Grid Services Research Articles

Spatial Aggregation of Local Flexibility – Horizon2020 project experiences

With the growth of renewables, the increased interconnection of European grids, the development of local energy initiatives, and the specific requirements on TSO–DSO cooperation as set forth in the different Network Codes and Guidelines, TSOs and DSOs face new challenges that will require greater coordination. The aforementioned measures encourage procurement of services at both the transmission and the distribution level, recognizing that this will enable more efficient and effective network management and will increase the level of demand response and the capacity of renewable generation. Digitalization is a key driver for coordination and active system management in the electricity grid, enabling TSOs and DSOs to optimize the use of distributed resources and ensure a costeffective and secure supply of electricity. It also empowers endusers to become active market participants, supporting selfgeneration and providing demand flexibility. To support the transformation, the INTERRFACE project, started in 2019, will design, develop and exploit an Interoperable pan-European Grid Services Architecture (IEGSA) to act as the interface between the power system (TSO and DSO – transmission system operator, distribution system operator) and the customers, and allow the seamless and coordinated operation of all stakeholders to use and procure common services. This paper describes the approach of one INTERRFACE demonstration, the spatial aggregation of local flexibility and its realization that contributes providing a clear market approach to include local constraints into the already well-established and working wholesale energy market solutions

A Dynamic Optimal Battery Swapping Mechanism for Electric Vehicles Using an LSTM-Based Rolling Horizon Approach

This paper proposes a new approach for optimal operation of an Electric Vehicle (EV) battery-swapping station (BSS) based on Rolling-Horizon optimization (RHO). The BSS has several swapping bays such that each can accommodate an EV for swapping single or multiple battery units. The proposed BSS model considers serving different types of EVs using a heterogeneous battery stock. The charging of the depleted batteries (DBs) is performed using continuously controlled variable chargers which makes it more flexible for providing grid services. While previous studies focused on day-ahead modeling of BSSs, our study considers BSS dynamic scheduling. The goal is to maximize the daily profit using an RHO mechanism to provide optimal swapping and charging/discharging processes. The problem is defined as mixed-integer nonlinear programming (MINLP), then it’s linearized into a mixed-integer linear problem (MILP) to reduce the computational complexity. To predict the EV’s swapping demand, a long short-term memory (LSTM) recurrent neural network is utilized as a time series forecasting engine. The proposed model is validated through a set of case studies comparing the LSTM-based RHO mechanism versus unscheduled operation and day-ahead scheduling. Simulation results demonstrate that the proposed dynamic scheduling mechanism increases the profit between 10% and 25.7% compared to the day-ahead scheduling. Furthermore, the number of EVs served using the proposed approach increases between 11% and 14% compared to the day-ahead model.

Electric Water Heaters for Transactive Systems: Model Evaluations and Performance Quantification

Electric water heaters (EWHs) are opportune appliances for implementing demand-side control. EWH models serve as a fundamental step toward accurately estimating EWH flexibility potential and designing proper control strategies. Existing studies have adapted numerous modeling approaches in evaluating the potential of EWHs for a variety of grid applications. This article presents an analytical study that evaluates the performance of state-of-art EWH models in terms of accuracy and computational complexity for adaptation in evaluation studies for transactive systems. The work also proposes a novel transactive control strategy that optimally utilizes the thermal inertia of EWHs for providing grid services. The performance of the control strategy and the impact of modeling accuracy are evaluated for device-level and feeder-level use cases using the IEEE 123 node distribution system. The simulation results illustrate the effectiveness of the control strategy in reducing the feeder demand during the peak period by 15% and also quantifying the impact of using simplified modeling approaches for determining the potential of EWHs for providing grid services.

Comparison of Li-ion battery chemistries under grid duty cycles

Li-ion batteries are the most widely deployed battery energy storage system (BESS) today but understanding the benefits and cost-effectiveness for a wide range of grid services needs to be fully validated to further expand the market. Hence, various Li-ion battery chemistries currently deployed must be evaluated and compared in terms of performance, lifecycle, economics, and safety under grid services using standardized testing protocols. In this work, commercially available cylindrical cells with four different chemistries from major Li-ion battery manufacturers are subjected to standardized tests developed by the U.S. Department of Energy Office of Electricity (DOE-OE) for frequency regulation, peak shaving, and electric vehicle drive cycles. The results of 15 months of continuous grid service tests on different chemistries are presented, analyzed, and compared in terms of capacity retention, resistance, open-circuit voltage (OCV), cyclic voltammetry (CV), incremental capacity (dQ/dV), differential voltage (dV/dQ), and AC impedance.

Long-duration energy storage in a decarbonized future: Policy gaps, needs, and opportunities

The future U.S. electric grid is being transformed with deep decarbonization of generation (i.e., removing or reducing reliance on fossil fuels and replacing them with renewable and clean energy resources), which in practice is not achievable without a dramatic increase in the reliance on long-duration energy storage (LDES) technologies. Regulators at both the state and federal level are well advised to take steps to address current policy gaps, build frameworks that will enable a greater role for LDES to contribute to grid reliability and be fairly compensated for its grid services..Decarbonization by definition is dependent on an increasing reliance on variable renewable energy, primarily wind and solar resources, that needs to be stored for longer durations to maintain electric grid reliability and provide operational flexibility to grid operators. However, despite the growing realization of the need for long-duration energy storage (LDES) technologies, a persistent gap of policy levers at the federal and state level creates a vacuum in terms of defining how and where LDES technologies can be utilized to support the electric grid, along with an inadequate regulatory framework wherein these resources will need to be valued and compensated for the services they can provide. This paper—which is primarily intended for US decision makers, but should be of value for all energy professionals and the general public—addresses policy gaps, needs, and opportunities for LDES that require urgent attention from US-based policymakers at the federal and state level. This paper also provides background information on how the US E&U industry is structured and regulated, along with perspectives on LDES technologies and applications, all of which have direct relevance to the paper’s primary focus on the need for LDES policymaking.DiscussionDespite a generally accepted future need for long-duration energy storage (LDES) technologies that is directly tied to the rapid of renewable resources on the U.S. electric grid, there is a lack of policymaking, market designs, and compensation mechanisms for LDES technologies. Decarbonization (i.e., the goal of removing or reducing reliance on fossil fuels) cannot be achieved at the aggressive levels envisioned without utilizing LDES. Policymakers must take steps now to build frameworks that recognize the unique ways in which LDES will increasingly contribute to grid reliability and resilience, and receive appropriate compensation for the services it provides.Graphical abstract

Home energy management under realistic and uncertain conditions: A comparison of heuristic, deterministic, and stochastic control methods

Home energy management systems (HEMS) have been shown to reduce energy bills and to provide grid services including peak demand reduction and demand flexibility. However, uncertainty in residential energy systems is a significant issue and can reduce the benefits of a HEMS to the homeowner or grid operator. Sources of uncertainty include weather forecasts, predictions of energy-related occupant activities (e.g., hot water draws), and parameter estimation for the building envelope and energy-consuming equipment.This paper tackles the problem of uncertainty by developing a framework that simulates HEMS in uncertain conditions and evaluates the performance of multiple control strategies. A linear, reduced-order residential building model for model predictive control applications is derived and compared to a full-order model. Stochastic model predictive control is shown to perform better than deterministic and heuristic methods when considering realistic forecasts with uncertainty. The framework can evaluate the performance of HEMS in real-world applications, which can help de-risk HEMS deployment.

What Incentives Are Required for Renewables to Provide Redispatch Services? A Simulation in a Multi-Market Setting

Renewable energy sources (RES) can provide valuable flexibility potential for multiple markets and grid services in the future. In this paper, the focus lies on the development of algorithms for an optimal dispatch and bidding of a RES-based virtual power plant (VPP) by considering current short-term and balancing market conditions in Austria, as well as a proposed redispatch market. Specifically, different pricing and bidding strategies for a redispatch market are compared and their feasibility is analyzed. The attractiveness of different pricing models and remuneration mechanisms for redispatch, as well as the influence of the redispatch call probability on the bidding behavior of the VPP, is investigated. The simulation of the bidding behavior is carried out using linear optimization techniques. The paper describes the algorithms as well as the assumptions for market rules in redispatch, balancing and short-term electricity markets. The results show that the probability to be activated for redispatch and thus the required incentives depend highly on the location of the plant. Pre-curtailment of the plant to offer positive redispatch is not recommended as the prices would need to be set at up to 200–300% of the day-ahead price.

How will retired electric vehicle batteries perform in grid-based second-life applications? A comparative techno-economic evaluation of used batteries in different scenarios

The adoption of electric vehicles is increasing in a global trend toward decarbonization, yet the overall sustainability of these vehicles still poses many questions. The sourcing of many critical raw materials in battery production and their poorly-defined end-of-life management are among the underlined environmental challenges. The sustainability of battery placement in the transportation system in this sense needs to be supported by circular end-of-life strategies. While battery recycling for a secondary supply can be more favourable for producers, reuse disposition is regarded as being more environmentally attractive from a holistic lifecycle perspective. Cascading the life of batteries as such to reduce the environmental burdens is considered by both regulatory and industrial bodies. However, the uncertainties in cost breakdown and reliability of repurposed batteries reflected in naïve second-life market alignment and financial justification are barriers to current reuse policies and practices. For batteries with a variable lifetime subject to technical constraints in every application, the financial dynamics which underpin the choices of how and where to deploy them are challenging to investigate. While present studies make static assumptions about battery lifetime and operating costs, this work develops a dynamic investigation by incorporating battery degradation in hosts of different second-life applications into the financial analysis. This goal is approached by estimating the lifetime of the batteries in cascaded applications, and further evaluating their second-life contribution to energy and non-energy services in power systems. Findings indicate that settling on a lower purchase price for retired batteries and reducing the number of energy trading results in a justifiable return-on-investment. For batteries in early-failure vehicles such as 4-years-old, this arrangement is equally profitable compared to new ones when going below 26% of the original battery cell price, yet for older batteries such as 10-year-olds, the best performance is expected when contributing to grid services while keeping the investment value below 15%. • Evaluating the techno-economic viability of reusing EV batteries. • 80% State-of-health is expected for batteries in 12-year-old disposed of vehicles. • Battery life decreases to a quarter when cycling numerously compared to grid-services. • Grid services were found to be the best reuse candidate in the grid-based stationery. • Retired battery values are below 26% and 15% in four and ten-year-old ones.

BBNP: A Blockchain-Based Novel Paradigm for Fair and Secure Smart Grid Communications

As the future energy infrastructure, smart grid aims to overcome the disadvantages of traditional power grid, e.g., low efficiency and unstable service. However, the frequent collection and analysis of the user’s electricity data may bring various security and privacy threats. Besides, the traditional centralized data storage model in the smart grid is prone to the single point of failure. To address these challenges, in this article, for fair and secure smart grid communication, a blockchain-based novel paradigm, named BBNP, is proposed. Specifically, based on the pseudorandom function and auxiliary information generation and sharing technology, a lightweight data aggregation protocol is designed first to protect the user’s data privacy and ensure communication confidentiality. Then, a novel efficient authentication mechanism is proposed to generate and share session keys in a noninteractive way, which is leveraged for MAC authentication to achieve data integrity of the transmitted data. After that, based on the subjective logic reputation model, a blockchain node consensus mechanism is studied to efficiently store smart grid big data and effectively solve the single point failure problem. By constructing the long-term reputation model for consensus nodes (CNs) and integrating batch verification technology, the problems of CN fair selection and scalability of large-scale nodes are solved simultaneously. Finally, the performance evaluation indicates that BBNP outperforms the state-of-the-art similar schemes in computing complexity, communication cost, system availability, and fairness of block generation.

Research on energy sharing ability and adaptability of building Complex: A case study with smart community in Japan

Energy sharing within building complexes depends greatly on the complex load adaptability and policy support. However, most building complex microgrid research concentrates on configuration and operation optimization, rather than energy contributions from different users. The objective of this study is to analyze energy sharing ability and load adaptability for a building complex comprising multiple building types, under transaction liberalization restrictions in different energy markets. The specific research for this study is a smart community in Kitakyushu, Japan, with 49 buildings of different types. The complex's microgrid system in this study comprises photovoltaic, battery, and vehicle to grid services. We obtained six representative typical clusters by reducing the 49 buildings’ dimensionality using principal components and clustering, then Monte Carlo simulation to predict permissible discharge capacities for vehicle to grid (V2G) services. We propose three demand-side liberalization scenarios: self-use, photovoltaic feed-in tariffs (FITs), and free trade; optimizing system configuration under different scenarios using a genetic algorithm. Results confirm that shopping malls achieve best economic benefits with self-use scenario; whereas office building adaptability is more significant for the other scenarios. These results will contribute to user selection and system design for building complex microgrid systems.

Identification and quantification of the flexibility potential of a complex industrial process for ancillary services provision

As the share of variable renewable energy sources in the electricity generation mix increases, so does the need for power system flexibility. Demand side flexibility has been identified as a great opportunity to facilitate the integration of this volatile production. An important part of demand side flexibility is the modulation of the consumption of industrial processes. With the emergence of ancillary services markets, operating industrial processes in a flexible manner might generate additional value. This paper proposes a flexibility assessment tool to quantify the potential of a given industrial plant for grid services provision. We start by formulating the feasible operating region of a given industrial asset, generating a set of operating points and their corresponding power ramping potential. A weekly simulation is then performed which leads to a set of most-observed operating points, themselves leading to a set of potential ancillary services bids from the industrial process.

Exploring energy storage methods for grid-connected clean power plants in case of repetitive outages

Inadequate access and unstable grid service are among the most severe difficulties developing communities face. As a result, these communities depend on fossil fuel-based technology to supply their electrical demands, which has significant socioeconomic consequences for society. The objective of this paper was to investigate greener, more sustainable technologies, such as solar battery storage systems. This research proposed a sizing technique for grid-connected photovoltaic systems with different battery technologies through techno-economic feasibility analysis. In the suggested method, the techno-economic performance of photovoltaic energy systems with five different battery technologies was compared: lead-acid battery, lithium-ion battery, vanadium redox battery, and nickel-iron battery. For different grid outage frequencies, this investigation was conducted (0 to 500 interruptions per year. The key comparison indices considered are the investment cost, renewable energy fraction, and surplus electricity. The grid-connected photovoltaic/nickel-iron battery system was shown to be the most efficient at high main grid failure frequencies.

A Hybrid Model for Analysing Disturbance Propagation in Cyber–Physical Energy Systems

Future power systems are expected to depend more on ICT for essential grid services such as voltage and frequency control, increasing the interdependencies between both systems. Therefore, disturbances from one system could propagate and impact the other, degrading the state of the interconnected system. This paper proposes a formalised hybrid model for analysing the impact and propagation of disturbances in a cyber–physical energy system. The states representing the performance of ICT-enabled grid services are modelled using a finite-state automaton. The impact of power system operational decisions in response to disturbances using these grid services are modelled using an optimisation considering situational awareness. The output from both models is used as input to a hybrid automaton that determines the state of the overall cyber–physical energy system. The model is verified by a proof of concept using state estimation and congestion management as exemplary grid services.

Flexible drinking water pumping to provide multiple grid services

Drinking water distribution networks (WDNs) can be operated as flexible, controllable loads. In this paper, we consider using WDNs to provide local and grid level services simultaneously to the power grid. We formulate a robust water pumping problem to determine the amount of voltage support and frequency regulation that can be provided subject to network constraints while managing power demand uncertainty. We tractably reformulate the problem as a sequential optimization problem and solve for the scheduled water pumping operation, the frequency regulation capacity, and the optimal control policy parameters that update the pump operation based on the frequency regulation signal and power distribution network demand forecast error. We demonstrate our approach through detailed case studies. Additionally, we evaluate the performance of the reformulation and discuss the benefits and trade-offs of WDNs providing multiple services.

(Invited) The Pathway to Widespread Commercialization of Redox Flow Batteries

Large-scale electric-energy-storage (EES) systems have the potential to transform the electric grid. The deployment of recent EES systems have predominately been Li-ion batteries (LiBs), which are being used primarily for Ancillary Services (e.g., frequency regulation) and other grid applications (e.g., load following) that require relatively short discharge durations (i.e., ≤ 4-h at rated power). At these relatively low energy/power ratios, LiBs can be lower cost than the historical EES baseline, which is pumped-storage hydropower (PSH). However, since the cost of most conventional batteries scales linearly with energy capacity, the energy-storage cost ($/kWh) is not necessarily lower for uses that require longer discharge times. For these long-duration energy-storage (LDES) applications (i.e., ≥ 5-h at rated power), EES systems with independent power and energy components, e.g., PSH, redox flow batteries (RFBs), reversible fuel cells, and electrofuels, have an inherent cost advantage, since one can readily increase the energy/power ratio by selectively adding more energy capacity, without the added cost of excessive power components. LDES applications have the potential to be a substantially larger market than short-duration EES; however, the requirements for LDES systems are also inherently more challenging. The primary commercialization challenge is capital cost (i.e., $/kWh installed). In addition to fully decoupled power and energy, RFBs have other unique attributes that make this battery architecture ideally suited for grid-scale LDES, namely: long cycle life even with deep cycles, inherently superior safety, and easy recyclability. Largo Clean Energy (LCE) is a vertically-integrated developer of all-V RFBs (VRFBs), and LCE’s MW-scale product is arguably the most advanced RFB system available on the market today. This talk will primarily focus on: 1) what LCE has already done to substantially reduce the cost of VRFBs, 2) how VRFBs can potentially enable the commercialization of other types of RFB systems, and 3) future opportunities to further reduce the capital cost of a variety of RFB systems. Acknowledgements Many thanks to my flow-battery collaborators at Largo Clean Energy and at Raytheon Technologies Research Center (formerly known as UTRC), as well as many other outstanding colleagues on multiple UTRC-led ARPA-E projects and JCESR collaborations on flow batteries.

Model predictive control for demand flexibility: Real-world operation of a commercial building with photovoltaic and battery systems

Hundreds of studies have investigated Model Predictive Control (MPC) for the optimal operation of building energy systems in the past two decades. However, MPC field tests are still uncommon, especially for small- and medium-sized commercial buildings and for buildings integrated with onsite renewables. This paper describes the implementation and the long-term performance evaluation of an MPC controller in a small commercial building equipped with behind-the-meter photovoltaics and electrochemical batteries. MPC controls space conditioning, commercial refrigeration, and the battery system. We tested two types of demand flexibility applications in the field: electricity bill minimization under time-of-use tariffs and responses to grid flexibility events. Results show that the proposed controller achieves 12% of annual electricity cost savings and 34% peak demand reduction against the baseline, while respecting thermal comfort and food safety. The field tests also demonstrate the ability of the MPC controller to provide a multitude of grid services including real-time pricing, demand limiting, load shedding, load shifting, and load tracking, using the same optimization framework.

Business Model Development for a High-Temperature (Co-)Electrolyser System

There are increasing international efforts to tackle climate change by reducing the emission of greenhouse gases. As such, the use of electrolytic hydrogen as an energy carrier in decentralised and centralised energy systems, and as a secondary energy carrier for a variety of applications, is projected to grow. Required green hydrogen can be obtained via water electrolysis using the surplus of renewable energy during low electricity demand periods. Electrolysis systems with alkaline and polymer electrolyte membrane (PEM) technology are commercially available in different performance classes. The less mature solid oxide electrolysis cell (SOEC) promises higher efficiencies, as well as co-electrolysis and reversibility functions. This work uses a bottom-up approach to develop a viable business model for a SOEC-based venture. The broader electrolysis market is analysed first, including conventional and emerging market segments. A further opportunity analysis ranks these segments in terms of business attractiveness. Subsequently, the current state and structure of the global electrolyser industry are reviewed, and a ten-year outlook is provided. Key industry players are identified and profiled, after which the major industry and competitor trends are summarised. Based on the outcomes of the previous assessments, a favourable business case is generated and used to develop the business model proposal. The main findings suggest that grid services are the most attractive business sector, followed by refineries and power-to-liquid processes. SOEC technology is particularly promising due to its co-electrolysis capabilities within the methanol production process. Consequently, an “engineering firm and operator” business model for a power-to-methanol plant is considered the most viable option.

Building flexibility revenue in modeled future bulk power systems with varying levels of renewable energy

The buildings sector can provide important demand-side flexibility for the integration of renewable energy. With the changing power system and rapid advancement in building energy control technology, technology providers and demand response aggregators need to know what potential revenues could be obtained by providing grid services. We dispatch a normalized and parameterized model of building load shifting against marginal service prices from grid investment and operational models to produce a database of the capacity, energy, and ancillary service revenues (gross value of providing bulk power system services) for a marginal kilowatt-hour (kWh) of shiftable load. The database is geographically disaggregated, hourly, and parameterized so that flexibility value can be estimated for a wide range of building technologies. The database covers the contiguous United States under three 2030 grid scenarios. Given the high degree of uncertainty in such grid projections, the results are perhaps best interpreted in terms of regional climate and grid mixes and are thus potentially applicable in non-U.S. contexts. Upon analyzing the results, we find the monthly mean gross value of building load shifting is 0–38 cents/kWh-day and the daily gross value of shifting the highest-value hour each day can be up to 620 cents/kWh-day. The different revenues obtained by aggregating results in different ways, as well as observed regional and seasonal differences, suggest different building technologies and grid environments might call for demand response program designs and business models.

Sizing of Hybrid Energy Storage Systems Using Recurring Daily Patterns

A hybrid Energy Storage Systems (ESS) consists of two or more energy storage technologies, with different power and energy characteristics. Using a hybrid ESS, both high-frequency and low-frequency power variations can be addressed at the same time. For an accurate sizing of a hybrid ESS, the use of high-resolution data is required. However, high-resolution data over long periods leads to large data sets, which are difficult to handle. In this paper, an improved motif discovery algorithm is introduced to find the most recurring daily consumption patterns within the time series of interest. The most recurring pattern is selected as the representative of the time series for sizing the hybrid ESS. Next, a simple optimization framework is proposed for selecting the cut-off frequency of a low-pass filter, used for allocating the power to different storage technologies. Finally, the proposed sizing approach is applied for sizing a hybrid battery-flywheel ESS at four different low voltage distribution grids in southern Germany using real measurement data. It is demonstrated that a hybrid ESS, with the characteristics derived from the most recurring patterns only, can effectively provide their intended grid services for most of the days during the whole period of the time series.

Offering of Variable Resources in Regulation Markets With Performance Targets: An Analysis

To date, wind and solar power plants have mainly provided energy. Going forward, with the application of appropriate controls, they can offer additional grid services, such as regulating reserves. Additional grid services can present an opportunity for additional value streams to wind and solar power plants. However, the additional value might not be realized when operator-determined performance targets and settlement adjustments for the delivery of regulation capacity are disregarded. This article studies the participation of variable resources in centralized regulation markets. We derive analytical results for regulation offers in terms of quantity and price considering that a variable resource does not want to risk being disqualified as a regulation provider, incurring losses, or foregoing more profitable opportunities. Our analysis suggests that U.S. operator-determined performance targets impose an upper bound on the quantity of a variable resource's regulation offer; and settlement adjustments for the delivery of regulation affect the price at which variable resources offer and the calculations of opportunity costs in case of imperfect regulation delivery.