Charge Of Energy Storage System Research Articles

Multi-objective parameter design and economic analysis of VSG-controlled hybrid energy systems in islanded grids

Photovoltaic (PV) systems offer cost-effective power solutions for outlying islands but often compromise system stability due to reduced inertia. This study introduces a Virtual Synchronous Generator (VSG) control strategy, integrated with Energy Storage Systems (ESS) and PV, to enhance system inertia. By optimizing coordination between these energy sources, the proposed method mitigates oscillations and improves grid stability. However, PV-VSG systems are generally not favored by energy providers due to the requirement for pre-curtailment of power output. To address this, the paper proposes a parameter design method for VSG control of ESS and PV, utilizing multi-objective genetic algorithm (MOGA) optimization to simultaneously increase the frequency nadir and minimize the settling time after disturbances. Additionally, an adaptive curtailment decision and parameter design method based on artificial neural networks is introduced to enhance the feasibility of PV-VSG systems by reducing PV pre-curtailment and prioritizing PV power release and ESS charging during frequency oscillations. Real data from the Penghu Archipelago in Taiwan are used to build a dynamic model in DIgSILENT, enabling interaction with MOGA. The Value at Risk (VaR) method with dual stochastic variables is employed to assess the allowable PV installed capacity. The results show that when VaR is set at 1%, the proposed PV-VSG method can increase PV penetration by 57.5% compared to scenarios without VSG. Furthermore, compared to traditional PV-VSG methods, the proposed approach achieves a 16.8% increase in PV penetration and reduces annual PV curtailment by 25 MWh. This study also evaluates the economic impact of planners choosing different risk levels, offering valuable insights for grid development in remote or island regions.

Energy Management of Green Port Multi-Energy Microgrid Based on Fuzzy Logic Control

The green port multi-energy microgrid, featuring renewable energy generation, hydrogen energy, and energy storage systems, is an important gateway to achieve the net-zero emission goal. But there are many forms of energy in green port multi-energy microgrid systems, the power fluctuates frequently, and the port loads with large fluctuations and fast changes. These factors can easily lead to the problem of the state of charge exceeding the limit of the energy storage system. To distribute the fluctuating power in the green port multi-energy microgrid system reasonably and maintain the state of charge (SOC) of the hybrid energy storage system in an moderate range, an energy management strategy (EMS) based on dual-stage fuzzy control with a low pass-filter algorithm is proposed in this paper. First, the mathematical model of a green port multi-energy microgrid system is established. Then, fuzzy rules are designed, and the dual-stage fuzzy controller is used to change the time constant of the low-pass filter (LPF) and modify the initial power distribution by an LPF algorithm. Finally, simulation models are built in Matlab 2016a/Simulink. The simulation results demonstrate that, compared with other algorithms under the control of the EMS proposed in this paper, the high-frequency component in the flywheel power is smaller, and the SOC of the supercapacitor is maintained in a reasonable range of 34–78%, which extends the lifespan of the flywheel and supercapacitor. Additionally, it has a faster automatic adjustment ability for the state of charge of the energy storage system, which is conducive to better maintaining the stable operation of green port multi-energy microgrid systems.

A deep reinforcement learning approach based energy management strategy for home energy system considering the time-of-use price and real-time control of energy storage system

To enhance the flexibility of the home load optimization dispatching strategy and ensure the safe operation of the energy storage system, an optimization dispatching strategy for home energy management system (HEMS) based on time-of-use pricing and real-time energy storage system control is proposed. Firstly, a HEMS dispatching model is established with the constraints of dispatchable load and energy storage system working status, aiming to minimize the total cost for home users. This dispatching strategy controls the energy storage system charging and discharging behavior based on time-of-use pricing and real-time battery state of charge, which helps to reduce the power cost for home users and ensure the safe operation of the battery. Then, the optimal dispatching problem of HEMS is modeled as a Markov decision process (MDP) and solved by a deep reinforcement learning algorithm called soft actor-critic (SAC). The example results verify the effectiveness and superiority of the proposed method compared with other benchmark methods, which the system cost can be reduced by 15.87% at least.

Coordinated Control Strategy of Railway Multisource Traction System With Energy Storage and Renewable Energy

Influenced by the growing daily travel demand of citizens and extension of urban land, the construction of the urban railway transit (URT) system is gradually increasing nowadays. This situation implies more electricity consumption in URT and more challenges to URT operation stability. Therefore, based on the existing traction substation, this research proposes a configuration of multi-source traction system (MSTS) for URT with a coordinated control strategy according to power profile of the system. The proposed MSTS, including conventional traction system (CTS), renewable energy source (RES), and energy storage system (ESS), have been modelled first. After that, considering the system dynamic performance and ESS capacity, a coordinated control strategy is designed to manage the MSTS energy flow. The control strategy considers the substation voltage as control signal which is optimized by proposed performance index. The case study is conducted on a three-station railway route with two substations. The proposed MSTS with a coordinated control strategy is compared with normal CTS considering multi-train timetables with different departure intervals. The simulation result illustrates that the proposed MSTS can maintain the state of charge (SOC) of ESS and reduce the substation peak power and voltage fluctuation, which is verified both in short-term and long-term simulation. Meanwhile, it can improve the substation capacity by decreasing the energy consumption of CTS to deal with the increasing demand for URT. the case study denotes that the energy-saving rate can be up to 36.25%, and the peak power is reduced up to 46.32%.

An ultimate peak load shaving control algorithm for optimal use of energy storage systems

Peak load shaving is one of the applications of energy storage systems (ESS) that will play a key role in the future of smart grid. Peak shaving is done to prevent the increase of network capacity to the amount of peak demand and also increase its reliability. Although the development of diverse ESS with high round-trip efficiency is very important and is considered a necessary condition for optimal peak load shaving, its sufficient condition is to follow an appropriate control algorithm. In this study, a new control algorithm called ultimate peak load shaving (UPLS) is developed for the optimal use of ESS for the peak shaving and valley filling purposes. The UPLS control algorithm is not only able to guarantee peak load shaving in any condition considering any load profile but also can ingeniously control ESS charging and discharging rates. This feature distinguishes it from other algorithms developed in the field.In this study, first, the UPLS control algorithm was explained in detail. Then, by comparing it with two other common control algorithms, its four unique features were investigated. After that, by applying these three control algorithms on different hypothetical load demand profiles A, B, C, and D, these features were demonstrated. Through sensitivity analysis and changing the allowed range for ESS charging and discharging rates (other parameters are assumed constant), how to design the grid power, Pg(t) by the UPLS control algorithm is described. It was found that the mentioned parameters are completely under the control of this algorithm. The optimization results show that peak demand can be reduced by 28.12 % and 27.82 % for load profiles B and C, respectively and the ESS discharging rate is significantly reduced to 55.28 % and 77.93 %, respectively compared to their corresponding values in basic design.

Research on the Energy Management Strategy of a Hybrid Energy Storage Type Railway Power Conditioner System

High-speed railways generate a large amount of regenerative braking energy during operation but this energy is not utilized efficiently. In order to realize the recycling of regenerative braking energy of high-speed railways, the hybrid energy storage type railway power conditioner (RPC) system is proposed. The working principle and the control strategy of the system are studied. The energy management strategy consisting of a hybrid energy storage system charging and discharging strategy and variational modal decomposition (VMD) power allocation strategy is proposed. Three system operation modes are proposed: the power of the hybrid energy storage system is decomposed by VMD and an interrelationship number is proposed to determine the lithium battery and supercapacitor power. The hardware-in-the-loop test experiments are conducted by the StarSim power electronics small-step real-time simulator from Modeling Tech and the validation analysis is carried out on MATLAB/Simulink with the actual measurement data of a traction substation on the Lanzhou–Xinjiang line. The results verify that the proposed strategy can effectively recycle the regenerative braking energy, realize the peak-shaving effect on the load, and reduce the energy consumption of the train.

A novel unified planning model for distributed generation and electric vehicle charging station considering multi-uncertainties and battery degradation

Achieving the goal of sustainable development is dependent on the widespread integration of renewable energy sources, energy storage systems (ESSs), and electric vehicles (EVs). However, a continuous increase in the penetration of such elements would bring more complexities to the distribution network. Accordingly, this paper presents a unified planning model comprising renewable energy-based distributed generation (DG), ESS, and electric vehicle charging stations (EVCSs). In this regard, a Latin Hypercube Sampling method is utilized to generate multi-scenario for describing the uncertainty of renewable energy and load demand. The stochastic EV charging behaviors are represented by various probability density functions (PDF). In addition, an exploitable capacity loss of ESS and EV batteries is calculated by the battery degradation model based on the depth of discharge (DOD). Furthermore, the battery degradation cost is incorporated into the objective of the planning model to identify the optimal decision for candidate assets. A piecewise linearization approach is introduced to convert the problem into a mix-integer linear programming (MILP) model. Numerical results demonstrate that the exploitable capacity loss of batteries plays a key role in asset planning and provides potential contributions to the optimal decisions of the distribution network. In the meantime, by considering battery degradation in the optimization model, the sustainability and lifetime of the battery can be preserved.

A Rolling-Adaptive Peak Clipping Control Strategy Coordinating RBE Recycling and PV Consumption

Electrified railway is one of the major users of electricity in the power system. The access of energy storage system (ESS) effectively solves the problems of peak traction load (TL) shock and regenerative braking energy (RBE) recycling, and the access of photovoltaic (PV) system improves environmental and economic benefits. However, in the current control strategy based on the electrified railway with ESS and PV, there are still problems of less optimal overall peak clipping effect of TL and insufficient penetration of renewable energy sources (RES) due to low utilization and lack of pre-processing capacity of ESS. Therefore, this paper proposes a rolling-adaptive peak clipping (RAPC) control strategy coordinating RBE recycling and PV consumption. Firstly, perform rolling peak clipping through dynamically divided implementation windows to make full use of the available energy in each time period. Secondly, according to the state of charge (SOC) of ESS and the specific working condition of TL, the ESS output is modified accordingly to perform adaptive deep peak clipping maximizing the consumption of PV energy and RBE. The simulation verified that the proposed strategy can improve the overall peak-clipping effect of TL, and achieve full recovery of RBE and complete consumption of PV energy under the same ESS configuration compared to the global optimization method. The results show that the RAPC control strategy can realize optimal peak-clipping and RBE utilization, and increase the PV penetration with limited ESS capacity, thus improving the economic benefits of the traction power supply system (TPSS) and promoting the energy transition of TPSS.

Online Reactive Power Minimization and Soft Switching Algorithm for Triple-Phase-Shift Modulated Dual Active Bridge Converter

In a dc microgrid, a bidirectional dc–dc converter is an essential link between voltage bus and energy storage system (ESS). When galvanic isolation and high efficiency are required, dual active bridge (DAB) converter is preferred. Due to the instability of renewable energy sources, the DAB converter may transmit high or low power to ESS. To improve the efficiency across wide power range, this article proposes a reactive power minimization and soft switching optimization algorithm that combines the augmented Lagrangian method and triple-phase-shift (TPS) modulation. Unlike an offline algorithm with look-up table, the proposed algorithm can compute phase-shift-angle online, avoiding a large database for various working conditions. A prototype with an 800 W rated power is built, and the efficiency test results validate the efficiency improvement over other TPS-based algorithms. Furthermore, a current regulation experiment demonstrates that the proposed algorithm is suitable for ESS charging, which must deal with varying output voltage and power demand.

Two-stage Optimization for Active Distribution Systems Based on Operating Ranges of Soft Open Points and Energy Storage System

Due to the lack of flexible interconnection devices, power imbalances between networks cannot be relieved effectively. Meanwhile, increasing the penetration of distributed generators exacerbates the temporal power imbalances caused by large peak-valley load differences. To improve the operational economy lowered by spatiotemporal power imbalances, this paper proposes a two-stage optimization strategy for active distribution networks (ADNs) interconnected by soft open points (SOPs). The SOPs and energy storage system (ESS) are adopted to transfer power spatially and temporally, respectively. In the day-ahead scheduling stage, massive stochastic scenarios against the uncertainty of wind turbine output are generated first. To improve computational efficiency in massive stochastic scenarios, an equivalent model between networks considering sensitivities of node power to node voltage and branch current is established. The introduction of sensitivities prevents violations of voltage and current. Then, the operating ranges (ORs) of the active power of SOPs and the state of charge (SOC) of ESS are obtained from models between networks and within the networks, respectively. In the intraday corrective control stage, based on day-ahead ORs, a receding-horizon model that minimizes the purchase cost of electricity and voltage deviations is established hour by hour. Case studies on two modified ADNs show that the proposed strategy achieves spatiotemporal power balance with lower cost compared with traditional strategies.

Deep Reinforcement Learning-Based Optimal Control of DC Shipboard Power Systems for Pulsed Power Load Accommodation

To accommodate the pulsed power load (PPL) in the dc shipboard power system, the charging performance of the energy storage system (ESS) specialized for the PPL needs to be guaranteed, which leads to a challenging power system optimal control problem due to multiple objectives, operational constraints, complex nonlinear system structure, and uncertainties. This paper addresses this problem by using a model-free optimal control method based on the deep reinforcement learning (DRL). First, a dc shipboard power system optimal control problem with three control objectives and the input constraints is formulated, where three objectives include the fast ESS charge, the dc bus voltage regulation, and the proportional load current sharing. Then, to solve this problem, a DRL control framework based on the improved twin-delayed deep deterministic policy gradient (TD3) algorithm is developed, which adopts a modified critic network predicting technique and a stack-based data sampling strategy that are suitable for this fast-dynamic power system. The proposed method links the DRL framework with the optimal control. With the reward function being properly designed, the presented DRL control can well realize three control objectives. Case studies considering various operating conditions of the power system verify its effectiveness.

주파수 변화율 측정 기반 에너지저장장치 효과적인 제어 전략

The paper describes an effective control strategy for energy storage system (ESS) according to the severity of occurring in power system. ESS is generally used to mitigate the frequency oscillation against the power system disturbances. However, ESS can frequently operate even in situation where it is not necessary to operate due to small load change or small fluctuations in renewable energy. It can cause the frequency variations of the state of charge (SOC) of ESS and shorten the lifespan of the ESS. Therefore, the paper proposes effective control method to reduce the unnecessary operation of ESS by categorizing three control modes based on measured rate of change of frequency (ROCOF). The performance of the proposed method is verified through time domain simulation by applying it to the microgrid system.

A Day-Ahead Energy Management for Multi MicroGrid System to Optimize the Energy Storage Charge and Grid Dependency—A Comparative Analysis

Microgrid (MG) is a combination of distributed generators (DGs), energy storage systems (ESSs), and loads connected to distribution network that can either be in islanded mode or grid-tied mode. Similarly, a multi-microgrid (MMG) system is a number of interconnected MGs connected with a larger and complex distribution network. Recently, the MMG energy management has created new challenges due to the inherent intermittency, uncertainty, and probabilistic nature of renewable based DGs output and varying load demands. To ensure the efficient operation and optimal energy management in the MMGs, this work proposes a two-stage, a day-ahead, simultaneous energy management strategy (EMS) of the MMG system as well as the MG system. At the first stage, each MG assumes a day-ahead predicted load demand and DGs output. At the second stage, through EMS, the energy scheduling, minimization of the main grid dependency, and maximization of the stored energy in the ESS are managed simultaneously. Four case studies are considered with four interconnected MGs with different DGs output and different initial state of charge (SOC) of ESS along with varying load demand. The proposed optimization model is formulated in the standard form using MATLAB OptimProblem, and compared with heuristic state flow-based EMS. Results show that the total grid dependency will be reduced to zero and ESS depth of discharge (DoD) will be increased up to 50% with the proposed optimization model.

Large Scale Multi-Period Optimal Power Flow With Energy Storage Systems Using Differential Dynamic Programming

With theincreased penetration of renewable sources, power gridsare becoming stressed due to fluctuating generation. To alleviate stress from inconsistent sources, utilities employ energy storage systems alongside renewable sources and rely on dispatching synchronous generators. However, optimal dispatch of such devices is limited by traditional AC optimal power flow methods that do not account for time-dependent constraints, such as the state of charge of energy storage systems and generator ramping constraints. Multi-period optimal power flow is proposed as a large non-convex non-linear problem to optimally dispatch and control generators and energy storage elements across multiple time periods. In this paper, we introduce a scalable, robust framework to solve multi-period optimal power flow using a differential dynamic programming scheme that makes it capable of scaling to large systems containing energy storage devices. We demonstrate the efficacy of this solution by optimizing the SyntheticUSA testcase over a set of time periods. A robust homotopy method is applied to achieve fast simulation times that can be parallelized for further improvements.

White-Model Predictive Control for Balancing Energy Savings and Thermal Comfort

To save energy consumed by a building, utilizing optimal predictive control with model predictive control (MPC) makes the most of energy storage systems (ESSs) to reduce the electrical energy consumption of peak and heavy loads. This study evaluated MPC applicability in a multi-zone commercial building using the EnergyPlus model and conducted multi-objective optimization of thermal comfort and energy savings. As a result of the simulation, optimal ESS charging scenarios responded to the fluctuating electricity pricing system, and changing the peak load time reduced the electricity bill of the grid by 55% compared to the existing operating method. At the same time, room temperatures stayed within the thermal comfort range, and the Pareto curve showed a proper balance between energy saving and thermal comfort. Especially, the proposed method with a white model is applicable for MPC applications in commercial buildings, as it gave optimal solutions within the target time interval.

Long Short-Term Memory Recurrent Neural Network for Estimating State of Charge of Energy Storage System for Grid Services

Long Short-Term Memory Recurrent Neural Network for Estimating State of Charge of Energy Storage System for Grid Services

Optimal State-Constrained Control of DC Shipboard Power Systems for Online Pulsed Power Load Accommodation

Pulsed power loads (PPLs) consume a huge amount of energy within a short time. An energy storage system (ESS) is usually installed in a shipboard power system (SPS) for PPL accommodation. Online accommodation strategies, where the PPL is connected to the SPS through the ESS all the time, have great advantages in reducing the hardware cost and increasing the PPL deployment frequency. But there is a lack of online accommodation solutions to deal with the operational requirements. This article presents a barrier-Lyapunov-function-based optimal control solution for online PPL accommodation in dc SPSs. By introducing the state-constrained technique, the operational constraints on the supply current for PPL accommodation and the bus voltage are all respected. Three control objectives, including safe and fast ESS charging, high-quality regulation of the dc bus voltage, and generation cost dynamic minimization, are realized simultaneously. The stability and optimality of the control solution are guaranteed rigorously via the Lyapunov synthesis. Simulations based on a switch-level SPS model demonstrate the effectiveness of the proposed control solution.

수요자원 인센티브를 통한 태양광발전 출력제한 대응 ESS 최적운영스케줄링에 관한 연구

The installation of energy storage system (ESS) is increased to solve the instability of the transmission system due to variable renewable energies such as Photovoltaic power generation (PV) and wind power generation. Meanwhile, recently, PV output is frequently curtailed since PV output produced after the ESS charging is finished causes transmission line congestion. For this congestion, it is common to increase line capacity or allocate the output curtailment for each power plant. However, since the unsettled loss occurs due to the output curtailment, the power plant operator should be given an incentive to induce the output curtailment for a specific time. Therefore, in this paper, the ESS optimal operation scheduling using the demand response transaction mechanism as an incentive is presented. From the perspective of PV operator with ESS, the charge/discharge capacity is determined in units of 15 minutes reflecting the PV curtailment and the demand response trading mechanism through mixed-integer linear programming. Uncertainty in the D-Day actual PV power output is considered as three scenarios such as Day-ahead forecasting PV power output and Day-ahead forecasting PV power output scenario with mean absolute percentage errors. Through optimal operation scheduling of ESS, transmission line overload and system congestion are suppressed by ESS charging and PV operators can secure additional benefits through the demand response trading mechanism without PV curtailment. Namely, total expected benefits can be maximized by the proposed method. As a result, an additional expansion of transmission lines or the introduction of a new incentive system can be avoided by the flexibility of PV operators.

태양광 발전소의 발전량 예측을 고려한 최적의 ESS 충전 스케줄링에 관한 연구

ESS (Energy Storage System) is being installed as a core equipment to resolve intermittent output characteristics according to weather conditions of photovoltaic power plants (PV). Generally, the ESS installed in the PV is charged from 10:00 to 16:00 and discharged after 16:00. Namely, ESS operation can be seen as maximizing the benefits of REC (Renewable Energy Certificate) weights rather than solving intermittent PV output. Meanwhile, utility line congestion occurs because a large amount of PV output supplied to the utility line at the time that ESS charging is completed. Through participation in the forecast of renewable energy generation, the PV output will be charged with the ESS when it is out of the PV forecast error range (± 6%). By applying the scenario technique and mixed-integer linear programming (MILP) for irregular PV output forecast error, the ESS charging scheduling method is determined to maximize the expected total daily benefit of the ESS operator. As a result of the simulation, it was confirmed that additional benefits of PV operator could be secured through the renewable energy generation forecasting system.

Optimization of Fuzzy Energy-Management System for Grid-Connected Microgrid Using NSGA-II.

This article proposes a fuzzy logic-based energy-management system (FEMS) for a grid-connected microgrid with renewable energy sources (RESs) and energy storage system (ESS). The objectives of the FEMS are reducing the average peak load (APL) and operating cost through arbitrage operation of the ESS. These objectives are achieved by controlling the charge and discharge rate of the ESS based on the state of charge of ESS, the power difference between load and RES, and electricity market price. The effectiveness of the fuzzy logic greatly depends on the membership functions (MFs). The fuzzy MFs of the FEMS are optimized offline using a Pareto-based multiobjective evolutionary algorithm, nondominated sorting genetic algorithm (NSGA-II). The best compromise solution is selected as the final solution and implemented in the fuzzy-logic controller. A comparison with other control strategies with similar objectives is carried out at a simulation level. The proposed FEMS is experimentally validated on a real microgrid in the energy storage test bed at Newcastle University, U.K.