Provide Frequency Regulation Research Articles

The design of a model predictive control strategy and performance analysis for pumped storage units and super capacitors in power systems

IntroductionSuper capacitors were regarded as one of the most effective frequency regulation technologies in power systems. The major issue of applying super capacitors for frequency regulation is the limited energy capacity and the high construction costs. The pumped storage units have been developed and applied in power systems for decades, whereas it was rarely operated to regulate the system frequency due to the operation limitations. The pumped storage units could be operated as a hydro generator under the generation mode and provide effective frequency regulation service with the conventional automatic generation control (AGC) system while its output power was constant under the pumping mode.MethodIn this paper, the pumped storage unit was controlled to participate in frequency regulation under the generation mode with super capacitors. Considering the AGC system could not consider the state of charging (SOC) of super capacitors for frequency regulation, an optimization model was proposed to determine the operation points for super capacitors and pumped storage units under a model predictive control (MPC) strategy to regulate the system frequency for power systems.Results and DiscussionBased on the various operation scenarios, the effectiveness and costs of pumped storage units and super capacitors to provide frequency regulation services were discussed. It was indicated that pumped storage units and super capacitors were effective technologies for power systems.

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

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

Multi-agent deep reinforcement learning based fully decentralized aggregation frequency regulation of electric vehicle

Electric vehicles (EVs) are high-quality flexible resources to provide frequency regulation services. However, the EV real-time participation in ancillary service market for frequency regulation (FRASM) under the existing aggregation framework faces data privacy and interest conflicts problems. To solve these issues, this paper proposes the fully-decentralized aggregator (FDA) as a trusted and non-profit agent to replace the traditional aggregator and help EVs interact with FRASM in a decentralized manner. Then a fully decentralized aggregation framework based on consortium blockchain is constructed to protect the interaction data security during frequency regulation process. Furthermore, the inference network is introduced so that EVs can learn better performance and optimize regulation mileage without sharing their private data. This paper proposes an inference network based multi-agent soft actor-critic (IN-MASAC) method that realizes the decentralized interaction between EVs and FRASM and considers multiple user preferences. The case studies demonstrate the efficiency and scalability of the fully decentralized aggregation framework and IN-MASAC method.

Frequency regulation with storage: On losses and profits

Low-carbon societies will need to store vast amounts of electricity to balance intermittent generation from wind and solar energy, for example, through frequency regulation. Here, we derive an analytical solution to the decision-making problem of storage operators who sell frequency regulation power to grid operators and trade electricity on day-ahead markets. Mathematically, we treat future frequency deviation trajectories as functional uncertainties in a receding horizon robust optimization problem. We constrain the expected terminal state-of-charge to be equal to some target to allow storage operators to make good decisions not only for the present but also the future. Thanks to this constraint, the amount of electricity traded on day-ahead markets is an implicit function of the regulation power sold to grid operators. The implicit function quantifies the amount of power that needs to be purchased to cover the expected energy loss that results from providing frequency regulation. We show how the marginal cost associated with the expected energy loss decreases with roundtrip efficiency and increases with frequency deviation dispersion. We find that the profits from frequency regulation over the lifetime of energy-constrained storage devices are roughly inversely proportional to the length of time for which regulation power must be committed.

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 .

Encouraging thermostatically controlled loads to provide frequency regulation for smart grid: A comfort-level trading mechanism

This paper presents a comfort-level trading mechanism for thermostatically controlled loads (TCLs), aiming to incentivize customers to provide more frequency regulation service for smart grid. This mechanism firstly divides aggregators composed of TCLs into the leader layer and the follower layer according to comfort adjustment level produced by the fuzzy neural network controller (FNC). The aggregator at leader layer has a low regulation level, while aggregators at follower layer are high. Then we establish a Stackelberg game between leader layer and follower layer, which can optimize the customer utility. Meanwhile, we build a noncooperative game model among aggregators at follower layer, which can maximize the customer utility while supplying regulation capacity for leader layer. Finally, simulation results validate the effectiveness of FNC and the feasibility of trading mechanism. The FNC can improve tracking accuracy to 2.05%, reduce absolute tracking error to 208.4 kW, and decrease average switching times to 9 per hour. The trading mechanism can make the temperature setpoints of aggregators within their comfort range and improve the customer satisfaction.

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.

Real-time operation strategy of virtual power plants with optimal power disaggregation among heterogeneous resources

The virtual power plant (VPP) can aggregate flexible resources on the demand side to provide frequency regulation for the grid, helping address the supply–demand balance challenges. When deploying regulation, the VPP disaggregates the requested power adjustment in real time among its internal heterogeneous resources. Achieving optimal power disaggregation in this process is challenging due to the temporal coupling characteristics of the resources, the uncertain regulation signals, and the requirement for fast response. Therefore, existing research relies on heuristic methods, such as proportional disaggregation, and fails to leverage the heterogeneity of multiple resources. Here, we propose an optimal operation strategy for VPPs to provide regulation, exploiting the complementary characteristics of heterogeneous resources by prioritising the use of low-cost resources while considering temporal coupling. To reduce the computational overhead of online deployment, we further propose a fast disaggregation algorithm to eliminate the reliance on optimisation solvers. We conducted case studies on the operation of a VPP composed of resources including thermostatically controlled loads and industrial production processes. The results verified the reduced operation cost and increased profit of the VPP under the proposed strategy, with only milliseconds of online computation time. We believe that our work can help better exploit demand-side flexibility.

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.

Evaluating Machine-Learning-Based Methods for Modeling a Digital Twin of Battery Systems Providing Frequency Regulation

Battery energy storage systems (BESSs) have the potential to become providers of frequency regulation services in future energy systems. In this case, the reliable operation of BESS has a significant impact on system stability. One of the tools that ensures the secure BESS operation is applying a digital twin to forecast the BESS state of charge (SOC). It allows to predict BESS behavior and identify potential failures or cyberattacks in BESS. In this article, we compare multiple data-driven approaches to forecast the SOC of a BESS based on realistic dataset and real battery operation. Recurrent neural networks, e.g., feedforward neural network, gated recurrent unit, long short-term memory, support vector regression, random forest, and AdaBoost methods are applied to evaluate each method's performance. We compare the methods based on the maximum and average forecast errors, the required computational capacity, as well as expected training speed. To validate the results, in addition to the data gathered from simulations, we used experimental setup with a BESS of 79 kWh providing containment reserve for normal operation, which is a BESS service with a high economical potential in Nordic Region. Furthermore, we provide recommendations for the methods that are suitable to be applied for modeling the digital twin of a utility-scale battery digital twin.

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.

Economic evaluation of a power-to-hydrogen system providing frequency regulation reserves: a case study of Denmark

Operating costs are dominant in the hydrogen production of a power-to-hydrogen system. An optimal operational strategy or bidding framework is effective in reducing these costs. However, it is still found that the production cost of hydrogen is high. As the electrolysis unit is characterized by high flexibility, providing ancillary service to the grid becomes a potential pathway for revenue stacking. Recent research has demonstrated the feasibility of providing such a service, but the related economics have not been well evaluated. In this work, we propose a comprehensive operation model to enable participation in the day-head, balancing and reserve markets. Three types of reserves are considered by using different operational constraints. Based on the proposed operation framework, we assess the economic performance of a power-to-hydrogen system in Denmark using plentiful actual market data. The results reveal that providing frequency containment reserve and automatic frequency restoration reserve efficiently raises the operational contribution margins. In parallel, by investing in the cash flows, net present value, and break-even hydrogen prices, we conclude that providing reserves makes the power-to-hydrogen project more profitable in the studied period and region.

Research on Fast Frequency Response Control of PV Power Stations

In this paper, the frequency support capacity of power system integrated by A large number of PV generation is considered. Based on the conventional inverter PQ control strategy, virtual droop control and inertia control are introduced, and a fast frequency response control strategy of PV inverter similar to the synchronous generator is proposed. Meanwhile, in simulation software of power system, a three-machine nine-node simulation model including the PV power station is established, and the simulation verification is carried out. The simulation results show that when the system is disturbed, PV station can actively provide frequency regulation and enhance frequency stability of the power system.

Benefits of integrating power-to-heat assets in CHPs

Integrating power-to-heat (P2H) assets in combined heat and power plants (CHPs) is an attractive option, which can improve the flexibility in CHPs. This paper compares the potential benefits of integrating an electrical boiler (EB) and a heat pump (HP) in a CHP from providing flexibility services in both the day-ahead market and the frequency regulation market. An optimization model is developed for the operation of P2H assets and the CHP to maximize the profit. A case study is carried out using the data of a real CHP and electricity prices of Nord Pool. It is found that when an EB or a HP is integrated, the annual profit of the studied CHP from providing frequency regulation can be increased by 3.1 % (EB) or 27.7 % (HP) respectively compared to the CHP without P2H. Despite the high capital cost, a HP can increase the net present value up to 21.8 %, and achieve a payback period of 3 year, which are better than an EB (0.8 % and 5 year). Sensitivity analysis shows that prices of fuel and electricity have significant impacts on the net present value and payback period for the integration of P2H assets. Even though the increase of the fuel price decreases the NPV, it can lead to a decline in the payback period. Meanwhile, the increase of the electricity price results in a large growth in the profit and NPV, but a big reduction in payback period.

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 coordinated control method of aggregate AC load for auxiliary frequency regulation service

Large-scale fixed frequency air-conditioners (FFAC) and inverter air-conditioners (IAC) are high-quality flexible load resources in the power system. This paper proposes a coordinated control method of aggregate air-conditioner (AC) load for auxiliary frequency regulation services. Firstly, a new method for evaluating the frequency regulation potential (FRP) of aggregate AC is proposed. This method can rapidly evaluate the FFAC and IAC’s potential of providing frequency regulation at all stages. Then the day-ahead frequency regulation capacity (FRC) of aggregate AC at each time period is determined based on the frequency and unit commitment constraints. Afterwards, a coordinated control method considering users’ comfort level is developed to ensure the sequential operation of each AC and support the frequency regulation of the power grid. Finally, a simulation is conducted to demonstrate that the proposed method can realize the coordinated control of FFAC and IAC and improve the frequency stability of the power system.