Ramp Rate Limits Research Articles

Module‐Based Supercapacitors: Potential Energy Storage Solutions for Large‐Scale Photovoltaic Systems

Intermittency is an inherent characteristic of photovoltaic (PV) power generation and results in high ramp rates of the generated power. This article explores the feasibility of integrating supercapacitors at the PV module level, aiming to reduce the power fluctuations of PV systems and control the power ramp rate into the power grid. First, an equivalent circuit model of a single‐phase grid‐connected PV system based on module‐based supercapacitors is proposed, and a power ramp rate control scheme is established. Then, experimental setups for a single‐phase grid‐connected PV system based on module‐based supercapacitors are implemented, and the computational model is verified through experiments. Finally, using the verified computational model and the proposed control scheme, the module‐based supercapacitor sizes for different PV system sizes (PV module, rooftop, small system, large system) that meet specific ramp rate requirements under different ramp rate limits (5, 10, 15% min−1) are compared. Case studies show that large‐scale PV systems with geographical smoothing effects help to reduce the size of module‐based supercapacitors per normalized power of installed PV, providing the possibility for the application of modular supercapacitors as potential energy storage solutions to improve power ramp rate performance in large‐scale PV systems.

Solving power system economic emission dispatch problem under complex constraints via dimension differential learn butterfly optimization algorithm with FDC-based

The economic emission dispatch (EED) aims to minimize the fuel and pollutant emission costs of generator units under various complex constraints. Optimizing the EED problem is of crucial importance for alleviating the current energy and environmental pressures. In this work, nearly all known complex constraints in the EED problem, including the valve-point effect, transmission line power loss, prohibited operating zones, and ramp-rate limits, are taken into account, and an enhanced version of butterfly optimization algorithm (FDCDLBOA) is proposed to solve it. First, a new adaptive fragrance is employed to optimize the instability caused by target differences and improve the convergence performance. Second, the proposed dimension differential learning strategy evolves the position of individuals with the help of superior dimensional information in the population, and this extensive learning exchange can balance global and local search, maintain diversity, and get rid of local optima. Third, the Fitness-Distance-Constraint (FDC) guide selection method is employed for the first time to handle the complex constraints of EED problems, enhancing the ability of individuals to bypass the infeasible search areas. After evaluating the proposed FDCDLBOA on CEC 2022 test suite, it is applied to solve 8 EED cases, encompassing small-, medium- and large-scale systems. Notably, the 280-generator case is the first large-scale test to exceed 200 generators. Compared with 9 representative algorithms, FDCDLBOA performs outstandingly in terms of robustness, improvement index (IF), mean constraint violation (MV), feasibility rate (FR) and Quade multiple comparison, among which IF, MV, FR, and Quade are all employed for evaluating the EED problem for the first time. The presented results confirm that the proposed method effectively enhances the robustness of high-quality solutions and the ability to handle complex constraints, demonstrating strong competitiveness and potential in solving the EED problem.

Optimal Solution of the Dynamic Economic Dispatch by Improved Teaching-Learning-Based Artificial Bee Colony Algorithm

Dynamic economic dispatch is one of the most handled problem in modern power system operations. It aims to optimize the output power from thermal generating units over a specified time period to minimize the total fuel cost, while satisfying the several constraints such as generation limits, ramp rate limits, and power balance. In addition to these constraints, the prohibited operating zones and the valve-point loading effect are included the DED problem. In this case, the complexity, nonlinearity, and non-convexity of the DED problem are increases. Therefore, in order to solve the DED problem, a powerful meta-heuristic search (MHS) algorithm are proposed. In this study, an improved teaching-learning-based artificial bee colony (TLABC) algorithm, where the fitness-distance balance based TLABC (FDB-TLABC) and natural-survivor method based TLABC (NSM-TLABC) algorithms were hybridized. To prove the performance of the proposed algorithm, it was applied to solve the DED problem and benchmark problem suites. In the simulation study carried out on benchmark problems, the results of the proposed algorithm and five MHS algorithms were evaluated statistically. According to Friedman test results, the proposed algorithm ranked first with 2.2836 values among them. On the other hand, the proposed algorithm and its rival algorithms were applied to solve the two DED cases. The results of them show that the proposed algorithm achieved superior performance to find the best objective values for both case studies.

Modified Opposition-Based Particle Swarm Optimization for Combined Economic and Emission Dispatch Problem

Modification of the particle swarm optimization (PSO) method is proposed with an opposition-based learning strategy to find the optimal solution to electrical power dispatch problems. The objective of the proposed algorithm is to address the combined economic and emissions dispatch problem (CEED) of thermal power plants. This problem includes constraints such as the valve point effect, prohibited zones of operation, and ramp rate limits. In order to assess its performance, the proposed algorithm is first evaluated using a set of benchmark functions. Later, three thermal generating systems having 6, 10, and 40 units respectively are regarded as the test systems to validate the proposed method. The proposed method is tested, and a comparison of the results are made with popular optimization techniques reported in the literature such as PDE, MODE, NSGA II, and MOSSA. Promising results have been obtained with opposition-based PSO in comparison with their current equivalents. A comparison was made between the fuel cost, emissions, and CPU time of the proposed method with the two other PSO variants: inertia factor PSO (IFPSO) and constriction factor PSO (CFPSO). The results showed a decline in the overall cost by approximately 3.73% and a decrease in CPU time by as much as 2.6 s. Furthermore, the obtained predictions consistently exhibit a high level of accuracy, typically approaching 100%.

Hybrid team game algorithm for dynamic economic-emission electric power generation scheduling

The rising greenhouse gases, insufficient electricity production, and growing demand call for optimal thermal power generation scheduling to minimise fuel costs and emissions. Traditional optimisation methods are unable to find solutions due to non-convex objective functions and constrained convergence. Meta-heuristic techniques gain attention to address static and dynamic economic-emission generation scheduling challenges, owing to their adaptable nature and derivative-free structures. This paper proposes a Hybrid Team Game Algorithm (HTGA) that incorporates learning and exchange operators, simplex search, passing, mistake, substitution, and player within the field operators to solve the dynamic economic-emission generation scheduling (DEEPGS) of thermal units while considering multiple fuel options, valve loading effect, prohibited zone avoidance, meeting ramp-rate limits, and spinning reserve constraints. By evaluating generation scheduling at initial intervals, the forward strategy provides a solution to the dynamic EEPGS problem. A non-interactive approach uses the price penalty method to combine the objective functions, and heuristics are employed to explore feasible solutions using a replacement technique and proportional power sharing for unmet load demand. The algorithm's versatility is demonstrated across benchmark problems and electric power systems, as affirmed by Friedman and Wilcoxon's tests. Notably, HTGA requires fewer sensitive parameters, enhancing its practical applicability in addressing the DEEPGS of thermal units.

Ekonomik yük dağıtım problemi için elektrik baliği optimizasyonu

The Economic Load Dispatch (ELD) problem is an essential aspect of power system planning and operational scheduling. Different techniques and algorithms have been recommended to solve it, aiming to minimize the cost of power generation with satisfying the load requirements. In this paper, a new algorithm called Electric Fish Optimization (EFO) is used to solve the ELD problem by considering the line losses, ramp rate limits, maximum and minimum capacities of the generators and prohibited operating zones (POZ). The algorithm has been utilized in test systems consisting of 6 and 15 units and its outcomes have been compared to those from previous research studies. The proposed algorithm has been shown to achieve minimum cost, indicating its superiority and effectiveness in addressing power system planning challenges. It is evident that the presented algorithm offers a valuable solution for optimizing ELD problems.

An application of Gorilla troops optimizer in solving the problem of economic load dispatch considering valve point loading effect

Economic load dispatch (ELD) is one of the fundamental and important optimization problems in an electrical power system. The goal of the ELD problem is to minimize the overall cost of fuel used by generators while satisfying the different constraints like power balance limitations, operational capacity limits, ramp-rate limits, and prohibited operating zones and valve point loading effect (VPLE). The paper introduces a novel optimization technique, termed Gorilla Troop Optimization (GTO), inspired by the mimic’s behavior of gorilla troops, for solving the Economic Load Dispatch (ELD) Problem. The ELD Problem is assessed both with and without the valve-point effect, considering best, average, and worst fuel costs in dollars per hour ($/hr). The proposed GTO is applied to four different case studies to find the best global solution. The effectiveness of the GTO is evaluated using four distinct IEEE unit systems, including the IEEE 3-unit, 6-unit, 10-unit, and 40-unit test systems. From the simulation results, it has been realized that GTO show superior results as compared to other algorithms in terms of the total fuel cost minimization, convergence speed, and computation time to achieve the global optimal solution in all the case studies.

A Dynamic Equivalent Method for PMSG-WTG Based Wind Farms Considering Wind Speeds and Fault Severities

The dynamic security assessment of power systems needs to scan contingencies in a preselected set through time-domain simulations. With more and more wind power generation integrated into power systems, the complexity of simulation will increase greatly if wind farms are modeled in detail. Thus, it is critical to develop dynamic equivalent methods for wind farms. The dynamic response characteristics of permanent magnet synchronous generator-wind turbine generators (PMSG-WTGs) are not only influenced by their control strategies, but also by the operating wind speeds and the fault severities. Thus, this paper proposes a dynamic equivalent method for a PMSG-WTG based wind farm considering the wind speeds and the fault severities. Firstly, we propose a clustering method based on the operating wind speed and the terminal voltage of each PMSG-WTG at the end of the fault. Then, a single-machine equivalent method is introduced for each cluster of PMSG-WTGs. For the cluster of PMSG-WTGs with active power ramp recovery process, an equivalent model with segmented ramp rate limitation for active current is designed. In order to obtain the clustering indicators, a simulation-based iterative method is put forward. Eventually, the efficiency and accuracy of the proposed method are verified by the simulation results.

A modified white shark optimizer for optimal power flow considering uncertainty of renewable energy sources

This paper presents a novel approach to solve the optimal power flow (OPF) problem by utilizing a modified white shark optimization (MWSO) algorithm. The MWSO algorithm incorporates the Gaussian barebones (GB) and quasi-oppositional-based learning (QOBL) strategies to improve the convergence rate and accuracy of the original WSO algorithm. To address the uncertainty associated with renewable energy sources, the IEEE 30 bus system, which consists of 30 buses, 6 thermal generators, and 41 branches, is modified by replacing three thermal generators with two wind generators and one solar PV generator. And the IEEE 57-bus system, which consists of 57 buses, 7 thermal generators, and 80 branches, is also modified by the same concept. The variability of wind and solar generation is described using the Weibull and lognormal distributions, and its impact on the OPF problem is considered by incorporating reserve and penalty costs for overestimation and underestimation of power output. The paper also takes into account the unpredictability of power consumption (load demand) by analyzing its influence using standard probability density functions (PDF). Furthermore, practical conditions related to the thermal generators, such as ramp rate limits are examined. The MWSO algorithm is evaluated and analyzed using 23 standard benchmark functions, and a comparative study is conducted against six well-known techniques using various statistical parameters. The results and statistical analysis demonstrate the superiority and effectiveness of the MWSO algorithm compared to the original WSO algorithm for addressing the OPF problem in the presence of generation and demand uncertainties.

Inertia and Governor Ramp Rate Constrained Economic Dispatch to Assess Primary Frequency Response Adequacy

The contribution of non-dispatchable, renewable generation to both total grid inertia and grid frequency response is significantly lesser than that of dispatchable, thermal generation. In large non-dispatchable generation penetration scenarios, the effect of inertia and frequency response diminution on primary frequency control reserves adequacy will be important. This paper develops a constraint on the economic dispatch problem that assures that frequency nadirs after a given sudden loss of generations will not be lower than a given limit. This constraint considers system inertia and governor ramp rate limits. A simulation using the IEEE Reliability Test System is presented.

On sizing of battery energy storage systems for PV plants power smoothing

The high variability of solar irradiance causes fluctuations in the generation of photovoltaic (PV) power plants. This characteristic affects power system operation, thus, strategies are necessary to mitigate the intermittent characteristics of PV power plants. Several countries adopt grid codes incorporating ramp rate (RR) limitations for PV power injection into the grid, also known as power smoothing control. In this sense, battery energy storage systems (BESS) with coordinated RR control algorithms are commonly applied to mitigate current fluctuations from the PV system to the grid. Therefore, studies on the effect of local solar irradiance on BESS requirements for PV power smoothing applications are very important. It is also worth highlighting that in the literature there is a gap in the evaluation of aspects of battery lifetime, BESS volume, price and BESS oversizing for selecting an ideal battery model, among a catalog of batteries, constituting an industrial concern. Therefore, this work presents a detailed BESS sizing methodology, evaluating aspects of the energy requirements, power, and discharging rate (C-rate), based on the mission profile (featuring a customized solution for each project). In addition, the effect of the battery temperature control on the BESS sizing is evaluated. The case study is based on a 1 MW PV system, assessed using a one-year mission profile of solar irradiance and ambient temperature from Goiânia-Brazil. The PV plant is connected to a 440 V grid associated with a BESS that mitigates oscillations of 10%/min. The BESS sizing is evaluated in terms of BESS volume, lifetime, and oversizing factor for power and energy. The proposed methodology reduces the BESS volume by 57.14% in relation to the traditional BESS design of literature.

Impact of Battery Ramp Rate Limit on Virtual Synchronous Machine Stability During Frequency Events

Impact of Battery Ramp Rate Limit on Virtual Synchronous Machine Stability During Frequency Events

Peak Shaving and Duck Curve Mitigation With BES Based Ramp Rate Limit Strategy for Power Quality Enhancement of SPV-BES Microgrid

Peak Shaving and Duck Curve Mitigation With BES Based Ramp Rate Limit Strategy for Power Quality Enhancement of SPV-BES Microgrid

Optimal sizing toolbox for energy generation and storage for a nuclear hybrid microgrid

Background In recent years, renewable energy sources, such as wind, have contributed to a decrease in grid stability. This has created the need for flexible and reliable back-up energy generation. Currently this role is fulfilled by natural gas-fired power plants that are able to quickly adjust power output based on present needs. Nuclear power presents an option for a clean and reliable alternative to these natural gas-fired power plants. However, nuclear power alone is unable to provide flexible enough power generation to fill this role. Instead, nuclear power plants must be combined with strategically sized energy storage systems to effectively complement the renewable power generation. Grids such as these are generally simulated using deterministic models which are unable to account for quickly fluctuating wind power generation. This in turn creates unsafe ramp rates for the nuclear power plant. Methods The approach taken in this paper utilizes a continuous-time stochastic model to simulate a grid with wind generation, nuclear power, and an energy storage system as the only energy sources. This paper focuses on this model and a development of a GUI to form a grid sizing toolbox. This toolbox was then demonstrated with an isolated microgrid using year-long wind and load data. Wind penetration, nuclear ramp rate limitations, and storage types were all varied to observe different scenarios and determine optimal storage sizing. Results There were several outcomes from this study that can inform grid planning. Nuclear power plant size trends downwards with greater wind power generation. However, high variability of wind power limits the size reduction. Nuclear capacity factor is between 41% to 57%, dropping further as wind penetration was increased. This highlights the difficulty of maintaining high utilization in these scenarios. Conclusions All storage types had similar performance in all categories except size, in which pumped hydroelectric and compressed air storage required smaller storage sizes.

Provision of ramp-rate limitation from distribution networks to transmission systems as preventive action towards frequency disturbances

The high proliferation of Converter-Interfaced Renewable Energy Sources (CIRES) poses several challenges to electric power system operators regarding frequency stability and control. Some of these issues are related with the high active power Ramp Rates (RRs) of CIRES, stemming from their inertia-less and stochastic nature. To deal with such problems, Transmission System Operators (TSOs) often commit excessive amounts of conventional spinning reserves, while in weak Transmission Systems (TSs) regulations are imposed, aiming to mitigate RRs of CIRES. Moreover, several technical studies have proposed control approaches for RR Limitation (RRL). Nevertheless, the influence of RRL on frequency quality and mainly the impact of RRL control capability of small-scale CIRES, connected to Distribution Networks (DNs), on frequency deviations have not been fully explored and quantified. To fill this gap, a detailed investigation is performed in this paper. For this purpose, simulations are conducted on a simplified System Frequency Response (SFR) model as well as on an analytical power system model. The latter is a modified version of the IEEE 9-bus Test System, consisting of three conventional Synchronous Generators (SGs) and three Medium Voltage (MV) DNs, which include CIRES equipped with SuperCapacitors (SCs), thus presenting RRL control capability. To thoroughly evaluate the impact of RRL on frequency dynamics, a set of metrics is introduced and frequency duration curves are created for various scenarios. Finally, the influence of several factors, such as the total system inertia, the RRL target value, the SC size, etc, is quantified by using the proposed indices.

A novel distributed approach for event-triggered economic dispatch of energy hubs under ramp-rate limits integrated with sustainable energy networks

This paper investigates a new consensus-oriented distributed approach for the event-triggered (ET) economic dispatch problem (EDP) over a smart grid under ramp-rate limits (RRLs) integrated with green power sources (GPSs) such as solar and wind energy for demand response strategies over hybrid energy power systems. To address the RRL condition, the authors have transformed the RRLs as minimum and maximum bounds on the derivative of generation for a generator. Then, a Karush–Kuhn–Tucker (KKT) condition and a more practical approximate KKT condition are developed for determining the optimality conditions. A practical ET protocol is proposed over a topology between generators by application of the proposed approximate KKT condition. In contrast to existing distributed optimization methods, this paper provides both optimally condition and distributed optimization scheme for dealing with RRLs integrated with sustainable hybrid energy systems. In addition, a computationally efficient ET mechanism, eliminating Zeno behaviour, has been considered for dealing with the efficient utilization of communication resources. This study incorporates the real-time input data from thermal production plants and GPSs for experimental analysis. RETScreen software having data-set of over 6,700 local meteorological stations is applied to obtain input data for GPSs. Furthermore, Weibull and Beta distribution functions have been applied for dealing with uncertainty in wind and solar energy sources. Two case studies are examined (with and without GPSs), and simulation results demonstrated the suitable performance of the proposed distributed ET EDP approach.

The dynamic economic emission dispatch of the combined heat and power system integrated with a wind farm and a photovoltaic plant

Two renewable energies are included in the combined heat and power (CHP) system to optimize its energy configuration, and they are wind power generation and photovoltaic power generation, respectively. Furthermore, a global nondominated sorting genetic algorithm II (GNSGA-II) is proposed to confront the combined heat and power dynamic economic emission dispatch (CHPDEED) with renewable energies. GNSGA-II produces offspring individuals by a crossover with negative exponential distribution, and it is able to carry out global search in the decision space. GNSGA-II also assigns an adaptive weight to original crowding distance to give consideration to both crowding degree and evenness of each individual in the objective space, which is beneficial for improving the evenness of the Pareto set. In addition, a constraint handling approach is proposed to satisfy all constraints, such as power generation limits, heat generation limits, capacity limits of the CHP units, power balances, heat balances, ramp rate limits and spinning reserve requirements. Seven multi-objective evolutionary algorithms (MOEAs) are used to solve the four CHPDEED scenarios with or without renewable energies, and GNSGA-II outperforms the other six MOEAs. It does not only obtain larger hypervolumes and coverage rates, but also obtain relatively small spacings. For the four compromise solutions of GNSGA-II, the generation costs of Scenario 2, Scenario 3 and Scenario 4 are, respectively, 0.51%, 0.34% and 4.1% higher than that of Scenario 1. In the meantime, the pollutant emissions of Scenario 2, Scenario 3 and Scenario 4 are, respectively, 54.78%, 19.05% and 71.45% lower than that of Scenario 1.

Impact of residential photovoltaic systems on net load intermittency

The addition of solar photovoltaic (PV) generation can negatively impact electricity grid stability due to both the intermittency of generation and temporal misalignment of generation and load. This has led to some jurisdictions enforcing ramp rate limitations on PV generation, and a growing interest in PV intermittency mitigation using energy storage. We evaluate residential PV and load intermittency using measured data from a municipality. Power ramp rates caused by residential loads and PV generation were compared. The datasets were also used to generate synthetic net load profiles to evaluate how adding PV to a home impacts ramp rates as seen by the electricity grid. Additionally, an energy storage model was used to estimate the capacity of battery required to mitigate ramp rates. Residential load profiles were found to have more extreme normalized ramp rate distributions than residential PV systems. The addition of PV systems to homes resulted in a 0.012% increase in ramp rates exceeding 10% of the maximum annual load per 5 minutes when aggregated, meaning PV variability is negligible compared to residential load variability. Consequently, mitigating PV ramp rates using energy storage had almost no impact on residential net load variability.

Optimal sizing and lifetime investigation of second life lithium-ion battery for grid-scale stationary application

The renewable energy sources (RESs) integration into the grid system aims to solve the problem of power shortage and satisfy the increasing demand with the production of surplus energy. However, the intermittent nature of these RESs (solar and wind) is a challenge to integrate with the grid system without the deployment of mitigating solutions. The re-use of first life-end-of-life (FL_EoL) electric vehicle batteries known as second life batteries (SLBs) is therefore proposed as a reliable solution to resolve this problem, satisfying the techno-economic requirements of stationary applications. Though various studies performed on the technical viability evaluation of SLBs, most of them have not considered the field data of existing stationary plants and were found to be limited with simulation-based aging results. On the other hand, few of the studies have performed the second life aging test with limited cycles considering the end of test conditions rather than using the cells reached to their end of first life criteria. Therefore, in this paper, the prolonged cycling aging of SLBs is conducted (both cell-level and module-level aging), focusing on aging characteristics of SLBs by using a real-life renewable power smoothing profile extracted from an existing photovoltaic grid-connected system (PVGCS) installed in Ethiopia. Prior to the aging characterization of SLBs, assessment of the selected stationary application requirement, optimal sizing of the storage battery and cycling profile definition are performed using advanced moving average ramp rate controller (MARRC) algorithm. The proposed method of MARRC is used to determine the optimal SLBs capacity by analyzing the relationship between ramp-rate, initial battery capacity and window sizes of moving average control method in terms of time series. The algorithm addresses the main objectives of reducing unnecessary battery cycling, mitigating ramp-rate violations and meeting minimum storage requirements for the second-life application. From the result of the battery sizing study, the desired optimal battery capacity and the permissible ramp-rate limit below 10 %/minute is achieved. Moreover, the aging characterization and lifetime model validation results with a root mean square error (RMSE) of 1.5 % shows that, the technical performance of the SLBs is found to be promising with an efficiency of >90 % interpreted in terms of the service year that the SLBs can provide. Therefore, SLBs can be regarded as a viable solution for integrating RESs with the grid system for the power variability smoothing application.

Novel Heuristic Optimization Technique to Solve Economic Load Dispatch and Economic Emission Load Dispatch Problems

The fundamental objective of economic load dispatch is to operate the available generating units such that the needed load demand satisfies the lowest generation cost and also complies with the various constraints. With proper power system operation planning using optimized generation limits, it is possible to reduce the cost of power generation. To fulfill the needs of such objectives, proper planning and economic load dispatch can help to plan the operation of the electrical power system. To optimize the economic load dispatch problems, various classical and new evolutionary optimization approaches have been used in research articles. Classical optimization techniques are outdated due to many limitations and are also unable to provide a global solution to the ELD problem. This work uses a new variant of particle swarm optimization techniques called modified particle swarm optimization, which is effective and efficient at finding optimum solutions for single as well as multi-objective economic load dispatch problems. The proposed MPSO is used to solve single and multi-objective problems. This work considers constraints like power balance and power generation limits. The proposed techniques are tested for three different case studies of ELD and EELD problems. (1) The first case is tested using the data of 13 generating unit systems along with the valve point loading effect; (2) the second case is tested using 15 generating unit systems along with the ramp rate limits; and (3) the third case is tested using the economic emission dispatch (EELD) as a multi-objective problem for 6 generating unit systems. The outcomes of the suggested procedures are contrasted with those of alternative optimization methods. The results show that the suggested strategy is efficient and produces superior optimization outcomes than existing optimization techniques.