Smooth Power Fluctuations Research Articles

Economic Optimal Dispatch of Distribution Networks Considering the Stochastic Correlation of Wind and Solar Energy

The traditional optimization scheduling of distribution networks has often only considered the volatility and randomness of wind and solar output. When estimating the prediction errors of wind and solar output, wind turbines and photovoltaics are typically considered separately, overlooking the correlation between them. Accurate modeling of wind and solar output prediction errors is crucial for enhancing the reliability and economy of distribution network scheduling. To address this, this paper proposes a new modeling method. First, based on the volatility and randomness of wind and solar output, it considers the characteristic that wind and solar outputs in the same region at the same time are correlated. A multivariate nonparametric kernel density estimation is introduced to fit the joint prediction error distribution of wind and solar output using historical data. Next, the impact of joint prediction errors on system scheduling costs is considered by introducing a penalty cost in the economic objective function for the errors caused by wind and solar predictions. Additionally, energy storage devices are integrated into the system to smooth power fluctuations, thereby constructing an economically optimized scheduling model for wind–solar–storage distribution networks based on stochastic correlations. Finally, testing is conducted using an improved IEEE-33 node system. The results indicate that the model considering the correlation between wind and solar output significantly improves the fitting accuracy of prediction errors compared to traditional models that only consider randomness. It also enhances the utilization rate of wind and solar energy and improves the economic performance of the distribution network.

Adaptive Disturbance Rejection and Power Smoothing Control for Offshore Hydraulic Wind Turbines Based on Pitch and Motor Tilt Angles

This paper investigates an adaptive disturbance rejection control (ADRC) strategy for dual-variable power smoothing for hydraulic wind turbine systems deployed in marine environments. Initially, fluctuations in wind speed induce variations in the output torque and rotational speed of the wind turbine; this study examines the interaction between these two variables and subsequently decouples them. An innovative dual-variable anti-disturbance control strategy is proposed, which independently regulates the pitch angle of the rotor and the swing angle of the variable motor to mitigate fluctuations in both speed and torque, thereby achieving a smoother system output power. The simulation results obtained through MATLAB/Simulink (Version R2022a) indicate that employing the proposed control strategy leads to an 8.31% reduction in power generation compared to optimal power tracking strategies while enhancing output power stability by 56.67%. Furthermore, the effective smoothing of power fluctuations is accomplished without necessitating energy storage devices. Finally, the effectiveness of the power smooth output control strategy proposed in this paper was verified based on a semi-physical simulation experimental platform for a 30 kW hydraulic wind turbine. The control method proposed in this paper provides a theoretical basis for the promotion and application of hydraulic wind turbines with stable power output.

Control Strategy for Power Fluctuation Smoothing at Distribution Network Substations Considering Multiple Types of Adjustment Resources

With the proposal of the dual carbon target, the distributed photovoltaic (PV) industry has rapidly developed in recent years. However, the randomness and volatility of photovoltaic energy can be transmitted to the main grid through distribution network substations, posing challenges to the stable operation of the power system. Therefore, this paper considers tapping into the regulation potential of feeder loads on the distribution network side, as well as distributed energy storage and distributed PV resources, to enhance the grid’s control methods. A power fluctuation smoothing control strategy for substations in distribution networks, accounting for multiple types of regulation resources, is proposed. In the day-ahead stage, traditional voltage regulation devices such as the OLTC (on-load tap changer) and CB (circuit breaker) are pre-dispatched based on source–load forecasts, optimizing the fluctuation range of substation power and the number of device operations. This provides optimal substation power values for day-to-day optimization. During the intraday phase, fast regulation devices such as PV (photovoltaic), SVC (static var compensator), and energy storage systems are coordinated, and an optimization model is established with the goal of reducing power curtailment while closely tracking substation trends. This model quickly calculates the active power regulation and device operations of various adjustable resources, improving the economic efficiency of the distribution network system while achieving power fluctuation smoothing at the substation level. Finally, the feasibility and effectiveness of the power fluctuation smoothing control model are verified through simulations on an improved standard distribution system.

A Q‐Learning and Fuzzy Logic Control of Hybrid Energy Storage System Using Two Stage Low‐Pass Filter to Smooth Power Fluctuations in Microgrid

ABSTRACTThe intermittent and fluctuating nature of wind turbine output power is increasingly being recognized as a significant issue adversely impacting the power quality and stability of electrical grids. With the increasing integration of wind power, this challenge cannot be underestimated. However, a potential solution for mitigating the adverse effects of such fluctuations lies in the Hybrid Energy Storage System (HESS), which encompasses battery energy storage systems (BESS) and supercapacitors (SC). The HESS is equipped with flexible operational modes for charging and discharging, thus enabling grid‐connected microgrids to possess the ability to counteract these oscillations. In this article, a control strategy based on the combination of Q‐learning and fuzzy logic control approaches is presented for tuning the parameters of a utilized two‐stage variable time constant low‐pass filter (LPF) in a grid‐connected microgrid. The proposed strategy adaptively tunes the time constants of LPFs to mitigate wind power fluctuations. Furthermore, practical constraints for the energy storage systems and their interfaced converters, such as preventing overcharge/discharge, ramp rate requirements, and certain maximum power conversion ranges, have been taken into account. Numerical simulation results verify the effectiveness of the proposed two‐stage variable time constant LPF for output wind power fluctuation reduction considering practical constraints of HESS.

Maximum power point tracking control and power distribution of marine current power system based on hybrid energy storage

Abstract With the development of industry in modern society leading to the energy crisis and the problem of the earth’s environmental pollution, it is especially important to generate electricity from renewable and clean energy sources. Marine current power generation, as a form of marine energy use, is also the object of widespread concern at home and abroad. A system consisting of supercapacitors and batteries can smooth out the fluctuations of the generating power. Power distribution is a necessary step to achieve smooth grid-connected power fluctuation by using the hybrid energy storage system (HESS). In the article, low-pass filter (LPF) and empirical mode decomposition (EMD) algorithms are used to distribute the marine current power generation, and the simulation experimental model of the present system is constructed in the Matlab/Simulink environment. Results of the simulation experiments show that the power curve of the generator side is obtained, and the generation power distribution is realized. Power signals of different frequencies are separated through the power distribution, so as to obtain the reference power of supercapacitors and the batteries.

Research on priority scheduling strategy for smoothing power fluctuations of microgrid tie‐lines based on PER‐DDPG algorithm

AbstractThe variability of renewable energy within microgrids (MGs) necessitates the smoothing of power fluctuations through the effective scheduling of internal power equipment. Otherwise, significant power variations on the tie‐line connecting the MG to the main power grid could occur. This study introduces an innovative scheduling strategy that utilizes a data‐driven approach, employing a deep reinforcement learning algorithm to achieve this smoothing effect. The strategy prioritizes the scheduling of MG's internal power devices, taking into account the stochastic charging patterns of electric vehicles. The scheduling optimization model is initially described as a Markov decision process with the goal of minimizing power fluctuations on the interconnection lines and operational costs of the MG. Subsequently, after preprocessing the historical operational data of the MG, an enhanced scheduling strategy is developed through a neural network learning process. Finally, the results from four scheduling scenarios demonstrate the significant impact of the proposed strategy. Comparisons of reward curves before and after data preprocessing underscore its importance. In contrast to optimization results from deep deterministic policy gradient, soft actor‐critic, and particle swarm optimization algorithms, the superiority of the deep deterministic policy gradient algorithm with the addition of a priority experience replay mechanism is highlighted.

A hybrid energy storage array group control strategy for wind power smoothing

AbstractWith the increase of wind power generation, the safety and economy of power system operations are greatly influenced by the intermittency and fluctuation of wind power. To take the advantage of the complementary characteristics between different energy storage devices, a Hybrid Energy Storage System (HESS) consisting of Battery Energy Storage System (BESS) and Flywheel Energy Storage System (FESS) can alleviate the uncertainty of wind power. This article has proposed a coordinated control strategy through group consensus algorithm based on Model Predictive Control (MPC) for Hybrid Energy Storage Array (HESA) to smooth wind power fluctuations. To allocate power commands to the FESS and BESS, the fluctuation of wind power output is extracted with different frequency domain characteristics as instructions by Empirical Mode Decomposition (EMD) technology. Moreover, a group consensus algorithm based on MPC is proposed to complete the adaptive power allocation of energy storage units. Eventually, the actual wind farm data is used for the simulation to verify the effect of control strategy proposed in this paper. It can be seen that the developed group consensus algorithm based on MPC can cope with different frequency power commands, avoid overcharging and discharging of energy storage media, and smooth wind power effectively.

A Wind Power Fluctuation Smoothing Control Strategy for Energy Storage Systems Considering the State of Charge

With the significant increase in the scale of energy storage configuration in wind farms, improving the smoothing capability and utilization of energy storage has become a key focus. Therefore, a wind power fluctuation smoothing control strategy is proposed for battery energy storage systems (BESSs), considering the state of charge (SOC). First, a BESS smoothing wind power fluctuation system model based on model predictive control (MPC) is constructed. The objective function aims to minimize the deviation of grid-connected power from the target power and the deviation of the BESS’s remaining capacity from the ideal value by comprehensively considering the smoothing effect and the SOC. Second, when the wind power’s grid-connected power exceeds the allowable fluctuation value, the weight coefficients in the objective function are adjusted in real time using the first layer of fuzzy control rules combined with SOC partitioning. This approach smooths wind power fluctuations while preventing overcharging and overdischarging of the BESS. When the grid-connected power is within the allowable fluctuation range, the charging and discharging power of the BESS is further refined using a second layer of fuzzy control rules. This enhances the BESS’s capability and utilization for smoothing future wind power fluctuations by preemptively charging and discharging. Finally, the proposed control strategy is simulated using MATLAB R2021b with actual operational data from a wind farm as a case study. Compared to the traditional MPC control method, the simulation results demonstrate that the proposed method effectively controls the SOC within a reasonable range, prevents the SOC from entering the dead zone, and enhances the BESS’s ability to smooth wind power fluctuations.

A novel two-stage multi-objective dispatch model for a distributed hybrid CCHP system considering source-load fluctuations mitigation

Small-scale distributed energy systems with combined cooling, heating, and power (DES-CCHP) production have attracted international interest. However, fluctuating loads and renewable energies continuously disturb the real-time operation of DES-CCHP and even the connected grid, hindering the broad application of grid-connected DES-CCHP. In this paper, a novel model predictive control (MPC) based two-stage strategy is developed for DES-CCHP, with multiple timescales consideration. In the first stage, a multi-objective MPC is built with inheritance and fusion mechanisms of multi-step scheduling instructions, simultaneously minimizing operation costs and power fluctuations. The second stage performs intelligent decision-making on the results of the first stage, which allows appropriate state-switching instructions to ensure economy. Meanwhile, the decision-making rejects the unnecessary one and triggers a second-round flexibility-based interventional optimization to revise scheduling planning. The case studies show that compared to dispatch without MPC, the economic single-objective MPC, single-stage multi-objective MPC (only applies the first-stage models), and two-stage multi-objective MPC save costs by 5.31 %, 4.80 %, and 4.57 %, respectively. As for fluctuations mitigation, the single-stage and two-stage models significantly smooth power fluctuations by 51.66 % and 69.93 %, respectively. The proposed strategy operates DES-CCHP economically, stably, and grid-friendly under a rugged operating environment.

Optimization of Ship Power Supply Network and Intelligent Energy Management Strategy Under Multiple Energy Modes

<p>Purpose: This article aims to address the issues of incomplete management models and slow convergence speed of optimization models in energy management in ship multi energy systems, and to construct a comprehensive dynamic optimization objective model. Method: Firstly, establish an optimization design model with the objective functions of energy storage system cost, grid power fluctuation smoothing, and energy supply and demand balance; Then, in the optimization process of the objective function, a bus voltage coordination control strategy is adopted, and for the parameter optimization problem in the strategy, a cuckoo search algorithm based on population feature feedback is used for optimization. Result: Through simulation experiments, the method proposed in this article provides effective guidance for capacity configuration and energy management of multi energy ship microgrids, improving quality and efficiency.</p> <p> </p>

Hybrid energy storage system control strategy to smooth power fluctuations in microgrids containing photovoltaics

Abstract The use of a hybrid energy storage system (HESS) consisting of lithium-ion batteries and supercapacitors (SCs) to smooth the power imbalance between the photovoltaics and the load is a widespread solution, and a reasonable probabilistic allocation of the batteries and SCs affects the performance of the HESS. This paper focuses on developing a simple and effective power allocation strategy for the HESS. The moving average filtering (MAF) is considered to effectively decompose the low-frequency components as well as the high-frequency components in the signal and is simple to implement, and we spoil the low-frequency components decomposed by MAF several times to obtain more excellent low-frequency components; moreover, during HESS charging, when the state of charge (SOC) of the SC is high, we reduce the SC’s charging power by decreasing the number of times of MAF usage while ensuring the performance. During the HESS discharging, when the SOC of the SC is low, this strategy is also used to reduce the SC’s discharging power to ensure the SC’s safe operation. The proposed approach is fully validated, and it has excellent performance.

Look-Ahead Rolling Economic Dispatch Approach for Wind-Thermal-Bundled Power System Considering Dynamic Ramping and Flexible Load Transfer Strategy

With a wind-thermal-bundled power system (WTBPS) under high wind penetration levels, the sharp power fluctuations of tie-lines for interconnected grids trigger a significant challenge of security-constrained power system operation. Smoothing power fluctuations with economic dispatch is widely concerned against this challenge. In this paper, an appropriate look-ahead economic dispatch framework for WTBPS considering the variable ramp rate of retrofitted coal-fired units and the flexible load transfer strategy (LTS) in high voltage distribution networks (HVDNs) is proposed. To cope with this issue, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</i> ) we derive a new family of tightened ramping constraints of retrofitted coal-fired units in the linear and second-order conic (SOC) forms, respectively. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ii</i> ) Using graph characterization of typical HVDNs, the simplified voltage-constrained LTS via topological structures can be developed. This proposed look-ahead economic dispatch model is cast as a mixed-integer second-order cone programming (MISOCP) problem, which can be reformulated by Multi-cut Generalized Benders Decomposition (MGBD). This MGBD enables multiple sub-problems can be solved in parallel and accelerates the solving process. Finally, simulation results of a large-scale practical system validate the computational accuracy and efficiency of the proposed approach.

Comparison of Variable Speed Wind Turbine Control Strategies.

Variable speed operation of wind turbines presents certain advantages over constant speed operation. Basically, variable speed wind turbines use the high inertia of the rotating mechanical parts of the system as a flywheel; this helps to smooth power fluctuations and reduces the drive train mechanical stress. Also, variable speed systems could lead to maximise the capture of energy during partial load operation. In this paper alternative control strategies for variable speed operation are considered and compared from the point of view of energy yield and power quality. Control aim is always maximum power generation. Maximum power tracking and power fluctuation will be analysed when exciting the different configurations with a particular wind profile. Also fixed speed wind turbine are considered for the comparison.

Day-ahead schedule optimization of household appliances for demand flexibility: Case study on PV/T powered buildings

Employing the demand flexibility strategy in PV powered buildings can effectively balance solar energy supply and building energy demand, thereby increasing the self-consumption ratio of PV electricity. Despite this, its solar energy utilization is still low due to the limit of the PV efficiency. On the other hand, PV/T modules not only generate electricity but also produce domestic hot water, thus providing higher solar efficiency. In this study, the demand flexibility of various shiftable appliances in a PV/T powered building was investigated. An optimization-based demand flexibility strategy was proposed to reduce electricity cost and maximum grid power. The case study showed that the proposed strategy could reduce the electricity cost by 23 % and smooth grid power fluctuation. Moreover, compared with the PV powered building, the PV/T powered building could reduce the electricity cost by 10 % and significantly improve utility grid friendliness. Furthermore, the forecast error of boundary conditions negatively affected the electricity cost and grid power fluctuations. The sensitivity analysis revealed that the ambient temperature and solar irradiation on the PV/T modules had a greater impact on the optimization objective. Overall, this work aims to provide guidance for planning the flexibility operation of PV/T powered buildings.

Design and control of modular multilevel matrix converter with symmetrically integrated energy storage for low frequency AC system

AbstractIntegrating energy storage units (ESUs) into part of sub‐modules (SMs) enables the decoupling active power control for the modular multilevel matrix converter (M3C). The low frequency AC (LFAC) system based on M3C with symmetrically integrated energy storage (SI‐AM3C) can provide functions such as renewable power fluctuation smoothing and other power grid auxiliary services, which is attractive in engineering practices. This paper focuses on the design and control of SI‐AM3C. For the converter design, an evaluation method for the number of required active SMs (ASMs) is presented. On this basis, the influences of different system operating conditions on the ASM requirement are analyzed in case studies, and the effects of specific circulating injection strategies are also investigated. The control system targeted at decoupled AC side active power for SI‐AM3C is introduced. A modified inner‐arm capacitor voltage balance strategy is introduced to reduce the capacitor voltage deviation between ASMs and passive SMs (PSMs). The conclusions related to the presented estimation method and converter control strategy are verified by time‐domain simulations in PSCAD/EMTDC.

A comparative study of the LiFePO4 battery voltage models under grid energy storage operation

Lithium iron phosphate (LFP) batteries are widely used in energy storage systems (EESs). In energy storage scenarios, establishing an accurate voltage model for LFP batteries is crucial for the management of EESs. This study has established three energy storage working conditions, including power fluctuation smoothing, peak shaving, and frequency regulation. Four voltage models for commercial LFP batteries are developed, including the second-order resistor-capacitor equivalent circuit model, hysteresis voltage reconstruction model (HVRM), one-state hysteresis voltage model, and back-propagation neural network model. To evaluate model suitability in energy storage working conditions, we compare terminal voltage simulation accuracy, SOC estimation accuracy using the extended Kalman filter algorithm, and SOC calculation time. Overall, among the four models, the HVRM proves more suitable for energy storage scenarios, offering guidance for selecting an LFP voltage model in such conditions. Using the hysteresis model, we analyze the hysteresis open-circuit voltage (OCV) variations of LFP batteries in three energy storage scenarios. Research findings indicate that under frequency regulation, OCV exhibits high-frequency, small-amplitude variations, while under power fluctuation smoothing and peak shaving scenarios, OCV takes on a cyclic pattern. Considering the hysteresis OCV enhances Kalman gain matching, thereby improving SOC estimation accuracy.

Numerical modelling of a hydro-pneumatic energy storage system for smoothing power fluctuations from offshore wind

Energy storage systems are imperative in tackling the regulation of electricity supply and demand mismatch to avoid curtailment of wind energy. Co-locating energy storage in the same operating region as the offshore wind farms allows for an unobtrusive environment while also steering away from rising land prices. The presented work involves an offshore Hydro-Pneumatic Energy Storage (HPES) system made up of a subsea accumulator pre-charged with compressed air. The Energy Conversion Unit (ECU) of the system consists of a megawatt-scale hydraulic pump and a turbine. This paper discusses a novel numerical model developed in Python™ for simulating the operation of the ECU pump and turbine of the offshore HPES system by using a simple moving average, based on a time window of wind data, whilst also introducing a one-step-ahead forecasting model. The results of the research are split into two parts; Firstly, the analysis and validation of the Seasonal Auto Regressive Integrated Moving Average (SARIMA) forecasting model is performed. Secondly, results on the pump and turbine performance based on the smoothened power are shown. The research found that despite the centrifugal pump limitations due to variable head operation, a smoothened power output is efficiently (> 90 %) attained in retaining the intermittent power generated.

Hybrid Energy Storage Power Allocation Strategy for Stabilizing Wind Power Fluctuations

In order to solve the problem of energy shortage, renewable energy such as wind power has developed rapidly worldwide. However, due to its time-varying and uncertain nature, wind power is prone to local fluctuations and operational safety hazards when connected to the power grid. In response to the stable grid connection problem of wind power generation, a hybrid energy storage system is proposed A method was proposed to achieve smooth power fluctuations in wind power generation using a model, and a charging and discharging control and allocation method for energy storage was proposed. Firstly, the preliminary power of hybrid energy storage was obtained using the model predictive control method, Then, an adaptive variational mode decomposition method was used to achieve energy distribution between batteries and supercapacitors. Finally, the measured data of a 100MW wind power plant in Xinjiang was analyzed as an example. The proposed method has been validated to not only achieve reasonable power allocation between hybrid energy storage systems, but also effectively reduce the impact of wind power generation on the power grid, achieving long-term safe operation of hybrid energy storage systems.

Research on the Stability of Grid Connected Wind Turbine Combined with Energy Storage Power System Based on a New Wind Power Fluctuation Smoothing Strategy

Wind power equipped with an energy storage system (ESS) has been demonstrated as the best potential configuration for a rapid global energy transition in the future. In the traditional anti-pulse average filtering control strategy for controlling wind power fluctuations, the smoothing window interval is a constant value, when the forecast output power error is considerable, the fixed window interval width may not be able to meet the smoothing demand, so the traditional strategy has the problems of bad smoothing effect and high probability for power fluctuations exceeding the limit. We propose an optimization strategy for energy storage operation in this paper. Firstly, this strategy can dynamically adjust the window interval width according to the power prediction error, and find the optimal window interval width that makes the output power volatility satisfy requirements for grid connection, so as to achieve the effect of improving the smoothing effect. Secondly, by optimizing hydrogen storage systems operation to reduce the demand for storage system capacity, the odds of output power volatility exceeding the limits are reduced. The outcomes indicate that this new smoothing strategy is effective in improving the stability of the wind power grid connection.

A technical feasibility study of a liquid carbon dioxide energy storage system: Integrated component design and off-design performance analysis

Liquid carbon dioxide (CO2) energy storage (LCES) system is emerging as a promising solution for high energy storage density and smooth power fluctuations. This paper investigates the design and off-design performances of a LCES system under different operation strategies to reveal the coupling matching regulation mechanism of the charging and discharging processes. An off-design performance prediction model is developed based on preliminary designs of turbomachinery components and heat exchangers to evaluate the feasibility of the LCES system. The performances of the charging and discharging processes are analyzed under different load levels with two operation strategies: constant pressure charging and constant pressure discharging (CP-CP) operation strategy, and constant pressure charging and sliding pressure discharging (CP-SP) operation strategy. Results show that the LCES system has a round trip efficiency of 61.83% and an energy storage density of 21.92 kW·h·m−3 under the rated condition. As the input load level increases from 80% to 120%, the maximum charging time of the system decreases from 5.15 h to 3.36 h under the constant pressure operation strategy. When the output load level increases from 70% to 120%, the maximum discharging time of the system decreases from 5.48 h to 3.31 h under the constant pressure operation strategy and from 5.60 h to 3.30 h under the sliding pressure operation strategy. Besides, the round trip efficiency of the LCES system increases first and then decreases as both input and output load levels rise, while the energy storage density of the system follows a parabolic curve only with an increase in the output load levels. For better system performance, the CP-SP operation strategy and CP-CP operation strategy are more suitable for the system with output load levels of 70–100% and 100–120%, respectively. These achievements will provide a valuable reference for the stable operation of the LCES under off-design conditions.