Grid Support Services Research Articles

Adaptive VSG control of flywheel energy storage array for frequency support in microgrids

The application of virtual synchronous generator (VSG) control in flywheel energy storage systems (FESS) is an effective solution for addressing the challenges related to reduced inertia and inadequate power supply in microgrids. Considering the significant variations among individual units within a flywheel array and the poor frequency regulation performance under conventional control approaches, this paper proposes an adaptive VSG control strategy for a flywheel energy storage array (FESA). First, by leveraging the FESA model, a variable acceleration factor is integrated into the speed-balance control strategy to effectively achieve better state of charge (SOC) equalization across units. Furthermore, energy control with a dead zone is introduced to prevent SOC of the FESA from exceeding the limit. The dead zone parameter is designed based on the SOC warning intervals of the flywheel array to mitigate its impact on regular operation. In addition, VSG technology is applied for the grid-connected control of the FESA, and the damping characteristic of the VSG is decoupled from the primary frequency regulation through power differential feedback. This ensures optimal dynamic performance while reducing the need for frequent involvement in frequency regulation. Subsequently, a parameter design method is developed through a small-signal stability analysis. Consequently, considering the SOC of the FESA, an adaptive control strategy for the inertia damping and the P/ω droop coefficient of the VSG control is proposed to optimize the grid support services of the FESA. Finally, the effectiveness of the proposed control methods is demonstrated through electromagnetic transient simulations using MATLAB/Simulink.

Quantifying the impact of electricity pricing on electric vehicle user behavior: a V2G perspective for smart grid development

ABSTRACT This research introduces an innovative analytical framework for studying the behavior of electric vehicles (EVs) in power grids. The approach primarily examines the dynamic State of Charge (SOC) and energy reserves of EV batteries. By thoroughly examining patterns of EV charging demand and operational states (charging, discharging, idling, and waiting) across various time intervals, including day and night periods, this study aims to provide a comprehensive understanding of EV dynamic behavior in power networks. The proposed model goes beyond traditional assessments by incorporating human behavioral patterns and responsiveness to economic signals, specifically investigating the impact of electricity pricing on user behavior. The core analysis centers on exploring the Vehicle-to-Grid (V2G) concept, viewing EVs not only as energy consumers but also as potential contributors to grid stability and efficiency. The study delves into how the interplay of dynamic EV behavioral characteristics and owner responses to pricing influences the overall energy consumption curve of the grid, thereby shaping the capability of EVs in providing grid support services. To quantitatively validate the effectiveness of the model, a detailed case study is conducted, offering empirical evidence of its practical utility. The research findings are quantified, highlighting the significant implications for decision-makers, grid managers, and stakeholders in the energy sector. The paper emphasizes the main outcomes, demonstrating how the proposed analytical framework can empower stakeholders in making informed decisions about policy and infrastructure development. The robust methodological approach presented in this paper serves as a valuable tool for evaluating the impacts of adopting V2G, providing strategic insights for a more sustainable and economically viable paradigm.

On the Use of Battery-Electric Locomotive as a Grid-Support Service in Electric Power Systems

On the Use of Battery-Electric Locomotive as a Grid-Support Service in Electric Power Systems

Frequency support by wave farms in low inertia power systems

New ancillary services and additional requirements for the grid integration of variable renewable energy (VRE) are being defined worldwide, in response to the technical challenges caused by increasing levels of VRE utilization in electric power grids. Currently, the use of wave energy is still limited to a few applications or demonstrations, where the level of penetration of wave power into the grid is not significant. To anticipate the future requirements for wave power integration, and the possibilities for provision of services, this paper considers the lessons being learned through the challenges caused by high penetration levels of other VRE sources into the grid, particularly wind power. On this basis, this paper presents an overview of grid support services that wave power plants can be expected to provide in power systems dominated by converter-interfaced generation, i.e. low inertia systems. Specifically, the focus is on services that support the active power balance in the power system. Then, the current capabilities and future perspectives for the provision of frequency support by wave farms are discussed.

Real-Time Allocation of Multi-Mobile Resources in Integrated Distribution and Transportation Systems for Resilient Electrical Grid

This article proposes a novel real-time optimization-based critical load restoration strategy for the post-event stages. The key idea is to integrate multi-mobile and stationary resources into the emergency tools of the system operation as grid-support services. The role of the demand response (DR) program and distributed generation (DG) from the resiliency-boosting points of view is investigated. To prevent prolonged and extensive electric outages, mobile emergency generator (MEG), emergency vehicle energy storage (EVES), and electric vehicles (EVs) are aimed to be exploited by forming microgrids. Power system constraints are modelled according to the linearized DistFlow equations. The framework's main aim is to serve critical loads, weighted by their priority, with the coordinated operation of multiple resources. Mixed-integer quadratic programming-based model is solved by a state-of-the-art solver within 30-minute time slots while considering uncertainties in real-time traffic congestion. Different case studies are carried out on the IEEE-33 bus coupled distribution-transportation system and the results are discussed in detail on the load restoration basis.

Decarbonization of the chemical industry through electrification: Barriers and opportunities

The chemical industry is a major source of economic productivity and employment globally and among the top 3 industrial sources of greenhouse gas (GHG) emissions, along with steel and cement. As global demand for chemical products continues to grow, there is an urgency to develop and deploy sustainable chemical production pathways and to reconsider continued investment in current emission-intensive production technologies. This perspective describes the challenges and opportunities to decarbonize the chemical industry via electrification powered by low-carbon electricity supply, both in the near term and long term, and it discusses four technological pathways ranging from the more mature direct substitution of heat with electricity and use of hydrogen to technologically less mature, yet potentially more selective, approaches based on electrochemistry and plasma. Finally, we highlight the key elements of integrating an electrified industrial process with the power sector to leverage process flexibility to reduce energy costs of chemical production and provide valuable power grid support services. Unlocking such plant-to-grid coordination and the four electrification pathways has significant potential to facilitate rapid and deep decarbonization of the chemical industry sector. The chemical industry is a major source of economic productivity and employment globally and among the top 3 industrial sources of greenhouse gas (GHG) emissions, along with steel and cement. As global demand for chemical products continues to grow, there is an urgency to develop and deploy sustainable chemical production pathways and to reconsider continued investment in current emission-intensive production technologies. This perspective describes the challenges and opportunities to decarbonize the chemical industry via electrification powered by low-carbon electricity supply, both in the near term and long term, and it discusses four technological pathways ranging from the more mature direct substitution of heat with electricity and use of hydrogen to technologically less mature, yet potentially more selective, approaches based on electrochemistry and plasma. Finally, we highlight the key elements of integrating an electrified industrial process with the power sector to leverage process flexibility to reduce energy costs of chemical production and provide valuable power grid support services. Unlocking such plant-to-grid coordination and the four electrification pathways has significant potential to facilitate rapid and deep decarbonization of the chemical industry sector.

Resiliency-Driven Multi-Step Critical Load Restoration Strategy Integrating On-Call Electric Vehicle Fleet Management Services

In order to enhance the restoration capability of the distribution system during emergency conditions, a resiliency-driven critical load restoration strategy is propounded in this paper. Electric vehicles (EVs) are considered for the grid-support services to deal with challenges on such occasions, in order to maintain the power supply continuity of critical loads by reducing the number of outage periods. The collaboration between fleet operator and distribution system operator is considered in the proposed scheme, making it possible to direct available EVs to the damaged areas. The random characteristic of the seismic event is captured by generating numerous hazard scenarios using a probabilistic approach with the Monte Carlo Simulation (MCS) technique. Afterwards, the unavailability of overhead distribution branches is determined within the fragility curve concept. Besides, the uncertainties caused by EV mobility are considered by performing learning-based analyses for forecasting the location and amount of EVs in the related zone. The obtained data is processed as input parameters in a mixed-integer linear programming (MILP) framework-based stochastic model. Besides, the conceptually developed interfaces for all stakeholders in the proposed scheme are described in detail for bridging the gap between the theoretical background of the concept and practical real-world implementation.

Local Electricity Markets for Electric Vehicles: An Application Study Using a Decentralized Iterative Approach

Local electricity markets are emerging solutions to enable local energy trade for the end users and provide grid support services when required. Various models of local electricity markets (LEMs) have been proposed in the literature. The peer-to-peer market model appears as a promising structure among the proposed models. The peer-to-peer market structure enables electricity transactions between the players in a local energy system at a lower cost. It promotes the production from the small low–carbon generation technologies. Energy communities can be the ideal place to implement local electricity markets as they are designed to allow for larger growth of renewable energy and electric vehicles, while benefiting from local transactions. In this context, a LEM model is proposed considering an energy community with high penetration of electric vehicles in which prosumer-to-vehicle (P2V) transactions are possible. Each member of the energy community can buy electricity from the retailer or other members and sell electricity. The problem is modeled as a mixed-integer linear programing (MILP) formulation and solved within a decentralized and iterative process. The decentralized implementation provides acceptable solutions with a reasonable execution time, while the centralized implementation usually gives an optimal solution at the expense of reduced scalability. Preliminary results indicate that there are advantages for EVs as participants of the LEM, and the proposed implementation ensures an optimal solution in an acceptable execution time. Moreover, P2V transactions benefit the local distribution grid and the energy community.

Engineering Microgrids With Control Co-Design: Principles, methods, and metrics [Technology Leaders

The vulnerability of electrical grids to natural disasters, physical and cyberattacks, and other potential failures has become an increasingly concerning issue. Microgrids can provide the necessary resilience to critical public and private infrastructures while also offering grid-support services and economic and environmental benefits.

Quantifying the challenge of reaching a 100% renewable energy power system for the United States

Quantifying the challenge of reaching a 100% renewable energy power system for the United States

Post-Event restoration strategy for coupled distribution-transportation system utilizing spatiotemporal flexibility of mobile emergency generator and mobile energy storage system

In this paper, a mixed integer quadratic programming (MIQCP) based post-event distribution restoration framework in response to high impact low probable events is propounded to maintain the power supply continuity of critical loads particularly in emergency conditions. Mobile power sources (MPSs) including mobile energy storage system (MESS) and mobile emergency generator (MEG) are considered as grid-support services in resiliency enhancement strategy due mainly to their spatiotemporal flexibility. Power system constraints are modelled according to the linearized DistFlow equations; on the other hand, traffic congestion is also considered while directing MPSs from staging locations to damaged areas. Repair crew dispatching schedule for maximizing the objective function is provided in a deterministic fashion. Numerical results based on the conducted case studies on modified version of the 15-bus distribution system show the capabilities of the model. Hereby power demand of all critical loads is served after an optimal allocation of MPS even though the damages are not completely cleared in the branches. Further, the decisions are close to what can be realistically expected in practical implementation, that fast-responded resources are exploited depending on traffic-related constraints and economic issues.

A topological overview of microgrids: from maturity to the future

The concept of microgrid (MG) has attracted great attention from the system operators for increasing operational effectiveness as well as providing more reliable, sustainable and economic power system. In this paper, a comprehensive investigation is presented to shine new light on evaluating changes in MG operation from maturity to the future. A great deal of literature studies consisting of the traditional MG architecture, encountered challenges and proposed solutions for overcoming them are all examined in detail. Also, the impact of highly integrated renewable-based energy sources into the power system is analysed by current studies. Moreover, modern MG architecture is extensively investigated from the point of operational flexibility such as energy storage systems (ESSs), combined structure of networked-MGs (NMGs) and demand-side management (DSM). Furthermore, incorporating MG architecture as a gridsupport service in power system resiliency enhancement strategies is investigated with current literature studies. As a result of this detail investigation, it can be deduced that the power system has witnessed radically new changes and outstanding developments in both generation and consumption side. Renewable power sources have been accepted as the major mile stone in the harnessing electricity and there have a strong trend towards penetrating these types of generation units in MG structure. On the other hand, increased concerns about safety problems and challenges in MG have triggered a huge amount of discussions in the literature.

Causative Cyberattacks on Online Learning-Based Automated Demand Response Systems

Power utilities are adopting Automated Demand Response (ADR) to replace the costly fuel-fired generators and to preempt congestion during peak electricity demand. Similarly, third-party Demand Response (DR) aggregators are leveraging controllable small-scale electrical loads to provide on-demand grid support services to the utilities. Some aggregators and utilities have started employing Artificial Intelligence (AI) to learn the energy usage patterns of electricity consumers and use this knowledge to design optimal DR incentives. Such AI frameworks use open communication channels between the utility/aggregator and the DR customers, which are vulnerable to causative data integrity cyberattacks. This paper explores vulnerabilities of AI-based DR learning and designs a data-driven attack strategy informed by DR data collected from the New York University (NYU) campus buildings. The case study demonstrates the feasibility and effects of maliciously tampering with (i) real-time DR incentives, (ii) DR event data sent to DR customers, and (iii) responses of DR customers to the DR incentives.

Local flexibility market framework for grid support services to distribution networks

The increasing volume of distributed resources and user-dependent loads in local networks has increased the concern for congestion and voltage management in distribution networks. To mitigate these issues, the implementation of local flexibility markets has been proposed to assist distribution system operators (DSOs) to manage their networks efficiently. This paper presents the framework of a local flexibility market, including the market participants and their roles. This framework aims to empower DSOs with a market-based instrument for the alleviation of congestion incidents by exploiting the flexibility of local resources. The proposed market aims to provide a tool for the holistic management of distribution networks by trading both reservation and activation of flexibility services, indifferent of the type and the timeline of the needed service. Three market modes are proposed, i.e., long-term, short-term and real-time market, and the interactions among those modes are shown. The operation of the market is explained in detail, including the identification of the needed services, the activation of the market as well as the proposed bidding, clearing and settlement mechanisms. The modelling of the long-term and real-time markets is also presented, along with some indicative simulation results for long-term and real-time services. Finally, the future developments as well as the major conclusions are discussed.

Cooperative optimal power flow with flexible chemical process loads

AbstractPower systems operations involve dispatching generation capacity and ancillary services as required to sustain reliable functioning of the electric grid. The dispatch schedules are determined by solving power flow calculations that incorporate models of the network components in real time as the power grid conditions change. Electricity intensive chemical processes can potentially provide grid support services and a closer coordination between process and grid operations can be beneficial. This cooperation is hindered by the lack of suitable process models, which appropriately protect process information. We propose a means to model the grid‐relevant dynamics of chemical processes, and a framework for embedding such models in optimal power flow calculations. We present an extensive case study concerning cooperative demand‐response operation for an electricity‐intensive chemical process, chlor‐alkali production. The results indicate significant flexibility and economic benefits both at the utility and at the end‐user levels.

Cascaded- and Modular-Multilevel Converter Laboratory Test System Options: A Review

The increasing importance of cascaded multilevel converters (CMCs), and the sub-category of modular multilevel converters (MMCs), is illustrated by their wide use in high voltage DC connections and in static compensators. Research is being undertaken into the use of these complex pieces of hardware and software for a variety of grid support services, on top of fundamental frequency power injection, requiring improved control for non-traditional duties. To validate these results, small-scale laboratory hardware prototypes are often required. Such systems have been built by many research teams around the globe and are also increasingly commercially available. Few publications go into detail on the construction options for prototype CMCs, and there is a lack of information on both design considerations and lessons learned from the build process, which will hinder research and the best application of these important units. This paper reviews options, gives key examples from leading research teams, and summarizes knowledge gained in the development of test rigs to clarify design considerations when constructing laboratory-scale CMCs.

Optimal Virtual Power Plant Management for Multiple Grid Support Services

A hierarchical control architecture is proposed for the optimal day-ahead commitment of multiple grid support services within a virtual power plant (VPP). The day-ahead optimization considers pricing and cost data to determine the commitment schedule, and a robust Model Predictive Control (MPC) approach is included to minimize the unbalance fees during real-time operations. The multi-level control has been demonstrated experimentally using a hybrid test system, where the VPP is formed of a commercial 240 kW, 180 kWh battery energy storage system (BESS), while the additional assets are modelled in a real-time digital simulator (RTDS). Two case studies are analyzed: the first assumes a purely-electrical VPP, with a single connection to the public network; the second involves a multi-energy approach, with the introduction of a gas-supplied Combined Heat and Power unit (CHP). Both winter and summer price scenarios are tested. The results show the superiority of the multiple-service operation compared to providing a single grid support service. For example, the net revenue is increased by 30% (winter) and 7% (summer) when compared to just frequency regulation, and by +99% (winter) and 30% (summer) when compared to only energy arbitrage.

On-Board Single-Phase Integrated Electric Vehicle Charger With V2G Functionality

Mass adoption of electric vehicles (EVs) is contingent on the availability of charging infrastructure. One solution to this issue is the introduction of on-board fast chargers, but such solutions typically require the installation of additional magnetic components that increase EV mass. An alternative approach is the dynamic redeployment of drivetrain components for charging when the vehicle is stationary. This article proposes an on-board single-phase charger that reuses the traction inverter and motor. The system consists of a dual-inverter drivetrain, which affords higher voltage charging compared to conventional systems. In addition, the system is able to operate bidirectionally and operate at any power factor for grid support services with real and reactive power exchange without subjecting the motor to low frequency harmonic currents. Experimental tests demonstrated operation at 19.2 kW using a 110-kW EV motor and a full-scale, state-of-the-art, dual-inverter drive prototype. Measured peak efficiencies of over 97% demonstrate the viability of integrated charging in a real-world scenario.

Data-Driven Learning and Load Ensemble Control

Demand response (DR) programs aim to engage distributed small-scale flexible loads, such as thermostatically controllable loads (TCLs), to provide various grid support services. Linearly Solvable Markov Decision Process (LS-MDP), a variant of the traditional MDP, is used to model aggregated TCLs. Then, a model-free reinforcement learning technique called Z-learning is applied to learn the value function and derive the optimal policy for the DR aggregator to control TCLs. The learning process is robust against uncertainty that arises from estimating the passive dynamics of the aggregated TCLs. The efficiency of this data-driven learning is demonstrated through simulations on Heating, Cooling & Ventilation (HVAC) units in a testbed neighborhood of residential houses.

A Delta-Connected Modular Multilevel STATCOM With Partially-Rated Energy Storage for Provision of Ancillary Services

This paper proposes a delta-connected Modular Multilevel STATCOM with partially rated storage (PRS-STATCOM), capable of providing both reactive, and active power support. The purpose is to provide short-term energy storage enabled grid support services such as inertial, and frequency response, either alongside or temporarily instead of standard STATCOM voltage support. The topology proposed here contains two types of sub-modules (SM) in each phase-leg: standard sub-modules (STD-SMs), and energy storage element sub-modules (ESE-SMs) with a dc-dc interface converter between the SM capacitor, and the ESE. A control structure has been developed that allows energy transfer between the SM capacitor, and the ESE resulting in active power exchange between the converter, and the grid. Injecting <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\text{3}^{rd}$</tex-math></inline-formula> harmonic current into the converter waveforms can be used to increase the amount of power that can be extracted from the ESE-SMs, and so reduce the required ESE-SMs fraction in each phase-leg. Simulation results demonstrate that for three selected active power ratings, 1 pu, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\frac{2}{3}$</tex-math></inline-formula> pu, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\frac{1}{3}$</tex-math></inline-formula> pu, the fraction of SMs that need be converted to ESE-SMs are only 69%, 59% & 38%. Thus, the proposed topology is effective in adding real power capability to a STATCOM without a large increase in equipment cost.