Power System Resources Research Articles

A Simplified Dynamic Model of DFIG-based Wind Generation for Frequency Support Control Studies

In recent years, wind generation has been fast growing and become one of the major generation resources in power systems. Since the wind generation does not inherently equip with frequency support functions, the power system frequency response degrades as the wind penetration increases. Therefore, frequency support control of wind generation has gained increasing focus. However, the dynamic models of wind generation systems, which are required by the control design and analysis, are commonly very complicated and difficult to obtain, especially for the doubly-fed induction generator based wind turbine (DFIG-WT). In this paper, the authors propose a simplified dynamic model for the DFIG-WT. The proposed analytical model is derived from the detailed model by selectively neglecting very fast dynamics while keeping the critical ones. It is suitable for frequency control studies. The model accuracy is first validated against the detailed DFIG-WT model in Simulink. Then, the wind frequency control function is studied based on the proposed model.

Flexible resource allocation optimization model considering global K-means load clustering and renewable-energy consumption

Abstract Vigorously developing flexible resources in power systems will be the key to building a new power system and realizing energy transformation. The investment construction cost and operation cost of various flexible resources are different, and the adjustment ability is different in different timescales. Therefore, the optimization of complementary allocation of various resources needs to take into account the economy and adjustment ability of different resources. In this paper, the global K-means load clustering model is proposed and the 365-day net load is reduced to eight typical daily net loads by clustering. Secondly, a two-level optimization model of flexible resource complementary allocation considering wind power and photovoltaic consumption is constructed. The flexible resources involved include the flexible transformation of thermal power, hydropower, pumped storage, energy storage, and demand response. The upper-layer model optimizes the capacity allocation of various flexible resources with the minimum investment and construction cost as the goal and the lower layer optimizes the operating output of various units with the minimum operating cost as the goal. The results of the example analysis show that the flexible capacity of thermal power units has nothing to do with the abandonment rate of renewable energy. As the abandonment rate of renewable energy decreases, the optimal capacity of pumped storage, electrochemical energy storage, and hydropower units increases. When the power-abandonment rate of renewable energy is 5%, the optimal allocation capacity of thermal power flexibility transformation, pumped storage, electrochemical energy storage, hydropower unit, and adjustable load in Province A is 5313, 17 090, 5830, 72 113, and 4250 MW, respectively. Under the condition that the renewable-energy abandonment rate is 0, 5%, and 10% respectively, the configured capacity of pumped storage is 20 000, 17 090, and 14 847 MW, respectively.

Control strategies applied to distribution networks with the integration of distributed energy resources - A review

The continuous integration of distributed energy resources (DERs) in power systems has opened the need to propose control strategies that tend to mitigate the possible impacts that an unlimited integration can produce. Authors have presented studies that present different control strategies that take advantage of the devices already present in the network or of new elements added, such as DERs. Also, different control architectures that can take full advantage of the capabilities that DERs can offer are studied. Therefore, this article focuses on reviewing the recent literature related to control strategies and architectures applied to distribution networks with DERs as protagonists. The control strategies that exploit the characteristics of DERs to perform control actions are described, and the control architectures adapted to the integration of DERs in distribution networks are detailed. Device modeling approaches are also mentioned, considering that some DERs depend on random variables and can introduce uncertainty in the analyses if their behavior is not modeled correctly. Finally, current and future trends in this area are discussed. Keywords. Control architectures, control strategies, deterministic approach, Distributed Energy Resources, stochastic approach.

Electrifying thermal loads vs. installing batteries: A case study on fast frequency resource potentials of the Victorian power system

Literature suggests different technologies and control algorithms to counteract the decrease in system damping and inertia caused by the increasing penetration of inverter-interfaced resources in renewable power systems. Published studies generally focus on comparing different control mechanisms for a single technology and examine a limited number of operating conditions. Technology comparisons are typically limited to small test cases, base on assumptions that no longer hold in renewable-rich systems or are solely based on literature reviews. This work thoroughly compares fast-frequency reserve provision from inverter-based active thermal loads (ATLs) against battery storage. As such, we employ a large-scale multi-level test case representing the future Victorian power system and link steady-state and dynamic analysis. We examine the frequency and voltage performance following distinct disturbances for five days with hourly resolutions. The results suggest that the potential for electrifying residential heating demand in Victoria is enormous and can significantly enhance frequency metrics. Generally, the improvements achieved with the aggregation of ATLs are similar or even higher than employing a large battery.

Novel estimation framework for short-circuit current contribution of type IV wind turbines at transient and steady-state of the faults

Given the increasing penetration of converter-interfaced resources in power systems, properly estimating the short-circuit current (SCC) contribution in large networks has become a growing challenge and necessity to ensure the security and stability of the systems and assets. This paper presents two novel methods to estimate the SCC contribution of type IV wind turbines at both the transient and steady-state stages of unbalanced and balanced faults: (1) a machine learning-based method trained with electromagnetic transient (EMT) simulations and capable of estimating some of the initial peak and transient current magnitudes; (2) an analytical approach to estimate the steady-state SCC based on the voltage and grid code dependency of the converter during the fault. The methods are coupled into a single framework and compared to field-validated EMT models of a real turbine. The results show that the majority of the estimated currents in the transient stages present errors below 5%. In steady-state, the errors are not greater than 1.21%. Given the complexity of the problem, these margins may be deemed acceptable for short-circuit studies.

On representation of energy storage in electricity planning models

This paper considers the representation of energy storage in electricity sector capacity planning models. The incorporation of storage in long-term systems models of this type is increasingly relevant as the costs of storage technologies, particularly batteries, and of complementary variable renewable technologies decline. To value energy storage technologies appropriately in optimization models, a representation of linkages between time periods is required, breaking classical temporal aggregation strategies that greatly improve computation time. Our paper reviews approaches to address the problem of compressing chronology for large-scale electricity planning models and provides a generalized conceptual model, conditions for lossless aggregation, and key principles to evaluate aggregation methods. We propose a novel approach, which we call the “expected value” method, to maintain key economic characteristics of energy storage, variable renewables, dispatchable generation, and other power system resources at a relatively low computational cost and conduct numerical experiments to compare its accuracy and computational performance with other temporal aggregation methods.

Multi-objective optimization of solar resource allocation in radial distribution systems using a refined slime mold algorithm

The integration of distributed generation resources in power systems offers various advantages, such as peak load management and reduced transmission line congestion. However, it also introduces challenges related to voltage stability. This paper presents a novel multi-objective model for optimizing the allocation of solar resources in radial distribution systems. The model aims to achieve an optimal voltage profile, minimize losses, and maximize penetration levels. To address the conflicting nature of these objectives, a refined multi-objective slime mold algorithm (MOSMA) is proposed. This algorithm demonstrates exceptional capabilities in finding Pareto fronts, avoiding local optima, and effectively solving multi-objective problems compared to other optimization methods. Additionally, the corrected social hierarchy method is integrated to enhance performance. The proposed method is evaluated using a standard system under various operational conditions, showing superior results in terms of maintaining an acceptable voltage profile and significantly reducing losses. The study reveals that while losses decrease for penetration levels ranging from low to medium, they start to increase for levels exceeding 100 %. Notably, the proposed method achieves approximately 12 % system efficiency improvement, as measured by the voltage profile, at a penetration level of 300 %. These findings highlight the effectiveness of the proposed method, even at high penetration levels, surpassing other optimization approaches based on the inverse generation distance parameter.

RETRACTED: Optimizing energy and quantifying dispatchable potential of holistic HVAC systems

The increasing demand for flexible resources in power systems, coupled with the evolution of smart buildings, underscores the significance of optimizing energy use and quantifying dispatchable potential in heating, ventilation, and air conditioning systems. This work develops an energy optimization framework for these systems, accounting for external factors like solar radiation, temperature, and humidity. The goal is to minimize the energy consumption of holistic systems and devise optimal control strategies for each subsystem. The framework includes a mechanistic model that accurately describes the performance of cooling towers, a three-damper control strategy for air-side economizers, and three operational modes for air handling units. Furthermore, the relationship between the fresh air ratio and the supply/return air duct pressure drop obtained through analysis simplifies the optimization problem and reduces the difficulty of solving it. Subsequently, the framework utilizes indoor temperature and humidity ranges to ascertain optimal control strategies for both maximal and minimal energy consumption in the system. Simulation results for a hypothetical building reveal that dispatchable potential of the system can account for up to 13.055% of maximal energy consumption.

Two-stage day-ahead and intra-day scheduling considering electric arc furnace control and wind power modal decomposition

As the uncertainty in energy supply increases, engaging various flexible resources in power systems has emerged as an effective strategy to address wind curtailment issues. Existing research insufficiently explores how EAFs can participate in reducing wind curtailment or optimizing flexible power system resources to decrease CO2 emissions from TTPs and enhance wind energy absorption across various timescales. This study introduces a dual timescale, dual-tier scheduling methodology incorporating EAF regulation and wind power modal decomposition. The day-ahead model integrates EAF demand response to decrease wind curtailment, a comprehensive wind power allocation, and a TTP carbon minimization model. The intra-day model employs wind power modal decomposition for optimizing BESSs within WFs and schedules TTPs to minimize CO2 emissions. Implemented through iterative genetic algorithms and CPLEX solver techniques, simulation results from a real-case scenario indicate that incorporating EAF loads reduces wind curtailment by 40.49 % and cuts CO2 emissions by 2.5 % in the day-ahead phase. Furthermore, by applying modal decomposition, TTPs and BESSs absorb fluctuating wind power components, ensuring maximal wind utilization and substantial CO2 reduction at TTPs. This approach offers vast potential to enhance power system flexibility, advance energy-intensive industries' transition, and foster low-carbon initiatives at TTPs.

Machine Learning and Weather Model Combination for PV Production Forecasting

Accurate predictions of photovoltaic generation are essential for effectively managing power system resources, particularly in the face of high variability in solar radiation. This is especially crucial in microgrids and grids, where the proper operation of generation, load, and storage resources is necessary to avoid grid imbalance conditions. Therefore, the availability of reliable prediction models is of utmost importance. Authors address this issue investigating the potential benefits of a machine learning approach in combination with photovoltaic power forecasts generated using weather models. Several machine learning methods have been tested for the combined approach (linear model, Long Short-Term Memory, eXtreme Gradient Boosting, and the Light Gradient Boosting Machine). Among them, the linear models were demonstrated to be the most effective with at least an RMSE improvement of 3.7% in photovoltaic production forecasting, with respect to two numerical weather prediction based baseline methods. The conducted analysis shows how machine learning models can be used to refine the prediction of an already established PV generation forecast model and highlights the efficacy of linear models, even in a low-data regime as in the case of recently established plants.

Multi-objective RIME algorithm-based techno economic analysis for security constraints load dispatch and power flow including uncertainties model of hybrid power systems

In recent times, the landscape of power systems has undergone significant evolution, particularly with the integration of diverse renewable energy sources (RESs). This advancement presents an invaluable opportunity to enhance energy efficiency in the modern power grid, primarily by bolstering the role of stochastic RESs. The challenge lies in the optimal power flow (OPF), a multifaceted and non-linear optimization challenge that grows more complex with the inclusion of stochastic RESs that aims to optimize the allocation of power system resources to minimize the operational cost while maintaining the stability and security of the system. Addressing this, the current study introduces an innovative optimization approach, the Multi-Objective RIME (MORIME) algorithm. Drawing inspiration from the physical phenomenon of rime-ice, called the RIME, the MORIME seeks to effectively tackle OPF issues. This algorithm enhances solution accuracy by smartly dividing with non-dominated sorting and crowding distance mechanism. The proposed OPF model incorporates three types of RESs: solar photovoltaic, wind and small-scale hydropower units. While uncertainties in wind speed and solar irradiation are managed through Monte Carlo simulations, the small hydro unit is considered a constant power source. The efficacy of the MORIME algorithm is tested on IEEE 30 bus systems and results indicate that the MORIME method identifies the optimal solution for the multi-objective OPF problem while satisfying the power system constraints, thereby proving its effectiveness and superiority over MOWOA, MOGWO, MOALO, MOMRFO and MOAGDE in terms of Hyper Volume (HV) and reciprocal of Pareto Sets Proximity (1/PSP) metrices. The MORIME source code is available at: https://github.com/kanak02/MORIME

Grid‐forming control for inverter‐based resources in power systems: A review on its operation, system stability, and prospective

AbstractThe increasing integration of inverter based resources (IBR) in the power system has a significant multi‐faceted impact on the power system operation and stability. Various control approaches are proposed for IBRs, broadly categorized into grid‐following and grid‐forming (GFM) control strategies. While the GFL has been in operation for some time, the relatively new GFMs are rarely deployed in the IBRs. This article aims to provide an understanding of the working principles and distinguish between these two control strategies. A survey of the recent GFM control approaches is also delivered here, expanding the existing classification. It also explores the role of GFM control and its types in power system dynamics and stability like voltage, frequency etc. Practical insight into these stabilities is provided using case studies, making this review article unique in its comprehensive approach. Lacking elsewhere, the GFMs' real‐world demonstrations and their applications in several IBRs like wind farms, photovoltaic power generation stations etc., are also analyzed. Finally, the research gaps are identified, and the prospect of GFM is presented based on the system needs, informed by GFM real‐world projects. This work is a potential road map for the GFM large‐scale deployment in the decarbonized IBR‐based bulk power system.

Coordinated, Centralized, and Simultaneous Control of Fast Charging Stations and Distributed Energy Resources

The growing penetration of fast charging stations (FCSs) to electric vehicles (EVs) and distributed energy resources (DERs) in the electrical power system brings technical issue changes in the voltage profile throughout grid nodes and feeder current overload. The provision of ancillary services by DERs and FCSs arises as an appealing solution to reduce these adverse effects, enhancing the grid hosting capacity. The control of microgrids is essential for the coordinated implementation of these services. Although microgrid control is widely applied to DERs, few studies address the coordinated control of DERs and FCSs to obtain benefits for the electrical power system. This paper proposes a coordinated and simultaneous control of DERs and FCSs based on the power-based control (PBC) strategy, efficiently exploiting FCSs in a microgrid model previously unaddressed in the literature. The results show that, with the coordinated control of DERs and FCSs, the control of the power flow in a minigrid (MG) is achieved both in moments of high generation and in moments of high load, even with the maximum operation of DERs. This method allows for the maintenance of voltage levels within values considered acceptable by technical standards (above 0.93 pu). The maintenance of voltage levels is derived from reducing the overload on the point of common coupling (PCC) of the minigrid by 28%, performing the peak shaving ancillary service. Furthermore, the method allows for the control of zero power flow in the PCC of the minigrid with the upstream electric grid in periods of high generation, performing the ancillary service of valley filling. The method performs this control without compromising vehicle recharging and power dispatch by DERs.

A network search space reduction method for robust coordinated energy storage and transmission expansion planning

AbstractThe development of renewable energy will increase the demand for flexible resources in power systems due to the strong uncertainties. To allocate resources and cope with these uncertainties, it is beneficial to apply robust coordinated energy storage and transmission expansion planning (ES&TEP). The large candidate line set can significantly increase the computational complexity of robust optimization models. However, there is currently no suitable network search space reduction (NSSR) method to address this problem. This paper designs a NSSR method based on redundancy constraint identification that only requires information on component power ranges rather than fixed power curves, which can be well adapted to robust optimization. Additionally, this method requires only basic numerical calculations and ensures convergence within a finite number of iterations. The classical stochastic robust ES&TEP model and the column and constraint generation (C&CG) algorithm are used to validate the effectiveness of the NSSR method. In the case studies, the numerical results confirm the rationality and effectiveness of the approach proposed in this paper. By selecting appropriate parameters, the candidate line set can be reduced by over 70% and the computational efficiency can be improved by an order of magnitude.

Frequency Stability-Based Penetration Limit Evaluation of Variable Energy Resources in Power Systems for Online Application

Frequency Stability-Based Penetration Limit Evaluation of Variable Energy Resources in Power Systems for Online Application

Development Strategy of Flexible Resources in China's Power System under the Carbon Peaking and Carbon Neutrality Goals

Development Strategy of Flexible Resources in China's Power System under the Carbon Peaking and Carbon Neutrality Goals

Collaborative allocation model and balanced interaction strategy of multi flexible resources in the new power system based on Stackelberg game theory

For realizing environment protection and carbon emission reduction strategy, the dominant position of renewable energy in the future power system has been clarified. Planning economic and efficient flexible resource system is the basis to balance multi fluctuations caused by large amount of renewable energy integration and ensure the safe operation of the whole power system. Considering that the flexibility adjustment demands of different links of the power system are not independent, but influence and restrict each other, this paper focuses on how to collaboratively allocate multi-flexible resources and achieve corresponding balancing interaction strategy. Firstly, a flexible resource system is composed through a fuzzy TODIM improved PROMETHEE-II model. Secondly, a multi-flexible resource collaborative allocation optimization model is proposed, which considers the Stackelberg game relationship of flexibility adjustment demand and ability in different links of the power system under varied fluctuation scenarios. The scientificity and effectiveness of the proposed model are verified by a practical case, and key incentive factors and specific development suggestions for improving flexible resources application efficiency are identified simultaneously. Since the collaborative planning of flexible resource system have not been deeply discussed by scholars yet, thus, this research contributes to the literature and expand the knowledge.

OPTIMIZATION OF THE USE OF DISTRIBUTED ENERGY RESOURCES IN LOCAL ELECTRICAL POWER SYSTEMS ACCORDING TO THE CRITERION OF MINIMUM LOSS OF ELECTRICITY

The issue of optimizing the use of distributed energy resources in local electric power systems (LES) based on the criterion of minimum power loss is considered. It was determined that an important step in optimizing the use of distributed energy resources in the LES is the development of an energy router that allows devices that generate, store, and consume electricity to be combined into a single system at low voltage (without integration into medium voltage electrical networks). The energy router itself can be positioned as a basic device that ensures the operation of LES, the interaction of neighboring LES at the level of energy and information exchange, and the integration of specific LES to medium voltage distribution networks.
 It is shown that the basic component of the evaluation of the effective functioning of energy routers and the platform approach in the LES, in accordance with the requirements of the Energy Internet, is the analysis of the components of electricity losses, the influence on their level of various factors, as well as the formation of appropriate energy efficiency criteria and the assessment of the partial impact of LES elements. It is proposed to carry out a description of energy processes during the decomposition of Frize reactive power with the allocation of reactive and active current in the controlled intersections of the system. The decomposition of the QF power into components under the action of various causes (factors) of additional electricity losses in the LES, in particular, due to different modes of operation of generators and LES loads (voltage and current spectra), both during the period of system operation and for an arbitrary period of time, was considered. which is determined by technological factors of system operation.
 The use of Frize power made it possible to take into account the energy supply processes of LES loads on alternating and direct current from a single point of view, in particular, to assess the impact of a combination of various factors of the appearance of additional electricity losses; comparison of electricity losses for different time intervals through a selected controlled intersection, when the direction of the flow of electricity is constant or the direction of the flow of electricity changes during separate time intervals; analysis of electricity losses in direct and alternating current systems of LES or hybrid systems, where modified capacities are introduced, as integral estimates of the discrepancy, which determines the influence of distorting factors; assessment of the impact of changes in voltage and current in the intersection of LES of limited power on the components of additional losses; analysis of changes in load consumption of active power according to the first harmonic and higher harmonics as integral indicators.

Cooperation Mode and Operation Strategy for the Union of Thermal Generating Unit and Battery Storage to Improve AGC Performance

With the growing penetration of intermittent renewable energy resources in power systems, it is a challenge for automatic generation control (AGC) to maintain the required control performance. In recent years, battery energy storage systems (BESSs) have been installed in thermal power plants to provide frequency regulation service bundled with the traditional thermal generating units (TGUs). However, in real projects, the installed BESS typically works as parasitic equipment of the generating unit, which limits the coordination between the TGU and the BESS. In this paper, a complementary cooperation pattern is proposed for the TGU-BESS union to improve the dispatchability of its response to the AGC signal. Then, the correlation between the BESS's energy demand and the TGU's operation states is described by a data-driven method, which combines fuzzy c-means cluster, r-vine copula, and random forest. Finally, a rolling-horizon energy balance strategy is developed to make the control decisions for the TGU-BESS union in response to the stochastic AGC signal. The effectiveness of the proposed method is demonstrated using the historical data from a real union with a 9MW/4.5MWh BESS and a 350MW coal-fired generator.

A real time peer-to-peer energy trading for prosumers utilizing time-varying building virtual energy storage

With the increasing penetration of distributed energy resources (DER) in the electric power system, Peer-to-Peer (P2P) energy trading has become a promising paradigm for future electric power systems. Building thermal load, which is an important demand side resource, should be considered carefully in the design of a P2P trading method. In this paper, we investigate the application of building thermal energy storage capability in P2P energy trading. We aggregate and model building thermal loads as a virtual energy storage and derive a time-varying virtual energy storage system (T-VESS) model to quantify the flexibility of a building. Key parameters of T-VESS, including charging/discharging rate, energy capacity, and state of charge (SOC), are analyzed so that T-VESS can be embedded in the building prosumer model to participate effectively in electricity-oriented P2P energy trading. We propose a real time P2P energy trading method of prosumers based on model predictive control (MPC). This method consists of an energy supply and demand quantification stage and a transaction price optimization stage, which can effectively capture the time-varying nature of T-VESS. To preserve the trading preference for prosumers, we propose a distributed P2P energy trading actions implementation method based on the continuous double auction (CDA) considering multiple trading preference grades. Numerical results demonstrate that the proposed P2P energy trading method considering T-VESS can reduce the operational cost of prosumers by 3.7%, while also promoting the local integration of renewable energy by 3.1%. Furthermore, compared to existing P2P trading methods considering single preference grade, the proposed method greatly enhances the utilities of both individual prosumers and the overall community.