Provide Ancillary Services Research Articles

Increasing Renewable Energy Penetration on Low-Voltage Networks: An Expert Knowledge Approach

While South Africa is deemed one of the countries with the highest irradiation levels, it still utilises coal as its primary energy source due to its abundance. Due to the world-wide drive towards carbon neutrality, residential, commercial, agricultural, and industrial consumers are considering small-scale embedded generation systems. The National Rationalised Specifications 097-2-3 document specifies the scale of the embedded generation capacity a consumer is allowed to install. However, specifications do not yet make the required provisions for the addition of energy storage. The effective collective management of the grouped small-scale embedded generation systems could provide a high level of energy security and increase the percentage of renewable energy generation in the total energy mix. Potential challenges come into play when considering the stochastic nature of photovoltaic generation and its effect on the storage capacity and the dispersion in load profiles of the residential units typically present on a low-voltage network. This paper contributes by investigating the utilisation of photovoltaic generation in conjunction with storage as the basis for virtual power plant control, with the aim to safely increase renewable energy penetration and improve energy security, all while remaining within the South African low-voltage regulatory limits. A two-level virtual power plant controller is proposed with the dispersed energy storage units as the primary controllable resources and the dispersed photovoltaic generation as the secondary controllable resources. The objective of the controller is to achieve nodal energy management, energy sharing, and ancillary service provision and finally to increase renewable energy penetration. A representative single-feeder low-voltage network is simulated, and test cases of 50% and 75% renewable energy penetration are investigated as the basis for evaluation. The proposed controller architecture proved to maintain network integrity for both test cases. The adaptability of the controller architecture was also confirmed for a changed feeder topology; in this case, it was a multi-feeder topology. Future work is warranted to inform policy on the allowed levels of renewable energy penetration to be based not only on demand but also on the level of energy storage present in a network.

Perceptions towards practicability of humanitarian principles for emergency cases in Ethiopia: case of Tikur Anbessa Specialized Hospital

IntroductionThe health of people is negatively impacted by emergencies and catastrophes, and many lives are lost as a result. The guidelines for humanitarian action during times of crisis, whether brought on by armed war or natural disasters, are provided by the humanitarian principles. The purpose of this study was to evaluate how the staff and clients at Tikur Anbessa Specialized Hospital felt about the applicability of the four humanitarian principles when responding to emergency conditions.MethodologyFacility-based descriptive and explanatory cross-sectional study designs were employed. The study intended to collect data with structured questionnaires from a total of 209 individuals in Tikur Anbessa Specialized Hospital. The participants were selected randomly based on, their willingness. Of the 209 participants, 105 included staff and the rest 104 included clients (patients and caregivers in the emergency room) of Tikur Anbessa Specialized Hospital. The collected data were analyzed using Statistical Package for Social Science version 23.ResultsThe descriptive analysis shows that principles of humanity are practiced better with (mean = 3.5) and principles of impartiality are practiced less with (mean = 3.2) as to the perception of the participants. Correlational analysis was used to examine the relationship between the ages, profession, gender, status, work experience, and familiarity with humanitarian principles, and the results indicated a non-significant correlation between variables, r (209) = 0.01 (2 tailored), “p = ns”. This suggests that familiarity with humanitarian principles or other independent variables does not affect the applicability of humanitarian principles in healthcare settings.ConclusionFrom the results of this study, it can be concluded that the state of being a staff (health care practitioners and those providing ancillary services) and client of Tikur Anbessa Specialized Teaching Hospital has nothing to do with developing a certain perception towards the practicability of the four humanitarian principles (humanity, impartiality, neutrality, and independence).

Dynamic model of integrated electricity and district heating for remote communities

District heating networks offer promising solutions for remote communities, providing centralized heat supply, improved efficiency, and diverse energy sources, especially with existing diesel generation. Hence, this paper bridges gaps in the existing literature by developing comprehensive dynamic models of combined district heating networks within existing electric power networks in remote communities, which allows identifying challenges and benefits of district heating networks for these communities. It is shown that district heating networks allow utilizing waste energy to enable energy exchanges between the electricity and heating systems, enabling the provision of necessary ancillary services for remote microgrids with renewable energy sources. The presented dynamic district heating network model incorporates particular considerations in remote, northern communities such as soil limitations, extreme cold conditions, and piping insulation to minimize heat loss. It also addresses accurate sizing of heat pumps based on realistic thermal load requirements, weather conditions, and consumer profiles, proposing demand management controls to enhance frequency regulation for the integration of variable renewable energy sources. The main contributions of the paper include detailed dynamic modeling for district heating network operation, heat pump demand response control system design, and a comparative analysis between centralized district heating networks and decentralized electric thermal storage units that have been deployed for thermal supply in remote areas. The presented dynamic models are applied, tested, and validated in an existing electric microgrid at Kasabonika Lake First Nation in Northern Ontario, showcasing the role of a potential district heating network in facilitating renewable energy sources integration in isolated microgrids.

Performance optimization of VPP in fast frequency control ancillary service provision

This paper proposes a computation offloading method for performance optimization in the fast frequency control ancillary service (FFCAS) provision of a virtual power plant (VPP). VPP aggregates massive demand-side resources to provide power systems with the FFCAS. It faces excessive communication and computation tasks. Edge computing addresses these issues through computation offloading. Heavy tasks can be partially offloaded to the edge to relieve the burden of the central server. Existing offloading methods, however, are not specific for the VPP. To fill the gap, first, an age of information (AoI) model is introduced to characterize the data flow of an edge-enabled VPP. Next, an AoI-based FFCAS performance evaluation model is proposed considering the impacts of communication delay, communication failures, and computational delay. Then an FFCAS performance optimization model is formulated. It aims to maximize VPP’s profit through communication offloading and the DSR portfolio determination. Simulation results show that the proposed method can efficiently improve VPP’s profit.

Predictive Models for Aggregate Available Capacity Prediction in Vehicle-to-Grid Applications

The integration of vehicle-to-grid (V2G) technology into smart energy management systems represents a significant advancement in the field of energy suppliers for Industry 4.0. V2G systems enable a bidirectional flow of energy between electric vehicles and the power grid and can provide ancillary services to the grid, such as peak shaving, load balancing, and emergency power supply during power outages, grid faults, or periods of high demand. In this context, reliable prediction of the availability of V2G as an energy source in the grid is fundamental in order to optimize both grid stability and economic returns. This requires both an accurate modeling framework that includes the integration and pre-processing of readily accessible data and a prediction phase over different time horizons for the provision of different time-scale ancillary services. In this research, we propose and compare two data-driven predictive modeling approaches to demonstrate their suitability for dealing with quasi-periodic time series, including those dealing with mobility data, meteorological and calendrical information, and renewable energy generation. These approaches utilize publicly available vehicle tracking data within the floating car data paradigm, information about meteorological conditions, and fuzzy weekend and holiday information to predict the available aggregate capacity with high precision over different time horizons. Two data-driven predictive modeling approaches are then applied to the selected data, and the performance is compared. The first approach is Hankel dynamic mode decomposition with control (HDMDc), a linear state-space representation technique, and the second is long short-term memory (LSTM), a deep learning method based on recurrent nonlinear neural networks. In particular, HDMDc performs well on predictions up to a time horizon of 4 h, demonstrating its effectiveness in capturing global dynamics over an entire year of data, including weekends, holidays, and different meteorological conditions. This capability, along with its state-space representation, enables the extraction of relationships among exogenous inputs and target variables. Consequently, HDMDc is applicable to V2G integration in complex environments such as smart grids, which include various energy suppliers, renewable energy sources, buildings, and mobility data.

BESS and the ancillary services markets: A symbiosis yet? Impact of market design on performance

The evolution of ancillary services markets (ASM) and balancing products is ongoing. The aim of the evolution is to integrate the products over the national boundaries and to open the ASM to distributed energy resources (DERs). Among DERs, battery energy storage systems (BESS) are increasing their importance. In this work, we investigate by means of numerical simulations the effect of different evolutions in the regulatory framework on the performance of a BESS providing ancillary services. The analyzed regulatory barriers are selected based on ongoing evolution in EU market design. The following parameters are involved: power vs energy-intensive services, symmetry of procurement, time definition and distance to delivery. The considered case study is a BESS associated to a large-scale energy district including load, a cogeneration plant, and a PV plant. Results are given in terms of energy flows, economics, operational efficiency, and reliability of service provision. Where both reliability, provision of large flexibility volumes, and good economic performance are achieved, we say that there is a symbiosis between BESS and the markets. This way, the best ASM arrangements abating regulatory barriers for BESS are defined.

Empowering offshore wind with ES-STATCOM for stability margin improvement and provision of grid-forming capabilities

Offshore wind power plants (WPPs) play a pivotal role in achieving a CO2-neutral electricity sector. However, their grid connection presents significant challenges and costs. High-voltage alternating current (HVAC) offers a cost-effective solution, but it may necessitate the installation of a static synchronous compensator (STATCOM) at the onshore bus to ensure stability and compliance with connection requirements. Integrating energy storage into the STATCOM (ES-STATCOM) enables the provision of ancillary services, such as inertial response and frequency support, which require active power. This paper utilizes the generalized Nyquist criterion to demonstrate that operating the ES-STATCOM with grid-forming control enhances the stability margin of the grid-connected WPP when compared to operating it with grid-following control. Furthermore, it illustrates through network frequency perturbation (NFP) plots that the overall WPP, comprising grid-following wind turbines and a grid-forming ES-STATCOM, imparts grid-forming behavior at the grid-connection point.

Dynamic ancillary services: From grid codes to transfer function-based converter control

Conventional grid-code specifications for dynamic ancillary services provision such as fast frequency and voltage regulation are typically defined by means of piece-wise linear step-response capability curves in the time domain. However, although the specification of such time-domain curves is straightforward, their practical implementation in a converter-based generation system is not immediate, and no customary methods have been developed yet. In this paper, we thus propose a systematic approach for the practical implementation of piece-wise linear time-domain curves to provide dynamic ancillary services by converter-based generation systems, while ensuring grid-code and device-level requirements to be reliably satisfied. Namely, we translate the piece-wise linear time-domain curves for active and reactive power provision in response to a frequency and voltage step change into a desired rational parametric transfer function in the frequency domain, which defines a dynamic response behavior to be realized by the converter. The obtained transfer function can be easily implemented e.g. via a proportional-integral (PI)-based matching control in the power loop of standard converter control architectures. We demonstrate the performance of our method in numerical grid-code compliance tests, and reveal its superiority over classical droop and virtual inertia schemes which may not satisfy the grid codes due to their structural limitations.

Analyzing impact of network constraints on feasible operation region of the radial distribution networks

Recently, there has been lot of research on finding the feasible operating region (FOR) of the active distribution network (ADN) to provide ancillary services to the transmission grid. The current focus of research is on finding methods that can accurately determine the shape of the FOR and have fast computation time. However, there is little focus on analyzing how network constraints impact the shape of the FOR. This paper attempts to address this issue and provides a mathematical derivation of how network constraints, such as cable loading and voltage limits, would influence FOR. The approach is based on a linear distflow mathematical analysis. The relations derived in the analysis are verified with numerical simulation on radial low-voltage (LV) and medium-voltage (MV) networks. The results show that cable loading constraints cut the FOR in a circular way and voltage constraints cut the FOR with a straight line of slope −ϵ, which is the ratio Rn/Xn of the path from root node to the node under analysis of the radial distribution network.

Optimal trading with regime switching: Numerical and analytic techniques applied to valuing storage in an electricity balancing market

Accurately valuing storage in the electricity market recognizes its role in enhancing grid flexibility, integrating renewable energy, managing peak loads, providing ancillary services and improving market efficiency. In this paper we outline an optimal trading problem for an Energy Storage Device trading on the electricity balancing (or regulating) market. To capture the features of the balancing (or regulating) market price we combine stochastic differential equations with Markov regime switching to create a novel model, and outline how this can be calibrated to real market data available from NordPool. By modelling a battery that can be filled or emptied instantaneously, this simplifying assumption allows us to generate numerical and quasi analytic solutions.We implement a case study to investigate the behaviour of the optimal strategy, how it is affected by price and underlying model parameters. Using numerical (finite-difference) techniques to solve the dynamic programming problem, we can estimate the value of operating an Energy Storage Device in the market given fixed costs to charge or discharge. Finally we use properties of the numerical solution to propose a simple quasi-analytic approximation to the problem. We find that analytic techniques can be used to give a benchmark value for the storage price when price variations during the day are relatively small.

Optimal scheduling of electric vehicle aggregators for frequency regulation and cost efficiency in renewable-powered grids

The growing integration of intermittent renewable energy sources (RESs), such as wind and solar, into power grids presents significant operational challenges, particularly in maintaining grid stability and managing generation forecasting errors. This study underscores the critical role of Electric Vehicles (EVs) as a flexible load in providing ancillary services, especially for secondary frequency regulation, to address these challenges. Moreover, a novel scheduling model for EV aggregators is introduced that aims to optimize frequency regulation and cost efficiency. This model strategically manages the State of Charge (SOC) to ensure EV owner satisfaction while minimizing operational costs. Employing Mixed-Integer Linear Programming (MILP), solved via CPLEX-GAMS in both deterministic and stochastic scenarios, the proposed approach evaluates various EV integration strategies, demonstrating potential reductions in charging costs and enhancing owner participation by considering parking constraints. This research highlights the importance of innovative grid management solutions in the era of renewable energy proliferation, offering significant insights into cost-effective and reliable energy practices. The results in the deterministic show that by considering the EV parking constraint, the total charging cost will be increased by about 20%. Also, by applying the proposed strategy to the down-regulation value of 18%, the total cost of EV charging is decreased to 11,400 $ which shows about a 5% decrement in comparison with the down-regulation value of 32%.

Enhancing Grid Stability and Sustainability: Energy-Storage-Based Hybrid Systems for Seamless Renewable Integration

In the face of escalating global energy demand, the shift towards renewable energy sources has emerged as a sustainable solution. However, the integration of renewable energy into the electrical grid introduces challenges such as intermittent and instability. The concept of energy-storage-based hybrid systems, which combines renewable energy systems with energy storage, presents a promising approach to overcome these hurdles. These hybrid systems enhance grid stability by ensuring a consistent energy supply, compensating for the variable output of renewable energy sources, and providing ancillary services to the grid. Furthermore, they pave the way for a more resilient and reliable energy infrastructure, fostering the seamless integration of a substantial share of renewable energies. This paper offers a comprehensive exploration of energy-storage-based hybrid systems, discussing their structure, functioning, and the pivotal role they play in bolstering grid stability and promoting the unobstructed integration of renewable energy sources.

Decentralized control strategy of thermostatically controlled loads considering the energy efficiency ratio and measurement error correction

The elimination of traditional thermal generators and the increase of renewable energy source access reduced the inertia of the power system, and thermostatically controlled loads have the potential to provide ancillary services to the power system. This paper introduces a decentralized control strategy for thermostatically controlled loads (TCLs), which focuses on assisting large-scale TCLs in providing frequency support to power systems while considering energy efficiency ratio and measurement error correction. Initially, the nonlinear relationship between heat flow and TCLs’ power is modeled using a polynomial relationship, and the coefficients are determined through a data-driven method to enhance accuracy in TCLs modeling and assess their regulation potential precisely. Subsequently, a decentralized control strategy tailored for power system frequency regulation is developed. To account for measurement errors in TCLs controllers, a measurement error back-correction method is proposed. Simulation examples demonstrate the effectiveness of the proposed control strategy in achieving accurate modeling and control of TCLs and reducing power system frequency fluctuations.

Assessing the Provision of Ancillary Services by PV-BES Systems Considering BES Capacity Degradation

Assessing the Provision of Ancillary Services by PV-BES Systems Considering BES Capacity Degradation

Stability analysis and application of a synchronverter-based control approach to DFIG-based wind energy conversion systems

This work proposes an enhanced control approach relying on the synchronverter concept for wind energy conversion systems (WECSs) based on the doubly-fed induction generator (DFIG). It consists of incorporating a virtual filter resistor into the control loop of the synchronverter, which is associated with the grid-side converter (GSC) aiming at emulating the behavior of a synchronous generator when providing ancillary services such as frequency support. A comprehensive state-space representation is derived to demonstrate that the virtual filter resistor is essential to ensure stability. After the proper adjustment of the model, it is possible to verify the effectiveness of the synchronverter-based control applied in the IEEE 14-bus modified system employing parameters obtained from a real power plant. The results show that the application of the introduced method results in less severe frequency dips and faster transient response during frequency support even when compared with the case in which only synchronous machines are connected to the grid.

Load shifting versus manual frequency reserve: Which one is more appealing to thermostatically controlled loads in Denmark?

This paper investigates demand-side flexibility provision in two distinct forms of manual Frequency Restoration Reserve (mFRR) services and load shifting, and explores which one is financially more appealing to Thermostatically Controlled Loads (TCLs) in Denmark. While mFRR is an ancillary service required for the system and being bought by the system operator, load shifting is an individual act of the TCL in response to the variation of hourly electricity prices. Without loss of generalization, we consider a supermarket freezer as a representative TCL, and develop a grey-box model describing its temperature dynamics using real data from a supermarket in Denmark. Taking into account price and activation uncertainties, a stochastic mixed-integer linear program is formulated to maximize the flexibility value from the freezer. Examined on an ex-post simulation based on Danish spot and balancing market prices in 2022, load shifting shows to be almost as profitable as mFRR provision, although it could be more consequential for temperature deviations in the freezer. This indicates the need for regulatory measures by the Danish system operator to make the attraction of ancillary service provision more obvious for TCLs in comparison to the load shifting alternative.

Stochastic economic model predictive control for renewable energy and ancillary services trading with storage

The provision of renewable-based ancillary services (AS) is paramount for the stable operation of power systems featuring high renewable penetration. The combined operation of storage with renewables enables aggregators to increase the reliability of their energy and frequency control AS offers. Existing dispatch strategies for the supply of both energy and AS are usually rule-based or involve tracking a technical reference signal, hence economically suboptimal for aggregators. This study proposes a comprehensive decision framework in which first a stochastic optimization derives bids on energy and AS markets, then stochastic economic model predictive control (SEMPC) optimizes the storage dispatch in order to maximize the profit and minimize the storage degradation, as a function of the predicted renewable production and the expected AS activation. The framework is applied to a real-world case study where storage combined with wind power participates in the energy market, the frequency containment market and the frequency restoration reserve market. The SEMPC-based approach increases market revenue by 15% compared to a standard reference-tracking MPC, and reduces storage degradation by 23%. The stochastic formulation lowers the sensitivity of the economic objectives to renewable energy forecast errors, compared to deterministic approaches.

Unmet Needs for HIV Ancillary Services Among Persons With Diagnosed HIV Aged 55 years and Older.

Approximately 2 in 5 persons with HIV (PWH) in the United States are aged 55 years or older. HIV ancillary services, such as case management and transportation services, can help older PWH remain engaged in care. We used data from the Medical Monitoring Project (MMP) to describe the prevalence of unmet needs for ancillary services among persons with diagnosed HIV aged 55 years or older. MMP is an annual cross-sectional study that reports representative estimates on adults with diagnosed HIV in the United States. We used MMP data collected during June 2019-May 2021 to calculate weighted percentages of cisgender men and cisgender women with HIV aged 55 years or older with unmet needs for ancillary services, overall and by selected characteristics (N = 3200). Unmet need was defined as needing but not receiving a given ancillary service. We assessed differences between groups using prevalence ratios (PRs) and 95% confidence intervals with predicted marginal means. Overall, 37.7% of cisgender men and women with HIV aged 55 years or older had ≥1 unmet need for ancillary services. Overall, 16.6% had ≥1 unmet need for HIV support services, 26.9% for non-HIV medical services, and 26.7% for subsistence services. There were no statistically significant differences in unmet needs for services by gender. The prevalence of ≥1 unmet need was higher among non-White persons (PR range: 1.35-1.63), persons who experienced housing instability (PR = 1.70), and those without any private insurance (PR range: 1.49-1.83). A large percentage of older PWH have unmet needs for ancillary services. Given the challenges that older PWH face related to the interaction of HIV and aging-associated factors, deficits in the provision of ancillary services should be addressed.

Coordinated, Centralized, and Simultaneous Control of Fast Charging Stations and Distributed Energy Resources

The growing penetration of fast charging stations (FCSs) to electric vehicles (EVs) and distributed energy resources (DERs) in the electrical power system brings technical issue changes in the voltage profile throughout grid nodes and feeder current overload. The provision of ancillary services by DERs and FCSs arises as an appealing solution to reduce these adverse effects, enhancing the grid hosting capacity. The control of microgrids is essential for the coordinated implementation of these services. Although microgrid control is widely applied to DERs, few studies address the coordinated control of DERs and FCSs to obtain benefits for the electrical power system. This paper proposes a coordinated and simultaneous control of DERs and FCSs based on the power-based control (PBC) strategy, efficiently exploiting FCSs in a microgrid model previously unaddressed in the literature. The results show that, with the coordinated control of DERs and FCSs, the control of the power flow in a minigrid (MG) is achieved both in moments of high generation and in moments of high load, even with the maximum operation of DERs. This method allows for the maintenance of voltage levels within values considered acceptable by technical standards (above 0.93 pu). The maintenance of voltage levels is derived from reducing the overload on the point of common coupling (PCC) of the minigrid by 28%, performing the peak shaving ancillary service. Furthermore, the method allows for the control of zero power flow in the PCC of the minigrid with the upstream electric grid in periods of high generation, performing the ancillary service of valley filling. The method performs this control without compromising vehicle recharging and power dispatch by DERs.

Data-driven approach for day-ahead System Non-Synchronous Penetration forecasting: A comprehensive framework, model development and analysis

This article presents a comprehensive, innovative, and data-driven approach for predicting System Non-Synchronous Penetration (SNSP) levels. It consists of iterative steps that involve data analytics and forecasting model development to overcome the challenges associated with forecasting, such as data mining or overfitting. The approach starts by defining the problem domain and identifying relevant features using the Pearson correlation method. The framework ensures that all forecasting models carry out data pre-processing uniformly. The hyperparameters, understood as adjustable external factors not learned during the training process that affect the performance and predictive ability of the forecasting model are optimized using the random search algorithm to enhance the models’ performance. The study compares the performance of classical models, such as Random Forest and Light Gradient Boosting, with advanced machine learning-based models, such as Feed-forward, Gate Recurrent Unit, Short-Term Long Memory, and Convolutional Neural Network. Data from the Irish power system is chosen as a case study. The results indicate that the Feed-forward model produces the lowest errors. It has a Mean Absolute Error of about 4.09, a Root Mean Squared Error of 5.37 and a Mean Absolute Percentage Error of 18.17% respectively. This systematic and practical approach can be applied to other regions with similar challenges. This study also highlights the potential of advanced machine learning-based models in improving SNSP forecasting accuracy. The approach is beneficial for network and market operators, and ancillary service providers in smart grid network operations, with a 15-minute resolution. It provides a promising direction for future research in this area.