Battery Energy Storage Control Research Articles

Optimal sitting, sizing and control of battery energy storage to enhance dynamic stability of low‐inertia grids

AbstractAs inverter‐based resources like wind turbines increase, grid inertia and stability decrease. Optimal placement and control of energy storage systems can stablise low‐inertia grids. This paper investigates how optimal battery energy storage systems (BESS) enhance stability in low‐inertia grids after sudden generation loss. The sitting, sizing and control of BESS are determined simultaneously in each genetic algorithm (GA) population, then voltage and frequency stability is evaluated based on the network simulation. This continues until the optimal solution is found. A network based on Kundur's four‐machine system is modelled for the first study and two of the four synchronous generators (SGs) have been replaced with wind farms. Then, the production of the third SG has been decreased by 13%. According to the results, addition of wind farms causes the frequency drop below 49.6 Hz for more than 5 min, indicating instability. It is also demonstrated that with optimal control parameters and placement, a 60 MW BESS can alleviate the voltage and frequency fluctuations, leading to enhanced stability. This method has also been tested on the IEEE 39‐bus network, where the installation of a BESS with a capacity of 9 MVA could restore the frequency stability.

Aggregator control of battery energy storage in wind power stations to maximize availability of regulation service

Battery energy storage systems can produce very fast bi-directional power flows, which makes them suitable for providing wind power regulation and frequency control services. Though battery systems can provide fast regulation services, their energy storage capacities are quite low in comparison to other generation sources, so regulation responses from them should be optimized to maximize availability of service. This paper proposes an aggregator that optimizes frequency control responses from battery energy storage systems to maximize service availability. The frequency control response from the aggregated system is defined by a single frequency-droop characteristic. This provides the predictability of response required to comply with grid codes and allows the aggregated system to participate in frequency control markets as a single entity. The method of implementation is fail-safe as failure to receive an optimized order from the aggregator does not prevent the battery energy storage systems from responding to frequency events. This mitigates stability concerns relating to communication delays between the aggregator and battery energy storage systems. Battery systems that provide multiple functions, such as frequency control system services and wind power regulation, can participate in the aggregator scheme by assigning a proportion of the battery’s capacity to the aggregator scheme. Simulations performed in DIgSILENT PowerFactory show that the aggregator successfully extends the duration of full regulation service from the battery systems during frequency excursion events.

An improved rule-based control of battery energy storage for hourly power dispatching of photovoltaic sources

This paper presents an improved rule-based control scheme for battery energy storage (BES) system with the goal of minimising the fluctuation output from photovoltaic (PV) sources while ensuring the operational constraints of BES are regulated at the specified ranges for safety purposes. The control scheme is formulated in accordance with the intended operational limitations of the BES, including charge/discharge current limits and state-of-charge (SOC). The simulation studies were carried out using MATLAB/Simulink to evaluate the effectiveness of an improved rule-based control scheme on the 1.2 MW PV system data acquired from a location in Malaysia. Furthermore, a comparative study on the effectiveness of an improved rule-based control scheme compared with the conventional rule-based control scheme has been carried out. The simulation results show that an improved rule-based control scheme can effectively reduce the fluctuations in the output power of the PV sources and dispatch the output to the utility grid on an hourly basis with an efficiency of up to 94.47%. Finally, the comparison results on the effects of the BESS capacity also illustrate that an improved rule-based control scheme is more effective compared to the conventional rule-based control scheme in the previous study.

A reinforcement learning approach using Markov decision processes for battery energy storage control within a smart contract framework

With the increasing penetration of renewable energy sources (RESs), the necessity for employing smart methods to control and manage energy has become undeniable. This study introduces a real-time energy management system based on a multi-agent system supervised by a smart contract, employing a bottom-up approach for a grid-connected DC micro-grid equipped with solar photovoltaic panels (PV), wind turbines (WT), micro-turbines (MT), and battery energy storage (BES). Each unit is controlled and managed through a distributed decision-making structure. The BES agent is governed by an intelligent structure based on a reinforcement learning model. To facilitate interaction and coordination among agents, a tendering process is employed, where each agent, under its supervised control structure, presents its offer for the tendered item at each time period. The tendering organization allocates demand using the first-price sealed-bid algorithm among bidders to optimize energy cost in the Microgrid. The proposed approach offers a real-time intelligent system capable of ensuring fault tolerance, stability, and reliability in the Microgrid. The main achievement of this study is the development of a robust real-time energy management system that integrates various renewable energy sources and battery storage while ensuring efficient operation and resilience in the face of faults or disruptions.

Utilizing Artificial Neural Networks for Intelligent Battery Energy Storage Control in Microgrid Energy Management

Utilizing Artificial Neural Networks for Intelligent Battery Energy Storage Control in Microgrid Energy Management

Optimal Battery Energy Storage Control for Multi-Service Provision Using a Semidefinite Programming-Based Battery Model

Battery energy storage systems (BESS) capable of simultaneously providing multiple grid services can assist a distribution grid operator in overcoming various challenges due to the high penetration of distributed solar photovoltaics and accelerated electrification. The seamless provision of multiple services can be ensured with BESS control decisions based on an accurate model reflecting the battery characteristics. Thus, this paper presents a novel Li-ion battery model based on linear matrix inequalities along with a semidefinite programming (SDP)-based model to determine the optimal BESS control decisions for the provision of multiple services in distribution systems. Peak shaving, power factor improvement, and electricity cost savings are considered for the services. Moreover, the mathematical analysis reveals that the original nonconvex problem can be equivalently transformed into the SDP model. The effectiveness of using BESS in the distribution system is verified through simulations using real-world data. Ultimately, a comparative analysis with a conventional linear model demonstrates that the proposed battery model reduces the energy losses in the batteries, in the simulation condition, and help maintain the battery states within normal operating limits.

Implementation of hybrid optimized battery controller and advanced power management control strategy in a renewable energy integrated DC microgrid.

In Renewable Energy (RE) integrated DC Microgrid (MG), the intermittency of power variation from RE sources can lead to power and voltage imbalances in the DC network and have an impact on the MG's operation in terms of reliability, power quality, and stability. In such case, a battery energy storage (BES) technology is widely used for mitigating power variation from the RE sources to get better voltage regulation and power balance in DC network. In this study, a BES based coordinated power management control strategy (PMCS) is proposed for the MG system to get effective utilization of RE sources while maintaining the MG's reliability and stability. For safe and effective utilization of BES, a battery management system (BMS) with inclusion of advanced BES control strategy is implemented. The BES control system with optimized FOPI controllers using hybrid (atom search optimization and particle swarm optimization (ASO-PSO)) optimization technique is proposed to get improved overall performance in terms of control response and voltage regulation in DC network under the random change in load profile and uncertain conditions of RE sources in real time.

Coordinated PSO-ANFIS-Based 2 MPPT Control of Microgrid with Solar Photovoltaic and Battery Energy Storage System

The microgrid is a group of smaller renewable energy sources (REs), which act in a coordinated manner to provide the required amount of active power and additional services when required. This article proposes coordinated power management for a microgrid with the integration of solar PV plants with maximum power point tracking (MPPT) to enhance power generation and conversion using a hybrid MPPT method based on particle swarm optimization-adaptive neuro-fuzzy inference system (PSO-ANFIS) to acquire rapid and maximum PV power along with battery energy storage control to maintain the stable voltage and frequency (V-f) of an isolated microgrid. In addition, it is proposed to provide active and reactive power (P-Q) regulation for the grid connected. The approach used provides more regulation due to the least root mean square error (RMSE), which improves photovoltaic (PV) potential extraction. The comparison results of the PSO-ANFIS and P&O controllers of the MPPT and the controller of the energy storage devices combined with the V-f (or P-Q) controller of the inverter all show effective coordination between the control systems. This is the most important need for contemporary microgrids, considering the potential of changing irradiance in the grid following mode, the grid forming mode under an island scenario, and back-to-grid synchronization. With the test model, the islanded and grid-islanded-grid connected modes are investigated separately. The results demonstrate conclusively that the proposed strategies are effective. To run the simulations, MATLAB and SimPowerSystems are utilized.

Research on a Multi-Agent Cooperative Control Method of a Distributed Energy Storage System

For the flexible regulation requirements of new power systems with a high proportion of new energy, this paper proposes a multi-point distributed energy storage system control method based on the idea of multi-agent cooperative control. On the one hand, the method transforms and upgrades the strategies of each distributed battery energy storage control system to make it a terminal agent with active response and control functions; on the other hand, a coordination agent is set between the power grid and each terminal agent, the coordinated control of the distributed energy storage system is realized by the information interaction between the coordination agent and the terminal agent. The simulation results show that the method can fully exploit the aggregate regulation ability of the distributed energy storage system, quickly stabilize the frequency fluctuation of new energy, and support the safe and economical operation of the system.

Optimal Control of BESS for Improved Grid Integration of the Hybrid DFIG/PV System Using Dual-Layer Adaptive Control

In view of the stochastic nature of wind and solar energy, maintaining quality power in grid-connected hybrid energy system is crucial for efficient energy utilization, especially in the presence of non-linear loads. This paper deals with the control and performance assessment of grid-integrated hybrid energy system that comprises of doubly fed induction generator (DFIG) and photovoltaic (PV) system including battery energy storage (BES) as a stand-by supply to meet utility and load demand. In order to maintain power balance and address various power quality issues encountered with hybrid renewable integration along the source and the grid/load end, a dual-layer variable-step size adaptive control is implemented for the grid-interfaced converter of the proposed system. The variable step-size based dual-layer least mean square (LMS) adaptive control functions by extracting weights of various control signals (stator and load current) where the step-size parameter adjusts according to the conditions that limit the maladjustment error and increase the rate of convergence. The DC-link voltage control realized by bidirectional BES control reduces the steady-state error and fluctuations in the voltage. An artificial bee colony (ABC) optimization is adopted for optimal gain tuning of the DC-link voltage controller as a result of which the battery performance and grid-side converter performance is enhanced with the increased power exchange to the grid. The system model is developed in MATLAB/Simulink and controller performance is evaluated for diverse operating conditions including steady-state, unbalanced non-linear load conditions, wind-speed and solar irradiation variation and specified grid power modes.

Enhancing static voltage stability of a power system in the presence of large-scale PV plants using a battery energy storage control scheme by the probabilistic technique

This paper aims to investigate the impact of large-scale photovoltaic plant (LS-PVP) controllers on the voltage stability (VS) of a power system. Besides, a new control strategy is proposed for the large-scale battery energy storage system (LS-BESS) to enhance a power system integrated with LS-PVPs, especially VS. To this end, a probabilistic technique, based on the stochastic nature of LS-PVPs and loads, is employed to accurately evaluate the impact of LS-PVPs on the system. Next, a new coupling between the optimization algorithm and the probabilistic technique is proposed to determine the optimal number, location, and capacity of the LS-BESSs by the probabilistic particle swarm optimization (PPSO) algorithm. The objective function is then used to simultaneously maximize the total VS and minimize the total power losses, line loading, as well as investment costs of LS-BESSs. Furthermore, technical performance indices (TPIs) are used for a more comprehensive investigation to compare the proposed solution to the synchronous generators. Finally, several disturbances occurring in the Nordic system are used to validate the effectiveness of the proposed approach, using the Monte Carlo simulation (MCS) method. According to the results, when a fault occurs, a system integrated with LS-PVPs in the stressed area does not operate better than synchronous generators. Therefore, an advanced control method is required to reduce undesirable effects of LS-PVPs. Using the proposed solution, voltage instability was prevented, LS-BESS investment costs were minimized, and negative effects of LS-PVPs on the system were reduced. Besides, costs required for expanding the transmission network as well as communication between controllers and reactive power compensation devices were reduced.

Real-Time Scheduling for Optimal Energy Optimization in Smart Grid Integrated With Renewable Energy Sources

Load scheduling, battery energy storage control, and improving user comfort are critical energy optimization problems in smart grid. However, system inputs like renewable energy generation process, conventional grid generation process, battery charging/discharging process, dynamic price signals, and load arrival process comprise controller performance to accurately optimize real-time battery energy storage scheduling, load scheduling, energy generation, and user comfort. Thus, in this work, the virtual queue stability based Lyapunov optimization technique (LOT) is adopted to investigate real-time energy optimization in a grid-connected sustainable smart home with a heating, ventilation, and air conditioning (HVAC) load considering unknown system inputs dynamics. The main goal is to minimize overall time average energy cost and thermal discomfort cost in a long time horizon for sustainable smart home accounting for changes in home occupancy state, the most comfortable temperature setting, electrical consumption, renewable generation output, outdoor temperature, and the electricity costs. The employed algorithm creates and regulates four queues for indoor temperature, electric vehicle (EV) charging, and energy storage system (ESS). Extensive simulations are conducted to validate the employed algorithm. Simulation results illustrate that the proposed algorithm performs real-time energy optimization and reduces the time average energy cost of 20.15% while meeting the user’s energy and comfort requirement.

Intelligent control of battery energy storage for microgrid energy management using ANN

<span>In this paper, an intelligent control strategy for a microgrid system consisting of Photovoltaic panels, grid-connected, and li-ion battery energy storage systems proposed. The energy management based on the managing of battery charging and discharging by integration of a smart controller for DC/DC bidirectional converter. The main novelty of this solution are the integration of artificial neural network (ANN) for the estimation of the battery state of charge (SOC) and for the control of bidirectional converter. The simulation results obtained in the MATLAB/Simulink environment explain the performance and the robust of the proposed control technique.</span>

Battery energy storage control using a reinforcement learning approach with cyclic time-dependent Markov process

Scheduling efficient energy management system operations to respond to the unstable customer demand, electricity prices, and weather increases the complexity of the control systems and requires a flexible and cost-effective control policy. This study develops an intelligent and real-time battery energy storage control based on a reinforcement learning model focused on residential houses connected to the grid and equipped with solar photovoltaic panels and a battery energy storage system. Because the reinforcement learning’s performance is very dependent on the design of the underlying Markov decision process, a cyclic time-dependent Markov Process is uniquely designed to capture existing daily cyclic patterns in demand, electricity price, and solar energy. The Markov Process is successfully used in the Q-learning algorithm, resulting in more efficient battery energy control and saving electricity costs. The proposed Q-learning algorithm is compared with benchmark models of a deterministic equivalent solution and a One-step Roll-out algorithm. Numerical experiments show the gap between the deterministic equivalent solution and Q-learning approaches for one-month electricity cost decreased from 7.99% to 3.63% for house 27 and 6.91% to 3.26% for house 387 when the discrete size of demand, solar energy, price, and battery energy level adjusted to 20. Accordingly, the better performance of the proposed Q-learning is demonstrated compared to the One-step Roll-out algorithm. Moreover, the effect of discrete size of state-space parameters on the adaptive Q-learning performance and computational time are investigated. Variations in the electricity price significantly affect the Q-learning algorithm’s performance more than other parameters.

Enhancing long-term voltage stability of a power system integrated with large-scale photovoltaic plants using a battery energy storage control scheme

Due to distinct differences in dynamic characteristics of large-scale photovoltaic plants (LS-PVPs) compared to conventional generation units, utilities face some new non-traditional operational problems, more specifically in terms of voltage stability. This paper analyzes the impact of various control methods of LS-PVPs on long-term (LT) voltage stability. A new corrective voltage control (CVC) scheme is proposed for the large-scale battery energy storage system (LS-BESS) to prevent LT voltage instability in a power system integrated with LS-PVPs. For this purpose, a new coupling between optimization and dynamic simulations is proposed to treat the problem as an objective function, which uses the particle swarm optimization (PSO) algorithm. This objective function is used to minimize investment costs and voltage drops of the system by determining the optimal number, location, and capacity of LS-BESSs. Furthermore, the effects of local voltage control modes and power priority settings of LS-BESSs are investigated. Besides, effectiveness of the proposed approach is validated through several scenarios using time-domain simulations of a more realistic system, namely the Nordic system. According to the results, upon using the proposed approach, negative effects of LS-PVPs on voltage stability and costs required for expanding and reinforcing the transmission network decreased. In addition, voltage collapse was prevented, investment costs of LS-BESSs were minimized, and costs required for reactive power compensation devices and communication between controllers decreased.

Deep reinforcement learning‐based optimal data‐driven control of battery energy storage for power system frequency support

A battery energy storage system (BESS) is an effective solution to mitigate real-time power imbalance by participating in power system frequency control. However, battery aging resulted from intensive charge–discharge cycles will inevitably lead to lifetime degradation, which eventually incurs high-operating costs. This study proposes a deep reinforcement learning-based data-driven approach for optimal control of BESS for frequency support considering the battery lifetime degradation. A cost model considering battery cycle aging cost, unscheduled interchange price, and generation cost is proposed to estimate the total operational cost of BESS for power system frequency support, and an actor–critic model is designed for optimising the BESS controller performance. The effectiveness of the proposed optimal BESS control method is verified in a three-area power system.

Bottom-up modeling of residential batteries and their effect on system-level generation cost

The declining cost of residential battery energy storage (BES) and photovoltaic (PV) systems enable customers to significantly reduce their energy dependency from the grid. By absorbing excess PV generation, BES systems could reduce impacts associated with the ‘duck curve’. However, since off-the-shelf (OTS) BES systems are controlled for customer benefits, this absorption might be limited at times, resulting in similar duck curve effects. This work proposes a bottom-up modeling approach to demonstrate the system-level generation cost effects from residential BES systems. Using a modified IEEE 9-bus system with real anonymized smart meter data, results show that with the OTS BES control, the overall reduction in system net demand due to peak PV generation still occurs which leads to baseload generation shutting off, incurring additional costs. Alternatively, the adoption of smarter BES controls aimed at mitigating impacts on distribution networks by reducing PV exports can bring significant system-level generation cost reductions.

Multi-Agent-Based Voltage Regulation Scheme for High Photovoltaic Penetrated Active Distribution Networks Using Battery Energy Storage Systems

This paper develops a distributed voltage regulation scheme for high Photovoltaic (PV) penetrated distribution networks by utilizing battery energy storage (BES) units. In this study, multiple BES units form a multi-agent network, in which each BES unit acts as an individual agent and can communicate with its neighbors to perform the distribution network's voltage regulation in a cooperative manner. To cope with the uncertainties of PV power and load demand, a receding horizon-based approach is proposed, where BES control decisions for regulating bus voltages are updated with the update of the system's operational conditions (PV power, load, etc.). An efficient solving method - Distributed Alternating Direction Method of Multipliers Algorithm (D-ADMM) is applied to solve the proposed model under a colored network, where the BES agents with the same color synchronously update their states. Case studies are conducted on modified IEEE benchmark systems to validate the performance of the proposed method.

Nonlinear Sliding Mode and Distributed Control of Battery Energy Storage and Photovoltaic Systems in AC Microgrids With Communication Delays

This paper proposes a distributed fixed-time multiagent control strategy for the frequency restoration, voltage regulation, state of charge balancing, and proportional reactive power sharing between photovoltaic battery systems distributed in a microgrid with communication time delays. First, the feedback linearization method is applied to find the direct relationships between explicit states and control inputs. Then, based on the model, the distributed fixed-time cooperative control system restores the frequency, regulates the average voltage to the nominal value, and achieves accurate power sharing. For the state of charge balancing, a fixed-time observer is proposed to estimate the average state of charge of a battery using only information from neighbors. Based on the estimated value, a local fixed-time sliding mode control is applied to achieve the balanced state of charge. Due to robustness of the fixed-time control strategy, the balanced state of charge can be maintained despite intermittent photovoltaic generation and variable loads. The Artstein's transformation is applied to ensure the stability of the time delayed system. The dynamic performance is verified with an RTDS Technologies real-time digital simulator, using switching converter models, nonlinear lead-acid battery models, photovoltaic generation, and communication delays in a European benchmark microgrid.

Optimal Battery Control Under Cycle Aging Mechanisms in Pay for Performance Settings

We study the optimal control of battery energy storage under a general "pay-for-performance" setup such as providing frequency regulation and renewable integration. In these settings, batteries need to carefully balance the trade-off between following the instruction signals and their degradation costs in real-time. Existing battery control strategies either do not consider the uncertainty of future signals, or cannot accurately account for battery cycle aging mechanism during operation. In this work, we take a different approach to the optimal battery control problem. Instead of attacking the complexity of battery degradation function or the lack of future information one at a time, we address these two challenges together in a joint fashion. In particular, we present an electrochemically accurate and trackable battery degradation model called the rainflow cycle-based model. We prove the degradation cost is convex. Then we propose an online control policy with a simple threshold structure and show it achieve near-optimal performance with respect to an offline controller that has complete future information. We explicitly characterize the optimality gap and show it is independent to the duration of operation. Simulation results with both synthetic and real regulation traces are conducted to illustrate the theoretical results.