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Dimensioning battery energy storage systems for peak shaving based on a real-time control algorithm
Abstract
In order to reduce power peaks in the electrical grid, battery systems are used for peak shaving applications. Under economical constraints, appropriate dimensioning of the batteries is essential. A dimensioning process is introduced consisting of a simulation environment to determine the behavior of the energy system, a real-time peak shaving control algorithm and an optimization process for detection of battery and algorithm parameters. The dimensioning process is investigated on the basis of four exemplary load profiles and in comparison to a conventional approach. Deviations between -7% and 75% for capacity and up to 43% for discharging power indicate undersized batteries using the conventional approach. The proposed approach relies on 1-min measurement data and does not require prediction data, leading to accurate dimensioning results for a given load profile, as verified in simulation. The practical use and effectiveness of the control algorithm is proven in a real-world laboratory. A battery system of 60 kWh capacity and 65 kW maximum power achieved successful peak load reduction by 50 kW (8%) for an a priori defined limit of 570 kW. The comparison with simulation shows only small deviations below 17 kW (4.1%) for the resulting load profile proving the realistic representation of an energy system in simulation.
... Battery technology may become more important for grid stability as the energy market changes [28]. A dimensioning adjustment for battery energy storage systems utilized for peak shaving based on a real-time control algorithm [29] increases peak shaving performance. In [30], an additional PLS strategy was presented that allowed for dynamic adjustments in EVB discharging rates without affecting battery usage for electric vehicle travel. ...
... No. [20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35] According to the authors' knowledge, PLS utilizing ANNs and EVBs for islanded SG was not studied until recently, and no prior research has discussed the use of decentralized secondary control with ANNs-optimized GA-based PI controllers to simultaneously restore frequency/voltage and share active/reactive power over low-voltage DCT for eliminating reactive power issues and reducing power losses. ...
Renewable energy penetration increases Smart Grid (SG) instability. A power balance between consumption and production can mitigate this instability. For this, intelligent and optimizing techniques can be used to properly combine and manage storage devices like Electric Vehicle Batteries (EVBs) with Demand-Side Management (DSM) strategies. The EVB helps distribution networks with auxiliary services, backup power, reliability, demand response, peak shaving, lower renewable power production’s climate unpredictability, etc. In this paper, a new energy management system based on Artificial Neural Networks (ANNs) is developed to maximize the performance of islanded SG-connected EVBs. The proposed ANN controller can operate at specified periods based on the demand curve and EVB charge level to implement a peak load shaving (PLS) DSM strategy. The intelligent controller’s inputs include the time of day and the EVB’s State of Charge (SOC). After the controller detects a peak demand, it alerts the EVB to start delivering power. This decrease in peak demand enhances the load factor and benefits both SG investors and end users. In this study, the adopted SG includes five parallel Distribution Generators (DGs) powered by renewable resources, which are three solar Photovoltaics (PVs) and two Wind Turbines (WTs). Sharing power among these DGs ensures the SG’s stability and efficiency. To fulfill demand problem-free, this study dynamically alters the power flow toward equity in power sharing using virtual impedance-based adaptive primary control level. This study proposes a decentralized robust hierarchical secondary control system employing Genetic Algorithm (GA)-optimized Proportional-Integral (PI) controller parameters with fine-grained online tuning using ANNs to restore frequency and voltage deviations. The proposed system is evidenced to be effective through MATLAB simulations and real-time data analysis on the ThingSpeak platform using internet energy technology. Our presented model not only benefits users by enhancing their utility but also reduces energy costs with robust implementation of a control structure by restoring any frequency and voltage deviations by distributing power equally among DGs regardless of demand condition variations.
... BESS deployment for peak shaving is a frequently investigated usecase in the context of the distribution network [18][19][20] or industrial zones [5,6]. To increase utilization and profitability, peak shaving is often combined with other services, such as load leveling, integrating renewable generation or frequency regulation. ...
... This gives the operator time to charge the battery in preparation for future peaks, which is done in lines (21)(22). The battery control decisions in lines (17)(18)(19)(20)(21)(22) are adapted from [3,6] and have been proven to be close to (economically) optimal and computationally efficient. We expand on the approaches of [3,6], because we are taking bidding windows into account. ...
Industrial peak shaving is a regularly discussed application of battery storage. We introduce the notion of risk attitude in the context of joint industrial peak shaving and frequency containment reserve provision with battery storage. To this end, we combine a probabilistic quantile forecast with a rolling-horizon battery control mechanism. Probabilistic forecasts incorporate prediction uncertainty by generating a distribution of future load. An industrial consumer has an incentive to plan conservatively when reserving battery capacities for peak shaving, as a single missed peak can drive up annual electricity costs steeply in the presence of peak-load charges. However, this limits the potential use of battery storage capacity for other financially attractive applications. We find that extremely risk averse planning behavior can lead to a decrease of up to 10% in economic performance of a battery investment. This loss might be tolerated in exchange for the significantly reduced risk of missing a critical peak. Moreover, moderately risk averse planning behavior does not lead to financial losses in most cases and can even improves economic performance by up to 3% in certain of the evaluated cases.
... 5.6: Ablauf der Auslegung von Batteriesystemen, vgl. [137] eine sehr große Kapazität E Bat,nenn → ∞ angenommen. Die Entladeleistung der Batterie werden wie folgt festgelegt: Erreicht der thermische Speicher während einer Lastspitze den Grenzwert für den Ladezustand (zum Beispiel "Speicher voll" bei einem BHKW mit Wärmespeicher), so ist die Speicherkapazität zu gering und muss erhöht werden. ...
... Die Dauer dieses Bereichs wird als ∆t LS bezeichnet. Die Differenz zwischen zwischen der höchsten Leistung der Lastspitze und der13 Die Definition der Kennzahlen wurden von Lange et al. in[137] veröffentlicht. ...
Peak loads cause high and unpredictable loads on the power grids and increased transmission losses in the distribution networks. The compensation of short-term high electrical energy demand also requires inefficient and expensive peak load power plants as well as oversized grid components. In order to achieve an electrical energy demand by larger consumers that is as even as possible, the grid operators set financial incentives. These include a demand rate, which depends on the maximum power value during the billing period. A reduction of the peak loads thus opens up high savings potentials for industrial and commercial companies.
The methodology and application of peak shaving across different energy sectors as well as the influence on the energy system are investigated in the present thesis. In addition to electrical energy storage systems, thermal plants are also used for peak shaving to increase the reduction potential, which is different from existing literature. This includes heat and cold water supply systems, which are often part of industrial energy systems. The thesis focuses on a combined heat and power plant with heat energy storage and a cooling plant with cold thermal energy storage. However, the approach can be transferred to similar components. The energy storages provide the flexibility, which is needed for the additional usage of the plants for peak shaving. Data-based models are used to represent the behavior of the components (plants, energy storages and peripherals) in simulations, which enable a non-invasive investigation of the cross-sectorial peak shaving.
Peak shaving with the plants and storages requires algorithms and operational strategies for the detection of relevant peak loads, calculation of setpoints as well as for the operation in normal and peak modes. In comparison to the state of the art, relevant characteristics of the components (e.g. startup procedures) are fully taken into account. The methods can be transferred to fields, which were not considered in the thesis, e.g., compressed air and air reservoir. The overall objective is to comply with a power limit, which can be varied over time to apply for individual network fees like atypical network usage.
The models and operational strategies are merged in an expandable and flexible simulation environment. This is used to present and investigate various scenarios as well as to optimize components and parameters. The components are linked dynamically within the program via a netlist, which greatly simplifies extensions and thus enables extensive investigations. The simulations show that a battery is necessary to observe a predefined load limit, as it can be operated very dynamically and provides a continuously variable output power. In the first scenario, a battery system is considered. A peak shaving potential of about 10 % is determined for the parameters of a reference system, which shows load peaks in the range of one Megawatt. This leads to a payback time of less than five years. With the additional consideration of a combined heat and power plant with thermal energy storage this value increases to approx. 18 %. A combination with a cold thermal energy storage shows a potential of 21 %. This leads to an annual saving of 21 thousand euros assuming a demand rate of 100 euros per kilowatt. A second annual data set from the reference system confirms a similar impact of the measures on total savings. If the normal operation of the CHP is also taken into account, the savings are in the range of 139 thousand euros per year, which results in a payback period of less than three years. As the absolute results are strongly dependent on the plant dimensions, a method for the calculation of the reduction potential with variation of nominal power and capacity of the plants and storages is shown and applied for numerous parameter sets.
The algorithms and operational strategies were implemented into a reference system. It provides measurements, which are used for validation of the simulations. Compared to the measurements, only minor differences with a mean absolute error of four kilowatts occur for the resulting transformer power. The present thesis thus provides an approach for planning and realization of the successful cross-sectorial peak shaving. The thesis also illustrates the exemplary application of component dimensioning, optimization of algorithm parameters and implementation of operation strategies in a real system.
... In [24], a management algorithm is proposed that utilises a heuristic rulebased approach in controlling the BESS in real-time for peak shaving in islanded microgrids. Another BESS heuristic control approach is introduced in [25] for peak shaving by dimensioning BESS parameters. Both studies utilise power threshold in controlling the BESS power for peak-shaving. ...
... In this paper, a heuristic strategy is proposed that controls the BESS setpoints in a real-time to flatten the grid power. This strategy utilises power thresholds to control the BESS as done previously in [24,25]. However, the coordination optimization of multiple BESS is considered by conducting online OPF to determine the multiple BESS dispatch with minimum losses. ...
The integration of battery energy storage systems (BESS) in the electrical grid is accelerating to mitigate the challenges associated with the rapid deployment of low carbon technologies (LCTs). This work investigates the ability of BESS to provide the power networks with important ancillary services such as peak shaving and grid power levelling through two case studies conducted in collaboration with Northern Ireland's distribution network operator. A powerful approach consisting of two strategies is developed to operate the BESS powerfully to enhance the operation of the distribution network. The first strategy is day-ahead scheduling that aims to dispatch the distributed BESS to smooth the grid power and mitigate voltage and lines stresses. A powerful demand forecasting algorithm is utilised to efficiently apply the day-ahead scheduling. The second strategy is a real-time operation to flatten the grid power which can be used separately or to adjust the results obtained from the day-ahead strategy against the forecasting errors. The proposed approach was validated using real measurements and applied to an 11 kV distribution network located in Northern Ireland, UK. The BESS expected revenues from the participation in different services available in the island of Ireland are quantified and the degradation is considered.
... Size of the BESS Peak Reduction Strategy Performance of the Controller [24] Simulation and experimental 65 kW/ 60 kWh • The controller uses a battery dimensioning method to schedule the charging and discharging operations of the batteries at a real-world laboratory in Fraunhofer, Germany. • This controller does not require any load prediction. ...
Battery-based energy storage systems (BESS) can be installed and controlled by a control strategy to reduce daily peak demands. Majority of the existing controllers for BESS is developed and studied on a simulation platform. Hence, an innovative adaptive threshold-based controller is developed and implemented on a real 200 kWh BESS installed on a university campus in Malaysia to reduce daily peak demands. This controller is developed on the free, open-source Node-RED platform using a deep learning-based one-dimensional convolution neural network (1D-CNN) to forecast the load profile for one day ahead. The controller uses the forecasted load profile to define a threshold or specific power demand level above which the BESS will start discharging power to meet the demand. The threshold is actively adjusted according to the actual and forecasted power demand of the customer as well as preceding peak of the grid power. The performance of the controller is initially assessed through six months of comprehensive simulations, where its daily peak reduction factor (Kpdr) and monthly peak reduction failure rate (ηfailure) are compared with that of two fixed-threshold controllers, namely forecasted threshold and historical threshold-based controllers, and another two advanced controllers, namely active and fuzzy controllers. The adaptive threshold-based controller achieves an average Kpdr of 41.62% and ηfailure of just 16.55%, which are better than that of other four controllers. The controller is implemented to BESS in a university building to evaluate its practical performance over 21 days under a real operating condition. The controller achieves an average Kpdr of 49.45% with a ηfailure of just 4.76%, which are better than the simulation results.
... The supposed aging behaviour is mainly applied if the storage model represents a battery energy storage system. Figure 8 shows the LuT for an exemplary parameter set of the storage model (see also [7]). The top diagram represents the efficiency based on the relative power. ...
A Marine Vessel Energy Management System (MVEMS) is a sophisticated technology designed to optimize energy usage and distribution onboard ships, ensuring efficient operation while minimizing fuel consumption and emissions. The main aim of T2.3 described in D2.3 is the development of a flexible energy management system that will be applied to the two demo ships DEMO-1 Gunnerus and DEMO-2 Atatürk. A Python based simulation framework was established in which the on-board electrical system is modelled and combined with various EMS strategies. In the first step, input data for the simulation were acquired, consisting of synthetic load profiles covering typical cases, as well as measurement data from the two DEMO vessels. Subsequently, the components (battery, generator, and onshore charging) were modelled, and the model library was subsequently generalized to fit a wide variety of applications. The EMS strategies were developed, tested, and compared within the simulation environment. A distinction is made between rule-based and optimization-based strategies. Methods such as deterministic finite state machines (FSM), mixed-integer linear programming (MILP), and equivalent consumption minimisation strategy (ECMS) are used. The EMS strategies were applied to the load profiles based on the simulated on-board electrical system and the fuel reduction was evaluated. The DC switchboard serves as a central hub for distributing direct current (DC) power throughout the vessel. It receives power from various sources, such as batteries and/or generators, distribute it to different onboard systems and equipment. This centralized distribution system allows for better control and monitoring of power flow, enabling operators to allocate power where it's needed most efficiently. The Power Management System (PMS) is connected to the EMS by a communication interface and is responsible for monitoring and optimizing the entire vessel's power network. It continuously analyses power demand and availability, considering factors like propulsion requirements, electrical loads from equipment, and energy storage levels. By dynamically adjusting power distribution and consumption based on the setpoints provided by EMS, the PMS ensures that the vessel operates at maximum efficiency (as few losses as possible) while maintaining safety and reliability. It reports all states and measurements to the EMS. Together, the DC switchboard and PMS form the backbone of a Marine Vessel EMS, providing seamless integration of power sources, distribution, and consumption to optimize energy usage and enhance overall vessel performance.
... This can impact electrical networks, making power control and power flow calculations challenging [10]. Battery energy storage system (BESS) is one of the energy storage methods in PV technology, primarily utilized to mitigate power peaks in electrical grids [11]. The management of batteries, including those within PV systems, entails distinct risks and potential hazards. ...
Thermal energy storage technologies have been widely used to mitigate intermittency from renewable energy sources such as solar energy. Phase change material (PCM) is a material that can be used as a heat storage medium and is available in a wide range of operating temperatures. Molten salt is one of the PCMs that has the advantage of a very high operating temperature. The PCM solidification simulation based on HitecXL molten salt using COMSOL Multiphysics software was carried out with variations in heat absorption of 1–5 kW/m², assuming constant heat absorption. The results showed that the PCM solidification process started from the surface of the Stirling engine heat exchanger pipe. The part of the PCM that is solidified falls due to gravity, causing a phenomenon similar to a droplet. The flow that occurred was natural, driven by the buoyancy force resulting from density changes due to temperature gradients in the solidification process. The time required for the PCM to completely solidify was closely related to the amount of heat absorption; the greater the heat absorption from the pipe, the faster the PCM is fully solidified.
... In another Excel table, the charging capacity limits are defined as a function of the aircraft battery's state of charge (SoC); see also [18,19]. Here, a factor ( ) is defined in % in steps of 5 %, which defines the upper limit of the charging power. ...
... In [14], Lange et al. developed a model to simulate the behavior of the BESS when providing peak shaving. The model developed uses Look-Up Tables (LUT) to model the efficiency of converters and the capability limits of the BESS. ...
The Battery Energy Storage System (BESS) is one of the possible solutions to overcoming the non-programmability associated with these energy sources. The capabilities of BESSs to store a consistent amount of energy and to behave as a load by releasing it ensures an essential source of flexibility to the power system. Nevertheless, BESSs have some drawbacks that pose limitations to their utilization. Indeed, effectively managing the stored and released energy is crucial, considering the degradation of performance associated with these systems over time. The substantial capital expenditure (CAPEX) required to install these systems represents a current constraint, impeding their broader adoption. This work evaluates a techno-economic analysis of a 2MW/2MWh BESS providing multiple services, namely participating in capacity and balance markets. The analysis is based on a BESS model implemented in SIMULINK, adopting online data gathered from a Lithium Iron Phosphate (LFP) battery facility. The model evaluates the auxiliary power consumption, state-of-charge (SoC), state of health (SoH), and the round-trip efficiency (RTE) of the overall system. The analysis is based on three price profiles: 2019 (Business-As-Usual), 2020 (COVID-19), and 2022 (Gas Crisis). Furthermore, this work conducts a case study to analyze the behavior of the BESS. It entails a sensitivity analysis, specifically evaluating the influence of CAPEX and upward bid price on the economic viability of the project. The results show a strong relation between the CAPEX variation and the Internal Rate of Return (IRR) of the project.
... The control technique for charge-discharge operations of BESS needs to be specific and clear which is not well discussed in this research. Another peak shaving technique is presented in [12]- [14]. This technique can reduce peak demand based on the system demand and predefined demand limits. ...
... Its implementation seeks financial benefits from reducing generation costs ). On the other hand, Battery Energy Storage System (BESS) has been known to have many benefits for power system applications (Abdullah et al. 2021;Davies et al. 2019;Hosseini et al. 2022;Hu, Armada, and Sánchez 2022;Jufri et al. 2021;, and it has become massively utilized due to the maturity of their technologies and the price fall (Dejvises 2016;Ioakimidis et al. 2018;Lange et al. 2020;Lee et al. 2021;Mair et al. 2021;Park et al. 2015;Shi et al. 2018;Tchagang and Yoo 2020;Wang et al. 2019;Zheng, Meinrenken, and Lackner 2015). Figure 1 illustrates the application of BESS for load shifting. ...
Advances in energy storage technology have allowed the application of load shifting in the utility grid for a more efficient power system operation. However, the economy of the Battery Energy Storage System (BESS) application is not linear to the cost reduction obtained from excluding the costly generators. This paper presents the economic analysis of cost-based load shifting implementation and an approach to determine the generation units to be deactivated and replaced by BESS on three large-scale power grids in Indonesia: Sumbagut, Sumbagteng, and Sumbagsel systems. The economic feasibility is evaluated using the Net Present Value (NPV), considering the savings and the BESS investment cost. The result shows that the proposed approach is more robust than the conventional methods, such as removing the most expensive units, units with the highest cost reduction, units with the best economic return contribution, or the maximum number of units that can be replaced.
... These forecasts use production planning data and weather forecasts to predict load curves for the electricity, heating and cooling sectors. They can be used to generate an operating strategy for the EGI components in order, for example, to charge or discharge storage facilities in an optimized manner, to run energy plants at the optimal operating point and to reduce load peaks [9,12]. ...
The ability of manufacturing companies to compete depends strongly on the efficient use of production resources and the flexibility to adapt to changing production conditions. Essential requirements for the energetic infrastructure (EGI) result from the production itself, e.g., security of supply, efficiency and peak shaving. Since production always takes priority and must not be disturbed, the flexibility potential in terms of energy efficiency lies primarily in the EGI. Based on this, strategies will be developed that support companies in increasing their efficiency and flexibility by optimizing the configuration and operation of the EGI, while production processes are reliably supplied and not adapted. This is reached with intelligent operation strategies for the heating and cooling network based on forecasts, the use of energy storage systems, and the coupling of energy sectors. This paper presents an approach for energy forecasts used for the optimization of operation strategies. Hence, an energy-forecast-tool was developed, which is used for the prediction of electrical and thermal loads depending on the expected production. Therefore, machine learning models are trained with past weather, energy, and production data. Using production planning data and weather forecasts, the model can predict energy demands as input for an EGI optimization.
KeywordsEnergy efficiencyForecastingMachine learning
... Different complex methods for optimal sizing of BESS have been proposed in recent years. In Ref. [4] author proposed a real-time peak-shaving control algorithm and an optimization process to detect the battery and algorithm parameters. In Ref. [5], the author proposed a two-step approach model that developed the linear optimizer with detailed modelling of non-linear effects on the battery. ...
A techno-economic model is provided in this research to assess the viability of using building-integrated battery energy storage systems (BI-BESS) in industries. The factor of β as the ratio of energy to power is introduced in both economic and technical calculations to quantify the economic feasibility of the project with the help of a simple iterative algorithm for battery sizing. The load profile from an industrial building in Malaysia is considered for checking the effectiveness of the results. In addition, the optimum size of the battery achieved by comparing the optimum β from the benefit-cost ratio against β graph, and the ∆ E against β graph. The results show that the optimum battery sizing achieved 84.05 kWh at β = 1.6. Furthermore, the battery parameters that affect the feasibility of using BI-BESS are evaluated. Finally, the results show that the proposed method is independent of the building load profile, and the same graph will apply to all buildings using the industrial tariff and current BESS technology for checking the feasibility of projects.
... One of the key notes we have to keep in mind is that it is nearly impossible to totally changeover the existing topology. So, we have to make do with the current topology and find an almost perfect solution [1]. The current scenario of the electrical board has been highly towards the point of maximum stress over generation of power. ...
The objective and purpose of this study is to implement a peak shaving algorithm in an islanded microgrid for economic power consumption. The simulation of this study has been created through MATLAB/Simulink. This system works for different peak demands and can easily be manipulated, providing us a dynamic approach to the system for maximum efficiency and maximum power generation for the required demand. Added to this, we have also included grid-tied solar panels which are renewable and eco-friendly. The results from the simulation show us clearly that the peak shaving algorithm gets activated as soon as the supply surpasses the peak demand. When this activation happens, the grid is islanded and it goes into self-generation mode. The current use of the grids is aged and is not able to support the current population from time to time. This paper will prove to be a viable, cost-effective and simple solution for the power stations. The simulation, functioning, observation, inference, advantages and disadvantages have been completely provided in this paper with the help of reference papers which have been mentioned towards the end.KeywordsIntentional islandingEconomic solutionPeak shaving algorithm
... For example, EVs powered by electricity storage units (batteries) can be charged during offpeak hours [18], likewise as an electrified heating supply applying thermal energy storage [19]. Moreover, peak shaving [20] (as a smart control approach to mitigate demand peaks) is enabled by installing electricity storage units on electric power networks. A study, evaluating residential heating demands met by HPs, indicates that using batteries for peak shaving could keep demand peaks at the current level [15]. ...
Electrification in energy supply-demand plays a critical role in domestic heating and road transport, delivering an electrified community to reduce carbon emissions. This solution, however, places a significant power demand increase on the distribution networks. To ensure the security of electricity supply, an efficient energy system and energy demand reduction are essential. In this paper, a multi-vector community energy system, applying an electrified heating network, electric vehicle smart charging, community-scale peak shaving and photovoltaic (PV) generation, is demonstrated in three models to manage an electrified community. Firstly, a heating network model, comprising a central ground source heat pump, low-temperature district heating system, electric heaters and thermal storage, is established to measure the optimum distribution temperature. Next, an electrified community model illustrates hourly electricity demands and performances of a community energy system, which is then used to identify the required degree of housing thermal efficiency improvement (i.e., heating demand reduction). The third model evaluates decentralised PV/storage units to maintain the power demand below a targeted power. Modelling results show that the demand ratio of domestic hot water to space heating determines the distribution temperature, which indicates the temperature is increasing with growing housing thermal efficiency. Moreover, the electrification of a community could increase the peak power demand on the highest demand day by over five times, converting heating demands into electricity directly. This significant peak demand can be possibly reduced to only a 33% increase by employing a community energy system. The model of PV/storage units is validated through a 12-week assessment. Ultimately, a modelling tool is developed by assembling the mentioned models, providing four pathways to attain electri-fication. Users can adjust specific parameters and database to align with the local conditions. The results indicate the requirements of building a community energy system and electricity demands in the highest consumption period. Ó 2022 Published by Elsevier B.V.
... cf. [Lan20], [Lan21a] Basics Efficiency increase ◼ Shift operation points to time periods with higher efficiency → energy storages needed ...
Intelligent energy management is the basis for economical and low-emission operation of decentral energy systems. This presentation gives a brief overview on the basics of an intelligent energy management system (iEMS) and explains some of its aspects (e.g., operational strategies, modeling and simulation) using concrete examples. An industrial-scale real-world laboratory was set up at Fraunhofer IISB, where, among other things, many aspects of an iEMS were implemented and demonstrated. In the framework ToSyCo an approach for the control of energy systems was developed. Two levels have been defined, which divide the operational strategy into a global level (storage schedules, load forecasts, etc.) and a local level (for real-time control, security, etc.).
... Many complicated strategies for optimal BESS size have been discussed in recent years. Lange et al. [7], and Chua, Lim, and Morris [8] identified the battery and algorithm parameters; the authors suggested a real-time peak shaving control technique and an optimization procedure. Englberger et al. [9] provided a two-step technique for developing the linear optimizer with extensive modelling of non-linear effects on the battery. ...
... The integration of these units into the distribution system provides many advantages to costumer and utilities. Improved voltage profiles [2][3][4][5], lower power/energy losses [6][7][8][9], peak load shaving [10][11][12][13], voltage stability enhancement [14][15][16][17], and network expansion are just examples of such benefits. However, unless the compensating devices (DG or SRC) are optimally installed, they may lead to technical problems such as overvoltages, reversing power flow, and overloading. ...
This work proposes an intelligent allocation of distributed generation (DG) units and shunt reactive com-pensators (SRC) with high penetration capacities into distribution systems for power loss mitigation using the Bald Eagle Search (BES) optimization algorithm. The intelligent allocation causes a reduction in voltage variations and enhances the voltage stability of the systems. The SRC units include shunt capacitors (SC), Static Var Compensators (SVC), and Distribution Static Compensators (DSTATCOM), which are determined according to their capacities. The optimization study includes the 33-bus and the 118-bus distribution systems as medium to large systems. Performance parameters, including the reactive power loss, Total Voltage Deviation (TVD), and Stability Index (SI), besides the power loss, are recorded for each optimization case study. When the BES algorithm optimizes 1, 2, and 3 DG units operating at optimal power factor (OPF) into the 33-bus systems, percentage reductions of power loss reach 67.84%, 86.49%, and 94.44%, respectively. Reductions of 28.26%, 34.47%, 35.24%, and 35.44% are achieved in power loss while optimizing 1, 3, 5, and 7 SRC units. With a combination of DG/SRC units, the power loss reductions achieve 72.30%, 93.89%, and 97.49%, optimizing 1, 3, and 5 pairs of them. Similar reductions are achieved for the rest of the performance parameters. With high penetration of compensators into the 118-bus system, the percentage reductions of power loss are 29.14%, 73.27%, 83.72%, 90.14%, and 93.41% for optimal allocations of 1, 3, 5, 7, and 9 DG units operating at OPF. The reduction reaches 11.15%, 39.08% with 1 and 21 devices when optimizing the SRC. When DG SRC units are optimized together, power loss turns out to be 32.83%, 73.31%, 83.32%, 88.52%, and 91.29% with 1, 3, 5, 7, and 9 pairs of them. The approach leads to an enhanced voltage profile near an acceptable range of bus voltages, reduces the voltage fluctuation substantially, and enhances the system stability. The study also ensures the BES algorithm's capability to solve these nonlinear optimization problems with high decision-variable numbers. Ó 2022 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Ain Shams University This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
... However, not factoring in the actual range of SoC variations in the sizing strategy can result in oversizing of the batteries and underutilization of the battery assets. Lange et al. [11] discuss a BESS sizing method for peak shaving applications based on a real-time control algorithm. However, the distribution network was not explicitly modeled, but the sizing strategy was only based on electricity demand readings at a specific site. ...
Deployment of renewable energy sources in Malta is limited by grid integration constraints. Photovoltaic (PV) systems pose a significant risk to grid stability due to their inherent intermittency and result in overvoltages at the medium-voltage and low-voltage networks. Investments in utility-scale battery energy storage systems (BESS) will facilitate further deployment of renewables and will help to achieve energy security. This study proposed a novel sizing strategy for utility-scale battery energy storage systems (BESS) based only on technical considerations to find the minimum required storage capacity based on historical electricity demand and PV generation. The modeling and simulation were constrained to a section of the Gozitan 11 kV electrical distribution network and the results showed that the utility-scale storage can reduce the impact of PV systems on the grid infrastructure by avoiding reverse power flows and improve the local energy security by reducing the peak electricity demand. The central BESS and the decentralized coordinated BESS with “equal sizing” stored 3.4 MWh while the decentralized coordinated BESSs with “optimal sizing” stored 5.307 MWh. In all three cases, the evening peak demand was reduced by 30.5% from 2.62 MW down to a defined limit of 1.82 MW. From the results presented in this paper, the “optimal sizing” strategy showed that the BESSs have most benefit when installed next to the local PV generation. Hence, by deploying coordinated utility-scale BESSs sized according to the PV generation potential, it is expected that the penetrations of PV generation can be increased even with the present distribution network infrastructure.
... Another approach is to focus on the improvement of the pumping operation by enhancing the pumping control through optimization techniques. More specifically, digital and control techniques have emerged, like monitoring systems and advanced optimization modeling, which have the advantage of not requiring major investments like system renovations, making them more attractive (Wanjiru et al., 2016;Kernan et al., 2017;Lange et al., 2020). ...
This study introduces an energy management method that smooths electricity consumption and shaves peaks by scheduling the operating hours of water pumping stations in a smart fashion. Machine learning models are first used to accurately forecast the electricity consumed and produced by renewable energy sources on an hourly level. Then, the forecasts are exploited by an algorithm that optimally allocates the operating hours of the pumps with the objective to minimize predicted peaks. Constraints related with the operation of the pumps are also considered. The performance of the proposed method is evaluated considering the case of a Greek remote island, Tilos. The island involves an energy management system that facilitates the monitoring and control of local water pumping stations that support residential water supply and irrigation. Results indicate that smart scheduling of water pumps in a small-scale island environment can reduce the daily and weekly deviation of electricity consumption by more than 15% at no monetary cost. It is also concluded that the potential gains of the proposed approach are strongly connected with the amount of load that can be shifted each day, the accuracy of the forecasts used, and the amount of electricity produced by renewable energy sources.
... The work of [5] investigated the profitability of storage systems in combination with solar power, but used only weather data from one part of Germany and no power data such as power demand or wind power. Both investigations of [6] and [7] used real measured demand power time series, but focussed on the peak shaving capability of storage systems in an industrial environment and omitted calculating the uncertainties of the results. Lastly [8] investigated a combined usage of storage systems with power data from 100 dwellings with a focus on a higher economical revenue using control algorithms but not dimensioning the storage systems. ...
The fluctuations of the renewable energies request storage systems as a significant part of the future energy system. Here we introduce a measurement approach regarding the regional coverage of the power demand and the influence of a storage system on it. The approach is based on measured power data with given uncertainties. The resolution is 15 minutes in the time domain and 2-digit ZIP code regions in the spatial domain. An idealized storage model is introduced, and one storage is implemented for each region. As a result, the regional coverage is measured for different storage parameters and the respective measurement uncertainty is assessed to be below the set goal of 2 %. Furthermore, the inverse problem, i. e. the storage dimensioning, is investigated by estimating the necessary storage parameters to achieve a desired regional coverage. Findings: electrical self-sufficiency can be reached in 23 out of 95 regions, in contrast in 56 regions the regional coverage is increased insignificantly regardless of the parameters of the storage system. This illustrates the necessity of power transport to enable the increase of regional coverage. Concluding, the proposed measurement approach enables the estimation of storage parameters and can be used for storage dimensioning.
... 3,000 20,000 1,000 1,000,000 2,000,000 have been presented in [48]. This paper presents the case study of the autonomous mode of VRLA ES operation providing voltage regulation with active and reactive power in an industrial LV grid containing distorting loads [49][50][51]. The system service has been performed according to the operation flowchart presented in Fig. 11 The procedure for the provision of system service providing voltage regulation with active and reactive power, begins when the assumed limit value of the relative voltage change ( % ) at the connection point is exceeded. ...
Microgrids with renewable power are becoming a widespread alternative for decarbonizing the electrical sector in light of climate change and global warming. However, such widespread penetration of renewables degrades some parameters of power quality along the low voltage utility grid. This research conducts an experiment with an advanced metering infrastructure of a power utility grid with hundreds of thousands of smart grid devices. The experiment identifies the location of the power quality disturbances. The results of the measurements have shown that the power quality parameters are mainly degraded along the downstream section of the low voltage power utility grid, despite the fulfillment of the regulatory quality requirements in the upstream high voltage substation. Thus, the novelty of the approach presented in the paper consists in the use of relatively low capacity energy storage units to locally mitigate the power quality disturbances, keeping them as close as possible to their source in the low voltage grid, instead of using large energy storage units connected to the medium voltage grid. Moreover, the research evaluated the parameters of power capacity, charge–discharge rate, weight, size, and the Levelized Cost Of Energy (LCOE) implemented with five types of energy storage technology. Lithium-Ion Capacitor (LIC) presents the lowest LCOE. However, service type and location parameters also play an essential role in selecting other different types of energy storage technology.
This research shows that locating small energy storage units close to the source of power quality disturbance is cost-effective and offers excellent potential for widespread implementation in the low voltage distribution grid.
... Meanwhile, PV technology also brings challenges to the stability of the grid [5]. The battery energy storage system (BESS) is beneficial to eliminate the mismatch of renewable energy power generation and alleviate the power grid pressure [6], especially in the grid-connected mode. ...
The optimal capacity of a battery energy storage system (BESS) is significant to the economy of energy systems and photovoltaic (PV) self-consumption. In this study, considering the long-term battery degradation, a mixed-integer nonlinear programming (MINLP) model was proposed for the PV-battery systems which aim to minimize the life cycle cost (LCC) and was solved by a novel two-layer optimization method. This study compared optimal battery capacities, LCCs, and self-consumption ratios (SCR) under two types of tariffs with and without considering battery degradation. The effects of feed-in tariffs, feed-in limits and PV degradation on the optimization results are discussed. The results showed that the battery degradation could cause an increase in operation cost. The cases considering battery degradation have larger battery capacity and more life cycle cost than the cases without considering battery degradation. Compared with the feed-in tariff profile of flat rate, the cases with feed-in tariff profile of time-of-use have smaller optimal battery capacity, higher SCR, and lower LCC. The feed-in tariff, feed-in limit and PV degradation have an important impact on optimal battery capacity and total life cycle cost. This study provides an optimization method and theoretical analysis for the capacity and operation optimization of PV-battery systems.
... proposes a bi-level optimal location and scale model for grid-side battery energy storage system with coordinated planning and operation, which effectively reduces the operation cost and loss. Based on a real time control algorithm [23], proposes a dimensioning optimization to battery energy storage systems used for peak shaving, which improves the peak shaving performance of the energy storage system. In [24], the impact of demand side management is considered, and a more economical energy storage scale design scheme is provided for the residential battery energy storage system. ...
The rapid development of battery energy storage technology provides a potential way to solve the grid stability problem caused by the large-scale construction of nuclear power. Based on the case of Hainan, this study analyses the economic feasibility for the joint operation of battery energy storage and nuclear power for peak shaving, and provides an effective solution framework for construction scale and battery type determination. Under the proposed framework, a novel cost model for the large-scale battery energy storage power station is proposed. Then, economic analysis is conducted to get the most economical battery type and construction scale by considering the comprehensive economic benefit of the joint operation under the limit of the load factor. On this basis, sensitivity analysis of economic indicators to control parameters and economic parameters is performed to further demonstrate the economic feasibility of the joint operation. Comparative analysis shows that 270MW lithium iron phosphate battery energy storage power station has the best and stable comprehensive performance in terms of the IRR, PBP and LCOE, which are 16.27%, 6.27 year and 0.464¥/kWh, respectively. In comparison with the pumped storage, the battery energy storage has lower initial investment, faster capital recovery and smaller floor area under the joint operation mode. Moreover, sensitivity analysis illustrates that the large-scale application of battery energy storage still depends on the trade-off between the cost performance of battery and the real-time electricity price.
With the development of renewable energy, the character of building energy system gradually transitions from electricity consumer to an integrated role that considers both consume and supply side. Abundant flexible resources within the buildings remain to be exploited and utilized. This paper proposes a distributed energy management system of DC microgrid in office buildings based on DC bus signaling. The system enables to control decentralized terminals in office buildings including photovoltaic (PV), battery, un-shiftable load (UL), thermostatically controlled load (TCL), and electric vehicle (EV). The DC bus voltage is regulated in real-time according to the difference between actual grid power and the grid requirement power, which is arranged as a constant power curve in this paper to grid relief. The viability and effectiveness of the proposed system are validated by the simulation results. The DC bus voltage is varied dynamically and the power of utility grid is almost the same as the demand power. In addition, the impact of terminal capacity and arranged control sequence with the proposed strategy are analyzed and evaluated. For a typical operating condition, the annual adjustment contribution of PV, battery, EV, and TCL among all the terminals take 9.3 %, 70.3 %, 19.0 %, and 1.4 %, respectively.
The presentation discussed the use of simulations for optimising energy systems, focusing on the challenges and strategies for efficient operational of energy systems, involved plants and storages and the application on heating grids. Several realisation types for operational strategies where presented followed by a concrete example consisting of a combined heat and power plant, a thermal energy storage and a battery from the real-world laboratory for decentral intelligent energy systems at Fraunhofer IISB. Simulations are the backbone of the optimisation process, as they enable the non-inversive investigation of the system. They are used for planning and optimisation, for load prognosis, grid analysis and management and energy efficiency measures. Additionally, the presentation covered the optimization of heat networks through multi-stage simulation-based operational enhancements based on the services in leveraging flexibility for the energy transition of VK Energie. Overall, the presentation provided insights into the complexities of energy system optimisation and the practical applications of simulation tools in this context.
The presentation covered the project ProEnergie - Bayern and the energy system at Eirenschmalz. In ProEnergie, strategies and software tools were developed that support companies in increasing the efficiency and flexibility of their energetic building infrastructure. Industrial companies from various branches (automotive engineering, metal, lightweight construction, technical textiles, plastics, ceramics) as well as the two Fraunhofer institutes IISB and IPA worked on the project.
Subsequently, the company Eirenschmalz presented their energy concept and the expanded energy system at the Schwabsoien site, which showed a real example of applications of the software tools from ProEnergie.
Frequent electricity shortages undermine economic activities and social well-being, thus the development of sustainable energy storage systems (ESSs) becomes a center of attention. This study examines the environmental and economic feasibility of using repurposed spent electric vehicle (EV) lithium-ion batteries (LIBs) in the ESS of communication base stations (CBS) for load shifting. Methodologically, life cycle assessment (LCA) and net present value (NPV) are used to evaluate the environmental and economic performance of such system, respectively. Comparing with the conventional system without load shifting and the use of new LIBs in CBS, our analysis indicates that the proposed system is economically appealing, saving 17.6 % of life cycle cost. Nonetheless, the environmental performance of our system is almost identical to the conventional one, due to the relatively low round-trip efficiency of spent EV LIBs. Sensitivity analysis suggests that the differences of peak and valley electricity prices determine the economic potential of this system, and cleaner energy sources such as nuclear power could largely improve its overall environmental performance. By highlighting the importance of improving round-trip efficiency and using clean energy resources, our work offers implications for stakeholders like policymakers and researchers.
The penetration of lithium-ion batteries (LIBs) in transport, energy, and communication systems is increasing rapidly. A meticulous but simplified LIB model for non-uniform internal state monitoring and online control is sought in practice. Based on the pseudo-two-dimensional (P2D) model, a simplified electro-chemical model for LIBs is proposed. Specifically, a rigorous model of the non-uniform reaction rates inside the battery is derived. Sub-models that capture the non-uniformity of current densities, potentials and concentrations are developed synchronously. Time-variant parameters and a lumped thermal model are incorporated as well. A full-cycle simulation framework, including the discretization, initialization, stabilization and closed-loop correction methods, is designed for ease of online control. Numerical experiments on the widely used NCM and LFP 18650 batteries under standard charge and discharge protocols and dynamic protocols during the peak-shaving or regulation service are conducted for validation. Generally, the speed of the proposed model increases hundreds of times compared to the P2D model. The estimation accuracy of internal and external states increases around 10% to 100% compared to state-of-art electro-chemical models. In addition, the correction speed and accuracy of the closed-loop framework increase around ten times and around 100% respectively compared to the widely used ensemble Kalman filter.
This paper presents a three-stage inverter-based peak shaving and Volt-VAR control (VVC) framework in active distribution systems using the online safe deep reinforcement learning (DRL) method. The proposed framework aims to reduce the peak load, voltage violations, and real power loss by coordinating three stages with different control timescales. In the first stage, a day-ahead charging/discharging scheduling of energy storage systems (ESSs) with a 30 min resolution is performed via their inverters for peak shaving. In the second stage, the discharging power of ESSs is adjusted through measurements with a 1 min resolution to completely shave peak loads. A model-free DRL algorithm integrated with a safety module is also implemented in the second stage. Using this algorithm, the reactive powers of photovoltaic (PV) systems and ESSs are controlled by the DRL agent to reduce the voltage violation and real power loss, whereas no voltage violation occurs during the online training process. In the third stage, a proportional-integral controller with real-power compensation is integrated into inverters of PV systems and ESSs to rapidly mitigate local voltage violations with a 0.1 s resolution. The high efficiency and safety of the proposed method were validated on the IEEE 33-bus and IEEE 123-bus systems.
Demand response with battery energy storage systems (BESS) provides the most flexible peak reduction solution for different markets. One of the major challenges is the optimization of the demand threshold that controls the charging and discharging powers of BESS. To increase its tolerance to day-ahead prediction errors, state-of-art controllers utilize rigid parameters that are determined from long-term historical data. However, long-term historical data may be unavailable at implementation, and rigid parameters cause them unable to adapt to evolving load patterns. To tackle this issue, this article proposes a novel dynamic two-stage maximum demand reduction controller using BESS that incorporates 1-h-ahead load profiles to refine the threshold found based on day-ahead load profile and prevent peak reduction failure if necessary. The dynamic controller needs no rigid parameters and can begin its daily peak reduction with just 30 days of historical data. Compared to the conventional fixed threshold, single-stage, and fuzzy controllers, the proposed two-stage controller achieves up to 6.82% and 306.23% higher in average maximum demand reduction and total maximum demand charge savings, respectively, on two different datasets. The proposed controller also achieves a 0% peak demand reduction failure rate in both datasets, demonstrating its peak demand reduction prevention capability.
Due to China's power supply structure, the conventional power units are responsible for the deep load shaving regulation to meet the high penetration challenge of renewable energy. To improve the capability of the peaking load shaving and the power regulation quality, battery energy storage systems (BESS) can be used to cooperate power units to satisfy the multi-objective regulation needs. This paper proposes a novel unified control scheme to smooth the power output of the power plant and meet the strict power load demands distributed from the automatic generation centre (AGC). The proposed coordination control strategy consists of unit load demand scheduler, multi-objective reference governor, fuzzy logic based model predictive control (FMPC) for the boiler-turbine unit, and one-step model predictive control for battery energy storage system. Based on the control scheme, we can achieve: 1) The operation of the boiler-turbine unit is more energy-saving and reliable while the service life of the valves is extended; 2) With the participation of battery energy storage system, the power output of the boiler-turbine unit is smooth and the tracking performances of the unified generation unit, including tracking accuracy and transient behaviour, are improved; 3) Various constraints can be explicitly coped with under the framework of model predictive control.
Because the efficiency of the hydrogen production is closely related to the optimal scheduling strategy and economy of the wind-hydrogen coupling system, an optimal scheduling method for the wind-hydrogen system considering the efficiency of the wind power hydrogen production is proposed in this paper. Taking hydrogen production from alkaline electrolysers as an example, a method to improve the efficiency of electrolyser arrays based on the segmented fuzzy control is proposed. An optimization scheduling model of wind-hydrogen system considering the efficiency of the wind power hydrogen production is built, and the optimal hydrogen production power is solved by the Artificial Bee Colony algorithm. The sensitivity analysis of the factors that affect the efficiency of wind-hydrogen system is carried out. The validity and correctness of the proposed method is verified by the simulation analysis of the actual operation data of the power grid. Compared with the traditional simple start-stop scheme, the control strategy proposed in this paper can produce more hydrogen of 6.18 tons and improve the hydrogen production efficiency by 4.8%, which can provide a theoretical basis for the large-scale application of hydrogen energy storage systems in the power grid.
Das vorliegende White Paper rückt das aufkommende Thema „Rightsizing von Batteriespeichern“ in den Fokus. Es skizziert die Eckpunkte einer Batteriespeicher-Auslegung, die über ein bloßes „Weniger genügt auch“ deutlich hinausgeht. Zusätzlich werden die einschlägigen Grundlagen der Batterieauslegung und Ökobilanzierung praxisrelevant vermittelt, da diese für eine Nachhaltigkeitsbewertung von Batteriespeichern unerlässlich sind.
This white paper focuses on the emerging topic of rightsizing battery storage systems. It outlines key points of a battery storage design that clearly goes beyond a mere "less is also enough". In addition, the relevant fundamentals of battery design and life cycle assessment are presented in a practically relevant manner, as these are essential for a
sustainability assessment of battery storage systems.
Integrated energy system is efficient and flexible in distributed energy supply, but the coupling of energy flows and the inaccuracy of prediction bring challenges to its economic operation. Peak shaving and demand management have become a necessary part of its operation method. In this paper, a cooperative dispatch method is proposed to optimize daily operations that consider the coupling characteristics of multi-energy flow in integrated energy systems. In addition, given the limitations of long-term and short-term prediction, a dynamic correction operation mode is proposed to improve the accuracy of the dispatch plan. In the case study, domestic hot water systems and charging piles are taken as dispatchable electricity demand, and indoor temperature is dispatchable cooling and heating demands. Electric energy storage is taken as the peak-shaving tool for electricity, and air conditioning circulation water is taken for cooling and heating. Based on this, daily operations over five winter days and five summer days are compared. The results show that, after applying this cooperative dispatch method, the energy supply costs are reduced by 10.82%, and carbon emissions are reduced by 9.71%. Meanwhile, the average energy efficiency is increased by 6.42%, and the average exergy loss rate is reduced by 5.46%.
The renewable energy transition has introduced new electricity tariff structures. With the increased penetration of photovoltaic and wind power systems, users are being charged more for their peak demand. Consequently, peak shaving has gained attention in recent years. In this paper, we investigated the potential of peak shaving through battery storage. The analyzed system comprises a battery, a load and the grid but no renewable energy sources. The study is based on 40 load profiles of low-voltage users, located in Belgium, for the period 1 January 2014, 00:00–31 December 2016, 23:45, at 15 min resolution, with peak demand pricing. For each user, we studied the peak load reduction achievable by batteries of varying energy capacities (kWh), ranging from 0.1 to 10 times the mean power (kW). The results show that for 75% of the users, the peak reduction stays below 44% when the battery capacity is 10 times the mean power. Furthermore, for 75% of the users the battery remains idle for at least 80% of the time; consequently, the battery could possibly provide other services as well if the peak occurrence is sufficiently predictable. From an economic perspective, peak shaving looks interesting for capacity invoiced end users in Belgium, under the current battery capex and electricity prices (without Time-of-Use (ToU) dependency).
The peak-valley characteristic of electrical load brings high cost in power supply coming from the adjustment of generation to maintain the balance between production and demand. Distributed energy storage system (DESS) technology can deal with the challenge very well. However, the number of devices for DESS is much larger than central energy storage system (CESS), which brings challenges for solving the problem of location selection and capacity allocation with large scale. We formulate the charging/discharging model of DESS and economic analysis. Then, we propose a simulation optimization method to determine the locations to equip with DESSs and the storage capacity of each location. The greedy algorithm with Monte Carlo simulation is applied to solve the location and capacity optimization problem of DESS over a large scale. Compared with the global optimal genetic algorithm, the case study conducted on the load data of a district in Beijing validates the efficiency and superiority of our method.
The interest in modeling the operation of large-scale battery energy storage systems (BESS) for analyzing power grid applications is rising. This is due to the increasing storage capacity installed in power systems for providing ancillary services and supporting nonprogrammable renewable energy sources (RES). BESS numerical models suitable for grid-connected applications must offer a trade-off, keeping a high accuracy even with limited computational effort. Moreover, they are asked to be viable in modeling for real-life equipment, and not just accurate in the simulation of the electrochemical section. The aim of this study is to develop a numerical model for the analysis of the grid-connected BESS operation; the main goal of the proposal is to have a test protocol based on standard equipment and just based on charge/discharge tests, i.e., a procedure viable for a BESS owner without theoretical skills in electrochemistry or lab procedures, and not requiring the ability to disassemble the BESS in order to test each individual component. The BESS model developed is characterized by an experimental campaign. The test procedure itself is framed in the context of this study and adopted for the experimental campaign on a commercial large-scale BESS. Once the model is characterized by the experimental parameters, it undergoes the verification and validation process by testing its accuracy in simulating the provision of frequency regulation. A case study is presented for the sake of presenting a potential application of the model. The procedure developed and validated is replicable in any other facility, due to the low complexity of the proposed experimental set. This could help stakeholders to accurately simulate several layouts of network services.
In this study, a novel algorithm for the management of the power flows of an islanded power system was developed, capable of simultaneously achieving steadier conventional unit operation and shaving the demand peak values, for the days of the year that present a night peak in their load curve. The under investigation system is composed of Diesel Generators, a PV farm and a Battery Energy Storage System (BESS) with the power system’s consumption to be relatively higher than its RES production. The proposed algorithm combines the use of a load forecasting methodology, a pattern recognition procedure and a custom optimal power flow scheduling algorithm. The prediction module was based on a feedforward artificial neural network, capable of short-term day ahead load forecasting. The forecasted day ahead load profile was then used as an input to the developed pattern recognition algorithm, in order to be classified based on its load curve shape (pattern). Subsequently, in case that the classification resulted in a clear night peak pattern, it was possible to estimate an hourly based trajectory for the diesel generators operation and derive the BESS charging setpoints, which result in the desired peak shaving and smoothing level simultaneously. In this way, it is possible to replace or substitute the highest power demand with stored renewable energy and to operate the diesel engines as steady as possible, diminishing the ramp up and the steep gradients before the night hours’ peak. The algorithm was integrated in the overall system model in APROS software, where dynamic simulations were performed. The simulation results proved that by applying the proposed algorithm, a combined effect of smoother diesel generator operation and peak shaving with renewable energy is achievable even with the absence of PV overproduction, diminishing the variability of the load to be covered from the conventional units. Such an operation aims at enabling diesel engines to be rated at a lower, than currently, maximum capacity while increasing the share of the renewable energy penetration into the grid.
Intensifying market pressure, extended environmental legislations, increasing environmental consciousness and rising energy prices are a major concern for production companies worldwide. Production of goods is responsible for about one-third of the global greenhouse gas emissions. As a consequence the energy demand in production and with this the energy costs for the production of a product are moving more into focus of decision makers. Depending on the shift and working system of a company, the energy demand during planned non-production times like free shifts, weekends or holidays can be significant. However, a lack of knowledge about realistically achievable electrical load levels in non-production times due to missing benchmarks can be observed in practice. As a consequence, related energy saving potentials remain undetected. Against this background, this paper presents a methodology to analyze the electrical load during non-production times using load duration curves. Performance indicators are developed allowing for a comparison between factories in order to identify energy saving potentials. Within this paper a tool is developed to easily compare different automotive factories and tested using real data of two large car manufacturers.
Battery pack modeling is essential to improve the understanding of large battery energy storage systems, whether for transportation or grid storage. It is an extremely complex task as packs could be composed of thousands of cells that are not identical and will not degrade homogeneously. This paper presents a new approach toward battery pack modeling by combining several previously published models into a comprehensive framework. This work describes how the sub-models are connected, their basic principles, what adjustments were necessary, and what new parameters needed to be introduced. Overall, this paper introduces an open modular framework for future work on, among others, the impact of cell-to-cell variations, inhomogeneous degradation, SOC and SOH tracking, balancing and performance forecast.
Recent attention to industrial peak shaving applications sparked an increased interest in battery energy storage. Batteries provide a fast and high power capability, making them an ideal solution for this task. This work proposes a general framework for sizing of battery energy storage system (BESS) in peak shaving applications. A cost-optimal sizing of the battery and power electronics is derived using linear programming based on local demand and billing scheme. A case study conducted with real-world industrial profiles shows the applicability of the approach as well as the return on investment dependence on the load profile. At the same time, the power flow optimization reveals the best storage operation patterns considering a trade-off between energy purchase, peak-power tariff, and battery aging. This underlines the need for a general mathematical optimization approach to efficiently tackle the challenge of peak shaving using an energy storage system. The case study also compares the applicability of yearly and monthly billing schemes, where the highest load of the year/month is the base for the price per kW. The results demonstrate that batteries in peak shaving applications can shorten the payback period when used for large industrial loads. They also show the impacts of peak shaving variation on the return of investment and battery aging of the system.
As a critical indictor in the Battery Management System (BMS), State of Charge (SOC) is closely related to the reliable and safe operation of lithium-ion (Li-ion) batteries. Model-based methods are an effective solution for accurate and robust SOC estimation, the performance of which heavily relies on the battery model. This paper mainly focuses on battery modeling methods, which have the potential to be used in a model-based SOC estimation structure. Battery modeling methods are classified into four categories on the basis of their theoretical foundations, and their expressions and features are detailed. Furthermore, the four battery modeling methods are compared in terms of their pros and cons. Future research directions are also presented. In addition, after optimizing the parameters of the battery models by a Genetic Algorithm (GA), four typical battery models including a combined model, two RC Equivalent Circuit Model (ECM), a Single Particle Model (SPM), and a Support Vector Machine (SVM) battery model are compared in terms of their accuracy and execution time.
This paper presents an approach to determine the optimal capacity of battery energy storage system (BESS) for peak shaving of the electric power load in Naresuan University (NU), Phitsanulok, Thailand. The topology of the system consists of main grid, loads and the proposed BESS. Experimental data are daily load profiles, which were recorded for every 15 minutes over the last year. The consumed electricity energy can well correlate with the temperature as well as the schedules of NU activities for both annual and daily scales. Peak shaving is proposed to reduce the electricity cost contributed from the high load peak during the daytime. Realistic parameters for both AC/DC converter and battery are taken into account. An optimal BESS capacity for saving the electricity cost by peak shaving is calculated by first considering the date when the highest energy demand is recorded. Our results show that the optimal BESS can shave the peak load efficiently. Oversized BESS can further decrease the load peak but the reduced cost per battery capacity is not optimal. In addition, we present and discuss two different management strategies, i.e., time-based and differentiated power criteria, for operating the BESS in this system. BESS with different storage capacity is included into the system and the equivalent electricity cost is estimated. Both time-based and differentiated power criteria can reduce the cost.
In the last few years, several investigations have been carried out in the field of optimal sizing of energy storage systems (ESSs) at both the transmission and distribution levels. Nevertheless, most of these works make important assumptions about key factors affecting ESS profitability such as efficiency and life cycles and especially about the specific costs of the ESS, without considering the uncertainty involved. In this context, this work aims to answer the question: what should be the costs of different ESS technologies in order to make a profit when considering peak shaving applications? The paper presents a comprehensive sensitivity analysis of the interaction between the profitability of an ESS project and some key parameters influencing the project performance. The proposed approach determines the break-even points for different ESSs considering a wide range of life cycles, efficiencies, energy prices, and power prices. To do this, an optimization algorithm for the sizing of ESSs is proposed from a distribution company perspective. From the results, it is possible to conclude that, depending on the values of round trip efficiency, life cycles, and power price, there are four battery energy storage systems (BESS) technologies that are already profitable when only peak shaving applications are considered: lead acid, NaS, ZnBr, and vanadium redox.
This study provides an overview of available techniques for on-board State-of-Available-Power (SoAP)
prediction of lithium-ion batteries (LIBs) in electric vehicles. Different approaches dealing with the onboard
estimation of battery State-of-Charge (SoC) or State-of-Health (SoH) have been extensively discussed
in various researches in the past. However, the topic of SoAP prediction has not been explored
comprehensively yet. The prediction of the maximum power that can be applied to the battery by discharging
or charging it during acceleration, regenerative braking and gradient climbing is definitely one
of the most challenging tasks of battery management systems. In large lithium-ion battery packs because
of many factors, such as temperature distribution, cell-to-cell deviations regarding the actual battery
impedance or capacity either in initial or aged state, the use of efficient and reliable methods for battery
state estimation is required. The available battery power is limited by the safe operating area (SOA),
where SOA is defined by battery temperature, current, voltage and SoC. Accurate SoAP prediction allows
the energy management system to regulate the power flow of the vehicle more precisely and optimize
battery performance and improve its lifetime accordingly. To this end, scientific and technical literature
sources are studied and available approaches are reviewed.
The interest in self-consumption of electricity generated by rooftop photovoltaic systems has grown in recent years, fueled by decreasing levelized costs of electricity and feed-in tariffs as well as increasing end customer electricity prices in the residential sector. This also fostered research on grid-connected PV-battery storage systems, which are a promising technology to increase self-consumption. In this paper a mixed-integer linear optimization model of a PV-battery system that minimizes the total discounted operating and investment costs is developed. The model is employed to study the effect of the temporal resolution of electrical load and PV generation profiles on the rate of self-consumption and the optimal sizing of PV and PV-battery systems. In contrast to previous studies high resolution (10 s) measured input data for both PV generation and electrical load profiles is used for the analysis. The data was obtained by smart meter measurements in 25 different households in Germany. It is shown that the temporal resolution of load profiles is more critical for the accuracy of the determination of self-consumption rates than the resolution of the PV generation. For PV-systems without additional storage accurate results can be obtained by using 15 min solar irradiation data. The required accuracy for the electrical load profiles depends strongly on the load profile characteristics. While good results can be obtained with 60 s for all electrical load profiles, 15 min data can still be sufficient for load profiles that do not exhibit most of their electricity consumption at power levels above 2 kW. For PV-battery systems the influence of the temporal resolution on the rate of self-consumption becomes less distinct. Depending on the load profile, temporal resolutions between 5 min and 60 min yield good results. For optimal sizing of the PV power and the storage capacity a resolution of 60 min is found to be sufficient. For the sizing of the battery inverter power of the storage system, a finer temporal resolution of at least 300 s is necessary.
Purpose
– The main purpose of this study is to provide an effective sizing method and an optimal peak shaving strategy for an energy storage system to reduce the electrical peak demand of the customers. A cost-savings analytical tool is developed to provide a quick rule-of-thumb for customers to choose an appropriate size of energy storage for various tariff schemes.
Design/methodology/approach
– A novel sizing method is proposed to obtain the optimum size of energy storage for commercial and industrial customers based on their historical load profile. An algorithm is developed to determine the threshold level for peak shaving. One of the buildings at Universiti Tunku Abdul Rahman (UTAR), Malaysia, is chosen for this study. A three-phase energy storage system rated at 15 kVA is developed and connected to the low-voltage electrical network in the building. An adaptive control algorithm is developed and implemented to optimize the peak shaving.
Findings
– The sizing analysis shows that the customer under the C2 tariff rate yields the highest saving, followed by E2, C1 and E1. The experimental results presented indicate that the proposed adaptive control algorithm has effectively optimized the peak demand to be shaved.
Research limitations/implications
– This study demonstrates the potential of energy storage in reducing the peak demand and cost of electricity. One of the main challenges of real-time peak shaving is to determine an appropriate threshold level such that the energy stored in the energy storage system is sufficient during the peak shaving process.
Originality/value
– The originality of the paper is the optimal sizing method of the energy storage system based on the historical load profile and adaptive control algorithm to optimize the peak demand deduction.
An adaptive control method is proposed for applying “peak shaving” to the grid electrical demand of a single building, using a battery energy storage system to reduce the maximum demand. The objective is to save cost by reducing the monthly “demand charges” commonly levied on commercial power customers. Multiple demand forecasts are evaluated at every time step during each day, and combined to calculate a maximum limit to which the grid demand is controlled. Whereas prior methods apply peak shaving to every day alike, our method does not attempt to reduce peak demand below the maximum reached in the same month. The results show greater monthly demand cost reduction than a previously published method.
Battery Energy Storage System (BESS) can be utilized to shave the peak load in power systems and thus defer the need to upgrade the power grid. Based on a rolling load forecasting method, along with the peak load reduction requirements in reality, at the planning level, we propose a BESS capacity planning model for peak and load shaving problem. At the operational level, we consider the optimal control policy towards charging and discharging power with two different optimization objectives: one is to diminish the difference between the peak load and the valley load, the other is to minimize the daily load variance. Particularly, the constraint of charging and discharging cycles, which is an important issue in practice, is taken into consideration. Finally, based on real load data, we provide simulation results that validate the proposed optimization models and control strategies.
Energy management means to optimize one of the most complex and important technical creations that we know: the energy system. While there is plenty of experience in optimizing energy generation and distribution, it is the demand side that receives increasing attention by research and industry. Demand Side Management (DSM) is a portfolio of measures to improve the energy system at the side of consumption. It ranges from improving energy efficiency by using better materials, over smart energy tariffs with incentives for certain consumption patterns, up to sophisticated real-time control of distributed energy resources. This paper gives an overview and a taxonomy for DSM, analyzes the various types of DSM, and gives an outlook on the latest demonstration projects in this domain.
This paper presents a sizing methodology and optimal operating strategy for a battery energy storage system (BESS) to provide a peak load shaving. The sizing methodology is used to maximize a customer's economic benefit by reducing the power demand payment with a BESS of a minimum capacity, i.e. a system with a lowest cost. The BESS optimal operating strategy is based on dynamic programming and is aimed to minimize the energy cost while satisfying battery physical constraints.
Advances in battery technology have not kept pace with rapidly growing energy demands. Most laptops, handheld PCs, and cell phones use batteries that take anywhere from 1.5 to 4 hours to fully charge but can run on this charge for only a few hours. The battery has thus become a key control parameter in the energy management of portables. To meet the stringent power budget of these devices, researchers have explored various architectural, hardware, software, and system-level optimizations to minimize the energy consumed per useful computation. Research in battery-aware optimization is now moving from stand-alone devices to networks of wireless devices, specifically, ad hoc and distributed sensor networks. Computationally feasible mathematical models are now available that capture battery discharge characteristics in sufficient detail to let designers develop an optimization strategy that extracts maximum charge.
Battery systems can reduce the peak electrical consumption through proper charging and discharging strategies. To this end, consumers often rely on historic consumption data to select a cost-efficient battery system. However, historic data is an imperfect mapping of the real consumption, because of a coarse sampling rate or measurement inaccuracies. This can result in non-optimal decisions, e.g., by underestimating the battery capacity required. In this article, we analyze how aggregation affects a state-of-the-art battery sizing algorithm for an industrial production site. We then use machine learning on a short period of high-resolution data to correct this error from historic data. Our experiments indicate that machine learning models can correct this error in some cases. However, adding a safety margin obtained from historic data to the battery size is a more reliable way of reducing the error.
Industrial facilities usually need multiple energy subsystems, e.g. for heat, cold and electric power supply. Normally those energy subsystems are controlled locally and independent of each other. Coupling of the different subsystems can open up additional potentials. Fraunhofer IISB developed a demonstration and research platform for investigation of the benefits of such sector coupling. A major precondition is to understand the energy flows in the system as well as establishing an overall and flexible system control to realize the required algorithms for setting up an intelligent decentralized energy system. Major components of the overall system are various storages, which extend the degree of freedom for sector coupling as well as increase the effectiveness of the different subsystems.
In this study, a significant literature review on peak load shaving strategies has been presented. The impact of three major strategies for peak load shaving, namely demand side management (DSM), integration of energy storage system (ESS), and integration of electric vehicle (EV) to the grid has been discussed in detail. Discussion on possible challenges and future research directions for each type of the strategy has also been included in this review. For the energy storage system, different technologies used for peak load shaving purpose, which include their methods of operation and control have been elaborated further. Finally, the sizing of the ESS storage system is discussed. For the demand side management system, various management methods and challenges associated with DSM implementation have been thoroughly explained. A detailed discussion on the electric vehicle strategy has also been included in the review, which considers the integration, control and operation techniques for implementing the peak load shaving.
Commercial and industrial customers are subject to the monthly maximum demand charges which can be as high as 30% of the total electricity bills. Battery-based energy storage system (BESS) can be used to reduce the monthly maximum demand charges. A number of control strategies have been developed for the BESS to reduce the daily peak demands. A fuzzy control algorithm is developed to reduce the daily peak demands with the limited capacity of the BESS. Its performance is evaluated at two different buildings, namely building A and B. The fuzzy controller forecasts the load profile one day in advance using the historical load data. Then during the day of peak reduction, the controller will adjust the power output of BESS using the latest state of charge and operation time. The performance of the fuzzy controller is compared with other two controllers developed in the past. The experimental results show that fuzzy controller is the most effective approach for the peak reduction under the limited capacity of the BESS.
For reflecting grid connected operation control strategies, modeling of Battery Energy Storage System (BESS) was studied. The BESS models include two parts according to the infection to control results of State of Charge (SOC) of batteries. One is the electromechanical transient model, and the other is the long-term dynamic model. The convertor section of the models consider power control segment in normal operating mode, and fault crossing and protection segment reflecting output external characteristics in abnormal operating mode. In the capacity limit aspect, the actual voltage-SOC curve of battery is introduced in the models. Meanwhile, the charging and discharging characteristics can be simulated accurately. The validity of the proposed models is proved according to calculation results after taking BESS models into single generator system for simulation. In this paper, BESS models are established from the perspective of power grid and to realize the target of grid connected control, the models have generality, and they are suitable for electromechanical transient and long-term dynamic simulation. Therefore, the problems of present BESS models, that is being complex, reflecting charging / discharging characteristics of different batteries but no grid connected characteristics, and being not suitable for engineering application, can be solved.
Peak shaving is one of the most important applications of battery energy storage system. In order to prolong battery life or to study the relationship between the battery lifetimes, the charge-discharge cycles and the depth of discharge, constraints concerning charge-discharge cycles and depth of discharge should be added to the optimization model. Therefore, algorithms based on continuous models fail to solve this kind of problem. This paper presents a dynamic programming algorithm containing multi-modules of states. The remaining capacity is discretized and modularized. Tests are carried out using a set of predicted load data. The results show the effectiveness of the method.
This paper discusses a simple method to perform peak load shaving through the means of energy storage systems owned by a utility. Peak load shaving, also referred to as load leveling or peak shifting, consists of the schemes used to eliminate the peaks and valleys in the load profile. This practice offers direct and indirect benefits to utilities in generation costs, line loss reduction, and volt support. Prior work in peak load shaving has been mainly focused on optimization approaches implemented through methods such as non-linear and dynamic programming, or heuristic approaches such as particle-swarm optimization. The proposed algorithm for peak load shaving in this paper is based on a simple approach which compares the aggregated load profile with its average in a certain utilization period and shares the charge/discharge amongst energy storage devices based on the definition of energy bars and weighting factors. In particular, the paper focuses on the usage of Battery Energy Storage Systems (BESS) to accomplish this task. Results show that the proposed algorithm offers a simple, fast and effective way for peak-load shaving without heavy computational burdens often needed in other methods. As a result, it can be easily implemented in a utility main substation for controlling the charge/discharge of storage devices throughout the distribution system.
This article presents the modeling, simulation, and sizing results of battery energy storage systems for residential electricity peak shaving. Realistic 5 min time-step electricity profiles were input to an energy storage model with the objective of reducing the peak electricity demand seen by the electricity grid. The model simulates and provides performance results of a range of battery and inverter sizes specific to a variety of residential houses. The results suggest typical system sizes range from 5 kWh/2.6 kW for low electricity intensity homes to 22 kWh/5.2 kW for electricity intense homes with electric space heating. Based on the resulting storage system cycling profile and data provided by other researchers, the battery life is estimated to be in the range of 10–20 years for this application.
The accurate estimation of internal parameters and state-of-charge (SoC) of battery, which greatly depends on proper models and corresponding high-efficiency, high-accuracy algorithms, is one of the critical issues for the battery management system. A model-based online estimation method of a LiFePO4 battery is presented for application in electric vehicles (EVs) by using an adaptive extended Kalman filter (AEKF) algorithm. The Thevenin equivalent circuit model is selected to model the LiFePO4 battery and its mathematics equations are deduced to some extent. Additionally, an implementation of the AEKF algorithm is elaborated and employed for the online parameters’ estimation of the LiFePO4 battery model. To illustrate advantages of the online parameters’ estimation, a comparison analysis is performed on the terminal voltages between the online estimation and the offline calculation under the Hybrid pulse power characteristic (HPPC) test and the Urban Dynamometer Driving Schedule (UDDS) test. Furthermore, an efficient online SoC estimation approach based on the online estimation result of open-circuit voltage (OCV) is proposed. The experimental results show that the online SoC estimation based on OCV–SoC can efficiently limit the error below 0.041.
The goal of any load-management program is to maintain, as nearly as possible, a constant level of load, thereby allowing the system load factor to approach 100%. The important benefits of load management are reduction in maximum demand, reduction in power loss, better equipment utilisation and saving through reduced maximum demand charges. Load shifting, one of the simplest methods of load management, is to reduce customer demand during the peak period by shifting the use of appliances and equipment to partial peak and off-peak periods. Here no loads are being switched off, but only shifted or rescheduled, and hence the total production is not affected. In this paper, a fully fledged program is developed for load shifting and the same has been tried with the actual load data collected from a typical fertiliser and chemical industry plant.
A dynamic programming algorithm for the optimal charge/discharge
scheduling of BESS energy storage is presented. It ensures the
minimisation of the electricity bill for a given battery capacity, while
reducing stress on the battery and prolonging battery life. Optimal
scheduling of the battery charge state is in itself unique; the methods
of multipass dynamic programming are used to accomplish this. Maximum
payoff for load redistribution and peak load shaving is determined while
accounting for charging rate, battery voltage fluctuation and internal
losses as a function of charge state. The optimal charging curve is
significantly different from the curve conventionally published for BESS
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