Frequency Regulation Service Research Articles

A hybrid control strategy for frequency regulation with variable refrigerant flow air conditioning system

Heating, ventilation and air conditioning (HVAC) systems of buildings are abundant sources for the Frequency Regulation (FR) ancillary service, whose actuating devices are controlled to follow the real-time power reference signals with the contractual capacity. For HVAC practice, model-based FR controls are hindered by expensive model acquisition. This paper presents a two-layer control strategy for HVAC systems to perform energy efficient FR service, with a rather low model requirement. At the lower level, the FR operation with a selected actuator is realized with a sliding mode control which is robust to power map variation. At the higher level, system efficiency is optimized in real time via a model-free constrained extremum seeking control (CESC). A conceptual variable refrigerant flow (VRF) system is used as the illustrative application, for which the outdoor unit fan offers the FR power reference tracking with SMC. At the outer loop, the compressor discharge pressure is adjusted by a CESC to minimize the total power consumption while the zonal temperature regulation is reinforced as equality constraint. The proposed strategy is evaluated with simulations on a Modelica simulation, and the results validate its effectiveness. The Net Energy Cost can reduce by 15% to 23% under different load levels.

Cyber-physical system planning for VPPs supporting frequency regulation considering hierarchical control and multidimensional uncertainties

Renewable energy resources with uncertain output are being connected to the power grid, which puts great pressure on the grid frequency regulation (FR). Virtual power plants (VPPs), which aggregate numerous flexible resources, have significant potential for participation in FR services. However, the FR capability of VPPs is often unreliable due to uncertain resource potential and unreliable communications, which limits their FR applications. Therefore, a cyber-physical system (CPS) planning method considering multidimensional uncertainties for hierarchical VPPs to provide reliable FR capacity is proposed in this paper. Firstly, a fast and reliable hierarchical architecture for VPPs is presented. Then, the reliable capacity model of VPPs for service planning is established within this architecture. The minimum probability scenario method based on the Pauta criterion and the failure mode and effects analysis (FMEA) method are used to quantify the impact of information-physical uncertainty in the model. Based on this, a CPS planning model for VPPs with the objective of minimizing investment and the constraints of normal capacity, reliable capacity and delay under various scenarios is constructed. By linearizing the model, the optimal solution for coordinating flexible resource pool composition, edge terminal allocation and communication can be solved. Numerical simulations on the improved IEEE 33-node and 123-node systems demonstrate the effectiveness of the proposed method.

Co-Optimizing Bidding and Power Allocation of an EV Aggregator Providing Real-Time Frequency Regulation Service

The rapidly expanding scale of electric vehicle (EV) fleets and continuously decreasing battery costs are making vehicle-to-grid services a reality. In this paper, we study the interaction between the problems of an EV aggregator’s bidding in the regulation market and power allocation (i.e., determining the (dis)charging powers of the EVs in regulation deployment). Although the two problems are coupled, they are often regarded as decoupled and optimized separately for complexity issues. However, failing to consider the coupling of bidding and power allocation can lead to a decline in the profit of the EV aggregator (EVA). In this paper, we propose a framework for co-optimizing EVA bidding and power allocation in the regulation market. The bidding model is formulated as a stochastic programming problem with embedded power allocation in discretized regulation signal scenarios. To meet the solution time requirement for regulation deployment, we further propose a power allocation model that can be solved online. It utilizes the Lagrange multipliers from the bidding problem to ensure that the allocation results correspond to the optimal solution of the bidding problem. The effect of the proposed framework on improving EVA profits and reducing degradation costs is verified in the case study.

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.

Lithium ion batteries participating in frequency regulation for power grid under the thermoelectric coupling degradation mechanisms

Lithium-ion batteries (LIBs) play an important role for the global net-zero emission trend. They are suitable for the power interaction with the power grid with high penetration renewable energy. However, the detail evolution of the LIBs participating in frequency regulation (FR) service at low temperature is critical for the all-climate application, especially the capacity decay and the related economic loss. This study reveals that the primary degradation mechanisms for FR operation at low temperature include lithium plating of anode and lattice distortion of cathode. Surprisingly, FR with appropriate parameters for batteries at low temperature does not introduce additional capacity decay due to the great temperature rise brought about and the optimized interfacial mass transfer. This study then analyses the economy of electric vehicles (EVs) participating in FR service, which is called vehicle-to-grid (V2G). A better temperature control can improve the profit of 35.88 $/kW. An appropriate capability is also vital to improve the profit of FR service. Moreover, suitable FR conditions for LIBs can even bring a certain degree of capacity improvement at low temperature. This work guides the design criteria of non-destructive LIB interaction for future grid.

Fast Frequency Regulation From a Wind Farm-BESS Unit by Model Predictive Control: Method and Hardware-in-the-Loop Validation

The integration of Renewable Energy Sources (RES) into the power system requires an upgrade of the adopted management and control solutions. Focusing on frequency regulation, the same RES, as well as loads, both possibly coupled with energy storage systems, are called to provide a contribution through suitable regulation services. In this context, this paper proposes a control strategy for enabling a unit composed by a Wind Farm and Battery Energy Storage Systems (BESSs) to provide a fast frequency regulation service. The control is based on Model Predictive Control technique, whereas the regulation service is defined according to the technical requirements of the Italian Transmission System Operator (TSO). The approach is also tested via Hardware-In-the-Loop simulations. More specifically, tests are carried out by implementing the control algorithm on a Raspberry Pi board that communicates with a real BESS and with a real-time simulator implementing a benchmark power system. The validation results prove the practical effectiveness of the proposed control method since they demonstrate that the designed algorithms can be implemented on a low-cost hardware and applied, without computational and communication issues, in a real-field framework.

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.

Decentralized frequency regulation service provision for virtual power plants: A best response potential game approach

The high-renewable-penetrated power system frequently requires frequency regulation services. By aggregating heterogeneous demand-side flexible resources, the virtual power plants (VPP) are able to quickly respond to the frequency regulation signal, enabling them promising frequency regulation service providers. However, due to the difference in ownership, it is unlikely that the self-interested demand-side resources are willing to follow the VPP-oriented dispatch results. To bridge this gap, this paper proposes a decentralized game-based mechanism for the frequency regulation service provision inside the VPP. Firstly, the supply function-based bidding models of aggregated demand-side resources, namely, demand-side flexible resources and renewable distributed generations, are established. Then, a non-cooperative generalized Nash game for frequency regulation service provision is formulated. The existence, uniqueness, and competitive efficiency loss of the game are analyzed by designing an equivalent best response potential game of the original game. Additionally, a decentralized look-ahead real-time market clearing mechanism for frequency regulation service provision inside the VPP is proposed based on dual decomposition algorithm. Finally, the effectiveness and computation efficiency of the proposed method are validated in the case studies. Meanwhile, it is shown that the competition efficiency loss can be effectively reduced from around 80% to 6% when increasing the demand-side resource population from 20 to 2000 as well as balancing the demand-side resource composition.

Joint energy-frequency regulation electricity market design for the transition towards a renewable-rich power system

Striving to develop renewable energy generation (REG) has become a global trend. Due to high carbon emissions, fossil-fueled generation units (FFGUs) in an electrical energy market such as the day-ahead and real-time electricity markets are facing unprecedented constraints. From the perspective of FFGUs, an effective way is to change their market roles from energy suppliers with high environmental costs to secure and reliable frequency regulation service providers. To help FFGUs successfully complete the transition, a bi-level optimization model for the joint energy and regulation service market is proposed in this paper. Different from existing joint markets, both the environmental cost of FFGUs and the frequency regulation service (FRS) cost of REG units are considered in this research for market operators to determine the scheduling priorities of submitted bids from generation units. In the frequency regulation service market model, a dynamic approach to determining the FRS demand is applied, which not only incentivizes REG units to initiatively manage their outputs, but also derives the price signals through the equilibrium of the FRS demand and supply to ensure the revenue of FFGUs in the FRS market. Meanwhile, the decision-making model of the FFGUs under the proposed market mechanism is also presented. Finally, extensive numerical experiments demonstrate the feasibility and efficiency of the mechanism. Simulation results show that the proposed mechanism can stimulate a smooth transition of FFGUs by adapting to actual situations and policy changes.

Stochastic bidding for VPPs enabled ancillary services: A case study

Strategic bidding which aims to optimally harvest the price difference in the wholesale electricity market can efficiently allocate VPPs’ aggregated resources to provide large flexibility for energy and frequency regulation (FR), aiding in the real-time rebalancing of supply and demand. However, the uncertainty of renewable energy results in substantial imbalances between supply and demand, which causes high randomness of system frequency deviation and significant price fluctuations in the electricity market, making bidding challenging. To tackle these issues, a risk-averse optimal bidding strategy is proposed for VPPs to participate in both energy and FR markets. Moreover, a payment recovery mechanism is designed to recover the cost of FR undersupply. Specifically, each VPP submits bids to maximize profit, while the market operator clears the market and penalizes undersupply through the payment recovery mechanism. The existence of Nash equilibrium is proved, and a distributed best response algorithm is implemented to calculate the risk-averse optimal bidding strategy, taking into account Denial-of-Service attacks. The mean-convergence and variance-convergence of our proposed algorithm are derived. A case study of Australian National Electricity Market (NEM) validates the effectiveness of the proposed method in reducing overbidding risk, strengthening FR service reliability and improving profits for VPPs.

Life-Aware Operation of Battery Energy Storage in Frequency Regulation

The rapid growth of renewable generation in power systems imposes unprecedented challenges on maintaining power balance in real time. With the continuous decrease of thermal generation capacity, battery energy storage is expected to take part in frequency regulation service. However, accurately following the automatic generation control (AGC) signal leads to more frequent switching between charging and discharging states, which may shorten battery life. Because battery life is a consequence of long-term operation depending on the depth of discharge, it is difficult to model battery health in frequency regulation problems. This paper establishes an online operation policy in response to the real-time AGC signal considering battery health. Based on the empirical relation between cycling number and depth of discharge, a cost function is suggested to approximate the impact of charging-discharging action on battery life in the long run. Then, Lyapunov drift-plus-penalty technique is employed to derive an explicit online policy compromising the revenue of frequency regulation and battery life without requiring future AGC data. The offline operation problem after knowing the entire sequence of AGC signal is cast as a difference-of-convex program and solved by a sequential convexification algorithm. The gap between the offline and online optimum is shown to be a constant, which quantifies the value of knowing future information. Case studies based on AGC data from the PJM market validate the proposed method.

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.

Optimal Electric Vehicle Fleet Charging Management with a Frequency Regulation Service

Electric vehicles are able to provide immediate power through the vehicle-to-grid function, and they can adjust their charging power level when in the grid-to-vehicle mode. This allows them to provide ancillary services such as frequency control. Their batteries differ from conventional energy storage systems in that the owner’s energy requirement constraint must be met when the vehicles participate in a frequency control system. An optimization problem was defined by considering both the owner satisfaction and frequency control performance. The main contribution of the proposed paper, compared to the literature, are (1) to keep the total available energy stored in the batteries connected to a charging station in an optimal region that favors the frequency regulation capability of the station and the proposed QoS and (2) to consider the optimal region bounded by the efficiency thresholds of the charger to allow for maximum regulation power. The problem is expressed as a multi-criteria optimization problem with time-dependent references. The paper presents an energy management strategy for frequency control, describes a concept of an optimal time-dependent state of charge for electric vehicle charging demands, and considers the power dependence of the electric vehicle charger efficiency. Finally, the simulation results are presented via Matlab/Simulink to prove the effectiveness of the proposed algorithm.

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.

Multi-timescale interaction of a coupled electricity-hydrogen-gas system with a distribution network considering dynamic games

The large-scale access of new energy sources to distribution networks has led to its safe and stable operation. Therefore, this study considers a multi-timescale interaction of a coupled electricity-hydrogen-natural gas (EHG) system with a distribution grid for dynamic games. First, a multifaceted interaction mechanism between the coupling system and the distribution network balancing service mechanism, demand response service mechanism, and frequency regulation service mechanism was designed. Furthermore, an interaction framework between the coupled system and the distribution network was proposed, and a three-level time-scale interaction strategy between the distribution network and the coupled system was constructed considering the dynamic game in the medium- and long-term, short-term, and ultrashort terms. Subsequently, the presence and uniqueness of the interactive equilibrium solution of the dynamic game was proven. Finally, a region was used as an example for arithmetic analysis. The results of the calculation example indicate the following: 1) the ranges of interaction deviation under the medium-to long-term, short-term, and ultra-short-term time scales are [-3.76%, 2.84%], [-2.37%, 1.43%], and [-0.43%, 0.87%], respectively, indicating that the interaction deviation between the coupling system and the distribution grid can be reduced via adjusting and correcting the medium-to long-term, short-term, and ultra-short-term time scales. 2) Compared with the scenario in which only a single service is provided, the distribution network operation cost in the present study decreased by 15.92% and the revenue increased by 30.59%, signifying that the coupling system and distribution network diversification mechanism can expand revenue channels. 3) Compared with Scenario 1, without considering the cost of energy storage loss, and Scenario 3, with a single energy storage configuration, the actual service life of the battery in this study was the longest, 23.72% longer than Scenario 1, and 47.90% longer than Scenario 3, revealing that hybrid energy storage can achieve coordinated optimization and optimal allocation of resources at different time scales. 4) The interaction between the distribution network and coupling system, considering dynamic games, can form a fair price and guide to improved interaction between the distribution network and coupling system.

Lithium‐Ion Batteries Participating in Frequency Regulation through Low‐Destructive Bidirectional Pulsed Current Operation

AbstractLithium‐ion batteries (LIBs) have enormous potential to participate in the frequency regulation (FR) of future high‐penetration renewable energy grids. This study reports the development of non‐destructive LIBs that supply FR service through controllable input–output power. The low‐destructive operation does not impair the cells and even provides a fast solid inter‐electrolyte phase for the LIB to ensure rapid diffusion of Li+ and suppress Li deposition. Cracks of the cathode and Li deposition on the anode appear to varying degrees when different destructive operations are adopted. The extraordinary capability of such non‐destructive operations for LIBs is demonstrated in historical dynamic signals from the regulation market of Pennsylvania‐New Jersey‐Maryland, where the matching degree achieved 86.7% of annual FR demand. This study guides the design criteria of low‐destructive operations of LIBs for future grid and contributes to the energy greening and electrification through a vehicle‐to‐grid solution.

Strategic behavior modeling and energy management for electric-thermal-carbon-natural gas integrated energy system considering ancillary service

Carbon capture system (CCS) and power to gas (P2G), as key low-carbon technologies, have obtained the widespread attention. Thus, it is necessary to explore the operation pattern and benefits of CCS and P2G. This paper investigates their roles in the electric-thermal-carbon-natural gas integrated energy system (IES) and focuses on the multi-market participation and multi-energy management. IES participates in the electricity, carbon trading, and natural gas markets. In the electricity market, IES can provide two types of ancillary services: deep peak regulation and frequency regulation services. Then, owing to the small scale of the ancillary service market, the strategic behaviors of IES are discussed. A bi-level model is established to deal with the complex couplings in the peak regulation market. IES's strategic behavior in the frequency regulation market is depicted by the price quota curve. Next, a solution method based on the particle swarm optimization algorithm is proposed to deal with the nonconvexity of the lower-level model. Finally, case studies show that the strategic behavior modeling in this paper can increase the profits of the deep peak regulation and the frequency regulation services. When IES is equipped with CCS and P2G, the operation cost decreases by 16.6%.

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.

Capacity of Virtual Energy Storage System for Frequency Regulation Services via a Data-Driven Distributionally Robust Optimization Method

Due to large thermal inertia of buildings and flexibility of interruptible loads, smart buildings pose a remarkable potential for developing virtual energy storage systems (VESSs). However, current literature lacks advanced models to quantify and thus properly optimize available capacity of VESS for power system ancillary services, especially frequency regulation services (FRS). This paper, firstly, presents a novel probabilistic model for explicitly quantifying the VESS capacity in charging and discharging modes, which can further be optimized by scheduling building loads. While the optimized capacity is fully available for FRS, building customer economic benefit and comfort could considerably be impaired by various uncertainties including weather and user related parameters, as well as FRS requests. Due to limited information of the uncertainties, common model-based stochastic and robust optimization approaches are inefficient, leading to either poor out-of-sample performance or over-conservative solutions. Thus, secondly, this paper develops a data-driven distributionally robust optimization (DRO) method to robustly optimize the capacity of VESS against the worst distribution of the uncertainties with a finite training dataset. The proposed data-driven DRO-based model can well match modern data-driven operation environment. Numerical simulations validate the high efficiency and out-of-sample performance of the proposed VESS capacity optimization method under the uncertainties.

P2P flexibility markets models to support the coordination between the transmission system operators and distribution system operators

The increasing integration of Distributed Energy Resources (DER) in the distribution network has brought more importance to Peer-to-Peer (P2P) markets. However, energy traded in P2P markets can lead to voltage and congestion constraints in distribution networks operated by Distribution System Operators (DSOs). At the same time, Transmission System Operators (TSOs) may need to solve system problems, requesting the participation of DERs in frequency regulation services. To ensure competitive participation in P2P markets, as well as to ensure a correct operation of distribution networks and to contribute to mitigate problems at the system level, coordination mechanisms between the P2P market and the System Operators (SOs) are required. This paper introduces a set of mathematical models considering P2P flexibility trading at the distribution system, while assisting the DSO and TSO in solving the congestion, voltage and frequency problems, respectively. The models are assessed on an IEEE 37-bus distribution network with high DER penetration. The first and second models are based on product differentiation to avoid violating the lines’ thermal limits and the nodes’ voltage limits, respectively. The second model also considers reactive power control in order to impact voltage constraints. The third model uses a virtual load, connected to the TSO network (before the power transformer), to model frequency regulation services. The last model proposes the integration of all methods. Results showed that each model was effective in solving its constraint. However, they do not dismiss the use of the peers’ flexibility assets to assure an overall feasible techno-economic solution. The use of the methodology proposed in the present paper can significantly facilitate the adoption of full P2P markets as well as the confidence of the system operators in the integration of these markets.