Participation In Ancillary Services Research Articles

A comprehensive framework for predicting electric vehicle's participation in ancillary service markets

AbstractElectric vehicles (EVs) have significant potential to offer unused capacity in ancillary service markets, providing unique opportunities for market operators to utilise these resources. EVs have a rapid response and high availability, making them a good fit for the frequency containment reserve (FCR) market. However, EV aggregators (EVAGs) must aggregate capacity blocks due to the limited capacity of individual EVs. An application of a supervised machine learning method named XGBoost is suggested to help EVAGs predict the amount of EV participation in the FCR market. The objective is to forecast yearly involvement using data from only a single week, using the game theory method SHapley Additive exPlanations (SHAP) to minimise extra data. The proposed strategy helps aggregators and uses feature engineering to select EVs with high potential to boost revenue. The proposed framework is effective in predicting EV performance in the DK‐2 market, as shown by multiple analyses.

Modelling of Solar Thermal Electricity Plants in the POSYTYF Research Project for an Extensive Integration of Renewable Energy Sources

This article presents a simplified simulation model of a concentrated solar thermal power plant developed in the framework of the European research project POSYTYF (POwering SYstem flexibiliTY in the Future through RES). Increasing the share of Renewable Energy Sources (RES) in modern power grids is of critical importance for the transformation of the energy markets worldwide. However, the stability of the grid and the limited participation in ancillary services of RES limit their use, especially when high penetration is expected from them. A solution to overcome these issues is to increase the share of so-called dispatchable RES (hydropower, biomass, concentrating solar thermal power). The main objective of the POSYTYF project is to group several renewable and non-renewable energy sources into a Dynamic Virtual Power Plant (DVPP). The simplified simulation model of a parabolic-trough solar thermal power plant developed consists of sub-models for the solar field, thermal energy storage system and power block and it has been validated with real DNI profiles and production data of a commercial STE plant in Spain. The differences between the simulation and real data of daily net production for the days analysed are lower than 1%. Parts of this paper were published as journal article “Using time-windowed solar radiation profiles to assess the daily uncertainty of solar thermal electricity production forecasts”, Journal of Cleaner Production, Volume 379, Part 2, 2022. Mario Biencinto, Lourdes González, Loreto Valenzuela (https://doi.org/10.1016/j.jclepro.2022.134821).

An open-data based framework to estimate EV demand and attainable flexibility and application to the case of Singapore

The electrification of the transport sector will have consequences that go beyond the transportation system itself, for example by also impacting power distribution networks and their operation. This both comes with potential threats and opportunities, such as sharp stochastic load changes that further burden already stressed power transmission and distribution systems. But opportunities arise from the flexibility that the vehicles batteries charging process could provide, eventually helping in better managing such infrastructures. This paper presents a framework that leverages openly available vehicles mobility-related data sources to define and simulate scenarios of electric mobility adoption, quantifying load and available demand flexibility in such scenarios. In particular, demand flexibility potential is assessed according to different charging strategies, including vehicle to grid, and different vehicles stationing behaviours. The goal is to provide a framework that is potentially of use to a set of different stakeholders, from city planners to grid operators and aggregators, to assess flexibility sources towards several applications. These range from planning for resilience by evaluating flexibility sources for provision of services to the grid, to enabling participation in ancillary services markets. The framework leverages a set of data sources, particularly carparks’ occupancy data in the form of live availability information at high temporal resolution.The framework is applied to a test case in the city of Singapore with the goal to estimate the consumption flexibility available from electric vehicles in carparks throughout the city. Maps that visualize the availability of such flexibility both in space and time are generated based on this assessment.

A comparative analysis of charging strategies for battery electric buses in wholesale electricity and ancillary services markets

The application of smart charging to battery electric buses can provide opportunities for bus operators to reduce the operational costs of their bus fleet. This research aims to create insight into the impact of different charging strategies for battery electric bus fleets on charging costs and the grid load. It proposes a novel framework to model the charging process of battery electric buses for different charging strategies: charging-on-arrival, peak-shaving, day-ahead market optimization with and without vehicle-to-grid (V2G) functions, including the provision of Frequency Containment Reserves (FCR) and automatic Frequency Restoration Reserves (aFRR) for system balancing in ancillary services markets. Model simulations are conducted to compare the charging costs and grid impact of different charging strategies, using three depots of bus operator Qbuzz in the Netherlands as a case study. Results indicate that the application of smart charging algorithms can considerably reduce charging costs for bus operators. Application of the peak-shaving strategy was found to reduce charging costs by 23–32% compared to the reference case of charging-on-arrival. Charging costs can be further reduced by 6–11% when considering day-ahead market optimization. Participation in ancillary services markets for system balancing is economically attractive for bus operators, particularly in the aFRR market, characterized by a cost reduction potential of 90–¿100% compared to the charging-on-arrival strategy. The grid impact analysis indicates that charging-on-arrival can result in high charging demand peaks, which can be drastically reduced by the application of peak-shaving or day-ahead market optimization charging strategies. However, the provision of aFRR and FCR using the battery electric bus charging process can have a severe impact on the local grid in terms of high peak demand.

Control Strategies of Electric Vehicles Participating in Ancillary Services: A Comprehensive Review

With the emergence of the electric vehicle (EV) era in which the vehicle’s embedded batteries can be exploited for grid support purposes, the role of EVs participating in ancillary services via vehicle-to-grid (V2G) technology cannot be disregarded. Although there are many forms of ancillary services, the most common services delivered by EVs are frequency regulation, frequency contingency, inertia, and voltage regulation. Numerous research studies have been conducted to propose the most effective control strategies for electric vehicle ancillary services (EVASs). In this paper, a comprehensive review is carried out on various control strategies for EVs with respect to their participation in ancillary services. The methodology applied for this review comprises a combination of thematic and historical reviews. The review explores the benefits and limitations of these control strategies and provides a clear understanding of the research gaps in the EVAS area. This review will provide a useful framework and a strong point of reference for researchers working in V2G controls for providing EVASs to a grid. V2G will be a way forward for future grids to accommodate more renewable resources and achieve sustainability pathways.

Optimal Management of an Energy Community with PV and Battery-Energy-Storage Systems

Most of current prosumer-energy-management approaches are focused on economic optimization by self-consumption maximization. Nevertheless, a lack of energy management strategies (EMS) that tackle different interaction possibilities among community-clustered solar plus battery prosumers has been detected. Furthermore, such active prosumer EMS may include participation in ancillary service markets such as automatic frequency restoration reserves (aFRR) through an optimized battery-energy storage-system (BESS) operation, as well as incorporating community-level energy management. In this study, an optimal EMS that includes aggregated aFRR-market participation of five solar plus battery prosumers participating in an energy community (EC), with the aim of reducing total costs of ownership for each individual prosumer is proposed. For its validation, different scenarios have been analyzed. The results show that the proposed EMS allows a levelized cost of energy (LCOE) reduction for all community members with respect to the base-case scenario. Moreover, the most profitable scenario for all prosumers is still the only PV.

Distribution locational marginal price based hierarchical scheduling framework for grid flexibility from virtual energy storage systems

• VESS's are modeled using residential air-conditioners for DSM. • Local ancillary services TSO-DSO coordination scheme for cooperative management in grids. • Hierarchical framework for scheduling aggregated VESS. • Network constrained optimal VESS participation in ancillary service markets. • DSM based practical market model for SOs, maximizing overall market benefits. Maintaining generation and demand equilibrium under large scale renewable energy integration is challenging. Conventional flexibility providers such as grid-scale Energy Storage Systems (ESS) have potential but are costly solutions. With evolving smart grid scenario, aggregated demand response from Thermostatically Controlled Loads (TCLs) is utilized as Virtual Energy Storage Systems (VESS) and considered as innovative and cost efficient solution to provide fast response reserves. VESS units are often scheduled and controlled in a centralized manner considering limited distribution system constraints. However, accurate operational flexibility potential of diversified VESS units for various grid services needs to consider distribution network constraints. It can be fully utilized when Distribution System Operators (DSO) coordinate with Transmission System Operators (TSO) to transfer grid services from VESS of its area. Local ancillary service TSO-DSO coordination scheme ensures in this perspective significant flexibility of TSO and allows both TSO and DSO to access VESS flexibility. For such controlling of diversified VESS units where DSO is responsible for aggregate flexibility and local area balancing, a hierarchical scheduling framework is more practical. In this context, this paper proposes a two-stage hierarchical approach for optimal scheduling of TCLs as VESS to optimize energy and provide grid flexibility. At the lower stage, TCLs are scheduled based on Distribution Locational Marginal Price (DLMP) provided by DSO, while ensuring consumers’ comfort. At the upper level, DSO provides additional flexibility to TSO through optimal scheduling of aggregated VESS considering local ancillary services TSO-DSO coordination scheme. The simulation results indicate that VESS scheduling improves system flexibility by maintaining a generation-demand balance.

Q-Learning-Based Model Predictive Control for Energy Management in Residential Aggregator

This article presents a demand response scheduling model in a residential community using an energy management system aggregator. The aggregator manages a set of resources, including photovoltaic system, energy storage system, thermostatically controllable loads, and electrical vehicles. The solution aims to dynamically control the power demand and distributed energy resources to improve the matching performance between the renewable power generation and the consumption at the community level while trading electricity in both day-ahead and real-time markets to reduce the operational costs in the aggregator. The problem can be formulated as a mixed-integer linear programming problem in which the objective is to minimize the operation and the degradation costs related to the energy storage system and the electric vehicles batteries. To mitigate the uncertainties associated with system operation, a two-level model predictive control (MPC) integrating <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -learning reinforcement learning model is designed to address different time-scale controllers. MPC algorithm allows making decisions for the day-ahead, based on predictions of uncertain parameters, whereas <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -learning algorithm addresses real-time decisions based on real-time data. The problem is solved for various sets of houses. Results demonstrated that houses can gain more benefits when they are operating in the aggregate mode. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners:</i> Residential buildings besides commercial and public sectors are among the building sectors responsible for high-energy consumption. Numerous measures have been considered to construct more energy-efficient buildings, such as implementing new effective insulation materials and increasing the utilization ratio of sunlight. However, there is also a need for practical solutions to reduce the greenhouse gas emissions and avoid power peak from the residential sector. Under this situation, energy management system aggregator (EMSA) offers the opportunity to exploit the flexibility potential of various houses and other available distributed energy resources, promoting their participation in ancillary services and benefiting from rewards and lower energy bills. In this article, an innovative and comprehensive model predictive control-based scheduling optimization that considers uncertainties of renewable resources and weather conditions is formulated. It can be considered as a practical solution in order to optimally control the operation of a residential community. We proposed a curtailable demand response (DR), where customers agree to participate in DR programs defined by the EMSA in response to price changes.

Spatial-economic framework to estimate small biomass plant remuneration in reactive power markets

With growing shares of renewable power generation, ancillary services such as reactive power become increasingly important to balance power systems. In this work, we provide a framework to assess the suitability of small biomass power plants, i.e., less than 5 MW, for reactive power markets. The proposed methodology consists of two-stages and considers the spatial relationship between the biomass plants’ location and the regional transmission system and tests different plant remuneration scenarios. In the first step, the reactive energy potentials available from biomass plants are quantified and located through spatial analysis, considering locational factors that influence the economic value that these plants bring to the ancillary services markets. Second, a financial analysis is performed to determine how each biomass plant’s electricity bill can be reduced through participation in ancillary services markets. Resulting layers of geographic information provide system operators and policymakers with the available reactive power potential and required financial compensation of biomass plant owners close to the electricity transmission system. The methodology is applied to a case study in São Paulo, Brazil, using data from sugar and alcohol plants installed near the regional transmission system’s 138 kV lines to assess the implementation of a seasonal reactive power market.

Modeling an electric vehicle parking lot with solar rooftop participating in the reserve market and in ancillary services provision

Electric vehicles (EVs) are seen as a crucial tool to reduce the polluting emissions caused by the transport and power systems (PS) sector and the associated shift to a cleaner and more sustainable energy sector. The combination of EVs and solar photovoltaics (PV) in PS, specifically through the aggregation of EVs in parking lots (PLs), may improve the reliability and flexibility of the PS, assisting the power network in critical moments. This work proposes a novel aggregator agent in the energy system which is an EV charging station with an installed PV system. In this work, an optimal operation strategy for the solar-powered EV PL (EVSPL) operation is presented. The model optimizes the EVSPL's participation in various energy and ancillary services markets, including the effects of capacity payments. The results show that the EVSPL leads to higher profits. The EVSPL's participation in ancillary services is highly influenced by the prices. The results of this work show that this novel agent can actively participate in the energy system in an economically viable manner while respecting the technical constraints of the network and providing important ancillary services to the system operator.

The Research on System of Charging Load Aggregator Participating in Market Transaction based on Internet of Vehicles

The electric vehicle market has gained rapid growth in various countries. Electric vehicles will be one of the largest loads in the future power grid. This paper sorts out the typical models of charging load aggregators’ participation in ancillary services, demand response, and valley drop electricity curve tracing, and formulates the trading strategies of charging load aggregators in the electricity direct trading market, ancillary service market and electricity derivatives market. And then the strategy of aggregators participating in multi market coordination is proposed. Based on the contribution of aggregators to participate in market transactions, considering the willingness of different entities to participate in charging load aggregated transactions, a reasonable game model as well as a fair and reasonable value sharing mechanism is established to achieve the transmission of market trading dividends and promote the charging load aggregators to participate in the electricity market transactions.

Day-ahead optimal bidding of microgrids considering uncertainties of price and renewable energy resources

Unpredictable faults always reduce the stability and reliability of the electrical system. The increasing use of renewable energy sources (RES) in recent decades has exacerbated power system problems. Microgrids (MG) participation in Ancillary Services (AS) market is a suitable solution for the optimal performance of power systems in these conditions. MGs can also maximize their profits by participating in the AS market. In this paper, the optimal stochastic bidding strategy in joint energy and AS (regulation up and regulation down, spinning reserve and non-spinning reserve) market is modeled. Uncertainties of wind speed and solar radiation are modeled using Weibull and Beta probability distribution functions (PDFs) and probability of call AS is computed for all available AS. Therefore, the risk of the bidding strategy is controlled using conditional value at risk (CVaR). ERCOT market simulation has been carried out in order to determine the participation of each generator in all of the mentioned markets for different prices of energy and also to present the bidding curve, based on real-world data.

A Critical Review of Demand Response Products as Resource for Ancillary Services: International Experience and Policy Recommendations

Demand response is a key element of future power systems due to its capacity to defer grid investments, improve demand participation in the market, and absorb renewable energy source variations. In this regard, demand response can play an important role in delivering ancillary services to power systems. The lack of standardization and ancillary services programs prepared for traditional generators have blocked the participation of demand in these services. Nowadays, increasing needs to ensure the security of supply, renewable fluctuations, and information and communication technology advances are boosting the interest in demand response products to deliver ancillary services. While countries have had lengthy experience with these programs, others are starting from almost zero to develop these programs. To our knowledge, no analysis or standardized comparison exists of the different parameters and prices of demand response in ancillary services among different countries. Our study reviews more than 20 power systems around the world and their programs to classify them according to standard demand response parameters. At the end of the paper we discuss the main characteristics and prices that face demand response in ancillary services markets and a series of policy recommendations to policymakers to improve the deployment on demand participation in ancillary services.

A Novel Framework for Optimizing Ramping Capability of Hybrid Energy Storage Systems

Hybrid Energy Storage System has been widely applied in aerospace, electric vehicle, and microgrid applications. The advantages are that they include complimentary technologies with both high power and energy capabilities. HESS have the potential to be useful to the bulk power systems, for example to increase the value of energy produced by variable generation resource through enabling participation in ancillary service markets. Actualizing the benefits of HESSs requires optimizing the combined ramping capability of aggregated HESS resources. To provide quality ancillary service, source of HESSs locating at multiple sites of variable generation resource must be optimally sized and cohesively controlled. Optimizing aggregated resources can be formulated as an optimization problem. Successfully solving this problem not only requires using effective solution methods but also depends on accurately and rapidly setting the necessary parameters in the problem formula. This article proposes a novel framework with double-layer structure to solve the optimization problem of aggregating ramping capability. Program developed on the upper layer focuses on solving the optimization of aggregating ramping capability among multiple HESSs and is compatible with most existing optimization algorithms. Program on lower layer of the framework targets at optimizing the local control of a single HESS based on a thorough analytics of HESS operation strategy presented in this article. Result of local optimization is also provided to upper layer program for updating the parameters in formula of optimization problem. Real time hardware-in-the-loop test is conducted to verify the performance of the optimization framework developed. The work presented is expected to provide guidance for implementing this framework in practical operation.

Bidirectional smart charging of electric vehicles considering user preferences, peer to peer energy trade, and provision of grid ancillary services

Electric vehicles (EVs) are witnessing increased utilization throughout the world as an alternative to fossil–fueled vehicles. The extensive deployment of EVs can bring challenges to the grid if not properly integrated. Such challenges, however, could be exploited as opportunities if the huge unused capacity of the battery storage in millions of EVs are utilized for ancillary services to the grid and peer–to–peer (PtP) energy trade. Given that there is at least one human user per vehicle, human input must be considered to improve the scheduling process. To that end, this paper presents a new algorithm for bidirectional smart charging of EVs considering user preferences, PtP energy trade, and provision of ancillary services to the grid. The preferences of an EV user as input to the model are embedded into the scheduling process enabling the model to be adaptive to various conditions. Optimization slack variables are utilized for optimal management of EV battery SOC and energy allocation for multiple services. New indices are developed and introduced for quantification of the EV participation in ancillary services and PtP transactions. Real–world data has been collected and utilized for model specification and simulation to make the assumptions more realistic. The efficacy and feasibility of the proposed model are validated using numerical studies. The results indicate that incorporating users’ preferences into the scheduling process would improve the aggregated revenue generated by the EV scheduling model which in turn could offset the charging costs by up to 100%. Further, an increase of about 90% in peer-to-peer energy transactions among EVs and 11% in ancillary services provision to the grid are achieved through the developed user-centric smart charging model.

Model predictive control of microgrids for real-time ancillary service market participation

This study develops two model predictive control approaches to optimize microgrid dispatch, one with participation in real-time ancillary service markets and the other without participation. Results are compared to a baseline logic-based control with case study data taken from a grid-tied 326 kW solar photovoltaic, 634 kW/634 kWh battery, and 350 kW diesel generator microgrid portfolio designed to serve an office building. Annual performance evaluations show that model predictive control algorithms can reduce operating expenses by up to 13.73% when compared to logic-based controls, and through participation in ancillary service markets, model predictive control can reduce net operating expenses by up to 23.47%. Revenue from ancillary service equated to 12.03% of operating costs, with approximately two-thirds of revenue from spinning reserve and one-third from non-spinning reserve. Model predictive control with ancillary services maintained battery state of charge an average of 38.78% higher than batteries dispatched by model predictive control without market participation. This reduced battery cycling losses, minimized battery operation and maintenance expenses, and improved battery lifetime. Sensitivity analyses indicate that model predictive control with more granular time steps and longer prediction horizons changes the dispatch schedule to further reduce operating costs. Intraday simulations indicate that both model predictive control algorithms can adapt to differences in environmental conditions and pricing signals to minimize operational costs. This generalizable finding suggests the inherent modularity, scalability, and robustness of the proposed algorithms can benefit a variety of microgrid configurations and use cases.

FERC Order 841 levels the playing field for energy storage

ABSTRACTFERC Order 841 focused on standardizing electric storage resource (ESR) participation in wholesale energy, ancillary services, and capacity market ruleset, by treating storage as a generation resource. Treatment of storage as a transmission asset (SATA) is up in the air. Expect to see FERC action on ISO/RTO compliance plans in 2019.Energy storage is finally getting its due at the wholesale grid level, thanks to FERC Order 841. All the grid operators within FERC jurisdiction must comply with FERC order to allow “electric storage resource” to participate in their markets. Storage increases capacity value of renewables and decreases variability as the grid makes way for more renewables such as wind and solar. Market Monitors must understand that storage could “withhold” its capacity in early morning ramp hours for evening peak ramp hours, or participate in ancillary services regulation market without bidding into the energy market. At the same time, this resource could be a transmission asset, adding an additional level of complexity. This FERC Order 841 has its own challenges: (i) it treats storage as a generation asset, (ii) some (such as National Association for Utility Regulators) think FERC stepped on their toes, (iii) it does not address all the value stack benefits for storage-like transmission for example, and (iv) aggregation of distribution connected storage is side stepped. So the industry is watching for clear direction from their Federal regulator on this important technology, which is finally getting its due.

Modelling and evaluation of PEM hydrogen technologies for frequency ancillary services in future multi-energy sustainable power systems

This paper examines the prospect of PEM (Proton Exchange Membrane) electrolyzers and fuel cells to partake in European electrical ancillary services markets. First, the current framework of ancillary services is reviewed and discussed, emphasizing the ongoing European harmonization plans for future frequency balancing markets. Next, the technical characteristics of PEM hydrogen technologies and their potential uses within the electrical power system are discussed to evaluate their adequacy to the requirements of ancillary services markets. Last, a case study based on a realistic representation of the transmission grid in the north of the Netherlands for the year 2030 is presented. The main goal of this case study is to ascertain the effectiveness of PEM electrolyzers and fuel cells for the provision of primary frequency reserves. Dynamic generic models suitable for grid simulations are developed for both technologies, including the required controllers to enable participation in ancillary services markets. The obtained results show that PEM hydrogen technologies can improve the frequency response when compared to the procurement with synchronous generators of the same reserve value. Moreover, the fast dynamics of PEM electrolyzers and fuel cells can help mitigate the negative effects attributed to the reduction of inertia in the system.

Valuation of an American Option for the Spanish Secondary Reserve Market Using a Machine Learning Model

This paper presents an original methodology to design a financial product that could enhance the demand side participation in ancillary services, especially for industrial consumers. The financial product consists in an American option on the Spanish secondary reserve market for the following day, where the buyer has the right, but not the obligation to offer part of their capacity to the system operator. Considering this approach, an industrial consumer would receive an economic incentive to offer its flexibility to the system without changing its production planning, paying an upfront premium. The computation of the American option is based on a Monte Carlo simulation approach where the random paths are obtained from a machine learning model. The machine learning model attempts to forecast the 24 hour secondary band prices of the following day using a combination of different algorithms; the output and the error of the model are used as a baseline to perform the Monte Carlo simulation that computes the option value.

The impact of ancillary services in optimal DER investment decisions

Microgrid resource sizing problems typically include the analysis of a combination of value streams such as peak shaving, load shifting, or load scheduling, which support the economic feasibility of the microgrid deployment. However, microgrid benefits can go beyond these, and the ability to provide ancillary grid services such as frequency regulation or spinning and non-spinning reserves is well known, despite typically not being considered in resource sizing problems. This paper proposes the expansion of the Distributed Energy Resources Customer Adoption Model (DER-CAM), a state-of-the-art microgrid resource sizing model, to include revenue streams resulting from the participation in ancillary service markets. Results suggest that participation in such markets may not only influence the optimum resource sizing, but also the operational dispatch, with results being strongly influenced by the exact market requirements and clearing prices.