Real-time Power Research Articles

An improved A-ECMS energy management for plug-in hybrid electric vehicles considering transient characteristics of engine

The plug-in hybrid electric vehicles (PHEVs) are playing an increasingly important role in urban public transportation systems for their unique potential for energy saving and emission reduction. However, as an enabling technology for the cost-efficient operation of PHEVs, the current energy management strategies (EMSs) rarely consider the transient characteristics of the engine, especially the limit of engine transient performance and the extra fuel consumption due to engine state changes. To improve the energy-saving effect of EMSs, an optimization study on energy management considering engine transient characteristics for PHEV is carried out in this paper. Firstly, a high-precision transient fuel consumption model is established based on artificial neural network (ANN) to accurately evaluate the real fuel consumption of engine under steady and unsteady states. Secondly, an adaptive equivalent consumption minimization strategy (A-ECMS) is constructed for PHEV, and the engine transient performance boundary is defined in the strategy to avoid unreasonable engine power surge decision. Thirdly, the transient fuel consumption model is incorporated into the equivalent fuel consumption cost function of A-ECMS to fully consider the impact of engine transient fuel consumption on the real-time power allocation of PHEV. The results show that the improved strategy weakens the state fluctuation of the engine, and makes the engine run more smoothly, resulting in a 99.16% reduction in the extra fuel consumption due to engine state changes. Finally, the fuel economy of the PHEV under the combined driving cycle based on the C-WTVC improved by 3.37%.

An effective solution to boost generation from waves: Benefits of a hybrid energy storage system integration to wave energy converter in grid-connected systems.

Background: Wave energy represents one of the most promising renewable energies due to its great theoretical potential. Nevertheless, the electrical compliance of grid-connected systems is a great issue nowadays, due to the highly stochastic nature of wave energy. Methods: In this paper, a Hybrid Energy Storage System (HESS) consisting of a Li-ion battery and a flywheel is coupled to a Wave Energy Converter (WEC) that operates in grid connected mode. The study is performed using real yearly wave power profiles relating to three different sites located along the European coasts. The Simultaneous Perturbation Stochastic Approximation (SPSA) principle is implemented as real-time power management strategy for HESS in wave energy conversion systems. Results: Obtained results demonstrate how the proposed HESS and the implementation of the SPSA power management coupled to a WEC allow a reduction of more than 80% of power oscillations at the Point of Common Coupling (PCC), while proving the robustness of the developed management strategy over the investigated sites. Moreover, the average energy penalty due to the HESS integration results slightly higher than 5% and battery solicitation is reduced by more than 64% with respect to the flywheel solicitation, contributing to extend its lifetime. Conclusions: HESS integration in renewable generation systems maximizes the WEC production while smoothing the power at the PCC. Specifically, flywheel-battery HESS together with the implemented power management strategy could provide a great flexibility in the view of increasing power production from waves, strongly mitigating the variability of this source while enhancing grid safety and stability.

A Real-Time Adaptive Energy Optimization Method for Urban Rail Flexible Traction Power Supply System

In urban rail traction power supply systems, the optimal power flow method is mostly utilized for energy optimization. Conventional power flow optimization adopts offline calculation methods to obtain the optimal strategy under given scenarios. However, this method cannot continuously realize the ideal optimization effect for the time-varying flexible traction power supply system. For this reason, this paper proposes a real-time adaptive energy optimization method to solve the problem above. Firstly, the control objective of the optimal power flow is determined based on analyzing the system power flow. Secondly, a hierarchical control system is designed to achieve adaptive optimal control for the flexible traction power supply system. Moreover, a swarm intelligence optimization algorithm-based optimal power flow calculation method is employed to realize the real-time optimal power flow. Finally, some simulations are performed to verify the effectiveness and accuracy of the proposed real-time optimal power flow. It is demonstrated that the real-time optimal power flow method makes it possible to consider the low delay and high accuracy of control commands at the same time. Compared with the conventional offline method, the proposed real-time method can reduce the input electricity consumption by 1.47%, which is valuable in practical applications.

A Real-Time Sinusoidal Voltage-Adjustment Power Supply Based on Interleaved Buck Converters With Enhanced Reference-Tracking Capability

In order to satisfy the requirement of fast dynamic response, high linearity and low distortion for the power supply of piezoelectric actuators, this paper presents a real-time sinusoidal voltage-adjustment power supply (RTSPS) with enhanced reference-tracking capability. The proposed power supply is composed of a pair of interleaved buck converters and an analog power amplifier. Since interleaved buck converters have an adequately fast response, the supply voltage of the analog power amplifier can follow its output voltage to reduce the power loss. The operating principles of the proposed power supply are presented, and its improved reference-tracking capability is analyzed and verified by experimental results. An application demonstration of the RTSPS with the proposed interleaved buck converters is shown, which provides a biased 500-Hz quasi-sinusoidal voltage for the piezoelectric actuator.

Robust Generation Dispatch With Strategic Renewable Power Curtailment and Decision-Dependent Uncertainty

As renewable energy sources replace traditional power sources (such as thermal generators), uncertainty grows while there are fewer controllable units. To reduce operational risks and avoid frequent real-time emergency controls, a preparatory schedule of renewable generation curtailment is required. This paper proposes a novel two-stage robust generation dispatch (RGD) model, where the preparatory curtailment threshold is optimized in the pre-dispatch stage. The curtailment threshold will then influence the variation range of real-time renewable power outputs, resulting in a decision-dependent uncertainty (DDU) set. In the re-dispatch stage, the controllable units adjust their outputs within their reserve contributions to maintain power balancing. To overcome the difficulty in solving the RGD with DDU, an adaptive column-and-constraint generation (AC&CG) algorithm is developed. We prove that the proposed algorithm can generate the optimal solution in a finite number of iterations. Numerical studies show the advantages of the proposed model and algorithm and validate their practicability and scalability.

Real-Time Energy Management for Marine Applications Using Markov Approximation

All-electric and hybrid-electric ships have become the centerpiece for reducing greenhouse gas emissions and improving fuel efficiency in the maritime industry. Real-time power management of multiple power sources in both design and operation becomes critical due to the uncertainty and randomness of the vessel's power demand. Furthermore, the vessel operators may have additional requirements for operational flexibility, such as fully recharging the battery before the next trip and limiting engine switch on/off to reduce wear-and-tear, which lead to complex power management systems requiring domain-expert knowledge. In this paper, a hybrid solution for real-time power management is proposed. The model-based approach encapsulates the power system, and the data-driven approach (a machine learning technique called Markov approximation) handles the uncertainty. First, an offline deterministic optimization problem for power management is formulated. Second, the deterministic problem is transformed into a maximum weighted independent set (MWIS) problem. Next, the Markov approximation framework is applied to the transformed problem to utilize the machine learning techniques widely used in wireless communications and computing industries. Finally, a reinforcement learning-based real-time solution is proposed. Extensive simulations are performed on a ferry case study, and the results are within the proven theoretical bounds of machine learning techniques.

Demand Response Potential Evaluation of Aggregated High-Speed Trains Toward Power System Operation

The integration of a high proportion of intermittent renewable energy sources and the continuous fluctuation of loads in power systems have created an urgent need for flexible reserves. It has been demonstrated both theoretically and experimentally that high-speed trains (HSTs), with booming electricity consumption, can flexibly change their speed to achieve real-time power climbing, reduction, or even feedback from the train to the grid. In this paper, the flexibility of HSTs is investigated from a power system-oriented perspective, and the demand response (DR) potential of HSTs is exploited based on the kinematics and dynamics equations of trains. The lagging power rebound (LPR) effect after the DR period of HSTs is enclosed for the first time, and a duration-capacity-speed-LPR index system is established to quantify the DR performance of HSTs. Considering the operation requirements of both power and railway systems, a novel operation optimization model for aggregated HSTs is proposed to calculate their maximum DR potential. In the proposed model, the LPR of HSTs is constrained to ensure power system security, and the minimum distance limit between HSTs sharing the same railway is also included to ensure railway safety. In addition, an adaptive solution method based on pseudo-spectral is used to solve the optimization model. This approach balances the requirement for calculation speed in the power system with the need for temporal accuracy in the railway system. Finally, the proposed model and technique are verified using a 3-railway 6-HST system.

Real-Time Power Tracking Control for Supercapacitor-Based Regenerative Braking System Using Interacting Multiple Model Filter

The mismatch between the charging power of the supercapacitor and the braking power of the motor will lead to redundant energy cycling. To accurately track the braking power, a real-time tracking control of braking power using interacting multiple model filter is proposed. Three supercapacitor models are established, and a novel interacting multiple model filter is developed accordingly. The main capacitor voltages of three supercapacitor models interact to obtain the terminal voltage of the supercapacitor. Then the loss function is established with power tracking as the objective. The reference current of the supercapacitor is obtained under the constraints. The comparative experimental results show that with the proposed control strategy, the redundant energy cycling of the system is reduced by 55% within one operating cycle.

Monitoring-Based Localization of Unbalances and Root Cause Analysis in Low-Voltage Distribution Systems

The dynamic nature of power systems operation, lack of sufficient monitoring initiatives, and the continuous nature of the power quality phenomena are the primary reasons behind a distribution system operator's inability to localize and understand the root cause behind unbalance. Additionally, there is a deficiency of algorithms that can leverage the grid monitoring data, even if limited, to present a sufficiently useful overview of unbalance propagation which can lead to appropriate corrective measures. This article presents: a new unbalance index derived from feeder active power measurements, an algorithm for localization of unbalanced contributions, and an algorithm for root cause analysis that can distinguish whether the unbalance problem is loading, asymmetry, and/ or fault-oriented. The end goals of this work are twofold: to present a holistic view of the grid unbalanced situation using EN50160 type monitoring, and to assist the distribution system operators in unbalance reduction. The usability of the proposed algorithms and indices is shown using a hybrid European MV-LV model simulated in OpenDSS and on two actual LV distribution systems with real-time power quality monitoring.

A novel variable power microwave (VPM) drying technology for lowering energy consumption and improving the in vitro protein digestibility of black solider fly larvae

In this study, a novel variable power microwave (VPM) drying process that can automatically and continuously adjust microwave power according to temperature feedback was developed to dry black soldier fly larvae (BSFL). The influence of VPM and constant power microwave (CPM) drying on the drying time, energy consumption, and product quality of BSFL and the potential relationship between BSFL quality and microstructural properties were investigated. The results showed that VPM drying could reduce 17.14% ∼ 27.50% of the drying time and 3.37% ∼ 12.24% of the energy consumption compared with CPM drying. BSFL protein produced by VPM drying exhibited higher digestibility. VPM drying decreased the formation of β-sheets and disulfide bonds within the protein, reducing the aggregation of protein molecules. Scanning electron microscopy and differential scanning calorimetry analysis also demonstrated that VPM-dried BSFL products had smaller protein particle sizes and less protein denaturation. Industrial relevanceVPM drying can reduce energy consumption while improving BSFL drying efficiency and help to maintain the quality of dried products. The control system that can adjust the microwave power in real time according to the temperature feedback can be easily applied to the continuous tunnel microwave drying equipment widely used in the food industry to realize large-scale VPM drying of insects. The results obtained in this study can help to improve insect-based product quality by using VPM drying and provide technical support for edible insects as an alternative protein source in the future.

Simultaneous AC/DC distribution for the sustainable operation of smart building clusters in hybrid urban microgrids

Hybrid AC/DC urban microgrids (HUMG) have emerged as a candidate solution to reliably, efficiently, and economically meet the increasing consumer electric demand and to ensure the quality of delivered power. Smart buildings (SB) are prevalent prosumers in HUMGs. To effectively utilise renewable resources in SBs, it is imperative to have assistance from battery energy storage systems (BESSs). Also, uncertainty in generation and demand necessitates real-time ancillary power support for SBs. Smart building interconnection forming SB clusters is an emerging solution for providing ancillary power support. Existing SB interconnection topologies require multiple power conversion stages, additional infrastructure, and underutilise the existing AC grid infrastructure. Most of these SB interconnections are not sustainable due to higher losses from more power conversion stages, increased cost, and excessive dependence on BESSs. A composite transmission system-based ancillary power dispatching architecture (CTS-APDA) for the sustainable operation of SB clusters is proposed in this paper. A multi-agent system based hierarchical control and decision logic in the CTS-APDA ensures the optimal ustilisation of battery resources. The operation of the proposed CTS-APDA and its efficacy as a fast-acting ancillary support is validated through extensive case studies. The proposed CTS-APDA will foster participation of electric vehicles in the transactive energy market of emerging SBs.

Time-series quasi-dynamic load flow analysis with seasonal load variation to resolve energy nexus for a practical distribution network in Puducherry smart grid system incorporating harmonic analysis and mitigation

Time-series quasi-dynamic load flow analysis is an important methodology to estimate the voltage profiles across various nodes in modern distribution networks. This paper describes the necessity of time-series load flow analysis (LFA) for a real-time power distribution network in Puducherry smart grid system by considering seasonal load variations in the union territory of Puducherry, India to obtain optimal performance. In this study, this load flow analysis has been applied to test systems such as IEEE 69, IEEE 37, IEEE 34 and the Indian utility 29 node distribution network (IU29NDN) in Puducherry smart grid system with unbalanced load patterns and energy sources in close proximity. Modified Decision Making (MDM) algorithm is proposed in this paper to improve voltage profiles with in distribution networks by choosing the size and location of solar photovoltaic (SPV) systems. The variations in the node voltages, real power, and reactive power flows are estimated for distribution networks subjected to seasonal load variations by quasi-dynamic load flow simulations conducted over a period of 24 h with 15-minute step size. Furthermore, a harmonic power flow is illustrated and extended to mitigate the harmonics by optimal placement of shunt capacitors (SCs) in all the test systems by incorporating MDM algorithm.

Real-time power optimization based on PSO feedforward and perturbation & observation of fuel cell system for high altitude

The control technology of the air supply system is key to the performance and reliability of the fuel cell system (FCS). However, due to the low oxygen pressure in high altitude environment, the power consumption of the air compressor is large, and the output power of FCS is seriously degraded. To effectively increase the output power of the FCS under changeable altitude conditions, this article introduces a real-time power optimization strategy based on the particle swarm optimization (PSO) feedforward and perturbation and observation (P&O) algorithm. Considering the change of total entropy and loss of centrifugal air compressor with altitude, the working characteristics of centrifugal compressors at different altitudes are studied. Aiming at the long optimization time of the traditional P&O method and the misjudgment problem when the external conditions change drastically, the PSO feedforward-based P&O method is designed to adjust the air compressor voltage. The results show that the proposed strategy has real-time power optimization capability and can adapt to altitude change. The average setting time is 1.8 s when the operating condition changes and the power decreases to 9.1% at 3000 m altitude under a higher load (300 A).

Data driven approach for the management of wind and solar energy integrated electrical distribution network with high penetration of electric vehicles

With the increased penetration of fluctuating renewables and growing population of contemporary loads such as electric vehicles, the uncertainties in the generation and demand in the electric power grids are increasing. This makes the efficient operation and management of these systems challenging. Objective of this study is to propose a real-time management system for EV charging, which maximises the renewable energy utilization. An electric power distribution network with an average and peak demands of 1.51 MW, and 3.6 MW respectively, was chosen for the study. The real time power flow through the network components were analyzed using the OpenDSS model. With a wind power density of 574.51 W/m2 and a solar insolation of 4.14 kWh/m2/day, an optimized renewable energy system consisting of a 2.3 MW wind turbine and 2.61 MWp photovoltaic power plant are proposed for the network. Models based on k-Nearest Neighbors algorithms were developed for predicting the performances of these renewable energy systems at the network area. Based on the load profile, power flow analysis, and the predicted generation from solar and wind systems, a demand side management algorithm has been developed for the charge/discharge scheduling of the electric vehicles connected within the network. The basic objective of the algorithm is to maximize the renewable energy utilization by triggering the charging cycle during the periods of excess renewable energy generation. With an annual contribution of renewables is estimated as 12.61 GWh out of which 9.33 GWh is from wind and 3.29 GWh from solar. Wind from wind and solar energy systems, the proposed scheduling algorithm could contribute 71.56 percent of the charging load demand by the EVs.

Optimal sources rating of electric vehicle based on generic battery storage system model

Batteries are the foundation stone of the hybrid-electric vehicle, where the powertrain is made of a battery and an energy source. An accurate battery model is a necessary tool for a successful sizing procedure. This paper presents a new generic battery model for the sizing process; it utilizes different methods of battery mocking up into one model. Firstly, a database is created based on battery tests performed under various conditions. Then, the model receives the real-time power demand and signals of the environment status; then, the algorithm characterizes the parameter changes. The algorithm interpolates and extrapolates the data to find the predicted operating point. The findings are fed into a battery-equivalent circuit where some basic parameters are revealed. Then, analytical equations are employed to supply all battery parameters. The outcomes show that the proposed generic model can better predict the battery parameters through all operating regions under varying conditions. The suggested algorithm could be designed for all types of batteries by reentering the data for any specific battery. A case study was made on the lithium ferro-phosphate (LiFePo4) battery. The experimental results demonstrate that the proposed model is more accurate than the others; thus, the sizing results are more optimal.

Multi-stage risk-based assessment for wind energy accommodation capability: A robust and non-anticipative method

The volatility and uncertainty of wind energy has brought great challenges to its accommodation in electric power systems. To determine a wind power trading strategy in the real-time power market, the accurate assessment of wind energy accommodation capability (WAC) is of great significance. However, most current studies investigate the wind energy accommodation potential from the perspective of dispatch, but rarely provide quantitative assessment results. Additionally, the non-anticipativity of decisions has not been considered in the assessment yet. This paper proposes a novel quantitative assessment method for WAC based on the multi-stage robust optimization (RO), which addresses the anticipativity issues in the traditional two-stage method while maintaining the decision-making process' robustness. In the assessment method, the operational risk is integrated via a tractable scheme to obtain dynamic admissible WAC boundaries. To obtain the optimal solution of the proposed multi-stage RO problem, a fast robust dual dynamic programming (FRDDP) algorithm is employed, where the adaptive techniques are developed to accelerate the computation. Numerical studies on the modified IEEE 14-Bus system and a real-world system in Zhejiang Province of China validate the effectiveness of the proposed assessment model and adaptive acceleration techniques. The simulation results demonstrate the presented method brings a 18.87% reduction in operational cost, and reduces 29.34% curtailment of wind energy. Compared with the original FRDDP, the adaptive technique significantly reduces the computational consumption by 73.34% on the real-world test system.

Tracking Photovoltaic Power Output Schedule of the Energy Storage System Based on Reinforcement Learning

The inherent randomness, fluctuation, and intermittence of photovoltaic power generation make it difficult to track the scheduling plan. To improve the ability to track the photovoltaic plan to a greater extent, a real-time charge and discharge power control method based on deep reinforcement learning is proposed. Firstly, the photovoltaic and energy storage hybrid system and the mathematical model of the hybrid system are briefly introduced, and the tracking control problem is defined. Then, power generation plans on different days are clustered into four scenarios by the K-means clustering algorithm. The mean, standard deviation, and kurtosis of the power generation plant are used as the features. Based on the clustered results, the state, action, and reward required for reinforcement learning are set. In the constraint conditions of various variables, to increase the accuracy of the hybrid system for tracking the new generation schedule, the proximal policy optimization (PPO) algorithm is used to optimize the charging/discharging power of the energy storage system (ESS). Finally, the proposed control method is applied to a photovoltaic power station. The results of several valid experiments indicate that the average errors of tracking using the Proportion Integral Differential (PID), model predictive control (MPC) method, and the PPO algorithm in the same condition are 0.374 MW, 0.609 MW, and 0.104 MW, respectively, and the computing time is 1.134 s, 2.760 s, and 0.053 s, respectively. The consequence of these indicates that the proposed deep reinforcement learning-based control strategy is more competitive than the traditional methods in terms of generalization and computation time.

Configuration and control strategy of flexible traction power supply system integrated with energy storage and photovoltaic

To mitigate voltage unbalance (VU) and eliminate the neutral sections while reducing the energy consumption of railways, a flexible traction power supply system (FTPSS) with the power hub is proposed. Different from conventional schemes, the proposed system provides a friendly interface to realize the on-site access of distributed photovoltaic (PV) generation along the railway line and concentrated integration of energy storage systems. The power flow is centrally managed by the power hub to reduce energy consumption and the proposed three-port VU compensation method is coupled with the power hub to suppress negative sequence current caused by trains. Considering the volatility of PV generation and traction loads, four operation modes including grid-connected, valley filling, VU compensation, and peak clipping mode, are proposed to dynamically regulate the operation of FTPSS. Moreover, a coordinated control strategy with a hierarchical structure is developed to enable the operation modes. The strategy is implemented with a global controller and multiple local controllers, which can realize real-time power management and flexible transition among different modes. Finally, different simulation and experimental case studies based on the field data are carried out to comprehensively verify the feasibility and effectiveness of the proposed FTPSS and control strategy.

Two-stage scheduling of smart electric vehicle charging stations and inverter-based Volt-VAR control using a prediction error-integrated deep reinforcement learning method

Smart electric vehicle charging stations (EVCSs) having distributed energy resources (DERs), including photovoltaic (PV) systems and energy storage systems (ESSs), are becoming vital devices for increasing their profit and maintaining stable distribution grid operations by scheduling the real/reactive power of DERs. However, prediction errors of PV generation outputs and electric vehicle (EV) loads from EVCSs may decrease their profit and destabilize the distribution grid owing to incorrect EV charging scheduling and voltage regulation. To address this issue, we propose a two-stage framework for smart EVCS scheduling and inverter-based Volt-VAR control (VVC) using prediction error-integrated deep reinforcement learning (DRL). In the first stage, the EVCS agents train their neural network model with a 30-min resolution to maximize their profit through a day-ahead charging/discharging scheduling of ESSs in the EVCSs while responding to various prediction errors of PV generation outputs and EV loads. The total real power consumption of each EVCS, including the charging/discharging schedules of the ESSs calculated in the first stage, is delivered to the second stage, in which the VVC agent trains its neural network model with a 5-min resolution to minimize the real power loss and voltage violations through real-time reactive power scheduling of the ESSs in the EVCSs via their inverters. The proposed approach was tested in the IEEE 33-node and IEEE 123-node distribution systems. The results show that the proposed approach outperforms DRL methods that do not consider the prediction errors in terms of profitability of the EVCS and reduction of real power loss.

Joint Control Strategy of Energy Storage System and Cutting Machine for Transient Stability of Direct Current Locking Rear Delivery Terminal System

With the gradual operation of large-capacity HVDC transmission, HVDC), the characteristics of the “strong and weak communication” of the power grid are increasingly obvious. The power impact of the DC line after locking has a great impact on the power angle stability of the system and seriously threatens the transient stability of the delivery end system. By establishing the equivalent model of the AC/DC system with the energy storage power station and analyzing the transient process after DC locking, we propose a control strategy for the transient stability of the energy storage system and the delivery system after DC locking. This strategy controls the charge and discharge of the energy storage system by collecting real-time power angle and voltage data of the grid, uses the equal area rule, and initiates the cutting machine after the energy storage system is withdrawn. To verify the validity and correctness of the proposed method, a simulation analysis was performed in a modified cross-flow CEPRI-36 node AC–DC system.