Abstract:With the increased penetration of renewable energy sources, the grid-forming (GFM) energy storage (ES) has been considered to engage in primary frequency regulation (PFR), often necessitating the use of a frequency deadband (FDB) to prevent excessive battery charging cycling and mitigate frequency oscillations. Implementing the FDB is relatively straightforward in grid-following (GFL) control. However, implementing the FDB in GFM control presents a significant challenge since the inverter must abstain from providing active power at any frequency within the FDB. Therefore, in this paper, the performance of PFR control in the GFM-ES inverter is analyzed in detail first. Then, the FDB is implemented for GFM inverters with various types of synchronization methods, and the need for inertia response is also considered. Moreover, given the risk of oscillations near the FDB boundary, different FDB setting methods are proposed and examined, where an improved triangular hysteresis method is proposed to realize the fast response and enhanced stability. Finally, the simulation and experiment results are provided to verify the effectiveness of the above methods.

Abstract:The hybrid photovoltaic (PV)-battery energy storage system (BESS) plant (HPP) can gain revenue by performing energy arbitrage in low-carbon power systems. However, multiple operational uncertainties challenge the profitability and reliability of HPP in the day-ahead market. This paper proposes two coherent models to address these challenges. Firstly, a knowledge-driven penalty-based bidding (PBB) model for HPP is established, considering forecast errors of PV generation, market prices, and under-generation penalties. Secondly, a data-driven dynamic error quantification (DEQ) model is used to capture the variational pattern of the distribution of forecast errors. The role of the DEQ model is to guide the knowledge-driven bidding model. Notably, the DEQ model aims at the statistical optimum, but the knowledge-driven PBB model aims at the operational optimum. These two models have independent optimizations based on misaligned objectives. To address this, the knowledge-data-complementary learning (KDCL) framework is proposed to align data-driven performance with knowledge-driven objectives, thereby enhancing the overall performance of the bidding strategy. A tailored algorithm is proposed to solve the bidding strategy. The proposed bidding strategy is validated by using data from the National Renewable Energy Laboratory (NREL) and the New York Independent System Operator (NYISO).

Abstract:To address environmental concerns, there has been a rapid global surge in integrating renewable energy sources into power grids. However, this transition poses challenges to grid stability. A prominent solution to this challenge is the adoption of battery energy storage systems (BESSs). Many countries are actively increasing BESS deployment and developing new BESS technologies. Nevertheless, a crucial initial step is conducting a comprehensive analysis of BESS capabilities and subsequently formulating policies. We analyze the current roles of BESS and review existing BESS policies worldwide, which focuses on key markets in Asia, Europe, and the U.S.. Using collected survey data, we propose a comprehensive three-phase framework for policy formulation, providing insights into future policy development directions.

Abstract:Oscillation accidents emerge in power systems integrated with increasing penetration of renewable energy sources. The impedance of electromagnetic dynamics is investigated in recent years, where the mechanical dynamics are neglected. So far, the low-frequency oscillations are not well addressed with the impedance analysis method. A novel analytical impedance is formulated and implemented for wind energy conversion system consisting of wind turbine generators (WTGs) and wind farm, which fills the gap in the mechanical dynamics of the impedance. Instead of assuming constant values, the electromechanical dynamics of the rotor speed and the pitch angle are involved in the WTG impedance. Besides, the impedance framework is generally and modularly designed and is adaptive to different operating regions. With the developed analytical impedance, the stability assessment can cover the low-frequency oscillations, providing an in-depth insight into the mechanical parameters influencing the small-signal stability performance. As an application, the impedance characteristic and stability performance of systems with active power reserve for grid supporting are analyzed and optimized. Furthermore, the shafting torsional vibrations of WTGs in wind farms are analyzed with modal decomposition and the low-frequency impedance model. The improved accuracy of the developed analytical impedance is illustrated by comparison with commonly used impedance, which ignores the coupling between the electrical and mechanical dynamics. It is proven that the mechanical dynamics have a significant influence on the impedance, particularly in the low-frequency range. Experimental validation is carried out to validate the low-frequency impedance model and the stability performance.

Abstract:Energy storage can smooth the fluctuations of wind power integrated into the grid. Due to the strong adaptability of the empirical mode decomposition (EMD) algorithm to non-stationary signals, it is widely used in wind power smoothing control strategies. However, traditional EMD algorithms cannot guarantee that the upper and lower areas of the calculated intrinsic mode functions (IMFs) are equal, which tends to result in imbalanced calculated energy storage power and thus exceeding the limit of energy storage capacity. Focusing on wind power smoothing control by energy storage, this paper proposes a strategy based on the area-equilibrium EMD, which modifies the upper and lower areas of the IMFs to achieve a more balanced distribution. As a result, the IMFs contain less energy, and consequently, the energy contained in the calculated smoothing power is also reduced. This makes the energy storage capacity less likely to exceed the limit, thereby achieving better wind power smoothing performance under given energy storage capacity. Case studies show that the proposed strategy results in more balanced upper and lower areas of the IMFs, reduces the fluctuating range of calculated energy storage, and improves the wind power smoothing effectiveness.

Abstract:Fault current limiting is a critical technology to ensure the safe operation of modular multilevel converter based multi-terminal direct current (MMC-MTDC) grids. This paper proposes a fault severity classification based coordination control strategy of fault current limiter (FCL) and MMC for adaptive fault current limiting. The proposed strategy reduces the investment in FCL, and keeps the bus voltages of non-faulty lines at reasonable values. Firstly, a rapid fault circuit parameter estimation (FCPE) method using initial fault current information is proposed. With this method, the fault distance and fault transition resistance can be quickly estimated, which are used for a quantitative indication of the fault severity. Subsequently, the coordination control strategy of FCL and MMC is proposed, in which the FCL action is prioritized, while the control of MMC is complementary for current limiting. Based on the proposed strategy, fault severity phase planes (FSPPs) are constructed to assess fault severity and calculate the activation time of FCL and voltage regulation factor of MMC. Therefore, the FCL activation and MMC control are matched to the fault severity. The effectiveness and advantages of the proposed strategy are validated by the simulations in PSCAD/EMTDC.

Abstract:A mathematical programming approach rooted in distributionally robust optimization (DRO) provides an effective data-driven strategy for battery energy storage system (BESS) planning. Nevertheless, the DRO paradigm often lacks interpretability in its results, obscuring the causal relationships between data distribution characteristics and the outcomes. Furthermore, the current approach to battery type selection is not included in traditional BESS planning, hindering comprehensive optimization. To tackle these BESS planning problems, this paper presents a universal method for BESS planning, which is designed to enhance the interpretability of DRO. First, mathematical definitions of interpretable DRO (IDRO) are introduced. Next, the uncertainties in wind power, photovoltaic power, and loads are modeled by using second-order cone ambiguity sets (SOCASs). In addition, the proposed method integrates selection, sizing, and siting. Moreover, a second-order cone bidirectional-orthogonal strategy is proposed to solve the BESS planning problems. Finally, the effectiveness of the proposed method is demonstrated through case studies, offering planners richer decision-making insights.

Abstract:Data centers are promising demand-side flexible resources that can provide frequency regulation services to power grids. While most existing studies focus on individual data centers, coordinating multiple geo-distributed data centers can significantly enhance operational flexibility and market participation. However, the inherent uncertainty in both data center workloads and regulation signals pose significant challenges to maintaining effective operations, let alone determining regulation capacity offerings. To address these challenges, this paper proposes a coordinated bidding strategy for electricity purchases and regulation capacity offerings for multiple geo-distributed data centers in electricity markets. This strategy expands the feasible region of operational decisions, including workload dispatch, server activation, and cooling behaviors. To enhance the participation of data centers in frequency regulation services under uncertainty, chance-constrained programming is adopted. This paper presents explicit models for these uncertainties involved, starting with the Poisson-distributed workloads and then addressing the unpredictable regulation signals. Numerical experiments based on real-world datasets validate the effectiveness of the proposed strategy compared with state-of-the-art strategies.

Abstract:This paper proposes a method for obtaining nonlinear models of distribution grid based on available measurements from the power grid. We formulate a maximum likelihood estimation (MLE) problem that estimates unknown line parameters—specifically, the impedance between nodes—using measured voltage magnitudes and net active and reactive power injections at each node. The nonlinear model for the distribution grid uses a nonlinear approximation of the DistFlow model, which includes line losses and is parameterized by the unknown line impedances. We solve the resulting MLE problem using an expectation maximization (EM) algorithm, tailored for the nonlinear model, and provide a numerically robust implementation. The proposed method is demonstrated on the IEEE 37-node test network, and we compare it with the state-of-the-art methods. The proposed method achieves a 70% reduction in voltage error and an error for state variables that is more than 10000 times smaller. A final comparison uses data from a real network, and the proposed method achieves parameter estimates with errors 100 times smaller than competing methods.

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: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 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:The unbalanced state of charge (SOC) of distributed energy storage systems (DESSs) in autonomous DC microgrid causes energy storage units (ESUs) to terminate operation due to overcharge or overdischarge, which severely affects the power quality. In this paper, a fuzzy droop control for SOC balance and stability analysis of DC microgrid with DESSs is proposed to achieve SOC balance in ESUs while maintaining a stable DC bus voltage. First, the charge and discharge modes of ESUs are determined based on the power supply requirements of the DC microgrid. One-dimensional fuzzy logic is then applied to establish the relationship between SOC and the droop coefficient R_d in the aforementioned two modes. In addition, when integrated with voltage-current double closed-loop control, SOC balance in different ESUs is realized. To improve the balance speed and precision, an exponential acceleration factor is added to the input variable of the fuzzy controller. Finally, based on the average model of converter, the system-level stability of microgrid is analyzed. MATLAB/Simulink simulation results verify the effectiveness and rationality of the proposed method.

Abstract:Energy storage (ES), as a fast response technology, creates an opportunity for microgrid (MG) to participate in the reserve market such that MG with ES can act as an independent reserve provider. However, the potential value of MG with ES in the reserve market has not been well realized. From the viewpoint of reserve provider, a novel day-ahead model is proposed comprehensively considering the effect of the real-time scheduling process, which differs from the model that MG with ES acts as a reserve consumer in most existing studies. Based on the proposed model, MG with ES can schedule its internal resources to give reserve service to other external systems as well as to realize optimal self-scheduling. Considering that the proposed model is just in concept and cannot be directly solved, a multi-stage robust optimization reserve provision method is proposed, which leverages the structure of model constraints. Next, the original model can be converted into a mixed-integer linear programming problem and the model is tractable with guaranteed solution feasibility. Numerical tests in a real-world context are provided to demonstrate efficient operation and economic performance.

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:In this paper, an operation model for distribution systems with energy storage (ES) is proposed and solved with the aid of machine learning. The model considers ES applications with uncertainty realizations. It also considers ES applications for economy and security purposes. Considering the special features of ES operations under day-ahead decision mechanisms of distribution systems, an ES operation scheme is designed for transferring uncertainties to later hours through ES to ensure the secure operation of distribution system. As a result, uncertainties from different time intervals are assembled and may counteract each other, thereby alleviating the uncertainties. As different ES applications rely on ES flexibility (in terms of charging and discharging) and interact with each other, by coordinating different ES applications, the proposed operation model achieves efficient exploit of ES flexibility. To shorten the computation time, a long short-term memory recurrent neural network is used to determine the binary variables corresponding to ES status. The proposed operation model then becomes a convex optimization problem and is solved precisely. Thus, the solving efficiency is greatly improved while ensuring the satisfactory use of ES flexibility in distribution system operation.

Abstract:High penetration of renewable energy sources (RESs) induces sharply-fluctuating feeder power, leading to voltage deviation in active distribution systems. To prevent voltage violations, multi-terminal soft open points (M-SOPs) have been integrated into the distribution systems to enhance voltage control flexibility. However, the M-SOP voltage control recalculated in real-time cannot adapt to the rapid fluctuations of photovoltaic (PV) power, fundamentally limiting the voltage controllability of M-SOPs. To address this issue, a full-model-free adaptive graph deep deterministic policy gradient (FAG-DDPG) model is proposed for M-SOP voltage control. Specifically, the attention-based adaptive graph convolutional network (AGCN) is leveraged to extract the complex correlation features of nodal information to improve the policy learning ability. Then, the AGCN-based surrogate model is trained to replace the power flow calculation to achieve model-free control. Furthermore, the deep deterministic policy gradient (DDPG) algorithm allows FAG-DDPG model to learn an optimal control strategy of M-SOP by continuous interactions with the AGCN-based surrogate model. Numerical tests have been performed on modified IEEE 33-node, 123-node, and a real 76-node distribution systems, which demonstrate the effectiveness and generalization ability of the proposed FAG-DDPG model.