving average (EWMA) scheme for finer time interval forecast adjustment. To circumvent the effects of forecasted apparent temperature bias, the forecasted temperatures issued by the weather bureau are adjusted using the average of the forecast errors over the preceding 28 days. The proposed RNN model is trained using 15-min interval load data from the Taiwan Power Company (TPC) and has been used by system operators since 2019. Forecast results show that the spline bases-assisted RNN-STLF method accurately predicts the short-term variations in power demand over the studied time period. The proposed real-time short-term load calibration scheme can help accommodate unexpected changes in load patterns and shows great potential for real-time applications.

Compact Steam Bottoming Cycles: Minimum Weight Design Optimization and Transient Response of Once-Through Steam Generators

Compactness and weight minimization are key aspects for successful and widespread implementation of waste heat recovery steam cycles in off-shore oil and gas platforms due to the site weight and volume footprint constraints. The power plant off-shore must be designed for flexibility in its operations to provide varying power demands across multiple time scales. Reliability of the heat and power production units is crucial. Within a case study in an off-shore platform in the Norwegian Continental Shelf, this article conducts design optimization of compact and low-weight steam cycles for power production from gas turbine exhaust and transient analysis of the core of heat recovery steam generators (HRSGs) via dynamic modeling and simulation, considering once-through steam generators (OTSGs) for the HRSGs. A method for simultaneous thermodynamic and heat exchanger geometry optimization design for bottoming cycles is applied, with the main objective being weight minimization and compactness of the cycle heat exchangers. Ten different optimal minimum weight bottoming cycle designs are provided by selecting ten different manufacturable tubes. The resulting bottoming cycle designs are compared in terms of weight, OTSG core weight distribution, heat transfer area, and footprint. The resulting bottoming cycle weight varies from 48.4 to ca. 87.10 ton for designs sensible for off-shore applications, and from 95.8 to 178.9 ton when selecting outer tube diameters typical of onshore applications. Smaller outer tube diameter selection in OTSG bundles is a key driver for low-weight and compact steam cycle designs. Three different designs representing light, normal, and heavy OTSG designs are compared by dynamic trajectory and response time analysis under transient scenarios by means of dynamic modeling and simulation. More compact and lighter designs respond faster to changes in the gas turbine (GT) operation upstream the OTSG. The results in this analysis indicate the need for feedforward control. Feedback control alone is probably not a good option due to the high OTSG open loop stabilization time and large sensitivity to GT exhaust gas variations. More compact and low-weight designs based on the OTSG can reduce potential challenges in controlling and stabilizing bottoming cycles for power production.

Techno-economic evaluation and optimization of CCGT power Plant: A multi-criteria decision support system

A key objective of the power generation industry is to achieve maximum economic benefit without over-consuming the life of power plants and over-maintaining its assets. From a CCGT power plant operator’s perspective, the stand-alone performance analysis of the plant is not enough to support the decision-making process due to the plethora of possible scenarios characterized by variable ambient conditions, engine health (fouling, erosion), electricity prices, and power demand. This study proposes a novel methodology to support decision-making for a CCGT power plant’s operational optimization. The comprehensive techno-economic performance evaluation is conducted by multidisciplinary optimization and decision-making to enhance information integration for the combined cycle power plant operated by Manx Utilities in the Isle of Man, UK. The decision support system has the capability to recommend the optimal operation schedules to plant operators. It recommends that the more severely degraded engine should run at a relatively lower power setting to decrease creep life consumption. The established power plant optimization framework has the potential to assist power plant operators in deciding the total power output and power split between gas turbines based on optimization results that considers both immediate thermo-economic benefits and life consumption. Finally, the proposed system can facilitate similar power plants to adjust daily operations to achieve thermo-economic and lifing benefits.

An MPC Based Algorithm for a Multipurpose Grid Integrated Solar PV System With Enhanced Power Quality and PCC Voltage Assist

The continuously fluctuating energy output and varying power demands in the renewable energy systems have led to the degradation of power quality. This work presents a model predictive based control for a solar PV system integrated to the grid for optimal management and control of the power transfer. The double stage three-phase configuration is controlled using model predictive control (MPC) strategy, which considers the power converters' switching states to predict the next control variable. The control uses a modified-dual second-order generalized-integrator for estimation of the power requirements based on the continuously varying system parameters. The PCC voltages assist and the ride through operation are performed based on the drops in voltage levels and optimum switching state is selected based on the minimization of the cost function to deliver the required active and reactive powers to the grid. The performance of the controller is validated through simulation and is also shown using hardware implementation. The IEEE-519 standard is followed throughout and a comparative analysis shows the remarkable performance of the presented grid controller.

Adaptive Power Factor Regulation Under Asymmetrical and Non-Sinusoidal Grid Condition With Distributed Energy Resource

Active and reactive power regulation, unbalanced current compensation, and harmonic current mitigation are the most significant functionalities typically embedded to a three-phase multifunctional grid-connected inverter. However, a vital control feature minimally addressed in the literature is the capability to adjust the grid power factor to the minimum value required by standards or grid codes. Hence, this paper presents an adaptive compensation approach to perform dynamic power factor regulation under varying power demand and unpredictable energy generation, also withstanding non-ideal voltage conditions. To demonstrate such an approach, a global power factor definition is first introduced, being validated upon bidirectional power flow conditions and under unbalanced and distorted voltages. Secondly, a simple algorithm is devised to attain scaling coefficients used on compensation purposes, allowing to instantaneously weigh up reference control signals to track a desired grid-side power factor value. As a result, the strategy can be used to retrofit the controllers of grid-connected inverters with little effort, limiting distribution losses and improving power quality. Simulations and analyses of a representative real study case are conducted to illustrate how the proposed approach copes with unpredictable distributed energy resources and variable load demands. Moreover, experimental results considering a grid-connected inverter prototype are shown to validate the feasibility of the control approach to real-life implementations.