Provide Frequency Regulation Service Research Articles

Collaborative Vehicle-to-Grid Operations in Frequency Regulation Markets

Problem definition: We study the operations of electric vehicles (EVs) providing frequency regulation services to the electric grid in vehicle-to-grid (V2G) systems. In particular, individually owned EVs collaboratively bid in the regulation market, coordinated by a platform that operates the network of charging equipment. We study how the platform determines optimal pricing incentives for drivers to plug in their EVs, accounting for heterogeneous driving schedules. Methodology/results: We model the platform’s pricing optimization problem as a bilevel program: At the upper level, the platform determines hourly rebates for EV owners to plug in their EVs and capacity bids in the regulation market; at the lower level, individual travelers optimize their travel and charging schedules in response to pricing incentives. To account for uncertainties and heterogeneity in regulation market prices and travel patterns, we adopt distributionally robust optimization techniques to formulate the problem as a mixed-integer second-order cone program. We conduct a computational study based on the California Household Travel Survey data set and actual frequency regulation prices. Our results show that the ability to offer time-varying rebates and install workplace chargers can significantly improve the V2G platform’s expected profits. Managerial implications: As EV adoption progresses past the nascent stage, V2G business models become more viable. Successful implementation of V2G provides economic incentive for switching to EVs, potentially helps sustain adoption growth, and complements the growth of renewable power by helping stabilize the grid. Our findings shed light on the design of driver incentives for V2G systems. Funding: R. Tang acknowledges support from the National Natural Science Foundation of China [Grant 72201206]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2022.0133 .

Data-Driven Emergency Frequency Control for Multi-Infeed Hybrid AC-DC System

With the continuous development of large-scale complex hybrid AC-DC grids, the fast adjustability of HVDC systems is required by the grid to provide frequency regulation services. This paper develops a fully data-driven linear quadratic regulator (LQR) for the HVDC to provide temporal frequency support. The main technical challenge is the complexity and the nonlinearity of multi-infeed hybrid AC-DC (MIDC) systems dynamics that make the LQR intractable. Based on Koopman operator (KO) theory, a Koopman eigenpairs construction method is developed to fit a global linear dynamic model of MIDC systems. Once globally linear representation of uncontrolled system dynamics is obtained offline, the control term is constituted by the gradient of the identified eigenfunctions and the control matrix <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\boldsymbol{B}$</tex-math></inline-formula> . In case that <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\boldsymbol{B}$</tex-math></inline-formula> is unknown, we propose a method to identify it based on the verified Koopman eigenfunctions. The active power reference is optimized online for LCC-HVDC in a moving horizon fashion to provide frequency support, with only locally measurable frequency and transmission power. The robustness of the proposed control method against approximation errors of the linear representation in eigenfunction coordinates is analyzed. Simulation results show the effectiveness, robustness and adaptability of the proposed emergency control strategy.

Incentivizing EVs to Provide Frequency Regulation Services by Aggregative Game-Based Mechanism

Incentivizing EVs to Provide Frequency Regulation Services by Aggregative Game-Based Mechanism

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.

Modeling and Transient Response Analysis of Doubly-Fed Variable Speed Pumped Storage Unit in Pumping Mode

Doubly-fed variable speed pumped storage (VSPS) unit is an advanced hydropower system. In pumping mode, its input power can continuously be adjusted within a certain range via changing speed. Thus, it overcomes the major defect that the pumped storage unit cannot provide frequency regulation services to the power grid in pumping mode. However, for the pumping mode VSPS, how to establish its model is still a problem. In this article, an electromagnetic-electromechanical transient multi-time scale model of pumping mode VSPS is presented. This model depicts the intrinsic speed-power regulation characteristics and the extrinsic power-frequency coupling characteristics of pumping mode VSPS, which can be used for the power grid frequency regulation analysis. Firstly, the hydraulic subsystem model of VSPS is established within the electromechanical transient time scale. The model is built based on the variable speed characteristics of the reversible pump-turbine (RPT) in pumping mode and considers the hydrodynamics and additional friction effects of the wicket gate in the water conveyance system. Secondly, the electric subsystem model of VSPS is established within the electromagnetic transient time scale. The model mainly describes the operation characteristics of a doubly-fed induction machine (DFIM) and the control properties of the inner and outer loops of the back-to-back (BTB) converter system. Finally, comprehensive simulation and experimental results have fully verified the established model and theoretical analysis.

Frequency Regulation Capacity Offering of District Cooling System: An Intrinsic-Motivated Reinforcement Learning Method

District cooling system (DCS), a type of large-capacity air conditioning system that supplies cooling for multiple buildings, is an ideal resource to provide frequency regulation services for power systems. In order to provide high-quality services and maximize DCS’s revenue from the electricity market, an accurate estimation of DCS’s regulation capacity is indispensable. Inaccurate regulation capacity estimation may lead to unsatisfactory cooling supply for buildings and/or poor regulation service quality that may be penalized by the market. However, estimating a DCS’s regulation capacity is quite challenging, because a DCS usually has complex thermal dynamics to model and its cooling demands and regulation signals are usually highly stochastic. To address the above challenges, this paper proposes a DCS regulation capacity offering strategy based on deep reinforcement learning. It is model-free and can effectively tackle various uncertainties. Furthermore, considering that the training process of DRL needs lots of “trial and errors," which may harm the actual physical system by making “bad" decisions. We propose a novel intrinsic-motivated method based on pseudo-count to improve the efficiency of the training. Numerical studies based on a realistic DCS system illustrate the effectiveness of the proposed method.

Changes in the electricity market under the participation of diverse participants and its supervision necessity analysis

With the demand for energy sustainability and the development of the low-carbon power system, distributed energy, energy storage, demand response, and other diverse market participants will participate in the electricity market, which brings new changes to the transaction organization process of the electricity market. This paper first introduces the changes in the electricity market under the participation of diverse market participants from two aspects of market transactions and market power. Diverse participants can provide a variety of auxiliary services in the electricity market while they have potential market power in the process. Secondly, the current supervision measures used in the foreign electricity market in the order of ex-ante, interim, and ex-post are reviewed and four common supervision indexes are introduced. Finally, the market power and the supervision necessity of the electricity market under the participation of energy storage are analyzed by case studies. As shown by the simulation results, energy storage with excellent regulating performance has market power in providing frequency regulation services, but the conventional supervision indexes fail to detect the market power of energy storage effectively. The purpose of this paper is to illustrate the necessity of the market supervision method for adapting to the changes under the participation of diverse participants.

A Model Predictive Control Based Optimal Task Allocation among Multiple Energy Storage Systems for Secondary Frequency Regulation Service Provision

Power system stability has been suffering increasing threats with the ever-growing penetration of intermittent renewable generation such as wind power and solar power. Battery energy storage systems (BESSs) could mitigate frequency fluctuation of the power system because of their accurate regulation capability and rapid response. By dividing the Area Control Error (ACE) signal and the State of Charge (SOC) of battery energy storage systems into different intervals, the frequency control task of BESSs could be determined by considering the frequency control demand of the power grid in each interval and SOC self-recovery. The well-developed model predictive control can be employed to attain the optimal control variable sequence of BESSs in the following time, which can determine the output depths of BESSs and action timing at different intervals. The simulation platform MATLAB/Simulink is used to build two secondary frequency control scenarios of BESSs for providing frequency regulation service. The feasibility of the presented strategy is demonstrated by simulation results of a sample system.

Distributed Economic Dispatch for Microgrids Tracking Ramp Power Commands

When in grid-connected mode of operation, distributed generators (DGs) within the microgrid (MG) can coordinate to act as a single entity to provide services to the bulk grid. The DGs can coordinate their power production to minimize the total operating cost, which is known as the distributed economic dispatch. Various methods have been proposed to solve the MG economic dispatch problem (EDP) in a distributed fashion, under the assumption that DGs’ power output, in aggregate, follows a constant or slowly varying power command. However, when the MG is providing frequency regulation service, or the internal load is highly dynamic, the MG EDP becomes dynamic, and the state-of-the-art distributed approaches cannot guarantee optimality. In this paper, we propose a distributed economic dispatch algorithm for MGs providing frequency regulation service, as an example of a dispatch profile with ramp commands. A consensus protocol guaranteeing zero steady-state error for ramp inputs is integrated into the EDP to find the optimal solution in a distributed way. With the proposed algorithm, the MG is able to tightly follow a time-varying regulation signal while maintaining an optimal economic dispatch for all the DGs within. We validate the proposed method using regulation signals from PJM and demonstrate the algorithm on a hardware-in-the-loop testbed.

An Incentive-Compatible Frequency Regulation Market for Flexible Resources in Microgrid

Frequency regulation services can be provided by emerging flexible resources such as electric vehicles and energy storage which can rapidly adjust the output in response to regulation signals, as long as there is an effective market to provide proper incentives. However, current regulation markets were designed for conventional thermal generating units, and it is difficult for the mileage-based bidding structure to express the cost characteristics of flexible resources. Therefore, the current mechanisms are not conducive to encouraging the participation of flexible resources in providing frequency regulation services. This paper proposes an incentive-compatible frequency regulation market for flexible resources, whose revisions to the existing mechanism include the bidding structure, clearing rules, and settlement rules that adapt to the characteristics of new resources. Compared with the current mechanism, the proposed mechanism can reduce the costs of deploying regulation and more reasonably allocate the revenues for regulation services. The case studies verify the effectiveness of the proposed mechanism.

A dynamic bidding strategy of hybrid energy storage system participating in day-ahead frequency regulation market

The rapid proliferation of intermittent and unpredictable renewable resources poses an unprecedented challenge to frequency stability in the modern system. A hybrid energy storage system (HESS) typically comprised of battery and ultracapacitor has better performance in quick response. In this context, this paper elaborates on a dynamic bidding strategy for an independent HESS operator to provide frequency regulation service in a day-ahead performance-based market. The proposed framework aims to maximize the net profit of the HESS operator based upon a two-part settlement mechanism considering the HESS degradation. Furthermore, the separated optimization of the battery and ultracapacitor can be realized by introducing a VSQF frequency division algorithm to leverage the full advantage of the HESS. Also, the optimization process in each stage can be innovatively driven by an improved snake optimizer and a penalty function is utilized to fulfilling the HESS-related and market-related constraints. The comparison between the HESS and the battery energy storage system (BESS) shows that HESS has indeed economic superiority and yields an 18.59 % higher profit. Moreover, the economic benefits under conservative SoC management by the flexible adjustment of the penalty coefficient are 9.06 % higher than that with the strict terminal SoC constraint which verifies the effectiveness of the dynamic bidding.

Frequency Regulation Service Provision for Virtual Power Plants Through 5G RAN Slicing

The virtual power plant (VPP) has the potential to provide frequency regulation services by aggregating demand-side distributed energy resources (DERs). Cellular communication networks are commonly utilized for connecting DERs. Nevertheless, conventional cellular networks prior to 5G were unreliable, while private cellular networks incur huge investment costs. Radio access network (RAN) slicing in 5G communications can address these issues by providing the VPP with virtual private networks in public cellular networks. The VPP must evaluate the benefits of 5G RAN slicing and determine the size of slices before implementing it. This paper proposes to apply 5G RAN slicing to assist the VPP in participating in frequency regulation services. First, an optimization model is developed for the VPP to maximize the profit of engaging DERs in various frequency regulation services. Its solution determines the VPP’s DER portfolio schedule of regulation services and communication needs. Then, the effects of communication reliability on the regulation performance are quantified. 5G RAN slicing enhances the predictability of communication reliability, and its benefit is explicitly modeled as its contribution to reducing VPP’s risk profit loss. Next, based on the 5G RAN slicing benefit, an optimization model for determining the size of slices is further established, considering the cost of purchasing slices from mobile service providers. Additionally, an online solution method is developed, such that DERs’ regulation service portfolio can be determined in real-time. Numerical case studies are conducted to demonstrate the benefit of 5G RAN slicing for the VPP’s frequency regulation services.

Hour-Ahead Optimization Strategy for Shared Energy Storage of Renewable Energy Power Stations to Provide Frequency Regulation Service

With the rapid growth of intermittent renewable energy sources, it is critical to ensure that renewable power generators have the capability to perform primary frequency response (PFR). This paper proposes a framework for using a shared battery energy storage system (BESS) to undertake the PFR obligations for multiple wind and photovoltaic (PV) power plants and provide commercial automatic generation control (AGC) service in the ancillary service market at the same time. Furthermore, an optimal bidding strategy in the hour-ahead energy and ancillary service markets is proposed to maximize the BESS' benefits, considering frequency regulation performance, battery degradation cost, and the state of charge (SoC) limits. The optimization model is first built as a chance-constrained optimization problem and then transformed into mixed-integer quadratically constrained programming using Wasserstein metric-based distributionally robust optimization technique. Simulation results show that the proposed optimal bidding strategy and solution method can effectively take advantage of a BESS to perform PFR and provide AGC services simultaneously and improve its economic benefits.

An overview of load frequency control for grid using various control techniques

Nowadays Electrical power generation using renewable energy systems brings more novel ideas to satisfy the power demand worldwide. Microgrid is one of the ideas that ensures stability and reliability of the power system. Due to seasonal or climatic effect, the power generated by the renewable energy sources has issue in providing a constant power output and hence produces the power imbalance between the source to load. Furthermore, the system frequency cannot be kept constant in all cases. In this case, an electric vehicle (EV) can serve as a power generation source to support the power grid and meet the Load Frequency Control requirement (LFC). Due to their fast-regulating characteristics, electric vehicles provide frequency regulation services. Electric vehicles are used in both primary and secondary frequency management in power systems to quickly suppress frequency oscillations caused by changes in load. The control centre gathers and updates real-time Electric Vehicle data, such as information regarding state of charge and the state of each Electric Vehicle, in order to decide on load frequency regulation dispatch to Electric Vehicles. This paper compares and consolidate the performance of Load frequency control through Conventional plants and Renewable energy systems. The graphical abstract of power system grid is shown in Figure.1

A machine learning-based control strategy for improved performance of HVAC systems in providing large capacity of frequency regulation service

Heating, ventilation and air-conditioning systems (HVAC), at demand side, have been regarded increasingly as promising candidates to provide frequency regulation service to smart power grids. In many control systems, chilled water outlet temperature setpoint is reset to change the power use of HVAC systems after the regulation capacity is determined. However, the conflict between changed power use and unchanged cooling/heating demand could become a prominent problem when a large regulation capacity is provided. This problem can deteriorate the performance of frequency regulation service provided by HVAC systems. In this study, a machine learning-based control strategy is proposed to solve this problem for improved performance of HVAC systems in providing large capacity of frequency regulation service. It adjusts the power use of HVAC systems by simultaneously resetting chilled water outlet temperature setpoint and indoor temperature setpoint. The proposed control strategy is validated on a simulation platform. Results show that the strategy can significantly increase the performance of service when an HVAC system provides different regulation capacities. Moreover, the robustness of the strategy is studied. The results show that the strategy can still work effectively even the machine learning algorithms has a relatively low prediction performance in real application due to practical difficulties.

Adaptive under‐frequency load shedding scheme for power systems with high wind power penetration considering operating regions

With the ever-increasing penetration of wind power into power systems, wind turbine generators (WTGs) are required to provide frequency regulation services to maintain the system frequency stability. For variable-speed WTGs, the adaptability of dissimilar frequency control strategies to different operating regions is distinct, which impacts the system frequency response. However, existing under-frequency load shedding (UFLS) schemes do not take that impact into account, causing improper load shedding after a loss of generation. To this end, this paper proposes an adaptive UFLS scheme for power systems with high wind power penetration considering operating regions. Based on the WTG operating characteristics in different operating regions, the appropriate frequency control strategy for each operating region is elaborated. The simplified frequency response model of the power system considering WTGs participating in frequency regulation is then established. According to the WTG active power response characteristics in different operating regions, the impact of WTGs participating in frequency regulation on UFLS is analysed. Accordingly, the method of accurately estimating the magnitude of the power deficiency and calculating the amount of load to be shed is proposed. Finally, the feasibility and effectiveness of the proposed UFLS scheme are verified through simulations on the IEEE 39-bus test system.

PowerMorph: QoS-Aware Server Power Reshaping for Data Center Regulation Service

Adoption of renewable energy in power grids introduces stability challenges in regulating the operation frequency of the electricity grid. Thus, electrical grid operators call for provisioning of frequency regulation services from end-user customers, such as data centers, to help balance the power grid’s stability by dynamically adjusting their energy consumption based on the power grid’s need. As renewable energy adoption grows, the average reward price of frequency regulation services has become much higher than that of the electricity cost. Therefore, there is a great cost incentive for data centers to provide frequency regulation service. Many existing techniques modulating data center power result in significant performance slowdown or provide a low amount of frequency regulation provision. We present PowerMorph , a tight QoS-aware data center power-reshaping framework, which enables commodity servers to provide practical frequency regulation service. The key behind PowerMorph is using “complementary workload” as an additional knob to modulate server power, which provides high provision capacity while satisfying tight QoS constraints of latency-critical workloads. We achieve up to 58% improvement to TCO under common conditions, and in certain cases can even completely eliminate the data center electricity bill and provide a net profit.

An Improved Two-Stage Deep Reinforcement Learning Approach for Regulation Service Disaggregation in a Virtual Power Plant

Managing numerous distributed energy resources (DERs) within the virtual power plant (VPP) is challenging due to inaccurate parameters and unknown dynamic characteristics. To address these obstacles, a two-stage deep reinforcement learning approach is proposed for the VPP to provide frequency regulation services and issue the disaggregation commands to DER aggregators in real-time operation. In the offline-stage, an offline simulator is formulated to learn the dynamic characteristics of DER aggregators, through which the soft actor-critic (SAC) algorithm is employed to train the control policy. In the online-stage, the trained control policy is updated continuously in the practical environment, which can ameliorate the performance of the start-up process with prior knowledge. Moreover, a novel sharpness-aware minimization based soft actor-critic (SAM-SAC) algorithm is proposed to improve the robustness and adaptability of the deep reinforcement learning approach. Simulation results illustrate that the proposed approach enables the VPP to manage the DER aggregators to track the regulation requests more accurately and economically than the state-of-the-art methods.

Data-Driven Model Predictive Control Method for Wind Farms to Provide Frequency Support

As the wind power penetration increases, wind farms are required by the grid codes to provide frequency regulation services. This article develops a fully data-driven model predictive control (DMPC) scheme for the wind farm to provide temporal frequency support. The main technical challenge is the complexity and the nonlinearity of wind turbine dynamics that make the DMPC intractable. Based on Koopman operator (KO) theory, a specialized dynamic mode decomposition (SDMD) algorithm is proposed, which fits a global linear dynamic model of the wind turbines. The performance of learning dynamics is powered through integrating the physical knowledge of the wind turbine into the specialized observables of KO. To stabilize the rotor speeds in frequency regulation, the active power contribution is optimally dispatched in a moving horizon fashion. Simulation results show that the DMPC can efficiently learn and predict the wind turbine dynamics. During the frequency response process, the proposed method can effectively track the frequency support order specified by the utility grid operator while significantly stabilizing the rotor speeds.

Coordinated control of Incentive-Based Demand Response Program and BESS for frequency regulation in low inertia isolated grid

With the increase in penetration of inverter-based renewable energy resources (RES) to meet the increasing demand, frequency regulation is a major concern in low inertia isolated microgrids. Conventionally, a battery energy storage system (BESS) regulates the frequency in coordination with RES. Using only BESS for frequency regulation may not be sufficient during high penetration of RES. Therefore, maintaining the BESS state of charge (SOC) at a nominal value for up/down frequency regulation services becomes daunting. Customers’ participation through the Demand Response (DR) programs is investigated to provide frequency regulation services when the existing BESS and other resources are insufficient for regulation services. The coordination between the Incentive-Based Demand Response (IBDR) program and BESS is used to provide frequency regulation services and maintain the nominal SOC of the battery. For the IBDR program, the power consumption of commercial buildings’ heating ventilating and air-conditioning (HVAC) systems are controlled by changing air handling units’ fan speed (AHUs). The speed of the fan in AHUs is tracked to regulate the frequency. A decentralized control scheme with a simple PI and PD controller is proposed. The simulated results show that smooth frequency regulation is achieved through coordinated control of HVAC loads and BESS.