Large-scale Renewable Energy Integration Research Articles

A new optimization approach considering demand response management and multistage energy storage: A novel perspective for Fujian Province

Due to the inherent uncertainty and the mismatch between renewable energy output and load demand, renewable energy-based system optimization is increasingly important in China. Therefore, electricity demand response (EDR) is critical to the stability and efficiency of an integrated renewable energy system (IRES). The customers' demand response under the time-of-use (TOU) mechanism is the key to the entire process. However, how to optimize integrated renewable energy system based on accurately identifying the electricity demand response participation of customers is one of the main issues at this stage. To solve the problem, this paper presents a novel approach for integrated renewable energy system optimization considering electricity demand response management and multistage energy storage systems from the perspective of Fujian Province, China. To enable large-scale renewable energy integration, the original output is processed using the Hampel identifier (HI)-Savitzky-Golay filter (SGF) technology. A machine learning (ML)-based identification model is developed for EDR to determine whether the customers are participating or not. Driven by the time-of-use mechanism, customers can make dynamic adjustments based on the Logistics function. After that, an integrated renewable energy system model optimization strategy is proposed based on satisfying customers’ electricity demand response. Throughout the optimization process, the multistage energy storage system plays a vital role in the residual fluctuation absorption for renewable energy filtering, the dynamic adjustment process of the demand response, and the integrated renewable energy system optimization. Through extensive case studies and discussions, it is demonstrated that random forest (RF) and support vector machine (SVM) reach 80 % identification accuracy with a 20 % testing ratio. According to the optimization approach proposed in this paper, the minimum operation cost can be calculated to be 9.336*106 CNY. The methodology presented in this paper establishes a special relationship between electricity demand response identification and dynamic adjustments, and provides an optimization model for Fujian Province, China, which is a reliable reference for future work.

Large-scale integration of renewable energies by 2050 through demand prediction with ANFIS, Ecuador case study

The growing reliance on hydroelectric power and the risk of future droughts pose significant challenges for power systems, especially in developing countries. To address these challenges, comprehensive long-term energy planning is essential. This paper proposes an optimized electrical system for 2050, using Ecuador as a case study. For forecasting electricity demand, a Neuro-Fuzzy Adaptive Inference System is employed, utilizing real historical data. Subsequently, the EnergyPlan software constructs a long-term energy consumption model, exploring three scenarios based on Ecuador's energy potential. The first scenario represents a 'business as usual’ approach, mirroring the current trend in the Ecuadorian electricity system. In contrast to the second scenario, it encompasses a broader range of renewable sources, including offshore wind, pumped storage, biomass, and geothermal energy. The third scenario extends the second one by incorporating demand response systems, such as vehicle-to-grid and hydrogen-to-grid technologies. In terms of novelty, this study highlights the innovative use of the Neuro-Fuzzy Adaptive Inference System for demand forecasting, along with a comprehensive exploration of multiple scenarios to optimize the electrical system. Research findings indicate that the integration of these new renewable energy sources not only reduces electricity import costs but also ensures surplus electricity production. Consequently, it is anticipated that the 2050 electricity system will reduce its dependence on hydroelectric energy while adopting photovoltaic and wind energy with penetration rates of 65 %, 11.2 %, and 9 %, respectively. This transition will be facilitated by a pumped storage system with a 28 % penetration rate and enhanced connectivity with neighboring countries, enabling the seamless integration of electric and hydrogen vehicles.

Risk-averse energy management of hydro/thermal/pumped storage complementarily operating with wind/solar: Balancing risk, cost and carbon emission

Large-scale grid integration of variable renewable energy is crucial for achieving decarbonized development. However, this integration requires frequent regulation of flexible power sources for complementary operation, which can lead to wear-and-tear and fatigue damage to key components. This poses potential risks to flexible power sources. Existing studies have primarily focused on limiting unit startups, while have neglected the risk of frequent power regulation. Thus, this work proposes a risk-averse short-term scheduling method for a Wind-Solar-Cascade hydro-Thermal-Pumped storage hybrid energy system to balance frequent regulation risk, cost, and carbon emission: (1) a risk-averse short-term scheduling model is proposed, considering multilayer constraints; (2) a multi-objective hybrid African vulture optimization algorithm is proposed to effectively solve the scheduling problem including continuous and discrete variables. A case study in the Songhua River basin, China shows that: (1) compared with traditional models, the proposed model reduces the risk by 31.4% and enhances the comprehensive performance in balancing the three objectives by 22.4%; (2) the proposed algorithm performs robustness and search capability advantages, with improvements of 33.01% and 21.44% respectively, in solving the problem of challenging constraints and mixed decision variables. Overall, this work contributes to enhancing the management of large hybrid energy systems.

Power investment and transmission network expansion in China and its neighbouring countries towards carbon neutrality

Transnational power cooperation contributes to promoting the large-scale use of renewable energies in Asia, but how to develop the structure of power transmission network deserves careful investigation. This paper thoroughly investigates the structure of power investment and transmission network in China and neighbouring countries, aiming to promote the large-scale use of renewable energies around Asia. Various power-generation and power-transmission technologies are considered in detail, and transnational power interconnection risk cost and carbon emission cost are embedded into a bottom-up optimization model. The future power-generation structures and networks installed under various policy scenarios are analysed in detail, and the corresponding economic, carbon emission reduction and renewable energy consumption benefits are evaluated. The results show that power transmission between China's internal regions and neighbouring countries can save $56.7 billion for the entire power system through the large-scale grid integration of renewable energy. The installed capacities of wind power, solar power, and hydropower renewable energy power generation could be increased by 141 GW, 163 GW, and 58 GW, respectively. The construction of transnational transmission lines from Central Asia to Northwest China, Mongolia to North China, the Russian Far East to Northeast China, and Northeast China to South Korea could effectively promote the utilization of renewable energy in the Russian Far East, Mongolia and Central Asia and further reduce carbon emissions in China, Japan and South Korea.

Geothermal energy exploitation in an island-based 100% renewables strategy. Case study of Tenerife (Spain)

The large-scale integration of non-dispatchable renewable energy must be carefully planned, especially in poorly robust electrical energy systems. Tenerife is the most populated of the islands in the Canary Archipelago (Spain) and has an isolated electrical system. It is envisaged for the horizon of 2040 that the island will have a high contribution of non-dispatchable renewable energies (fundamentally wind and solar photovoltaic). Tenerife also has exploitable high enthalpy geothermal resources. An energy planning model is implemented in this paper based on which different scenarios with high non-dispatchable renewable energy penetration in the island's electrical system are simulated. Geothermal energy is also incorporated in the simulations as a dispatchable renewable energy source, with the power required sized according to the hypotheses of each scenario. The results are compared according to different technical and economic parameters. An evaluation is also made of the influence that the capacity of the electrical system to take on higher non-dispatchable renewable power and the presence of energy accumulation systems might have on the minimization of losses in the non-dispatchable renewable energy capacity factor. The results of the simulated scenarios show that geothermal energy could meet up to 28.8% of the island's electrical energy demand, increasing the stability, flexibility and supply guarantee of system. A 100% renewable system could be possible through the incorporation of this energy source and the planning of energy accumulation systems. In the case study, the economic saving for Spain's electrical system could exceed 400 €million per year.

Comprehensive benefit evaluations for integrating off-river pumped hydro storage and floating photovoltaic

Integrating pumped hydro storage with wind-solar power is an effective method for large-scale integration of renewable energy. The integration of floating photovoltaics with pumped hydro storage solves the issues of unstable output from photovoltaic generation and limited land resources. However, traditional pumped hydro storage has limitations in terms of siting and structure, resulting in environmental issues and opposition when integrated with floating photovoltaics. To overcome these limitations, a novel integrated generation system that combines off-river pumped hydro storage and floating photovoltaic is proposed. Additionally, an optimized scheduling model is developed for the hybrid energy power system with the objective of minimizing operating costs. Finally, a case study of a large clean energy project in Qinghai, China is conducted to quantitatively evaluate the comprehensive benefits of this integrated system in hybrid renewable energy power generation from economic, technical, and environmental perspectives. The results indicate that the combination of off-river pumped hydro storage and floating photovoltaics provides improved technological, economic, and environmental benefits. Specifically, under the premise of meeting the power supply rate and ensuring power transmission reliability, the integration of off-river pumped hydro storage and floating photovoltaic generation system surpasses the traditional integration of pumped hydro storage and photovoltaics in multiple aspects. The integrated system can increase power transmission by 26%, decrease investment costs by 15.04%, lower operating costs by 31.75%, save land use by 4.78 km2, and evaporation reduction efficiency by 44.6%. The OFPHS + FPV operation scheme can provide a reference for the development of pumped hydro storage and photovoltaic industries in resource-constrained areas and provide technical guidance to improve the flexibility of regional power systems.

A decision-making method for the operation flexibility enhancement of hybrid cascaded MTDC

To enable the integration of large-scale renewable energy, hybrid HVDC technology, which combines the technical advantages of LCC-HVDC and VSC-HVDC, is being gradually deployed in the power grid nowadays. The operation of the Wu-dong-de Hybrid DC Project and the Jian-su Hybrid cascaded MTDC Project has proved its advantages. However, for the simultaneous application of different converter station technologies in the system, the control strategies become complex. Issuing appropriate control instructions to ensure system stability according to operational requirements is an issue that cannot be ignored in decision-making. Even under abnormal conditions, when the topology changes due to various failure scenarios, reasonable decision-making and precise control instruction definitions are required. To achieve flexible planning of the MTDC system, this paper presents a decision-making method for control strategies of a hybrid cascaded MTDC system, which analyzes the control strategy combinations selected for normal and abnormal conditions of the MTDC system. In addition, a control instruction calculating method and decision-making process for precise control in normal and abnormal control conditions is proposed. Simulation results based on a five-terminal hybrid cascaded MTDC in PSCAD/EMTDC have verified the effectiveness of the proposed method.

A combined wind-PV power prediction method based on clustering prescreening

Due to the volatility caused by the high uncertainty of renewable energy, the large-scale integration of renewable energy into the electric power grid brings huge challenges to the scheduling and operation of the electric power system. Effective power prediction of power source can not only relieve the pressure of power system peaking and frequency regulation, but also improve the accuracy of decision-making. So as to make the most of the information between wind and photovoltaic power source, this paper puts forward a wind-PV joint power forecasting model. First, the raw power data is pre-screened by clustering to select the closer connection between wind and PV power; next, the screened data are passed through the upper CNN structure respectively after pre-processed. Then, the outputs from CNN modules are fed together into the prediction layer, which include LSTM layers and fully connected layers. Finally, the proposed wind-PV joint prediction model is verified by simulation experiments.

Evaluation of Renewable Energy Accommodation Capacity Considering Multiple Flexibility Resources of Source-Load-Storage

The utilization of multiple flexibility resources (MFR), such as source-load-storage (SLS-sides), plays a vital role in promoting the integration of large-scale grid-connected renewable energy (RE). Analyzing the renewable energy accommodation capacity (REAC) and assessing the impact of flexibility resources on enhancing REAC is vital for the coordinated development of SLS sides. This study focuses on the technical mechanisms of thermal power (TP) deep peak regulation, load demand response (DR), and energy storage (ES). Considering the operational constraints of these MFR, an evaluation model for REAC is established. The model is a multi-objective optimization framework that encompasses the maximized installed capacity of RE while minimizing the comprehensive expenses of the power system. A hierarchical decoupling solution method is used to solve the evaluation model, obtaining the Pareto optimal solution set for RE installed capacity and power system operating cost. Subsequently, a fuzzy membership function is applied to select a compromise solution, quantitatively characterizing the REAC. The case study results demonstrate that by effectively coordinating the “SLS-sides” MFR, the REAC of the IEEE-RTS96 system can be improved from 1, 758 MW to 2, 731 MW, indicating a remarkable enhancement of 55.35%.

A Joint Clearing Model of Energy-Frequency Modulation Based on Flexible Block Order

The large-scale integration of renewable energy into the grid has led to a gradual diversification of power generation trading units on the power-side, with varying operational characteristics, costs, and trading needs among diverse power generation trading units. Traditional power system clearing models face challenges. Block order is a bidding type that allows for multiple volume–price combinations. At the same time, the randomness and volatility of renewable energy poses challenges to the safe and stable operation of the system. Building a power system clearing model that meets the diverse and flexible needs of the power system has become an important consideration. Therefore, this paper considers the design of three flexible energy blocks: a sustaining block, flux block, and adjustment block to meet the differential needs of diverse trading units and establishes a flexible block order clearing model. Secondly, it establishes a joint clearing model of electrical energy and frequency modulation (FM) to ensure the stable and reliable operation of the system, and solves the model based on relevant constraints to determine the winning electrical energy price and FM price. The CPLEX and Yalmip algorithms are used to solve the model. Finally, a case study was conducted based on an improved IEEE 14-bus system. The results showed that the model proposed in this paper can satisfy the differential needs of diverse trading units, effectively improve the renewable energy consumption capacity, and reduce the prices of the energy market and frequency modulation market. Also, the standard deviation of the net load of the system is relatively low, which improves the reliability of the power system’s operation.

Adaptive H∞ event-triggered load frequency control in islanded microgirds with limited spinning reserve constraints

Using an islanded microgrid (MG) with large-scale integration of renewable energy is the most popular way of solving the reliable power supply problem for remote areas and critical electrical users. However, compared with traditional power systems, the limited spinning reserves and network communication bandwidth may cause weak frequency stability in the presence of stochastic renewable active outputs and load demand fluctuations. In this paper, an adaptive event-triggered control (ETC) strategy for a load frequency control (LFC) system in an islanded MG is proposed. First, a bounded adaptive event-triggered communication scheme is designed. This not only saves on network resources, but also ensures that the control center has a sensitive monitoring ability for the MG operating status when the frequency deviations have been effectively damped. Secondly, by fully considering the spinning reserve constraints and uncertain communication delays, the LFC system is described as a nonlinear model with saturation terms. Design criteria for ETC parameters are strictly deduced based on Lyapunov stability theory. Finally, an ETC parameter optimization algorithm based on random direction search is developed to reconcile the bandwidth occupancy and control performance. The effectiveness of the proposed method is verified in an MG test system.

Efficient and Robust Power Flow Algorithm for Asynchronous Grids Coupled Through a VSC-MTDC System and Its Probability Analysis

With the interconnection of asynchronous ac grids through a voltage source converter-based multiterminal direct current (AGs/VSC-MTDC) system and the large-scale integration of renewable energy, it is important to resolve the power flow calculation problems efficiently and robustly for secure and economic operation of such system under stochastic context. Therefore, an improved sequential algorithm for deterministic power flow (DPF) of AGs/VSC-MTDC system is proposed first. This enhances the conventional manner of updating power losses of VSCs, which leads to a one-shot DPF solution of the ac/dc grid, thus boosting the efficiency; while the coupling of ac/dc systems is considered to remain high accuracy. In combination with the efficient DPF method for each sample, probabilistic power flow (PPF) analysis of the AGs/VSC-MTDC system is performed by using Zhao's three-point estimate method (Z-3PEM) with much fewer samples, thus achieving a higher efficiency with good accuracy. In addition, the PPF analysis of AGs/VSC-MTDC systems being more prone to the poor robustness in stochastic scenarios is effectively addressed, benefitting from the decreased nonconvergence of improved DPF and fewer extreme samples of Z-3PEM. Finally, the effectiveness and performance of works are verified through the results on an 11-terminal VSC-MTDC system with three asynchronous ac grids modified from different IEEE test systems.

Holistic approach for microgrid planning for e-mobility infrastructure under consideration of long-term uncertainty

Integrating renewable energy sources in sectors such as electricity, heat, and transportation has to be planned economically, technologically, and emission-efficient to address global environmental issues. Microgrids appear to be the solution for large-scale renewable energy integration in these sectors. The microgrid components must be optimally planned and operated to prevent high costs, technical issues, and emissions. Existing approaches for optimal microgrid planning and operation in the literature do not include a solution for e-mobility infrastructure development. Consequently, the authors provide a compact new methodology that considers the placement and the stochastic evolution of e-mobility infrastructure. In this new methodology, a retropolation approach to forecast the rise in the number of electric vehicles, a monte-carlo simulation for electric vehicle (EV) charging behaviors, a method for the definition of electric vehicle charging station (EVCS) numbers based on occupancy time, and public EVCS placement based on monte-carlo simulation have been developed. A deterministic optimization strategy for the planning and operation of microgrids is created using the abovementioned methodologies, which additionally consider technical power system issues. As the future development of e-mobility infrastructure has high associated uncertainties, a new stochastic method referred to as the information gap decision method (IGDM) is proposed. This method provides a risk-averse strategy for microgrid planning and operation by including long-term uncertainty related to e-mobility. Finally, the deterministic and stochastic methodologies are combined in a novel holistic approach for microgrid design and operation in terms of cost, emission, and robustness.The proposed method has been tested in a new settlement area in Magdeburg, Germany, under three different EV development scenarios (negative, trend, and positive). EVs are expected to reach 31 percent of the total number of cars in the investigated settlement area. Due to this, three public electric vehicle charging stations will be required in the 2031 trend scenario. Thus, the investigated settlement area requires a total cost of 127,029 €. In association with possible uncertainties, the cost of the microgrid must be raised by 80 percent to gain complete robustness against long-term risks in the development of EVCS.

An extended SFR model based fast frequency security assessment method for power systems

The large-scale integration of renewable energy and the increasing access to power electronic equipment lead to the low inertia of power grid, which raises severe challenges to frequency security. The low inertia makes power system more sensible to power disturbances, and therefore, evaluating frequency stability is crucial. To address this issue, a fast frequency security assessment method for power grid based on the extended SFR (system frequency response) model is proposed. In this method, an extended SFR model is established based on the characteristics of future new power systems, which are comprised of multiple frequency response sources, including induction motor load, renewable energy, and other asynchronous resources. To obtain the parameters of the proposed model, the principle and applicability of model parameter aggregation methods are explained. Considering the difficulty of obtaining all the required information in real-world situations, the parameters are obtained by parameter identification method through optimization algorithms. Finally, the effectiveness of the proposed extended SFR model is verified in the WSCC-9 bus system and the parameter identification method is validated in a regional power grid. The calculation time is less than 0.03s and the accuracy is more precise than conventional SFR. The results demonstrate that the proposed extended SFR model and parameter identification method show characteristics of both rapidity and accuracy in judging the security of power systems.

Quantifying flexibility provisions of the ladle furnace refining process as cuttable loads in the iron and steel industry

The large-scale integration of renewable energy into power grids brings new problems and challenges to the flexible and stable operation of power systems. Providing flexibility from the industrial load side is an effective way to maintain the balance between power supply and demand in a power grid. Ladle furnaces (LFs) in the iron and steel industry consume copious power resources yet can provide flexible potential in changing power consumption rates. If the flexibility of LFs can be quantified, the iron and steel sites can response the demand signals from the power grid by adjusting their production plans. However, the real-time regulation capability of LFs in the refining process has not been clearly quantified. To fill in the research gaps, an evaluation model to quantify the provisions of flexibility of LFs, as cuttable loads, is proposed. The regulation capacity of LFs is evaluated, and the electricity costs before and after power adjustments are compared. The results of a case study shows that the maximum cuttable load can reach 23.3 MW, and the maximum load-cutting process duration can reach 34 min in the production cycle. The electricity cost may increase by 232 yuan under the background of the time-of-use electricity price and decrease by 797 yuan under the background of the peak electricity price.

Modeling and operation optimization of hydrogen-based integrated energy system with refined power-to-gas and carbon-capture-storage technologies under carbon trading

As a promising solution to realize the energetic complementarity and energy cascade utilization, integrated energy system with a large-scale integration of renewable energy garners considerable attention recently. However, the renewable energy curtailment and uncertainty become two prominent challenges. To the end, we establish a hydrogen-based integrated energy system (HIES) operation framework comprising a refined power to gas (P2G) and carbon capture system (CCS) under carbon trading. Subsequently, a Wasserstein metric based distributional robust optimization method (WDRO) is developed to address wind power uncertainty. Finally, numerical analysis is performed. Simulations results show that: (1) the proposed approach can realize a low-carbon electric energy supply and consumption mode and satisfies the hydrogen demand through internal equipment of the system; (2) compared with the benchmark, the proposed model reveals outstanding economic, environmental and energy performance by obtaining 31.81% decline in operation cost, a 20.19% mitigation in CO2 emission, 7.89% reduction of wind power curtailment and a higher energy efficiency; (3) the validation of WDRO model is demonstrated by the data-driven and robustness test. Moreover, the model exhibits advantages due to its trade-off between conservativeness and computational complexity. The optimization results provide a good reference for the HIES investment strategy and government carbon pricing decision.

Fast frequency response constrained electric vehicle scheduling for low inertia power systems

Reduction in System Inertia (SI) and Primary Frequency Response (PFR) driven by the large-scale integration of uncertain and variable Renewable Energy (RE) sources disturbs the post-fault frequency dynamics. This necessitates the identification of potential solutions that can restrict high Rate of Change of Frequency (RoCoF). Electric vehicles (EVs), due to their fast response capability and instantaneous power delivery, are a viable source for the Fast Frequency Response (FFR) service. Potential assessment of EVs for FFR service is required to restrict high RoCoF and maintain secure and reliable system operation in real-time. The main contribution of this work is to assess the potential of EVs for FFR support through both Grid-to-vehicle (G2V) and Vehicle-to-grid (V2G) modes. In this work challenges like EVs aggregation, related netload uncertainty characterization, and post-fault frequency dynamics are modelled in a stochastic security-constrained scheduling framework. Further, a case study is carried out to investigate the role of EVs and their techno-economic impact on the overall system: (a) only in dumb charging mode and (b) participating in FFR service by scheduling aggregated EVs for both G2V and V2G with different level of RE integration. Numerical results indicate that the aggregated EVs scheduling can provide FFR support of around 71%, by simultaneously reducing system operating cost by 62% and 87% reduction in RE curtailment.

A robust flexibility evaluation method for distributed multi-energy microgrid in supporting power distribution system

Today’s power system is facing various challenges brought by large-scale renewable energy (RE) integration, which brings higher demand for flexibility. With the energy network gradually showing its distributed structural characteristics, multi-energy microgrids (MEMG) become an important component to effectively utilize distributed energy sources and supplement the flexibility of power distribution system (PDS). To effectively harness the operational flexibility of distributed MEMGs, we propose in this paper an evaluation method to quantify the flexibility capability of MEMG. A virtually established MG flexibility bus (MG-FB) is endowed with MG flexibility parameters (MG-FPs), which can reflect the flexibility characteristics of MEMG. To consider the impact of operational uncertainty on MG-FPs, a two-stage adaptive robust optimization (ARO) model is proposed, which can be solved by the C&CG algorithm. The results of a typical test system show the influence of system configuration, operator’s risk preference, and other factors on the values of MG-FPs. Besides, we illustrate the effectiveness and applicability of the proposed framework in modeling and quantifying the operational flexibility of MEMG to support the operation of the upstream network.

Dynamic pricing and energy management of hydrogen-based integrated energy service provider considering integrated demand response with a bi-level approach

Integrated energy system with a large-scale integration of renewable energy has witnessed an enormous growth recently. Besides, traditional electric demand response has gradually developed into integrated demand response (IDR). However, the renewable energy curtailment becomes a prominent problem. In addition, a reasonable trading mode between integrated energy service provider (IESP) and demander is required to ensure the benefits of both sides. To the end, the operation framework of an IESP comprising power to gas, fuel cell (FC) and carbon capture system (CCS) is established in the paper. Then the bilevel energy trading mode between IESP and users is proposed, wherein the upper level considers the IESP profit maximization while the lower level is to minimize consumers' cost. By utilizing Karush-Kuhn Tucker optimality condition and strong duality theory, the two-level model is recast into a tractable single-level mixed integer programming model. The simulation results show that: corresponding to the models with removing bilevel trading, IDR, FC and CCS, as well as CCS, 1) the formulated price can achieve peak cutting and valley filling, as well as improve the flexibility of the system operation. 2) the IESP and users can take benefit in the proposed model. For IESP, profit experiences an increase with a rate by 20.41 %, 3.2 % -16.8 % and − 15.9 %. For demander, cost witnesses a decline by 4.07 %, 3.69 %, 1.12 % and 0.89 %. 3) In terms of environmental benefit, the proposed model can not only achieve a carbon emissions mitigation but also maintain a relatively reasonable and acceptable economic advantage.

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.