Power System Energy Management Research Articles

Research on the adaptability of energy management to thermal management of PEMFCs

Abstract The stable output of power and the effective control of stack temperature play a very important role in the durability and stability of fuel cell hybrid vehicles. In actual working conditions, energy management of power system and thermal management of Proton Exchange Membrane Fuel Cells (PEMFCs) need to be coordinated with each other to jointly ensure the efficient and stable operation of vehicles, however, most of the research in these two directions is independent. In response to the research gaps mentioned above, the concept of adaptability has been proposed for the first time with the aim of combining these two systems for research. This paper takes the fuel cell hybrid vehicle as the research object and establishes the energy management system and thermal management system model based on Matlab/Simulink software. In order to investigate the adaptability of energy management strategies to the temperature control system, two representative types of rule-based and optimization-based energy management strategies (power-following strategy, PFS, and adaptive equivalent hydrogen consumption minimization strategy, A-EHMS) are designed to be numerically simulated in the driving cycle condition of the UDDS, and then combined with the temperature control system of the fuel cell to comparatively analyze the above two types of energy management strategies from the perspectives of power allocation and the impact on the thermal management system. The results show that the PFS provides better protection for the battery under the same operating conditions. And from the perspective of fuel economy and adaptability to the temperature control system, the fuel consumption of A-EHMS is reduced by 12.5% and the adaptability to the temperature control system is improved by about 13.5% compared to PFS.

Analysis of energy management in a hybrid renewable power system using MOA technique

Analysis of energy management in a hybrid renewable power system using MOA technique

Coupling energy management of power systems with energy hubs through TSO-DSO coordination: a review

Abstract The transition towards a sustainable energy system requires the integration of high-penetration renewable energy sources (RESs) and the use of new energy management frameworks to handle RES variability and uncertainty. The evolution of novel energy management technologies has made it possible to design and operate integrated energy systems (IESs) that couple various energy carriers such as electricity, heat, and gas. The emergence of energy hubs (EHs) offers the potential for greater efficiency, reliability, and flexibility in energy supply and demand. However, integrating EHs into the power system operation remains a significant challenge due to the complex interaction between the transmission system operator (TSO), distribution system operator (DSO), and EHs. Enhanced coordination between TSOs and DSOs is of the utmost importance within this context. This paper first aims to review and highlight the most practical features of EHs including the EH concept, topology, elements, classifications, flexibility resources, and optimization approaches. Subsequently, it describes TSO-DSO roles and all concepts that are in line with the aims of this subject, such as conceptual framework, TSO-DSO coordination schemes, formulation, and solution techniques. Meanwhile, it will identify any remaining research gaps and prospective trends in this respect.

Hybrid Power System Topology and Energy Management Scheme Design for Hydrogen-Powered Aircraft

Hybrid Power System Topology and Energy Management Scheme Design for Hydrogen-Powered Aircraft

Comprehensive Analysis on Topological Structure of Modular Multilevel Converters

This paper discusses the importance and application of modular multilevel converter (MMC) in the field of power conversion. Firstly, the article starts with the basic concept of MMC and introduces its key role in high-power electrical energy conversion. Subsequently, to address the complexity of MMC, the article describes in detail the common topologies of MMC, including diode-clamped multilevel topology, flyover capacitor topology, H-bridge cascade topology, and switched-capacitor multilevel topology, and also emphasizes the different scenarios in which they are applied to power systems. The last section describes the capacitor voltage balancing strategy in MMC. Several common capacitor voltage balancing strategies, including hardware clamping method, capacitor voltage closed-loop method, and capacitor voltage sequencing method, are comprehensively discussed in this paper, and the principles, advantages, disadvantages, and applicability of each strategy are thoroughly analyzed. Ultimately, this article offers academics and engineers a thorough review of MMC and offers valuable references for further study and real-world applications in the areas of power system conversion and energy management.

A Novel Sequence-to-Sequence-Based Deep Learning Model for Multistep Load Forecasting.

Load forecasting is critical to the task of energy management in power systems, for example, balancing supply and demand and minimizing energy transaction costs. There are many approaches used for load forecasting such as the support vector regression (SVR), the autoregressive integrated moving average (ARIMA), and neural networks, but most of these methods focus on single-step load forecasting, whereas multistep load forecasting can provide better insights for optimizing the energy resource allocation and assisting the decision-making process. In this work, a novel sequence-to-sequence (Seq2Seq)-based deep learning model based on a time series decomposition strategy for multistep load forecasting is proposed. The model consists of a series of basic blocks, each of which includes one encoder and two decoders; and all basic blocks are connected by residuals. In the inner of each basic block, the encoder is realized by temporal convolution network (TCN) for its benefit of parallel computing, and the decoder is implemented by long short-term memory (LSTM) neural network to predict and estimate time series. During the forecasting process, each basic block is forecasted individually. The final forecasted result is the aggregation of the predicted results in all basic blocks. Several cases within multiple real-world datasets are conducted to evaluate the performance of the proposed model. The results demonstrate that the proposed model achieves the best accuracy compared with several benchmark models.

Optimal Energy Management in Autonomous Power Systems With Probabilistic Security Constraints and Adaptive Frequency Control

The decarbonization of many heavy power-consuming industries is dependent on the integration of renewable energy sources and energy storage systems in isolated autonomous power systems. The optimal energy management in such schemes becomes harder due to the increased complexity and stability requirements, the rapidly varying operating conditions and uncertainty of renewable sources, the conflicting objectives across different timescales, the limited amount of reliable power sources and energy storage. The state of charge management when energy storage is used for multiple services, such as optimal scheduling and frequency support, is one of the most notorious problems in this context. To address this issue, an optimal energy management system is proposed in this paper. It co-optimizes the primary frequency control layer and the dispatch schedule of conventional generators and energy storage by taking advantage of an algorithm that provides adaptive active power demand uncertainty quantification, theoretical guarantees for frequency stability, and bounds for the reserves for frequency support assigned to the energy storage system. A pattern-based reformulation of the frequency stability constraints is derived enabling the efficient solution of the involved optimization problem, being a test case of an isolated offshore oil and gas platform presented for validation.

Security Event-Trigger-Based Distributed Energy Management Of Cyber-Physical Isolated Power System With Considering Nonsmooth Effects.

Due to cyber-physical fusion and nonsmooth characteristics of energy management, this article proposes a security event-trigger-based distributed approach to address these issues with developed smoothing technique. To tackle with nonconvex and nondifferentiable issue, a randomized gradient-free-based successive convex approximation is developed to smooth economic objective function. Due to resilience ability against security issue, a security event-triggered mechanism-based distributed energy management is proposed to optimize social welfare, which coordinately controls both power generators and load demand. The security event-triggered mechanism is designed to reduce power system security risks, and relieve communication burden caused by smoothing calculation, the convergence of proposed distributed algorithm is also properly proved. According to those obtained results on both IEEE 9-bus and IEEE 39-bus systems, it reveals that the proposed approach can achieve good convergence performance and have less security risks than other alternatives, which also proves that the proposed approach can be a viable and promising way for tackling with energy management issue of cyber-physical isolated power system.

Traceless Kalman filter threshold estimation for distributed power loads

Abstract Load forecasting is an important part of smart grid construction, energy management, and sustainable design of power systems, and has a great impact on the reliable operation of power grids, facility planning and other decisions. In this paper, we utilize the traceless transform of the UKF algorithm to obtain the sigma feature points generated by the mean value of the high-frequency wavelet components of the power signals and combine them with the EKF algorithm. We put forward a novel neural network hybrid Kalman TUKF algorithm, which will be used to carry out simulation experiments on the distributed electric loads and to estimate the threshold value of the loads in the numerical experiments. The results show that in comparison with the actual measurements, the TUKF algorithm improves by 34.7% in the RMSE metrics, 38.7% in the MAE metrics, and 40.6% in the MAPE metrics compared to the PFWNN. The TUKF algorithm is closer to the real curves and has the best prediction performance for all the time intervals of the prediction. The change of the threshold value has a more obvious effect on the prediction accuracy, and the best effect is in the scale δ = 0.5, i.e., the threshold frequency is selected as the middle value of the intermediate frequency.

RSM-Based Approximate Dynamic Programming for Stochastic Energy Management of Power Systems

The stochastic energy management (SEM) of power systems is computationally intractable due to its randomness, nonconvexity, and nonlinearity. To solve this problem, a response surface method (RSM)-based approximate dynamic programming (ADP) algorithm is proposed in this paper. Since the value function can be directly obtained by RSM, the proposed algorithm does not need to iteratively approach them as existing ADP algorithms, which facilitates reducing the computing time. In addition, an improved generalized polynomial chaos (IgPC) method (i.e., an extension of RSM) is proposed to calculate the expectation of the value function of ADP in the stochastic environment. Compared with the Monte Carlo method, which is commonly used in existing ADP algorithms, IgPC requires fewer sampling scenarios while providing similar results. Simulation results with two modified IEEE test systems and a real 2778-bus system demonstrate the effectiveness of the proposed algorithm in terms of accuracy and computation efficiency.

Distributionally Robust Energy Management for Islanded Microgrids With Variable Moment Information: An MISOCP Approach

The ever-increasing penetration of renewable energy sources (RESs) has brought tremendous challenges to power system energy management problems. In this paper, we develop a distributionally robust (DR) joint chance-constrained microgrid energy management model while the uncertainties stemming from RESs are embedded. The proposed framework minimizes the expected cost function and ensures that the DR joint chance constraints (CCs) will satisfy for any distribution over a promising ambiguity set, which is designed based on the variable moments. The employed ambiguity set can describe the uncertainties more accurately compared with the commonly-used moment metric (i.e., fixed moments). By deriving equivalent second-order cone programming (SOCP) reformulations of the expected objective function and introducing effective approximation methods such as the optimized Bonferroni approximation to deal with the DR joint CCs, the proposed model is finally reduced to a tractable mixed-integer SOCP problem that can readily be solved. Case studies are conducted on a representative test system to corroborate the effectiveness of the suggested approach.

Multiple satellite and ground clock sources‐based high‐precision time synchronization and lossless switching for distribution power system

AbstractPrecise energy management in distribution power system requires high‐precision time synchronization among large‐scale deployed devices. Multiple clock sources‐based time synchronization possesses advantages of reliability, high precision, and robustness, but still faces several challenges such as coupling between time synchronization error and delay, as well as different timescales between clock source and clock weight optimization. In this paper, a multi‐clock source time synchronization model is constructed and a problem is formulated to minimize the synchronization error and delay through jointly optimizing large‐timescale clock source selection and small‐timescale weight selection. A reinforcement learning‐based multi‐timescale multi‐clock source time synchronization algorithm named RL‐M2 is proposed to solve the formulated problem from a learning perspective. Besides, a lossless switching method is proposed to address the switching problem for multiple clock sources. Simulation results demonstrate the superior performance of RL‐M2 and the lossless switching method in time synchronization delay and error.

Economic energy managementof networked flexi-renewable energy hubs according to uncertainty modeling by the unscented transformation method

The utilization of energy hubs (EHs) in the power system and simultaneous energy management of several energy networks is of high importance to increase energy efficiency. Therefore, this paper focuses on the energy management of grid-connected flexible energy hubs. Scheme includes electrical and heating networks, where renewables and storage devices are in connection using the hub as a coordinator. Problem minimizes the expected operating cost of the mentioned networks subject to networks optimal power flow model and the mathematical representation of flexible EHs. Uncertainties related to the amount of demand, price of electrical and heating energy, and renewable power have also been considered in the problem formulation. The unscented transformation method has been adopted to reduce the volume of the problem to obtain the optimal solution in low computation time, along with the precise modeling of flexibility and modeling of the aforementioned uncertainties. Modeling the flexibility of EHs in the presence of renewable sources, modeling bio-waste energy unit for the simultaneous generation of electric and heat energy, and modeling the various uncertainties of load using the unscented transformation are among the contributions of the paper. The obtained numerical results demonstrate the capability of the proposed scheme in improving the economic and operating status of electrical and heating networks, along with improving the flexibility of EHs in such a way that energy storages in renewable hubs can extract 100% flexibility conditions. In these conditions, the hubs are able to reduce energy costs, energy losses, voltage drop, and temperature by about 25%, 24%, 60%, and 59%, respectively, compared with the power flow studies of the networks under study. Also, based on sensitivity analysis results, the increase in load and energy price leads to an increase in the operating cost of the networks, while the increase in the size of renewable resources and storage devices reduces the networks’ energy cost.

A GM-Based Energy Management Strategy of Hybrid Power System for Hydrogen Fuel Cell Buses

Hydrogen energy is a clean, carbon-free, flexible, efficient, and widely used secondary energy source, which is an ideal alternative to promote the clean and efficient use of traditional fossil fuels. Hydrogen fuel cell bus has the advantages of a high-energy conversion rate, absolute pollution-free, sufficient raw materials, and convenient filling. The hybrid power system, composed of fuel cell and auxiliary energy source, is one of the key technologies to promote the development of hydrogen fuel cell vehicle. This study aims to propose an energy management strategy by analyzing the output characteristics and power allocation of fuel cell and power battery in the hybrid power mode with fuel cell as the main and battery as the auxiliary. A GM (1, N) power prediction strategy was proposed and compared with other strategies as an on-off control strategy and logical threshold value strategy in this study. The variation curves of the battery SOC and fuel cell output power under two working conditions of CCBC and real vehicle conditions were analyzed by using these three strategies, when the initial SOC of power battery is 30%, 70%, and 90%, respectively. Results showed that the power prediction strategy based on GM (1, N) has a better performance in output efficiency and fuel economy when compared to the other two strategies by analyzing the aspects of the battery in the SOC variation and equivalent hydrogen consumption and the fuel cell in the output power variation and hydrogen consumption. This research can be helpful to provide the suggested solution for energy management of the hybrid power system for hydrogen fuel cell buses.

Optimal Energy Management for Virtual Power Plant Considering Operation and Degradation Costs of Energy Storage System and Generators

Even though generating electricity from Renewable Energy (RE) and electrification of transportation with Electric Vehicles (EVs) can reduce climate change impacts, uncertainties of the RE and charged demand of EVs are significant challenges for energy management in power systems. To deal with this problem, this paper proposes an optimal energy management method using the Virtual Power Plant (VPP) concept for the power system considering solar PhotoVoltaics (PVs) and Electric Vehicle Charging Stations (EVCS). The Differential Evolution (DE) algorithm is applied to manage energy in the power system to minimize the operation cost of generators and degradation costs in Energy Storage Systems (ESS) and generators. The effectiveness of the proposed approach is examined and tested on the IEEE 24 bus Reliability Test System (RTS 24) using the MATPOWER tool on the MATLAB program for calculating Optimal Power Flow (OPF). In this study, two situations before and after applying the proposed method are considered. The simulation results demonstrate that a branch constraint violation occurs before using optimal energy management using the VPP concept. In order to solve this issue, the DE algorithm for optimal energy management using the VPP concept is applied by dividing the studied case into two subcases as follows. For the first subcase, two objectives consisting of the minimization of the generator operation cost and the minimization of the battery degradation cost in ESS are considered. In the second case, three objectives comprising the two mentioned objectives with the minimization of generator degradation cost are considered. The results demonstrate that branch constraint violations can be avoided by applying optimal energy management using the VPP concept. This study also suggests considering the generator degradation cost in the objective function, which can minimize the total costs by 7.06% per day compared with the total cost of the first case.

A new rule-based self-reconfigurable energy management control of grid and batteries connected hybrid regenerative solar power system for critical loads

Renewable energy sources are increasing and diversifying day by day. For this reason, new and more efficient controller designs are being developed on diversified renewable energy sources. Thus, renewable energy power systems can vary with the development of both topological, power system element controllers and energy management system controllers in line with the desired demands. In this paper, Hybrid Regenerative Power System (HRPS) can operate at low power with a designing a novel Central Energy Management System (CEMS) that is suitable for the critical AC & DC loads and it is efficient thanks to the regenerative effect. Equipments in this power system were firstly created and they are connected each other to obtain HRPS at MATLAB-Simulink environment. After the HRPS topology emerged, power flow scenarios are automatically operated according to the proposed Energy Management System (EMS) on the HRPS topology with using real irradiance and temperature spectrums. Then, according to the consequences obtained, how efficient the power system is in itself was calculated and when compared to the literature, it was emphasized in which cases it was advantageous compared to other EMS studies.

Detection of False Data Injection Attacks in Smart Grid Based on Joint Dynamic and Static State Estimation

Power system state estimation is an essential component of the modern power system energy management system (EMS), and accurate state estimation is an indispensable basis for subsequent work. However, the attacker can inject biases into measurements to launch false data injection attacks (FDIAs) in smart grids, which ultimately cause state estimates to deviate from security values. This paper proposed the joint use of static state estimation and dynamic state estimation to detect the FDIA, i.e. the joint use of weighted least squares (WLS) and extended Kalman filter (EKF) with exponential weighting function (WEKF), which improves the robustness of state estimation. Since the WLS estimation considers only the measurements at the current moment, the recursive feature of the WEKF enables the estimation process to involve both historical state and current measurements. Therefore, consistency tests and residual tests were performed using the estimations of WLS and WEKF to effectively detect FDIA. In addition, a cluster partitioning approach with approximate equal redundancy of subsystems is proposed to locate the FDIA. The detection of FDIA triggers the partitioning of the network system, and then the chi-square test is used separately in each sub-network to determine the location of FDIA. Finally, the experimental results in the IEEE-14 bus system and the IEEE-30 bus system demonstrate that the approach can effectively detect and locate FDIAs.

Assessment the hydrogen-electric coupled energy storage system based on hydrogen-fueled CAES and power-to-gas-to-power device considering multiple time-scale effect and actual operation constraints

Energy storage technology provides efficient energy management in renewable driven power system. The long duration time-scale fluctuation in unbalance power becomes more obvious and prominent with the elevated renewable penetration level. However, this issue is not widely considered in current energy storage system. In this paper, a green hydrogen-electric coupled energy storage system based on hydrogen-fueled compressed air energy storage (CAES) and power-to-gas-to-power (PtGtP) device is proposed. The hydrogen-based PtGtP device, including proton exchange membrane fuel cell (PEMFC) and PEM electrolzyer, is employed to smooth out the long duration time-scale fluctuation. Whereas, the hydrogen-fueled CAES is used to settle the remaining time-scale fluctuations. Moreover, the coupled feature is reflected by the hydrogen medium. The hydrogen only generates in PtGtP device, but consumes in both PtGtP device and hydrogen-fueled CAES. The performance assessment by considering the actual operation constraints is conducted based on historical data from real world. The simulated results show that the proposed system can provide an effective and flexible power management in the high share renewable power system. The loss of power supply probability (LPSP) is 5.40%, which is higher than that of any single energy storage system. However, the wind curtailment ratio (WCR) is 8.81%, illustrating an insufficient energy storage capacity. Furthermore, the energy shifting occurs in both several days scale and seasonal scale. This is obvious evidence for function of long duration energy storage (LDES) for the proposed coupled energy storage.

Novel Functionalities of Smart Home Devices for the Elastic Energy Management Algorithm

Energy management in power systems is influenced by such factors as economic and ecological aspects. Increasing the use of electricity produced at a given time from renewable energy sources (RES) by employing the elastic energy management algorithm will allow for an increase in “green energy“ in the energy sector. At the same time, it can reduce the production of electricity from fossil fuels, which is a positive economic aspect. In addition, it will reduce the volume of energy from RES that have to be stored using expensive energy storage or sent to other parts of the grid. The model parameters proposed in the elastic energy management algorithm are discussed. In particular, attention is paid to the time shift, which allows for the acceleration or the delay in the start-up of smart appliances. The actions taken by the algorithm are aimed at maintaining a compromise between the user’s comfort and the requirements of distribution network operators. Establishing the value of the time shift parameter is based on GMDH neural networks and the regression method. In the simulation studies, the extension of selected activities related to the tasks performed in households and its impact on the user’s comfort as well as the response to the increased generation of energy from renewable energy sources have been verified by the simulation research presented in this article. The widespread use of the new functionalities of smart appliance devices together with the elastic energy management algorithm is planned for the future. Such a combination of hardware and software will enable more effective energy management in smart grids, which will be part of national power systems.

An autonomous control technology based on deep reinforcement learning for optimal active power dispatch

The large-scale renewable energy integration has brought challenges to energy management in modern power systems. Due to the strong randomness and volatility of renewable energy, traditional model-based methods may become insufficient for optimal active power dispatch. To tackle the challenge, this paper proposes an autonomous control method based on soft actor–critic (SAC), a deep-reinforcement learning (DRL) strategy recently developed, which provides an optimal solution for active power dispatch without a mathematical model while improving the renewable energy consumption rate under stable operation. A Lagrange multiplier is introduced to the SAC (LM-SAC) to promote algorithm performance in optimal active power dispatch. A pre-trained scheme based on imitation learning (IL-SAC) is also designed to further improve the training efficiency and robustness of the DRL agent. Simulations on the IEEE 118-bus system with the open platform Grid2Op verify that the proposed algorithm effectively achieves better renewable energy consumption rate and robustness compared with existing DRL algorithms.