Abstract:We consider a power system whose electric demand pertaining to freshwater production is high (high freshwater electric demand), as in the Middle East, and investigate the tradeoff of storing freshwater in tanks versus storing electricity in batteries at the day-ahead operation stage. Both storing freshwater and storing electricity increase the actual electric demand at valley hours and decrease it at peak hours, which is generally beneficial in term of cost and reliability. But, to what extent? We analyze this question considering three power systems with different generation-mix configurations, i.e., a thermal-dominated mix, a renewable-dominated one, and a fully renewable one. These generation-mix configurations are inspired by how power systems may evolve in different countries in the Middle East. Renewable production uncertainty is compactly modeled using chance constraints. We draw conclusions on how both storage facilities (freshwater and electricity) complement each other to render an optimal operation of the power system.

Abstract:Community batteries (CBs) are emerging to support and even enable energy communities and generally help consumers, especially space-constrained ones, to access potential techno-economic benefits from storage and support local grid decarbonization. However, the economic viability of CB projects is often uncertain. In this regard, typical feasibility studies assess CB value for behind-the-meter (BTM) operation or wholesale market participation, i.e., front-of-meter (FOM). This work proposes a novel techno-economic operational framework that allows systematic assessment of the different options and introduces a two-meter architecture that co-optimizes both BTM and FOM benefits. A real CB project application in Australia is used to demonstrate the significant two-meter co-optimization opportunities that could enhance the business case of CB and energy communities by multi-service provision and value stacking.

Abstract:The scale of distributed energy resources is increasing, but imperfect business models and value transmission mechanisms lead to low utilization ratio and poor responsiveness. To address this issue, the concept of cleanness value of distributed energy storage (DES) is proposed, and the spatiotemporal distribution mechanism is discussed from the perspectives of electrical energy and cleanness. Based on this, an evaluation system for the environmental benefits of DES is constructed to balance the interests between the aggregator and the power system operator. Then, an optimal low-carbon dispatching for a virtual power plant (VPP) with aggregated DES is constructed, wherein energy value and cleanness value are both considered. To achieve the goal, a green attribute labeling method is used to establish a correlation constraint between the nodal carbon potential of the distribution network (DN) and DES behavior, but as a cost, it brings multiple nonlinear relationships. Subsequently, a solution method based on the convex envelope (CE) linear reconstruction method is proposed for the multivariate nonlinear programming problem, thereby improving solution efficiency and feasibility. Finally, the simulation verification based on the IEEE 33-bus DN is conducted. The simulation results show that the multidimensional value recognition of DES motivates the willingness of resource users to respond. Meanwhile, resolving the impact of DES on the nodal carbon potential can effectively alleviate overcompensation of the cleanness value.

Abstract:As renewable energy continues to be integrated into the grid, energy storage has become a vital technique supporting power system development. To effectively promote the efficiency and economics of energy storage, centralized shared energy storage (SES) station with multiple energy storage batteries is developed to enable energy trading among a group of entities. In this paper, we propose the optimal operation with dynamic partitioning strategy for the centralized SES station, considering the day-ahead demands of large-scale renewable energy power plants. We implement a multi-entity cooperative optimization operation model based on Nash bargaining theory. This model is decomposed into two subproblems: the operation profit maximization problem with energy trading and the leasing payment bargaining problem. The distributed alternating direction multiplier method (ADMM) is employed to address the subproblems separately. Simulations reveal that the optimal operation with a dynamic partitioning strategy improves the tracking of planned output of renewable energy entities, enhances the actual utilization rate of energy storage, and increases the profits of each participating entity. The results confirm the practicality and effectiveness of the strategy.

Abstract:Battery energy storage systems (BESSs) serve a crucial role in balancing energy fluctuations and reducing carbon emissions in net-zero power systems. However, the efficiency and cost performance have remained significant challenges ， which hinders the widespread adoption and development of BESSs. To address these challenges, this paper proposes a real-time energy management scheme that considers the involvement of prosumers to support net-zero power systems. The scheme is based on two shared energy storage models, referred to as energy storage sale model and power line lease model. The energy storage sale model balances real-time power deviations by energy interaction with the goal of minimizing system costs while generating revenue for shared energy storage providers (ESPs). Additionally, power line lease model supports peer-to-peer (P2P) power trading among prosumers through the power lines laid by ESPs to connect each prosumer. This model allows ESP to earn profits from the use of power lines while balancing power deviations and better consuming renewable energy. Experimental results validate the effectiveness of the proposed scheme, ensuring stable power supply for net-zero power systems and providing benefits for both the ESP and prosumers.

Abstract:The concept of utilizing microgrids (MGs) to convert buildings into prosumers is gaining massive popularity because of its economic and environmental benefits. These prosumer buildings consist of renewable energy sources and usually install battery energy storage systems (BESSs) to deal with the uncertain nature of renewable energy sources. However, because of the high capital investment of BESS and the limitation of available energy, there is a need for an effective energy management strategy for prosumer buildings that maximizes the profit of building owner and increases the operating life span of BESS. In this regard, this paper proposes an improved energy management strategy (IEMS) for the prosumer building to minimize the operating cost of MG and degradation factor of BESS. Moreover, to estimate the practical operating life span of BESS, this paper utilizes a non-linear battery degradation model. In addition, a flexible load shifting (FLS) scheme is also developed and integrated into the proposed strategy to further improve its performance. The proposed strategy is tested for the real-time annual data of a grid-tied solar photovoltaic (PV) and BESS-powered AC-DC hybrid MG installed at a commercial building. Moreover, the scenario reduction technique is used to handle the uncertainty associated with generation and load demand. To validate the performance of the proposed strategy, the results of IEMS are compared with the well-established energy management strategies. The simulation results verify that the proposed strategy substantially increases the profit of the building owner and operating life span of BESS. Moreover, FLS enhances the performance of IEMS by further improving the financial profit of MG owner and the life span of BESS, thus making the operation of prosumer building more economical and efficient.

Abstract:Base station (BS) backup batteries (BSBBs), with their dispatchable capacity, are potential demand-side resources for future power systems. To enhance the power supply reliability and post-contingency frequency security of power systems, we propose a two-stage stochastic unit commitment (UC) model incorporating operational reserve and post-contingency frequency support provisions from massive BSBBs in cellular networks, in which the minimum backup energy demand is considered to ensure BS power supply reliability. The energy, operational reserve, and frequency support ancillary services are co-optimized to handle the power balance and post-contingency frequency security in both forecasted and stochastic variable renewable energy (VRE) scenarios. Furthermore, we propose a dedicated and scalable distributed optimization framework to enable autonomous optimizations for both dispatching center (DC) and BSBBs. The BS model parameters are stored and processed locally, while only the values of BS decision variables are required to upload to DC under the proposed distributed optimization framework, which safeguards BS privacy effectively. Case studies on a modified IEEE 14-bus system demonstrate the effectiveness of the proposed method in promoting VRE accommodation, ensuring post-contingency frequency security, enhancing operational economics, and fully utilizing BSBBs ’ energy and power capacity. Besides, the proposed distributed optimization framework has been validated to converge to a feasible solution with near-optimal performance within limited iterations. Additionally, numerical results on the Guangdong 500 kV provincial power system in China verify the scalability and practicality of the proposed distributed optimization framework.

Abstract:Accurate prediction of the state-of-charge (SOC) of battery energy storage system (BESS) is critical for its safety and lifespan in electric vehicles. To overcome the imbalance of existing methods between multi-scale feature fusion and global feature extraction, this paper introduces a novel multi-scale fusion (MSF) model based on gated recurrent unit (GRU), which is specifically designed for complex multi-step SOC prediction in practical BESSs. Pearson correlation analysis is first employed to identify SOC-related parameters. These parameters are then input into a multi-layer GRU for point-wise feature extraction. Concurrently, the parameters undergo patching before entering a dual-stage multi-layer GRU, thus enabling the model to capture nuanced information across varying time intervals. Ultimately, by means of adaptive weight fusion and a fully connected network, multi-step SOC predictions are rendered. Following extensive validation over multiple days, it is illustrated that the proposed model achieves an absolute error of less than 1.5% in real-time SOC prediction.

Abstract:The offering strategy of energy storage in energy and frequency response (FR) markets needs to account for country-specific market regulations around FR products as well as FR utilization factors, which are highly uncertain. To this end, a novel optimal offering model is proposed for stand-alone price-taking storage participants, which accounts for recent FR market design developments in the UK, namely the trade of FR products in time blocks, and the mutual exclusivity among the multiple FR products. The model consists of a day-ahead stage, devising optimal offers under uncertainty, and a real-time stage, representing the storage operation after uncertainty is materialized. Furthermore, a concrete methodological framework is developed for comparing different approaches around the anticipation of uncertain FR utilization factors (deterministic one based on expected values, deterministic one based on worst-case values, stochastic one, and robust one), by providing four alternative formulations for the real-time stage of the proposed offering model, and carrying out an out-of-sample validation of the four model instances. Finally, case studies employing real data from UK energy and FR markets compare these four instances against achieved profits, FR delivery violations, and computational scalability.

Abstract:Photovoltaic (PV) and battery energy storage systems (BESSs) are key components in the energy market and crucial contributors to carbon emission reduction targets. These systems can not only provide energy but can also generate considerable revenue by providing frequency regulation services and participating in carbon trading. This study proposes a bidding strategy for PV and BESSs operating in joint energy and frequency regulation markets, with a specific focus on carbon reduction benefits. A two-stage bidding framework that optimizes the profit of PV and BESSs is presented. In the first stage, the day-ahead energy market takes into account potential real-time forecast deviations. In the second stage, the real-time balancing market uses a rolling optimization method to account for multiple uncertainties. Notably, a real-time frequency regulation control method is proposed for the participation of PV and BESSs in automatic generation control (AGC). This is particularly relevant given the uncertainty of grid frequency fluctuations in the optimization model of the real-time balancing market. This control method dynamically assigns the frequency regulation amount undertaken by the PV and BESSs according to the control interval in which the area control error (ACE) occurs. The case study results demonstrate that the proposed bidding strategy not only enables the PV and BESSs to effectively participate in the grid frequency regulation response but also yields considerable carbon emission reduction benefits and effectively improves the system operation economy.

Abstract:The vehicle-to-grid (V2G) technology enables the bidirectional power flow between electric vehicle (EV) batteries and the power grid, making EV-based mobile energy storage an appealing supplement to stationary energy storage systems. However, the stochastic and volatile charging behaviors pose a challenge for EV fleets to engage directly in multi-agent cooperation. To unlock the scheduling potential of EVs, this paper proposes a source storage cooperative low-carbon scheduling strategy considering V2G aggregators. The uncertainty of EV charging patterns is managed through a rolling-horizon control framework, where the scheduling and control horizons are adaptively adjusted according to the availability periods of EVs. Moreover, a Minkowski-sum based aggregation method is employed to evaluate the scheduling potential of aggregated EV fleets within a given scheduling horizon. This method effectively reduces the variable dimension while preserving the charging and discharging constraints of individual EVs. Subsequently, a Nash bargaining based cooperative scheduling model involving a distribution system operator (DSO), an EV aggregator (EVA), and a load aggregator (LA) is established to maximize the social welfare and improve the low-carbon performance of the system. This model is solved by the alternating direction method of multipliers (ADMM) algorithm in a distributed manner, with privacy of participants fully preserved. The proposed strategy is proven to achieve the objective of low-carbon economic operation.

Abstract:The push for renewable energy emphasizes the need for energy storage systems (ESSs) to mitigate the unpredictability and variability of these sources, yet challenges such as high investment costs, sporadic utilization, and demand mismatch hinder their broader adoption. In response, shared energy storage systems (SESSs) offer a more cohesive and efficient use of ESS, providing more accessible and cost-effective energy storage solutions to overcome these obstacles. To enhance the profitability of SESSs, this paper designs a multi-time-scale resource allocation strategy based on long-term contracts and real-time rental business models. We initially construct a life cycle cost model for SESS and introduce a method to estimate the degradation costs of multiple battery groups by cycling numbers and depth of discharge within the SESS. Subsequently, we design various long-term contracts from both capacity and energy perspectives, establishing associated models and real-time rental models. Lastly, multi-time-scale resource allocation based on the decomposition of user demand is proposed. Numerical analysis validates that the business model based on long-term contracts excels over models operating solely in the real-time market in economic viability and user satisfaction, effectively reducing battery degradation, and leveraging the aggregation effect for SESS can generate an additional increase of 10.7% in net revenue.

Abstract:The increasing penetration of variable renewable energy (VRE) generation along with the decommissioning of conventional power plants in Chile, has raised several operational challenges in the Chilean National Power Grid (NPG), including transmission congestion and VRE curtailment. To mitigate these limitations, an innovative virtual transmission solution based on battery energy storage systems (BESSs), known as grid booster (GB), has been proposed to increase the capacity of the main 500 kV corridor of the NPG. This paper analyzes the dynamic performance of the GB using a wide-area electromagnetic transient (EMT) model of the NPG. The GB project, composed of two 500 MVA BESS units at each extreme of the 500 kV corridor, allows increasing the transmission capacity for 15 min during N - 1 contingencies, overcoming transmission limitations under normal operation conditions while maintaining system stability during faults. The dynamic behavior of the GB is also analyzed to control power flow as well as voltage stability. The results show that the GB is an effective solution to allow greater penetration of VRE generation while maintaining system stability in the NPG.

Abstract:To provide guidance for photovoltaic (PV) system integration in net-zero distribution systems (DSs), this paper proposes an analytical method for delineating the feasible region for PV integration capacities (PVICs), where the impact of battery energy storage system (BESS) flexibility is considered. First, we introduce distributionally robust chance constraints on network security and energy/carbon net-zero requirements, which form the upper and lower bounds of the feasible region. Then, the formulation and solution of the feasible region is proposed. The resulting analytical expression is a set of linear inequalities, illustrating that the feasible region is a polyhedron in a high-dimensional space. A procedure is designed to verify and adjust the feasible region, ensuring that it satisfies network loss constraints under alternating current (AC) power flow. Case studies on the 4-bus system, the IEEE 33-bus system, and the IEEE 123-bus system verify the effectiveness of the proposed method. It is demonstrated that the proposed method fully captures the spatio-temporal coupling relationship among PVs, loads, and BESSs, while also quantifying the impact of this relationship on the boundaries of the feasible region.

Abstract:In rural territories, the communities use energy sources based on fossil fuels to supply themselves with electricity, which may address two main problems: greenhouse gas emissions and high fuel prices. Hence, there is an opportunity to include renewable resources in the energy mix. This paper develops an optimization model to determine the optimal sizing, the total annual investment cost in renewable generation, and other operating costs of the components of a hybrid microgrid. By running a k-means clustering algorithm on a meteorological dataset of the community under study, the hourly representative values become input parameters in the proposed optimization model. The method for the optimal design of hybrid microgrid is analyzed in six operating scenarios considering ① 24-hour continuous power supply; ② load shedding percentage; ③ diesel power generator (genset) curtailment; ④ the worst meteorological conditions; ⑤ the use of renewable energy sources including battery energy storage systems (BESSs)； and ⑥ the use of genset. A mathematical programming language (AMPL) tool is used to find solutions of the proposed optimization model. Results show that the total costs of microgrid in the scenarios that cover 100% of the load demand (without considering the scenario with 100% renewables) increase by over 16% compared with the scenario with genset operation limitation. For the designs with power supply restrictions, the total cost of microgrid in the scenario with load shedding is reduced by over 27% compared with that without load shedding.

Abstract:—This paper presents a control strategy for residential battery energy storage systems, which is aware of volatile electricity markets and uncertain daily cycling loads. The economic benefits of energy trading for prosumers are achieved through a novel modification of a conventional model predictive control (MPC). The proposed control strategy guarantees an optimal global solution for the applied control action. A new cost function is introduced to model the effects of volatility on customer benefits more effectively. Specifically, the newly presented cost function models a probabilistic relation between the power exchanged with the grid, the net load, and the electricity market. The probabilistic calculation of the cost function shows the dependence on the mathematical expectation of market price and net load. Computational techniques for calculating this value are presented. The proposed strategy differs from the stochastic and robust MPC in that the cost is calculated across the market price and net load variations rather than across model constraints and parameter variations.

Abstract:The series line-commutated converter (LCC) and modular multilevel converter (MMC) hybrid high-voltage direct current (HVDC) system provides a more economical and flexible alternative for ultra-HVDC (UHVDC) transmission, which is the so-called Baihetan-Jiangsu HVDC (BJ-HVDC) project of China. In one LCC and two MMCs (1+2) operation mode, the sub-module (SM) capacitors suffer the most rigorous overvoltage induced by three-phase-to-ground fault at grid-side MMC and valve-side single-phase-to-ground fault in internal MMC. In order to suppress such huge overvoltage, this paper demonstrates a novel alternative by employing the MMC-based embedded battery energy storage system (MMC-BESS). Firstly, the inducements of SM overvoltage are analyzed. Then, coordinated with MMC-BESS, new fault ride-through (FRT) strategies are proposed to suppress the overvoltage and improve the FRT capability. Finally, several simulation scenarios are carried out on PSCAD/EMTDC. The overvoltage suppression is verified against auxiliary device used in the BJ-HVDC project in a monopolar BJ-HVDC system. Further, the proposed FRT strategies are validated in the southern Jiangsu power grid of China based on the planning data in the summer of 2025. Simulation results show that the MMC-BESS and proposed FRT strategies could effectively suppress the overvoltage and improve the FRT capability.

Abstract:By collecting and organizing historical data and typical model characteristics, hydrogen energy storage system (HESS)-based power-to-gas (P2G) and gas-to-power systems are developed using Simulink. The energy transfer mechanisms and numerical modeling methods of the proposed systems are studied in detail. The proposed integrated HESS model covers the following system components: alkaline electrolyzer (AE), high-pressure hydrogen storage tank with compressor (CM & H ₂ tank), and proton-exchange membrane fuel cell (PEMFC) stack. The unit models in the HESS are established based on typical U-I curves and equivalent circuit models, which are used to analyze the operating characteristics and charging/discharging behaviors of a typical AE, an ideal CM & H ₂ tank, and a PEMFC stack. The validities of these models are simulated and verified in the MicroGrid system, which is equipped with a wind power generation system, a photovoltaic power generation system, and an auxiliary battery energy storage system (BESS) unit. Simulation results in MATLAB/Simulink show that electrolyzer stack, fuel cell stack and system integration model can operate in different cases. By testing the simulation results of the HESS under different working conditions, the hydrogen production flow, stack voltage, state of charge (SOC) of the BESS, state of hydrogen pressure (SOHP) of the HESS, and HESS energy flow paths are analyzed. The simulation results are consistent with expectations, showing that the integrated HESS model can effectively absorb wind and photovoltaic power. As the wind and photovoltaic power generations increase, the HESS current increases, thereby increasing the amount of hydrogen production to absorb the surplus power. The results show that the HESS responds faster than the traditional BESS in the microgrid, providing a solid theoretical foundation for later wind-photovoltaic-HESS-BESS integration.

Abstract:This paper proposes a distribution locational marginal pricing (DLMP) based bi-level Stackelberg game framework between the internet service company (ISC) and distribution system operator (DSO) in the data center park. To minimize electricity costs, the ISC at the upper level dispatches the interactive workloads (IWs) across different data center buildings spatially and schedules the battery energy storage system temporally in response to DLMP. Photovoltaic generation and static var generation provide extra active and reactive power. At the lower level, DSO calculates the DLMP by minimizing the total electricity cost under the two-part tariff policy and ensures that the distribution network is uncongested and bus voltage is within the limit. The equilibrium solution is obtained by converting the bi-level optimization into a single-level mixed-integer second-order cone programming optimization using the strong duality theorem and the binary expansion method. Case studies verify that the proposed method benefits both the DSO and ISC while preserving the privacy of the ISC. By taking into account the uncertainties in IWs and photovoltaic generation, the flexibility of distribution networks is enhanced, which further facilitates the accommodation of more demand-side resources.

Abstract:The optimal planning and operation of multi-type flexible resources (FRs) are critical prerequisites for maintaining power and energy balance in regional power grids with a high proportion of clean energy. However, insufficient consideration of the multi-dimensional and heterogeneous features of FRs, such as the regulation characteristics of diversified battery energy storage systems (BESSs), poses a challenge in economically relieving imbalance power and adequately sharing feature information between power supply and demand. In view of this disadvantage, an optimal planning and operation method based on differentiated feature matching through response capability characterization and difference quantification of FRs is proposed in this paper. In the planning stage, a model for the optimal planning of diversified energy storages (ESs) including Lithium-ion battery (Li-B), supercapacitor energy storage (SCES), compressed air energy storage (CAES), and pumped hydroelectric storage (PHS) is established. Subsequently, in the operating stage, the potential, direction, and cost of FR response behaviors are refined to match with the power and energy balance demand (PEBD) of power grid operation. An optimal operating algorithm is then employed to quantify the feature differences and output response sequences of multi-type FRs. The performance and effectiveness of the proposed method are demonstrated through comparative studies conducted on an actual regional power grid in northwest China. Analysis and simulation results illustrate that the proposed method can effectively highlight the advantages of BESSs compared with other ESs, and economically reduce imbalance power of the regional power grid under practical operating conditions.

Abstract:Battery energy storage stations (BESSs) pose several challenges for both phasor-based differential protection and the newly-proposed time-domain differential protection. These challenges include low sensitivity and even rejection. Besides, the negative impact of various nonideal conditions, including current transformer (CT) saturation, errors, and outliers, on the security of differential protection remains an important problem. Motivated by the aforementioned issues, this study accounts for the trajectory distribution discrepancy on Cartesian plane under various conditions and proposes a time-domain differential protection method. In this paper, the trajectory formed by operating and restraining current samples is developed. Subsequently, after considering different operating states, the fault severity levels, and nonideal conditions, the variances in trajectory distribution between internal and external faults are extensively analyzed. On this basis, the Cartesian plane is divided into operating, uncertainty, and restraining zones. Further, the operating and restraining trajectory indices are meticulously designed and a protection criterion based on these indices is formed to accurately separate internal faults from other events, unaffected by CT saturation, errors, and outliers. The exceptional performance of the proposed protection method is extensively validated through PSCAD simulations and a hardware-in-the-loop testing platform. Regarding the dependability, sensitivity, and security, the proposed protection method outperforms three state-of-the-art differential protection methods.

Abstract:As the proportion of renewable energy (RE) increases, the inertia and the primary frequency regulation (FR) capability of the power system decrease. Thus, ensuring frequency security in the scheduling model has become a new technical requirement in power systems with a high share of RE. Due to a shortage of conventional synchronous generators, the frequency support of multi-source converters has become an indispensable part of the system frequency resources, especially variable-speed wind turbine generation (WTG) and battery energy storage (BES). Quantitative expression of the FR capability of multi-source converters is necessary to construct frequency-constrained scheduling model. However, the frequency support performance of these converter-interfaced devices is related to their working states, operation modes, and parameters, and the complex coupling of these factors has not been fully exploited in existing models. In this study, we propose an integrated frequency-constrained scheduling model considering the coordination of FR capabilities from multi-source converters. Switchable FR control strategies and variable FR parameters for WTG with or without reserved power are modeled, and multi-target allocation of BES capacity between tracking dispatch instruction and emergency FR is analyzed. Then, the variable FR capabilities of WTG and BES are embedded into the integrated frequency-constrained scheduling model. The nonlinear constraints for frequency security are precisely linearized through an improved iteration-based strategy. The effectiveness of the proposed model is verified in a modified IEEE 24-bus standard system. The results suggest that the coordinated participation of BES and WTG in FR can effectively reduce the cost of the scheduling model while meeting frequency security constraints.

Abstract:Nowadays, public policies in Chile are geared towards the promotion of distributed energy resources (DERs) such as distributed photovoltaic (PV) systems. However, the prevailing socioeconomic context and the lack of incentive to invest in DERs have posed a challenge to achieving the established goals in the coming years. This paper develops a three-entity architecture model and decision-making algorithms for peer-to-peer (P2P) PV energy trading. It seeks to conduct a sensitivity analysis of a P2P PV energy trading system in a community microgrid, to assess the potential benefits for local communities and to encourage the development of new local public policies aimed at enhancing the profitability of DERs. Various scenarios are compared, both with and without P2P market, considering residential customers (RCs), encompassing both consumers and prosumers with PV systems, with or without battery energy storage systems (BESSs), an aggregator (AG), and utility grid (UG). Daily energy and economic transactions are examined with the aim of reducing the annual electricity bills for each RC, enhancing the profitability of DERs for prosumers, increasing incomes for the AG, and exploring potential benefits for the UG. The load profiles and meteorological data are collected from publicly available databases, and a novel electricity pricing scheme is proposed based on current rates offered by the local UG. The results demonstrate that the P2P market could lead to a reduction in the annual electricity bills by as much as 1.76% for consumers, an increase in annual income of up to 149% for prosumers, and a reduction in the payback period for their DERs by up to 0.4 years. This paper contributes to improving the investment in DER projects and provides a guide for extending the work to different regions of Chile and global emerging economies with DER potential.

Abstract:Battery energy storage systems (BESSs) are an important asset for power systems with high integration levels of renewable energy, and they can be controlled to provide various critical services to the power grid. This paper presents the real-world experience of using a megawatt-scale BESS with grid-following (GFL) and grid-forming (GFM) controls and a run-of-river (ROR) hydropower plant to restore a regional power system. To demonstrate this, we carry out power-hardware-in-the-loop experiments integrating an actual GFL- or GFM-controlled BESS and a load bank. Both the simulation and experimental results presented in this paper show the different roles of GFL- or GFM-controlled BESS in power system black starts. The results provide further insight for system operators on how GFL- or GFM-controlled BESS can enhance grid stability and how an ROR hydropower plant can be converted into a black-start-capable unit with the support of a small-capacity BESS. The results show that an ROR hydropower plant combined with a BESS has the potential of becoming one of enabling elements to perform bottom-up black-start schemes as opposed to conventional bottom-down method, thus enhancing the system resiliency and robustness.

Abstract:With the increasing proportion of renewable energy sources (RESs) in power grid, the reserve resource (RR) scarcity for correcting power deviation of RESs has become a potential issue. Consequently, the power curve of RES needs to be more rigorously assessed. The RR scarcity varies during different time periods, so the values of assessment indicators should be dynamically adjusted. The assessment indicators in this paper include two aspects, i.e., deviation exemption ratio and penalty price. Firstly, this paper proposes a method for dynamically calculating the supply capacity and RR cost, primarily taking into account the operating status of thermal units, forecast information of RES, and load curve. Secondly, after clarifying the logical relationship between the degree of RR scarcity and the values of assessment indicators, this paper establishes a mapping function between them. Based on this mapping function, a dynamic setting method for assessment indicators is proposed. In the future, RES will generally be equipped with battery energy storage systems (BESSs). Reasonably utilizing BESSs to reduce the power deviation of RESs can increase the expected income of RESs. Therefore, this paper proposes a power curve optimization strategy for RESs considering self-owned BESSs. The case study demonstrates that the dynamic setting method of assessment indicators can increase the revenue of RESs while ensuring that the penalty fees paid by RESs to the grid are sufficient to cover the RR costs. Additionally, the power curve optimization strategy can help RESs further increase income and fully utilize BESSs to reduce power deviation.